This Electronic Thesis Or Dissertation Has Been Downloaded from Explore Bristol Research

This electronic thesis or dissertation has been downloaded from Explore Bristol Research, http://research-information.bristol.ac.uk

Author: Smith, Melissa Title: The effects of chronic pain from spontaneous canine osteoarthritis on working memory and sleep

General rights Access to the thesis is subject to the Creative Commons Attribution - NonCommercial-No Derivatives 4.0 International Public License. A copy of this may be found at https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode This license sets out your rights and the restrictions that apply to your access to the thesis so it is important you read this before proceeding. Take down policy Some pages of this thesis may have been removed for copyright restrictions prior to having it been deposited in Explore Bristol Research. However, if you have discovered material within the thesis that you consider to be unlawful e.g. breaches of copyright (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please contact [email protected] and include the following information in your message:

•Your contact details •Bibliographic details for the item, including a URL •An outline nature of the complaint

Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. The effects of chronic pain from spontaneous canine osteoarthritis on working memory and sleep

Melissa Isobelle Smith

A dissertation submitted to the University of Bristol in accordance with the requirements for award of the degree of PhD in the Faculty of Health Sciences

Bristol Veterinary School September 2018

Word Count: 77,791

Abstract:

In humans, chronic pain is associated with impaired working memory and sleep, however it is not known if this is the case in dogs. The aim of this thesis was to assess whether chronic pain from spontaneous osteoarthritis is associated with impaired working memory and sleep disturbance in dogs.

Initially, two behavioural assays (the holeboard and novel object recognition tasks) previously used in other species were adapted for rapid, single session use in osteoarthritic and healthy control dogs. Surprisingly, osteoarthritic dogs spent proportionally more time interacting with a novel object than a familiar one compared to control dogs, indicating possible increased non-spatial working memory or increased neophilia. However, dogs did not appear to successfully learn the holeboard task. Therefore a study was performed in dogs with no known health problems using a holeboard task with more trials to assess spatial working and reference memory, and a disappearing object task to assess spatial working memory, both of which dogs learned successfully. The spatial working memory of osteoarthritic and control dogs was compared using the disappearing object task, and dogs’ sleep was monitored for one month using proxy measures obtained from actigraphy and owner questionnaires.

Female (but not male) osteoarthritic dogs had a lower predicted probability of success in the disappearing object task compared to control dogs of the same sex. Osteoarthritic dogs spent less of the night resting than control dogs, though there were no significant differences between groups in scores on an owner questionnaire designed to measure sleep quality. These studies are the first to find evidence that a chronically painful condition impairs spatial working memory in dogs, and to find differences in night-time activity between osteoarthritic and healthy control dogs. However, further studies are required to confirm that these findings are generalisable to the wider canine population.

For Lily, who believed in my abilities from the very start. And for P.K. and Frankie, who will always be a piece of the family.

Dedication and Acknowledgements:

I would like to thank my supervisors Dr. Jo Murrell and Prof. Mike Mendl for the plethora of advice and support they have provided throughout the PhD project. I would also like to thank Dr. Liz Paul & Prof. Becky Whay for their practical advice regarding project planning, Dr. Suzanne Held for providing pragmatic emotional support when the project was going less smoothly, the BBSRC SWBio DTP for funding this project, and Dr. Samantha Southern for patiently responding to all of my DTP-related queries.

I would also like to thank Dr. Lauren Harris, Hannah Myall, Juan Carlos Arenas-Salcedo and Natasja van der Woude for their help with animal handling and/or blinded video observation tasks during the course of this study, and for Lauren's helpful advice with respect to the PhD assessment process.

I also appreciate the assistance of the University of Bristol School of Mathematics, who provided practical advice regarding statistical modelling techniques and their assumptions, and the StackOverflow community for their advice on specific data manipulation techniques, debugging non-functional code and use of obscure R packages and functions. I would also like to thank the friendly and supportive staff of the Public Health England Southwest Field Epidemiology Service, with whom I performed an internship during my third year, for allowing me the opportunity to greatly develop my skills in R coding, data manipulation and presentation, and logistic regression analyses, and for improving my confidence in my own skills and abilities. Without these skills the analyses performed in Chapters 3-5 of this thesis would have been far beyond my ability.

This project would not have been possible without the participation of members of the public, whose enthusiasm for the study and love for their dogs and the wider dog community renewed my faith in humanity at times when such belief often seemed unreasonable. Similarly, the apparent eagerness with which their dogs engaged with unusual tasks at the request of a stranger in their home, and their apparent enjoyment from retrieval of a hidden object, acquisition of food rewards or just positive attention from an unfamiliar human, made the difficulties involved in planning, authorising and organising the experiments seem worthwhile even before the data were analysed.

iii

I would also like to thank my parents, Vivienne and Terry, and my grandparents, Lily and Fred, for their continuing support throughout my life. In supporting me through school and college, encouraging me to go to university and agreeing to get the dog I’d always wanted as a child, they allowed me to develop my skills and interests in animal health, behaviour and welfare.

Finally, I would like to thank Phil Wadey for supporting my choice to pursue a PhD despite his complete lack of interest in or enthusiasm for academia. The superhuman efficiency and willingness with which he performed mundane household and administrative tasks afforded me the time to work long into the evenings when necessary, to rest and recover afterwards, and to maintain social relationships even through the most intensive periods of study. He approached difficult situations with a rare combination of personal emotional detachment and a seemingly intuitive awareness of and consideration for the emotional states of others, allowing me to identify and pursue the most reasonable courses of action when my own anxiety would have otherwise made this difficult. Furthermore, his anchoring to and passion for the world beyond academia and veterinary science allowed me to become aware of events, activities and potential development and career opportunities that I would otherwise have missed and without which my life would have been far less interesting and enjoyable. Though all of the anxiety, frustration and doubt I experienced throughout the PhD was my own choice to endure, my workload, exhaustion and emotional inconsistency impacted on his own lifestyle and emotional state too. Despite this, he recognised that completing this PhD was important to me even when I couldn’t clearly explain why, and chose to be practically and emotionally supportive even when this inconvenienced his own plans and activities. In short, Phil is a thoroughly improbable person whose skills, dedication and resilience in all areas of life are far too often underappreciated and overlooked because of the resolve, efficiency and quiet consideration with which he performs every activity, and because of his tendency to put the concerns and insecurities of others before his own. I have not thanked him enough for his support during the course of this project, and I am not sure that it is possible to do so.

Author’s Declaration:

I declare that the work in this dissertation was carried out in accordance with the requirements of the University's Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate's own work. Work done in collaboration with, or with the assistance of, others, is indicated as such. Any views expressed in the dissertation are those of the author.

SIGNED: ...... DATE: 30/03/2019

Table of Contents:

Abstract: ...... i

Dedication and Acknowledgements: ...... iii

Author’s Declaration: ...... v

Table of Contents: ...... vi

List of Figures: ...... xiii

List of Tables: ...... xvii

List of Appendices: ...... xix

Table of Abbreviations and Acronyms: ...... xxi

Chapter 1 Introduction and Literature Review ...... 1

1.1 Why is it important to assess the effect of spontaneous canine osteoarthritis on working memory and sleep? ...... 1

1.2 Chronic pain – definitions, mechanisms and prevalence: ...... 3

1.2.1 What is chronic pain? ...... 3

1.2.2 Pathophysiological changes in chronic pain: ...... 4

1.2.3 Evidence for chronic pain in non-human animals: ...... 5

1.3 Osteoarthritis: ...... 9

1.3.1 Osteoarthritis and chronic pain:...... 10

1.3.1.1 Osteoarthritis causes chronic pain in humans: ...... 10

1.3.1.2 The pathogenesis of osteoarthritic pain: ...... 11

1.3.1.3 Painful osteoarthritis in dogs: ...... 11

1.3.2 Diagnosis and treatment of osteoarthritis: ...... 13

1.3.3 The epidemiology of osteoarthritis: ...... 14

1.3.4 Developmental disorders associated with osteoarthritis in the dog: ...... 17

1.3.5 Animal models of osteoarthritis: ...... 18

1.3.5.1 Current animal models of osteoarthritis: ...... 18

1.3.5.2 Spontaneous canine osteoarthritis as a model for human osteoarthritis: ...... 19

1.4 The effect of chronic pain on cognition: ...... 20

1.4.1 The effect of chronic pain on cognition in humans: ...... 21

1.4.2 Potential causes of cognitive impairment in chronic pain conditions: ...... 23

1.4.3 The effects of chronic pain on cognition in rodents: ...... 26

1.5 Assessment of working memory in non-human animals: ...... 27

1.5.1 Reinforced maze tasks of spatial working memory: ...... 27

1.5.2 Unreinforced (exploratory) tasks of working memory: ...... 29

1.5.3 Delayed non-matching to sample tasks: ...... 30

1.6 Chronic pain and sleep disturbance: ...... 32

1.6.1 The chronic pain-sleep disturbance relationship: ...... 33

1.6.2 Sleep disturbance in osteoarthritis: ...... 35

1.6.3 Methods of sleep assessment: ...... 36

1.7 How will this thesis project investigate the effect of spontaneous canine osteoarthritis on working memory and sleep? ...... 38

1.7.1 Summary of literature: ...... 38

1.7.2 Aims, hypotheses and scope of the project: ...... 41

1.7.3 Thesis structure: ...... 41

Chapter 2 Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks ...... 43

2.1 Abstract: ...... 43

2.2 Introduction: ...... 43

2.3 Clinical and owner assessment of chronic pain in dogs with and without signs of osteoarthritis: ...... 44

2.3.1 Background: ...... 44

2.3.2 Materials and Methods: ...... 48

vii

2.3.2.1 Ethics:...... 48

2.3.2.2 Animals: ...... 48

2.3.2.3 Data acquisition: ...... 50

2.3.2.4 Data analysis: ...... 51

2.3.3 Results: ...... 54

2.3.4 Discussion: ...... 65

2.4 The Novel Object Recognition task: Comparing the non-spatial working memory of osteoarthritic and healthy control dogs: ...... 67

2.4.1 Background:...... 67

2.4.2 Materials and Methods: ...... 69

2.4.2.1 Pilot Study: ...... 69

2.4.2.2 Animals: ...... 70

2.4.2.3 Apparatus: ...... 71

2.4.2.4 Procedure: ...... 73

2.4.2.5 Recording and data analysis: ...... 75

2.4.3 Results: ...... 78

2.4.4 Discussion: ...... 90

2.5 The Holeboard task: Comparing the spatial working memory of osteoarthritic and healthy control dogs: ...... 95

2.5.1 Background:...... 95

2.5.2 Materials and Methods: ...... 98

2.5.2.1 Pilot Study: ...... 98

2.5.2.2 Animals: ...... 99

2.5.2.3 Apparatus: ...... 100

2.5.2.4 Procedure: ...... 102

2.5.2.5 Data Recording and Analysis: ...... 104

2.5.3 Results: ...... 105

2.5.4 Discussion: ...... 112

viii

2.6 Summary:...... 115

Chapter 3 Comparing assays of spatial working memory in dogs ...... 117

3.1 Abstract: ...... 117

3.2 Introduction: ...... 117

3.3 The Holeboard task: ...... 118

3.3.1 Background: ...... 118

3.3.2 Materials and Methods: ...... 122

3.3.2.1 Animals: ...... 122

3.3.2.2 Apparatus: ...... 122

3.3.2.3 Procedure: ...... 124

3.3.2.4 Analysis: ...... 125

3.3.3 Results: ...... 127

3.3.4 Discussion: ...... 135

3.4 The Disappearing Object task: ...... 138

3.4.1 Background: ...... 138

3.4.2 Materials and Methods: ...... 140

3.4.2.1 Animals: ...... 140

3.4.2.2 Apparatus: ...... 140

3.4.2.3 Procedure: ...... 141

3.4.2.4 Analysis: ...... 144

3.4.3 Results: ...... 146

3.4.4 Discussion: ...... 154

3.5 Summary:...... 160

Chapter 4 Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task ...... 162

4.1 Abstract: ...... 162

4.2 Introduction: ...... 162

4.3 Materials and Methods: ...... 165

4.3.1 Animals: ...... 165

4.3.2 Questionnaires: ...... 167

4.3.3 Apparatus: ...... 167

4.3.4 Procedure: ...... 170

4.3.5 Analysis: ...... 170

4.4 Results: ...... 173

4.4.1 Signalment and Week 0 questionnaire scores: ...... 173

4.4.2 Probability of Success: ...... 175

4.4.3 Adjusted latency to reach box: ...... 186

4.5 Discussion:...... 192

4.5.1 Discussion of findings: ...... 192

4.5.1.1 Signalment factors: ...... 192

4.5.1.2 The effects of group and sex on probability of success: ...... 193

4.5.1.3 The effect of group on adjusted latency: ...... 195

4.5.1.4 The effect of interval duration on probability of success: ...... 196

4.5.1.5 The effect of interval duration on adjusted latency: ...... 198

4.5.1.6 The effect of trial number on adjusted latency: ...... 199

4.5.1.7 The effect of box on probability of success: ...... 200

4.5.1.8 The lack of significant effects of age on task performance: ...... 201

4.5.1.9 The lack of significant effects of or between-group differences in SNoRE scores: ...... 203

4.5.2 Methodological limitations: ...... 204

4.5.2.1 Risk of Collinearity: ...... 204

4.5.2.2 Assumption that relationships are linear: ...... 205

4.5.2.3 Risk of overfitting: ...... 206

4.5.2.4 Model selection techniques: ...... 206

4.5.3 Conclusion: ...... 210

Chapter 5 Comparison of resting behaviour in dogs with and without osteoarthritis ...... 211

5.1 Abstract: ...... 211

5.2 Introduction: ...... 211

5.3 Materials and Methods: ...... 222

5.3.1 Animals: ...... 222

5.3.2 Apparatus: ...... 222

5.3.3 Procedure: ...... 223

5.3.4 Data Preparation: ...... 224

5.3.5 Data Analysis: ...... 224

5.4 Results: ...... 226

5.4.1 Signalment and questionnaire scores: ...... 226

5.4.2 Proportion of night period spent resting: ...... 228

5.4.3 Proportion of daytime period spent resting: ...... 230

5.4.4 Mean duration of an uninterrupted bout of rest during the night: ...... 233

5.4.5 Mean duration of an uninterrupted bout of rest during the daytime: ...... 236

5.4.6 SNoRE score: ...... 236

5.5 Discussion: ...... 239

5.5.1 Discussion of findings: ...... 239

5.5.2 Methodological limitations: ...... 244

5.5.3 Conclusion: ...... 248

Chapter 6 Conclusion ...... 249

6.1 Overview of findings: ...... 249

6.2 Contribution to literature: ...... 250

6.3 Implications: ...... 254

6.4 Limitations: ...... 257

6.5 Future Work: ...... 260

6.6 Summary: ...... 264

References: ...... 266

Appendices: ...... 304

xii

List of Figures:

Figure 1.1: Diagram summarising the mechanisms by which chronic pain is hypothesised to impair cognition...... 25 Figure 2.1: Body condition scores of recruited dogs...... 55 Figure 2.2: The effect of group on clinical questionnaire scores...... 57 Figure 2.3: The effect of group on C-BARQ component scores...... 60 Figure 2.4: The effect of group on clinical lameness and joint function scores...... 62 Figure 2.5: The vet-assigned (A) and owner-assigned (B) osteoarthritis severity ratings of the dogs recruited...... 63 Figure 2.6: Clinical questionnaire component scores for each vet-assigned osteoarthritis severity score...... 64 Figure 2.7: (A): Diagram of the arena used within the novel object recognition task...... 72 Figure 2.8: The “blue” (left) and “yellow” (right, with blue objects in background) pairs of objects constructed for this study...... 73 Figure 2.9: The effect of age on Investigation Ratio (IR - the time spent investigating the novel object or object in its future position as a proportion of the total time spent investigating either object) in each phase of the novel object recognition task...... 79 Figure 2.10: (A) The Proximity Laterality Ratios (PLR - the time spent in close proximity to the left object as a proportion of the total time spent in close proximity to either object) for each phase of the novel object task for dogs that were assigned each colour of sample object (initial identical object pairs). (B) The numbers of dogs assigned each sample object which then had the novel object (of the alternative colour) placed in the right or left position (see Figure 2.7) during the test phase...... 81 Figure 2.11: The effect of group and phase on investigation ratio (IR - the time spent investigating the novel object or object in its future position as a proportion of the total time spent investigating either object)...... 83 Figure 2.12: The effect of group and phase on Proximity Ratio (PR - the time spent in close proximity to the novel object or object in its future position

xiii

as a proportion of the total time spent in close proximity to either object)...... 84 Figure 2.13: (A) Investigation Laterality Ratio (the time spent investigating the left object as a proportion of the total time spent investigating either object), (B), Proximity Laterality Ratio (the time spent in close proximity to the left object as a proportion of the total time spent in close proximity to either object), (C) Proportion of time spent interacting with either object and (D) Proportion of total time spent in proximity to either object for each group during each phase...... 85 Figure 2.14: The numbers of dogs which first visited each object during (A) the sample phase and (B) the test phase...... 87 Figure 2.15: The durations of time that control and osteoarthritic dogs spent with their ears held back during the sample and test phases...... 88 Figure 2.16: The durations of time dogs spent (A) looking through the arena fence, (B) lying down, and (C) walking during the novel object recognition task...... 89 Figure 2.17: A typical holeboard setup used in rodent studies, showing baited and unbaited holes. (Modified from van der Staay et al. (2012))...... 97 Figure 2.18: Diagram (A) and photograph (B) showing the arena setup for the holeboard task...... 101 Figure 2.19: Buckets used for the holeboard task...... 102 Figure 2.20: Mean values for each group and trial for each outcome...... 107 Figure 2.21: The effect of age and phase on (A) time taken to find all treats and (B) search rate...... 109 Figure 3.1: Arena setup during the holeboard task (A) when the gate is open and (B) during rebaiting of buckets...... 124 Figure 3.2: Diagram showing the configuration of baited buckets for the initial (sessions 1-4) and reversal (sessions 5-6) phases of the holeboard task...... 125 Figure 3.3: Graphs showing the effect of session on (A) reference memory score, (B) working memory score (baited buckets), (C) working memory score (all buckets), (D) time taken to find all food rewards and (E) search rate...... 129

xiv

Figure 3.4: The mean effect of trial on (A) reference memory score and (B) mean time taken to find all food rewards, showing regression lines (red) with 95% confidence intervals (grey)...... 131 Figure 3.5: The effect of trial on time taken to find all food rewards for each session...... 132 Figure 3.6: (A) Perseveration rates for sessions 5 and 6. (B) Proportion of visits to buckets that were baited in the initial configuration...... 134 Figure 3.7: The arena setup for the disappearing object task...... 141 Figure 3.8: Mean predicted probability of success (black) and interquartile range (grey) for each interval...... 148 Figure 3.9: Mean predicted probability of success (black) and interquartile range (grey) for each trial within a trialblock...... 149 Figure 3.10: Graphs related to target box location during the disappearing object task...... 150 Figure 3.11: Mean proportions (with 95% confidence intervals) of visits to boxes at each distance from the target box (grey)...... 152 Figure 4.1: Diagrammatic representation of the transportable barrier used in the disappearing object task (A) from the front and (B) overhead whilst open...... 168 Figure 4.2: The portable testing arena...... 169 Figure 4.3: Boxplots showing the differences between groups in questionnaire scores for week 0 (the week in which the disappearing object task was performed)...... 175 Figure 4.4: Effect of group on proportion of successful visits...... 179 Figure 4.5: Effect of group and sex on proportion of successful visits...... 180 Figure 4.6: Clinical questionnaire component scores for male and female osteoarthritic dogs...... 181 Figure 4.7: The effect of interval on predicted probability of success (A) overall and (B) for each group...... 183 Figure 4.8: The effect of box on predicted probability of success (A) overall and (B) for each group...... 185 Figure 4.9: Boxplots showing adjusted latency to reach the box for each group...... 186 Figure 4.10: Boxplots showing the relationship between interval and mean adjusted latency to reach the box visited for each dog...... 188

Figure 4.11: Boxplots showing adjusted latency to reach the box...... 189 Figure 4.12: A scatterplot to show the correlation between trial number and adjusted latency to reach the box visited...... 191 Figure 5.1: Boxplots showing the differences between groups in questionnaire scores (means of weeks 1-4 for each dog)...... 227 Figure 5.2: Effect of (A) group, (B) vet-assigned severity score and (C) owner- assigned severity score on the proportion of the night period spent resting...... 230 Figure 5.3: The proportion of the daytime period spent resting for each group of dogs...... 231 Figure 5.4: The effect of HCPI score on proportion of daytime period spent resting (A) overall and (B) for each group...... 232 Figure 5.5: Boxplots showing the mean duration of a bout of rest during the (A) night and (B) daytime periods for each group...... 235 Figure 5.6: The effect of HCPI score on SNoRE score (A) overall and (B) for each group...... 238 Figure 5.7: Mean SNoRE scores for each group of dogs...... 239

xvi

List of Tables:

Table 1.1: Types of memory described in Section 1.4.1...... 21 Table 2.1: A description of the clinical questionnaires used in this study and their validation...... 45 Table 2.2: Demographic compositions of each group of dogs...... 49 Table 2.3: Contents and reasoning behind components of the clinical checklist...... 51 Table 2.4: Summary of the F-statistics and p-values (rounded to three decimal places) for effects of group on clinical questionnaire and CCDR scores...... 57 Table 2.5: Pearson correlation coefficients and p-values between scores for clinical questionnaire component scores and between clinical questionnaire component scores and CCDR score...... 58 Table 2.6: Counterbalancing table used for assigning each pair of dogs a sample phase object pair colour and novel object location...... 73 Table 2.7: Behaviours included in the ethogram for the Novel Object Recognition task...... 75 Table 2.8: Summary of the outcome measures calculated for the novel object recognition task...... 77 Table 2.9: Counterbalancing table used for assigning each pair of dogs a pattern of baited buckets for the holeboard task...... 103 Table 2.10: Summary of the outcome measures calculated for the holeboard task. .... 105 Table 2.11: Predicted sample sizes (per group) required for a statistical power of ≥0.8, for each outcome measure of the holeboard task...... 110 Table 3.1: Summary of the number of trials and trial spacing used in several previous holeboard studies...... 119 Table 3.2: Outcome measures for the holeboard task...... 126 Table 3.3: Odds ratios with 95% confidence limits calculated from model estimates, and z- and p-values generated by the model output, for each continuous factor and level of categorical factor included in the model...... 146 Table 3.4: The number of (erroneous) visits to the visited and target box from the previous trial, the total number of erroneous visits to all boxes and

xvii

the results of the binomial tests comparing each of these to values expected by chance (0.25) for each dog...... 151 Table 3.5: Binomial test results comparing observed and expected proportions of visits to boxes at each distance from the target box...... 153 Table 3.6: Spearman’s rank correlations between mean holeboard task outcome measures and disappearing object task success rate for each dog...... 154 Table 4.1: Numbers of dogs with each subjective osteoarthritis severity score, as assigned by the owner and the examining veterinary surgeon...... 173 Table 4.2: List of predictor variables used in analyses...... 176 Table 4.3: Variables that significantly (p<0.1) predicted the probability of success in initial models...... 177 Table 4.4: Odds ratios with 95% confidence limits (CI), z and p-values for the multivariate model of probability of success...... 178 Table 5.1: Methods and findings of studies found to be relevant to sleep quality in dogs in the literature search described in Appendix 5.1, and their implications for this study...... 217 Table 5.2: Numbers of dogs with each subjective osteoarthritis severity score, as assigned by the owner and the examining veterinary surgeon...... 228

xviii

List of Appendices:

Appendix 1 – Introduction to appendices: ...... 304

Appendix 2 – Material associated with Chapter 2: ...... 304

Appendix 2.1 – Clinical checklist: ...... 304

Appendix 2.2 – Types of questionnaire validity: ...... 308

Appendix 2.3 – The Helsinki Chronic Pain Index (HCPI): ...... 310

Appendix 2.4 – The Canine Brief Pain Inventory (CBPI): ...... 312

Appendix 2.5 – The ACVS Canine Orthopedic Index (COI): ...... 314

Appendix 2.6 – The Liverpool Osteoarthritis in Dogs (LOAD) questionnaire:...... 316

Appendix 2.7 – The Canine Cognitive Dysfunction Rating (CCDR) Scale: ...... 320

Appendix 2.8 – The Canine Behavioral Assessment and Research Questionnaire (C- BARQ): ...... 321

Appendix 2.9 – Owner information sheet: ...... 327

Appendix 2.10 – Owner consent form: ...... 329

Appendix 2.11 – Ethogram for Novel Object Recognition Task: ...... 330

Appendix 2.12 – Signalments of pilot study dogs: ...... 334

Appendix 2.13 – Investigation ratios for pilot study dogs: ...... 334

Appendix 2.14 – Pilot study results for the Holeboard task: ...... 335

Appendix 3 – Material associated with Chapter 3: ...... 336

Appendix 3.1 – Owner information sheet: ...... 336

Appendix 3.2 – VBA Macro (Days 1-2): ...... 338

Appendix 3.3 – VBA Macro (Day 3): ...... 341

Appendix 3.4 – Data entry sheet: ...... 345

Appendix 3.5 – R script: ...... 346

Appendix 3.6 – Trialblock 1 model results: ...... 364

Appendix 4 – Material associated with Chapter 4: ...... 365

xix

Appendix 4.1 – Owner information sheet: ...... 365

Appendix 4.2 – Trial order within the sample phase: ...... 367

Appendix 4.3 – Scoring sheet for disappearing object task: ...... 368

Appendix 4.4 – Model results (excluding owner-assigned severity score): ...... 369

Appendix 4.5 – Relationship between age and osteoarthritis severity: ...... 370

Appendix 5 – Material associated with Chapter 5: ...... 370

Appendix 5.1 – Literature search for factors affecting sleep quality in dogs: ...... 370

Appendix 5.2 – Sleep diary sheet: ...... 371

Appendix 5.3 – Cover sheet for owner questionnaire packs: ...... 373

Appendix 5.4 – The SNoRE questionnaire: ...... 374

Appendix 5.5 – Limbs affected in osteoarthritic dogs recruited: ...... 375

Appendix 5.6 – Models for night-time rest bout length: ...... 376

Appendix 5.7 – Effect of HCPI Score Squared on Daytime Rest: ...... 377

Table of Abbreviations and Acronyms:

Acronym: Definition: ACTH Adrenocorticotropic hormone ACVS American College of Veterinary Surgeons ADHD Attention deficit hyperactivity disorder AIC Akaike Information Criterion AWERB Animal Welfare and Ethical Review Body BCS Body Condition Score BIC Bayesian Information Criterion BORIS Behavioural Observation Research Interactive Software BrOAD Bristol Osteoarthritis in Dogs (questionnaire) C-BARQ Canine Behavioral Assessment and Research Questionnaire CBPI Canine Brief Pain Inventory CBT Cognitive Behavioural Therapy CCDR Canine Cognitive Dysfunction Rating (questionnaire) CCLR Cranial Cruciate Ligament Rupture CI Confidence Interval CNS Central Nervous System COI Canine Orthopedic Index DMS Delayed Matching to Sample DNIC Diffuse Noxious Inhibitory Controls DNMP Delayed Non-Matching to Position DNMS Delayed Non-Matching to Sample EEG Electroencephalography/Electroencephalogram EMG Electromyography/Electromyogram EOG Electrooculography/Electrooculogram FMCP Fragmented Medial Coronoid Process GDP Gross Domestic Product HCPI Helsinki Chronic Pain Index HRQL Health Related Quality of Life (questionnaire) IASP International Association for the Study of Pain ID Identity ILR Investigation Laterality Ratio IR Investigation Ratio LCL Lower Confidence Limit LOAD Liverpool Osteoarthritis in Dogs (questionnaire) MRI Magnetic Resonance Imaging MS Melissa Smith (PhD Candidate) NMDA N-methyl-D-aspartate NOR Novel Object Recognition NREM Non-Rapid Eye Movement

xxi

Acronym: Definition: OR Odds Ratio PLR Proximity Laterality Ratio PPL Home Office Project License PR Proximity Ratio QOL Quality Of Life REM Rapid Eye Movement RM Reference Memory s Seconds (if following a number as a unit of measurement) SNoRE Sleep and Night Time Restlessness Evaluation (questionnaire) SPSS Statistical Package for Social Sciences SWS Slow-wave Sleep tA Time taken to find all food rewards tC Time taken (capped at 300s) TGF-β Transforming growth factor beta UAP Ununited Anconeal Process UCL Upper Confidence Limit VIN Veterinary Investigation Number

WMA Working Memory (score) for all buckets

WMB Working Memory (score) for baited buckets WSAVA World Small Animal Veterinary Association

xxii

“Everyone thinks that their dog is the best dog in the world. And none of them are wrong.”

– W. R. Pursche, Lessons To Live By: The Canine Commandments

xxiii

Chapter 1: Introduction and Literature Review

Chapter 1 Introduction and Literature Review

1.1 Why is it important to assess the effect of spontaneous canine osteoarthritis on working memory and sleep?

Chronic pain in humans can cause deficits in working memory (see Section 1.4) and sleep (see Section 1.6), but it is not yet known whether chronic pain in dogs causes similar deficits. Since osteoarthritis is a common cause of chronic pain in both humans and dogs (see Section 1.3), this thesis will investigate the effect of chronic pain from osteoarthritis on working memory and sleep in dogs.

Working memory deficits are a clinically important factor in human chronic pain (though they have not been studied in osteoarthritis patients specifically) because they could cause impairments in academic and workplace performance and in everyday life (Klingberg, 2010) and thus may contribute to the loss or reduction of working hours seen in chronic pain patients (Raftery et al., 2012). Since training to improve working memory causes improved self-esteem in schizophrenia patients (Vogt et al., 2009) and improved functioning in daily life in stroke patients (Westerberg et al., 2007), it is possible that working memory deficits could cause reduced self-esteem and daily functioning in chronic pain patients also. Sleep problems in chronic pain patients (including those with osteoarthritis) are also clinically important because poor sleep is associated with subsequent increased pain (Affleck et al., 1999), and may be associated with reduced quality of life, as seen in insomnia patients without chronic pain (LeBlanc et al., 2007, Léger et al., 2012).

If chronic pain from osteoarthritis in dogs also causes working memory deficits, these could potentially impair dogs’ quality of life, engagement with their owner and environment, and ability to respond to training or novel situations. Impaired sleep could also affect dogs’ quality of life and potentially worsen their pain, as observed in humans (Affleck et al., 1996, Morin et al., 1998). Recognition that working memory and sleep deficits may be a symptom of osteoarthritis in dogs could also assist with the diagnosis of osteoarthritis in dogs presenting with such clinical signs, and may prevent these clinical signs from being erroneously attributed to other disorders, such as age-related

Chapter 1: Introduction and Literature Review cognitive decline or systemic diseases. Understanding more about the effects of osteoarthritis on sleep and working memory could also elucidate the pathophysiological changes that occur in chronic pain in dogs, which may ultimately help to direct the development of novel treatments for chronic pain in dogs. Additionally, if dogs with chronic pain from osteoarthritis show similar deficits in sleep and working memory to human chronic pain patients, it would be possible to use them as a model for human chronic pain and osteoarthritis. This has many advantages compared to experimentally induced models of osteoarthritis as it is likely to be more translationally valid and is compliant with the “3Rs” framework for more ethical animal use, as described in Section 1.3.5.2. Therefore this research may help to elucidate the mechanisms involved in osteoarthritis in humans as well as in dogs.

This literature review will cover a range of relevant topics: Section 1.2 gives an overview of chronic pain, describing the pathophysiological changes that occur and providing evidence that chronic pain is likely to occur in non-human species. This is necessary to appreciate that chronic pain is fundamentally subjective in nature yet caused by physical mechanistic processes, to understand the phenomena described in papers cited throughout the thesis, and to show that animals such as dogs are likely to be capable of experiencing pain as a subjectively negative phenomenon that may adversely affect their welfare. Section 1.3 describes osteoarthritis as a cause of chronic pain as well as its diagnosis, treatment and epidemiology in both humans and dogs, and details existing animal models of human osteoarthritis, in order to show that spontaneous human and canine osteoarthritis share many similarities, that osteoarthritis is likely to be an important cause of chronic pain in dogs as well as in humans, and that canine spontaneous osteoarthritis may be a promising model for human osteoarthritis. Section 1.4 details the effects of chronic pain on attention and working memory in humans and rodents, which suggests that similar deficits may also be possible in dogs with chronic pain. Section 1.5 describes existing methods for assessing working memory in non- human animals, with the aim of devising similar tasks for use in dogs. Section 1.6 explores the association between chronic pain and sleep disturbance in humans, including those with osteoarthritis, and describes commonly-used methods of assessing sleep, in preparation for a study examining the effect of canine osteoarthritis on sleep (described in Chapter 5). Section 1.7 gives the overall aim, hypotheses and scope of the thesis project, and details the contents of subsequent chapters.

Chapter 1: Introduction and Literature Review

1.2 Chronic pain – definitions, mechanisms and prevalence:

1.2.1 What is chronic pain?

The International Association for the Study of Pain (IASP) defines pain as “An unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage” (Merskey and Bogduk, 1994). Because pain therefore has both sensory and emotional components, it is distinct from nociception, which has been defined as “the neural processes of encoding and processing noxious stimuli” (Barrot, 2012).

Chronic pain is sometimes defined as “pain that persists past the healing phase following an injury” (Apkarian et al., 2009), however this definition is somewhat problematic given that different injuries take different and not necessarily predictable lengths of time to heal (Verhaak et al., 1998), and that often it is difficult to ascertain whether healing is complete (Apkarian et al., 2009). Additionally in some chronic pain disorders, such as osteoarthritis and rheumatoid arthritis (Smolen et al., 2007, Sofat et al., 2011), the underlying pathology is not curable and tissue damage is ongoing (Woolf and Doubell, 1994), with pain occurring alongside continuous tissue damage rather than following apparent recovery from a disease process (Keefe et al., 1989, Sofat et al., 2011). Furthermore, some chronic pain disorders, such as fibromyalgia, can occur without any obvious peripheral injury (Staud et al., 2001), therefore this definition is not always suitable. For these reasons, it is common in clinical studies to define chronic pain as pain that persists beyond a defined period of time, though this is inconsistent with various studies defining it as pain persisting beyond one month (Magni et al., 1990, Magni et al., 1992), three months (Frølund and Frølund, 1986, Andersson, 1994), or six months (Von Korff et al., 1988, Brattberg et al., 1989). Additionally, these definitions do not relate to the pathophysiological changes that occur in chronic pain (Apkarian et al., 2009).

Because there is no definitive diagnostic test for chronic pain (Verhaak et al., 1998), its prevalence is difficult to ascertain, though it is thought to be a widespread clinical problem, with prevalence estimates varying from 2%-40% in humans (with a median prevalence of 15%) in one systematic review (Verhaak et al., 1998), and similar prevalences (17.1% of men and 20.0% of women, though prevalence increased with age) being found in a large-scale Australian study (Blyth et al., 2001). Chronic pain is also an

Chapter 1: Introduction and Literature Review important public health issue, estimated to cost €5,665 per patient (€5.34 billion overall) annually in Ireland, accounting for 2.86% of Ireland’s GDP (Raftery et al., 2012) and costing around $40 billion and 93 million lost working days annually in the USA in 1983 (Aronoff et al., 1983).

1.2.2 Pathophysiological changes in chronic pain:

Chronic pain, like acute pain, involves peripheral changes such as hyperalgesia (increased pain sensitivity) (Loeser and Treede, 2008) and allodynia (the perception of pain as a result of usually innocuous stimuli) (Woolf and Doubell, 1994). However acute pain, which occurs shortly after tissue damage, has a protective function in that it induces hypersensitivity to a range of stimuli, discouraging the affected human or animal from using or contacting the environment with the affected area and thus preventing further damage and facilitating tissue repair (Woolf and Doubell, 1994). Because chronic pain occurs for prolonged periods of time and is sometimes distinct from the initial physical cause of the pain (Verhaak et al., 1998), instead of providing a useful physiological functional it is maladaptive, interfering with daily life and causing physical disability (Blyth et al., 2001, Peters et al., 2005) and inability to work (Raftery et al., 2012), as well as being associated with increased prevalence of mood disorders (Von Korff et al., 1988, Fishbain et al., 1997, Elliott et al., 2003, Kroenke et al., 2013), impaired cognition (Dick and Rashiq, 2007, Berryman et al., 2013) and poor sleep (Menefee et al., 2000, Smith and Haythornthwaite, 2004).

Chronic pain can be categorised into neuropathic pain from nerve injury and inflammatory pain from prolonged inflammation (Woolf and Doubell, 1994), though some chronic pain syndromes involve both (Apkarian et al., 2009). A key aspect of chronic pain is central sensitisation; an increase in central nervous system (CNS) responsiveness to various stimuli (Nijs et al., 2012, Cagnie et al., 2014), causing pain perception to become influenced by CNS changes and thus decoupled from physical nociceptive stimuli (Woolf, 2011). Many chronic pain disorders show evidence of central sensitisation including osteoarthritis (Gwilym et al., 2009), rheumatoid arthritis (Meeus et al., 2012), fibromyalgia (Cagnie et al., 2014) and chronic lower back pain (Smart et al., 2012). Central sensitisation involves increased spinal cord excitability (caused by increased activity in nociceptive neurones following peripheral injury) (Woolf, 2011), changes to microglia (Chacur et al., 2009), astrocytes (Gao et al., 2009), gap junction

Chapter 1: Introduction and Literature Review signalling (Chiang et al., 2010) and cell membrane excitability (Rivera-Arconada and Lopez-Garcia, 2010). Processing in the brain is also altered in regions associated with the sensory component of pain, (e.g. thalamus and secondary somatosensory cortex) (Staud et al., 2007, Seifert and Maihöfner, 2009), as well as in areas that are involved in cognition (e.g. anterior cingulate and prefrontal cortex) (Apkarian et al., 2004b, Staud et al., 2007, Seifert and Maihöfner, 2009) and those involved in emotional processing (e.g. insula, anterior cingulate and medial prefrontal cortex) (Baliki et al., 2006, Staud et al., 2007, Seifert and Maihöfner, 2009).

Central sensitisation results in measurable changes such as allodynia (Woolf, 1984, Price et al., 1992), hyperalgesia (Woolf, 1983, Woolf, 1984), secondary hyperalgesia (hyperalgesia in regions adjacent to the injured area) (Cook et al., 1987), temporal summation (“windup”), where repeated application of noxious stimuli causes increased magnitude and duration of nociceptive responses (Staud et al., 2007). It also involves reduced inhibition of pain by descending pathways (Nijs et al., 2012), such as DNIC (diffuse noxious inhibitory controls; a process by which peripheral nociceptive stimuli applied to a location on the body of a healthy animal inhibit the nociceptive response to noxious stimuli applied elsewhere on the body (Ossipov et al., 2010)). Impaired DNIC has been identified in irritable bowel syndrome (a painful gastrointestinal disorder (Nobaek et al., 2000, Akbar et al., 2008)) (King et al., 2009), temporomandibular disorder (King et al., 2009), tension-type headache (Pielsticker et al., 2005), fibromyalgia (Julien et al., 2005) and osteoarthritis (Arendt-Nielsen et al., 2010), but DNIC may remain intact in rheumatoid arthritis (Leffler et al., 2002) and chronic lower back pain (Julien et al., 2005).

1.2.3 Evidence for chronic pain in non-human animals:

Because pain is fundamentally a subjective state characterised by a negative emotional experience (Merskey and Bogduk, 1994) and non-human animals are unable to report their subjective experiences, it is much more difficult to assess pain in animals than humans. However, animals’ lack of ability to verbally express negative emotional states such as pain does not imply that they are incapable of experiencing such states, just that techniques other than self-reporting are required to measure them. Whilst the ability to experience pain is disputed in some non-human animals, such as fish (Sneddon, 2009, Rose et al., 2014) and invertebrates (Elwood, 2011), there is a wide range of evidence

Chapter 1: Introduction and Literature Review suggesting that mammalian species are capable of experiencing pain: They exhibit many of the same physiological changes seen in human pain when exposed to stimuli or situations that would be expected to be painful, such as pupil dilation (Holton et al., 1998, Chapman et al., 1999), increased heart rate (White et al., 1995, Loggia et al., 2011) and increased plasma cortisol concentrations (Talbert et al., 1976, Schwartzkopf- Genswein et al., 1997), as well as functional impairments consistent with pain, such as lameness (Mlacnik et al., 2006, Marshall et al., 2010). Experimentally-induced rodent models of human pain conditions also show behavioural changes such as increased grooming of the affected area, spontaneous lifting and shaking of affected paws, ultrasonic vocalisation, and reduced feeding and locomotion behaviours (Mogil and Crager, 2004), which may indicate pain.

There is also evidence that chronic pain exists in animals in a similar way to that observed in humans, such that animal models are often used to investigate human chronic pain conditions (Mogil, 2009) where doing so in humans would be impractical or unethical. For example, animals display hyperalgesia and allodynia in response to mechanical (Chaplan et al., 1994, Christensen et al., 1996) and thermal (Christensen et al., 1996) stimuli following pain model induction, and may show secondary hyperalgesia (Coderre and Melzack, 1991), temporal summation (Chen et al., 2001), impaired DNIC (Danziger et al., 1999), changes in spinal cord activity (Woolf, 1983), and structural changes in areas of the brain in regions associated with sensory and emotional processing, including the anterior cingulate cortex (Seminowicz et al., 2009) and medial prefrontal cortex (Metz et al., 2009).

However, these changes do not show that animals consider pain to be unpleasant. There is evidence that animals attempt to avoid painful stimuli, showing a preference for less- painful alternatives: Rats induced with an ostensibly painful osteoarthritis model (intraarticular mycobacterial injection) self-administered significantly higher amounts of oral fentanyl (an opioid analgesic) than non-arthritic control rats (Colpaert et al., 2001). Administration of corticosteroids or systemic fentanyl to osteoarthritic rats reduced this preference, and administration of naloxone (an opioid antagonist) reduced self- administration of fentanyl in osteoarthritic but not control rats, indicating that the preference for opioid analgesia in osteoarthritic rats was due to pain-relieving effects rather than other effects, such as reward or dependency (Colpaert et al., 2001). Similarly, rats with nerve injury, but not rats without nerve injury, learned to self-

Chapter 1: Introduction and Literature Review administer intrathecal clonidine (which is often prescribed for the relief of neuropathic pain in humans (Woolf and Mannion, 1999)), implying they were motivated to reduce their pain (Martin et al., 2006). Danbury et al. (2000) trained lame and sound (not lame) broiler hens to discriminate between food containing a nonsteroidal analgesic and food without, and found that when given the choice between two feeds, lame broilers chose to eat significantly more of the analgesic-containing feed than sound birds (which showed a preference for analgesic-free feed), and that the consumption of analgesic- containing food increased with increasing lameness severity (Danbury et al., 2000). Sufka (1994) found that rats showed an increased conditioned place preference for an environment in which they had previously been administered morphine compared to environments in which they had been administered vehicle or received no stimulus, whether or not they had inflammation of the hindpaw (probably because of morphine’s euphoric effect), but that rats with inflammation of the hindpaw showed a stronger preference than rats without hindpaw inflammation (Sufka, 1994), suggesting that they had a preference for the pain-relieving effect of morphine in addition to the baseline preference for its euphoric effect seen in rats without hindpaw inflammation.

Whilst these studies suggest that animals will attempt to avoid pain by choosing to treat it with analgesic drugs, they still do not confirm whether the pain causes a negative emotional change in the animals, as it does in humans. It has been shown that animals in ostensibly more negative emotional states display negative judgement bias (tendency to perceive ambiguous cues as more negative), which can be used as an indirect indicator of emotional state (Mendl et al., 2009). Animals can be trained to associate a particular “positive” cue with a reward stimulus and a “negative” cue with an aversive stimulus, and to perform behaviours specific to the cue they had experienced in order to gain the anticipated reward or avoid the anticipated punishment, before exposure to ambiguous stimuli. When presented with an ambiguous stimulus, animals in a more negative affective state will be more likely to interpret the stimulus as negative and perform the behaviours they had been trained to perform in order to avoid the punishment associated with the negative cue, whereas animals in a more positive affective state will be more likely to interpret an ambiguous stimulus as positive and perform the behaviours they had been trained to perform in order to gain the reward associated with the positive cue (Mendl et al., 2009).

Chapter 1: Introduction and Literature Review

There is some evidence that animals exposed to ostensibly painful stimuli display negative judgement bias: Neave et al. (2013) trained calves to touch a screen when it was illuminated in one colour (e.g. red) in order to receive milk, and to avoid touching the screen when illuminated in another colour (e.g. white) to avoid receiving a time-out period in which no milk was available, before being allowed to start the next trial by touching a start-box positioned on an adjacent wall. When shown an ambiguously- coloured screen (e.g. pink) several hours after removal of the horn buds via thermocautery disbudding, which is considered to be painful (Heinrich et al., 2010), calves were significantly more likely to refrain from touching the screen compared to prior to surgery, though their responses to the ‘positive’ and ‘negative’ cues were not altered (Neave et al., 2013). The authors concluded that this was “indicative of a negative emotional state in calves following hot-iron disbudding” (Neave et al., 2013), and similar negative judgement biases have been observed in ostensibly negatively- valenced affective (emotional) states in other species, such as in dogs that tend to perform more separation-related behaviours (and are thus assumed to have greater generalised anxiety than dogs that do not) (Mendl et al., 2010a), and in rat models of anxiety and depression (Enkel et al., 2010).

Judgement bias is indicative of a negatively-valenced affective state (Mendl et al., 2009, Neave et al., 2013) which is generally thought to indicate a negative emotional state (Mendl et al., 2009), however this requires that non-human animals are able to perceive emotional valence (positivity/negativity) (Mendl et al., 2010b), which would require the capacity for conscious experience. It is currently not possible (and may never be possible) to definitively conclude that non-human animals have this capacity (Mendl et al., 2010b) given their inability to report on their emotional state using language. However, it is highly probable that humans’ capacity for conscious experience is encoded neurologically (Searle, 2000, Cooney and Gazzaniga, 2003, Tononi and Koch, 2008), and certain features of this conscious experience in humans, such as high- frequency EEG activity and thalamocortical activation, also occur in other mammals (Seth et al., 2005). Furthermore, no neural correlates of consciousness have been discovered that are specific to human brains (Griffin and Speck, 2004). Some non-human species also show high levels of behavioural versatility when encountering novel scenarios, such as future planning and episodic-like memory in scrub jays (Clayton and Dickinson, 1998), creation and use of novel tools in crows (Weir et al., 2002), and the ability to choose to avoid starting a reinforced recall task (giving a lesser reward

Chapter 1: Introduction and Literature Review compared to no reward if the task was started and an incorrect response selected) after long but not short retention intervals in macaques (Hampton, 2001), suggesting that macaques are aware of whether or not they are able to recall particular information and can use this awareness to make decisions. This behavioural versatility is often regarded as most efficiently explained by consciousness (Griffin and Speck, 2004). Additionally, consciousness is often attributed to humans but not to other species on the basis that humans can communicate such experiences using language and other species cannot (Griffin and Speck, 2004). However some animals are able to communicate in ways somewhat reminiscent of language, for example some chimpanzees can use sign language-derived gestures to communicate with other chimpanzees in the absence of humans (Fouts and Jensvold, 2002, cited in Griffin and Speck, 2004), and some grey parrots appear to be able to use imitated human language to express thoughts and answer questions (Pepperberg and Lynn, 2000). Non-human species may also emit specific vocalisations in situations that would be expected to be associated with high- arousal, negatively-valenced emotional states, such as piglets in response to pain from castration (Weary et al., 2006) and chicks in response to social isolation (Marx et al., 2001). Human infants similarly lack the ability to express subjective experience using language, however it is generally accepted by paediatric specialists that human infants can feel pain (Purcell-Jones et al., 1988) and that increased vocalisation is an important clinical sign of this (Levine and Gordon, 1982), therefore it is possible that animals are similarly able to use vocalisations to communicate their subjective state to conspecifics (and potentially to humans) in a similar way. Even though it is not possible to be certain that animals are capable of subjective experience, this evidence collectively suggests that subjective experience is likely in at least some non-human species, and combined with the evidence described in this section of the similarities between humans and other mammals in pain processing systems and pain-related behaviours and physiological changes, the tendency of animals to avoid ostensibly painful stimuli, and that an ostensibly painful stimulus induces a negative judgement bias in calves, it appears more likely than not that mammalian species such as dogs experience pain as an unpleasant subjective experience in a similar way to that observed in humans.

1.3 Osteoarthritis:

This section aims to show that osteoarthritis is a common and important cause of chronic pain in both dogs and humans, and that both species share similarities in the

Chapter 1: Introduction and Literature Review pathogenesis, epidemiology, diagnosis and treatment of osteoarthritis, such that spontaneous canine osteoarthritis could be considered a useful clinical model of human osteoarthritis and more generally of chronic pain in humans. Therefore it would be interesting to assess whether canine osteoarthritis is also similar to human osteoarthritis in that it is associated with disturbed sleep (see Section 1.6), and whether canine osteoarthritis is associated with impaired working memory, as is the case in many other human chronic pain disorders but has not yet been specifically investigated in osteoarthritis in humans (see Section 1.4).

1.3.1 Osteoarthritis and chronic pain:

1.3.1.1 Osteoarthritis causes chronic pain in humans:

Osteoarthritis is a degenerative condition characterised by progressive focal cartilage loss with initial, transient mild synovial inflammation, followed by osteophyte formation, bone remodelling and thickening of the joint capsule (Dieppe and Lohmander, 2005). It is thought to be a common final pathway of several diseases rather than a single disease in its own right (Felson et al., 2000). Osteoarthritis is the most common cause of musculoskeletal disability (Linaker et al., 1999) and the second most common cause of chronic pain (Johannes et al., 2010) in human patients, causing clinical signs in 10% of men and 18% of women over 60 years (Peat et al., 2001), and disability in 10% of people over 55 years (Peat et al., 2001). Chronic pain is an important symptom of osteoarthritis (Arendt-Nielsen et al., 2010) and pain is the most common reason for patients to seek clinical treatment (Sofat et al., 2011). Patients report two kinds of pain; a “dull, aching pain” which becomes more constant as osteoarthritis progresses, and shorter periods of intense and unpredictable pain which become increasingly frequent and may prevent social and recreational activities (Hawker et al., 2008).

Patients with painful knee osteoarthritis also show signs of central sensitisation, showing significantly lower pressure pain thresholds than pain-free age-matched controls even at sites distant from the affected joint, suggesting hyperalgesia, with pressure pain thresholds being significantly negatively associated with increasing self- reported pain severity Arendt-Nielsen et al. (2010). Patients also displayed significant temporal summation of pain and impaired DNIC compared to controls (Arendt-Nielsen et al., 2010), indicating central sensitisation (see Section 1.2.2).

Chapter 1: Introduction and Literature Review

1.3.1.2 The pathogenesis of osteoarthritic pain:

The pathogenesis of pain in osteoarthritis is not fully understood, however it is believed to arise from a range of sources including peripheral factors such as local inflammation (Conaghan and Felson, 1999, Brooks, 2003, Abramson, 2004, Sofat et al., 2011) causing the release of local inflammatory mediators (Martel-Pelletier et al., 1999, Kojima et al., 2004) which are thought to activate and sensitise peripheral nociceptors (Wajed et al., 2012), changes in chondrocyte NMDA receptor expression (Ramage et al., 2008), and angiogenesis (blood vessel growth) within normally avascular cartilage tissue (Walsh et al., 2010), causing increased expression of nerve growth factor (Walsh et al., 2010) which is involved in peripheral nociceptive neurone sensitisation (Ma and Woolf, 1997). Changes in the brain also occur: Whilst experimentally-induced acute pain and osteoarthritic pain both activate regions associated with physiological pain processing in brains of osteoarthritic patients, osteoarthritic pain also increases activation in regions associated with fear and emotion, especially the anterior cingulate cortex (Kulkarni et al., 2007), implying that the differences in brain region involvement between osteoarthritic and acute pain may be associated with changes in emotional state. There is also evidence of neuropathic pain in osteoarthritis: Osteoarthritic patients not only showed lower pain thresholds in response to punctate stimuli in referred pain areas than control participants, but also showed significantly greater brainstem activation, the extent of which was positively correlated with their score on a neuropathic pain-specific questionnaire (Gwilym et al., 2009), suggesting that these brainstem changes were due to neuropathic pain. In an osteoarthritic rat model, nonsteroidal analgesics (which reduce inflammation) only provided effective analgesia for up to a fortnight, whereas centrally-acting analgesics amitriptyline and gabapentin remained active following this point (Ivanavicius et al., 2007), implying that joint inflammation contributes to initial osteoarthritic pain but lessens over time as pain becomes neuropathic and sustained by central mechanisms.

1.3.1.3 Painful osteoarthritis in dogs:

Despite the difficulties inherent in assessing pain in animals, there is some evidence that osteoarthritis also causes pain in dogs: Owners of osteoarthritic dogs report changes in their behaviour such as decreased activity, appetite, sociability and playfulness and increased aggression, anxiety, vocalisation frequency, daytime sleep and fearfulness (Wiseman et al., 2001). Whilst it cannot be proven that these changes occur as a result

Chapter 1: Introduction and Literature Review of pain, similar changes in behaviour are often taken as indicating pain in non-verbal human infants (McGrath and Unruh, 1994, cited in Wiseman et al., 2001), so it is likely that they represent pain in dogs also. Behavioural measures have also been used to develop owner questionnaires to assess pain in dogs with osteoarthritis (Hielm- Björkman et al., 2003, Brown et al., 2007, Brown, 2014b, Brown, 2014a), which were able to detect differences between healthy control dogs and those with osteoarthritis (Brown et al., 2007) or congenital hip dysplasia (a common precursor of osteoarthritis in dogs) (Hielm-Björkman et al., 2003), and showed significant reductions in pain scores following nonsteroidal analgesia (Brown et al., 2008, Hielm-Björkman et al., 2009, Brown, 2014c) in randomised placebo-controlled double-blinded trials. The responsiveness of these questionnaire scores to analgesic treatment suggests that the difference in scores between osteoarthritic and healthy control dogs was due to (or influenced by) pain. Whilst these owner questionnaire scores are a useful measure of behavioural aspects of pain, they have the disadvantage of relying on owners’ subjective judgement and ability to monitor dogs’ behaviour. However, more objective measures also suggest that osteoarthritis is painful in dogs: Dogs with osteoarthritis showed significantly different vertical ground reaction forces (weight bearing) in affected compared to unaffected limbs (Conzemius et al., 2003), and compared to the joints of healthy control dogs (Moreau et al., 2003), and these differences were reduced by total joint arthroplasty (Conzemius et al., 2003) or nonsteroidal analgesia (Moreau et al., 2003), suggesting they were pain-related.

Additionally, canine osteoarthritis appears to cause central sensitisation and hyperalgesia, with osteoarthritic dogs displaying significantly decreased mechanical and thermal nociceptive thresholds across several sites than healthy control dogs (Knazovicky et al., 2016). Hunt et al. (2018) also found signs of central sensitisation in dogs with osteoarthritis, including decreased nociceptive withdrawal thresholds and augmented stimulus-response curves suggesting hyperalgesia, increased temporal summation, and impaired DNIC compared to healthy control dogs (Hunt et al., 2018). This suggests that dogs with osteoarthritis exhibit many of the same electrophysiological changes associated with chronic pain as those seen in osteoarthritic human patients (Arendt-Nielsen et al., 2010).

Chapter 1: Introduction and Literature Review

1.3.2 Diagnosis and treatment of osteoarthritis:

Osteoarthritis is generally diagnosed based on radiographic changes in both humans (Zhang and Jordan, 2010) and dogs (Gordon et al., 2003, Innes et al., 2004). In humans, radiographic scoring of osteoarthritis severity is most commonly performed using the Kellgren-Lawrence grading scheme (Zhang and Jordan, 2010), which assigns the joint a grade from 0-4 with joints that have a score of two or more (defined by the presence of osteophytes) being diagnosed as osteoarthritic, and increasing grades being assigned based on the appearance of other radiographic signs including joint space narrowing, sclerosis, cysts and deformation (Kellgren and Lawrence, 1963, cited in Zhang and Jordan, 2010). However, radiographic changes are not always closely associated with the presence or severity of pain in human osteoarthritis patients: In a systematic literature search, Bedson and Croft (2008) found that only 15-76% of study participants reporting knee pain showed radiographic signs of osteoarthritis, and only 15-81% of participants with radiographic knee osteoarthritis reported knee pain, with large variations in agreement between studies due to differences in radiographic views, grading systems, pain definitions, and participant demographics (Bedson and Croft, 2008). Even studies using the most sensitive radiographic views could only successfully detect osteoarthritis in 79-80% of symptomatic patients (Lanyon et al., 1998, Williams et al., 2004), implying that radiographic changes do not fully correlate with the severity of pain experienced by osteoarthritic patients. Similarly, in osteoarthritic dogs there was no significant relationship between force platform lameness recordings and radiographic severity (Gordon et al., 2003), nor between any of 13 radiological variables and either owner- assessed chronic pain questionnaire scores or veterinarian-assessed lameness scores (Hielm-Björkman et al., 2003). Modern imaging techniques such as MRI may be able to detect lesions that are undetectable via radiography such as bone marrow lesions, which were found in 78% of patients with painful radiographic osteoarthritis but only 30% of patients with non-painful radiographic osteoarthritis (Felson et al., 2001), and were

2 more frequently >1cm in size in painful than non-painful osteoarthritic patients (Sowers et al., 2003). Whilst bone marrow lesions are also seen in experimentally-induced canine osteoarthritis (Nolte-Ernsting et al., 1996, Martig et al., 2007), further research is required to determine whether they occur in spontaneous canine osteoarthritis and if so, whether they are associated with pain.

Chapter 1: Introduction and Literature Review

There are currently no curative treatments for osteoarthritis, and no drugs that slow or modify its progression (Sofat and Kuttapitiya, 2014). In humans, treatment largely focuses on pain management with analgesic drugs, which include nonsteroidal analgesics, paracetamol and tramadol (Jordan et al., 2003, Brandt, 2004). However, patients prescribed nonsteroidal analgesics show low levels of compliance (Scholes et al, 1995, cited in Brandt, 2004), and many osteoarthritic people continue to experience pain even with analgesic treatment (Hawker et al., 2008, Sofat and Kuttapitiya, 2014). Nutraceuticals such as chondroitin and glucosamine are available over-the-counter (Brandt, 2004) and appear to be effective in most small-scale studies (Jordan et al., 2003), however their efficacy in larger, higher quality trials is questionable (Brandt, 2004). Non-pharmacological treatments such as patient education into the nature, management and coping strategies for osteoarthritis (Superio-Cabuslay et al., 1996, Jordan et al., 2003) and exercise regimes (Jordan et al., 2003, Brandt, 2004) also appear to be effective for pain reduction. Surgical total joint arthroplasty can be performed in patients with severe uncontrolled pain or disability (Hawker et al., 1998, Hawker, 2006), which provides significant reductions in pain and disability and increases in patient satisfaction for several years in most patients (Hawker et al., 1998), although up to 34% report dissatisfaction with their surgical outcomes (Hawker, 2006, Beswick et al., 2012). Intra-articular injections of corticosteroid (Dieppe et al., 1980, Gaffney et al., 1995, Jones and Doherty, 1996, Ravaud et al., 1999) or hyaluronic acid (Wobig et al., 1999, Jordan et al., 2003) also appear to provide effective pain reduction when compared to placebo (Jordan et al., 2003), however the effect of corticosteroid administration appears to be short-lived (Dieppe et al., 1980, Ravaud et al., 1999). Treatment of osteoarthritis in dogs similarly involves the use of nonsteroidal analgesics (Brown et al., 2008, Hielm-Björkman et al., 2009) and total joint arthroplasties (Conzemius et al., 2003, Lascelles et al., 2010). Surgical treatment of developmental abnormalities that lead to secondary osteoarthritis (see Section 1.3.4) may also be performed (Remedios and Fries, 1995, Evers et al., 1997, Rayward et al., 2004), though these are unlikely to prevent osteoarthritis (Hayashi et al., 2003, Michelsen, 2013).

1.3.3 The epidemiology of osteoarthritis:

Little information is available regarding the prevalence or epidemiology of osteoarthritis in dogs (Henrotin et al., 2005). It is estimated that up to 20% of dogs above one year of age are affected (Johnston, 1997), however since the original data relating to this claim

Chapter 1: Introduction and Literature Review are unavailable (Pfizer Animal Health proprietary research, 1996, Study no. RI19960IV) the methods used to obtain this often-quoted statistic cannot be investigated. In a recent single-year cohort study of 455,557 dogs, Anderson et al. (2018) found that the estimated prevalence of clinically diagnosed or managed appendicular (limb) osteoarthritis in the UK dog population in 2013 was 2.5% (around 200,000 individual dogs), with affected dogs having osteoarthritis for around 11% of their lifespan. However because conservative criteria were used to identify probable osteoarthritis cases, it is possible that this is an underestimate (Anderson et al., 2018). Although there is limited information available regarding the epidemiology of osteoarthritis in dogs, it may be possible to identify possible risk factors based on what is known about the epidemiology of human osteoarthritis.

In humans, one of the most important risk factors for osteoarthritis is age (Van Saase et al., 1989, Oliveria et al., 1995). Since osteoarthritis is a degenerative condition (Felson et al., 2000) it is probable that this is the case in dogs also, as supported by the finding that odds ratios for having osteoarthritis have also been shown to significantly increase with increasing age in dogs (Anderson et al., 2018).

Sex is also a risk factor in humans: Whilst men under the age of 50 are more likely to develop osteoarthritis than women, this trend is reversed in older people (Oliveria et al., 1995, Felson and Zhang, 1998). Women have a greater incidence and prevalence of osteoarthritis in several sites (Srikanth et al., 2005), and tend to have more severe osteoarthritis of the knee (Srikanth et al., 2005). In dogs however, males displayed a significantly higher odds ratio (1.19) for having osteoarthritis compared to females (Anderson et al., 2018), though it is not yet known whether this effect of sex interacts with age as it does in humans (Oliveria et al., 1995), or whether it interacts with neutering status given that many dogs are neutered.

Osteoarthritis in human women often occurs at around the time of the menopause (Silman and Newman, 1996, Wluka et al., 2000) and oestrogen-only (though not when combined with progesterone) hormone-replacement therapy significantly decreased the risk of requiring joint replacement surgery (Cirillo et al., 2006). This suggests that hormonal changes may play a role in osteoarthritis development, therefore it is possible that neutering may similarly affect the development of osteoarthritis in dogs. The odds ratio for having osteoarthritis was significantly higher in neutered dogs (1.80) than

Chapter 1: Introduction and Literature Review entire dogs (Anderson et al., 2018), although this study did not examine whether the effect of neutering differed between male and female dogs.

Race is also a risk factor in humans, with Chinese, African American and white American people showing differences in the prevalence of osteoarthritis at various sites (Zhang et al., 2001, Nevitt et al., 2002, Nelson et al., 2010, Zhang et al., 2001, cited in Zhang and Jordan, 2010). Since human races show much less variation in body and limb shape than dog breeds, it is likely that any breed effect on the incidence and severity of osteoarthritis will be more pronounced than the racial effect seen in humans. This is supported by the findings of Anderson et al. (2018) that purebred dogs had a significantly higher odds ratio of having osteoarthritis compared to crossbred dogs, with certain breeds such as the Old English Sheepdog, Rottweiler and Dogue de Bordeaux showing greatly elevated (2.81-3.11) odds ratios for osteoarthritis, and smaller breeds, such as the Shih Tzu, Yorkshire terrier and Jack Russell terrier, displaying reduced odds ratios (0.40-0.47) of osteoarthritis compared to crossbred dogs. Several specific genes have been associated with increased osteoarthritis risk in humans (Stefánsson et al., 2003, Forster et al., 2004, Loughlin et al., 2004, Meulenbelt et al., 2004, Smith et al., 2004, Valdes et al., 2004, Kizawa et al., 2005), so these may also play a role in dogs.

Bodyweight is also a risk factor in humans, with obesity/high body mass index increasing the risk of bilateral hip osteoarthritis (Heliövaara et al., 1993) and hip replacement surgery (Karlson et al., 2003), and weight loss reducing the risk of symptomatic (Felson et al., 1992, cited in Zhang and Jordan, 2010) and radiographic (Felson et al., 1992, cited in Zhang and Jordan, 2010) osteoarthritis and the severity (Messier et al., 2004, Christensen et al., 2007) of osteoarthritis symptoms. Since overweight or obese dogs with osteoarthritis showed reduced lameness severity following dietary restriction (Mlacnik et al., 2006, Marshall et al., 2010), and being of average or higher bodyweight was associated with a more than doubled odds ratio for having osteoarthritis in dogs (Anderson et al., 2018), it is likely that bodyweight also affects osteoarthritis incidence and severity in dogs. This is particularly relevant given that more than half of pet dogs in the UK are overweight or obese (Courcier et al., 2010).

Other risk factors in humans include occupational hazards such as standing and lifting (Croft et al., 1992, Coggon et al., 2000), fine motor skills (Lawrence, 1961, Hadler et al., 1978) and intense (but not moderate (Lane et al., 1993)) athletic activities (Kujala et al.,

Chapter 1: Introduction and Literature Review

1995, Spector et al., 1996, McAlindon et al., 1999), which may have implications for working dogs or those competing in events such as agility or flyball. Injuries such as cranial cruciate ligament ruptures (Lohmander et al., 2004) and meniscal tears (Roos et al., 2001, Englund et al., 2008) are also associated with increased osteoarthritis risk. Whilst acquired disorders such as fractures, joint luxations and traumatic ligamentous damage can similarly lead to osteoarthritis in the dog (Martinez and Coronado, 1997), these usually occur due to traumatic injury such as road traffic accidents (Martinez and Coronado, 1997) and therefore are unlikely to provide much information relating to the epidemiology of most cases of spontaneous canine osteoarthritis. Developmental disorders of the joint such as hip dysplasias may also cause osteoarthritis in humans (Stulberg et al., 1981, Lau et al., 1995, Lane et al., 2000, Ganz et al., 2008, Nunley et al., 2011), though there appears to be no significant association between hip dysplasia prevalence and osteoarthritis prevalence in humans (Lau et al., 1995, Smith et al., 1995, Lane et al., 1997).

1.3.4 Developmental disorders associated with osteoarthritis in the dog:

In dogs, osteoarthritis is generally thought to occur secondary to musculoskeletal disorders (Henrotin et al., 2005), therefore the prevalence of and risk factors for these disorders may provide some information regarding risk factors for osteoarthritis itself. Some common developmental disorders thought to increase the risk of osteoarthritis in dogs are hip dysplasia, elbow dysplasia and cranial cruciate ligament rupture (Clements et al., 2010).

Hip dysplasia is one of the most common orthopaedic disorders of the dog (Fries and Remedios, 1995, Martinez, 1997), especially in large and giant breeds (Martinez, 1997), with 12.6% of Labrador retrievers affected (Morgan et al., 1999). It is thought to develop due to joint laxity in puppies, causing abnormal loading and bone growth in the hips (Fries and Remedios, 1995), causing joint deformity and secondary osteoarthritis (Fries and Remedios, 1995, Martinez, 1997). Though there is a genetic component to its development (Martinez, 1997, Mäki et al., 2004, Malm et al., 2008), nutrition and growth rates are also a factor: Reduced food intake was associated with reduced risk of hip dysplasia development (Kealy et al., 1992), reduced incidence and delayed onset of osteoarthritis (Kealy et al., 2000, Smith et al., 2006), and decreased lameness severity in

Chapter 1: Introduction and Literature Review dogs with secondary osteoarthritis (Impellizeri et al., 2000). Sex may also have an effect, as Hays et al. (2007) found that male dogs had more severe hip dysplasia scores than female dogs at necropsy.

Elbow dysplasia may also lead to secondary osteoarthritis in dogs (Martinez, 1997, Michelsen, 2013). ‘Elbow dysplasia’ is a collective term comprising fragmented medial coronoid process (FMCP), ununited anconeal process (UAP), osteochondrosis dissecans (Martinez, 1997), articular cartilage injury and elbow incongruity (Trostel et al., 2003, Michelsen, 2013). It is most common in large and giant breeds (Martinez, 1997) including Labrador retrievers (Morgan et al., 1999), Rottweilers (Mäki et al., 2000) and Bernese mountain dogs (Ubbink et al., 1999), though screening and selective breeding programmes appear to be reducing its incidence (Ubbink et al., 1999, Malm et al., 2008). Elbow dysplasia progresses to osteoarthritis (Bouck et al., 1995, cited in Cook and Cook, 2009, Michelsen, 2013), even with treatment (Michelsen, 2013). Whilst lag screw fixation of UAP may potentially reduce osteoarthritis incidence (Krotscheck et al., 2000, Meyer-Lindenberg et al., 2001) this is difficult to confirm since these studies had follow- up periods of 40 months or less, and it is possible that osteoarthritis developed later than this.

Cranial cruciate ligament rupture (CCLR) also leads to osteoarthritis in dogs, even if treated surgically (Hurley et al., 2007, Innes et al., 2010). The pathogenesis of CCLR is poorly understood (Hayashi et al., 2003), though rupture occurs progressively over time in most dogs (Hayashi et al., 2004). CCLR is significantly more common in larger breeds, neutered dogs (especially males), and dogs older than four years (Witsberger et al., 2008). Because dogs with CCLR will develop secondary osteoarthritis (Hayashi et al., 2003), these risk factors may be important in the epidemiology of osteoarthritis also.

1.3.5 Animal models of osteoarthritis:

1.3.5.1 Current animal models of osteoarthritis:

Animal models of osteoarthritis include surgical models, such as cranial cruciate ligament transection (DeGroot et al., 2004, Boyd et al., 2005, Kamekura et al., 2005, Hayami et al., 2006, Inoue et al., 2006), meniscectomy (Cake et al., 2004, Kamekura et al., 2005, Moore et al., 2005), transarticular impact trauma (Lahm et al., 2004), subchondral bone trauma (Lahm et al., 2004, Lahm et al., 2005, Mrosek et al., 2006), and

Chapter 1: Introduction and Literature Review articular cartilage damage (Yamamoto et al., 2004, Kuroda et al., 2006, Mastbergen et al., 2006). A range of species have been used for these experimental surgical models including mice (Kamekura et al., 2005), rats (Moore et al., 2005, Hayami et al., 2006, Kuroda et al., 2006), rabbits (Yamamoto et al., 2004, Inoue et al., 2006), sheep (Cake et al., 2004), cats (Boyd et al., 2005), and dogs (DeGroot et al., 2004, Lahm et al., 2005, Mastbergen et al., 2006). Other rodent models induce damage to the joint via intraarticular injection of collagenase (Blom et al., 2004), TGF-β (Van Lent et al., 2004), iodoacetate (Bove et al., 2003, Fernihough et al., 2004), or heat-killed mycobacteria (Landis et al., 1989), and several transgenic mouse models have been used to investigate the genes and pathways involved in osteoarthritis development (Rountree et al., 2004, de Hooge et al., 2005, Glasson et al., 2005, Morko et al., 2005, Wadhwa et al., 2005, Xu et al., 2005, Zhang et al., 2005). Ovariectomised rats (Høegh-Andersen et al., 2004) and sheep (Cake et al., 2005) also show osteoarthritis-like changes, which may mirror those in postmenopausal women (Cake et al., 2005). Osteoarthritic-like changes generally progress rapidly in induced models (Ameye and Young, 2006), facilitating faster, less expensive research, however their aetiologies differ from slowly degenerative human osteoarthritis and thus their pathogenesis may differ also, potentially reducing their translational validity (Ameye and Young, 2006). Spontaneous models of osteoarthritis, in which the disease develops without any experimental induction procedure, include age- related degenerative joint changes in the rat (Jallali et al., 2005), mouse (Davidson et al., 2005) and horse (McIlwraith et al., 2012), and specific strains predisposed to developing osteoarthritis at younger ages, such as the STR/ort mouse (Regan et al., 2005) and Hartley guinea pig (Johnson et al., 2004, Quasnichka et al., 2005). The slow progression, spontaneously-developing nature and lack of an identifiable aetiology in these animal models is more similar to spontaneous osteoarthritis in humans (Ameye and Young, 2006) and dogs (Liu et al., 2003), therefore the pathogenesis may be similar also.

1.3.5.2 Spontaneous canine osteoarthritis as a model for human osteoarthritis:

It may also be useful to consider spontaneous osteoarthritis in dogs as a model for human osteoarthritis: Spontaneous osteoarthritis in both species shows similar radiographic changes (Innes et al., 2004, Zhang and Jordan, 2010), behavioural or emotional changes (Wiseman et al., 2001, Hielm-Björkman et al., 2003, Brown et al., 2007, Hawker et al., 2008, Arendt-Nielsen et al., 2010) consistent with pain (McGrath and Unruh, 1994, cited

Chapter 1: Introduction and Literature Review in Wiseman et al., 2001), disability/lameness (Peat et al., 2001, Hielm-Björkman et al., 2003, Hawker et al., 2008, Marshall et al., 2010), and responses to nonsteroidal analgesia (Jordan et al., 2003, Brown et al., 2008, Hielm-Björkman et al., 2009) and total joint arthroplasty (Conzemius et al., 2003, Lascelles et al., 2010, Beswick et al., 2012). Similar changes in gene expression also occur in the cartilage of dogs and humans with spontaneous osteoarthritis (Clements et al., 2006), and dogs with spontaneous osteoarthritis also show very similar changes in cartilaginous proteoglycan levels to those found in osteoarthritic humans, whereas cartilage from dogs with experimentally-induced osteoarthritis (cranial cruciate ligament transection) showed very different changes in proteoglycan levels (Liu et al., 2003), suggesting that spontaneous canine osteoarthritis may have better translational validity. Domestic dogs also tend to live in the same environments as humans, so are probably exposed to many of the same risk factors (and protective factors), whereas experimental animals, including those that develop osteoarthritis spontaneously, tend to live in standardised, barren environments (Würbel, 2001) that bear little resemblance to those of human patients. Dogs also have considerably shorter lifespans than humans (Silva and Annamalai, 2009, Dreschel, 2010), therefore it should be possible to complete epidemiological cohort studies in dogs much more quickly than in humans. The spontaneous canine osteoarthritis model would also be compliant with the “3Rs” framework (Reduction, Refinement and Replacement) (Guhad, 2005) for ethical use of animals in research in that it does not require the usage of laboratory-housed experimental animals (Replacement) and does not require the performance of invasive and painful experimental procedures to induce the model (Refinement). Further research is required to investigate the similarities and potential differences in pathogenesis, epidemiology and responses to treatment in humans and dogs, in order to show this model’s translational validity.

1.4 The effect of chronic pain on cognition:

This section examines the evidence for impaired cognition in chronic pain conditions in humans and rodents, and potential reasons for this. Because working memory and attention appear to be impaired in human chronic pain conditions and in rodent pain models, it is possible that chronic pain may be associated with impaired cognition in osteoarthritic dogs also.

Chapter 1: Introduction and Literature Review

1.4.1 The effect of chronic pain on cognition in humans:

Human chronic pain patients may experience cognitive deficits: For example, patients with high-intensity chronic pain showed impaired performance scores (Eccleston, 1995, Dick and Rashiq, 2007) and longer response latencies (Grisart and Plaghki, 1999) in attention-based tasks. Similarly, chronic pain patients performed worse than age- matched controls in tests of working memory, recall and verbal fluency (Park et al., 2001), and performed consistently and significantly worse than healthy controls in a meta-analysis of 24 studies assessing working memory (see Table 1.1) (Berryman et al., 2013). Given that performance in attentional tasks appears to be related to ability to manipulate information during performance of the tasks (Dick and Rashiq, 2007), and that working memory has been described in the context of controlling attention in order to utilise or refrain from utilising specific information (Engle, 2002), it is unsurprising that both attention and working memory would be similarly impaired by the same phenomenon, such as chronic pain. Chronic pain is associated with deficits in both spatial and non-spatial working memory (Berryman et al., 2013). In terms of non-spatial working memory, chronic pain was significantly associated with decreased verbal working memory across 14 studies (Berryman et al., 2013), and decreased “nonverbal” working memory across six comparisons in four studies (however whilst five of these comparisons were numeric tasks (Berryman et al., 2013), one used visuospatial sequencing (Luerding et al., 2008) which requires aspects of spatial working memory), and deficits in “attention and working memory” across six studies in which these were considered the same construct (Berryman et al., 2013). Chronic pain patients also showed significant deficits in immediate verbal recall, running memory (recall of the last few words of a sequence that ends unpredictably), and immediate visual memory (Berryman et al., 2013). Spatial working memory may also be impaired in human chronic pain patients (though fewer studies have examined this (Berryman et al., 2013)), with chronic pain patients showing lower accuracy at recalling the order of symbol movements on a grid (Antepohl et al., 2003) and at recalling the order in which blocks arranged on a screen were “tapped” (Luerding et al., 2008) than healthy control participants.

Table 1.1: Types of memory described in Section 1.4.1.

Chapter 1: Introduction and Literature Review

Short-term memory that can be accessed and manipulated during performance of a task (van der Staay et al., 2012) and is forgotten soon afterwards (Dudchenko, 2004), such as an animal remembering locations in which it has already Working Memory searched for food during a single experimental trial (van der Staay et al., 2012). In humans, working memory facilitates short-term storage and manipulation of information required for tasks such as understanding language, learning, and reasoning (Baddeley, 1992). Allows the storage and recall of longer-term information, such as locations of food that remain constant between trials (van der Staay et al., 2012) or memory of the rules of a task, such as that pushing a lever results in a food reward (Dudchenko, 2004). In humans, this is often referred to as Reference Memory “long-term memory” (Faw, 2003, Blumenfeld and Ranganath, 2007, Cowan, 2008), which has been described “as a vast store of knowledge and a record of prior events” (Cowan, 2008), thereby encompassing both semantic and episodic memory (shown below).

Stores information relating to specific events, including Episodic Memory when and where events occurred (Tulving, 1972).

Stores knowledge about the meanings of and relationships between words and symbols, as well as rules and Semantic Memory algorithms for the manipulation of symbols, concepts and relationships (Tulving, 1972).

The evidence on whether chronic pain is associated with deficits in longer-term memory such as reference memory (see Table 1.1) is inconclusive: Luerding et al. (2008) found that long-term non-verbal recall was significantly impaired in fibromyalgia patients, but that long-term verbal recall and recognition, and non-verbal recognition were within normal ranges. Landrø et al. (1997) and Grace et al. (1999) found impaired long-term recall in fibromyalgia patients, however this impairment appeared to be associated with anxiety (Grace et al., 1999) or depression (Landrø et al., 1997). Therefore it is difficult to assess whether pain or comorbid anxiety/depression is responsible for long-term recall deficits in chronic pain patients. Oosterman et al. (2011) also found impaired verbal episodic memory in patients with various chronic pain conditions in comparison to controls, implying that this may also be impaired in human chronic pain, though this would be difficult to test in animals because of their inability to use languages.

Chapter 1: Introduction and Literature Review

1.4.2 Potential causes of cognitive impairment in chronic pain conditions:

There are several possible explanations for how chronic pain could cause cognitive impairments, as shown in Figure 1.1: Firstly, if more neural processing power is used for attention to pain, less is available for other cognitive processes (Apkarian et al., 2004a), potentially impairing performance at cognitive tasks. This is supported by the findings of Oosterman et al. (2011), who found that working memory and verbal episodic memory deficits in human chronic pain patients were partially (though not entirely) explained by impaired sustained attention. Additionally, chronic pain patients may show hypervigilance (Crombez et al., 2004) (increased attentiveness to somatic sensations), which further diverts attention towards pain and away from other cognitive functions (Legrain et al., 2011). Attention and memory impairments in chronic pain patients are also associated with increased pain-related fear and pain catastrophising (Crombez et al., 1999, Grisart and Van der Linden, 2001, Sharpe et al., 2009), which increases the salience of and prevents disengagement from pain-related stimuli (Sharpe et al., 2009). Furthermore, it appears that chronic pain causes grey matter atrophy (Apkarian et al., 2004b) and also disrupts cortical inhibitory mechanisms in the medial prefrontal cortex, amygdala and posterior cingulate cortex; part of a network that is also disrupted in autism, Alzheimer’s disease, depression, attention-deficit disorder and schizophrenia (Baliki et al., 2008), both of which may contribute to impaired cognitive function. A recent study by Dick and Rashiq (2007) found that chronic pain patients with poor performance on an attentional task also showed significant impairment at a task requiring maintenance and manipulation of spatial information whilst performing another spatial task, suggesting that working memory deficits, in particular impaired ability to manipulate spatial information during problem solving, may be a cause of impaired attention in human chronic pain patients (Dick and Rashiq, 2007).

Chapter 1: Introduction and Literature Review

Figure 1.1: Diagram summarising the mechanisms by which chronic pain is hypothesised to impair cognition.

Chronic pain, impaired sleep, affective disorders and impaired cognition are shown in red, hypothesised links between these are shown in blue, and citations for sources providing evidence for these links (described in further detail within this chapter) are shown in green.

Since studies examining the association between chronic pain and cognition in humans have not been able to determine a causal relationship (since inducing chronic pain in humans would be ethically unacceptable), it is also possible that the working memory and attention deficits that occur in chronic pain conditions may not be caused by chronic pain itself but by another comorbid factor, such as affective disorders, which are known to be associated with both chronic pain (Leino and Magni, 1993, McWilliams et al., 2003, Ohayon and Schatzberg, 2003) and impaired working memory (Harvey et al., 2004, Christopher and MacDonald, 2005, Rose and Ebmeier, 2006). Anxiety impairs working memory in humans (Ashcraft and Kirk, 2001, Shackman et al., 2006), and memory deficits also occur in patients with post-traumatic stress disorder (Bremner, 1999). Chronic pain patients show elevated levels of anxiety (Gaskin et al., 1992, Holzberg et al., 1996), and muscle pain patients reported more stress during a series of cognitive tests than control participants (Røe et al., 2001). Chronic pain is often associated with symptoms of depression (Gaskin et al., 1992, Leino and Magni, 1993, Holzberg et al., 1996, Ohayon and Schatzberg, 2003), which is associated with a range of cognitive deficits in humans (Ravnkilde et al., 2002), so it is also possible that depression may contribute to the cognitive impairments seen in chronic pain patients. In particular, the long-term recall deficits observed in some studies of fibromyalgia patients appear to be associated with anxiety (Grace et al., 1999) or depression (Landrø et al., 1997).

Chronic pain disorders are associated with impaired sleep, as discussed in Section 1.6.1, which is also associated with working memory deficits: Children with lower sleep efficiency and longer sleep latencies (measured actigraphically) had lower scores in a test of working memory than children with higher sleep efficiency and shorter sleep latencies (Steenari et al., 2003). Similarly, older adults who slept for less than 5 hours a

Chapter 1: Introduction and Literature Review night (measured actigraphically) had reduced accuracy in a number-matching working memory task than older adults who slept for more than 7 hours a night (Miyata et al., 2013). Patients with obstructive sleep-disordered breathing also showed significantly reduced accuracy and increased response times in a task of verbal working memory compared to healthy controls (Thomas et al., 2005). Impaired working memory has also been observed following induced sleep deprivation in healthy participants: Sleep deprivation of just one hour past participants’ usual sleeping time was sufficient to cause impaired accuracy and increased response latencies in a virtual location-matching test of spatial working memory in human volunteers (Smith et al., 2002). Additionally, 24 hours of sleep deprivation caused an increase in response latencies in two working memory tasks in young adults (Chee and Choo, 2004). Sleep deprivation also appears to be associated with impaired working memory in non-human animals: Twelve hours of sleep deprivation significantly impaired performance in a novel arm recognition task of spatial working memory in mice (Hagewoud et al., 2010), and rats deprived of sleep for eight hours (but not for only four hours) showed significantly decreased accuracy in a T- maze task of spatial working memory compared to control rats (Xie et al., 2015). Therefore it is possible that sleep deficits associated with chronic pain (see Section 1.6.1) may contribute to impaired working memory in chronic pain patients.

1.4.3 The effects of chronic pain on cognition in rodents:

There is also evidence that chronic pain impairs cognition in rats: Rats showed deficits in sustained attention (reduced accuracy and more omissions) in a 5-choice serial response time task following experimentally-induced osteoarthritis from intraarticular Freund’s adjuvant (Pais-Vieira et al., 2009). Whereas healthy rats show a preference for novel objects over familiar ones and will usually spend more time interacting with these (Antunes and Biala, 2012), rats with experimentally-induced colitis showed significantly reduced proportions of time interacting with a novel object placed in a familiar environment containing three familiar objects compared to control rats (Millecamps et al., 2004), and the proportion of time colitic rats spent investigating the novel object was close to that expected by chance, implying that colitis causes rats to show reduced attention to their environment such that they do not appear to recognise that the novel object is novel (Millecamps et al., 2004). Alternatively, the colitic rats may have had impaired non-spatial working memory and thus did not recognise the familiar objects, interacting with all objects as if they were equally novel, as hypothesised in a similar

Chapter 1: Introduction and Literature Review paradigm devised by Ennaceur and Delacour (1988). Rats with neuropathic pain from nerve ligation also showed significantly more spatial working memory errors (increased number of revisits to previously-entered arms on a radial maze) compared to sham- operated rats, but there was no significant difference in spatial reference memory errors (visits to arms that were not baited with food) between groups (Ren et al., 2011). Nerve- ligated rats also showed a significantly reduced proportion of correct choices on a figure-of-eight maze task of spatial working memory than sham-operated rats (Cardoso- Cruz et al., 2013a, Cardoso-Cruz et al., 2013b), providing further evidence that their spatial working memory is impaired.

1.5 Assessment of working memory in non-human animals:

This section will focus on the assessment of working memory in non-human animals, as there is much evidence that this is impaired in human chronic pain patients (see Section 1.4), and exploration of existing working memory assays in animals is necessary in order to identify suitable paradigms for assessing working memory in dogs.

Because of non-human animals’ inability to use language, it is not possible to assess the verbal/auditory or numeric working memory constructs more commonly used in humans (Berryman et al., 2013). Therefore most working memory assays in laboratory rodents have focused on either spatial working memory or non-spatial working memory in terms of short-term recognition of objects or odours (Dudchenko, 2004).

1.5.1 Reinforced maze tasks of spatial working memory:

Many tests of spatial working memory in rats have used maze-type tasks, such as the radial maze in which the rat is placed in a central chamber connected to several arms (usually eight (Olton and Samuelson, 1976), but four (Olton and Feustle, 1981), 12 (Burešová and Bureš, 1982, Cook et al., 1985), 17 (Olton et al., 1977) and 24 arms (Burešová and Bureš, 1982) have been used) each of which is baited with a food reward. The animal uses its spatial working memory to avoid revisiting arms it has already visited (and thus that currently contain no food) on that particular trial, generally making very few errors (revisits to previously visited arms) (Olton and Samuelson, 1976). Whilst success rates decrease with increasing numbers of arms (Olton et al., 1977, Burešová and Bureš, 1982), the absence or rearrangement of external landmarks (Suzuki et al.,

Chapter 1: Introduction and Literature Review

1980) or enforced delays between visits (Cook et al., 1985, Bolhuis et al., 1986), performance does not reach chance levels until delays reach more than one hour (Bolhuis et al., 1986), and rats are capable of performing the task successfully even without external landmarks (Suzuki et al., 1980). Whilst it is possible that rats’ inherent preference to explore previously-unexplored areas (Dudchenko, 2004) may partially explain their success at this task, rats are also able to successfully perform the task if trained that food reinforcement only occurs if they select the same arm that they visited on the initial trial of the day (which changes each day), rather than a different arm (Jarrard et al., 1987, cited in Jarrard, 1993), implying that rats can use spatial working memory to solve these tasks rather than relying on alternation strategies or preference for unexplored areas. The radial maze task can also be adapted to assess reference memory as well as working memory by consistently placing food rewards in specific arms and not others, thus visiting an arm that is never baited constitutes a reference memory error whereas visiting a previously-visited arm that was baited constitutes a working memory error (Wirsching et al., 1984, Smith et al., 1998, Fader et al., 1999). Water maze tasks can also be used to assess working memory, relying on negative reinforcement rather than food rewards: A rat is placed in a water-filled pool which contains a submerged platform, the location of which changes each day, and several visual landmarks located outside the pool are available to aid in navigation (Morris et al., 1986). The time taken to locate the platform decreases between the first and second trial, implying that the rodent learns the location of the platform for that day (Morris et al., 1986), even when delays are present (Steele and Morris, 1999). A radial water maze has also been used, with rats swimming to the end of each maze arm to reach a submerged platform which then disappears after 20 seconds, requiring them to swim to an unvisited arm to find a platform that is still present (Burešová et al., 1985). Rats showed a 97.5% success rate on their first eight visits (Bolhuis et al., 1985), and whilst enforced delays between visits decreased performance, it did not reach levels expected by chance unless the delay exceeded 10 hours (Bolhuis et al., 1985).

Similar tasks have previously been used in dogs: Macpherson and Roberts (2010) used an eight-arm radial maze task to assess spatial working memory in dogs. However whilst the dogs did show improvements (fewer visits to arms they had entered previously, and earlier visits to arms that contained larger amounts of food) over time (Macpherson and Roberts, 2010), suggesting that spatial working memory in dogs works similarly to that in rats, the performance of dogs in this task was significantly worse (83% mean correct

Chapter 1: Introduction and Literature Review responses) to that previously seen in rats (90.21% mean correct responses, p<0.05) (Olton and Samuelson, 1976, Macpherson and Roberts, 2010). It was therefore suggested that the radial maze may not be an appropriate test of spatial memory in dogs (Macpherson and Roberts, 2010). Meanwhile Craig et al. (2012) reported success with this paradigm, finding that dogs were able to perform significantly better than chance and also that they displayed a significant primacy effect (recalling the locations of earlier visits better than later ones) when they had to remember which arms they had previously visited, however their study required a prolonged training period (10 habituation trials followed by up to 23 trials to reach a learning criterion, with only one trial per day (Craig et al., 2012)) which may not be feasible for studies involving animals owned by members of the public due to the limited contact time available with each animal (although it may be possible to condense this period by using multiple trials per day). Additionally, animals with osteoarthritis may struggle to travel between the arms of a maze due to locomotor impairment or pain. The appetitively motivated holeboard task is an alternative to the radial maze task in which food is placed in holes/containers arranged in a square grid rather than at the ends of arms (van der Staay et al., 2012), which has shorter walking distances between containers so that osteoarthritic dogs could more easily participate, and for which the apparatus should be easier to construct, place and store than that required for a radial maze. It also has the advantage of providing separate scores for spatial working memory and spatial reference memory (van der Staay et al., 2012), allowing both to be assessed simultaneously. The holeboard task is described in more detail in Chapter 2.

1.5.2 Unreinforced (exploratory) tasks of working memory:

Because rats have a natural preference for exploring unexplored areas rather than recently explored ones (Dudchenko, 2004), unreinforced maze tasks can also be used to assess working memory: Rats placed in a T-maze (Tolman, 1925), plus-maze (Montgomery, 1952), square maze (with two parallel routes between the same start and endpoints) (Dennis, 1939), or other shapes of maze with spatially-distinct route choices (Ladieu, 1944, Whishaw et al., 1995), consistently choose to visit different arms in subsequent trials to those they visited on previous trials. This occurs independently of the direction in which the rat needs to turn in order to reach an unexplored arm (Dennis, 1939, Montgomery, 1952), so is more likely to be based on motivation to explore than alternation of body movements (Montgomery, 1952). Additionally, rats

Chapter 1: Introduction and Literature Review were able to correctly select the alternate arm from the previous trial when placed in an identical maze in an identical featureless room, suggesting rats are not reliant on odour cues to solve the task (Dudchenko and Davidson, 2002), but not if the maze was rotated 90 degrees, suggesting that rats used a “sense of direction” or spatial working memory rather than a turning strategy to select the alternate arm (Dudchenko and Davidson, 2002). Rats’ success at selecting the novel arm rather than the previously-explored arm of a T-maze (and thus, it is assumed, their working memory for which arm they explored on the previous trial) is decreased with increasing delays between trials (Dudchenko, 2001), and removal of external landmarks if rats were exposed to these in training (Dudchenko, 2001). However, rats trained without landmarks performed as well as those trained (and tested) with landmarks, implying that rats can learn to recall the arm they have not previously visited using strategies other than external (allocentric) cues (Dudchenko, 2001). Rats do not appear to significantly prefer unexplored arms over previously explored arms if the arms are adjacent and parallel (and thus not spatially distinct from each other) (Douglas et al., 1972), however.

Rats’ tendency to explore novel stimuli over familiar ones can also be applied to assess their working memory for objects (non-spatial working memory): A task developed by Ennaceur and Delacour (1988) involved placing a rat in a chamber containing two identical sample objects which they were allowed to explore, removing the rat for a short delay period and placing them back into the chamber which now contained one object from the previous trial and another, novel object. The rats spent more time interacting with the novel object than the familiar object (Ennaceur and Delacour, 1988), indicating that they recognised the familiar object from the previous trial and were thus less motivated to explore it. The ability to perform this task in a single session of two trials without training, and the lack of specialised equipment required, would make this task an ideal candidate for the assessment of non-spatial working memory in companion animal dogs, therefore this “novel object recognition task” is explored in greater detail in Chapter 2.

1.5.3 Delayed non-matching to sample tasks:

Non-spatial working memory in rats can also be assessed using delayed matching to sample (DMS - where the animal is rewarded for responding to a previously- encountered stimulus rather than a novel one) or delayed non-matching to sample

Chapter 1: Introduction and Literature Review

(DNMS - where the animal is rewarded for responding to a novel stimulus rather than a previously-encountered one) tasks (Elliott and Dolan, 1999, Dudchenko, 2004). However, since DMS tasks require inhibition of animals’ innate tendency to preferentially respond to novel stimuli over familiar ones (Elliott and Dolan, 1999) and DNMS tasks do not (Diamond, 1991, cited in Elliott and Dolan, 1999), DNMS tasks may be considered more “pure” tests of working memory (Elliott and Dolan, 1999)

In two such studies using similar paradigms, rats were trained to displace an object in return for a food reward on the first trial, but on the second trial they were presented with this object and another, novel object, and were only rewarded if the novel object was displaced (Rothblat and Hayes, 1987, Mumby et al., 1990). Each pair of objects was unique to a specific two-trial session (Rothblat and Hayes, 1987, Mumby et al., 1990) so the task was assessing working rather than reference memory for the object, and rats showed success rates of 75-90%, though performance decreased with increasing delays between trials (Rothblat and Hayes, 1987, Mumby et al., 1990). Since rats tested using an identical copy of the original object in the second trial performed similarly to rats tested using the same original object in both trials at each delay interval (Mumby et al., 1990), it is likely that rats were relying on working memory rather than on odour cues left on the previously-encountered object in order to recognise it.

A variant of this task, the delayed non-matching to position (DNMP) task, has also been used to assess spatial working memory in rats (Chudasama and Muir, 1997). Rats are placed in an operant chamber containing two levers on the left and right sides of a food or water reward dispenser, and are initially presented with a lever in one position, which they push to activate a retention interval in which the lever disappears and after which the rat has to push its nose into either the central food-dispenser (Chudasama and Muir, 1997) or a well on the wall opposite the lever (Hampson et al., 1999), to begin the next trial. At this point, both levers are presented, and the rat has to push the lever that was not presented in the previous trial to receive a reward of food (Chudasama and Muir, 1997) or water (Hampson et al., 1999). However, animals may not be relying solely on spatial working memory to solve this task, as Chudasama and Muir (1997) found that observers were reliably able to predict (R≥0.7, p<0.01) which lever a rat was going to select by their behaviour during the delay interval (e.g. orienting the head or nose towards the location at which the correct lever would appear whilst nose-poking the food dispenser), therefore it seems that rats use an orienting strategy to successfully

Chapter 1: Introduction and Literature Review select the correct lever, using their body position as a cue to select the correct lever rather than relying on their spatial working memory of the correct location. This could be avoided by using tasks in which the rat is unable to detect what the correct response will be until after the delay interval (Pontecorvo et al., 1996); for example, Pontecorvo (1983) used a non-spatial DNMS task in which rats were presented with either a bright or dim light for a brief period followed by an intertrial interval and then either a light of the same brightness or a light of a different brightness. If the brightness was different, the rat had to push a lever and respond in order to receive a reward, whereas pushing a lever when the light was the same brightness as previously resulted in no reward. This concept could be applied to the DNMP task in order to test spatial memory by using a chamber containing multiple potential lever locations that differ from the original lever, such that the correct response location would not be obvious during the retention interval. Delayed non-matching to sample tasks have not only been used to assess working memory for objects and locations but also for odours, with rats able to perform nose-pokes if presented with an odour that differed from the previously-presented odour (Otto and Eichenbaum, 1992), and able to learn to dig for food pellets in sand only if the sand was scented with spices that had not been present in previous trials (Wood et al., 1999, Dudchenko et al., 2000). DNMS tasks have also been used to assess non-spatial working memory in monkeys (Mishkin and Delacour, 1975, Zola-Morgan and Squire, 1986, Baxter and Murray, 2001) and human infants (Diamond, 1990).

Several studies by N. W. Milgram’s research group have also used DNMP tasks to investigate spatial working memory in dogs (Adams et al., 2000, Tapp et al., 2003, Zanghi et al., 2015). This method appears to be sensitive in that it is able to detect age-related deficits in spatial working memory in dogs (Adams et al., 2000, Tapp et al., 2003, Zanghi et al., 2015), but the long durations of training required, especially in older dogs (which are more likely than younger dogs to have osteoarthritis), means that such a training- intensive method may be unsuitable for studies using companion animal dogs owned by members of the public, where contact time with the subjects is limited.

1.6 Chronic pain and sleep disturbance:

Patients with chronic pain often report disturbed sleep or insomnia: 74% of rheumatoid arthritis patients reported that arthritis interfered with their sleep to some extent, 14% claiming it severely interfered with their sleep (Nicassio and Wallston, 1992), with a

Chapter 1: Introduction and Literature Review significant correlation between pain severity and sleep problems even after accounting for age, demographics and functional impairment (Nicassio and Wallston, 1992). 77% of orofacial pain patients also reported decreased sleep since disease onset, with 41% reporting a decrease in sleep duration of 50% or more (Riley et al., 2001).

1.6.1 The chronic pain-sleep disturbance relationship:

Whilst many chronic pain patients report sleep disturbances, the directional nature of this relationship is still unclear. It is possible that increased chronic pain severity causes increased sleep disturbance, as in many studies chronic pain was a predictor of subsequent sleep problems (Nicassio and Wallston, 1992, Drewes et al., 2000, Riley et al., 2001), and cognitive behavioural therapy (CBT) for chronic pain improved subsequent sleep measures in ankylosing spondylitis patients (Basler and Rehfisch, 1991), though this may have been due to improved mood outcomes. However another study found no evidence of improved sleep following successful CBT for chronic pain (Becker et al., 2000). It is much more difficult to assess whether pharmacological treatment of chronic pain leads to a decrease in sleep disturbance (or vice-versa), because many drugs have both analgesic and sedative-hypnotic properties (Menefee et al., 2000, Smith and Haythornthwaite, 2004), for example, whilst amitriptyline reduced pain severity and increased sleep in chronic pain patients (Zitman et al., 1990), this is probably because it has both analgesic and sedative effects (Menefee et al., 2000), and use of “off-label” sedative-hypnotic benzodiazepine drugs for analgesic purposes in chronic pain patients is common (King and Strain, 1990, Hardo and Kennedy, 1991, Hardo et al., 1992). Therefore it is difficult to use responsiveness to drug treatment to investigate the relationship between chronic pain and sleep. There is also some evidence that impaired sleep causes increased pain: Chronic pain patients reporting poor sleep also report greater pain severity and unpleasantness (Affleck et al., 1996, Morin et al., 1998), with impaired sleep appearing to occur prior to increased daytime pain (Affleck et al., 1996), though this could be explained by an association between increased attention to pain and decreased sleep (Affleck et al., 1996). Stone et al. (1997) found that self-reported pain intensity and fatigue were correlated with perceived sleep quality but not sleep duration, suggesting that subjective quality of sleep is more important than sleep duration in terms of reducing pain perception and/or being reduced by pain perception.

Chapter 1: Introduction and Literature Review

Experimentally depriving healthy subjects of SWS (slow-wave sleep) can cause changes in alpha-brainwave activity during sleep and decreased acute musculoskeletal nociceptive thresholds afterwards (Moldofsky and Scarisbrick, 1976, cited in Moldofsky and Lue, 1980) both of which may also occur in fibromyalgia patients (Moldofsky and Scarisbrick, 1975, cited in Moldofsky and Lue, 1980), and normal acute nociceptive thresholds can be restored following SWS-specific recovery sleep (but not REM recovery sleep) (Onen et al., 2001). However, Arima et al. (2001) found no changes in nociceptive thresholds following SWS disruption in healthy volunteers. Additionally, the prolonged (40 hours (Onen et al., 2001) to three days (Arima et al., 2001)) SWS deprivation in these studies does not resemble sleep deprivation in chronic pain patients, as orofacial pain patients report a mean of 5.9 hours sleep a night, with only 19% of patients sleeping for four hours or less (Riley et al., 2001). Additionally, these studies rely on acute nociceptive threshold testing, and whilst chronic pain causes decreased nociceptive thresholds (Woolf and Doubell, 1994) it also involves mechanisms distinct from those involved in nociception (see Section 1.2.2), therefore the mechanisms by which sleep disturbance appears to acutely exacerbate pain in chronic pain patients may not be comparable to those by which severe sleep deprivation causes decreased mechanical nociceptive thresholds in healthy volunteers.

Both chronic pain (Leino and Magni, 1993, Ohayon and Schatzberg, 2003), and sleep disorders (Casper et al., 1985, Alvaro et al., 2013), are associated with depressive disorders. Pain duration and severity ratings, as well as depression ratings, were significantly associated with impaired sleep measures on a sleep diary in chronic pain patients (Haythornthwaite et al., 1991), and depression patients with no chronic pain disorder showed increased activation of the anterior cingulate cortex and insula when anticipating an experimentally-evoked acute painful stimulus (Strigo et al., 2008), both of which are generally coactivated with the amygdala during the emotional processing of pain and other emotion-evoking stimuli (Craig, 2002). Furthermore, similar neurophysiological and neurochemical changes occur in pain, depression and sleep disorders (Boakye et al., 2016), suggesting that depression may be involved in the relationship between chronic pain and sleep. Other studies have also found stronger associations between depression and insomnia than chronic pain and insomnia (Riley et al., 2001), and higher effect scores for depression than pain as a predictor of sleep disturbance (though both were significant) (Pilowsky et al., 1985). It has been suggested that chronic pain may induce depression and this may induce sleep disturbances,

Chapter 1: Introduction and Literature Review causing the observed sleep deficits in chronic pain patients (Smith and Haythornthwaite, 2004), though since each of the three conditions appear to increase the probability or severity of the others, it is difficult to identify the true directionality of this relationship.

1.6.2 Sleep disturbance in osteoarthritis:

People with painful osteoarthritis also experience sleep disturbances, reporting twice the overall population prevalence of insomnia and unrefreshing sleep (Power et al., 2005) (note that this study did not distinguish between osteoarthritic and rheumatoid arthritic participants), as well as high prevalences of reporting poor sleep maintenance (Wilcox et al., 2000), sleep onset (Wilcox et al., 2000), sleep quality (Taylor‐Gjevre et al., 2011) and night-time or early morning awakenings (Wilcox et al., 2000, Taylor‐Gjevre et al., 2011). Whilst self-report measures show impaired sleep in osteoarthritis, the results from actigraphy studies (a more objective measure of sleep, discussed in Section 1.6.3) are mixed: Total hip arthroplasty in osteoarthritic patients improved subjective sleep measures and reduced actigraphic measures of time spent in bed, activity during sleep and fragmented sleep (Fielden et al., 2003), however another study found no significant actigraphic differences between osteoarthritic patients and healthy demographically matched-controls (Leigh et al., 1988) and whilst yoga significantly improved self- reported measures of sleep in women with osteoarthritis, it had no significant effect on actigraphic outcomes (Taibi and Vitiello, 2011). This discrepancy may be due to the relatively low specificity of actigraphy in comparison to self-report measures (Rogers et al., 1993, Paquet et al., 2007), as discussed in Section 1.6.3. Rat osteoarthritis models (intraarticular iodoacetate (Silva et al., 2008) or killed mycobacteria (Landis et al., 1989)) also showed increased time awake and greater sleep fragmentation (Landis et al., 1989), reduced total sleep time, decreased SWS and REM sleep, and increased microarousals (periods of arousal that do not result in waking) (Silva et al., 2008), and similar findings occurred in a gout arthritis model (intraarticular uric acid) (Guevara‐López et al., 2009).

At the time of writing, there are no studies assessing the effects of pain from osteoarthritis on sleep in dogs, in comparison with healthy control dogs. However, Knazovicky et al. (2015) found that nonsteroidal analgesic (meloxicam) administration in osteoarthritic dogs resulted in improved sleep quality as assessed by an owner questionnaire, but did not decrease night-time activity as assessed by actigraphy, compared to placebo or no treatment. This suggests that osteoarthritic dogs may also

Chapter 1: Introduction and Literature Review experience sleep disturbances due to pain, as seen in osteoarthritic humans. Whilst depressive disorders are prevalent in chronic pain conditions and may affect sleep, Harris (2017) found no evidence of negative judgement bias (see Section 1.2.3) in dogs with osteoarthritis compared to healthy control dogs, indicating that osteoarthritis may not be associated with depressive-like clinical signs in dogs. However more severely osteoarthritic dogs were less likely to successfully learn the task than less severely affected osteoarthritic dogs (Harris, 2017), so the osteoarthritic dogs that performed this task may have had less pain and perhaps a more positive affective state than osteoarthritic dogs in the general population. Additionally, there was a significant association between osteoarthritis severity and right-hemisphere laterality bias (tendency to pass a novel object on their right side) (Harris, 2017), a novel measure that may indicate increased emotional intensity (Sullivan, 2004, Siniscalchi et al., 2008) and negative affective (emotional) state (Rohr et al., 2013). Therefore it is possible that depressive-like states may occur in osteoarthritic dogs and that this may complicate the relationship between chronic pain and sleep.

1.6.3 Methods of sleep assessment:

The accurate study of sleep in chronic pain patients is difficult, partially because of the drawbacks of the various methods of assessing sleep. The gold standard measure of sleep assessment is via polysomnography (Sadeh et al., 1995), which records EEG (electroencephalogram – measuring brain activity), EMG (electromyogram – measuring muscle activity) and EOG (electro-oculogram - measuring eye movement) measurements during sleep (Menefee et al., 2000). This allows identification of different sleep stages: Stages 1-4 of NREM (non-rapid-eye-movement) followed by REM (rapid eye movement) sleep. Stages 3 and 4 show high voltage waves and are collectively known as slow-wave sleep (SWS) (Moldofsky and Lue, 1980, Menefee et al., 2000), whereas REM sleep is characterised by low voltage waves and rapid eye movements (Menefee et al., 2000). This may be useful as certain stages of sleep may be affected more by chronic pain conditions than others; for example increased rheumatoid arthritis pain and severity measures were associated with increased SWS but decreased stage 2 sleep (Drewes et al., 2000), osteoarthritis patients showed more stage 1 sleep but less stage 2 sleep (Leigh et al., 1988) and fibromyalgia patients showed increased stage 1 sleep but decreased SWS (Roizenblatt et al., 2001) compared to healthy control participants. However, polysomnography requires expensive equipment and analytical

Chapter 1: Introduction and Literature Review expertise thus is infeasible for prolonged monitoring of patients at home, therefore relatively few polysomnographic studies in chronic pain patients exist (Menefee et al., 2000).

Actigraphy is an alternative measure of objectively monitoring sleep which can be performed in the patient’s home and allows continuous data collection over longer periods of time (Sadeh et al., 1995): A device containing an accelerometer is placed on the wrist or ankle and records the participant’s movements over each period or “epoch” allowing the distinction between sleep (low movement) and wakefulness (high movement) to be determined either through manual scoring (Kripke et al., 1978, Mullaney et al., 1980) or more recently through automated algorithms (Webster et al., 1982, Sadeh et al., 1994) Whilst initial validation studies in healthy participants showed agreement rates with polysomnography of over 80% (Kripke et al., 1978, Mullaney et al., 1980, Webster et al., 1982), more recent evidence suggests this may be misleading: Whilst the sensitivity (ability to correctly detect sleep) of actigraphy often exceeds 90%, its specificity (ability to detect wakefulness) is lower at 24-65% (de Souza et al., 2003, Paquet et al., 2007, Sitnick et al., 2008). Because more of the measurement period is generally spent asleep than awake (and thus is more likely to be correctly scored), the minute-by-minute agreement with polysomnography is often high (Sadeh, 2011), therefore this agreement rate is decreased in participants with poorer sleep who spend more time awake (Mullaney et al., 1980, Sadeh et al., 1989, Sivertsen et al., 2006, Paquet et al., 2007). Despite this, actigraphy could differentiate between healthy adult participants, insomniacs and participants with sleep apnoea with 65-74% accuracy (Sadeh et al., 1989), between children with sleep disturbance and healthy control children with 79-91% accuracy (Sadeh et al., 1991), and between medicated and unmedicated narcolepsy patients with 81% accuracy (Bruck et al., 2005), and has also identified sleep-impairing adverse effects of methylphenidate in children with ADHD (Corkum et al., 2008). These findings imply that despite its low specificity, actigraphy remains useful in participants with impaired sleep.

Though less objective, several self-reporting techniques are regularly used to assess sleep duration and quality, which do not require expensive equipment and are straightforward to record and analyse. These include sleep diaries, in which patients record their sleep duration and onset, waking frequency and other factors pertinent to the study (Haythornthwaite et al., 1991, Åkerstedt et al., 1994, Monk et al., 1994).

Chapter 1: Introduction and Literature Review

Rogers et al. (1993) found 87% agreement between polysomnographic and sleep diary recordings of time spent asleep, with 92.3% sensitivity and 95.6% specificity, much higher than the specificity identified in actigraphic studies (de Souza et al., 2003, Paquet et al., 2007). Sleep diaries and actigraphy provided similar measures of total sleep time, sleep efficiency and time spent awake following onset of sleep, but patients reported longer sleep onset latencies and fewer awakenings than were detected actigraphically (Wilson et al., 1998). This may not reflect a problem with the sleep diary method itself however, as some actigraphic algorithms overestimate awakening frequency (Paquet et al., 2007). In addition to sleep diaries, there are also self-report questionnaires that ask patients about their sleep duration and quality retrospectively (Buysse et al., 1989, Nicassio and Wallston, 1992, Douglass et al., 1994, Riley et al., 2001). One questionnaire score was able to classify two thirds of patients into the correct category of five based on the presence and type of (polysomnographically diagnosed) sleep disorder (Sweere et al., 1998), and another was significantly correlated with polysomnographically- recorded sleep efficiency, sleep maintenance and delta brainwaves in younger, but not in elderly, participants (Buysse et al., 1991). Because of their ease of use, affordability and ability to assess participants’ subjective views on their sleep, they are a useful additional measure in studies also using more objective measures.

1.7 How will this thesis project investigate the effect of spontaneous canine osteoarthritis on working memory and sleep?

1.7.1 Summary of literature:

This literature review has brought together information from previous studies relating to chronic pain, osteoarthritis, working memory and sleep impairments in chronic pain patients, and methods of assessing working memory and sleep.

Sections 1.2.1-1.2.2 of this literature review discussed the definitions of and pathophysiological changes in chronic pain, showing that chronic pain, whilst difficult to define, is a subjective phenomenon characterised by specific, measurable pathophysiological changes. Section 1.2.3 summarised evidence from previous studies showing that chronic pain is likely to be experienced as an unpleasant subjective

Chapter 1: Introduction and Literature Review experience by non-human mammalian species, implying that dogs with chronic pain from osteoarthritis are likely to experience this as a subjectively unpleasant phenomenon which could affect their welfare. This emphasises the importance of studying the effects of chronic pain disorders such as osteoarthritis in dogs from a canine welfare perspective.

Section 1.3.1 reviewed the evidence that osteoarthritis causes chronic pain in both humans and companion animal dogs, which suggests that osteoarthritic dogs may be subject to the same unpleasant effects associated with chronic pain as those previously observed in human chronic pain patients, potentially including working memory and sleep deficits. This shows that chronic pain from osteoarthritis is an important area for research with the potential to affect the welfare of both dogs and humans. Sections 1.3.2-1.3.4 discussed the diagnosis and treatment of osteoarthritis in dogs and identified potential risk factors for its development, using information from limited epidemiological and experimental studies on canine osteoarthritis, known risk factors for osteoarthritis in humans, and risk factors for developmental joint disorders predisposing to osteoarthritis in the dog. This suggested that osteoarthritis is likely to be a prevalent and important cause of chronic pain in dogs and may affect certain breeds or demographics more than others, highlighting the importance of studying the effects of osteoarthritis in dogs as an important and prevalent welfare concern.

Section 1.3.5 reviewed the types and limitations of existing animal models of osteoarthritis, and proposed that spontaneous canine osteoarthritis may have potential as a translationally valid and ethically advantageous model of human osteoarthritis. This also suggests that because similar mechanisms occur in human and canine osteoarthritis, dogs may experience similar clinical signs and pathophysiological changes as those reported in humans. This section also highlights potential implications for the findings of this thesis project in terms of human chronic pain and osteoarthritis: If it could be shown that spontaneous canine osteoarthritis causes impaired sleep, this would provide further evidence for its translational validity as a model of human chronic pain and osteoarthritis, in which sleep impairments have been reported. Additionally, if it could be shown that canine osteoarthritis causes impaired working memory, this would provide further evidence for the translational validity of spontaneous canine osteoarthritis as a model of human chronic pain. This finding would also imply that

Chapter 1: Introduction and Literature Review chronic pain may cause impaired working memory in human osteoarthritis patients, in which the effects of chronic pain on cognition have not been specifically investigated.

Section 1.4 discussed the evidence for impaired working memory as a result of chronic pain in humans and laboratory rodents, and the hypothesised causes of this effect. This showed that working memory deficits occur in chronic pain conditions in both humans and non-human mammals, and thus that it is plausible that chronic pain in dogs with osteoarthritis may also be associated with such deficits. Section 1.5 reviewed existing methods of assessing working memory in non-human animals and the feasibility of adapting these methods for use in dogs owned by members of the public, facilitating the identification of tasks that could be used to compare the working memory of dogs with and without chronic pain from osteoarthritis. Section 1.6 discussed the relationship between chronic pain and sleep disturbance, as well as the evidence that sleep is impaired in human osteoarthritis patients and rodent osteoarthritis models, and that nonsteroidal analgesia improves scores on an owner questionnaire assessing sleep quality in osteoarthritic dogs. It also discussed the advantages and limitations of common methods used to measure sleep. This suggests that spontaneous canine osteoarthritis could potentially be associated with impaired sleep, as reported by human osteoarthritis patients, and highlights potential methods that could be adapted for investigating sleep in dogs.

From the evidence discussed in this chapter that human chronic pain is associated with impaired working memory and sleep, that human osteoarthritis is associated with sleep deficits, and that osteoarthritis causes chronic pain in both humans and dogs and appears to share many similarities between these species, it therefore seems possible that dogs with chronic pain from osteoarthritis may experience impaired working memory and sleep. If working memory impairments exist in osteoarthritic dogs, they may impair dogs' everyday functioning as seen in human chronic pain patients (Klingberg, 2010), which could potentially include reduced abilities to navigate spatial environments (van der Staay et al., 2012) or locate objects (Fiset et al., 2003), recall and respond appropriately to familiar situations (Ennaceur and Delacour, 1988), and learn, recall or focus attention on trained behaviours (Adams et al., 2000, Tapp et al., 2003, Snigdha et al., 2012), which may adversely affect the relationship between dogs and their owners. Sleep deficits may cause negative affective states (Casper et al., 1985,

Chapter 1: Introduction and Literature Review

Alvaro et al., 2013) and increased pain (Affleck et al., 1996, Morin et al., 1998), as seen in humans, which may adversely affect dogs' welfare.

Because impaired working memory and sleep could therefore lead to impaired welfare, it is important to investigate whether these phenomena occur in dogs with chronic pain from osteoarthritis. As discussed in Sections 1.5 and 1.6.3, these could be investigated using adaptations of working memory assays used in rodents, such as the holeboard and novel object recognition tasks, and using adaptations of sleep assays used in humans, such as actigraphy and owner questionnaires (as used previously by Knazovicky et al. (2015) to assess the effect of nonsteroidal analgesia on sleep in osteoarthritic dogs). This thesis project therefore aimed to investigate the effects of chronic pain from osteoarthritis on working memory and sleep in dogs, by comparing measures of working memory and sleep in dogs with chronic pain from osteoarthritis to those in healthy control dogs.

1.7.2 Aims, hypotheses and scope of the project:

The aim of this thesis was to investigate the effects of chronic pain from osteoarthritis on working memory and sleep in dogs. It was hypothesised that dogs with chronic pain from osteoarthritis would show reduced measures of working memory in behavioural tasks, as well as decreased sleep quality (assessed via owner questionnaire) and potentially decreased night-time rest (assessed actigraphically) compared to healthy control dogs.

Because an investigation into all potential effects of chronic pain from osteoarthritis on cognition and behaviour of dogs would be prohibitively vast in scope, this project will focus on the effects of spontaneous canine osteoarthritis on working memory and sleep. Other aspects of canine osteoarthritis are beyond the scope of this project.

1.7.3 Thesis structure:

The thesis structure is as follows: Chapter 2 describes a study comparing osteoarthritic and healthy control dogs’ performance in two single-session tasks of working memory; the novel object recognition task and the holeboard task. Chapter 3 describes a study investigating companion animal dogs’ performance in a holeboard task with an increased number of trials and a disappearing object task, in order to assess whether the

Chapter 1: Introduction and Literature Review tasks could be learned by the dogs and which task would be more suitable to compare working memory in dogs with and without chronic pain from osteoarthritis. Chapter 4 describes a study comparing the performance of dogs with and without chronic pain from osteoarthritis in the disappearing object task of working memory, and Chapter 5 describes a study comparing actigraphic measures of time spent resting and subjective owner-assessed sleep quality scores between the same groups of dogs. The thesis conclusion is presented in Chapter 6.

Appendices are also provided for further information; these are grouped and numbered according to which chapter the information within them is most relevant to, for example Appendix 2 contains material that is most relevant to the study described in Chapter 2.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks Chapter 2 Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

2.1 Abstract:

In order to investigate whether chronic pain from osteoarthritis is associated with impaired working memory in dogs, the study described in this chapter attempted to compare the performance of dogs with and without osteoarthritis in a single-session task of non-spatial working memory (the novel object recognition task) and a single- session task of spatial working memory (the holeboard task). Unexpectedly, osteoarthritic dogs spent proportionally more time interacting with a novel compared to a familiar object in comparison to healthy control dogs, suggesting that osteoarthritis may be associated with increased non-spatial working memory or increased neophilia. However, the proportions of time dogs spent interacting with either object were very low, suggesting that dogs had minimal interest in these objects. There were no significant differences in holeboard task performance between osteoarthritic and control dogs, however the lack of expected increases in reference and working memory scores suggest that neither group of dogs was able to successfully learn this task.

2.2 Introduction:

Chapter 1 discussed the prevalence of chronic pain from osteoarthritis in dogs and the cognitive impairments seen in human patients with chronic pain syndromes. Since it is unknown whether dogs with chronic pain also have similar cognitive impairments, the experimental work described in this chapter attempted to accurately differentiate dogs with chronic pain from osteoarthritis from healthy control dogs without chronic pain, and then to assess the performance of each of these groups of dogs in a task of non- spatial working memory; the novel object recognition task, and a task of spatial working memory; the holeboard task. Because the tasks were to be performed by companion animal dogs brought to the hospital by their owners, it was also necessary to design the

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks tasks such that they could be completed within a short period during a single visit by the dog’s owner.

For ease of reading, this chapter is divided into three main sections: Section 2.3 describes the methods used to differentiate osteoarthritic and healthy control dogs, and to compare owners’ assessment of chronic pain in these groups using questionnaires. Section 2.4 describes the use of the novel object recognition task to compare non- spatial working memory in these two groups of dogs, and Section 2.5 describes the use of the holeboard task to compare spatial working and reference memory in the same groups of dogs. The findings are then summarised in Section 2.6.

2.3 Clinical and owner assessment of chronic pain in dogs with and without signs of osteoarthritis:

2.3.1 Background:

Prior to cognitive testing, osteoarthritis severity was assessed for each dog, to ensure that dogs were correctly assigned to the osteoarthritis or healthy control groups and to assess the effect of osteoarthritis severity on cognitive task results. Whilst the presence and severity of osteoarthritis in dogs is generally assessed radiographically (Morgan et al., 1999, Kealy et al., 2000, Rayward et al., 2004, Hurley et al., 2007) radiographic changes do not correlate well with assessments of limb function (Gordon et al., 2003) or pain (Hielm-Björkman et al., 2003) (see Section 1.3.2). Therefore it was considered that anaesthesia and radiography would have been unnecessarily invasive, and would also have required a Project License (PPL) from the Home Office (Animals (Scientific Procedures) Act, 1986). Instead, a verbal history was taken from the owner of any signs of osteoarthritis (stiffness, pain, slowing down during walks or difficulty jumping or climbing), and a veterinary surgeon performed a clinical orthopaedic examination using a standardised “clinical checklist” (See Appendix 2.1).

Since a clinical examination only assesses osteoarthritis severity at that specific time and is not a validated assessment tool, a method of assessing the dogs’ pain intensity, demeanour, mobility and quality of life at home was also required. Several clinical questionnaires have been designed and validated for this purpose (relevant types of validity and methods of assessment are summarised in Appendix 2.2), including the

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Liverpool Osteoarthritis in Dogs questionnaire (LOAD) (Hercock et al., 2009, Walton et al., 2013), the Helsinki Chronic Pain Index (HCPI) (Hielm-Björkman et al., 2003, Hielm- Björkman et al., 2009), the Canine Brief Pain Inventory (CBPI) (Brown et al., 2007, Brown et al., 2008) and the ACVS Canine Orthopaedic Index (COI) (Brown, 2014a, Brown, 2014b, Brown, 2014c). Since these questionnaires each have their own strengths and weaknesses and have been validated in different ways (see Table 2.1), all four were used in this study. This also allowed an assessment of the correlation between different questionnaire scores, to give further information about their criterion validity, and by comparing osteoarthritic to control dogs also provided a measure of construct validity (extreme groups comparison, see Appendix 2.2), which has not yet been assessed for the LOAD and COI, as shown in Table 2.1. Whilst other pain-related questionnaires have previously been used in dogs, including the Bristol Osteoarthritis in Dogs (BrOAD) questionnaire which uses visual analogue scales rather than numeric responses (Innes and Barr, 1998), and the Health Related Quality of Life in dogs (HRQL) questionnaire which focuses on effects of pain on quality of life rather than pain itself (Wiseman-Orr et al., 2006), these were not included in this study in order to limit the number of questionnaires to a more reasonable number for the owners to complete.

Table 2.1: A description of the clinical questionnaires used in this study and their validation.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

In addition to these, owners were also given the Canine Cognitive Dysfunction Rating (CCDR) scale questionnaire, to identify cognitive dysfunction that might affect a dog’s performance in the tasks (Salvin et al., 2011). This is especially important as this study was focused on older dogs (since osteoarthritis is an age-related condition (Van Saase et

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks al., 1989, Oliveria et al., 1995)), a population in which cognitive dysfunction is highly prevalent (Azkona et al., 2009).

Owners were also given the Canine Behavioural Assessment and Research Questionnaire (C-BARQ) (Hsu and Serpell, 2003, Nagasawa et al., 2011), to assess whether any behavioural traits were more/less common in osteoarthritic dogs than in healthy controls. This was performed because differences between groups in certain behaviours might be indicative of motivational or affective state differences that might affect their performance in the behavioural tasks. For example, dogs with high scores in C-BARQ categories associated with fear or aggression might indicate increased anxiety, which decreases the time spent interacting with novel objects in rats (Ennaceur et al., 2005), dogs with increased scores for energy or excitability might show increased movement around the arena which may affect their interaction with objects or their movement between buckets, and dogs with increased scores for attachment/attention-seeking may be more motivated to stay near the owner rather than to explore the arena and the objects or buckets.

2.3.2 Materials and Methods:

2.3.2.1 Ethics:

The use of animals within this study was approved by the University of Bristol Animal Welfare and Ethical Review Body (VIN Number: VIN/15/042), and the involvement of owners was approved by the University of Bristol Faculty of Health Sciences Research Ethics Committee. Owners were provided with an information sheet about the study (shown in Appendix 2.9), given the opportunity to ask any questions, and asked to sign a consent form (shown in Appendix 2.10) permitting their dog’s involvement. Trials were terminated early if the dog appeared to the researcher to be in excessive distress. This occurred in the case of only one dog during the test phase of the novel object recognition task.

2.3.2.2 Animals:

Sixteen privately-owned companion animal dogs (8 control, 8 osteoarthritic) were recruited via an advertising campaign on Facebook (https://en-gb.facebook.com) and internal emails to university staff members. The dogs were matched into eight control-

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks osteoarthritis pairs of the same age (±1 year) with entire (un-neutered) dogs matched to a dog of the same sex, to reduce the effect of any sexual/hormonal differences in cognition or pain, as previously seen in humans (Affleck et al., 1999, Cánovas et al., 2008). Dogs were not matched by breed due to the wide variety of breeds recruited (see Table 2.2). These matched pairs were used only to ensure a similar distribution of ages and sexes (if entire) between the two groups, and to appropriately counterbalance object colours and locations during the novel object recognition task (see Table 2.6) and configurations of baited buckets in the holeboard task (see Table 2.9). Because differences in breed and life experience could not adequately be controlled for, dogs were not paired during analyses. It was assumed that neutered dogs would not have sex-dependent differences in outcome measures due to their lack of sexual hormones, though the effect of sex on the outcome measures for the cognitive tasks was investigated to confirm this (see Sections 2.4 and 2.5). The demographics of each group are shown in Table 2.2. An additional three dogs (one eight-year-old neutered male healthy control, two ten-year-old entire males with osteoarthritis) were recruited and performed the study, but their results were not analysed as it was not subsequently possible to match them with a dog of the same age and neutering status from the other group. Participating owners were given a £10 gift card (John Lewis Partnership, London, UK) following completion of the study.

Table 2.2: Demographic compositions of each group of dogs.

Sex & Sex Neutering status

Breeds recruited:

Sample size

Weight (kg)

Description

Age Age (years)

Grou

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Four neutered females, oneentire female,

bordercollie (x2),Staffordshire bull terrier

greyhound, mixed breed, Weimaraner,

Cocker

Healthypain

three neuteredthree males

spaniel, golden retriever,

25.1 ±5.9

7.6 7.6 ±0.8

Control

freecontrol dogs

Mixed Mixed

borderterrier, Irish terrier, springer

Dogs with chronic pain from osteo

Two neutered females, one entire

spaniel, Staffordshirebull terrier

female,five neutered males.

breed (x2), Wels

Osteoarthritis

17.2 ±3.8

7.4 7.4 ±0.9

arthritis

h corgi, lurcher,

Values given are means ± 95% confidence limits.

2.3.2.3 Data acquisition:

Prior to the tasks described in Sections 2.4 and 2.5, a clinical checklist was used by a veterinary surgeon (MS) to perform a standardised clinical examination. This checklist (shown in Appendix 2.1) was adapted from that described by Harris (2017) and used to collect information described in Table 2.3. An anxiety checklist based on that used by Harris (2017) and aimed at identifying behaviours likely to be associated with anxiety was also included, however due to practical difficulties associated with accurate completion of the checklist during history taking and clinical examination, as well as because the novel object recognition task involved more quantitative assessments of anxiety-related behaviours, this checklist was not included in analyses.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Owners were then given the six printed questionnaires (HCPI, LOAD, CBPI, COI, CCDR and C-BARQ – shown in Appendix 2.3 to Appendix 2.8) and a pencil and asked to fill in the questionnaires whilst their dog performed the cognitive tasks.

2.3.2.4 Data analysis:

Data from each dog’s completed clinical checklist (see Table 2.3), as well as the group to which the dog was assigned, were entered into a dataset.

Table 2.3: Contents and reasoning behind components of the clinical checklist.

Data recorded on clinical examination Reason for recording: form:

Signalment (breed, To assess whether these factors have an effect on task sex, age, neutering outcomes and to match dogs for age. status, weight)

Dogs that have eaten more recently may be less motivated Time last fed to obtain food rewards.

Any current Analgesics may reduce dogs’ pain severity and may medication, when last potentially have adverse cognitive effects. taken

Any food allergies or To ensure the food rewards used are safe. intolerances

Brief history of signs associated with To inform categorisation into control/osteoarthritis groups. arthritis

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Brief description of any other health To ensure that the dog is healthy enough to participate in issues the study and rule-out any diseases that might affect their performance (neurological, other orthopaedic, cardiac, Routine clinical exam blindness). to assess overall health

Body condition scoring (WSAVA Weight is likely to be a factor in osteoarthritis severity and Nutritional progression (Impellizeri et al., 2000, Smith et al., 2006). Assessment Body condition score was recorded as dogs were of Guidelines Task Force different breeds and thus different optimal weights. Members, 2011)

A straightforward method of visually assessing dogs' ease Lameness score of movement. This 1-5 scale was selected because each (Gordon-Evans et al., score is well defined and thus robust to variations in 2013) subjective judgement.

Difficulty of A subjective measure of stiffness and ease of movement to lying/standing from assist the vet in categorising the dog and scoring their sitting severity.

Standardised joint To identify signs of osteoarthritis such as stiffness, swelling examination including heat and crepitus (Hart et al., 1991, Sellam and sites and details of any Berenbaum, 2010). abnormalities

Joint Function score Detects the presence of a pain response on manipulation (modified from of a joint, which is often reported in human osteoarthritis Impellizeri et al. (Hart et al., 1991). (2000))

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Subjective opinion of the examining vet after taking into account all of the recorded information – “none”, “mild”, Vet assessment of “moderate” or “severe”. Dogs with a severity of “none” severity were assigned to the control group, all others to the osteoarthritic group.

To assess to what extent the vet’s assessment of Owner assessment of osteoarthritis severity was associated with that of the severity owner.

Scores were processed as described on each questionnaire or in the supporting literature: For the HCPI, LOAD and CCDR, a single score for each was obtained by adding the owner scores across the questionnaire. The four component scores of the COI (see Table 2.1) were given by the sum of the scores within each subsection. The two component scores of the CBPI (see Table 2.1) were given by the mean of the scores within each subsection. Additionally, the owner’s rating of the dog’s quality of life (“CBPI QOL Score”) was recorded as it was present on the CBPI questionnaire, despite not being a validated part of the questionnaire itself (Brown et al., 2007). For the C-BARQ, question scores were entered into the C-BARQ’s official website (http://vetapps.vet.upenn.edu/cbarq) and a range of scores for behavioural traits were generated automatically, these were: Stranger-directed aggression, owner-directed aggression, dog-directed aggression, dog-directed fear, familiar-dog aggression, trainability, chasing, stranger-directed fear, non-social fear, separation-related problems, touch sensitivity, excitability, attachment/attention seeking and energy. Each of the behavioural trait scores for each dog were entered into the dataset.

The effect of group on clinical questionnaire scores, CCDR score, C-BARQ scores and clinical joint function and lameness scores were analysed in SPSS using the Multivariate General Linear Model function. Three models were created in total: One multivariate model was specified with HCPI score, LOAD score, CBPI Pain Severity score, CBPI Pain Interference score, COI Stiffness score, COI Function score, COI Gait score, COI Quality of Life score and CCDR score as dependent (outcome) variables and group as a fixed factor, to investigate the effect of group on questionnaire scores. A second multivariate model

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks was specified containing all of the C-BARQ outcomes (stranger-directed aggression, owner-directed aggression, dog-directed aggression, dog-directed fear, familiar-dog aggression, trainability, chasing, stranger-directed fear, non-social fear, separation- related problems, touch sensitivity, excitability, attachment/attention seeking and energy) as dependent variables and group as a fixed factor, to assess the effect of group on behavioural traits assessed by the C-BARQ. A third multivariate model was created with lameness score and joint function score as dependent variables and group as a fixed factor.

Bonferroni-adjusted Pearson correlation coefficients were calculated to assess correlations between different clinical questionnaire (HCPI, CBPI, COI and LOAD) component scores and between clinical questionnaire scores and CCDR score. T-tests (for numeric signalment factors) and Fisher’s exact tests (for categorical signalment factors) were performed to investigate group differences in signalment factors.

Bonferroni adjustments for multiple testing were performed for all post-hoc comparisons (Fisher’s Least Significant Difference) where a significant effect of a factor with more than two levels or an interaction between two factors was identified. SPSS performs these automatically by multiplying the p-value associated with each outcome statistic by the number of comparisons, rather than the conventional method of dividing the threshold for p-value significance (α) by the number of comparisons (IBM Corporation, 2016), therefore the p-value threshold for significance (α) remained at α=0.05 for these results.

Results were reported as a test statistic, degrees of freedom (shown as a subscript) and a p-value. Graphs show means with error bars representing 95% confidence intervals and asterisks representing significant differences (α=0.05) between indicated columns, unless stated otherwise.

2.3.3 Results:

The demographic composition of each group is shown in Table 2.2, with body condition scores shown in Figure 2.1. There was no significant difference in sex (Fisher’s Exact

Test, p=0.619), age (t(14)=0.202, p=0.843) or body condition score (t(14) =1.986, p=0.067) between groups. A significant difference in weight between groups (t(14)=2.202, p=0.045)

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks was identified, with healthy control dogs tending to be heavier than osteoarthritic dogs (see Table 2.2). Since only two entire dogs were recruited, neutering status was not included in analyses.

Figure 2.1: Body condition scores of recruited dogs.

No dogs had a body condition score that was lower than three, exactly six, or greater than seven.

A significant (α=0.05) effect of group was found on all clinical questionnaire scores with the exception of CBPI QOL Score (shown in Figure 2.2 with statistical test outcomes summarised in Table 2.4). The range of possible scores for each clinical questionnaire component is shown in Table 2.1. All clinical questionnaire scores were highly correlated with each other, with the exception of CBPI Quality of Life Score (which despite being on the questionnaire is not considered a validated component of the CBPI – see Table 2.1) which did not correlate with any other clinical questionnaire score, as summarised in Table 2.5. Mean CCDR scores were not significantly different between osteoarthritic

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

(26.75±1.83) and control (26.19±1.30) dogs (see Table 2.4) and did not correlate with any clinical questionnaire score. All dogs had CCDR scores well below the diagnostic threshold (50 points) suggestive of cognitive dysfunction (Salvin et al., 2011). C-BARQ component scores did not significantly differ between treatment groups (α=0.05), with the exception of Excitability (F(1,14)=5.710, p=0.031) which was significantly lower in osteoarthritic dogs than in control dogs, as shown in Figure 2.3.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.2: The effect of group on clinical questionnaire scores.

Graphs show means with error bars representing 95% confidence intervals.

(ns = not significant; α=0.05. QOL = “Quality of Life”) Asterisks denoting significance were omitted for clarity; significant (α=0.05) between-group differences exist unless indicated otherwise. Test outcome statistics for individual component scores are shown in Table 2.4.

Table 2.4: Summary of the F-statistics and p-values (rounded to three decimal places) for effects of group on clinical questionnaire and CCDR scores.

Effect of Group on: Degrees of Freedom F-Statistic p-value CBPI Pain Severity Score 1,14 6.308 0.025 CBPI Pain Interference Score 1,14 9.412 0.008 CBPI Quality of Life Score 1,14 0.2 0.662 COI Stiffness Score 1,14 10.085 0.007 COI Function Score 1,14 9.218 0.009 COI Gait Score 1,14 29.346 <0.001 COI Quality of Life Score 1,14 6.615 0.022 HCPI Score 1,14 13.186 0.003 LOAD Score 1,14 22.309 <0.001 CCDR Score 1,14 0.351 0.563

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Table 2.5: Pearson correlation coefficients and p-values between scores for clinical questionnaire component scores and between clinical questionnaire component scores and CCDR score.

Pearson Correlation Coefficients:

CBPI CBPI Pain Interferen QOLCBPI COI Stiffness COI Function COI Gait COI QOL HCPI LOAD CCDR

Component Score:

0 - 0 0 0 0 0 0 -

0.319 0.017

CBPI Pain .915 .869 .846 .795 .784 .732 .811

Severity

- 0 0 0 0 0 0 0.269

0.368

CBPI Pain .793 .770 .825 .859 .733 .826

Interference

------

CBPI QOL 0.23 0.211 0.194 0.435 0.281 0.303 0.064

0 0 0 0 0 0.1

COI Stiffness .966 .883 .782 .8 .785

0 0 0 0 0.007

COI Function .830 .700 .777 .715

0 0 0 0.263

COI Gait .800 .844 .876

0 0 0.382

COI QOL .740 .801

0 0.339

HCPI .796

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

0.261 LOAD

All correlations are statistically significant (α=0.05) except for those shaded in red. Due to the large number of comparisons performed, a Bonferroni correction was applied to the threshold for significance (p=0.001111). Only the correlations of the COI function score with the COI QOL score and with the LOAD score became non- significant once this correction was applied (shaded in blue), however this correction is highly conservative (Curtin and Schulz, 1998) and both of these correlations are considered strong (≥0.7) (Taylor, 1990).

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.3: The effect of group on C-BARQ component scores.

Graphs show means with error bars representing 95% confidence intervals. Asterisks represent significant differences (α=0.05) between indicated columns. SDA = Stranger-Directed Aggression, ODA = Owner-Directed Aggression, DDA = Dog- Directed Aggression, DDF = Dog-Directed Fear, FDA = Familiar Dog Aggression, SDF =

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Stranger-Directed Fear, SRP = Separation-Related Problems, A/AS = Attachment/Attention-Seeking.

Both the lameness score (F(1,14)=14.538, p=0.002) and joint function score (F(1,14)=28.493, p=0.0001) were significantly higher in osteoarthritic than control dogs, as shown in Figure 2.4. Summaries of owner-assigned and vet-assigned severity ratings are shown in Figure 2.5. No dogs were judged to have “severe” osteoarthritis by their owner or the vet. All owners of control dogs (vet-assigned severity = “none”) also felt their dog had no osteoarthritis, however, two dogs assessed by the vet as “moderate” were assessed by their owners as “mild”, and one dog assessed by the vet as “mild” was assessed by the owner as “moderate”, as shown in Figure 2.5C. Whilst a significant overall effect of vet- assigned severity score was found for all clinical questionnaire factor scores (α=0.05), with the exception of CBPI QOL (F(2,13)=0.093, p=0.912), CBPI Pain Severity (F(2,13)=3.193, p=0.074) and COI QOL (F(2,13)=3.071, p=0.081) scores, Bonferroni-adjusted Fisher’s Least Significant Difference post-hoc tests found no significant differences (Bonferroni- adjusted outcome p-values, significance threshold remained at α=0.05 (IBM Corporation, 2016)) between any clinical questionnaire scores of dogs classified as “mild” and dogs classified as “moderate” (whilst there were several significant (α=0.05) differences between dogs classified as “none” and those classified as “mild” or “moderate”, these comparisons were already considered when analysing the effect of group on questionnaire scores). Clinical questionnaire scores of dogs of each vet- assigned severity category are shown in Figure 2.6.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.4: The effect of group on clinical lameness and joint function scores.

Graphs show means with error bars representing 95% confidence intervals.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.5: The vet- assigned (A) and owner-assigned (B) osteoarthritis severity ratings of the dogs recruited.

Frequency of agreement/ disagreement between the vet and owner ratings are is shown in (C), with higher scores referring to increasing severity.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.6: Clinical questionnaire component scores for each vet-assigned osteoarthritis severity score.

Graphs show means with error bars representing 95% confidence intervals.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks 2.3.4 Discussion:

Both groups of dogs were demographically similar: There were no significant sex differences between groups and age matching successfully resulted in highly similar age distributions for both groups. Whilst control dogs had significantly higher bodyweights than osteoarthritic dogs, a wide range of breeds of different sizes and optimum healthy weights were recruited. Therefore body condition score may be a more generalisable indicator of whether a dog is overweight, which may increase clinical signs of osteoarthritis (Impellizeri et al., 2000). Body condition score did not significantly differ between groups and most dogs recruited had an “ideal” score (4-5) (WSAVA Nutritional Assessment Guidelines Task Force Members, 2011) implying they were of a healthy body condition. The two dogs with a BCS of 3 were a whippet-cross (osteoarthritis) and a greyhound (control), which naturally tend to have a lower proportion of body fat than most breeds (Gunn, 1978), and since the WSAVA BCS criteria are non-breed specific (Mawby et al., 2004) these dogs may not actually be underweight for their breed.

All clinical questionnaire component scores (see Table 2.1 for a description of the clinical questionnaires used and their components) significantly differed between groups (the quality of life question on the CBPI is not a validated component of the questionnaire), with osteoarthritic dogs having higher scores than controls (see Figure 2.2). Given that these questionnaire results were not available to the veterinary surgeon when categorising the dogs into groups, the significant differences in clinical questionnaire scores between groups provides evidence that the veterinary surgeon had correctly assigned the dogs to the appropriate group. Furthermore, the difference in clinical questionnaire scores between groups provides further evidence of construct validity, and appears to be the first time that construct validity has been assessed by comparison of extreme groups (see Appendix 2.2 for definitions of these terms) for the LOAD and COI (see Table 2.1).

Additionally, all clinical questionnaire scores were highly correlated, with almost all correlations remaining significant following the application of a highly conservative Bonferroni correction. This provides evidence of their criterion validity and supports the findings of Walton et al. (2013) who similarly found correlations between the LOAD score and the HCPI and CBPI scores. This study also found that all four COI component scores were correlated with all other validated clinical questionnaire component scores,

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks which is important as such evidence to support its criterion validity was previously lacking. Interestingly, whilst it is not part of the CBPI itself, the quality of life rating on the CBPI questionnaire did not correlate with any CBPI score, despite being found to correlate moderately well with CBPI scores during validation of the CBPI in both dogs with osteoarthritis (Brown et al., 2007) and bone cancer (Brown et al., 2009), nor did it correlate with any other clinical questionnaire score. This may have been due to insufficient sensitivity of a single-question to detect quality of life differences in dogs with only mild or moderate osteoarthritis. The validated, multi-question quality of life component of the COI did correlate with other clinical questionnaire scores and was significantly different between groups, so it is reasonable to assume that the recruited osteoarthritic dogs did have significant quality of life impairments compared to healthy controls (higher scores indicate lower quality of life (Brown, 2014b)).

All dogs had very similar CCDR scores, at approximately half of the diagnostic threshold value for cognitive dysfunction (Salvin et al., 2011). This suggests that none of the dogs had significant age-related cognitive dysfunction.

No differences in C-BARQ factor scores were found between groups, with the exception of excitability. This seems reasonable given that human osteoarthritis patients with severe pain or joint dysfunction are more likely to be inactive (Lee et al., 2013), and whilst these dogs are only in mild to moderate pain, this may have had some effect on their activity levels such that fewer of them displayed sufficient activity to be considered “excitable”. Furthermore, human osteoarthritis patients report substantial levels of fatigue (Power et al., 2008), and if dogs with osteoarthritis also experience fatigue it may cause them to behave less excitably.

Joint function score and Lameness score were significantly different between groups, indicating that dogs with arthritis had both a significantly altered gait and significantly increased presence of a pain response on manipulation of the affected joint(s). Both of these are common signs of osteoarthritis in human patients (Hart et al., 1991, Kaufman et al., 2001), so a significant between-group difference in these scores provides further evidence suggesting the dogs were correctly assigned to groups based on the presence or absence of osteoarthritis.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

There were no significant differences in clinical questionnaire scores between dogs assessed by the veterinary surgeon as having “mild” compared to “moderate” osteoarthritis, indicating that the subjectively-assessed difference between these may be minor and of little clinical significance. Since there were no severely affected dogs recruited, possibly due to owners’ reluctance to allow a dog in “severe” pain to participate, it may be more salient to compare all osteoarthritic dogs (the “osteoarthritis” group) to all dogs with a severity of “none” (the “control” group), rather than assessing dogs categorised as having “mild” or “moderate” osteoarthritis separately. Additionally, owner scores of dogs’ osteoarthritis severity were not always the same as those of the veterinary surgeon. This may be because the distinction between the subjective categories of “mild” and “moderate” is unclear, as these are subjective judgements which may vary between individuals. Furthermore, since these categories did not affect the dogs’ clinical questionnaire scores, the differences between them are likely to be minor and of little clinical significance.

2.4 The Novel Object Recognition task: Comparing the non- spatial working memory of osteoarthritic and healthy control dogs:

2.4.1 Background:

Whilst humans with chronic pain may experience non-spatial working memory deficits (see Section 1.4.1), it is not yet known whether this is the case in dogs. A common test of non-spatial working memory in laboratory rodents is the one-trial novel object recognition (NOR) task (Ennaceur and Delacour, 1988, Cohen and Stackman Jr, 2015). This consists of a sample phase where rodents are exposed to two identical objects in an arena, a variable delay (the “retention interval”), and a test phase where the rodent is reintroduced to the arena containing one novel object and one familiar object from the sample phase (Dere et al., 2007). The amount of time spent interacting with each object is measured, and a greater time spent interacting with the novel object than the familiar object is interpreted as evidence that the rodent has recognised the object from the sample phase as familiar (Ennaceur and Delacour, 1988). This task is advantageous for several reasons: It is based on rodents’ natural exploratory behaviour and preference for novel objects (and their habituation to and comparative lack of interest in familiar

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks objects) and therefore requires no training or learning of rules, thus testing the rodents’ working memory without involving a reference memory component (Ennaceur and Delacour, 1988). It does not involve positive or negative reinforcement, so animals do not experience the hunger or fear required for motivation to acquire food rewards or to avoid an aversive stimulus in other testing paradigms (Ennaceur and Delacour, 1988), giving greater similarity to human studies which do not generally occur in these altered emotional states (Ennaceur and Delacour, 1988). It is also thought to cause less stress than reinforced tasks (Silvers et al., 2007, Cohen and Stackman Jr, 2015), which is known to impair working memory (Oei et al., 2006, Qin et al., 2009), as well as being an important welfare concern. The NOR task is also particularly appealing for potential use in client-owned dogs because it can be completed very quickly in a single session (Antunes and Biala, 2012), making it more convenient for dogs’ owners than tasks requiring multiple visits to the research site.

The novel object recognition (NOR) paradigm is widely used in rats (Sutcliffe et al., 2007, Karasawa et al., 2008, Broadbent et al., 2010, Gaskin et al., 2010) and mice (Hale and Good, 2005, Taglialatela et al., 2009). Variants of the task have also been performed in pigs, with Moustgaard et al. (2002) finding that laboratory pigs prefer novel objects to familiar objects, suggesting neophilia, and Hemsworth et al. (1996) finding that farmed pigs were reluctant to approach an object unless they had previously been exposed to it, suggesting neophobia. This discrepancy could be due to differences in outcome measures (durations of interaction time vs. latency to approach an object) or due to differences in the husbandry or biology of farmed and laboratory pigs. Presenting a novel object (but not a familiar object) to trout increased their opercular beat rate (analogous to respiratory rate), suggesting that trout are neophobic and can recognise familiar objects (Sneddon et al., 2003). However, latency of cattle to approach an object was not altered by prior exposure to the object (Hemsworth et al., 1996), possibly because cattle either do not readily recognise familiar objects, take longer to become familiar with an object than the time given, or do not display marked neophilia or neophobia, or because the objects used were not of particular salience to cattle.

The novel object recognition task has not previously been used in dogs, however puppies shown videos of objects between 3-5 weeks of age subsequently visited the objects less and showed less exploratory behaviour overall than puppies that were not shown the videos, and showed more exploratory behaviour in response to objects that

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks were not shown in the videos than those that were (Pluijmakers et al., 2010). However unexposed puppies showed more fearful behaviour and postural changes in response to the objects than exposed puppies (Pluijmakers et al., 2010), suggesting that the increased attention shown to unfamiliar objects in dogs may be due to fear or anxiety evoked by the objects rather than a desire to explore or play with them. Nevertheless, dogs appear to show a differential response to familiar and unfamiliar objects, suggesting that the novel object recognition task is feasible to attempt in dogs.

Therefore in this study the novel object recognition paradigm was adapted for use in companion-animal dogs, with the aim of investigating whether dogs with chronic pain from osteoarthritis experience deficits in short-term non-spatial working memory for objects compared to age-matched healthy controls. It was hypothesised that control dogs would recognise the familiar object in the test phase and thus spend less time interacting with it than with the novel object, whereas the dogs with chronic pain from osteoarthritis would have impaired non-spatial working memory for objects and thus would spend more equivalent amounts of time interacting with each object.

2.4.2 Materials and Methods:

2.4.2.1 Pilot Study:

For an initial pilot study, a group of seven university staff-owned dogs were recruited by internal email for initial proof-of-concept testing, to ensure that these testing paradigms could be applied to dogs. The signalments of the pilot dogs are shown in Appendix 2.12 and the pilot study novel object recognition task results are shown in Appendix 2.13. Each dog participated in the pilot study for both the novel object recognition and holeboard tasks within the same session.

The methods for the pilot study of the novel object recognition task were the same as those described in Sections 2.4.2.3-2.4.2.5 for the main study comparing osteoarthritic and control dogs, with the following exceptions: The holeboard task pilot study (described in Section 2.5.2.1) was performed before the novel object recognition task pilot study, with a short break of approximately 10 minutes between tasks, in which the dog was walked outside and offered drinking water. This was also the case for dogs C1 and T1 in the main study, but the order of tasks was subsequently reversed because C1 (the first dog to perform the tasks in the main study) appeared to become distressed

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks when performing the novel object recognition task after the holeboard task, possibly as she was frustrated by the removal of food rewards that had previously been present. It was thought that reversing the order of tasks so that food rewards were introduced later during the holeboard task may have prevented or reduced this distress, so the order of tasks was changed for subsequent pairs of dogs. Additionally, video footage from the pilot study was event-logged by the thesis author (MS), whereas in the main study video footage was event-logged by an assistant who was blinded with respect to group (osteoarthritis/control), to reduce the effects of observer bias, however the pilot study was not aimed at detecting between-group differences (and only one pilot study dog was known to have osteoarthritis) and no formal statistical analyses were performed, so the recruitment of a blinded assistant for event-logging was not deemed necessary.

The only outcome variable examined for the pilot study of the novel object recognition task was the investigation ratio during the test phase (the time spent investigating the novel object as a proportion of the total time spent investigating either object). Behaviours not relating to object investigation were not recorded or analysed.

Whilst two dogs did not investigate either object at all, the remaining five dogs had investigation ratios greater than 0.5 (see Appendix 2.13), suggesting that they were neophilic and had recognised the object. Because this suggested that dogs were able to recognise novel objects and preferentially investigate them in the same way as previously observed in rats (Ennaceur and Delacour, 1988), it was decided to proceed with the main study to compare the investigation ratios of osteoarthritic and control dogs.

2.4.2.2 Animals:

Following the pilot study, the dogs that undertook clinical and owner assessment in Section 2.3 (8 per group – see Table 2.2) were then tested using the same novel object recognition paradigm described below.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks 2.4.2.3 Apparatus:

The arena used for cognitive tasks is shown in Figure 2.7. Since dogs owned by members of the public were likely to have experienced a wide variety of objects previously, completely original objects were created for use in this study. Two pairs of such objects were created as shown in Figure 2.8: The “blue” object was constructed from a shoebox wrapped in a plastic polka-dot tablecloth and covered with five polka-dot cardboard cups attached to the surface with blue electrical tape. The “yellow” object was constructed from an empty 1L cordial bottle wrapped in gold wrapping paper, with three white polystyrene bowls threaded through the bottle structure and taped in place with transparent tape, likewise a polystyrene cup was attached to the tapered end of the bottle. Blue and yellow objects were chosen as it was thought that the dichromatic vision of dogs should allow these colours to be easily distinguished (Miller and Murphy, 1995), and dogs have been shown to use colour cues preferentially to brightness cues when distinguishing visual stimuli (Kasparson et al., 2013). Two copies of each object were constructed to counterbalance which object was presented as the “novel” or “familiar” object between pairs, controlling for an inherent preference amongst dogs for one object type over the other.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.7: (A): Diagram of the arena used within the novel object recognition task.

The grey area represents the holding area in which the dog was held between trials. “S” and “O” represent the locations of the researcher and owner respectively. “L” and “R” represent the locations of the objects during the novel object recognition task. Yellow areas represent the areas in which the dog is in “close proximity” to the

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

nearest object. Photographs show the arena set up for the novel object sample phase (B) and test phase (C).

Figure 2.8: The “blue” (left) and “yellow” (right, with blue objects in background) pairs of objects constructed for this study.

2.4.2.4 Procedure:

Each age-matched control-osteoarthritis pair of dogs was assigned an initial object pair (blue/yellow) for the sample phase (in which a pair of identical sample objects were presented), and a side of the arena in which the object was to be switched in the test phase (left/right), based on a counterbalancing table; shown in Table 2.6.

Table 2.6: Counterbalancing table used for assigning each pair of dogs a sample phase object pair colour and novel object location.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

“Test subject” dogs were those assigned to the osteoarthritis group. The three dogs that were excluded from the study due to lack of an appropriate matched dog (T4, C9 and T11) are shown in grey.

Following clinical examination, the appropriate pair of objects for the sample phase (see Table 2.6) was placed in the arena at the locations shown in Figure 2.7. The researcher and owner(s) sat quietly in the holding area without communicating with the dog, at the locations shown in Figure 2.7. The dog was then allowed to enter the arena containing the object pair and was video recorded for 5 minutes during the sample phase. The dog was then removed from the arena and the objects stored in an opaque closet. The dog then spent 10 minutes in the holding area, in which they were able to interact with the owner and researcher, and drinking water and bedding was provided. After 10 minutes, the objects were replaced within the arena, with one of the objects replaced with one from the other item pair (the “novel object”). The object that was replaced (left/right) for each pair of dogs is shown in Table 2.6. The dog was allowed to return to the arena and was video recorded for 5 minutes during the test phase, before being allowed to leave the arena and reunite with their owner in the holding area.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks 2.4.2.5 Recording and data analysis:

Video footage was recorded using a Canon Legria HF R206 camcorder mounted onto the wall of the holding area (see Figure 2.7). Footage was analysed using Behavioural Observation Research Interactive Software (BORIS) (Friard and Gamba, 2016) according to an ethogram shown in Appendix 2.11. The list of behaviours included in the ethogram is shown in Table 2.7. The time spent interacting with each object in each trial of the novel object paradigm was calculated as the sum of the time spent pawing, nosing, mouthing or in “other contact” with the object.

Table 2.7: Behaviours included in the ethogram for the Novel Object Recognition task.

Anxiety- Postural/ related or Object interaction locomotor vocal behaviours: behaviours: behaviours: Other behaviours: Sniff/nose right object, Lie down, Spin, Groom, Sniff/nose left object, Sit, Ears back, Cock leg, Close proximity to right Stand, Tail under, Defecate, object, Walk, Hunched/ Rear, Close proximity to left Trot or run, tense Sniff environment object, Squatting, posture, /air, Paw left object, Beg, Trembling, Look out of Paw right object, Climb wall or Licks lips, window, Mouth left object, window ledge Whine, Interact with fence, Mouth right object, Growl, Look through fence, Other contact with left Howl, Panting object, Bark Other contact with right object

The ethogram used is shown in full in Appendix 2.11 including behaviour types (point/state), descriptions and end-states. Due to its length and complexity it has been omitted from this chapter.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

The time spent in close proximity to each object (as described in Appendix 2.11), as well as the durations (for state behaviours) and frequencies (for point behaviours) of other behaviours performed by the dog during the task (listed in Table 2.7 and described in Appendix 2.11), were also recorded. Behaviours were recorded to assess whether there were differences in ambulation between groups, which may affect exploration of objects, as well as to record anxiety-related behaviours, as dogs may display behavioural signs of fear and anxiety when presented with novel objects (Pluijmakers et al., 2010) and there is some evidence that anxiety may adversely affect cognitive performance in humans (Chapell et al., 2005, Wood, 2006). Because observer bias can be induced by provision of information about the animals and hypothesis under examination and this can affect the frequency of behaviours recorded by the observer (Tuyttens et al., 2014), video analysis in BORIS was performed by another researcher who was blinded with respect to group (control/osteoarthritis) and provided with no information about the dogs’ signalments, using an ethogram devised by MS (see Appendix 2.11). All other aspects of analysis were performed by MS.

The raw data were exported to Microsoft Excel and the outcome measures for each task calculated (by MS) as shown in Table 2.8. Dog C1 was removed from the arena during the test phase after 180s as she showed signs of distress, therefore the results for C1 were divided by 0.6 so that the durations and frequencies of behaviours performed would be proportionally equivalent to those recorded for dogs that had spent the full 300s in the arena.

Categories were created for weight (12-15kg (n=4); 15-20kg (n=5), 20-30kg (n=4), 30- 40kg (n=3)) and age (6-7 years (n=9) and 8-10 years (n=7)) so that the sample size in each category was large enough for analyses to be performed in order to assess their effect on outcome measures. Data were analysed using the “General Linear Model – Repeated Measures” function in IBM SPSS, and comparisons between observed results and those expected by chance were calculated using one-sample t-tests.

As previously, Bonferroni adjustments to Fisher’s Least Significant Difference post-hoc tests were calculated automatically in SPSS by multiplying the p-value associated with each outcome statistic by the number of comparisons, and not by dividing the threshold for p-value significance by the number of comparisons (IBM Corporation, 2016), therefore the p-value threshold for significance is still given as α=0.05 for these results.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Table 2.8: Summary of the outcome measures calculated for the novel object recognition task.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

2.4.3 Results:

Results are reported as a test statistic, degrees of freedom (shown as a subscript) and p- value. Graphs show means with error bars representing 95% confidence intervals. Letters above bars represent no significant difference (α=0.05) between bars sharing the same letter. Letters are omitted where no significant differences are present between any factors.

Results from the pilot dogs are shown in Appendix 2.13. Since the IR (see Table 2.8 for definition) of all pilot dogs that interacted with any objects during the test phase was above that expected by chance (50%), implying all of the dogs were neophilic, the task was performed using the age-matched pairs of osteoarthritic and healthy control dogs (See Table 2.2).

There was no significant (α=0.05) effect of sex or bodyweight on any of the measures listed in Table 2.8, and no significant interaction with phase for any of these measures. A significant age*phase interaction was found for IR (F(1,11)=7.217, p=0.0212) with older (8- 10 year-old) dogs preferentially investigating the novel object compared to the familiar object in the test phase, but not the object in the same location as the future novel object compared to the object in the same location as the future familiar object in the sample phase (Mean difference =0.438 ±0.247, Bonferroni-adjusted p=0.0246), as shown in Figure 2.9. There were no significant differences between phases for younger (6-7-year-old) dogs.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.9: The effect of age on Investigation Ratio (IR - the time spent investigating the novel object or object in its future position as a proportion of the total time spent investigating either object) in each phase of the novel object recognition task.

Graphs show means with error bars representing 95% confidence intervals. The dotted line represents an IR of 50%, the value expected by chance.

Whilst no significant differences were found in any of the measures summarised in Table 2.8 between dogs that had the novel object located on the right compared to the left side of the arena, a significant difference for PLR (see Table 2.8) was detected between dogs that were assigned a yellow familiar and blue novel object than dogs assigned a blue familiar object and yellow novel object, as shown in Figure 2.10. PLR (proportion of

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks time spent exploring the left object compared to the right object) was significantly higher for dogs that were assigned a yellow sample object and a blue novel object than those that were assigned a blue sample object and yellow novel object (F(1,9)=5.539, p=0.043), as shown in Figure 2.10A. There was no significant phase by sample object colour interaction. Whilst a greater proportion of the dogs that were assigned a yellow sample object had the novel object presented on the left than the right, and a greater proportion of the dogs that were assigned a blue sample object had the novel object presented on the left as shown in Figure 2.10B (due to incomplete counterbalancing as a result of three unmatched dogs which were removed from the dataset following task performance), this difference was not statistically significant (Fisher’s Exact test, p=0.608).

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.10: (A) The Proximity Laterality Ratios (PLR - the time spent in close proximity to the left object as a proportion of the total time spent in close proximity to either object) for each phase of the novel object task for dogs that were assigned each colour of sample object (initial identical object pairs). (B) The numbers of dogs assigned each sample object which then had the novel object (of the alternative colour) placed in the right or left position (see Figure 2.7) during the test phase.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Graph (A) shows means with error bars representing 95% confidence intervals. Asterisks represent significant differences (α=0.05) overall between dogs assigned one type of sample object and dogs assigned the other.

A significant group by phase interaction was found on Investigation Ratio (IR – the proportion of the total time spent interacting with the objects that was spent interacting with the novel object (test phase) or object in the same location as the future novel object (sample phase)) (F(1,11)=9.729, p=0.00977), as shown in Figure 2.11. Bonferroni- adjusted Fisher’s Least Significant Difference post-hoc tests revealed that osteoarthritic dogs, but not control dogs, had a higher IR in the test phase than the sample phase (mean difference ± 95% CI (confidence interval) =0.483±0.203, Bonferroni-corrected p=0.0003). Osteoarthritic dogs also had a significantly higher IR during the test phase than control dogs (mean difference =0.357±0.279, p=0.017). The IR of the osteoarthritic dogs during the test phase was significantly higher than that expected by chance

(t(5)=6.601, p=0.000581) but the IRs of the osteoarthritic dogs during the sample phase and of the control dogs during either phase did not significantly differ from that expected by chance (α=0.05).

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.11: The effect of group and phase on investigation ratio (IR - the time spent investigating the novel object or object in its future position as a proportion of the total time spent investigating either object).

Graphs show means with error bars representing 95% confidence intervals. The dotted line represents an IR of 50%, the value expected by chance.

A significant group by phase interaction was found on Proximity Ratio (PR – the proportion of the total time spent in close proximity to the objects that was spent in close proximity to the novel object (test phase) or object in the same location as the future novel object (sample phase)) (F(1,11)=6.179, p=0.0303), as shown in Figure 2.12. Whilst osteoarthritic dogs had a higher PR on the test phase than the sample phase and control dogs had a lower PR on the test phase than the sample phase, this difference was not statistically significant for osteoarthritic (mean difference ± 95% CI =0.175±0.232, Bonferroni-adjusted p=0.125) or control dogs (mean difference ± 95% CI =0.210±0.250, Bonferroni-adjusted p=0.091). No significant differences were detected

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks between groups at either phase (α=0.05). The PR of neither group significantly differed from that expected by chance (50%) at either phase (α=0.05).

Figure 2.12: The effect of group and phase on Proximity Ratio (PR - the time spent in close proximity to the novel object or object in its future position as a proportion of the total time spent in close proximity to either object).

Graphs show means with error bars representing 95% confidence intervals. The dotted line represents a PR of 50%, the value expected by chance. Whilst a significant group by phase interaction was found on PR, there were no significant (α=0.05) differences between phases for either group or between groups for either phase.

No significant differences were found between groups for investigation laterality ratio

(ILR) (F(1,11)=0.046, p=0.835), proximity laterality ratio (PLR) (F(1,11)=0.489, p=0.499), or the time spent interacting with (F(1,11)=0.891 p=0.365) or in close proximity to

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

(F(1,11)=0.633, p=0.433) the objects, as shown in Figure 2.13. No significant effects of or interactions with phase were detected.

No significant differences were found between the proportions of osteoarthritic and control dogs that first visited the object in the same location in which the novel object would be placed during the test phase and those that first visited the object in the alternative location (Fisher’s exact test, p=0.619), as shown in Figure 2.14A. All dogs explored at least one object during the sample phase. No significant differences were found between the proportions of osteoarthritic and control dogs that first explored the familiar object, novel object or explored neither object during the test phase (Fisher’s exact test, p=0.429), as shown in Figure 2.14B.

Figure 2.13: (A) Investigation Laterality Ratio (the time spent investigating the left object as a proportion of the total time spent investigating either object), (B), Proximity Laterality Ratio (the time spent in close proximity to the left object as a

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

proportion of the total time spent in close proximity to either object), (C) Proportion of time spent interacting with either object and (D) Proportion of total time spent in proximity to either object for each group during each phase.

Graphs show means with error bars representing 95% confidence intervals. Time is expressed as a proportion of the total time (300s), such that 0.01 is equivalent to 3 seconds.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.14: The numbers of dogs which first visited each object during (A) the sample phase and (B) the test phase.

A significant effect of group was found on the time dogs spent with their ears back during the novel object recognition task (F(1,14)=4.905, p=0.044) as shown in Figure 2.15, with osteoarthritic dogs spending significantly more time with their ears held back than control dogs in both phases of the task. None of the other behaviours analysed (see Table 2.7) showed a significant (α=0.05) difference between groups.

Dogs spent significantly more time looking through the fence (F(1,14)=8.321, p=0.012) and lying down (F(1,14)=4.658, p=0.049), and less time walking (F(1,14)=16.231, p=0.001), during the test phase compared to the sample phase, as shown in Figure 2.16. This effect did not significantly interact with that of group, nor was any significant effect of group detected on these behaviours. No other behaviours analysed (see Table 2.7) showed a significant (α=0.05) difference between phases.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.15: The durations of time that control and osteoarthritic dogs spent with their ears held back during the sample and test phases.

Graphs show means with error bars representing 95% confidence intervals.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.16: The durations of time dogs spent (A) looking through the arena fence, (B) lying down, and (C) walking during the novel object recognition task.

Graphs show means with error bars representing 95% confidence intervals.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks 2.4.4 Discussion:

Contrary to our hypothesis, osteoarthritic dogs showed a significantly higher mean IR during the test phase than they had during the sample phase, and than control dogs had during either phase (see Figure 2.11). The IR of osteoarthritic dogs during the test phase was also significantly higher than expected by chance. Conversely, the healthy control dogs showed no difference in IR between phases, and neither phase had an IR that was different from that expected by chance. This implies that the osteoarthritic dogs were either able to recognise the familiar object and the control group were not, or that the osteoarthritic dogs were neophilic whereas the control dogs were ambivalent about novelty. This challenges the hypothesis that osteoarthritic dogs would have impaired non-spatial working memory compared to control dogs and thus would recognise the familiar object less well: If this hypothesis was true the control dogs would have been expected to have recognised the object well and thus had a higher IR during the test phase than chance, and the osteoarthritic dogs that did not recognise the familiar object as well would have a lower IR than the control dogs, and one more similar to that expected by chance.

It is possible that non-spatial working memory for the familiar object was enhanced in osteoarthritic compared to control dogs. One potential cause of this is mild acute stress; the osteoarthritic dogs spent more time with their ears held back compared to control dogs, which is thought to be a sign of anxiety (Schilder and van der Borg, 2004), indicating that the osteoarthritic dogs were likely to have experienced higher levels of stress during the task. Whilst prolonged or intense stress may cause impaired performance on cognitive tasks, milder acute stress can improve performance (Yerkes and Dodson, 1908, Hebb, 1955, Mendl, 1999). This is supported by findings that increased anxiety/arousal in rats via forced swimming improves working memory performance on a T-maze task (Yuen et al., 2009), and that increased anxiety/arousal in humans via telling an emotive story involving a medical emergency compared to a similar but non-emotive story (Cahill et al., 1994) or by increasing participants’ muscle tension (Nielson and Jensen, 1994) improved recall. This improvement was prevented by administration of adrenergic antagonists (Cahill et al., 1994, Nielson and Jensen, 1994), suggesting the stress-mediated increase in catecholamines (Peter et al., 1997) was responsible for improved performance. Induction of stress-like states via acute corticosteroid or low-dose ACTH (causes corticosteroid secretion) administration also

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks enhances learning in rats (Gold and Van Buskirk, 1976, Roozendaal and McGaugh, 1996), whereas prolonged corticosteroid (Bodnoff et al., 1995) or higher-dose ACTH administration (Gold and Van Buskirk, 1976) impaired performance. In this study, the osteoarthritic dogs held their ears back more than the control group during the sample phase, implying that they were experiencing more anxiety compared to the control dogs, however none of the osteoarthritic dogs were prematurely removed from the arena for displaying signs of intense distress. Therefore, it is possible that the osteoarthritic dogs were experiencing sufficient anxiety to improve their memory of the familiar object but not high enough levels of anxiety to decrease their memory, whereas the control dogs were experiencing less anxiety and so did not show improved memory for the familiar object.

Acute stress also reduces the perceived intensity of pain (Butler and Finn, 2009), so the acute stress experienced by the osteoarthritic dogs may have reduced the detrimental effects of pain on learning: Vachon-Presseau et al. (2013) found that acutely painful thermal stimuli caused an increase in blood cortisol levels, and individuals with stronger cortisol responses reported less pain unpleasantness in response to the thermal stimuli (Vachon-Presseau et al., 2013). Acute analgesia might reduce the amount of neural processing used for pain, allowing more of it to be devoted to problem solving (Apkarian et al., 2004a). Therefore osteoarthritic dogs may not have experienced the predicted non-spatial working memory impairment because of acute stress-induced analgesia, preventing pain from distracting them during the task. However, chronic pain-associated attention deficits persist after acute analgesia in humans (Dick and Rashiq, 2007), so stress-induced analgesia may not improve chronic pain-associated cognitive deficits, and whilst this phenomenon could explain why the osteoarthritic dogs did not display the predicted pain-related decrease in non-spatial working memory, it would not explain why the control dogs failed to recognise the familiar object or show a preference for the novel object and the osteoarthritic dogs did not.

It may be that osteoarthritic dogs did not recognise the familiar object better than control dogs, but that they were more neophilic whereas control dogs were more ambivalent about novelty. Exposure to novelty has been shown to cause analgesia in rats (Netto et al., 1987, Rochford and Dawes, 1993, Torres et al., 2001). The mechanisms involved in this process are not fully understood but appear to indirectly involve opioidergic mechanisms: Control rats no-longer experienced novelty-induced

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks hypoalgesia following exposure and habituation to a previously-novel object, however rats treated with naloxone (an opioidergic antagonist) continued to experience novelty- induced hypoalgesia following exposure to the object (Rochford and Dawes, 1993). However because morphine tolerance did not inhibit novelty-induced hypoalgesia in either control or naloxone-treated rats, the involvement of opioidergic mechanisms is likely to be indirect (Rochford and Dawes, 1993). It is possible that osteoarthritic dogs showed a preference for the novel object due to experiencing novelty-induced analgesia in its presence and not in the presence of the familiar object (to which they had already habituated), and that this preference did not occur in control dogs since they were not experiencing pain. It is also feasible that if osteoarthritic dogs were walked less often, for less time or in less varied environments than control dogs (perhaps due to owner concerns regarding poorer mobility or increased pain or stiffness after exercise), they may have had less exposure to novelty in their daily lives than the control dogs, which may have caused them to value novelty more highly.

Despite the statistically significant differences between the IRs of the osteoarthritic group in the test phase compared to the sample phase, and between the IR of the osteoarthritic dogs during the test phase and that expected by chance, the proportions of the total time in each phase that dogs of either group spent interacting with either object were extremely low (see Figure 2.13). This suggests that dogs were not particularly motivated to explore either object, possibly because the objects were not particularly salient to dogs, because the arena itself was novel (especially in the sample phase) and the dogs therefore explored the arena rather than the objects, or because the dogs were more motivated to leave the arena and reunite with their owner than to explore the objects. On average, dogs spent more than half of the total time in both phases looking through the fence towards the holding area (see Figure 2.16), which supports this hypothesis, but they also spent far greater amounts of time walking around the arena than they did interacting with the objects in either phase, suggesting that they may also have been more motivated to explore the arena than the objects. The very low amounts of time spent investigating the objects could adversely affect the reliability of these results: Because the start and stop times for each state behaviour were input manually by an observer watching the videos, any variability in the delays between observing a behaviour start or finish and pressing the relevant key would have more of an effect on the results if the animal performed the behaviour for a short amount of time than for a longer amount of time, therefore there may be greater

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks human error in these recordings based on the observer’s reaction time than would be expected. Because the observer was blinded with respect to group and signalment factors, this effect should be random and should not have introduced any bias relating to these factors, but it still could have adversely affected the accuracy of recordings. Furthermore, dogs’ interactions with these objects may not be representative of interactions with novel objects that are salient to dogs and which they are more motivated to spend time interacting with. It also may be the case that if dogs had spent more time investigating objects during the sample phase they would be more likely to recall that object type in the test phase. The mean proportion of time spent interacting with the objects in the sample phase was slightly higher for osteoarthritic dogs than control dogs (though this effect was not statistically significant), which may partially explain why osteoarthritic dogs were able to better recognise the familiar object in the test phase than control dogs. The time spent interacting with objects could potentially be improved by performing a pilot study exposing dogs to a range of bespoke objects (to prevent accidental use of an object with which a dog is already familiar) and selecting two objects which dogs spent similar and high amounts of time interacting with for use in the task. Performing the experiment in the owner’s home with the owner present may also encourage the dog to explore the objects rather than seeking the owner. However, it may be worthwhile investigating alternative tests of working memory that dogs are likely to be more motivated to participate in, such as those involving the use of food rewards.

Osteoarthritic dogs spent far more of the task with their ears held back than control dogs, implying that the osteoarthritic dogs found the experience more anxiety-inducing or stressful (Schilder and van der Borg, 2004). In human patients, chronic pain and anxiety disorders commonly occur together (Kroenke et al., 2013), with chronic pain patients having almost double the prevalence of anxiety disorders compared to the general population (McWilliams et al., 2003), so it is possible that dogs with chronic pain from osteoarthritis also have increased anxiety. Since footage was only taken during the task, it is not possible to determine whether these dogs were chronically anxious or whether they became acutely anxious during the task. However, the C-BARQ component scores of osteoarthritic dogs did not significantly differ from those of control dogs in factors that would be expected to relate to anxiety (such as fear or aggression), and if the osteoarthritic dogs were experiencing chronically elevated anxiety it might be expected that these would be higher than for control dogs. Whilst there is insufficient

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks information to suggest that osteoarthritic dogs were chronically anxious, the difference in ear position between groups suggests that osteoarthritic dogs at least showed signs of increased anxiety during the task itself.

Time spent walking was significantly higher during the sample phase than the test phase (for both groups of dogs considered together), whereas time spent lying down and time spent looking through the fence were significantly higher during the test phase. This may be because the environment itself would have been more novel during the sample phase and thus the dogs spent more time exploring it than during the test phase, when it was more familiar, during which time the dog spent more time looking through the fence at the owner and researcher and lying down resting. Many novel object recognition studies in rodents have a familiarisation phase to allow the animals to investigate the environment and become familiar with it prior to the introduction of objects (Dere et al., 2007, Antunes and Biala, 2012). In this study it was originally intended that the holeboard task, described in Section 2.5, would be performed prior to the novel object recognition task and act as a familiarisation phase, to reduce the time required to perform the tasks and encourage more owners to visit and participate. However, as the first dog tested became distressed during the test phase and had to be prematurely removed from the arena, possibly because of frustration associated with the absence of food rewards present in the holeboard task, all other pairs of dogs participated in the novel object recognition task before the holeboard task, which made the arena unfamiliar to the dogs during the novel object recognition task.

Older dogs (and not younger dogs) had a higher IR in the test phase than the sample phase. However, the IR during the test phase was similar for both older and younger dogs, with the difference being that older dogs spent less time investigating the object located in the future novel object location during the sample phase than younger dogs. It is possible that older dogs investigated the novel object more during the test phase than they did during the sample phase because the location was more novel to them, since they had not investigated that location as much during the sample phase. Burke et al. (2010) found that aged rats showed decreased novel object preference compared to younger adult rats, but further testing (presenting the rats with two novel objects or two familiar objects during a modified test phase) showed that older rats behaved as if the novel object was familiar, rather than behaving as if the familiar object was novel (Burke et al., 2010). It was not possible to assess this using our protocol, but both younger and

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks older dogs showed a tendency to interact more with the novel object than the familiar object during the test phase, as both age-groups had a mean IR of over 50% (though the younger dogs also interacted with the object in the same location in a similar way during the sample phase), implying that older dogs showed a preference for the novel object in the test phase. However because the younger dogs already displayed a preference for the object in the same location as the future novel object during the sample phase, it may be that the younger dogs tested had an inherent tendency to prefer that location in the arena, so it cannot be confirmed that they displayed a preference for the novel object. However, all dogs were 6-10 years old, and age matching between the two groups was very successful, therefore age was very unlikely to have had an effect on the comparisons between osteoarthritic and control dogs.

PLR (tendency to explore the left object over the right object) was higher for dogs assigned a yellow sample object and blue novel object compared to those assigned a blue sample object and yellow novel object. This may be due to the fact that dogs assigned a yellow sample object were more likely to have the novel object presented on the left side, so at least during the test phase were more likely to approach this side. This occurred despite counterbalancing attempts because three dogs that performed the study and were included in the counterbalancing table could not be matched and so were later excluded, therefore certain object type-location combinations occurred more frequently than others. Since both dogs in each control-osteoarthritis pair had the same colour of sample object and side of novel object presentation, this should not have affected any comparison between osteoarthritic and control dogs.

2.5 The Holeboard task: Comparing the spatial working memory of osteoarthritic and healthy control dogs:

2.5.1 Background:

Although there are fewer studies examining spatial working memory compared to non- spatial working memory in human chronic pain patients (Berryman et al., 2013), it appears that chronic pain may impair spatial working memory in humans (Antepohl et al., 2003, Luerding et al., 2008), as described in Section 1.4. Whilst spatial working memory tasks such as the radial arm maze (Macpherson and Roberts, 2010, Craig et al., 2012) and delayed non-matching to position tasks (Adams et al., 2000, Tapp et al., 2003,

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Zanghi et al., 2015) have previously been performed in dogs (as described in Section 1.5), dogs’ performance in the radial arm maze was described as “surprisingly low” (Macpherson and Roberts, 2010), and all of these tasks required several weeks of habituation and/or training (Adams et al., 2000, Tapp et al., 2003, Craig et al., 2012, Zanghi et al., 2015), which would not be ideal for use in dogs owned by members of the public due to the practical difficulties associated with requiring owners to bring the dogs to the testing facility on a daily basis for prolonged periods of time.

A test of spatial working memory that does not require extensive training and relies on natural exploratory and appetitive behaviours is the holeboard task, which is well established in rodents (Van der Staay et al., 1989, Van der Staay et al., 1990, van der Staay, 1999, Kuc et al., 2006, Arndt et al., 2009). This task consists of a small enclosed open field with several (usually sixteen) holes placed in the floor. Within each hole a small amount of food is placed below a mesh screen to prevent olfactory discrimination, and one quarter of the holes, randomly selected, are additionally baited with food accessible to the animal (van der Staay et al., 2012). A typical setup for this experiment in rodents is shown in Figure 2.17.

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.17: A typical holeboard setup used in rodent studies, showing baited and unbaited holes. (Modified from van der Staay et al. (2012)).

After several training trials, the animal is re-released into the same testing arena for a specified duration, and the holes it enters with its head are recorded (van der Staay, 1999). From this, it is possible to obtain a measure of spatial working memory (the animal’s ability to remember short-term spatial information pertaining to the current trial (Dudchenko, 2004, Berryman et al., 2013), in this case which baited holes it has already visited this trial) calculated by the number of head-dips into previously unentered baited holes as a ratio of the total number of head-dips into baited (‘correct’) holes (van der Staay et al., 2012). Additionally, a measure of spatial reference memory (the animal’s ability to remember the pattern of baited holes between trials) can be calculated by expressing the number of head-dips into baited (‘correct’) holes (even if previously entered on that trial) as a ratio of the total number of head-dips into holes (van der Staay et al., 2012). Spatial working memory and spatial reference memory are not thought to be highly correlated, with different rat strains performing better for one construct than the other (van der Staay, 1999), and unlike working memory, there do not appear to have been many studies specifically assessing the effect of pain on reference memory (see Section 1.4), so whilst it is predicted that spatial working memory would be impaired in osteoarthritic dogs on this task in comparison to healthy controls, it is currently not possible to predict whether reference memory will also be impaired. However, in a human virtual reality task inspired by the holeboard task it was found that fibromyalgia patients made more reference memory errors than healthy control participants (Cánovas et al., 2009) (the display revealed previously-visited locations to participants during the task so it was not possible to assess working memory), therefore it is possible that chronic pain may also decrease spatial reference memory scores in the holeboard task in dogs.

Whilst this paradigm does not appear to have been previously studied in dogs, it has been adapted for use in humans (Cánovas et al., 2008, Cánovas et al., 2009), cats (Steigerwald et al., 1999, McCune et al., 2008), gerbils (Wappler et al., 2009), tree

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks shrews (Keuker et al., 2004), chickens (Nordquist et al., 2011) and goldfish (Durán et al., 2010), and has recently been used in several studies in pigs (Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Haagensen et al., 2013a, Haagensen et al., 2013b, Antonides et al., 2015): In one study, an individual pig was placed in an arena 8m*7.6m with sixteen buckets screwed into the arena floor, each bucket having a lower meshed compartment containing chocolate raisins and an upper accessible compartment which could be baited also (Arts et al., 2009). Pigs were given several familiarisation sessions before testing began. They were then placed individually in the testing arena with four specific buckets baited for 25 trials, before switching to a different configuration of baited buckets for 13 trials, subjected to an experimental treatment and switching to a third configuration of baited buckets for 13 trials to compare learning and spatial reference and working memory before and after the experimental treatment (Arts et al., 2009). Video footage of the pigs’ performance was analysed and their working memory and reference memory scores assessed as for rats in each of the three periods (Arts et al., 2009, van der Staay et al., 2012). It has been found that an enriched home environment improves working memory but not reference memory in pigs (Bolhuis et al., 2013), and that diets rich in fats and sucrose impaired both working and reference memory (Haagensen et al., 2013b), therefore the holeboard test shows sufficient sensitivity to detect between-group differences in working and reference memory in pigs. Because of the broadly similar size of dogs and pigs, this scaled-up method is feasible to attempt in dogs, however chocolate raisins must be replaced with a suitable reward due to their toxicity (Crnic´ et al., 2015).

2.5.2 Materials and Methods:

2.5.2.1 Pilot Study:

The same dogs described in Section 2.4.2.1 also participated in the pilot study for the holeboard task. The signalments of these dogs are given in Appendix 2.12 and the pilot study holeboard task results are shown in Appendix 2.14.

The methods for the holeboard task pilot study were as described in the methods for the main study (see Sections 2.5.2.3-2.5.2.5), with the following differences: As described in Section 2.4.2.1, the holeboard task was performed before the novel object recognition task during the pilot study, and event-logging of video files was performed by the thesis author (MS) rather than a blinded assistant, in order to develop a rapid and

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks simple key-binding system for recording bucket visits whilst watching the video footage that could then be used by an assistant with no prior experience using BORIS.

Additionally, during the pilot study dogs were not removed from the holding arena during rebaiting as described in Section 2.5.2.4, but instead the researcher (MS) visited all buckets, tapping a food reward against the inside of unbaited buckets and placing a reward inside baited buckets, such that the dog within the holding area could not detect which buckets had been baited and which had not. Dogs that participated in the main study (with the exception of C1 and T1, where the task procedure was as described in the pilot study) were removed from the holding area into the adjacent corridor during rebaiting of buckets in the holeboard task, as it was thought that observing the researcher tapping or placing a food reward in every bucket might cause the dog to perceive that all buckets were baited and as such not learn the pattern of baited buckets. Additionally, in the main study it was possible for the owners to lead the dogs into the adjacent corridor, whereas in the pilot study owners were not present to remove dogs from the holding area during rebaiting.

Outcome measures recorded for the holeboard task pilot study were reference memory score, working memory score (for baited buckets), and total time taken to find all food rewards, as shown in Table 2.10. Formal analyses were not performed due to small sample sizes and large confidence intervals, but because there appeared to be a visually discernible trend for reference memory score to increase and time taken to find all rewards to decrease with subsequent sessions, as shown in Appendix 2.14, this was considered a promising indicator that dogs may be able to learn the holeboard task. Therefore it was decided to proceed with the holeboard task using age-matched control- osteoarthritis pairs of dogs.

2.5.2.2 Animals:

Animals that underwent clinical assessment in Section 2.3 (see Table 2.2) and participated in the novel object recognition task described in Section 2.4 also participated in the task described in this section. All dogs shown in Table 2.6, with the exception of C1 and T1, participated in the holeboard task after a 10-minute break and walk outside following the novel object recognition task. Dogs C1 and T1 participated in the holeboard task first, and had a 10-minute break before participating in the novel

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks object recognition task. As C1, the first dog to be tested, became distressed during the test phase of the novel object recognition task, possibly as she could no-longer obtain the food rewards she had obtained during the holeboard task, the order of tasks was reversed for all subsequent pairs of dogs tested.

2.5.2.3 Apparatus:

The same arena was used for testing as in the novel object recognition task, which was set up as shown in Figure 2.18. Bucket containers were constructed as shown in Figure 2.19. Treats (food rewards) used were individual pieces of Hill’s Vet Essentials Adult Medium Dog kibble (Hill’s Pet Nutrition Inc., Topeka, Kansas, USA).

100

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.18: Diagram (A) and photograph (B) showing the arena setup for the holeboard task.

101

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

(A) The grey area represents the holding area in which the dog is held between trials. “S” and “O” represent the locations of the researcher and owner respectively. Coordinates marked with green crosses represent locations of buckets in the holeboard task, which were spaced 75cm apart and 75cm from the wall or fence.

Figure 2.19: Buckets used for the holeboard task.

(Left): Photograph of “bucket” container for the holeboard task, comprised of 5L plant pot (17.6*23cm) with perforated base, inside 5L bucket (20*21cm), held together by clamps over the pot rim and bucket handle. (Right): Cross-sectional diagram of a “bucket” (not to scale) showing six food treats and a bag of ~800g gravel (for stability) placed inside the lower (inaccessible) compartment of the bucket, and a possible additional food treat in the upper (accessible) compartment of baited buckets.

2.5.2.4 Procedure:

Each of 16 bucket containers, shown in Figure 2.19, was placed on each coordinate of the central 4*4 grid shown in Figure 2.18. Each pair of dogs was assigned a holeboard configuration by randomly selecting one bucket (1-4) from each row (A-D) using the “RANDBETWEEN” function in Microsoft Excel, which was re-ran for that pair if three or

102

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks more consecutive rows returned the same bucket number. The pattern of baited buckets for each pair is shown in Table 2.9.

Table 2.9: Counterbalancing table used for assigning each pair of dogs a pattern of baited buckets for the holeboard task.

The three dogs that were excluded from the study due to lack of an appropriate matched dog (T4, C9 and T11) are shown in grey.

For dogs C1 and T1, the researcher visited all buckets, tapping a treat against the inside of unbaited buckets and placing a treat inside listed baited buckets, such that the dog within the holding area could not detect which buckets had been baited. However this method was discontinued for subsequent pairs as it was thought that the dog might perceive that all buckets were baited, rather than only the ones baited in previous trials. All other dogs were briefly removed from the room into an adjacent corridor by the owner whilst the buckets were baited, before returning to the holding area.

Each dog was allowed into the arena and footage of their bucket visits was recorded. Dogs were removed after 5 minutes had elapsed or immediately after finding all four rewards, as performed in previous studies in rodents and pigs (Kuc et al., 2006, Arts et al., 2009, Bolhuis et al., 2013), because it was thought that visiting now-empty buckets once all rewards were found might lead to extinction of the learned pattern. They were then removed into the holding area for 5 minutes, during this time the same buckets

103

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks were re-baited. The dog was then allowed back in the arena as previously. This was repeated until the dog had completed four trials.

2.5.2.5 Data Recording and Analysis:

Video footage was recorded using a Canon Legria HF R206 camcorder mounted onto the wall of the holding area (see Figure 2.18). Footage was analysed using Behavioural Observation Research Interactive Software (BORIS) (Friard and Gamba, 2016) by another researcher who was blinded with respect to group (control/osteoarthritis) and provided with no information about the dogs’ signalments, all other aspects of analyses were performed by MS.

The coordinates of each baited bucket, the number of treats found and the trial number (1-4) were manually added to each dataset exported from BORIS. From these the outcomes described in Table 2.10 were calculated. The same categories for weight (12- 15kg (n=4); 15-20kg (n=5), 20-30kg (n=4), 30-40kg (n=3)) and age (6-7 years (n=9) and 8- 10 years (n=7)) were used as in the novel object recognition task, so that the sample size in each category was large enough for analyses to be performed in order to assess their effect on outcomes. Analyses were performed using the Repeated Measures General Linear Model function in SPSS in order to assess the effects of trial number, group, signalment factors and clinical examination measures on the outcomes described in Table 2.10. Where degrees of freedom were sufficiently high to do so, Mauchly’s test of sphericity was performed. If this statistic significantly differed from sphericity (α=0.05), a Greenhouse-Geisser correction was applied to the degrees of freedom for that F- distribution.

104

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Table 2.10: Summary of the outcome measures calculated for the holeboard task.

2.5.3 Results:

Results are reported as a test statistic, degrees of freedom (shown as a subscript) and a p-value. Graphs show means with error bars representing 95% confidence intervals. Letters above bars represent no significant difference (α=0.05) between bars sharing the same letter. Letters are omitted where no significant differences are present between any factors.

Results of the pilot study are shown in Appendix 2.14. Whilst there was no significant effect of phase, it seemed that there was at least a general trend for the time taken to find all the treats (food rewards) to decrease with subsequent trials, therefore it was

105

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks decided to proceed with the study using the matched control-osteoarthritis pairs of dogs.

No significant differences were found between groups for any measure shown in Table

2.10 (reference memory score (F(1,14)=0.525, p=0.481), working memory score for baited buckets (F(1,14)=0.14, p=0.908), working memory score for all buckets (F(1,14)=0.008, p=0.928), time taken to find all treats (F(1,14)=1.52, p=0.238), number of bucket visits

(F(1,14)=0.39, p=0.846) or search rate (F(1,14)=0.703, p=0.416)), as shown in Figure 2.20.

Significant effects of trial were found for time taken to find all treats (F(3,42)=7.939,

-8 p=0.000263) and search rate (F(3,42)=18.674, p=7.5381x 10 ), but not for any other measure. There were no significant interactions between trial and group for any measure. There was no effect on the significance of any results whether the measure used for “time taken” was that to find all treats (tA), which excluded dogs that did not find all treats from the analysis, or that capped at 300s if not all treats were found (tC)

(see Table 2.10), therefore tC was used since it was the measure used to calculate the search rate, and because excluding the dogs that failed to find all treats would artificially reduce the mean time taken to find all rewards by excluding the dogs that were slowest. There were similarly no significant (α=0.05) differences in outcome measures between dogs with different osteoarthritis severities (numbers of dogs with each severity score are summarised in Figure 2.5) either when assessed by the owner or when assessed by the veterinary surgeon.

106

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.20: Mean values for each group and trial for each outcome.

(A) reference memory score, (B) working memory score for baited buckets (WMB),

(C) working memory score for all buckets (WMA), (D) time taken to find all treats, (E) number of bucket visits and (F) search rate. Graphs show means with error bars representing 95% confidence intervals.

107

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

There was no significant (α=0.05) effect of weight or sex on any of the outcome measures from the holeboard task (see Table 2.10), nor any interactions between trial and weight or sex. There was a significant interaction between age and trial for time taken to find all of the treats (F(3,42)=3.889, p=0.015) and search rate (F(3,42)=3.667, p=0.02) as shown in Figure 2.21. In earlier trials, older dogs explored buckets at a similar rate to younger dogs and took more time than younger dogs to find all treats, but in later trials, older dogs explored buckets at a faster rate than younger dogs and found all of the treats in a similar time to younger dogs. The total number of buckets visited, which along with the time taken determined the search rate, was not significantly affected by age (F(1,14)=0.538, p=0.467), trial (F(3,42)=2.124, p=0.112) or the interaction between these (F(3,42)=2.294, p=0.092), neither was any other factor described in Table 2.10.

108

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

Figure 2.21: The effect of age and phase on (A) time taken to find all treats and (B) search rate.

Graphs show means with error bars representing 95% confidence intervals.

109

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

A sample size estimation was then performed. Measures of effect size (partial eta- squared) were calculated in SPSS for between-group effects, within-group (between- trial) effects, and the interaction between these, for each measure shown in Table 2.10. Using these, the sample size required to obtain a statistical power of ≥0.8 was estimated using G*Power (Faul et al., 2007), assuming an approximate correlation amongst repeated measures of 0.5 (moderate-strong). The results are shown in Table 2.11. For Reference Memory Score, a Greenhouse-Geisser correction (ε=0.623) was applied to degrees of freedom due to a probable lack of sphericity (Mauchly’s W(14)=0.315, p=0.012) for the effect of trial. Data for all other measures did not significantly differ from those expected if data were spherical (α=0.05).

Table 2.11: Predicted sample sizes (per group) required for a statistical power of ≥0.8, for each outcome measure of the holeboard task.

110

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks

111

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks 2.5.4 Discussion:

There were no significant differences in working memory scores between control and osteoarthritic dogs, regardless of whether visits to baited buckets only (as in previous studies (van der Staay et al., 2012)) or visits to all buckets (which may give a measure less related to reference memory) were considered. However, the lack of an effect of trial on reference or working memory scores implies the dogs did not learn the task successfully: Reference memory and working memory scores are expected to increase with subsequent trials as animals learn the locations of food rewards (and that rewards replenish between but not during trials) and preferentially visit buckets containing them (van der Staay et al., 2012), as seen in other studies (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012). There are several potential reasons for this: Dogs had only four trials in which to familiarise themselves with the task and learn the pattern of baited buckets, as it was advantageous to use a protocol that could be completed in a single visit. However in other studies involving mice (Kuc et al., 2006), rats (van der Staay, 1999) and pigs (Haagensen et al., 2013a, Haagensen et al., 2013b), animals were given a habituation phase to familiarise themselves with the environment and handlers, and a learning/acquisition phase consisting of 5 or more trials prior to their retention phase or any experimental treatments or holeboard pattern changes. A habituation phase was considered unnecessary because dogs were already familiar with humans and indoor environments, and owners were present during testing, therefore this was avoided to limit the session duration. However, dogs may require more than four trials to learn the pattern of baited buckets.

Another possibility is that dogs do not have comparable spatial working or reference memory skills to other species. Macpherson and Roberts (2010) found that whilst dogs could learn tasks within an eight-arm radial maze, their performance was significantly worse than that of rats and primates, leading them to conclude that “Dogs’ memory capacity in these studies was found to be surprisingly low” (Macpherson and Roberts, 2010). Since dogs also performed less well in a problem solving task than wolves (Frank and Frank, 1985), it is possible that domestication has reduced the selective pressure for spatial cognitive skills in dogs (Macpherson and Roberts, 2010), due to their close bond with human care-givers and reduced need to roam for food and resources. Furthermore, the brain of domestic dogs is proportionally around 30% smaller than that of wolves (Kruska, 2005), with the hippocampus, a structure regarded as highly important for

112

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks spatial reasoning skills (Macpherson and Roberts, 2010), being 42% smaller (Kruska, 2005). Since the hippocampus of domestic rats is only 12% smaller than that of wild rats (Kruska, 2005), it seems reasonable that rats could have retained more of their ancestral spatial memory skills compared to dogs. However, Fiset and Plourde (2013) found that dogs performed as well as wolves in a test of spatial working memory involving finding a hidden object, suggesting that domestication may not have had such a marked effect on the spatial working memory of dogs as previously thought. Whilst it is possible that dogs would be unable to learn any variant of the holeboard task due to limited spatial working or reference memory, it is also possible that they may be able to learn the task given more trials, and it would be appropriate to investigate this before concluding that this task is not possible for dogs to learn.

Dogs may not have had an incentive to preferentially visit buckets baited on previous trials or to learn the pattern of baited buckets, because of the lack of a cost associated with visiting unbaited buckets. Laughlin and Mendl (2004) found that pigs that incurred costs (stepping over a rope barrier) to enter the arms of a radial maze during learning trials made fewer errors during recall trials than those that incurred no costs to enter arms, suggesting that the presence of costs associated with choices may improve performance in spatial working memory tasks. In a holeboard task in piglets, Antonides et al. (2015) covered buckets with plastic balls so that piglets could not visually locate food rewards, however since piglets had to lift the balls with their snouts to access the buckets, this may have added an increased cost in terms of difficulty or effort to a bucket visit, which may incentivise animals to preferentially investigate baited rather than unbaited buckets. Whilst it may be possible to do something similar to increase the cost of visiting a bucket in dogs, three dogs (all osteoarthritic) did not find all four food rewards in earlier trials, with some dogs initially appearing disinterested in visiting the buckets, therefore increasing the cost of bucket visits may discourage dogs from learning the task or initially exploring the buckets to learn that some of them contain food.

It is possible that the dogs were relying on olfactory cues rather than memory and were visiting and returning to unbaited or previously-visited buckets because they could smell the food rewards in the inaccessible compartment of the buckets, and persisted in attempting to retrieve these inaccessible treats rather than learning to visit the accessible compartments of other buckets to retrieve those treats. This would be

113

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks difficult to resolve as if the inaccessible compartments were not baited, the dogs would be able to rely on olfactory stimuli rather than memory to locate baited buckets, which would not provide useful information about their cognition.

One possibility that may explain why there were no differences seen between dogs with and without osteoarthritis is that the dogs recruited may not have experienced a high enough intensity of pain to cause the cognitive effects seen in human chronic pain patients. All of the osteoarthritic dogs recruited were assessed by their owner and the veterinary surgeon as having “mild” or “moderate” osteoarthritis severity and none had “severe” severity. Grisart and Plaghki (1999) found that chronic pain patients with “low pain” (assessed by visual analogue scale) had no differences in selective attention compared to healthy controls, however those with “high pain” had significantly higher response times than both healthy controls and “low pain” patients in all of the tests of selective attention performed (Grisart and Plaghki, 1999). Eccleston (1995) also found that patients with high intensity pain had significantly impaired performance on an attentional task compared to patients with low-intensity pain and healthy controls, whereas low-intensity pain patients showed no significant differences in reaction times compared to healthy controls (Eccleston, 1995). This suggests that there is an intensity threshold at which chronic pain begins to affect cognition, and that if more severely affected dogs could be recruited, these might show significant cognitive differences compared to less-severely affected dogs and to healthy controls.

It is also possible that the lack of significant changes in reference memory over time and/or differences in working memory between osteoarthritic and control dogs may be due to insufficient sample sizes and thus low statistical power. From Table 2.11, it is evident that some factors, most notably the time taken to find the rewards, had a reasonable effect size, but would require 14 dogs per group rather than 8 to give the test sufficient power to detect a significant effect. It also appears from Figure 2.20 that the time taken appeared to be non-significantly but consistently higher for osteoarthritic dogs than control dogs, supporting the idea that increased sample size may lead to the detection of a significant difference between groups for this measure. Such a difference could be explained by impaired mobility and thus increased time to reach each bucket in the osteoarthritic dogs rather than any cognitive impairment, however. Of principal interest to this study was working memory, due to its impairment in human chronic pain patients, however both measures of working memory showed a between-groups effect

114

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks size of 0.001, requiring an estimated 4904 dogs per group to detect a significant between-group difference. This strongly suggests that there is no difference in spatial working memory between osteoarthritic and control dogs that is detectable using this paradigm, regardless of sample size. The relatively higher effect sizes and lower required sample sizes of less than 50 dogs per group seen for between-trial comparisons imply that between-trial effects may also exist (suggesting that dogs may learn how to perform the task to some extent) and be detectable with higher numbers of dogs, however it may ultimately be preferable to use a paradigm with greater effect sizes and the ability to detect between-group differences in spatial working memory.

Another interesting aspect of these results is the lack of effect of age on reference or working memory scores: Several studies by N. W. Milgram’s research group have found age-related deficits in spatial learning and working memory in dogs (Adams et al., 2000, Tapp et al., 2003, Snigdha et al., 2012), suggesting that dogs show age-related cognitive decline in a similar way to humans. However this is possibly because of the narrow age- range of dogs in this study; dogs younger than 6 years were excluded as it was unlikely that many osteoarthritic dogs younger than six could be found, and dogs older than 10 were excluded because of findings that “senior” dogs above the age of 11 years showed significant deficits in working memory compared to younger dogs (Snigdha et al., 2012). Whilst “old” dogs of 8-9.5 years also showed (less pronounced and non-significant) impairments in working memory compared to younger dogs (Snigdha et al., 2012), the younger dogs they were compared to were 3-4.5 years old, younger than any of the dogs in this sample. Therefore it is possible that no significant age-related differences in spatial working or reference memory would occur between 6-7-year-old dogs and 8-10- year-old dogs as these dogs are too similar in age. This is supported by the fact that the CCDR scores for dogs tested were all well below the threshold for concern (Salvin et al., 2011), suggesting that none of them had significant age-related cognitive dysfunction.

2.6 Summary:

Though analyses of clinical assessment and owner questionnaires would suggest dogs were correctly assigned to the osteoarthritic and control groups, osteoarthritic dogs did not perform substantially differently on the holeboard task compared to control dogs. However the lack of increased reference or working memory scores over time would suggest that neither group of dogs had successfully learned the task. More surprisingly,

115

Chapter 2: Assessment of working memory in dogs with and without chronic pain from osteoarthritis using two rapid assays: The novel object recognition and holeboard tasks dogs with chronic pain from osteoarthritis appeared to recognise the familiar object and thus preferentially interact with a novel object, whereas control dogs did not. This may be because of increased non-spatial working memory for objects or increased preference for novelty, however these findings should be interpreted cautiously as the amount of time that dogs spent interacting with either object in either phase was very low.

Because the dogs in this study did not appear to have successfully learned the holeboard task, it would be interesting to develop a version of the holeboard task comprising a greater number of trials, as seen in other species, to assess whether the holeboard task is suitable for use in dogs, as well as to investigate potential alternative assays for assessing spatial working memory in dogs.

116

Chapter 3: Comparing assays of spatial working memory in dogs

Chapter 3 Comparing assays of spatial working memory in dogs

3.1 Abstract:

The study described in this chapter assesses the performance of companion animal dogs in two tasks of spatial working memory, with the aim of selecting an appropriate task to compare the spatial working memory of dogs with and without chronic pain from osteoarthritis. The tasks were a three-day version of the holeboard task (described in Chapter 2) with an increased number of trials and a configuration change, and a disappearing object task. Dogs were able to learn both tasks successfully: Working and reference memory scores in the holeboard task increased as dogs learned the configuration of baited buckets and decreased following a configuration change, and dogs’ success at recalling the location of a hidden object decreased with increasing durations between observing the researcher hide the object and being allowed to retrieve it. There was no significant correlation between dogs’ performance in the two tasks, possibly because the holeboard task required a greater degree of independent learning and was exclusively food-reinforced whereas the disappearing object task required observation of and interaction with a researcher and was not purely food- reinforced. Because it could be performed in a single session, the disappearing object task was selected as a more appropriate task for comparing the spatial working memory of osteoarthritic and healthy control dogs owned by members of the public.

3.2 Introduction:

The holeboard task described in Chapter 2 was not only unable to differentiate osteoarthritic from healthy control dogs, but since the dogs showed no significant improvement in reference or working memory scores over time, it is likely that the dogs were not able to successfully learn the task over four trials. Therefore in this chapter a version of the holeboard task consisting of 40 trials was devised and performed by 10 dogs, in order to assess whether dogs are capable of learning the holeboard task when more trials are given. Additionally, a modified version of the “disappearing object task” first described by Fiset et al. (2003) was performed by the same dogs, as a shorter test of

117

Chapter 3: Comparing assays of spatial working memory in dogs working memory that should be more adaptable to use with visiting owners in order to assess differences in working memory score between dogs with and without osteoarthritis. Finally, correlations between the outcome measures of these tasks were analysed to assess whether they were likely to be measures of the same construct (spatial working memory).

For ease of reading, this chapter is structured such that the background, materials and methods, results and discussion for the holeboard task are presented first in Section 3.3, followed by the same sections for the disappearing object task in Section 3.4, with a summary presented in Section 3.5.

3.3 The Holeboard task:

3.3.1 Background:

In Chapter 2, a version of the modified holeboard task that was short enough to be performed by dogs visiting the hospital with their owners was described, but it appeared that neither osteoarthritic nor healthy control dogs were able to learn the task successfully in the four trials allowed. However, many holeboard studies performed in rodents and pigs have allowed far more trials than this (as summarised in Table 3.1), as their husbandry more easily facilitates repeated testing over a longer period. Whilst it would not be feasible to test pet dogs daily over the course of several weeks, it should be possible to achieve similar numbers of trials as those achieved in previous studies within a single week, by using larger numbers of massed trials per day. Previous studies have shown that using massed rather than spaced trials decreases the efficiency of long- term spatial memory (spatial reference memory) acquisition in rats (Spreng et al., 2002, Commins et al., 2003), however this does not appear to impair the acquisition of short- term information reliant on spatial working memory (Spreng et al., 2002, Commins et al., 2003), and therefore should be suitable for this study in which the primary outcome measure of interest is reliant on spatial working memory. However it is possible that using massed trials may affect the learning of the task “rules” (e.g. that the same buckets are baited between trials, buckets are not rebaited during a trial, and the food in the bottom compartment of the bucket is not accessible), as this is likely to involve reference memory (Dudchenko, 2004).

118

Chapter 3: Comparing assays of spatial working memory in dogs

Table 3.1: Summary of the number of trials and trial spacing used in several previous holeboard studies.

Number of trials (excluding habituation trials and Trials reversal/ performed Study intervention phases) each day Sample size Species 24 or 32 (two Kuc et al. experiments (2006) performed) 6 or 4 24 or 8 Mouse van der Staay (1999) 46 4 30 per group Rat Haagensen et al. (2013a) 20 2 12 Pig Haagensen et al. (2013b), 10 2 8 per group Pig Gieling et al. (2012) 26 2 9 per group Pig 32 overall Bolhuis et (group sizes al. (2013) 30 2 not apparent) Pig Arts et al. (2009) 25 2 10 pairs Pig Antonides 46-54 (dependent on et al. when learning (2015) criterion reached) 4 9 per group Pig

Another limitation of the rapid holeboard assay described in Chapter 2 was the lack of a “reversal” phase, in which the holeboard configuration learned by the animal is replaced with a new configuration (van der Staay, 1999, Kuc et al., 2006, Antonides et al., 2015) and further trials are performed. (Whilst this is often described as a “reversal” phase (Kuc et al., 2006, van der Staay et al., 2012, Antonides et al., 2015), and it fulfils a criterion for reversal learning in that it involves the alteration of behaviour in response to a change in stimulus-reward relationships (Clark et al., 2004), it does not involve the switching of the reward value of two stimuli (Fellows and Farah, 2003) (since there are multiple possible previously unrewarded configurations and some previously-unbaited buckets/holes remain unbaited during the reversal phase), therefore it may not be

119

Chapter 3: Comparing assays of spatial working memory in dogs regarded as a true “reversal” paradigm, however the term “reversal phase” will be used to describe this phase of the task in this thesis document in order to maintain consistent terminology with the existing literature.) Incorporating a reversal phase allows an assessment of whether the animals were genuinely using spatial reference memory to locate the food rewards: If the reference memory score decreases following the configuration change, it is likely that the animals’ prior performance was due to their spatial reference memory for the previous configuration (Kuc et al., 2006), rather than other factors such as scent cues. Additionally, adding a reversal phase would also facilitate the acquisition of outcome measures relating to perseveration; the tendency of an animal to repeat a previously-learned behaviour that is no-longer appropriate (McNamara and Albert, 2004).

Many previous studies using the holeboard test have included a reversal phase: Kuc et al. (2006) introduced mice that had previously learned a particular holeboard configuration to 6 trials with a different holeboard configuration, and found that the number of reference memory errors increased in all mice, suggesting mice were using spatial reference memory to solve the task. They also found that mice overexpressing amyloid precursor protein made more working memory errors than wild-type mice even though both strains showed similar outcomes when performing the task with the initial configuration (Kuc et al., 2006), suggesting the reversal phase can highlight differences in spatial working or reference memory that were not apparent in the initial learning phase. Similarly van der Staay (1999) found that the extent to which the reference memory score dropped following a change in configuration was higher in Brown Norway strain rats than albino WAG strain rats, but that the reference memory score of Brown Norway rats increased more quickly than WAG strain rats after the configuration change whereas working memory score was similarly disrupted in both strains, indicating that there are strain differences in perseveration in rats that can be detected by the reversal phase. This suggests that it may be possible to detect differences in perseveration between other groups of animals also. Gieling et al. (2012) found that whilst low- birthweight and normal-birthweight piglets performed similarly pre-reversal, low- birthweight piglets took slightly longer to learn the new configuration following reversal than normal-birthweight piglets. Whilst Arts et al. (2009) did not find an effect of group- mixing on performance, both groups of pigs showed a decrease in reference and working memory scores following each configuration change, suggesting that they had successfully remembered the previous configuration and weren’t using non-spatial cues

120

Chapter 3: Comparing assays of spatial working memory in dogs to find the food rewards.

Given that the holeboard task described in this chapter will no longer be short enough to perform in a single session, it is therefore reasonable to include a reversal phase in order to confirm that the dogs were using spatial reference memory to locate the food rewards, as well as to increase the similarity with holeboard protocols used in previous studies in order to assess whether dogs perform similarly to other species in this task. This would also create a task in which the perseveration tendency of different groups of dogs, such as dogs with and without osteoarthritis, could then be compared. Whilst there is currently no evidence linking chronic pain itself to perseveration, and one study by Apkarian et al. (2004a) found that patients with chronic lower back pain did not make more perseveration errors than healthy control subjects despite deficits in emotional decision-making, little research examining the effect of chronic pain on perseveration has been performed. Furthermore, chronic pain patients have been found to show decreased grey matter density in the prefrontal cortex (a region of the frontal lobe) (Apkarian et al., 2004b), disorders affecting the frontal lobe can cause perseveration errors in human patients (Lombardi et al., 1999) and lesions to the prefrontal cortex have been shown to induce perseverative behaviour in marmosets (Iversen and Mishkin, 1970), therefore it is not infeasible that chronic pain may lead to increased perseveration. Additionally, chronic pain patients are thought to show perseveration of attempting to escape or cure chronic pain even when it is inescapable or incurable, which may cause increased vigilance or worrying (Aldrich et al., 2000), however it is not known whether this phenomenon is related to perseverance of non-pain-related behaviours such as continuing to search in a previously-correct location for food. Use of the holeboard as a novel method of assessing perseveration behaviours in dogs may also be useful in future to assess the effects of other conditions such as damage to the central nervous system following trauma, neoplasia, surgery or disease.

It may have been possible that using different configurations for different pairs of dogs in the holeboard protocol described in Chapter 2 may have led to some dogs having easier or more difficult configurations than others. Therefore in the adapted holeboard task described in this chapter, all dogs had the same initial and reversal holeboard configurations.

121

Chapter 3: Comparing assays of spatial working memory in dogs

3.3.2 Materials and Methods:

3.3.2.1 Animals:

Ethical approval for animal use was obtained from the University of Bristol AWERB (VIN/15/042). Ten staff-owned dogs between the ages of one and thirteen years (7.4±3.0 years; mean ± 95% confidence interval) were recruited: Six females and four males. Six dogs were neutered (three females and three males) and four were unneutered (three females and one male). The mean weight of dogs recruited was 22.5±5.5kg. Nine different breeds were recruited: Six dogs were gundog-type, two terrier-type, and one each of pastoral- and hound-type. Where age was to be included in analyses, dogs were grouped by age such that there were three dogs aged 1-5 years, four dogs aged 6-10 years and three dogs aged 11-15 years. These categories were chosen since they are of approximately equal size and approximately represent the boundaries between “old” and “senior” dogs used by Snigdha et al. (2012), who identified age-related differences in working memory between these groups. Participating owners were given a £10 gift card (John Lewis Partnership, London, UK) following completion of the study.

3.3.2.2 Apparatus:

The apparatus for the holeboard task was largely identical to that described in Chapter 2. The arena setup is shown in Figure 3.1: A leash was attached to the closet and a dog bed placed in front of it, to restrict the dog’s movement whilst buckets were being baited. Two sliding wooden boards (120cm high, 160cm long and 3mm thick) were placed in front of the fencing such that they could be moved to block the dogs’ view of the arena whilst the buckets were being baited.

122

Chapter 3: Comparing assays of spatial working memory in dogs

123

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.1: Arena setup during the holeboard task (A) when the gate is open and (B) during rebaiting of buckets.

S represents the seat upon which the researcher sat whilst the dog was in the arena.

3.3.2.3 Procedure:

The dog was tethered using a leash to the closet within the holding area, with wooden boards placed against the arena fencing such that the dog could not see into the arena (see Figure 3.1). The researcher then placed a food reward in buckets A1, B4, C2 and D3 as shown in Figure 3.2, all other buckets remained unbaited (though all buckets contained inaccessible food placed in the lower compartment). The dog was then allowed into the arena and footage of their bucket visits was recorded. Dogs were removed after 180s had elapsed or immediately after finding all four rewards (the duration of each trial was reduced from the 300s used in the study described in Chapter 2 in order to ensure that there was sufficient time to perform two sessions each with two dogs each day, and because in the majority of trials in the study described in Chapter 2 in which the dog found all of the food rewards in 300s, all food rewards were found in the first 180s of the trial (51/58 trials; 87.9%). Each session lasted for 10 trials, with two sessions per day, three hours apart, over two consecutive days (40 trials in total). On the third day, a further 20 trials were completed (2 sessions of 10 trials, with each session 3 hours apart) with the configuration of baited buckets changed to A3, B2, C4, and D1; a mirror image of the original configuration (so that both configurations should be of similar difficulty). These specific configurations used were the same as those successfully used by Arts et al. (2009) in pigs. Between each trial there was a retention interval of three minutes during which dogs were led to the bed adjacent to the closet and leashed to the closet door whilst wooden boards were moved in front of the arena fencing to completely obstruct the dogs’ view of the arena, as shown in Figure 3.1. During this period the buckets were rebaited before moving the wooden boards to their initial position, releasing the dog into the holding area and offering them a drink of water before the next trial. The outcome measures for this study are shown in Table 3.2.

124

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.2: Diagram showing the configuration of baited buckets for the initial (sessions 1-4) and reversal (sessions 5-6) phases of the holeboard task.

The locations of baited buckets are marked with red crosses and unbaited buckets with white crosses.

3.3.2.4 Analysis:

Video footage was recorded using a Canon Legria HF R206 camcorder mounted onto the wall of the holding area (see Figure 3.1). The identity of each bucket visited (e.g. A1, B2 – see Figure 3.1) and the time of each visit was recorded using Behavioural Observation Research Interactive Software (BORIS) (Friard and Gamba, 2016) by a paid assistant who viewed the footage after all experimental sessions were complete and received no information about the dogs’ signalments, the study hypothesis or how outcome measures would be obtained from the recorded bucket visits. The assistant was aware of which buckets were baited so that they were able to input when each trial had been completed (at the first point at which the dog had visited all four baited buckets in that trial). All other aspects of data manipulation and analysis were performed by MS. Macros (See Appendix 3.2 and Appendix 3.3) were developed and applied to each of the BORIS output spreadsheets for each trial of each dog to calculate outcome measures (see Table 3.2), which were then imported into a single dataset to be analysed in SPSS.

125

Chapter 3: Comparing assays of spatial working memory in dogs

Table 3.2: Outcome measures for the holeboard task.

126

Chapter 3: Comparing assays of spatial working memory in dogs

Data were analysed in SPSS using the repeated measures general linear model function, with session and trial as within-subject factors and age group as a between-subjects factor. In order to test that the data met assumptions, and where degrees of freedom were sufficient to allow their performance, Mauchly’s tests of sphericity and Levene’s tests of homogeneity of error variances were performed, and Shapiro-Wilk tests of normality were performed on model residuals. Figures were plotted in RStudio (RStudio Team, 2016) using the package ggplot2 (Wickham, 2009).

3.3.3 Results:

Results are reported as a test statistic, degrees of freedom (shown as a subscript) and a p-value, except in cases where not all of these were given as outputs from statistical testing software. Graphs show means with error bars representing 95% confidence intervals.

Since only 17/300 (5.67%) of residuals significantly deviated from normality (p<0.05), no corrections were applied to account for this. 47/300 (15.67%) sets of datapoints (each set consisting of data from all dogs for each outcome variable at each trial of each session) showed significant heterogeneity of variance as assessed via Levene’s test (p<0.05), however, since the majority of sets of datapoints did show homogeneity of variance, this was considered acceptable. A significant deviation from sphericity was

2 found only for the effect of session on search rate (W=0.01, X (14)=36.7, p=0.02), therefore a Greenhouse-Geisser correction was applied to the degrees of freedom for this effect.

A significant effect of session was found on all measures; reference memory score

(F(5,35)=10.549, p=0.000003), working memory score (both considering only baited buckets (F(5,35)=4.877, p=0.002) and considering all buckets (F(5,35)=3.587, p=0.010)), time

-7 taken to find all food rewards (F(5,35)=13.540, p=2.24*10 ) and search rate

(F(1.52,35)=6.590, p=0.0182). These results are summarised in Figure 3.3. Significant effects of trial were found on reference memory score and time taken to find all food rewards, with dogs showing an improvement in reference memory scores and a decrease in time taken to find all rewards with successive trials within a session, as shown in Figure 3.4. There was also a significant trial by session interaction for time taken to find all food rewards, as shown in Figure 3.5; whilst for most sessions dogs showed a similar decrease

127

Chapter 3: Comparing assays of spatial working memory in dogs in time taken to find all of the rewards with increasing trial number, this was more pronounced in session 1, when dogs were first exposed to the task, and was not the case for session 3, in which there was a slight increase in the mean time taken to perform the task. No significant effect of age group or session by age group interaction was found on any outcome (p>0.05).

128

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.3: Graphs showing the effect of session on (A) reference memory score, (B) working memory score (baited buckets), (C) working memory score (all buckets), (D) time taken to find all food rewards and (E) search rate.

Graphs show means with error bars representing 95% confidence intervals. The red dotted line separates sessions 1-4, performed with the initial configuration, and sessions 5-6 (reversal phase) with the second configuration. (A) The black dotted

129

Chapter 3: Comparing assays of spatial working memory in dogs line represents the reference memory score expected by chance if dogs were visiting buckets at random (0.25).

130

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.4: The mean effect of trial on (A) reference memory score and (B) mean time taken to find all food rewards, showing regression lines (red) with 95% confidence intervals (grey).

131

Chapter 3: Comparing assays of spatial working memory in dogs

Graphs show means with error bars representing 95% confidence intervals. The dotted line represents the reference memory score expected by chance.

Figure 3.5: The effect of trial on time taken to find all food rewards for each session.

Perseveration rate (see Table 3.2) was also calculated for sessions 5 and 6, as shown in Figure 3.6. One-sample T-tests performed in R using mean perseveration rate values for each session and each dog (to prevent pseudoreplication) found that the mean perseveration rate for session 5 (t(9) = 3.854, p=0.00388), but not session 6 (t(9) = 1.4939, p=0.169), was significantly higher than 0.25; the value expected if dogs were equally likely to search in all buckets. A repeated measures general linear model with session (5 and 6 only) and trial as between-subjects factors found that there was no significant difference between the perseveration rate of sessions 5 and 6 (F(1,9) = 0.714, p = 0.420),

132

Chapter 3: Comparing assays of spatial working memory in dogs

nor was there any significant effect of trial (F(9,81)= 0.610, p=0.785) or session*trial interaction (F(4.267,38.4)=1.266, p=0.300) on perseveration rate.

133

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.6: (A) Perseveration rates for sessions 5 and 6. (B) Proportion of visits to buckets that were baited in the initial configuration.

Graphs show means with error bars representing 95% confidence intervals. The black dotted line represents the rate/proportion expected by chance if dogs visited

134

Chapter 3: Comparing assays of spatial working memory in dogs

each bucket at random. (B) For sessions 1-4 (left of the red dotted line), buckets baited in the initial configuration were the currently-baited buckets and thus the data are equivalent to reference memory scores. For sessions 5-6 (right of the red dotted line), these were the previously-baited buckets and thus the data are equivalent to perseveration rates.

3.3.4 Discussion:

These results indicate that the holeboard task is adaptable for use in dogs when sufficient trials are given. Results are similar to those seen in other species (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Haagensen et al., 2013a, Haagensen et al., 2013b, Antonides et al., 2015), with both reference and working memory scores increasing with successive sessions, decreasing suddenly following implementation of a new configuration, and then increasing again as dogs begin to learn the new configuration.

This study used two different working memory scores; the WMB score representing visits to previously unvisited baited buckets as a proportion of the total number of visits to baited buckets, and the WMA score representing visits to all previously unvisited buckets as a proportion of the total number of visits to all buckets. Whilst the method represented by the WMB score is used by many other holeboard studies (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Haagensen et al., 2013a, Haagensen et al., 2013b, Antonides et al., 2015) and therefore allows comparisons to be made, the focus exclusively on baited buckets means that the score may be affected by the dogs’ reference memory score for which buckets were baited, therefore the WMA score was also included as an alternative measure that allowed all revisits to buckets to be accounted for, rather than only visits to baited buckets. However, both measures of working memory showed a similar pattern of changes with successive sessions and following reversal, implying that they are likely to represent the same construct.

Whilst Macpherson and Roberts (2010) found that the working memory capabilities of dogs in a radial maze task was “surprisingly low” compared to rodents, in this task the mean working memory score (considering only baited buckets, as in other studies) during the last session before reversal (session 4) was within the range of 0.8-0.95

135

Chapter 3: Comparing assays of spatial working memory in dogs previously recorded in other species (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Haagensen et al., 2013a, Haagensen et al., 2013b, Antonides et al., 2015). This implies that the holeboard task may be more suitable for assessing working memory in dogs than radial maze tasks.

However, the mean reference memory score of the dogs during session 4 was unexpectedly low. Whilst Haagensen et al. (2013a, 2013b) found mean pre-reversal reference memory scores of around 0.3-0.4 in pigs, most other studies found higher mean reference memory scores of 0.45-0.7 (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Antonides et al., 2015). This is possibly because this study used a large number of massed trials spaced over only two days (excluding reversal), whereas other studies generally used 2-4 trials per day spaced over periods longer than a single week. Since usage of spaced rather than massed trials improves reference but not working memory (Spreng et al., 2002, Commins et al., 2003), it makes sense that the reference but not working memory scores of the dogs in this study would be lower than those previously observed in other species even if the cognitive capacities of those species were comparable.

Dogs showed perseveration with visiting the previous configuration of buckets during session 5, but not during session 6, when the proportion of visits to these buckets was not significantly different from those expected by chance. Whilst perseverative behaviours may occur in pathological situations such as frontal lobe injury (Lombardi et al., 1999) or obsessive-compulsive disorders (Voon et al., 2015), where behaviours become less goal-oriented and more habitual, short-term perseveration with visiting the formerly correct locations from a previously-learned holeboard configuration may represent persistent reference memory for the original configuration, which then becomes extinguished when the behaviour is unrewarded. This has previously been observed in other species: Trouche et al. (2010) found that older mouse lemurs made fewer perseverative errors in a maze task, but made more reference memory errors than younger lemurs (Trouche et al., 2010). Conversely, Beatty et al. (1985) found that 26-month-old rats made a higher proportion of correct choices on a radial maze task than 6-month-old rats, indicating superior reference memory abilities, but prior radial maze training interfered with their ability to learn a cross-maze task, causing them to make more errors and require more training trials to reach a learning criterion than 6- month-old rats and non-pre-trained 26-month-old rats. Both studies would indicate that

136

Chapter 3: Comparing assays of spatial working memory in dogs increased reference memory could interfere with the learning of new or changed task rules and thus increase perseveration behaviours. In this study, since there was no significant perseveration with visiting the previous configuration during session 6, it seems more likely that the perseveration observed in session 5 was due to persistent reference memory that had not yet been extinguished, rather than the formation of a persistent habitual behaviour. However, this task could be used to compare the tendencies of different groups of dogs to persevere with visiting the initial baited bucket configuration, especially if a larger number of post-reversal sessions were used to assess the duration of perseveration after such visits were no-longer rewarded.

There was also no effect of age group on probability of success, which was surprising as other studies have found age-dependent differences in working memory (Adams et al., 2000, Tapp et al., 2003, Snigdha et al., 2012). It is possible that there were insufficient dogs of each age group within this sample to detect an effect, or that the task itself is less well suited to detection of age-related cognitive decline compared with more complex tasks that require training, such as the delayed non-matching to position tasks that have been previously used in studies of age-related cognitive decline in dogs (Adams et al., 2000, Zanghi et al., 2015). Alternatively, the dogs recruited may have been less likely to have experienced age-related cognitive decline than dogs within the general population: The staff-owned dogs involved in this study were habituated to handling and participation in studies and clinical teaching exercises, so they may receive more regular and varied cognitive and environmental enrichment than dogs within the general population or laboratory dogs used in other studies. Studies have found that cognitive and environmental enrichment can reduce the effect of age-related cognitive decline in humans (Ball et al., 2002, Wilson et al., 2002, Bosma et al., 2003), mice (Kempermann et al., 2002), and dogs (Milgram et al., 2005, Nippak et al., 2007), so it is possible that the older dogs in this study were less cognitively impaired than older dogs in general due to the greater amount of cognitive enrichment they received due to being used in clinical training and experimental work.

This study shows that the holeboard task can successfully be adapted to dogs and performed in a short timeframe of three days, with the potential to add additional post- reversal trials in order to monitor perseveration of visits to buckets that were baited in the initial configuration over a longer period. This task also has the advantage of allowing spatial working and reference memory capabilities of dogs to be assessed

137

Chapter 3: Comparing assays of spatial working memory in dogs separately. As well as having the potential to compare working and reference memory scores of dogs with and without chronic pain from osteoarthritis, this task could be used to investigate the effects of other disease states or of environmental enrichment factors on working and reference memory, as well as allowing comparisons to be made between dog breeds or to compare the working and reference memory abilities of dogs with those of other species. Whilst there was no significant effect of age in this study, it is possible that with an increased sample size or a sample of dogs that more accurately represents the general population, the holeboard task could also be used to investigate age-related cognitive decline using a canine model, and as it requires no operant conditioning prior to the onset of testing it can be performed much more quickly than other tasks that have been used previously for this purpose, which typically take weeks to months (Tapp et al., 2003, Milgram et al., 2005, Snigdha et al., 2012, Zanghi et al., 2015).

3.4 The Disappearing Object task:

3.4.1 Background:

The holeboard task described in this chapter requires three continuous days of practice, with two sessions each day. Therefore it would be difficult to perform with visiting owners who are unlikely to have sufficient amounts of free time to participate, thus it would be advantageous to use a task that can be performed in a shorter period of time in order to compare dogs with and without chronic pain from osteoarthritis.

The disappearing object task developed by Fiset et al. (2003) was able to be successfully performed in only two days, though because the trial durations and intertrial intervals were relatively short, it should be possible to condense training and testing into a single day. This task is also advantageous because it was developed and successfully used in companion animal dogs (Fiset et al., 2003), unlike the holeboard task which had not been used in dogs prior to the studies described in this thesis document. The equipment required for the study should also be fairly transportable and does not require large amounts of space, allowing for it to be performed in owners’ homes (Fiset et al., 2003) which is likely to yield results that are more representative of dogs’ working memory in everyday life than if testing was performed in an unfamiliar setting.

138

Chapter 3: Comparing assays of spatial working memory in dogs

The task consisted of three phases: In an initial shaping phase, the dog was trained to touch an object in exchange for a reward (a combination of food, verbal praise and petting), and then the object was gradually moved closer to, and eventually between, four identical open-backed wooden boxes arranged in a semicircle around the dog’s position (Fiset et al., 2003), before the dog was allowed to retrieve the object and receive the reward each time. In the training phase, the dog was restrained by an experimenter and watched another experimenter place the object, suspended by a length of nylon string, behind one of the four boxes. In order to prevent the dog from using body orientation or maintained eye contact as cues to remember the location of the object, and to prevent visual cues associated with the boxes to allow memory reactivation of the object’s location during the retention interval (Fiset et al., 2003), an opaque screen was placed in front of the dog’s position. The screen was then immediately removed and the dog released and allowed to approach a box. If the dog approached the correct box first, they were rewarded. If the dog approached an incorrect box, they were interrupted and led back to their starting position without receiving any reward. If the dog made no search attempt within 60s they were called back to the starting position to begin the next trial (Fiset et al., 2003). During the testing phase, which was performed the following day, a similar protocol was followed, however the opaque screen remained in place for a variable retention interval between 0s and 240s, and trials were separated by a 30s intertrial retention period (Fiset et al., 2003).

Fiset et al. (2003) found that dogs’ success rate (proportion of visits that were to the correct box) was significantly higher than expected by chance at all retention intervals from 0s to 120s, but was significantly higher at shorter retention intervals than at longer retention intervals. Furthermore, when dogs searched in an incorrect box, they were significantly more likely to search in boxes near to the target box than boxes further away (Fiset et al., 2003), as would be expected if the dogs were relying on spatial working memory to recall the location of the object and thus retained an approximate representation of its location (Bjork and Cummings, 1984). Therefore this task appears to be a fast but effective method of assessing spatial working memory in dogs.

139

Chapter 3: Comparing assays of spatial working memory in dogs

3.4.2 Materials and Methods:

3.4.2.1 Animals:

This task used the same animals that participated in the holeboard task described in experiment 3.1. The disappearing object task was performed on either the day immediately after the reversal phase of the holeboard study (sessions 5 and 6) or two days afterwards (in order to allow two dogs to be tested each week).

3.4.2.2 Apparatus:

Four identical wooden boxes, cuboidal-shaped but with an open back, were placed in the arena as shown in Figure 3.7, such that each was 150cm from the starting position (centre point of the arena gate) and evenly-spaced. Each box was filled with 750ml aquarium gravel inside plastic cups, to increase stability. One experimenter, E1 (MS), remained within the arena, whereas another, E2, remained in the holding area. The object consisted of a tennis ball or squeaky rubber toy attached to a 125cm, 1mm thick nylon thread. Tennis balls and boxes were cleaned between uses with F10 disinfectant spray (Health and Hygiene (Pty) ltd., Florida Hills, South Africa).

140

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.7: The arena setup for the disappearing object task.

Red dotted lines represent the arc at 150cm from the start position, with boxes placed at 36, 72, 108 and 144 degrees from the line of the fence and at tangents to the arc. Boxes are numbered as shown. The positions of E1 and E2, the two experimenters, are given at the point when the dog is released to retrieve the object, which in this example is behind box 2. During the retention interval, the wooden board (120cm high, 160cm long and 3mm thick) was slid in front of the gate to prevent the dog from maintaining eye contact with the location of the object. Grey lines represent walls of the arena, which was 3.75m long on each side (full length not shown).

3.4.2.3 Procedure:

The dog was brought into the holding area and allowed to explore for at least 5 minutes whilst the apparatus was set up. E1 showed a tennis ball and rubber squeaky toy to the

141

Chapter 3: Comparing assays of spatial working memory in dogs dog and allowed the dog to interact with both objects. The object most preferred by the dog was selected for use, according to E1’s subjective opinion which was influenced by which object the dog initially approached, which object they showed more tail-wagging and jumping behaviour towards when the objects were presented, and which object they interacted with for longer following presentation of both objects.

The procedure was similar to that used by Fiset et al. (2003): In the Shaping Phase, E2 held the dog by the collar whilst E1 placed the object on the floor near the dog and verbally encouraged the dog to approach the object. When the dog touched the object with their paw, mouth or nose, the object was removed by E1 and the dog was rewarded with a food reward, petting and verbal praise. The dog was then called back by E2 who held their collar at the designated point to start the next trial. Once the dog had touched the object 5 times successfully, the object was gradually moved towards the boxes with each successful trial. Then the object was placed between two of the four boxes, such that it was still visible. Once the dog touched the object 10 times successively, the Training Phase was initiated.

In the Training Phase, E2 held the dog in the starting position as previously. E1 placed the object in front of and between the two middle boxes and called the dog to get their attention. The target box for each trial was pseudorandomised using the RAND() function in Excel, such that each target box occurred exactly six times, and the same target box was not selected twice in succession. E1 then lifted the object using the end of the cord that was not attached to the object, such that the object dangled in the air in front of E1, and moved the object in front of all four boxes to reach the target box (i.e. if the target box was box 3 or 4, the object was moved in front of boxes 1, 2, 3 and 4 and then into the target box; if the target box was 1 or 2 the object was moved in front of boxes 4, 3, 2 and 1 and then into the target box). Once the object reached the target box it was placed behind the target box. If it was unclear whether the dog was still observing the object at that point, the object was lifted from behind the box into the dog’s view and replaced behind the box once more, with E1 visually tracking the dog’s eyes to ensure they followed the object. If the dog appeared to stop following the object with their eyes at any point, the movement of the object between all four boxes was repeated. E1 then moved the wooden board in front of the gate aperture and immediately moved it back again, in order to habituate the dog to its presence. E1 then stood in the position indicated in Figure 3.7, and E2 released the dog to retrieve the

142

Chapter 3: Comparing assays of spatial working memory in dogs object. At this point E1 looked towards the adjacent wall and E2 was instructed to look towards E1, so as not to provide cues about the object’s location to the dog. Once the dog began to move towards the boxes, E1 followed the dog such that they could reach the dog’s collar to prevent them from visiting multiple boxes per trial. If the dog did not search behind any of the boxes within 1 minute, they were called back by E2 and the next trial began. If the dog searched behind a non-target box, E1 restrained the dog by the collar and led them back out of the arena, where they were held by E2 ready for the next trial. If the dog searched behind the target box and touched the object, they were rewarded with a food reward, petting and verbal praise. Dogs were not allowed to visit multiple boxes, and the first box visited by the dog was recorded as the outcome. Occasionally, dogs moved between boxes too quickly to be restrained and were able to retrieve the object before being led out of the arena. In these cases, the outcome was recorded as an incorrect visit (to the first box visited), but the dog still received a food reward so that their training to touch the object would not be extinguished. After 24 training trials, the Testing Phase was initiated.

The Testing Phase consisted of three trialblocks of 20 trials each. E2 held the dog at the designated location as previously, and E1 hid the object behind one of the four boxes as in the Training Phase. The wooden board was then moved in front of the gate and remained there for a specific retention interval (0, 30, 60, 120 or 240 seconds). The interval and target box for each trial were pseudorandomised using the RAND() function in Excel, such that each combination of target box and interval duration occurred exactly once per trialblock, and the same target box or interval duration did not occur twice in succession. The order of trials with each interval and target box is given in Appendix 3.4. Each trial then proceeded as in the Training Phase. Dogs were taken for a walk outside for 5 minutes between each of the three phases.

For each trial, the box visited by each dog and the outcome (Success/Failure/No visit) were recorded by E1 using pencil and paper. An attempt was made to record footage of the task using a camcorder in order to obtain the latency to approach each box in addition to the success rate data, as whilst this was not performed by Fiset et al. (2003) it may have provided additional information for analysis. Furthermore this would also have allowed each dog’s visits to be recorded independently by an assistant who would

143

Chapter 3: Comparing assays of spatial working memory in dogs have been blinded with respect to dogs’ signalment and the study hypothesis. However due to technical issues relating to the camcorder this was ultimately not possible.

3.4.2.4 Analysis:

The analysis method for this study was adapted from the example mixed effects logistic regression model given by UCLA: Statistical Consulting Group (2017). A mixed effects logistic regression model was fitted in R (R Core Team, 2014) using the glmer() function from the lme4 package (Bates et al., 2015), which automatically identifies the nesting structure of variables (UCLA: Statistical Consulting Group, 2017), in order to identify factors affecting the probability of a successful visit. The model was specified as 'binomial' and included the fixed effects of interval, trialblock, box number (behind which the object was hidden) and trial number. The individual dog being tested was included as a random effect, but between-dog factors were not included due to concerns about insufficient power and to reduce the risk of overfitting (Babyak, 2004). A reference category was defined for the categorical factors of target box (box 1) and trialblock (trialblock 1), with which to compare the effect of other categories of each factor.

The developer version of lme4 (version 1.1-15) was installed using the install_github() function from the package devtools (Wickham and Chang, 2017) in order to successfully make predictions from this type of model, as the default version at the time of writing (version 1.1-14) does not possess this functionality (UCLA: Statistical Consulting Group, 2017). The predict.merMod() function was then used to predict the probability of success over the range of possible values for each continuous variable that was found to have a significant effect on the probability of successfully visiting the correct box.

The function dispersion_glmer() from the R package blmeco (Korner-Nievergelt et al., 2015) was used to test for overdispersion. Additional packages used were Hmisc (Harrell, 2017), for the binconf() function which allows confidence interval calculations for the observed outcomes, and dplyr (Wickham et al., 2017) for various data manipulation functions.

In order to assess whether the dogs were experiencing proactive interference, whereby the memory of the object location in the previous trial interferes with the dog’s memory

144

Chapter 3: Comparing assays of spatial working memory in dogs of the object location in the current trial (Hampton and Shettleworth, 1998, Fiset et al., 2003), binomial tests were performed for each dog to assess whether the proportion of erroneous visits in which the dog visited the same box as they visited the previous trial differed from what would be expected by chance (0.25; assuming dogs were randomly selecting boxes to visit), as performed by Fiset et al. (2003). Additionally, binomial tests were also performed for each dog to assess whether the proportion of erroneous visits in which the dog visited the target box of the previous trial differed from what would be expected by chance, as it is possible that the dog could have learned and remembered the location of the object when it was removed from the previous box between trials even if they did not successfully visit that box during the previous trial.

If dogs were truly relying on spatial working memory to locate the object, it would be expected that they would search “as a function of proximity to the target object” (Fiset et al., 2003), i.e. that the dogs would be more likely to search for the object in boxes closer to the target box rather than boxes that were further away from it, rather than searching randomly (Bjork and Cummings, 1984). Dogs could search in boxes that were 0 (target box), 1, 2 or 3 boxes away from the target box, however the proportions of visits to each of these that would be expected by chance are not equal: If the object was hidden behind boxes 1 or 4, the probability of dogs randomly searching in a box 0, 1, 2, or 3 boxes from the target box is 0.25. However if the object was hidden behind boxes 2 or 3, the probability of dogs randomly searching in a box 1 box away is 0.5 (as two boxes meet this criteria), whereas the probability of searching in the correct box (0 boxes away) or a box 2 boxes away is 0.25, and the probability of searching in a box 3 boxes away is 0, as no such boxes exist. Because the experimental design was balanced such that there were equal numbers of trials with each target box for each interval, the mean of the probabilities for target boxes 1 and 4 and target boxes 2 and 3 was used as the expected probability for each distance from the target box. The number of visits each dog made to boxes at each distance was calculated, mean values were obtained for each distance, and binomial tests were performed to compare these to numbers of visits expected by chance.

In order to assess whether the holeboard and disappearing object tasks measured the same construct, a Spearman’s rank correlation test was performed (a Pearson correlation test was not used as the relationship may not be linear) between the outcomes of the holeboard task (Working memory score (all buckets), working memory

145

Chapter 3: Comparing assays of spatial working memory in dogs score (baited buckets) and reference memory score) and the overall success rate for the disappearing object task for each dog. Whilst the disappearing object task is thought to rely on working memory, it is not clear which working memory score is superior, and it would be interesting to assess whether there was any correlation with reference memory score in order to confirm that the disappearing object task did not rely on reference memory. Because dogs may not have fully learned the task in early sessions, this was repeated using only the data from holeboard session 4. A Bonferroni adjustment was applied to the p-value threshold for significance to account for multiple testing.

Figures were produced in RStudio (RStudio Team, 2016) using various functions from the R packages ggplot2 (Wickham, 2009) and GridExtra (Auguie, 2017). The R script used to analyse and plot the data for this experiment is given in Appendix 3.5.

3.4.3 Results:

The model estimates were based on an adaptive Gaussian Hermite approximation (lme4 automatically selects an appropriate approximation method for the data unless otherwise specified) with 100 integration points, and from these estimates odds ratios for each continuous variable and factor category were calculated, as well as z-values and associated p-values (as shown in Table 3.3). The factors with a significant (p<0.05) effect on the probability of success were interval (z=-7.17, p=7.49*10-13), with dogs being less likely to successfully find the object with increasing intervals (Odds Ratio (OR)=0.992, 95% confidence interval (CI)=0.990-0.994), trial (z=-2.53, p=0.0115), with dogs being less likely to find the object with progressive trials within a trialblock (OR=0.962, CI=0.933- 0.991) and box, with dogs being less likely to successfully find the object when it was hidden behind box 2 (but not when it was hidden behind boxes 3 or 4) compared with box 1 (OR=0.593, CI=0.362-0.972; z=-2.07, p=0.0383).

Table 3.3: Odds ratios with 95% confidence limits calculated from model estimates, and z- and p-values generated by the model output, for each continuous factor and level of categorical factor included in the model.

146

Chapter 3: Comparing assays of spatial working memory in dogs

Lower 95% Upper 95% Variable/ Odds confidence confidence category ratio (OR) limit (LCL) limit (UCL) z-value p-value Interval 0.992 0.990 0.994 -7.17 7.49x10-13 Trial 0.962 0.933 0.991 -2.53 0.011 Trialblock 2 1.225 0.808 1.858 0.95 0.340 Trialblock 3 1.256 0.809 1.950 1.01 0.310 Box 2 0.593 0.362 0.972 -2.07 0.038 Box 3 0.789 0.484 1.286 -0.95 0.342 Box 4 1.211 0.745 1.970 0.77 0.440

Factors with a significant effect on probability of success (p<0.05) are shown in orange.

Whilst the model generated warning messages (see Appendix 3.5), these were initially removed following rescaling of continuous variables (interval and trial) to values between 0 and 1 using the rescale() function, so that they were within the same scale as the binary outcome variable (success/failure to reach the correct box). However, since this did not affect the p-values of the model, it was decided to retain the non-rescaled version of the model for ease of making and interpreting predictions from the model. The dispersion_glmer() function returned a scale parameter (1.11) within the expected range for models that are not overdispersed (0.75-1.4) according to the package documentation (Korner-Nievergelt et al., 2015), therefore neither the model or analysis method were amended to account for overdispersion.

For the continuous variables that were found to have a significant effect on the probability of success (interval and trial), the model was used to predict the average marginal probability of success (the average change in probability of success for a given variable across all groups of all other variables (UCLA: Statistical Consulting Group, 2017)) at a series of values between the minimum and maximum recorded value (240 datapoints between 0 and 240s for interval; 20 datapoints between 1 and 20 for trial). These predictions were plotted together with the observed results, as shown in Figure 3.8 and Figure 3.9. The lowest integer value of interval at which the probability of success was less than or equal to 0.25 (the value expected by chance if the dogs were selecting a box at random each trial) was 207 seconds.

147

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.8: Mean predicted probability of success (black) and interquartile range (grey) for each interval.

Points in red show the mean observed success rate at each experimental interval, with 95% confidence intervals. The dotted line represents the probability of success expected by chance if dogs were randomly selecting one of the four boxes to visit each trial.

148

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.9: Mean predicted probability of success (black) and interquartile range (grey) for each trial within a trialblock.

Points in red show the mean observed rate of success for each trial (averaged across all three trialblocks), with 95% confidence intervals. The dotted line represents the probability of success expected by chance if dogs were randomly selecting one of the four boxes to visit each trial.

Because the probability of success when the object was hidden behind box 2 was significantly lower than when it was hidden behind box 1, average marginal probabilities of success for each interval were also calculated separately for each box, as shown in Figure 3.10. A graph showing proportion of visits to each box for each target box (behind which the object was hidden) was also plotted (see Figure 3.10).

149

Chapter 3: Comparing assays of spatial working memory in dogs

Figure 3.10: Graphs related to target box location during the disappearing object task.

(A): Observed proportion of successful visits (with 95% confidence intervals) for each target box (behind which the object was hidden). (B): Mean predicted effect of interval on probability of success for each target box. Translucent areas represent interquartile ranges. The dotted line represents the probability of success expected by chance if dogs were randomly selecting one of the four boxes to visit each trial. (C): Observed proportion of visits to each box for each target box (with 95% confidence intervals).

150

Chapter 3: Comparing assays of spatial working memory in dogs

Only one dog visited the box visited in the previous trial more often than would be expected by chance, and only three of ten dogs visited the target box from the previous trial more than would be expected by chance, as shown in Table 3.4.

Table 3.4: The number of (erroneous) visits to the visited and target box from the previous trial, the total number of erroneous visits to all boxes and the results of the binomial tests comparing each of these to values expected by chance (0.25) for each dog.

Incorrect visits to: Total number of Box visited in previous trial Target box of previous incorrect Dog trial visits (to any non- Number of Binomial Number Binomial target box) visits test p-value of visits test p-value

1 26 10 0.11669 12 0.02092

2 43 7 0.21985 18 0.02027

3 23 10 0.05237 10 0.05237

4 26 7 0.82175 11 0.06592

5 33 9 0.84057 12 0.15733

6 36 9 1 12 0.25053

7 40 9 0.85564 11 0.71593

8 35 14 0.04994 14 0.04994

151

Chapter 3: Comparing assays of spatial working memory in dogs

9 32 11 0.22335 10 0.41634

10 30 6 0.67446 6 0.67446

Rows in orange represent statistically significant (p<0.05) results.

A greater proportion of visits were made to boxes the closer they were to the target box, as shown in Figure 3.11. Binomial tests were performed to compare the observed number of visits to boxes at each distance from the target box to those expected by chance. Significant differences (p<0.05) were found only for boxes that were 0 or 2 boxes from the target box, as shown in Table 3.5.

Figure 3.11: Mean proportions (with 95% confidence intervals) of visits to boxes at each distance from the target box (grey).

Transparent cyan bars show the expected proportion of visits to boxes at each distance if dogs were searching by chance.

152

Chapter 3: Comparing assays of spatial working memory in dogs

Table 3.5: Binomial test results comparing observed and expected proportions of visits to boxes at each distance from the target box.

Distance Number Total Observed Expected Binomial from of visits number of proportion proportion test p- target (mean trials of visits of visits value box per dog) (mean per dog)

0 28 59 0.475 0.25 0.000217

1 19 59 0.322 0.375 0.424

2 8 59 0.136 0.25 0.0493

3 4 59 0.068 0.125 0.237

p-values shaded in gold were significantly different (p<0.05) from those expected by chance. Trials in which the dog did not visit a box were omitted.

None of the outcomes of the holeboard task significantly correlated with the success rate in the disappearing object task (at either p<0.05 or p<0.00833 (Bonferroni-adjusted) thresholds), either when all holeboard trials were considered or when only holeboard session 4 was considered, as shown in Table 3.6.

153

Chapter 3: Comparing assays of spatial working memory in dogs

Table 3.6: Spearman’s rank correlations between mean holeboard task outcome measures and disappearing object task success rate for each dog.

Spearman's Sessions Correlation between disappearing rho included: object task success rate and: statistic: p-value:

Working memory score (all buckets) 0.079 0.828

All sessions Working memory score (baited buckets) -0.061 0.868

Reference memory score 0.438 0.206

Working memory score (all buckets) -0.024 0.947

Session 4 Working memory score (baited buckets) -0.073 0.841 only

Reference memory score 0.225 0.532

3.4.4 Discussion:

As hypothesised, the dogs were less likely to correctly recall the location of the hidden object as the interval between watching the object get hidden and being allowed to retrieve it increased. This result is similar to the findings of Fiset et al. (2003). Dogs were also slightly less likely to recall the location of the hidden object with increasing trials within the same trialblock. This may be because the dogs became disinterested or inattentive after several trials had occurred without a break. It is also possible that the dogs were becoming satiated from successive food rewards, however if this was the case it would be expected that dogs would also show a reduced probability of success with successive trialblocks, which was not the case.

154

Chapter 3: Comparing assays of spatial working memory in dogs

Dogs were also less likely to successfully find the object when it was hidden behind box 2 compared to when it was hidden behind box 1, and were also slightly (but not significantly) less successful at locating the object when it was hidden behind box 3 than box 1 (See Figure 3.10A). Whilst dogs did most commonly search behind box 2 when the object was hidden there, this effect was less pronounced than for the other boxes (see Figure 3.10C). Dogs also searched behind box 3 more often than would be expected by chance when the object was hidden behind box 2, and dogs also searched behind box 2 slightly more than would be expected by chance when the object was hidden behind box 3. This implies that dogs may find it more difficult to distinguish the two middle boxes (2 and 3) compared to the outer boxes (1 and 4). Since boxes 1 and 4 were closer to the arena walls, these may have acted as an environmental (allocentric) cue and facilitated the dog’s memory for the nearby object location. Milgram et al. (1999) found that dogs were capable of using allocentric cues in order to solve spatial tasks, so the presence of a cue (such as a wall adjacent to one side of the box) may have facilitated their learning in this case. Additionally, there may have been something about the location of box 2 in the arena that made dogs less willing to approach it; although the boxes and arena were cleaned between uses and each wooden box was likely to have changed locations several times during the study, box 2 was always placed at the same location within the arena, and there may have been something undetectable to experimenters about this spatial location that was aversive or less appealing to the dogs than the other locations.

In a spatial working memory task performed in a chamber containing an array of several holes, Gutnikov et al. (1994) similarly found that rats were more accurate in trials where the target hole was an outer hole compared to where the target hole was a central hole. They concluded that this was probably because rats had used a body-orienting strategy involving turning from the food dispenser in the direction of the target hole and selecting the first eligible hole they encountered (which would be an outer rather than an inner hole). Because of technical problems preventing successful video recording and analysis of the trials in this study, it was not possible to investigate whether dogs were more likely to turn in the direction of the correct box following release or whether they oriented their bodies towards that direction prior to release. This could be investigated by repeating the experiment with a superior camcorder setup in order to allow the examination of the dogs’ body orientation during the retention interval and routes taken by dogs between the holding pen and the boxes. However, each dog was restrained by

155

Chapter 3: Comparing assays of spatial working memory in dogs

E2 so that they could not overtly aim their body towards the target box, and the presence of the wooden boards during the retention interval should have prevented the dog from maintaining visual contact with the target box, in order to prevent the dog from using an orienting strategy when selecting which box to visit (though it is possible that dogs could have used more subtle strategies involving orienting of the head or eyes). Additionally, when the target box was a central box, incorrect visits were more commonly due to visiting the alternative (incorrect) central box than either of the outer boxes, whereas the turning strategy proposed by Gutnikov et al. (1994) would predict that dogs would be more likely to visit the outer box that was nearest to the correct central box instead, therefore it is perhaps more likely that the dogs were more likely to successfully locate the object when it was hidden behind an outer box than an inner box due to allocentric cues such as the position of the target box relative to the arena walls than using this strategy.

One limitation of this experiment was that each testing session lasted for around 5-6 hours, which is too long to reasonably use with dogs owned by members of the public in order to compare osteoarthritic and healthy control dogs. Ideally the task would take a maximum of around two hours, as this would better fit into owners’ schedules and thus would be likely to improve recruitment, and would also ensure the dog remained motivated to participate throughout the session. There are several potential changes that could be made to the study design in order to reduce the duration of a study session: Since there was no effect of trialblock, and the effect of interval was still statistically significant when only data from trialblock 1 were included in the model (p<0.05; see Appendix 3.6), it would be feasible to use only one trialblock rather than three. Additionally, since the dogs’ performance reached chance levels at around 207 seconds, it would be reasonable to omit the 240s trialblock from future studies as dogs would be expected to be performing no better than expected by chance at this point.

Additionally, the model used assumes that the effect of each variable on probability of success is linear, however this is unlikely to truly be the case, as realistically the probability of success would be expected to tend towards that expected by chance (estimated as 0.25 assuming that dogs were selecting a box to visit at random each trial) rather than continue to decrease beyond this, and as such the model fits less well at lower probabilities of success. For example, at an interval of 240s, which is beyond the point that the predicted model has crossed P(Success)=0.25, the predicted value

156

Chapter 3: Comparing assays of spatial working memory in dogs

(0.2033) is considerably below the observed value (0.2583). However, since values of interval at which the probability of success was predicted to be less than 0.25 will be omitted from the subsequent study, this issue is likely to have less of an effect when comparing dogs with and without osteoarthritis.

This experiment was adapted from a study by Fiset et al. (2003), which also found a significant effect of interval on their measure of success (measured as proportion of successful visits rather than predicted probability of success). However the success rate of the dogs in their study was much higher, with dogs visiting the correct box on ~90% of visits at intervals of 0s, and still performing at higher than expected chance levels of success after intervals of 240s (Fiset et al., 2003). There are several possible reasons for this; the study by Fiset et al. (2003) was performed over two days, with the shaping and training phase performed on day 1 and the three trialblocks of the testing phase performed on day 2. It is possible that spreading sessions over two days may have increased the dog’s attentiveness as the testing sessions were shorter, and allowing the dog to sleep between the training and testing phase may have allowed memory consolidation (Rasch and Born, 2007), which has been shown to improve the performance of human participants in a range of cognitive tasks (Stickgold, 2005). Additionally, the study by Fiset et al. (2003) was performed in the owners’ homes, a familiar environment in which the dog is likely to be more comfortable and less anxious or fearful than an unfamiliar arena containing many novel stimuli, which may cause neophobia and anxiety in dogs (Pluijmakers et al., 2010). Since anxiety and fear are known to impair working memory in humans (Calvo et al., 1992, Lavric et al., 2003) and rats (Diamond et al., 1999, Woodson et al., 2003), it seems likely that dogs would perform better in their home environment than in an unfamiliar one in which they may be anxious. Fiset et al. (2003) also specifically selected dogs that appeared to be highly motivated to play with the objects, whereas in order to maximise recruitment this study used as many staff-owned dogs as could be recruited within a short period. Whilst most of the dogs appeared to be highly motivated to touch the ball or squeaky toy without food reinforcement, two of the dogs did not appear motivated to touch the object until after it was associated with a food reward, which may have affected their motivation to retrieve it during the testing phase.

It is possible that trials were not truly independent; studies have found that in tasks such as these with short intertrial intervals and a range of trials of different durations

157

Chapter 3: Comparing assays of spatial working memory in dogs presented together (Edhouse and White, 1988, Fiset et al., 2003) animals are likely to experience intertrial proactive interference, whereby animals retain the memory of the object location from the previous trial which interferes with the memory of the location in the current trial (Hampton and Shettleworth, 1998, Fiset et al., 2003). However only one of ten dogs visited the same box that they visited on the previous trial, indicating that there was not a marked effect of proactive intertrial interference. Slightly more dogs than this visited the target box from the previous trial significantly more often than would be expected by chance. This could be because dogs were more likely to remember the “correct” location of the object in the previous trial than the place in which they looked for it. However this effect occurred in only three of the dogs tested, so is unlikely to have a marked effect on the results.

It is possible that dogs could use alternative strategies rather than spatial working memory to find the object. One possibility is that the dogs used scent cues to locate the object, however the object, arena and boxes were cleaned with the same disinfectant in order to eliminate any scent associated with the object as much as possible, and if dogs were relying on scent cues to locate the object they would have been similarly likely to successfully locate it at both longer and shorter intervals, which was not the case. As noted by Fiset et al. (2003), it is also possible that dogs were orienting their head or body towards the box behind which the object was hidden and remaining in that position until the barrier was removed. However since the dogs were restrained by an experimenter the extent to which they could orient themselves towards a single box was limited. Additionally, dogs did not appear to consistently face a single direction during the interval, and would often move around in an apparent attempt to move around the barrier (which was prevented by the experimenter restraining them), turn around to interact with the experimenter, or lie down on the arena floor. Therefore it seems the most reasonable explanation is that the dogs were using spatial working memory to locate the hidden object. Furthermore, dogs visited boxes that were closer to the target box more often than boxes that were further away. Whilst the proportions of these visits significantly differed from those expected by chance for only boxes that were 0 and 2 boxes from the target box, the sample size in this study was low and the 95% confidence intervals at each proximity were relatively large. The overall tendency of the dogs to visit boxes more often the closer they were to the target box suggests that dogs were searching “as a function of the proximity to the target box” (Fiset et al., 2003), and this combined with the significant effect of interval on probability of success as found by

158

Chapter 3: Comparing assays of spatial working memory in dogs

Fiset et al. (2003) suggests that dogs were likely to be using spatial working memory to locate the object (Bjork and Cummings, 1984).

However, it was hypothesised that if the dogs were using spatial working memory to locate the object, their performance would have correlated with their performance in the holeboard task; a more established test of spatial working memory in other species, which was not the case. There are several potential reasons for this: The dogs may have differed in their motivation to acquire the different rewards in each task, with dogs that were more food-oriented potentially being more motivated to directly obtain the food rewards in the holeboard task and dogs that preferred toys potentially being more motivated to obtain the ball or squeaky toy in the disappearing object task. This would fit with observations that some dogs appeared to find the acquisition of the object more rewarding than the food reward such that they would not readily drop the object to obtain the food reward, and other dogs did not appear initially motivated to touch the object until it was paired with food in the shaping phase. It is also possible that since the location of the baited buckets in the holeboard task is constant between trials whereas the location of the target object in the disappearing object task varies between trials, performance on the disappearing object task may be more reflective of true spatial working memory, whereas working memory scores in the holeboard task may be affected to some extent by the task’s reference memory component.

Additionally, the disappearing object task required the dog to observe a researcher hiding an object with rewards provided by the researcher, which has a social learning component, whereas recalling previously visited buckets in the holeboard task required independent learning and reward acquisition without any human interaction. Dogs appear readily able to use observed human behaviour during behavioural tasks; Merola et al. (2012) found that the latency of dogs to approach a novel object was decreased if a human used a positive tone of voice and facial expressions in relation to the object, and increased if the human used a negative tone of voice and facial expressions in relation to it, showing that dogs’ observation of human behaviour can affect motivation and performance in behavioural tasks. Additionally, several studies have shown that dogs can use observations of human locomotion (Pongrácz et al., 2001) and cues such as gaze, body position, pointing or verbal communication (Miklösi et al., 1998, Hare and Tomasello, 1999, Pongrácz et al., 2004), to solve spatial tasks more effectively. However, looking to humans to provide cues may not always improve dogs’ performance if the

159

Chapter 3: Comparing assays of spatial working memory in dogs task requires independent problem solving: Topál et al. (1997) found that companion animal dogs showed impaired performance on an independent problem-solving task compared to working dogs; they spent more time following and gazing at their owner, showed a greater latency to begin interacting with the task objects, obtained fewer food rewards in total, and tended to wait for the owner’s encouragement before attempting the task. It was also found that this effect was stronger in dogs whose owners regarded them with more anthropomorphic attitudes (i.e. more similarly to human family members) (Topál et al., 1997). Whilst the owners were not present for the holeboard or disappearing object tasks in this study, this suggests that the dogs that are more likely to observe humans and rely on them for provision of cues, which may be advantageous in maintaining attention and learning the object location during the disappearing object task, may be impaired on individual problem-solving tasks such as the holeboard task.

3.5 Summary:

The modified holeboard task is able to be successfully used in dogs providing that they are given a sufficient number of trials to learn the task. The gradual increases in reference and working memory scores over repeated sessions with the same configuration, followed by a decrease in the initial session of a novel configuration and a gradual increase afterwards, is a similar pattern to that observed in other studies and in other species (van der Staay, 1999, Bolhuis et al., 2013), indicating that the underlying cognitive processes are also likely to be similar. Furthermore, the decrease in reference memory score following the change in configuration meant that the dogs were likely to have truly relied on spatial reference memory to locate the rewards prior to the configuration change, rather than relying on scent cues, which would have allowed them to locate the rewards in a similar way even following the configuration change.

The disappearing object task adapted from that used by Fiset et al. (2003) was also successful. As expected, the probability of the dogs successfully finding the hidden object decreased with the length of retention interval. However the dogs performed less well than those of Fiset et al. (2003), who reported higher success rates at each interval, with dogs still more likely to find the object than would be expected by chance even at retention intervals of 240s (Fiset et al., 2003). Whilst this could be due to genuine differences in the dog populations sampled, Fiset et al. (2003) performed the study within participating owners’ own homes in which the dogs were likely to be more

160

Chapter 3: Comparing assays of spatial working memory in dogs comfortable, which may have reduced their anxiety and increased their attention and probability of success.

Since the disappearing object task can be performed in a single day per dog, this task will be used to compare the working memory of dogs with and without chronic pain from osteoarthritis. Because the disappearing object task took longer than expected from initial calculations (up to 6 hours per dog), and because there was no significant effect of trialblock on success rate, only one trialblock will be used. Additionally, because the staff-owned dogs used in this study are regularly used for behavioural studies and veterinary training workshops, they were likely to have been less anxious in the testing environment when separated from their owners than companion animal dogs from the general population. Therefore the following study will be performed within the dogs’ and owners’ own homes, with the owner present.

161

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task Chapter 4 Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

4.1 Abstract:

The study described in this chapter compared the performance of osteoarthritic and healthy control dogs in the disappearing object task of spatial working memory. There was a significant effect of group by sex interaction on probability of success, with female neutered osteoarthritic dogs, but not male neutered osteoarthritic dogs, showing decreased proportions of successful visits compared to healthy control dogs of the same sex and neutering status (no entire dogs participated in this study). This may possibly reflect post-menopausal increases in osteoarthritis severity in human women and increased osteoarthritis-like changes in ovariectomised animal models of osteoarthritis, or may be due to the combination of female mammals’ reduced proficiency in spatial tasks compared with that of male mammals and working memory impairment from osteoarthritis-related pain, neither of which were sufficient to impair task performance alone. However due to limitations of the analysis methods used, in particular the risk of overfitting, these findings should be interpreted cautiously until replicated in a larger sample.

4.2 Introduction:

Since the disappearing object task from Chapter 3 was successfully able to assess spatial working memory in dogs, this chapter describes a study adapting this task to compare the spatial working memory of companion-animal dogs with and without chronic pain from osteoarthritis. The dogs used were owned by members of the public and therefore may not be habituated to unusual situations or settings or to separation from owners compared to the staff-owned dogs used in the study described in Chapter 3. Therefore dogs were tested within their owners' homes with the owner present. This was done to

162

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task reduce potential anxiety or fear which as well as having a negative welfare impact on the dog may also reduce spatial working memory (Lavric et al., 2003), and to improve recruitment of owners who were unable or unwilling to travel to the veterinary school for testing.

Because the study described in Chapter 3 took around 5-6 hours, it was necessary to alter the method such that the task could be performed within 2 hours whilst visiting an owner, as this would allow several dogs to be tested in a single day and would also increase the probability that the study session could fit within owners’ schedules, potentially improving recruitment rates. In order to achieve this, a single trialblock (set of trials containing one instance of each possible combination of target box and interval duration) was used, as in the study from chapter 3 there was no effect of trialblock as a variable and the effect of interval was still significant when only data from the first trialblock were analysed. Intervals of 0, 60 and 120s were used, as by 240s in the previous study dogs were performing as expected by chance. Additionally, removal of this and the 30s interval allowed the task to be performed in a shorter amount of time, and also allowed the trialblock to contain fewer trials, which may reduce the negative effect of successive trials within the trialblock on probability of success. Because dogs were less likely to successfully find the object when it was hidden behind the central boxes, especially box 2, it was important to continue to balance the study so that there were the same number of trials at each box for each interval. Therefore 12 testing trials were performed; one at each interval for each box. The training phase was also reduced to 12 trials in order to reduce the time taken for the session and ensure the dogs remained motivated.

In addition to the probability of success, the latency to reach the box visited is also of interest. Many studies have used the response latency in addition to accuracy as a measure of cognitive ability in humans (Lavric et al., 2003, Schmiedek et al., 2007) and dogs (Nippak et al., 2007, Snigdha et al., 2012, Zanghi et al., 2015), and this could also give a measure of motivation, with animals in many studies taking longer to reach a food reward with increased satiety and thus decreased motivation to obtain the reward (Berkson, 1962, Lawrence and Illius, 1989, Ostlund et al., 2011, Patterson-Kane et al., 2011). Additionally, longer latencies may indicate that the task requires greater use of cognitive strategies and problem solving (Nippak et al., 2007). Because of the technical difficulties experienced when recording was attempted using a camcorder in the

163

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task previous study, and because of the ethical issues concerning privacy that arise when recording video footage of a participant's home, in this study latency values were recorded using a stopwatch and pencil instead.

Human patients with chronic pain from osteoarthritis report impaired sleep (Wilcox et al., 2000, Taylor‐Gjevre et al., 2011), and osteoarthritic dogs appear to show improved sleep following nonsteroidal analgesia (Knazovicky et al., 2015), implying that sleep may also be impaired in canine osteoarthritis. The relationship between chronic pain and sleep in osteoarthritic dogs is explored further in Chapter 5, however decreased sleep is also associated with impaired working memory in humans (Smith et al., 2002, Steenari et al., 2003, Chee and Choo, 2004, Thomas et al., 2005, Miyata et al., 2013) and rodents (Hagewoud et al., 2010, Xie et al., 2015), as described in Section 1.4.2. It was therefore considered important to include a measure of dogs' sleep in this study, to investigate whether osteoarthritic dogs showed impaired sleep prior to task performance and whether this affected their performance in the disappearing object task.

Whilst polysomnography is the gold-standard method of measuring sleep (Sadeh et al., 1995) and has been used in laboratory-housed dogs (Takeuchi and Harada, 2002), the complex apparatus required would be infeasible to use in companion animal dogs in their home environments. Actigraphic recording of night time rest/inactivity is feasible to perform in dogs and has previously been used by Knazovicky et al. (2015), however whilst actigraphy was used in the study described in Chapter 5, it was necessary to perform the study described in this chapter prior to setting up the actigraphic device due to time and logistical constraints. Therefore it was necessary to use an owner questionnaire that could retrospectively provide information about each dog's sleep in the week prior to the study. Only one such questionnaire appears to have been designed for the assessment of sleep in dogs; the SNoRE (Sleep and Night Time Restlessness Evaluation) used by Knazovicky et al. (2015), which assesses dogs' sleep quality as reported by their owners. Whilst the SNoRE does not appear to have undergone prior validation, it was able to successfully detect improved sleep quality in osteoarthritic dogs following nonsteroidal analgesia administration compared to baseline levels or placebo administration in a blinded study (Knazovicky et al., 2015). Therefore the SNoRE shows promise in its ability to measure sleep quality in dogs, and was used in this study due to the absence of validated alternatives.

164

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.3 Materials and Methods:

4.3.1 Animals:

Ethical approval for animal use was obtained from the University of Bristol AWERB (VIN/17/005), and ethical approval for recruitment of members of the public (dog owners) was obtained from the University of Bristol Faculty of Health Sciences Research Ethics Committee (Application Ref: 31623).

Inclusion criteria required dogs to be between 5-11 years of age, to reduce any effects of age-related cognitive decline on task performance and to prevent the control group from being significantly younger than the osteoarthritic group (since osteoarthritis is an age-related condition it was considered unlikely that dogs younger than 5 years of age would have had osteoarthritis). Dogs were also required to be of breeds or breed combinations such they were unlikely to weigh less than 12kg. This weight restriction was applied due to the hypothesis that the clinical presentation and pathogenesis of osteoarthritis may differ between larger dogs and smaller dogs weighing less than 12kg (J Hunt 2016, personal communication, 27 April), and was consistent with other studies performed within the research group (Harris, 2017, Hunt et al., 2018). Because the study was performed in owners' homes where dogs could not be weighed, a combination of owner reports and visual examination of the dog by a veterinary surgeon (MS) was used to confirm that each dog was unlikely to weigh less than 12kg. Dogs were also to be excluded if their clinical history or veterinary examination indicated any ongoing health condition where participation in the study could have caused health or welfare concerns, or any ongoing health condition (other than osteoarthritis) with the potential to cause pain or impair cognition, however no such conditions were identified in any dogs examined. Participation in the task was terminated if dogs showed signs of distress; this occurred in the case of one (male osteoarthritic) dog. This dog was excluded from this study, but participated in the study described in Chapter 5.

Twenty dogs with signs of osteoarthritis (12 female, 8 male) and 21 healthy control dogs without signs of osteoarthritis (6 female, 15 male) were recruited via a marketing campaign consisting of distribution of posters and flyers to veterinary practices within Bristol and North Somerset and the use of a study Facebook page (https://www.facebook.com/dog.arthritis). All of the dogs were neutered. 19 dogs were

165

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task from breeds within the Kennel Club Gundog group, five within the Pastoral group, four within each of the Terrier and Working groups, one from each of the Utility and Hound groups, and seven were crossbred dogs descended from multiple Kennel Club groups. Participating owners were given a £10 gift card (John Lewis Partnership, London, UK) following completion of the study described in Chapter 5.

Due to delays in obtaining ethical approval and the timing of an industrial internship placement attended by MS, the study was performed in two phases: Data from 25 dogs (13 with osteoarthritis and 12 control dogs) were collected during the “Summer” period (May-June 2017) and data from a further 16 dogs (7 with osteoarthritis and 9 healthy control dogs) were collected during the “Winter” period (November-December 2017). All owners were given an information sheet describing the study, as shown in Appendix 4.1. A standardised clinical examination was performed on each dog and a verbal veterinary history was obtained from the owners as described in Chapter 2, using the standardised clinical examination form shown in Appendix 2.1. As part of the clinical examination process, dogs were assigned a subjective severity score by the vet and owner independently (see Chapter 2).

Because of the difficulty experienced in recruiting dogs with moderate-severe osteoarthritis that were not receiving analgesic medication during the study described in Chapter 2, it was decided not to exclude dogs that were taking analgesic medication from this study. Nine of the dogs with osteoarthritis (45%) received some form of analgesic medication as reported by their owners: Six dogs received daily non-steroidal analgesic treatment, two dogs received occasional non-steroidal analgesic treatment (given by the owner only when they perceived an increase in the dog’s pain), two dogs (including one on daily non-steroidal analgesic treatment) received intermittent tramadol treatment, and one dog (also receiving occasional nonsteroidal treatment) received daily paracetamol and gabapentin treatment. No control dogs were receiving analgesic medication or had received analgesic medication within a fortnight prior to the study. Dogs were clinically examined by the same veterinary surgeon and using the same clinical examination sheet and criteria described in Chapter 2, and were assigned to either the “Osteoarthritis” or “Control” groups accordingly.

166

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.3.2 Questionnaires:

Prior to the task, Owners completed the CCDR questionnaire (described in Chapter 2 and shown in Appendix 2.7) in order to screen dogs for signs of age-related cognitive decline. After completing the task, owners were given a set of 5 identical packs of questionnaires, with the instruction to complete the first pack, relating to the week at the end of which the disappearing object task was performed ("week 0"), on the same day as the disappearing object task or as soon as possible afterwards. The questionnaire pack included HCPI and CBPI questionnaires (described in Chapter 2 and shown in Appendix 2.3 and Appendix 2.4) to assess the severity of their dog's pain or motor impairment, as well as the SNoRE (Sleep and Night Time Restlessness Evaluation) questionnaire (Knazovicky et al., 2015), which gives a measure of the owner's perception of the dog's sleep quality (described in greater detail in Chapter 5 and shown in Appendix 5.4). This was used in order to assess the effect that sleep quality may have had on dogs' working memory in the disappearing object task, as well as to ensure that all questionnaire packs for each week were the same, as the SNoRE was required as part of a related study described in Chapter 5, for which the remaining questionnaire packs for weeks 1-4 were collected.

4.3.3 Apparatus:

The same four cuboidal open-backed, gravel-weighted wooden boxes and objects attached to nylon string were used as in the disappearing object task described in Chapter 3. The cups containing aquarium gravel used for weighting the boxes were sealed inside four identical plastic carrier bags (Lidl UK GmbH, London) to prevent spillage during use or transport. A transportable barrier consisting of a folding two-panel laundry airer covered with opaque black plastic sheeting (shown in Figure 4.1) was used instead of the wooden barriers described in Chapter 3. The testing arena was set up in an area of the owner’s home as shown in Figure 4.2.

167

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.1: Diagrammatic representation of the transportable barrier used in the disappearing object task (A) from the front and (B) overhead whilst open.

When placed in front of the dog for the interval duration the barrier was opened to an angle between 90° and 120° in order to maximise stability but prevent vision of any of the boxes. The optimal angle varied depending on the space available within each owner’s home, the flooring material and the size of the dog.

168

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.2: The portable testing arena.

Red dotted lines represent the arc at 100cm from the start position, with boxes placed at 36, 72, 108 and 144 degrees from the starting position at tangents to the arc, measured using a tape-measure and whiteboard protractor. Boxes are numbered as shown. Grey lines represent the position of the folding barrier during the retention interval. The positions of E1 and E2, the two experimenters, are given at the point when the dog is released to retrieve the object, which in this example is behind box 2. If insufficient space was available to allow E1 to stand behind the testing arena during testing, E1 stood adjacent to the edge of the room furthest from the arena and faced away from the arena when verbally prompting the dog to find the object, to avoid providing cues about the object’s location.

169

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.3.4 Procedure:

The overall procedure for the task was similar to that of the disappearing object task described in Chapter 3. Two experimenters were present, E1 (MS) and E2. The dog was shown the ball and squeaky toy, and the toy most preferred by them (in E1’s opinion) was selected for use. One of the osteoarthritic dogs showed little interest in these toys so their own toy (a blue plastic ring) was used instead. The shaping phase occurred as described in Chapter 3, however before the onset of the training phase the object was hidden behind each box twice (pseudorandomised so that the target box was not the same twice in a row, with the order shown in Appendix 4.2), and the dog allowed to retrieve it and receive the reward without the presence of the barrier. This was done so that the dog could learn to search behind the boxes prior to the introduction of the barrier, which appeared to be distracting or aversive to some dogs when it first appeared during the training phase of the study described in Chapter 3. The training phase occurred as described in Chapter 3, however in this study it consisted of 12 rather than 20 trials (3 for each target box), as shown in Appendix 4.3. Dogs were released into the owner’s garden for 2-5 minutes and offered drinking water between the training and testing phase. The testing phase also occurred largely as described during Chapter 3, with some differences: A single 12-trial trialblock was used with four trials (one for each target box) at each interval (0, 60 and 120s), in order to complete testing within a two- hour visit period. E2 measured the latency between releasing the dog and the dog visiting a box using a stopwatch, and recorded the latency and box visited using a pencil. If the first box visited by the dog was at all ambiguous (for example if the dog quickly ran between two boxes or ran past the boxes and turned around such that they could see behind multiple boxes), the visit was scored as ambiguous and removed from the dataset prior to analysis. All analyses were performed by MS.

4.3.5 Analysis:

Probability of success data were analysed using mixed-effects logistic regression in R, using the method described in Chapter 3. Because there were a relatively large number (22) of potential predictor variables (shown in Table 4.2), it was beneficial to select a smaller subset of these variables to include in the final multivariate model, in order to reduce the number of unnecessary factors added and thus decrease the computational power required. Individual “pre-screening” models were first generated for each

170

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task potential predictor variable. Because the interaction of each variable with group was of interest as well as the main effect of each variable, each initial model contained the potential predictor variable, group and the interaction between the potential predictor and group, with outcome (success/failure) as the binary outcome variable, rather than the predictor variable and outcome alone. Group and interaction with group were removed for initial models of severity score (vet- and owner-assigned) and all measures relating to analgesia, due to all dogs with a score of "none" for vet-assigned severity (and all but one for owner-assigned severity) being included in the control group, and all control dogs receiving no analgesia. All potential predictor variables for which the main effect and/or interaction with group was significant at the p<0.1 level were added simultaneously to the final multivariate model along with group, which was the main factor of interest. This reduced inclusion threshold was selected because using a threshold of p<0.05 can sometimes fail to identify important variables (Bursac et al., 2008). Stepwise addition/removal of variables following generation of the multivariate model was not performed.

The mean latency to approach the box during the training phase was calculated to give the baseline latency for each dog. The latency to approach each box during the testing phase was expressed as a proportion of the baseline latency for that dog and referred to as “adjusted latency”, to avoid between-dog differences in latency due to factors unrelated to working memory, such as the size of the dog or impaired mobility from osteoarthritis. The adjusted latency data (as well as the raw latency data) were highly positively skewed, and general linear models produced using these generated residuals showing significant non-normality and heteroscedasticity, which were not corrected by transformation (log10(adjusted latency), ln(adjusted latency), 1/(adjusted latency), √(adjusted latency) and 1/√(adjusted latency) were all attempted). Because the outcome data consisted of continuous positive non-integers, use of a generalised linear model fitted using a Poisson, binomial or logistic distribution was inappropriate. Therefore nonparametric comparisons were used to analyse these data.

To analyse within-subject variables (Interval, Box, Trial) the dataset was grouped by dog ID and by the variable of interest, and the mean adjusted latency for each dog at each interval, box or trial was calculated, and to analyse between-subjects variables the mean adjusted latency was calculated per dog, in order to avoid pseudoreplication. Mann- Whitney U tests were performed to assess the effect of group on latency. Because the

171

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task dispersion of latency measurements between groups also appeared to be different, a Siegel-Tukey test was used to assess dispersion. Friedman tests were performed to assess the effect of within-subjects variables, and where these were significant at the given α-threshold for factors that consisted of more than two levels, individual levels of the factor were compared using Wilcoxon signed-rank post-tests. The effects of between-subjects factors were assessed using Kruskal-Wallis tests. Because of the large number of tests performed, Holm-Bonferroni adjustments (which have greater power and thus a lower risk of Type II errors than conventional Bonferroni adjustments (Holm, 1979)) were made to the p-value significance thresholds (α) for each set of comparisons: For each result starting from that with the lowest (most significant) p-value (rank = 1), the α-threshold for significance was given by 0.05/((number of tests) – (p-value rank) +1) until the first non-significant result was reached, after which comparisons were stopped and all results with higher (less significant) p-values were declared non-significant (Holm, 1979).

To assess the differences between age, body condition score and initial questionnaire scores for each group, Holm-Bonferroni-adjusted Mann-Whitney U tests were performed. To assess the differences between initial questionnaire scores for each owner-assigned and vet-assigned severity score of osteoarthritic dogs only (since all control dogs were assigned “none”), Holm-Bonferroni-adjusted Kruskal-Wallis tests were performed.

Data were plotted in R using the ggplot2 package (Wickham, 2009). Effects of categorical factors analysed using nonparametric methods are expressed in the form of boxplots, with each box representing the median and interquartile range and whiskers representing data within 1.5 interquartile ranges from the upper or lower quartile. Outliers (more than 1.5 interquartile ranges from the upper or lower quartile) are not shown in order to restrict axis scales and allow clearer visualisation of data distributions, unless otherwise specified. Results of non-parametric tests are given as a relevant test statistic, p-value and degrees of freedom where provided by the test output. Model results are given as odds ratios with 95% confidence intervals (note that the odds ratio statistic is asymmetric and thus not at the centre of its confidence interval (Bland and Altman, 2000)), z- and p-values.

172

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.4 Results:

4.4.1 Signalment and Week 0 questionnaire scores:

The mean age for control group dogs was 7.85 (95% CI =7.35-8.36) years and the mean age of osteoarthritic dogs was 8.10 (95% CI =7.42-8.78) years. The mean body condition score of control dogs was 4.67 (95% CI =4.25-5.08) and the mean body condition score of osteoarthritic dogs was 5.45 (95% CI =4.82-6.08). There were no significant differences in sex (Fisher’s exact test, p=0.0616) between groups. Owner and vet- assigned severity scores are shown in Table 4.1. For 36 out of 41 dogs (87.8%), the owner and vet assigned the same severity score. One owner did not successfully complete the CCDR questionnaire, but the clinical history, task performance and observed behaviour of the dog were not suggestive of age-related cognitive decline, so the data collected for this dog were not excluded from the study. All other dogs had a CCDR score below the diagnostic threshold of 50, indicating that age-related cognitive decline was unlikely (Salvin et al., 2011), and were thus included in the study. 11/492 trials (2.24%) were removed from the dataset prior to analysis due to ambiguity over which box was visited first by the dog.

Table 4.1: Numbers of dogs with each subjective osteoarthritis severity score, as assigned by the owner and the examining veterinary surgeon.

Vet-Assigned Severity Score None Mild Moderate Severe Total

None 21 1 0 0 22

Owner- Mild 0 9 0 0 9 Assigned Moderate 0 1 6 2 9 Severity Score Severe 0 0 1 0 1 Total 21 11 7 2 41

-5 A significant difference between groups was found for HCPI score (U=39, p=1.39*10 , α= 0.00714), CBPI Severity score (U 56, p=2.57*10-5, α=0.01) and CBPI Interference score (U=49.5, p=1.84*10-5, α=0.00833), as shown in Figure 4.3. There was no significant difference between groups with respect to age (U=186, p=0.5284, Holm-Bonferroni

173

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task testing threshold reached), body condition score (U=140.5, p=0.0554, α=0.0125), SNoRE score (U=181, p=0.8109, Holm-Bonferroni testing threshold reached) or CBPI quality of life score (U=250, p=0.01497, Holm-Bonferroni testing threshold reached). There were no significant (Holm-Bonferroni-adjusted α-thresholds) differences in any week 0 questionnaire score between osteoarthritic dogs of differing owner-assigned severity

2 scores, and only CBPI Severity score (X (2)=10.6, p=0.00503, α=0.01) was significantly different between osteoarthritic dogs of different vet-assigned severity scores. There was a significant difference in CBPI Severity scores of dogs with mild compared to moderate vet-assigned severity scores (U=3, p=0.00458, α=0.0167) but not between dogs with mild compared to severe (U=1, p=0.0491, α=0.025) or moderate compared to severe (U=4, p=0.8422, Holm-Bonferroni testing threshold reached) vet-assigned severity scores (note that only two dogs had a severe vet-assigned severity score).

174

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.3: Boxplots showing the differences between groups in questionnaire scores for week 0 (the week in which the disappearing object task was performed).

Significant differences (Holm-Bonferroni-adjusted α-values) are marked with asterisks. Outliers (values more than 1.5 interquartile ranges from the upper and lower quartiles) are shown as individual points.

4.4.2 Probability of Success:

The potential predictor variables for which initial models were created are shown in Table 4.2. Of these, the only significant (p<0.1) predictors are shown in Table 4.3.

175

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Table 4.2: List of predictor variables used in analyses.

Predictor Reference level Levels (if categorical) Variable (if categorical) Group Control/Osteoarthritis Control Interval Box 1/2/3/4 1 Trial Object Choice Ball/Squeaky toy/Own toy Ball Season Summer/Winter Summer SNoRE score HCPI score CBPI Severity score CBPI Interference score CBPI Quality of Life (QOL) score Sex Female/Male Female Age Body condition score (BCS) Analgesia provision Yes/No No Analgesia frequency None/Daily/Occasional None Analgesia type None/Non-steroidal/Other None Vet-assigned severity score None/Mild/Moderate/Severe None Owner-assigned severity score None/Mild/Moderate/Severe None

Lameness score Joint Function score Levels and reference levels are given for categorical factors (shown in white). Continuous variables are shown in grey.

176

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Table 4.3: Variables that significantly (p<0.1) predicted the probability of success in initial models.

Odds Variable Lower CI Upper CI z p Ratio Interval 0.993 0.987 0.998 -2.337 0.0174

Interval * Group 0.99 0.981 0.999 -2.248 0.0246 Box 2 0.196 0.0754 0.511 -3.334 0.000857

Box 2 * Group 6.27 1.82 21.6 2.908 0.00364 Box 3 0.151 0.0585 0.392 -3.891 9.98*10-5 Box 3 * Group interaction 4.38 1.31 14.7 2.394 0.0167 Box 4 * Group interaction 3.74 0.996 14.5 1.911 0.056 Trial 0.896 0.823 0.976 -2.531 0.0114 Sex * Group 3.07 1.26 7.43 2.479 0.0132 Age 0.782 0.589 1.04 -1.705 0.0882 Owner-assigned severity score = "mild" 0.65 0.394 1.073 -1.684 0.0921 Odds ratios with 95% confidence limits (CI), z and p-values are shown.

The results of the multivariate model, including odds ratios, z-statistics and p-values, are shown in Table 4.4. Significant predictors (p<0.05) were group, interval, and box. Significant interactions with group were found for box, age and sex. The model had a dispersion statistic of 1.01, which is within the expected range for non-overdispersed models (0.75-1.4) (Korner-Nievergelt et al., 2015). Since the effects of owner-assigned severity score and group were unlikely to be independent (since all but one dog with a score of “None” were control dogs), the model was repeated without including owner severity to assess the effect of its inclusion (as shown in Appendix 4.4), however there was no change to the variables and factor levels that significantly predicted success at the threshold of p<0.05, so owner-assigned severity score was retained in the model.

177

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Table 4.4: Odds ratios with 95% confidence limits (CI), z and p-values for the multivariate model of probability of success.

Lower Upper Variable Odds ratio CI CI z p Interval 0.992 0.985 0.999 -2.276 0.0228 Box 2 0.190 0.072 0.502 -3.351 0.0008 Box 3 0.145 0.052 0.408 -3.663 0.0002 Box 4 0.655 0.225 1.912 -0.774 0.4392 Trial 0.998 0.883 1.129 -0.027 0.9783 Age 0.755 0.568 1.003 -1.939 0.0525 Sex 0.724 0.358 1.464 -0.899 0.3686 Owner-Assigned Severity Score: Mild 0.444 0.074 2.658 -0.889 0.3742 Owner-Assigned 0.813 0.131 5.048 -0.222 0.8241 OwnerSeverity-Assigned Score: SeverityModerate Score: Severe 1.198 0.116 12.380 0.152 0.8793 Group 0.028 0.001 1.318 -1.819 0.0689 Interval*Group 0.992 0.982 1.002 -1.619 0.1053 Box 2*Group 6.630 1.793 24.522 2.835 0.0046 Box 3*Group 6.027 1.503 24.165 2.535 0.0112 Box 4*Group 4.063 0.976 16.911 1.927 0.0540 Trial*Group 0.908 0.767 1.075 -1.118 0.2635 Age:Group 1.471 0.992 2.180 1.920 0.0549 Sex:Group 4.048 1.475 11.109 2.715 0.0066 Statistically significant p-values (p<0.05) are highlighted in yellow.

Whilst osteoarthritic dogs had a lower proportion of successful visits (mean = 0.674, 95% CI = 0.612-0.730) than control dogs (mean = 0.727, 95% CI = 0.668-0.779) overall, as shown in Figure 4.4, this was not significant. However there was a significant sex-by- group interaction (see Figure 4.5). Mann-Whitney U tests found that female (U=58, p=0.0412), but not male (U=46, p=0.375) osteoarthritic dogs had significantly lower proportions of successful visits than control dogs of the same sex, as shown in Figure 4.5. Whilst median scores for all clinical questionnaire components except for the CBPI QOL score were higher in female osteoarthritic dogs than in males, this effect was not significant (Holm-Bonferroni-adjusted α-thresholds) for any questionnaire score (as shown in Figure 4.6), nor was there any significant difference between the vet-assigned severity scores of male and female osteoarthritic dogs (Fisher’s exact test, p= 0.796).

178

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.4: Effect of group on proportion of successful visits.

Graph shows means with 95% confidence intervals.

179

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.5: Effect of group and sex on proportion of successful visits.

Graph shows means with 95% confidence interval error bars. Asterisks denote significant differences (p<0.05) compared to control dogs of the same sex.

180

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.6: Clinical questionnaire component scores for male and female osteoarthritic dogs.

Outliers (values more than 1.5 interquartile ranges from the upper and lower quartiles) are shown as individual points.

181

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Probability of success decreased with increasing interval as shown in Figure 4.7 (mean proportion of successful visits at 0s=0.859, 95% CI=0.797-0.904; mean at 60s=0.663, 95% CI=0.586-0.731; mean at 120s=0.576, 95% CI=0.498-0.650)), and there was no significant interaction between interval and group, with both osteoarthritic and control dogs showing similar decreases in probability of success with increasing interval duration, as shown in Figure 4.7. The model predicted that dogs would reach a probability of success of 0.25, the value expected if they were selecting which box to visit at random, at an interval of 269s. Control dogs were predicted to reach a probability of success of 0.25 (equivalent to the success rate expected by chance if dogs were randomly selecting a box to visit each trial) at 340s, whereas osteoarthritic dogs were predicted to reach this threshold at a shorter interval of 223s. However, these predictions rely on extending the model beyond the range of observed data and thus may be inaccurate.

182

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.7: The effect of interval on predicted probability of success (A) overall and (B) for each group.

Graphs show prediction lines in black or group-specific colours with grey or pale interquartile ranges. Points with error bars represent mean observed proportions of successful visits with 95% confidence intervals. Equations of linear regression lines for predicted values are also given.

183

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Dogs were less likely to successfully find the object when it was hidden behind box 2 or 3 compared to box 1, however this effect was only seen for control dogs and not for osteoarthritic dogs, who showed similarly low success rates when the object was hidden behind boxes 1, 2 and 3, as shown in Figure 4.8.

184

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.8: The effect of box on predicted probability of success (A) overall and (B) for each group.

Graphs show means with 95% confidence intervals. Asterisks denote that a significant difference (p<0.05) compared to box 1 was found on the probability of success. † symbols denote that a significant group*box interaction was found for this box.

185

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.4.3 Adjusted latency to reach box:

There was no significant effect of group on adjusted latency to reach the box (U=191, p=0.633), however the distributions of each group did significantly differ (Siegel-Tukey test; p=0.01185), with the adjusted latencies of osteoarthritic dogs showing greater dispersion and appearing much more positively skewed than those of control dogs, as shown in Figure 4.9.

Figure 4.9: Boxplots showing adjusted latency to reach the box for each group.

2 -5 There was a significant effect of interval on adjusted latency (X (2)=19.7, p=5.39*10 ), with significant differences between intervals of 0s and 60s (V=156, p=0.0214, α=0.025)

-5 and 0 and 120s (V=112, p=1.08*10 , α=0.0167) but not between 60 and 120s (V=331, p=0.202, α=0.05). The effect of interval on adjusted latency was also significant for

2 2 osteoarthritic dogs (X (2)= 9.10, p=0.0106) and control dogs (X (2)=12.9, p=0.00215) considered separately. The difference in adjusted latencies between 0s and 60s intervals

186

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task was significant for both osteoarthritic (V=50, p=0.0399, α=0.05) and control dogs (V=27, p=0.0118, α=0.025), and the difference in adjusted latencies between 0s and 120s intervals was also significant for both osteoarthritic (V=30, p=0.00365, α=0.0167) and control dogs (V=25, p=0.000852, α=0.0167), however the difference in adjusted latencies between 60s and 120s intervals was only significant for osteoarthritic dogs (V=42, p=0.0172, α=0.025) and not for control dogs (V=148, p=0.273, α=0.05).

187

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.10: Boxplots showing the relationship between interval and mean adjusted latency to reach the box visited for each dog.

Significant differences occur between bars of the same colour that are not marked by the same letter.

There was a significant effect of target box on adjusted latency to reach the box visited

2 (Friedman X (3) =11.25, p=0.0104). Wilcoxon signed-rank post-tests found a significant effect of hiding the object behind box 3 (V=203, p=0.00261, α=0.0167), but not boxes 2

188

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

(V=404, p=0.739, Holm-Bonferroni testing threshold reached) or 4 (V=356, p=0.341, α=0.025) on the adjusted latency to visit a box when compared to when the object was hidden behind box 1, with dogs taking longer to visit a box when the object was hidden behind box 3 than box 1, as shown in Figure 4.11. There were no significant differences between groups in the adjusted latency to reach the box visited for any target box at either the p<0.05 or Holm-Bonferroni-adjusted α-thresholds (Mann-Whitney U tests).

Figure 4.11: Boxplots showing adjusted latency to reach the box.

Asterisks denote significant differences (Holm-Bonferroni-adjusted α-values) compared to target box 1.

2 There was a significant effect of trial on adjusted latency overall (X (11)=48.6,

-6 2 p=1.13*10 ), and for both osteoarthritic (X (11)=30.0, p=0.00157) and control dogs

2 (X (11)=22.3, p=0.0219) (see Figure 4.12) using the Prentice rank sum test; a modified version of the Friedman test that allows for incomplete datasets (due to the exclusion of trials with ambiguous outcomes) (Prentice, 1979, Wittkowski and Song, 2012). However

189

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task it was not possible to perform Wilcoxon signed-rank post-hoc tests to compare the adjusted latency to reach the box for individual pairs of trials due to missing values and unequal variable lengths between trials.

190

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Figure 4.12: A scatterplot to show the correlation between trial number and adjusted latency to reach the box visited.

Random horizontal jitter was applied to better differentiate points at the same trial. Regression lines with 95% confidence intervals (shaded areas) are shown to more easily visualise the effect of trial on adjusted latency for each group. 13 outlying points (twelve points from osteoarthritic dogs and one point from a healthy control dog) were excluded from this graph (but not from analyses) to allow clearer visualisation of the majority of points.

191

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

Kruskal-Wallis tests revealed no significant differences between adjusted latencies to reach the box for any between-subjects categorical variable (choice of object, season, sex, analgesia provision, analgesia frequency, analgesia type, vet-assigned severity score or owner-assigned severity score; Holm-Bonferroni-adjusted α-values). Of these, only

2 season would have been significant at p<0.05 (X (1)=5.5314, p=0.01868). There were no significant correlations between adjusted latency to reach the box and any continuous variable (body condition score, age, joint function score, lameness score, week 0 SNoRE score, week 0 HCPI score, week 0 CBPI Severity score, week 0 CBPI Interference score or week 0 CBPI Quality of life score) at either Holm-Bonferroni adjusted α-values or at α =0.05.

4.5 Discussion:

Because of the complexity of the analyses performed in this chapter, it is important to discuss not only the findings obtained but also the limitations of using mixed effects linear modelling to analyse such data. Therefore Section 4.5.1 of this discussion will discuss the findings in the context of existing literature, and Section 4.5.2 will discuss the methodological limitations relating to the analyses.

4.5.1 Discussion of findings:

4.5.1.1 Signalment factors:

Osteoarthritic dogs had significantly higher week 0 CBPI Severity & Interference scores and HCPI scores than controls. This was expected as these questionnaires have previously been validated in their ability to detect pain and functional impairment due to osteoarthritis (Brown et al., 2007, Brown et al., 2008, Hielm-Björkman et al., 2009), therefore this shows that the dogs assigned by the vet to the "osteoarthritic" group based on clinical examination were experiencing more chronic pain and mobility impairments than those assigned to the control group, suggesting the vet was successfully able to distinguish dogs with chronic pain from osteoarthritis and healthy, pain-free control dogs.

192

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.5.1.2 The effects of group and sex on probability of success:

Whilst osteoarthritic dogs did not have a significantly lower probability of successfully locating the object than control dogs overall, female osteoarthritic dogs did have a significantly lower probability of successfully finding the object than female control dogs, though there was no significant difference between male osteoarthritic dogs and male control dogs. This difference between sexes was unexpected, particularly as all of the dogs were neutered. It has been suggested that chronic pain may impair working memory by increasing the attention allocated to pain and decreasing that allocated to cognitive tasks (Apkarian et al., 2004a), and women have been found to experience significantly greater pain and physical disability due to osteoarthritis than men (Affleck et al., 1999, Keefe et al., 2000). Therefore it appears plausible that female osteoarthritic dogs may similarly experience more severe pain than male dogs and that this may impair their spatial working memory on the disappearing object task. Whilst the clinical questionnaire scores and vet-assigned severity scores were not significantly different between female and male osteoarthritic dogs, female osteoarthritic dogs did have higher median scores than males on the HCPI and both validated components of the CBPI, so it may be that female dogs tended to be in slightly more pain than males and that this slight effect may have caused sufficient distraction to the female dogs to have had a significant effect on their probability of success. Whilst all of these dogs were neutered, human women generally retain their ovaries, so are likely to have differences in gonadal hormonal concentrations compared to men, and these hormones may persist following the menopause even in older women (Key, 2011) who are more likely to have osteoarthritis (Felson, 1990, Hart et al., 1999). However, osteoarthritis in human women becomes more common, severe and generalised following the menopause (Lawrence et al., 1966, Silman and Newman, 1996, Wluka et al., 2000), and ovariectomised rats (Høegh-Andersen et al., 2004) and sheep (Cake et al., 2005) have been used as induced models of osteoarthritis, suggesting that ovariectomy in animals may potentially have similar osteoarthritis-inducing effects as the menopause in human women. Additionally, Ma et al. (2007) found that induction of a surgical model of osteoarthritis led to more severe osteoarthritic changes in female ovariectomised (neutered) mice than female entire (non-neutered) mice, but led to less severe osteoarthritic changes in male castrated mice compared to male entire mice, suggesting that neutering may have different effects on osteoarthritis severity depending on sex. Therefore it seems plausible that neutered female dogs may similarly have an increased risk of developing

193

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task osteoarthritis and potentially more severe symptoms (although whilst the osteoarthritic group in this study contained a higher proportion of female dogs than the control group, the differences in distribution were not significantly different from those expected from chance, and whilst osteoarthritic female dogs had slightly higher median scores on validated clinical questionnaire components than male osteoarthritic dogs, this difference was not statistically significant).

However, the effect of gender on osteoarthritis pain severity in humans may be due to increased pain catastrophising (the tendency to focus disproportionately on pain as a threat and to underestimate one's ability to cope with pain) in women (Keefe et al., 2000), a phenomenon which has not been shown to occur in non-human animals. Furthermore, human women may also experience differences in pain perception or expression due to cultural gender norms that are unlikely to exist in non-human species such as dogs. Sex-related differences in nociception have been found to occur in rodents: Female rats demonstrate increased responses to several nociceptive assays compared to males (Wiesenfeld-Hallin, 2005), with female entire rats showing more prolonged behavioural responses to formalin injection (leg-flexing and licking) than males (Aloisi et al., 1994), though nociceptive sensitivity in female rats varies throughout the oestrus cycle (Frye et al., 1993). Additionally, the administration of oestrogen and progesterone was found to increase nociception (rats showed decreased weightbearing and increased paw lifting and licking), whilst the administration of testosterone was found to decrease nociception, in response to formalin injection in both male and female gonadectomised rats (Gaumond et al., 2005), suggesting that gonadal hormones may be responsible for sex-related differences in nociception. However, in a separate study it was found that whilst entire male and female rats differed in their responses to formalin, there was no difference between castrated male rats and ovariectomised female rats (Gaumond et al., 2002), suggesting that there are no persistent sex-related changes in nociception following neutering. However, the mechanisms involved in nociception differ from those involved in chronic pain (see Chapter 1), so these findings may not be generalisable to the current study.

Whilst it is theoretically possible that neutered animals may retain sex-related differences in chronic pain due to the persistence of congenital or developmental changes that occurred prior to neutering, or that female neutered dogs may undergo processes similar to the menopause in humans that increase their risk or severity of

194

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task osteoarthritis, further research is needed to test this hypothesis. Additionally, recruiting entire dogs as well as neutered dogs in future studies would allow investigation into whether the sex differences observed in this study differ in entire compared to neutered dogs.

In both humans and rodents, performance in spatial tasks is generally lower in females than males (Jones et al., 2003), and in a virtual-reality holeboard task women made more spatial reference memory errors than men (this version of the task did not assess spatial working memory) (Cánovas et al., 2008). Therefore it is possible that female dogs found the task more difficult than male dogs and that the distraction provided by pain in female osteoarthritic dogs was sufficient to impair their performance, whereas osteoarthritic male dogs found the task easier and thus the distraction provided by pain was not sufficient to impair their performance. Whilst there was no significant difference between the performance of male and female control dogs, this could be because without the distraction provided by pain, the task may not have been sufficiently difficult that sex differences in spatial reasoning caused impaired performance in female dogs, alternatively female control dogs that were not experiencing any distraction due to pain may have been able to focus more attention on the task than female osteoarthritic dogs, and thus perform similarly to males. However it is not known whether female dogs display similar deficits in spatial task performance compared to male dogs as those seen in humans and rodents, nor whether these deficits would persist after neutering, therefore further research is needed on the effects of sex and neutering on spatial task performance in dogs to assess whether this explanation for the interaction between sex and group on score is plausible.

Because of the surprising nature of this result, it would also be useful to repeat this experiment using a different cohort of dogs to assess whether the observed difference between probabilities of success in female and male osteoarthritic dogs was generalisable to a wider population, or whether it was merely a characteristic of this particular group of recruited dogs.

4.5.1.3 The effect of group on adjusted latency:

There was no overall effect of group on adjusted latency to approach the box visited, though adjusted latencies for osteoarthritic dogs showed significantly greater dispersion

195

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task than those of control dogs. This could be because of the relative lack of homogeneity of pain within the osteoarthritic group: Whilst all control dogs would be expected to have similarly negligible levels of pain, the osteoarthritic dogs varied in the severity of their osteoarthritis-related pain, as shown by their differing owner- and vet-assigned severity scores. Whilst there was no overall effect of osteoarthritis on the adjusted latency to approach a box, with the majority of osteoarthritic dogs behaving similarly to control dogs, some dogs with osteoarthritis did show increased adjusted latencies to reach the box, as shown by the positive skew of the osteoarthritic group's adjusted latencies. However there was no significant effect of owner- or vet- assigned severity score, or of any of the questionnaire scores designed to give a more quantitative score of the severity of chronic pain from osteoarthritis, on the adjusted latency. This implies that there may be an unknown factor other than osteoarthritis severity that differed more within the osteoarthritic than the control group and decreased the adjusted latency to reach a box in a subset of osteoarthritic dogs. It is possible that future large-scale studies on the epidemiology of canine spontaneous osteoarthritis may be able to identify additional factors of importance that were omitted from this study. It is also possible that there were insufficient numbers of more severely affected osteoarthritic dogs recruited to show an effect of these measures of severity on adjusted latency in statistical tests even if such an effect did exist; over half of the dogs recruited had a vet- assigned severity score of "mild" and only two were assigned "severe". It may be worthwhile repeating the study with larger numbers of moderately to severely affected dogs in order to better identify the effects of osteoarthritis severity. However it appears from the very small numbers of severely affected dogs recruited in this study and the study described in Chapter 2 that such dogs are much more difficult to recruit, possibly because most dogs that would otherwise be severely affected generally receive analgesic medication that reduces the severity of their osteoarthritis as assessed during the study (or are excluded due to receiving such medication, as in Chapter 2), and possibly because owners may be reluctant to volunteer more severely affected dogs to participate in studies, perhaps due to welfare concerns or beliefs that these dogs would be less motivated to participate in behavioural tasks.

4.5.1.4 The effect of interval duration on probability of success:

Both groups of dogs showed a reduced probability of success with increasing duration of the interval between the object being hidden and the dog being allowed to retrieve it.

196

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

This shows that the task functioned as expected, with dogs' spatial working memory for the object location decreasing over time. This also suggests that the lower success rate observed in osteoarthritic dogs compared to control dogs (although this was significant only in female dogs and not males or overall) was consistent and did not increase with increasing intervals. This would suggest that the mechanism by which working memory is decreased in female dogs with chronic pain from osteoarthritis may not be due to hypervigilance to pain and a corresponding decrease in attention to cognitive processes, one mechanism proposed to explain the negative cognitive effects of chronic pain in humans (Crombez et al., 2004, Legrain et al., 2009): If osteoarthritic dogs were more inattentive than control dogs it would be expected that their success rate would be more similar to that of control dogs at lower intervals when only short periods of attention are required, and comparatively lower at longer intervals when dogs must pay attention for longer to successfully recall the object location. Similarly, in one human study, Apkarian et al. (2004a) found that whilst chronic pain patients showed similar attention, short-term memory and general intelligence skills as healthy demographically- matched controls, their ability to recognise patterns and thus select the most profitable outcomes in the Iowa Gambling Task was markedly lower, with chronic pain patients making more apparently random decisions than controls (Apkarian et al., 2004a). It is possible that the female osteoarthritic dogs in this study were behaving similarly and, whilst they were similarly capable of paying attention as the female control dogs, were less able to learn and remember the relevant associations between choice of box and outcome (i.e. that visiting the box behind which the object was hidden resulted in a food reward and/or obtaining the object (which some dogs appeared to find more rewarding), whereas visiting another box resulted in being restrained ready for the next trial and being unable to continue searching), even after the training phase. Apkarian et al. (2004a) attributed this change to deficits in the prefrontal cortex, as the Iowa Gambling Task was designed for and validated in its ability to distinguish patients with prefrontal cortex injury from those without (Bechara et al., 1994). In another study, Apkarian et al. (2004b) also showed that humans with chronic back pain have decreased grey matter in the prefrontal cortex, the extent of which was proportional to the duration of chronic pain experienced by these patients (Apkarian et al., 2004b). In this study, dogs with osteoarthritis were not behaving truly randomly, as their success rates at all intervals were above that expected by chance and their success rate did decrease with increasing interval in a similar pattern as control dogs, suggesting that they were

197

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task primarily using their working memory to perform the task (Fiset et al., 2003). However, it is possible that an increased randomness in choices, or decreased ability to learn and consistently recall the associations between their choices and outcomes, as seen in the human patients studied by Apkarian et al. (2004a), could explain the relative poorer performance of female osteoarthritic compared to female control dogs at each interval. It would therefore be interesting to use MRI techniques in order to compare the prefrontal cortex sizes of dogs with and without osteoarthritis to assess whether grey matter in the prefrontal cortex is also reduced in dogs with chronic pain, and whether this varies between male and female neutered dogs. Due to the variation in size and shape of dogs' heads, this would require osteoarthritic dogs to be matched with controls of the same breed and approximate size and would probably require sedation to prevent excessive movement during scanning, but this should not be infeasible to perform given that MRI in dogs is routinely performed for veterinary diagnostic purposes.

An alternative and perhaps more straightforward hypothesis is that the difference in success rate between female osteoarthritic and control dogs is genuinely due to impaired working memory in female osteoarthritic dogs, but that this impairment is not proportional to attention span, and thus has a similar effect at each interval. Whilst evidence suggests that working memory and attention are closely linked in humans (Lavric et al., 2003, Martinussen et al., 2005, Dick and Rashiq, 2007), these studies did not differentiate selective, sustained and divided attention as is commonly done in animal studies (Muir, 1996), and Zanghi et al. (2015) found that dogs that had higher spatial working memory scores as assessed in a delayed non-matching to position task did not show higher selective attention scores as assessed using a novel visual search task with incorrect distractor objects (however, in humans selective attention appears to be a distinct construct from sustained attention (Underbjerg et al., 2013), which is required in the disappearing object task, so these attentional constructs may also be distinct in dogs).

4.5.1.5 The effect of interval duration on adjusted latency:

There were significant differences in time taken to reach the box between intervals of 0s and intervals of 60s and 120s, both for all dogs combined and for both control dogs and osteoarthritic dogs considered separately. This is unsurprising as increased reaction time

198

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task in human cognitive studies is thought to occur due to lapses in attention or working memory (Schmiedek et al., 2007), and since the success rate of dogs also decreases with interval in this task it appears that dogs’ working memory for the object location is indeed decreased with increasing interval. It is also possible that the increase in latency with longer intervals could be due to dogs becoming distracted from the task during longer intervals and then requiring a period of time to refocus their attention on the task – a process known as “set-shifting” (Lang et al., 2014), which increases the time taken to perform cognitive tasks (Alport et al., 1994).

4.5.1.6 The effect of trial number on adjusted latency:

The adjusted latency to reach the box increased with subsequent trials, overall and for both groups assessed separately, however there was no significant effect of trial on probability of success in the multivariate model. It is possible that dogs were becoming more physically exhausted with subsequent trials and thus taking progressively longer to reach the box as a proportion of their mean training phase latency, whilst still maintaining sufficient attention to perform the task effectively. Alternatively, dogs may have become less motivated to obtain the object in later trials compared with training trials (from which the baseline latency for each dog was calculated), as dogs received successive food rewards and became more satiated as the session progressed. Findings from previous studies support this concept: Pigs fed a lower proportion of their usual food intake show increased performance of food-rewarded behaviours than pigs fed their full ration (Lawrence and Illius, 1989), and the maximum “cost” (measured in number of nose pokes to a panel) that pigs are willing to incur in order to obtain food increases as the proportion of the pig’s ad-lib food intake they are fed beforehand decreases (Patterson-Kane et al., 2011). Similar effects have also been observed in food- restricted rats (Ostlund et al., 2011) and gibbons (Berkson, 1962), as well as in genetically obese mice (which have impaired satiety mechanisms and are thought to be in a persistent hunger-like state) (Atalayer and Rowland, 2011). Therefore it is possible that the dogs in this study became less motivated to obtain further food rewards the more trials they had completed and the more food rewards they had already obtained, and thus approached the box more slowly in later trials.

199

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.5.1.7 The effect of box on probability of success:

Overall, and in the control group, dogs were less likely to successfully find the object when it was hidden behind one of the middle boxes than when it was hidden behind one of the outer boxes. As in the study described in Chapter 3, this is likely to be because the outermost boxes were likely to be nearer to environmental cues such as walls or furniture, which better allowed the dog to distinguish them. Similarly, the boxes in the middle were adjacent to identical boxes on both sides whereas the outer boxes were only adjacent to an identical box on one side, which may have meant that it was more difficult to distinguish the inner boxes from each other. Dogs have previously been found to be able to use allocentric cues (cues relating one object location, such as a box, to another object location, such as a wall, independently of the organism location or orientation (Klatzky, 1998)) to solve spatial tasks (Milgram et al., 1999), so it would not be surprising for them to do so in this case. Additionally, the owner’s homes generally contained more identifiable features that could be used as allocentric cues than the arena used in Chapter 3, and these are also likely to have been more familiar to the dog than those present in an unfamiliar arena, so it would have been difficult to remove this effect. Alternatively, as described in Chapter 3, it may be that dogs were using body- orienting or turning strategies to visit the target box: Gutnikov et al. (1994) found that rats were more likely to correctly nose-poke an outer target chamber than an inner target chamber in a nine-hole box because they used a strategy that involved turning in a certain direction and choosing the first location they encountered. During the interval, dogs were restrained by E2 to prevent movement or overt orienting of the body and the barrier prevented dogs from having visual contact with the target box, but dogs may have been able to use more subtle orienting strategies to aim towards the location of the target box even when restrained and unable to see that location. However, since each box was used the same number of times for each interval, and the same order of box and interval combinations was used for all dogs, the effect of box should not have affected the probability of success for interval or for between-subjects factors.

It was somewhat surprising that this effect did not occur for osteoarthritic dogs, however. It is possible that osteoarthritic dogs were less able to use allocentric cues to solve spatial working memory tasks than control dogs and thus their proportion of successful visits for box 1 was not significantly higher than boxes 2 or 3. Christie et al. (2005) found that dogs’ learning of a landmark-based spatial task using allocentric cues

200

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task

(the position of a container relative to a small wooden peg) and reversal learning in a spatial discrimination task using egocentric cues (cues relating to the dog’s orientation relative to the environment (Klatzky, 1998), in this case the position of a container relative to the dog) decreased with age, and there was significant intra-subject correlation between performance in each task, suggesting that there may be a common mechanism that influences both forms of learning and is affected by age (Christie et al., 2005). The prefrontal cortex is particularly sensitive to age-related changes in humans (Raz et al., 1997) and dogs (Tapp et al., 2003, cited in Tapp et al., 2003), and changes in this region are thought to be responsible for the spatial memory impairments seen in aged dogs (Head et al., 1995, Tapp et al., 2003, Christie et al., 2005). Because decreased prefrontal cortex volume also occurs in humans with chronic pain (Apkarian et al., 2004b), who also show impaired working memory (Berryman et al., 2013), it is possible that chronic pain from osteoarthritis in dogs may have an effect similar to that of age, reducing both spatial working memory in a task using egocentric cues, causing an decreased success rate in female dogs in the disappearing object task, and also reducing the ability to use allocentric cues to recall an object location, causing a decreased ability to recall the object location when hidden behind one of the outer boxes. The effect of osteoarthritis on spatial tasks requiring use of allocentric cues could be investigated by comparing performance of osteoarthritic and healthy control dogs in the landmark- based task described by Christie et al. (2005), however since this task required prolonged training of up to 400 trials (40 days) (Christie et al., 2005), it was infeasible to perform in this study.

4.5.1.8 The lack of significant effects of age on task performance:

The probability of success did not significantly decrease with increased age and there was no significant correlation between age and adjusted latency to reach the box. This was surprising given previous findings that older dogs have impaired working memory: Snigdha et al. (2012) found that older dogs performed more errors in a two-choice discrimination task than younger dogs, and made a lower proportion of correct choices in a visual search task (Snigdha et al., 2012). Tapp et al. (2003) also found that older dogs made more errors on a spatial list learning paradigm. This was a form of delayed non- matching to position task in which dogs had to recall several previous stimulus locations and instead select the stimulus presented at a novel location to receive a reward) at intervals of 10 seconds between stimulus presentation events. Old dogs (but not young

201

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task dogs) were unable to successfully learn the task when longer intervals between stimulus presentation events were used (Tapp et al., 2003). Similarly, Rypma and D'Esposito (2000) found that older human volunteers had longer response times in a letter recall- based task of working memory than younger volunteers, and fMRI analysis found that younger participants had increased dorsolateral prefrontal cortex activation than older participants during recall (Rypma and D'Esposito, 2000), suggesting that the observed working memory deficits were due to age-related dorsolateral prefrontal cortex changes (Rypma and D'Esposito, 2000).

It is possible that the effect of chronic pain from osteoarthritis on probability of success may have masked the effect of age in osteoarthritic dogs: Apkarian et al. (2004b) found that human chronic back pain patients had 5-11% less grey matter in the prefrontal cortex than age-matched healthy controls, representing the equivalent of 10-20 years of normal aging, and that the extent of grey matter loss was proportional to the duration of chronic pain (Apkarian et al., 2004b). It is possible therefore that older osteoarthritic dogs were not the most severely affected (there was no significant relationship between age and HCPI score or owner- or vet-assigned severity scores, as shown in Appendix 4.5) or may not have been the dogs that had experienced chronic pain for the longest periods, and these factors may have had more of an effect on the osteoarthritic dogs' probabilities of success than age. However, chronic pain from osteoarthritis only had a significant effect on probability of success in female osteoarthritic dogs and not in males or overall, so its seems unlikely this effect would have masked the effect of age in all osteoarthritic dogs, and no effect of chronic pain would have been present in control dogs. Therefore this phenomenon would not explain why there was no significant effect of age in control dogs (since there was neither an overall effect of age nor an age-by- group interaction).

It is also possible that the age range of dogs recruited for this study was sufficiently narrow to prevent detectable effects of age on working memory. In studies that found an effect of age on working memory in dogs, there was a considerably greater age difference between the older and younger groups of dogs than the range of ages of dogs in this study: Snigdha et al. (2012) compared dogs aged 3-5 years with those aged 11-16 years, Tapp et al. (2003) compared dogs aged 3-7 years with those aged 10-15 years, and Adams et al. (2000) compared dogs aged 1-3 years with those aged 8-12 years. Since this study contained no dogs younger than 5 years and no dogs older than 11 years, it

202

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task seems reasonable that such effects of age on working memory would not be detectable within such a narrow range of ages. This study restricted recruitment to dogs no older than 11 years in order to reduce the risk of age-related cognitive decline, as this is likely to affect working memory in dogs (Adams et al., 2000, Tapp et al., 2003, Snigdha et al., 2012). Since all dogs recruited had CCDR scores much lower than the threshold indicative of probable age-related cognitive decline, it appears this age restriction was successful at preventing dogs with age-related cognitive decline from being recruited, therefore this is likely to explain the lack of a significant effect of age on probability of success or adjusted latency to reach the box in the disappearing object task.

4.5.1.9 The lack of significant effects of or between-group differences in SNoRE scores:

There was no significant effect of SNoRE score on the predicted probability of success and no significant correlation between SNoRE score and adjusted latency to reach the box. This was surprising as previous studies in humans indicate that sleep impairment is associated with impaired working memory (Steenari et al., 2003, Chee and Choo, 2004). It was also surprising that there were no significant differences in SNoRE scores between osteoarthritic and control dogs, with both groups having very similar median scores and score distributions, given that previous studies in humans (Wilcox et al., 2000, Taylor‐ Gjevre et al., 2011) and rodent models (Landis et al., 1989 , Silva et al., 2008) have found evidence of impaired sleep in osteoarthritis, and that nonsteroidal analgesia increased sleep quality as measured by the SNoRE score in osteoarthritic dogs (Knazovicky et al., 2015), implying that chronic pain from osteoarthritis is likely to impair sleep in dogs also.

However, whilst the SNoRE was successfully able to detect a significant difference in sleep quality in osteoarthritic dogs receiving nonsteroidal analgesia compared to placebo or baseline (Knazovicky et al., 2015), it is not a fully validated questionnaire and therefore may not be an ideal method of sleep assessment in dogs. It relies on subjective owner judgements of their dogs’ sleep quality and is therefore not an objective method of assessing dogs' sleep, and additionally owners are also likely to have been sleeping during the night and as such would have been unable to continuously monitor their dogs, thus they may potentially have missed some of the signs of poor sleep quality described on the SNoRE questionnaire, such as pacing and vocalising. Additionally, it is likely that many owners did not sleep in the same room as

203

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task their dogs overnight, and may have made assumptions about their dogs overnight sleep behaviour based on observations of their behaviour whilst resting earlier in the day, which may not be representative of overnight sleep. It may be possible that other methods of assessing sleep, such as actigraphy or polysomnography, may have been sensitive enough to have detected an effect of sleep on working memory and differences in sleep between osteoarthritic and control dogs, however because polysomnography requires advanced apparatus, facilities and analysis techniques, it is not feasible to perform in dogs owned by members of the public. Actigraphic measurement of rest duration in the same sample of dogs (with the exception of one osteoarthritic dog participating in this study and not the study described in Chapter 5, and one osteoarthritic dog participating in the study described in Chapter 5 and not in this study) over a period of four weeks was performed in the month-long study described in Chapter 5 alongside the SNoRE questionnaire. However, due to logistical and time constraints it was decided to perform the disappearing object task first and to show owners how to set up the actigraphic recording system (and thus begin the period of actigraphic recording) afterwards, so that only one visit was required per dog for both studies.

4.5.2 Methodological limitations:

4.5.2.1 Risk of Collinearity:

As well as the findings, it is also important to discuss the methodological issues with this study. One potential problem with multivariate analyses is the potential for collinearity; where the predictors are not independent and the effect of one predictor can be predicted from the effect of another, therefore making it statistically impossible to separate the effects of these variables (Dormann et al., 2013). This increases the variance of parameter estimates made using regression algorithms, potentially leading to increased type II errors and the failure to identify significant predictor variables (Dormann et al., 2013). Whilst the car package (Fox and Weisberg, 2011) contains the ability to identify collinear variables using the vif() (Variable Inflation Factors) function, this is currently only applicable to linear and general linear models (lm and glm object types) and not to mixed-effects models (lmer or glmer object types) (Fox and Weisberg, 2011). However, of the variables added to the multivariate model, only three were continuous (age, interval and trial). Because trial and interval were within-subjects measures and each dog had the same number of trials at each interval, and in the same

204

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task order, age could not have been collinear with these. Whilst correlation does not necessarily imply collinearity, factors that are highly correlated are more likely to be collinear (Dormann et al., 2013), and there was not a significant correlation between trial number and interval (t(10) = 1.44, p = 0.181; Pearson’s rather than Spearman’s test was used as collinearity is only a problem if the variables are highly linearly correlated (Dormann et al., 2013)), indicating that collinearity is not likely to be a problem for this model.

4.5.2.2 Assumption that relationships are linear:

As with the study described in Chapter 3, the mixed effects generalised linear model method assumes that the relationships between the predictors and outcome variable are linear, therefore one limitation of this method is that it does not have the ability to identify higher-order (quadratic, cubic etc.) effects on the outcome variable. One software package that has the ability to investigate these relationships is MLwiN (Charlton et al., 2017), however this requires prolonged training to learn and its user interface requires interpretation and manipulation of complex mathematical formulae. In comparison, the lme4 package (Bates et al., 2015) is advantageous in that it is relatively straightforward to generate and manipulate models, runs within the simple command-line user interface of RStudio (RStudio Team, 2016), and is compatible with many other data manipulation, exploration and analysis packages and functions for R (R Core Team, 2014, Bates et al., 2015, Koller, 2016). This allows data import, cleaning, manipulation, exploration, model generation, assumption testing and figure generation to be performed flexibly within the same script file using the same widely-used and documented software and coding language (R Core Team, 2014, RStudio Team, 2016). Additionally, it is not entirely clear whether the detection of non-linear effects would meaningfully add to the interpretation of these data; in this study the primary interest was in assessing whether certain variables (especially group and interval) had an effect on probability of success and if so in which direction, rather than using the models generated to predict the probability of success of a different group of dogs in the future, which might require a more accurate identification of the exact nature of the relationships between predictors and success rate.

205

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.5.2.3 Risk of overfitting:

One limitation of studies of this type with many potential predictor variables is the potential for overfitting, which refers to the generation of a model that describes the data from the sample well but is not generalisable to the wider population; i.e. apparently significant findings that are in reality due to variation within the sample recruited rather than a genuine population-level effect of the variable(s) that appear significant. Overfitting becomes more likely with the more predictors that are involved in the model, because fewer observations exist per predictor (Babyak, 2004). Babyak (2004) suggests that there should optimally be around 10-15 observations per predictor variable included in the model to limit this risk, however recruiting such large samples is likely to be more feasible for ecological or epidemiological studies than for studies such as this requiring behavioural testing of individual animals. Additionally, quantifying the number of “observations” within a mixed-effects model is not straightforward due to the nested variable structure: Whilst this dataset contained 481 observations overall (12 trials for each of 41 dogs, with 11 trials in which the outcome was ambiguous being removed prior to analyses) and 14 fixed-effects and one random effect were included in the final model, many of these predictors were between-subjects factors and would therefore have had fewer observations each than within-subjects factors such as interval, which decreases further when subjects are divided between smaller subgroups to assess the effect of categorical variables. Additionally, whilst this study has merit as the first study to identify preliminary evidence of spatial working memory deficits in dogs with osteoarthritis, and to show that the disappearing object task is able to detect such differences between groups of dogs, it used only 20-21 dogs per group and all of these came from Southwest England. Therefore it would be wise to treat these results as more suggestive than confirmatory until a similar study can be performed with a larger number of recruited dogs, as it is possible that these findings may not be generalisable to the wider population.

4.5.2.4 Model selection techniques:

The use of initial “univariate” (usually bivariate with one predictor and one outcome variable) models for each potential predictor variable in order to select the variables with p-values below a certain threshold for addition into multivariate models is a well- established practice within the biomedical sciences (for example: Alves et al., 2002, Kooby et al., 2003, Bogaert et al., 2005, Sterling et al., 2006, Wai et al., 2006) as it allows

206

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task an easy method for screening variables likely to have a significant effect on the outcome of the study, decreasing the number of unnecessary variables, reducing the likelihood of including collinear variables and decreasing the computational power required for the multivariate model. Because in this study we were also interested in the effect of the interaction between each variable and group, this interaction was also included in the initial models so that factors without a significant main effect but a significant effect of the interaction with group on the probability of success would not be omitted from the multivariate model.

Many researchers have historically used "stepwise regression" following univariate pre- screening to further simplify the final model (for example: Alves et al., 2002, Kooby et al., 2003, Bogaert et al., 2005, Sterling et al., 2006). This involves using an automated procedure to either iteratively add variables to the multivariate model and retain those that are significant (usually at p<0.05) (forward selection), iteratively remove variables until only those that are significant remain (backward elimination) or a combination of adding factors and assessing whether factors that then become non-significant can be removed (often called stepwise regression – however this term is also used to refer to all three methods more generally) (Whittingham et al., 2006). However there are many problems with this method of variable selection: Firstly, the specific type of algorithm used (forward, backward or stepwise) and the order in which variables are added and/or removed can have a significant effect on the final model that is selected (Derksen and Keselman, 1992, cited in Whittingham et al., 2006), which means that the findings may not be reproducible even when repeating analyses using the same dataset. Additionally, the sampling distribution in a multivariate model fitted by stepwise regression is often unrepresentative of the true expected sampling distribution (Whittingham et al., 2006). Furthermore, stepwise regression would increase the total number of tests performed in the analysis (Babyak, 2004, Whittingham et al., 2006), leading to a reduction in the degrees of freedom that is not reported in the output from the final model (Babyak, 2004), and increasing the risk of overfitting (Babyak, 2004, Whittingham et al., 2006). In a simulation study, Steyerberg et al. (2001) found that stepwise regression methods were more prone to overfitting (incorrectly identifying a significant effect of a variable that had no relationship with the outcome) than use of a simultaneous model (containing all covariates), even when large numbers of observations per variable were used (Steyerberg et al., 2001). Additionally, the "best model" identified by a particular

207

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task stepwise regression method may not fit the data markedly better than other models that may show different results (Whittingham et al., 2006).

Using a simultaneous model based on adding factors that were significant when considered in initial models does not avoid all of the pitfalls of stepwise regression: It still generates a single model that may not be much better than alternative models, and there is also the issue that factors that are significant in initial "univariate" models may become insignificant when other variables are added, and likewise variables that are not significant in univariate models may become significant when other variables are added and thus their effects could be missed (Babyak, 2004, Dormann et al., 2013). Additionally, whilst univariate pre-screening initially appears to increase the observation:predictor ratio in the final model and increase the degrees of freedom, Babyak (2004) argues that any prior calculation using the same dataset as the final model, including univariate pre-screening, effectively reduces the degrees of freedom in a largely unnoticed manner and thus does not actually reduce the risk of overfitting in comparison to an a priori model specification. However, avoiding the additional stepwise regression stage of variable selection reduces the number of statistical tests performed and thus avoids further increasing the risk of overfitting (Steyerberg et al., 2001, Babyak, 2004). Additionally, assessing the significance of each variable in a separate initial model as the criterion for adding to the multivariate model, rather than assessing each successive factor added to the multivariate model once it is added (as is performed in stepwise regression), means that the significance of the variables added to the model is not altered by the order in which the variables are added and thus avoids the potential for inconsistent and non-replicable results. In addition, the methods used in this analysis had the advantage of being relatively straightforward to perform without the need for further training courses, statistical, mathematical or computer programming qualifications, specialist software or hardware, or cross-departmental collaborations with specialists in the statistical or computer sciences. Additionally, these methods were capable of being described in terms that could be easily understood by other researchers within the behavioural and veterinary sciences, who may be unfamiliar with more novel and specialised analysis techniques and who also may lack advanced training in mathematics or computer science.

There are also other more modern methods of model selection that were not utilised in this study: The Akaike information criterion (AIC) avoids many of the issues with

208

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task stepwise regression by combining a measure of model fit with penalisation for inclusion of large numbers of variables to assess models both on quality of fit and simplicity, with lower AIC values representing higher quality models (Whittingham et al., 2006). The R package glmulti (Calcagno, 2013) can be used for automated generation of all possible models including some-all of the variables provided and ranking of these via their AIC values. However, whilst the package is compatible with lmer-type models generated in lme4 (Calcagno, 2013), it does not appear to have an inbuilt ability to specify a mixed- effects logistic regression model (all attempts to run the relevant function with a glmer- type model failed), and also does not include a straightforward method to include only a small number of interaction criteria, such as those with group, as it would be advantageous to exclude all variable interactions that did not make sufficient sense from a scientific standpoint. However, the use of AIC-driven model selection from large numbers of potential candidate models would be an interesting potential use of these data, as it would inform whether the model selected was truly the "best" model and whether some models described the data markedly better than others, and would provide an excellent opportunity to collaborate with computer scientists and/or statisticians with the ability to write the functions and packages required for this purpose and to assess their validity within modern statistical understanding.

An alternative to the AIC is the Bayesian Information Criterion or BIC. This identifies the best model (or average of several best models (Posada and Buckley, 2004)) using Bayesian statistics (Weakliem, 1999); an alternative to frequentist statistics that is particularly useful for revising probability distributions from previous studies (“Bayesian Priors”) based on new information (Ellison, 1996), and also has the advantage of accounting for uncertainty by providing probability parameters for each finding, rather than assessing them against a particular statistical significance threshold (Ellison, 1996). Since there have been no previous studies assessing the probability of success in this task in dogs with osteoarthritis or other sources of chronic pain, or how the presence/absence of chronic pain or osteoarthritis interacts with the effect of other factors on probability of success, there is not any sufficiently relevant prior information to make the use of Bayesian statistics particularly advantageous in this case, and since the previous studies described in this thesis have been analysed using frequentist statistics it makes sense to continue to use frequentist approaches rather than Bayesian approaches in this study also, so that results can be more easily compared.

209

Chapter 4: Comparison of spatial working memory in dogs with and without chronic pain from osteoarthritis using the disappearing object task 4.5.3 Conclusion:

The findings of this study suggest that osteoarthritis is associated with impaired spatial working memory in female neutered dogs with osteoarthritis, but not in male neutered dogs with osteoarthritis. However, these findings should be interpreted with caution, due to the relatively small sample size and methodological limitations associated with the analysis techniques used. Repeating this study with a larger sample would provide evidence to assess whether these findings are likely to be representative of the general canine population, and the inclusion of entire dogs in future studies would allow investigation into whether neutering status affects the observed sex differences in spatial working memory in dogs with chronic pain from osteoarthritis.

210

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Chapter 5 Comparison of resting behaviour in dogs with and without osteoarthritis

5.1 Abstract:

The study described in this chapter investigated two proxy measures of sleep in dogs with and without chronic pain from osteoarthritis; rest/inactivity measured using an actigraphic device (the FitBark activity monitor system) and owner-assessed sleep quality measured using a questionnaire (the Sleep and Night Time Restlessness Evaluation (SNoRE)). Osteoarthritic dogs spent significantly less of the night-time period resting compared to healthy control dogs, though there were no significant differences in SNoRE scores between groups. This difference in time spent resting overnight suggests that canine osteoarthritis may be associated with impaired sleep, as is often reported in studies of osteoarthritis and other chronic pain conditions in humans. Due to the limitations of the analysis methods used, particularly the risk of overfitting, these results should be interpreted cautiously until they can be replicated in a larger sample in order to confirm that they are generalisable to the wider canine population.

5.2 Introduction:

In Chapter 4 it was found that female dogs with chronic pain from osteoarthritis showed deficits in spatial working memory, as seen in human chronic pain patients (Antepohl et al., 2003, Luerding et al., 2008). Human patients with osteoarthritis and other chronic pain conditions also show impaired sleep, as described in Section 1.6. This study aimed to investigate whether dogs with chronic pain from osteoarthritis show impaired sleep in comparison with healthy control dogs, using two proxy measures of sleep similar to those used in humans and described in Section 1.6.3; actigraphic measurement of proportion of time spent resting/inactive during the night, and scores on an owner questionnaire intended to measure sleep quality (Knazovicky et al., 2015).

Knazovicky et al. (2015) performed a study in osteoarthritic dogs to assess the effect of nonsteroidal analgesia (meloxicam) on night-time activity, measured via accelerometry using a previously-validated actigraphic device attached to the dog’s collar (Hansen et

211

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis al., 2007), and sleep quality, measured using a novel questionnaire developed for the study; the Sleep and Night Time Restlessness Evaluation (SNoRE). Each dog was given meloxicam and placebo in a random order with a washout period in between. Owners were blinded with respect to treatment and completed the SNoRE questionnaire as well as CBPI and HCPI questionnaires every seven days to monitor the dog’s sleep quality and pain. Whilst the study did not show an effect of meloxicam analgesia (in comparison to no treatment or to placebo treatment) on accelerometry-assessed night-time activity, there were significant differences between the SNoRE scores of the dogs when they were on meloxicam analgesia in comparison to when they were on placebo treatment or no treatment. They concluded that it may be possible that nonsteroidal analgesic treatment improves sleep quality but not sleep duration, or that actigraphy may not be particularly sensitive as a measure of sleep in dogs, as decreased sleep may not significantly affect movement during the night (Knazovicky et al., 2015). Similarly, Taibi and Vitiello (2011) found that whilst a yoga-based exercise programme significantly improved self-reported measures of sleep in human osteoarthritic patients (though participants were not blinded and no control interventions were used), there was no effect on actigraphic outcomes, and Leigh et al. (1988) found no significant differences in night-time movements between osteoarthritic patients and healthy demographically- matched controls. However, Fielden et al. (2003) found that surgical treatment of hip osteoarthritis significantly improved a range of both-self reported and actigraphic measures of sleep quality, suggesting that treatment of osteoarthritis does have the potential to improve actigraphic measures of sleep quality in humans.

One limitation of the study by Knazovicky et al. (2015) is that there was no comparison of dogs with and without osteoarthritis, in order to examine whether osteoarthritic dogs show deficits in sleep (as assessed using the proxy measures of owner-assessed sleep quality and actigraphic measurement of night time activity) compared to control dogs. Whilst meloxicam treatment appeared to show improvements in owner-reported sleep quality but not actigraphic measures of night-time activity (Knazovicky et al., 2015), it is not known whether this is genuinely due to inhibition of the adverse effects of chronic pain from osteoarthritis on sleep. Comparing the sleep quality and night-time activity of dogs with chronic pain from osteoarthritis and healthy control dogs would better elucidate the effects of canine spontaneous osteoarthritis on sleep and night time rest, and if these mirror the differences between dogs on meloxicam treatment and dogs on

212

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis placebo/no treatment it is more likely that the effects of meloxicam on these measures were mediated by reduction of the pain caused by osteoarthritis.

In this study, two methods were used to measure sleep-related phenomena in dogs: Actigraphy via the use of the FitBark™ activity monitoring system; a commercially available, minimally invasive dog activity monitor that attaches to the dog’s collar and records activity continuously on a minute-by-minute basis, thus giving a measure of inactivity or rest, and a pre-existing owner questionnaire designed to measure owner- perceived sleep quality; the SNoRE (Sleep and Night Time Restlessness Evaluation). Since these methods do not include electroencephalographic recordings, they do not measure sleep directly (Bloch, 1997), and instead assess proxy measures of sleep; rest/inactivity as measured by actigraphy and owner-perceived sleep quality as measured by the SNoRE.

Wearable activity monitor devices such as the FitBark contain accelerometers; sensors which measure acceleration of the body along reference axes (Chen and Bassett, 2005, Yang and Hsu, 2010). These devices contain a piezoelectric material, which generates electrical current when compressed or deformed, connected to a weight of known mass. When the body (and worn device) accelerates, the acceleration of the weight deforms the piezoelectric material, generating charge and creating a voltage signal proportional to the acceleration (Chen and Bassett, 2005). Some devices can detect acceleration in up to three orthogonal planes (Chen and Bassett, 2005). These data are then integrated with respect to time in order to provide information about velocity and distance moved (Chen and Bassett, 2005) which is stored in the device’s memory, from which it can be downloaded to a computer for analysis (Chen and Bassett, 2005).

Accelerometers have been used in human studies for a wide variety of functions including investigations of population activity levels (Troiano et al., 2008), evaluation of activity levels in clinical patients including those with stroke (Rand et al., 2009) and multiple sclerosis (Snook et al., 2009), distinguishing between psychogenic and Parkinsonian tremor (Zeuner et al., 2003), and evaluating time spent sitting in the workplace (Sudholz et al., 2018). Although they measure the inactivity associated with sleep rather than recording sleep directly, accelerometers have been widely used in actigraphic studies to measure inactivity as a proxy measure of sleep in humans, as described in Section 1.6.3. This involves placing the accelerometer on the wrist or ankle

213

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis and recording movement over each epoch (unit of time), using automated algorithms to distinguish periods of (probable) sleep (characterised by low movement) and (probable) wakefulness (characterised by high movement) (Webster et al., 1982, Sadeh et al., 1994). Actigraphy has demonstrated the ability to differentiate between healthy participants, insomniacs and those with sleep apnoea (Sadeh et al., 1989) and between medicated and unmedicated narcolepsy patients (Bruck et al., 2005), and to detect adverse effects of methylphenidate on sleep in children with ADHD (Corkum et al., 2008). Actigraphy shows very high overall agreement with polysomnography (Kripke et al., 1978, Mullaney et al., 1980, Webster et al., 1982), which is considered the gold- standard method of sleep assessment (Sadeh et al., 1995, Menefee et al., 2000) as it directly measures the neurological changes occurring in the brain during sleep (Bloch, 1997). However whilst the sensitivity (ability to detect sleep) of actigraphy is often over 90%, its specificity (ability to identify wakefulness) is often much lower (de Souza et al., 2003, Paquet et al., 2007, Sitnick et al., 2008). Therefore the accuracy of actigraphy is often lower in participants with impaired sleep, since these participants spend a greater proportion of the night period awake than healthy control participants (Paquet et al., 2007). Actigraphy has also been attempted in dogs (Knazovicky et al., 2015), though it was unable to detect significant differences in mean night-time activity counts per hour in osteoarthritic dogs receiving nonsteroidal analgesia compared to baseline or placebo treatment.

Three of the most common clinically validated accelerometers used in human studies are the ActiGraph, Actical and RT3 (Bassett and John, 2010). The validity of the ActiGraph (Yam et al., 2011) and Actical (Hansen et al., 2007, Olsen et al., 2016) has also been investigated for the assessment of day-time activity in dogs: The Actical showed high correlation with videographic assessment of activity in healthy dogs (Hansen et al., 2007), however whilst Hansen et al. (2007) found acceptably low inter-device variability, Olsen et al. (2016) found that inter-device variability in measurements was high. The ActiGraph was able to detect significant differences between different intensities of activity and showed high reliability as assessed by the Spearman–Brown prophecy method, which encompasses both inter-device and intra-device reliability (Yam et al., 2011). However, these devices have several limitations: They were originally devised for use in humans so are not optimised for measuring activity in dogs. They are also incredibly expensive (whilst obtaining a quote for each device currently requires contacting the manufacturer, in 2010 each Actical device cost $327 with an additional

214

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

$738 for the required analysis software, whereas each ActiGraph device cost around $300 (Bassett and John, 2010)). Such costs would not be feasible for this study as large numbers of devices were required (45 devices were used in total), so that several weeks of data from several dogs could be obtained within a relatively short period. Furthermore, the Actical and ActiGraph devices must be manually connected to a computer running the required proprietary software in order to obtain the stored data (ActiGraph, 2017, Philips Respironics, 2018b), which would either require a second visit to each owner to retrieve the devices or risking losing data and expensive hardware if they became lost or damaged in the post.

There are also commercially-available accelerometry devices which have been designed specifically for use in dogs. These include the Whistle (Yashari et al., 2015), PetPace (Belda et al., 2018) and FitBark (Di Cerbo et al., 2017, Patel et al., 2017). These devices are primarily aimed at providing information about dogs' activity to their owners rather than for use in research, and since they use proprietary and unpublished algorithms, it is more difficult to assess their relative strengths and weaknesses and applications for research in comparison to devices designed for research purposes (Ladha et al., 2017). However, they also have many advantages: The FitBark was selected for this study because it was significantly more affordable (each FitBark device purchased for this study cost £45.46 and supporting apps and software are available free-of-charge), it was designed for use in dogs rather than humans, and is incredibly easy for both owners and researchers to use; the FitBark was (subjectively) rated “Easiest Dog Activity Monitor To Use” by the dog owner-oriented website CanineJournal.com (Canine Journal, 2017), ensuring that owners should be easily able to use the interface and upload their dogs’ data. This is advantageous as the owner can also view summary data about the dogs’ rest and activity patterns, providing an incentive for owners to participate with their dog. Furthermore, because data is uploaded from the device to an online database via a Bluetooth connection to the owner's smartphone, it is possible for researchers to obtain the data remotely without having to visit each owner a second time to retrieve the device and transfer the data manually, which was advantageous as it increased the number of dogs that could be recruited in short time periods. The algorithms for determining whether the dog is active, resting or "playing" (high intensity activity) at each minute are also very simple to apply to the raw activity counts, decreasing the time required to process the data prior to statistical analysis, however these algorithms are the intellectual property of FitBark and cannot be disseminated in this thesis. Whilst the

215

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

FitBark system has not been validated in the published literature, it has been used in previous studies to investigate the effects of a specialised nutraceutical diet on activity levels (Di Cerbo et al., 2017) and in an investigation into how dogs' rest and activity patterns affected those of their owners (Patel et al., 2017). FitBark also publish findings from collected data on their website (FitBark, 2018a).

The SNoRE (Sleep and Night Time Restlessness Evaluation) questionnaire was designed for assessing sleep quality in dogs and was successfully able to detect improved sleep quality in osteoarthritic dogs following nonsteroidal analgesia administration compared to placebo treatment or baseline levels (Knazovicky et al., 2015). Whilst the SNoRE has not yet been formally validated, it currently appears to be the only existing questionnaire designed for the assessment of sleep quality (as perceived by owners) in dogs, and it was used as an additional proxy measure of sleep in this study both because of concerns about the low specificity of actigraphy in human studies and the lack of published validation of FitBark algorithms, and in order to better compare the results of this study with those of the study performed by Knazovicky et al. (2015), in which a combination of actigraphy and SNoRE scores were also used.

Polysomnography is considered the “gold standard” measure of sleep assessment (Sadeh et al., 1995, Menefee et al., 2000) and can more accurately distinguish periods of sleep and wakefulness than either actigraphy (Paquet et al., 2007) or self-report questionnaires (Buysse et al., 1989) in humans, and would have the advantage of allowing the time spent in different stages of sleep to be compared (Bloch, 1997) in osteoarthritic and healthy control dogs. It would also have the advantage of measuring the neurological changes associated with sleep itself (Bloch, 1997), rather than relying upon proxy measures of sleep such as inactivity/rest as measured by the FitBark and owner perceptions of sleep quality as measured by the SNoRE. However, whilst polysomnography has been successfully used in laboratory-housed dogs (Takeuchi and Harada, 2002), it requires prohibitively expensive facilities and apparatus and complex recording and analysis techniques (Menefee et al., 2000), making it infeasible for monitoring large numbers of dogs owned by members of the public in their owner's homes. Polysomnography is also somewhat invasive (Bloch, 1997), meaning that a home office project license would be required to perform the study (Animals (Scientific Procedures) Act, 1986), which would be time-consuming to obtain and thus reduce the time available to recruit a suitably large sample of dogs. Therefore it was decided that a

216

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis combination of actigraphy and SNoRE questionnaires would be used rather than polysomnography, to assess the proxy sleep measures of time spent resting and owner- perceived sleep quality in this study, despite their limitations.

In addition to chronic pain, it is likely that other factors affect sleep quality in companion animal dogs, and that these would need to be controlled for or at least included in analyses in a study investigating the effect of chronic pain from osteoarthritis in dogs on measures related to sleep. However, there are very few published papers investigating this: A systematic literature search (the methods of which are described in Appendix 5.1) revealed only five relevant papers that investigated factors associated with sleep quality in dogs, these are shown in Table 5.1.

Table 5.1: Methods and findings of studies found to be relevant to sleep quality in dogs in the literature search described in Appendix 5.1, and their implications for this study.

Findings relevant to sleep Paper Methods quality in dogs Implications for this study Knazovicky Methods, findings and implications are described extensively in the et al. introduction and discussion of this chapter, as well as in Chapter 1. (2015) Dietary treatment of pruritis reduced frequency of scratching and head-shaking Accelerometry/actigraphy behaviour and using the Vetrak activity improved sleep monitor to monitor quality (reduced Pruritic skin diseases are scratching behaviour, night-time likely to reduce sleep quality Wernimont sleep quality and other disturbance as in dogs, however no dogs in et al. activity measures in dogs measured using this study had clinical signs or (2018) with pruritic skin disease Vetrak's owner reports of ongoing due to environmental proprietary pruritis. allergens before and after algorithm). switching to a novel Clinical signs of dermatological diet. pruritis as assessed on veterinary examination were significantly

217

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

predicted by sleep quality as well as scratching and headshaking.

Assessment of the effects of 8 weeks of dietary Ginkgo extract supplementation in 42 elderly dogs with age- Age-related behavioural related behavioural change may have an effect on changes. A range of sleep in dogs; therefore in Ginkgo extract behavioural scores were this study dogs above the age significantly recorded including "sleep of 10 years were excluded, improved sleep and activity changes" (a age was added into initial Reichling and activity single score models, and the CCDR was et al. change score in encompassing the used to screen dogs. None of (2006) dogs with age- severity of awakening the dogs in this study were related and vocalising during the receiving Gingko extract (all behavioural night, sleeping more owners were asked about changes. during the day and complementary therapies sleeping less at night, during the history-taking assessed by a researcher process). before, during and at the end of treatment). (Note: This study had no control treatment.) Aged dogs showed Polysomnographic increased recording in four elderly Takeuchi wakefulness (16-18 years) and four and during the night young (3-4 years) See above. Harada and increased laboratory dogs in order (2002) slow wave sleep to identify the effect of and reduced aging on sleep in dogs. REM sleep during the day. Dogs with Sleep-wake changes due to Interviewed the owners impairments in age do not necessarily of 110 dogs aged the sleep-wake progress in the same between 11-14 years of cycle at the first predictable manner as age twice within an 18- Bain et al. interview were changes in cognitive factors month period in order to (2001) not significantly such as disorientation. Since assess which behavioural more likely to dogs older than 10 years and factors (including sleep- develop further those with CCDR scores of 50 wake cycle changes) were impairments in or higher were excluded from significantly associated sleep-wake cycle this study, sleep changes

218

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

with worsening of these compared to occurring in elderly dogs with factors as the dogs aged. dogs that had no age-related cognitive changes impairments in should not be a concern, sleep-wake cycle though age was added to on initial initial models to confirm this. interview, although dogs with initial disorientation were more likely to develop more severe disorientation compared to dogs without initial disorientation.

It was hypothesised that dogs with osteoarthritis would show higher SNoRE scores (indicating lower sleep quality) than control dogs, given that Knazovicky et al. (2015) found that meloxicam analgesia significantly reduced SNoRE scores in osteoarthritic dogs, and several studies have found that self-report measures of sleep quality are lower in human osteoarthritic patients than controls (Leigh et al., 1988, Wilcox et al., 2000, Power et al., 2005, Taylor‐Gjevre et al., 2011).

It was also hypothesised that dogs with chronic pain from osteoarthritis would show reduced time spent resting during the night, as reported in human patients (Leigh et al., 1988, Wilcox et al., 2000, Power et al., 2005, Taylor‐Gjevre et al., 2011). Whilst Knazovicky et al. (2015) found that nonsteroidal analgesia did not cause significantly decreased night-time activity counts in osteoarthritic dogs compared to placebo or baseline, the difference in time spent resting during the night between osteoarthritic and control dogs may not be directly comparable to the difference in mean activity counts per hour during the night in osteoarthritic dogs given placebo and those given nonsteroidal analgesia: Firstly, it is unlikely that nonsteroidal analgesia provides complete pain relief leaving osteoarthritic dogs pain-free, given that 63% of human osteoarthritic patients prescribed nonsteroidal analgesia who then re-presented to their GP cited inadequate pain relief as their reason for presentation (Crichton and Green, 2002), and 47% of osteoarthritis patients used additional nonsteroidal analgesics to those they were prescribed, with incomplete pain relief as the most common main reason given for this (Cavagna et al., 2013). It is also possible that nonsteroidal analgesia

219

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis has effects other than reversal of the effects of chronic pain from osteoarthritis, for example drowsiness, a known effect of nonsteroidal analgesics (Cooper, 1984, Edwards et al., 1999), is also likely to increase resting behaviour and decrease night-time activity. It is also possible that dogs with chronic pain from osteoarthritis could show decreased total time spent resting (i.e. time spent with activity counts per minute below the threshold for “activity” as determined by the algorithms applied to the raw activity counts) without having significantly reduced mean activity counts per hour throughout the night, as measured by Knazovicky et al. (2015). Since the former outcome measure has been used to successfully detect changes in sleep duration in human chronic pain patients (Wilson et al., 1998, Law et al., 2012), it may be a better approximation of the proportion of the night period spent sleeping. However, neither measure truly represents sleep itself, as methods involving electroencephalography (such as polysomnography (Menefee et al., 2000)) are required to detect the neurological changes required to objectively distinguish sleep from wakefulness (Bloch, 1997).

It was also hypothesised that osteoarthritic dogs would show reduced activity/increased rest during the day compared to healthy control dogs, partially due to their impaired mobility and reduced physical activity and partially due to increased daytime sleep. Wiseman et al. (2001) found that 85% of owners of osteoarthritic dogs reported that their dogs showed disturbed mobility, and Brown et al. (2010) found that nonsteroidal analgesia caused increased activity counts as recorded by accelerometry in osteoarthritic dogs, suggesting that chronic pain from osteoarthritis is likely to cause decreased activity counts which can be alleviated by analgesic treatment. Similarly, human osteoarthritis patients report decreased mobility and slowing down (Hawker et al., 2008), and osteoarthritic patients showed lower mean activity levels measured by accelerometry than healthy controls (Murphy et al., 2008), suggesting that similar changes may also occur in osteoarthritic dogs. Humans with chronic pain, including those with osteoarthritis, also report increased daytime sleep: Hawker et al. (2010) found that 85% of osteoarthritis patients reported napping during the day, with 43% of patients reporting napping during the day four or more times a week. Rats with an induced model of osteoarthritis also showed a loss of diurnal rhythms in sleep and wakefulness and were unable to sustain long periods of sleep (Landis et al., 1989), and adolescent human chronic pain patients showed significantly more daytime sleep than healthy control adolescents (Law et al., 2012). Therefore it seems likely that dogs with chronic pain from osteoarthritis will show decreased activity and increased daytime

220

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis sleep, and thus would be likely to spend less time active and more time resting than control dogs during the daytime.

It was also hypothesised that dogs with osteoarthritis would show shorter mean rest bout durations both during the night and daytime periods compared to healthy control dogs, due to increased sleep fragmentation (in which sleep is interrupted by short periods of wakefulness known as "arousals" (Smurra et al., 2001)). Osteoarthritic rat models show significantly more fragmented sleep compared to control rats (Landis et al., 1988, Landis et al., 1989), and sleep fragmentation in osteoarthritic human patients was significantly decreased by total hip arthroplasty (Fielden et al., 2003), therefore it seems possible that increased sleep fragmentation may occur in canine osteoarthritis also.

It was hypothesised that older dogs in this study would show decreased rest during the night and more rest during the day, as age-related behavioural changes in dogs include owner-reported changes in the sleep-wake cycle (Bain et al., 2001, Reichling et al., 2006) as well as increased wakefulness during the night and fragmented wakefulness (increased numbers of short periods of sleep) during the day as measured polysomnographically (Takeuchi and Harada, 2002). Since osteoarthritis is more prevalent with increasing age in humans (Van Saase et al., 1989, Oliveria et al., 1995) and dogs (Anderson et al., 2018), age could confound the effect of osteoarthritis on sleep. Whilst the CCDR was completed by owners to screen and exclude dogs with signs of age-related cognitive decline, sleep-wake cycle changes do not appear to progress with increasing age in the same way as changes in cognition (Bain et al., 2001), and may not be strongly associated with age-related declines in memory and learning (Bain et al., 2001). Therefore excluding dogs with signs of age-related cognitive decline may not prevent the effect of age on sleep and thus on time spent resting as measured actigraphically in this study. However, limiting recruitment to dogs aged between 5-11 years should have prevented the recruitment of overly young control dogs (since other researchers within the research group have anecdotally reported difficulties recruiting sufficient numbers of osteoarthritic dogs under the age of five years) and the recruitment of overly elderly osteoarthritic dogs (since other researchers within the research group have anecdotally reported difficulty recruiting sufficient numbers of control dogs aged over 11 years). Therefore recruiting only dogs aged between 5-11 years should have ensured that both groups recruited were of similar ages, so whilst it

221

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis was likely that age would have an effect on the sleep-wake cycles of dogs within the study, this effect was expected to be similar for both groups, and therefore was not expected to mask or confound the effect of osteoarthritis on time spent resting during the night or daytime periods. The difference in age between groups and the effect of age on each outcome measure were assessed to investigate this.

5.3 Materials and Methods:

5.3.1 Animals:

Twenty dogs with osteoarthritis and 21 healthy control dogs participated in this study, all but one of which also participated in the study described in Chapter 4 and all of which were recruited using the same methods. One of the dogs (male, osteoarthritic, terrier) that participated in the study described in Chapter 4 was excluded from this study as contact with the owner was lost shortly after the disappearing object task was performed, and FitBark data were not uploaded. An additional dog (male, osteoarthritic, gundog) participated in this study but was unable to participate in the disappearing object task described in Chapter 4 due to appearing excessively anxious when introduced to the apparatus. The sample size for this study was based on the finding of Knazovicky et al. (2015) that a sample size of nineteen dogs was sufficient to detect significant differences in SNORE scores of osteoarthritic dogs treated with nonsteroidal analgesia compared to placebo treatment or baseline, therefore the study was designed with the aim of recruiting at least nineteen dogs in each group within the data collection period. Ten osteoarthritic dogs and one control dog received analgesic medication during the study (the control dog received meloxicam for a period of several days due to a mild respiratory infection). Of the osteoarthritic dogs on medication, seven were on daily treatment and three on occasional treatment. Seven osteoarthritic dogs were receiving only nonsteroidal analgesics, two were receiving tramadol (one of whom was also receiving nonsteroidal analgesia) and one dog was receiving gabapentin and paracetamol alongside nonsteroidal analgesia. Participating owners were given a £10 gift card (John Lewis Partnership, London, UK) following completion of the study.

5.3.2 Apparatus:

The FitBark system (FitBark Inc., Kansas City, MO) consisted of a recording device; the FitBark activity monitor, and an online user interface; the FitBark smartphone app. The

222

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

FitBark activity monitor is an electronic accelerometry device (dimensions: 3.9cm parallel to the collar, 2.8cm perpendicular to the collar, 1.2cm thick) which is attached to the dog’s collar and records their activity levels each minute. The FitBark smartphone app allows owners to create an account for themselves and each of their dogs, and upload (“synchronise”) the data from their FitBark activity monitor(s) using Bluetooth technology (Bluetooth, 2003). Owners could also view a range of user-friendly summary data regarding their dog’s recent activity. The app also allowed owners to give access to the “Bristol Canine Arthritis Study” account (which was set up prior to study onset) as a veterinarian user, allowing the raw activity data for each minute of each day to be downloaded for use in the study.

5.3.3 Procedure:

Following the disappearing object task described in Chapter 4, owners were shown the FitBark activity monitor and instructed in how to attach the device to the dog’s collar, how to charge and synchronize the device, and how to use the FitBark Smartphone app such that a researcher (MS) could access and download the data remotely, with instructions summarised on an owner information sheet which was retained by the owner (see Appendix 4.1).

Owners were given five identical sets of questionnaires, containing the SNoRE (shown in Appendix 5.4), HCPI and CBPI (described in detail in Chapter 2, questionnaire forms shown in Appendix 2.3 and Appendix 2.4), to complete on days 0, 7, 14, 21 and 28 of the study (day 0 questionnaires were analysed within the study described in Chapter 4), each with a cover sheet describing on which day they were to be filled in (as shown in Appendix 5.3). Owners were also given a sleep diary sheet (see Appendix 5.2) in order to record the times at which they got up and went to bed over the 28-day period of the study and any notes which they felt may have affected the dog’s activity or rest behaviour on a particular day. Data collection continued for 28 days because collecting actigraphy data over periods of several weeks has been shown to result in increased reliability (Van Someren, 2007). The sheet also contained prompts for the owner to fill in the SNoRE, HCPI and CBPI questionnaires and to recharge and synchronise the FitBark activity monitor once every seven days.

223

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

5.3.4 Data Preparation:

Raw activity results for each minute of the month of interest were downloaded from the FitBark website (https://web.fitbark.com/dashboard) for each dog, consisting of the date and time of each recording and a recorded activity value. For each dog, algorithms provided by FitBark (these remain the intellectual property of FitBark and cannot be disseminated) were used to determine whether for each minute the dog was in a state of "rest", "activity" or "play" (high-energy activity). Because of the difficulties in assessing what "play" truly represented, and because the distinction between rest and activity was the focus of this study, "play" was combined with activity so that for each minute a dog was either resting or active. The true night-time period was calculated for each dog as the period between one hour after the owner's mean bedtime and one hour before the owner's mean getting up time, calculated from values recorded on the owner's sleep diary sheet, as used previously by Knazovicky et al. (2015).

For each dog at each minute, the following values were calculated using Microsoft Excel: Whether the dog was resting or active according to the algorithm, the day (1-28) and week (1-4) of the recording, whether the recording was made during the night period or during the daytime, whether the recording represented a switch from resting to active or active to resting, and for each point that represented a switch from active to resting, the duration in minutes of the subsequent uninterrupted period of rest before the next switch from resting to active. In RStudio (RStudio Team, 2016) the following outcomes were calculated for each dog at each day: The proportion of the night period that was spent resting (Night Rest Time), the proportion of the daytime period that was spent resting (Daytime Rest Time), and the mean duration of a bout of uninterrupted rest during the night period (Night Rest Mean Duration) and during the daytime period (Daytime Rest Mean Duration) separately. Questionnaire scores were calculated for each dog and each week from owner questionnaire responses.

5.3.5 Data Analysis:

The following predictor variables were included in analyses (many of which were also included in the analyses for chapter 4): Group, season, length of night period, age, body condition score, sex, breed class, SNoRE score (except in models in which this was the outcome variable), HCPI score, CBPI Severity score, CBPI Interference score, CBPI QOL score, analgesia provision, analgesia frequency, analgesia type, vet-assigned severity

224

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis score, owner-assigned severity score, joint function score, and lameness score. Questionnaire scores differ from those described in Chapter 4 as in this study the scores for weeks 1-4 were used, rather than the scores for week 0. Models included dog and week as random factors. Reference levels for categorical factors were the same as those described in Chapter 4. As in Chapter 4, initial models included group and the interaction with group (with the exception of those for vet-assigned and owner-assigned severity scores) as well as the factor of interest, and any factors that were significant at p<0.1 were then added to a multivariate model.

For the proportions of time spent resting during the night and day, the residuals of initial mixed-effects linear models were significantly non-normal (e.g. Shapiro-Wilk test for initial model for season for night period: U=0.75627, p<2.2*10-16; for daytime period: U=0.96548, p=1.095*10-15). Because of this, and because the data appeared approximately Gaussian on initial examination, the rlmer() function from the robustlmm package (Koller, 2016) was used to perform a robust mixed-effects linear model. This modifies the lmer() function from lme4 to use more robust scoring functions that are resistant to non-normality of residuals and the presence of outliers (Koller, 2016), the default of which (used in these analyses) is a smoothed version of the Huber function (Koller, 2016). t- or z-values and p-values for hypothesis testing were calculated from model estimates and their 95% confidence intervals. Data for the mean duration of a bout of rest during the night and daytime periods were highly positively skewed, and since these data represent an amount of time before an event occurs (in this case the onset of a period of activity), a generalised mixed-effects linear model using a gamma distribution, which is often used for interval and waiting time data (McGill and Gibbon, 1965), was considered appropriate. For these models, diagnostic plots of the residuals against fitted values were performed to confirm that there was no relationship between these, and that there were no obvious outliers. For the mean duration of a bout of rest during the night, three obvious outliers were identified with mean rest durations (64.0, 101.0 and 143.0 minutes) that were more than 10 times the interquartile range (4.64 minutes) and more than twice the value of any of the remaining 1144 points. These were removed as it was believed that such long mean durations without movement were unlikely to reflect genuine bouts of rest and may have occurred due to technical issues such as the device becoming detached from the collar or running out of power during the night. For the mean duration of a bout of rest during the daytime, the data appeared to show many outliers upon visual inspection, and this was still the case after

225

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis removing all points that were not within 1.5 interquartile ranges of the median. Whilst the rlmer() function is robust to outliers it is only appropriate for approximately Gaussian data (Koller, 2016), therefore these data were analysed using rank-based methods (Kruskal-Wallis tests for categorical variables and Spearman correlation tests for continuous variables, with Holm-Bonferroni adjustments applied to the α value (p- value threshold for statistical significance) to account for multiple testing). SNoRE scores appeared approximately Gaussian, though residuals were non-normal (Shapiro-Wilk test: U=0.39152, p<2.2e-16) and there appeared to be some potential outliers. Therefore these were also analysed by robust mixed-effects linear regression using the rlmer() function. As in Chapter 4, differences in age, body condition score and questionnaire scores (using the mean score for each dog) between groups were assessed using Holm-Bonferroni-adjusted Mann-Whitney U tests, and the difference in these between vet- or owner assigned severity scores were assessed using Holm- Bonferroni-adjusted Kruskal-Wallis tests.

Model results for each variable or level of factor are given as the beta-estimate (β) with 95% confidence interval, t- or z-statistic and associated p-value estimate. Unless specified otherwise, data are given as mean values with 95% confidence intervals (CIs).

5.4 Results:

5.4.1 Signalment and questionnaire scores:

The mean age (± 95% confidence limits) of osteoarthritic dogs was 7.80±0.77 years and the mean age of control dogs was 7.86±0.50 years. The mean body condition score of osteoarthritic dogs was 5.50±0.62 and the mean body condition score of control dogs was 4.67±0.42. Significant differences between groups were found for HCPI score (U=60, p=9.45*10-5, α=0.0125), CBPI Severity score (U=42.5, p=1.12*10-5, α=0.01) and CBPI interference score (U=34, p=5.07*10-6, α=0.00833) as shown in Figure 5.1, but not for age, body condition score or CBPI QOL score (Holm-Bonferroni-adjusted α-values). None of these factors significantly differed between different vet-assigned or owner-assigned severity scores (Holm-Bonferroni-adjusted α-values). The numbers of dogs with each vet-assigned and owner-assigned severity score are shown in Table 5.2. 36 out of 41 dogs (87.8%) were assigned the same score by both the owner and the vet.

226

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.1: Boxplots showing the differences between groups in questionnaire scores (means of weeks 1-4 for each dog).

Significant differences (Holm-Bonferroni-adjusted α-values) are marked with asterisks. Individual points represent outliers more than 1.5 interquartile ranges from the upper and lower quartiles.

227

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Table 5.2: Numbers of dogs with each subjective osteoarthritis severity score, as assigned by the owner and the examining veterinary surgeon.

Vet-Assigned Severity Score None Mild Moderate Severe Total

None 21 1 0 0 22

Owner-Assigned Severity Mild 0 10 0 0 10 Score Moderate 0 1 5 2 8 Severe 0 0 1 0 1 Total 21 12 6 2 41

Note that severity scores differ slightly from those shown in Chapter 4.

5.4.2 Proportion of night period spent resting:

Factors and factor levels that had a significant (p<0.1) effect on proportion of the night period spent resting in the initial models were group (β=-0.0248 ± 0.0220, z=2.21, p=0.0268) moderate vet-assigned severity score (β =-0.0547 ± 0.0306, z=3.51, p=0.000454), mild owner-assigned severity score (β=-0.0249 ± 0.0259, z=1.88, p=0.0599), and moderate owner-assigned severity (β=-0.02961 ± 0.0281, z=2.07, p=0.0388). The levels of these three factors overlapped almost completely, with all control dogs and no osteoarthritic dogs having a vet-assigned severity score of "none", and all control dogs and only one osteoarthritic dog having an owner-assigned severity score of "none", therefore a model containing all of these factors would not be informative and there would be marked non-independence of predictor variables. Because of this, a significant result at p<0.05 on the initial models was taken as indicating a significant effect on the proportion of the night period spent resting, as shown in Figure 5.2.

228

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

229

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.2: Effect of (A) group, (B) vet-assigned severity score and (C) owner-assigned severity score on the proportion of the night period spent resting.

Graphs show means with 95% confidence interval error bars. Asterisks denote a significant difference (p<0.05) between bars.

5.4.3 Proportion of daytime period spent resting:

Factors that had a significant (p<0.1) effect on proportion of the daytime period spent resting in the initial models were group (β=-0.0419 ± 0.0432, z=1.90, p=0.0572), HCPI score (β=0.0216 ± 0.0147, z=3.69, p=0.000229), HCPI score by group interaction (β=-0.00457 ± 0.00308, z=2.91, p=0.00363), CBPI Severity score (β=1.022 ± 0.015, z=2.88, p=0.0390), CBPI Severity score by group interaction (β=-0.0197 ± 0.0165, z=2.40, p=0.0164), CBPI Interference score (β=0.0104 ± 0.00654, z=3.11, p=0.00187). Since these scores were very highly correlated (HCPI score and CBPI Severity score: t(37)=9.24,

-11 p=3.77*10 , R=0.836; HCPI score and CBPI Interference score: t(37)=8.6155,

-10 p=2.26*10 , R=0.817; CBPI Severity score and CBPI Interference score: t(37)=13.307, p=1.12*10-15, R=0.909), it was considered probable that including all three in the model would be likely to lead to collinearity (Dormann et al., 2013), therefore HCPI score was selected for addition to the model, due to having the lowest p-value for the main effect in the initial model. Because visual inspection of the relationship between HCPI score and proportion of time spent resting during the day implied the possible presence of a parabolic (n-shaped) relationship, as shown in Figure 5.4, a model was generated containing HCPI score squared and the interaction between group and HCPI score squared, alongside group, HCPI score and the interaction between group and HCPI score, as shown in Appendix 5.7. As neither the effect of HCPI score squared or the interaction between group and HCPI score squared on the proportion of time spent resting during the day was significant, these terms were then removed from the model.

The multivariate model therefore contained only group, HCPI score and HCPI score by group interaction. There was no significant (p<0.05) overall effect of group (β=-0.00457 ± 0.00308, z=0.55, p=0.550) as shown in Figure 5.3, however significant effects of HCPI

230

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis score (β=0.0216 ± 0.0147, z=3.69, p=0.000229) and HCPI score*group interaction (β=- 0.00457 ± 0.00308, z=2.91, p=0.00363) were found: Whilst dogs with increased HCPI scores tended to rest for a greater proportion of the daytime period overall, this was only the case for control dogs, as shown in Figure 5.4, however, predictions from this model for the control group exceed the range of observed data: The highest observed weekly HCPI score for a control dog was 20, whereas the highest observed weekly HCPI score for an osteoarthritic dog was 29.

Figure 5.3: The proportion of the daytime period spent resting for each group of dogs.

Graphs show means with 95% confidence interval error bars. The difference between groups was significant in the initial model (p<0.1) but not in the multivariate model (p<0.05).

231

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.4: The effect of HCPI score on proportion of daytime period spent resting (A) overall and (B) for each group.

Graphs show prediction lines in black or group-specific colours with grey or pale interquartile ranges. Each point represents the observed mean HCPI score and mean proportion of the daytime spent resting for each dog. Equations of linear regression lines for predicted values are also given.

232

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

5.4.4 Mean duration of an uninterrupted bout of rest during the night:

Factors that had a significant (p<0.1) effect on mean duration of an uninterrupted bout of rest during the night in the initial models were length of night period (β=-7.00*10-6 ± 7.00*10-6, t=-2.27, p=0.0230), length of night period by group interaction (β=-1.10*10-5, ± 1.20*10-5, t=-1.85, p=0.0639), age by group interaction (β=-0.00613 ± 0.00537, t=-2.25, p=0.0246), daily analgesia frequency (β=0.0105 ± 0.004378, t=4.70, p=2.58*10-6), occasional analgesia frequency (β=-0.00856 ± 0.00457, t=-3.67, p=0.000246), occasional analgesia frequency by group interaction (β=0.006669 ± 0.004588, t=2.58, p=0.00439; note that only one control dog received this treatment) and moderate vet-assigned severity score (β=0.0240 ± 0.0221, t=2.13, p=0.0329). The effect of group was not significant when considered alone (β=0.0111 ± 0.0165, t=1.317, p=0.188), though it was sometimes significant when included in models with other variables, and as the primary factor of interest was also added to the multivariate model.

The multivariate model therefore contained group, length of night period, length of night period by group interaction, age, age by group interaction, analgesia frequency, analgesia frequency by group interaction, and vet-assigned severity score. None of these factors significantly predicted mean duration of an uninterrupted bout of rest during the night (p>0.05 for all).

However, because all control dogs (and no osteoarthritic dogs) had a vet-assigned severity score of "none", and only one control dog was in receipt of analgesia (“occasional” frequency), it was thought that it may not be informative or sensible to include these factors as predictors in the same model. To explore whether this would affect the significance of outcomes, three separate models were created (see Appendix 5.6), each containing length of night period, age, and one of group, analgesia frequency or vet-assigned severity score, as well as the interactions of this variable with length of night period and age. None of the predictors were significant (p>0.05) for the models containing vet-assigned severity score and analgesia frequency. Only length of night period was significant at the threshold of α=0.05 in the model containing group, (β=-6.40*10-6 ± 6.05*10-6, t=-2.071, p=0.0383) however it was not significant at a Holm- Bonferroni-adjusted threshold for significance of α=0.0167 (since three separate models were used), nor at the α=0.05 threshold in the models that included analgesia frequency and vet-assigned severity score. Therefore it was concluded that none of these factors

233

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis had a significant effect on mean duration of an uninterrupted bout of rest during the night. The median values for each group of mean duration of an uninterrupted bout of rest during the night are shown in Figure 5.5.

234

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.5: Boxplots showing the mean duration of a bout of rest during the (A) night and (B) daytime periods for each group.

Boxplots show the median, interquartile range and extent of values up to 1.5 interquartile ranges from the upper or lower quartile, for each group, calculated from the mean duration of a bout of rest in each period per dog. Outliers more than

235

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

1.5 interquartile ranges from the upper and lower quartiles were omitted for ease of visualisation, these consisted of values for three osteoarthritic dogs during the daytime period (two were above the median and one below).

5.4.5 Mean duration of an uninterrupted bout of rest during the daytime:

There were no significant differences in mean duration of an uninterrupted bout of rest

2 during the daytime between groups (X (1)= 0.479 p=0.489), as shown in Figure 5.5, or between levels of any other categorical variable (season, sex, breed class, analgesia provision, analgesia frequency, analgesia type, vet-assigned severity score or owner- assigned severity score), at either Holm-Bonferroni-adjusted α values or at α=0.05. There were no significant correlations between mean duration of an uninterrupted bout of rest during the daytime and any continuous variable (age, body condition score, length of night period, SNoRE score, HCPI score, CBPI Severity score, CBPI Interference score, CBPI QOL score) at Holm-Bonferroni-adjusted α values.

5.4.6 SNoRE score:

Factors that had a significant (p<0.1) effect on SNoRE score in the initial models were HCPI score (β=0.141 ± 0.0713, z=-3.86, p=0.000115), HCPI score by group interaction (β=-0.105 ± 0.0983, z=2.09, p=0.0362), CBPI Severity Score (β=0.451 ± 0.420, z=-2.10, p=0.0354), CBPI Interference score (β=0.171 ± 0.187, z=-1.78, p=0.0742) and CBPI QOL score (β=-0.449 ± 0.392, z=2.25, p=0.0246). There was no significant effect of group (β=2.620 ± 5.21, z=-0.869, p=0.385). CBPI QOL score was significantly negatively correlated with HCPI score (t(36)=-3.79, p=0.00055, R=-0.534), CBPI Severity score

(t(36)= -2.23, p=0.0326, R=-0.347) and CBPI interference score (t(36)=-2.46, p=0.0188, R=-0.379). Since all other clinical questionnaire scores were also correlated (see Section 5.4.3) and appeared to measure biologically similar phenomena, HCPI score was selected for addition to the multivariate model to represent osteoarthritis severity as assessed via owner questionnaire, as it had the lowest main effect p-value in the initial model. The final model therefore contained only the predictors HCPI score, group and HCPI score by group interaction. There was a significant effect of HCPI score (β=0.141 ± 0.0713, z=-3.86, p=0.000115) and HCPI score by group interaction (β=-0.105 ± 0.0983,

236

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis z=2.09, p=0.0362); dogs with higher HCPI scores tended to have higher SNoRE scores, but the magnitude of this relationship (gradient of regression line) was greater for control dogs than for osteoarthritic dogs, as shown in Figure 5.6. There was no significant effect of group (β=2.78 ± 5.38, z=-1.01, p=0.312) overall, despite dogs with osteoarthritis having slightly higher SNoRE scores, as shown in Figure 5.7.

237

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.6: The effect of HCPI score on SNoRE score (A) overall and (B) for each group.

238

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Figure 5.7: Mean SNoRE scores for each group of dogs.

Graphs show means with 95% confidence interval error bars.

5.5 Discussion:

As with the discussion in Chapter 4, because of the complex nature of the analyses used in this chapter, this discussion is divided into two sections. Section 5.5.1 discusses the study findings, and Section 5.5.2 discusses the methodological limitations of the analysis techniques used in this study.

5.5.1 Discussion of findings:

The significant difference between groups for overall HCPI score and CBPI severity and interference scores implies that the osteoarthritic dogs were in more pain and had more mobility issues than dogs without osteoarthritis, suggesting that the dogs had been assigned to groups appropriately. Whilst there was no significant effect on CBPI quality of life score, this is not a validated component of the questionnaire (Brown et al., 2009), and thus may not significantly differ between groups, though the group mean CBPI QOL

239

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis score was higher (more positive) for control dogs (3.48 ± 0.084) than osteoarthritic dogs (3.18 ± 0.069).

A significant effect of group on total proportion of the night period spent resting was found, with osteoarthritic dogs spending a significantly lower proportion of the night resting in comparison to control dogs. This suggests that dogs with osteoarthritis may experience sleep disturbance in that they spend a lower proportion of the night resting and show increased periods of night-time activity, as seen in human patients with sleep disturbances (Fielden et al., 2003, Law et al., 2012). Additionally, dogs with moderate osteoarthritis severity (whether assessed by the vet or owner), but not dogs with mild or severe osteoarthritis severity, spent significantly less of the night time period resting than dogs without osteoarthritis/dogs believed by the owner not to have osteoarthritis. This suggests that increasing osteoarthritis severity decreases rest/inactivity duration, which is believed to be indicative of decreased sleep duration (Fielden et al., 2003, Law et al., 2012). Whilst there was no significant difference between severely affected osteoarthritic dogs and control dogs/dogs believed by the owner not to have osteoarthritis, there were only two dogs with a “severe” vet-assigned severity score and one dog with a “severe” owner-assigned severity score, and no dogs were assigned as “severe” by both the vet and owner, so it is likely that the size of this category was too low to detect an effect. This fits with the findings of Fielden et al. (2003) that total hip replacement was able to significantly reduce actigraphic measures of sleep disturbance in human patients with osteoarthritis, implying the pain from osteoarthritis had been contributing to their poor sleep (Fielden et al., 2003). Whilst Leigh et al. (1988) did not find significant differences in actigraphic measures of sleep disturbance between osteoarthritic and healthy control participants, they did observe a non-significant trend for osteoarthritic participants to move around more during the night than control participants. Unlike the study described in Chapter 4, there was no significant effect of group*sex interaction on proportion of the night spent resting (or any other outcome measure investigated).

The mean SNoRE score of osteoarthritic dogs was higher than that of control dogs, but this effect was not significant in the multivariate model. Whilst there was a significant effect of HCPI score (which was correlated with all other pain-related questionnaire scores) on SNORE score, potentially indicating that dogs with more severe pain had higher SNoRE scores, the effect size (gradient of regression line)was small and more

240

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis pronounced for control dogs than those with osteoarthritis, so it is unlikely that this was the case.

These findings are surprising given those of Knazovicky et al. (2015) who found that sleep quality as measured by the SNoRE, but not total night-time activity, was improved by nonsteroidal analgesic treatment in osteoarthritic dogs. However Knazovicky et al. (2015) did not include a healthy control group with which to compare osteoarthritic dogs, and it is quite possible that the differences in night time activity of osteoarthritic dogs given meloxicam and placebo may not mirror the differences in night time activity between healthy control and osteoarthritic dogs. It may be that nonsteroidal analgesics treat sleep disturbances in osteoarthritic dogs by increasing their sleep quality but not their sleep duration (of which decreased time spent active during the night is often used as a proxy measure (Fielden et al., 2003, Law et al., 2012)), whereas osteoarthritis may decrease sleep duration in a way that is not ameliorated by meloxicam analgesia. Alternatively, nonsteroidal analgesics may themselves improve sleep quality by mechanisms other than treating pain, as nonsteroidal analgesia in humans is known to cause drowsiness (Cooper, 1984, Edwards et al., 1999) and this could potentially facilitate sleep. However, studies in human osteoarthritis patients have found changes in sleep quality as well as sleep duration (Wilcox et al., 2000, Taylor‐Gjevre et al., 2011), so it would seem surprising that osteoarthritis does not cause impaired sleep quality in dogs, especially as nonsteroidal analgesia appears to improve sleep quality in osteoarthritic dogs (Knazovicky et al., 2015).

It may be that owners were not regularly present in the same room as the dog whilst the dog was sleeping, and so may have guessed the answers to the SNoRE or based their answers predominantly on sleep behaviours performed by the dog earlier in the day when the owner was awake, thus giving an inaccurate measurement of the dog’s true night-time sleep quality. However Knazovicky et al. (2015) did not describe the regular presence of the owner whilst the dog was sleeping as a recruitment criterion, and owners were instructed not to change the management of their dogs during the study, so it is likely this risk was present in their study also. Since owners were aware that the study aim was to investigate the effects of osteoarthritis on sleep (as assessed using proxy measures) and that osteoarthritis can affect sleep in humans (as this information was provided in the owner information sheet shown in Appendix 4.1), it is possible that this information coupled with their awareness of their dog's symptoms (or lack of

241

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis symptoms) of osteoarthritis may have affected their responses in the SNoRE questionnaire, however, if present, this effect would have been expected to have caused a greater difference in SNoRE scores between osteoarthritic and control dogs, and no such difference between groups was found (whereas in the study by Knazovicky et al. (2015) a significant effect of treatment on SNoRE score was found even though owners were blinded with respect to treatment). Additionally, the SNoRE score is not fully validated and has only previously been used to detect a difference in sleep quality between osteoarthritic dogs on nonsteroidal analgesic treatment compared to placebo (Knazovicky et al., 2015); it may not be as sensitive for detecting changes in sleep quality due to osteoarthritis itself.

Furthermore, the study may not have had a sufficiently high sample size to detect changes in sleep quality due to osteoarthritis: The sample size of this study was based on calculations by Knazovicky et al. (2015) who calculated that at least 16 dogs would be required in order to sufficiently detect the expected changes in pain scores following meloxicam treatment in a repeated-measures study design, and then found that a sample size of 19 dogs was successfully able to detect significant differences in SNoRE scores between treatments, therefore this study was designed to recruit a target sample size of at least 19 dogs per group. However, the effect of meloxicam compared to placebo or no treatment on osteoarthritis-related pain may be larger than that of the difference between dogs with and without osteoarthritis, especially since 50% and 60% of the dogs recruited were assigned “mild” owner- and vet-assigned severity scores respectively, 10/20 osteoarthritic dogs (50%) were on analgesic treatment (all but one including nonsteroidal analgesics), seven of which (35%) received analgesic treatment daily. Additionally, Knazovicky et al. (2015) recruited only dogs with radiographic evidence of osteoarthritis in addition to owner reports of osteoarthritis symptoms (Knazovicky et al., 2015), whereas in this study radiographic evidence of osteoarthritis was not required, which could have affected the assessment of osteoarthritis severity in the dogs or caused dogs to be placed in different groups than if they had been categorised by the presence or absence of radiographic signs of osteoarthritis. However, there is evidence that the severity of radiographic symptoms of osteoarthritis is not particularly correlated with the severity of chronic pain from osteoarthritis, in humans (Bedson and Croft, 2008) or dogs (Hielm-Björkman et al., 2003), and furthermore radiography would have required sedation or anaesthesia of the dogs, requiring a project license from the Home Office (Animals (Scientific Procedures) Act, 1986), and

242

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis would have meant that owners would have been required to visit the veterinary hospital rather than have the study performed in their own homes during a single visit, which combined with the inconvenience and risks associated with anaesthesia would have been likely to discourage recruitment of owners and their dogs.

Additionally, Knazovicky et al. (2015) also found that dogs with predominantly forelimb impairment showed higher night-time activity (less rest) but lower SNoRE scores than those with predominantly-hindlimb impairment, so the location of osteoarthritis may have an effect on movement during the night that is not necessarily attributable to altered wakefulness. In this study dogs were not considered predominantly forelimb or hindlimb impaired, though the clinical examination forms used to classify the group, osteoarthritis severity and signalment of each dog did assess the presence/absence and nature (but not severity) of abnormalities at every appendicular joint (see Appendix 2.1). Examination of these forms revealed that more than a third of osteoarthritic dogs recruited had signs of osteoarthritis in at least one forelimb joint and at least one hindlimb joint (see Appendix 5.5), therefore in future studies it would be useful to include a question to record whether the vet and/or owner considered the dog to be more severely affected in their forelimbs or hindlimbs, so that this variable could be included in analyses.

Whilst there was no effect of group on the mean time spent resting during the day in the multivariate model, there was a significant effect of HCPI score, with higher HCPI scores predicting slightly longer periods of the day spent resting. Although this may appear to suggest that dogs with higher HCPI scores and thus more severe pain from osteoarthritis showed increased amounts of rest (and thus decreased activity) during the day, this effect was only seen in control and not osteoarthritic dogs and therefore was not due to the effect of pain from osteoarthritis on the HCPI score (in fact dogs with osteoarthritis on average spent less time resting and therefore more time active during the day, though this was not significant). This effect could have occurred for several reasons: It is possible that control dogs with higher HCPI scores had undiagnosed illnesses that were not detectable on clinical examination but caused both pain and fatigue, and therefore increased daytime rest compared to control dogs with lower HCPI scores. It is also possible that the effect was specific to the dogs studied and not generalisable to the wider canine population; this study involved several outcome measures and as such overfitting of the data and detection of significant effects that are due to variation

243

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis within the sample rather than population-level differences is a possibility. Additionally, control dogs had a smaller range of HCPI scores compared to osteoarthritic dogs; and as such the predicted data for this effect required extrapolating the prediction beyond the available range of data for control dogs. It is possible that this effect would not be observed if healthy control dogs with a greater range of HCPI scores were recruited, however this would be difficult as it is unlikely that healthy dogs with no obvious painful pathology would show significant chronic pain and thus would have HCPI scores as high as those observed in osteoarthritic dogs. Because of these reasons, and because the effect size (beta estimate) was so small, this finding should be considered tentative and viewed with caution unless it can be confirmed in a larger study.

This study also assessed the effect of group and other factors on the mean duration of a period of uninterrupted rest during the day and night. However no factors, including group, had a significant effect on the mean duration of a period of uninterrupted rest during the night or day. Whilst human osteoarthritis patients report high rates of awakening during the night or early morning (Wilcox et al., 2000, Taylor‐Gjevre et al., 2011), Mullaney et al. (1980) found that wrist actigraphy in humans was poorly correlated with polysomnographic measures of night-time awakenings. It may be that dogs with osteoarthritis do awaken during the night but do not significantly change their movement such that the FitBark activity monitor does not accurately record this change, or it may be that events other than awakenings (such as movement during sleep) may cause an increase in activity that is then recorded as an interruption to a period of rest in both control and osteoarthritic dogs. This seems likely given that the group medians of the individual mean durations of uninterrupted rest during the night were less than 15 minutes for both groups, indicating regular switching from rest to activity as recorded by the FitBark activity monitor.

5.5.2 Methodological limitations:

There are some methodological limitations to this study. Firstly, the absence or relatively low levels of activity recorded as "rest" using the FitBark device and algorithms may not be fully representative of sleep itself: Some human studies report high levels of agreement between actigraphic measurements of rest/inactivity (which is generally assumed to represent sleep (Fielden et al., 2003, Law et al., 2012)) and direct polysomnographic measures of sleep itself (Kripke et al., 1978, Mullaney et al., 1980,

244

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

Webster et al., 1982, Gale et al., 2005). However, agreement appears to be lower in patients with disturbed sleep or poor sleep quality than in healthy participants (Sadeh et al., 1989, Sivertsen et al., 2006). This may be because whilst actigraphy has high sensitivity for detecting sleep, it generally has relatively poor specificity and therefore often fails to detect periods of wakefulness (Mullaney et al., 1980, de Souza et al., 2003, Gale et al., 2005, Paquet et al., 2007, Sitnick et al., 2008), which tend to occur more often and/or for longer periods in participants with disturbed sleep (Gale et al., 2005, Sivertsen et al., 2006).

Polysomnography is considered the “gold standard” method for sleep monitoring (Sadeh et al., 1995) and has been used in laboratory-housed dogs previously (Takeuchi and Harada, 2002). However, because of the invasiveness of the procedure and associated licensing requirements (Animals (Scientific Procedures) Act, 1986), expensive equipment and specialist expertise required for generating and analysing the polysomnographic traces, as well as the inability of this method to collect data over a long period within the dog’s own home environment, it was not considered a suitable technique for this study.

Additionally, the algorithms that distinguish whether a particular minute was spent in activity or rest using the raw activity recordings are the intellectual property of FitBark and have not yet been validated in peer-reviewed literature, so may not be entirely accurate. Knazovicky et al. (2015) appear to have used Actical activity monitors (Wernham et al., 2011, Knazovicky et al., 2015), which have been validated against videographic measurements of activity during the day in a laboratory setting (Hansen et al., 2007), and therefore may be more accurate than the FitBark system, which may explain the discrepancy in results obtained between this study and that of Knazovicky et al. (2015). However the Actical has not yet been validated for night-time use within owners’ homes, and the FitBark system was selected primarily for its ease of use by participating owners (Canine Journal, 2017) as well as its ability to upload each dog’s data remotely and allow these data to be accessed online, rather than requiring the data to be imported from the device directly, as required with the Actical (Wernham et al., 2011). Therefore instead of visiting each owner’s home multiple times to acquire data from their device or requiring owners to visit the research facility, only a single initial visit to each participating owner’s home was required. This dramatically reduced the time required to perform the study and decreased demands on owners’ time, potentially increasing recruitment and retention of participating dogs and owners.

245

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

It is also possible that for certain periods the FitBark activity monitor was removed from the collar, for charging or to avoid damage e.g. if the dog was being bathed or walked in an area where they were likely to swim. Owners were advised to charge the activity monitor once a week for approximately 90 minutes, which was considered acceptable as it constituted only 0.89% of the total recording time and since the same information sheet (see Appendix 4.1) was given to all owners it was assumed that this period of unrecorded activity would be similar for all dogs, however it is possible that the monitor was not replaced on the dog’s collar immediately after charging was complete in all cases. Owners were also asked to remove the FitBark temporarily if the dog was being bathed, undergoing hydrotherapy or was off the leash in an area in which they were likely to swim, in order to protect the device from water damage. FitBark have recently released a new device, the FitBark2, which is waterproof (FitBark, 2018b), however this was not available at the time of the study.

There is also the issue that dogs receiving analgesia treatment were included in the study, which may have limited the ability to detect differences in rest duration or sleep quality that truly exist between control dogs and dogs with untreated pain from osteoarthritis. The decision to include these dogs was made because in the study described in Chapter 2, it took several months to recruit sufficient numbers of osteoarthritic dogs that were not receiving analgesic medication, and there were no dogs recruited with a “severe” owner- or vet-assigned severity score. Since it seems likely that more severely-affected osteoarthritic dogs are more likely to be receiving analgesic treatment, recruiting dogs that were taking analgesic medication seemed reasonable in order to ensure more severely-affected dogs were included in the study, and to increase the number of osteoarthritic dogs that were eligible to participate overall. In order to reduce the extent to which analgesic provision affected the results, factors related to the presence, type and frequency of analgesia were pre-screened for addition to each multivariate model, and each dog’s osteoarthritis severity was assessed without changing their normal analgesic provision. However, whilst including these variables was important in order to assess whether analgesia provision had any effect on outcome measures (no factors relating to analgesia were significant in any multivariate model), this did result in more factors being added to initial pre-screening models, increasing the risk of overfitting (Babyak, 2004).

246

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis

As discussed in Chapter 4, studies such as this using mixed-effects models to analyse datasets containing large numbers of variables are prone to overfitting. The use of stepwise regression was avoided in order to prevent further reductions to the degrees of freedom (Babyak, 2004), however it would have been optimal to avoid initial pre- screening models and specify a smaller group of variables of interest a priori (Babyak, 2004, Whittingham et al., 2006) in order to reduce the probability of overfitting. However, this study was the first to compare rest duration and quality in dogs with and without osteoarthritis, and a systematic search revealed that studies (other than that by Knazovicky et al. (2015)) that have identified factors likely to have an effect on sleep quality in companion animal dogs are small in number and have focused on age (Bain et al., 2001, Takeuchi and Harada, 2002, Reichling et al., 2006) and pruritis (Wernimont et al., 2018). Therefore it was important to investigate other factors that differed between the dogs recruited in order to explore whether any changes were likely to be due to osteoarthritis or other variables (or to investigate whether osteoarthritis interacted with other variables). For these reasons, this study should be seen as exploratory rather than confirmatory, and its findings should be considered suggestive until further studies with larger sample sizes and/or fewer variables that differ between animals can be performed, or until sufficient information is available on the factors likely to affect sleep- related outcomes in dogs such that a model can be specified a priori.

As with all linear model analyses with large numbers of continuous predictors, there was also the concern that collinearity could be a problem. To combat this, where highly correlated continuous measures met the criteria to be included in a multivariate model, all but one of these was removed – thus in the models for proportion of time spent resting during the day and SNoRE score HCPI score was used to represent severity of pain from osteoarthritis assessed via questionnaire and the other questionnaire scores were omitted. Whilst age and length of night period were also significantly correlated, the correlation was weak (Pearson test: t(39)=-2.04, p=0.0480, R=-0.311), and collinearity is thought to be likely with correlation coefficients of R≥0.7 (Dormann et al., 2013). Therefore both of these were included together in the models for mean duration of an uninterrupted period of rest during the night.

The main outcomes in this study (SNoRE score and proportions of time spent resting during the night and daytime) were analysed using robust general linear models using the robustlmm package in R, which uses a smoothed Huber function to increase

247

Chapter 5: Comparison of resting behaviour in dogs with and without osteoarthritis robustness to outliers and other violations of the assumptions of linear models (Koller, 2016), but still assumes the data are approximately Gaussian in distribution (Koller, 2016). This was advantageous as it allowed the data for these outcomes to be analysed using a mixed-effects model framework, and meant that it was possible to include interactions of other factors with group. It would have been possible to analyse these data using nonparametric rank-based analyses on the mean value of each variable for each dog, which would not allow the potential for interactions between group and other variables to be investigated, and would reduce the amount of information contained in the data analysed to a single value per dog, rather than several values nested within the variables “Week” and “Dog”. However, the robustlmm package is relatively novel and much of the information available on its usage is from a single peer-reviewed paper (Koller, 2016). The rlmer() function used in these analyses increases robustness at the cost of decreasing asymptotic efficiency (Koller, 2016) (a measure of model quality in terms of precise parameter estimation (Everitt and Skrondal, 2002; pp. 24 and 149)), thus the fit of the models is less good than those fit using a conventional mixed effects linear model on data that would meet all of the relevant assumptions. The extent to which robustness is favoured over asymptotic efficiency can be specified within the function (Koller, 2016), however without advanced statistical knowledge or prior information relating to the area of study it would be difficult to ascertain the optimal settings for each outcome variable. The robust models created also have the disadvantage of being incompatible with AIC or BIC measures of model quality (as the robust equations do not correlate with measures of likelihood or pseudo-likelihood) (Koller, 2016), thus these methods cannot be used to identify the best models from a selection as they can with general linear models.

5.5.3 Conclusion:

In conclusion, whilst this is an exploratory study and further research is required to assess whether these findings are generalisable to the canine population at large, this study found evidence that suggests that dogs with osteoarthritis spend less time resting and more time active during the night.

248

Chapter 6: Conclusion

Chapter 6 Conclusion

The aim of this thesis was to investigate whether dogs with chronic pain from osteoarthritis show deficits in working memory and sleep. Using a modification of the disappearing object task designed by Fiset et al. (2003), it was found that female neutered osteoarthritic dogs showed spatial working memory deficits compared to female neutered control dogs, though the performance of male neutered osteoarthritic dogs did not significantly differ from male neutered control dogs. Using actigraphic measures and owner questionnaires, it was also found that dogs with osteoarthritis spent significantly less time resting during the night than control dogs, but did not show significant differences in sleep quality as assessed by their owners. There was no significant effect of sex on SNoRE scores or time spent resting during the day or night.

6.1 Overview of findings:

Chapter 2 described a study comparing the performance of dogs with and without chronic pain from osteoarthritis in the novel object recognition task, which assesses non-spatial working memory for objects (Dudchenko, 2004, Ennaceur et al., 2005) and the holeboard task, which assesses spatial working memory (van der Staay et al., 2012). Dogs with clinical signs of osteoarthritis showed higher scores on all validated pain- related questionnaire scores than healthy control dogs, suggesting that the clinical checklist was effective at correctly distinguishing osteoarthritic and control dogs. Surprisingly, osteoarthritic dogs spent a greater proportion of time interacting with the novel object than the familiar object compared to control dogs in the novel object recognition task, which may indicate increased neophilia or increased non-spatial working memory for objects. However, the total time spent interacting with objects for both groups of dogs was very low, suggesting that dogs were not particularly interested in them. Spatial working and reference memory scores in the holeboard task did not significantly increase with successive trials, indicating that neither group of dogs had successfully learned how to perform the task within the number of trials given. Therefore Chapter 3 described a study assessing dogs’ performance on a version of the holeboard task containing a greater number of trials and on a disappearing object task similar to that previously described by Fiset et al. (2003), in order to assess whether

249

Chapter 6: Conclusion dogs were able to learn the tasks and if so, which would be more feasible to use in order to compare the spatial working memory capabilities of dogs with and without chronic pain from osteoarthritis. It was found that dogs were able to learn how to complete the holeboard task if given sufficient trials over two days (three if a configuration change was included), and could successfully perform the disappearing object task in a single day. Therefore the disappearing object task was selected to compare the spatial working memory of osteoarthritic and control dogs in Chapter 4. Whilst osteoarthritic dogs had a slightly lower probability of successfully locating the object than control dogs, this effect was not significant overall, however female neutered dogs but not male neutered dogs showed significant differences in probability of success compared to control dogs of the same sex. There was no overall difference between groups in adjusted latency to approach the box visited, though osteoarthritic dogs had a wider distribution of latencies than control dogs, possibly due to heterogeneity of osteoarthritis severity. Chapter 5 compared two proxy measures of sleep in dogs with and without osteoarthritis; rest/inactivity duration measured using actigraphy and owner-perceived sleep quality measured using the SNoRE questionnaire, developed by Knazovicky et al. (2015). Osteoarthritic dogs overall spent significantly less time resting and more time active during the night than control dogs, this was also true for dogs with moderate osteoarthritis severity but not those with mild osteoarthritis severity or severe osteoarthritis severity (although only 1-2 dogs were considered to have “severe” osteoarthritis by the owner or the vet, and none by both). Whilst the mean SNoRE score of osteoarthritic dogs was slightly higher than that of control dogs, this effect was not statistically significant.

6.2 Contribution to literature:

This work provides the first evidence of working memory impairments associated with a chronic pain-causing condition in the dog, supporting previous findings that working memory impairments are associated with chronic pain in humans (Dick and Rashiq, 2007, Berryman et al., 2013) and rats (Ren et al., 2011, Cardoso-Cruz et al., 2013a, Cardoso-Cruz et al., 2013b). Surprisingly, this effect appeared to occur in female neutered osteoarthritic dogs but not in male neutered osteoarthritic dogs, which is interesting in the context of previous findings that human women generally have more severe osteoarthritis symptoms than men (Affleck et al., 1999, Keefe et al., 2000), and that osteoarthritis incidence increases following menopause (Silman and Newman,

250

Chapter 6: Conclusion

1996, Wluka et al., 2000). Whilst dogs and most other mammalian species do not experience a menopause (Johnstone and Cant, 2010), ovariectomy induces osteoarthritis-like changes in sheep (Cake et al., 2005) and rats (Høegh-Andersen et al., 2004) that may mirror the changes seen in the joints of post-menopausal women (Høegh-Andersen et al., 2004). Therefore it seems plausible that ovariectomy/ovariohysterectomy could also induce similar changes in female dogs, causing increased severity of osteoarthritis clinical signs and possibly increased impairment of spatial working memory. Because human men do not undergo a menopause analogue and are rarely gonadectomised, it is unknown how castration would affect the severity of spontaneous osteoarthritis in humans, though in one study castrated male mice developed less severe osteoarthritic changes in the stifle following meniscal destabilisation (a surgically induced model of osteoarthritis) than entire males, whereas ovariectomised female mice developed more severe changes following meniscal destabilisation than entire females (Ma et al., 2007), suggesting that neutering may be protective against osteoarthritis-like changes in males but facilitative of such changes in females. This could explain how osteoarthritis severity could be greater in female dogs compared to male dogs even when the dogs were neutered, which could potentially lead to greater effects from osteoarthritis on the working memory of female neutered dogs compared to males.

However, whilst female osteoarthritic dogs did have higher median HCPI and CBPI Interference and Severity scores (owner-assessed measures of osteoarthritis severity) compared to males, this difference was not statistically significant, therefore it is possible that factors other than sex differences in osteoarthritis severity are responsible for the different effects of osteoarthritis on spatial working memory in male and female neutered dogs. Keefe et al. (2000) found that differences in pain perception and pain behaviours in human women compared to men were largely due to increased pain catastrophising (the tendency of patients to disproportionately focus on pain and more negatively perceive their ability to cope with their pain) in women (Keefe et al., 2000). Previous studies have also shown that pain catastrophising is associated with impaired attention (Crombez et al., 1999, Grisart and Van der Linden, 2001) and short-term recall (Grisart and Van der Linden, 2001) in chronic pain patients. Whilst it is technically possible that female dogs with osteoarthritis may also be more likely to focus on their pain than male dogs, perhaps due to congenital differences in anatomy or physiology that are not inhibited by neutering, and that this may explain their impaired spatial

251

Chapter 6: Conclusion working memory in the disappearing object task, it seems more likely that sex differences in pain catastrophising in humans would be due to hormonal or societal differences between human men and women. Additionally, it is not currently known whether catastrophising is possible in non-human animals or if so how it could be tested. Since female mammals generally show impaired performance in spatial tasks compared to males (Jones et al., 2003, Cánovas et al., 2008), it is also possible that female dogs found the disappearing object task more difficult than male dogs, and this increased difficulty combined with distraction from osteoarthritis-related pain caused impaired performance in female osteoarthritic dogs, but that the effects of these factors individually were not sufficient to impair performance in female control dogs or male osteoarthritic dogs.

This project also provides evidence that osteoarthritis in dogs is associated with decreased night-time rest (and therefore with increased night-time activity) but not with changes in owner-assessed sleep quality assessed via the SNoRE questionnaire. This is in contrast to the findings of Knazovicky et al. (2015) who found that nonsteroidal analgesic treatment of dogs with osteoarthritis was not associated with significant differences in night-time activity compared to no treatment or placebo, but was associated with significantly decreased SNoRE scores. However, Knazovicky et al. (2015) did not compare the SNoRE scores or actigraphy results of osteoarthritic dogs with those of a healthy control group, therefore these differences could be due to differences in the independent variables being compared (nonsteroidal analgesic/placebo administration c.f. presence/absence of osteoarthritis): It is possible that nonsteroidal analgesics are able to improve sleep in dogs with osteoarthritis by improving sleep quality and not by reversing the osteoarthritis-mediated increase in night-time activity. However, many of the osteoarthritic dogs recruited in the study described in Chapter 5 were receiving various analgesic drugs at differing frequencies, and whilst the variables associated with analgesia did not have a significant effect on night-time rest or on SNoRE scores, analgesia could still have had a confounding effect in some way. Additionally, some dogs may not have been sleeping in a location that allowed for easy observation by owners, such that SNoRE responses may not have been particularly accurate for all dogs, and given that owners would not have been able to observe their dogs continuously throughout each night it is possible that dogs may have performed behaviours associated with higher SNoRE scores such as moving, pacing or vocalising whilst sleeping, and that these may not have been observed by owners. Since night-time

252

Chapter 6: Conclusion inactivity is considered representative of sleep in human actigraphy studies (Fielden et al., 2003, Law et al., 2012), the findings of this study also support the findings from existing literature that sleep is impaired in chronic pain patients (Nicassio and Wallston, 1992, Drewes et al., 2000, Riley et al., 2001), including those with osteoarthritis when assessed by self-report measures (Wilcox et al., 2000, Power et al., 2005, Taylor‐Gjevre et al., 2011). The discrepancy in findings between this study and that of Knazovicky et al. (2015) in terms of actigraphic measures is also interesting in the context of conflicting results from actigraphic studies on sleep in human osteoarthritic participants: Whilst Fielden et al. (2003) found that total hip arthroplasty improved actigraphic measures relating to sleep (including night-time activity) in osteoarthritic patients, Leigh et al. (1988) found no significant difference in nocturnal body movement between osteoarthritic patients and healthy controls, and Taibi and Vitiello (2011) found that whilst yoga improved subjective measures of sleep quality in osteoarthritic patients, there was no effect on actigraphic sleep measures. Since the specificity (ability to detect time spent awake) of actigraphy is generally low (Sadeh, 2011) and varies between algorithms (Paquet et al., 2007), it is possible that the discrepancy between actigraphic sleep outcomes in human osteoarthritis studies and the discrepancy in actigraphic findings between this project and the study by Knazovicky et al. (2015) may have been due to differences in the algorithms used. Knazovicky et al. (2015) used an Actical actigraphic device (Wernham et al., 2011, Knazovicky et al., 2015), which has been validated against videographic measurements of dogs’ activity during the day in a laboratory setting (Hansen et al., 2007), whereas the FitBark system has not been validated against video recordings of dogs’ activity in the accessible peer-reviewed literature, so may not be as accurate as the Actical, which may explain the differing results of these studies.

The findings of this study also build on the existing literature on cognitive assessment in dogs: The study described in Chapter 3 provides the first evidence that the holeboard task can be successfully performed in dogs, which show similar working memory scores to those previously observed in rats (van der Staay, 1999), and pigs (Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Haagensen et al., 2013a, Haagensen et al., 2013b, Antonides et al., 2015). Whilst dogs’ reference memory scores were generally lower than those recorded in rats and pigs (van der Staay, 1999, Arts et al., 2009, Gieling et al., 2012, Bolhuis et al., 2013, Antonides et al., 2015) some studies found similar reference memory scores in pigs (Haagensen et al., 2013a, Haagensen et al., 2013b).

253

Chapter 6: Conclusion

Furthermore, due to time and recruitment constraints this study used trials that were more closely spaced together than most others, and since this is known to impair reference but not working memory (Spreng et al., 2002, Commins et al., 2003) this could explain the comparably low reference memory scores observed in this study compared to others. The findings that dogs’ holeboard performance was comparable to that previously seen in rats (van der Staay, 1999) is surprising given that Macpherson and Roberts (2010), reported that dogs showed “surprisingly low” performance on a radial maze task in comparison to rats. However, the radial maze task relies on travelling through narrow enclosed arms rather than searching a grid of holes/buckets placed in an open space, and laboratory rats show an aversion to open spaces (Candland and Campbell, 1962, Denenberg, 1969) and a preference for enclosed spaces (Pellow et al., 1985). It is possible that dogs do not show such a preference, which may explain why dogs perform similarly well (in terms of working memory score) to rats in the holeboard task but less well in the radial maze task.

6.3 Implications:

The finding that chronic pain from osteoarthritis may be associated with impaired night- time rest and working memory in dogs has implications for dogs’ behaviour. In humans, impaired working memory is associated with inattention and regular distraction from everyday tasks (Kane et al., 2007, Spencer-Smith and Klingberg, 2015) as well as decreased ability to find locations or objects and to do more than one task at the same time (Johansson and Tornmalm, 2012). Therefore dogs may also have impaired attention (which could manifest as impaired ability to learn or perform trained behaviours or to respond to the owner) and decreased ability to navigate in space, which could manifest as the dog appearing to be lost, having difficulty efficiently navigating their home environment or appearing less able to find and retrieve objects such as toys or food. This also has implications for the veterinary profession. For example, it would be useful to encourage recognition amongst veterinary professionals that osteoarthritis may be a potential cause of sleep disturbance and impaired working memory, so that osteoarthritis could be considered a differential diagnosis in dogs presenting to veterinary practices with these clinical signs, and to prevent sleep or working memory problems in dogs from being misattributed to other factors such as age related cognitive decline, poor training or systemic disease. These findings also have implications for owners of dogs with osteoarthritis, as increased owner awareness that

254

Chapter 6: Conclusion osteoarthritic dogs may experience sleep or working memory difficulties along with their other clinical signs (such as pain and reduced mobility), may allow owners to take steps to accommodate these changes. For example, owners could provide comfortable and quiet sleeping locations and analgesic treatment when necessary (as Knazovicky et al. (2015) found that nonsteroidal analgesia significantly improved owner-assessed measures of sleep quality), and could maintain a consistent, open environment free from clutter that dogs with working memory deficits may find easier to navigate. However, these findings should be confirmed in a different, larger sample of dogs before they are disseminated as fact to owners and veterinary surgeons, to confirm their validity in the general canine population and not just the sample recruited.

There are also potential implications for human chronic pain and osteoarthritis patients: Because it appears that chronic pain from osteoarthritis may cause working memory deficits in some dogs, similar to those that occur in human chronic pain (though it is not yet known whether this is the case in osteoarthritis), as well as increased night-time activity and therefore probable sleep deficits, similar to those reported in human chronic pain patients (including those with osteoarthritis), this provides evidence that spontaneous canine osteoarthritis has good translational validity as a model of human chronic pain and osteoarthritis. Spontaneous canine osteoarthritis shows many other similarities to human osteoarthritis including radiographic changes (Innes et al., 2004, Zhang and Jordan, 2010), disability/lameness (Peat et al., 2001, Hielm-Björkman et al., 2003, Hawker et al., 2008, Marshall et al., 2010), behavioural changes or subjective reports consistent with pain (Wiseman et al., 2001, Hawker et al., 2008, Hielm-Björkman et al., 2009), gene expression (Clements et al., 2006), cartilaginous proteoglycan levels (Liu et al., 2003), and responses to analgesic (Jordan et al., 2003, Brown et al., 2008, Hielm-Björkman et al., 2009) and surgical (Conzemius et al., 2003, Lascelles et al., 2010, Beswick et al., 2012) treatment. Therefore the understanding that both canine and human osteoarthritis appear to be associated with impaired night time sleep adds to this body of evidence suggesting that canine osteoarthritis is a translationally valid model for human osteoarthritis. Whilst no published studies appear to have investigated whether chronic pain specifically from osteoarthritis is associated with impaired working memory in humans, the evidence from this study that spatial working memory may be impaired in female dogs with spontaneous osteoarthritis, and from previous studies showing that chronic pain from other conditions impairs working memory in humans (Berryman et al., 2013), suggests that it is possible that humans with osteoarthritis may

255

Chapter 6: Conclusion experience deficits in working memory also. The findings of this study also support the use of chronic pain from spontaneous canine osteoarthritis as a model for human chronic pain more generally, as both sleep (Nicassio and Wallston, 1992, Riley et al., 2001) and working memory (Berryman et al., 2013) are known to be impaired in human chronic pain, and canine spontaneous osteoarthritis appears to be one of the more common causes of chronic pain in dogs. There are also implications for research ethics: These results support the translational validity of spontaneous canine osteoarthritis as a model for human osteoarthritis and/or chronic pain, and more widespread use of this model could lead to a reduction in the use of laboratory animals and painful experimentally induced osteoarthritis-like conditions (or other induced chronic pain models), which would be compatible with the “3Rs” framework for ethical animal use (Guhad, 2005).

The findings of this project also have implications for the assessment of working memory in dogs. The holeboard task has not been previously described in dogs, and the finding that dogs’ working memory scores are similar to those in other species suggests that this task may be more appropriate for the assessment of working memory in dogs than the radial maze task, in which Macpherson and Roberts (2010) described dogs’ performance as “surprisingly low”. Dogs were able to learn and perform the holeboard task in only three days, whereas the delayed non-matching to position tasks used by N.W. Milgram’s research group (Adams et al., 2000, Tapp et al., 2003, Zanghi et al., 2015) require prolonged training periods of several weeks. Therefore the holeboard task appears to be advantageous in situations where researcher access to animals is limited to short periods, such as in studies using animals owned by members of the public.

Additionally, the holeboard task has the advantages of allowing dogs’ spatial working and spatial reference memory to be assessed simultaneously, which could provide more information about learning and memory in dogs, and because the holeboard task does not require a researcher to be present in the arena during the task performance, it may also be suitable for the assessment of spatial working and reference memory in non- domesticated canid (and potentially non-canid) species such as those kept in zoos or wildlife parks, providing a new method for comparative memory research in non- domesticated species. This project was also the first to use the disappearing object task devised by Fiset et al. (2003) in two distinct groups of dogs in order to successfully identify differences in spatial working memory; whilst Fiset and Plourde (2013) have

256

Chapter 6: Conclusion used a different disappearing object paradigm featuring a moving container to compare object permanence and ability to track moving hidden objects in dogs and wolves, that paradigm used and the research questions it aimed to answer were quite different from the disappearing object task used in this study and by Fiset et al. (2003). The disappearing object task described in Chapter 4 of this project is advantageous as training and testing can be performed in a single session of under two hours, and all of the equipment is mobile and can be transported to and set up within the dogs home environment, allowing easier recruitment of dogs and owners since they do not have to visit the research site in order to participate, and is potentially more representative of their working memory in everyday life than a task performed in an unfamiliar novel setting. This project was also the first to attempt in dogs the single session novel object recognition task first described by Ennaceur and Delacour (1988), however the total time spent interacting with the objects was very low and dogs appeared more motivated to leave the arena and be reunited with their owners than to explore the objects. This information could be useful in informing future attempts at performing this task in dogs, as it suggests that objects may need to be different or more interesting to attract the attention of dogs compared to laboratory rats (which are generally housed in more barren environments and probably have had fewer experiences of novelty or unusual objects in general (Würbel, 2001)), and that tasks should perhaps be performed in the owners’ home with the owner present in order to prevent the dog from being motivated to escape.

6.4 Limitations:

There are several limitations to the studies undertaken within this project, some of which relate to the experimental design and others which relate to the analysis techniques used. One limitation relating to the experimental design is that the use of client-owned dogs meant that it was necessary to use assays that could be completed in a single session or remotely, as having access to dogs for several regularly-spaced consecutive sessions was very unlikely. This limited the range of tasks and measurements that it was possible to perform in these animals. Whereas studies in laboratory-housed rodents (Wirsching et al., 1984, Gutnikov et al., 1994, van der Staay, 1999) and dogs (Tapp et al., 2003, Snigdha et al., 2012, Zanghi et al., 2015), and in livestock species such as pigs (Mendl et al., 1997, Gieling et al., 2012, Bolhuis et al., 2013) often use more prolonged tasks requiring several weeks of habituation, training

257

Chapter 6: Conclusion and testing (as the animals are available throughout such periods), this is not feasible in companion animal dogs.

Another limitation affecting the experimental design was the inability to thoroughly blind recording and analysis, despite attempts to blind data collection and recording as much as possible: The assistants who performed the event logging from the video files in the studies described in Chapters 2 and 3 were blinded in that dogs were identified by a code (e.g. “A1”) and no information about each dog was provided beyond that which was apparent from watching the videos. Additionally, whilst the person performing the event logging for the study described in Chapter 2 was aware that there were two groups of dogs and that the study was about dogs with osteoarthritis, they were not informed which group consisted of osteoarthritic dogs and which group consisted of control dogs. Similarly, the experimenter responsible for restraining the dogs and recording their disappearing object task performance in the study described in Chapter 4 was also not aware of the group to which each dog had been assigned following clinical examination, though in some cases information provided in comments from the owner or by observation of the dog’s gait may have revealed whether they were likely to have osteoarthritis. However, not all aspects of the study were thoroughly blinded: The clinical examination of each dog and analysis of the outcomes for each study (following recording or event logging) were performed by the same person (MS), therefore the analysis itself was not blinded. Blinding was also not possible for owner questionnaires because owners already knew their dogs well and were likely to have informed opinions on whether their dog was likely to have osteoarthritis (this differed from the veterinary surgeon’s (MS) assessment in only one case in each of Chapters 4 and 5). Optimally, different researchers would have been responsible for clinical examination of the dogs and scoring and analysis of their task performance, with the groups, severity scores, medication categories etc. referred to by a code such that the person performing the analysis would not be able to ascertain which dogs were in each category. However, as these studies were undertaken within a PhD project, it was considered important for the candidate/thesis author (MS) to be heavily involved in both data collection and analysis, and since recruitment of animal handling and event logging assistants was difficult, it seemed infeasible to recruit another qualified veterinary surgeon to examine the dogs. Additionally, not knowing how many dogs had been recruited in each group until after each study’s completion would have made it much more difficult to know when the target sample size had been reached and to recruit an appropriate number of remaining

258

Chapter 6: Conclusion dogs in each group, and may have necessitated the recruitment of an additional assistant to monitor this.

Another limitation associated with this project is that the FitBark system (device and algorithms) has not been validated for sleep-monitoring purposes in the peer-reviewed accessible literature, so it is not possible to know how accurately it is able to distinguish periods of activity and rest, or how accurately inactivity/rest actually represents sleep. The Actical activity monitor used by Knazovicky et al. (2015) showed strong correlations with videographic measurements of dogs’ activity during the day in a laboratory setting (Hansen et al., 2007), so this activity monitor system may be more accurate, though it does not appear to have been validated against videographic analysis during the night whilst the dogs were resting, or within the dogs’ own homes. Additionally, the FitBark system was selected primarily for its reported ease of use by dog owners at home (Canine Journal, 2017) and the ability to remotely access recorded data without the owner having to physically bring the device to the research site during the study, which was advantageous as dogs were recruited from a relatively wide area of Southwest England, and requiring owners to travel to the research site may have discouraged their participation. The Actical was developed for use in humans and is primarily aimed at researchers (Philips Respironics, 2018a) rather than dogs’ owners, and requires manual transfer of data via placement on a telemetric reader (Wernham et al., 2011) with no capacity for remote transfer of data such as via Bluetooth (Philips Respironics, 2018b), therefore it would not have been ideal for use in this study despite its prior validation.

One major limitation regarding the analysis techniques used is that the modelling methods used in Chapters 3-5 are prone to overfitting (Babyak, 2004). This increases the likelihood that whilst the findings of this project describe the sample group well, they may not be generalisable to the wider canine population (Babyak, 2004). Efforts were taken to limit the risk of overfitting by avoiding the use of a “stepwise” regression protocol, which is known to increase the risk of overfitting (Steyerberg et al., 2001). Whilst larger samples with 10-15 observations per variable are more resistant to overfitting (Babyak, 2004), such large samples are not particularly feasible in studies requiring the behavioural testing of live animals owned by members of the public, because of the time required for testing, marketing and recruitment. Decreasing the number of variables entered into initial models would have also reduced the overfitting risk, however since these studies were the first to examine many of these outcomes

259

Chapter 6: Conclusion using this particular study design, it was important to include potential confounding factors to assess whether they had any effect that might be erroneously attributed to osteoarthritis. Because many of the signalment and environmental factors examined did not have an effect on any outcomes in this study, it should be possible to omit these from analyses in future studies.

Another limitation of the analysis methods used is that the model selection methods used may not result in the best fitted model, and other models that are an equally good or better fit might potentially show very different results (Whittingham et al., 2006). The AIC and BIC are quantitative measures of model fit (Posada and Buckley, 2004, Whittingham et al., 2006) that could be useful in comparing how well different models fit the data, and the R package glmulti can be used with some regression methods to automatically generate all possible models that include all permutations of the variables provided and rank these by AIC value (Calcagno, 2013). However, the rlmer() function from the robustlmm package (Koller, 2016) used to analyse the proportions of time spent resting and SNoRE scores, are incompatible with the AIC and BIC (Koller, 2016), and the glmulti package does not appear to offer built-in support for glmer-type models created using the lme4 package (Bates et al., 2015) either, as all attempts to run it with glmer-type models failed during this study.

6.5 Future Work:

The findings of this project reveal several avenues for potential future research. Firstly, because of the limitations of the analysis methods used, it would be appropriate to confirm these findings in a different and preferably larger sample of dogs, perhaps collaborating with computer science or mathematics departments to ensure that the risk of overfitting is minimal and that the model selected best fits the data. Because it was surprising that female but not male neutered osteoarthritic dogs displayed decreased working memory, especially since osteoarthritic female dogs did not have significantly different owner questionnaire scores from osteoarthritic male dogs, it would also be worthwhile investigating this particular outcome within a larger sample, to confirm whether it is the case for the wider canine population rather than just the sample recruited.

260

Chapter 6: Conclusion

It would also be worthwhile repeating the disappearing object task performed in chapters 3 and 4 whilst video recording the dogs’ body position during the retention interval, to assess whether the opaque barrier and physical restraint by the experimenter were sufficient to prevent dogs from using bodily orientation strategies rather than/in addition to spatial working memory in order to locate the object. Because dogs without osteoarthritis were more likely to successfully find the object if it was hidden behind an outer box compared to if it was hidden behind an inner box, and Gutnikov et al. (1994) similarly found that rats that had to recall one of an array of several holes were more likely to be successful when the correct location was an outer compared to a central hole because they were using a body-turning strategy, it would be interesting to analyse whether dogs’ body position during the interval and the direction in which they turned following entry to the arena was dependent on the target box location, and if some dogs did use such a strategy, whether this was associated with increased probability of successfully finding the object compared to dogs that did not.

It would also be interesting to compare the spatial working and reference memory scores of osteoarthritic and control dogs using the holeboard task described in Chapter 3. Since dogs’ holeboard performance did not significantly correlate with their disappearing object task performance in the study described in Chapter 3, possibly because of differences in the kind of learning required or the motivation to acquire food compared to toys, it may be that osteoarthritis has different effects on spatial working memory depending on the assay, and if dogs with osteoarthritis perform worse than those without osteoarthritis in the holeboard assay this provides further evidence that dogs’ spatial working memory is affected by osteoarthritis. Furthermore, the few studies examining reference memory (long term recall) in humans with chronic pain are often confounded by anxiety or depression (Landrø et al., 1997, Grace et al., 1999), therefore it would be interesting to assess whether spatial reference memory is decreased in dogs with osteoarthritis also, as this may suggest that humans with chronic pain may also be likely to have reference memory deficits and that this should be investigated more thoroughly. Whilst the holeboard task may be difficult to perform in dogs owned by members of the public, as access to the dogs would be required for three consecutive days, it may be possible to recruit dogs owned by people who do not work or work from home for this purpose.

261

Chapter 6: Conclusion

Impaired long-term recall is associated with depression in chronic pain patients (Landrø et al., 1997), sleep disturbance is associated with both chronic pain (Nicassio and Wallston, 1992, Riley et al., 2001) and depressive disorders (Casper et al., 1985, Alvaro et al., 2013), and depressive disorders occur frequently in chronic pain conditions (Leino and Magni, 1993, Ohayon and Schatzberg, 2003). Therefore, it would also be useful to assess whether depressive-like clinical signs were correlated with impaired spatial reference memory and decreased night-time rest in osteoarthritic dogs. This could be investigated using the spatial judgement bias and visual laterality tasks described by Harris (2017) alongside the holeboard task, actigraphic recordings and SNoRE questionnaires in the same cohort of dogs.

Further studies using the novel object recognition task are also warranted: The results of the novel object recognition task, which assesses non-spatial working memory for objects (Dudchenko, 2004, Ennaceur et al., 2005), are intriguing as they suggest that osteoarthritic dogs may show increased neophilia or increased non-spatial working memory relating to objects. However the proportion of the total time that was spent interacting with either object was very low for both groups, and most dogs appeared to be more motivated to leave the arena and return to their owner on the other side of the fence rather than to interact with the objects, which calls the validity of these findings into question. It would therefore be interesting to perform a pilot study to assess which object characteristics dogs consider more interesting, and thus spend more time interacting with, and subsequently to repeat the novel object recognition task in the owner’s home with the owner present (such that the dog is not motivated to leave the area containing the objects), using a pair of objects that dogs appear motivated to interact with for prolonged but approximately equal periods, in order to assess whether the findings described in Chapter 2 were genuinely due to increased neophilia/non- spatial working memory in osteoarthritic dogs.

It would also be useful to compare the performance of dogs with and without osteoarthritis and humans with and without osteoarthritis on similar tasks. Most studies assessing working memory in humans have focused on verbal, written or numeric tasks which can be explained verbally (Berryman et al., 2013), whereas animal working memory tasks have primarily focused on memory for spatial locations, objects or odours (Dudchenko, 2004). Whilst the holeboard task has been adapted for use in humans (Cánovas et al., 2008, Sturz and Brown, 2009) and has successfully found spatial memory

262

Chapter 6: Conclusion deficits in fibromyalgia patients (Cánovas et al., 2009), the experimental design for these tests was somewhat different; with baited buckets arranged in a specific pattern that moved around the grid between trials (Sturz and Brown, 2009), or revealing the locations of previous visits to the participant throughout the trial (Cánovas et al., 2008, Cánovas et al., 2009), such that outcome measures were comparable with reference memory scores rather than working memory scores in the rodent paradigm (van der Staay et al., 2012). Therefore designing a virtual (or physical) task that is more similar to the holeboard or disappearing object task used in dogs and comparing the performance of these species and the effect of osteoarthritis on performance in each species would provide further evidence regarding the translational validity of canine osteoarthritis as a model for human osteoarthritis, and would also be useful for comparing the spatial working memory of humans and dogs. This study would also provide information on whether spatial working memory is impaired in human osteoarthritis patients, which is currently lacking.

It would also be interesting to perform a randomised placebo-controlled trial to assess the effects of nonsteroidal analgesia on activity and rest patterns in osteoarthritis, similar to the study performed by Knazovicky et al. (2015) but with the inclusion of a healthy control group. The findings of the study described in Chapter 5 (dogs with osteoarthritis showed increased night-time activity but not decreased owner-assessed sleep quality compared to control dogs) do not mirror the findings of Knazovicky et al. (2015) (osteoarthritic dogs treated with nonsteroidal analgesia showed increased owner-assessed sleep quality but not decreased night-time activity compared to placebo and baseline). Therefore it is possible that the effects of nonsteroidal analgesia on sleep quality and night-time activity are not mediated by simply inhibiting the effects of painful osteoarthritis on sleep quality and night-time activity. This proposed study would allow the effects of osteoarthritis and the effects of nonsteroidal analgesia in osteoarthritis to be distinguished within the same testing protocol, and would prevent the potential confounding effects of the various analgesic treatments provided to dogs in the study described in Chapter 5.

The findings of Chapter 5 were also surprising in that dogs with osteoarthritis did not spend more time resting (and thus less time active) during the day than control dogs, which seemed unlikely given that both dogs and people with osteoarthritis experience impaired mobility/lameness (Hawker et al., 2008, Marshall et al., 2010) and this would

263

Chapter 6: Conclusion be expected to reduce daytime activity. Because the FitBark system has the ability to differentiate less strenuous “active” periods from more strenuous “play” periods (this distinction was not used in this study as it was considered unlikely that all periods of strenuous activity were truly representative of play behaviour, and the distinction between time spent active and time spent resting was more relevant to the study hypotheses), it may be useful to analyse these two categories separately in future studies to assess whether there is a difference in the intensity of exercise performed in osteoarthritic compared to control dogs, as well as to assess whether the duration of time spent resting during the day would be affected by osteoarthritis in a different sample of dogs.

Additionally, whilst it does not directly follow from the findings of this study, it would be beneficial to perform a widespread cohort study examining the incidence, prevalence and risk factors for osteoarthritis in the wider canine population, as very little information currently exists regarding the epidemiology of canine osteoarthritis (Henrotin et al., 2005), with only one published cohort study investigating risk factors for canine osteoarthritis as determined from retrospective examination of veterinary clinical records (Anderson et al., 2018). Without epidemiological information it is difficult to ascertain the true scale of the impact of canine osteoarthritis on the health and welfare of the canine population and thus to appreciate the extent to which studies investigating the changes that occur in canine osteoarthritis are likely to provide benefit to dogs, their owners and the veterinary profession.

6.6 Summary:

The project described in this thesis found that female but not male dogs with osteoarthritis showed spatial working memory deficits compared to healthy control dogs, that osteoarthritic dogs of either sex showed decreased time spent resting at night but not decreased owner-assessed sleep quality measures compared to control dogs, and that the holeboard and disappearing object task are suitable for the assessment of spatial working memory in dogs. These findings contribute to the existing literature on working memory deficits in chronic pain, sex differences in osteoarthritis, sleep deficits in chronic pain and osteoarthritis and cognitive assessment in animals, and have implications for dogs with osteoarthritis, their owners and the veterinary surgeons treating them, as well as for the study of human osteoarthritis and thus for human

264

Chapter 6: Conclusion osteoarthritic patients and laboratory animals. Limitations of this research include a lack of blinding of the researcher performing the analyses (MS) and of the dogs’ owners (though assistants observing the animals and entering the data were blinded as much as it was possible to do so), risks of overfitting and suboptimal model fit during analyses, and restrictions on the types of behavioural test that can be feasibly performed in animals owned by members of the public. Potential avenues for future research include recruitment of larger samples and collaboration with statistical or computer science researchers in order to improve model fit and reduce overfitting risks, thus confirming whether the findings of this project are truly generalisable to the wider canine population, comparing the performance of dogs with and without osteoarthritis in different behavioural tasks, and performing a randomised placebo-controlled trial to differentiate the differences between osteoarthritic and control dogs and between osteoarthritic dogs treated with nonsteroidal analgesia in comparison to placebo.

265

References

References:

ABRAMSON, S. B. 2004. Inflammation in osteoarthritis. Journal of Rheumatology- Supplements, 31, 70-76. ACTIGRAPH. 2017. CentrePoint Initializing Devices [Online]. Available: https://www.actigraphcorp.com/tutorial/centrepoint-initializing-devices/ [Accessed 20/03/2019]. ADAMS, B., CHAN, A., CALLAHAN, H., SIWAK, C., TAPP, D., IKEDA-DOUGLAS, C., ATKINSON, P., HEAD, E., COTMAN, C. W. & MILGRAM, N. W. 2000. Use of a delayed non-matching to position task to model age-dependent cognitive decline in the dog. Behavioural Brain Research, 108, 47-56. AFFLECK, G., TENNEN, H., KEEFE, F. J., LEFEBVRE, J. C., KASHIKAR-ZUCK, S., WRIGHT, K., STARR, K. & CALDWELL, D. S. 1999. Everyday life with osteoarthritis or rheumatoid arthritis: independent effects of disease and gender on daily pain, mood, and coping. PAIN®, 83, 601-609. AFFLECK, G., URROWS, S., TENNEN, H., HIGGINS, P. & ABELES, M. 1996. Sequential daily relations of sleep, pain intensity, and attention to pain among women with fibromyalgia. Pain, 68, 363-368. AKBAR, A., YIANGOU, Y., FACER, P., WALTERS, J. R. F., ANAND, P. & GHOSH, S. 2008. Increased capsaicin receptor TRPV1 expressing sensory fibres in irritable bowel syndrome and their correlation with abdominal pain. Gut. ÅKERSTEDT, T., HUME, K. E. N., MINORS, D. & WATERHOUSE, J. I. M. 1994. The subjective meaning of good sleep, an intraindividual approach using the Karolinska Sleep Diary. Perceptual and motor skills, 79, 287-296. ALDRICH, S., ECCLESTON, C. & CROMBEZ, G. 2000. Worrying about chronic pain: vigilance to threat and misdirected problem solving. Behaviour Research and Therapy, 38, 457-470. ALOISI, A. M., ALBONETTI, M. E. & CARLI, G. 1994. Sex differences in the behavioural response to persistent pain in rats. Neuroscience letters, 179, 79-82. ALPORT, A., STYLES, E. A. & HSIEH, S. 1994. 17 Shifting Intentional Set: Exploring the Dynamic Control of Tasks. ALVARO, P. K., ROBERTS, R. M. & HARRIS, J. K. 2013. A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36, 1059-1068. ALVES, A., PANIS, Y., TRANCART, D., REGIMBEAU, J.-M., POCARD, M. & VALLEUR, P. 2002. Factors Associated with Clinically Significant Anastomotic Leakage after Large Bowel Resection: Multivariate Analysis of 707Patients. World Journal of Surgery, 26, 499-502. AMEYE, L. G. & YOUNG, M. F. 2006. Animal models of osteoarthritis: lessons learned while seeking the ‘Holy Grail’. Current opinion in rheumatology, 18, 537-547. ANDERSON, K. L., O’NEILL, D. G., BRODBELT, D. C., CHURCH, D. B., MEESON, R. L., SARGAN, D., SUMMERS, J. F., ZULCH, H. & COLLINS, L. M. 2018. Prevalence, duration and risk factors for appendicular osteoarthritis in a UK dog population under primary veterinary care. Scientific Reports, 8, 5641. ANDERSSON, H. I. 1994. The epidemiology of chronic pain in a Swedish rural area. Quality of Life Research, 3, S19-S26. ANIMALS (SCIENTIFIC PROCEDURES) ACT. 1986. Available: https://www.legislation.gov.uk/ukpga/1986/14/section/24 [Accessed 09/04/2018].

266

References

ANTEPOHL, W., KIVILOOG, L., ANDERSSON, J. & GERDLE, B. 2003. Cognitive impairment in patients with chronic whiplash-associated disorder–a matched control study. NeuroRehabilitation, 18, 307-315. ANTONIDES, A., SCHOONDERWOERD, A. C., NORDQUIST, R. E. & VAN DER STAAY, F. J. 2015. Very low birth weight piglets show improved cognitive performance in the spatial cognitive holeboard task. Front Behav Neurosci, 9, 43. ANTUNES, M. & BIALA, G. 2012. The novel object recognition memory: neurobiology, test procedure, and its modifications. Cogn Process, 13, 93-110. APKARIAN, A. V., BALIKI, M. N. & GEHA, P. Y. 2009. Towards a theory of chronic pain. Progress in neurobiology, 87, 81-97. APKARIAN, A. V., SOSA, Y., KRAUSS, B. R., THOMAS, P. S., FREDRICKSON, B. E., LEVY, R. E., HARDEN, R. N. & CHIALVO, D. R. 2004a. Chronic pain patients are impaired on an emotional decision-making task. Pain, 108, 129-36. APKARIAN, A. V., SOSA, Y., SONTY, S., LEVY, R. M., HARDEN, R. N., PARRISH, T. B. & GITELMAN, D. R. 2004b. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J Neurosci, 24, 10410-5. ARENDT-NIELSEN, L., NIE, H., LAURSEN, M. B., LAURSEN, B. S., MADELEINE, P., SIMONSEN, O. H. & GRAVEN-NIELSEN, T. 2010. Sensitization in patients with painful knee osteoarthritis. Pain, 149, 573-81. ARIMA, T., SVENSSON, P., RASMUSSEN, C., NIELSEN, K. D., DREWES, A. M. & ARENDT‐NIELSEN, L. 2001. The relationship between selective sleep deprivation, nocturnal jaw‐muscle activity and pain in healthy men. Journal of oral rehabilitation, 28, 140-148. ARNDT, S. S., LAARAKKER, M. C., VAN LITH, H. A., VAN DER STAAY, F. J., GIELING, E., SALOMONS, A. R., VAN'T KLOOSTER, J. & OHL, F. 2009. Individual housing of mice— impact on behaviour and stress responses. Physiology & behavior, 97, 385-393. ARONOFF, G. M., EVANS, W. O. & ENDERS, P. L. 1983. A review of follow-up studies of multidisciplinary pain units. Pain, 16, 1-11. ARTS, J. W., VAN DER STAAY, F. J. & EKKEL, E. D. 2009. Working and reference memory of pigs in the spatial holeboard discrimination task. Behav Brain Res, 205, 303-6. ASHCRAFT, M. H. & KIRK, E. P. 2001. The relationships among working memory, math anxiety, and performance. Journal of experimental psychology: General, 130, 224. ATALAYER, D. & ROWLAND, N. E. 2011. Structure of motivation using food demand in mice. Physiology & behavior, 104, 15-19. AUGUIE, B. 2017. gridExtra: Miscellaneous Functions for "Grid" Graphics. R package version 2.3. Available: https://CRAN.R-project.org/package=gridExtra [Accessed 12/09/2018]. AZKONA, G., GARCÍA-BELENGUER, S., CHACÓN, G., ROSADO, B., LEÓN, M. & PALACIO, J. 2009. Prevalence and risk factors of behavioural changes associated with age-related cognitive impairment in geriatric dogs. Journal of Small Animal Practice, 50, 87-91. BABYAK, M. A. 2004. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic medicine, 66, 411-421. BADDELEY, A. 1992. Working memory. Science, 255, 556-559. BAIN, M. J., HART, B. L., CLIFF, K. D. & RUEHL, W. W. 2001. Predicting behavioral changes associated with age-related cognitive impairment in dogs. Journal of the American Veterinary Medical Association, 218, 1792-1795.

267

References

BALIKI, M. N., CHIALVO, D. R., GEHA, P. Y., LEVY, R. M., HARDEN, R. N., PARRISH, T. B. & APKARIAN, A. V. 2006. Chronic pain and the emotional brain: specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain. Journal of Neuroscience, 26, 12165-12173. BALIKI, M. N., GEHA, P. Y., APKARIAN, A. V. & CHIALVO, D. R. 2008. Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics. J Neurosci, 28, 1398-403. BALL, K., BERCH, D. B., HELMERS, K. F. & ET AL. 2002. Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA, 288, 2271- 2281. BARROT, M. 2012. Tests and models of nociception and pain in rodents. Neuroscience, 211, 39-50. BASLER, H.-D. & REHFISCH, H. P. 1991. Cognitive-behavioral therapy in patients with ankylosing spondylitis in a German self-help organization. Journal of psychosomatic research, 35, 345-354. BASSETT, D. R. & JOHN, D. 2010. Use of pedometers and accelerometers in clinical populations: validity and reliability issues. Physical therapy reviews, 15, 135-142. BATES, D., MÄCHLER, M., BOLKER, B. & WALKER, S. 2015. Fitting Linear Mixed- Effects Models Using lme4. Journal of Statistical Software; Vol 1, Issue 1 (2015). BAXTER, M. G. & MURRAY, E. A. 2001. Opposite relationship of hippocampal and rhinal cortex damage to delayed nonmatching‐to‐sample deficits in monkeys. Hippocampus, 11, 61-71. BEATTY, W. W., BIERLEY, R. A. & BOYD, J. G. 1985. Preservation of accurate spatial memory in aged rats. Neurobiology of Aging, 6, 219-225. BECHARA, A., DAMASIO, A. R., DAMASIO, H. & ANDERSON, S. W. 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15. BECKER, N., SJØGREN, P., BECH, P., OLSEN, A. K. & ERIKSEN, J. 2000. Treatment outcome of chronic non-malignant pain patients managed in a Danish multidisciplinary pain centre compared to general practice: a randomised controlled trial. PAIN®, 84, 203-211. BEDSON, J. & CROFT, P. R. 2008. The discordance between clinical and radiographic knee osteoarthritis: a systematic search and summary of the literature. BMC musculoskeletal disorders, 9, 116. BELDA, B., ENOMOTO, M., CASE, B. C. & LASCELLES, B. D. X. 2018. Initial evaluation of PetPace activity monitor. The Veterinary Journal, 237, 63-68. BERKSON, G. 1962. Food motivation and delayed response in gibbons. Journal of comparative and physiological psychology, 55, 1040. BERRYMAN, C., STANTON, T. R., JANE BOWERING, K., TABOR, A., MCFARLANE, A. & LORIMER MOSELEY, G. 2013. Evidence for working memory deficits in chronic pain: a systematic review and meta-analysis. Pain, 154, 1181-96. BESWICK, A. D., WYLDE, V., GOOBERMAN-HILL, R., BLOM, A. & DIEPPE, P. 2012. What proportion of patients report long-term pain after total hip or knee replacement for osteoarthritis? A systematic review of prospective studies in unselected patients. BMJ open, 2, e000435. BJORK, E. L. & CUMMINGS, E. M. 1984. Infant search errors: Stage of concept development or stage of memory development. Memory & Cognition, 12, 1-19. BLAND, J. M. & ALTMAN, D. G. 2000. The odds ratio. BMJ : British Medical Journal, 320, 1468-1468. BLOCH, K. E. 1997. Polysomnography: a systematic review. Technology and health care, 5, 285-305.

268

References

BLOM, A. B., VAN LENT, P. L. E. M., HOLTHUYSEN, A. E. M., VAN DER KRAAN, P. M., ROTH, J., VAN ROOIJEN, N. & VAN DEN BERG, W. B. 2004. Synovial lining macrophages mediate osteophyte formation during experimental osteoarthritis. Osteoarthritis and cartilage, 12, 627-635. BLUETOOTH, S. I. G. 2003. Bluetooth specification. BLUMENFELD, R. S. & RANGANATH, C. 2007. Prefrontal cortex and long-term memory encoding: an integrative review of findings from neuropsychology and neuroimaging. The Neuroscientist, 13, 280-291. BLYTH, F. M., MARCH, L. M., BRNABIC, A. J. M., JORM, L. R., WILLIAMSON, M. & COUSINS, M. J. 2001. Chronic pain in Australia: a prevalence study. Pain, 89, 127- 134. BOAKYE, P. A., OLECHOWSKI, C., RASHIQ, S., VERRIER, M. J., KERR, B., WITMANS, M., BAKER, G., JOYCE, A. & DICK, B. D. 2016. A critical review of neurobiological factors involved in the interactions between chronic pain, depression, and sleep disruption. The Clinical journal of pain, 32, 327-336. BODNOFF, S. R., HUMPHREYS, A. G., LEHMAN, J. C., DIAMOND, D. M., ROSE, G. M. & MEANEY, M. J. 1995. Enduring effects of chronic corticosterone treatment on spatial learning, synaptic plasticity, and hippocampal neuropathology in young and mid- aged rats. Journal of Neuroscience, 15, 61-69. BOGAERT, D., HERMANS, P. W. M., BOELENS, H., SLUIJTER, M., LUIJENDIJK, A., RÜMKE, H. C., KOPPEN, S., BELKUM, A. V., DE GROOT, R. & VERBRUGH, H. A. 2005. Epidemiology of Nasopharyngeal Carriage of Neisseria meningitidis in Healthy Dutch Children. Clinical Infectious Diseases, 40, 899-902. BOLHUIS, E. J., OOSTINDJER, M., HOEKS, C. W., DE HAAS, E. N., BARTELS, A. C., OOMS, M. & KEMP, B. 2013. Working and reference memory of pigs (Sus scrofa domesticus) in a holeboard spatial discrimination task: the influence of environmental enrichment. Anim Cogn, 16, 845-50. BOLHUIS, J. J., BIJLSMA, S. & ANSMINK, P. 1986. Exponential decay of spatial memory of rats in a radial maze. Behavioral and neural biology, 46, 115-122. BOLHUIS, J. J., BURESOVA, O. & BURES, J. 1985. Persistence of working memory of rats in an aversively motivated radial maze task. Behavioural Brain Research, 15, 43- 49. BOSMA, H., VAN BOXTEL, M. P. J., PONDS, R. W. H. M., HOUX, P. J., BURDORF, A. & JOLLES, J. 2003. Mental work demands protect against cognitive impairment: MAAS prospective cohort study. Experimental aging research, 29, 33-45. BOVE, S. E., CALCATERRA, S. L., BROOKER, R. M., HUBER, C. M., GUZMAN, R. E., JUNEAU, P. L., SCHRIER, D. J. & KILGORE, K. S. 2003. Weight bearing as a measure of disease progression and efficacy of anti-inflammatory compounds in a model of monosodium iodoacetate-induced osteoarthritis. Osteoarthritis Cartilage, 11, 821- 30. BOYD, S. K., MÜLLER, R., LEONARD, T. & HERZOG, W. 2005. Long-term periarticular bone adaptation in a feline knee injury model for post-traumatic experimental osteoarthritis. Osteoarthritis and cartilage, 13, 235-242. BRANDT, K. D. 2004. Non-surgical treatment of osteoarthritis: a half century of “advances”. Annals of the rheumatic diseases, 63, 117-122. BRATTBERG, G., THORSLUND, M. & WIKMAN, A. 1989. The prevalence of pain in a general population. The results of a postal survey in a county of Sweden. Pain, 37, 215-222. BREMNER, J. D. 1999. Does stress damage the brain? Biological psychiatry, 45, 797- 805.

269

References

BROADBENT, N. J., GASKIN, S., SQUIRE, L. R. & CLARK, R. E. 2010. Object recognition memory and the rodent hippocampus. Learning & Memory, 17, 5-11. BROOKS, P. 2003. Inflammation as an important feature of osteoarthritis. Bulletin of the World Health Organization, 81, 689-690. BROWN, D. C. 2014a. The Canine Orthopedic Index. Step 1: Devising the items. Vet Surg, 43, 232-40. BROWN, D. C. 2014b. The Canine Orthopedic Index. Step 2: Psychometric Testing. Veterinary Surgery, 43, 241-246. BROWN, D. C. 2014c. The Canine Orthopedic Index. Step 3: Responsiveness testing. Vet Surg, 43, 247-54. BROWN, D. C., BOSTON, R., COYNE, J. C. & FARRAR, J. T. 2009. A Novel Approach to the Use of Animals in Studies of Pain: Validation of the Canine Brief Pain Inventory in Canine Bone Cancer. Pain Medicine, 10, 133-142. BROWN, D. C., BOSTON, R. C., COYNE, J. C. & FARRAR, J. T. 2007. Development and psychometric testing of an instrument designed to measure chronic pain in dogs with osteoarthritis. American Journal of Veterinary Research, 68, 631-637. BROWN, D. C., BOSTON, R. C., COYNE, J. C. & FARRAR, J. T. 2008. Ability of the Canine Brief Pain Inventory to detect response to treatment in dogs with osteoarthritis. Journal of the American Veterinary Medical Association, 233, 1278- 1283. BROWN, D. C., BOSTON, R. C. & FARRAR, J. T. 2010. Use of an activity monitor to detect response to treatment in dogs with osteoarthritis. Journal of the American Veterinary Medical Association, 237, 66-70. BRUCK, D., KENNEDY, G. A., COOPER, A. & APEL, S. 2005. Diurnal actigraphy and stimulant efficacy in narcolepsy. Human Psychopharmacology: Clinical and Experimental, 20, 105-113. BUREŠOVÁ, O. & BUREŠ, J. 1982. Radial maze as a tool for assessing the effect of drugs on the working memory of rats. Psychopharmacology, 77, 268-271. BUREŠOVÁ, O., BUREŠ, J., OITZL, M. S. & ZAHALKA, A. 1985. Radial maze in the water tank: an aversively motivated spatial working memory task. Physiology & behavior, 34, 1003-1005. BURKE, S., WALLACE, J., NEMATOLLAHI, S., UPRETY, A. & BARNES, C. 2010. Pattern Separation Deficits May Contribute to Age-Associated Recognition Impairments. Behav Neurosci, 124, 559-573. BURSAC, Z., GAUSS, C. H., WILLIAMS, D. K. & HOSMER, D. W. 2008. Purposeful selection of variables in logistic regression. Source Code for Biology and Medicine, 3, 17-17. BUTLER, R. K. & FINN, D. P. 2009. Stress-induced analgesia. Progress in Neurobiology, 88, 184-202. BUYSSE, D. J., REYNOLDS, C. F., MONK, T. H., BERMAN, S. R. & KUPFER, D. J. 1989. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research, 28, 193-213. BUYSSE, D. J., REYNOLDS III, C. F., MONK, T. H., HOCH, C. C., YEAGER, A. L. & KUPFER, D. J. 1991. Quantification of subjective sleep quality in healthy elderly men and women using the Pittsburgh Sleep Quality Index (PSQI). Sleep, 14, 331-338. CAGNIE, B., COPPIETERS, I., DENECKER, S., SIX, J., DANNEELS, L. & MEEUS, M. 2014. Central sensitization in fibromyalgia? A systematic review on structural and functional brain MRI. Seminars in arthritis and rheumatism, 44, 68-75. CAHILL, L., PRINS, B., WEBER, M. & MCGAUGH, J. L. 1994. β-Adrenergic activation and memory for emotional events. Nature, 371, 702.

270

References

CAKE, M. A., APPLEYARD, R. C., READ, R. A., SMITH, M. M., MURRELL, G. A. C. & GHOSH, P. 2005. Ovariectomy alters the structural and biomechanical properties of ovine femoro-tibial articular cartilage and increases cartilage iNOS. Osteoarthritis and cartilage, 13, 1066-1075. CAKE, M. A., READ, R. A., APPLEYARD, R. C., HWA, S.-Y. & GHOSH, P. 2004. The nitric oxide donor glyceryl trinitrate increases subchondral bone sclerosis and cartilage degeneration following ovine meniscectomy. Osteoarthritis and cartilage, 12, 974- 981. CALCAGNO, V. 2013. glmulti: Model selection and multimodel inference made easy. R package version 1.0.7 ed.: Available: https://CRAN.R-project.org/package=glmulti [Accessed 12/09/2018]. CALVO, M. G., RAMOS, P. M. & ESTEVEZ, A. 1992. Test anxiety and comprehension efficiency: The role of prior knowledge and working memory deficits. Anxiety, Stress, & Coping, 5, 125-138. CANDLAND, D. K. & CAMPBELL, B. A. 1962. Development of fear in the rat as measured by behavior in the open field. Journal of comparative and physiological psychology, 55, 593. CANINE JOURNAL. 2017. Best Dog Activity Monitor: Fitbark vs Tagg vs Whistle vs Link AKC & More! [Online]. Available: https://www.caninejournal.com/dog-activity- monitor/ [Accessed 03/04/2018]. CÁNOVAS, R., ESPÍNOLA, M., IRIBARNE, L. & CIMADEVILLA, J. M. 2008. A new virtual task to evaluate human place learning. Behavioural brain research, 190, 112-118. CÁNOVAS, R., LEÓN, I., ROLDÁN, M. D., ASTUR, R. & CIMADEVILLA, J. M. 2009. Virtual reality tasks disclose spatial memory alterations in fibromyalgia. Rheumatology, 48, 1273-1278. CARDOSO-CRUZ, H., LIMA, D. & GALHARDO, V. 2013a. Impaired spatial memory performance in a rat model of neuropathic pain is associated with reduced hippocampus–prefrontal cortex connectivity. Journal of Neuroscience, 33, 2465- 2480. CARDOSO-CRUZ, H., SOUSA, M., VIEIRA, J. B., LIMA, D. & GALHARDO, V. 2013b. Prefrontal cortex and mediodorsal thalamus reduced connectivity is associated with spatial working memory impairment in rats with inflammatory pain. PAIN®, 154, 2397-2406. CASPER, R. C., REDMOND, D. E., KATZ, M. M., SCHAFFER, C. B., DAVIS, J. M. & KOSLOW, S. H. 1985. Somatic symptoms in primary affective disorder: presence and relationship to the classification of depression. Archives of General Psychiatry, 42, 1098-1104. CAVAGNA, L., CAPORALI, R., TRIFIRO, G., ARCORACI, V., ROSSI, S. & MONTECUCCO, C. 2013. Overuse of prescription and OTC non-steroidal anti-inflammatory drugs in patients with rheumatoid arthritis and osteoarthritis. International journal of immunopathology and pharmacology, 26, 279-281. CHACUR, M., LAMBERTZ, D., HOHEISEL, U. & MENSE, S. 2009. Role of spinal microglia in myositis‐induced central sensitisation: An immunohistochemical and behavioural study in rats. European Journal of Pain, 13, 915-923. CHAPELL, M. S., BLANDING, Z. B., SILVERSTEIN, M. E., TAKAHASHI, M., NEWMAN, B., GUBI, A. & MCCANN, N. 2005. Test anxiety and academic performance in undergraduate and graduate students. Journal of educational Psychology, 97, 268. CHAPLAN, S. R., BACH, F. W., POGREL, J. W., CHUNG, J. M. & YAKSH, T. L. 1994. Quantitative assessment of tactile allodynia in the rat paw. Journal of neuroscience methods, 53, 55-63.

271

References

CHAPMAN, C. R., OKA, S., BRADSHAW, D. H., JACOBSON, R. C. & DONALDSON, G. W. 1999. Phasic pupil dilation response to noxious stimulation in normal volunteers: relationship to brain evoked potentials and pain report. Psychophysiology, 36, 44- 52. CHARLTON, C., RASBASH, J., BROWNE, W. J., HEALY, M. & CAMERON, B. 2017. MLwiN Version 3.00. Centre for Multilevel Modelling, University of Bristol. CHEE, M. W. L. & CHOO, W. C. 2004. Functional imaging of working memory after 24 hr of total sleep deprivation. Journal of Neuroscience, 24, 4560-4567. CHEN, K. Y. & BASSETT, D. R. 2005. The technology of accelerometry-based activity monitors: current and future. Medicine & Science in Sports & Exercise, 37, S490- S500. CHEN, H.-S., CHEN, J., CHEN, J., GUO, W.-G. & ZHENG, M.-H. 2001. Establishment of bee venom-induced contralateral heat hyperalgesia in the rat is dependent upon central temporal summation of afferent input from the site of injury. Neuroscience letters, 298, 57-60. CHIANG, C. Y., LI, Z., DOSTROVSKY, J. O. & SESSLE, B. J. 2010. Central sensitization in medullary dorsal horn involves gap junctions and hemichannels. Neuroreport, 21, 233. CHRISTENSEN, M. D., EVERHART, A. W., PICKELMAN, J. T. & HULSEBOSCH, C. E. 1996. Mechanical and thermal allodynia in chronic central pain following spinal cord injury. Pain, 68, 97-107. CHRISTENSEN, R., BARTELS, E. M., ASTRUP, A. & BLIDDAL, H. 2007. Effect of weight reduction in obese patients diagnosed with knee osteoarthritis: a systematic review and meta-analysis. Annals of the rheumatic diseases, 66, 433-439. CHRISTIE, L.-A., STUDZINSKI, C. M., ARAUJO, J. A., LEUNG, C. S. K., IKEDA-DOUGLAS, C. J., HEAD, E., COTMAN, C. W. & MILGRAM, N. W. 2005. A comparison of egocentric and allocentric age-dependent spatial learning in the beagle dog. Progress in Neuro- Psychopharmacology and Biological Psychiatry, 29, 361-369. CHRISTOPHER, G. & MACDONALD, J. 2005. The impact of clinical depression on working memory. Cognitive neuropsychiatry, 10, 379-399. CHUDASAMA, Y. & MUIR, J. L. 1997. A behavioural analysis of the delayed non- matching to position task: the effects of scopolamine, lesions of the fornix and of the prelimbic region on mediating behaviours by rats. Psychopharmacology, 134, 73- 82. CIRILLO, D. J., WALLACE, R. B., WU, L. & YOOD, R. A. 2006. Effect of hormone therapy on risk of hip and knee joint replacement in the Women's Health Initiative. Arthritis & Rheumatology, 54, 3194-3204. CLARK, L., COOLS, R. & ROBBINS, T. W. 2004. The neuropsychology of ventral prefrontal cortex: Decision-making and reversal learning. Brain and Cognition, 55, 41-53. CLAYTON, N. S. & DICKINSON, A. 1998. Episodic-like memory during cache recovery by scrub jays. Nature, 395, 272. CLEMENTS, D. N., CARTER, S. D., INNES, J. F., OLLIER, W. E. R. & DAY, P. J. R. 2006. Analysis of normal and osteoarthritic canine cartilage mRNA expression by quantitative polymerase chain reaction. Arthritis research & therapy, 8, R158. CLEMENTS, D. N., SHORT, A. D., BARNES, A., KENNEDY, L. J., FERGUSON, J. F., BUTTERWORTH, S. J., FITZPATRICK, N., PEAD, M., BENNETT, D., INNES, J. F., CARTER, S. D. & OLLIER, W. E. R. 2010. A Candidate Gene Study of Canine Joint Diseases. Journal of Heredity, 101, 54-60.

272

References

CODERRE, T. J. & MELZACK, R. 1991. Central neural mediators of secondary hyperalgesia following heat injury in rats: neuropeptides and excitatory amino acids. Neuroscience letters, 131, 71-74. COGGON, D., CROFT, P., KELLINGRAY, S., BARRETT, D., MCLAREN, M. & COOPER, C. 2000. Occupational physical activities and osteoarthritis of the knee. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, 43, 1443- 1449. COHEN, S. J. & STACKMAN JR, R. W. 2015. Assessing rodent hippocampal involvement in the novel object recognition task. A review. Behavioural Brain Research, 285, 105-117. COLPAERT, F. C., TARAYRE, J. P., ALLIAGA, M., SLOT, L. A. B., ATTAL, N. & KOEK, W. 2001. Opiate self-administration as a measure of chronic nociceptive pain in arthritic rats. Pain, 91, 33-45. COMMINS, S., CUNNINGHAM, L., HARVEY, D. & WALSH, D. 2003. Massed but not spaced training impairs spatial memory. Behavioural Brain Research, 139, 215-223. CONAGHAN, P. G. & FELSON, D. T. 1999. Structural associations of osteoarthritis pain: lessons from magnetic resonance imaging. In: CHADWICK, D. J. & GOODE, J. (eds.) Osteoarthritic Joint Pain. Chichester; New York; : John Wiley; . CONZEMIUS, M. G., APER, R. L. & CORTI, L. B. 2003. Short‐term outcome after total elbow arthroplasty in dogs with severe, naturally occurring osteoarthritis. Veterinary surgery, 32, 545-552. COOK, A. J., WOOLF, C. J., WALL, P. D. & MCMAHON, S. B. 1987. Dynamic receptive field plasticity in rat spinal cord dorsal horn following C-primary afferent input. Nature, 325, 151-153. COOK, C. R. & COOK, J. L. 2009. Diagnostic imaging of canine elbow dysplasia: a review. Veterinary Surgery, 38, 144-153. COOK, R. G., BROWN, M. F. & RILEY, D. A. 1985. Flexible memory processing by rats: Use of prospective and retrospective information in the radial maze. Journal of Experimental Psychology: Animal Behavior Processes, 11, 453. COONEY, J. W. & GAZZANIGA, M. S. 2003. Neurological disorders and the structure of human consciousness. Trends in Cognitive Sciences, 7, 161-165. COOPER, S. A. 1984. Five studies on ibuprofen for postsurgical dental pain. The American journal of medicine, 77, 70-77. CORKUM, P., PANTON, R., IRONSIDE, S., MACPHERSON, M. & WILLIAMS, T. 2008. Acute Impact of Immediate Release Methylphenidate Administered Three Times a Day on Sleep in Children with Attention-Deficit/Hyperactivity Disorder. Journal of Pediatric Psychology, 33, 368-379. COURCIER, E. A., THOMSON, R. M., MELLOR, D. J. & YAM, P. S. 2010. An epidemiological study of environmental factors associated with canine obesity. Journal of Small Animal Practice, 51, 362-367. COWAN, N. 2008. What are the differences between long-term, short-term, and working memory? Progress in brain research, 169, 323-338. CRAIG, A. D. 2002. How do you feel? Interoception: the sense of the physiological condition of the body. Nature reviews neuroscience, 3, 655. CRAIG, M., RAND, J., MESCH, R., SHYAN-NORWALT, M., MORTON, J. & FLICKINGER, E. 2012. Domestic Dogs (Canis familiaris) and the Radial Arm Maze: Spatial Memory and Serial Position Effects. Journal of Comparative Psychology, 126, 233-242. CRICHTON, B. & GREEN, M. 2002. GP and patient perspectives on treatment with non-steroidal anti-inflammatory drugs for the treatment of pain in osteoarthritis. Current medical research and opinion, 18, 92-96.

273

References

CRNIC´, A. P., ŠANTEK, E. & ŠURAN, J. 2015. Foodstuffs toxic to dogs: chocolate and grapes. Veterinarska Stanica, 46, 311-315. CROFT, P., COOPER, C., WICKHAM, C. & COGGON, D. 1992. Osteoarthritis of the hip and occupational activity. Scandinavian journal of work, environment & health, 59- 63. CROMBEZ, G., ECCLESTON, C., BAEYENS, F., VAN HOUDENHOVE, B. & VAN DEN BROECK, A. 1999. Attention to chronic pain is dependent upon pain-related fear. Journal of Psychosomatic Research, 47, 403-410. CROMBEZ, G., ECCLESTON, C., VAN DEN BROECK, A., GOUBERT, L. & VAN HOUDENHOVE, B. 2004. Hypervigilance to pain in fibromyalgia: the mediating role of pain intensity and catastrophic thinking about pain. The Clinical journal of pain, 20, 98-102. CURTIN, F. & SCHULZ, P. 1998. Multiple correlations and bonferroni’s correction. Biological Psychiatry, 44, 775-777. DANBURY, T. C., WEEKS, C. A., CHAMBERS, J. P., WATERMAN-PEARSON, A. E. & KESTIN, S. C. 2000. Self-selection of the analgesic drug carprofen by lame broiler chickens. The Veterinary Record, 146, 307-311. DANZIGER, N., WEIL-FUGAZZA, J., LE BARS, D. & BOUHASSIRA, D. 1999. Alteration of descending modulation of nociception during the course of monoarthritis in the rat. Journal of Neuroscience, 19, 2394-2400. DAVIDSON, E. N. B., SCHARSTUHL, A., VITTERS, E. L., VAN DER KRAAN, P. M. & VAN DEN BERG, W. B. 2005. Reduced transforming growth factor-beta signaling in cartilage of old mice: role in impaired repair capacity. Arthritis research & therapy, 7, R1338. DE HOOGE, A. S. K., VAN DE LOO, F. A. J., BENNINK, M. B., ARNTZ, O. J., DE HOOGE, P. & VAN DEN BERG, W. B. 2005. Male IL-6 gene knock out mice developed more advanced osteoarthritis upon aging. Osteoarthritis and cartilage, 13, 66-73. DE SOUZA, L., BENEDITO-SILVA, A. A., PIRES, M. L. N., POYARES, D., TUFIK, S. & CALIL, H. M. 2003. Further validation of actigraphy for sleep studies. Sleep, 26, 81-85. DEGROOT, J., VERZIJL, N., WIJK, W. V., MARION, J. G., JACOBS, K. M. G., VAN EL, B., VAN ROERMUND, P. M., BANK, R. A., BIJLSMA, J. W. J. & TEKOPPELE, J. M. 2004. Accumulation of advanced glycation end products as a molecular mechanism for aging as a risk factor in osteoarthritis. Arthritis & Rheumatology, 50, 1207-1215. DENENBERG, V. H. 1969. Open‐field behavior in the rat: what DOES it mean? Annals of the New York Academy of Sciences, 159, 852-859. DENNIS, W. 1939. Spontaneous alternation in rats as an indicator of the persistence of stimulus effects. Journal of Comparative Psychology, 28, 305. DERE, E., HUSTON, J. P. & DE SOUZA SILVA, M. A. 2007. The pharmacology, neuroanatomy and neurogenetics of one-trial object recognition in rodents. Neurosci Biobehav Rev, 31, 673-704. DI CERBO, A., SECHI, S., CANELLO, S., GUIDETTI, G., FIORE, F. & COCCO, R. 2017. Behavioral disturbances: an innovative approach to monitor the modulatory effects of a nutraceutical diet. Journal of visualized experiments: JoVE. DIAMOND, A. 1990. Rate of Maturation of the Hippocampus and the Developmental Progression of Children's Performance on the Delayed Non‐Matching to Sample and Visual Paired Comparison Tasks. Annals of the New York Academy of Sciences, 608, 394-433. DIAMOND, D. M., PARK, C. R., HEMAN, K. L. & ROSE, G. M. 1999. Exposing rats to a predator impairs spatial working memory in the radial arm water maze. Hippocampus, 9, 542-552.

274

References

DICK, B. D. & RASHIQ, S. 2007. Disruption of attention and working memory traces in individuals with chronic pain. Anesth Analg, 104, 1223-9, tables of contents. DIEPPE, P. A. & LOHMANDER, L. S. 2005. Pathogenesis and management of pain in osteoarthritis. Lancet, 365, 965-73. DIEPPE, P. A., SATHAPATAYAVONGS, B., JONES, H. E., BACON, P. A. & RING, E. F. J. 1980. Intra-articular steroids in osteoarthritis. Rheumatology, 19, 212-217. DORMANN, C. F., ELITH, J., BACHER, S., BUCHMANN, C., CARL, G., CARRÉ, G., MARQUÉZ, J. R. G., GRUBER, B., LAFOURCADE, B. & LEITÃO, P. J. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27-46. DOUGLAS, R. J., MITCHELL, D. & KENTALA, D. 1972. Spontaneous alternation as a function of maze configuration. Psychonomic Science, 27, 285-286. DOUGLASS, A. B., BOMSTEIN, R., NINO-MURCIA, G., KEENAN, S., MILES, L., ZARCONE JR, V. P., GUILLEMINAULT, C. & DEMENT, W. C. 1994. The sleep disorders questionnaire I: creation and multivariate structure of SDQ. Sleep, 17, 160-167. DRESCHEL, N. A. 2010. The effects of fear and anxiety on health and lifespan in pet dogs. Applied Animal Behaviour Science, 125, 157-162. DREWES, A. M., NIELSEN, K. D., HANSEN, B., TAAGHOLT, S. J., BJERREGÅRD, K. & SVENDSEN, L. 2000. A longitudinal study of clinical symptoms and sleep parameters in rheumatoid arthritis. Rheumatology, 39, 1287-1289. DUDCHENKO, P. A. 2001. How do animals actually solve the T maze? Behavioral neuroscience, 115, 850. DUDCHENKO, P. A. 2004. An overview of the tasks used to test working memory in rodents. Neurosci Biobehav Rev, 28, 699-709. DUDCHENKO, P. A. & DAVIDSON, M. 2002. Rats use a sense of direction to alternate on T-mazes located in adjacent rooms. Animal cognition, 5, 115-118. DUDCHENKO, P. A., WOOD, E. R. & EICHENBAUM, H. 2000. Neurotoxic hippocampal lesions have no effect on odor span and little effect on odor recognition memory but produce significant impairments on spatial span, recognition, and alternation. Journal of Neuroscience, 20, 2964-2977. DURÁN, E., OCAÑA, F. M., BROGLIO, C., RODRÍGUEZ, F. & SALAS, C. 2010. Lateral but not medial telencephalic pallium ablation impairs the use of goldfish spatial allocentric strategies in a “hole-board” task. Behavioural brain research, 214, 480- 487. ECCLESTON, C. 1995. Chronic pain and distraction: An experimental investigation into the role of sustained and shifting attention in the processing of chronic persistent pain. Behaviour Research and Therapy, 33, 391-405. EDHOUSE, W. V. & WHITE, K. G. 1988. Sources of proactive interference in animal memory. Journal of Experimental Psychology: Animal Behavior Processes, 14, 56. EDWARDS, J. E., OLDMAN, A. D., SMITH, L. A., CARROLL, D., WIFFEN, P. J., MCQUAY, H. J. & MOORE, R. A. 1999. Oral aspirin in postoperative pain: a quantitative systematic review. Pain, 81, 289-297. ELLIOTT, R. & DOLAN, R. J. 1999. Differential neural responses during performance of matching and nonmatching to sample tasks at two delay intervals. Journal of Neuroscience, 19, 5066-5073. ELLIOTT, T. E., RENIER, C. M. & PALCHER, J. A. 2003. Chronic Pain, depression, and quality of life: Correlations and predictive value of the SF‐36. Pain medicine, 4, 331- 339. ELLISON, A. M. 1996. An introduction to Bayesian inference for ecological research and environmental decision‐making. Ecological applications, 6, 1036-1046. ELWOOD, R. W. 2011. Pain and Suffering in Invertebrates? ILAR Journal, 52, 175-184.

275

References

ENGLE, R. W. 2002. Working memory capacity as executive attention. Current directions in psychological science, 11, 19-23. ENGLUND, M., GUERMAZI, A., GALE, D., HUNTER, D. J., ALIABADI, P., CLANCY, M. & FELSON, D. T. 2008. Incidental meniscal findings on knee MRI in middle-aged and elderly persons. New England Journal of Medicine, 359, 1108-1115. ENKEL, T., GHOLIZADEH, D., VON BOHLEN UND HALBACH, O., SANCHIS-SEGURA, C., HURLEMANN, R., SPANAGEL, R., GASS, P. & VOLLMAYR, B. 2010. Ambiguous-cue interpretation is biased under stress- and depression-like states in rats. Neuropsychopharmacology, 35, 1008-15. ENNACEUR, A. & DELACOUR, J. 1988. A new one-trial test for neurobiological studies of memory in rats. 1: Behavioral data. Behavioural Brain Research, 31, 47-59. ENNACEUR, A., MICHALIKOVA, S., BRADFORD, A. & AHMED, S. 2005. Detailed analysis of the behavior of Lister and Wistar rats in anxiety, object recognition and object location tasks. Behavioural brain research, 159, 247-266. EVERITT, B. & SKRONDAL, A. 2002. The Cambridge dictionary of statistics, Cambridge University Press Cambridge. EVERS, P., KRAMEK, B. A., WALLACE, L. J., JOHNSTON, G. R. & KING, V. 1997. Clinical and radiographic evaluation of intertrochanteric osteotomy in dogs: a retrospective study of 18 dogs. Veterinary Surgery, 26, 217-222. FADER, A. J., JOHNSON, P. E. M. & DOHANICH, G. P. 1999. Estrogen improves working but not reference memory and prevents amnestic effects of scopolamine on a radial-arm maze. Pharmacology Biochemistry and Behavior, 62, 711-717. FAUL, F., ERDFELDER, E., LANG, A.-G. & BUCHNER, A. 2007. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191. FAW, B. 2003. Pre-frontal executive committee for perception, working memory, attention, long-term memory, motor control, and thinking: a tutorial review. Consciousness and cognition, 12, 83-139. FELLOWS, L. K. & FARAH, M. J. 2003. Ventromedial frontal cortex mediates affective shifting in humans: evidence from a reversal learning paradigm. Brain, 126, 1830- 1837. FELSON, D. T. 1990. The epidemiology of knee osteoarthritis: results from the Framingham Osteoarthritis Study. Seminars in arthritis and rheumatism, 20, 42-50. FELSON, D. T., CHAISSON, C. E., HILL, C. L., TOTTERMAN, S. M. S., GALE, M. E., SKINNER, K. M., KAZIS, L. & GALE, D. R. 2001. The association of bone marrow lesions with pain in knee osteoarthritis. Annals of internal medicine, 134, 541-549. FELSON, D. T., LAWRENCE, R. C., DIEPPE, P. A., HIRSCH, R., HELMICK, C. G., JORDAN, J. M., KINGTON, R. S., LANE, N. E., NEVITT, M. C., ZHANG, Y., SOWERS, M., MCALINDON, T., SPECTOR, T. D., POOLE, A. R., YANOVSKI, S. Z., ATESHIAN, G., SHARMA, L., BUCKWALTER, J. A., BRANDT, K. D. & FRIES, J. F. 2000. Osteoarthritis: New Insights. Part 1: The Disease and Its Risk Factors. Annals of Internal Medicine, 133, 635-646. FELSON, D. T. & ZHANG, Y. 1998. An update on the epidemiology of knee and hip osteoarthritis with a view to prevention. Arthritis & Rheumatology, 41, 1343-1355. FERNIHOUGH, J., GENTRY, C., MALCANGIO, M., FOX, A., REDISKE, J., PELLAS, T., KIDD, B., BEVAN, S. & WINTER, J. 2004. Pain related behaviour in two models of osteoarthritis in the rat knee. Pain, 112, 83-93. FIELDEN, J. M., GANDER, P. H., HORNE, J. G., LEWER, B. M. F., GREEN, R. M. & DEVANE, P. A. 2003. An assessment of sleep disturbance in patients before and after total hip arthroplasty. The Journal of arthroplasty, 18, 371-376.

276

References

FISET, S., BEAULIEU, C. & LANDRY, F. 2003. Duration of dogs'(Canis familiaris) working memory in search for disappearing objects. Animal Cognition, 6, 1-10. FISET, S. & PLOURDE, V. 2013. Object permanence in domestic dogs (Canis lupus familiaris) and gray wolves (Canis lupus). Journal of Comparative Psychology, 127, 115. FISHBAIN, D. A., CUTLER, R., ROSOMOFF, H. L. & ROSOMOFF, R. S. 1997. Chronic pain-associated depression: antecedent or consequence of chronic pain? A review. The Clinical journal of pain, 13, 116-137. FITBARK. 2018a. Explore Dog Health [Online]. Available: https://www.fitbark.com/explore/ [Accessed 03/04/2018]. FITBARK. 2018b. Is FitBark 2 Waterproof? [Online]. Available: https://www.fitbark.com/articles/fitbark2-waterproof [Accessed 05/04/2018]. FORSTER, T., CHAPMAN, K. & LOUGHLIN, J. 2004. Common variants within the interleukin 4 receptor α gene (IL4R) are associated with susceptibility to osteoarthritis. Human genetics, 114, 391-395. FOX, J. & WEISBERG, S. 2011. An {R} Companion to Applied Regression. Second Edition ed. Thousand Oaks CA: Sage [Online]. Available: http://socserv.socsci.mcmaster.ca/jfox/Books/Companion [Accessed 12/09/2018]. FRANK, H. & FRANK, M. G. 1985. Comparative manipulation-test performance in ten-week-old wolves (Canis lupus) and Alaskan malamutes (Canis familiaris): A Piagetian interpretation. Journal of Comparative Psychology, 99, 266-274. FRIARD, O. & GAMBA, M. 2016. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods in Ecology and Evolution, n/a-n/a. FRIES, C. L. & REMEDIOS, A. M. 1995. The pathogenesis and diagnosis of canine hip dysplasia: a review. The Canadian Veterinary Journal, 36, 494-502. FRØLUND, F. & FRØLUND, C. 1986. Pain in general practice: Pain as a cause of patient-doctor contact. Scandinavian journal of primary health care, 4, 97-100. FRYE, C. A., CUEVAS, C. A. & KANAREK, R. B. 1993. Diet and estrous cycle influence pain sensitivity in rats. Pharmacology Biochemistry and Behavior, 45, 255-260. GAFFNEY, K., LEDINGHAM, J. & PERRY, J. D. 1995. Intra-articular triamcinolone hexacetonide in knee osteoarthritis: factors influencing the clinical response. Annals of the rheumatic diseases, 54, 379. GALE, J., SIGNAL, T. L. & GANDER, P. H. 2005. Statistical artifact in the validation of actigraphy. Sleep, 28, 1017-1018. GANZ, R., LEUNIG, M., LEUNIG-GANZ, K. & HARRIS, W. H. 2008. The etiology of osteoarthritis of the hip. Clinical orthopaedics and related research, 466, 264-272. GAO, Y.-J., ZHANG, L., SAMAD, O. A., SUTER, M. R., YASUHIKO, K., XU, Z.-Z., PARK, J.- Y., LIND, A.-L., MA, Q. & JI, R.-R. 2009. JNK-induced MCP-1 production in spinal cord astrocytes contributes to central sensitization and neuropathic pain. Journal of Neuroscience, 29, 4096-4108. GASKIN, M. E., GREENE, A. F., ROBINSON, M. E. & GEISSER, M. E. 1992. Negative affect and the experience of chronic pain. Journal of Psychosomatic Research, 36, 707-713. GASKIN, S., TARDIF, M., COLE, E., PITERKIN, P., KAYELLO, L. & MUMBY, D. G. 2010. Object familiarization and novel-object preference in rats. Behav Processes, 83, 61- 71. GAUMOND, I., ARSENAULT, P. & MARCHAND, S. 2002. The role of sex hormones on formalin-induced nociceptive responses. Brain Research, 958, 139-145.

277

References

GAUMOND, I., ARSENAULT, P. & MARCHAND, S. 2005. Specificity of female and male sex hormones on excitatory and inhibitory phases of formalin-induced nociceptive responses. Brain research, 1052, 105-111. GIELING, E. T., PARK, S. Y., NORDQUIST, R. E. & VAN DER STAAY, F. J. 2012. Cognitive performance of low- and normal-birth-weight piglets in a spatial hole-board discrimination task. Pediatr Res, 71, 71-6. GLASSON, S. S., ASKEW, R., SHEPPARD, B., CARITO, B., BLANCHET, T., MA, H.-L., FLANNERY, C. R., PELUSO, D., KANKI, K. & YANG, Z. 2005. Deletion of active ADAMTS5 prevents cartilage degradation in a murine model of osteoarthritis. Nature, 434, 644. GOLD, P. E. & VAN BUSKIRK, R. 1976. Enhancement and impairment of memory processes with post-trial injections of adrenocorticotrophic hormone. Behavioral Biology, 16, 387-400. GORDON-EVANS, W., KNAP, K. & SCHULZ, K. S. 2013. Fundamentals of Physical Rehabilitation. In: FOSSUM, T. W. (ed.) Small Animal Surgery Fourth edition ed. St. Louis: Elsevier Health Sciences. GORDON, W. J., CONZEMIUS, M. G., RIEDESEL, E., BESANCON, M. F., EVANS, R., WILKE, V. & RITTER, M. J. 2003. The relationship between limb function and radiographic osteoarthrosis in dogs with stifle osteoarthrosis. Veterinary Surgery, 32, 451-454. GRACE, G. M., NIELSON, W. R., HOPKINS, M. & BERG, M. A. 1999. Concentration and memory deficits in patients with fibromyalgia syndrome. Journal of clinical and experimental neuropsychology, 21, 477-487. GRIFFIN, D. R. & SPECK, G. B. 2004. New evidence of animal consciousness. Animal cognition, 7, 5-18. GRISART, J. M. & PLAGHKI, L. H. 1999. Impaired selective attention in chronic pain patients. European Journal of Pain, 3, 325-333. GRISART, J. M. & VAN DER LINDEN, M. 2001. Conscious and automatic uses of memory in chronic pain patients. Pain, 94, 305-313. GUEVARA‐LÓPEZ, U., AYALA‐GUERRERO, F., COVARRUBIAS‐GÓMEZ, A., LÓPEZ‐ MUÑOZ, F. J. & TORRES‐GONZÁLEZ, R. 2009. Effect of acute gouty arthritis on sleep patterns: a preclinical study. European journal of pain, 13, 146-153. GUHAD, F. 2005. Introduction to the 3Rs (refinement, reduction and replacement). Journal of the American Association for Laboratory Animal Science, 44, 58-59. GUNN, H. M. 1978. Differences in the histochemical properties of skeletal muscles of different breeds of horses and dogs. Journal of Anatomy, 127, 615-634. GUTNIKOV, S. A., BARNES, J. C. & RAWLINS, J. N. P. 1994. Working memory tasks in five-choice operant chambers: Use of relative and absolute spatial memories. Behavioral neuroscience, 108, 899. GWILYM, S. E., KELTNER, J. R., WARNABY, C. E., CARR, A. J., CHIZH, B., CHESSELL, I. & TRACEY, I. 2009. Psychophysical and functional imaging evidence supporting the presence of central sensitization in a cohort of osteoarthritis patients. Arthritis Care & Research, 61, 1226-1234. HAAGENSEN, A. M., GRAND, N., KLASTRUP, S., SKYTTE, C. & SORENSEN, D. B. 2013a. Spatial discrimination and visual discrimination: two methods evaluating learning and memory in juvenile Gottingen minipigs. Behav Pharmacol, 24, 172-9. HAAGENSEN, A. M., KLEIN, A. B., ETTRUP, A., MATTHEWS, L. R. & SORENSEN, D. B. 2013b. Cognitive performance of Gottingen minipigs is affected by diet in a spatial hole-board discrimination test. PLoS One, 8, e79429.

278

References

HADLER, N. M., GILLINGS, D. B., IMBUS, H. R., LEVITIN, P. M., MAKUC, D., UTSINGER, P. D., YOUNT, W. J., SLUSSER, D. & MOSKOVITZ, N. 1978. Hand structure and function in an industrial setting. Arthritis & Rheumatology, 21, 210-220. HAGEWOUD, R., HAVEKES, R., NOVATI, A., KEIJSER, J. N., VAN DER ZEE, E. A. & MEERLO, P. 2010. Sleep deprivation impairs spatial working memory and reduces hippocampal AMPA receptor phosphorylation. Journal of sleep research, 19, 280- 288. HALE, G. & GOOD, M. 2005. Impaired visuospatial recognition memory but normal object novelty detection and relative familiarity judgments in adult mice expressing the APPswe Alzheimer's disease mutation. Behav Neurosci, 119, 884-91. HAMPSON, R. E., JARRARD, L. E. & DEADWYLER, S. A. 1999. Effects of ibotenate hippocampal and extrahippocampal destruction on delayed-match and-nonmatch- to-sample behavior in rats. Journal of Neuroscience, 19, 1492-1507. HAMPTON, R. R. 2001. Rhesus monkeys know when they remember. Proceedings of the National Academy of Sciences, 98, 5359-5362. HAMPTON, R. R. & SHETTLEWORTH, S. J. 1998. Proactive interference, recency, and associative strength: Comparisons of black-capped chickadees and dark-eyed juncos. Animal Learning & Behavior, 26, 475-485. HANSEN, B. D., LASCELLES, B. D. X., KEENE, B. W., ADAMS, A. K. & THOMSON, A. E. 2007. Evaluation of an accelerometer for at-home monitoring of spontaneous activity in dogs. American journal of veterinary research, 68, 468-475. HARDO, P. G. & KENNEDY, T. D. 1991. Night sedation and arthritic pain. Journal of the Royal Society of Medicine, 84, 73. HARDO, P. G., WASTI, S. A. & TENNANT, A. 1992. Night pain in arthritis: patients at risk from prescribed night sedation. Annals of the rheumatic diseases, 51, 972. HARE, B. & TOMASELLO, M. 1999. Domestic dogs (Canis familiaris) use human and conspecific social cues to locate hidden food. Journal of Comparative Psychology, 113, 173. HARRELL, F. E. J. 2017. Hmisc: Harrell Miscellaneous. R package version 4.0-3. Available: https://CRAN.R-project.org/package=Hmisc [Accessed 12/09/2018]. HARRIS, L. 2017. Developing and validating novel techniques of quantifying chronic pain in dogs using osteoarthritis as a spontaneous pain model. University of Bristol. HART, D. J., DOYLE, D. V. & SPECTOR, T. D. 1999. Incidence and risk factors for radiographic knee osteoarthritis in middle-aged women. Arthritis Rheum, 42, 17-24. HART, D. J., SPECTOR, T. D., BROWN, P., WILSON, P., DOYLE, D. V. & SILMAN, A. J. 1991. Clinical signs of early osteoarthritis: reproducibility and relation to x ray changes in 541 women in the general population. Annals of the Rheumatic Diseases, 50, 467-470. HARVEY, P. O., LE BASTARD, G., POCHON, J. B., LEVY, R., ALLILAIRE, J. F., DUBOIS, B. & FOSSATI, P. 2004. Executive functions and updating of the contents of working memory in unipolar depression. Journal of psychiatric research, 38, 567-576. HAWKER, G., WRIGHT, J., COYTE, P., PAUL, J., DITTUS, R., CROXFORD, R., KATZ, B., BOMBARDIER, C., HECK, D. & FREUND, D. 1998. Health-related quality of life after knee replacement: results of the knee replacement patient outcomes research team study. Jbjs, 80, 163-173. HAWKER, G. A. 2006. Who, when, and why total joint replacement surgery? The patient's perspective. Current opinion in rheumatology, 18, 526-530. HAWKER, G. A., FRENCH, M. R., WAUGH, E. J., GIGNAC, M. A. M., CHEUNG, C. & MURRAY, B. J. 2010. The multidimensionality of sleep quality and its relationship to fatigue in older adults with painful osteoarthritis. Osteoarthritis and Cartilage, 18, 1365-1371.

279

References

HAWKER, G. A., STEWART, L., FRENCH, M. R., CIBERE, J., JORDAN, J. M., MARCH, L., SUAREZ-ALMAZOR, M. & GOOBERMAN-HILL, R. 2008. Understanding the pain experience in hip and knee osteoarthritis – an OARSI/OMERACT initiative. Osteoarthritis and Cartilage, 16, 415-422. HAYAMI, T., PICKARSKI, M., ZHUO, Y., WESOLOWSKI, G. A., RODAN, G. A. & DUONG, L. T. 2006. Characterization of articular cartilage and subchondral bone changes in the rat anterior cruciate ligament transection and meniscectomized models of osteoarthritis. Bone, 38, 234-243. HAYASHI, K., FRANK, J. D., DUBINSKY, C., HAO, Z., MARKEL, M. D., MANLEY, P. A. & MUIR, P. 2003. Histologic changes in ruptured canine cranial cruciate ligament. Veterinary Surgery, 32, 269-277. HAYASHI, K., MANLEY, P. A. & MUIR, P. 2004. Cranial cruciate ligament pathophysiology in dogs with cruciate disease: a review. Journal of the American Animal Hospital Association, 40, 385-390. HAYS, L., ZHANG, Z., MATEESCU, R. G., LUST, G., BURTON-WURSTER, N. I. & TODHUNTER, R. J. 2007. Quantitative genetics of secondary hip joint osteoarthritis in a Labrador Retriever–Greyhound pedigree. American journal of veterinary research, 68, 35-41. HAYTHORNTHWAITE, J. A., HEGEL, M. T. & KERNS, R. D. 1991. Development of a sleep diary for chronic pain patients. Journal of Pain and Symptom Management, 6, 65-72. HEAD, E., MEHTA, R., HARTLEY, J., KAMEKA, M., CUMMINGS, B. J., COTMAN, C. W., RUEHL, W. W. & MILGRAM, N. W. 1995. Spatial learning and memory as a function of age in the dog. Behavioral neuroscience, 109, 851. HEBB, D. O. 1955. Drives and the CNS (conceptual nervous system). Psychological review, 62, 243. HEINRICH, A., DUFFIELD, T. F., LISSEMORE, K. D. & MILLMAN, S. T. 2010. The effect of meloxicam on behavior and pain sensitivity of dairy calves following cautery dehorning with a local anesthetic. J Dairy Sci, 93, 2450-7. HELIÖVAARA, M., MÄKELÄ, M., IMPIVAARA, O., KNEKT, P., AROMAA, A. & SIEVERS, K. 1993. Association of overweight, trauma and workload with coxarthrosis: a health survey of 7,217 persons. Acta Orthopaedica Scandinavica, 64, 513-518. HEMSWORTH, P. H., PRICE, E. O. & BORGWARDT, R. 1996. Behavioural responses of domestic pigs and cattle to humans and novel stimuli. Applied Animal Behaviour Science, 50, 43-56. HENROTIN, Y., SANCHEZ, C. & BALLIGAND, M. 2005. Pharmaceutical and nutraceutical management of canine osteoarthritis: Present and future perspectives. The Veterinary Journal, 170, 113-123. HERCOCK, C. A., PINCHBECK, G., GIEJDA, A., CLEGG, P. D. & INNES, J. F. 2009. Validation of a client-based clinical metrology instrument for the evaluation of canine elbow osteoarthritis. J Small Anim Pract, 50, 266-71. HIELM-BJÖRKMAN, A. K., KUUSELA, E., LIMAN, A., MARKKOLA, A., SAARTO, E., HUTTUNEN, P., LEPPÄLUOTO, J., TULAMO, R.-M. & RAEKALLIO, M. 2003. Evaluation of methods for assessment of pain associated with chronic osteoarthritis in dogs. Journal of the American Veterinary Medical Association, 222, 1552-1558. HIELM-BJÖRKMAN, A. K., RITA, H. & TULAMO, R.-M. 2009. Psychometric testing of the Helsinki chronic pain index by completion of a questionnaire in Finnish by owners of dogs with chronic signs of pain caused by osteoarthritis. American Journal of Veterinary Research, 70, 727-734. HØEGH-ANDERSEN, P., TANKÓ, L. B., ANDERSEN, T. L., LUNDBERG, C. V., MO, J. A., HEEGAARD, A.-M., DELAISSÉ, J.-M. & CHRISTGAU, S. 2004. Ovariectomized rats as a

280

References model of postmenopausal osteoarthritis: validation and application. Arthritis Res Ther, 6, R169. HOLM, S. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65-70. HOLTON, L. L., SCOTT, E. M., NOLAN, A. M., REID, J. & WELSH, E. 1998. Relationship between physiological factors and clinical pain in dogs scored using a numerical rating scale. Journal of small animal practice, 39, 469-474. HOLZBERG, A. D., ROBINSON, M. E., GEISSER, M. E. & GREMILLION, H. A. 1996. The effects of depression and chronic pain on psychosocial and physical functioning. The Clinical journal of pain, 12, 118-125. HSU, Y. & SERPELL, J. A. 2003. Development and validation of a questionnaire for measuring behavior and temperament traits in pet dogs. Journal of the American Veterinary Medical Association, 223, 1293-1300. HUNT, J. R., GOFF, M., JENKINS, H., HARRIS, J., KNOWLES, T. G., LASCELLES, B. D. X., ENOMOTO, M., MENDL, M., WHAY, B. & MURRELL, J. C. 2018. Electrophysiological Characterisation of Central Sensitisation in Canine Spontaneous Osteoarthritis. Pain. HURLEY, C. R., HAMMER, D. L. & SHOTT, S. 2007. Progression of radiographic evidence of osteoarthritis following tibial plateau leveling osteotomy in dogs with cranial cruciate ligament rupture: 295 cases (2001–2005). Journal of the American Veterinary Medical Association, 230, 1674-1679. IBM CORPORATION. 2016. The calculation of Bonferroni-adjusted p-values - United States [Online]. Available: http://www- 01.ibm.com/support/docview.wss?uid=swg21476685 [Accessed 04/06/2018]. IMPELLIZERI, J. A., TETRICK, M. A. & MUIR, P. 2000. Effect of weight reduction on clinical signs of lameness in dogs with hip osteoarthritis. Journal of the American Veterinary Medical Association, 216, 1089-1091. INNES, J. F. & BARR, A. R. S. 1998. Can owners assess outcome following treatment of canine cruciate ligament deficiency? Journal of Small Animal Practice, 39, 373- 378. INNES, J. F., CLAYTON, J. & LASCELLES, B. D. 2010. Review of the safety and efficacy of long-term NSAID use in the treatment of canine osteoarthritis. Vet Rec, 166, 226- 30. INNES, J. F., COSTELLO, M., BARR, F. J., RUDORF, H. & BARR, A. R. S. 2004. Radiographic progression of osteoarthritis of the canine stifle joint: a prospective study. Veterinary radiology & ultrasound, 45, 143-148. INOUE, A., TAKAHASHI, K. A., ARAI, Y., TONOMURA, H., SAKAO, K., SAITO, M., FUJIOKA, M., FUJIWARA, H., TABATA, Y. & KUBO, T. 2006. The therapeutic effects of basic fibroblast growth factor contained in gelatin hydrogel microspheres on experimental osteoarthritis in the rabbit knee. Arthritis & Rheumatology, 54, 264- 270. IVANAVICIUS, S. P., BALL, A. D., HEAPY, C. G., WESTWOOD, F. R., MURRAY, F. & READ, S. J. 2007. Structural pathology in a rodent model of osteoarthritis is associated with neuropathic pain: increased expression of ATF-3 and pharmacological characterisation. Pain, 128, 272-282. IVERSEN, S. D. & MISHKIN, M. 1970. Perseverative interference in monkeys following selective lesions of the inferior prefrontal convexity. Experimental Brain Research, 11, 376-386. JALLALI, N., RIDHA, H., THRASIVOULOU, C., UNDERWOOD, C., BUTLER, P. E. M. & COWEN, T. 2005. Vulnerability to ROS-induced cell death in ageing articular cartilage: the role of antioxidant enzyme activity. Osteoarthritis and cartilage, 13, 614-622.

281

References

JARRARD, L. E. 1993. On the role of the hippocampus in learning and memory in the rat. Behavioral and neural biology, 60, 9-26. JOHANNES, C. B., LE, T. K., ZHOU, X., JOHNSTON, J. A. & DWORKIN, R. H. 2010. The prevalence of chronic pain in United States adults: results of an Internet-based survey. J Pain, 11, 1230-9. JOHANSSON, B. & TORNMALM, M. 2012. Working memory training for patients with acquired brain injury: effects in daily life. Scandinavian Journal of Occupational Therapy, 19, 176-183. JOHNSON, K., SVENSSON, C. I., ETTEN, D. V., GHOSH, S. S., MURPHY, A. N., POWELL, H. C. & TERKELTAUB, R. 2004. Mediation of spontaneous knee osteoarthritis by progressive chondrocyte ATP depletion in Hartley guinea pigs. Arthritis & Rheumatology, 50, 1216-1225. JOHNSTON, S. A. 1997. Osteoarthritis: Joint Anatomy, Physiology, and Pathobiology. Veterinary Clinics of North America: Small Animal Practice, 27, 699-723. JOHNSTONE, R. A. & CANT, M. A. 2010. The evolution of menopause in cetaceans and humans: the role of demography. Proceedings of the Royal Society of London B: Biological Sciences, rspb20100988. JONES, A. & DOHERTY, M. 1996. Intra-articular corticosteroids are effective in osteoarthritis but there are no clinical predictors of response. Annals of the rheumatic diseases, 55, 829. JONES, C. M., BRAITHWAITE, V. A. & HEALY, S. D. 2003. The evolution of sex differences in spatial ability. Behavioral neuroscience, 117, 403. JONES, C. M. & HEALY, S. D. 2006. Differences in cue use and spatial memory in men and women. Proceedings of the Royal Society B: Biological Sciences, 273, 2241. JORDAN, K. M., ARDEN, N. K., DOHERTY, M., BANNWARTH, B., BIJLSMA, J. W. J., DIEPPE, P., GUNTHER, K., HAUSELMANN, H., HERRERO-BEAUMONT, G. & KAKLAMANIS, P. 2003. EULAR Recommendations 2003: an evidence based approach to the management of knee osteoarthritis: Report of a Task Force of the Standing Committee for International Clinical Studies Including Therapeutic Trials (ESCISIT). Annals of the rheumatic diseases, 62, 1145-1155. JULIEN, N., GOFFAUX, P., ARSENAULT, P. & MARCHAND, S. 2005. Widespread pain in fibromyalgia is related to a deficit of endogenous pain inhibition. Pain, 114, 295-302. KAMEKURA, S., HOSHI, K., SHIMOAKA, T., CHUNG, U., CHIKUDA, H., YAMADA, T., UCHIDA, M., OGATA, N., SEICHI, A. & NAKAMURA, K. 2005. Osteoarthritis development in novel experimental mouse models induced by knee joint instability. Osteoarthritis and cartilage, 13, 632-641. KANE, M. J., BROWN, L. H., MCVAY, J. C., SILVIA, P. J., MYIN-GERMEYS, I. & KWAPIL, T. R. 2007. For whom the mind wanders, and when: An experience-sampling study of working memory and executive control in daily life. Psychological science, 18, 614-621. KARASAWA, J., HASHIMOTO, K. & CHAKI, S. 2008. D-Serine and a glycine transporter inhibitor improve MK-801-induced cognitive deficits in a novel object recognition test in rats. Behav Brain Res, 186, 78-83. KARLSON, E. W., MANDL, L. A., AWEH, G. N., SANGHA, O., LIANG, M. H. & GRODSTEIN, F. 2003. Total hip replacement due to osteoarthritis: the importance of age, obesity, and other modifiable risk factors. The American journal of medicine, 114, 93-98. KASPARSON, A. A., BADRIDZE, J. & MAXIMOV, V. V. 2013. Colour cues proved to be more informative for dogs than brightness. Proceedings of the Royal Society B: Biological Sciences, 280.

282

References

KAUFMAN, K. R., HUGHES, C., MORREY, B. F., MORREY, M. & AN, K.-N. 2001. Gait characteristics of patients with knee osteoarthritis. Journal of Biomechanics, 34, 907-915. KEALY, R. D., LAWLER, D. F., BALLAM, J. M., LUST, G., BIERY, D. N., SMITH, G. K. & MANTZ, S. L. 2000. Evaluation of the effect of limited food consumption on radiographic evidence of osteoarthritis in dogs. Journal of the American Veterinary Medical Association, 217, 1678-1680. KEALY, R. D., OLSSON, S. E., MONTI, K. L., LAWLER, D. F., BIERY, D. N., HELMS, R. W., LUST, G. & SMITH, G. K. 1992. Effects of limited food consumption on the incidence of hip dysplasia in growing dogs. J Am Vet Med Assoc, 201, 857-63. KEEFE, F. J., BROWN, G. K., WALLSTON, K. A. & CALDWELL, D. S. 1989. Coping with rheumatoid arthritis pain: catastrophizing as a maladaptive strategy. Pain, 37, 51-56. KEEFE, F. J., LEFEBVRE, J. C., EGERT, J. R., AFFLECK, G., SULLIVAN, M. J. & CALDWELL, D. S. 2000. The relationship of gender to pain, pain behavior, and disability in osteoarthritis patients: the role of catastrophizing. Pain, 87, 325-334. KEMPERMANN, G., GAST, D. & GAGE, F. H. 2002. Neuroplasticity in old age: Sustained fivefold induction of hippocampal neurogenesis by long‐term environmental enrichment. Annals of neurology, 52, 135-143. KEUKER, J. I. H., DE BIURRUN, G., LUITEN, P. G. M. & FUCHS, E. 2004. Preservation of hippocampal neuron numbers and hippocampal subfield volumes in behaviorally characterized aged tree shrews. Journal of Comparative Neurology, 468, 509-517. KEY, T. J. 2011. Endogenous oestrogens and breast cancer risk in premenopausal and postmenopausal women. Steroids, 76, 812-815. KING, C. D., WONG, F., CURRIE, T., MAUDERLI, A. P., FILLINGIM, R. B. & RILEY 3RD, J. L. 2009. Deficiency in endogenous modulation of prolonged heat pain in patients with irritable bowel syndrome and temporomandibular disorder. PAIN®, 143, 172- 178. KING, S. A. & STRAIN, J. J. 1990. Benzodiazepine use by chronic pain patients. The Clinical journal of pain, 6, 143-147. KIZAWA, H., KOU, I., IIDA, A., SUDO, A., MIYAMOTO, Y., FUKUDA, A., MABUCHI, A., KOTANI, A., KAWAKAMI, A. & YAMAMOTO, S. 2005. An aspartic acid repeat polymorphism in asporin inhibits chondrogenesis and increases susceptibility to osteoarthritis. Nature genetics, 37, 138. KLATZKY, R. L. 1998. Allocentric and egocentric spatial representations: Definitions, distinctions, and interconnections, Springer. KLINGBERG, T. 2010. Training and plasticity of working memory. Trends in cognitive sciences, 14, 317-324. KNAZOVICKY, D., HELGESON, E. S., CASE, B., GRUEN, M. E., MAIXNER, W. & LASCELLES, B. D. X. 2016. Widespread somatosensory sensitivity in naturally occurring canine model of osteoarthritis. Pain, 157, 1325. KNAZOVICKY, D., TOMAS, A., MOTSINGER-REIF, A. & LASCELLES, B. D. X. 2015. Initial evaluation of nighttime restlessness in a naturally occurring canine model of osteoarthritis pain. PeerJ, 3, e772. KOJIMA, F., NARABA, H., MIYAMOTO, S., BEPPU, M., AOKI, H. & KAWAI, S. 2004. Membrane-associated prostaglandin E synthase-1 is upregulated by proinflammatory cytokines in chondrocytes from patients with osteoarthritis. Arthritis Res Ther, 6, R355. KOLLER, M. 2016. robustlmm: an R package for robust estimation of linear mixed- effects models. Journal of statistical software, 75, 1-24. KOOBY, D. A., STOCKMAN, J., BEN-PORAT, L., GONEN, M., JARNAGIN, W. R., DEMATTEO, R. P., TUORTO, S., WUEST, D., BLUMGART, L. H. & FONG, Y. 2003.

283

References

Influence of transfusions on perioperative and long-term outcome in patients following hepatic resection for colorectal metastases. Annals of surgery, 237, 860. KORNER-NIEVERGELT, F., ROTH, T., VON FELTEN, S., GUÉLAT, J., ALMASI, B. & KORNER-NIEVERGELT, P. 2015. blmeco: Data Files and Functions Accompanying the Book "Bayesian Data Analysis in Ecology using R, BUGS and Stan". Elsevier, New York KRIPKE, D. F., MULLANEY, D. J., MESSIN, S. & WYBORNEY, V. G. 1978. Wrist actigraphic measures of sleep and rhythms. Clinical Neurophysiology, 44, 674-676. KROENKE, K., OUTCALT, S., KREBS, E., BAIR, M. J., WU, J., CHUMBLER, N. & YU, Z. 2013. Association between anxiety, health-related quality of life and functional impairment in primary care patients with chronic pain. General Hospital Psychiatry, 35, 359-365. KROTSCHECK, U., HULSE, D. A., BAHR, A. & JERRAM, R. M. 2000. Ununited anconeal process: lag-screw fixation with proximal ulnar osteotomy. Veterinary and Comparative Orthopaedics and Traumatology, 13, 212-216. KRUSKA, D. C. T. 2005. On the Evolutionary Significance of Encephalization in Some Eutherian Mammals: Effects of Adaptive Radiation, Domestication, and Feralization. Brain, Behavior and Evolution, 65, 73-108. KUC, K. A., GREGERSEN, B. M., GANNON, K. S. & DODART, J. C. 2006. Holeboard discrimination learning in mice. Genes Brain Behav, 5, 355-63. KUJALA, U. M., KETTUNEN, J., PAANANEN, H., AALTO, T., BATTIÉ, M. C., IMPIVAARA, O., VIDEMAN, T. & SARNA, S. 1995. Knee osteoarthritis in former runners, soccer players, weight lifters, and shooters. Arthritis & Rheumatology, 38, 539-546. KULKARNI, B., BENTLEY, D. E., ELLIOTT, R., JULYAN, P. J., BOGER, E., WATSON, A., BOYLE, Y., EL‐DEREDY, W. & JONES, A. K. P. 2007. Arthritic pain is processed in brain areas concerned with emotions and fear. Arthritis & Rheumatology, 56, 1345-1354. KURODA, R., USAS, A., KUBO, S., CORSI, K., PENG, H., ROSE, T., CUMMINS, J., FU, F. H. & HUARD, J. 2006. Cartilage repair using bone morphogenetic protein 4 and muscle‐derived stem cells. Arthritis & Rheumatology, 54, 433-442. LADHA, C., O'SULLIVAN, J., BELSHAW, Z. & ASHER, L. 2017. GaitKeeper: A System for Measuring Canine Gait. Sensors (Basel), 17. LADIEU, G. 1944. The effect of length of delay interval upon delayed alternation in the albino rat. Journal of Comparative Psychology, 37, 273. LAHM, A., UHL, M., EDLICH, M., ERGGELET, C., HABERSTROH, J. & KREUZ, P. C. 2005. An experimental canine model for subchondral lesions of the knee joint. The Knee, 12, 51-55. LAHM, A., UHL, M., ERGGELET, C., HABERSTROH, J. & MROSEK, E. 2004. Articular cartilage degeneration after acute subchondral bone damage an experimental study in dogs with histopathological grading. Acta Orthopaedica Scandinavica, 75, 762- 767. LANDIS, C. A., LEVINE, J. D. & ROBINSON, C. R. 1989. Decreased slow-wave and paradoxical sleep in a rat chronic pain model. Sleep, 12, 167-177. LANDIS, C. A., ROBINSON, C. R. & LEVINE, J. D. 1988. Sleep fragmentation in the arthritic rat. Pain, 34, 93-99. LANDRØ, N. I., STILES, T. C. & SLETVOLD, H. 1997. Memory functioning in patients with primary fibromyalgia and major depression and healthy controls. Journal of psychosomatic research, 42, 297-306. LANE, N. E., LIN, P., CHRISTIANSEN, L., GORE, L. R., WILLIAMS, E. N., HOCHBERG, M. C. & NEVITT, M. C. 2000. Association of mild acetabular dysplasia with an increased risk of incident hip osteoarthritis in elderly white women: the study of osteoporotic fractures. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, 43, 400-404.

284

References

LANE, N. E., MICHEL, B., BJORKENGREN, A., OEHLERT, J., SHI, H., BLOCH, D. A. & FRIES, J. F. 1993. The risk of osteoarthritis with running and aging: a 5-year longitudinal study. J Rheumatol, 20, 461-8. LANE, N. E., NEVITT, M. C., COOPER, C., PRESSMAN, A., GORE, R. & HOCHBERG, M. 1997. Acetabular dysplasia and osteoarthritis of the hip in elderly white women. Annals of the rheumatic diseases, 56, 627-630. LANG, K., STAHL, D., ESPIE, J., TREASURE, J. & TCHANTURIA, K. 2014. Set shifting in children and adolescents with anorexia nervosa: An exploratory systematic review and meta‐analysis. International Journal of Eating Disorders, 47, 394-399. LANYON, P., O’REILLY, S., JONES, A. & DOHERTY, M. 1998. Radiographic assessment of symptomatic knee osteoarthritis in the community: definitions and normal joint space. Annals of the rheumatic diseases, 57, 595-601. LASCELLES, B. D., FREIRE, M., ROE, S. C., DEPUY, V., SMITH, E. & MARCELLIN-LITTLE, D. J. 2010. Evaluation of functional outcome after BFX total hip replacement using a pressure sensitive walkway. Vet Surg, 39, 71-7. LAU, E. M., LIN, F., LAM, D., SILMAN, A. & CROFT, P. 1995. Hip osteoarthritis and dysplasia in Chinese men. Annals of the rheumatic diseases, 54, 965. LAUGHLIN, K. & MENDL, M. 2004. Costs of acquiring and forgetting information affect spatial memory and its susceptibility to interference. Animal Behaviour, 68, 97-103. LAVRIC, A., RIPPON, G. & GRAY, J. R. 2003. Threat-Evoked Anxiety Disrupts Spatial Working Memory Performance: An Attentional Account. Cognitive Therapy and Research, 27, 489-504. LAW, E. F., DUFTON, L. & PALERMO, T. M. 2012. Daytime and nighttime sleep patterns in adolescents with and without chronic pain. Health Psychology, 31, 830. LAWRENCE, A. B. & ILLIUS, A. W. 1989. Methodology for measuring hunger and food needs using operant conditioning in the pig. Applied Animal Behaviour Science, 24, 273-285. LAWRENCE, J. S. 1961. Rheumatism in cotton operatives. Occupational and Environmental Medicine, 18, 270-276. LAWRENCE, J. S., BREMNER, J. M. & BIER, F. 1966. Osteo-arthrosis. Prevalence in the population and relationship between symptoms and x-ray changes. Annals of the Rheumatic Diseases, 25, 1-24. LEBLANC, M., BEAULIEU-BONNEAU, S., MÉRETTE, C., SAVARD, J., IVERS, H. & MORIN, C. M. 2007. Psychological and health-related quality of life factors associated with insomnia in a population-based sample. Journal of psychosomatic research, 63, 157- 166. LEE, J., SONG, J., HOOTMAN, J. M., SEMANIK, P. A., CHANG, R. W., SHARMA, L., VAN HORN, L., BATHON, J. M., EATON, C. B., HOCHBERG, M. C., JACKSON, R., KWOH, C. K., MYSIW, W. J., NEVITT, M. & DUNLOP, D. D. 2013. Obesity and other modifiable factors for physical inactivity measured by accelerometer in adults with knee osteoarthritis. Arthritis Care & Research, 65, 53-61. LEFFLER, A. S., KOSEK, E., LERNDAL, T., NORDMARK, B. & HANSSON, P. 2002. Somatosensory perception and function of diffuse noxious inhibitory controls (DNIC) in patients suffering from rheumatoid arthritis. European Journal of Pain, 6, 161-176. LÉGER, D., MORIN, C. M., UCHIYAMA, M., HAKIMI, Z., CURE, S. & WALSH, J. K. 2012. Chronic insomnia, quality-of-life, and utility scores: comparison with good sleepers in a cross-sectional international survey. Sleep medicine, 13, 43-51. LEGRAIN, V., DAMME, S. V., ECCLESTON, C., DAVIS, K. D., SEMINOWICZ, D. A. & CROMBEZ, G. 2009. A neurocognitive model of attention to pain: behavioral and neuroimaging evidence. Pain, 144, 230-2.

285

References

LEGRAIN, V., IANNETTI, G. D., PLAGHKI, L. & MOURAUX, A. 2011. The pain matrix reloaded: a salience detection system for the body. Prog Neurobiol, 93, 111-24. LEIGH, T. J., HINDMARCH, I., BIRD, H. A. & WRIGHT, V. 1988. Comparison of sleep in osteoarthritic patients and age and sex matched healthy controls. Annals of the rheumatic diseases, 47, 40. LEINO, P. & MAGNI, G. 1993. Depressive and distress symptoms as predictors of low back pain, neck-shoulder pain, and other musculoskeletal morbidity: a 10-year follow-up of metal industry employees. Pain, 53, 89-94. LEVINE, J. D. & GORDON, N. C. 1982. Pain in prelingual children and its evaluation by pain-induced vocalization. Pain. LINAKER, C. H., WALKER-BONE, K., PALMER, K. & COOPER, C. 1999. Frequency and impact of regional musculoskeletal disorders. Baillieres Best Pract Res Clin Rheumatol, 13, 197-215. LIU, W., BURTON‐WURSTER, N., GLANT, T. T., TASHMAN, S., SUMNER, D. R., KAMATH, R. V., LUST, G., KIMURA, J. H. & CS‐SZABO, G. 2003. Spontaneous and experimental osteoarthritis in dog: similarities and differences in proteoglycan levels. Journal of orthopaedic research, 21, 730-737. LOESER, J. D. & TREEDE, R.-D. 2008. The Kyoto protocol of IASP Basic Pain Terminology☆. Pain, 137, 473-477. LOGGIA, M. L., JUNEAU, M. & BUSHNELL, M. C. 2011. Autonomic responses to heat pain: Heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity. PAIN®, 152, 592-598. LOHMANDER, L. S., ÖSTENBERG, A., ENGLUND, M. & ROOS, H. 2004. High prevalence of knee osteoarthritis, pain, and functional limitations in female soccer players twelve years after anterior cruciate ligament injury. Arthritis & Rheumatology, 50, 3145-3152. LOMBARDI, W. J., ANDREASON, P. J., SIROCCO, K. Y., RIO, D. E., GROSS, R. E., UMHAU, J. C. & HOMMER, D. W. 1999. Wisconsin Card Sorting Test performance following head injury: dorsolateral fronto-striatal circuit activity predicts perseveration. Journal of Clinical and Experimental Neuropsychology, 21, 2-16. LOUGHLIN, J., DOWLING, B., CHAPMAN, K., MARCELLINE, L., MUSTAFA, Z., SOUTHAM, L., FERREIRA, A., CIESIELSKI, C., CARSON, D. A. & CORR, M. 2004. Functional variants within the secreted frizzled-related protein 3 gene are associated with hip osteoarthritis in females. Proceedings of the National Academy of Sciences of the United States of America, 101, 9757-9762. LUERDING, R., WEIGAND, T., BOGDAHN, U. & SCHMIDT-WILCKE, T. 2008. Working memory performance is correlated with local brain morphology in the medial frontal and anterior cingulate cortex in fibromyalgia patients: structural correlates of pain– cognition interaction. Brain, 131, 3222. MA, H. L., BLANCHET, T. J., PELUSO, D., HOPKINS, B., MORRIS, E. A. & GLASSON, S. S. 2007. Osteoarthritis severity is sex dependent in a surgical mouse model. Osteoarthritis and cartilage, 15, 695-700. MA, Q.-P. & WOOLF, C. J. 1997. The progressive tactile hyperalgesia induced by peripheral inflammation is nerve growth factor dependent. Neuroreport, 8, 807-810. MACPHERSON, K. & ROBERTS, W. A. 2010. Spatial memory in dogs (Canis familiaris) on a radial maze. J Comp Psychol, 124, 47-56. MAGNI, G., CALDIERON, C., RIGATTI-LUCHINI, S. & MERSKEY, H. 1990. Chronic musculoskeletal pain and depressive symptoms in the general population. An analysis of the 1st National Health and Nutrition Examination Survey data. Pain, 43, 299-307.

286

References

MAGNI, G., ROSSI, M. R., RIGATTI-LUCHINI, S. & MERSKEY, H. 1992. Chronic abdominal pain and depression. Epidemiologie findings in the United States. Hispanic health and nutrition examination survey. Pain, 49, 77-85. MÄKI, K., JANSS, L. L. G., GROEN, A. F., LIINAMO, A. E. & OJALA, M. 2004. An indication of major genes affecting hip and elbow dysplasia in four Finnish dog populations. Heredity, 92, 402. MÄKI, K., LIINAMO, A. E. & OJALA, M. 2000. Estimates of genetic parameters for hip and elbow dysplasia in Finnish Rottweilers. Journal of animal science, 78, 1141-1148. MALM, S., FIKSE, W. F., DANELL, B. & STRANDBERG, E. 2008. Genetic variation and genetic trends in hip and elbow dysplasia in Swedish Rottweiler and Bernese Mountain Dog. Journal of animal breeding and genetics, 125, 403-412. MARSHALL, W. G., HAZEWINKEL, H. A. W., MULLEN, D., DE MEYER, G., BAERT, K. & CARMICHAEL, S. 2010. The effect of weight loss on lameness in obese dogs with osteoarthritis. Veterinary research communications, 34, 241-253. MARTEL-PELLETIER, J., ALAAEDDINE, N. & PELLETIER, J.-P. 1999. Cytokines and their role in the pathophysiology of osteoarthritis. Front Biosci, 4, D694-D703. MARTIG, S., BOISCLAIR, J., KONAR, M., SPRENG, D. & LANG, J. 2007. MRI characteristics and histology of bone marrow lesions in dogs with experimentally induced osteoarthritis. Veterinary radiology & ultrasound, 48, 105-112. MARTIN, T. J., KIM, S. A. & EISENACH, J. C. 2006. Clonidine maintains intrathecal self- administration in rats following spinal nerve ligation. Pain, 125, 257-263. MARTINEZ, S. A. 1997. Congenital Conditions that Lead to Osteoarthritis in the Dog. Veterinary Clinics of North America: Small Animal Practice, 27, 735-758. MARTINEZ, S. A. & CORONADO, G. S. 1997. Acquired Conditions That Lead to Osteoarthritis in the Dog. Veterinary Clinics of North America: Small Animal Practice, 27, 759-775. MARTINUSSEN, R., HAYDEN, J., HOGG-JOHNSON, S. & TANNOCK, R. 2005. A meta- analysis of working memory impairments in children with attention- deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 44, 377-384. MARX, G., LEPPELT, J. & ELLENDORFF, F. 2001. Vocalisation in chicks (Gallus gallus dom.) during stepwise social isolation. Applied Animal Behaviour Science, 75, 61-74. MASTBERGEN, S. C., MARIJNISSEN, A. C., VIANEN, M. E., VAN ROERMUND, P. M., BIJLSMA, J. W. & LAFEBER, F. P. 2006. The canine ‘groove’model of osteoarthritis is more than simply the expression of surgically applied damage. Osteoarthritis and cartilage, 14, 39-46. MAWBY, D. I., BARTGES, J. W., D’AVIGNON, A., LAFLAMME, D. P., MOYERS, T. D. & COTTRELL, T. 2004. Comparison of Various Methods for Estimating Body Fat in Dogs. Journal of the American Animal Hospital Association, 40, 109-114. MCALINDON, T. E., WILSON, P. W. F., ALIABADI, P., WEISSMAN, B. & FELSON, D. T. 1999. Level of physical activity and the risk of radiographic and symptomatic knee osteoarthritis in the elderly: the Framingham study. The American journal of medicine, 106, 151-157. MCCUNE, S., STEVENSON, J., FRETWELL, L., THOMPSON, A. & MILLS, D. S. 2008. Ageing does not significantly affect performance in a spatial learning task in the domestic cat (Felis silvestris catus). Applied animal behaviour science, 112, 345-356. MCGILL, W. J. & GIBBON, J. 1965. The general-gamma distribution and reaction times. Journal of Mathematical Psychology, 2, 1-18. MCILWRAITH, C. W., FRISBIE, D. D. & KAWCAK, C. E. 2012. The horse as a model of naturally occurring osteoarthritis. Bone and Joint Research, 1, 297-309.

287

References

MCNAMARA, P. & ALBERT, M. L. Neuropharmacology of verbal perseveration. 2004 2004. Copyright© 2004 by Thieme Medical Publishers, Inc., 333 Seventh Avenue, New York, NY 10001, USA., 309-321. MCWILLIAMS, L. A., COX, B. J. & ENNS, M. W. 2003. Mood and anxiety disorders associated with chronic pain: an examination in a nationally representative sample. Pain, 106, 127-133. MEEUS, M., VERVISCH, S., DE CLERCK, L. S., MOORKENS, G., HANS, G. & NIJS, J. 2012. Central sensitization in patients with rheumatoid arthritis: a systematic literature review. Seminars in arthritis and rheumatism, 41, 556-567. MENDL, M. 1999. Performing under pressure: stress and cognitive function. Applied Animal Behaviour Science, 65, 221-244. MENDL, M., BROOKS, J., BASSE, C., BURMAN, O., PAUL, E., BLACKWELL, E. & CASEY, R. 2010a. Dogs showing separation-related behaviour exhibit a ‘pessimistic’ cognitive bias. Current Biology, 20, R839-R840. MENDL, M., BURMAN, O. H. P., PARKER, R. M. A. & PAUL, E. S. 2009. Cognitive bias as an indicator of animal emotion and welfare: Emerging evidence and underlying mechanisms. Applied Animal Behaviour Science, 118, 161-181. MENDL, M., BURMAN, O. H. P. & PAUL, E. S. 2010b. An integrative and functional framework for the study of animal emotion and mood. Proceedings of the Royal Society of London B: Biological Sciences, 277, 2895-2904. MENDL, M., LAUGHLIN, K. & HITCHCOCK, D. 1997. Pigs in space: spatial memory and its susceptibility to interference. Animal Behaviour, 54, 1491-1508. MENEFEE, L. A., COHEN, M. J. M., ANDERSON, W. R., DOGHRAMJI, K., FRANK, E. D. & LEE, H. 2000. Sleep disturbance and nonmalignant chronic pain: a comprehensive review of the literature. Pain Medicine, 1, 156-172. MEROLA, I., PRATO-PREVIDE, E. & MARSHALL-PESCINI, S. 2012. Dogs' social referencing towards owners and strangers. PloS one, 7, e47653. MERSKEY, H. & BOGDUK, N. 1994. Classification of chronic pain. International Association for the Study of Pain Press, 210-213. MESSIER, S. P., LOESER, R. F., MILLER, G. D., MORGAN, T. M., REJESKI, W. J., SEVICK, M. A., ETTINGER, W. H., PAHOR, M. & WILLIAMSON, J. D. 2004. Exercise and dietary weight loss in overweight and obese older adults with knee osteoarthritis: the Arthritis, Diet, and Activity Promotion Trial. Arthritis & Rheumatology, 50, 1501- 1510. METZ, A. E., YAU, H.-J., CENTENO, M. V., APKARIAN, A. V. & MARTINA, M. 2009. Morphological and functional reorganization of rat medial prefrontal cortex in neuropathic pain. Proceedings of the National Academy of Sciences, 106, 2423-2428. MEULENBELT, I., SEYMOUR, A. B., NIEUWLAND, M., HUIZINGA, T. W. J., VAN DUIJN, C. M. & SLAGBOOM, P. E. 2004. Association of the interleukin‐1 gene cluster with radiographic signs of osteoarthritis of the hip. Arthritis & Rheumatology, 50, 1179- 1186. MEYER-LINDENBERG, A., FELIR, M. & MOLTE, I. 2001. Short-and long-term results after surgical treatment of an ununited anconeal process in the dog. Veterinary and Comparative Orthopaedics and Traumatology, 14, 101-110. MICHELSEN, J. 2013. Canine elbow dysplasia: Aetiopathogenesis and current treatment recommendations. The Veterinary Journal, 196, 12-19. MIKLÖSI, Á., POLGÁRDI, R., TOPÁL, J. & CSÁNYI, V. 1998. Use of experimenter-given cues in dogs. Animal Cognition, 1, 113-121. MILGRAM, N. W., ADAMS, B., CALLAHAN, H., HEAD, E., MACKAY, B., THIRLWELL, C. & COTMAN, C. W. 1999. Landmark discrimination learning in the dog. Learning & Memory, 6, 54-61.

288

References

MILGRAM, N. W., HEAD, E., ZICKER, S. C., IKEDA-DOUGLAS, C. J., MURPHEY, H., MUGGENBURG, B., SIWAK, C., TAPP, D. & COTMAN, C. W. 2005. Learning ability in aged beagle dogs is preserved by behavioral enrichment and dietary fortification: a two-year longitudinal study. Neurobiology of aging, 26, 77-90. MILLECAMPS, M., ETIENNE, M., JOURDAN, D., ESCHALIER, A. & ARDID, D. 2004. Decrease in non-selective, non-sustained attention induced by a chronic visceral inflammatory state as a new pain evaluation in rats. Pain, 109, 214-224. MILLER, P. E. & MURPHY, C. J. 1995. Vision in dogs. Journal-American Veterinary Medical Association, 207, 1623-1634. MISHIMA, N., HIGASHITANI, F., TERAOKA, K. & YOSHIOKA, R. 1986. Sex differences in appetitive learning of mice. Physiology & behavior, 37, 263-268. MISHKIN, M. & DELACOUR, J. 1975. An analysis of short-term visual memory in the monkey. Journal of Experimental Psychology: Animal Behavior Processes, 1, 326. MIYATA, S., NODA, A., IWAMOTO, K., KAWANO, N., OKUDA, M. & OZAKI, N. 2013. Poor sleep quality impairs cognitive performance in older adults. Journal of sleep research, 22, 535-541. MLACNIK, E., BOCKSTAHLER, B. A., MÜLLER, M., TETRICK, M. A., NAP, R. C. & ZENTEK, J. 2006. Effects of caloric restriction and a moderate or intense physiotherapy program for treatment of lameness in overweight dogs with osteoarthritis. Journal of the American Veterinary Medical Association, 229, 1756- 1760. MOGIL, J. S. 2009. Animal models of pain: progress and challenges. Nat Rev Neurosci, 10, 283-94. MOGIL, J. S. & CRAGER, S. E. 2004. What should we be measuring in behavioral studies of chronic pain in animals? Pain, 112, 12-15. MOLDOFSKY, H. & LUE, F. A. 1980. The relationship of alpha and delta EEG frequencies to pain and mood in ‘fibrositis’ patients treated with chlorpromazine and L-tryptophan. Clinical Neurophysiology, 50, 71-80. MONK, T. H., REYNOLDS, C. F., KUPFER, D. J., BUYSSE, D. J., COBLE, P. A., HAYES, A. J., MACHEN, M. A., PETRIE, S. R. & RITENOUR, A. M. 1994. The Pittsburgh sleep diary. Journal of sleep research, 3, 111-120. MONTGOMERY, K. C. 1952. A test of two explanations of spontaneous alternation. Journal of Comparative and Physiological Psychology, 45, 287. MOORE, E. E., BENDELE, A. M., THOMPSON, D. L., LITTAU, A., WAGGIE, K. S., REARDON, B. & ELLSWORTH, J. L. 2005. Fibroblast growth factor-18 stimulates chondrogenesis and cartilage repair in a rat model of injury-induced osteoarthritis. Osteoarthritis and Cartilage, 13, 623-631. MOREAU, M., DUPUIS, J., BONNEAU, N. H. & DESNOYERS, M. 2003. Clinical evaluation of nutraceutical, carprofen and meloxicam for the treatment. The Veterinary Record, 152, 323-329. MORGAN, J. P., WIND, A. & DAVIDSON, A. P. 1999. Bone dysplasias in the labrador retriever: a radiographic study. Journal of the American Animal Hospital Association, 35, 332-340. MORIN, C. M., GIBSON, D. & WADE, J. 1998. Self-reported sleep and mood disturbance in chronic pain patients. The Clinical journal of pain, 14, 311-314. MORKO, J., KIVIRANTA, R., JORONEN, K., SÄÄMÄNEN, A. M., VUORIO, E. & SALMINEN‐MANKONEN, H. 2005. Spontaneous development of synovitis and cartilage degeneration in transgenic mice overexpressing cathepsin K. Arthritis & Rheumatology, 52, 3713-3717. MORRIS, R. G. M., HAGAN, J. J. & RAWLINS, J. N. P. 1986. Allocentric spatial learning by hippocampectomised rats: a further test of the “spatial mapping” and “working

289

References memory” theories of hippocampal function. The Quarterly journal of experimental psychology, 38, 365-395. MOUSTGAARD, A., LIND, N. M., HEMMINGSEN, R. & HANSEN, A. K. 2002. Spontaneous Object Recognition in the Göttingen Minipig. Neural Plasticity, 9. MROSEK, E. H., LAHM, A., ERGGELET, C., UHL, M., KURZ, H., EISSNER, B. & SCHAGEMANN, J. C. 2006. Subchondral bone trauma causes cartilage matrix degeneration: an immunohistochemical analysis in a canine model. Osteoarthritis and cartilage, 14, 171-178. MUIR, J. L. 1996. Attention and stimulus processing in the rat. Brain Res Cogn Brain Res, 3, 215-25. MULLANEY, D. J., KRIPKE, D. F. & MESSIN, S. 1980. Wrist-actigraphic estimation of sleep time. Sleep, 3, 83-92. MUMBY, D. G., PINEL, J. P. J. & WOOD, E. R. 1990. Nonrecurring-items delayed nonmatching-to-sample in rats: A new paradigm for testing nonspatial working memory. Psychobiology, 18, 321-326. MURPHY, S. L., SMITH, D. M., CLAUW, D. J. & ALEXANDER, N. B. 2008. The impact of momentary pain and fatigue on physical activity in women with osteoarthritis. Arthritis Care & Research: Official Journal of the American College of Rheumatology, 59, 849-856. NAGASAWA, M., TSUJIMURA, A., TATEISHI, K., MOGI, K., OHTA, M., SERPELL, J. A. & KIKUSUI, T. 2011. Assessment of the Factorial Structures of the C-BARQ in Japan. Journal of Veterinary Medical Science, 73, 869-875. NEAVE, H. W., DAROS, R. R., COSTA, J. H., VON KEYSERLINGK, M. A. & WEARY, D. M. 2013. Pain and pessimism: dairy calves exhibit negative judgement bias following hot-iron disbudding. PLoS One, 8, e80556. NELSON, A. E., BRAGA, L., RENNER, J. B., ATASHILI, J., WOODARD, J., HOCHBERG, M. C., HELMICK, C. G. & JORDAN, J. M. 2010. Characterization of individual radiographic features of hip osteoarthritis in African American and White women and men: the Johnston County Osteoarthritis Project. Arthritis care & research, 62, 190-197. NETTO, C. A., SIEGFRIED, B. & IZQUIERDO, I. 1987. Analgesia induced by exposure to a novel environment in rats: Effect of concurrent and post-training stressful stimulation. Behavioral and Neural Biology, 48, 304-309. NEVITT, M. C., XU, L., ZHANG, Y., LUI, L. Y., YU, W., LANE, N. E., QIN, M., HOCHBERG, M. C., CUMMINGS, S. R. & FELSON, D. T. 2002. Very low prevalence of hip osteoarthritis among Chinese elderly in Beijing, China, compared with whites in the United States: the Beijing osteoarthritis study. Arthritis & Rheumatology, 46, 1773- 1779. NICASSIO, P. M. & WALLSTON, K. A. 1992. Longitudinal relationships among pain, sleep problems, and depression in rheumatoid arthritis. Journal of abnormal psychology, 101, 514. NIELSON, K. A. & JENSEN, R. A. 1994. Beta-adrenergic receptor antagonist antihypertensive medications impair arousal-induced modulation of working memory in elderly humans. Behavioral and neural biology, 62, 190-200. NIJS, J., MEEUS, M., VAN OOSTERWIJCK, J., ICKMANS, K., MOORKENS, G., HANS, G. & DE CLERCK, L. S. 2012. In the mind or in the brain? Scientific evidence for central sensitisation in chronic fatigue syndrome. European journal of clinical investigation, 42, 203-212. NIPPAK, P. M. D., MENDELSON, J., MUGGENBURG, B. & MILGRAM, N. W. 2007. Enhanced spatial ability in aged dogs following dietary and behavioural enrichment. Neurobiology of Learning and Memory, 87, 610-623.

290

References

NOBAEK, S., JOHANSSON, M.-L., MOLIN, G., AHRNÉ, S. & JEPPSSON, B. 2000. Alteration of intestinal microflora is associated with reduction in abdominal bloating and pain in patients with irritable bowel syndrome. The American journal of gastroenterology, 95, 1231-1238. NOLTE-ERNSTING, C. C. A., ADAM, G., BÜHNE, M., PRESCHER, A. & GÜNTHER, R. W. 1996. MRI of degenerative bone marrow lesions in experimental osteoarthritis of canine knee joints. Skeletal radiology, 25, 413-420. NORDQUIST, R. E., HEERKENS, J. L. T., RODENBURG, T. B., BOKS, S., ELLEN, E. D. & VAN DER STAAY, F. J. 2011. Laying hens selected for low mortality: behaviour in tests of fearfulness, anxiety and cognition. Applied Animal Behaviour Science, 131, 110- 122. NUNLEY, R. M., PRATHER, H., HUNT, D., SCHOENECKER, P. L. & CLOHISY, J. C. 2011. Clinical presentation of symptomatic acetabular dysplasia in skeletally mature patients. JBJS, 93, 17-21. OEI, N. Y. L., EVERAERD, W. T. A. M., ELZINGA, B. M., VAN WELL, S. & BERMOND, B. 2006. Psychosocial stress impairs working memory at high loads: An association with cortisol levels and memory retrieval. Stress, 9, 133-141. OHAYON, M. M. & SCHATZBERG, A. F. 2003. Using chronic pain to predict depressive morbidity in the general population. Archives of general psychiatry, 60, 39-47. OLIVERIA, S. A., FELSON, D. T., REED, J. I., CIRILLO, P. A. & WALKER, A. M. 1995. Incidence of symptomatic hand, hip, and knee osteoarthritis among patients in a health maintenance organization. Arthritis & Rheumatology, 38, 1134-1141. OLSEN, A. M., EVANS, R. B. & DUERR, F. M. 2016. Evaluation of accelerometer inter- device variability and collar placement in dogs. Veterinary Evidence, 1. OLTON, D. S., COLLISON, C. & WERZ, M. A. 1977. Spatial memory and radial arm maze performance of rats. Learning and Motivation, 8, 289-314. OLTON, D. S. & FEUSTLE, W. A. 1981. Hippocampal function required for nonspatial working memory. Experimental Brain Research, 41, 380-389. OLTON, D. S. & SAMUELSON, R. J. 1976. Remembrance of places passed: spatial memory in rats. Journal of Experimental Psychology: Animal Behavior Processes, 2, 97. ONEN, S. H., ALLOUI, A., GROSS, A., ESCHALLIER, A. & DUBRAY, C. 2001. The effects of total sleep deprivation, selective sleep interruption and sleep recovery on pain tolerance thresholds in healthy subjects. Journal of sleep research, 10, 35-42. OOSTERMAN, J. M., DERKSEN, L. C., VAN WIJCK, A. J. M., VELDHUIJZEN, D. S. & KESSELS, R. P. C. 2011. Memory functions in chronic pain: examining contributions of attention and age to test performance. The Clinical journal of pain, 27, 70-75. OSSIPOV, M. H., DUSSOR, G. O. & PORRECA, F. 2010. Central modulation of pain. The Journal of clinical investigation, 120, 3779-3787. OSTLUND, S. B., WASSUM, K. M., MURPHY, N. P., BALLEINE, B. W. & MAIDMENT, N. T. 2011. Extracellular dopamine levels in striatal subregions track shifts in motivation and response cost during instrumental conditioning. Journal of Neuroscience, 31, 200-207. OTTO, T. & EICHENBAUM, H. 1992. Complementary roles of the orbital prefrontal cortex and the perirhinal-entorhinal cortices in an odor-guided delayed- nonmatching-to-sample task. Behavioral neuroscience, 106, 762. PAIS-VIEIRA, M., LIMA, D. & GALHARDO, V. 2009. Sustained attention deficits in rats with chronic inflammatory pain. Neuroscience letters, 463, 98-102. PAQUET, J., KAWINSKA, A. & CARRIER, J. 2007. Wake detection capacity of actigraphy during sleep. Sleep, 30, 1362-1369.

291

References

PARK, D. C., GLASS, J. M., MINEAR, M. & CROFFORD, L. J. 2001. Cognitive function in fibromyalgia patients. Arthritis & Rheumatism, 44, 2125-2133. PATEL, S. I., MILLER, B. W., KOSIOREK, H. E., PARISH, J. M., LYNG, P. J. & KRAHN, L. E. The Effect of Dogs on Human Sleep in the Home Sleep Environment. 2017 2017. Elsevier, 1368-1372. PATTERSON-KANE, E. G., KIRKDEN, R. D. & PAJOR, E. A. 2011. Measuring motivation in swine: the food-metric scale. Journal of applied animal welfare science, 14, 175- 186. PEAT, G., MCCARNEY, R. & CROFT, P. 2001. Knee pain and osteoarthritis in older adults: a review of community burden and current use of primary health care. Ann Rheum Dis, 60, 91-7. PELLOW, S., CHOPIN, P., FILE, S. E. & BRILEY, M. 1985. Validation of open: closed arm entries in an elevated plus-maze as a measure of anxiety in the rat. Journal of neuroscience methods, 14, 149-167. PEPPERBERG, I. M. & LYNN, S. K. 2000. Possible levels of animal consciousness with reference to grey parrots (Psittacus erithacus). American Zoologist, 40, 893-901. PETER, H. Y., LAI, C.-T. & ZUO, D.-M. 1997. Formation of formaldehyde from adrenaline in vivo; a potential risk factor for stress-related angiopathy. Neurochemical research, 22, 615-620. PETERS, M. L., VLAEYEN, J. W. S. & WEBER, W. E. J. 2005. The joint contribution of physical pathology, pain-related fear and catastrophizing to chronic back pain disability. Pain, 113, 45-50. PHILIPS RESPIRONICS. 2018a. Philips Respironics | Actical [Online]. Available: https://en.wikipedia.org/wiki/Vertical_bar [Accessed 20/07/2018]. PHILIPS RESPIRONICS. 2018b. Philips Respironics | Actical specifications [Online]. Available: http://www.actigraphy.com/solutions/actical/specifications.html [Accessed 20/07/2018]. PIELSTICKER, A., HAAG, G., ZAUDIG, M. & LAUTENBACHER, S. 2005. Impairment of pain inhibition in chronic tension-type headache. Pain, 118, 215-223. PILOWSKY, I., CRETTENDEN, I. & TOWNLEY, M. 1985. Sleep disturbance in pain clinic patients. Pain, 23, 27-33. PLUIJMAKERS, J. J. T. M., APPLEBY, D. L. & BRADSHAW, J. W. S. 2010. Exposure to video images between 3 and 5 weeks of age decreases neophobia in domestic dogs. Applied Animal Behaviour Science, 126, 51-58. PONGRÁCZ, P., MIKLÓSI, Á., KUBINYI, E., GUROBI, K., TOPÁL, J. & CSÁNYI, V. 2001. Social learning in dogs: the effect of a human demonstrator on the performance of dogs in a detour task. Animal Behaviour, 62, 1109-1117. PONGRÁCZ, P., MIKLÓSI, Á., TIMÁR-GENG, K. & CSÁNYI, V. 2004. Verbal attention getting as a key factor in social learning between dog (Canis familiaris) and human. Journal of comparative psychology, 118, 375. PONTECORVO, M. J. 1983. Effects of proactive interference on rats’ continuous nonmatching-to-sample performance. Animal Learning & Behavior, 11, 356-366. PONTECORVO, M. J., SAHGAL, A. & STECKLER, T. 1996. Further developments in the measurement of working memory in rodents. Cognitive Brain Research, 3, 205-213. POSADA, D. & BUCKLEY, T. R. 2004. Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests. Systematic Biology, 53, 793-808. POWER, J. D., BADLEY, E. M., FRENCH, M. R., WALL, A. J. & HAWKER, G. A. 2008. Fatigue in osteoarthritis: a qualitative study. BMC Musculoskeletal Disorders, 9, 63.

292

References

POWER, J. D., PERRUCCIO, A. V. & BADLEY, E. M. 2005. Pain as a mediator of sleep problems in arthritis and other chronic conditions. Arthritis Care & Research, 53, 911-919. PRENTICE, M. 1979. On the problem of m incomplete rankings. Biometrika, 167-170. PRICE, D. D., LONG, S. & HUITT, C. 1992. Sensory testing of pathophysiological mechanisms of pain in patients with reflex sympathetic dystrophy. Pain, 49, 163- 173. PURCELL-JONES, G., DORMON, F. & SUMNER, E. 1988. Paediatric anaesthetists' perceptions of neonatal and infant pain. Pain, 33, 181-187. QIN, S., HERMANS, E. J., VAN MARLE, H. J. F., LUO, J. & FERNÁNDEZ, G. 2009. Acute Psychological Stress Reduces Working Memory-Related Activity in the Dorsolateral Prefrontal Cortex. Biological Psychiatry, 66, 25-32. QUASNICHKA, H. L., ANDERSON‐MACKENZIE, J. M., TARLTON, J. F., SIMS, T. J., BILLINGHAM, M. E. J. & BAILEY, A. J. 2005. Cruciate ligament laxity and femoral intercondylar notch narrowing in early‐stage knee osteoarthritis. Arthritis & Rheumatology, 52, 3100-3109. R CORE TEAM 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing Available: http://www.R- project.org [Accessed 12/09/2018]. RAFTERY, M. N., RYAN, P., NORMAND, C., MURPHY, A. W., DE LA HARPE, D. & MCGUIRE, B. E. 2012. The economic cost of chronic noncancer pain in Ireland: results from the PRIME study, part 2. The Journal of Pain, 13, 139-145. RAMAGE, L., MARTEL, M. A., HARDINGHAM, G. E. & SALTER, D. M. 2008. NMDA receptor expression and activity in osteoarthritic human articular chondrocytes. Osteoarthritis and cartilage, 16, 1576-1584. RAND, D., ENG, J. J., TANG, P.-F., JENG, J.-S. & HUNG, C. 2009. How active are people with stroke? Use of accelerometers to assess physical activity. Stroke, 40, 163-168. RASCH, B. & BORN, J. 2007. Maintaining memories by reactivation. Current Opinion in Neurobiology, 17, 698-703. RAVAUD, P., MOULINIER, L., GIRAUDEAU, B., AYRAL, X., GUERIN, C., NOEL, E., THOMAS, P., FAUTREL, B., MAZIERES, B. & DOUGADOS, M. 1999. Effects of joint lavage and steroid injection in patients with osteoarthritis of the knee: results of a multicenter, randomized, controlled trial. Arthritis & Rheumatology, 42, 475-482. RAVNKILDE, B., VIDEBECH, P., CLEMMENSEN, K., EGANDER, A., RASMUSSEN, N. A. & ROSENBERG, R. 2002. Cognitive deficits in major depression. Scandinavian journal of psychology, 43, 239-251. RAYWARD, R. M., THOMSON, D. G., DAVIES, J. V., INNES, J. F. & WHITELOCK, R. G. 2004. Progression of osteoarthritis following TPLO surgery: a prospective radiographic study of 40 dogs. Journal of Small Animal Practice, 45, 92-97. RAZ, N., GUNNING, F. M., HEAD, D., DUPUIS, J. H., MCQUAIN, J., BRIGGS, S. D., LOKEN, W. J., THORNTON, A. E. & ACKER, J. D. 1997. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cerebral cortex (New York, NY: 1991), 7, 268-282. REGAN, E., FLANNELLY, J., BOWLER, R., TRAN, K., NICKS, M., CARBONE, B. D., GLUECK, D., HEIJNEN, H., MASON, R. & CRAPO, J. 2005. Extracellular superoxide dismutase and oxidant damage in osteoarthritis. Arthritis & Rheumatology, 52, 3479-3491. REICHLING, J., FRATER-SCHRÖDER, M., HERZOG, K., BUCHER, S. & SALLER, R. 2006. Reduction of behavioural disturbances in elderly dogs supplemented with a standardised Ginkgo leaf extract. Schweizer Archiv für Tierheilkunde, 148, 257-263.

293

References

REMEDIOS, A. M. & FRIES, C. L. 1995. Treatment of canine hip dysplasia: a review. The Canadian Veterinary Journal, 36, 503. REN, W.-J., LIU, Y., ZHOU, L.-J., LI, W., ZHONG, Y., PANG, R.-P., XIN, W.-J., WEI, X.-H., WANG, J. & ZHU, H.-Q. 2011. Peripheral nerve injury leads to working memory deficits and dysfunction of the hippocampus by upregulation of TNF-α in rodents. Neuropsychopharmacology, 36, 979. RILEY, J. L., BENSON, M. B., GREMILLION, H. A., MYERS, C. D., ROBINSON, M. E., SMITH JR, C. L. & WAXENBERG, L. B. 2001. Sleep disturbance in orofacial pain patients: pain-related or emotional distress? CRANIO®, 19, 106-113. RIVERA-ARCONADA, I. & LOPEZ-GARCIA, J. A. 2010. Changes in membrane excitability and potassium currents in sensitized dorsal horn neurons of mice pups. Journal of Neuroscience, 30, 5376-5383. ROCHFORD, J. & DAWES, P. 1993. Effect of naloxone on the habituation of novelty- induced hypoalgesia: The collateral inhibition hypothesis revisited. Pharmacology Biochemistry and Behavior, 46, 117-123. RØE, C., BJØRKLUND, R. A., KNARDAHL, S., WAERSTED, M. & VØLLESTAD, N. K. 2001. Cognitive performance and muscle activation in workers with chronic shoulder myalgia. Ergonomics, 44, 1-16. ROGERS, A. E., CARUSO, C. C. & ALDRICH, M. S. 1993. Reliability of sleep diaries for assessment of sleep/wake patterns. Nursing research. ROHR, C. S., OKON-SINGER, H., CRADDOCK, R. C., VILLRINGER, A. & MARGULIES, D. S. 2013. Affect and the brain's functional organization: a resting-state connectivity approach. PLoS One, 8, e68015. ROIZENBLATT, S., MOLDOFSKY, H., BENEDITO‐SILVA, A. A. & TUFIK, S. 2001. Alpha sleep characteristics in fibromyalgia. Arthritis & Rheumatology, 44, 222-230. ROOS, E. M., ÖSTENBERG, A., ROOS, H., EKDAHL, C. & LOHMANDER, L. S. 2001. Long- term outcome of meniscectomy: symptoms, function, and performance tests in patients with or without radiographic osteoarthritis compared to matched controls. Osteoarthritis and Cartilage, 9, 316-324. ROOZENDAAL, B. & MCGAUGH, J. L. 1996. Amygdaloid nuclei lesions differentially affect glucocorticoid-induced memory enhancement in an inhibitory avoidance task. Neurobiology of learning and memory, 65, 1-8. ROSE, E. J. & EBMEIER, K. P. 2006. Pattern of impaired working memory during major depression. Journal of affective disorders, 90, 149-161. ROSE, J. D., ARLINGHAUS, R., COOKE, S. J., DIGGLES, B. K., SAWYNOK, W., STEVENS, E. D. & WYNNE, C. D. L. 2014. Can fish really feel pain? Fish and Fisheries, 15, 97-133. ROTHBLAT, L. A. & HAYES, L. L. 1987. Short-term object recognition memory in the rat: Nonmatching with trial-unique junk stimuli. Behavioral Neuroscience, 101, 587. ROUNTREE, R. B., SCHOOR, M., CHEN, H., MARKS, M. E., HARLEY, V., MISHINA, Y. & KINGSLEY, D. M. 2004. BMP receptor signaling is required for postnatal maintenance of articular cartilage. PLoS biology, 2, e355. RSTUDIO TEAM 2016. RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. Available: http://www.rstudio.com [Accessed 12/09/2018]. RYPMA, B. & D'ESPOSITO, M. 2000. Isolating the neural mechanisms of age-related changes in human working memory. Nature Neuroscience, 3, 509. SADEH, A. 2011. The role and validity of actigraphy in sleep medicine: an update. Sleep medicine reviews, 15, 259-267. SADEH, A., ALSTER, J., URBACH, D. & LAVIE, P. 1989. Actigraphically based automatic bedtime sleep-wake scoring: validity and clinical applications. Journal of Ambulatory Monitoring, 2, 209-216.

294

References

SADEH, A., HAURI, P. J., KRIPKE, D. F. & LAVIE, P. 1995. The role of actigraphy in the evaluation of sleep disorders. Sleep, 18, 288-302. SADEH, A., LAVIE, P., SCHER, A., TIROSH, E. & EPSTEIN, R. 1991. Actigraphic home- monitoring sleep-disturbed and control infants and young children: a new method for pediatric assessment of sleep-wake patterns. Pediatrics, 87, 494-499. SADEH, A., SHARKEY, M. & CARSKADON, M. A. 1994. Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep, 17, 201-207. SALVIN, H. E., MCGREEVY, P. D., SACHDEV, P. S. & VALENZUELA, M. J. 2011. The canine cognitive dysfunction rating scale (CCDR): A data-driven and ecologically relevant assessment tool. The Veterinary Journal, 188, 331-336. SCHILDER, M. B. H. & VAN DER BORG, J. A. M. 2004. Training dogs with help of the shock collar: short and long term behavioural effects. Applied Animal Behaviour Science, 85, 319-334. SCHMIEDEK, F., OBERAUER, K., WILHELM, O., SÜß, H.-M. & WITTMANN, W. W. 2007. Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136, 414. SCHWARTZKOPF-GENSWEIN, K. S., STOOKEY, J. M., PASSILLÉ, A. M. D. & RUSHEN, J. 1997. Comparison of hot-iron and freeze branding on cortisol levels and pain sensitivity in beef cattle. Canadian Journal of Animal Science, 77, 369-374. SEARLE, J. R. 2000. Consciousness. Annual Review of Neuroscience, 23, 557-578. SEIFERT, F. & MAIHÖFNER, C. 2009. Central mechanisms of experimental and chronic neuropathic pain: findings from functional imaging studies. Cellular and molecular life sciences, 66, 375. SELLAM, J. & BERENBAUM, F. 2010. The role of synovitis in pathophysiology and clinical symptoms of osteoarthritis. Nature Reviews Rheumatology, 6, 625-635. SEMINOWICZ, D. A., LAFERRIERE, A. L., MILLECAMPS, M., JON, S. C., CODERRE, T. J. & BUSHNELL, M. C. 2009. MRI structural brain changes associated with sensory and emotional function in a rat model of long-term neuropathic pain. Neuroimage, 47, 1007-1014. SETH, A. K., BAARS, B. J. & EDELMAN, D. B. 2005. Criteria for consciousness in humans and other mammals. Consciousness and cognition, 14, 119-139. SEYMOURE, P., DOU, H. & JURASKA, J. M. 1996. Sex differences in radial maze performance: influence of rearing environment and room cues. Psychobiology, 24, 33-37. SHACKMAN, A. J., SARINOPOULOS, I., MAXWELL, J. S., PIZZAGALLI, D. A., LAVRIC, A. & DAVIDSON, R. J. 2006. Anxiety selectively disrupts visuospatial working memory. Emotion, 6, 40. SHARPE, L., DEAR, B. F. & SCHRIEBER, L. 2009. Attentional Biases in Chronic Pain Associated With Rheumatoid Arthritis: Hypervigilance or Difficulties Disengaging? The Journal of Pain, 10, 329-335. SILMAN, A. J. & NEWMAN, J. 1996. Obstetric and gynaecological factors in susceptibility to peripheral joint osteoarthritis. Annals of the rheumatic diseases, 55, 671. SILVA, A., ANDERSEN, M. L. & TUFIK, S. 2008. Sleep pattern in an experimental model of osteoarthritis. Pain, 140, 446-455. SILVA, C. A. & ANNAMALAI, K. 2009. Entropy generation and human aging: lifespan entropy and effect of diet composition and caloric restriction diets. Journal of Thermodynamics, 2009.

295

References

SILVERS, J. M., HARROD, S. B., MACTUTUS, C. F. & BOOZE, R. M. 2007. Automation of the novel object recognition task for use in adolescent rats. Journal of Neuroscience Methods, 166, 99-103. SINISCALCHI, M., QUARANTA, A. & ROGERS, L. J. 2008. Hemispheric specialization in dogs for processing different acoustic stimuli. PLoS One, 3, e3349. SITNICK, S. L., GOODLIN-JONES, B. L. & ANDERS, T. F. 2008. The use of actigraphy to study sleep disorders in preschoolers: some concerns about detection of nighttime awakenings. Sleep, 31, 395. SIVERTSEN, B., OMVIK, S., HAVIK, O. E., PALLESEN, S., BJORVATN, B., NIELSEN, G. H., STRAUME, S. & NORDHUS, I. H. 2006. A comparison of actigraphy and polysomnography in older adults treated for chronic primary insomnia. Sleep, 29, 1353-1358. SMART, K. M., BLAKE, C., STAINES, A., THACKER, M. & DOODY, C. 2012. Mechanisms- based classifications of musculoskeletal pain: part 1 of 3: symptoms and signs of central sensitisation in patients with low back (±leg) pain. Manual therapy, 17, 336- 344. SMITH, A. J. P., KEEN, L. J., BILLINGHAM, M. J., PERRY, M. J., ELSON, C. J., KIRWAN, J. R., SIMS, J. E., DOHERTY, M., SPECTOR, T. D. & BIDWELL, J. L. 2004. Extended haplotypes and linkage disequilibrium in the IL1R1–IL1A–IL1B–IL1RN gene cluster: association with knee osteoarthritis. Genes and immunity, 5, 451. SMITH, C. T., CONWAY, J. M. & ROSE, G. M. 1998. Brief paradoxical sleep deprivation impairs reference, but not working, memory in the radial arm maze task. Neurobiology of learning and memory, 69, 211-217. SMITH, G. K., PASTER, E. R., POWERS, M. Y., LAWLER, D. F., BIERY, D. N., SHOFER, F. S., MCKELVIE, P. J. & KEALY, R. D. 2006. Lifelong diet restriction and radiographic evidence of osteoarthritis of the hip joint in dogs. Journal of the American Veterinary Medical Association, 229, 690-693. SMITH, M. E., MCEVOY, L. K. & GEVINS, A. 2002. The impact of moderate sleep loss on neurophysiologic signals during working-memory task performance. Sleep, 25, 56-66. SMITH, M. T. & HAYTHORNTHWAITE, J. A. 2004. How do sleep disturbance and chronic pain inter-relate? Insights from the longitudinal and cognitive-behavioral clinical trials literature. Sleep medicine reviews, 8, 119-132. SMITH, R. W., EGGER, P., COGGON, D., CAWLEY, M. I. & COOPER, C. 1995. Osteoarthritis of the hip joint and acetabular dysplasia in women. Annals of the Rheumatic Diseases, 54, 179. SMOLEN, J. S., ALETAHA, D., KOELLER, M., WEISMAN, M. H. & EMERY, P. 2007. New therapies for treatment of rheumatoid arthritis. The Lancet, 370, 1861-1874. SNEDDON, L. U. 2009. Pain Perception in Fish: Indicators and Endpoints. ILAR Journal, 50, 338-342. SMURRA, M. V., DURY, M., AUBERT, G., RODENSTEIN, D. O. & LIISTRO, G. 2001. Sleep fragmentation: comparison of two definitions of short arousals during sleep in OSAS patients. European Respiratory Journal, 17, 723-727. SNEDDON, L. U., BRAITHWAITE, V. A. & GENTLE, M. J. 2003. Novel object test: examining nociception and fear in the rainbow trout. The Journal of Pain, 4, 431- 440. SNIGDHA, S., CHRISTIE, L. A., DE RIVERA, C., ARAUJO, J. A., MILGRAM, N. W. & COTMAN, C. W. 2012. Age and distraction are determinants of performance on a novel visual search task in aged Beagle dogs. Age (Dordr), 34, 67-73.

296

References

SNOOK, E. M., MOTL, R. W. & GLIOTTONI, R. C. 2009. The effect of walking mobility on the measurement of physical activity using accelerometry in multiple sclerosis. Clinical rehabilitation, 23, 248-258. SOFAT, N., EJINDU, V. & KIELY, P. 2011. What makes osteoarthritis painful? The evidence for local and central pain processing. Rheumatology (Oxford), 50, 2157-65. SOFAT, N. & KUTTAPITIYA, A. 2014. Future directions for the management of pain in osteoarthritis. International Journal of Clinical Rheumatology, 9, 197-216. SOWERS, M. F., HAYES, C., JAMADAR, D., CAPUL, D., LACHANCE, L., JANNAUSCH, M. & WELCH, G. 2003. Magnetic resonance-detected subchondral bone marrow and cartilage defect characteristics associated with pain and X-ray-defined knee osteoarthritis. Osteoarthritis and Cartilage, 11, 387-393. SPECTOR, T. D., HARRIS, P. A., HART, D. J., CICUTTINI, F. M., NANDRA, D., ETHERINGTON, J., WOLMAN, R. L. & DOYLE, D. V. 1996. Risk of osteoarthritis associated with long‐term weight‐bearing sports: a radiologic survey of the hips and knees in female ex‐athletes and population controls. Arthritis & Rheumatology, 39, 988-995. SPENCER-SMITH, M. & KLINGBERG, T. 2015. Benefits of a working memory training program for inattention in daily life: a systematic review and meta-analysis. PloS one, 10, e0119522. SPRENG, M., ROSSIER, J. & SCHENK, F. 2002. Spaced training facilitates long-term retention of place navigation in adult but not in adolescent rats. Behavioural Brain Research, 128, 103-108. SRIKANTH, V. K., FRYER, J. L., ZHAI, G., WINZENBERG, T. M., HOSMER, D. & JONES, G. 2005. A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis and cartilage, 13, 769-781. STAUD, R., CRAGGS, J. G., ROBINSON, M. E., PERLSTEIN, W. M. & PRICE, D. D. 2007. Brain activity related to temporal summation of C-fiber evoked pain. Pain, 129, 130- 142. STAUD, R., VIERCK, C. J., CANNON, R. L., MAUDERLI, A. P. & PRICE, D. D. 2001. Abnormal sensitization and temporal summation of second pain (wind-up) in patients with fibromyalgia syndrome. Pain, 91, 165-175. STEELE, R. J. & MORRIS, R. G. M. 1999. Delay‐dependent impairment of a matching‐ to‐place task with chronic and intrahippocampal infusion of the NMDA‐antagonist D‐ AP5. Hippocampus, 9, 118-136. STEENARI, M.-R., VUONTELA, V., PAAVONEN, E. J., CARLSON, S., FJÄLLBERG, M. & ARONEN, E. T. 2003. Working memory and sleep in 6-to 13-year-old schoolchildren. Journal of the American Academy of Child & Adolescent Psychiatry, 42, 85-92. STEFÁNSSON, S. E., JÓNSSON, H., INGVARSSON, T., MANOLESCU, I., JÓNSSON, H. H., ÓLAFSDÓTTIR, G., PÁLSDÓTTIR, E., STEFÁNSDÓTTIR, G., SVEINBJÖRNSDÓTTIR, G. & FRIGGE, M. L. 2003. Genomewide scan for hand osteoarthritis: a novel mutation in matrilin-3. The American Journal of Human Genetics, 72, 1448-1459. STEIGERWALD, E. S., SARTER, M., MARCH, P. & PODELL, M. 1999. Effects of feline immunodeficiency virus on cognition and behavioral function in cats. Journal of acquired immune deficiency syndromes and human retrovirology: official publication of the International Retrovirology Association, 20, 411-419. STERLING, R. K., LISSEN, E., CLUMECK, N., SOLA, R., CORREA, M. C., MONTANER, J., S SULKOWSKI, M., TORRIANI, F. J., DIETERICH, D. T. & THOMAS, D. L. 2006. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology, 43, 1317-1325.

297

References

STEYERBERG, E. W., EIJKEMANS, M. J. C., HARRELL JR, F. E. & HABBEMA, J. D. F. 2001. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Medical Decision Making, 21, 45-56. STICKGOLD, R. 2005. Sleep-dependent memory consolidation. Nature, 437, 1272- 1278. STONE, A. A., BRODERICK, J. E., PORTER, L. S. & KAELL, A. T. 1997. The experience of rheumatoid arthritis pain and fatigue: Examining momentary reports and correlates over one week. Arthritis & Rheumatology, 10, 185-193. STRIGO, I. A., SIMMONS, A. N., MATTHEWS, S. C., ARTHUR, D. & PAULUS, M. P. 2008. Association of major depressive disorder with altered functional brain response during anticipation and processing of heat pain. Archives of general psychiatry, 65, 1275-1284. STULBERG, S. D., COOPERMAN, D. R. & WALLENSTEN, R. 1981. The natural history of Legg-Calve-Perthes disease. J Bone Joint Surg Am, 63, 1095-108. STURZ, B. R. & BROWN, M. F. 2009. Facilitation of learning spatial relations among locations by visual cues: Implications for theoretical accounts of spatial learning. Psychonomic bulletin & review, 16, 306-312. SUDHOLZ, B., RIDGERS, N. D., MUSSAP, A., BENNIE, J., TIMPERIO, A. & SALMON, J. 2018. Reliability and validity of self-reported sitting and breaks from sitting in the workplace. Journal of science and medicine in sport, 21, 697-701. SUFKA, K. J. 1994. Conditioned place preference paradigm: a novel approach for analgesic drug assessment against chronic pain. Pain, 58, 355-366. SULLIVAN, R. M. 2004. Hemispheric asymmetry in stress processing in rat prefrontal cortex and the role of mesocortical dopamine. Stress, 7, 131-143. SUPERIO-CABUSLAY, E., WARD, M. M. & LORIG, K. R. 1996. Patient education interventions in osteoarthritis and rheumatoid arthritis: a meta-analytic comparison with nonsteroidal antiinflammatory drug treatment. Arthritis Care and Research, 9, 292-301. SUTCLIFFE, J. S., MARSHALL, K. M. & NEILL, J. C. 2007. Influence of gender on working and spatial memory in the novel object recognition task in the rat. Behav Brain Res, 177, 117-25. SUZUKI, S., AUGERINOS, G. & BLACK, A. H. 1980. Stimulus control of spatial behavior on the eight-arm maze in rats. Learning and motivation, 11, 1-18. SWEERE, Y., KERKHOF, G. A., DE WEERD, A. W., KAMPHUISEN, H. A. C., KEMP, B. & SCHIMSHEIMER, R. J. 1998. The validity of the Dutch sleep disorders questionnaire (SDQ). Journal of Psychosomatic Research, 45, 549-555. TAGLIALATELA, G., HOGAN, D., ZHANG, W.-R. & DINELEY, K. T. 2009. Intermediate- and long-term recognition memory deficits in Tg2576 mice are reversed with acute calcineurin inhibition. Behavioural Brain Research, 200, 95-99. TAIBI, D. M. & VITIELLO, M. V. 2011. A pilot study of gentle yoga for sleep disturbance in women with osteoarthritis. Sleep medicine, 12, 512-517. TAKEUCHI, T. & HARADA, E. 2002. Age-related changes in sleep-wake rhythm in dog. Behavioural brain research, 136, 193-199. TALBERT, L. M., KRAYBILL, E. N. & POTTER, H. D. 1976. Adrenal cortical response to circumcision in the neonate. Obstetrics and Gynecology, 48, 208-210. TAPP, P. D., SIWAK, C. T., ESTRADA, J., HOLOWACHUK, D. & MILGRAM, N. W. 2003. Effects of age on measures of complex working memory span in the beagle dog (Canis familiaris) using two versions of a spatial list learning paradigm. Learn Mem, 10, 148-60.

298

References

TAYLOR‐GJEVRE, R. M., GJEVRE, J. A., NAIR, B., SKOMRO, R. & LIM, H. J. 2011. Components of sleep quality and sleep fragmentation in rheumatoid arthritis and osteoarthritis. Musculoskeletal care, 9, 152-159. TAYLOR, R. 1990. Interpretation of the correlation coefficient: a basic review. Journal of diagnostic medical sonography, 6, 35-39. THOMAS, R. J., ROSEN, B. R., STERN, C. E., WEISS, J. W. & KWONG, K. K. 2005. Functional imaging of working memory in obstructive sleep-disordered breathing. Journal of Applied Physiology. TOLMAN, E. C. 1925. Purpose and cognition: the determiners of animal learning. Psychological Review, 32, 285. TONONI, G. & KOCH, C. 2008. The neural correlates of consciousness. Annals of the New York Academy of Sciences, 1124, 239-261. TOPÁL, J., MIKLÓSI, Á. & CSÁNYI, V. 1997. Dog-Human Relationship Affects Problem Solving Behavior in the Dog. Anthrozoös, 10, 214-224. TORRES, I. L. S., VASCONCELLOS, A. P., SILVEIRA CUCCO, S. N. & DALMAZ, C. 2001. Effect of repeated stress on novelty-induced antinociception in rats. Brazilian Journal of Medical and Biological Research, 34, 241-244. TROIANO, R. P., BERRIGAN, D., DODD, K. W., MASSE, L. C., TILERT, T. & MCDOWELL, M. 2008. Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40, 181-188. TROSTEL, C. T., MCLAUGHLIN, R. M. & POOL, R. R. 2003. Canine Elbow Dysplasia: Anatomy and Pathogenesis. Compendium. TROUCHE, S. G., MAURICE, T., ROULAND, S., VERDIER, J.-M. & MESTRE-FRANCÉS, N. 2010. The three-panel runway maze adapted to Microcebus murinus reveals age- related differences in memory and perseverance performances. Neurobiology of Learning and Memory, 94, 100-106. TULVING, E. 1972. Episodic and semantic memory. Organization of memory, 1, 381- 403. TUYTTENS, F. A. M., DE GRAAF, S., HEERKENS, J. L. T., JACOBS, L., NALON, E., OTT, S., STADIG, L., VAN LAER, E. & AMPE, B. 2014. Observer bias in animal behaviour research: can we believe what we score, if we score what we believe? Animal Behaviour, 90, 273-280. UBBINK, G. J., HAZEWINKEL, H. A., VAN DE BROEK, J. & ROTHUIZEN, J. 1999. Familial clustering and risk analysis for fragmented coronoid process and elbow joint incongruity in Bernese Mountain Dogs in The Netherlands. American journal of veterinary research, 60, 1082-1087. UCLA: STATISTICAL CONSULTING GROUP. 2017. Mixed Effects Logistic Regression | R Data Analysis Examples. [Online]. Available: https://stats.idre.ucla.edu/r/dae/mixed- effects-logistic-regression/ [Accessed 28/12/2017]. UNDERBJERG, M., GEORGE, M. S., THORSEN, P., KESMODEL, U. S., MORTENSEN, E. L. & MANLY, T. 2013. Separable sustained and selective attention factors are apparent in 5-year-old children. PloS one, 8, e82843. VACHON-PRESSEAU, E., MARTEL, M. O., ROY, M., CARON, E., ALBOUY, G., MARIN, M. F., PLANTE, I., SULLIVAN, M. J., LUPIEN, S. J. & RAINVILLE, P. 2013. Acute stress contributes to individual differences in pain and pain-related brain activity in healthy and chronic pain patients. J Neurosci, 33, 6826-33. VALDES, A. M., HART, D. J., JONES, K. A., SURDULESCU, G., SWARBRICK, P., DOYLE, D. V., SCHAFER, A. J. & SPECTOR, T. D. 2004. Association study of candidate genes for the prevalence and progression of knee osteoarthritis. Arthritis & Rheumatology, 50, 2497-2507.

299

References

VAN DER STAAY, F. J. 1999. Spatial Working Memory and Reference Memory of Brown Norway and WAG Rats in a Holeboard Discrimination Task. Neurobiology of Learning and Memory, 71, 113-125. VAN DER STAAY, F. J., GIELING, E. T., PINZON, N. E., NORDQUIST, R. E. & OHL, F. 2012. The appetitively motivated "cognitive" holeboard: a family of complex spatial discrimination tasks for assessing learning and memory. Neurosci Biobehav Rev, 36, 379-403. VAN DER STAAY, F. J., RAAIJMAKERS, W. G. M., LAMMERS, A. & TONNAER, J. 1989. Selective fimbria lesions impair acquisition of working and reference memory of rats in a complex spatial discrimination task. Behavioural brain research, 32, 151-161. VAN DER STAAY, F. J., VAN NIES, J. & RAAIJMAKERS, W. 1990. The effects of aging in rats on working and reference memory performance in a spatial holeboard discrimination task. Behavioral and neural biology, 53, 356-370. VAN LENT, P., BLOM, A. B., VAN DER KRAAN, P., HOLTHUYSEN, A. E. M., VITTERS, E., VAN ROOIJEN, N., SMEETS, R. L., NABBE, K. & VAN DEN BERG, W. B. 2004. Crucial role of synovial lining macrophages in the promotion of transforming growth factor β–mediated osteophyte formation. Arthritis & Rheumatology, 50, 103-111. VAN SAASE, J. L., VAN ROMUNDE, L. K., CATS, A., VANDENBROUCKE, J. P. & VALKENBURG, H. A. 1989. Epidemiology of osteoarthritis: Zoetermeer survey. Comparison of radiological osteoarthritis in a Dutch population with that in 10 other populations. Annals of the rheumatic diseases, 48, 271. VAN SOMEREN, E. J. W. 2007. Improving actigraphic sleep estimates in insomnia and dementia: how many nights? Journal of sleep research, 16, 269-275. VERHAAK, P. F. M., KERSSENS, J. J., DEKKER, J., SORBI, M. J. & BENSING, J. M. 1998. Prevalence of chronic benign pain disorder among adults: a review of the literature. Pain, 77, 231-239. VOGT, A., KAPPOS, L., CALABRESE, P., STÖCKLIN, M., GSCHWIND, L., OPWIS, K. & PENNER, I.-K. 2009. Working memory training in patients with multiple sclerosis– comparison of two different training schedules. Restorative neurology and neuroscience, 27, 225-235. VON KORFF, M., DWORKIN, S. F., LE RESCHE, L. & KRUGER, A. 1988. An epidemiologic comparison of pain complaints. Pain, 32, 173-183. VOON, V., BAEK, K., ENANDER, J., WORBE, Y., MORRIS, L. S., HARRISON, N. A., ROBBINS, T. W., RÜCK, C. & DAW, N. 2015. Motivation and value influences in the relative balance of goal-directed and habitual behaviours in obsessive-compulsive disorder. Translational psychiatry, 5, e670. WADHWA, S., EMBREE, M. C., KILTS, T., YOUNG, M. F. & AMEYE, L. G. 2005. Accelerated osteoarthritis in the temporomandibular joint of biglycan/fibromodulin double-deficient mice. Osteoarthritis and cartilage, 13, 817-827. WAI, C. T., CHENG, C. L., WEE, A., DAN, Y. Y., CHAN, E., CHUA, W., MAK, B., OO, A. M. & LIM, S. G. 2006. Non‐invasive models for predicting histology in patients with chronic hepatitis B. Liver international, 26, 666-672. WAJED, J., EJINDU, V., HERON, C., HERMANSSON, M., KIELY, P. & SOFAT, N. 2012. Quantitative sensory testing in painful hand osteoarthritis demonstrates features of peripheral sensitisation. International journal of rheumatology, 2012. WALSH, D. A., MCWILLIAMS, D. F., TURLEY, M. J., DIXON, M. R., FRANSÈS, R. E., MAPP, P. I. & WILSON, D. 2010. Angiogenesis and nerve growth factor at the osteochondral junction in rheumatoid arthritis and osteoarthritis. Rheumatology, 49, 1852-1861. WALTON, M. B., COWDEROY, E., LASCELLES, D. & INNES, J. F. 2013. Evaluation of construct and criterion validity for the 'Liverpool Osteoarthritis in Dogs' (LOAD)

300

References clinical metrology instrument and comparison to two other instruments. PLoS One, 8, e58125. WAPPLER, E. A., SZILÁGYI, G., GÁL, A., SKOPÁL, J., NYAKAS, C., NAGY, Z. & FELSZEGHY, K. 2009. Adopted cognitive tests for gerbils: validation by studying ageing and ischemia. Physiology & behavior, 97, 107-114. WEAKLIEM, D. L. 1999. A critique of the Bayesian information criterion for model selection. Sociological Methods & Research, 27, 359-397. WEARY, D. M., NIEL, L., FLOWER, F. C. & FRASER, D. 2006. Identifying and preventing pain in animals. Applied animal behaviour science, 100, 64-76. WEBSTER, J. B., KRIPKE, D. F., MESSIN, S., MULLANEY, D. J. & WYBORNEY, G. 1982. An activity-based sleep monitor system for ambulatory use. Sleep, 5, 389-399. WEIR, A. A. S., CHAPPELL, J. & KACELNIK, A. 2002. Shaping of hooks in New Caledonian crows. Science, 297, 981-981. WERNHAM, B. G., TRUMPATORI, B., HASH, J., LIPSETT, J., DAVIDSON, G., WACKEROW, P., THOMSON, A. & LASCELLES, B. D. 2011. Dose reduction of meloxicam in dogs with osteoarthritis-associated pain and impaired mobility. J Vet Intern Med, 25, 1298-305. WERNIMONT, S. M., THOMPSON, R. J., MICKELSEN, S. L., SMITH, S. C., ALVARENGA, I. C. & GROSS, K. L. 2018. Use of Accelerometer Activity Monitors to Detect Changes in Pruritic Behaviors: Interim Clinical Data on 6 Dogs. Sensors, 18, 249. WESTERBERG, H., JACOBAEUS, H., HIRVIKOSKI, T., CLEVBERGER, P., ÖSTENSSON, M. L., BARTFAI, A. & KLINGBERG, T. 2007. Computerized working memory training after stroke–a pilot study. Brain Injury, 21, 21-29. WHISHAW, I. Q., COLES, B. L. K. & BELLERIVE, C. H. M. 1995. Food carrying: a new method for naturalistic studies of spontaneous and forced alternation. Journal of neuroscience methods, 61, 139-143. WHITE, R. G., DESHAZER, J. A., TRESSLER, C. J., BORCHER, G. M., DAVEY, S., WANINGE, A., PARKHURST, A. M., MILANUK, M. J. & CLEMENS, E. T. 1995. Vocalization and physiological response of pigs during castration with or without a local anesthetic. Journal of Animal Science, 73, 381-386. WHITTINGHAM, M. J., STEPHENS, P. A., BRADBURY, R. B. & FRECKLETON, R. P. 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of animal ecology, 75, 1182-1189. WICKHAM, H. 2009. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York WICKHAM, H. & CHANG, W. 2017. devtools: Tools to Make Developing R Packages Easier. R package version 1.13.4. Available: https://CRAN.R- project.org/package=devtools [Accessed 12/09/2018]. WICKHAM, H., FRANCOIS, R., HENRY, L. & MÜLLER, K. 2017. dplyr: A Grammar of Data Manipulation. R package version 0.7.3. Available: https://CRAN.R- project.org/package=dplyr [Accessed 12/09/2018]. WIESENFELD-HALLIN, Z. 2005. Sex differences in pain perception. Gender medicine, 2, 137-145. WILCOX, S., BRENES, G. A., LEVINE, D., SEVICK, M. A., SHUMAKER, S. A. & CRAVEN, T. 2000. Factors related to sleep disturbance in older adults experiencing knee pain or knee pain with radiographic evidence of knee osteoarthritis. Journal of the American Geriatrics Society, 48, 1241-1251. WILLIAMS, D. A., FARRELL, M. J., CUNNINGHAM, J., GRACELY, R. H., AMBROSE, K., CUPPS, T., MOHAN, N. & CLAUW, D. J. 2004. Knee pain and radiographic osteoarthritis interact in the prediction of levels of self‐reported disability. Arthritis Care & Research, 51, 558-561.

301

References

WILSON, K. G., WATSON, S. T. & CURRIE, S. R. 1998. Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain, 75, 75-84. WILSON, R. S., BENNETT, D. A., BIENIAS, J. L., AGGARWAL, N. T., DE LEON, C. F. M., MORRIS, M. C., SCHNEIDER, J. A. & EVANS, D. A. 2002. Cognitive activity and incident AD in a population-based sample of older persons. Neurology, 59, 1910-1914. WIRSCHING, B. A., BENINGER, R. J., JHAMANDAS, K., BOEGMAN, R. J. & EL- DEFRAWY, S. R. 1984. Differential effects of scopolamine on working and reference memory of rats in the radial maze. Pharmacology Biochemistry and Behavior, 20, 659-662. WISEMAN-ORR, M. L., SCOTT, E. M., REID, J. & NOLAN, A. M. 2006. Validation of a structured questionnaire as an instrument to measure chronic pain in dogs on the basis of effects on health-related quality of life. American Journal of Veterinary Research, 67, 1826-1836. WISEMAN, M. L., NOLAN, A. M., REID, J. & SCOTT, E. M. 2001. Preliminary study on owner-reported behaviour changes associated with chronic pain in dogs. The Veterinary Record, 149, 423-424. WITSBERGER, T. H., VILLAMIL, J. A., SCHULTZ, L. G., HAHN, A. W. & COOK, J. L. 2008. Prevalence of and risk factors for hip dysplasia and cranial cruciate ligament deficiency in dogs. Journal of the American Veterinary Medical Association, 232, 1818-1824. WITTKOWSKI, K. M. & SONG, T. 2012. muStat: Prentice Rank Sum Test and McNemar Test. R package version 1.7.0. WLUKA, A. E., CICUTTINI, F. M. & SPECTOR, T. D. 2000. Menopause, oestrogens and arthritis. Maturitas, 35, 183-199. WOBIG, M., BACH, G., BEKS, P., DICKHUT, A., RUNZHEIMER, J., SCHWIEGER, G., VETTER, G. & BALAZS, E. 1999. The role of elastoviscosity in the efficacy of viscosupplementation for osteoarthritis of the knee: a comparison of hylan GF 20 and a lower-molecular-weight hyaluronan. Clinical therapeutics, 21, 1549-1562. WOOD, E. R., DUDCHENKO, P. A. & EICHENBAUM, H. 1999. The global record of memory in hippocampal neuronal activity. Nature, 397, 613. WOOD, J. 2006. Effect of anxiety reduction on children's school performance and social adjustment. Developmental psychology, 42, 345. WOODSON, J. C., MACINTOSH, D., FLESHNER, M. & DIAMOND, D. M. 2003. Emotion- induced amnesia in rats: working memory-specific impairment, corticosterone- memory correlation, and fear versus arousal effects on memory. Learning & memory, 10, 326-336. WOOLF, C. J. 1983. Evidence for a central component of post-injury pain hypersensitivity. Nature, 306, 686. WOOLF, C. J. 1984. Long term alterations in the excitability of the flexion reflex produced by peripheral tissue injury in the chronic decerebrate rat. Pain, 18, 325- 343. WOOLF, C. J. 2011. Central sensitization: Implications for the diagnosis and treatment of pain. PAIN, 152, S2-S15. WOOLF, C. J. & DOUBELL, T. P. 1994. The pathophysiology of chronic pain— increased sensitivity to low threshold Aβ-fibre inputs. Current opinion in neurobiology, 4, 525-534. WOOLF, C. J. & MANNION, R. J. 1999. Neuropathic pain: aetiology, symptoms, mechanisms, and management. The lancet, 353, 1959-1964. WSAVA NUTRITIONAL ASSESSMENT GUIDELINES TASK FORCE MEMBERS 2011. WSAVA nutritional assessment guidelines. J Feline Med Surg, 13, 516-25.

302

References

WÜRBEL, H. 2001. Ideal homes? Housing effects on rodent brain and behaviour. Trends in neurosciences, 24, 207-211. XIE, M., YAN, J., HE, C., YANG, L., TAN, G., LI, C., HU, Z. & WANG, J. 2015. Short-term sleep deprivation impairs spatial working memory and modulates expression levels of ionotropic glutamate receptor subunits in hippocampus. Behavioural brain research, 286, 64-70. XU, L., PENG, H., WU, D., HU, K., GOLDRING, M. B., OLSEN, B. R. & LI, Y. 2005. Activation of the discoidin domain receptor 2 induces expression of matrix metalloproteinase 13 associated with osteoarthritis in mice. Journal of Biological Chemistry, 280, 548-555. YAM, P. S., PENPRAZE, V., YOUNG, D., TODD, M. S., CLONEY, A. D., HOUSTON‐ CALLAGHAN, K. A. & REILLY, J. J. 2011. Validity, practical utility and reliability of Actigraph accelerometry for the measurement of habitual physical activity in dogs. Journal of Small Animal Practice, 52, 86-91. YAMAMOTO, T., WAKITANI, S., IMOTO, K., HATTORI, T., NAKAYA, H., SAITO, M. & YONENOBU, K. 2004. Fibroblast growth factor-2 promotes the repair of partial thickness defects of articular cartilage in immature rabbits but not in mature rabbits. Osteoarthritis and cartilage, 12, 636-641. YANG, C.-C. & HSU, Y.-L. 2010. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10, 7772-7788. YASHARI, J. M., DUNCAN, C. G. & DUERR, F. M. 2015. Evaluation of a novel canine activity monitor for at-home physical activity analysis. BMC veterinary research, 11, 146. YERKES, R. M. & DODSON, J. D. 1908. The relation of strength of stimulus to rapidity of habit‐formation. Journal of comparative neurology, 18, 459-482. YUEN, E. Y., LIU, W., KARATSOREOS, I. N., FENG, J., MCEWEN, B. S. & YAN, Z. 2009. Acute stress enhances glutamatergic transmission in prefrontal cortex and facilitates working memory. Proc Natl Acad Sci U S A, 106, 14075-9. ZANGHI, B. M., ARAUJO, J. & MILGRAM, N. W. 2015. Cognitive domains in the dog: independence of working memory from object learning, selective attention, and motor learning. Anim Cogn, 18, 789-800. ZEUNER, K. E., SHOGE, R. O., GOLDSTEIN, S. R., DAMBROSIA, J. M. & HALLETT, M. 2003. Accelerometry to distinguish psychogenic from essential or parkinsonian tremor. Neurology, 61, 548-550. ZHANG, Y.-W., SU, Y., LANNING, N., SWIATEK, P. J., BRONSON, R. T., SIGLER, R., MARTIN, R. W. & WOUDE, G. F. V. 2005. Targeted disruption of Mig-6 in the mouse genome leads to early onset degenerative joint disease. Proceedings of the National Academy of Sciences of the United States of America, 102, 11740-11745. ZHANG, Y. & JORDAN, J. M. 2010. Epidemiology of osteoarthritis. Clinics in geriatric medicine, 26, 355-369. ZHANG, Y., XU, L., NEVITT, M. C., ALIABADI, P., YU, W., QIN, M., LUI, L. Y. & FELSON, D. T. 2001. Comparison of the prevalence of knee osteoarthritis between the elderly Chinese population in Beijing and whites in the United States: The Beijing Osteoarthritis Study. Arthritis & Rheumatology, 44, 2065-2071. ZITMAN, F. G., LINSSEN, A. C. G., EDELBROEK, P. M. & STIJNEN, T. 1990. Low dose amitriptyline in chronic pain: the gain is modest. Pain, 42, 35-42. ZOLA-MORGAN, S. & SQUIRE, L. R. 1986. Memory impairment in monkeys following lesions limited to the hippocampus. Behavioral neuroscience, 100, 155.

303

Appendices

Appendices:

Appendix 1 – Introduction to appendices:

Appendices are grouped and numbered according to the chapter they are most relevant to or are first described in. For example Appendix 2 contains the materials that were predominantly/first described in Chapter 2. There are no appendices associated with Chapters 1 or 6.

Please note that because most printed documents used in these studies were printed on A4 pages with narrow margins, images of these pages are reduced in size when provided in the appendices due to the relatively wider margins of this document.

Appendix 2 – Material associated with Chapter 2:

Appendix 2.1 – Clinical checklist: For examination and history taking of dogs (adapted from Harris (2017)):

304

Appendices

305

Appendices

306

Appendices

307

Appendices

Appendix 2.2 – Types of questionnaire validity: A description of different types of clinical questionnaire validity and how they are assessed, using information from Hielm-Björkman et al. (2009):

Term: Description: Assessed by:

Extent to which the questionnaire Subjective opinion rather than Face validity subjectively appears to measure evidence base. chronic pain.

Subjective opinion, consulting with Extent to which the questionnaire experts and reading relevant Content validity covers all of the aspects of chronic literature to best ensure all pain. potential aspects are covered.

Extent to which the questionnaire Measurement of chronic pain Criterion score(s) correlate(s) with other using an established method, validity existing measurements of chronic assessing correlation with pain. questionnaire scores.

Extent to which the components of the questionnaire are actually Construct measuring aspects of chronic pain, Principal component analysis or validity rather than another phenomenon extreme groups comparison. (some questionnaires measure multiple constructs/phenomena).

Assesses relationships between Identify factors with an eigenvalue Principal question responses to identify how >1, confirm that these account for Component many individual a large proportion of total Analysis constructs/phenomena are included variation. in the questionnaire

308

Appendices

Assesses the ability of the Extreme questionnaire to distinguish Presence of statistically significant Groups between two different groups e.g. differences in score(s) between Comparison healthy pain free dogs and dogs extreme groups. with chronic pain.

Extent to which the questionnaire Includes repeatability and internal gives the same results whenever it is Reliability consistency. Longer questionnaires used, assuming all other factors also tend to be more reliable. remain the same.

Repeatability/ Extent to which the questionnaire Repeating the test with the same gives the same results when used in cohort, generating an intraclass Test-retest the same cohort multiple times. correlation statistic. reliability

Extent to which results for each question in the questionnaire that Internal Cronbach's alpha score - a measure the same consistency measure of overall correlation. construct/phenomenon (e.g. joint stiffness) correlate with each other.

Extent to which the questionnaire Ability to detect significant can detect changes in chronic pain Responsiveness differences between scores over over time or in response to clinical time or following intervention interventions etc.

309

Appendices

Appendix 2.3 – The Helsinki Chronic Pain Index (HCPI): As described by Hielm-Björkman et al. (2003, 2009) (English Translation).

310

Appendices

311

Appendices

Appendix 2.4 – The Canine Brief Pain Inventory (CBPI): As described by Brown et al. (2007, 2008):

312

Appendices

313

Appendices

Appendix 2.5 – The ACVS Canine Orthopedic Index (COI): As described by Brown (2014a, 2014b, 2014c):

314

Appendices

315

Appendices

Appendix 2.6 – The Liverpool Osteoarthritis in Dogs (LOAD) questionnaire: As described by Hercock et al. (2009) and Walton et al. (2013):

316

Appendices

317

Appendices

318

Appendices

319

Appendices

Appendix 2.7 – The Canine Cognitive Dysfunction Rating (CCDR) Scale: As described by Salvin et al. (2011):

320

Appendices

Appendix 2.8 – The Canine Behavioral Assessment and Research Questionnaire (C-BARQ): As described by Nagasawa et al. (2011) and Hsu and Serpell (2003):

321

Appendices

322

Appendices

323

Appendices

324

Appendices

325

Appendices

326

Appendices

Appendix 2.9 – Owner information sheet: Owner information sheet for the study described in Chapter 2:

327

Appendices

328

Appendices

Appendix 2.10 – Owner consent form: Owner consent form for the study described in Chapter 2:

329

Appendices

Appendix 2.11 – Ethogram for Novel Object Recognition Task: Ethogram used to record behaviours during the Novel Object Recognition (NOR) task described in Chapter 2. Animals were considered to be interacting with an object when they were pawing, mouthing or in “other contact” with the object:

Behaviour Type Description Terminates As soon as animal Animal directs eyes and nose directs eyes and Sniff/Nose towards object, less than 30cm nose away from RIGHT object State away object As soon as animal Animal directs eyes and nose directs eyes and Sniff/nose LEFT towards object, less than 30cm nose away from object State away object Part of animal's body excluding Once all parts of the tail is within the four crosses animal excluding Close proximity demarcating the immediate area tail leave the to RIGHT object State of the object demarcated area Part of animal's body excluding Once all parts of the tail is within the four crosses animal excluding Close proximity demarcating the immediate area tail leave the to LEFT object State of the object demarcated area Once neither of the animal's forelimbs Animal extends forelimb towards are in contact with, object <30cm from object or is or being moved Paw LEFT object State contacting object with forelimb(s) towards, the object Once neither of the animal's forelimbs Animal extends forelimb towards are in contact with, Paw RIGHT object <30cm from object or is or being moved object State contacting object with forelimb(s) towards, the object Once dog retracts tongue or closes Animal licks the object or places mouth without Mouth LEFT part of the object within its object enclosed for object State mouth >1s Once dog retracts tongue or closes Animal licks the object or places mouth without Mouth RIGHT part of the object within its object enclosed for object State mouth >1s Dog contacts object in way not covered by other behaviours Other contact listed (e.g. sitting, lying or Once no longer in with LEFT standing on object, mounting physical contact object State object etc.) with object

330

Appendices

Dog contacts object in way not covered by other behaviours Other contact listed (e.g. sitting, lying or Once no longer in with RIGHT standing on object, mounting physical contact object State object etc.) with object When vocalisation Prolonged, high-pitched, smooth stops or another Whine vocalisation. Mouth may be open vocalisation state (undirected) State or closed begins When vocalisation stops or another vocalisation state Growling State Prolonged rough guttural sound begins When vocalisation stops or another Prolonged, low-pitched vocalisation state Howl State vocalisation. begins Bark Individual short vocalisation (undirected) Point (<0.5s) N/A - point Animal has mouth open and rhythmic movements of the tongue and thorax are apparent, may be audible respiratory Once mouth is Panting State sounds. closed for >5s Dog adopts posture in which no limbs are weightbearing and body Once another is in contact with ground. Includes postural or lateral, dorsal or ventral locomotor state Lie down State recumbency. begins Dog adopts posture in which rump is brought nearer to ground, Once another hindlimbs flexed under body, and postural or a least one forelimb is extended locomotor state Sit State and weightbearing. begins Dog adopts posture in which at Once another least one forelimb and one postural or hindlimb is weightbearing. No locomotor state Stand State locomotion. begins Once another Relatively slow locomotion in postural or which more than two paws are on locomotor state Walk State the ground at any time. begins Once another Rapid locomotion in which at postural or least two paws are removed from locomotor state Trot or run State the ground at any time. begins Dog adopts hunched posture with hindlimbs forward and forelimbs Once another back under body, at least 3 limbs postural or bearing weight, possibly visible locomotor state Squatting State straining. begins

331

Appendices

Dog adopts posture in which Once another rump is brought nearer to ground, postural or hindlimbs flexed under body, locomotor state Beg State both forelimbs raised into the air. begins Dog has hindpaws on the ground, places at least one forelimb Once another against wall and raises body to postural or Climbs wall or become more parallel with wall or locomotor state window ledge State window. begins Dog licks, bites or scratches with After >1s of not its paw a part of its body. (Lip- contacting body licking mutually exclusive and with mouth, Groom State takes priority) tongue or paws Dog has one hindlimb abducted at When hindlimb is the hip and not weight bearing, replaced under the Cock leg Point stifle flexed. Urine may be visible. animal's body Animal defecates whilst squatting From when faeces - measure from when faeces no longer emerging Defecate Point become visible. from animal Dog raises forelimbs into the air Once one forelimb with hindpaws still placed on is placed back on Rear State ground and weightbearing ground Dog makes slight, rapid movements of head and muzzle Sniff (interacting with fence or object is >2s after rapid environment/ mutually exclusive and takes movements of air State priority) head cease. Once head is Dog is still for >1s, looks towards directed away from Look out of rear wall with head pointed window or window State upwards towards window locomotion begins After the dog has Dog moves rapidly in a tight not performed circular motion as if "chasing its circular locomotion Spin State tail" for >1s After >1s of bringing ears Dog's ears move caudodorsally rostroventrally into and may be held flat against the relaxed or forward- Ears back State head. facing position. Dogs tail curves such that it is >1s after tail is angled such that the tip is at an raised above the angle between the floor and the plane from the tail plane from the tail base base perpendicular Tail under State perpendicular to the arena walls to the arena walls Once another Dog adopts a hunched posture in postural or Hunched/tense which its muscles are noticeably locomotor state posture State tensed begins

332

Appendices

>2s after rapid movements cease. Can occur with A part of the dog's body other other than its head or tail makes rapid, postural/locomoto Trembling State slight movements. r states Dogs tongue protrudes and licks Licks lips Point around its muzzle N/A - point As soon as animal turns away (>=90 Animal faces fence and contacts degrees) from fence with head or forelimbs. fence, or when Includes nudging, jumping, animal has not climbing, pawing, biting. Animal’s interacted with the Interact with eyes are pointed towards fence fence in any way fence State and not through it. for >2s Animal's forelimb crosses Start Point threshold of gate to enter arena N/A - point 5 minutes have elapsed since Complete Point entry to arena N/A - point As soon as animal turns away (>=90 degrees) from fence or when Animal points head towards fence interaction with with eyes directed at a point the fence or Look through through the fence rather than at environment fence State the fence itself. begins. A sound audible on the recording occurs that originated outside the Immediately Loud external arena. Does not include dialogue following cessation noise State by the owner or researcher. of the sound The trial was terminated due to excessive distress or danger to Terminated Point the dog, researcher or owner. N/A - point

333

Appendices

Appendix 2.12 – Signalments of pilot study dogs: Signalments of dogs used in the pilot study for the novel object recognition and holeboard tasks in Chapter 2:

Appendix 2.13 – Investigation ratios for pilot study dogs: Investigation ratios of pilot dogs during the test phase of the novel object recognition task. All dogs were healthy controls, with the exception of dog 6, who had osteoarthritis. Dogs 3 and 7 did not interact with either object in either phase of the study so their investigation ratios could not be calculated.

334

Appendices

Appendix 2.14 – Pilot study results for the Holeboard task: Showing the reference memory score (A), working memory score (B) and time taken to find all treats (C) (n=6). There was no effect of trial on any of these measures (assessed by one-way ANOVAs). Dog 6, the only pilot dog with osteoarthritis, did not attempt to search the buckets at all, and as such was excluded from the results.

335

Appendices

Appendix 3 – Material associated with Chapter 3:

Appendix 3.1 – Owner information sheet: For the study described in Chapter 3:

336

Appendices

337

Appendices

Appendix 3.2 – VBA Macro (Days 1-2): Macro to generate holeboard outcomes for days 1 and 2 (sessions 1-4) of the holeboard task described in Chapter 3:

Sub HoleboardDay1() ' ' HoleboardDay1 Macro '

Range("A17:A324").Select Selection.NumberFormat = "0.00" Selection.TextToColumns Destination:=Range("A17:A324"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=True, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 1), TrailingMinusNumbers:=True Range("C17").Select ActiveWindow.SmallScroll Down:=270 Range("C17:C303").Select Selection.NumberFormat = "0.00" Selection.TextToColumns Destination:=Range("C17:C324"), DataType:=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=False, Tab:=True, _ Semicolon:=False, Comma:=False, Space:=False, Other:=False, FieldInfo _ :=Array(1, 1), TrailingMinusNumbers:=True

Range("J1").Select Selection.Font.Bold = True ActiveCell.FormulaR1C1 = "Row" Range("J2").Select ActiveCell.FormulaR1C1 = "Value" Range("J2").Select Selection.Font.Bold = True Range("K1").Select ActiveCell.FormulaR1C1 = "A" Range("L1").Select ActiveCell.FormulaR1C1 = "B"

338

Appendices

Range("M1").Select ActiveCell.FormulaR1C1 = "C" Range("N1").Select ActiveCell.FormulaR1C1 = "D" Range("K1:N1").Select Selection.Font.Bold = True Range("O1").Select ActiveCell.FormulaR1C1 = "Treats found" Range("P1").Select ActiveCell.FormulaR1C1 = "Trial number" Range("O2").Select ActiveCell.Formula = "= IF(COUNTIF(F17:F300,""A1"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""B4"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""C2"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""D3"")>0,""1"",""0"")" Range("P2").Select ActiveCell.FormulaR1C1 = "" Range("P1").Select Selection.ClearContents Range("N2").Select Range("K2").Select Range("K2").Select ActiveCell.FormulaR1C1 = "A1" Range("L2").Select ActiveCell.FormulaR1C1 = "B4" Range("M2").Select ActiveCell.FormulaR1C1 = "C2" Range("N2").Select ActiveCell.FormulaR1C1 = "D3" Range("J4").Select ActiveCell.FormulaR1C1 = "Number of visits to previously baited buckets" Range("J5").Select ActiveCell.FormulaR1C1 = "Total number of bucket visits" Range("J6").Select ActiveCell.FormulaR1C1 = "Reference memory score" Range("J7").Select ActiveCell.FormulaR1C1 = "Working memory score (baited)" Range("J8").Select ActiveCell.FormulaR1C1 = "Total number of unique bucket visits (all)"

339

Appendices

Range("J9").Select ActiveCell.FormulaR1C1 = "working memory score (all)" Range("J10").Select ActiveCell.FormulaR1C1 = "Time taken to find all treats" Range("J11").Select ActiveCell.FormulaR1C1 = "Number of treats found" Range("J13").Select ActiveCell.FormulaR1C1 = "WM Errors (re-entries):" Range("J14").Select ActiveCell.FormulaR1C1 = "RM Errors (visit to never baited)" Range("J15").Select ActiveCell.FormulaR1C1 = "Omission Errors (holes missed)" Range("K4").Select ActiveCell.Formula = "=SUM(COUNTIF(F17:F300, K2), COUNTIF(F17:F300, L2), COUNTIF(F17:F300, M2), COUNTIF(F17:F300, N2))" Range("K5").Select ActiveCell.Formula = "=COUNTIF(F17:F300,""??"")" Range("K6").Select ActiveCell.Formula = "=K4/K5" Range("K7").Select ActiveCell.Formula = "=(O2/K4)" Range("K8").Select ActiveCell.Formula = "=SUMPRODUCT((F17:F300<>"""")/COUNTIF(F17:F300,F17:F300&""""))-2" Range("K9").Select ActiveCell.Formula = "=K8/K5" Range("K10").Select ActiveCell.Formula = "=IF(R[-8]C[4]=4, ((LOOKUP(2,1/(R[7]C[-10]:R[290]C[-10]<>""""),R[7]C[- 10]:R[290]C[-10]) - R[7]C[-10])), ""Incomplete"")" Range("K11").Select ActiveCell.Formula = "=O2" Range("K13").Select ActiveCell.Formula = "=K5-K8" Range("K14").Select ActiveCell.Formula = "=K5-K4" Range("K15").Select ActiveCell.Formula = "=4-K11" Range("J16").Select

340

Appendices

ActiveCell.FormulaR1C1 = "Total Time Taken" Range("K16").Select ActiveCell.Formula = "=MAX(A17:A300)-MIN(A17:A300)"

End Sub

Appendix 3.3 – VBA Macro (Day 3): Macro to generate holeboard outcomes for day 3 (sessions 5-6) of the holeboard task described in Chapter 3:

Sub HoleboardDay3() ' ' HoleboardDay3 Macro '

Range("J1").Select

341

Appendices

Selection.Font.Bold = True ActiveCell.FormulaR1C1 = "Row" Range("J2").Select ActiveCell.FormulaR1C1 = "Value" Range("J2").Select Selection.Font.Bold = True Range("K1").Select ActiveCell.FormulaR1C1 = "A" Range("L1").Select ActiveCell.FormulaR1C1 = "B" Range("M1").Select ActiveCell.FormulaR1C1 = "C" Range("N1").Select ActiveCell.FormulaR1C1 = "D" Range("K1:N1").Select Selection.Font.Bold = True Range("O1").Select ActiveCell.FormulaR1C1 = "Treats found" Range("P1").Select ActiveCell.FormulaR1C1 = "Trial number" Range("O2").Select ActiveCell.Formula = "= IF(COUNTIF(F17:F300,""A3"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""B2"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""C4"")>0,""1"",""0"")+ IF(COUNTIF(F17:F300,""D1"")>0,""1"",""0"")" Range("P2").Select ActiveCell.FormulaR1C1 = "" Range("P1").Select Selection.ClearContents Range("N2").Select Range("K2").Select Range("K2").Select ActiveCell.FormulaR1C1 = "A3" Range("L2").Select ActiveCell.FormulaR1C1 = "B2" Range("M2").Select ActiveCell.FormulaR1C1 = "C4" Range("N2").Select ActiveCell.FormulaR1C1 = "D1"

342

Appendices

Range("J4").Select ActiveCell.FormulaR1C1 = "Number of visits to previously baited buckets" Range("J5").Select ActiveCell.FormulaR1C1 = "Total number of bucket visits" Range("J6").Select ActiveCell.FormulaR1C1 = "Reference memory score" Range("J7").Select ActiveCell.FormulaR1C1 = "Working memory score (baited)" Range("J8").Select ActiveCell.FormulaR1C1 = "Total number of unique bucket visits (all)" Range("J9").Select ActiveCell.FormulaR1C1 = "working memory score (all)" Range("J10").Select ActiveCell.FormulaR1C1 = "Time taken to find all treats" Range("J11").Select ActiveCell.FormulaR1C1 = "Number of treats found" Range("J13").Select ActiveCell.FormulaR1C1 = "WM Errors (re-entries):" Range("J14").Select ActiveCell.FormulaR1C1 = "RM Errors (visit to never baited)" Range("J15").Select ActiveCell.FormulaR1C1 = "Omission Errors (holes missed)" Range("K4").Select ActiveCell.Formula = "=SUM(COUNTIF(F17:F300, K2), COUNTIF(F17:F300, L2), COUNTIF(F17:F300, M2), COUNTIF(F17:F300, N2))" Range("K5").Select ActiveCell.Formula = "=COUNTIF(F17:F300,""??"")" Range("K6").Select ActiveCell.Formula = "=K4/K5" Range("K7").Select ActiveCell.Formula = "=(O2/K4)" Range("K8").Select ActiveCell.Formula = "=SUMPRODUCT((F17:F300<>"""")/COUNTIF(F17:F300,F17:F300&""""))-2" Range("K9").Select ActiveCell.Formula = "=K8/K5" Range("K10").Select

343

Appendices

ActiveCell.Formula = "=IF(R[-8]C[4]=4, ((LOOKUP(2,1/(R[7]C[-10]:R[290]C[-10]<>""""),R[7]C[- 10]:R[290]C[-10]) - R[7]C[-10])), ""Incomplete"")" Range("K11").Select ActiveCell.Formula = "=O2" Range("K13").Select ActiveCell.Formula = "=K5-K8" Range("K14").Select ActiveCell.Formula = "=K5-K4" Range("K15").Select ActiveCell.Formula = "=4-K11" Range("J16").Select ActiveCell.FormulaR1C1 = "Total Time Taken" Range("K16").Select ActiveCell.Formula = "=MAX(A17:A300)-MIN(A17:A300)"

End Sub

344

Appendices

Appendix 3.4 – Data entry sheet: Raw data entry sheet showing order of trials performed for the disappearing object task described in Chapter 3:

345

Appendices

Appendix 3.5 – R script: R script used for the analyses described in Chapter 3:

#### FOREWORD: ####

# Melissa Smith, University of Bristol, 27/12/2017

# This is an R-script used to generate, plot and predict outcomes from # the mixed-effects logistic regression model used in the analysis # of the disappearing object data for thesis chapter 3.

# It is divided into sections that begin and end with four "#" symbols. # Using RStudio, these can be collapsed and expanded for ease of reading. # Comments and currently-unused code are prefaced by the "#" symbol.

#### 1. LOAD PACKAGES: ####

# Clear any existing data from memory: rm(list=ls())

# Install new required packages # (note, installed packages are commented-out to prevent reinstallation; # to install, remove "#" symbol and re-run):

# install.packages("devtools") # library("devtools"); install_github("lme4",user="lme4") # need developer version of lme4 for predict() to work with glmer() models # install.packages("GGally") # install.packages("Hmisc") # install.packages("gridExtra") # Load required packages once installed: library(tidyverse) require(ggplot2) require(reshape2) require(lme4) require(compiler) require(parallel) library(Hmisc) library(blmeco) library(gridExtra)

#### 2. IMPORT AND FORMAT DATA: ####

# Load the data from Microsoft Excel .csv file: data <- read.csv("DOresults_proximity.csv")

# Examine the data structure: str(data)

# Correct Excel's alteration of years to dates: data$AgeGroup <- recode(data$AgeGroup, "06-Oct" = "6-10 years") data$AgeGroup <-recode(data$AgeGroup, "01-May" = "0-5 years") data$AgeGroup <-recode(data$AgeGroup, "Nov-15" = "11-15 years")

346

Appendices data$Dog<- data$ï..Dog # To correct character change on import

# Box, dog, trialblock, agegroup, sex should be categorical ("factor"). # Change these to the correct variable types:

data <- within(data, { Dog <- factor(Dog) Block <- factor(Block) AgeGroup <- factor(AgeGroup) Box <-factor(Box) Sex <- factor(Sex) })

#### 3. RUN THE MODEL: ####

# With "Dog" as a random effect, all other variables as fixed effects: # (lme4 automatically detects variable nesting from dataset structure)

model <- glmer(SuccessBinary ~ Interval + Block + Box + Trial + AgeGroup + Sex + Weight + (1 | Dog), data = data, family = binomial, nAGQ=100)

# Generates the error messages:

# Warning messages: # 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : # Model failed to converge with max|grad| = 0.0023924 (tol = 0.001, component 1) # 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : # Model is nearly unidentifiable: very large eigenvalue # - Rescale variables?

#### 4.0. DEBUGGING & ASSUMPTION TESTING - RESCALE VARIABLES: ####

# Rescale the variables for continuous numeric variables # (interval, trial and weight) so they are between 0 and 1: numcols <- data %>% dplyr::select(Interval, Trial, Weight) numnames <-names(numcols) datascaled <- data datascaled[,numnames] <- scale(datascaled[,numnames]) model1 <- (update(model,data=datascaled))

summary(model1, corr=FALSE)

# No more warning messages in the rescaled model, suggesting that # the warnings were just due to a scale difference between variables summary(model, corr=FALSE)

# However the p-values for the non-rescaled model # are almost identical, despite the warnings.

347

Appendices

# Therefore keep the non-rescaled model as it will be more straightforward # not to rescale the variables when predicting outcomes from the model.

#### 4.1. DEBUGGING AND ASSUMPTION TESTING - TEST FOR OVERDISPERSION: ####

# Use ?dispersion_glmer() # from package "blmeco" to test model for overdispersion: dispersion_glmer(model)

# 1.11 is between the recommended values of 0.75 and 1.4, # therefore model is unlikely to be overdispersed.

#### 5. CALCULATE ODDS RATIOS: ####

# First calculate standard errors from the coefficients: se <- sqrt(diag(vcov(model))) (tab <- cbind(Est = fixef(model), LL = fixef(model) - 1.96 * se, UL = fixef(model) + 1.96 * se))

# Then exponentiate these to get the odds ratios: oddsratios <- exp(tab) oddsratios

#### 6. OBSERVED DATA PLOTS: ####

# In this section, the observed (non-modelled) outcome data will be plotted # For the factors that had a significant effect on the probability of success. # Once the model has been used to predict outcomes, these will be plotted # on the same axes, in order to compare observed and predicted outcomes.

# First ensure Success is a logistic (binary) variable so it will plot correctly: data$SuccessBinary <- as.logical(data$SuccessBinary)

# Calculate means and confidence intervals by interval: intervalmeans <- data %>% group_by(Interval) %>% summarise(mean = mean(SuccessBinary), count_success = sum(SuccessBinary), count_total = n(), count_fail = count_total - count_success) %>% group_by(Interval) %>% mutate(proportion_success_lci = binconf (count_success, count_total) [2], proportion_success_uci = binconf (count_success, count_total) [3])

# Plot results by interval: ggplot(data = intervalmeans, mapping=aes(x=Interval, y=mean, ymin=proportion_success_lci, ymax=proportion_success_uci)) + geom_point(stat="identity") + geom_errorbar() + geom_smooth(method="auto", color="red", size=0.5) + geom_hline(yintercept=0.25)

348

Appendices

# Calculate means and confidence intervals by trial: trialmeans <- data %>% group_by(Trial) %>% summarise(mean = mean(SuccessBinary), count_success = sum(SuccessBinary), count_total = n(), count_fail = count_total - count_success) %>% group_by(Trial) %>% mutate(proportion_success_lci = binconf (count_success, count_total) [2], proportion_success_uci = binconf (count_success, count_total) [3])

#Plot results by trial: ggplot(data = trialmeans, mapping=aes(x=Trial, y=mean, ymin=proportion_success_lci, ymax=proportion_success_uci)) + geom_point(stat="identity") + geom_errorbar() + geom_smooth(colour="red", size = 0.5)

# Because of the variability between trials it is difficult to assess # the overall trend, however plotting the predicted probability of success # for each trial using the model should elucidate this.

# Calculate means and confidence intervals by box: boxmeans <- data %>% group_by(Box) %>% summarise(mean = mean(SuccessBinary), count_success = sum(SuccessBinary), count_total = n(), count_fail = count_total - count_success) %>% group_by(Box) %>% mutate(proportion_success_lci = binconf (count_success, count_total) [2], proportion_success_uci = binconf (count_success, count_total) [3])

# Plot data by box: box_observed <- ggplot(data = boxmeans, mapping=aes(x=Box, y=mean, fill=Box, ymin=proportion_success_lci, ymax=proportion_success_uci)) + geom_bar(stat="identity", color="black") + geom_errorbar() + theme_bw() + scale_fill_manual(values=c("yellow", "cyan", "magenta","green")) + ylab("Proportion of successful visits") + xlab("Target box") + theme_bw() + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + theme(legend.position="none") + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,0.65), expand = c(0, 0))

# It looks like the dogs successfully found the toy more often # when it was hidden between boxes 1 and 4 rather than 2 and 3. # This seems reasonable as the probability of success for box 2

349

Appendices

# significantly differed from box 1 in the model.

# Bar chart of correct box against box visited by dog: boxmeans_visit <- data %>% group_by(Box, Visit) %>% summarise(count_total = n()) boxmeans_visit <- boxmeans_visit %>% mutate(proportion=count_total/150, total=150) %>% group_by(Box, Visit) %>% mutate(proportion_success_lci = binconf (count_total, total) [2], proportion_success_uci = binconf (count_total, total) [3])

Boxvisits <- ggplot(data = boxmeans_visit, mapping=aes(x=Box, y=proportion, fill=Visit)) + geom_bar(stat="identity", position="dodge", colour="black") + geom_errorbar(position="dodge", mapping=aes(ymax=proportion_success_uci, ymin=proportion_success_lci)) + theme_bw() + scale_color_manual(values=c("yellow", "cyan", "magenta","green", "grey")) + scale_fill_manual(values=c("yellow", "cyan", "magenta","green", "grey"), name = "Box visited:") + ylab("Proportion of visits") + xlab("Target Box") + theme_bw() + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + theme(legend.title = element_text(face="bold", size=14)) + theme(legend.text = element_text(face="bold", size=13)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,0.63), expand = c(0, 0))

#### 7.0 PREDICT AND PLOT EFFECT OF INTERVAL ON P(SUCCESS): ####

# Make a temporary dataset containing only the predictor and random variables: tmpdat <- data[, c("Interval", "Block", "Box", "Trial", "AgeGroup", "Sex", "Dog", "Weight")]

# Check the min and max of the main variable of interest (Interval): summary(data$Interval)

# Calculate the j-values (sequence of possible interval values between min and max) jvalues <- with(data, seq(from = min(Interval), to = max(Interval), length.out = 240))

# calculate predicted probabilities for these and store in a list pp <- lapply(jvalues, function(j) { tmpdat$Interval <- j predict(model, newdata = tmpdat, type = "response") })

# Get the mean and quartiles for the predictions: plotdata <- t(sapply(pp, function(x) { c(M = mean(x), quantile(x, c(0.25, 0.75))) }))

350

Appendices

# Append the interval j-values and convert to data frame # This is so the plotting package, ggplot2, can read it: plotdata <- as.data.frame(cbind(plotdata, jvalues))

# Name the variables: colnames(plotdata) <- c("PredictedProbability", "LQ", "UQ", "Interval")

# View first few fields to check data looks as expected: head(plotdata)

# Plot the data: (Intervalpredict <- ggplot(plotdata, aes(x = Interval, y = PredictedProbability)) + geom_linerange(aes(ymin = LQ, ymax = UQ)) # Plots the quartiles + geom_line(size = 2) + ylim(c(0, 1)))

# Plot the observed datapoints on the same axes:

intervalpredictionplot <- ggplot(plotdata, aes(x = Interval)) + # ylim(c(0, 1)) + geom_point(data = intervalmeans, mapping=aes(x=Interval, y=mean), color="red", stat="identity", size=3) + geom_errorbar(data = intervalmeans, mapping=aes(x=Interval, ymin=proportion_success_lci, ymax=proportion_success_uci), color="red", size=1) + geom_ribbon(aes(ymin = LQ, ymax = UQ, y = PredictedProbability), fill="grey", alpha=0.6) + # Plots the quartiles geom_line(size = 2, aes(y = PredictedProbability)) + ylab("Predicted probability of success") + xlab("Interval (seconds)") + theme_bw() + scale_x_continuous(breaks=seq(0,240,30)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + geom_hline(yintercept=0.25, linetype="dashed")

#### 7.1. PREDICT VALUE OF INTERVAL WHEN P(SUCCESS) = 0.25: ####

# Work out at what interval the predicted probability reaches 0.25, # as this is the value expected by chance assuming the dogs # are randomly selecting a box to search behind each trial:

# I.e. find the smallest value of Interval where # the probability is less than or equal to 0.25: chance_intercept <- plotdata %>% filter(PredictedProbability<=0.25) %>% summarise(intercept=min(Interval))

# Returns: # intercept # 1 206.8619

# So the model predicts that the dogs' probability of success first reaches # that which would be expected by chance after an interval of 207 seconds

351

Appendices

# (rounded to the nearest second).

#### 8. PREDICT AND PLOT EFFECT OF TRIAL ON P(SUCCESS): ####

# Trial also has a significant effect on P(Success) in this model. # So predict and plot the effect of trial also:

# Calculate the j-values (sequence of possible trial values between min and max) jvalues_trial <- with(data, seq(from = min(Trial), to = max(Trial), length.out = 20))

# calculate predicted probabilities and store in a list pp_trial <- lapply(jvalues_trial, function(j) { tmpdat$Trial <- j predict(model, newdata = tmpdat, type = "response") })

# Get the mean and quartiles for the predictions: plotdata_trial <- t(sapply(pp_trial, function(x) { c(M = mean(x), quantile(x, c(0.25, 0.75))) }))

# add in Trial j-values and convert to data frame plotdata_trial <- as.data.frame(cbind(plotdata_trial, jvalues_trial))

# Name the variables: colnames(plotdata_trial) <- c("PredictedProbability", "LQ", "UQ", "Trial")

#View first few fields: head(plotdata_trial)

# Plot the data: (Trialpredict <- ggplot(plotdata_trial, aes(x = Trial, y = PredictedProbability)) + geom_linerange(aes(ymin = LQ, ymax = UQ)) # Plots the quartiles + geom_line(size = 2) + ylim(c(0, 1)))

# Overlay the observed data for trial over the modelled data:

Trialcumulative <- ggplot(plotdata_trial, aes(x = Trial)) + # ylim(c(0, 1)) + geom_point(data = trialmeans, mapping=aes(x=Trial, y=mean), color="red", stat="identity", size=3) + geom_errorbar(data = trialmeans, mapping=aes(x=Trial, ymin=proportion_success_lci, ymax=proportion_success_uci), color="red", size=1) + geom_ribbon(aes(ymin = LQ, ymax = UQ, y = PredictedProbability), color="gray", alpha=0.3) + # Plots the quartiles geom_line(size = 2, aes(y = PredictedProbability)) + ylab("Predicted probability of success") + xlab("Trial") + theme_bw() + scale_x_continuous(breaks=seq(0,20,1)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + geom_hline(yintercept=0.25, linetype="dashed")

352

Appendices

# It seems that as the dog performs more trials, P(Success) decreases.

#### 9. PREDICT AND PLOT EFFECT OF INTERVAL SEPARATELY FOR EACH BOX: ####

# The model also found a significant effect of box 2 compared to box 1. # Therefore it may be beneficial to plot the effect of interval # separately for each of the four boxes.

# Separate into levels according to box: biprobs <- lapply(levels(data$Box), function(box) { tmpdat$Box[] <- box lapply(jvalues, function(j) { tmpdat$Interval <- j predict(model, newdata = tmpdat, type = "response") }) })

# get means and quartiles for all Interval jvalues for each level of Box: plotdata_2 <- lapply(biprobs, function(X) { temp <- t(sapply(X, function(x) { c(M=mean(x), quantile(x, c(.25, .75))) })) temp <- as.data.frame(cbind(temp, jvalues)) colnames(temp) <- c("PredictedProbability", "LQ", "UQ", "Interval") return(temp) })

# collapse from list into data frame format: plotdata_2 <- do.call(rbind, plotdata_2)

# add box as a variable: plotdata_2$Box <- factor(rep(levels(data$Box), each = length(jvalues))) head(plotdata_2)

#First try plotting separately as four panels: ggplot(plotdata_2, aes(x = Interval, y = PredictedProbability)) + geom_ribbon(aes(ymin = LQ, ymax = UQ, fill = Box), alpha = .15) + geom_line(aes(colour = Box), size = 2) + ylim(c(0, 1)) + facet_wrap(~ Box)

# They all seem to follow a similar pattern, but it's difficult # to compare them on different axes. # Plot them all on the same axes instead:

Box_model <- ggplot(plotdata_2, aes(x = Interval, y = PredictedProbability)) + geom_ribbon(aes(ymin = LQ, ymax = UQ, fill = Box), alpha = .25) + geom_line(aes(colour = Box), size = 2) + geom_hline(yintercept=0.25, linetype="dashed") + theme_bw() + scale_color_manual(values=c("yellow", "cyan", "magenta","green")) + scale_fill_manual(values=c("yellow", "cyan", "magenta","green")) + ylab("Predicted probability of success") + xlab("Interval (seconds)") + theme_bw() +

353

Appendices

scale_x_continuous(breaks=seq(0,240,30)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + theme(legend.title = element_text(face="bold", size=14)) + theme(legend.text = element_text(face="bold", size=13))

#### 10. NAMES OF PLOT AND RESULTS VARIABLES: ####

# Please note: The above sections must be run first! #

# Run the following code to show the respective plot: # These can be copied from the Rstudio Plot window # And pasted into e.g. MSPaint, for figure creation. intervalpredictionplot #Plot of predicted effect of interval on P(Success)

Trialcumulative #Plot of predicted effect of trial on P(Success)

Box_model #Plot of predicted effect of interval on P(Success) for each box box_observed #Plot of observed proportion of correct visits to each box

Boxvisits #Plot of visits to each box by which box was correct grid.arrange(box_observed, Box_model, ncol=2) #Plots 2 graphs in same panel

#Run the following to get odds ratios: oddsratios

#Run the following to get p-values for model factors: summary(model, corr=FALSE)

#Run the following to get the first interval at which P(Success) <0.25: chance_intercept

#### 11. CITING PACKAGES: ####

# This section gives the functions to run to get # citations for the packages used. # "parallel" and "compiler" are part of base R and do not need to be cited. # To cite the other packages listed in section 1: citation(package="tidyverse") citation(package="dplyr") citation(package="ggplot2") citation(package="reshape2") citation(package="lme4") citation(package="devtools") citation(package="Hmisc") citation(package="blmeco") citation(package="gridExtra") RStudio.Version()

354

Appendices

sessionInfo() # current (developer) version of lme4 is 1.1-15 citation(package="lme4") # doesn't give version

#### 12. ADDITIONAL TESTS: CALCULATING EFFECT OF PROXIMITY ON VISIT RATE: ####

# Fiset et al. (2003) state that the task relies on spatial working memory # because the dogs "search as a function of the proximity to the target box" # i.e. they are more likely to search an adjacent box than a further box

# However the expected chance probabilities are not the same: # Only for target box 1 or 4 could the dogs visit a box 3 spaces away # And for target boxes 2 or 3 they could only visit boxes 0, 1 or 2 spaces away.

# For boxes 1 or 4 - the probability of visiting a box each distance away # (0, 1, 2, or 3) would be 0.25 if the dogs were searching randomly. # For boxes 2 or 3 - the probability of visiting a box 0 or 2 boxes away would be 0.25 # but the probability of visiting a box 1 box away would be 0.5 and 3 boxes away would be 0. # Because there are the same number of boxes for each interval, # let's find the mean of these: prob_0 = mean(c(0.25, 0.25)) prob_1 = mean(c(0.25, 0.5)) prob_2 = mean(c(0.25, 0.25)) prob_3 = mean(c(0.25, 0))

# Remove trials where no visit was attempted: data_visit <- filter(data, Visit != "None") #589 records

# Count the number of trials at each proximity per dog - avoid pseudoreplication! dogdata_proximity <- data_visit %>% group_by(Dog, ProximityScore) %>% summarise(countprox=n())

# View (dogdata_proximity)

# Find the average of all dogs at each proximity: proximity_means <- dogdata_proximity %>% group_by(ProximityScore) %>% summarise(countprox=mean(countprox))

# View (proximity_means)

#proximity_means$countprox <- as.integer(proximity_means$countprox) # doesn't work - attenuates instead of rounding proximity_means$countprox <- round(proximity_means$countprox, 0) # must be integers for binomial test to work sumprox<- sum(proximity_means$countprox) proximity_means$sumprox <- sumprox proximity_means <- proximity_means %>% mutate(proprox=countprox/sumprox)

355

Appendices

binom_0_dog <- binom.test(28, n=59, p=0.25, alternative="two.sided", conf.level=0.95) binom_1_dog <- binom.test(19, n=59, p=0.375, alternative="two.sided", conf.level=0.95) binom_2_dog <- binom.test(8, n=59, p=0.25, alternative="two.sided", conf.level=0.95) binom_3_dog <- binom.test(4, n=59, p=0.125, alternative="two.sided", conf.level=0.95)

#Get values expected by chance for each location: expected_0_dog = prob_0*59 expected_1_dog = prob_1*59 expected_2_dog = prob_2*59 expected_3_dog = prob_3*59

# Put expected and observed counts into a table: proximity_means_2 <- proximity_means%>% group_by(ProximityScore) %>% mutate(fails = sumprox - countprox, proportion_lci = binconf (countprox, sumprox) [2], proportion_uci = binconf (countprox, sumprox) [3])

proximity_means_2$expected_count <- c(expected_0_dog, expected_1_dog, expected_2_dog, expected_3_dog) proximity_means_2$expected_proportion <- c(prob_0, prob_1, prob_2, prob_3)

# plot graph: ggplot(proximity_means_2, mapping=aes(x=ProximityScore)) + geom_bar(mapping=aes(y=proprox), stat="identity") + geom_errorbar(mapping=aes(ymax=proportion_uci, ymin=proportion_lci)) + geom_bar(mapping=aes(y=expected_proportion), stat="identity", fill="cyan", alpha=0.3) + ylab("Proportion of visits") + xlab("Distance from target box") + theme_bw() + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14))

# Put them in the table so they can be copied into the thesis chapter: binomial_table <- function(a, b, p = p) {binom.test(a, b, p, alternative= c("two.sided"), conf.level = 0.95)$p.value} proximity_means_2$binomial_pval <- mapply(binomial_table, proximity_means_2$countprox, proximity_means_2$sumprox, p=proximity_means_2$expected_proportion)

#### 13. ADDITIONAL TESTS: MODEL FOR TRIALBLOCK 1 ONLY: ####

# Would the effect of interval still be significant if only one trialblock was used?

356

Appendices

trialblock1 <- filter(data, Block==1) modeltb1 <- glmer(SuccessBinary ~ Interval + Box + Trial + AgeGroup + Sex + Weight + (1 | Dog), data = trialblock1, family = binomial, nAGQ=100) summary(modeltb1)

# Yes, interval is still statistically significant.

#### 14. ADDITIONAL TESTS: ASSESSING EFFECT OF INTERFERENCE FROM PREVIOUS TRIAL: ####

# Focussing only on incorrect visits:

PreviousTable <- data %>% filter (SuccessBinary == FALSE) %>% group_by(Dog) %>% summarise(PreviousVisitCount = sum(IsPreviousVisit==TRUE, na.rm=TRUE), PreviousTargetCount = sum(IsPreviousTarget==TRUE, na.rm=TRUE), NumberOfErrors = sum(SuccessBinary==FALSE, na.rm=TRUE)) %>% group_by(Dog) %>% mutate (PreviousVisitProportion = PreviousVisitCount/NumberOfErrors, PreviousTargetProportion = PreviousTargetCount/NumberOfErrors, ExpectedProportion = 0.25) # assume dogs visiting by chance

# Perform binomial tests:

# Create function: binomial_table <- function(a, b, p = 0.25) {binom.test(a, b, 0.25, alternative= c("two.sided"), conf.level = 0.95)$p.value}

# Apply it to the values and add the results to the table: PreviousTable$Previous_Visit_pval <- mapply(binomial_table, PreviousTable$PreviousVisitCount, PreviousTable$NumberOfErrors) PreviousTable$Previous_Target_pval <- mapply(binomial_table, PreviousTable$PreviousTargetCount, PreviousTable$NumberOfErrors) PreviousTable$Previous_Visit_pval <- round(PreviousTable$Previous_Visit_pval,digits=5) PreviousTable$Previous_Target_pval <- round(PreviousTable$Previous_Target_pval,digits=5)

# Put significance marker in table: PreviousTable <- PreviousTable %>% mutate(Previous_Visit_Sig = ifelse(Previous_Visit_pval < 0.05, "Yes", "No"), Previous_Target_Sig = ifelse(Previous_Target_pval < 0.05, "Yes", "No"))

#### 15. ADDITIONAL TESTS: ASSESSING CORRELATION WITH HOLEBOARD DATA: ####

# Read the summarised holeboard data by dog:

HBdogs <-read.csv("Holeboard_ByDog_Summary.csv")

HBdogs$Dog <- gsub("[^0-9]", "", HBdogs$Dog) #Remove letters so same as other dataset

DO_success_bydog <- data %>% group_by(Dog) %>% summarise(SuccessRate = mean(SuccessBinary))

357

Appendices

# Use the session 4 data as dogs are likely to have learned the task by then

HBdogs_session4 <- HBdogs %>% filter(session_number==4)

DO_success_bydog$WMA_4 <- HBdogs_session4$WMA_Score DO_success_bydog$WMB_4 <- HBdogs_session4$WMB_Score DO_success_bydog$RM_4 <- HBdogs_session4$WMB_Score

# It cannot perform the test because of ties. # However the data are not tied; must be another problem. # Performed test in SPSS instead, see relevant SPSS output. # Plot data: ggplot(DO_success_bydog, mapping=aes(x=WMA_4, y=SuccessRate)) + geom_point() + geom_smooth(method="lm") # USe lm rather than loess as should be linear

# What if all the sessions were pooled?

HBdogs_allsessions <- HBdogs %>% group_by(Dog) %>% summarise(WMA=mean(WMA_Score), WMB=mean(WMB_Score), RM=mean(RM_Score))

HBdogs_allsessions$Dog <- gsub("[^0-9]", "", HBdogs_allsessions$Dog)

HB_DO_Connect <- full_join(DO_success_bydog, HBdogs_allsessions, by = "Dog", copy = FALSE, suffix = c(".x", ".y"))

# Move this table to SPSS for Spearman correlation test (as error messages in R) # See chapter 3 text to view SPSS results.

#### Post-Viva correction: Run the model with within dog factors only: #### model_new <- glmer(SuccessBinary ~ Interval + Block + Box + Trial + (1 | Dog), data = data, family = binomial, nAGQ=100) summary(model_new)

# interval, box and trial are still significant and trialblock still isn't. # Make sure to change the statistics in the chapter text and the table. # Also update the appendix. # And the methods!

# Work out odds ratios:

# First calculate standard errors from the coefficients: se2 <- sqrt(diag(vcov(model_new))) (tab2 <- cbind(Est = fixef(model_new), LL = fixef(model_new) - 1.96 * se2, UL = fixef(model_new) + 1.96 * se2))

# Then exponentiate these to get the odds ratios:

358

Appendices

oddsratios2 <- exp(tab2) oddsratios2

# Use broom::tidy() to more easily get model summary into dataframe: install.packages("broom") library(broom) z_values <- tidy(model_new) dispersion_glmer(model_new)

## try rescaling

# Rescale the variables for continuous numeric variables # (interval, trial and weight) so they are between 0 and 1:

model_new_1 <- (update(model_new,data=datascaled))

summary(model1, corr=FALSE)

# No more warning messages in the rescaled model, suggesting that # the warnings were just due to a scale difference between variables summary(model, corr=FALSE)

#### Redo chapter graphs and predictions for this new model ####

# Make a temporary dataset containing only the predictor and random variables: tmpdat <- data[, c("Interval", "Block", "Box", "Trial", "AgeGroup", "Sex", "Dog", "Weight")]

# Check the min and max of the main variable of interest (Interval): summary(data$Interval)

# Calculate the j-values (sequence of possible interval values between min and max) jvalues <- with(data, seq(from = min(Interval), to = max(Interval), length.out = 240))

# calculate predicted probabilities for these and store in a list pp <- lapply(jvalues, function(j) { tmpdat$Interval <- j predict(model_new, newdata = tmpdat, type = "response") })

# Get the mean and quartiles for the predictions: plotdata <- t(sapply(pp, function(x) { c(M = mean(x), quantile(x, c(0.25, 0.75))) }))

# Append the interval j-values and convert to data frame

359

Appendices

# This is so the plotting package, ggplot2, can read it: plotdata <- as.data.frame(cbind(plotdata, jvalues))

# Name the variables: colnames(plotdata) <- c("PredictedProbability", "LQ", "UQ", "Interval")

# View first few fields to check data looks as expected: head(plotdata)

# Plot the data: (Intervalpredict_new <- ggplot(plotdata, aes(x = Interval, y = PredictedProbability)) + geom_linerange(aes(ymin = LQ, ymax = UQ)) # Plots the quartiles + geom_line(size = 2) + ylim(c(0, 1)))

# Plot the observed datapoints on the same axes:

intervalpredictionplot_new <- ggplot(plotdata, aes(x = Interval)) + # ylim(c(0, 1)) + geom_point(data = intervalmeans, mapping=aes(x=Interval, y=mean), color="red", stat="identity", size=3) + geom_errorbar(data = intervalmeans, mapping=aes(x=Interval, ymin=proportion_success_lci, ymax=proportion_success_uci), color="red", size=1) + geom_ribbon(aes(ymin = LQ, ymax = UQ, y = PredictedProbability), fill="grey", alpha=0.6) + # Plots the quartiles geom_line(size = 2, aes(y = PredictedProbability)) + ylab("Predicted probability of success") + xlab("Interval (seconds)") + theme_bw() + scale_x_continuous(breaks=seq(0,240,30)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + geom_hline(yintercept=0.25, linetype="dashed")

# Work out at what interval the predicted probability reaches 0.25, # as this is the value expected by chance assuming the dogs # are randomly selecting a box to search behind each trial:

# Returns: # intercept # 1 206.8619

# So the model_predicts that the dogs' probability of success first reaches # that which would be expected by chance after an interval of 207 seconds # (rounded to the nearest second).

360

Appendices

# Trial also has a significant effect on P(Success) in this model. # So predict and plot the effect of trial also:

# Calculate the j-values (sequence of possible trial values between min and max) jvalues_trial <- with(data, seq(from = min(Trial), to = max(Trial), length.out = 20))

# calculate predicted probabilities and store in a list pp_trial <- lapply(jvalues_trial, function(j) { tmpdat$Trial <- j predict(model_new, newdata = tmpdat, type = "response") })

# Get the mean and quartiles for the predictions: plotdata_trial <- t(sapply(pp_trial, function(x) { c(M = mean(x), quantile(x, c(0.25, 0.75))) }))

# add in Trial j-values and convert to data frame plotdata_trial <- as.data.frame(cbind(plotdata_trial, jvalues_trial))

# Name the variables: colnames(plotdata_trial) <- c("PredictedProbability", "LQ", "UQ", "Trial")

#View first few fields: head(plotdata_trial)

# Plot the data: (Trialpredict_new <- ggplot(plotdata_trial, aes(x = Trial, y = PredictedProbability)) + geom_linerange(aes(ymin = LQ, ymax = UQ)) # Plots the quartiles + geom_line(size = 2) + ylim(c(0, 1)))

# Overlay the observed data for trial over the modelled data:

Trialcumulative_new <- ggplot(plotdata_trial, aes(x = Trial)) + # ylim(c(0, 1)) + geom_point(data = trialmeans, mapping=aes(x=Trial, y=mean), color="red", stat="identity", size=3) + geom_errorbar(data = trialmeans, mapping=aes(x=Trial, ymin=proportion_success_lci, ymax=proportion_success_uci), color="red", size=1) + geom_ribbon(aes(ymin = LQ, ymax = UQ, y = PredictedProbability), color="gray", alpha=0.3) + # Plots the quartiles geom_line(size = 2, aes(y = PredictedProbability)) + ylab("Predicted probability of success") + xlab("Trial") + theme_bw() + scale_x_continuous(breaks=seq(0,20,1)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) + theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + geom_hline(yintercept=0.25, linetype="dashed")

# It seems that as the dog performs more trials, P(Success) decreases.

361

Appendices

# The model also found a significant effect of box 2 compared to box 1. # Therefore it may be beneficial to plot the effect of interval # separately for each of the four boxes.

# collapse from list into data frame format: plotdata_2 <- do.call(rbind, plotdata_2)

# add box as a variable: plotdata_2$Box <- factor(rep(levels(data$Box), each = length(jvalues))) head(plotdata_2)

# They all seem to follow a similar pattern, but it's difficult # to compare them on different axes. # Plot them all on the same axes instead:

Box_model_new <- ggplot(plotdata_2, aes(x = Interval, y = PredictedProbability)) + geom_ribbon(aes(ymin = LQ, ymax = UQ, fill = Box), alpha = .25) + geom_line(aes(colour = Box), size = 2) + geom_hline(yintercept=0.25, linetype="dashed") + theme_bw() + scale_color_manual(values=c("yellow", "cyan", "magenta","green")) + scale_fill_manual(values=c("yellow", "cyan", "magenta","green")) + ylab("Predicted probability of success") + xlab("Interval (seconds)") + theme_bw() + scale_x_continuous(breaks=seq(0,240,30)) + scale_y_continuous(breaks=seq(0,1,0.1), limits = c(0,1), expand = c(0, 0)) +

362

Appendices

theme(axis.text.x = element_text(face="bold", size=13)) + theme(axis.text.y = element_text(face="bold", size=13)) + theme(axis.title.x = element_text(face="bold", size=14)) + theme(axis.title.y = element_text(face="bold", size=14)) + theme(legend.title = element_text(face="bold", size=14)) + theme(legend.text = element_text(face="bold", size=13))

#### Compare graphs and predictions (they are identical as previously) ####

Intervalpredict Intervalpredict_new intervalpredictionplot intervalpredictionplot_new

Trialcumulative Trialcumulative_new

Box_model Box_model_new

# These are the same, leave them as they are. # chance_intercept is still exactly the same despite being updated with the new model.

# Therefore there is no need to update predictions or plots in the chapter.

363

Appendices

Appendix 3.6 – Trialblock 1 model results: Results of a modified model using only data from Trialblock 1. (All other model specifications are the same as those described within Chapter 3.)

Odds Lower 95% Upper 95% Variable/ ratio confidence confidence z-value p-value category (OR) limit (LCL) limit (UCL) Interval 0.9935 0.9896 0.9974 -3.2490 0.0012 Box 2 0.4576 0.1869 1.1205 -1.7110 0.0871 Box 3 0.3260 0.1286 0.8266 -2.3610 0.0182 Box 4 0.9750 0.4017 2.3666 -0.0560 0.9554 Trial 0.9330 0.8814 0.9875 -2.3920 0.0168 Age 6-10 1.8431 0.7214 4.7092 1.2780 0.2014 years Age 11-15 1.2975 0.4454 3.7798 0.4770 0.6330 years Male sex 0.7258 0.3396 1.5512 -0.8270 0.4082 Weight 0.9407 0.8857 0.9992 -1.9860 0.0470 Neutered 1.1234 0.5007 2.5206 0.2820 0.7778

364

Appendices

Appendix 4 – Material associated with Chapter 4:

Appendix 4.1 – Owner information sheet: Owner information sheet for the study described in Chapter 4:

365

Appendices

366

Appendices

Appendix 4.2 – Trial order within the sample phase:

Shaping Phase

Trial Box 1 4 2 2 3 4 4 3 5 1 6 3 7 1 8 2

367

Appendices

Appendix 4.3 – Scoring sheet for disappearing object task:

368

Appendices

Appendix 4.4 – Model results (excluding owner-assigned severity score): Odds ratios for the multivariate model of probability of success in the disappearing object task with owner-assigned severity score excluded:

Variable Odds ratio Lower CI Upper CI z p Interval 0.99185254 0.984919 0.998835 -2.28584 0.022264 Box 2 0.18739112 0.070577 0.49755 -3.36113 0.000776 Box 3 0.14217925 0.050388 0.401189 -3.68567 0.000228 Box 4 0.65474112 0.223563 1.917519 -0.7725 0.439817 Trial 0.99878179 0.882487 1.130402 -0.0193 0.984602 Age 0.75369826 0.55808 1.017885 -1.84437 0.065129 Sex 0.72348904 0.345502 1.515005 -0.85835 0.390702 Group 0.04000571 0.001273 1.257669 -1.82968 0.067298 Interval*Group 0.99191485 0.981912 1.002019 -1.5699 0.116439 Box 2*Group 6.70030829 1.809945 24.80413 2.848456 0.004393 Box 3*Group 6.10747642 1.521525 24.51571 2.55191 0.010713 Box 4*Group 4.01554651 0.964666 16.71522 1.910561 0.056061 Trial*Group 0.90903406 0.767897 1.076111 -1.10789 0.267909 Age:Group 1.32358478 0.906443 1.932694 1.451442 0.146657 Sex:Group 3.67898714 1.32898 10.18446 2.50747 0.01216

95% confidence intervals and z- and p-values are shown. Statistically significant p- values (p<005) are highlighted in yellow. The exact same variables, factor levels and interactions were statistically significant as those in the model that included owner- assigned severity score, so owner-assigned severity score was retained in the model to improve prediction accuracy.

369

Appendices

Appendix 4.5 – Relationship between age and osteoarthritis severity:

There was no significant correlation between age and HCPI score (S = 11380, rho = - 0.0676, p = 0.679) and no significant differences between the ages of dogs with different owner-assigned (Kruskal-Wallis X2(5) = 6.41, p = 0.269) or vet-assigned severity scores (Kruskal-Wallis X2(5) = 3.85, p = 0.572), in the study described in Chapter 4.

Appendix 5 – Material associated with Chapter 5:

Appendix 5.1 – Literature search for factors affecting sleep quality in dogs:

A brief systematic search was performed to investigate potential factors affecting/associated with sleep quality in companion dogs, using PubMed (https://www.ncbi.nlm.nih.gov/pubmed). The search terms entered were: ((dog[Title] OR dogs[Title] OR canine[Title]) AND (sleep disorder* OR sleep quality OR sleep disturb*) Words relating to "dog" were included in the title only rather than the full text, in order to reduce the amount of papers returned that described studies in other species but mentioned dogs within the body text. A total of 46 papers were returned. 41 of these were rejected; four described outcomes measured in humans rather than dogs, two described studies in companion dogs that did not describe sleep quality, seven referred to experimental procedures performed on laboratory dogs that were not relevant to sleep quality in companion animal dogs, 27 papers focused on specific sleep disorders in dogs (canine narcolepsy or rapid eye movement sleep behaviour disorder) and one paper was a duplicate of another. Five papers were therefore identified as relevant to sleep quality in dogs. The relevant findings from these papers are summarised in the introduction section of Chapter 5 (see Table 5.1]).

370

Appendices

Appendix 5.2 – Sleep diary sheet: The two-sided A4 Sleep diary sheet given to owners as part of the study described in chapter 5.

371

Appendices

372

Appendices

Appendix 5.3 – Cover sheet for owner questionnaire packs: The sheet shown is the A4 cover sheet for Week 0 questionnaire packs, which was stapled to one copy of each of the SNoRE, HCPI and CBPI questionnaires. Week 1, 2, 3 and 4 questionnaire packs listed “Day 7”, “Day 14”, “Day 21” and “Day 28” instead of “Day 0”.

373

Appendices

Appendix 5.4 – The SNoRE questionnaire: This was devised by Knazovicky et al. (2015) and used within the studies described in Chapter 4 (Week 0) and Chapter 5 (Weeks 1-4):

374

Appendices

Appendix 5.5 – Limbs affected in osteoarthritic dogs recruited: Table showing counts and percentages of dogs with hindlimb only, forelimb only and both hindlimb and forelimb signs of osteoarthritis within the osteoarthritic group of dogs recruited for the study described in Chapter 5.

Number of Percentage of Limb affected: dogs: group:

Hindlimb only: 9 45%

Forelimb only: 4 20%

Both hindlimb and forelimb: 7 35%

375

Appendices

Appendix 5.6 – Models for night-time rest bout length: Table showing the three separate models used to explore the variables that predicted mean duration of an uninterrupted bout of rest during the night:

Outcome: Mean duration of an uninterrupted bout of rest during the night

Model 1 Model 2 Model 3 AIC = 5213.6 AIC = 5213.0 AIC = 5218.6 Age Age Age

Length of Night Length of Night period Length of Night period period

Vet -assigned severity Group Analgesia frequency score

Predictors Included Age * Vet-assigned severity Age * Analgesia Age * Group score frequency

Length of Night Length of Night Period * Length of Night Period * Period * Group Vet-assigned severity score Analgesia frequency

Factors included in each of the models used in the analysis of mean duration of an uninterrupted bout of rest during the night. AIC = Akaike Information Criterion; a relative estimate of model quality dependent on goodness of fit and model simplicity (lower AIC values represent higher quality models relative to others (Posada and Buckley, 2004).

376

Appendices

Appendix 5.7 – Effect of HCPI Score Squared on Daytime Rest: The following table shows the coefficient estimates with confidence intervals, z-values and p-values for the robust linear mixed effects regression (rlmer) model of the effects of HCPI score squared, HCPI score squared by group interaction, HCPI score, HCPI score by group interaction and group on proportion of time spent resting during the day. Because the effects of HCPI score squared and HCPI score squared by group interaction were not statistically significant (α=0.05), they were removed and not included in the final model, which is described in Section 5.4.3. Interactions are denoted by asterisks between variables.

Variable Estimate Lower 95% Upper 95% z p confidence confidence limit limit

Group 0.992 0.937 1.050 0.283 0.777

HCPI Score 1.006 1.000 1.012 -1.849 0.064

Group *HCPI Score 1.000 1.000 1.000 0.525 0.599

(HCPI Score)2 0.993 0.985 1.001 1.685 0.092

Group*(HCPI Score)2 1.000 1.000 1.000 -0.601 0.548

377