Visual Predictors for Poor Mobility Performance in People with Age Related Macular Degeneration

VISUAL PREDICTORS FOR POOR MOBILITY PERFORMANCE IN PEOPLE WITH AGE RELATED MACULAR DEGENERATION

Remy Sheena Mathew, B.Sc

A thesis submitted in partial fulfilment of the requirements for admission to the degree of Doctor of Philosophy

School of Optometry and Vision Science, University of New South Wales, Sydney, Australia

September 2009

Visual predictors for poor mobility performance in people with AMD

Originality statement

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

Signed …………………………...... Remy Sheena Mathew.

Visual predictors for poor mobility performance in people with AMD

‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

Signed ……………………………………………...... Date ……………………………………………......

AUTHENTICITY STATEMENT

‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’

Signed ……………………………………………...... Date ……………………………………………......

iii

Visual predictors for poor mobility performance in people with AMD

Abstract Age-related macular degeneration is the leading cause of visual impairement in older people in developed countries. Although, several studies have considered aspects of quality of life and depression in people with AMD, case control studies and investigations of visual function and its impact on mobility have not been extensively explored; these studies form the focus for this study. This thesis aimed to analyze the quality of life in people with moderate to moderately severe AMD compared to age-and-gender matched controls. Further, the study examined different aspects of visual function, sensorimotor function and mobility performance in AMD and controls. Analysis of these findings was conducted to determine whether poor mobility performance in people with AMD could be predicted using particular measures of visual function. A quality of life profile was mapped for AMD (n=145) and Control (n=136) subjects using the Short Form general health (SF-36), Low Luminance Questionnaire (LLQ), Goldberg Anxiety and Depression Scale (GADS), Incidental and Planned Activity questionnaire (IPAQ) and Assistance for daily living activities. Visual function (high contrast visual acuity, contrast sensitivity, photostress test, visual fields and dark adaptation), sensorimotor function (proprioception, contrast sensitivity, muscle strength, reaction time and balance) and mobility performance (under five levels of illumination) were assessed for a subset of 68 subjects (AMD: 33; Control: 35). The differences between the two groups were assessed using independent t- tests and multi-variate analysis. Visual predictors for poor mobility performance were modelled using stepwise linear regression. This study showed that people with AMD have reduced quality of life on both functional and emotional domains, with more anxiety and depressive symptoms when compared to age-and-gender-matched controls. People with AMD also required more assistance for daily living activities. Significant functional impairments for visual acuity, contrast sensitivity, photostress test, visual field indices and dark adaptation indices (cone threshold, rod threshold and rod-cone break up time) were also seen in people with AMD compared to controls. Multivariate analysis showed similar performances for the sensorimotor functions in both groups after correcting for age, gender and visual input (factored in terms of visual acuity). Mobility performance for people with AMD was significantly reduced for all the obstacle courses when compared with controls. Poor iv

Visual predictors for poor mobility performance in people with AMD mobility performance in people with AMD, particularly with changes in surrounding illumination could be best predicted from the presence of focal defects in the central visual field, delayed recovery of macular function and delayed dark adaptation. Taken together, this study indicates the need for assessment and improved intervention to maintain independence and quality of life in people with AMD.

Visual predictors for poor mobility performance in people with AMD

Acknowledgements

I am grateful to Dr Michele Madigan for her expert guidance through the most difficult times of my candidature. Her positive outlook and constant support kept me going to the point of completion of this thesis. Although she became involved in my supervision towards the end of my candidature, her guidance and inputs have been invaluable- without which this thesis would not be completed.

I am extremely grateful to Dr Vaegan for all the thought provoking discussions we had and for inspiring me to ask the why’s and how’s of research during the time of his supervision, before his passing away late last year. He is much missed. I am extremely grateful to Dr Paul Beaumont, you were always there for me. Your support and guidance was of great help. Prof. Stephen Lord, your valuable feedback, constant support and understanding has helped me complete this journey; you were always there for me besides your hectic schedule. Dr Peter Herse, thank you for helping me through the initial phase of my candiature and helping me to embark on this intellectual journey.

I am grateful to the friendly staff at the School of Optometry and Vision Science, UNSW. You made my stay in Australia very pleasant. My special thanks go to Professor Stephen Dain for loaning photometric instruments from ORLAB, and to Dr. Alexander for helpful discussions related to psychophysics and vision. I am grateful to Thomas John for the discussions on statistics during my candidature.

I gratefully acknowledge the University of New South Wales for providing financial assistance, through the PhD Student Completion Scholarships. Thanks are due to my friends and colleagues at Retina and Vitreous Centre for their patience and kindness that made this study possible. Special thanks to Netty for sorting out the subject folders and Nikki for proof reading the references for me. Jane, Netty, Nikki, Jen, Christine and Sapna you all were simply great- Thank you.

Visual predictors for poor mobility performance in people with AMD

My family has always been the source of inspiration to my studies all my life. A prayer of grattitude to the almighty for giving me a father who inspired me to make my life worthwhile to the community I live in, my mother for her silent constant encouragement, my elder sister who is my strength, my jijaji (brother-in-law) for his encouragement and my younger sister who is the pillar of my support. Special thanks is also due for my better half, Ginu for understanding my dream and for battling the hardship of financial burden due to the tuition fees with a smile, never complaining. You have been very patient and a real motivating factor. Your love and support have inspired my continued efforts to complete this project. My family’s unconditional love through some very difficult phases has made completion of this thesis possible.

vii

Visual predictors for poor mobility performance in people with AMD

Table of contents

Originality statement ...... ii

AUTHENTICITY STATEMENT ...... iii

Abstract ...... iv

Table of contents ...... viii

List of Figures ...... xii

List of Tables ...... xiv

List of Appendices ...... xvi

Glossary of Abbreviations ...... xviii

Chapter 1: Introduction ...... 1

1.1 Background ...... 1

1.2 Rationale ...... 2

1.3 Hypotheses & Aims ...... 4

Chapter 2: Understanding the Disease Process in Age-Related Macular Degeneration ...... 5

2.1 Anatomy of Retina ...... 5

2.2 Historical Perspective ...... 7

2.3 Definition: Age-Related Macular Degeneration ...... 7

2.3.1 Non- Exudative AMD ...... 8 viii

Visual predictors for poor mobility performance in people with AMD

2.3.2 Exudative AMD ...... 8

2.4 AMD Prevalence ...... 16

2.5 Classification and Grading for AMD ...... 18

2.6 Pathogenesis and Pathophysiology of AMD ...... 18

2.7 Risk factors for macular degeneration ...... 25

Chapter 3: Functional Implications of AMD in Quality of Life ...... 28

3.1 Introduction ...... 28

3.2 Materials and Methodology ...... 33

3.2.1 Research synopsis ...... 33 3.2.2 Quality of life evaluation ...... 35 3.2.3 Statistics ...... 39

3.3 Results ...... 39

3.4 Discussion ...... 44

3.5 Conclusion ...... 49

Chapter 4: Psychophysical Aspects of Vision in people with AMD .... 50

4.1 Introduction ...... 50

4.1.1 Visual Acuity ...... 50 4.1.2 Contrast sensitivity ...... 53 4.1.3 Visual fields ...... 55 4.1.4 Visual adaptation ...... 57

4.2 Materials and Methods ...... 60

4.2.1 Study Design ...... 60 4.2.2 Psychophysical evaluation ...... 64 4.2.3 Statistics ...... 67

4.3 Results ...... 69

Visual predictors for poor mobility performance in people with AMD

4.3.1 Visual acuity ...... 69 4.3.2 Contrast sensitivity ...... 70 4.3.3 Photostress test ...... 70 4.3.4 Visual fields ...... 71 4.3.5 Dark adaptation ...... 73

4.4 Discussion ...... 76

4.4.1 Visual acuity ...... 76 4.4.2 Contrast sensitivity ...... 76 4.4.3 Photostress test ...... 77 4.4.4 Dark adaptation ...... 78 4.4.5 Visual fields ...... 80

4.5 Conclusion ...... 81

Chapter 5: Sensorimotor Functions and Mobility Performance in People with AMD ...... 83

5.1 Introduction ...... 83

5.2 Materials and Methods ...... 85

5.2.1 Materials ...... 85 5.2.2 Methods ...... 88 5.2.3 Statistics ...... 94

5.3 Results ...... 95

5.4 Discussion ...... 100

5.5 Conclusions...... 104

Chapter 6: Visual Predictors for Poor Mobility Performance in People with AMD 106

6.1 Introduction ...... 106

6.2 Methods ...... 109

6.2.1 Statistics ...... 111 x

Visual predictors for poor mobility performance in people with AMD

6.3 Results ...... 111

6.4 Discussion ...... 117

6.5 Limitations ...... 123

6.6 Conclusions...... 123

Chapter 7: Summary ...... 125

7.1 Summary ...... 125

7.2 Conclusions...... 127

7.3 Limitations ...... 128

7.4 Significance ...... 129

7.5 Future directions ...... 130

References ...... 132

APPENDICES ...... 175

Visual predictors for poor mobility performance in people with AMD

List of Figures

Figure 2-1: Schematic diagram of the retina showing its layers and corresponding cells. ... 6

Figure 2-2 A: Fundus photograph of AMD lesion. (Copyright Dr Paul Beaumont) ...... 10

Figure 2-2 B (a): Classic CNV in the early phase on fundus fluorescien angiography...... 11

Figure 2-2 B (b): Classic CNV in the mid phase on fundus fluorescien angiography...... 11

Figure 2-2 B (c): Classic CNV in the late phase on fundus fluorescien angiography...... 12

Figure 2-3 A: Fundus photograph of AMD lesion. (Copyright Dr Paul Beaumont).) ...... 12

Figure 2-3 B (a): Occult CNV (Type 1) on the early phase of fundus fluorescien angiography...... 13

Figure 2-3 B (b): Occult CNV (Type 1) on the mid-phase of fundus fluorescein angiography...... 13

Figure 2-3 B (c): Occult CNV (Type 1) on the late phase of fundus fluorescein angiography...... 14

Figure 2-3 C (a): Occult CNV (Type 2) on the early phase of fundus fluorescein angiography...... 14

Figure 2-3 C (b): Occult CNV (Type 2) on the mid phase of fundus fluorescein angiography...... 15

Figure 2-3 C (c): Occult CNV (Type 2) on the late phase of fundus fluorescein angiography...... 15

Figure 3-1: Effect of visual acuity on depressive symptoms in people with AMD...... 43

Figure 4-1: Dark adaptation curve ...... 58

Figure 4-2: Standard photographs used for grading the nuclear lens opacities...... 62

Figure 4-3: Effect of disease severity on visual acuity...... 70

Figure 4-4: Effect of luminance on retinal sensitivity...... 72

Figure 4-5: Effect of luminance on density of the scotoma...... 73

Figure 4-6: Representative dark adaptation curve for the control group ...... 74

Figure 4-7: Representative dark adaptation curve for the AMD group ...... 74

xii

Visual predictors for poor mobility performance in people with AMD

Figure 4-8: Comparison of the mean dark adaptation curve for control and AMD group. 75

Fig 5-1: Calibration of the luminance levels of the complex course (luminance cd/m2) ... 86

Figure 5-2: Schematic representation of the complex mobility course sequence followed by the subjects...... 87

Figure 5-3: Schematic representation of obstacles in the mobility course...... 88

Fig. 5-4: Comparison of PPWS in Controls and people with AMD ...... 97

xiii

Visual predictors for poor mobility performance in people with AMD

List of Tables

Table 2-1: Racial differences in the prevalance of AMD...... 17

Table 2-2: AREDS AMD Classification (AREDS research group, 2001) ...... 19

Table 2-3: Components of drusen deposits (Donoso et al, 2006) ...... 25

Table 3-1: Summary of Questionnaires used...... 38

Table 3-2: Participant Demographics...... 40

Table 3-3: Descriptives for GADS, SF-36, LLQ, Assistance for daily tasks, FES-I and IPAQ...... 41

Table 3-4: Table of co-efficients for linear regression model for depressive symptoms in people with AMD...... 43

Table 3-5: Pearson’s correlation co-efficients (R) for SF 36, IPAQ and GAD scale...... 44

Table 4-1: Nuclear and cortical grading classification...... 63

Table 4-2: Demographic details of the study sample ...... 69

Table 4-3: Visual field Mean Deviation (MD) and Pattern Standard Deviation (PSD) data for AMD and control groups...... 71

Table 4-4: Descriptives for dark adaptation indices ...... 73

Table 5-1: Descriptives of sensorimotor functions...... 95

Table 5-2: Descriptives of the percentage preferred walking speed (PPWS)...... 96

Table 5-3: Descriptives of the Error Score...... 97

Table 5-4: Mobility Performance Score (%) ...... 98

Table 5-5: Comparison between MPS and PPWS scores for AMD and Control groups. .. 99

Table 5-6: Pearson’s correlation co-efficients (R) for sensorimotor functions and mobility performance in people with AMD ...... 99

Table 6-1: Table of co-efficients for linear regression model of Reduction in speed from brighter luminance to dim luminance...... 112

Table 6-2: Table of co-efficients for linear regression model of Reduction in speed from dim luminance to brighter luminance...... 112

xiv

Visual predictors for poor mobility performance in people with AMD

Table 6-3: Table of co-efficients for linear regression model PPWS through course A (155 cd/m2)...... 113

Table 6-4: Table of co-efficients for linear regression model PPWS through course B (3 cd/m2)...... 113

Table 6-5: Table of co-efficients for linear regression model PPWS through course C (1500 cd/m2)...... 114

Table 6-6: Table of co-efficients for linear regression model PPWS through course D (12 cd/m2)...... 114

Table 6-7: Table of co-efficients for linear regression model PPWS through course E (0.2 cd/m2)...... 115

Table 6-8: Table of co-efficients for linear regression model PPWS through course F (0.06 cd/m2)...... 115

Table 6-9: Table of co-efficients for linear regression model of ES made through photopic conditions (Course A and Course D)...... 116

Table 6-10: Table of co-efficients for linear regression model of ES made through mesopic conditions (Course B and Course E)...... 116

Table 6-11: Table of co-efficients for linear regression model of ES made through scotopic conditions (Course F)...... 117

Visual predictors for poor mobility performance in people with AMD

List of Appendices

Appendix 1: Consent form used for the study...... 175

Appendix 2 : Study invitation poster ...... 179

Appendix 3: Comparison between the FES-1 and Short version FES-1 ...... 180

Appendix 4: Demographic details of the Control data...... 181

Appendix 5: Demographic details of the AMD data...... 182

Appendix 6: Data on Visual Acuity, Contrast sensitivity and Photostress for Controls. . 183

Appendix 7: Data on Visual Acuity, Contrast sensitivity and Photostress for AMD...... 184

Appendix 8: Specifications for the Medmont M700W Automatic perimeter...... 185

Appendix 9: Example of visual field for a control subject in 8 cd/m2 background luminance...... 186

Appendix 10: Example of visual field for AMD subject in 8 cd/m2 background luminance...... 187

Appendix 11: Visual field data for controls...... 188

Appendix 12: Visual field data for AMD...... 189

Appendix 13: IDL Non-Linear Regression Model (Adapted from Herse, 1995)...... 190

Appendix 14: IDL Non-Linear Regression Model output...... 192

Appendix 15: Examples of Control subject’s DA function modelled by IDL software. .. 193

Appendix 16: Examples of AMD subject’s DA function modelled by IDL software...... 195

Appendix 17: Results derived from IDL program for controls...... 197

Appendix 18: Results derived from IDL program for AMD...... 198

Appendix 19: Pearson’s Correlation co-efficients (R) for Visual function variables...... 199

Appendix 20: Normal range for sensorimotor functions in people aged 60 and above ... 200

Appendix 21: Instructions given before mobility performance assessment...... 201

Appendix 22: Mobility performance measures for Controls ...... 202

xvi

Visual predictors for poor mobility performance in people with AMD

Appendix 23: Mobility performance measures for AMD ...... 203

Appendix 24: Error score (ES) data for Controls ...... 204

Appendix 25: Error score (ES) data for Controls ...... 205

Appendix 26: Data from trial 1 and 2 for preferred walking speed and percentage preferred walking speed...... 206

Appendix 27: Pearson’s correlation coefficient (R) for mobility performance measures and visual function measures...... 207

Appendix 28: Pearson’s correlation co-efficient (R) for Error scores and visual function variables ...... 210

xvii

Visual predictors for poor mobility performance in people with AMD

Glossary of Abbreviations

AMD Age Related Macular Degeneration ARM Age-Related Maculopathy BMES Blue Mountains Eye Study RPE Retinal Pigment Epithelium ONL Outer Nuclear Layer INL Inner Nuclear Layer GCL Ganglion Cell Layer OPL Outer Plexiform Layer IPL Inner Plexiform Layer BRB Blood-Retinal Barrier FAZ Foveal Avascular Zone GA Geographic Atrophy CNV Choroidal Neovascularisation FFA Fundus Fluorescein Angiography SRPED Serous Retinal Pigment Epithelial Detachment PCV Polypoidal Choroidal Vasculopathy ICG Indocyanine Green RVAC Retinal Vascular Anomalous Complex PED Pigment Epithelial Detachment VIP Visual Impairment Project AREDS Age-Related Eye Diseases Study NHANES III National Health and Nutrition Examination Survey III BDES Beaver Dam Eye Study RS Rotterdam Study SEE Project Salisbury Eye Evaluation Project BES Baltimore Eye Study AD Alzheimer disease QoL Quality of Life SF 36 Medical Outcomes Study Short Form 36 xviii

Visual predictors for poor mobility performance in people with AMD

QWB Quality of Well Being MacDQol Measure of Impact of Macular Degeneration on Quality of Life DLTV Daily Living Tasks dependent on Vision NEI-VFQ 25 National Eye Institute Function Questionnaire VF-14 14- item Visual Function questionnaire ADVS Activities of Daily living Scale VQOL Vision related Quality of Life IADL Instrumental Activities of Daily Living scale LLQ Low Luminance Questionnaire HADS Hospital Anxiety and Depression Scale GDS Geriatric Depression Scale POMS Profile of Mood States GADS Goldberg Anxiety and Depression scale FES-I Falls Efficacy Scale- International PCS Physical Component Score MCS Mental Component Score IPAQ Incidental and Planned Activity Questionnaire TIA Transient Ischaemic Attack SEIFA Socio-Economic Index for Areas LogMAR Logarithm of the Minimum Angle of Resolution VA Visual Acuity ETDRS Early Treatment Diabetic Retinopathy Study MET Melbourne Edge Test PST Photostress Test LOCS III Lens Opacity Classification System III MD Mean Deviation PSD Pattern Standard Deviation SD Standard Deviation SEM Standard Error Mean PWS Preferred Walking Speed PPWS Percentage Preferred Walking Speed xix

Visual predictors for poor mobility performance in people with AMD

ES Error Score MPS Mobility Performance Score RS Reduction in Speed PPWS (ce) Percentage Preferred Walking Speed through the course examined PPWS (ca) Percentage Preferred Walking Speed through the course adapted to RSp Reduction in Speed from a Photopic to scotopic environment RSs Reduction in Speed from a Scotopic to photopic environment ESp Error Score made at photopic conditions ESm Error Score made at mesopic conditions ESs Error Score made at scotopic conditions cpd cycles per degree mins minutes secs seconds cd/m2 candela per meter square asb apostilb dB Decibel mm millimetre

Visual predictors for poor mobility performance in people with AMD

Chapter 1: Introduction

1.1 Background

Current trends in medical research have shifted towards age-related conditions. One of the major reasons for this is increased life expectancy as a result of advancements in medicine. A recent census showed that people over the age of 55 years accounted for 37% of the world population (US Census Bureau, 2008). The Australian Bureau of Statistics reported in 2004 that 13% of the Australian population were aged 65 years and over, and it is projected that the proportion this age group makes up of the population will increase by 26 to 28% by the year

2051 (Australian Bureau of Statistics, 2004).

Age-related macular degeneration (AMD) is one of the major health concerns of older people and is reported to be one of the most prevalent causes of visual impairment in ageing

Western populations ((Kahn et al, 1977; Attebo et al, 1996; Klaver et al, 1998). The Blue Mountains Eye

Study (BMES) reported similar incidences in Australia (Foran et al, 2003) compared to other studies in western populations. Another major concern of the ageing population is the risk of falling, leading to loss of independence and increased mortality rates. Between July 1990 and June 2000, the number of admissions to NSW acute-care hospitals for hip fracture increased by 42% for men and by 31% for women (Boufous et al, 2004). Several studies have shown that mobility performance is a good predictor of falls (Thompson and Medley, 1995; Shumway-

Cook et al, 2000; Tiedemann et al, 2008). Elderly people with visual impairment are more likely to fall

(Lord and Dayhew, 2001), and in a study that looked at people admitted to hospital due to falls in

Australia, 76% of patients were visually impaired (Jack et al.1995).

In addition to the increased morbidity and mortality caused by the combined effects of visual loss and postural instability, there are major concerns related to the social and economic burden of these factors within the geriatric community. For example, the estimated costs for treatment of falls for people aged 65 years and over in Western Australia was shown to be $86.4 million for the year 2001-2002 and projected to increase to $181 1

Visual predictors for poor mobility performance in people with AMD

million by the year 2021 (Hendrie et al, 2004). If appropriate steps are not taken to reduce the rate of falls, the predicted cost for the treatment and security of people with AMD has been estimated to be $2.6 billion per year, and projected to increase to $6.5 billion by 2025 (Taylor et al, 2006). Therefore, further understanding of older people at high risk of falls, such as those with AMD, is required, so as to refine risk factor profiles to inform evidence based intervention strategies quality of life.

1.2 Rationale

Reduced quality of life has been reported in people with AMD related to difficulties encountered in performing daily living activities and leisure activities (Marotolli et al, 1997;

Williams et al, 1998). Quality of life is assessed on the basis of one’s physical and emotional health, which can be causally related. Physical disabilities that limit activities that involve one’s social life could result in withdrawal and have a negative impact on emotional health

(Fitzgerald, 1970). Although previous studies have reported reduction in quality of life; there are no case control studies that assess the impact of quality of life in people with AMD (Chapter 3).

Another serious problem for older people is the increased risk of falling. For people aged over 70 years living independently, >30% reported at least one fall per year (Lord and Dayhew,

2001, Dolinis et al 1997) and this number increased with increasing age (Dolinis et al 1997, Lord et al

1994). Mobility performance has been shown to be a good predictor of falls (Thompson and Medley,

1995; Shumway-Cook et al, 2000; Tiedemann et al, 2008). A large cohort study that assessed mobility performance using various measures of functional mobility reported people who performed poorly on two mobility measures were at an increased risk of multiple falls (Tiedemann et al,

2008). There is a strong association between visual impairment, and falls in the elderly population (Ivers et al, 2002; Black and Wood, 2005). Hence the identification of visual predictors for poor mobility performance could help with timely referral of people with visual impairment for appropriate assessments and training programs to reduce the risk of falling and also maintain social independence.

Visual predictors for poor mobility performance in people with AMD

Some previous studies have looked at the mobility performance in people with AMD. Early studies showed that subjects with retinal changes associated with the development of AMD (age-related maculopathy - ARM) had no measurable mobility impairment, compared with subjects of similar age with normal vision in ambient light levels (Brown et al, 1986; Wilcox and

Burdett, 1989). However, the design of the mobility courses may have been critical for these findings, as suggested by another study that found that subjects with ARM walked faster on simpler mobility courses than on more complex routes (Kuyk and Elliott, 1999). The significance of mobility course complexity on performance has also been demonstrated for subjects with other types of visual loss such as retinitis pigmentosa (Kuyk et al, 1996). Mobility in real life situations requires people to avoid obstacles to safely navigate and travel along a path, often in varying levels of illumination.

More specific studies into mobility performance in AMD have found that letter contrast sensitivity and visual field extent were the best predictors of the time taken and the number of contacts made with obstacles on indoor mobility courses (Kuyk and Elliott, 1999; Elliott et al, 1995). This contrasts with earlier studies that found no correlation between mobility, contrast sensitivity and visual acuity (Wilcox and Burdett, 1989). A more controlled study was performed by Hassan et al (2002) where mobility performance of subjects with ARM was assessed using an indoor, high density obstacle course. They explored the relationship between scotoma size and mobility performance, using well-defined paths and set illumination levels but did not examine the effects that reduced illumination and glare might have on the mobility performance. Patients with ARM also displayed compromised dark adaptation (Campbell et al,

1969; Lovie-Kitchen et al, 1983, Brown et al, 1986), suggesting an area of further study. Other visual factors linked to the increased incidence of falls include contrast sensitivity (Lord and Menz,

2000; De Boer et al, 2004), uncorrected refractive error (Buckley et al, 2005), postural/vestibular integration with vision and the effect of bifocal and progression addition spectacle lenses on peripheral vision (Elliott et al, 1995). However mobility performance under varying levels of illumination (Chapter 5) and the relationship between visual factors and mobility performance under varying levels of illumination have not been explored (Chapter 6).

Visual predictors for poor mobility performance in people with AMD

1.3 Hypotheses & Aims

Given these observations, the following hypotheses were considered: a) People with moderate to moderately severe AMD will have reduced quality of life when compared with an age- and gender-matched control population with similar co-morbidities, b) In addition to reduced visual function (as measured by tests of contrast sensitivity, recovery of macular function after photostress, visual field extent and dark adaptation) people with moderate to moderately severe AMD will exhibit reduction in visual field extent depending on the background luminance compared to age-matched controls, and c) Increased difficulties with mobility will be seen in people with moderate to moderately severe AMD when compared to age-matched controls, for similar levels of illumination.

To test these hypotheses, the studies in this thesis aimed to: a) Assess the quality of life in people with moderate to moderately severe AMD with validated questionnaires and compare quality of life measures with those of age-and gender- matched controls (Chapter 3), b) Compare the visual function characteristics of people with moderate to moderately severe AMD compared to that of age-matched normals (Chapter 4) and , c) Compare the mobility performance of moderate to moderately severe AMD patients with age-matched controls, including mobility through courses with varying levels of illumination (Chapter 5), d) Identify a visual predictor for poor mobility performance in people with moderate to moderately severe AMD, based on observations from Chapter 4 and 5 (Chapter 6).

Visual predictors for poor mobility performance in people with AMD

Chapter 2: Understanding the Disease

Process in Age-Related Macular

Degeneration

2.1 Anatomy of Retina

The retina covers the inner surface of the posterior eyeball and is composed of 10 layers; the retinal pigmented epithelium (RPE), rod and cone photoreceptors (consisting of inner and outer segments), external limiting membrane formd by Muller cell end foot processes, outer nuclear layer, outer plexiform layer, inner nuclear layer, inner plexiform layer, ganglion cell layer, nerve fibre layer and the internal limiting membrane (Duke-elder and Wybar, 1961).

The neural retina consists of the outer nuclear layer (ONL) comprising the rods and cones, the inner nuclear layer (INL) comprising horizontal, bipolar and amacrine cells and the ganglion cell layer (GCL) comprising the ganglion cells. The outer and the inner plexiform layers (OPL and IPL respectively) consist of the synaptic contacts between the retinal neurons (Duke-elder and Wybar, 1961). A graphical illustration of the structure of the human retina is given in Figure 2-1

Underlying the neural retina is the retinal pigmented epithelium (RPE), which is closely apposed to the neural retina and with the basement membrane firmly atached to Bruch’s membrane, below which is the choriocapillaris. The RPE is a layer of heavily pigmented cuboidal cells joined by tight junctions (Figure 2-1). . These cells have active transport pathways across their cell membranes but are otherwise impermeable to the easy flow of electrolytes, forming the outer blood-retinal barrier (BRB). Bruch’s membrane can inhibit the movement of ions and fluid between the choriocapillaris, RPE and neural retina and vice versa.

Visual predictors for poor mobility performance in people with AMD

The macula was first described by Polyak, and consists of the fovea centralis, parafoveal and perifovea (Provis et al, 2005) and is about 5-6 mm in diameter; the macula is located in the central area within the vascular arcade of the retina (Wood, 2000). It has a yellowish colour due to the presence of macular pigments which comprise lutein and zeaxanthin (Alexander, 1989;

Bone et al, 1985; Handelman et al, 1988; Bone et al, 1988). The central area of the macula is characterised by a shallow pit like depression, the fovea centralis. This is the region where the density of the cone photoreceptors is at its peak, ~199,000 cones / mm2, although this is highly variable between individuals (Curcio et al, 1990.). The area surrounding the fovea which is devoid of retinal capillaries is called the foveal avascular zone (FAZ) and primarily receives its nutrition from the choriocapillaris. These anatomical features, together with the synaptic relationship of the cone photoreceptors and midget ganglion cells present in the macular region (Penfold et al, 2001) enable us to resolve fine detail and relay optimal quality of visual information to the brain. This characteristic also makes the macular area more susceptible to degenerative diseases (Penfold et al, 2001).

Figure 2-1: Schematic diagram of the retina showing its layers and corresponding cells. http://webvision.med.utah.edu/sretina.html

Visual predictors for poor mobility performance in people with AMD

2.2 Historical Perspective

The dry/atrophic form of AMD was first described by Nettleship (1903) and Haab (1904) and the wet form of AMD was first described by Pagenstecher (1875) (Abdelsalam et al, 1999). Early literature shows that several terms were used to describe the condition, and the classification was based on the examiners’ observations, understanding and evidence or facts which sometimes included histological and/or clinical features (Ryan et al, 1980). Some of the earlier terms used were “circinate retinopathy” (Hutchinson and Tay, 1875), “Hutchinson’s change” (Goldzieher, 1887), “circinate retinitis” (Hartridge, 1896), “central choroidoretinitis resembling an optic disc” (Yarr, 1898-1899), “external exudative retinitis”

(Coats, 1908; Leber, 1916) etc (Abdelsalam et al, 1999). The term senile macular degeneration was first described by Haab (1904) (Abdelsalam et al, 1999).

2.3 Definition: Age-Related Macular Degeneration

There is generally a consensus in the literature that age related macular degeneration is defined in the early stages by the appearance of drusen and/or pigmentary changes after the age of 50yrs, and in the late stages by the appearance of areas of RPE and phototreceptor atrophy and/or the occurrences of new vessel growth arising from the choriocapillaris.

It is important that there is an accurate definition of what is meant by a drusen and that commonly used terms, such as wet and dry AMD, are clearly defined to enable comparison of epidemiological studies and to translate these findings into clinical practice. For this reason there has been an agreed international classification. In this classification the early stage is termed Age-Related Maculopathy (ARM) and defined by the presence of drusen, hyperpigmentation of the RPE, and sharply demarcated areas of depigmentation in the RPE without indication that they are secondary to another disorder. The late stage is referred to as

Age- Related Macular Degeneration (AMD) (The International ARM Epidemiological Study Group, 1995), and is considered to be characterised by two processes associated with visual loss, the loss of RPE and photoreceptors, and the formation of new vessels. The two processes are referred to by various names. The process where there is loss of RPE and photoreceptors cells is referred to as the atrophic, the dry or the nonexudative type of late AMD, and the

Visual predictors for poor mobility performance in people with AMD

process where new vessels are formed is referred to as the neovascular, wet or exudative type of late AMD.

2.3.1 Non- Exudative AMD

Non-Exudative/ Dry AMD is a slow progressive condition characterised by geographic atrophy (GA). Geographic atrophy is distinguished by the presence of sharply demarcated areas of hypopigmentation, an apparent loss of photoreceptors and absence of the RPE, in which the choroidal vessels are more visible in the surrounding areas that is at least 175µm in diameter (The International ARM Epidemiological Study Group, 1995). These lesions are usually bilateral and symmetrical.

2.3.2 Exudative AMD

Exudative AMD (wet AMD, disciform AMD or neovascular AMD) is characterised either by the presence of RPE detachments, subretinal or sub-RPE neovascular membranes, epiretinal, intraretinal subretinal/sub-pigment epithelial scar/ glial tissue or fibrin-like deposits, subretinal haemorrhages that may be nearly black, bright red or whitish yellow and not related to any other retinal vascular disease or hard exudates within the macular area related to any of the above (The international ARM epidemiological study group, 1995).

The clinical appearance is caused by choroidal neovascularisation (CNV) which originates from the choriocapillaris and grows through the defects in Bruch’s membrane to remain confined in the sub-RPE space or extends into the sub-retinal space (Alexander, 1989). The defects in Bruch’s membrane may pre-exist related to ageing changes or be associated with extracellular matrix breakdown, new vessel growth and leukocyte activity, particularly macrophages. Macrophages occur in increasing numbers in relation to Bruch’s membrane as exudative AMD progresses (Penfold et al, 1986). Serous or haemorrhagic detachment of the RPE and sensory retina can occur due to leakage of fluid or blood from the fragile and highly permeable new vessels. The fibrous tissue proliferation which accompanies neovascularisation over time becomes the predominant feature resulting in a disciform scar involving the choroid, RPE and the neurosensory retina (Sarks, 1976). At this point, the RPE may proliferate and together with the macrophages form a pigment ring which is seen

Visual predictors for poor mobility performance in people with AMD

clinically. The neural retina overlying the disciform scar becomes atrophic following the loss of photoreceptors and the formation of cystoid spaces and lamellar or full-thickness holes (Sarks, 1976).

Classification of subtypes of exudative AMD

CNV can be classified depending on the patterns of presentation seen during fundus fluorescein angiography (FFA).

“Classic CNV is characterized by an area of choroidal hyperfluorescence with well- demarcated boundaries that can be discerned in the early phase of the angiogram. In later phases of the angiogram, progressive pooling of dye leakage occurs in the overlying sub sensory retinal space and usually obscures the boundaries of the CNV.” (Macular

Photocoagulation Study Group, 1991). (Fig 2-2B)

“Occult CNV was defined as an irregularly elevated nonhomogeneous area of stippled hyperfluorescence in the middle phase with persistent hyperfluorescence in the late phase (type 1) (Fig 2-3B) or as late-phase leakage in which the source of the leakage cannot be determined from earlier-phase frames (type 2)” (Beaumont and Kang, 2006). (Fig 2-3C)

“Serous retinal pigment epithelial detachment (SRPED) was defined as a round or an oval lesion with a smooth border that shows early and persistent hyperfluorescence but little evidence of leakage” (Beaumont and Kang, 2006). The characteristic features of SRPED are the smoothness of its contours and the homogeneity of the fluorescence.

“Polypoidal choroidal vasculopathy (PCV) is characterized by the presence of dilated, choroidal vascular channels ending in orange, bulging, polyplike dilations in the peripapillary and macular area” which “commonly presents with an associated serosanguineous detachment of the retinal pigment epithelium which may or may not be vascularized” (Yannuzzi et al, 1990; Ciardella et al, 2004; Eandi et al, 2007). PCV is a distinct choroidal abnormality characterized by an inner choroidal vascular network of vessels with a polyplike structure on the indocyanine green angiography (ICG) (Gomi et al, 2008). While the PCV lesions appear as occult or minimally classic CNV with fundus fluorescein 9

Visual predictors for poor mobility performance in people with AMD

angiography (FFA), the ICG clearly demonstrates the polyplike vascular network within the choroid (Gomi et al, 2008). The PCV lesions vary in size according to the location, they are larger and are visible on biomicroscopy if they are in the outer choriodal vessels (Tateiwa et al,

2002) and are smaller and can be diagnosed only using an ICG if they are present in the middle choriodal vasculature (Yannuzzi et al, 1990; Yannuzzi et al, 2000).

“The Retinal vascular anomalous complex (RVAC) is often asymptomatic in the early stages, which starts with the proliferation of the retinal capillaries to form intraretinal and subretinal neovascularisation and is subsequently associated with Pigment epithelial detachment (PED) and CNV” (Yannuzzi et al, 2001; Schneider et al, 2005). “RVAC was defined by the retinal vessel that was noted to turn posteriorly to the posterior the retinal pigment epithelium and to proliferate beneath the neurosensory retina as demonstrated by a focal hyperfluorescence” on the FFA and ICG angiography (Schneider et al, 2005).

Area of the lesion

Figure 2-2 A: Fundus photograph of AMD lesion. (Copyright Dr Paul Beaumont)

Visual predictors for poor mobility performance in people with AMD

Classic New Vessel

Figure 2-2 B (a): Classic CNV in the early phase on fundus fluorescien angiography. (Copyright Dr Paul Beaumont).

Classic New Vessel

Figure 2-2 B (b): Classic CNV in the mid phase on fundus fluorescien angiography. (Copyright Dr Paul Beaumont).

Visual predictors for poor mobility performance in people with AMD

Classic New Vessel

Figure 2-2 B (c): Classic CNV in the late phase on fundus fluorescien angiography. (Copyright Dr Paul Beaumont).

Area of the lesion

Figure 2-3 A: Fundus photograph of AMD lesion. (Copyright Dr Paul Beaumont).)

Visual predictors for poor mobility performance in people with AMD

Possible site of new vessel activity

Figure 2-3 B (a): Occult CNV (Type 1) on the early phase of fundus fluorescien angiography. (Copyright Dr Paul Beaumont).

New Vessel that shows hyperfluorescence in the mid-phase

Figure 2-3 B (b): Occult CNV (Type 1) on the mid-phase of fundus fluorescein angiography. (Copyright Dr Paul Beaumont).

Visual predictors for poor mobility performance in people with AMD

Occult New Vessel (Type I)

Figure 2-3 B (c): Occult CNV (Type 1) on the late phase of fundus fluorescein angiography. (Copyright Dr Paul Beaumont).

Possible site of new vessel activity

Figure 2-3 C (a): Occult CNV (Type 2) on the early phase of fundus fluorescein angiography. (Copyright Dr Paul Beaumont).

Visual predictors for poor mobility performance in people with AMD

New Vessel that shows hyperflurescence in the mid-phase where the source of leak cannot be identified

Figure 2-3 C (b): Occult CNV (Type 2) on the mid phase of fundus fluorescein angiography. (Copyright Dr Paul Beaumont).

Occult New Vessel (Type II)

Figure 2-3 C (c): Occult CNV (Type 2) on the late phase of fundus fluorescein angiography. (Copyright Dr Paul Beaumont).

Visual predictors for poor mobility performance in people with AMD

2.4 AMD Prevalence

AMD is the leading cause of irreversible blindness in older people in developed countries

(Kahn, 1977; Attebo, 1996; Klaver, 1998). It is more prevalent in people of Caucasian ethnicity than people of African ethnicity (Sommer et al, 1991). AMD is one of the primary causes of visual loss in people over the age of 40 in the United States (Kahn et al, 1977; Klein et al, 1992) with increased prevalence ratio of 10:1 among people of European ethnicity compared to those with African ethnicity although the prevalence of drusen in both groups was comparable

(Friedman et al, 1999). A similar pattern in the prevalence of AMD has been observed in the

Caribbean and Latin American countries (Munoz and West, 2002). Although the prevalence of early AMD was reported to be higher in Hispanic people (17.8%) when compared to people of other ethnicities [Caucasian (7.57%) and African (6.53%)] the prevalence of late AMD was very low in Hispanics (0.58%) (Munoz et al, 2005). The prevalence has also been shown to increase dramatically with age and has been estimated to increase by 50% by the year 2020

(The Eye Diseases Prevalence Research Group, 2004).

According to the Blue Mountains Eye Study (BMES), AMD was the second most prevalent cause of visual impairment) in people over the age of 40 years (Foran et al, 2003; Wang et al, 2000). AMD was estimated to account for about 75% of bilateral blindness in Australia (Visual impairment for purpose of this study were defined as visual acuity less than 6/12 and blindness as visual acuity less than 6/60) (Wang et al, 2000). The Visual Impairment Project (VIP) reported AMD to be the leading cause of blindness in people over the age of 40 years of age and projected that visual impairment due to AMD would double in the next twenty years (Mukesh et al, 2004) in concurrence with an increase in the ageing population. This is also in agreement with the projections estimated by the Eye Diseases Prevalence Research Group

(2004). The discrepancy in the prevalence of AMD in Caucasians reported by studies in the US (BDES and NHANES III) compared to other studies in Caucasians (RS, BMES and SEE) is due to the difference in the type of AMD and age group reported. The US studies (BDES and NHANES III) reported prevalence of any type of AMD (ARM and AMD) while the other studies (RS, BMES and SEE) reported prevalence of AMD (also classified as late ARM by some studies [BDES, NHNESIII]). The racial differences in the prevalence of AMD are given Table 2-1.

Visual predictors for poor mobility performance in people with AMD

Table 2-1: Racial differences in the prevalance of AMD.

Country Study Ethnicity Prevalence* (%)

Australia BMES Caucasian 1.9 (Mitchell et al, 1995)

Australia VIP Diverse ethnicity 0.76 (VanNewkirk et al, 2000) Non Hispanic whites 10.1** US NHANES III Non Hispanic blacks 8.53** (Klein et al, 1999) Mexican Americans 7.66**

US BDES Caucasian 16.2 (Klein et al, 1992)

Netherlands RS Caucasian 1.7 (Vingerling et al, 1995)

US SEE Project Caucasian 3.5 (Bressler et al, 2008) African 2.4

US BES Caucasian 3.47 (Friedman et al, 1999) African 0.21

Barbados, West Barbados Eye Study African 0.6 Indies (Schachat et al, 1995)

Japan Hisayama Study Asian 0.72 (Kawasaki et al, 2010 )

India INDEYE Study Asian 1.2 (Krishnan et al, 2010) Abbreviations: BMES, Blue Mountains Eye Study; NHANES III, The National Health and Nutrition Examination Survey III; BDES, Beaver Dam Eye Study; RS, Rotterdam Study; SEE Project, The Salisbury Eye Evaluation Project; BES, Baltimore Eye Study. * The prevalence reported was that of only AMD; ** The prevalence is reported as the total of ARM and AMD

Visual predictors for poor mobility performance in people with AMD

2.5 Classification and Grading for AMD

Although a number of studies have looked at the histological and epidemiological pattern of the disease process in AMD, understanding of this condition has been difficult due to the absence of a standard definition and a universal classification of the disease process (The international ARM epidemiological study group, 1995). This issue was first addressed in 1991 by the development of the Wisconsin Age-Related Maculopathy grading system (Klein et al, 1991). The most commonly used classification for AMD is that by the Age-Related Eye Diseases Study (AREDS) (Table 2-2) which was developed using standards set by the Wisconsin age- related Maculopathy grading system in order to assess the progression of the disease process

(AREDS research group, 2001).

2.6 Pathogenesis and Pathophysiology of AMD

Verhoeff and Grossman (1937) provided the sentinel paper on the pathogenesis of disciform degeneration of the macula with a review of the condition up to that date. They were the first to suggest the underlying disease process that leads to the appearance of neovascular AMD.

They ascribed the first description of wet AMD to Pagenstecher (1875) who described the macroscopic and microscopic features of the lesion and gave it the name “Choroidoretinitis in regione maculae luteae” (Verhoeff and Grossman, 1937). They credited Oller (1905) with the term “disciform” when describing this lesion as “choroidoretinitis macula luteae disciformis” (Verhoeff and Grossman, 1937). They reviewed 84 cases available in the literature and 13 eyes were examined histologically. Not all the articles reviewed provided details other than a description of the lesion. The average age of the patients was 68 with a range from 39 to 83. 47/81 were male. The lesion was described as a mound in the central macula which usually distinct; in the early stages it may be associated with blood at the edges and was greyish greenish or sometime black but in the late stage it became white. The associated fundal changes included the finding of drusen and pigmentary changes (Verhoeff and Grossman,

1937). Verhoeff and Grossman (1937) described breaks in Bruchs membrane with vessels coming through them and lymphocytes around the choriocapillaris in the vicinity of these breaks. Their final conclusion was that AMD was possibly a condition in which the

Visual predictors for poor mobility performance in people with AMD

haemorrhage originated from the choroidal vessels and “senile pigmentary changes may predispose to disciform macular degeneration.”

Table 2-2: AREDS AMD Classification (AREDS research group, 2001)

AMD Level Criteria 1 Drusen maximum size< circle C-0 (63µm in diameter) and total area< circle C-1 (125µm diameter)

2 Presence of one or more of the following: (1) Drusen maximum size > circle C-0 but circle C-1. (3) Retinal pigment epithelial pigment abnormalities consistent with AMD, defined as one or more of the following in the central or inner subfields: (a) Depigmentation present. (b) Increased pigment > circle C-1. (c) Increased pigment present and depigmentation at least questionable.

3 Presence of one or more of the following: (1) Drusen maximum size > circle C-1. (2) Drusen maximum size > circle C-0 and total area > circle I-2 and type is soft indistinct. (3) Drusen maximum size > circle C-0 and total area > circle O-2 and type is soft distinct. (4) Geographic atrophy within the grid but none at the centre of macula. 4 Presence of one or more of the following: (Advanced) (1) Geographic atrophy in the central subfield with at least questionable involvement of centre of macula. (2) Evidence of neovascular AMD. (a) Fibrovascular/ serous pigment epithelial detachment. (b) Serous (haemorrhagic) sensory retinal detachment. (c) Subretinal/ subretinal pigment epithelial haemorrhage. (d) Subretinal fibrous tissue (e) Photocoagulation for AMD

Early fluorecein angiographic and histological studies suggested that the neovascualrisation from the choroid occurred in response to the abnormal deposits beneath the retinal pigment epithelium leading to the haemorrhage and exudation that lead to the disciform lesion (Gass,

1967). 19

Visual predictors for poor mobility performance in people with AMD

It has been stated that “Pathogenesis of AMD is a puzzle with many pieces not yet securely placed and with an unknown number of pieces missing” (Mcquire, 1997). Although many of the pieces have been reported over the years, this statement still holds true. In AMD the progressive morphological changes in the choroid, Bruch’s membrane, RPE and neural retina due to normal ageing become pathological resulting in a myriad of clinical and histological changes (Sarks 1973; Green and Enger, 1993).

A histological study of 378 eyes with a range of appearances from normal to the late manifestations of senile macula degeneration, delineated the initial histological lesions characteristic of AMD as opposed to the changes associated with ageing alone (Sarks, 1973). This study reported that “Although senile macula degeneration seems inextricably bound to the ageing process the lack of unerversality suggests it is not essential to ageing.” A hyaline change in Bruck’s membrane was noted as a normal accompaniment of ageing and another layer, described as “basal linear deposit” was a marker for the degeneration of RPE cells. Subsequently although there has been agreement that there are seperate pathological deposits within Bruch’s membrane, related to ageing and to AMD, there has been some confusion about the naming of the two abnormal layers.

Green and Enger (1993) redefined basal linear deposit as basal laminar deposit, based on electron microscopy observations. Basal linear deposit has been redefined as an abnormal layer external to the RPE basement membrane. The Basal linear deposit has been described as the electron microscopic diagnostic hallmark of ARM, whereas with light microscopy, it is a continuous layer of basal laminar deposit. This does still create confusion, although it is generally agreed that basal linear deposits appear more specific for AMD than basal laminar deposits (Curcio and Millican, 1999).

Early histopathological studies suggested the development of basal linear deposit under the

RPE initiates the disease process (Sarks, 1976). This was suggested to progress to form a thin continuous layer with grade 3 hyalinization of the Bruch’s membrane which thickened along with pronounced degenerative and proliferative changes in the RPE. At this stage, circumscribed areas of depigmentation occurred due to the gradual disappearance of cells. These deposits also disturbed the normal basement membrane attachment of the RPE 20

Visual predictors for poor mobility performance in people with AMD

causing spontaneous detachment of the pigment epithelium in some cases. In the absence of new vessels, collapse of the RPE and serous detachment was reported to result in areas of geographic atrophy. The incorporation of the basal linear deposit into the fibrovascular scar may contribute to disciform degeneration (Sarks, 1976). The presence of giant cells and mononuclear phagocyte series (MPS) using histological and electron microscopy observations of eyes obtained from individuals diagnosed with atrophic form of AMD or non-exudative AMD have led to conclusions that they may play a role in the pathogenesis of the disease process (Penfold et al, 1986). Although the precise mechanism is still not clear, it has been suggested that the rate of development or progression of the atrophic lesion is actively influenced by these cell types.

Clinically, the early stages of AMD are identified by the presence of drusen. Drusen have a complex composition including mucopolysaccharides and lipids. Pathophysiologically, they may represent an altered state of a metabolically healthy retina. The RPE/Bruch’s membrane complex is the area of accumulation of drusen. Soft indistinct drusen are considered more pathognomonic of early ARM than distinct drusen.Drusen over 125µm are more significant than smaller drusen. Hard small, <63µm drusen, are almost ubiquitous after the age of 50 yrs and particularly when present in small numbers, are not indicative or early ARM.

There are several theories postulated about the pathogenesis of ARM or drusen. One of the reasons for this could be the differences in the histological appearance of the different types of drusen. The major theories regarding the pathogenesis of drusen were the transformation theory by Donders (1854), deposition theory by Muller H (1856) (Abdelsalam et al, 1999)) and the vascular theory (Friedman et al, 1963). The earlier postulations by Donders and Muller were that drusen resulted from dysfunction of the RPE. While Donders proposed that drusen was the result of degenerated RPE cells, Muller theorized that drusen was a result of secretion from

RPE membranes on to the Bruch’s memebrane (Abdelsalam et al, 1999). Muller’s hypothesis stated that drusen was the representation of localized areas of RPE dysfunction where membraneous debris was acccumulated in the Bruch’s membrane by means of extrusion of a portion of the pigment epithelium cells (Burns and Feeney-Burns, 1980; Isibashi et al, 1986). The observation of cytoplasmic profiles, fibrous material and cell processes in drusen added evidence to this hypothesis. However, this hypothesis failed to explain the observed pattern of distribution of drusen and also did not account for the possibility that these observed 21

Visual predictors for poor mobility performance in people with AMD

components could be a consequence rather than a cause. Although several studies (Ulshafer et al, 1987; Abdelsalem et al, 1999) reported that the origin of drusen formation was RPE, the lack of documentation to determine the origin of drusen failed to provide scientific evidence of directional nature (Penfold et al, 2001). Observations that the drusoid material was laid on the sclerad aspect of the choriocapillaris away from the RPE led to reservations about this hypothesis and led to postulation that RPE was not the primary source of drusen formation. Clustering of drusen around the collecting venules of the choriodal vortex system and capillaries associated with them, led to the hypothesis that the origin of drusen was from the choriocapillaris, although this pattern of distribution was seen only in drusen present at the equator and a more random pattern was observed at the posterior pole (Friedman et al, 1963).

Friedmann (1963) postulated that this difference in distribution pattern could be a result of the architecture of choriocapillaris and due to the lack of perceptible polarization of the capillary network, the drusen present at this site appeared to be dispersed randomly.

Another plausible explanation suggests a role for inflammation in the pathogenesis of AMD, based on the composition of drusen (Donoso et al, 2006). The presence of drusen in membrane proliferative glomerulonephritis type II, a renal condition due to complement-mediated immune dysfunction, which were clinically, histologically and immunohistochemically identical to drusen in AMD, lead to the hypothesis that drusen form due to a localized inflammatory response following an RPE injury (Donoso et al, 2006).

A clear understanding of the defense mechanisms in humans is essential to understand the role of inflammation in the pathogenesis of AMD. The defense mechanisms can be broadly classified into physical barriers, the innate immune system and the adaptive immune system

(Donoso et al, 2006). The complement system is a part of the innate immune system and can be subdivided into the classical arm that involves antigen-antibody complexes and complement, the lectin- mediated arm and the alternative arm (Rodriguez de Cordoba et al, 2004;

Donoso et al, 2006). As a defence mechanism to invasion from pathogens, the complement system deposits C3b on both host and foreign cells. Factor H inactivates the C3b from the intact host cells by binding to heparin or sailic acid on the host cell to the domain 7 on its molecule, thus allowing the destruction of the foreign cells while preserving the intact host cells. Any alteration to either domain 7, heparin or the sialic acid binding area of Factor H

Visual predictors for poor mobility performance in people with AMD

could direct or enhance the destruction of normal cells along with the injured and foreign cells even in an appropriate environment (Donoso et al, 2006).

Identification of the components of drusen deposits has shed light into the possible pathogenesis of AMD (Table 2-3). This has led us to hypothesise that the pathogenesis of

AMD revolves around the immune-mediated and inflammatory events (Hageman et al, 2001). The dynamic interaction between those factors that induce and sustain chronic local inflammation at the level of the RPE - Bruch’s membrane – Choroicapillaris interface forms the foundation for this hypothesis.

The possibility of a role for inflammation in the pathogenesis of ARM is further supported by the findings of the role of complement activation in the biogenesis of drusen (Johnson et al,

2001; Luibl et al, 2006). These findings reported by Johnson et al (2001) were (a) presence of multifunctional molecules that can modulate the immune response such as Vitronectin, Clusterin, Membrane Cofactor Protien and Complement Receptor Type I in drusen, (b) the concentration of drusen associated molecules such as vitronectin in the RPE that are closely associated with abnormalities in Bruch’s membrane or drusen, and (c) the presence of vesicular profiles that contain high concentrations of C3 complement fragments that become bound at the primary sites of complement activation. Combining results from these investigations and those that reported the exhibition of compromised phenotype by the RPE cells that overlie drusen (a cellular response to complement attack (Johnson et al, 2000; 2001;

Anderson et al, 2002)), Johnson et al (2001) postulated that “drusen formation is a by-product of pro-inflammatory events and complement mediated attack on the RPE”.

Although the role of complement activation in the biogenesis of drusen has been hypothesized (Luibl et al, 2006), its role in the pathogenesis of ARM is controversial due to the presence of complement components in drusen of both ARM eyes and normal eyes (Johnson et al, 2001; Ambati et al, 2003; Hageman et al, 2005). The presence of amyloid oligomers in drusen similar to that found in amyloid disease suggests that these oligomers could be involved in the biogenesis of drusen (Luibl et al, 2006). Luibl (2006) reported that amyloid oligomers are present in drusen, RPE cells and basal deposits. Based on the toxicity of amyloid oligomers towards cells, they hypothesized that the close proximity of these oligomer cells to the RPE

Visual predictors for poor mobility performance in people with AMD

cells could lead to the activation of the complement cascade by compromising their function and thus initiating the formation of drusen.

The similarities between drusen present in AMD and deposits found in other systemic conditions provides yet another link to explore the pathogenesis of this disease process. The similarities in the components of drusen, which is a precursor to the development of AMD, and amyloid deposits found in diseases such as Alzheimer’s and Parkinson’s disease (Luibl et al, 2006) suggests the possibility of similar mechanisms or pathways to explain pathogenesis of these disease processes.

The presence of amyloid β in drusen of AMD retinas and not in that of normal retinas suggests an association in the pathogenesis of AMD (Dentchev et al, 2003). Amyloid receptors could be used as markers for eyes with drusen that could be at risk of developing AMD due to its absence in drusen of normal retinas, but whether these amyloid β receptors are the cause of the disease process or rather a consequence of the degenerative process is still not clear (Dentchev et al, 2003). The similarity in the components between amyloid deposits in Alzheimer disease (AD) and AMD suggests that perhaps the pathogenesis of AMD could be related to that of AD and other neuro-degenerative diseases (Dentchev et al, 2003; Luibl et al, 2006).

Another possibility is the phenomenon called immunological mimicry. Immunological mimicry between the host and microbial glycoprotein’s is a possibility for local immune responses in humans. This possibility of the role of autoimmune responses within the retina in the development of AMD was proposed by Donoso (2006) in his review based on reports regarding the innate ability of Streptococcus pneumoniae to escape destruction by the binding of the M protein (a bacterial cell wall protein) with the domain 7 of factor H protein

(complement cascade protein) (Duthy et al, 2002; Giannakis et al, 2003). This is mediated by the immunological similarities between the M protein of the bacterial cell wall and the Retinal

S- antigen (Photoreceptor cell protein) (Lerner et al, 1995).

Although these theories explain some of the features of the pathogenesis of ARM/AMD, they do not explain all the aspects of the disease process. Perhaps deriving a hypothesis by re-evaluating and combining the above observations and theories would enhance our understanding of the pathogenesis of the disease condition.

Visual predictors for poor mobility performance in people with AMD

Table 2-3: Components of drusen deposits (Donoso et al, 2006)

Biochemical components

α1- Antitrypsin Alzheimer amyloid β peptide Amyloid P Apolipoproteins B and E Cholesterol esters Clusterin Complement factors (C1Q, C4, C5) Complement receptor 1 Factor X Glycoprotein moieties recognised by various lectins Heparin sulphate proteoglycan Human leukocyte antigen DR Immunoglobulins Peroxidised lip derived material Phospholipids Tissue inhibitor of matrix metalloproteinases – 3 Transthyretin Ubiquitin Vitronectin

2.7 Risk factors for macular degeneration

AMD is a complex, multifactorial disease for which age, family history, anti-oxidant status, photic injury, nutritional deficiency, toxic injury, immunological disorders, smoking, cardiovascular and respiratory disturbances, apolipoprotein genotype associations, pre- existing eye diseases, hypertension, high cholesterol levels and sunlight exposure have been identified as potential risk factors (Klein et al, 2010). The evidence in regards to involvement of many of these risk factors has been conflicting ((Tso MOM, 1985; Leibowitz et al, 1980; Seddon et al,

1994; 1996; 1997; 2006; Christen et al, 1996; Souied et al, 1998; Delcourt et al, 1999).

Proposed genetic transmission of AMD is a complex issue due to the multifactorial basis of the disease process and the controversy as to whether the expression of the condition is 25

Visual predictors for poor mobility performance in people with AMD

caused by a single gene, several genes or an interaction between genetic and other risk factors. About 25% of cause and overall occurrence of ARM is attributed to genetic factors

(Klaver et al, 1998). A few studies that looked at familial cases of AMD have reported an autosomal-dominant inheritance (Silvestri et al, 1994; Gass, 1973; Klein et al, 1998).

Several genes have been identified as risk factors related to their potential associations with AMD. The first gene to be identified in a number of studies is the Y40211 allele of the

Compliment factor H (CFH) gene (Haines et al, 2005; Hageman et al, 2005; Klein et al, 2005). The heterozygous mutations in ABCA4 is another risk factor for AMD (Zhang et al, 1999), although its associations are challenged (Guymer et al, 2001) supported by a report of 13 different AMD- associated gene variations in one allele of ABCA4 (Allikamets et al, 1997).Reports by Majewski et al, identified Chromosome 1q25-31 and 10q26 to be associated with a risk of AMD (Tuo et al,

2004). Allele ε4 of the Apolipoprotien E (APOE) gene and the C-C chemokine Ccl2 (MCP1)/ its cognate receptor 2 (Ccr2), may be protective in function against AMD (Souied et al, 1998;

Ambati et al, 2003; Wong et al, 2006). The genetically engineered mouse models with C-C chemokine Ccl2 (MCP1)/ its cognate receptor 2 (Ccr2) knockouts developed both dry and wet AMD (Ambati et al, 2003).

Apolipoprotien E (APOE) gene is a major apolipoprotein of the central nervous system and one of the key regulators of lipid transport (Mahley, 1988). The influence of APOE on lipid metabolism and its association with a range of neurodegenerative conditions is well known

(Wong et al, 2006; Slooter et al, 1997; Evans et al, 1997). Several studies have demonstrated the reduced frequency of APOE allele ε4 in people with AMD compared to normals (Zareparsi et al, 2004;

Souied et al, 1998; Wong et al, 2006) which is suggestive of the protective effect of allele ε4 against the development or occurrence of AMD.

Cellular and tissue damage due to Xenobiotics and oxidants through exposure to environmental chemicals suggests the involvement of genetic polymorphisms as one of the underlying cause of AMD (Souied et al, 1998; Kimura at al, 2000). Involvement of genetic polymorphisms for manganese superoxide dismutase genotype and microsomal epoxide hydrolase genotype were shown by its increased susceptibily to AMD (Kimura at al, 2000).

Visual predictors for poor mobility performance in people with AMD

In addition to family history, tobacco smoking has been found to be a major risk factor for any form of AMD (Smith et al, 2001). Analysis of risk factors from data obtained in large population-based samples shows that current smokers have an increased risk of developing

AMD (Tomany et al, 2004) at an earlier age (Mitchell et al, 2002). Smoking more than 20 cigarettes a day has been shown to increase the risk 2.5 times by the Physician’s health study (Christen et al, 1996) and this has been supported by other studies (Seddon et al, 1996) with some discrepancies where smoking was not associated with the risk of developing AMD (Chaine et al, 1998). However the risk due to exposure to tobacco smoking is considered preventable, since recent studies have shown that current smokers are at more risk compared to past smokers

(Tomany et al, 2004). Susceptability of the retina to oxidative stress is one of the other risk factors (Gottsch et al, 1990) that is preventable by the protective effect of antioxidants (West et al,

1994).

The other potential risk factor is the gene-environmental interaction. Studies have shown that the risk of AMD increases in a genetically susceptible person when exposed to environmental factors that are identified as risk factors. Environmental factors that have been identified to interact with genetic vulnerability are smoking (Schmidt et al, 2005), systemic conditions such as atherosclerosis (Vingerling et al, 1995) and dietary intake (Seddon et al, 1994). Histopathological similarities between drusen and the artherosclerotic deposits within the arterial vessels lead to the possibility of an association between cardiovascular disease and

AMD (Donoso et al, 2006).

This review provides current evidence on the prevalence, pathogenesis, severity and risk factors associated with the tracjectory of the disease process in AMD. Understanding the disease process in AMD is very important as it provides a background for the possible functional consequences as evidenced with psychophysical measurements of visual function. Previous studies have shown associations between severity of AMD and psychophysical functional deficits (Hogg et al, 2003; Beirne et al, 2006). Chapter 3 assesses the quality of life implications related to AMD and Chapter 4 assesses the psychophysical visual functions in people with AMD in contrast to controls.

Visual predictors for poor mobility performance in people with AMD

Chapter 3: Functional Implications of AMD in

Quality of Life

Quality of life in this study is defined on the basis of the impact that a disability has on an individual’s life rather than the disability itself (Bradley et al, 1999; Mitchell and Bradley, 2006). Self-reporting outcome measures are now a vital part of all clinical trials in all divisions of medicine including ophthalmology, as secondary endpoints to physiological, anatomical and psychophysical measures (Mangione et al, 1996; Ellwein et al, 1995; Patrick and Deyo, 1989). Quality of life questionnaires aim to identify and quantify the impact of visual impairment on aspects of daily living such as self care, mobility and dependency (Wolffsohn and Cochran, 2000).

3.1 Introduction

Several instruments have been designed or validated to measure quality of life. The most commonly used instruments for measuring quality of life are the ‘medical outcomes study Short Form 36’ (SF 36) and the ‘Quality of Well Being scale’ (QWB). The SF-36 is a short form measure of generic health status (Stewart and Ware, 1992) and QWB uses preference- weighted measures combined with scales of functioning measuring the symptoms and problems with an expression of wellbeing (Kaplan et al, 1979). Another health-related quality of life measure that is popular among economic researchers and policy makers in the analysis of improvements to quality of life as a result of treatment is Utility Analysis (Brown et al, 2005). While there are three variants for this (time trade off, standard-gamble and willingness-to- pay), the most preferred is the time-trade off utility analysis. The basis for time trade off analysis is based on the individual’s responses to “how long they expect to live?” and “how much time they were willing to trade to return to normal health for the remaining years?” and is then calculated as the proportion of the trade off being subtracted from 1 (Brown et al,

2005). For instance, if an individual responded that they expected to live for another 20 years and were willing to trade 10 years to be rid of the existing condition, the time trade off in that case is 0.5 (calculated as 1.00 – 10/20). Analysis of quality of life using time trade-off utility analysis has found that while mild AMD caused a 17% reduction in quality of life, severe AMD caused a 63% reduction (Brown et al, 2005). This study categorised people with 28

Visual predictors for poor mobility performance in people with AMD

AMD based on Snellen visual acuity in the better eye (mild: 20/20 (6/6) to 20/40 (6/12); moderate: 20/50 (6/15) -20/100 (6/30); severe: 20/200 (6/60) or worse).

Function-specific questionnaires that have been validated for people with macular degeneration are: ‘Measure of impact of macular Degeneration on Quality of Life’

(MacDQol) (Mitchell et al, 2006), ‘Daily Living Tasks dependent on Vision scale’ (DLTV) (Hart et al, 1999), ‘National Eye Institute Function Questionnaire’ (NEI-VFQ 25) (Mangione et al, 1998),

‘14- item Visual Function questionnaire’ (VF-14) (Steinberg et al, 1994), ‘Activities of Daily living Scale’ (ADVS) (Mangione et al, 1992), ‘Vision related Quality of Life’ (VQOL),

‘Instrumental Activities of Daily Living scale’ (IADL) (Williams et al, 1998) and ‘Low

Luminance Questionnaire’ (LLQ) (Owsley et al, 2006).

Reports by Moore et al (2000) showed that the key to understanding the quality of life in people with AMD is in the assessment of the patient’s ability to cope and adapt rather than the assessment of implications of vision loss (Slakter and Stur, 2005). One of the major drawbacks of questionnaires measuring the functional status of patients has been that psychological domains have often been ignored. Furthermore, although items relevant to vision were included these did not specifically assess vision-related functions such as reading or watching television and sometimes did not corelate with the objective measures of vision

(Mitchell and Bradley, 2006). There have also been arguments that since the functional status questionnaires often investigated what an individual can or cannot do, rather than whether they need to do it or its impact on the individual; these questionnaires do not truly measure the quality of life. (Slakter and Stur, 2005; Mitchell and Bradley, 2006).

Other questionnaires that have been used and considered important for a better understanding of quality of life in people with different health conditions are those that assess, screen or demonstrate the mental health status of individuals. The most commonly used ones in a clinical environment are the ‘Hospital Anxiety and Depression Scale’

(HADS) (Zigmond and Snaith, 1983), ‘Geriatric Depression Scale’ (GDS) (Yesavage et al, 1982),

‘Profile of Mood States’ (POMS) (Williams et al, 1998) and ‘Goldberg Anxiety and Depression scale’ (GADS) (Goldberg et al, 1988).

Visual predictors for poor mobility performance in people with AMD

Fitzgerald and Parkes (1998) reported that the psychological phases that a patient passes through following loss of vision due to AMD is similar to that of the grief cycle demonstrated by the Kubler-Ross model and can be distinguished as “shock or disbelief” defined by reluctance to accept, “pining for what is lost” recognized by behavioural patterns associated with feelings of irritability and “anger” due to the intense frustration experienced. Depression almost always follows pinning which is often followed by resolution associated with increased self esteem and self-sufficient acts (Fitzgerald and Parkes, 1998). Resolution has been found to occur in only half the affected population while the other half persisted to have anxiety and depression leading to a decline in self-esteem and social withdrawal

(Fitzgerald RG, 1970; Fitzgerald and Parkes, 1998).

The use of generic health related quality of life questionnaires for people with AMD and their ability to interpret the impact of AMD on quality of life has been debated. While

Mangione et al (1999) and Cahill et al (2005) have shown that generic health-related quality of life questionnaires were not sensitive to the impact of AMD on quality of life, other studies by Knudtson et al (2005) and Scott et al (1999) have found that the SF-36 (generic health related quality of life questionnaire) was in fact sensitive to the impact of AMD on quality of life of affected individuals. Careful analysis of these results showed that while the SF-36 was not as sensitive as vision-related questionnaires to the severity of the condition and the impact of minor improvements to vision; the SF-36 did assist in the analysis of the impact of the condition on an individual’s quality of life. This finding depended on the functional abilities of the individual, highlighting the importance of the domains that are affected, rather than just the visual disability. This was confirmed by the findings of Knudtson et al

(2005), where they showed that the differences obtained on the mental health domain remained significant even after correcting for vision.

A number of studies to assess the extent to which AMD adversely affects emotional health and quality of life are reported in the literature, although the outcome of these is equivocal. Some studies have shown that AMD has adverse consequences on the quality of life of affected people associated with difficulties in performance of daily tasks, emotional distress

(Williams et al, 1998) and driving cessation (Marottoli et al, 1997). However, other studies of people with AMD reported that they enjoyed their privacy and independence in spite of difficulties with mobility, their hobbies and home activities such as watching television and reading and 30

Visual predictors for poor mobility performance in people with AMD

also found life interesting as it challenged them to find new coping strategies (William et al,

1998; Moore LW, 2000). These conflicting reports, and the absence of case control studies, provide a rationale for the current investigation.

Another study found that restriction of valued activities such as reading and driving is more depressing than the disease itself (Zeiss et al, 1996). Visual impairment has been identified as an important determinant in developing depression and a wish to die (Jorm et al, 1995). These studies differed in how the health and vision related cofactors were controlled. The study by

Moore (2000) comprised of subjects from one area only and the observed result may reflect good community support present in that area or could be a result of the interpretation of the interview by the researcher (Slater and Stur, 2005). These conflicting studies indicate that further study is required to determine the extent to which AMD adversely affects emotional health and quality of life.

The impact of vision on quality of life has been studied previously, in studies that examined vision-related quality of life and assessing the impact of the eye with the worse acuity (Meads et al, 2003) and eye with the better acuity (Brown, 1999; Berdeaux et al, 2005). Although there are discrepancies between these studies, their results do show the impact of reduced vision on quality of life particularly for domains of mental health due to anxiety relating to the fear of further loss of vision and handicap leading to loss of independence.

When the mental health status of people with AMD was studied specifically, both anxiety and depression were more common compared to those without AMD (Rovner et al, 2002; Brody et al, 2001; Banarjee et al, 2008). Although these studies used different methods and criteria for assessing the mental health status which makes comparisons difficult, these studies consistently showed that people with AMD were more likely to have depression compared to people of the same age group. Casten et al (2004) in their review on AMD and depression documented evidence showing that depression is not a normal phase of ageing but rather an outcome of disabilitating diseases. Depression has been reported to lead to decrease in functioning, increased rate of institutionalisation and mortality if left untreated (Penninx et al,

1999; Covinsky et al, 1999; Pulska et al, 1999).The reported prevalence of depression in people with

AMD varies from 17 to 33% (Augustin et al, 2007; Brody et al, 2001; Banarjee et al, 2008). The other interesting observation was that the emotional distress exhibited by people with AMD was 31

Visual predictors for poor mobility performance in people with AMD

similar to that of people with serious chronic illness such as melanoma or cancer (Williams et al,

1998; Brody et al, 2001).

The relationship between the severity of AMD, the disability it brings about and depression was further explored by Rovner and Casten (2002). This study demonstrated that the severity of the disease process was associated with depression only to the extent where normal activity was disrupted. Further exploration of this possibility pointed out that “dissatisfaction with the performance of a valued activity” was the first step towards developing depression

(Rovner et al, 2007). This was consistent with studies by Zeiss et al (1996), where they concluded that while the disease process in general is a vulnerability factor, it was the functional impairment caused by the disease which was the precursor of depression.

The major studies that explored the implications of AMD on quality of life and depression to date did not include age-matched control subjects in their studies. A cross-sectional study

(n=151) by Brody et al (2001) that analysed the associations between depression, disability and comorbidity reported that the number of people with AMD having depressive disorder was twice of that reported in the general population, however they did not have a control group in their study to substantiate this claim. Similarly reports by Williams et al (cross- sectional design; study group= AMD; n=86) (1998), Scott et al (cohort design; study group= low vision subjects of which 86 had AMD; n=156) (1999), Rovner et al (cohort design; study group= AMD; n=46) (2002), Cahill et al (cohort design; study group= AMD; n=70) (2005),

Rovner et al (cross-sectional; study group= AMD; n=206) (2006) and Augustin et al (cross- sectional design; study group= AMD ;n=360) (2007) did not include reference/control groups. Although the impact of AMD on quality of life and its effect on the mental health status has been studied, the lack of case-control studies does not provide an appropriate measure of the true significance of the impact of AMD, as it is often considered to be concommitant with the normal ageing process.

In this study, a range of quality of life measures derived from questionnaires related to generic health and the mental health status of individuals, reflecting both physical and emotional domains were compared in a large sample of people with AMD and age-matched controls. Functional and emotional domains were also analysed to identify factors associated with depressive symptoms. This information may assist with informing interventions and 32

Visual predictors for poor mobility performance in people with AMD

service provision that may enhance quality of life for the increasing population of people with AMD.

3.2 Materials and Methodology

3.2.1 Research synopsis Questionnaires that assessed different domains of quality of life were selected. The aspects of quality of life that were examined were physical and emotional health in terms of the individual’s general and ocular health. Each questionnaire set was comprised of the SF-36, GADS, LLQ and Falls Efficacy Scale- International (FES-I). All subjects participating in the study were asked to complete all the questionnaires. The subjects were given the option of completing the questionnaire during their visit at the clinic or completing it at their own pace and posting it back to the candidate. Subjects who opted to complete it at home were given a self-addressed (candidate’s details) envelope with the adequate postage.

3.2.1.1 Study Design

This was a cross-sectional study. Subjects who were willing to complete all the questionnaires of the study were recruited. As mentioned earlier, the subjects were allowed to complete the questionnaires on their visit to the clinic or at their own pace at home. In addition, questionnaires were mailed out to subjects who were recruited from the Macular Degeneration (MD) Foundation and the Optometry Clinic at the School of Optometry and Vision Science.

Only the self-administered mode was used even though all the questionnaires have shown to be effective with all modes of administration (interviewer based, by telephone and self administered); previous studies have shown that use of different modes of administration could bias results and that it was best to follow one mode throughout the entire study

(Wolffsohn and Cochran, 2000).

Visual predictors for poor mobility performance in people with AMD

3.2.1.2 Subject selection and Recruitment

Subjects

Ethics approval for the study was obtained from the Human Research Ethics Advisory Panel, University of New South Wales. Written informed consent was obtained from each subject. Subjects were divided into two groups: AMD and controls. The control group was further divided into sub-groups Control A and Control B according to their source of recruitment. Preliminary analysis of data from AMD and Control A subjects (Reported in section 3.3 and discussed in 3.4) indicated the need for a further control group to provide appropriate age-matched data. Hence, Control B data was included.

Recruitment

The AMD subjects were recruited from Sydney Retina Clinic, Sydney Eye Clinic and Macular Degeneration (MD) Foundation. The recruitment process at the Sydney Retina Clinic and Sydney Eye Clinic included the staff providing the information sheet about the study to potential subjects. The subjects were advised that if they were interested in participating in the study they could either contact the researcher individually or alternatively give their consent to the consulting staff to pass their details on to the researcher to be contacted. The recruitment process at the MD Foundation was done by identifying potential subjects from the database and sending out the information sheet and contact details of the researcher by mail.

Control A Subjects were recruited from the Optometry Clinic, School of Optometry and Vision Science, University of New South Wales. The recruitment process was similar to that at the MD Foundation. Potential subjects were identified from the database, limited to subjects seen in the clinic between 2006-2007. Information regarding the study and contact details of the researcher was mailed out to potential subjects. A further sub-group of control subjects (Control B) were collected by random selection matching for age and gender from the database for normal subjects of the Falls Research Group at the Prince of Wales Medical Research Institute, which comprised 3,000 controls aged between 65 yars and older. In addition to this, the subjects for control B also met the inclusion criteria given below.

Visual predictors for poor mobility performance in people with AMD

Inclusion criteria

Subjects were included in the study only if they met the following criteria: Age 55 years and above. AMD in either one or both eyes for AMD group; absence of AMD for Controls. (Confirmed from the clinical records of the subjects). Absence of any other ocular conditions for both groups. (Confirmed by asking the subject of his/her eye health.) The subjects that were recruited from the Falls Research Group at the Prince of Wales Medical Research Institute also fulfilled this criterion. This was confirmed on the basis of a questionnaire where the subjects were asked whether they had any existing eye problems including cataract, macular degeneration, diabetic retinopathy, glaucoma etc.

3.2.2 Quality of life evaluation The GADS questionnaire is designed for screening mental illness without the presence of a psychiatrist. It has been validated and found to be appropriate for detecting anxiety over a wide variety of conditions in the geriatric population across the world (Holm et al, 2001) and has also been used in Australian populations (Menz et al, 2006) (an overall sensitivity of 86% and overall specificity of 91% (Goldberg et al, 1988)). The other desirable qualities of GADS are that it is easy to administer, can be administered in general clinical setting and the questions asked are easily comprehendible for the patient. Although there are reports suggesting that

GADS is poor in detecting anxiety (Montero et al, 2006) there are other studies that demonstrate the psychometric characteristics of GADS are accurate (Nubukpo et al, 2004). The HAD scale was not used as it was designed as a clinical guide to determine whether depression or anxiety was a contributing factor to the distress of the patient attending the general clinic rather than a screening tool to detect depression or anxiety (Menz et al, 2006). It has also been found to be poor at detecting anxiety disorders in geriatric medical inpatients (Neal and Baldwin,

1994). The GADS questionnaire is an 18-item questionnaire which has two subscales designed for screening anxiety and depression. Each subscale has a set of 9-items where one point is given for every positive response. Both the subscales have a screening section of four items with a cut-off score greater than or equal to 2 on the anxiety scale and score greater than or equal to 1 on the depression scale. A score of 5 or greater on the anxiety

Visual predictors for poor mobility performance in people with AMD

scale and 2 or greater on the depression scale is clinically significant and warrants further psychological assessments (Goldberg et al, 1988).

The SF-36 is a generic health status questionnaire used for general assessment and provides measures of multiple operational health indicators such as behavioural function and dysfunction, distress and wellbeing, subjective ratings and objective reports. It includes not only the favourable but also the unfavourable self-evaluation reports about general health status (Ware et al, 1993). The instrument has been validated across general populations around the world and shown to have good internal consistency (α ~0.8) and test/re-test reliability

(Brazier et al, 1992; Jenkinson et al, 1993; Hawthorne G, 2003). The SF-36 has been validated for the

Australian population (Cronbach’s α > 0.82) (McCallum, 1995), and is widely used in studies in

Australia (Chia et al, 2004; Chua et al, 2006; Swamy et al, 2009). The SF-36 is usually preferred to other generic health surveys due to its comprehensiveness, flexibility in mode of administration and interpretation characteristics (Alonso et al, 1995). The normative data across the eight subscales of SF-36 in Australian population are of similar magnitude to that UK and USA

(Stevenson, 1996) and comparison of results obtained from studies around the globe are possible. The SF-36 has eight subscales: physical functioning, role-physical, role-emotional, bodily pain, general health, social functioning, mental health and vitality. Each subscale consists of a number of questions pertaining to the patient’s outlook. It is scored on a range of 1 to 100 where 0 correlates with worst possible score/function and 100 correlates with best possible score/function. Although SF-36 is often used as a profile, it can be summarised into a physical component score (PCS) and a mental component score (MCS) where PCS is comprised of physical functioning, role-physical, bodily pain and general health and MCS comprises of vitality, social functioning, role-emotional and mental health. In this study, we analysed data obtained from the SF-36 as a profile as it has been reported that summarising the subscales into two scores often leads to loss of information (Taft et al, 2001).

The LLQ was used as a function-specific questionnaire as it was designed and validated for assessing quality of life in people with macular degeneration (Owsley et al, 2006). This also provides a detailed profile of the subject’s assessment of their visual performance in different environments encountered in daily life. The LLQ comprises six subscales which are Extreme lighting, General dim light, Mobility, Driving, Peripheral vision and Emotional distress, and is scored on a scale of 0 to 100, where 0 is the worst possible score and 100 is 36

Visual predictors for poor mobility performance in people with AMD

the best possible score. It has good internal consistency (Cronbach’s α > 0.82) and test-retest reliability (0.74-0.88) (Owsley et al, 2006).

The Falls Efficacy Scale- International (FES-I) was designed for measuring fear of falling

(Yardley et al, 2005). The 16-item questionnaire summarises the individual’s assessment of their capabilities of both simple and more demanding activities encountered in daily life (Yardley et al, 2005). It has been validated in populations across the globe (Yardley et al, 2005; Skelton et al, 2004;

ProFANE, 2007; Kempen et al, 2007) with good internal consistency and test-retest reliability (0.96). It is scored on a scale of 1-4, where 1 implies that you are not at all concerned with the task and 4 implies that you that very concerned about doing the task. The short form FES-I has also been shown to have the good internal consistency (Cronbach’s α 0.92) and test–retest reliability (0.83) (Kempen et al, 2008).

Assistance for everyday tasks was assessed with questions relating to dependence on others (relatives, friends and community services) for basic tasks involved in daily living activities such as cooking, cleaning, and shopping. These questions were part developed by the Falls Research Group, Prince of Wales Medical Research Institute. It was scored in a binary fashion with any assistance for any task being given a score of 1

The Incidental and Planned Activity Questionnaire (IPAQ) was used to measure the physical activity. This was designed for use in older people (Delbaere et al, 2009). The IPAQ measures the number of self-reported hours of exercise including both planned and incidental activities and has good psychometric properties (test-retest reliability (0.77) and internal consistency

(Cronbach’s α = 0.6)) (Delbaere et al, 2009).

Co-morbid medical conditions were assessed as a count of the reported chronic medical conditions (Mangione et al, 1999). Conditions included were hypertension, diabetes mellitus (with or without medication), previous myocardial infarction, previous stroke, cancer, chronic obstructive pulmonary disease, transient ischaemic attack (TIAs), congestive heart failure, and arthritis (either knee or hip joints or both).

The socio-economic status of the subjects was extracted from the Socio-Economic Index for areas (SEIFA) 2001 using the “Index of Relative Socio-economic Advantage and 37

Visual predictors for poor mobility performance in people with AMD

Disadvantage (IRSAD)” where a low value indicates areas of disadvantage and a high value indicates areas of advantage (Australian Bureau of Statistics, 2008).

Table 3-1 summarises the questionaires that were used in this study for analysing the different domains that contribute towards quality of life. In cases where the subjects left one or more items on the questionnaire unanswered, the value for that item was calculated as the average of the other items on that subscale if the subject had completed 50% of the items on that subscale. If more than 50% of the items were unanswered the data for that subscale was treated as missing data.

Table 3-1: Summary of Questionnaires used.

Area assessed Measure Scoring General health ‘Medical outcomes study Short Form 36’ 0-100 (SF 36) Visual function Low Luminance Questionnaire 0-100 (LLQ) Mental health Goldberg Anxiety and Depression scale 0-9 (GADS) Fear of falling Falls Efficacy Scale- International 16-64 (FES-I) Physical activity Incidental and Planned 0-182 Activity Questionnaire (IPAQ) (Hrs/week)

The current trend/ gold standard for validation of questionnaires is Rasch analysis. Rasch model analysis is commonly used for developing items of a test or questionnaires. It is used to check the degree to which the scoring and summing of the questionnaires is defensible in the data collected (Tennant et al, 2004; Prieto et al, 2003). The practice of using validated questionnaires has been recommended by Streiner and Norman (1996). Rasch model analysis validates the questionnaire by creating a Rasch score followed by a t-test to analyse differences between the groups. The questionnaires used in this study have been previously validated and reported widely (Brazier et al, 1992; Jenkinson et al, 1993; Hawthorne G, 2003; Goldberg et al, 1988; Skelton et al, 2004; Yardley et al, 2005; Owsley et al, 2006; ProFANE, 2007; Kempen et al, 2007; Delbaere et al,

Visual predictors for poor mobility performance in people with AMD

2009). Hence although Rasch analysis is the test of choice in psychometric analysis when assessing new questionnaies for example, this was not performed in analysing the data in this study.

3.2.3 Statistics Statistical analysis was performed using SPSS (Version 17.0). The demographic differences between the two groups were analysed using ANOVA and Kruskal-Wallis test for parametric and non-parametric variables. Several variables were transformed due to non- normal distributions. A square root transformation was used for general health, physical functioning, mental health and IPAQ; a logmarithmic transformation was used for Role- physical, Role-Emotional, Social functioning subscales of the SF 36; FES-I; and an inverse transformation was used for the depression subscale of the GADS.

Differences between people with and without AMD were evaluated using independent t- tests. Multivariate analysis of co-variance was used to further analyse if the differences between the two groups was significant taking into consideration the effect of the demographic differences between the groups [age, sex, social support (living arrangement) and economic status].Associations among the quality of life measures were assessed with Pearson correlations and a stepwise linear regression analysis was used to elucidate physical, health and lifestyle factors associated with depression in the group with AMD, excluding all the subscales associated with psychological domains (to avoid issues relating to autocorrelation). The association between visual acuity and GADS score was analysed using scatter plot. A p-value of less than 0.05 was considered statistically significant. For collinearity statitistics a tolerance >0.1 and variance inflation factor (VIF) <5 was considered acceptable.

3.3 Results

Of the 350 subjects recruited (AMD [n=168]; Control A [n=40]; Controls B [n=142]), 98.28% of the subjects returned the questionnaires (AMD [n=168]; Controls A [n=34]; Controls B [n=142]). Of these, 62 were excluded due to glaucoma (AMD [n=23], Controls A [n=1]; Control B [n=39]). 39

Visual predictors for poor mobility performance in people with AMD

The mean age of the AMD group was 78.3 + 7.8 years, Control A 70.8 + 5.6 years and Control B 78.1 + 5.80 years. (Table 3-1). There were 63.4% females and 36.6 % males in the AMD group; 30.3% females and 69.7% males in Control A; 70.9% females and 29.1% males and Control B (Table 3-2).

Table 3-2: Participant Demographics.

Descriptives AMD Control A Control B (N=145) (N=33) (N=103) Age (years) (mean [SD]) 78.3[7.8] 70.8 [5.6] 78.1 [5.8]

Gender (N) Male 53 23 30 Female 92 10 73 Accommodation (N) House 103 27 63 Unit 28 6 33 Other 14 0 7 Living arrangement (N) Alone 62 6 54 With spouse 54 22 31 With spouse and children 21 3 12 Other 8 2 6 Economic status (N) Above average 111 32 102 Below average 29 1 1 Missing 5 0 0 Co morbidity (mean [SD]) 1.54 [1.21] 0.94 [1.03] 1.53 [1.07] Visual Acuity (mean [SD]) (LogMAR) 0.74 + 0.33 0.07 + 0.19 ND ND: Data for this section of the questionnaire could not be collected from the Control group B, as the data collection was done retrospectively. The N given in the above table is the population average of the study sample.

Visual predictors for poor mobility performance in people with AMD

People with AMD had significantly reduced scores from Control A on the SF-36 for general health (p=0.02), physical functioning (p=0.002) and vitality (p<0.001) domains even after adjusting for differences in their demographic details. They were also significantly different from the Controls A group for all the domains on LLQ (p<0.001), anxiety and depression (p<0.001) on the GADS scale and the FES-I (p<0.05) after adjusting for demographic factors other than vision.

Table 3-3: Descriptives for GADS, SF-36, LLQ, Assistance for daily tasks, FES-I and IPAQ.

Questionnaire Subscale AMD (N=145) Control Group A Control Group B (mean [SD]) (N=33) (mean [SD]) (N=104)(mean [SD]) GADS Depression 56.7%* 33.3% 29.1% (0-9) Anxiety 29.4%* 15.2% 12.6% SF-36 General health 62.68 (20.49) ** 74.67 (20.63) 67.65 (21.50) (0-100) Physical functioning 56.96 (29.53) ** 84.13 (14.44) 67.99 (24.43) Role - physical 58.64 (40.91) 81.67 (32.12) 64.71 (40.32) Role - emotional 71.61 (37.55) 90 (21.71) 81.08 (32.66) Vitality 56.33 (20.04) ** 71.83 (16.21) 59.80 (23.1) Bodily pain 65.27 (24.76)] 77(23.51) 67.18 (24.49) Mental health 77.71 (17.31) 81.6 (14.8) 80.86 (18.0) Social functioning 79.58 (25.37) 89.58 (18.88) 74.88 (21.72) LLQ* Extreme lighting 39.54 (22.99)** 84.66 (13.78) ND (0-100) Mobility 54.55 (28.94)** 94.33 (6.99) ND General dim light 54.17 (24.79)** 92.63 (9.46) ND Peripheral vision 58.69 (26.12)** 92.22 (9.77) ND Driving 26.9 (33.37)** 83.17 (18.26) ND Emotional Distress 57.89 (30.7)** 96.88 (6.3) ND FES-I Short FES- I 9.75 (3.22) 8.06 (1.6) 9.46 (3.95) (16-64) FES-I 23.76 (8.81)** 17.94 (2.61) ND Assistance with 38.6%** 9.1% 4.9% everyday tasks (0-1) IPAQ (0-182) 8.67 (4.28) ** 10.2 (4.9) 22.45 [10.36] ND: Data for this section of the questionnaire could not be collected from the control group B as the data was collected retrospectively; ** p<0.05; Multivariate analysis. Higher scores on SF 36, LLQ and IPAQ meant better performance. Higher scores on FES-I, GADS and Assistance with everyday tasks meant poor performance. 41

Visual predictors for poor mobility performance in people with AMD

People with AMD had significantly reduced scores from Control B participants on the SF- 36 for physical functioning domain (p=0.004). They also had significantly increased scores from controls on the anxiety (p<0.001) and depression (p<0.001) sections of the GADS and the assistance for daily living skills scale (p<0.005, Kruskal-Wallis test) (Table 3-2).

The assistance with everyday tasks questionnaire showed that 38.6% of people with AMD used assistance, while only 6% of the Control A and 4.9% of Control group B used assistance services (Table 3-2). The anxiety subscale of the GADS showed that 29.4% of the AMD group had a clinically significant number of symptoms compared to 13.3% of the Control A and 12.6% of Control B participants. The depression subscale of the GADS showed that 56.7 % of those with AMD met the criteria for a clinically significant score for depression compared to 30% Control A and 29.1% Control B participants

The FES-I score was significantly increased for people with AMD (p<0.001) compared to the control A although the short version FES-I was not statistically significant between the three groups. The difference between the two groups (AMD: Control A) remained statistically significant even after correcting for age using multi-variate analysis. Analysis of Variance using the Bonferroni test showed that the short FES-I was not significantly different between the three groups (p>0.05)

Correlation analysis of the different subscales evaluating mental health status from the various questionnaires for the AMD group and the Control A group showed that the Pearson’s correlation co-efficient for the subscale of mental health on the SF-36 and LLQ were positively correlated (r=0.52, p< 0.001). The mental health domain of the SF-36 correlated inversely with the GADS for anxiety (r=-0.56, p<0.001) and depression (r= 0.49, p< 0.001). Similarly, the emotional distress domain on the LLQ correlated inversely with the GADS anxiety (r= -0.4, p<0.001) and depression (r= 0.5, p< 0.001). Table 3-3 shows bivariate correlations among the depression, physical activity and quality of life scales in the AMD group. Depression scores were inversely correlated with the mental health, physical functioning, social functioning, and vitality subscales of the SF-36.

A stepwise linear regression analysis revealed that 33% of the variance in the depression scores could be explained by three health and physical measures: poorer general health, 42

Visual predictors for poor mobility performance in people with AMD

lower physical functioning levels and increased number of comorbidities (F3,141=24.65, P<0.001; Adjusted r2=0.330). The standardised beta weights were -0.36 for general health, -0.21 for physical functioning and 0.18 for comorbidity. The variables included in the regression analysis were the all the domains on SF- 36 except vitality and mental health (as they assess the mental health of the patient), number of comorbities, assistance for daily living tasks and the FES-1. The tolerance was > 0.1 and the variance inflation factor (VIF) was <5.

Table 3-4: Table of co-efficients for linear regression model for depressive symptoms in people with AMD.

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower Upper t Tolerance VIF Significance bound bound Constant 5.55 0.70 4.18 6.93 7.99 <0.001 GH -0.04 0.01 -0.06 -0.02 -4.64 0.758 1.320 <0.001 PF -0.02 0.01 -0.03 -0.004 -2.65 0.759 1.318 0.009 Comorbidity 0.35 0.15 0.05 0.64 2.34 0.827 1.209 0.021 Outcome: Depressive symptom score; Slope denotes the change in value of depressive symptom score for 1 unit increase in the independent factor.

Figure 3-1: Effect of visual acuity on depressive symptoms in people with AMD.

1.50 y = -0.003x + 1.0289 1.30 R2 = 0.0239 1.10 0.90

0.70 0.50 0.30

Visual acuity (LogMAR) 0.10

-0.10-113579111315 GADS (De pre ssion score )

Visual predictors for poor mobility performance in people with AMD

Table 3-5: Pearson’s correlation co-efficients (R) for SF 36, IPAQ and GAD scale.

2 3 4 5 6 7 8 9 10 11 12 13 1 0.20* 0.28# -0.29# -0.21* -0.41# -0.25# -0.09 -0.35 -0.20 0.41 -0.35# -0.33# 2 - 0.65# -0.53# -0.65# -0.27# -0.42# -0.19# -0.49# -0.32# 0.37# -0.35# 0.59# 3 - - -0.53# -0.54# -0.44# -0.53# -0.29# -0.61# -0.33# 0.38# -0.38# -0.55# 4 - - - 0.55# -0.38# 0.45# 0.18* 0.55# 0.31# -0.39# 0.48# -0.51# 5 - - - - 0.19* 0.35# 0.82# 0.56# 0.24# -0.24# 0.31# 0.50#- 6 - - - - - 0.43# 0.35# 0.46# 0.35# -0.48# 0.41# 0.24# 7 ------0.25# 0.57# 0.44# -0.28# 0.41# 0.32# 8 ------0.31# 0.22# -0.26# 0.18# 0.19# 9 ------0.42# -0.33# 0.47# 0.5# 10 ------0.16* 0.38# 0.24# 11 ------0.25# -0.26# 12 ------0.56# 13 ------

1 = ADLA: Assistance with Daily Living Activities; 2 = Anxiety; 3 = Depression; 4 = Social functioning; 5 = Mental health; 6 = Physical functioning; 7 = General health; 8 = IPAQ; 9 = Vitality; 10 = Body pain; 11 = MFES; 12 = Role – physical; 13 = Role – emotional; * p<0.05, #p< 0.01

3.4 Discussion

AMD is a common cause of visual impairment or loss in ageing populations, particularly in the developed world and its impact on the quality of life is not fully understood or often

underestimated (Williams et al, 1998). This study aimed to create a profile of Australian subjects with AMD across the physical, social and mental domains of quality of life. This study provides the data on domains that are more affected in AMD which in turn will guide health care professionals gain a better understanding of the support systems individuals with AMD would require to maintain their quality of life.

Visual predictors for poor mobility performance in people with AMD

Previous studies have shown that the use of different modes of administration within a study can bias the results (Wolffsohn and Cochran, 2000). As such, only one mode of admnsitration (self admin) was used in the current study. All the questionnaires used have been shown to be effective with all modes of administration (interviewer based, by telephone and self administered).

Preliminary analysis of the data collected for AMD and Control A groups showed that there was a significant difference between the groups for age and gender, the control group being significantly younger and with a higher proportion of male participants. These differences were considered to be significant confounding factors for the reduced general health, physical functioning, body pain and vitality observed in the AMD group.

Given these differences, a further study to explore the true impact of AMD on quality of life was undertaken using the control database of the Falls and Balance Research group, Prince of Wales Medical Research Institute. These data was collected retrospectively matching normal and AMD participants for age and gender.

From the analysis of AMD and Control B groups, despite similarities in social and physical roles, people with AMD had reduced scores on physical functioning subscale of the SF-36. This difference was interesting as while physical role related to the subject’s performance of the task, the physical functioning subscale related to the subject’s evaluation of their ability to perform the tasks.

These results showed that the profile obtained from the SF-36 can be used to assess the quality of life in people with AMD as it was able to differentiate between the two groups. This questionnaire deals with domains that are affected by an individual ability to define areas that are relevant to them, rather than assessing if they can or cannot perform a particular function. These findings are consistent with earlier studies that reported reduced quality of life in people with AMD by using the SF-12, which is the shorter version of SF 36

(Scott et al, 1999; Cahill et al, 2005; Knudtson et al, 2005; Hassell et al, 2006). However, there are studies that reported that SF-36 did not detect the difficulties experienced by people with AMD (Cahill et al, 2005; Mangione et al, 1999). This inconsistency may be because the latter studies were looking at the ability of the SF-36 to differentiate the difficulties experienced by the people with 45

Visual predictors for poor mobility performance in people with AMD

AMD in the context of the severity of the disease rather than its ability to differentiate difficulties experienced by people with AMD in comparison to the general population.

High scores on mental health and vitality domains of SF-36 reflects better mental health status and a high score on the GADS (anxiety and depression) reflects poor or altered mental health status. The negative correlation between the physical functioning, social functioning, mental health and vitality domains with the GADS (anxiety and depression) show their ability to detect changes brought about by the impact of AMD on the emotional domains of an individual’s life. This result provides further evidence to support the effectiveness of these measures as a screening tool in people with AMD.

High scores on IPAQ and physical functioning subscale indicate better performance while high score on the GADS indicate the number of depressive symptoms present. The negative correlation found on the IPAQ and physical functioning subscale with the GADS (anxiety and depression) and the positive correlation between IPAQ and physical functioning suggest that people who had more depressive symptoms tended to be less active and considered that they had reduced functional abilities. This inference is further supported by the fact that although people with AMD had similar comorbidity compared to the control group, they reported as being less active physically. This is consistent with previous studies that reported associations between depression and decreased physical activity and social interaction (Bruce et al, 1994; Penninx et al, 1998; Penninx et al, 1999). This may help explain why the two groups with similar physical roles differ in physical functioning.

The FES-I score was significantly greater for people with AMD even after adjusting for age compared to the Control A group. The short version FES-I was not significantly different between the three groups. One reason for this could be that the items that were excluded on the short version FES-I, such as walking around the neighbourhood were more relevant for people with AMD compared to getting in and out of a chair. For example, getting in and out of the chair would require more vestibular input for maintaining balance, while walking around the neighbourhood would require adequate visual input not just for avoiding obstacles but also for maintaining balance. (A comparison of items in the FES-I and short FES-I is given in Appendix 3). This assumption is consistent with the study of Kempen et al

(2008) where they recommended the use of the FES-I in the analysis of “specific fear of 46

Visual predictors for poor mobility performance in people with AMD

falling-related activities”. People with AMD would be expected to be more apprehensive when performing tasks that required visual input, hence the use of FES-I would be more appropriate.

Approximately 30% of people with AMD were found to have clinically significant levels of anxiety and approximately 60% had clinically significant levels of depression, with both AMD and controls groups having similar co-morbidity scores (p=0.143). The mean of co morbid conditions in this study was similar to that reported by Brody et al (2001) (1.33 + 1.02 vs. 1.54 + 1.21). Overall, these results suggest that people with AMD have greater emotional distress leading to depression and is consistent with previous observations (William et al, 1998; Brody et al, 2001).

The prevalence of depressive symptoms (60%) in people with AMD in the current study is notably higher than ~33% reported in previous studies (Brody et al, 2001; Rovner et al, 2002). This may be related to an increased awareness of the trajectory of the disease process in AMD and its impact on their visual outcome and may also be related to the difference in the tools used to diagnose depression. The previous study used a diagnostic process [Brody et al

(2001): The Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental

Disorders-IV (SCID-IV) and Rovner et al (2002): Center for Epidemiological Studies– Depression (CES-D) Scale] to identify people with depression. A screening process was used in the current study to identify people with a potential risk of having depression. This approach would be more feasible in a busy clinical setting with subsequent referral to other health care professionals as required.

Previous studies have shown a linear association between Visual Acuity (VA) severity and prevalence of depression scores (Albert et al, 2007). Contrary to this, no association was found between these two factors in the current study. This earlier study did not explore the possibility of functional visual deficits due to the presence of ocular co morbidities. Depressive symptoms have been reported to increase over time corresponding to a decrease in visual function, independent of the change in visual acuity consistent with this possibility

(Brody et al, 2001).

Visual predictors for poor mobility performance in people with AMD

Depression, defined as scoring 2 or more on the GADS questionnaire, rarely occurred in the control population (3.3%) unless these participants also had significant co-morbidities. In the presence of a comorbidity score of >1 a control participant had a 30% chance of being depressed. A participant with AMD but no comorbidity had a 36% chance of being depressed and a participant with AMD and a comorbidity score >1 had a 62% chance of being depressed. Brody et al (2001), relying on previous studies of community samples, suggested AMD was responsible for doubling the prevalence of depression. This case control study, with Control B participants, showed a much greater effect, indicating that the presence of AMD could potentially increase the risk of depression in a person without comorbidity by a factor of 10. These results also show that the true importance of AMD- associated depression may be masked by the effect of comorbidities.

As discussed above, depression is commonly associated with AMD and if left untreated, may lead to lack of motivation, worsening of functional abilities leading to institutionalization and greater mortality rate (Rovner et al, 2002). A recent review by Casten et al (2004) on AMD and depression also provided evidence that depression is not a normal phase of ageing, but rather an outcome of disabilitating diseases. The importance of self management programs including problem solving skills has been found to help in preventing and reducing emotional distress in people with AMD (Brody et al, 2005). The significance of early referral for low-vision rehabilitation services is emphasised in a recent study where they demonstrated that the improvement in visual functioning was twice that of the control group in people with visual impairment (Stelmack et al, 2008).

A potential source of bias in this study may relate to the consent being obtained from the subjects before sending out the questionnaire; this is reflected by the response rate of 98.28%. The other limitation of the study was related to the difficulty in obtaining an age- gender matched control group. The retrospective data collected from the Falls Group normal database, provided the opportunity for a case-matched analysis for AMD and Control B groups, although this was not possible for the LLQ data. The current study also provides a representation of people with mild to moderately severe AMD. For people with severe AMD, not included in the current study, it is assumed that the quality of life would be even further reduced. This assumption is consistent with the studies of Hassell et al, (2006) who reported that people with severe vision loss reported greater difficulty in functioning on all 48

Visual predictors for poor mobility performance in people with AMD

areas of daily life. The other limitation of this study is the inability to explore the relationship between the grief cycle and temporal change in coping with visual impairment due the cross-sectional design. This is an important area that could be explored further by conducting a longitudinal study. This information would be valuable in determining the appropriate services required by people with AMD

3.5 Conclusion

In this study, the most affected domains in quality of life for AMD patients were those in mental health, and these findings were highly correlated between the different questionnaires. The negative correlation between the mental health and vitality subscales and the GADS suggests that AMD has an impact in this area. Depression is commonly associated with AMD and if left untreated, may lead to lack of motivation, worsening of functional abilities, eventual institutionalization and an increased mortality rate (Rovner et al,

2002).

This study shows also highlights the need for further studies of the psychological health of the AMD patients, despite earlier studies identifying depression in people with AMD (Zeiss et al, 1996; Brody et al, 2001; Rovner et al, 2002; Rovner et al, 2002; Brody et al, 2002; Albert et al, 2007). Screening AMD patients for depression when they are concerned as to their ability to perform everyday tasks, with subsequent referral for further evaluation and support services could assist in maintaining quality of life in people with AMD. This is further supported by evidence from studies that have reported improvements in emotional distress and functional decline following interventions such as self-management programs (Brody et al, 2002).

Taken together, these observations highlight the importance of earlier recognition of mental health changes in people with AMD (particularly depression), and emphasise the need for earlier access to appropriate counselling and rehabilitation services so as to maintain independence and quality of life.

Visual predictors for poor mobility performance in people with AMD

Chapter 4: Psychophysical Aspects of Vision

in people with AMD

People with AMD generally report changes in the psychophysical aspects of vision related to the development of central scotomas, reduced acuity (De Boer et al, 2004), reduced contrast sensitivity, difficulty in adapting to changing levels of illumination (manifest as a delayed photo stress recovery time) (Grant et al, 2005), alterations in colour vision (Smith et al, 1988) and delayed dark adaptation (Owsley et al, 2001).

4.1 Introduction

Spatial vision is an integral aspect of human vision (Regan, 1991), defined as the ability to discriminate shapes while resolving them and perceiving them with respect to their spatial orientation. Visual acuity and contrast sensitivity are the most commonly used and key measures of spatial vision.

4.1.1 Visual Acuity

Visual acuity is defined with respect to the nature of the target (threshold and stimuli) used as a measure (Borish IM, 1970). Visual acuity was classified as minimum distinguishable, minimum separable, minimum cogniscible or legible and minimum separable angle by Low

(1951) and Weymouth (1955), Riggs (1965) categorized visual acuity in terms of detection, recognition, resolution and localization of the target. In the current study, visual acuity was defined as the resolving power of the eye which enabled two different objects to be viewed as being separate. The most commonly used measures for high contrast visual acuity are the Snellen fraction, logMAR (logarithm of the minimum angle of resolution) and decimal notation. Visual acuity is usually measured at two different distances which covers the range used for most daily visual tasks although people do perform visual tasks over a wide range 50

Visual predictors for poor mobility performance in people with AMD

of distances (distance visual acuity - usually measured at 6m and near visual acuity - usually measured at 40 cms). These are both considered measures of central vision for people with intact central fixation.

The Snellen acuity chart is the most commonly used visual acuity chart in clinical practice, as it is a simple test to perform and is sensitive to the most common causes of visual impairment such as refractive error, cataract and macular disease (McGraw et al, 1995). There are however several limitations with Snellen charts related to the irregular progression of letter size on the chart (Bailey et al, 1991), close proximity of letter contour (Flom et al, 1963), varying levels of complexity in the task throughout the chart (Bailey and Lovie-Kitchin, 1976) and restricted sensitivity towards changes over time arising from the scoring system used (Bailey et al, 1991). Newer charts such as the Sloan acuity chart, the ETDRS (Early Treatment Diabetic Retinopathy Study) chart and the Bailey-Lovie chart were introduced to overcome these shortcomings (Bailey and Lovie-Kitchin, 1976; Ferris et al, 1982). In order to make the complexity of the task uniform throughout the chart, an equal number of letters on each line was introduced. The idea that resolution thresholds, similar to other visual thresholds, follow a Weber’s law- like function was first proposed by Sloan (1951). If this was true, then letters that were just noticeably different in size would be different by a constant proportion regardless of the letter size (Westheimer, 1979). As the letter size was increased by n log units, the acuity score was found to increase by n/2 log units (Raasch et al, 1998).

The prime concern in the design of visual acuity charts was in the selection and design of the optotypes to be used (Bennett, 1965). Based on the concept of equal legibility of letters, two sets of letters were recommended by Sloan and British Standards institution known as Sloan letters and the British standard letters each of which consisted of 10 non-serifed, upper case letters with a stroke width of one fifth of the letter height (Raasch et al, 1998). An assumption of equal legibility of letters was considered an important factor (Alexander et al, 1997) until recently when it was reported as an insignificant factor since the variability caused by the inequality th of identification of letters was very small (about 1/20 of a letter) (Raasch et al, 1998).

The Bailey-Lovie chart uses logarithmic size progression with an equal number of letters on each line; this standardises the test task by creating uniform complexity throughout the whole chart (Bailey and Lovie-Kitchin, 1976; Ferris and Sperduto, 1982). Charts that utilise this principle 51

Visual predictors for poor mobility performance in people with AMD

have become the gold-standard for research purposes because of their accuracy, reliability and repeatability even in patients with low vision (Bailey and Lovie-Kitchin, 1976; Ferris et al, 1982;

Elliot and Sheridan, 1988; Blackhurst and Maquire, 1989; Kiser et al, 2005).

Previous research has shown that AMD is associated with a reduced high contrast and low contrast visual acuity although there was no linear association between the severity of AMD and the visual acuity performance (Brown and Lovie-Kitchin, 1983; Brown et al, 1984). Brown and

Lovie-Kitchin (1983) reported that the visual acuity performance of an individual with AMD is associated with the complexity of the task. They found that the visual acuity in people with AMD became worse when they increased the complexity of the task used to assess visual acuity. It has also been reported that the visual acuity performance in people with AMD was further reduced at low luminances and that increasing the contrast had no effect on the visual acuity outcome (Brown et al, 1984; Sunness et al, 1997). Studies that analysed the relationship between the decrease in visual acuity and the progression of the AMD identified the morphological changes responsible in cases of people with geographic atrophy (Maguire et al, 1986) and subretinal scarring, atrophy, exudates and haemorrhages in those with CNV (Hogg et al, 2003).

Klein et al (1995) found that people with ARM showed a decrease of two letters or fewer and that people with AMD (exudative macular degeneration or pure GA) had a decrease of approximately six to eight lines on their distance visual acuity. The relevance of this in terms of clinical significance and functional impairment is debatable considering that the test-retest variability of the logMAR chart is one to two lines and the range of visual acuity scores obtained when testing people with AMD that have comparable GA lesions (Sunness et al, 1999). Studies have also shown that distance visual acuity measurements lack the ability to predict or monitor the progression of the disease process in AMD (Sarks et al, 1988; Mayer et al,

1994). This could be due to the different morphological presentation of the disease at a particular stage including location or size of the lesion (See Chapter 2 for the morphological characteristics of different lesions). It has also been shown that distance visual acuity does not indicate the ability of AMD patients to perform visually demanding tasks (Rubin et al, 2000). Despite these limitations, high contrast distance visual acuity remains one of the most widely used tests of visual function in people with AMD in clinical practice and in clinical trials, as it is readily available, reproducible and easy to apply (Neelam et al, 2009). 52

Visual predictors for poor mobility performance in people with AMD

4.1.2 Contrast sensitivity

Contrast sensitivity is the measure of threshold contrast required for visual perception

(Owsley, 2003). In cases where a periodic pattern such as a sine-wave grating is used for testing, contrast (Michelson contrast) is defined as the ratio of the difference between the maximum

(Lmax) and minimum (Lmin) luminance of a test stimulus divided by the sum of the maximum

(Lmax) and minimum (Lmin) luminance:[Lmax - Lmin]/ [Lmax + Lmin] (Borish IM, 1970). The National Research Council Committee on Vision (1980), defined contrast for a non- periodic pattern (Weber contrast), as the difference between the maximum luminance and the minimum luminance divided by the minimum luminance: [Lmax - Lmin]/[Lmin] (Neelam et al,

2009).

Contrast sensitivity relies on both the optical and neural qualities of vision (Arundale, 1978). It can be measured using repetitive vertical square-wave or sine-wave gratings at different contrast levels or using variable contrast letter charts. A range of commercially-available contrast sensitivity tests using the above principles have been developed including electronically generated gratings, the Arden plate test, the Vistech chart, the Melbourne Edge Test (MET), the Cambridge Low Contrast Gratings Test, the Vector Vision CSV- 1000, the Mentor B- VAT II and B- VAT II SG, the Pelli-Robertson letter chart, the Bailey- Lovie chart, High/ Low contrast visual acuity chart and the Regan low contrast letter chart.

The limited range of contrast and high intersubject variance between individual plates precludes the use of Arden plates and Vistech test in low-vision subjects (Howell and Hess, 1978;

Brown, 1981; Singh et al, 1981; Veagan and Halliday, 1982). MET, a measure of edge contrast sensitivity is reported to be highly correlated with peak contrast sensitivity and has been shown to be independent of visual acuity (Greeves et al, 1987). In addition, several studies have reported that peak contrast sensitivity (Marron and Bailey, 1982) and edge contrast sensitivity were highly correlated with mobility and orientation (Verbaken and Johnston, 1986; Haymes and Chen, 2004; Lord and

Dayhew, 2001).

Many studies have shown that contrast sensitivity is one of the most useful psychophysical functions in detecting visual changes associated with AMD (Brown and lovie-Kitchin, 1987; Eisner et al, 1987; Kleiner et al, 1988; Sunness et al, 1997; Midena et al, 1997). Weale (1963) estimated that the contrast 53

Visual predictors for poor mobility performance in people with AMD

sensitivity in people aged 60 years would be about a third of that for those aged 20 years. Later studies showed that contrast sensitivity in elderly people was found to be reduced even with good visual acuity and good ocular health (Sekular et al, 1982). Since contrast sensitivity reduces with age (Crassini et al, 1988), caution is advised while analysing the contrast sensitivity in the early stages of the disease process in AMD (Neelam et al, 2009).

Reduced contrast sensitivity has also been associated with assessing quality of life due to its significance in performing daily living activities, reading performance, face and object recognition, driving and orientation and mobility (Legge et al, 1985; Scott et al, 2002; Cummings et al,

1995; Mones and Rubin, 2004; Neelam et el, 2009).

Contrast sensitivity has been shown to reflect reduced psychophysical function associated with the morphological changes that occur with progression in disease process of AMD

(Kleiner et al, 1988; Midena et al, 1997). Contrast sensitivity function was found to be decreased at all spatial frequencies in patients with ARM even those with normal visual acuity, although there were discrepancies related to the concept of “hidden vision” (Brown and Lovie- Kitchin, 1987;

Kleiner et al, 1988; Loshin and white, 1984; Hyvarinen et al, 1983). Hidden vision refers to the occurrence of normal or near normal contrast sensitivity at low and intermediate spatial frequencies in cases of reduced visual acuity (Hyvarinen et al, 1983). Direct comparison between these studies for interpretation of the inconsistencies in the results was not possible due to the different stimuli used to assess contrast function (Hogg and Chakravarthy, 2006).

As discussed above, contrast sensitivity was found to be reduced in people with ARM when compared with controls of the same age (Mei and Leat, 2007, Feigl et al, 2005). Furthermore, reduction in contrast sensitivity in people with AMD suggested the potential of this function to assess and monitor the progression of the disease process in AMD (Midena et al, 1997). However the failure of contrast sensitivity tests to predict the risk for progression from

ARM to AMD limits its application as a prognostic tool (Midena et al, 1997; Neelam et al, 2009). Taken together, these studies demonstrate that contrast sensitivity function does however have application in detecting subtle changes in psychophysical function, even when the visual acuity is normal (Neelam et al, 2009).

Visual predictors for poor mobility performance in people with AMD

4.1.3 Visual fields

Visual fields can be defined as the extent of space in which objects are visible to the eye in a given position (Traquair, 1949). When the eye is in the primary gaze position the boundaries of the visual field are 100° temporally, 60° nasally, 65° superiorly and 75° inferiorly (Millodot,

1986). The visual field is usually depicted as a “hill of vision” where fixation denotes the peak of sensitivity gradually decreasing towards the periphery. The technique of mapping visual sensitivity over a range of topographically defined loci is called perimetry. The two principles employed in the assessment of visual field are kinetic perimetry, where the position of a constant/static stimulus is varied to plot the area of visual sensitivity, and static perimetry, where the position of the stimulus is constant and the area of visual sensitivity is plotted using a stimulus of varying luminance (Bedwell, 1978; Johnson and Nelson- Quigg 1993).

Kinetic perimetry is more useful in plotting large scotomas and loss of peripheral visual field, while static perimetry plots both large and small field defects when the input of an approximate area of visual field loss is already known and can be used to monitor changes over a period of time (Bedwell, 1978; Johnson and Nelson- Quigg 1993). In addition to being a direct measure of visual function, perimetry also provides valuable insights into the disabling effects of particular conditions such as AMD and glaucoma (Wild, 1988; Holopigian et al, 1996).

Some of examples of kinetic perimetry methods include the confrontation field test, the Arc perimeter, the Bjerrum screen and the Goldmann perimeter. The Humphrey visual field analyser, Medmont M 600, Friedmann visual field analyser Mk II and Frequency doubling technology are examples of static perimetry instruments. A comparison of perimetric thresholds with full field rod ERGs in patients with retinitis pigmentosa showed that retinal sensitivities can be mapped with a high degree of accuracy using static perimetry (Birch et al,

1986; 1987). Hence the use of static perimetry is ideal for testing field defects in retinal disease. The visual field indices obtained by Medmont automated perimeter (Medmont M

600) have been shown to be comparable to that of Humphrey visual field analyser (Landers et al, 2007). A study by Zhang et al (1997) that looked at the ability of Medmont automated perimeter to detect threats to fixation reported that it was efficient in its performance of central threshold testing (sensitivity and specificity was 78% and 81% within 1 degree, and

Visual predictors for poor mobility performance in people with AMD

95% and 83% within 3 degrees in detecting the field defects for central 10-2) when compared with Humphrey visual field analyser.

Loss of the central visual field is one of the characteristic features of AMD. The location and density of the scotoma or the reduction in central retinal sensitivity depends on the stage and location of the disease process and the morphological changes at that stage (See Chapter 2). In the early stages (ARM), a reduction in the mean retinal sensitivity in the central visual field area, mainly in the parafoveal region, has been reported in some, but not all studies

(Atchison et al, 1990; Swann and Lovie-Kitchin, 1991; Midena et al, 1997; Cheng and Vingrys, 1993; Feigl et al, 2005). This variability may be related to differences in the morphology and location of the lesions present, and differences in the sensitivity of the instruments used to measure fields. As the degeneration progresses, the sensitivity is further reduced related to the underlying pathology with a relative scotoma that eventually deteriorates to an absolute scotoma in the central visual field, typically the size of the lesion in degrees (Fujii et al, 2003; Schneider et al, 1996;

Tezel et al, 1996; Sunness et al, 1997).

People with ARM often do not realise the impact that a reduction in retinal sensitivity has on visual function and will frequently interpret their inability to read in areas of reduced illumination as an indication for a stronger prescription for reading glasses. The improvement in visual acuity and increase in reading speed with increased illumination provides further evidence for the impact of reduced retinal sensitivity (Sloan, 1969; Cornelissen et al, 1991; Bowers et al, 2001). The appearance of a “black blob” in their vision due to an absolute scotoma is often when most people with AMD relate to a deficit in their visual field. This is associated with difficulty in reading (Legge et al, 1992; Whittaker and Lovie-Kitchin, 1993), daily living activities and mobility performance (Kuyk and Elliott, 1999; Hassan et al, 2002).

The effect of luminance changes on visual field defects in people with AMD has not been fully explored. The basic principle on which the perimetric measurement functions is the minimum stimulus luminance required to evoke a response against a constant background luminance. The visibility of the perimetric stimulus is reported to conform to Weber’s law, where the ratio between the stimulus luminance and the background luminance is constant

(Greve, 1973; Wood et al, 1988). The ability to assess retinal sensitivity across a dynamic range of background luminances enhances the ability to predict visual impairment due to the impact 56

Visual predictors for poor mobility performance in people with AMD

of these changes and adds a new dimension to visual field analysis (Fankhauser, 1979; Johnson et al, 1981; Barnes et al, 1985; Paige, 1985). Studies by Johnson et al (1981) demonstrated that retinal sensitivity was at its peak at the fovea for photopic background luminance; for scotopic background luminance, the retinal sensitivity was greater at 20° eccentricity, suggesting further evidence for both rod and cone contributions under various illumination levels. These observations also confirm the significance of visual field testing at different background luminances for conditions affecting the photoreceptors, as it can be used in predicting optimal illumination for desired visual function (Lovie-Kitchin and Feigl, 2005).

4.1.4 Visual adaptation

Visual adaptation is used to describe the ability of the visual system to adjust to a wide range of illumination levels via synchronized photochemical and neural action (Lamb and Pugh,

2004; Neelam et al, 2009). Visual adaptation has been described as light and dark adaptation. Light adaptation is considered as the ability of the visual system to adapt to luminance levels when moving from a dark to a lighted environment. This is normally a rapid process and takes about 1-2 minutes (Lamb and Pugh, 2004). Dark adaptation conversely, is the ability to adapt to a dark environment when coming from a lighted area, a slow process that usually requires more than 30 minutes (Adler et al, 1959). People with AMD generally take longer to adapt to changing light levels and can experience washout glare (i.e being blinded by a white non- seeing area) when going into bright environments, or are unable to see when entering dark environments (Hassan et al, 2002).

4.1.4.1 Dark adaptation Dark adaptation is a measure of the recovery process of the visual system in the dark, after being exposed to a light that effectively bleaches all the photoreceptors. This is conventionally plotted as a biphasic curve, referred to as the dark adapted (DA) curve after total bleach (Fig. 4-1). The first curve obtained during the early phase of recovery represents the cone function and on average takes about 8 minutes (Neelam et al, 2009). The second curve obtained during the latter phase of recovery represents the rod function and on an average takes about 30-40 minutes to complete (Neelam et al, 2009). The interception between the cone and the rod function curve is called the rod-cone break up time. The clinical standard for the

Visual predictors for poor mobility performance in people with AMD

measurement of dark adaptation has been the Goldmann Weekers dark adaptometer (Peters et al, 2000).

Figure 4-1: Dark adaptation curve

(http://www.medicine.mcgill.ca/physio/cullenlab/Notes209.htm)

Older adults without ocular disease require significantly more time to recover light sensitivity in the dark than younger adults (Owsley et al, 1999). A reduction in scotopic sensitivity appears gradually over the course of adulthood even in the absence of any retinal disease and has been estimated to be 0.08 log units per decade, while the decrease in photopic sensitivity has been reported as 0.04 log units per decade (Owsley et al, 2000). This is consistent with histological evidence where the spatial density of rods decreases during adulthood (34 to 90 years) in macroscopically healthy eyes (Curcio and Drucker, 1993). Scotopic dysfunction is a sign of early ARM, related to gradual loss of rod photoreceptors (Steinmetz et al, 1993, Owsley et al, 2000, Owsley et al, 2001 and Chen et al 2004; Curcio et al, 1996 and Medeiros et al, 2001). The ARM-associated loss of scotopic sensitivity and slow dark adaptation extend beyond the anatomically and clinically defined macula (Polyak, 1941; Klein et al, 1991) supporting the more recent observation that the pathology in ARM and AMD is not confined to macula (Owsley et al, 2001, 2004). Owsley et al (2001) also demonstrated that there was significant scotopic dysfunction in early ARM despite normal or near-normal cone mediated visual functions 58

Visual predictors for poor mobility performance in people with AMD

such as high contrast visual acuity, photopic sensitivity and contrast sensitivity. The kinetic aspects of scotopic dysfunction appeared to be more sensitive in detecting vision impairment caused by early ARM than were steady-state parameters in ARM patients (Owsley et al, 2001). These patients were more likely to fall outside the normal reference range for kinetic parameters of dark adaptation than the steady-state scotopic measurements (Owsley et al, 2001). Parafoveal rod function is known to be reduced in AMD leading to reduced dark adaptation

(Chen et al, 2004; Jackson et al, 2002; Owsley et al, 2000).

4.1.4.2 Photostress test

Macular recovery function is the ability of the photoreceptors to recover after exposure to bright light. The macular recovery function can be measured by the photostress test (PST). Several methods have been developed to measure photostress, some of which are the pupillographic photostress test (Zabriskie and Kardon, 1994), photostress recovery time using the

Eger Macular Stressometer (Bartlett et al, 2004), automated macular photostress test (Dhalla et al,

2007) and electroretinogram (ERG) photostress test (Binns and Margrain, 2007). A study by

Margrain and Thomson (2002) comparing different techniques of measuring photostress showed that bleaching of the photoreceptors using light from the direct ophthalmoscope for 30 seconds and then measuring the time taken for acuity to return to within one line of pre- bleach acuity was the best technique for performing PST.

The photostress test (PST) measures the time taken (in minutes) for retinal sensitivity to return to baseline using a predefined visual task (usually visual acuity), following exposure to a controlled glare source (Sandberg and Guadio, 1995). This is also known as glare recovery test

(Sandberg and Guadio, 1995; Neelam et al, 2009). The theoretical basis for the PST is considered as a three step process, w 0here bleaching of the retinal pigments is followed by a momentary state of insensitivity towards the visual stimulus and the visualisation of an afterimage and eventually the restoration of visual function as a result of retinal photoreceptor pigment regeneration (Alpern et al, 1971; Severin et al, 1967; Wu et al, 1990). The interval between the bleaching and regeneration of the retinal pigments is dependent on the anatomical and biochemical events that occur as a result of visual adaptation (Alpern et al, 1971; Wu et al, 1990).

Visual predictors for poor mobility performance in people with AMD

People with AMD often complain of problems with their visual function when changing from one light level to another, even when there are no reported vision problems in constant lighting conditions (Steinmetz et al, 1993). This has been explained clinically by demonstrating that the rate of light adaptation or photostress time is reduced in people with AMD compared to age-matched normals (Bartlett et al, 2004; Sandberg and Guadio, 1995). Several studies have shown that the PST can be used to assess the macular function in people with ARM, even when there is no change or decrease in high contrast visual acuity (Collins and Brown, 1989;

Severin et al, 1967; Wu et al, 1990). Some studies have shown a positive association between the

PST and the severity of ARM in the early stages of the disease (Collins and Brown, 1989; Midena et al, 1997; Sandberg and Guadio, 1995), although others have failed to find this association (Smiddy and

Fine, 1984; Wu et al, 1990; Glaser et al, 1977). Sandberg and Guadio (1995) reported that the PST can be used as a prognostic tool by demonstrating its ability to predict the development of CNV. Comparison of these studies so as to better understand the discrepancies is very difficult due to the lack of standardisation of methodologies and techniques used to assess PST.

In this study people with moderate to moderately severe AMD were assessed for their high contrast visual acuity, contrast sensitivity, glare recovery time, dark adaptation and visual fields and compared to age-matched normals. This study aimed to improve our understanding of the changes in the psychophysical aspects of vision under different illumination levels in normal ageing and associated with AMD.

4.2 Materials and Methods

4.2.1 Study Design

This study was conducted at the School of Optometry and Vision Science, University of New South Wales, as a cross-sectional study. Subjects who were willing to participate for the whole duration of the study were recruited.

All the visual functions were assessed monocularly (see Chapter 4.2.1.2). The basis for which eye to test is described in Inclusion Criteria. The subjects were allowed to rest between tests to avoid “noise” in the data due to the effect of fatigue. The tests were completed on a single visit, and are described in Chapter 4.2.2. All subjects were assessed

Visual predictors for poor mobility performance in people with AMD

for logMAR visual acuity, photostress, contrast sensitivity, dark adaptation, and visual fields. Subjects were given written information on the procedures involved in the study as well as verbal instructions on how to perform on each test. The verbal instructions were given prior to the commencement of each test to ensure that the subjects understood what was expected for the test. The procedure took 1.5 to 2.25 hours for each individual. The range in the time taken to complete the test was subject to individual factors such as the need for a break before each test and the duration of the break.

Subject selection and Recruitment

Subject selection

Ethics approval for the study was obtained from the Human Research Ethics Advisory Panel, University of New South Wales. Written informed consent was obtained from each subject, and people with moderate and moderately severe AMD and controls were examined. As noted in Section 3.2.1.2 and Table 4-2, although subjects were collected for the age range 55 years and above, since there was statistically significant differences between the average mean of the two groups (70.7 + 5.9 [Control]; 76.7 + 6.8 [AMD]) and hence, the control group for this study is a reference group and not truly age and gender matched.

Subjects were screened using slit-lamp biomicroscopy for assessment of the anterior chamber and with a 90D Volk lens for the posterior chamber. Several systems have been used to record the severity and presence of lens opacities (West and Taylor, 1986). The most widely accepted methodology for classification of lens opacities is the Lens Opacity

Classification System III (LOCS III). (Chylack et al, 1993). Although the photographic technique of grading cataract is more widely accepted, the clinical grading of lens opacities (Taylor and

West, 1989) was used as access to photographic records of subjects recruited from centres other than those from the Sydney Retina Clinic (Dr P. Beaumont) were not available. Along with being a simple and reliable system for the grading of presence and severity of lens opacities, the LOCS III system for grading of lens opacities has been shown to be reproducible and with good correlation to that of photographic grading (Taylor and West, 1989). The nucleus of the lens was examined by a thin slit beam of 0.1 width and 8mm height set at an angle of 30°.

Visual predictors for poor mobility performance in people with AMD

The cortex of the lens was then examined by retro illumination using a beam of 1mm width and 5mm height, angled first through one side of the pupil and then the other such that examination of the whole cortex was possible under retro illumination (Taylor and West, 1989). The lens appearance of the subjects was compared to the photographs recommended by the longitudinal study of cataract group and graded in accordance with the standards (Fig. 4-2). The grading characteristics for nuclear and cortical opacities are given in Table 4-1. The retina of the subjects was assessed using 90D Volk lens and the severity of AMD in the AMD group was graded according to the AREDS AMD classification (See Chapter 2, Table 2-1). Figure 4-2: Standard photographs used for grading the nuclear lens opacities.

Adapted from Taylor and West, 1989. Picture on the upper left represents the “standard photograph 1 where the faint opaque crescents can be seen in the nucleus but a clear central sulcus separates them. The opacities do not extend to the edges of the nuclear zone and are clearly demarcated”. Picture on the upper right represents standard photograph 2 showing “mild flocculant opacification with at least some opacity extending throughout most but not the entire nuclear zone. The opacities reach the edges of the nuclear zone in some areas”. Picture on the lower left represents the standard photograph 3 where “the increasing density of nuclear opacity with a uniform opacification of the whole nuclear zone which has no clear areas. The nucleus is clearly delineated from the rest of the lens”. Picture on the lower right represents the standard photograph 4 with “dense uniform nuclear opacity”. (Taylor and West, 1989) 62

Visual predictors for poor mobility performance in people with AMD

Table 4-1: Nuclear and cortical grading classification.

Grade Nuclear Cortical Grade 0 No opacity/opacities that was less dense or No opacities less extensive than standard Photograph1. Grade 1 Opacity that is at least as dense and as Opacities which combined extensive as standard photograph 1 but less occupy less than one dense or less extensive than standard eighth of circumference. photograph 2 Grade 2 Opacity at least as dense and as extensive as Opacities which combined standard photograph 2 but less dense or less occupy < one quarter of extensive than standard photograph 3 circumference. Grade 3 Opacity at least as dense and as extensive as Opacities which combined standard photograph 3 but less dense or less occupy < half of extensive than standard photograph 4 circumference. Grade 4 Opacity was at least as dense and extensive as Opacities which combined standard photograph 4. occupy > half of circumference. (Adapted from Taylor and West, 1989).

Recruitment

AMD subjects (n= 33) were recruited from Sydney Retina Clinic, Sydney Eye Clinic and Macular Degeneration (MD) Foundation. The recruitment process at the Sydney Retina Clinic and Sydney Eye Clinic was done by passing the information sheet about the study to potential participants who were told that if they were interested in participating in the study they could either contact the researcher individually or alternatively give their consent to the consulting staff to pass their details on to the researcher to be contacted. The recruitment process at the MD Foundation was done by identifying potential subjects from the database and sending out the information sheet and contact details of the researcher by mail.

Visual predictors for poor mobility performance in people with AMD

Control subjects (n= 35) were recruited from the Optometry Clinic of School of Optometry and Vision Science, University of New South Wales. The recruitment process was similar to that done at the MD Foundation. Potential subjects were identified from the database, limited to subjects who had been seen in the clinic between 2006 and 2007. Information regarding the study and contact details of the researcher was mailed out to potential subjects.

Inclusion criteria

Subjects were included in the study only if they met the following criteria: • Age 55 years and above • Moderate or moderately severe AMD in either one or both eyes for AMD group; graded 3.4 or 4 according to the AREDS classification (Chapter 2; section 2.5; table 2-2). • Absence of any other ocular conditions for both AMD and control groups. (Cataract with clinical grading 1 were included) The eye used for measurements was selected on the basis of the above criteria. In cases where both the eyes met the inclusion criteria, one eye was selected randomly.

4.2.2 Psychophysical evaluation

4.2.2.1 Visual acuity

Visual acuity was measured monocularly using a standard Bailey-Lovie chart at a distance 2 of 4m, with a 160 cd/m chart illumination (Bailey and Lovie-Kitchin, 1976; Ferris and Sperduto, 1982). The visual acuity was expressed in terms of LogMAR units. The test was terminated when the subject read the 6/6 line correctly or more than 3 letters in a line were read incorrectly. Visual acuity scored was based on the last line that the subject read three or more letters correctly; the score was reduced by 0.02 for each letter read incorrectly. Alternatively if the subject read only two letters on a line, visual acuity was scored as for the line above and

0.02 was added to the score for each letter read correctly (Ferris et al, 1982). The individual data is given in Appendix 6 and 7.

Visual predictors for poor mobility performance in people with AMD

4.2.2.2 Contrast sensitivity

Contrast sensitivity was measured using the Melbourne Edge Test (MET) at a distance of 40 cm with a 60 cd/m2 background luminance. The recommended background luminance for 2 MET is between 18-80 cd/m (Haymes and Chen, 2004). The MET is a non-grating contrast sensitivity chart which measures the contrast threshold for a single luminance profile edge

(Verbaken and Johnston, 1986). The MET was used as it evaluates edge contrast, which provides a better indication of the subject’s contrast performance in the real world situation and also because it has been reported to have high reliability and validity for contrast sensitivity function in people with low vision (Haymes and Chen, 2004).

The MET is a portable test chart and comprises 20 disks each measuring 25mm in diameter, each disc with an edge that decreases in contrast from the top to the bottom of the chart. It is arranged in four lines such that each line has five disks and possible orientations of 45°, 90°,

135° and 180° which are randomly arranged (Verbaken and Johnston, 1986; Haymes and Chen, 2004). Contrast sensitivity is measured in dB.

The subjects were seated at 40 cm from the MET and asked to identify the orientation of the edge of each disc. The subjects were prompted to guess the orientation of the disc and the test was stopped after two consecutive mistakes as per the recommended guidelines (Verbaken and Johnston, 1986). The individual data is given in Appendix 6 and 7.

4.2.2.3 Visual fields

Visual fields were tested using a Medmont M700 automated perimeter. This instrument was used as it provides rapid, reliable visual fields with the flexibility to customise visual field tests. This instrument also allowed the installation of a variable output transformer, which made adjustment of the background luminance of the bowl simple to control. The Medmont M700 utilises a 30 cm radius perimetric bowl with 164 green light emitting diodes (wavelength 565 nm) acting as the stimulus. Each stimulus is an equivalent Goldman size III with intensity from 0.03 asb to 1000 asb and duration adjustable from 0.1 to 9.9 sec (nom.

Visual predictors for poor mobility performance in people with AMD

0.2 sec). Although Central fixation was not directly video monitored, fixation was monitored with stimulus points presented within the blind spot (Vingrys and Helfrich, 1990).

The background luminance of the bowl was measured using a photometer (Minolta Luminance meter Ls-110). Visual fields were assessed with different background luminances to try and understand the impact of luminances on the visual fields of subjects with AMD and to compare these effects to the control group. The intensity for the background luminace was chosen to approximately match the luminances used through the mobility courses. The rationale for choice of luminances on the mobility course is given in Chapter 5; section 5.2.2 (page 90). The background luminance of the bowl was set at 155 cd/m2, 8 cd/m2, 3 cd/m2, 1 cd/m2, 0.2 cd/m2 and 0.06 cd/m2. The test was carried out in a completely darkened environment to avoid “noise” due to visual adaptation, which may reduce the sensitivity of the test, and to avoid uneven lighting on the bowl surface. A customised version of visual field test points was designed for purpose of this study with a total of 30 points.

All the subjects wore their reading correction as it has been shown to impact the visual field outcome (Wood et al, 1988). Testing was performed for one eye only (See Inclusion Criteria) at all the five background luminances. The subject was instructed to press the response button whenever they saw the stimulus presented in the bowl while fixating at the central fixation target. The subject response at each light level was recorded. The test duration for each light level was about 2.5 to 4 minutes. The subject was allowed to take breaks between each test to reduce the effect of fatigue as this has can influence the test results (Hudson et al, 1994). All the responses were recorded using the Medmont M700 software and a printout of the test result was obtained (See Appendix 9, 10, 11 and 12).

4.2.2.4 Visual adaptation

4.2.2.4.1 Photostress test

Methods for assessing macular function, measured the time for recovery of visual activity after adaptation to an intense light source; these have been called macular photostress tests or the photostress test (PST). The PST was performed monocularly, prior to pupil dilatation,

Visual predictors for poor mobility performance in people with AMD

using a direct ophthalmoscope. The subject was asked to look into the light from a direct ophthalmoscope held 5 cms in front of the subject for 30 seconds while the other eye was covered (Margrian and Thompson, 2002; Wu et al, 1990). The time for recovery was measured as the time taken to read the line above the best corrected visual acuity backwards (Wu et al, 1990) (See Appendix 6 and 7).

4.2.2.4.2 Dark adaptation

The rate of dark adaptation was measured using a Goldman-Weekers dark adaptometer (Haag Streit AG, Geneva). The luminance level of the test light of the instrument was standardised at 1.01 cd/m2. A white stimulus light was used on a test field of 5.5° illuminated opal glass. The dazzling lights used for bleaching were two 60-watt bulbs giving a measured luminance of 650 cd/m2, which was within the range recommended by the manufacturer (445-668 cd/m2), to ensure that no retinal damage was caused to the subjects participating in the study.

The subject was seated in normal room illumination for a minimum of 1 minute before starting the test. Pupils were dilated with 1% tropicamide and 2.5% phenylephrine, and a pupillary diameter of at least 6mm was achieved before commencement of the examination. The subject was pre-adapted with the dazzling lights for 5 minutes. After these lights were switched off, the subject was asked to respond as soon as they saw the white stimulus, while fixating on the red spot located 10º superior to the centre of the bowl. The patient’s response was marked on the chart attached to the instrument. The stimuli were presented below threshold level, gradually increasing the brightness, and readings were recorded at 1 minute intervals for 30 minutes (See Appendix 13-18).

4.2.3 Statistics Visual field indices used in this study were Mean Deviation (MD) and the Pattern Standard Deviation (PSD). MD and PSD were calculated using the formula for Humphrey perimetric analysis for comparison with other studies (Anderson, 1992). n n MD= [1/n ∑ (Zi-Xi)] [∑ (1/S2i)] i = 1 S2i i = 1

Visual predictors for poor mobility performance in people with AMD

Where n is the number of test location, Zi is the sensitivity of the retina at the test location i, Xi is the normative data for the sensitivity of the retina at the test location i and Si is the variance. m m PSD=√ [1/ (n-1) ∑ (Xi + MD - Zi)2] X [1/n ∑ (1/S2i)] i = 1 S2i i = 1

The data for Xi was taken as the average of the sensitivity of the retina at the test location i of the control group and Si is the average of the variance of the control group.

The dark adaptation curve was modelled as a biphasic exponential curve using the IDL (Interface Description Language) software (Student Version). The intersection of the biphasic exponential curve was defined as the Rod-Cone break up time (minutes). The IDL program used to perform the analysis and examples of the printout from the procedure are shown in Appendix 13 - 16.

The data was analysed using SPSS software (Version 17.0) and the distribution of the data was analysed using a Kolmogorov-Smirnov test. A logarithmic transformation was used for variables (PST, PSD at all bowl luminances and MD at bowl luminance of 8 cd/m2) that were not normally distributed. Differences in the demographic details of the subjects were assessed using independent t-tests for parametric and Chi-square tests for non-parametric variables. The difference in psychophysical performance between the AMD and control groups were assessed using Multivariate analysis after correcting for age and gender as a cofactors, as previous studies have shown age to be associated with a decline in visual function such as contrast sensitivity and visual adaptation ( Neelam et al, 2009). The difference in retinal sensitivity and density of scotoma within the two groups was tested using Analysis of Variance (ANOVA) for repeated measures. Pearson’s correlation test was done to analyse the correlation between the different variables of vision for the two groups. A p-value of less than 0.05 was considered statistically significant.

Visual predictors for poor mobility performance in people with AMD

4.3 Results

The demographics of the study sample are given in Table 4-2. The difference in the mean age of the two groups was statistically significant (AMD: 76.7 + 6.8 and Control: 70.7 + 5.9; p< 0.01; independent t-test) and the ratio of males and females in the two groups were also statistically significant (p<0.05; Chi-square test).

Table 4-2: Demographic details of the study sample

Demographics Control (n=35) AMD (n=33) Age (years) 70.7 + 5.9 76.7 + 6.8* Gender Male 25 12 Female 10 21 Lens Grade 1 (Nuclear/Cortical) 20 19 IOL 15 14 AMD (See Chapter 2: Table 2- 2) Grade 3.4 0 2 Grade 4.1 0 7 Grade 4.2 0 24

4.3.1 Visual acuity The distribution for high contrast visual acuity for both groups was not significantly different from a normal distribution (Kolmogorov-Smirnov test p=0.23). Visual acuity for people with AMD was significantly reduced compared to controls (AMD (n=33): 0.7 ± 0.4 LogMAR vs. Control (n=35): 0 ± 0 LogMAR; F=44.88; p<0.001, Multivariate analysis of variance). The association between visual acuity and the severity of the disease for the AMD group was analysed using scatter plot (R2=0.12) (Fig. 4-3).

Visual predictors for poor mobility performance in people with AMD

Figure 4-3: Effect of disease severity on visual acuity.

y = 0.6298x - 2.1023 R2 = 0.1161 1.2

0.8

0.6

0.4

0.2 Visual acuity (Log MAR) (Log Visual acuity 0 012345 AREDS GRADE

4.3.2 Contrast sensitivity The distribution for contrast sensitivity measurements for both groups was not significantly different from a normal distribution (Kolmogorov-Smirnov test, p=0.312). The mean contrast sensitivity for the control group and the AMD group was analysed using an independent t test and was found to be significantly reduced in people with AMD (p<0.001) when compared to controls (AMD (n=33): 9.8 + 5.7 dB vs. Control (n=35) 20.7 + 1.7 dB; F=96.19; p<0.001, Multivariate analysis of variance).

4.3.3 Photostress test

The photostress time for the study sample was normalised using logarithmic transformation (Kolmogorov-Smirnov test; p>0.05). People with AMD took a significantly longer time to recover the pre-bleach acuity compared to the controls (AMD (n=33): 87.2 + 5.7 secs. vs. Control (n=35) 21.7 + 8.4 secs; F=92.15; p<0.001, Multivariate analysis of variance). Further division of people with AMD depending on their visual acuity showed that the mean of photostress time taken by people with visual acuity less than 0.6 LogMAR was 101.11 + 59.09 seconds and that for those with people with visual acuity greater than 0.6 LogMAR was 91.93 + 64.54 seconds.

Visual predictors for poor mobility performance in people with AMD

4.3.4 Visual fields The average MD and PSD was analysed using ANOVA (Table 4-3) following logarithmic transformation of PSD at all background luminance and MD at 8 cd/m2, to meet conditions for normal a distribution. The MD at all the other bowl luminances had a normal distribution (p>0.05).

Table 4-3: Visual field Mean Deviation (MD) and Pattern Standard Deviation (PSD) data for AMD and control groups.

Visual field Control AMD F value Indices (n=35) (n=33) (Log units) dB dB MD 2.19 0.28 + 0.33 -2.43 + 0.42* 25.69 MD 1.08 0.1 + 0.22 -5.18 + 0.66* 59.86 MD 0.48 0.1 + 0.21 -5.57 + 0.75* 56.12 MD0 0.18 + 0.22 -4.79 + 0.89* 42.28 MD -0.70 0.22 + 0.25 -6.38 + 0.91* 51.61 MD -1.22 0.31 + 0.37 -7.23 + 0.92* 60.98 PSD 2.19 0.77 + 0.14 2.21 + 0.35* 15.03 PSD 1.08 0.34 + 0.08 8.40 + 1.58* 27.78 PSD 0.48 0.34 + 0.06 11.29 + 2.30* 24.02 PSD 0 0.44 + 0.09 10.37 + 2.51* 24.05 PSD -0.70 0.49 + 0.95 13.53 + 2.95* 20.70 PSD -1.22 0.91 + 0.30 15.31 + 3.01* 24.05 * p<0.001; Multivariate Analysis of Variance. MD: mean deviation; PSD: pattern standard deviation; 155, 8, 3, 1, 0.2 and 0.06 reflect the bowl luminance in cd/m2.

The effect of luminance on the retinal sensitivity was plotted (Fig. 4-3) and the mean retinal sensitivity was found to be significantly different between both the groups at all levels of background luminance. The decrease in retinal sensitivity in the control group was not statistically significant between the different bowl luminances (p=1.0). The decrease in retinal sensitivity in the AMD group was shown to be statistically significant at all bowl luminances (p<0.02) except between bowl luminance of 3 cd/m2 and 1 cd/m2 (p=0.147) and between bowl luminance of 3 cd/m2 and 0.2 cd/m2 (p=0.147). 71

Visual predictors for poor mobility performance in people with AMD

Figure 4-4: Effect of luminance on retinal sensitivity.

1 Bowl luminance in log units 0 ‐2 ‐101‐1 23 (dB) ‐2 ‐3 ‐4 Control Sensitivity ‐5 AMD ‐6 ‐7 ‐8 ‐9

Where the x-axis plots the log transformation of the bowl luminance and the y-axis plots the mean sensitivity (Standard Error Mean [SEM]); AMD n= 33; Control n = 35.

The PSD (focal defects) was also plotted and the mean PSD (focal defects) was found to be significantly different between the groups for all levels of background luminance (Fig.4-4). The increase in PSD (focal defects) in the control group was shown to be statistically significant between bowl luminance of 155 cd/m2 and 8 cd/m2 (p<0.01); bowl luminance of 155 cd/m2 and 3 cd/m2 (p<0.01); bowl luminance of 8 cd/m2 and 0.06 cd/m2 (p=0.05) and was not significant between all the other bowl luminances (p>0.1). For the controls, the increase in PSD with decreasing luminance is as expected. The PSD in normals was less than + 1 at all bowl luminances. The increase in PSD (focal defects) in the AMD group was shown to be statistically significant at bowl luminances (p<0.002) except between bowl luminance of 8 cd/m2 and 1 cd/m2 (p=0.147) and between bowl luminance of 3 cd/m2 and 0.2 cd/m2 (p=0.134).

Visual predictors for poor mobility performance in people with AMD

Figure 4-5: Effect of luminance on density of the scotoma.

20 18 16 [SEM ]) 14 12 (M ean 10 Control 8 C AMD defects

6 4 Focal 2 0 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5 2 2.5 Bowl luminance in log units

Where the x-axis is the log transformation of the bowl luminance and the y-axis is the mean of focal defects (SEM); AMD n= 33; Control n = 35.

4.3.5 Dark adaptation

The normality of the distribution of the study sample was analysed using the Kolmogorov- Smirnov test (p>0.05). The mean rod and cone thresholds (log units) are given in Table 4-4. The difference between the two groups was analysed by multivariate analysis of variance and was significantly reduced in people with AMD (p< 0.001).

Table 4-4: Descriptives for dark adaptation indices

Dark adaptation indices Control AMD (n=35) (n=33) Cone threshold (log units) 4.15 + 0.3 4.87 + 0.7* Rod threshold (log units) 0.75 + 1.1 3.64 + 1.0* Rod-Cone break-up time (mins) 8.77 + 2.6 13.16 + 3.4* Rate of Cone adaptation (log units/min) 0.36 + 0.1 0.18 + 0.1* Rate of Rod adaptation (log units/min) 0.09 + 0.1 0.08 + 0.1

Visual predictors for poor mobility performance in people with AMD

Figure 4-6: Representative dark adaptation curve for the control group (IDL software output).

Rod-Cone break-up time

Cone threshold

Rod threshold

Figure 4-7: Representative dark adaptation curve for the AMD group (IDL software output).

Rod-Cone break-up time Cone threshold

Rod threshold

Visual predictors for poor mobility performance in people with AMD

Figure 4-8: Comparison of the mean dark adaptation curve for control and AMD group.

8.00 Control 7.00 AMD

6.00 )

5.00

4.00

3.00

Threshold (Log units 2.00

1.00

0.00 0 5 10 15 20 25 30 35 Time (Mins)

The mean dark adaptation curve obtained for control and AMD subjects (Mean ± SD); control (n=35), AMD (n=33).

The Pearson’s correlation between visual function variables showed that visual acuity (LogMAR) was correlated with contrast sensitivity (r2=-0.752, p<0.001), visual field indices (MD 155 (r2=-0.600, p<0.001), PSD 155 (r2=0.603, p<0.001), MD 8 (r2=-0.777, p<0.001), PSD 8 (r2=0.763, p<0.001), MD 3 (r2=-0.801, p<0.001), PSD 3 (r2=0.770, p<0.001), MD 1 (r2=-0.657, p<0.001), PSD 1 (r2=0.667, p<0.001), MD 0.2 (r2=-0.766, p<0.001), PSD 0.2 (r2=0.657, p<0.001), MD 0.06 (r2=-0.781, p<0.001)and PSD 0.06 (r2=- 0.718, p<0.001)), rod threshold caluclated from dark adaptation (r2=-0.413, p=0.021). Visual acuity (LogMAR) was not correlated with macular recovery function (PST) (r2=- 0.119, p=0.51); and dark adaptation indices (cone threshold (r2=0.207, p=0.248) and Rod- Cone break-up time (r2=0.287, p=0.105). Macular recovery function (PST) did not correlate with any of the vision variables (r2<0.12, p>0.45). Contrast sensitivity correlated with all the vision variables (p<0.001) except macular recovery function (PST) (r2=-0.142, p=0.44); and dark adaptation indices (cone threshold (r2=-0.123, p=0.5018) and Rod-Cone break-up time (r2=0.288, p=0.110). The visual field indices correlated with each other at all background luminances (p<0.001) and also correlated with LogMAR and CS (p<0.001) (Appendix 19).

Visual predictors for poor mobility performance in people with AMD

4.4 Discussion

4.4.1 Visual acuity

People perform a wide variety of tasks daily, which require good resolution and high contrast visual acuity performance (Rubin et al, 1994; Fletcher and Schuchard, 2006). As expected, the current study showed a significant reduction in high contrast VA for people with moderate to moderately severe AMD compared to controls. Recent studies tend not to analyse visual acuity as a measure of visual dysfunction as it has been shown to have a poor correlation with the subtle changes in visual function due to the progression of AMD and the visual disabilities experienced by the patient (Marron and Bailey, 1982; Brown et al, 1986; Owsley and Sloane,

1987; Rubin et al, 2000), The current results are consistent with earlier studies of Brown and

Kitchen (1983) who demonstrated that visual acuity in people with AMD was reduced compared to age-matched controls. Furthermore, the results agree with previous studies, in that VA did not have a linear association with the severity of the disease process (Brown et al,

1986).

4.4.2 Contrast sensitivity

As discussed in Chapter 4.1, contrast sensitivity has been shown to be an important predictor of difficulties experienced with everyday activities, mobility, orientation and quality of life outcomes for people with visual impairments (Marron and Bailey, 1982; Owsley and Sloane, 1987; Carta et al, 1998). Contrast sensitivity was significantly reduced in the AMD group when compared with controls, as reported previously (Midena et al, 1997; Feigl et al, 2005; Mei and Leat, 2007). Although previous studies have reported reduced contrast sensitivity in people with AMD, quantitative comparison between this study and previous studies is difficult due to the different methods used for measuring contrast sensitivity and differences in the extent of severity of AMD.

Midena et al (1997) assessed the static and dynamic contrast sensitivity using computer software-controlled sinusoidal gratings for testing static contrast sensitivity and temporally modulated at a 16 Hz frequency for dynamic contrast sensitivity testing. Mei and Leat (2007) assessed the contrast threshold levels using computer Morphonome 3.5 software to generate a stimuli that consisted of vertical cosine gratings of at least four cycles in width with a circular vignette envelope at spatial frequencies of 0.26, 0.58, 1.11, 2.17, 4.29, and 8.53 cpd

Visual predictors for poor mobility performance in people with AMD

(cycles per degree). The present study assessed edge contrast sensitivity (MET) as previous research has shown that it correlates well with mobility and orientation (Verbaken, 1989; Haymes and Chen, 2004; Lord and Dayhew, 2001).

4.4.3 Photostress test

The photostress test (PST) has been shown to detect early adaptational changes in ARM

(Collins and Brown, 1989; Cheng and Vingrys, 1993; Sandberg and Guadio, 1995). In the current study, people with AMD took significantly longer to recover to their pre-bleach acuity level compared to controls. This is consistent with previous studies where an increased recovery time was also reported in people with AMD (Collins and Brown, 1989; Sandberg and Guadio, 1995; Midena et al, 1997;

Sandberg et al, 1998).

The correlation analysis showed that the PST was not correlated with VA, CS, visual field indices (MD 155, PSD 155, MD 8, PSD 8,MD 3, PSD 3, MD 1, PSD 1, MD 0.2, PSD 0.2, MD0.06 and PSD 0.06) and dark adaptation indices (cone threshold, rod threshold and rod- cone break up time). This means that the PST is a measure of visual function that is independent of visual acuity. People with AMD often have difficulty adapting to changing luminances in the envirenment. This study provides further evidence that the macular recovery function in people with AMD is slower. Hence it is important to assess the macular recovery function in people with AMD routinely in a clinical envirenemt inorder to be able to address issues that is concerning to them.

It was interesting to note in the current study that people with AMD and visual acuity worse than 0.6 Log MAR (Snellen acuity 6/24) did not take as long as patients with acuity of better than 0.6 Log MAR (Snellen acuity: 6/24) in performing the PST. This may be related to the magnification effect of the letter at the test distance, which does not require the same adaptation time as that required for resolution of a letter that is half the size (Borish, 1970). Another explanation for this could be the concept of auto-gain control of photoreceptors

(Enroth-Cugell and Shapley, 1973; Baylor, 1987). This is based on the idea that the central response mechanism acts as a unit and the impulse-quantum ratio (I/Q ratio) of the centre is set by signals from many photoreceptors to produce responses (Enroth-Cugell and Shapley, 1973). A graph plotting I/Q ratio against the background illumination showed a flat portion at low 77

Visual predictors for poor mobility performance in people with AMD

background illumination and a steeply rising portion for higher background illuminations

(Enroth-Cugell and Shapley, 1973). The break in the curve showed the retinal illumination required for the auto-gain control for the cell tested. The adaptive effect is constant for a central receptive field area of uniform sensitivity with constant retinal flux (retinal illumination x area) when the adapting field is smaller than the central receptive field area (Cleland and Enroth-

Cugell, 1968). Enroth-Cugell and Shapley (1973) demonstrated that cells with a larger receptive field centre must be more light adapted than those with smaller centre.

Based on the above concept, it may be proposed that the number functional photoreceptors in people with AMD and VA worse than 6/24, are less than for those with VA better than 6/24 and hence does not reach the sufficient level of flux required to stimulate or trigger the auto-gain process. The adaptive effect is thus minimally affected by the impact of bleaching during photostress. This observation does not appear to have been reported elsewhere.This area could be further assessed by analysing the macular recovery function in terms of resolution acuity, and classifying subjects into groups depending on their visual acuity level. Previous studies that assessed PST looked at VA ranging from 0.0 to 0.8 but did not compare if there were differences in PST within individual AMD patients (Collins and Brown,

1989; Sandberg and Guadio, 1995; Midena et al, 1997; Sandberg et al, 1998).

4.4.4 Dark adaptation

Several studies have shown that people with ARM and AMD have a delayed dark adaptation function (Brown and Lovie-Kitchin, 1983; Brown et al, 1986; Owsley et al, 2001). The present study showed that people with moderate to moderately severe AMD had a significant effect with delayed rod-cone break up time and also reduced thresholds for both rods and cones. This is consistent with previous studies which demonstrated rod and cone adaptation abnormalities

(Brown and Lovie-Kitchin, 1983; Brown et al, 1986; Owsley et al, 2001; Dimitrov et al, 2008).

The cone-mediated parameters were also impaired in the current study, in contrast to a recent study (Owsley et al, 2007). This may be related to differences in the severity of AMD between the two studies. The subject population in their study comprised people with ARM, while the subject group in this study consisted of people with moderate to moderately severe AMD (Table 4-2). Although all the subjects in the AMD group had at least moderate AMD, 78

Visual predictors for poor mobility performance in people with AMD

their cone threshold, rod threshold and rod-cone break up time varied between subjects as expected. This most likely reflects the progression of the disease process involved in AMD (See Chapter 2), and could be related to the size and location of the lesion present. Although all the lesions were foveal, estimation of the lesion size was not possible as subjects were assessed by biomicroscopy and access to the fluorescein angiography data for all the study subjects was not available. Steinmetz et al (1993) hypothesised that individual variability in psychophysical functions could be related to non-uniform disease progression.

The difference in rod threshold reduction was greater than that for cone threshold in the AMD group when compared with controls. This is consistent with previous psychophysical and histopathological studies that reported selective vulnerability of rods in people with

AMD (Curcio et al, 1996; Owsley et al, 2000). In AMD, the loss of rods is more severe and generally precedes that of cone loss (Curcio et al, 1996), although the differential loss of rods over cones is also associated with the normal ageing process (Gao and Hollyfield, 1992).

Previous studies have shown that the adaptation time required by people with AMD was even longer than with normal ageing (Owsley et al, 1999; Owlsey and Jackson, 2000; Owsley et al, 2000;

Owsley et al, 2001). Delayed visual adaptation in the normal ageing population most likely relates to additive effects of age-related changes such as loss of photoreceptors and ganglion cells (Gao and Hollyfield, 1992; Curcio and Drucker, 1993), the slowing of rhodopsin regeneration

(Jackson et al, 1999) and a reduction in neural transmission time or impaired neural integration

(Porciatti et al, 1992). In addition to these age-related changes, functional impairment of the rods and cones due to the loss of photoreceptors (Brown et al, 1986a; Brown et al, 1986b; Curcio et al, 1996;

Owsley et al, 2007), and retinoid deficiencies leading to functional impairment of the photoreceptors due to disruptions in metabolic exchange (Kemp et al, 1988; Kemp et al, 1989; Steinmetz et al, 1993; Owsley et al, 2007) further delays visual adaptation in people with AMD.

People with AMD often complain about functional difficulties in areas of changing luminances even in the presence of good high contrast visual acuity (Steinmetz et al, 1993). These functional difficulties could be explained by the impairments in visual adaptation. The delay in visual adaptation caused by AMD could also predispose this group to an increased risk of falls when moving through environments with changing light levels. The relevance of this aspect of visual function in relation to mobility performance is modelled in Chapter 6. 79

Visual predictors for poor mobility performance in people with AMD

4.4.5 Visual fields

The assessment of visual fields (retinal sensitivity) at different luminance levels showed a significant reduction in people with AMD compared to the control group. This is consistent with previous studies that reported reduced retinal sensitivity and focal field defects in people with AMD (Cheng and Vingrys, 1993; Tolentino et al, 1994; Midena et al, 1997).

While the sensitivity was linearly reduced in changing from photopic to scotopic conditions, there was an increase in the retinal sensitivity at the mesopic level (Fig. 4-3). This may be explained by the retinal anomaly of spatial summation, with both rods and cones receptive under mesopic illumination compared to only cones being receptive at photopic levels or only rods being receptive at scotopic levels at the parafoveal area. The PSD (focal defects) was also reduced at mesopic levels (Fig. 4-4) which may also be explained by spatial summation. The retinal sensitivity and focal field defects (PSD) in relation to different levels of luminance have not been previously assessed. However, histopathological studies showing the vulnerability of rods over cones in AMD (Curcio et al, 1996) indirectly support the current findings of reduced sensitivity at scotopic compared to photopic conditions.

Spatial summation can be explained by the relationship between the differential light threshold and the stimulus size (Sloan, 1961, Wilson, 1970). Earlier studies showed that that the summation zone area increases from the fovea to the peripheral retina (Glezer, 1965). Furthermore, looking at the receptive field sizes, cones in the foveal field are two orders less when compared to rods in the peripheral field. Correspondingly, when measurements are made with large area stimuli, the sensitivity of the rods is greater than cones (Wald, 1954). This is consistent with the assumption that the sensitivity is the same for single rods and cones and the differences in sensitivity exhibited is due to neural factors such as spatial summation

(Arden and Weale, 1954).

Further studies on the receptive fields has led to the hypothesis that reorganisation of the neural connections where the summation area increased with a decrease in illumination could explain the changes to light sensitivity and visual acuity (Lythgoe, 1940; Blackwell, 1946).

Observations by Barlow et al (1957) that the summation area was constant on changing the background illumination lead to the supposition that the increase in luminance caused a field 80

Visual predictors for poor mobility performance in people with AMD

of smaller area to respond (Pirenne and Denton, 1952), where the rod receptive fields replaced the cone receptive fields as the illumination was reduced (Glezer, 1965). The area of the receptive field is considered inversely proportional to the background luminance (Glezer, 1965). The summation area for a background luminance of 10-30 asb (3.18 – 9.55 cd/m2) is reported to be equal to the size of a single cone (Glezer, 1965). This indirectly supports the hypothesis in the present study that by reducing the background illumination, the receptive field is increased, and thus increasing the size of the summation area at low luminance levels. The response in the mesopic range can be considered to depend on both the density of both rods and cones and spatial summation. This area could be further explored in patients with ARM and AMD, and other pathologies with rod-cone degenerations. One of the limitations of using the Medmont M700 model for visual field assessment is that it is not possible to visually monitor the fixation stability of the patient although fixation was monitored by a blind-spot method (automatic tracking during the test with visual and audible warning of fixation errors) (Medmont International Pty Ltd.). Repeated verbal instructions were also given throughout the test to maintain fixation. A newer version of the Medmont M700 model is now available with built-in cameras for monitoring fixation stability (Medmount International Pty

Ltd.).

The differences in retinal sensitivity and focal defects at different light levels are also consistent with the difficulties experienced by people with AMD in performing various tasks over a dynamic range of luminance conditions.

4.5 Conclusion

These observations provide further evidence of the reduced psychophysical visual performance that occurs in people with moderate to moderately severe AMD compared to controls. This study also provided evidence that people with AMD take longer to adapt to changing luminances and that PST is a measure of visual function that is independent of visual acuity, contrast sensitivity and visual field defects. PST can be performed routinely in a clinical envirenment as sophisticated instruments are not required and this simple test is not onerous for either patient or practioner.

Visual predictors for poor mobility performance in people with AMD

The severity of the visual field defects in people with AMD was found to vary with the levels of illumination used during testing. This may be useful in practice for minimising the effects of visual field defects by manipulating ambient illumination levels for specific tasks. Impaired visual adaptation in people with AMD could be a significant factor in predicting functional impairments by its impact on mobility performance. These areas are further explored in Chapters 5 and 6.

Visual predictors for poor mobility performance in people with AMD

Chapter 5: Sensorimotor Functions and

Mobility Performance in People with AMD

5.1 Introduction

Mobility is a complex function involving coordinated processing and amalgamation of information and functions of different physiological systems. Mobility has been defined as “moving by changing body position or location or by transferring from one place to another”

(WHO International Classification of Functioning, Disability and Health) (Sturnieks et al., 2008). Mobility is intrinsically dependant on balance as this is required for maintaining a position, remaining stable while moving from one position to another, performing daily living activities or moving freely. The visual system plays an integral part in maintaining balance and remaining stable while standing still and moving around in the environment. Visual reference of position of oneself and objects within the surroundings is provided from the visual information, which is integrated with vestibular and proprioceptive or somatosensory

(muscle receptors and joint nerves) system inputs (Black and Wood, 2005).

Balance is maintained by the integrated relay of information from the sensory, somatosensory and motor systems to the central processing system (Horak et al, 1990; Sturnieks et al,

2008). The sensory system comprises of the visual and vestibular system where the visual input is used to process a spatial map of the surroundings and the vestibular input is used towards processing counteractive movements brought out by the vestibulo-ocular and vestibulospinal pathways (Sturnieks et al, 2008). The somatosensory input (proprioception) is received from the receptors in the muscle, tendons and joints and help towards the coordination of steps to achieve ideal foot placement (Sturnieks et al, 2008). The motor system input is generated from muscle strength and reaction time which contribute toward, and react quickly and efficiently, in maintaining or recovering disturbed balance (Sturnieks et al,

2008). Decline in balance ability occurs in old age and a correlation between impaired

Visual predictors for poor mobility performance in people with AMD

balance and risk of falls and increased mortality rate is reported in older people. (Bogle and

Newton, 1996).

Falls are one of the most common causes of increased mortality, morbidity and immobility in the older community. Falls are reported to account for about 75% of deaths occurring in about a quarter of the population aged 65 and over (Rubenstein et al, 1994). Lower extremity weakness, gait, visual and memory impairments, balance problems, mobility performance and the number of medications taken have all been identified as risk factors for falling

(Robbins et al, 1989; Rubenstein et al, 1994). The number of risk factors present is directly proportional to the risk of falling (Robbins et al, 1989; Rubenstein et al, 1994). This inturn depicts the significance of assessment of mobility performance in people with visual impairments.

The sensorimotor functions (somatosensory, visual and vestibular) provide a measure of functional capacity of the physiological systems that play an important role in maintaining postural stability (Lord and Dayhew, 2001). Sensorimotor functioning reduces with ageing in normal subjects (Kaplan et al, 1985). Reliance on vision for balance control was found to decline after 65 years of age, with increased reliance on peripheral input for balance control after this age (Lord and Ward, 1994).

Several studies have looked at the relationship between sensory systems and body sway and found that peripheral sensation has the strongest correlation with sway while on a firm surface; vision, strength and reaction time are significantly associated with sway when peripheral sensation is altered (Horak et al, 1990; Lord et al, 1991; Lord and Ward, 1994). Other studies that have looked at the relationship between sensorimotor input and stability by altering the sensory input report that body sway increases or postural stability decreases when the numbers of sensory inputs are reduced (Diener et al, 1984; Horak et al, 1990; Lord et al, 1991; Simoneau et al, 1992).

Various methods have been adopted to assess the mobility performance of an individual. Mobility performance is commonly measured in terms of walking speed. The Percent Preferred Walking Speed assesses an individual’s walking speed through the desired test course as a percentage of their general walking speed through an unchallenged environment

(Clark-Carter et al, 1986). This method has been mostly used to assess walking efficiency in 84

Visual predictors for poor mobility performance in people with AMD

subjects with visual impairment, as the subject is effectively their own control when assessed in a challenging environment (Clark-Carter et al, 1986; Haymes et al., 1996; Black et al., 1997;

Hassan et al., 2002; Soong et al., 2000) and takes individual variations such as age, height and physical status into account.

This study investigated differences in sensorimotor functions and mobility performance of people with AMD compared to age-matched controls, taking into account differences in visual function between the two groups. This information will be valuable for understanding the factors that impede mobility performance in people with AMD and help with designing orientation and mobility training services for this group.

5.2 Materials and Methods

5.2.1 Materials

Sensorimotor functions

The sensorimotor functions assessed were proprioception, muscle strength, reaction time and balance (adapted from the Falls and Balance Research Group at the Prince of Wales

Medical Research Institute) (Lord et al, 1991)).

Mobility course design

The mobility course used to assess the subject’s mobility performance consisted of a simple course and a complex course.

Simple course The simple course was 12m long and comprised of a straight path free of obstacles with illumination level of 155 cd/m2. This course was designed to measure the preferred walking speed of the subject.

Complex course The complex course was 48 m long with varying levels of illumination and obstacles of 10- 15% and 80-100% contrast. The different levels of luminance in the obstacle course were obtained by using flood lights, fluorescent tube light and night lamps (Fig 5-1). The luminance in the room was measured using a calibrated photometer (Minolta Luminance 85

Visual predictors for poor mobility performance in people with AMD

meter Ls-110) and an average of 10 measurements were obtained at five different locations from the floor and walls in the course.

2 Fig 5-1: Calibration of the luminance levels of the complex course (luminance cd/m )

1500 1300 ) 2 1100 900 700 500 300 Luminance (cd/m Luminance 100 -100 01234567 Source of Luminance

Type of source: 1= all the tubes + 4 flood lights; 2= all the tubes + 2 flood light; 3= Surface tubes + 1 flood light; 4= Surface tubes; 5= Table lamp; 6= Night lamps.

The obstacles were designed to mimic the effects of curbs or loose payment tiles and half pillars (made from foam, rolled into the shape of a pillar). The obstacles were made of foam to minimize the risk of injury from a trip or a fall. A schematic representation of the complex course followed by the subject is shown in Fig 5-2 and a schematic representation of the obstacles in the mobility course is shown in Fig 5-3.

Visual predictors for poor mobility performance in people with AMD

Figure 5-2: Schematic representation of the complex mobility course sequence followed by the subjects.

MobilityMobility coursecourse

Course A (155 cd/m2) Course B(3 cd/m2 Pre-adapted at 155 cd/m2 for 3mins

Pre-adapted at 155 cd/m2 Course C (1500 cd/m2) for 3mins Pre-adapted at 3 cd/m2 Course F (0.06 cd/m2) for 3mins

Course D (12 cd/m2) Pre-adapted at 1500 cd/m2 Course E (0.2 cd/m2) for 3mins Pre-adapted at 155 cd/m2 for 3mins

Visual predictors for poor mobility performance in people with AMD

Figure 5-3: Schematic representation of obstacles in the mobility course. Finish Start the cylindrical tubes represent half pillar of two different contrast (5-15% and 80-85%) and the small blocks represent small obstacles such as loose pavement stone, of two different contrast (5-15% and 80-85%). The black box represents the wall.

5.2.2 Methods

Subject selection and recruitment

Subject selection and recruitment are described in detail in Chapter 4.2. The subjects were assessed for their sensorimotor functions and mobility performance using the following tests.

Visual predictors for poor mobility performance in people with AMD

Sensorimotor functions

Sensorimotor functions assessed were proprioception, balance, muscle strength and reaction times. These tests were adapted from the Falls and Balance Research Group at the Prince of

Wales Medical Research Institute (Lord et al, 1991). These tests have good test-retest reliability

(Lord et al, 1991) and take into account normal age related functional declines (Nevitt et al, 1989). The normative data is taken from the study conducted Falls and Balance Research Group at the Prince of Wales Medical Research Institute (Lord et al, 2003).

Proprioception Proprioception was assessed in the lower limbs, where the subject was seated on a tall chair and asked to align the first metatarsal joints of both feet by extending both legs forwards with their eyes closed. The error in aligning their feet was measured in degrees with a protractor inscribed on a Perspex sheet (60 x 60 x 1 cm), which was placed between the legs. Five measurements were taken on each subject (Lord et al, 2003). The normal range for people aged 60 and above is given in Appendix 20.

Muscle strength Muscle strength was determined by assessing quadriceps strength (in kilogram force) from a seated position, with the angle of knees and hips set at 90°. The strength of the subject’s dominant leg was measured using a spring gauge. The dominant leg was considered as the leg the subject uses unintentionally to place the first step while walking or climbing steps

(Lord et al, 2003). The normal range for people aged 60 and above is given in Appendix 20.

Reaction time Reaction time was measured as a simple reaction task where a light stimulus was presented and the response to the stimulus indicated by the depression of a switch by hand was recorded in milliseconds. The subject was initially given 5 trial tests to become familiar with the task and then 10 repeat measures were taken (Lord et al, 2003). The normal range for people aged 60 and above is given in Appendix 20.

Visual predictors for poor mobility performance in people with AMD

Balance Balance was measured in terms of postural sway by attaching a swaymeter to the body at the level of the waist and recording the horizontal and vertical displacement of the body (in mm) while the subject was standing on a foam mat (70 x 62 x 15 cm) (Lord et al, 2003). The Fall Risk calculator estimates the sway path length by calculating the logarithm of the product of maximal anterior-posterior and lateral sway, multiplied by a constant determined from a regression analysis to estimate the sway path (Lord et al, 1991). The normal range for people aged 60 and above is given in Appendix 20.

Mobility performance

Two mobility courses were designed to measure the mobility performance of all subjects. The subjects were asked to walk through the simple course in order to estimate their Preferred Walking Speed. They were asked to walk at their normal pace and it was also emphasised that the performance measure was not based on their walking speed for this part of the test. For mobility performance they were pre-adapted to the required luminance level (Fig 5-2) for 3 mins. The subjects were then asked to follow a course of A, B, C, D, E and F as described below: Course A - the complex course with a luminance of 155 cd/m2; Course B - the complex course with a luminance of 3 cd/m2; Course C - the simple course with a luminance of 1500 cd/m2; Course D - the complex course with a luminance of 12 cd/m2; Course E - the complex course with a luminance of 0.2 cd/m2 and Course F- the complex course with a luminance of 0.06 cd/m2.

The luminance levels were chosen to represent the wide range of light levels that an individual may be exposed to in a real world setting. The 1500 cd/m2 represented illumination on a bright sunny day, 155 cd/m2 represents the illumination level in daytime (when it is not particularly sunny), 12 cd/m2 represents the daytime illumination level indoors, 3 cd/m2 represents the illumination levels in the evening, 0.2 cd/m2 represents a moonlit night and 0.06 cd/m2 represents the illumination level indoors on a moonlit night. The luminance level 1500 to 8 cd/m2 represented the photopic range, 3 to 0.2 cd/m2 was in the mesopic range and 0.06 cd/m2 represented the scotopic range. 90

Visual predictors for poor mobility performance in people with AMD

After completing course D, the subjects were adapted for 3 minutes in 155 cd/m2 luminance before entering course E and then course F (Fig. 5-2). The subjects were instructed that they could pause at the start of each course till they were comfortable with the current illumination and able to see obstacles in the path, so that they could manoeuvre their way through the course. The time taken to adapt to the light level they were exposed to was at the discretion of each subject and was not included in the pre-adaptation time, however this time was added to the time they took to complete the course. The subjects were followed closely by the investigator to minimize the risk of injury from fall due to tripping. The positions of the obstacles were altered after each course while the subject was pre-adapting to avoid learning effects. The short term memory that is associated with processing coding and storing of information for a short span is capable of remembering 7 + 2 items in the correct order (Miller G, 1956) and hence the possibility of subjects remembering the positioning of obstacles in the event the order of positioning the obstacles were repeated is highly unlikely. All subjects followed the same order of luminances for the mobility courses. This order was followed as it also helped to assess the individual’s mobility performance in association to the demands of changing illumination levels. Although previous studies (Soong et al, 2000; Clark-

Carter et al, 1986) have taken measurements from an average of two trials, the measurement of mobility performance was done for a single trial to avoid fatigue. Further, as shown in Appendix 24 data from five patients from each group showed that there were no statistically significant differences between single trial and average from trials for the mobility measures. The stop watch was started as soon as the subject entered the mobility course irrespective of whether the subject paused to adapt to the light level in that mobility course and was stopped when the subject reached the end of the mobility course

The mobility performances of individuals were quantified using three indices: Preferred Walking Speed (PWS), Percentage Preferred Walking Speed (PPWS) and Error Score (ES).

Preferred Walking Speed (PWS)

This is the walking speed of an individual in a stress-free environment, measured as the subject negotiates the simple mobility course using the non-sighted guide technique (Soong et -1 al, 2000). The PWS (m/sec ) was calculated as follows: PWS (msec-1) = (Distance)/ (Time taken) 91

Visual predictors for poor mobility performance in people with AMD

Percentage Preferred Walking Speed (PPWS)

This is assessed as the speed of the subject walking through the mobility Courses A to F, expressed as a percentage of the Preferred Walking Speed: PPWS = [(PWS through the complex course)/ (PWS)] x 100

This measure is reported to assess the individual’s performance against his or her own performance in a stress-free environment, thus allowing the individual to act as their own control (Clark-Carter et al, 1986; Haymes et al., 1996; Black et al., 1997; Hassan et al., 2002; Soong et al., 2000).

Error Score (ES)

Any contact made by the subject with the obstacles placed in the mobility course was counted as an error or mobility incident. Other behavioural tactics displayed by the subject such as trailing the wall with their hand or reaching out with their hand to feel for obstacles were also counted as errors. The subjects were informed about this prior to commencing the assessment. The error score (ES) (Hassan et al., 2002) was calculated as:

ES= Log10 [100 / (1+ number of errors/mobility incidents)]

Mobility Performance Score (MPS)

A new index for measuring mobility performance that combined the speed of the participant and a penalty for the number of errors made in navigating the mobility courses was also developed – the Mobility Performance Score (MPS).

A review of the literature for guidelines pertaining to penalising obstacle contacts made during the assessment of mobility performance in an obstacle course was conducted. Two evaluations were found to be relevant. The Physical Abilities Readiness Evaluation (PARE) performed by the Northern Alberta Institute of Technology (NAIT) campus security services gave a penalty of 2 secs for every obstacle knocked down by the participant (PARE, 2009). The guidelines set by the Law Enforcement Games for Florida penalised the contestant 5 secs for knocking the obstacles on the course during high jump (Florida Law enforcement games, 2009). These assessments however, did not mention the expected speed of the participants. 92

Visual predictors for poor mobility performance in people with AMD

The only other guidelines available were those set by the international body for equestrian contests - Federation Equestre Internationale (FEI, 2009). In these guidelines contestants were penalised by 4 faults for each contact made during the course of the contest for show jumping in the Olympic Equestrian Competitions (2009). Similar sporting events in humans, where the performance is measured in terms of speed, disqualify the contestant if contact with the obstacle is made during the event. When the above issues were taken into account it was decided to apply a modified version of the equine guidelines to the current study as a further measure of performance mobility, taking into account the number of errors.

The penalty calculation used by the FEI was based on the assumption that the average speed at which a horse moved during the contest was 400m/min. A horse moving at an average speed of 400 m/min, was given 4 penalties per error. i.e. 400m/min (6.67m/sec) = 4 penalty points

Furthermore, from the FEI Guidelines, each penalty was equivalent to 4 secs.

Therefore, for a speed of 6.67 m/sec, each error incurred a time penalty equivalent to: 4 penalty points x 4 secs =16 secs.

Based on these assumptions, a time penalty for each error or obstacle contact made by subjects in the current study could be calculated. The average preferred walking speed for the control group in this study = 1.18 m/sec.

Therefore, using FEI values, the time penalty incurred for each error (X) was calculated as:

X = 16 secs * 1.18m/sec/ 6.67 m/sec = 2.83 secs

The time penalty added for each error in the current study = 2.8 sec.

The Mobility Performance Score (%) was calculated by adding a penalty of 2.8 sec for each error made by the subject when navigating the complex mobility course to the time taken to complete the course, expressed as a percentage of their mobility performance. 93

Visual predictors for poor mobility performance in people with AMD

MPS (%) = [{(Time taken (sec) + error penalty (sec))/ (Distance (m))}/ PWS (m/sec)] 100

5.2.3 Statistics

The sensorimotor functions were analysed using a web-based software program (www.powmri.unsw.edu.au/FBRG/calculator.htm) designed by the Falls and Balance Research Group (Prince of Wales Medical Research Institute, Australia) that analyses and compares individual performance to the normative data. This program known as the

Physiological Profile Assessment (PPA) (Lord et al, 1991; Lord et al, 2003) contains five tests and is used to assess falls risk. Visual contrast sensitivity (discussed in chapter 4), proprioception, muscle strength, reaction time and balance (discussed in section 5.2) were five tests used in the Physiological Profile Assessment (PPA). In multivariate models, weighted contributions from these five variables provide a Falls Risk Score (expressed in standard z-score form) that can predict those at risk of falling with 75% accuracy in community settings.

Statistical analysis was performed using SPSS (Version 16). The demographic differences between the two groups were analysed using ANOVA and Kruskal-Wallis test for parametric and non-parametric variables) (See Chapter 4). The Kolmogorov-Smirnov test for normality was used to analyse the distribution of the study sample. Conditions for normality were met for all the variables (p> 0.05) except Balance, Reaction time, Muscle Strength, Proprioception and Preferred Walking Speed (PWS) and these variable were transformed due to non-normal distributions using logmarithmic transformation.

Differences between people with and without AMD for sensorimotor functions and their mobility performance (PPWS, MPS and ES) were evaluated using independent t-tests. Paired t tests were performed to assess if there were differences in mobility performance of the AMD and control groups using the standard PPWS index and the MPS index, for each Course. Multivariate analysis of variance was used to analyse the differences in sensorimotor functions using age, gender and visual acuity as co-variates as the differences between these factors were significantly different between the two groups (See Chapter 4) and previous studies have shown that there was a general decline in sensorimotor functions with age (Kaplan et al, 1985); the normal range for muscle strength was different for men and women (Lord et al, 1993) and interruption to visual input could cause a decline in balance 94

Visual predictors for poor mobility performance in people with AMD

performance (Paulus et al, 1984; Lord et al, 2002) . A p-value of less than 0.05 was considered statistically significant.

5.3 Results

The demographic details for the AMD and Controls are given in Table 4-2.

Sensorimotor functions

The descriptives of the sensorimotor functions measured for the AMD and Controls are given in Table 5-1. There were significant differences between the AMD and Controls for balance, reaction time and muscle strength (p<0.01). The average balance measurement (i.e. postural sway) (222.5 + 126.6 log units) and reaction time (292.43 + 82.24millisecs) was significantly increased in people with AMD and the average muscle strength (20.1 + 8.5 kgs) was significantly reduced in people with AMD. The Falls Risk Score was also significantly increased in people with AMD (Table 5-1). Multivariate analysis with age, gender and vision (LogMAR visual acuity used as the standardised measure of visual performance) as cofactors showed that the sensorimotor functions were not significantly different between the AMD and Controls.

Table 5-1: Descriptives of sensorimotor functions.

Function AMD Control (n=33) (n=35) Proprioception (degrees) 1.2 + 0.6 1.2 + 0.6 Reaction Time (millisecs) 292.43 + 82.24 252.28 + 33.66 Balance (postural sway – log units) 222.49 + 126.63 133.56 + 66.83* Muscle strength ( kgs) 20.09 + 8.49 32.09+ 11.43* Falls Risk Score# 2.39 + 1.14 0.37 + 0.79* *Independent t-test was used to analyse the differences between the two groups and p<0.01 was considered significant. #The normal population risk score is calculated on the basis of the age group, for example a risk score of 1 or less is the normal range expected for people aged 70 years and above. An increased score on proprioception, reaction time and balance indicates poor functioning and an increased score on muscle strength indicates better performance.

Visual predictors for poor mobility performance in people with AMD

Mobility performance

The PWS of people with AMD (1.2 + 0.1 msec-1) was significantly slower than that of controls (1. + 0.2 msec-1) (p=0.04). People with AMD had significantly reduced walking speeds compared to controls through all the courses except Course A, where they were pre- adapted to that light level (Table 5-2).

The Error Score was significantly decreased in subjects with AMD in contrast to the control group for Courses E and F (Table 5-3). Course C did not have Error Scores as it was a simple mobility course without obstacles. If there were no errors made during the mobility tests, an error score of 2 was recorded (For calculation see 5.2.2). The controls did not make any errors from courses A to E.

Table 5-2: Descriptives of the percentage preferred walking speed (PPWS).

Course AMD(n=33) Control(n=35) F P-value (Mean [%] + SD) (Mean [%] + SD) Course A 88.6 + 16.3 93.9 + 7.8 3.0 0.09 Course B 79.5 + 16.2 93.4 + 7.8 20.8 <0.001* Course C 88.1 + 7.2 99.5 + 2.2 80.0 <0.001* Course D 74.2 + 16.9 91.7 + 9.9 27.9 <0.001* Course E 63.8 + 18.7 91.1 + 8.5 61.6 <0.001* Course F 18.4 + 8.7 28.6 + 8.3 24.7 <0.001* * ANOVA was used to analyse the difference between the two groups and p<0.05 was considered significant.

A correlation analysis was performed to assess the relationship between the Error Score and the PPWS; these were significantly positively correlated for courses D (r2= 0.388), E (r2= 0.588) and F (r2= 0.388) (p<0.001).

Visual predictors for poor mobility performance in people with AMD

Table 5-3: Descriptives of the Error Score.

Course AMD (n=33) Control (n=35) F P-value (Mean [log units] + SD) (Mean [log units] + SD) Course A 1.97 + 0.09 2 + 0.0 3.4 0.07 Course D 1.9 + 0.16 2 + 0.0 13.9 <0.001* Course B 1.9 + 0.22 2 + 0.0 8.2 0.006* Course E 1.57 + 0.26 2 + 0.0 99.7 <0.001* Course F 0.94 + 0.27 1.57 + 0.22 112.2 <0.001* * ANOVA was used to analyse the difference between the two groups, where P<0.05 was considered significant. Higher values indicates less contacts with obstacles

Fig. 5-4: Comparison of PPWS in Controls and people with AMD

110.00

100.00

90.00

80.00

70.00

60.00 Controls AMD 50.00 PPWS (%) PPWS 40.00

30.00

20.00

10.00

0.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 Mobility course

1= Course A (155 cd/m2); 2= Course B (3 cd/m2); 3=Course C (1500 cd/m2); 4=Course D (12 cd/m2); 5=Course E (0.2 cd/m2); 6= Course F(0.06 cd/m2).

Visual predictors for poor mobility performance in people with AMD

Correlation analysis showed that for all subjects (Control or AMD), those who walked faster tended to make more errors, such as touching more obstacles on the mobility course. Based on this observation, a new index for measuring mobility performance was developed the Mobility Performance Score (MPS) - that combined factors relevant to mobility performance (i.e. speed and the number of errors made). The rationale for calculating this index is described in the Methods (Chapter 5.2).The average MPS (%) calculated for the AMD and control groups for each course is given in Table 5-4. The MPS was significantly decreased between the two groups (p<0.05) for their mobility assessment through courses A (155 cd/m2), B (3 cd/m2), D (12 cd/m2), E (0.2 cd/m2) and F (0.06 cd/m2).

The MPS index was statistically significantly reduced for AMD group (p< 0.01) compared to their PPWS score, except for course A in the AMD group (Table5-5) whereas the MPS index was significantly reduced for Course F only in the Control group.

Table 5-4: Mobility Performance Score (%)

Course AMD Control F P-value (n=33) (n=35) ( Mean [%] + SD) ( Mean [%] + SD) Course A 88.5 + 16.3 97.8 + 13.2 3.114 0.01* Course B 78.8 + 15.8 97.1 + 13.3 23.88 <0.001* Course D 73.8 + 16.8 95. 5 + 15.0 29.09 <0.001* Course E 62.1 + 17.6 93.7 + 12.6 76.08 <0.001* Course F 17.3 + 8.1 28.2 + 8.0 31.25 <0.001* Course A (155 cd/m2); Course B (3 cd/m2); Course D (12 cd/m2); Course E (0.2 cd/m2); Course F(0.06 cd/m2)* #The MPS score for Course C (1500 cd/m2)was not calculated as the course had no obstacles and thus no errors. ANOVA was performed to analyse differences between the two groups and p<0.05 was considered significant.

Visual predictors for poor mobility performance in people with AMD

Table 5-5: Comparison between MPS and PPWS scores for AMD and Control groups.

AMD Control Performance (n=33) (n=35) through MPS PPWS MPS PPWS (Mean [%] + SD) (Mean [%] + SD) (Mean [%] + SD) (Mean [%] + SD) Course A 88.5 + 16.3 88.6 + 16.3 97.8 + 13.2 93.9 + 7.8 Course B 78.8 + 15.8 79.5 + 16.2 97.1 + 13.3 93.4 + 7.8 Course D 73.8 + 16.8 74.2 + 16.9 95. 5 + 15.0 95.5 + 15.0 Course E 62.1 + 17.6 63.8 + 18.7 93.7 + 12.6 93.7 + 12.6 Course F 17.3 + 8.1 18.4 + 8.7 28.2 + 8.0 28.6 + 8.3* Course A (155 cd/m2); Course B (3 cd/m2); Course D (12 cd/m2); Course E (0.2 cd/m2); Course F(0.06 cd/m2)* paired t test between MPS (%) and PPWS for AMD and control groups, p<0.05 considered significant.

Pearson’s correlation analysis showed that the sensorimotor functions other than balance proprioception were not correlated to PPWS. Proprioception was correlated with PPWS through Course C (1500 cd/m2) (r2=0.46; p=0.007) and balance correlated with PWS (r2=0.46; p=0.007) PPWS through Course A (155 cd/m2) (r2=0.46; p=0.007)and Course D (12 cd/m2).

Table 5-6: Pearson’s correlation co-efficients (R) for sensorimotor functions and mobility performance in people with AMD

Reaction time Proprioception Muscle strength Balance PWS -0.26 0.063 0.27 0.37* Course A (PPWS) 0.04 0.08 -0.06 -0.47* Course B (PPWS) -0.06 0.14 0.01 -0.24 Course C (PPWS) -0.24 0.46* 0.09 -0.27 Course D (PPWS) <-0.01 0.22 -0.08 -0.50* Course E (PPWS) -0.03 -0.03 0.20 0.14 Course F (PPWS) -0.22 0.20 0.17 -0.07 Course A (155 cd/m2); Course B (3 cd/m2); Course C (1500 cd/m2); Course D (12 cd/m2); Course E (0.2 cd/m2); Course F (0.06 cd/m2); * p< 0.05

Visual predictors for poor mobility performance in people with AMD

5.4 Discussion

A major concern for older people is loss of independence, which has a negative impact on the quality of life. Conditions that impede mobility often lead to loss of independence due to lack of confidence (Williams, et al., 1998). Previous studies have shown that people with AMD have difficulty with mobility (Kuyk and Elliott, 1999).

This study examined the sensorimotor functions and mobility performance of people with AMD in contrast to a control sample and the influence of sensorimotor functions on their mobility performance. Comparison of the current study with the age-matched population normative data for PPA (Lord et al., 1994) showed that the proprioception and muscle strength of the AMD and control groups were within the normal range.

In this study, reaction time and proprioception were not statistically different between the AMD and control groups. However, muscle strength and balance (assessed as mm of postural sway) were significantly worse in the AMD group in comparison to the control group. Differences between the two groups in muscle strength may be explained by the differences in gender distribution between the two groups. The AMD group had 21 females and 12 males, whereas the control group had 10 females and 25 males; this was statistically significant (p=0.004; Chi-square test).

The reduced balance performance in subjects with AMD in this study is consistent with a recent study that found that older women with AMD have impaired balance (Szabo et al, 2008). Considering that balance is maintained by inputs from both the visual and the vestibular system, input from the visual system, measured as visual acuity (logMAR), was included as a cofactor in the analysis to differentiate whether the difference in balance between the two groups was because of impairments from visual or vestibular input. The control and AMD groups, after adjusting for vision (LogMAR visual acuity data - Chapter 4.3.1) showed similar proprioception and vestibular function inputs in both groups which implies the significance of the impact of visual impairment on functional balance. This finding is consistent with an earlier study that found impaired balance in visually impaired subjects 100

Visual predictors for poor mobility performance in people with AMD

(Harry and Rhonda, 2003). The current study findings also imply that people with AMD are at an increased risk of a fall, not only due to their inability to detect obstacles, but related to inadequate visual inputs for sensory processing required for detecting sway.

The mobility performance of both groups was reduced when moving from a brighter luminance environment to a dim luminance environment and vice-versa, although the AMD group had significantly reduced performance in contrast to the control group. The reduced mobility performance with varying luminance seen in both groups can be explained by an increased visual adaptation time related to photoreceptor function. Previous studies have demonstrated that dark adaptation was delayed in the normal ageing population (Owlsey and

Jackson, 2000; Owsley et al, 1999) with an even longer adaptation time required for people with

AMD (Owsley et al, 2000; Owsley et al, 2001). As discussed in Chapter 4, the delayed visual adaptation in the control group could be due to the additive effect of age-related changes such as loss of photoreceptors and ganglion cell layer (Gao and Hollyfield, 1992; Curcio and Drucker,

1993), the slowing of rhodopsin regeneration (Jackson et al, 1999) and reduced neural transmission time or impaired neural integration (Porciatti et al, 1992). In addition to these normal age-related changes, damage or loss of both rods and cones has functional implications for visual adaptation (Brown et al, 1986a; Brown et al, 1986b; Curcio et al, 1996; Owsley et al,

2007), retinoid deficiency leading to functional impairment of the photoreceptors and disruptions of metabolic exchange (Kemp et al, 1988; Kemp et al, 1989; Steinmetz et al, 1993; Owsley et al,

2007) further delays visual adaptation in people with AMD. People with AMD were observed to be much slower in mobility performance in courses with dim illumination compared to course with brighter illumination in the current study. This is indirectly consistent with previous studies that have found more severe rod dysfunction is found in AMD compared to cone dysfunction (Steinmetz et al, 1993; Curcio et al, 1996; Owsley et al, 2000; Owsley et al, 2007). Taken together, these results show that people with AMD had difficulty with mobility performance while moving from a brighter environment to a dimmer environment and from a dimmer environment to brighter environment. These findings imply that changes in the illumination levels in the environment would increase the risk of falls in people with AMD as mobility performance is a good indicator or falls. For example, while walking into a dimly lit room from outside on a sunny day, as the risk of having a fall is greater in people with AMD.

101

Visual predictors for poor mobility performance in people with AMD

The mean PPWS of subjects with AMD was slower than the control group as reported in earlier studies (Haymes et al, 2002; Kuyk and Elliott, 1999). The current study showed that the PPWS of people with AMD was significantly reduced compared to the control group throughout the obstacle courses and that the number of obstacle contact made was significantly higher in contrast to earlier studies (Haymes et al, 2002; Brown et al, 1986; Wilcox and Burdett, 1989). This may be related to the severity or extent of AMD in the subjects that participated in the different studies and also due to the difference in the design and complexity of the mobility course. The mobility course settings in the current study (controlled environment) were similar to those of Hassan et al (2002), although they did not examine the effect of changing illumination levels.

In another study subjects with ARM were selected and the majority had a visual acuity of 6/7.5 and the mobility course designed for the study was simple and did not have any obstacles scattered around the course (Brown et al, 1986). Similarly, the study by Wilcox and

Burdett (1989) used a simple mobility course without any obstacles. The impact of the complexity of the mobility course design has been demonstrated by Haymes et al (1996) for people with Retinitis pigmentosa, who demonstrated that the PPWS depended on the complexity of the mobility course; by showing that people with all levels of visual impairment had similar performances on the simple mobility course while their performances where greatly varied through the complex courses.

The observed increased number of errors made by AMD subjects compared to controls under conditions of reduced illumination was consistent with observations in earlier studies

(Haymes et al, 2002; Kuyk and Elliott, 1999). Bivariate correlation analysis showed that people who walked faster tended to touch more obstacles while moving from photopic to scotopic conditions and vice versa. It has been postulated that the increase in number of contacts made was as a result of a sacrificing “safety for speed” strategy adopted by subjects due to the awareness that their performance was being assessed by speed. This agrees with the postulation of Kuyk et al (1998), who suggested that the increase in number of contacts made was as a result of a sacrificing “safety for speed” strategy adopted by subjects due to the awareness that their performance was being assessed by speed.

102

Visual predictors for poor mobility performance in people with AMD

The MPS index showed that people with AMD were slower than the controls at all levels of illumination. The MPS data also showed that while the control group was slowed by 3-5 % when moving from courses with illumination 155, 12, 3 and 0.02 cd/m2 and by 65% at 0.06 cd/m2 illumination, people with AMD slowed by 10-25% while moving from courses with illumination 155, 12, 3 and 0.02 cd/m2and by 70% at 0.06 cd/m2 illumination level. The advantage of this scoring process over the PPWS is that, it also takes into account the number obstacle contacts made. This is significant, as contact with obstacles could lead to tripping, which might result in a fall. The significance of obstacle detection in helping an individual to improvise strategies for obstacle avoidance by providing the central processing system with the necessary visual input required for maintaining balance has been emphasised (Patla, 1997). This additional index (MPS) assists in overcoming the biases caused by the strategies used by the subject when subjected to scrutiny and provide a better overview of an individual’s performance in an unfamiliar environment. The statistically significant difference between PPWS and the MPS score on paired t- test, show the effectiveness of the MPS score to pick up subtle changes to mobility performance. This will potentially be useful for identifying conditions that impede mobility even when the impact is subtle and facilitate early mobility training thus preventing an event/ fall from occurring.

Correlation analysis showed that the PWS was correlated moderately with balance and did not correlate with any other sensorimotor function (CS, reaction time, proprioception and muscle strength). The PPWS through course C (1500 cd/m2) correlated with proprioception and did not correlate with the other sensory motor functions; PPWS through courses A (155 cd/m2) and D (12 cd/m2) correlated with balance and did not correlate with other sensorimotor functions; PPWS through courses B (3 cd/m2), E (0.2 cd/m2) and F (0.06 cd/m2) did not correlate with any of the sensorimotor functions. Balance was the only sensorimotor function that fell outside the normative range; this could be because of the lack of visual input that is necessary for balance and hence its correlation with mobility performance (PWS, PPWS through courses A (155 cd/m2) and D (12 cd/m2)). This provides further evidence to show that reduced mobility performance was not due to reduction in sensorimotor functioning and the importance of the relationship between visual factors and mobility.

103

Visual predictors for poor mobility performance in people with AMD

One of the limitations of the current study was that the mobility course design used a controlled environment, with stationary obstacles, and therefore did not mimic moving objects in a real world situation. However, studies by Kuyk et al (1998), have demonstrated that mobility performance measured under laboratory conditions correlated well with the performance in a real-world situation. They demonstrated that the effects of an individual performance in a real world situation and a controlled environment were similar and further, that the performance in a uncontrolled environment could be generalized to that of real world situation. The current study excluded people with end-stage AMD lesions (vision reported as count fingers, light perception or less), and so does not fully represent the range of mobility difficulties experienced by people with AMD in the general population. However, this study provides considerable insight into the potential mobility difficulties that may be experienced by people with AMD, particularly with regards to difficulties in adapting to variations in luminance typically encountered in real life. The level of difficulty experienced in moving around in the normal environment would presumably increase with progression of AMD in patients.

5.5 Conclusions

The sensorimotor functions in people with AMD were similar to those of controls when the impact of their visual impairment was taken into account. Similarities in the sensorimotor functions of the two groups emphasizes the importance of studying the impact of visual function on mobility of people with AMD, particularly with regard to developing strategies for the prevention of falls.

This study provides evidence for the impact of illumination on mobility performance of people with AMD. People with AMD showed reduced performance compared to controls, using the previously defined mobility indices (PWS, PPWS and ES) for the mobility courses with different light levels. This study shows that people with AMD had difficulty with mobility performance while moving from a brighter environment to a dimmer environment and vice versa.

The MPS (%) index developed in this study may provide a more appropriate measure of the overall mobility performance, as it reflects all the factors that enable an individual to move 104

Visual predictors for poor mobility performance in people with AMD

from place to another without the occurrence of an incident that may lead to a fall. Further validation of this measure using different mobility courses, luminances and other patient groups with various forms of visual dysfunction (for example, patients with early stage AMD, cystoid macula oedema associated with diabetic retinopathy, or retinitis pigmentosa) as well population-based studies are required. Whether this index can also provide a more sensitive measure for differentiating between people at different stages of AMD progression and their mobility performance remains to be determined.

105

Visual predictors for poor mobility performance in people with AMD

Chapter 6: Visual Predictors for Poor Mobility

Performance in People with AMD

6.1 Introduction

The effects of visual impairment on the mobility performance of people with impaired vision have been investigated for more than 2 decades (Marron and Bailey; 1982; Geruschat et al,

1998) and continue to be explored for possible implications related to rehabilitative assessment and training. Although people with visual impairments that are not correctable by conventional measures are collectively classified as having low-vision, their visual disabilities and their requirements for daily life differ according to the underlying disease process.

Different studies have identified visual acuity (Brown et al, 1986), visual field extent (Brown et al,

1986; Lovie-Kitchin et al, 1990; Haymes et al, 1996; Kuyk et al, 1998; Geruschat et al, 1998; Hassan et al, 2002), scanning ability (Kuyk et al, 1998), differential motion sensitivity (Brown et al, 1986) and contrast sensitivity (Marron and Bailey; 1982; Haymes et al, 1996; Geruschat et al, 1998; Hassan et al, 2002) as possible visual predictors of poor mobility performance in people with visual impairments. Visual factors linked to an increased risk of falls include contrast sensitivity (Lord and Menz, 2000; de

Boer et al, 2004), uncorrected refractive error (Buckley et al, 2005) and the effect of bifocal and progression addition spectacle lenses on tripping (Davies et al, 2001; Lord et al, 2002). However these studies varied with respect to the ocular conditions of the subjects. The varied nature of the ocular condition could have different implications for visual function, with variable effects on the predictor variables

Older people with moderate impairment in visual acuity have been reported to have poorer functional balance compared with those with no or mild visual impairment (Harry and Rhonda,

106

Visual predictors for poor mobility performance in people with AMD

2003). However, in the study by Harry and Rhonda (2003), visual acuity was the only measure of visual function included, whereas other functional aspects such as visual fields, contrast sensitivity, depth perception and glare impairment which are associated with balance performance were not measured. It also only included subjects from care homes, limiting the generalisability of the findings.Reduction in contrast sensitivity has been shown to increase the risk of hip fractures in people above the age of 65 (Cummings et al, 1995). A recent study looked at community dwelling older women with AMD and reported that the risk for falls was high in this group (Szabo et al., 2008). This study did not analyse which aspect of functional vision impairment was associated more with this increased risk.

It has also been reported that subjects with ARM have no measurable mobility impairments compared with subjects of similar age with normal vision in ambient light levels (Brown et al,

1986; Wilcox and Burdett, 1989). However, the simplicity of the mobility courses used may have contributed to these findings. This suggestion is supported by another study that found that subjects with ARM walked faster on simpler mobility courses than they did on more complex routes (Kuyk and Elliott, 1999). The significance of mobility course complexity on mobility performance has also been demonstrated for subjects with other types of ocular pathology such as retinitis pigmentosa (Kuyk and Elliott, 1999). Real world mobility requires subjects to avoid obstacles, to safely navigate and to travel along a path. Mobility performances of AMD subjects were significantly worse under reduced illumination compared with high-luminance conditions (Kuyk and Elliott, 1999). However, this study did not include an age-matched control group and thus it is not possible to conclude whether AMD subjects demonstrated impaired mobility compared with normally sighted subjects of similar age.

1995). In contrast, earlier studies found no correlation between mobility, contrast sensitivity and visual acuity and did not assess visual field (Wilcox and Burdett, 1989).This could be because the sample subjects chosen by Wilcox and Burdett (1989) had only minimal visual impairment and the design of the mobility course was simple. The study by Kuyk and Elliott

(1999) included both sensory and perceptual vision function variables and also examined the 107

Visual predictors for poor mobility performance in people with AMD

influence of reducing levels of illumination on mobility performance. Although this study conducted extensive assessment of functional vision variables and had mobility courses with sufficient complexity, this was not a controlled study and hence it is not possible to infere whether people with AMD had increased difficulty when compared to controls of the similar age-group.

A more controlled study was performed by Hassan et al (2002) in which they assessed the mobility performance of subjects with AMD using an indoor high density obstacle course. They explored the relationship between binocular visual acuity, contrast sensitivity, visual fields and mobility performance. In this study, contrast sensitivity was measured by MET and sine wave gratings at 2cpd spatial frequency and visual fields were assessed binocularly using both static and kinetic methods. This allows comparison across studies using either method. The measurements were recorded as the extent of central visual field and the position and size of any central scotoma. The mobility course included a step, ramp and open area and the subject were asked to perform simple task durng their mobility performance assessment. Their model predicted binocular scotoma size and contrast sensitivity measured at 2cpd to be the best predictor of mobility performance in people with AMD. However their study had well-defined paths and set luminance levels and did not examine the different effects that reduced illumination and glare might have on mobility performance. It is well known that dark adaptation is compromised in people with ARM/AMD and although several studies have suggested that functional dark adaptation impairments could impair mobility, this has not been objectively studied (Campbell et al, 1969;

Brown and Lovie-Kitchen, 1983; Brown et al, 1986).

This Chapter builds on the work from Chapters 4 and 5 to obtain a better understanding of the changes in visual function that occur in people with AMD. This will provide insight into the difficulties experienced by people with AMD and enable the clinician to provide and encourage appropriate mobility training programs.

This Chapter determines visual function measures that could be used clinically to predict mobility impairment in older people with AMD. In addition to this, this study explores the impact of changing illumination levels on visual function in people with AMD.

108

Visual predictors for poor mobility performance in people with AMD

6.2 Methods

The psychophysical aspects of vision that were examined in Chapter 4 and the indices of mobility assessed in Chapter 5 were used to develop a model for determining visual function predictors of mobility performance in people with AMD. Sensorimotor functions were not included in this model due to low correlation with mobility performance (Chapter 5; Section 5.3; Table 5-6).

The visual characteristics that were used as predictor variables were: Visual acuity (Log MAR), contrast sensitivity (dB), photostress test (secs), visual field indices (MD (mean deviation) and PSD (pattern standard deviation) tested at bowl luminance of 155 cd/m2, 8 cd/m2, 3 cd/m2, 0.2 cd/m2 and 0.06 cd/m2) and dark adaptation variables (cone threshold, rod threshold and the rod-cone break-up time).

Mobility performance was considered in terms of the PPWS through each course as well as participants’ Reduction in Speed (RS). Visual predictors were modelled for each course to gain a better understanding of functional difficulties experienced by subjects during mobility in performing daily routines. The PPWS was used to model a visual predictor as this would help in drawing comparisons with existing literature.

The PPWS in subjects with AMD was significantly reduced in all the courses when compared to controls (See Chapter 5). In order to analyse whether the subjects experienced difficulties while moving from one course to another, analysis of variance (ANOVA) for repeated measures using the Bonferroni’s test was performed (See Chapter 6.3). Since the subject’s PPWS were significantly different through each course, mobility performances were analysed in terms of reduction in speed. Reduction in Speed was calculated as the percentage of the ratio of difference between the PPWS on the course that they are examined on, and the PPWS on the course with the luminance level the subject is adapted to, divided by the sum of the PPWS through the course that they are examined and the PPWS through the course with the luminance level the subject is adapted to, which can be represented as: RS (%) = (PPWS (ce) – PPWS (ca))/ (PPWS (cc) + PPWS (ca)) *100

109

Visual predictors for poor mobility performance in people with AMD

PPWS (ce) is the percentage preferred walking speed through the course examined and PPWS (ca) is the percentage preferred walking speed through the course to which they are adapted. For example, Reduction in speed through course B (3 cd/m2) = (PPWS through course B- PPWS through course A)/ (PPWS through course B + PPWS through course A)*100

The change in luminance conditions for subjects’ performances on course B (3 cd/m2), D (12 cd/m2), E (0.2 cd/m2) and F (0.06 cd/m2) were classified as brighter luminance condition to dimmer luminance condition (See Chapter 5.2.2 for luminance levels and pre-adaptational levels through each course) and that on course C (1500 cd/m2) was classified as dimmer luminance condition to brighter luminance condition (Chapter 5.2.2). The reduction in speed from a brighter luminance condition to dimmer luminance condition (RS(B2D)) was calculated as the average reduction of speed when moving from a brighter luminance condition to dimmer luminance condition (RSp) and the reduction in speed from a dimmer luminance condition to brighter luminance condition (RS(D2B)) was the average reduction in speed in course C as this was the only condition where the envirement luminance changed from dim to bright.

RS (B2D) = [RS (Course B) + RS (Course D) + RS (Course E) + RS (Course F)]/ 4 and

RS (D2B)= RS (Course C)

Mobility performance was assessed in terms of “reduction in speed from brighter luminance conditions to dimmer luminance conditions” and “reduction in speed from dimmer luminance conditions to brighter luminance conditions” as this would provide information that is easily transferable from research laboratory to clinical settings and simple to understand by both practitioners and people with AMD.

The error score made by the individual was categorised into three classes: a) Error Score in photopic conditions (ESp), calculated as the average of the error score through course A (155 cd/m2), Course C (1500 cd/m2) and Course D (12 cd/m2); b) Error Score in mesopic conditions (ESm), calculated as the average of error score through Course B (3 cd/m2) and Course E (0.2 cd/m2); and 110

Visual predictors for poor mobility performance in people with AMD

c) Error Score in scotopic conditions (ESs), the Error score on Course F (0.06 cd/m2).

6.2.1 Statistics

Statistical analysis was performed using SPSS (Version 17.0). The general rule of thumb for multiple regression analysis is atleast 5 participants for each predictor variable in the model. The number of variables used in each model was reduced using correlation analysis (Appendix 23) and stepwise linear regression analysis. Visual predictors for poor mobility performance in people with AMD were modelled by multiple regression analysis using the stepwise method and forced backward selection method. Visual predictors were modelled for all the courses and also for their reduction in speed while moving from brighter luminance to dim luminance conditions and vice versa. The visual function variables used were VA, CS, PST, Visual field variables (MD 155, PSD 155, MD 8, PSD 8, MD 3, PSD3, MD1, PSD 1, MD 0.2, PSD 0.2, MD 0.06 and PSD 0.06 depending on the luminance level used for the course and correlation analysis), dark adaptation variables (cone threshold and rod threshold). Correlation affects between the predictor variables were analysed using tests for multicollinearity. A p-value of less than 0.05 was considered statistically significant. For collinearity statitistics a tolerance >0.1 and variance inflation factor (VIF) <5 was considered acceptable.

6.3 Results

The demographic details of the study sample are given in Chapter 4.3; Table 4-2. Analysis of variance for repeated measures using Bonferroni’s test showed that the mobility performance of people with AMD was sigbificantly reduced from one course to another (Course A (155 cd/m2) vs Course B (3 cd/m2); Course B (3 cd/m2) vs Course C (1500 cd/m2); Course C (1500 cd/m2) vs Course D (12 cd/m2); Course A (155 cd/m2) vs Course E (0.2 cd/m2); Course A (155 cd/m2) vs Course F (0.06 cd/m2); p<0.01)

Stepwise linear regression analysis revealed that 49.3% of the variance in the reduction of speed while walking from a brighter luminance environment to a dim luminance environment could be explained by the PSD 155 (Pattern Standard Deviation in 155 cd/m2 = 2 486.95 asb) and the photostress test (PST) (F2,18=10.718, P=0.001; Adjusted r =0.493)

111

Visual predictors for poor mobility performance in people with AMD

(Table 6-1). The standardised β weights were -0.656 for PSD 155 and -0.422 for PST. The tolerance and variance inflation factor were within acceptable range (tolerance =0.99 and VIF=1.01). Correlation between the visual function variables are given in Appendix 19.

Table 6-1: Table of co-efficients for linear regression model of Reduction in speed from brighter luminance to dim luminance.

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 4.054 9.372 -15.636 23.745 0.433 0.670 PSD 155 -2.579 0.631 -3.904 -1.254 -4.090 0.001 PST -12.359 4.692 -22.217 -2.501 -2.643 0.017 PSD 155: Pattern standard deviation at 155 cd/m2 bowl luminance; PST: Photostress test. Outcome: Reduction in speed when walking from brighter luminance condition to dim luminance condition; Slope denotes the amount of reduction in speed for 1 unit increase in the independent factor.

This analysis also showed that 36.1% of the variance in the reduction of speed while walking from a dim luminance environment to a brighter luminance environment could be explained by visual acuity and PSD 8 (Pattern Standard Deviation in 8 cd/m2 =25.13 asb) (F 2 2,30=10.02, P<0.001; Adjusted r =0.361) (Table 6-2). The standardised β weights were 0.95 for PSD 8 and -0.60 for VA. The tolerance and variance inflation factor were within acceptable range (tolerance=0.43and VIF=2.36). Correlation between the visual function variables are given in Appendix 19.

Table 6-2: Table of co-efficients for linear regression model of Reduction in speed from dim luminance to brighter luminance.

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 5.31 2.54 0.12 10.49 2.09 0.05 VA 17.86 6.45 -31.02 -4.70 3.81 0.01 PSD 8 1.12 0.26 0.60 1.64 -2.47 <0.001 PSD 8: Pattern standard deviation at 8 cd/m2 bowl luminance; VA: Visual acuity. Outcome: Reduction in speed when walking from dim luminance condition to brighter luminance condition; Slope denotes the amount of reduction in speed for 1 unit increase in the independent factor.

112

Visual predictors for poor mobility performance in people with AMD

A stepwise linear regression model for PPWS through course A found that 12.7% of the variance in the PPWS could be explained by the cone threshold of the subject. (F1,31=4.50, P=0.042; Adjusted r2=0.099) (Table 6-3). The standardised β weight was 0.356 for cone threshold. The tolerance and variance inflation factor were within acceptable range (tolerance=0.40 and VIF= 2.52). Correlation between the visual function variables are given in Appendix 19.

Table 6-3: Table of co-efficients for linear regression model PPWS through course A (155 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 47.925 19.376 8.408 87.441 2.473 0.019 Cone threshold 8.368 3.942 0.326 16.409 2.122 0.042 Outcome: PPWS through course A; Slope denotes the change in PPWS for 1 unit increase in the independent factor.

A stepwise linear regression model for PPWS through course B: 18.9% of the variance in the PPWS could be explained by the MD 3 (Mean Deviation in 3 cd/m2 = 9.42 asb) and 2 visual acuity (F 2,30=4.721, P=0.016; Adjusted r =0.189) (Table 6-4). The standardised β weights were 0.804 for MD 3 and 0.580 for visual acuity. The tolerance and variance inflation factor were within acceptable range (tolerance=0.365 and VIF=2.742). Correlation between the visual function variables are given in Appendix 19.

Table 6-4: Table of co-efficients for linear regression model PPWS through course B (3 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower Upper bound t Significance bound Constant 83.571 4.427 74.529 92.612 18.877 <0.001 MD 3 3.030 0.994 1.000 5.060 3.049 0.005 visual acuity 26.233 11.928 1.873 50.593 2.199 0.036 MD 3: Mean deviation at 3 cd/m2 bowl luminance; Outcome: PPWS through course B. Slope denotes the change in PPWS for 1 unit increase in the independent factor.

113

Visual predictors for poor mobility performance in people with AMD

A stepwise linear regression model for PPWS through course C: 24.4% of the variance in the PPWS could be explained by the photostress test (PST) (F1,31=11.310, P=0.002; Adjusted r2=0.244) (Table 6-5). The standardised β weight was -0.517 for PST. The tolerance and variance inflation factor were within acceptable range (tolerance=1.00 and VIF =1.00). Correlation between the visual function variables are given in Appendix 19.

Table 6-5: Table of co-efficients for linear regression model PPWS through course C (1500 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 115.628 8.261 98.780 132.475 13.997 <0.001 PST -14.414 4.286 -23.156 -5.673 -3.363 0.002 PST: Photostress test. Outcome: PPWS through course C; Slope denotes the change in PPWS for 1 unit increase in the independent factor.

A stepwise linear regression model for PPWS through course D: 17.4% of the variance in the PPWS could be explained by the VA and the MD 8 (Mean Deviation in 8 cd/m2 =25.13 2 asb) (F2,31=4.362, P=0.022; Adjusted r =0.174) (Table 6-6). The standardised β weights were 0.642 for VA and 0.740 for MD8. The tolerance and variance inflation factor were within acceptable range (tolerance=0.402 and VIF=2.486). Correlation between the visual function variables are given in Appendix 19.

Table 6-6: Table of co-efficients for linear regression model PPWS through course D (12 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 76.331 4.740 66.650 86.012 16.103 <0.001 MD 8 3.276 1.121 0.986 5.567 2.921 0.007 VA 30.287 11.945 5.892 54.682 2.536 0.017 MD 8: Mean deviation at 8 cd/m2 bowl luminance; VA: Visual acuity. Outcome: PPWS through course D; Slope denotes the change in PPWS for 1 unit increase in the independent factor.

114

Visual predictors for poor mobility performance in people with AMD

A stepwise linear regression model for PPWS through course E: 30.3% of the variance in the PPWS could be explained by the PSD 0.2 (Pattern Standard Deviation in 0.2 cd/m2 = 2 0.63 asb) and PST (F2,31=7.939, P=0.002; Adjusted r =0.303) (Table 6-7). The standardised β weights were -0.448 for PSD 0.2 and -0.413 for PST. The tolerance and variance inflation factor were within acceptable range (tolerance=0.995 and VIF=1.005). Correlation between the visual function variables are given in Appendix 19

Table 6-7: Table of co-efficients for linear regression model PPWS through course E (0.2 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 127.648 20.943 84.878 170.419 6.095 <0.001 PSD 0.2 -0.494 0.163 -0.826 -0.161 -3.030 0.005 PST -29.950 10.728 -51.859 -8.040 -2.792 0.009 PSD 0.2: Pattern Standard Deviation at 0.2 cd/m2 bowl luminance; PST: Photostress test. Outcome: PPWS through course E; Slope denotes the change in PPWS for 1 unit increase in the independent factor.

A stepwise linear regression model for PPWS through course F: 8.4 % of the variance in the 2 PPWS could be explained by PST (F 1, 31=3.93, P=0.05; Adjusted r =0.084) (Table 6-8). The standardised β weight was -0.335 for PST. The tolerance and variance inflation factor were within acceptable range (tolerance=1 and VIF=1). Correlation between the visual function variables are given in Appendix 19

Table 6-8: Table of co-efficients for linear regression model PPWS through course F (0.06 cd/m2).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 40.123 11.07 17.541 62.705 3.624 0.001 PST -11.39 5.75 -23.106 -0.327 -1.98 0.05 PST: Photostress test. Outcome: PPWS through course F; Slope denotes the change in PPWS for 1 unit increase in the independent factor.

115

Visual predictors for poor mobility performance in people with AMD

Stepwise linear regression model for ES (Error score) through photopic conditions: 9.3% of the variance in the ES could be explained by the Rod-Cone break point measured by the 2 dark adaptometer (F 1, 31=4.281, P=0.047; Adjusted r =0.093) (Table 6-9). The standardised β weight was 0.348 for the Rod-Cone break point. The tolerance and variance inflation factor were within acceptable range (tolerance=1 and VIF=1). Correlation between the visual function variables are given in Appendix 19.

Table 6-9: Table of co-efficients for linear regression model of ES made through photopic conditions (Course A and Course D).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 1.815 0.060 1.692 1.938 30.027 <0.001 Rod-Cone 0.009 0.005 0.0 0.019 2.069 0.047 break up point Outcome: Error Score made through photopic conditions; Slope denotes the change in value of error score for 1 unit increase in the independent factor.

Stepwise linear regression model for ES (Error score) through mesopic conditions: 16.5% of the variance in the ES could be explained by the Cone threshold measured by the dark 2 adaptometer (F1 21=5.355, P=0.031; Adjusted r =0.165) (Table 6-10). The standardised β weight was -0. 451 for cone threshold. The tolerance and variance inflation factor were within acceptable range (tolerance=1 and VIF=1). Correlation between the visual function variables are given in Appendix 19.

Table 6-10: Table of co-efficients for linear regression model of ES made through mesopic conditions (Course B and Course E).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 2.418 0.297 1.801 3.035 8.151 <0.001 Cone threshold -0.140 0.060 -0.266 -0.014 -2.314 0.031 Outcome: Error Score made through mesopic conditions; Slope denotes the change in value of error score for 1 unit increase in the independent factor.

116

Visual predictors for poor mobility performance in people with AMD

Stepwise linear regression model for ES (Error score) through scotopic conditions: 15.4% of the variance in the ES could be explained by the Rod threshold measured by the dark 2 adaptometer (F 1, 31=6.814, P=0.014; Adjusted r =0.154) (Table 6-11). The standardised β weight was -0. 424 for rod threshold. The tolerance and variance inflation factor were within acceptable range (tolerance=1 and VIF=1). Correlation between the visual function variables are given in Appendix 19.

Table 6-11: Table of co-efficients for linear regression model of ES made through scotopic conditions (Course F).

Unstandardised co-efficients 95% CI for Slope Model Slope Standard error Lower bound Upper bound t Significance Constant 1.775 0.203 1.361 2.189 8.740 <0.001 Rod threshold -0.139 0.052 -0.248 -0.030 -2.610 0.014 Outcome: Error Score made through scotopic conditions; Slope denotes the change in value of error score for 1 unit increase in the independent factor.

Visual predictor for error score was not modelled for each individual course as the correlation between the error score and the visual function variables were not statistically significant (Appendix 28).

6.4 Discussion

As already discussed in the previous chapters, one of the leading causes of increased institutionalisation and mortality in the elderly population are falls. Studies have shown that people with visual impairment are more likely to have an injury related to falls. AMD is one of the leading causes of visual impairment in Australia and other developed countries. It is therefore imperative to have an understanding of the extent of functional visual impairments in people with AMD and its impact on their mobility performance. This study models visual predictors for poor mobility performance in people with AMD by analysing the visual characteristics in people with AMD and their mobility performance through different illumination levels.

117

Visual predictors for poor mobility performance in people with AMD

The current study showed that a range of aspects of visual function are processed for adequate maintenance of balance and orientation during mobility, depending on the environmental demands. While the ability to adapt to the changes in illumination in the environment and the defects in visual field were modelled as the best predictors of mobility performance of an individual with AMD, VA and cone threshold were also identified as visual indicators of poor mobility performance. The effects of the photostress test on mobility performance of people with AMD have not been examined previously. Overall, this study showed that the focal defects in the visual field detected at a bowl luminance of 155 cd/m2 (this is approximately 15 times greater than the bowl illumination used in standard visual field examination) and the time required to adapt to changes in light levels were the best predictors for mobility performance in people with AMD when moving from an area with bright illumination to an area of dim illumination; while the focal defects in the visual field and VA were the best predictors for poor mobility performance (PSD performed at 8 cd/m2; this is equivalent to the standard bowl luminance used in standard clinical practice) while moving from an area of dim illumination to bright illumination..

Further analysis of mobility performance under different luminance conditions showed that the visual predictors for mobility performance differed according to the illumination condition that mobility was assessed. This could be because of the complexity and difference in the visual task at different light levels. For instance, the mobility performance of an individual with AMD in bright light (1500cd/m2: this was measured to be equivalent to the luminance on a bright sunny day) could be predicted in part by their macular recovery function (PST) (Table 6-5). Cone threshold was shown to be the best predictor in standard outdoor illumination (155 cd/m2) (Table 6-3). Visual acuity and focal defects in the visual field (PSD tested at bowl luminance of 8cd/m2 which is approximately equivalent to the bowl luminance used to perform perimetry in a clinical setting) were the best predictors for mobility performance indoors during daytime (Table 6-6). Focal defects in the visual field (PSD tested at bowl luminance of 3 cd/m2 where the bowl luminance was 3 times less than the bowl luminance used to perform perimetry clinically) and cone threshold were the best predictors of mobility under mesopic levels of illumination (3cd/m2: approximately equivalent to the luminance level outdoors in the evening) (Table 6-4). The MD (mean deviation) or the average defect depth measured by the visual fields (MD tested at bowl luminance of 0.2 cd/m2 where the bowl luminance is 50 times less than the bowl luminance 118

Visual predictors for poor mobility performance in people with AMD

used to perform perimetry clinically) and the macular recovery function (PST) were the best predictors of mobility performance in an environment with a luminance of 0.2 cd/m2 (the luminance outdoors of the light given by street lamps was measured by the examiner to be approximately equivalent to this level) (Table 6-7). Macular recovery function could also predict the mobility performance on a path lighted by night lamps indoors (measured to be approximately 0.06 cd/m2) (Table 6-8).

Visual field measures (MD and PSD) modelled to be the best predictor for mobility performance in most situations that the subject was exposed to in the current study. This is consistent with previous studies that have reported the significance of visual field defects in mobility performance (Marron and Bailey, 1982; Brown et al, 1986; Long et al, 1990; Haymes et al, 1996; Hassan et al, 2002). This is however the first study that assessed the relationship of different visual field indices to the impact of changing light levels on the mobility performance of people with AMD and also their performance in different light levels. This result is also indirectly consistent with previous studies (Lovie-Kitchin et al, 1990; Turano et al, 2004) that reported that the central visual field is an important predictor for mobility and orientation tasks and that visual field loss outside the central 20-40 degrees does not have much effect on mobility performance. This contrasts with Kuyk and Elliott (1999) who reported that visual field measures were not important for mobility performance in people with AMD. These different observations could be attributed to the methods used for measuring visual field extent and also related to the severity of AMD in their subject group compared to the moderately severe category in this study. Studies by Elliott et al (1995) have shown that as the severity of AMD increases the focal field defect becomes more extensive and the effectiveness of the measure reduces.

The current study showed that visual acuity was the best predictor for: (a) reduction in mobility performance in people with AMD while moving from dim luminance environment to a brighter luminance environment (the luminance in the brighter luminance environment was 1500 cd/m2) along with the pattern standard deviation (tested for bowl luminance of 8 cd/m2), and (b) along with the mean deviation (tested for bowl luminance of 8 cd/m2), for PPWS through course D (luminance of 12 cd/m2).

119

Visual predictors for poor mobility performance in people with AMD

(c) along with the mean deviation (tested for bowl luminance of 3 cd/m2), for PPWS through course B (luminance of 3 cd/m2).

This result is consistent with studies by Brown et al (1986) who reported that visual acuity carried the greatest weight in their predictive equations, although it is contrary to those reported by Wilcox and Burdett (1989). This may be related to the effect of the simplicity of their mobility course design on the visual demand required by the individual and the severity of AMD in the study samples.

Contrary to previous studies (Kuyk et al, 1998; Hassan et al, 2002), contrast sensitivity was not included in any models as a measure of visual function that could predict mobility performance, even though the contrast sensitivity in the study subjects was significantly impaired. This could be related to the difference in tests used to measure contrast sensitivity. Contrast sensitivity tests use principles of sine wave- or square wave gratings and the assessment can involve one or both aspects of visual function such as detection or resolution. MET, which was used in the current study, is based on a square-wave grating and is a forced-choice, preferential looking technique that assesses detection (Verbaken, 1989). Most of the previous studies have used a sine-wave grating at different spatial frequency, which also assesses resolution. The MET test was used as previous research has shown that it relates more to real world function of mobility and orientation, including edge contrast detection required for walking down stairs and elevated curbs in the pavement (Lord and

Dayhew, 2001; Haymes and Chen, 2004). One of the reasons for contrast sensitivity not being found as a visual measure predictive of mobility performance in the current study may be that although contrast sensitivity was impaired compared to the control group (Chapter 4; Section 4.4.2), the detection ability in people with AMD in the study sample was adequate to enable them to identify obstacles and path features provided they had sufficient resolution abilities. This is further supported by the results in this study where focal defects (pattern standard deviation) in the visual field and visual acuity were modelled as important predictors of mobility performance. The other reason for the absence of contrast sensitivity as a predictor in the current model could be the level ground used in the mobility course; this was regular and uniform and lacked tasks such as walking up and down the stairs, which relate more to visual detection.

120

Visual predictors for poor mobility performance in people with AMD

Dark adaptation variables (cone threshold, rod threshold and the rod-cone break up time) were the best predictors of Error Scores made by people with AMD on the mobility courses. The Rod-Cone break up time was the best predictor for the number of errors made by subjects in photopic conditions. Cone and Rod thresholds were the best predictors for errors made by people with AMD at mesopic and scotopic conditions respectively. Contrary to previous studies (Kuyk et al, 1998; Hassan et al, 2002) contrast sensitivity was not significantly associated with the number of errors made by subjects, again most likely related to the different tests used to asses contrast sensitivity. This is the first study to have explored the relationship between dark adaptation function and mobility performance.

The study by Hassan et al (2002) assessed contrast sensitivity using both MET and using sine wave gratings at different spatial frequencies reported that the sine wave contrast sensitivity at 2 cycles per degree was the best predictor for error scores while contrast sensitivity dervived from the MET did not model as a predictor. Studies by Kuyk et al (1998, 1999) measured contrast sensitivity functions using Pelli-Robson chart and sine wave gratings. The Pelli-Robson chart uses the 20/60 optotype (equalent to a 0.47 LogMAR) arranged in groups with varying contrast from high to low contrast. The mean VA for the study sample in the study by Kuyk and Elliott (1999) was 1.23 logMAR. Although, the correlation among the vision variables were not given in this study, it is likely that the contrast sensitivity measurement was influenced by the visual acuity, given the contrast sensitivity test used.

The current study shows that visual tests can be used to predict mobility performance in a given situation. For example, while cone threshold was the best predictor for mobility performance on the course with high light levels and when the individual is adapted to that light level, macula recovery assessed using the photostress test was the best predictor for mobility performance on the course with very bright light (Course C with 1500 cd/m2), if subjects were adapted to dim lights. This could be one reason for inconsistencies in the literature regarding visual predictors for mobility performance in people with AMD. Previous studies assessed visual predictors for mobility functions pertaining to only one functional aspect rather than a detailed assessment through a range of lighting conditions

(Brown et al, 1986; Kuyk et at, 1998; Hassan et al, 2002). The individual variability in the severity of AMD and the tests used for psychophysical measures could also be expected to contribute to variations between studies. 121

Visual predictors for poor mobility performance in people with AMD

This study provides evidence to show that people with AMD have difficulty with mobility performance in environment with changing illumination and are at risk of having a fall due to incidents such as tripping or stepping especially when moving from a brightly illuminated area to a dimly illuminated area (For example, entering a dimly lit room from outdoors on a sunny day). This study predicted reduced macular recovery function, increased focal defects and reduced retinal sensitivities could identify poor mobility performance in people with AMD. Routine assessment of macular recovery function could help clinicans identify people at risk and would help in the timely referral to appropriate orientation and mobility services. Macular recovery function test can be assessed using PST in a clinical environment. It is a simple to perform and does not require procurement of any additional instruments. Adequate counselling of people with AMD regarding the implications of slower macular recovery function and focal defects could help to reduce the risk of falling and help them maintain their independence.

Several studies have assessed the impact of orientation and mobility training in people with visual impairments (Geruschat and Del’Aune, 1989; Straw and Harley, 1991; Soong et al, 2001; Kuyk et al, 2004). Evidence with regards to the influence of orientation and mobility training in mobility performance of people with visual impairment is contradictory. Although the study by

Soong et al (2001) reported that there were no significant improvenments in the mobility performance after orientation and mobility training, studies by Straw et al (1991) reported otherwise. Straw et al (1991) reported that the mobility performance in people with visual impairment who had orientation and mobility training showed statistically significant improvements on their mobility performance. A recent study by Kuyk et al, (2004) showed that there was a statistically significant improvement in the mobility performance of people with visual impairment. They analysed performances 2 months after the orientation and rehabilitation training, which takes into consideration the time required for information learned from the training program stored in the short term memory to long term memory through repeated practice. This evidence taken together with the results from the current study, highlight that the referral for people with AMD with reduced macular recovery function and reduced visual fields to appropriate orientation and mobility training would help reduce the risk of falls due to poor mobility performance.

122

Visual predictors for poor mobility performance in people with AMD

6.5 Limitations

One of the limitations of this study was the laboratory settings used for the assessment of mobility functions, although measures were taken to incorporate variations or visual demands brought about by the real world. Kuyk et al (1998) assessed mobility performance by individuals with AMD in both laboratory and real-world settings and found that both performances were highly correlated, suggesting that the laboratory setting was sufficient to assess mobility performance. Another limitation of this study was that all the variables of vision were measured monocularly while mobility performance was assessed binocularly. A study by Turano et al (1999) where they modelled monocular and binocular MD (Mean deviation; visual field indices) to predict mobility reported that mobility performance was best predicted by monocular MD obtained from the more affected eye. As such the current study used monocular measuremts of the more affected eye for all the visual function tests, but allowed patients to use both eyes for mobility performance.

As mentioned in the previous chapters (Chapter 3, 4 and 5), generalisation of these results for people with severe AMD may not be appropriate as the subject group in this study consisted of individuals with moderate to moderately severe stages of AMD. It is assumed however, that people with severe AMD would have increased difficulties due to severe visual dysfunction.

6.6 Conclusions

The analytical models in this study show the significance of the different aspects of visual function on the mobility performance of an individual with AMD. Visual field measures (MD and PSD) and macular recovery function were found to be the best predictors for mobility performance. Measures of visual acuity and cone threshold also contributed towards the prediction of mobility performance under specific lighting conditions. This study not only highlights the variability of the effect of illumination on the visual field defects of an individual with AMD, but also signifies the importance of clinically assessing individuals with AMD for their visual field defects and macular recovery function. This

123

Visual predictors for poor mobility performance in people with AMD

would help timely referral of individuals with a high risk of falls (poor mobility performance) to appropriate mobility training programs. This may help to reduce the rate of falls and related injuries and thus enhance independence and quality of life in this ageing population.

124

Visual predictors for poor mobility performance in people with AMD

Chapter 7: Summary

Age-Related Macular Degeneration is the most common cause of irreversible visual impairment in developed countries (Kahn, 1977; Attebo, 1996; Klaver, 1998). This is one of the major concerns of the ageing population given the trajectory of the disease process (See Chapter 2). One of the major concerns of the older population is the risk of falling which is frequently associated with a loss of independence, eventual institutionalisation and increased morbidity (Marottoli et al, 1995; Meyer et al, 2000). Studies have shown that one of the risk factors for falls is visual impairment (Jack et al, 1995; Lord and Dayhew, 2001).

In addition to the disability brought about by the impact of visual impairment due to AMD and falls on the affected individual, there are major social and economic consequences for the community in general (Alexander et al, 1992). The costs related to health services involved in caring for people over the age of 65 who suffer a fall are estimated to be approximately $90 million/year in Australia, and projected to double by 2021 (Hendrie et al, 2004). This is an area of public health concern given the socio-economic burden it places on the community and emphasises the need for studies that assess risk factors associated with falls and implementation of prevention strategies. This study assesses the mobility performance of people with AMD in comparison to a control group in the same age range and modelled visual predictors for poor mobility performance as previous studies have shown that mobility performance is a good predictor for the risk of falls (Thompson and Medley, 1995; Shumway-

Cook et al, 2000; Tiedemann et al, 2008).

7.1 Summary

In Chapter 3, the quality of life profile of AMD and control subjects was examined using a generic health measure (SF-36), a mental health screen (GADS), a function specific questionnaire (LLQ), a physical activity level questionnaire (IPAQ) and questions relating to assistance used for daily living activities. This case-control study indicated that people with AMD had reduced levels of physical activity, increased anxiety and depressive symptoms 125

Visual predictors for poor mobility performance in people with AMD

along with increased dependence on assistance for daily living activities compared to the control subjects, independent of co-morbidities. A recent study by Brody et al (2005) has shown early interventions lead to a reduction in depressive symptoms and improved functioning in people with AMD. This evidence along with results from the current study shows that screening of people with AMD for depressive symptoms and timely referral to appropriate allied healthcare services would potentially allow them to maintain their independence and hence their quality of life. This study also highlighted the importance of AMD-associated depression - a topic that had not been extensively addressed in the literature.

Psychophysical aspects of vision analysed in Chapter 4 for a subset of AMD and control subjects were high contrast visual acuity (Bailey-Lovie chart; Log MAR), contrast sensitivity (Melbourne Edge Test; dB), macular recovery function (photostress test; seconds), visual fields for 5 different bowl luminances (Medmont; dB for pattern standard deviation and mean deviation) and dark adaptation (Goldman-Weekers dark adaptometer; log units for cone threshold and rod threshold; minutes for rod-cone break-up time). All the psychophysical functions assessed were significantly reduced in people with AMD when compared with controls. The increased time required for visual adaptation in people with AMD could cause significant visual impairments on task that are otherwise performed with ease when people are exposed to changing light levels. The other significant highlight of this Chapter was the influence of luminance on visual field defects and retinal sensitivity. People with AMD had significantly increased focal field defects and reduced retinal sensitivity with decreasing levels of luminance compared to controls where there were only minimal changes in retinal sensitivity with luminance changes.

The sensorimotor functions of this same subset of subjects were assessed in Chapter 5 and this consisted of proprioception, muscle strength, reaction time and balance. The mobility performance of these subjects was also assessed in obstacle courses and with different levels of illumination. These courses were designed to assess whether moving through areas of different illumination could be difficult for people with moderate to moderately severe AMD. Indices of preferred walking speed and number of errors were assessed for these courses (PPWS and ES respectively). A new index for measurement of mobility performance was also proposed- Mobility Performance Score (MPS)- that took into account 126

Visual predictors for poor mobility performance in people with AMD

both speed and safety (number of errors) during mobility performance. People with AMD had significantly reduced mobility performance (PPWS, ES and MPS indices) for all environmental conditions, although difficulties with mobility were exaggerated in these subjects during low illumination or when moving from light to dark or dark to light illumination. The sensorimotor functions between people with AMD and the control group were not statistically significant when vision, age and gender were added as covariates. The correlation between sensorimotor functions and mobility performance in people with AMD was not statistically significant except for balance which was moderately correlated with mobility performance. This implies that people with AMD are at an increased risk of poor mobility and that this increased risk is due visual impairment caused by AMD.

The best visual predictors of poor mobility performance were analysed in Chapter 6 and found to be: the presence of focal defects in the central visual field and the time for recovery of macular function after exposure to bright light. Delayed dark adaptation (indicated by increased cone and rod thresholds and delayed rod-cone break-up time) in terms of obstacles contacts made and visual acuity also contributed significantly to poor mobility performance. The difficulties in mobility performance were directly related to changing levels of illumination, highlighting the importance of considering the dynamic nature of visual function required for mobility performance without an incident (falls). This is the first study to analyse the dynamics of the involvement of visual input and also the significance of visual adaptation in mobility performance. This study provides evidence for the significance of testing the Macular recovery function routinely in people with AMD. This can be assessed by using the photostress test method routinely in a clinic setting as it is a simple to perform and does not require specialised and expensive instrumentation.

7.2 Conclusions

Taken together, the observations in this study indicated that people with AMD suffered more from anxiety and depressive symptoms than non-AMD controls. This was evident despite the AMD group having similar levels of physical abilities and co-morbidities compared to controls. The impact of reduced visual function is thus significant and needs to be considered in people with AMD. This case-control study also showed that people with 127

Visual predictors for poor mobility performance in people with AMD

AMD had approximately 10 fold more likelihood of having depressive symptoms, in the absence of other co-morbidities, when compared to the general population.

The visual predictor model for poor mobility performance showed a comprehensive representation of the different aspects of visual function in mobility performance. The main findings of this section were that: (a) mobility performance when moving from a brighter luminance environment to a dim luminance environment could be predicted by the focal field defects and that macular recovery function was the best predictor for mobility performance in people with AMD. (b) mobility performance when moving from a dim luminance environment to brighter luminance environment could be predicted by the focal field defects and that VA was the best predictor for poor mobility performance in people with AMD. (c) the number of errors made by subjects on mobility courses was best predicted by the dark adaptation variables (rod-cone break-up time under photopic levels of illumination; cone threshold under mesopic levels of illumination; and rod threshold under scotopic levels of illumination).

7.3 Limitations

One of the main difficulties encountered in this study was obtaining an age- and-gender- matched control group. For the quality of life study, this limitation was addressed by a retrospective collection of data from the Falls and Balance Research Group normative database (Prince of Wales Medical Research Institute). This was not possible, however, for comparison of the LLQ data and the study sample for modelling visual predictors for poor mobility performance. The influence of age and gender on visual and sensorimotor functions on the subset of subjects examined was addressed by analysing the data using multivariate analysis of variance with these factors as covariates in the model.

The other consideration relates to the AMD subject group. The participants with AMD had moderate to moderately severe AMD (See Chapter 4). As such the problems of people with severe AMD in relation to quality of life were not examined. This thesis does however provide considerable insight into the impact of AMD on quality of life and the potential 128

Visual predictors for poor mobility performance in people with AMD

mobility difficulties that may be experienced by people with AMD, particularly with regard to difficulties in adapting to variations in luminance typically encountered in everyday life. It is likely that people with severe AMD would experience even further reductions in quality of life especially in regard to anxiety and depression, and that the level of difficulty experienced in moving around in the environment would be more pronounced. The accessibility to FFA and fundus photographs was another limitation of the current study. Comparison of visual function in relation to individual lesions was not possible.

The mobility course design used in this study had stationary obstacles and was assessed in laboratory settings, which may be considered very different to a “real-life” situation confronting people with AMD. Studies by Kuyk et al (1998), have demonstrated that mobility performance measured under laboratory conditions in a controlled environment correlated well with the performance in a real-world situation. This observation would suggest that the current study findings can be translated to “real-life” when advising people with AMD on how to navigate their way during normal life activities.

7.4 Significance

Earlier identification and referral of individuals with AMD, who are at an increased risk of falling, may prevent the occurrence of falls and fall-related injuries. This would in turn assist continued independent living, as studies have shown that the incidence of institutionalisation and loss of independence are higher among those that fall, than in non-fallers (Marottoli et al,

1995).

This study provides evidence to show that people with AMD have slower macular recovery function, increased visual field abnormalities relative to the surrounding illumination, in addition to the well established fact of decreased visual acuity and contrast sensitivity. This study also showed that although the sensorimotor functions were similar when compared to a control group, people with AMD had reduced mobility performance and the impairment was more affected with changing levels of illumination in the envirenment. This study modelled visual field measures (MD and PSD) and slower macular recovery function to be the best predictor of mobility performance and also identified dark adaptation variables as 129

Visual predictors for poor mobility performance in people with AMD

predictors of incidents (error score) that could occur during mobility performance. Strategies and training programs for preventing the occurrence of fall injuries could assist in reducing the social and economic burden of costs relating to health services (Alexander et al, 1992; Hendrie et al, 2004). A comprehensive review by Gillespie et al (2009) has shown the effectiveness of a large number of intervention strategies in reducing the risk of falls.

7.5 Future directions

This thesis provides a comprehensive insight into the quality of life in people with AMD and the variations in visual functions that affect mobility performance. A new index for mobility performance was proposed, the Mobility Performance Score (MPS) that can take into account both speed and safety aspects of mobility. These are important factors for older people, especially those with AMD, who strive to maintain independence in their daily activities. Validation of this measure in population-based studies using patient groups with various forms of visual dysfunction (for example, people with early stage AMD, cystoid macula oedema associated with diabetic retinopathy, or retinitis pigmentosa) is needed. The potential for this index to provide a more sensitive discrimination of those who may experience mobility difficulties at very early stages of AMD could be important in implementing earlier intervention strategies for mobility training. The use of different mobility courses and luminance conditions could also be explored in this context.

The current study demonstrated that focal defects in the visual field, macular recovery function and dark adaptation variables were the best predictors of mobility performance in people with AMD. However, the extent of the focal field defect and delay in macular recovery function, at which mobility impairments begin to occur, remain to be explored. The other area that could be explored is the macular recovery function in terms of resolution acuity by classifying subjects into groups depending on their visual acuity level and lesion morphology in AMD.

An interesting finding in this study was the improvement in retinal sensitivity and reduction in focal defects at mesopic levels of illumination. It was hypothesised that this effect may be related to spatial summation, however further studies to test this hypothesis are needed. 130

Visual predictors for poor mobility performance in people with AMD

Future research attempts to quantitatively determine the photoreceptor status and analyse this compared to the retinal sensitivity are required and would provide further insights into this phenomenon. Another area that could be further explored is establishing the optimal conditions for successful performance of different tasks in a dynamic range of luminance levels.

Referring people with visual field defects and delayed macular recovery function for mobility and orientation training services could reduce the risk of falls for older people and help maintain their independence. The current study indicates that this is critical for maintaining quality of life, particularly with regard to the development of anxiety and depression. Studies have shown that early interventions increase the overall performance and quality of life in people with AMD. Hence it is of paramount importance to disseminate these results to clinical practice so that people with AMD are screened for symptoms of depression, visual field defects and macular recovery function as a routine practice. Early referral for appropriate services can then be arranged. A practical and effective approach for disseminating these findings into clinical practice needs to be designed and implemented. Further, assessment of the prevalence of depressive symptoms and fall related injuries is another area for further studies. Finally, follow-up of people with AMD who receive rehabilitation services, mobility and orientation training requires more extensive research.

131

Visual predictors for poor mobility performance in people with AMD

References

Abdelsalem A, Del Priore L and Zarbin MA. Drusen in Age-related Macular Degeneration: pathogenesis, natural course and laser photocoagulation induced regression. Survey of Ophthalmology. 1999; 44: 1-29.

Adler FH. Adaptation, Physiology of the eye-clinical application. 1959; 3rd edition: 618-639.

Age-Related Eye Disease Study Research group. The Age-Related Eye Disease Study system for classifying Age-Related Macular Degeneration from stereoscopic colour fundus photographs: The Age-Related Eye Disease Study Report number 6. American Journal of Ophthalmology. 2001; 132: 668-81.

Albert A, Sabel J-A, Bandello F, et al. Anxiety and depression prevalence rates in Age- related Macular Degeneration. Investigative Ophthalmology and Vision Science. 2007; 48: 1498-1503.

Alexander LJ. Exudative and Nonexudative Macular disorders. Primary care of the posterior segment. Appelton & Lange. E.Norwalk. 1989; 163-207.

Alexander BH, Rivara FP and Wolf ME. The Cost and Frequency of Hospitalization for Fall-Related Injuries in Older Adults. American Journal of Public Health. 1992; 82: 1020-3.

Alexander KR, Wei X and Derlacki DJ. Visual acuity and contrast sensitivity for individual Sloan letters. Vision Research. 1997; 37: 813-7.

Allikmets R, Shroyer NF, Singh N, et al. Mutation of the Stargardt disease gene (ABCR) in Age-related Macular Degeneration. Science. 1997; 277: 1805–7.

132

Visual predictors for poor mobility performance in people with AMD

Alonso J, Prieto L, Ferrer M, Anto JM. Comparison of two generic health measures: the Nottingham health profile (NHP) and the SF-36 Health survey (SF-36). Quality of Life Research. 1995; 4: 387-388.

Alpern M, Maaseidvaag F, Ohba N. The kinetics of cone pigments in man. Vision Research. 1971; 11: 539-548.

Ambati J, Anand A, Fernandez S, et al. An animal model of Age-related Macular Degeneration in senescent Ccl-2 orCcl-2-deficient mice. National Medicine. 2003; 9: 1390- 1397.

Anderson DH, Mullins RF, Hageman GS, et al. A role for local inflammation in the formation of drusen in the ageing eye. American Journal of Ophthalmology. 2002; 134: 411- 431.

Anderson DR. The single field printout with Statpac analysis. Automated Static Perimetry. 1992. Chapter 6: 76-90.

Arden GB and Weale RA. Nervous mechanisms and dark adaptations. Journal of Physiology. 1954; 125: 417-426.

Arundale K. An investigation into the variation of human contrast sensitivity with age and ocular pathology. British Journal of Ophthalmology. 1978; 62: 213-15.

Atchison DA, Lovie-Kitchin JE, Swann PG. Investigation of central visual fields in patients with age-related macular changes. Optometry and Vision Science. 1990; 67: 179-183.

Attebo K, Mitchell P, Smith W. Visual acuity and the causes of visual loss in Australia. The Blue Mountains Eye Study. Ophthalmology. 1996; 103:357-64.

Augustin A, Sahel J-A, Bandello F, et al. Anxiety and depression prevalence rates in in Age- related Macular Degeneration. Investigative Ophthalmology and Vision Science. 2007; 48: 1498-1503. 133

Visual predictors for poor mobility performance in people with AMD

Australian Bureau of Statistics. Scenarios for Australia's aging population. 2004 http://www.abs.gov.au/AUSSTATS/[email protected]/2f762f95845417aeca25706c00834efa/95560b 5d7449b135ca256e9e001fd879!OpenDocument (Viewed 10/012008)

Bailey IL and Lovie JE. New design principles for visual acuity letter charts. American Journal of Optometry and Physiological Optics. 1976; 53: 740-5.

Bailey I, Bullimore MA, Raasch T, Taylor HR. Clinical grading and the effects of scaling. Investigative Ophthalmology and Vision Science. 1991; 32:422-32.

Banerjee A, Kumar S, Kulhara P, Gupta A. Prevalence of depression and its effect on disability in patients with Age-related Macular Degeneration. Indian Journal of Ophthalmology. 2008; 56:469-74.

Barlow HB, Fitzhugh R and Kuffler SW. Change of organisation in the receptive fields of the cat’s retina during dark-adaptation. Journal of Physiology. 1957; 137: 338-354.

Barnes DA, Wild JM, Flanagan JG, et al. Manipulation of sensitivity in visual field investigation. Documenta Ophthalmologica. 1985; 59: 301-308.

Bartlett H, Davies LN, Eperjesi F. Reliability, normative data, and the effect of Age-related Macular Disease on the Eger Macular Stressometer photostress recovery time. Ophthalmic and Physiologic Optics. 2004; 24:594-9.

Baylor DA. Photoreceptor signals and vision: Proctor Lecture. Investigative Ophthalmology and Vision Science. 1987; 28: 34-49.

Beaumont PE and Kang HK. Lesion morphology in Age-Related Macular Degeneration and its therapeutic significance. Archives of Ophthalmology. 2006; 124: 807-12.

Bedwell CH. Recent developments in investigations of visual fields. American Journal of Optometry and Physiologic Optics. 1978; 55: 681-99. 134

Visual predictors for poor mobility performance in people with AMD

Beirne RO, Hogg RE, Stevenson MR, et al. Severity Staging by Early Features of Age- Related Maculopathy Exhibits Weak Relationships with Functional Deficits on SWS Grating Acuity. Investigative Ophthalmology and Vision Science. 2006; 47: 4624–4631.

Bennett AG. Ophthalmic test types. British Journal of Physiologic Optics. 1965; 22: 238-71.

Berdeaux GH, Nordmann J-P, Colin E, et al. Vision-related quality of life in patients suffering from Age-related Macular Degeneration. American Journal of Ophthalmology. 2005; 139: 271-279.

Binns AM and Margrain TH. Evaluating retinal function in age-related maculopathy with the ERG photostress test. Investigative Ophthalmology and Vision Science. 2007; 48 :2806- 2813.

Birch DG, deFaller JM, Herman WK and Disbrow DT. Relationship between retina wide rod perimetric thresholds and full-field rod ERG’s in Retinitis pigmentosa. ARVO, Sarasota, Florida, The Association for Research in Vision and Ophthalmology. Investigative Ophthalmology and Vision Science. 1986; 27 (Suppl. 3): 337.

Birch DG, Herman WK, deFaller JM, et al. The relationship between rod perimetric thresholds and full-field rod ERG’s in Retinitis pigmentosa. Investigative Ophthalmology and Vision Science. 1987; 28: 954-65.

Black A, Lovie-Kitchin J, Woods R, Arnold N, Byrnes J, Murrish J. Mobility performance with retinitis pigmentosa. Clinical and Experimental Optometry 1997; 80:1–12.

Black A and Wood J. Vision and falls. Clinical and Experimental Optometry. 2005; 88: 212- 22.

Blackhurst DW and Maguire MG. Reproducibility of refraction and visual acuity measurements under a standard protocol. The macular Photocoagulation Study Group. Retina. 1989; 9:163–9. 135

Visual predictors for poor mobility performance in people with AMD

Blackwell HR. Contrast thresholds of the human eye. Journal of the Optical Society of America. 1946; 36: 624- 643.

Bogle Thorbahn LD and Newton RA. Use of the Berg balance test to predict falls in elderly persons. Physical Therapy. 1996; 76: 576-585.

Bone RA, Landrum JT and Tarsis SL. Preliminary identification of human macular pigment. Vision Research. 1985; 25(11): 1531- 1535.

Bone RA, Landrum JT, Tarsis SL and Fernandez L. Analysis of the macular pigment by HPLC: Retinal distribution and age study. Investigative Ophthalmology and Vision Science. 1988; 29: 843-849.

Borrish IM. Clinical Refraction. 3rd Edition. The Professional Press, Chicago. 1970: 115– 122.

Boufous S, Finch CF, and Lord SR. Incidence of hip fracture in New South Wales: are our efforts having an effect? Medical Journal of Australia. 2004 ; 180:623-6.

Bowers AR, Meek C, Stewart N. Illumination and reading performance in Age-related Macular Degeneration. Clinical and Experimental Optometry. 2001; 84:139-47.

Bradley C, Todd C, Gorton T, et al. The development of an individualized questionnaire measure of perceived impact of diabetes on quality of life: the ADDQoL. Quality of Life Research. 1999; 8:79-91.

Brazier JE, Harper R, Jones NMB, O’Cathain A, Usherwood T AND Westlake J. Validating the SF-36 Health survey questionnaire: new outcome measure for primary care. British Medical Journal. 1992; 305: 160-164.

136

Visual predictors for poor mobility performance in people with AMD

Bressler SB, Munoz B, Solomon SD, et al. Racial differences in the prevalence of Age- Related Macular Degeneration: the Salisbury Eye Evaluation (SEE) Project. Archives of Ophthalmology. 2008; 126: 241-5. Brody BL, Anthony CG, Williams RA, et al. Depression, Visual acuity, Co morbidity and Disability associated with Age-Related Macular Degeneration. Ophthalmology. 2001; 108 (10): 1893- 1900.

Brody BL, Roch-Levecq AC, Gamst AC, et al.: Self-management of Age-related Macular Degeneration and quality of life: a randomized controlled trial. Archives of Ophthalmology. 2002; 120:1477–1483.

Brody BL, Roch-Levecq A-C, Thomas RG, et al. Self-management of Age-related Macular Degeneration at the 6-month follow-up: A randomized controlled trial. Archives of Ophthalmology. 2005; 123: 46-53.

Brown B, 1981. Reading performance in low-vision patients: relation to contrast and contrast sensitivity. American Journal of Optometry and Physiologic Optics.1981; 58: 218- 226.

Brown B and Garner LF. Effects on luminance on contrast sensitivity in Senile Macular Degeneration. American Journal of Optometry and Physiologic Optics.1983; 60: 788-793.

Brown B, Zadnik K, Bailey IL and Colenbrander A. Effect of luminance, contrast, and eccentricity on visual acuity in Senile Macular Degeneration. American Journal of Optometry and Physiologic Optics.1984; 61: 265- 270.

Brown B and Lovie-Kitchin J. Dark adaptation and the acuity/ luminance response in Senile Macular Degeneration. American Journal of Optometry and Physiologic Optics. 1983; 60:645-50.

137

Visual predictors for poor mobility performance in people with AMD

Brown B, Brabyn L, Welch L, Haegerstrom-Portney G, Colenbrander A, Contribution of vision variables to mobility in ARM patients. American Journal of Optometry and Physiologic Optics. 1986; 63:773-39.

Brown B and Lovie-Kitchin J. Contrast sensitivity in central and paracentral retina in Age- Related Maculopathy. Clinical and Experimental Optometry. 1987; 70:145-8.

Brown B, Adams AJ, Coletta NJ et al. Dark adaptation in Age-Related Maculopathy. Ophthalmic and Physiologic Optics. 1986; 6: 81-84.

Brown B, Tobin C, Roche N and Wolanowski A. Cone adaptation in age-related maculopathy. American Journal of Optometry and Physiologic Optics. 1986; 63:450-45

Brown GC. Vision and quality of life. Transactions of the American Ophthalmology Society.1999; 97: 473-511.

Brown MM, Brown GC, Stein JD, et al. Age-related macular degeneration: economic burden and value-based medicine analysis. Candian Journal of Ophthalmology. 2005; 40: 277-87.

Bruce ML, Seeman TE, Merrill SS, et al: The impact of depressive symptomatology on physical disability: MacArthur Studies of Successful Aging. American Journal of Public Health 1994; 84:1796–1799.

Buckley JG, Heasley K, Scally A, Elliott DB. The effects of blurring vision on medio-lateral balance during stepping up or down to a new level in the elderly. Gait Posture. 2005 Oct; 22:146-53.

Burns RP and Feeney-Burns L. Clinico-morphologic correlations of drusen of Bruch’s membrane. Transactions of the American Ophthalmology Society. 1980; 116: 206-225.

Cahill MT, Banks AD, Stimnettt SS, Toth CA. Vision-Related Quality of Life in patients with Bilateral severe Age-Related Macular Degeneration. Ophthalmology. 2005; 112:152-8. 138

Visual predictors for poor mobility performance in people with AMD

Campbell CJ, Rittler MC. Clinical adaptation studies of human retina. In: Hall BR, Allen MO, Crescitelli RA, Eds. The Retina: Morphology, Function and Clinical characteristics. Berkeley: University of California press, 1969:513-44.

Carta A, Braccio L, Belpoliti M, et al. Self-assessment of the quality of vision: association of questionnaire score with objective clinical tests. Current Eye Research. 1998; 17: 506-12.

Casten RJ, Rovner BW and Tasman W. Age-Related Macular Degeneration and depression: A review of recent research. Current Opinion in Ophthalmology. 2004; 15: 181-183.

Chaine G, Hullo A, Sahel J, et al. Case-control study of the risk factors for Age-Related Macular Degeneration. British Journal of Ophthalmology. 1998; 82: 996-1002.

Cheng AS and Vingrys AJ. Visual losses in early Age-Related Maculopathy. Optometry and Vision Science. 1993; 70:89-96.

Chen C, Wu L, Wu D, et al. The local cone and rod system function in early Age-Related Macular Degeneration. Documenta Ophthalmologica. 2004; 109: 1-8.

Chia EM, Wang JJ, Rochtchina E, et al. Impact of bilateral visual impairment on health- related quality of life: the Blue Mountains Eye Study. Investigative Ophthalmology and Vision Science. 2004; 45: 71-6.

Christen WG, Glynn RJ, Manson JE, et al. A prospective study of ciggarette smoking and the risk of Age-Related Macular Degeneration in men. Journal of American Medical Association. 1996; 276: 1147-1151.

Chua R, Keogh AM, Byth K, O’Loughlin A. Comparison and validation on three measures of quality of life in patients with pulmonary hypertension. Internal Medicine Journal. 2006; 705-710.

139

Visual predictors for poor mobility performance in people with AMD

Chylack JR, Wolfe JK, Singer DM, et al. The Lens Opacities Classification System III: the longitudinal study of Cataract Study Group. Archives of Ophthalmology. 1993; 111:831-6.

Ciardella AP, Donsoff IM, Huang SJ, et al. Polypoidal Choroidal Vasculopathy. Survey of Ophthalmology. 2004; 49:25-37.

Clark-Carter DD, Heyes AD, Howarth CI. The efficiency and walking speed of visually impaired people. Ergonomics 1986; 29:779–89.

Cleland BG and Enroth-Cugell C. Quantitative aspects of sensitivity and summation in the cat retina. Journal of Physiology. 1968; 198:17-38.

Coats G. Forms of retinal disease with massive exudation. Royal London Ophthalmic Hospital Report. 1980; 18: 440-525.

Collins M and Brown B. Glare recovery and Age-Related Maculopathy. Clinical Vision Sciences. 1989; 4:145-53.

Collins M and Brown B. Glare recovery and its relation to other clinical findings in age- related maculopathy. Clinical Vision Sciences. 1989; 4:155-63.

Cornelissen FW, Kooijman AC, Dumbar G, et al. Illumination research as part of a visual assessment of visually impaired individuals. Documenta Ophthalmologica. 1991; 78:195- 203.

Covinsky KE, Kahana E, Chin MH, et al. Depressive symptoms and 3-year mortality in older hospitalized medical patients. Annals of Internal Medicine 1999; 130:563–9.

Crassini B, Brown B and Bowman K. Age-related changes in contrast sensitivity in central and peripheral retina. Perception. 1988; 17: 315-32.

140

Visual predictors for poor mobility performance in people with AMD

Cummings SR, Nevitt MC, Browner WS. Risk factors for hip fracture in white women. Study of Osteoporotic fractures in research group. The New England Journal of Medicine. 1995; 332: 767-73.

Curcio CA, Sloan KR, Kalina RE and Hendrickson AE. Human Photoreceptor Topography. The Journal of Comparative Neurology. 1990; 292 (4): 497-523.

Curcio CA and Drucker DN. Retinal ganglion cells in Alzheimer’s disease and ageing. Annals of Neurology. 1993; 33: 248-257.

Curcio CA, Medeiros NE and Millican CL (1996). Photoreceptor loss in Age-Related Macular Degeneration. Investigative Ophthalmology and Vision Science. 1996; 37: 1236- 49.

Curcio CA and Millican CL. Basal linear deposit and large drusen are specific for Age- Related Maculopathy. Archives of Ophthalmology. 1999; 117:329- 39.

Davies JC, Kemp GJ, Stevens G, Frostick SP, Manning DP. Bifocal/ varifocal spectacles, lighting and missed-step accidents. Safety Science. 2001; 38:211–226.

De Boer MR, Pluijm SM, Lips P, Moll AC, Volker-Dieben HJ, Deeg DJ, van Rens GH. Different aspects of visual impairment as risk factors for falls and fractures in older men and women. Journal of Bone Mineral Research. 2004; 19:1539-47.

Delbaere K, Hauer K and Lord SR. Evaluation of the Incidental and Planned Activity Questionnaire (IPAQ) for older people. British Journal of Sports Medicine. 2009; May 26 [Epub ahead of print]

Delcourt C, Cristol J-P, Tessier F, et al. Age-related Macular Degeneration and Antioxidant Status in the POLA Study. Archives of Ophthalmology. 1999; 117: 1384-90.

141

Visual predictors for poor mobility performance in people with AMD

Dentchev T, Milam AH, Lee VM-Y, et al. Amyloid-β is found in drusen from some Age- Related Macular Degeneration retinas, but not in drusen from normal retinas. Molecular Vision. 2003; 9: 184-90.

Dhalla MS, Fantin A, Blinder KJ and Bakal JA. The Macular Automated Photostress Test. American Journal of Ophthalmology. 2007; 143: 596-600.

Diener HC, Dichgans J, Guschlbauer B and Mau H. The significance of proprioception on postural stability as assessed by ischaemia. Brain Research. 1984; 296: 103-9.

Dimitrov PN, Guymer RH, Zele AJ, et al. Measuring Rod and Cone dynamics in age-related maculopathy. Investigative Ophthalmology and Vision Science. 2008; 49:55-65.

Dolinis J, Harrison JE, Andrews GR. Factors associated with falling in older Adelaide residents. Australian and New Zealand Journal of Public Health 1997; 21: 462-468.

Donoso LA, Kim D, Frost A, et al. The role of inflammation in the pathogenesis of age- related macular degeneration. Survey of Ophthalmology. 2006; 51 (2): 137-152.

Duke-Elder WS. The anatomy of the eye. Text book of Ophthalmology. 1942; 1:82- 101.

Duthy TG, Ormsby RJ, Giannakis E, et al. The human complement regulator factor H binds pneumococcal surface protein PspC via short consensus repeats 13 to 15. Infection and Immunity. 2002; 70: 5604-11.

Eandi CM, Ober MD, Freund KB, et al. Selective photodynamic therapy for age-related macular degeneration with Polypoidal choroidal neovascularisation. Retina 2007; 27: 825- 31.

Eisner A, Fleming SA, Klein ML, Mauldin WM. Sensitivities in older eyes with good acuity: eyes whose fellow eye has exudative AMD. Investigative Ophthalmology and Vision Science. 1987;28(11):1832-1837.

142

Visual predictors for poor mobility performance in people with AMD

Elliot DB and Sheridan M. The use of accurate visual acuity measurements in clinical anti- cataract formulation trials. Ophthalmic and Physiologic Optics. 1988; 8:397–401.

Elliott DB, Patla AE, Flanagan JG, et al. The Waterloo vision and mobility study: postural control strategies in subjects with ARM. Ophthalmic and Physiologic Optics. 1995; 15: 553- 559.

Ellwein LB, Fletcher A, Negrel A, Thulsiraj R. Quality of life assessment in blindness prevention interventions. International Ophthalmology. 1995; 18:263-68.

Enroth-Cugell C and Shapley RM. Adaptation and dynamics of cat retinal ganglion cells. Journal of Physiology. 1973a; 233:271-309.

Enroth-Cugell C and Shapley RM. Flux, not retinal illumination, is what cat retinal ganglion cells really care about. Journal of Physiology. 1973b; 233:311-328.

Evans DA, Beckett LA, Field TS, et al. Apolipoprotein E epsilon4 and incidence of Alzheimer’s disease in a community population of older persons. Journal of American Medical Association. 1997; 277: 822-824.

Fankhauser F. Problems related to the design of automatic perimeters. Documenta Ophthalmologica. 1979; 47: 89-139.

Federation Equestre Internationale 2009. http://www.fei.org/Disciplines/Jumping/Documents/Championships_Games%20Rules_23rd %20Ed.pdf .

Feigl B, Brown B, Lovie-Kitchin J, et al. Monitoring retinal function in early age-related maculopathy; visual performance after 1 year. Eye. 2005; 19:1169-77.

Ferris FL and Sperduto RD. Standardised illumination for visual acuity testing in clinical research. American Journal of Ophthalmology. 1982; 94: 97-98.

143

Visual predictors for poor mobility performance in people with AMD

Ferris FL, Kassoff A, Bresnick GH AND Bailey IL. New visual acuity charts for clinical research. American Journal of Ophthalmology. 1982; 94: 91-92.

Fitzgerald RG. Reactions to blindness. An exploratory study of adults with recent loss of sight. Archives of general psychiatry. 1970; 22 (4): 370-9.

Fitzgerald RG, Parkes CM. Blindness and Loss of other sensory and cognitive functions. British Medical Journal.1998; 316: 1160-1163.

Fletcher DC and Schuchard RA. Visual function in patients with choriodal neovascularisation resulting from Age-Related Macular Degeneration: The importance of looking beyond visual acuity. Optometry and Vision Science. 2006; 83: 178-189.

Flom MC, Heath G, Takahaski E. Contour interaction and visual resolution: contralateral effects. Science. 1963; 142: 979-80.

Foran S, Wang JJ, Mitchell P, Causes of visual impairment in two older population cross sections: The Blue Mountains Eye Study. Ophthalmic Epidemiology, 2003; 10: 215-25.

Friedman E, Smith T, Kuwabara T: Senile choroidal vascular patterns and drusen. Arch Ophthalmology. 1963; 69: 220-30.

Friedman DS, Katz J, Bressler NM, et al. Racial Differences in the prevalence of Age- Related Macular Degeneration: The Baltimore Eye Survey. Ophthalmology. 1999; 106: 1049-1055.

Fujii GY, De Juan Jr E, Humayun MS, et al. Characteristics of visual loss by scanning laser ophthalmoscope microperimetry in eyes with subfoveal choriodal neovascularisation secondary to age-related macular degeneration. American Journal of Ophthalmology. 2003; 136: 1067-78.

Gao H and Hollyfield JG. Ageing of the human retina. Investigative Ophthalmology and Vision Science. 1992; 33: 1-17. 144

Visual predictors for poor mobility performance in people with AMD

Gass JDM. Pathogenesis of disciform detachment of the neuroepithelium. American Journal of Ophthalmology. 1967; 63: 573-711. Gass JDM. Drusen and disciform macular detachment and degeneration. Archives of Ophthalmology. 1973; 90:206–17.

Geruschat DR and Del'Aune W. Reliability and validity of O&M instructor observations. Journal of Visual Impairment and Blindness. 1989; 83:457-60.

Geruschat DR, Turano KA and Stahl JW. Traditional measures of mobility performance and retinitis pigmentosa. Optometry and Vision Science. 1998; 75: 525-37.

Giannakis E, Jokiranta TS, Male DA, et al. A common site within factor H SCR 7 responsible for binding heparin, C-reactive protein and streptococcal M protein. European Journal of Immunology. 2003. 33:962-9.

Gillespie LD, Robertson MC, Gillespie WJ, et al. Interventions for preventing falls in older people living in the community (Review). Cochrane Database of Systematic Reviews (Online). 2009; 15: CD007146.

Glaser JS, Savino PJ, Sumers KD. The photostress recovery test in the clinical assessment of visual function. American Journal of Ophthalmology. 1977; 83:255--60

Glezer VD. The receptive fields of the retina. Vision Research. 1965. 5: 497-525.

Goldberg D, Bridges K, Duncan-Jones P and Grayson D. Detecting anxiety and depression in general medical settings. British Medical Journal. 1988; 297: 897-99.

Gomi F, Ohji M, Sayanagi K, et al. One –year outcomes of photodynamic therapy in age- related macular degeneration and Polypoidal choroidal vacsulopathy in Japanese patients. Ophthalmology 2008; 115: 141-46.

145

Visual predictors for poor mobility performance in people with AMD

Gottsch JD, Pou S, Bynoe LA and Rosen GM. Hematogenous photosensitization. A mechanism for the development of age-related macular degeneration. Investigative Ophthalmology and Vision Science. 1990; 31(9):1674-82.

Grant M Comer, Thomas Ciulla, Mark Criswell and Alon Harris. AMD, Exudative, E- Medicine, 2005, Oct. 25.

Green J. Notes on the clinical determination of the acuteness of vision including the construction and graduation of optotypes. Transactions of the American Ophthalmology Society. 1905; 10: 644-654

Green WR, Enger C. Age-Related Macular Degeneration histopathologic studies. Ophthalmology. 1993; 100: 1519-1535.

Greeves AL, Cole BL and Jacobs RJ. Reliability and validity of simple photographic plate tests of contrast sensitivity. American Journal of Optometry and Physiologic Optics. 1987; 64: 832- 841.

Greve EL. Single and multiple stimulus static perimetry in glaucoma; the two phases of perimetry. Documenta Ophthalmologica. 1973; 36: 1-355.

Guymer RH, Heon E, Lotery AJ, et al. Variation of codons 1961 and 2177 of the Stargardt disease gene is not associated with age-related macular degeneration. Archives of Ophthalmology. 2001; 119: 745–751.

Hageman GS, Luthert PJ, Victor Chong NH, et al. An integrated hypothesis that considers drusen as biomarkers of immune-mediated processes at the RPE-Bruch’s membrane interface in aging and age-related macular degeneration. Progress in Retinal and Eye Research. 2001; 20: 705–32.

Hageman GS, Anderson DH, Johnson LV, et al. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular

146

Visual predictors for poor mobility performance in people with AMD

degeneration. Proceedings for the National Academy of Science, USA. 2005; 105:7227- 7232.

Haines JL, Hauser MA, Schmidt S, et al.Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration. Science. 2005; 308: 419-21.

Handelman GJ, Dratz EA, Reay CC, et al. Carotenoids in the human macula and whole retina. Investigative Ophthalmology and Vision Science. 1988; 29(6): 850-855.

Harry KML, Rhonda JS. Comparison of balance in older people with and with out visual impairment. Age and Aging. 2003; 32(6): 643-48.

Hart PM, Chakravarthy U, Stevenson MR, et al. A vision specific functional index for use in patients with age related macular degeneration. British Journal of Ophthalmology. 1999; 83:1115–1120.

Hartridge G. A case of retinitis circinata. Transactions of the Ophthalmological Society of United Kingdom. 1896; 16:90-93.

Hassan SE, Lovie-Kitchin JE, Woods RL. Vision and mobility performance of subjects with age-related macular degeneration. Optometry and Vision Science. 2002; 79:697-707.

Hassell JB, Lamoureux EL, Keeffe JL. Impact of Age-Related Macular Degeneration on quality of life. British Journal of Ophthalmology. 2006; 90: 593-6.

Hawthorne G. The effect of different methods of collecting data: mail, telephone and filter data collection issues in utility measurement. Quality of Life Research. 2003; 12: 1081-8.

Haymes S, Guest D, Heyes A, Johnston A. Mobility of people with retinitis pigmentosa as a function of vision and psychological variables. Optometry and Vision Science. 1996; 73:621–37.

147

Visual predictors for poor mobility performance in people with AMD

Haymes S and Chen J. Reliability and validity of the Melbourne Edge test and High/ Low contrast visual acuity chart. Optometry and Vision Science. 2004; 81: 308-316.

Hendrie D, Hall SE, Arena G, Legge M. Health system costs of falls of older adults in Western Australia. Australian Health Review. 2004; 28(3):363-73.

Hogan MJ. Bruch’s membrane and diseases of the macula: Role of elastic tissue and collagen. Transactions of the Ophthalmology Society of United Kingdom. 1967; 87: 113- 161.

Hogg R, Curry E, Muldrew A, et al. Identification of lesion component that influence visual function in age-related macular degeneration. British Journal of Ophthalmology. 2003; 87:609-614.

Hogg RE and Chakravarthy U. Visual function and dysfunction in early and late age-related maculopathy. Progress in Retinal and Eye Research. 2006; 25: 249-76.

Holm J, Holm L, Bech P. Monitoring improvement using a patient rated depression scale during treatment with anti-depressants in general practice. A validation study on the Goldberg Depression Scale. Scandinavian Journal of Primary Health Care. 2001; 19(4): 263-6.

Holopigian K, Greenstein V, Seiple W and Carr RE. Rates of change differ among measures of visual function in patients with retinitis pigmentosa. Ophthalmology. 1996; 103:398- 405.

Horak FB, Nashner LM and Diener HC. Postural strategies associated with somatosensory and vestibular loss. Experimental Brain Research. 1990; 82: 167-177.

Howell ER and Hess RF. The functional area for summation to threshold for sinusoidal gratings. Vision Research. 1978; 18: 369-374.

148

Visual predictors for poor mobility performance in people with AMD

Hudson D, Wild JM, O’Neill EC. Fatigue effects during a single session of automated static perimetry. Investigative Ophthalmology and Vision Science. 1994; 35:268–280.

Hutchinson J and Tay W. Symmetrical central choroidoretinal disease occurring in senile persons. Royal London Ophthalmic Hospital Report. 1875; 8: 224-231.

Hyvarinen L, Laurinen P and Rovamo J. Contrast sensitivity in evaluation of visual impairment due to macular degeneration and optic nerve lesions. Acta Ophthalmologica. 1983; 61: 161-70.

Ishibashi T, Sorgente N, Patterson R, Ryan SJ: Pathogenesis of drusen in the primate. Investigative Ophthalmology and Vision Science. 1986; 27: 184–93.

Ivers RQ, Cumming RG, Mitchell P, et al. Visual risk factors for hip fracture in older people. Journal of American Geriatric Society. 2003; 51: 356-363.

Jack CI, Smith T, Neoh C, et al. Prevalence of low vision in elderly patients admitted to an acute geriatric unit in Liverpool: elderly people who fall are more likely to have low vision. Gerontology 1995; 41:280–5.

Jackson GR, Owsley C and McGwin Jr G. Aging and dark adaptation. Vision Research. 1999; 39: 3975- 3982.

Jackson GR, Owsley C, Curcio CA. Photoreceptor degeneration and dysfunction in aging and age-related maculopathy. Ageing Research Reviews. 2002; 1:381-96.

Jenkinson C, Coulter A and Wright L. Short form 36 (SF-36 Health survey questionnaire: Normative data for adults of working age. British Medical Journal. 1993; 306: 1437-1440.

Johnson CA, Keltner JL and Balestrery FG. Static and acuity profile perimetry at various adaptation levels. Documenta Ophthalmologica. 1981; 50: 371-388.

149

Visual predictors for poor mobility performance in people with AMD

Johnson CA and Nelson –Quigg JM. A prospective three year study of response properties of normal subjects and patients during automated perimetry. Ophthalmology. 1993; 100: 269-274.

Johnson LV, Ozaki S, Staples MK, et al. A potential role for immune complex pathogenesis in drusen formation. Experimental Eye Research 2000; 70:441–449.

Johnson LV, Leitner WP, Staples MK and Anderson DH. Complement activation and inflammatory processess in drusen formation and age-related degeneration. Experimental Eye Research. 2001; 73: 887-896.

Jorm AF, Henderson AS, Scott R, et al. Factors associated with the wish to die in elderly people. Age and Ageing. 1995; 24: 389–392.

Kahn HA, Leibowitz HM, Ganley JP, Kini MM, Colton T, Nickerson RS, Dawber TR. The Framingham Eye Study. I. Outline and major prevalence findings. American Journal of Epidemiology. 1977; 106:17-32.

Kaplan RM, Bush JW, Berry CC. Health Status index: category versus magnitude estimation for measuring levels of well-being. Medical Care 1979; 17: 501-525.

Kaplan FS, Nixon JE, Reitz M, et al. Age-related changes in proprioception and sensation of joint position. Acta Orthopaedica. 1985; 56:72-4.

Kawasaki R, Yasuda M, Song SJ, et al.The Prevalence of Age-Related Macular Degeneration in Asians: A Systematic Review and Meta-Analysis. Ophthalmology. 2010; 117: 921-927.

Kempen GIJM, Todd CJ, van Haastregt JCM et al. Cross-cultural validation of the Falls Efficacy Scale International (FES-I) in older people: Results from Germany, the Netherlands and the UK were satisfactory. Disability Rehabilitation. 2007; 29: 155–62.

150

Visual predictors for poor mobility performance in people with AMD

Kempen GIJM, Yardley L, van Haastregt JCM et al. The Short FES-I: a shortened version of the falls efficacy scale-international to assess fear of falling. Age and Ageing. 2008; 37:45-50.

Kemp CM, Jacobson SG, Faulkner DJ and Walt RW. Visual function and rhodopsin levels in humans with vitamin A deficiency. Experimental Eye Research. 1988; 46:185–97.

Kemp CM, Jacobson SG, Borruat FX and Chaitin MH. Rhodopsin levels and retinal function in cats during recovery from vitamin A deficiency. Experimental Eye Research. 1989;49:49–65.

Kimura K, Isashiki Y, Sonoda S, et al. Genetic association of Manganese Superoxide Dismutase with exudative age related macular degeneration. American Journal of Ophthalmology. 2000; 130: 769-773.

Kiser AK, Mladenovich D, Eshraghi F, et al. Reliability and consistency of visual acuity and contrast sensitivity measures in advanced eye disease. Optometry and Vision Science. 2005; 82:946–54.

Klaver CC, Wolfs RC, Vingerling JR, Hofman A, de Jong PT. Age-specific prevalence and causes of blindness and visual impairment in an older population: the Rotterdam Study. Archives of Ophthalmology 1998; 116: 653-8.

Klaver CC, Wolfs RC, Assink JJ, et al. Genetic risk of Age-Related Maculopathy. Population-based familial aggregation study. Archives of Ophthalmology. 1998; 116:1646- 1651.

Klein R, Davis MD, Magli YL, et al. The Wisconsin age-related maculopathy grading system. Ophthalmology. 1991; 98: 1128-1134.

Klein R, Klein BEK and Linton KLP. Prevalence of age-related maculopathy. The Beaver Dam Eye Study. Ophthalmology. 1992; 99: 933–943.

151

Visual predictors for poor mobility performance in people with AMD

Klein R, Wang Q, Klein BE, et al. The relationship of age-related maculopathy, cataract and glaucoma to visual acuity. Investigative Ophthalmology and Vision Science. 1995; 36: 182- 91.

Klein R, Rowland ML and Harris MI. Racial/ethnic differences in age-related maculopathy: Third National Health and Nutrition Survey. Ophthalmology. 1995; 102: 371–381.

Klein R, Klein BE and Cruickshanks KJ. The prevalence of age-related maculopathy by geographic region and ethnicity. Progress in Retinal and Eye Research. 1999; 18: 371–389.

Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005; 308: 385- 389.

Klein R, Cruickshanks KJ, Nash SD, et al. The prevalence of Age-Related Macular Degeneration and associated risk factors. Archives of Ophthalmology. 2010; 128: 750-8.

Kleiner RC, Enger C, Alexander MF, et al. Contrast sensitivity in Age-Related Macular Degeneration. Archives of Ophthalmology. 1988; 106: 55-7.

Knudtson MD, Klein BEK, Klein R, et al. Age-Related Eye Disease, Quality of Life, and Functional Activity. Archives of Ophthalmology. 2005; 123:807-814.

Krishnan T, Ravindran RD, Murthy GVS, et al. Prevalence of Early and Late Age-Related Macular Degeneration in India: The INDEYE Study. Investigative Ophthalmology and Vision Science. 2010; 51: 701-707.

Kuyk T, Elliott JL, Biehl J, Fuhr PS. Environmental variables and mobility performance in adults with low vision. Journal of American Optometric Association. 1996; 67: 403-9.

Kuyk T, Elliott JL and Fuhr PS. Vision correlates of obstacle avoidance in adults with low- vision. Optometry and Vision Science. 1998 ; 75 :174-82.

152

Visual predictors for poor mobility performance in people with AMD

Kuyk T, Elliott JL and Fuhr PS. Vision correlates of mobility in real world settings in older adults with low-vision. Optometry and Vision Science. 1998 ; 75 :538-47.

Kuyk T and Elliott J. Visual factors and Mobility in persons with age related macular degeneration, Journal of Rehabilitation Research and Development. 1999; 36(4): 303-12.

Kuyk T, Elliott JL, Wesley J, et al. Mobility function in older veterans improves after blind rehabilitation. Journal of Rehabilitation Research and Development. 2004; 41: 337-346.

Lamb TD and Pugh Jr EN. Dark adaptation and the retinoid cycle of vision. Progress in Retinal and Eye Research. 2004; 23: 307-380.

Landers J, Sharma A, Goldberg I and Graham S. A comparison of global indices between the Medmont Automated Perimeter and the Humphrey Field Analyzer. British journal of Ophthalmology. 2007; 91: 1285-1287.

Law enforcement games, 2009. http://www.lawgames.org/Rules/TCA%20Rules.htm

Leat SJ and Lovie-Kitchin J. Visual impairment and the useful field of vision. Ophthalmic and Physiologic Optics. 2006; 26:392-403.

Legge GE, Rubin GS, Pelli DG, and Schleske MM. Psychophysics of reading. II. Low vision. Vision Research. 1985; 25:253- 266.

Legge GE, Ross JA, Isenberg LM, LaMay JM. Psychophysics of reading: Clinical predictors of low vision reading speed. Investigative Ophthalmology and Vision Science. 1992; 33: 677-87.

Leibowitz HM, Kruegger DE, Maunder LR, et al. The Framingham Eye Study Monograph. Survey of Ophthalmology. 1980; 24(Suppl): 335-610.

Lerner MP, Donoso LA, Nordquist RE, et al. Immunological mimicry between retinal S- antigen and group A streptococcal M proteins. Autoimmunity. 1995; 22:95-106.

153

Visual predictors for poor mobility performance in people with AMD

Long RG, Rieser JJ, Hill EW. Mobility in individuals with moderate visual impairments. Journal of Visual Impairment and Blindness. 1990; 84: 111–8.

Lord SR, Clark RD and Webster IW. Postural stability and associated physiological factors in a population of aged persons. Journal of Gerontology. 1991; 46: 69-76.

Lord SR, Clark RD and Webster IW. Physiological factors associated with falls in an elderly population. Journal of the American Geriatrics Society. 1991; 39 (12):1194-200.

Lord SR, Ward JA, Williams P and Anstey K. An epidemiological study of falls in older community-dwelling women: the Randwick falls and fractures study. Australian Journal of Public Health 1993; 17:240-245.

Lord SR and Ward JA. Age-associated differences in sensori-motor function and balance in community dwelling women. Age and Ageing. 1994; 23: 452-60.

Lord SR, Sambrook PN, Gilbert C, Kelly PJ, Nguyen T, Webster IW, Eisman JA. Postural stability falls and fractures in the elderly: results from the Dubbo. Osteoporosis Epidemiology Study. Med J Aust 1994; 160: 684-685, 688-691.

Lord SR, Menz HB. Visual contributions to postural stability in older adults. Gerontology. 2000 Nov-Dec; 46(6):306-10.

Lord SR, Dayhew J, Howland A. Multifocal glasses impair edge contrast sensitivity and depth perception and increase the risk of falls in older people. Journal of American Geriatric Society. 2002; 50: 1760–1766.

Lord SR, Dayhew J. Visual risk factors for falls in older people. Journal of American Geriatric Society. 2001; 49: 508-515.

Lord SR, Menz HB and Tiedemann A. A physiological profile approach to falls risk assessment and prevention. Physical Therapy. 2003; 83: 237-252.

154

Visual predictors for poor mobility performance in people with AMD

Loshin DS and White J. Contrast sensitivity: the visual rehabilitation of the patient with macular degeneration. Archives of Ophthalmology. 1984; 102: 1301-1306.

Lovie-Kitchin J, Mainstone J, Robinson J, Brown B. What areas of the visual field are important for mobility in low-vision patients? Clinical Vision Sciences. 1990; 5: 249-63.

Lovie-Kitchin J and Feigl B. Assessment of age-related maculopathy using subjective vision tests. Clinical and Experimental Optometry. 2005; 88: 292-303.

Low FN. Peripheral visual acuity. Archives of Ophthalmology. 1951; 45: 1.

Luibl V, Isas JM, Kayed R, et al. Drusen deposits associated with aging and age-related macular degeneration contain nonfibrillar amyloid oligomers. The Journal of Clinical Investigation. 2006; 116(2): 378-385.

Lythgoe RJ. The mechanism of dark adaptation. British Journal of Ophthalmology. 1940; 24: 21-43.

Macular Photocoagulation Study Group. Subfoveal neovascular lesions in age-related macular degeneration: guidelines for evaluation and treatment in the macular photocoagulation study. Archives of Ophthalmology. 1991; 109:1242-1257.

Maguire P, Vine AK. Geographic atrophy of the retinal pigment epithelium. American Journal of Ophthalmology. 1986; 102:621-5.

Mahley RW. Apolipoprotein E: cholesterol transport protein with expanding role in cell biology. Science. 1988; 240: 622-630.

Mangione CM, Phillips RS, Seddon JM, et al. Development of the 'Activities of Daily Vision Scale': A Measure of Visual Functional Status. Medical Care. 1992; 30(12): 1111- 1126.

155

Visual predictors for poor mobility performance in people with AMD

Mangione CM, Lee PP, Pitts J, et al. Psychometric Properties of the National Eye Institute Visual Function Questionnaire (NEI-VFQ). Archives of Ophthalmology. 1998; 116:1496- 1504.

Mangione CM, Gutierrez PR, Lowe G, et al. Influence of Age-Related Maculopathy on visual functioning and health-related quality of life. American Journal of Ophthalmology. 1999; 128: 45 -53.

Mangione CM, Lee PP, Hays RD. Measurement of visual functioning and health related quality of life in eye disease and cataract surgery. In: Spilker B, ed. 2nd ed. New York: Lippincott- Raven; 1996:1045-51.

Margrain TH and Thomson D. Sources of variability in clinical photostress test. Ophthalmic and Physiologic Optics. 2002; 22: 61-7.

Marottoli RA, Berkman LF, Leo-Summers L, Cooney LM. Predictors of mortality and institutionalization after hip fracture: The New Haven EPESE cohort. American Journal of Public Health. 1995; 84:1807–1812.

Marottoli RA, Mendes de Leon CF, Glass TA, et al. Driving cessation and increased depressive symptoms: prospective evidence from the New Haven EPESE. Established populations for epidemiologic studies of the elderly. Journal of American Geriatric Society. 1997; 45: 202-6.

Marron JA and Bailey IL. Visual factors and orientation- mobility performance. American Journal of Optometry and Physiologic Optics. 1982; 59: 413-26.

Maumenee AE. Clinical manifestations (Symposium: Macular diseases). Transactions of the American Academy of Ophthalmology and Otolaryngology. 1965; 69: 605-613.

Mayer MJ, Ward B, Klein R, et al. Flicker sensitivity and fundus appearance in pre- exudative age-related maculopathy. Investigative Ophthalmology and Vision Science. 1994; 35: 1138-1149. 156

Visual predictors for poor mobility performance in people with AMD

McCallum J. The SF-36 in an Australian sample: validating a new generic health status measure. Australian Journal of Public Health. 1995; 19: 160-6.

McCarty CA, Taylor KI, McKay R and Keeffe JE. Diabetic Retinopathy: effects of national guidelines on the referral examination and treatment practices of ophthalmologists and optometrists. Clinical and Experimental Ophthalmology. 2001; 29:52-8.

McGraw P, Winn B, Whitaker D. Reliability of the Snellen chart. British Medical Journal. 1995; 310: 1481-82.

Mcquire MD. More pieces for the age-related Macular Degeneration Puzzle. Ophthalmology. 1997; 104(1): 7-21.

Meads C, Salas C, Roberts T, et al. Clinical cost effectiveness and cost-utility of photodynamic therapy for wet age-related macular degeneration: a systematic review and economic evaluation. Health Technology Assessment. 2003; 7: 1-98.

Medeiros NE and Curcio CA. Preservation of ganglion cell layer neurons in age-related macular degeneration. Investigative Ophthalmology and Vision Science. 2001; 42: 795- 803.

Medmont International Pty Ltd. http://www.medmont.com.au/products/m700-automated- perimeter/m700-specifications.aspx (Viewed 31/5/2010).

Mei M and Leat SJ. Suprathreshold contrast matching in maculopathy. Investigative Ophthalmology and Vision Science. 2007; 48: 3419-24.

Menz HB, Tiedemann A, Kwan MMS, et al. Foot pain in community-dwelling older people: an evaluation of Manchester foot pain and disability index. Rheumatology. 2006; 45: 863- 67.

157

Visual predictors for poor mobility performance in people with AMD

Meyer HE, Tverdal A, Falch JA, Pedersen JI. Factors associated with mortality after hip fracture. Osteoporosis International. 2000; 11:228–232.

Midena E, Angeli CD, Blarzino MC, et al. Macular function impairment in eyes with early age-related macular degeneration. Investigative Ophthalmology and Vision Science. 1997; 38: 469-77.

Millodot M. Dictionary of Optometry.1986; 1st edition: 54-55.

Mitchell P, Smith W, Attebo K and Wang JJ. Prevalence of Age-Related Maculopathy. The Blue Mountains Eye Study. Ophthalmology. 1995; 102: 1450-60.

Mitchell P, Wang JJ, Smith W and Leeder SR. Smoking and the five year incidence of Age- Related Maculopathy: the Blue Mountains Eye Study. Archives of Ophthalmology. 2002; 120: 1357-1363.

Mitchell J, Wolffsohn JS, Woodcock A, et al. Psychometric evaluation of the MacDQol individualised measure of the impact of macular degeneration on quality of life. Health and Quality of Life Outcomes. 2006; 4: 97. (http://www.hqlo.com/content/3/1/25).

Mitchell J and Bradley C. Quality of life in age-related macular degeneration: A review of the literature. Health and Quality of Life Outcomes. 2006; 4: 97.

Mones J and Rubin GS. Contrast sensitivity as an outcome measure in patients with subfoveal choroidal neovascularisation due to age- related macular degeneration. Eye. 2004; 19: 1142-50.

Montero Monterroso JL, Perula de Torres LA, Larrea Barroso MJ. Feeling sick and its relationship with anxiety and depression at a primary care emergency service. Aten Primaria. 2006; 38(4): 200-5.

Moore LW. Severe visual impairment in older women. Western Journal of Nursing Research. 2000; 22:571-95. 158

Visual predictors for poor mobility performance in people with AMD

Mukesh BN, Dimitrov PN, Leikin S, Wang JJ, Mitchell P, McCarty CA, Taylor HR. Five- year incidence of age-related maculopathy: the Visual Impairment Project. Ophthalmology. 2004; 111(6):1176-82.

Munoz B and West SK. Blindness and Visual impairement in the Americas and the Caribbean. British Journal of Ophthalmology. 2002; 86: 498-504.

Munoz B, Klein R, Rodriguez J, et al. Prevalence of age-related macular degeneration in a population – based sample of Hispanic people in Arizona: Proyecto VER. Archives of Ophthalmology. 2005; 123:1575-1580.

Neal RM and Baldwin RC. Screening for anxiety and depression in elderly medical outpatients. Age and Ageing. 1994; 23(6): 461-4.

Neelam K, Nolan J, Chakravarthy U and Beatty S. Psychophysical function in age-related maculopathy. Survey of Ophthalmology. 2009; 54: 167-210.

Nevitt M, Cummings S, Kidd S, et al. Risk factors for recurrent for non-syncopal falls. Journal of American Medical Association. 1989; 261: 2663- 8.

Nubukpo P, Houinato D, Preux PM, et al. Anxiety and depression among the epileptics in general population in Benin (Western Africa). Encephale. 2004; 30(3): 214-19.

Olympic Equestrian Competitions, 2009. http://horses.about.com/od/horsesportsexplained/a/olympic_rules.htm.

Owsley C and Sloane ME. Contrast sensitivity, acuity and the perception of real world targets. British Journal of Ophthalmology. 1987; 71:791-796.

Owsley C, Jackson GR and McGwin GJr. Aging and dark adaptation. Vision Research.1999; 39:3975- 82.

159

Visual predictors for poor mobility performance in people with AMD

Owsley C and Jackson GR. Scotopic sensitivity during adulthood. Vision Research. 2000; 40:2467- 73.

Owsley C, Jackson GR, Cideciyan AV, Huang Y, Fine SL, Ho AC, Maguire MG, Lolley V, Jacobson SG. Psychophysical evidence for rod vulnerability in age-related macular degeneration. Investigative Ophthalmology and Vision Science. 2000; 41:267-73.

Owsley C, Jackson GR, Whit MF, Feist R, Edwards D. Delays in rod-mediated dark adaptation in early age related maculopathy. Ophthalmology. 2001; 108: 1196- 202.

Owlsey C. Contrast sensitivity. Ophthalmolgy Clinics of North America. 2003; 16:171-7.

Owsley C, Jackson GR, McGwin GJr, Phillips JM and Klein R. Impact of Aging and Age- related Maculopathy on the activation of the a-wave of the rod mediated electroretinogram. Investigative Ophthalmology and Vision Science. 2004; 45 (9): 3271- 278.

Owlsey C, McGwin G Jr, Scilley K and Kallies K. Development of a questionnaire to assess vision problems under low luminance in age related maculopathy. Investigative Ophthalmology and Vision Science. 2006; 47 (2): 528-35.

Owlsey C, McGwin G Jr, Jackson GR, et al. Cone and Rod mediated dark adaptation impairment in age related maculopathy. Ophthalmology. 2007; 114:1728-35.

Paige GD. Effects of increased background luminance on static threshold perimetry. ARVO, Sarasota, Florida, The association for research in vision and ophthalmology. Investigative Ophthalmology and Vision Science. 1985; 26 (Suppl. 3): 226.

PARE, 2009. http://www.nait.ca/docs/PARE_-_Guidelines_for_Applicants.pdf

Patla AE. Understanding the roles of vision in the control of human locomotion. Gait and Posture. 1997; 5: 54-69.

160

Visual predictors for poor mobility performance in people with AMD

Patrick DL, Deyo RA. Generic and disease specific measures in assessing health status and quality of life. Medical Care. 1989; 27(suppl) S217-32.

Paulus WM, Straube A, Brandt T. Visual stabilization of posture. Physiologic stimulus characteristics and clinical aspects. Brain. 1984; 107: 1143-63.

Penfold PL, Killingsworth MC and Sarks SH. Senile Macular Degeneration: The involvement of Gaint cells in Atrophy of the Retinal Pigment Epithelium. Investigative Ophthalmology and Vision Science. 1986; 27: 364-371 . Penfold PL, Madigan MC, Gillies MC and Provis JM. Immunological and aetiological aspects of Macular Degeneration. Progress in Retinal and Eye Research. 2001; 20: 385-414.

Penninx BWJH, Guralnik JM, Ferrucci L, et al: Depressive symptoms and physical decline in community-dwelling older persons. Journal of American Medical Association. 1998; 279:1720–1726.

Penninx BWJH, Leveille S, Ferrucci L, et al. Exploring the effect of depression on physical disability: longitudinal evidence from the established populations for epidemiologic studies of the elderly. American Journal of Public Health 1999; 89:1346 –52.

Peters AY, Locke KG and Birch DG. Comparison of the Goldmann- Weekers dark adaptometer and LKC Technologies scotopic sensitivity tester-1. Documenta Ophthalmologica. 2000; 101:1-9.

Pirenne MH and Denton EJ. Accuracy and sensitivity of the human eye. Nature. 1952; 1039- 1042.

Polyak SL. The Retina. Chicago: University of Chicago Press; 1941.

Porciatti V, Burr DC, Morrone C and Fiorentini A. The effects of ageing on pattern electroretinogram and visually evoked potentials in humans. Vision Research. 1992; 32: 1199- 1209. 161

Visual predictors for poor mobility performance in people with AMD

Prieto L, Alonso J and Lamarca R. Classical test theory versus Rasch analysis for quality of life questionnaire reduction. Health Qual Life Outcomes. 2003; 1: 27.

ProFANE: Prevention of Falls Network Europe, www.profane.eu.org. 12/12/2007

Provis JM, Penfold PL, Cornish EE, et al. Anatomy and development of the macula: specialisation and vulnerability to macular degeneration. Clinical and Experimental Optometry. 2005; 88: 269-281.

Pulska T, Pahkala K, Laippala P, Kivela SL. Follow up study of longstanding depression as predictor of mortality in elderly people living in the community. British Medical Journal 1999; 318:432–3.

Rasch analysis- http://www.rasch-analysis.com/rasch-courses-workshops.htm (downloaded on 15/5/2010)

Raasch TW, Bailey IL, Bullimore MA. Repeatability of visual acuity measurement. Optometry and Vision Science.1998; 75 (5): 342-348.

Regan D. Spatial Vision. Vision and Visual Dysfunction. CRC Press, Boa Raton, FL. 1991; Volume 10.

Riggs LA. Visual acuity. Vision and Visual Perception. In C.H. Graham (Ed.), New York: Wiley. 1965; Chap. 11: 321–349.

Robbins AS, Rubenstein LZ, Josephson KR, et al. Predictors of falls among elderly people: Results of two population based studies. Archives of Internal Medicine. 1989; 149: 1628- 1633.

Rodrıguez de Cordoba S, Esparza-Gordillo J, Goicoechea de Jorge E, et al. The human complement factor H: functional roles, genetic variations and disease associations. Molecular Immunology. 2004; 41: 355-367. 162

Visual predictors for poor mobility performance in people with AMD

Rovner BW, Casten RJ, Tasman WS. Effect of depression on vision function in age related macular degeneration. Archives of Ophthalmology. 2002; 120: 1041-44.

Rovner BW and Casten RJ. Activity loss and depression in Age-Related Macular Degeneration. American Journal of Geriatric Psychiatry. 2002; 10:305-310.

Rovner BW, Casten RJ, Hegel MT and Tasman WS. Minimal depression and vision function in Age-Related Macular Degeneration. Ophthalmology. 2006; 113: 1743-7.

Rovner BW, Casten RJ, Hegel MT, et al. Dissatisfication with performance of valued activities predicts depression in age-related macular degeneration. International Journal of Geriatric Psychiatry. 2007; 22: 789-793.

Rubenstein LZ, Josephson KR and Robbins AS. Falls in the nursing home. Annals of Internal Medicine. 1994; 121: 442-451.

Rubin GS, Bandeen–Roche K, Prasada–Rao P, Fried LP. Visual impairment and disability in older adults. Optometry and Vision Science. 1994; 71: 750–760.

Rubin GS, Munoz B, Bandeen-Roche K, et al. Monocular versus binocular visual acuity as measures of vision impairment and predictors of visual disability. Investigative Ophthalmology and Vision Science. 2000; 41: 3327-34.

Ryan SJ, Mittl RN and Maumenee AE. The Disciform response: a historical perspective. Albrecht von Graefe's Archive for Clinical and Experimental Ophthalmology. 1980; 215: 1- 20.

Sandberg MA, Gaudio AR. Slow photostress recovery and disease severity in age-related macular degeneration. Retina. 1995; 15:407-12.

Sandberg MA, Weiner A, Miller A and Gaudio AR. High-risk Characteristics of Fellow Eyes of Patients with Unilateral Neovascular Age-related Macular Degeneration. Ophthalmology 1998; 105: 441-447. 163

Visual predictors for poor mobility performance in people with AMD

Sarks SH. New vessel formation beneath the retinal pigment epithelium in senile eyes. British Journal of Ophthalmology. 1973; 57:951–65.

Sarks SH. Ageing and degeneration in the macular region: a clinco-pathological study. British Journal of Ophthalmology. 1976; 60: 324-341.

Sarks JP, Sarks SH and Killingsworth MC. Evolution of geographic atrophy of the retinal pigment epithelium. Eye. 1988; 2: 552-577.

Schachat AP, Hyman L, Leske MC, et al. Features of Age-Related Macular Degeneration in a black population. The Barbados Eye Study Group. Archives of Ophthalmology. 1995; 113: 728-35.

Schmidt S, Haines JL, Postel EA, et al. Joint effects of smoking history and APOE genotypes in age-related macular degeneration. Molecular Vision. 2005; 11: 941-949.

Schneider U, Inhoffen W, Gelisken F, Kreissig I. Assessment of visual function in choroidal neovascularisation with scanning laser microperimetry and simultaneous indocyanine green angiography. Graefe’s Archives of Clinical and Experimental Ophthalmology. 1996; 234: 612- 17.

Schneider U, Gelisken F, Inhoffen W, et al. Pigment epithelial detachments with Retinal vascular anomalous complex in age-related macular degeneration. Ophthalmologica. 2005; 219:303-308.

Scilley K, Jackson GR, Cideciyan AV, Maguire MG, Jacobson SG, Owsley C. Early Age- Related Maculopathy and Self-reported Visual Difficulty in Daily life. Ophthalmology. 2002; 109(7): 1235-1242.

Scott IU, Smiddy WE, Schiffman J, et al. Quality of life of low-vision patients and impact of low-vision services. American Journal of Ophthalmology. 1999; 128: 54-62.

164

Visual predictors for poor mobility performance in people with AMD

Scott IU, Feuer WJ and Jacko JA. Impact of visual function on computer task accuracy and reaction time in a cohort of patients with age-related macular degeneration. American Journal of Ophthalmology. 2002; 133: 350-7.

Seddon JM, Ajani UA, Sperduto RD, et al. Dietary carotinoids, vitamins A, C and E and adavanced age-related macular degeneration. Journal of American Medical Association. 1994; 272: 1413-1420.

Seddon JM, Hennekens CH. Vitamins, Minerals and macular degeneration, Promising but unproven hypotheses. Archives of Ophthalmology. 1994; 112(2): 176 – 179.

Seddon JM, Willett WC, Speizer FE, Hankinson SE. A prospective study on cigarette smoking and age-related macular degeneration in women. Journal of American Medical Association. 1996; 276: 1141-1146.

Seddon JM, Ajani UA, Mitchell BD. Familial aggregation of age- related maculopathy. American Journal of Ophthalmology. 1997; 123: 199-206.

Seddon JM, Chen C: Epidemiology of age-related macular degeneration, in Ryan SJ (ed): Medical Retina, Vol. 2. St. Louis, C.V. Mosby, 2006. ed 4, pp 1017—27.

SEIFA-2039.0 - Information Paper: An Introduction to Socio-Economic Indexes for Areas (SEIFA), 2006. (downloaded on 10/01/2008) http://www.abs.gov.au/Ausstats/[email protected]/7d12b0f6763c78caca257061001cc588/84d68d8d 34deecbaca2571800020da7f!OpenDocument .

Sekuler R, Owlsey C and Hutman L. Assessing spatial vision of older people. American Journal of Optometry and Physiological Optics. 1982; 59: 961-8.

Severin SL, Tour RL and Kershaw RH. Macular function and photostress test I. Archives of Ophthalmology. 1967; 77:2-7.

165

Visual predictors for poor mobility performance in people with AMD

Shelley EJ, Madigan MC, Natoli R, et al. Cone degeneration in aging and age-related macular degeneration. Archives of Ophthalmology. 2009; 127: 483-492.

Shumway-Cook A, Brauer S, Woollacott M. Predicting the Probability for falls in Community-Dwelling Older Adults Using the Timed Up & Go Test. Physical Therapy 2000;80:896-903.

Simoneau GG, Leibowitz HW, Ulbrecht JS, et al. The effects of visual factors and head orientation on postural steadiness in women 55 to 70 years of age. Journal of Gerontology. 1992; 47: 151-8.

Singh H, Cooper RL, Adler VA, et al. The Arden grating acuity: effect of age and optical factors in the normal patient, with prediction of the false negative rate in screening of glaucoma. British Journal of Ophthalmology. 1981; 65: 518-524.

Silvestri G, Johnston PB and Hughes AE. Is genetic predisposition an important risk factor in age-related macular degeneration? Eye. 1994; 8: 564-8.

Skelton DA, Becker C, Lamb SE et al. Prevention of Falls Network Europe: a thematic network aimed at introducing good practice in effective falls prevention across Europe. European Journal of Aging. 2004; 1: 89–94.

Slakter JS, Stur M. Quality of life in patients with age-related macular degeneration: Impact of the condition and benefits of treatment. Survey of Ophthalmology. 2005; 50: 263-73.

Sloan L. The measurement of visual acuity. A critical review. Archives of Ophthalmology. 1951; 45: 704-25.

Sloan L. New charts for the measurement at far and near distance. American Journal of Ophthalmology. 1959; 48: 807-13.

Sloan LL. Area and luminance of test object as variables in examination of the visual field by projection perimetry. Vision Research. 1961; 1: 121-138. 166

Visual predictors for poor mobility performance in people with AMD

Sloan LL. Variation of acuity with luminance in ocular diseases and anomalies. Documenta Ophthalmologica. 1969; 26: 384-93.

Slooter AJ, Tang MX, van Duijn CM, et al. Apolipoprotein E epsilon4 and the risk of dementia with stroke. A population-based investigation. Journal of American Medical Association. 1997; 277: 818-821.

Smiddy WE, Fine SL. Prognosis of patients with bilateral macular drusen. Ophthalmology. 1984; 91:271-7.

Smith VC, Pokorny J and Diddie KR. Color matching and the Stiles–Crawford effect in observers with early age-related macular changes. Journal of the Optical Society of America. A, Optics, Image, Science and Vision. 1988; 5:2113–2121

Smith W, Assink J, Klein R, et al. Risk factors for age-related macular degeneration: Pooled findings from three continents. Ophthalmology. 2001; 108(4): 697-704.

Sommer A, Tielsch JM, Katz J, et al. Racial differences in the cause – specific prevalence of blindness in east Baltimore. The New England Journal of Medicine. 1991; 325 (20): 1412- 1417.

Soong GP, Lovie-Kitchin JE, Brown B. Preferred walking speed for assessment of mobility performance: sighted guide versus non-sighted guide techniques. Clinical and Experimental Optometry. 2000; 83:279–82.

Soong GP, Lovie-Kitchin JE, Brown B. Does mobility performance of visually impaired adults improve immediately after orientation and mobility training? Optometry and Vision Science. 2001; 78:657–66.

Souied EH, Benlian P, Amouyel P, et al. The ε4 allele of the apolipoprotein E gene as a potential protective factor for exudative age related macular degeneration. American Journal of Ophthalmology. 1998; 125(3): 353-359.

167

Visual predictors for poor mobility performance in people with AMD

Steinberg EP, Tielsch JM, Schein OD, et al. The VF-14. An Index of Functional Impairment in Patients with Cataract. Archives of Ophthalmology. 1994;112:630-638.

Steinmetz RL, Haimovici R, Jubb C, Fitzke FW and Bird AC. Symptomatic abnormalities of dark adaptation in patients with age-related Bruch’s membrane change. British Journal of Ophthalmology. 1993; 77: 549- 54.

Stelmack JA, Tang XC, Reda DJ, et al. Outcomes of the Veterans Affairs Low Vision Intervention Trial (LOVIT). Archives of Ophthalmology. 2008; 126:608-17.

Stevenson C. SF-36: Interim norms for Australian data. Canberra: Australian Institute of Health and Welfare.1996.

Stewart AL, Ware JE. Measuring Functioning and Well-Being: The Medical Outcomes Study Approach. Durham, NC: Duke University Press, 1992.

Straw LB and Harley RK. Assessment and training in orientation and mobility for older persons: program development and testing. Journal of Visual Impairment and Blindness. 1991; 85: 291-96.

Streiner DL and Norman GR. Devising the items. Health Measurement Scales: a practical guide to their development and use. 2nd edition. 1996; 3: 15-27

Sturnieks DL, George R and Lord SR. Balance disorders in the elderly. Neurophysiologie Clinique. 2008; 38: 467-78.

Sunness JS, Rubin GS, Applegate CA, et al. Visual function abnormalities and prognosis in eyes in eyes with age-related geographic atrophy of the macula and good visual acuity. Ophthalmology. 1997; 104: 1677- 91.

Sunness JS, Gonzalez-Baron J, Applegate CA, et al. Enlargement of atrophy and visual acuity loss in the geographic atrophy form of age-related macular degeneration. Ophthalmology. 1999; 106: 1768-79. 168

Visual predictors for poor mobility performance in people with AMD

Swann PG and Lovie-Kitchin J. Age-related maculopathy II: The nature of central visual field loss. Ophthalmology and Physiologic Optics. 1991; 11: 59-70.

Swamy BN, Chia EM, Wang JJ, et al. Correlation between vision- and health-related quality of life scores. Acta Ophthalmologica. 2009; 87:335-9.

Szabo SM, Janssen PA, Khan K, et al. Older women with age-related macular degeneration have a greater risk of falls: a physiological profile assessment study. Journal of the American Geriatrics Society. 2008; 56: 800-7.

Taft C, Karlsson J and Sullivan M. Do SF-36 summary component scores accurately summarise subscale scores? Quality of Life Research. 2001; 10: 395-404.

Tateiwa H, Kuroiwa S, Gaun S. Polypoidal choroidal vasculopathy with large vascular network. Graefes Archives of Clinical and Experimental Ophthalmology. 2002; 240:354–61.

Taylor HR and West SK. The clinical grading of lens opacities. Australian and New Zealand Journal of Ophthalmology. 1989; 17(1): 81-86.

Taylor H, Guymer R, Keeffe J. The impact of Age-Related Macular Degenration. Access Economics Pty Limited. Melbourne:University of Melbourne, 2006: 31-39.

Tennant A, McKenna SP and Hagell P. Application of Rasch Analysis in the Development and Application of Quality of Life Instruments. Value in Health. 2004; 7 :22-26.

Tezel TH, Del Priore LV, Flowers BE, et al. Correlation between scanning laser ophthalmoscope microperimetry and anatomic abnormalities in patients with subfoveal neovascularisation. Ophthalmology. 1996; 103: 1829-36.

The Eye Diseases Prevalence Study Group. Prevalence of Age-Related Macular Degeneration in the United States. Archives of Ophthalmology. 2004; 122: 564-572.

169

Visual predictors for poor mobility performance in people with AMD

The International ARM Epidemiological Study Group. An international classification and grading system for age-related Maculopathy and Age-Related Macular Degeneration. Survey of Ophthalmology. 1995; 39(5): 367-374.

Thompson M and Medley A. Performance of Community Dwelling Elderly on the Timed Up and Go test. Physical and occupational therapy in geriatrics 1995; 13:17-30.

Tiedemann AC, Shimada H, Sherrington C, Murray SM, Lord SR. The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age and Ageing 2008; 37;430-435.

Tomany SC, Wang JJ, Van Leeuwen R, et al. Risk factors for incident age-related macular degeneration: Pooled findings from three continents. Ophthalmology. 2004; 111(7): 1280- 1287.

Traquair HM. The field of vision: perimetric instruments and methods introductory. An introduction to clinical perimetry. H.Kimpton, London. 1949:1-3.

Tso MOM. Pathogenetic factors of Aging Macular Degeneration. Ophthalmology. 1985; 92: 628-635.

Tuo J, Bojanowski CM, Chan C-C. Genetic factors of age-related macular degeneration. Progress in Retinal and Eye Research. 2004; 23: 229–249.

Turano KA, Rubin GS and Quigley HA. Mobility Performance in Glaucoma. Investigative Ophthalmology and Vision Science. 1999; 40:2803-2809.

Turano KA, Broman AT, Bandeen- Roche K, et al. Association of visual field loss and mobility performance in older adults: Salisbury Eye Evaluation Study. Optometry and Vision Science. 2004; 81: 298-307.

Ulshafer RJ, Allen CB, Nicolaissen B Jr and Rubin ML. Scanning electron microscopy of human drusen. Investigative Ophthalmology and Vision Science. 1987;28:683-9. 170

Visual predictors for poor mobility performance in people with AMD

VanNewkirk MR, Weih L, McCarty CA, Stanislavsky YL, Keeffe JE, Taylor HR. Visual impairment and eye diseases in elderly institutionalized Australians. Ophthalmology. 2000; 107:2203-8.

Veagan and Halliday BL. A forced-choice test improves contrast sensitivity testing. British Journal of Ophthalmology. 1982; 66: 477- 491.

Verbaken JH and Johnston AW. Population norms for edge contrast sensitivity. American Journal of Optometry and Physiologic Optics. 1986; 63:724–32.

Verhoeff FH and Grossman HP. Pathogenesis of disciform degeneration of the macula. Archives of Ophthalmology. 1937; 18:561–85.

Vingerling JR, Dielemans I, Bots ML, et al. Age-related macular degeneration is associated with atherosclerosis. The Rotterdam study. American Journal of Epidemiomology. 1995; 142: 404-409.

Vingrys AJ and Helfrich KA. The Opticom M600: a new LED automated perimeter. Clinical and Experimetal Optometry. 1990; 73:3–17.

Wald G. On the mechanism of the visual threshold and visual adaptation. Science. 1954; 119: 887 – 892.

Wang JJ, Foran S, Mitchell P. Age-specific prevalence and causes of bilateral and unilateral visual impairment in older Australians: the Blue Mountains Eye Study. Clinical and Experimental Ophthalmology. 2000; 28: 268-273.

Ware JE, Snow KK, Kosinski M, Gandek B. SF-36® Health Survey Manual and Interpretation Guide. Boston, MA: New England Medical Centre, The Health Institute, 1993.

Weale R. The aging eye. 1963. H.K. Lewis, London.

171

Visual predictors for poor mobility performance in people with AMD

West SK and Taylor HR. The Detection and Grading of Cataract: An Epidemiologic perspective. Survey of Ophthalmology. 1986; 31(3): 175-184.

West S, Vitale S, Hallfrisch J, et al. Are antioxidants or supplements protective for age- related macular degeneration? Archives of Ophthalmology. 1994; 112:222-7.

Westheimer G. The spatial sense of the eye. Investigative Ophthalmology and Vision Science. 1979; 18: 893- 912.

Weymouth FW. Visual acuity: an analysis of the stimulus situation. American Journal of Optometry and Archives of American Academy of Optometry. 1955; 32(10): 507- 519.

Whittaker SG and Lovie-Kitchin JE. Visual requirements for reading. Optometry and Vision Science. 1993; 70: 54-65.

Wilcox DT and Burdett R. Contrast sensitivity function and mobility in elderly patients with macular degeneration. Journal of American Optometric Association. 1989; 60: 504-7.

Wild JM. Techniques and developments in automated perimetry: A review. Ophthalmic and Physiologic Optics. 1988; 8: 295-308.

Williams RA, Brody BL, Thomas RG, et al. The psychosocial impact of macular degeneration. Archives of Ophthalmology. 1998; 116: 514-20.

Wilson ME. Invariant features of spatial summation with changing locus in the visual field. Journal of Physiology. 1970; 207: 611-622.

Wolffsohn JS and Cochran A. Design of the low vision quality of life questionnaire (LVQOL) and measuring the outcomes of low vision rehabilitation. American Journal of Ophthalmology. 2000; 130: 793-802.

172

Visual predictors for poor mobility performance in people with AMD

Wong TY, Shankar A, Klein R, et al. Apolipoprotein E gene and early age-related Maculopathy: The atherosclerosis risk in community study. Ophthalmology. 2006; 113:255- 259.

Wood JM, Wild JM, Bullimore MA and Gilmartin B. Factor affecting the normal perimetric profile derived by automated static threshold LED perimetry, I. Pupil size. Ophthalmic and Physiologic Optics. 1988; 8: 26-31.

Wood AJJ. Age-Related Macular Degeneration. The New England Journal of Medicine. 2000; 342(7): 483-492.

Wu G, Weiter JJ and Santos S. The macular photostress test in diabetic retinopathy and age- related macular degeneration. Archives of Ophthalmology. 1990; 108: 1556-8.

Yannuzzi LA, Sorenson J, Spaide RF, et al. Idiopathic polypoidal choroidal vasculopathy. Retina 10:1–8, 1990.

Yannuzzi LA, Freund KB, Goldbaum M. Polypoidal choroidal vasculopathy masquerading as central serous chorioretinopathy. Ophthalmology 107:767–77, 2000.

Yannuzzi LA, Negrao S, Lida T, et al. Retinal angiomatous proliferation in age-related macular degeneration. Retina 2001; 21: 416–434.

Yardley L, Beyer N, Hauer K, et al. Development and initial validation of the Falls Efficacy Scale- International (FES-I). Age and Ageing. 2005; 34: 614-619.

Yarr MT. Central choroidoretinitis, resembling an optic disc, in a patient suffering from glycosuria. Transactions of the Ophthalmologiy Society of United Kingdom. 1898-1899; 19:68-69.

Yesavage JA., Brink TL, Rose TL, et al. Development and validation of a Geriatric

Depression Screening Scale: A preliminary report. Journal of Psychiatric Research. 1983; 17: 37-49. 173

Visual predictors for poor mobility performance in people with AMD

Zabriskie NA and Kardon RH. The pupil photostress test. Ophthalmology. 1994; 101: 1122- 1130.

Zareparsi S, Reddick AC, Branham KEH, et al. Association of Apolipoprotein E alleles with susceptibility to age-related macular degeneration in a large cohort from a single center. Investigative Ophthalmology and Vision Science. 2004; 45:1306-1310.

Zeiss A, Lewinsohn PM, Rohde P, et al. Relationship of physical disease and functional impairment to depression in older people. Psychology and Aging. 1996; 11:572-581.

Zhang L, Drance SM and Douglas GR.The ability of Medmont M600 automated perimetry to detect threats to fixation. Journal of Glaucoma. 1997; 6: 259-262.

Zhang K, Kniazeva M, Hutchinson A, et al. The ABCR gene in recessive and dominant Stargardt diseases: a genetic pathway in macular degeneration. Genomics. 1999 ; 60: 234– 237.

Zigmond AS and Snaith RP. The Hospital Anxiety and Depression Scale. Acta psychiatrica scandanavica. 1983:67:361-370.

174

Visual predictors for poor mobility performance in people with AMD

APPENDICES

Appendix 1: Consent form used for the study.

PARTICIPANT INORMATION STATEMENT AND CONSENT FORM

Visual predictors for falls and fractures in

people with Age-related macular degeneration Approval number: (074006)

You are invited to participate in a study "Visual predictors for falls and fractures in people with Age-related macular degeneration". We hope to learn which aspect of visual function helps most in predicting the risk of falls and fractures in people with macular degeneration. You were selected as a possible participant in this study because you are of age group 60-80/ above and were diagnosed as having macular degeneration or do not have any ocular diseases (healthy eyes)

If you decide to participate, we will: 1. Measure a number of your visual functions on the eye which is most affected with macular degeneration. * Visual acuity - the smallest letter you can read on an eye chart * Contrast sensitivity - the lowest amount of contrast you can see on a chart * Photo stress - how rapidly you recover after having a bright light shone into your eyes * Dark adaptation - how rapidly you can see a dim light in a dark room. This test is done following dilatation of your pupil * Visual fields - how well you can see small objects in your peripheral vision *Falls Screening test - to assess your balance, reaction time, muscle strength and proprioception. 2. Ask you to tell us about your general health, thinking abilities and your history of falls and fractures * complete a general health questionnaire (SF36) * complete a visual function questionnaire (LLQ) * complete mental health questionnaire (GADS) 175

Visual predictors for poor mobility performance in people with AMD

* complete questionnaires to assess your cognitive skills (Everyday mistakes) * complete a Falls Predictor questionnaire (Prince of Wales Falls Predictor) We will be sending you out the questionnaires which you can complete in your own pace and bring along with you on your visit (or handing it out on your visit according to your preference). It would take about 15 to 20 minutes of your time to fill out the questionnaires. These questionnaires were taken as they have been previously used in studies that looked at mobility in the elderly population conducted at the Prince of Wales Hospital and would help us to have an idea about your overall general, cognitive and mental health status assisting us with knowledge of factors that could interfere with the variables we are investigating. We will be contacting you over phone on a three monthly interval over the next one year period. 3. Measure your ability to negotiate a mobility course * You will walk through a simple, non-threatening mobility course * There will be 5 light levels from bright to quite dark * You will also walk through one situation where you will be dazzled by a bright light and then asked to walk through a dimly lit mobility course. You can wait for as long as you wish before commencing your passage through the course There is a very small risk that you might trip during the mobility course. The mobility course will be made very safe by covering any edges with soft foam blocks. A researcher will be following you through the course to support you in case you trip over. It would take about 2.5 - 3 hours to complete the test (We would be happy to split the sessions to meet your convenience). Refreshments will be provided in between the study though travel expenses will not be reimbursed.

We cannot and do not promise that you will receive any benefits from this study. The data collected from this study will be used in analysing the best visual function predictor for falls in people with macular degeneration. This ultimately would help in increasing the quality of life in people with macular degeneration.

Any information that is obtained in connection with this study and that can be identified with you will remain confidential and will be disclosed only with your permission, except as required by law. If you give us your permission by signing this document, we plan to publish the results in scientific/clinical journals and the UNSW for the purpose of evaluation. In any publication, information will be provided in such a way that you cannot be identified. 176

Visual predictors for poor mobility performance in people with AMD

It is not anticipated that you will incur any additional costs if you participate in this study. There are no costs to you for any tests specifically related to this research study. You will not receive any payment for participation in this study.

Complaints may be directed to the Ethics Secretariat, The University of New South Wales, SYDNEY 2052 AUSTRALIA (phone 9385 4234, fax 9385 6648, email [email protected]). Any complaint you make will be treated in confidence and investigated, and you will be informed of the outcome.

Your decision whether or not to participate will not prejudice your future relations with The University of New South Wales. If you decide to participate, you are free to withdraw your consent and to discontinue participation at any time without prejudice. If you have any questions, please feel free to ask us. If you have any additional questions later, Mrs Remy Sheena Mathew (phone 0432 230171 or 96633505) will be happy to answer them.

You are making a decision whether or not to participate. Your signature indicates that, having read the Participant Information Statement, you have decided to take part in the study.

………………………………………… .……………………………………… Signature of Research Participant Signature of Witness

………………………………………… .…………………………………. (Please PRINT name) (Please PRINT name)

………………………………… .……………………………… Date Nature of Witness

……………………………………… .………………………………. Signature(s) of Investigator(s) Please PRINT Name

177

Visual predictors for poor mobility performance in people with AMD

REVOCATION OF CONSENT

Visual predictors for falls and fractures in people with Age-related macular degeneration

I hereby wish to WITHDRAW my consent to participate in the research proposal described above and understand that such withdrawal WILL NOT jeopardise any treatment or my relationship with The University of New South Wales.

…………………………………………………… .……………………………………………………. Signature Date

…………………………………………………… Please PRINT Name

The section for Revocation of Consent should be forwarded to Mrs Remy Sheena Mathew, School of Optometry and Vision Science, UNSW, Sydney. NSW. 2052.

178

Visual predictors for poor mobility performance in people with AMD

Appendix 2 : Study invitation poster

CALL FOR VOLUNTEERS

Are you of the age 70 or over 70 ??? Do you have good vision???? If so, We invite you to participate in our exciting venture to find predictors for falls in people with macular degeneration.

To complete this study we also need people without macular degeneration and good eye health. Your contribution could make a valuable difference to the lives of those that suffer from macular degeneration

The study includes: )Questions about general health, general wellbeing and any history of falls and fractures.

)Measurement of the visual functions.

)Measurement of your ability to walk through mobility course.

This study is a one-off session and will take about 2.5-3 hours of your time. If you are interested, please contact Mrs Remy Mathew (96633505 or 0432 230171) at the School of

Optometry and Vision Science at UNSW for further information.

179

Visual predictors for poor mobility performance in people with AMD

Appendix 3: Comparison between the FES-1 and Short version FES-1

FES-1 Short version FES-1

Cleaning the house Getting dressed or undressed Getting dressed/undressed Taking a bath or shower Preparing simple meals Getting in or out of a chair Taking a bath or shower Going up or down stairs Going to the shop Reaching for something above your head Getting in or out of a chair or on the ground Going up or down stairs Walking up or down a slope Walking around outside Going out to a social event (e.g. religious Reaching up or bending down service, family gathering or club meeting) Answering the telephone Walking on a slippery surface Visiting a friend/relative Going to a place with crowds Walking on an uneven surface Walking up or down a slope Going out to a social event

180

Visual predictors for poor mobility performance in people with AMD

Appendix 4: Demographic details of the Control data.

Subjects Age Gender Lens (Grade) AMD (Grade) C1 62 1 1 0 C2 69 2 1 0 C3 62 1 1 0 C4 63 1 1 0 C5 63 2 1 0 C6 64 1 1 0 C7 69 1 2 0 C8 81 1 2 0 C9 63 1 1 0 C10 67 1 2 0 C11 72 1 2 0 C12 79 1 2 0 C13 71 1 1 0 C14 72 1 1 0 C15 79 2 2 0 C16 71 1 1 0 C17 75 1 2 0 C18 65 1 1 0 C19 65 1 1 0 C20 77 1 1 0 C21 71 1 1 0 C22 69 2 1 0 C23 65 2 1 0 C24 72 2 1 0 C25 75 1 2 0 C26 82 1 2 0 C27 74 2 2 0 C28 72 1 2 0 C29 80 2 2 0 C30 80 1 2 0 C31 71 2 1 0 C32 66 2 1 0 C33 74 1 2 0 C34 69 1 2 0 C35 69 1 1 0

181

Visual predictors for poor mobility performance in people with AMD

Appendix 5: Demographic details of the AMD data.

Subjects Age Gender Lens (Grade) AMD (Grade) A1 80 1 2 4.2 A2 61 2 1 4.2 A3 78 2 1 4.1 A4 77 2 1 4.2 A5 63 1 1 3.4 A6 80 2 2 4.1 A7 77 1 1 4.1 A8 82 1 1 4.2 A9 81 2 1 4.2 A10 82 2 1 3.4 A11 69 2 1 4.2 A12 72 1 1 4.2 A13 74 2 1 4.2 A14 84 2 1 4.2 A15 80 2 2 4.2 A16 80 2 2 4.2 A17 76 2 2 4.2 A18 83 2 2 4.1 A19 74 1 2 4.2 A20 76 2 2 4.2 A21 86 2 2 4.2 A22 71 2 1 4.2 A23 71 2 1 4.1 A24 77 1 1 4.2 A25 69 1 1 4.2 A26 92 1 2 4.2 A27 78 1 1 4.2 A28 77 2 1 4.2 A29 69 2 2 4.1 A30 86 1 2 4.1 A31 78 1 2 4.2 A32 84 2 2 4.2 A33 67 2 1 4.2

182

Visual predictors for poor mobility performance in people with AMD

Appendix 6: Data on Visual Acuity, Contrast sensitivity and Photostress for Controls.

Subjects Category VA (Log MAR) PST (secs) CS (dB) C1 1 0 18 20 C2 1 0 50 23 C3 1 0 20 23 C4 1 0 30 21 C5 1 0 15 20 C6 1 0 15 20 C7 1 0 29 20 C8 1 0.1 20 20 C9 1 0 20 22 C10 1 0 17 21 C11 1 0 30 20 C12 1 0 10 19 C13 1 0 18 20 C14 1 0.06 3 19 C15 1 0 41 24 C16 1 0 11 19 C17 1 0 11 20 C18 1 0 20 21 C19 1 0 9 21 C20 1 0.08 30 21 C21 1 0 32 23 C22 1 0 23 23 C23 1 0 20 22 C24 1 0 29 23 C25 1 0 20 23 C26 1 0 15 23 C27 1 0 30 24 C28 1 0 18 21 C29 1 0 20 22 C30 1 0 15 21 C31 1 0 15 20 C32 1 0 16 23 C33 1 0 22 21 C34 1 0.08 40 22 C35 1 0 20 20

183

Visual predictors for poor mobility performance in people with AMD

Appendix 7: Data on Visual Acuity, Contrast sensitivity and Photostress for AMD.

VA Subjects Category (LogMAR) PST (secs) CS (dB) A1 2 0.3 197 15 A2 2 0.6 170 14 A3 2 0 60 18 A4 2 0 50 22 A5 2 0.2 240 9 A6 2 0.3 120 14 A7 2 0.1 117 14 A8 2 0.46 108 12 A9 2 0.9 38 10 A10 2 0 57 14 A11 2 0.2 86 15 A12 2 0.4 51 13 A13 2 1 33 7 A14 2 0.84 225 5 A15 2 1 35 5 A16 2 1 52 1 A17 2 0.04 62 15 A18 2 0.14 40 19 A19 2 0.6 116 7 A20 2 0.2 209 10 A21 2 0.5 110 10 A22 2 0.1 118 A23 2 0.74 100 12 A24 2 1 45 5 A25 2 0.54 70 11 A26 2 0.74 55 11 A27 2 0.7 40 16 A28 2 1.1 206 7 A29 2 0.1 65 14 A30 2 1 120 7 A31 2 0.6 102 5 A32 2 0.7 42 8 A33 2 0.36 60 20

184

Visual predictors for poor mobility performance in people with AMD

Appendix 8: Specifications for the Medmont M700W Automatic perimeter.

(Medmont International Pty Ltd.)

Stimulator bowl type: Part hemispherical radius 300mm integrated diffusing surface

Visual Field extend 100° Standard field test Full 50°- 164 points; Glaucoma 22°/50°- 104 points; Central 30°- 100 points; Peripheral 30° to 50°- 73 points; Flicker 15°/22°- 164 points; Macula 10°- 49 point; Quickscan 22°/30°- 40 points; Driving test 50°/80°- 103 points; Binocular 30°/40°- 21-128 points Stimulus source: Rear projection light emitting diode (LED) Stimulus colour: Pale green LED; wavelength 565nm Stimulus size: Goldmann size III (0.43°) Stimulus intensity: 0.003 asb to 1000 asb in 16 dB Exposure time: Adjustable- 0.1 to 9.9s Response time: Adjustable- 0.1 to 9.9s Inter test delay: Adjustable- 0.1 to 9.9s Background illumination 10 asb (3.2 cd/m2), automatic level control Fixation target: Yellow LED, wavelength 358 nm Fixation monitor: Heijl-Krakau blindspot method Test lens diameter: 38mm diameter Simulator unit: dimensions: 626mm W x 438mm D x 713mm H Power: 110/220/240 VAC 50/60 Hz 30W Fuse ratings: 110 VAC- 800mA 220/240 VAC- 400mA

185

Visual predictors for poor mobility performance in people with AMD

Appendix 9: Example of visual field for a control subject in 8 cd/m2 background luminance.

186

Visual predictors for poor mobility performance in people with AMD

Appendix 10: Example of visual field for AMD subject in 8 cd/m2 background luminance.

187

Visual predictors for poor mobility performance in people with AMD

Appendix 11: Visual field data for controls.

Subjects MD155 PSD155 MD8 PSD8 MD3 PSD3 MD1 PSD1 MD0.2 PSD0.2 MD0.06 PSD0.06 C1 -0.03 0.03 -0.27 0.02 -0.02 0.03 -0.16 0.04 -0.54 0.1 -1.42 0.47 C2 1.5 0.38 0.48 0.08 0.43 0.12 0.98 0.25 -0.29 0.17 0.47 0.12 C3 -0.43 0.15 -1.21 0.27 -1.25 0.31 -0.43 0.23 -0.7 0.2 -0.59 0.28 C4 3.81 2.69 2.51 1.2 1.93 0.72 1.73 0.81 2.74 1.56 1.89 0.71 C5 3.14 1.75 1.66 0.62 1.56 0.48 1.26 0.56 1.11 0.37 1.72 0.79 C6 -3.07 1.78 0.59 0.11 1.15 0.23 0.22 0.04 0.77 0.15 0.88 0.26 C7 0.6 0.1 0.5 0.1 0.86 0.17 0.47 0.12 0.43 0.13 0.68 0.11 C8 0.71 0.21 0.62 0.17 0.83 0.19 0.4 0.09 1.49 0.36 2.73 1.29 C9 0.91 0.29 0.23 0.04 0.77 0.18 1.34 0.37 0.96 0.25 0.27 0.11 C10 -1.56 0.47 -1.83 0.6 -1.62 0.58 -1.35 0.63 -1.18 0.26 -0.93 0.23 C11 0.6 0.28 0.33 0.06 -0.32 0.07 -0.58 0.14 0.85 0.18 0.49 0.15 C12 0.57 0.19 0.11 0.11 0.9 0.16 1.42 0.54 1.78 0.53 1.29 0.28 C13 -2.09 0.77 -0.15 0.11 -0.69 0.16 -0.48 0.1 -0.31 0.04 -0.53 0.09 C14 0.95 0.18 0.61 0.16 0.87 0.18 0.33 0.07 0.79 0.12 0.49 0.11 C15 -0.58 0.27 -0.75 0.1 -1.33 0.37 -0.93 0.25 -0.8 0.21 0.73 0.11 C16 0.46 0.4 -1.22 0.24 -0.65 0.22 -0.08 0.05 0.34 0.11 0.23 0.03 C17 2.38 1.01 0.04 0.05 0.08 0.1 -0.76 0.23 0.03 0.08 -6.06 6.88 C18 0.35 0.08 0.35 0.06 0.24 0.01 -0.95 0.18 0 0.03 -1.82 0.64 C19 0.57 0.1 0.72 0.12 0.45 0.14 -0.7 0.13 0.16 0.04 0.22 0.07 C20 -3.57 2.32 -1.67 0.46 -2.11 0.91 -1.41 0.46 -2.45 1.13 -2.39 0.97 C21 1.95 1.3 0.98 0.27 1.13 0.33 2.09 1.4 2.84 2.22 6.79 8.42 C22 -2.35 1.04 0.99 0.25 1.78 0.71 1.05 0.36 1.22 0.37 0.84 0.22 C23 1.08 0.37 0.27 0.15 0.21 0.13 0.95 0.4 1.1 0.27 0.93 0.26 C24 1.26 0.33 0.66 0.11 1.24 0.26 1.12 0.3 1.54 0.52 0.65 0.2 C25 -0.21 0.08 -0.62 0.1 -0.07 0.05 -0.1 0.06 -1.81 0.7 -0.85 0.16 C26 0.15 0.04 -0.45 0.06 0.24 0.04 0.24 0.04 0 0.06 1.26 0.38 C27 -2.92 1.88 -2.79 1.54 -2.6 1.46 -2.93 1.94 -2.8 1.65 -2.41 1.15 C28 -2.87 1.53 -2.34 0.91 -2.74 1.49 -1.54 0.62 -2.12 0.86 -1.95 0.76 C29 2.11 0.64 0.7 0.11 0.72 0.16 1.14 0.31 0.77 0.27 1.33 0.34 C30 0.35 0.04 0.51 0.1 1.18 0.26 0.55 0.17 0.87 0.18 2.35 1.09 C31 -1.58 0.82 -1.61 0.44 -1.59 0.48 -1.8 0.78 -1.57 0.55 -1.88 0.62 C32 4.13 2.95 3.27 1.97 0.97 0.22 2.76 1.86 1.69 0.76 4.01 2.94 C33 3.08 1.77 2.29 1.1 1.48 0.46 2.48 1.51 3.13 1.9 2.37 1.1 C34 1.5 0.38 0.48 0.08 0.43 0.12 0.98 0.25 -0.29 0.17 0.47 0.12 C35 -1.25 0.32 -0.46 0.14 -1.14 0.22 -1.02 0.25 -1.99 0.79 -1.41 0.38

188

Visual predictors for poor mobility performance in people with AMD

Appendix 12: Visual field data for AMD.

Subjects MD155 PSD155 MD 8 PSD 8 MD 3 PSD3 MD 1 PSD1 MD0.2 PSD0.2 MD0.06 PSD0.06 A1 -1.87 0.55 -1.38 0.45 -3.73 1.81 -2.57 1.39 -2.5 1.15 -1.81 0.99 A2 -1.42 0.34 -2.04 0.65 -0.49 0.12 -0.56 0.17 -2.09 0.89 -0.85 0.12 A3 1.14 0.53 -1.76 0.73 -1.97 0.54 0.07 0.09 1.39 0.45 -1.24 0.29 A4 -0.81 0.18 -1.57 0.62 -0.6 0.23 -0.03 0.06 1.16 0.64 2.37 1.15 A5 -0.91 0.43 -3.76 5.06 -4.4 8.53 -4.06 7.66 -4.86 7.41 -5.57 8.88 A6 -5.01 4.5 -7.89 11.07 -7.43 11.5 -8.86 19.17 -8.51 14.68 -10.2 19.42 A7 -1.51 0.49 -1.74 0.93 -3.03 1.98 -1.5 1.24 -3 1.87 -3.47 2.42 A8 -1.68 0.45 -3.88 3.05 -4.5 3.61 -0.32 0.47 -3.28 1.95 -5.29 6.62 A9 -5.2 4.62 -10.07 20.09 -9.34 23 -6.03 12.61 -15.82 45 -15.03 42.04 A10 0.8 0.28 0.24 0.05 0.03 0.03 -0.49 -2.3 0.71 0.2 -1.52 0.76 A11 3.55 2.33 -0.43 0.09 -0.23 0.1 -1.63 0.49 -1.91 0.7 -2.55 1.31 A12 1.38 0.35 -1.92 1.93 -1.65 1.69 -2.56 3.8 -3.39 2.89 -5.48 6.45 A13 -2.96 1.9 -7.91 11.74 -10.35 26 -5.67 11.63 -11.39 29.58 -14.41 38.87 A14 -5.73 5.68 -13.13 35.36 -13.74 45.75 -7.69 20.8 -17.59 63.95 -17.63 59.2 A15 -5.84 5.84 -10.81 23.02 -10.86 30.18 -10.27 25.55 -11.41 28.46 -15.16 47.02 A16 -3.81 3.15 -7.61 13.74 -6.58 10.86 -1.64 2.28 -10.66 21.3 -9.61 17.63 A17 -0.62 0.47 -3.99 2.85 -3.63 3.47 -3.2 4.69 -5.04 4.6 -5.93 6.5 A18 -3.34 2 -3.61 2.93 -3.6 2.47 -3.82 4.26 -4.03 3.09 -4.95 4.64 A19 -2.58 1.59 -5.38 6.47 -5.72 6.28 -0.18 -4.3 -6.06 6.64 -4.95 5.03 A20 -3.27 1.92 -2.83 2.34 -1.87 1.48 -3.45 5.21 -4.44 4.96 -5.2 6.06 A21 -4.74 3.91 -7.29 10.14 -6.28 10.19 -6.39 10.32 -9 16.94 -8.22 13.24 A22 -0.87 0.17 -0.64 0.17 0.18 0.08 -0.09 0.11 -0.77 0.16 -1.24 0.41 A23 -0.38 0.22 -4.08 6 -5.89 8.2 -5.38 9.95 -4.91 6.21 -9.62 17.6 A24 -3.1 2.04 -9.91 18.93 -9.64 20.64 -6.92 14.68 -8.4 14.21 -7.94 12.7 A25 -2.45 1.38 -6.73 9.49 -5.81 7.19 -7.83 16.61 -6.92 11.01 -7.36 10.44 A26 -4.61 4.13 -9.49 17.63 -9.72 20.63 -8.24 17.56 -9.01 19.27 -9.98 19.33 A27 -1.01 0.43 -1.1 0.33 -2.06 0.79 -0.7 0.22 -3.88 3.26 -5.29 5.23 A28 -5.78 6.12 -10.89 23.07 -12.75 34.4 -10.52 29.15 -12.54 29.43 -13.18 33.19 A29 -3.22 1.9 -2.59 1.19 -2.37 1.05 -2.61 1.62 -2.16 1.03 -1.92 0.8 A30 -4.43 3.81 -8.32 14.89 -13.9 42.41 -11.85 31.97 -10.82 26.12 -13.77 38.21 A31 -5.49 5.53 -7.47 11.76 -9.4 20.49 -10.59 26.26 -9.36 17.18 -10.17 19.88 A32 -5.4 5.31 -10.26 20.14 -11.52 26.66 -13.31 40.09 -18.64 61 -17.89 56.41 A33 1.01 0.28 -0.5 0.16 -1.09 0.25 -0.62 0.3 -1.26 0.32 -3.6 2.3

189

Visual predictors for poor mobility performance in people with AMD

Appendix 13: IDL Non-Linear Regression Model (Adapted from Herse, 1995).

Cone Exponential: T = B1 + I1* exp (R1*t)

Where T: model threshold in log units; B1: dark adapted cone threshold in log units; I1: initial cone threshold in log units after exposure to the light source; R1: cone exponential constant; t: time

Rod Exponential: T = B1 + I1* exp (-R1*min0) – I2* exp (R2 – min0) + I2* exp (-R2*t)

Where T: model threshold in log units; B1: dark adapted cone threshold in log units; I2: rod threshold in log units; R2: rod exponential constant; t: time in minutes; min0: Alpha point/ rod-cone break up time.

The program command string is given below:

;program for curve fitting darkadapt and to find alpha; to obtain the plot in a file; to include rod thresh output t = indgen(30) + 1.0 y=[6.4,6.05,5.68,5.32,5.08,5.0,4.85,4.6,4.6,4.5,4.48,4.35,4.22,4.08,3.8,3.4,3.25,3.12,2.9,2.55 ,2.38,2.28,2.15,2.12,1.8,1.6,1.5,1.4,1.55,1.5]; C1 a = [4.1,-1.47,4.13,7.5,4.1,-0.13] weights = 1.0d/y yfit = curvefit(t,y,weights,a,sigma,FUNCTION_NAME='darkadapt')

PRINT, 'I1', a(0) PRINT, 'Cone decay constant', a(1) PRINT, 'cone threshold', a(2) PRINT, 'Break point(alpha)', a(3) PRINT, 'I2', a(4)

190

Visual predictors for poor mobility performance in people with AMD

PRINT, 'Rod decay constant', a(5) plot,t,yfit,xtitle='TIME (min)',ytitle='LOG THRESHOLD' oplot,t,y,psym=1 rodthresh = a(0) * exp(a(1)*a(3)) + a(2) - a(4) * exp(a(5)*a(3)) print,'Rod threshold', rodthresh

;The following set of commands will generate a file called "darkadapt graph1.eps" in the folder: IDL50SV

;So, rename the filename soon after it is created before running the next data OR change the filename in the

;second line below each time.

set_plot, 'ps'

device, filename = 'C1.eps'; This is the file name in which graph will be stored

device, xsize=18.0; Here, 18.0 is the x axis length in cm device, ysize=18.0; Here, 18.0 is the y axis length in cm plot,t,yfit,xtitle='TIME (min)',ytitle='LOG THRESHOLD' oplot,t,y,psym=1 device,/close set_plot, 'win' end

191

Visual predictors for poor mobility performance in people with AMD

Appendix 14: IDL Non-Linear Regression Model output.

IDL Student Version 5.0.1 (Win32 x86). Research Systems, Inc. For basic information, enter "IDLInfo" at the IDL> prompt.

IDL> .run darkadapt % Compiled module: DARKADAPT.

IDL> .run cfit_darkadapt2[1] % Compiled module: $MAIN$. % Compiled module: CURVEFIT. I1 2.8491661 Cone decay constant -0.19401068 cone threshold 4.0680197 Break point(alpha) 13.495606 I2 12.959836 Rod decay constant -0.092502135

192

Visual predictors for poor mobility performance in people with AMD

Appendix 15: Examples of Control subject’s DA function modelled by IDL software.

Dark adaptation curve for subject C1

193

Visual predictors for poor mobility performance in people with AMD

Dark adaptation curve for subject C2

Dark adaptation curve for subject C3

Dark adaptation curve for subject C4 194

Visual predictors for poor mobility performance in people with AMD

Appendix 16: Examples of AMD subject’s DA function modelled by IDL software.

Dark adaptation curve for subject A1

Dark adaptation curve for subject A2

195

Visual predictors for poor mobility performance in people with AMD

Dark adaptation curve for subject A3

Dark adaptation curve for subject A4 196

Visual predictors for poor mobility performance in people with AMD

Appendix 17: Results derived from IDL program for controls.

Subjects Cone threshold Rate of cone Alpha Rate of Rod Rod threshold C1 3.58 0.26 8.6 0.22 1.63 C2 4.18 0.5 9.14 0.11 1.1 C3 4.18 0.49 7.89 0.07 0.32 C4 4.16 0.23 4.68 0.04 0.35 C5 4.26 0.3 10.91 0.08 0.93 C6 4.21 0.26 7.02 0.03 2 C7 4.43 0.42 5.99 0.03 0.54 C8 5.04 0.79 5.68 0.06 0.43 C9 4.28 0.42 11.58 0.06 0.1 C10 4.71 0.33 6.66 0.03 -1.63 C11 3.49 0.13 14.84 0.17 1.71 C12 4.04 0.15 7.61 0.06 -0.15 C13 4.46 0.45 8.74 0.08 0.98 C14 4.11 0.25 9.87 0.12 1.3 C15 4.44 0.29 5.55 0.03 -0.4 C16 4.07 0.19 13.5 0.09 0.56 C17 4.24 0.51 11.48 0.09 0.91 C18 4.51 0.51 11.94 0.55 0.11 C19 4.29 0.53 10.25 0.23 2.01 C20 3.77 0.29 7.41 0.12 1.51 C21 4.11 0.22 11.83 0.1 1.17 C22 4.04 0.42 8 0.07 1.69 C23 4.23 0.4 4.94 0.03 1.63 C24 4.31 0.45 7.39 0.03 -2 C25 3.77 0.26 6.64 0.1 1.4 C26 3.77 0.24 8.01 0.06 0.13 C27 4.4 0.78 8 0.03 2.06 C28 4.18 0.78 6.09 0.03 -1.41 C29 4.37 0.22 8.91 0.02 2.17 C30 4.14 0.32 11.93 0.03 1.66 C31 3.52 0.21 11.99 0.15 1.17 C32 4.3 0.4 5.04 0.03 1.87 C33 3.94 0.38 8.68 0.04 0.27 C34 4.09 0.51 12.51 0.03 -1.81 C35 4.67 0.48 7.56 0.08 1.84

197

Visual predictors for poor mobility performance in people with AMD

Appendix 18: Results derived from IDL program for AMD.

Subjects Cone threshold Rate of cone Alpha Rate of Rod Rod threshold A1 4.14 0.1 18.04 0.03 3.7 A2 5.83 0.11 13.91 0.02 4.88 A3 4.27 0.06 23.58 0.04 4.33 A4 5.45 0.11 16 0.03 4.48 A5 4.14 0.06 14.33 3.83 0.1 A6 4.8 0.18 10.79 0.1 3.14 A7 4.59 0.18 6.78 0.06 2.91 A8 4.38 0.33 10.64 0.1 2.70 A9 4.53 0.15 12.98 0.03 3.37 A10 6.05 0.13 13.37 0.01 4.16 A11 4.62 0.13 16.95 0.07 4.04 A12 4.74 0.12 15.08 0.03 4.08 A13 5.38 0.17 6.57 0.16 4.75 A14 5.28 0.17 7.03 0.04 4.01 A15 4.04 0.08 16.29 0.13 3.36 A16 5.79 0.13 11.99 0.01 4.04 A17 5.55 0.09 11.73 0.05 4.19 A18 3.8 0.14 14.48 0.22 3.45 A19 5.15 0.23 14.91 0.09 3.57 A20 3.824 0.11 17.5 0.18 3.43 A21 4.86 0.1 11.58 0.02 3.74 A22 3.85 0.16 13.73 0.13 2.02 A23 5.21 0.12 8.05 0.07 4.55 A24 4.75 0.12 14.43 0.11 3.11 A25 6.06 0.29 13.31 0.05 4.51 A26 4.5 0.54 10.89 0.15 2.14 A27 4.31 0.09 11.82 0.08 3.46 A28 4.86 0.25 14.7 0.02 3.57 A29 5.83 0.17 11.06 0.14 4.63 A30 5.11 0.3 13.86 0.11 3.97 A31 6.05 0.13 13.37 0.01 5.27 A32 4.76 0.19 11.95 0.11 3.48 A33 4.09 0.51 12.51 0.03 3.03

198

Visual predictors for poor mobility performance in people with AMD

Appendix 19: Pearson’s Correlation co-efficients (R) for Visual function variables.

2 3 4 5 6 7 8 9 10 1 VA ‐0.12 ‐0.75* ‐0.6* 0.6* ‐0.78* 0.76* ‐0.80* 0.77* ‐0.66* 2 PST ‐ ‐0.14 ‐0.07 0.02 0.05 ‐0.01 ‐0.01 0.05 ‐0.01 3 CS ‐ ‐ 0.63* ‐0.62* 0.75* ‐0.73* 0.74* ‐0.70* 0.74* 4 MD155 ‐ ‐ ‐ ‐0.83* 0.85* ‐0.78* 0.81* ‐0.73* 0.83* 5 PSD 155 ‐ ‐ ‐ ‐ ‐0.86* 0.85* ‐0.82* 0.81* ‐0.88* 6 MD8 ‐ ‐ ‐ ‐ ‐ ‐0.96* 0.95* ‐0.89* 0.91* 7 PSD8 ‐ ‐ ‐ ‐ ‐ ‐ ‐0.92* 0.92* ‐0.90* 8 MD3 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐0.96* 0.93* 9 PSD3 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐0.88* 10 MD1 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

Pearon’s correlation co-efficients (R) for Visual function variables (Contd)

11 12 13 14 15 16 17 18 1 VA 0.67* ‐0.77* 0.66* ‐0.78* 0.72* 0.21 0.41* 0.29 2 PST ‐0.01 0.06 ‐0.07 ‐0.1 ‐0.1 0.06 0.07 ‐0.1 3 CS ‐0.71* 0.74* ‐0.62* ‐0.62* ‐0.63* ‐0.12 ‐0.41* ‐0.29 4 MD155 ‐0.78* 0.80* ‐0.71* ‐0.71* ‐0.71* ‐0.17 ‐0.23 ‐0.17 5 PSD 155 0.86* ‐0.84* 0.79* 0.79* 0.80* 0.13 0.18 0.2 6 MD8 ‐0.89* 0.93* ‐0.86* ‐0.86* ‐0.86* ‐0.16 ‐0.33 ‐0.27 7 PSD8 0.90* ‐0.91* 0.90* 0.90* 0.90* 0.05 0.26 0.26 8 MD3 ‐0.92* 0.90* ‐0.84* ‐0.89* ‐0.89* ‐0.21 ‐0.43* ‐0.28 9 PSD3 0.91* ‐0.87* 0.86* 0.92* 0.92* 0.17 0.36* 0.21 10 MD1 ‐0.97* 0.94* ‐0.86* ‐0.89* ‐0.90* ‐0.41 ‐0.53* ‐0.41 11 PSD1 ‐ ‐0.94* 0.92* 0.92* 0.95* 0.36 0.51* 0.43* 12 MD0.2 ‐ ‐ ‐0.95* ‐0.93* ‐0.93* ‐0.13 ‐0.40* ‐0.37* 13 PSD 0.2 ‐ ‐ ‐ 0.96 0.96* 0.02 0.3 0.31 14 MD0.06 ‐ ‐ ‐ ‐ ‐0.95* ‐0.23 ‐0.44* ‐0.35 15 PSD0.06 ‐ ‐ ‐ ‐ ‐ 0.15 0.37* 0.3 16 CT ‐ ‐ ‐ ‐ ‐ ‐ 0.69* ‐0.31 17 RT ‐ ‐ ‐ ‐ ‐ ‐ ‐ 0.04 18 Alpha ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

199

Visual predictors for poor mobility performance in people with AMD

Appendix 20: Normal range for sensorimotor functions in people aged 60 and above

Age group Men Sensorimotor Function Women (years) (Aged 60 – 64) 60 – 64 0.2-1.8 Proprioception 0.2-1.8 30-50 Muscle strength 19-38 192-249 Reaction time 192-249 63-121 Balance 61-130 65 – 69 0.3-2.1 Proprioception 0.3-2.1 28-46 Muscle strength 18-33 194-258 Reaction time 194-258 65-127 Balance 62-143 70 – 74 0.4-2.4 Proprioception 0.4-2.4 25-46 Muscle strength 15-29 197-267 Reaction time 197-267 68-151 Balance 65-163 75 – 79 0.4-2.8 Proprioception 0.4-2.8 22-45 Muscle strength 13-26 203-284 Reaction time 203-284 74-198 Balance 75-190 80 – 84 0.6-3.2 Proprioception 0.6-3.2 17-43 Muscle strength 10-23 208-305 Reaction time 208-305 79-246 Balance 90-230 85 and above 0.8-3.8 Proprioception 0.8-3.8 16-40 Muscle strength 7-22 222-336 Reaction time 222-336 86-246 Balance 120-280

200

Visual predictors for poor mobility performance in people with AMD

Appendix 21: Instructions given before mobility performance assessment.

¾ The subjects were informed that their mobility performance would be assessed using a simple and complex course.

¾ They were informed that before their mobility performance was assessed they would be pre-adapted to the required luminance level (Fig 5-2) for 3 minutes.

¾ The Preferred Walking Speed was assessed using the simple course.

¾ They were asked to walk at their normal pace and it was also emphasised that the performance measure was not based on their walking speed for this part of the test.

¾ The subjects were then asked to follow a course of A, B, C, D, E and F after appropriate pre-adaptation.

¾ The subjects were instructed that they could pause at the start of each course till they were comfortable with the current illumination and able to see obstacles in the path, so that they could manoeuvre their way through the course.

¾ The subjects were instructed that the investigator would follow them closely to minimize the risk of injury from fall due to tripping.

¾ The subjects were informed that the positions of the obstacles were altered after each course while the subject was pre-adapting to avoid learning effects.

¾ The subjects were informed that the stop watch was started as soon as they entered the mobility and was stopped when the subject reached the end of the mobility course. They were also informed that this measurement along with the number of contacts that they made with the obstacles would be recorded for assessment of their mobility performance.

201

Visual predictors for poor mobility performance in people with AMD

Appendix 22: Mobility performance measures for Controls

PPWS PPWS PPWS Subject PWS 1500 155.00 MPS 155 12.00 MPS 12 PPWS 3.00 MPS 3 PPWS 0.20 MPS 0.02 PPWS 0.06 MPS 0.06

C1 1.33 100.00 83.93 83.93 82.02 82.02 78.46 78.46 73.65 73.65 18.99 18.99

C2 1.2 90.91 100.00 100.00 93.02 93.02 100.00 100.00 95.24 95.24 21.05 20.16

C3 1.2 100.00 88.89 88.89 86.96 86.96 88.89 88.89 86.96 86.96 44.44 39.53

C4 1.5 100.00 91.43 91.43 86.49 86.49 88.89 88.89 80.00 80.00 18.82 17.94

C5 1.33 100.00 83.93 83.93 82.02 82.02 80.20 80.20 76.79 76.79 40.10 36.68

C6 1.33 100.00 78.46 78.46 76.79 76.79 76.79 76.79 75.19 75.19 30.08 28.73

C7 1 100.00 94.12 94.12 90.57 90.57 94.12 94.12 88.89 88.89 40.00 36.59

C8 1.2 100.00 72.73 72.73 64.52 64.52 78.43 78.43 75.47 75.47 32.79 30.67

C9 1.09 100.00 91.74 91.74 78.64 78.64 89.87 89.97 89.87 89.87 44.94 44.94

C10 1 100.00 100.00 100.00 100.0 100.0 100.00 100.00 96.00 96.00 34.29 33.61

C11 1.09 100.00 100.00 100.00 100.0 100.0 100.00 100.00 100.00 100.00 27.52 26.59

C12 1 100.00 87.27 87.27 87.27 87.27 87.27 87.27 85.71 85.71 28.24 26.49

C13 1.33 90.23 94.97 94.97 80.20 80.20 94.97 94.97 90.23 90.23 34.05 30.79

C14 1.33 100.25 78.46 78.46 69.40 69.40 83.93 83.93 83.93 83.93 21.23 20.55

C15 1 100.00 92.31 92.31 90.57 90.57 92.31 92.31 90.57 90.57 20.87 20.37

C16 1 100.00 100.00 100.00 102.13 102.13 96.00 96.00 96.00 96.00 28.24 26.91

C17 1.09 100.08 97.86 97.86 100.08 100.08 95.73 95.73 95.73 95.73 25.90 25.48

C18 1.2 100.00 105.26 105.26 100.00 100.00 105.26 105.26 97.56 97.56 26.67 25.71

C19 1.5 100.00 88.89 88.89 84.21 84.21 84.21 84.21 82.05 82.05 37.65 34.26

C20 1.2 100.00 88.89 88.89 86.96 86.96 86.96 86.96 85.11 85.11 19.05 18.55

C21 1.09 100.00 125.82 125.82 125.82 125.82 125.82 125.82 110.09 110.09 22.94 22.29

C22 1.09 100.00 125.82 125.82 125.82 125.82 125.82 125.82 122.32 122.32 28.05 27.56

C23 1.2 100.00 97.56 97.56 97.56 97.56 97.56 97.56 97.56 97.56 48.78 47.17

C24 1.2 100.00 97.56 97.56 97.56 97.56 97.56 97.56 93.02 93.02 24.10 24.10

C25 1.33 100.00 90.23 90.23 90.23 90.23 90.23 90.23 90.23 90.23 31.38 31.38

C26 1.2 100.00 100.00 100.00 100.00 100.00 100.00 100.00 95.24 95.24 23.53 22.78

C27 1.2 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 105.26 22.22 21.55

C28 1.2 100.00 114.29 114.29 114.29 114.29 114.29 114.29 108.11 108.11 25.00 24.57

C29 1 100.00 109.09 109.09 109.09 109.09 109.09 109.09 104.35 104.35 28.24 27.33

C30 1.09 100.00 125.82 125.82 125.82 125.82 125.82 125.82 125.82 125.82 20.97 20.97

C31 1 100.00 106.67 106.67 102.13 102.13 102.13 102.13 102.13 102.13 37.50 25.93

C32 1.2 100.00 97.56 97.56 97.56 97.56 97.56 97.56 86.96 86.96 18.52 17.83

C33 1 100.00 117.07 117.07 117.07 117.07 117.07 117.07 109.09 109.09 22.86 21.98

C34 1.33 100.25 85.93 85.93 85.93 85.93 83.93 83.93 80.20 80.20 20.05 19.74

C35 1.09 100.00 110.09 110.09 110.09 110.09 110.09 110.09 104.85 104.85 31.45 30.84

202

Visual predictors for poor mobility performance in people with AMD

Appendix 23: Mobility performance measures for AMD

PPWS PPWS PPWS PPWS MPS MPS Subjects PWS 1500 155.00 MPS 155 12.00 MPS 12 PPWS 3.00 MPS 3 0.20 0.02 PPWS 0.06 0.06

A1 1.33 82.02 65.62 65.62 60.15 57.47 53.07 53.07 50.13 44.89 10.94 9.49

A2 0.75 80.00 114.29 114.29 80.00 74.77 110.34 100.63 45.71 42.33 9.85 9.29

A3 1.5 88.89 80.00 80.00 66.67 66.67 84.21 84.21 74.42 74.42 10.26 9.90

A4 1.33 90.23 94.97 94.97 82.02 77.12 82.02 82.02 64.45 58.59 13.27 12.38

A5 1.33 82.02 80.20 80.20 63.32 63.32 75.19 75.19 46.27 46.27 15.04 14.21

A6 0.86 82.08 74.42 71.74 54.72 54.72 62.02 62.02 48.53 44.23 7.18 7.01

A7 0.92 86.96 108.70 108.70 69.57 69.57 106.48 106.48 88.43 77.41 26.09 22.33

A8 1.09 84.69 75.93 75.93 68.81 68.81 75.93 72.43 69.90 64.19 33.62 24.28

A9 0.71 88.95 85.58 82.65 54.08 52.90 69.70 69.70 58.28 56.91 27.71 22.54

A10 1 92.31 94.12 89.22 80.00 76.43 88.89 73.62 78.69 72.07 15.95 14.23

A11 1.09 84.69 81.55 81.55 72.19 69.02 80.07 80.07 60.32 56.03 16.94 13.69

A12 0.8 100.00 98.36 98.36 93.75 89.82 81.08 81.08 93.75 89.55 29.70 26.09

A13 1.33 90.23 85.93 85.93 70.77 70.77 80.20 80.20 70.77 63.76 21.11 14.47

A14 1.33 75.19 37.21 37.21 34.70 34.70 37.99 37.99 21.48 19.83 12.53 11.13

A15 1 100.00 97.96 97.96 72.73 72.73 71.64 66.12 63.16 56.87 18.68 15.61

A16 1.2 100.00 71.43 71.43 71.43 71.43 71.43 71.43 59.70 59.70 29.20 21.67

A17 1.09 91.74 61.16 61.16 37.01 36.15 53.70 53.70 32.14 30.88 11.68 10.87

A18 0.8 100.00 101.69 101.69 86.96 86.96 88.24 88.24 82.19 82.19 45.45 39.58

A19 1.09 84.69 77.26 77.26 57.94 57.94 73.39 73.39 56.46 54.50 18.43 14.93

A20 0.8 83.33 100.00 100.00 73.17 73.17 88.24 88.24 76.92 71.77 16.22 15.08

A21 0.71 76.82 80.48 80.48 58.79 56.06 63.78 57.68 44.48 37.56 5.63 5.13

A22 1 85.71 96.00 96.00 87.27 87.27 81.36 81.36 68.57 63.49 19.59 18.53

A23 0.92 81.52 100.33 100.33 74.53 74.53 91.53 91.53 72.46 69.75 24.84 20.04

A24 1.33 90.23 78.46 78.46 60.15 60.15 75.19 75.19 75.19 71.04 14.44 12.98

A25 0.92 81.52 108.70 108.70 94.86 94.86 104.35 98.81 93.17 84.70 17.69 14.75

A26 1 92.31 88.89 88.89 80.00 76.43 64.86 64.86 46.60 45.37 12.00 7.58

A27 0.86 87.21 99.67 99.67 107.33 107.33 107.33 96.90 111.63 91.20 31.01 26.83

A28 0.86 99.67 111.63 111.63 101.48 101.48 94.60 94.60 69.77 63.14 22.42 21.45

A29 0.92 100.33 100.33 100.33 94.86 94.86 89.96 89.96 54.92 54.92 16.88 16.44

A30 0.86 93.02 101.48 101.48 96.23 96.23 93.02 93.02 50.74 48.28 10.53 9.81

A31 0.8 83.33 100.00 100.00 85.71 85.71 82.19 82.19 50.00 46.73 10.17 9.93

A32 0.86 87.21 93.02 93.02 79.73 79.73 66.45 66.45 51.68 49.13 10.69 10.58

A33 1.5 80.00 80.00 80.00 76.19 76.19 74.42 74.42 72.73 72.73 20.25 19.23

203

Visual predictors for poor mobility performance in people with AMD

Appendix 24: Error score (ES) data for Controls

Subjects ES 155 ES 12 ES 3 ES 0.2 ES 0.06 C1 2.00 2.00 2.00 2.00 2.00 C2 2.00 2.00 2.00 2.00 1.40 C3 2.00 2.00 2.00 2.00 1.30 C4 2.00 2.00 2.00 2.00 1.40 C5 2.00 2.00 2.00 2.00 1.40 C6 2.00 2.00 2.00 2.00 1.52 C7 2.00 2.00 2.00 2.00 1.30 C8 2.00 2.00 2.00 2.00 1.40 C9 2.00 2.00 2.00 2.00 2.00 C10 2.00 2.00 2.00 2.00 1.70 C11 2.00 2.00 2.00 2.00 1.52 C12 2.00 2.00 2.00 2.00 1.30 C13 2.00 2.00 2.00 2.00 1.30 C14 2.00 2.00 2.00 2.00 1.52 C15 2.00 2.00 2.00 2.00 1.52 C16 2.00 2.00 2.00 2.00 1.40 C17 2.00 2.00 2.00 2.00 1.70 C18 2.00 2.00 2.00 2.00 1.52 C19 2.00 2.00 2.00 2.00 1.40 C20 2.00 2.00 2.00 2.00 1.52 C21 2.00 2.00 2.00 2.00 1.52 C22 2.00 2.00 2.00 2.00 1.70 C23 2.00 2.00 2.00 2.00 1.70 C24 2.00 2.00 2.00 2.00 2.00 C25 2.00 2.00 2.00 2.00 2.00 C26 2.00 2.00 2.00 2.00 1.52 C27 2.00 2.00 2.00 2.00 1.52 C28 2.00 2.00 2.00 2.00 1.70 C29 2.00 2.00 2.00 2.00 1.52 C30 2.00 2.00 2.00 2.00 2.00 C31 2.00 2.00 2.00 2.00 1.52 C32 2.00 2.00 2.00 2.00 1.40 C33 2.00 2.00 2.00 2.00 1.40 C34 2.00 2.00 2.00 2.00 1.70 C35 2.00 2.00 2.00 2.00 1.70

204

Visual predictors for poor mobility performance in people with AMD

Appendix 25: Error score (ES) data for Controls

Subjects ES 155 ES 12 ES 3 ES 0.2 ES 0.06 A1 2.00 1.70 2.00 1.40 0.72 A2 2.00 1.52 1.52 1.30 0.82 A3 2.00 2.00 2.00 2.00 1.30 A4 2.00 1.70 2.00 1.52 1.10 A5 2.00 2.00 2.00 2.00 1.22 A6 1.70 2.00 2.00 1.30 1.10 A7 2.00 2.00 2.00 1.40 0.89 A8 2.00 2.00 1.70 1.52 0.72 A9 1.70 1.70 2.00 1.70 0.68 A10 1.70 1.70 1.30 1.52 0.85 A11 2.00 1.70 2.00 1.52 0.64 A12 2.00 1.70 2.00 1.40 0.96 A13 2.00 2.00 2.00 1.52 0.54 A14 2.00 2.00 2.00 1.22 0.85 A15 2.00 2.00 1.52 1.40 0.72 A16 2.00 2.00 2.00 2.00 0.74 A17 2.00 1.70 2.00 1.52 0.96 A18 2.00 2.00 2.00 2.00 1.10 A19 2.00 2.00 2.00 1.70 0.68 A20 2.00 2.00 2.00 1.52 0.96 A21 2.00 1.52 1.30 0.96 0.37 A22 2.00 2.00 2.00 1.52 1.22 A23 2.00 2.00 2.00 1.70 0.72 A24 2.00 2.00 2.00 1.70 0.96 A25 2.00 2.00 1.70 1.52 0.66 A26 2.00 1.70 2.00 1.70 0.89 A27 2.00 2.00 1.52 1.30 0.96 A28 2.00 2.00 2.00 1.40 1.30 A29 2.00 2.00 2.00 2.00 1.40 A30 2.00 2.00 2.00 1.52 0.82 A31 2.00 2.00 2.00 1.40 1.22 A32 2.00 2.00 2.00 1.52 1.52 A33 2.00 2.00 2.00 2.00 1.40

205

Visual predictors for poor mobility performance in people with AMD

Appendix 26: Data from trial 1 and 2 for preferred walking speed and percentage preferred walking speed.

Subject C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 Category 1 1 1 1 1 2 2 2 2 2 PWS 1.33 1.2 1.09 1.33 1.33 0.8 1.33 1.33 1 1.2 PWS1 1.34 1.18 1.06 1.35 1.35 0.82 1.3 1.33 1.12 1.2 PWS1500 100 100 100 90 100 100 90 75 100 100 PPWS1500A 100 100 100 92 100 100 92 73 100 100 PPWS155 83.93 72.73 91.74 94.97 78.46 98.36 85.93 37.21 97.96 71.43 PPWS155A 83.93 72.73 94.97 91.74 80.12 99.12 85.93 37.21 97.96 72.43 PPWS12 82.02 64.52 78.64 80.2 69.4 93.75 70.77 34.7 72.73 71.43 PPWS12A 82.02 65.02 79.15 81.32 70 93.15 72 31.12 73.41 71.52 PPWS3 80.2 78.43 89.87 94.97 83.93 81.08 80.2 37.99 71.64 71.43 PPWS3A 81 79.8 83.93 92.12 85 84.42 80.2 40 72.13 72 PPWS0.2 76.79 75.47 89.87 90.23 83.93 93.75 70.77 21.48 63.16 59.7 PPWS0.2A 77 75.5 89.97 91.23 85 94.53 71.42 20 62.13 60 PPWS0.06 40.1 32.79 44.94 34.05 21.23 29.7 21.11 12.53 18.68 29.2 PPWS0.06A 39.79 32.79 44.94 34.05 21.23 30.12 21.11 13 17.68 28.78

Comparison of PWS and PPWS from trial 1 and trial 2

95% Confidence Interval of the Difference

Lower Upper t df Sig. (2-tailed) 1 Pair 1 PWS - PWS1 -0.02912 0.02912 0 4 1 Pair 2 PPWS 1500 - -1.33374 0.72422 -0.822 4 0.457 PPWS1500A Pair 3 PPWS155 - PPWS155A -3.3147 2.65167 -0.309 4 0.773

Pair 4 PPWS12 - PPWS12A -1.04085 -0.05071 -3.061 4 0.038 Pair 5 PPWS3 - PPWS3A -2.85975 5.08284 0.777 4 0.48 Pair 6 PPWS0.2 - PPWS0.2A -1.11671 0.15132 -2.114 4 0.102

Pair 7 PPWS0.06 - PPWS0.06A -0.11389 0.23368 0.957 4 0.393

2 Pair 1 PWS - PWS1 -0.09354 0.04954 -0.854 4 0.441

Pair 2 PPWS 1500 - -1.66244 1.82786 0.132 4 0.902 PPWS1500A Pair 3 PPWS155 - PPWS155A -0.96013 0.25371 -1.616 4 0.181 Pair 4 PPWS12 - PPWS12A -1.90868 2.77793 0.515 4 0.634 Pair 5 PPWS3 - PPWS3A -2.9865 0.42296 -2.088 4 0.105

Pair 6 PPWS0.2 - PPWS0.2A -1.12592 1.43665 0.337 4 0.753

Pair 7 PPWS0.06 - PPWS0.06A -0.65731 0.86684 0.382 4 0.722

206

Visual predictors for poor mobility performance in people with AMD

Appendix 27: Pearson’s correlation coefficient (R) for mobility performance measures and visual function measures.

Visual Mobility performance measure function RSp RSs PPWS PPWS PPWS PPWS PPWS PPWS measures Course Course B Course Course Course E Course F A C D VA Pearson’s -0.02 0.12 0.02 0.06 0.14 0.07 -0.09 0.05 correlation p-value 0.93 0.50 0.92 0.74 0.45 0.70 0.61 0.79 N 33 33 33 33 33 33 33 33 CS Pearson’s 0.2 -0.26 0.16 0.23 -0.09 0.17 0.30 0.12 correlation p-value 0.27 0.14 0.39 0.21 0.62 0.35 0.10 0.52 N 33 33 33 33 33 33 33 33 PST Pearson’s -0.26 -0.08 -0.12 -0.6 -0.52 -0.19 -0.38 0.34 correlation p-value 0.14 0.66 0.50 0.76 0.002 0.29 0.03 0.05 N 33 33 33 33 33 33 33 33 MD155 Pearson’s 0.41 0.33 0.02 0.27 0.10 0.09 0.38 0.14 correlation p-value 0.02 0.06 0.91 0.13 0.60 0.63 0.03 0.43 N 33 33 33 33 33 33 33 33 MD8 Pearson’s 0.39 0.43 0.19 0.38 0.09 0.24 0.41 0.16 correlation p-value 0.03 0.01 0.29 0.03 0.64 0.17 0.02 0.39 N 33 33 33 33 33 33 33 33 MD3 Pearson’s 0.34 0.35 0.17 0.34 -0.09 0.15 0.38 0.17 correlation p-value 0.06 0.02 0.35 0.05 0.62 0.41 0.03 0.35 N 33 33 33 33 33 33 33 33

207

Visual predictors for poor mobility performance in people with AMD

Visual Mobility performance measure function RSp RSs PPWS PPWS PPWS PPWS PPWS PPWS measures Course A Course B Course C Course D Course E Course F MD1 Pearson’s 0.47 0.24 0.09 0.211 0.07 0.05 0.31 0.30 correlation p-value 0.01 0.18 0.63 0.24 0.62 0.80 0.08 0.09 N 33 33 33 33 33 33 33 33 MD0.2 Pearson’s 0.32 0.44 0.21 0.40 0.02 0.22 0.39 0.10 correlation p-value 0.07 0.01 0.34 0.02 0.91 0.22 0.03 0.60 N 33 33 33 33 33 33 33 33 MD0.06 Pearson’s 0.25 -0.39 0.17 0.35 -0.06 0.17 0.30 0.04 correlation p-value 0.16 0.02 0.35 0.04 0.74 0.35 0.03 0.81 N 33 33 33 33 33 33 33 33 PSD155 Pearson’s -0.37 -0.38 -0.12 -0.32 -0.16 -0.12 -0.37 -0.17 correlation p-value 0.03 0.03 0.50 0.07 0.37 0.52 0.03 0.33 N 33 33 33 33 33 33 33 33 PSD8 Pearson’s -0.35 -0.45 -0.21 -0.40 -0.09 -0.24 -0.40 -0.14 correlation p-value 0.05 0.01 0.24 0.03 0.64 0.19 0.02 0.43 N 33 33 33 33 33 33 33 33 PSD3 Pearson’s -0.28 -0.39 -0.19 0.36 -0.08 -0.22 -0.31 -0.05 correlation p-value 0.12 0.02 0.29 0.04 0.65 0.22 0.02 0.78 N 33 33 33 33 33 33 33 33

208

Visual predictors for poor mobility performance in people with AMD

Visual function Mobility performance measure measures RSp RSs PPWS PPWS PPWS PPWS PPWS PPWS Course Course Course Course Course E Course F A B C D PSD1 Pearson’s 0.45 -0.28 -0.3 -0.25 -0.08 -0.04 0.32 -0.28 correlation p-value 0.01 0.02 0.90 0.18 0.69 0.83 0.08 0.12 N 33 33 33 33 33 33 33 33 PSD0.2 Pearson’s -0.31 0.36 -0.15 -0.32 0.06 -0.20 0.32 -0.08 correlation p-value 0.08 0.04 040 0.07 0.76 0.26 0.07 0.68 N 33 33 33 33 33 33 33 33 PSD0.06 Pearson’s -0.15 -0.39 -0.18 -0.36 0.09 -0.16 0.19 0.07 correlation p-value 0.42 0.03 0.32 0.04 0.62 040 0.30 0.71 N 33 33 33 33 33 33 33 33 Cone threshold Pearson’s -0.17 -0.25 0.36 0.25 -0.06 0.20 0.11 0.13 correlation p-value 0.34 0.15 0.04 0.16 0.74 0.27 0.54 0.47 N 33 33 33 33 33 33 33 33 Rod threshold Pearson’s -0.17 -0.03 0.02 -0.02 -0.12 -0.06 -0.08 0.17 correlation p-value 0.35 0.88 0.90 0.93 0.52 0.72 0.66 0.34 N 33 33 33 33 33 33 33 33 Rod-Cone break- up time Pearson’s 0.98 0.06 -0.07 -0.10 -0.16 0.03 0.07 0.06 correlation p-value 0.59 0.75 0.71 0.58 0.37 0.88 0.69 0.73 N 33 33 33 33 33 33 33 33

209

Visual predictors for poor mobility performance in people with AMD

Appendix 28: Pearson’s correlation co-efficient (R) for Error scores and visual function variables

ES 155 ES12 ES3 ES0.20 ES0.06 Visual acuity 0.09 0.20 -0.02 -0.17 -0.30 Contrast sensitivity -0.08 -0.31 -0.04 0.14 0.26 PST 0.13 0.02 0.09 -0.28 <0.01 MD155 0.09 -0.24 -0.02 0.24 0.02 PSD155 -0.15 0.14 0.07 -0.27 0.02 MD8 0.06 -0.22 -0.11 0.19 0.13 PSD8 -0.07 0.21 0.10 -0.21 -0.09 MD3 <0.01 -0.31 -0.18 0.19 0.11 PSD3 -0.01 0.26 0.15 -0.23 -0.07 MD1 0.21 -0.15 -0.17 0.22 -0.05 PSD1 -0.16 0.20 0.20 -0.21 0.16 MD0.2 0.09 -0.20 -0.13 0.24 0.12 PSD0.2 -0.12 0.17 0.13 -0.22 -0.02 MD0.06 0.10 -0.29 -0.11 0.21 0.13 PSD0.06 -0.10 0.24 0.11 -0.24 -0.06 Cone threshold -0.33 -0.09 -0.29 -0.38* -0.27 Rod threshold -0.26 0.02 -0.10 -0.31 -0.41* Alpha 0.34 0.24 0.05 -0.21 -0.06

210