Digital Fall Prevention for Older Adults Feasibility of a Self-Managed Exercise Application and Development of a Smartphone Self-Test for Balance and Leg Strength

Digital fall prevention for older adults Feasibility of a self-managed exercise application and development of a smartphone self-test for balance and leg strength

Linda Månsson

Department of Community Medicine and Rehabilitation, Section of Physiotherapy Department of Radiation Sciences, Radiation Physics and Biomedical Engineering Umeå University, Sweden Umeå 2021 Responsible publisher under Swedish law: the Dean of the Medical Faculty This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD in Medicine Copyright © Linda Månsson, 2021 ISBN: 978-91-7855-449-2 (print) ISBN: 978-91-7855-450-8 (pdf) ISSN: 0346-6612 New Series Number: 2113 Front cover illustration: Rose Cooper, Valencia, www.rosecooper.com Electronic version available at: http://umu.diva-portal.org/ Printed by: Cityprint i Norr AB Umeå, Sweden 2021

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Table of Contents

Abstract ...... iii Enkel sammanfattning på svenska ...... v Abbreviations ...... viii Definitions ...... ix Original papers ...... x Preface ...... xii Introduction ...... 1 The ageing population and falls...... 1 Falls in older adults ...... 2 Balance the opposite to falls ...... 4 Exercise as fall prevention ...... 5 Adherence to fall prevention interventions ...... 6 Self-management of exercise to prevent falls ...... 7 Digital technology, eHealth, and falls in older adults ...... 9 Fall prevention with digital technology ...... 10 Smartphones and sensor measurements ...... 12 Balance function self-assessment with smartphone...... 13 User experience to facilitate eHealth ...... 15 Rationale ...... 17 Aims ...... 19 Materials and Methods ...... 20 Study design ...... 21 Safe Step feasibility study (Papers I & II) ...... 21 MyBalance project (Papers III & IV) ...... 24 Co-creation study for the self-test application development (Paper III) ...... 25 Concurrent validity study (Paper IV)...... 26 Setting ...... 27 Participants ...... 27 Safe Step feasibility study (Paper I & II) ...... 28 MyBalance co-creation study (Paper III) ...... 28 MyBalance validity study (Paper IV) ...... 29 Data collection ...... 29 Safe Step feasibility study (Paper I & II) ...... 29 Co-creation study for the self-test application development (Paper III) ...... 32 Concurrent validity study (Paper IV)...... 33 Data analyses ...... 36 Quantitative methods for data analysis (Papers I-II & IV) ...... 36 Qualitative methods for data analysis (Paper III) ...... 38 Ethical considerations ...... 40

Results ...... 41 The Safe Step feasibility study (Papers I & II) ...... 41 Description of the participants ...... 41 Attrition to the study...... 41 Adherence to the exercise intervention ...... 42 Feasibility of performance-based and self-reported outcome measures ...... 44 4-month survey ...... 46 12-month survey ...... 46 Co-creation application design with older adults (Paper III) ...... 48 Result of the qualitative content analysis ...... 48 Application development ...... 51 Concurrent validity testing of the MyBalance prototype (Paper IV) ...... 52 Participants’ characteristics ...... 52 Sensor measurement and clinical instrument correlation analyses ...... 54 Discussion ...... 56 Adherence to exercise programmes ...... 56 Feasibility of outcome measures ...... 58 User experience and involvement ...... 60 Self-assessment with digital technology ...... 62 Methodological reflections...... 64 Participants ...... 64 Data collection and data analysis ...... 66 Ethical reflections...... 69 Overall reflection ...... 70 Implications for fall prevention and physiotherapy ...... 70 Future research ...... 72 Conclusions ...... 73 Acknowledgements ...... 75 References ...... 77 Appendix ...... 89

Abstract

As the numbers of older adults grow, fall prevention is vital to reduce health care needs due to falls and to increase quality of life. Balance and strength exercises have been found to be effective in fall prevention, however, long-term adherence is often poor. The growth of digital technology in society has generated opportunities for fall prevention with eHealth. The aim of this thesis was to evaluate the feasibility and use of a new digital fall prevention exercise programme, and to develop and investigate a smartphone self-test application for balance and leg strength.

Three different studies included community-dwelling older adults ≥ 70 years, who were able to rise from a chair and stand without support. A feasibility study evaluated a new digital exercise programme (DP) compared to a paper booklet exercise programme (PB) for self-managed fall prevention in a four-month controlled participant preference trial (n = 67) (Paper I & II). Self-reported data on adherence, falls efficacy, and functional ability were collected and analysed, along with performance-based measures of gait speed, balance, and chair stand test. In Paper II the feasibility was explored of using the self-reported scales and performance-based outcome measures. A self-test application was also developed (Paper III) in co-creation with 10 participants, who met during five sessions to design the application’s instructions and user interface. The participants’ preference for and their contribution to the application design was analysed with qualitative content analysis with a deductive-inductive approach. A concurrent validity study (n = 31) (Paper IV) assessed the correlations between variables from the self-test prototype and outcome measures from clinical instruments.

Results from the feasibility study show that 43% chose the DP and 57% PB, and the attrition rate was 17% and 37% respectively. Both groups had similar adherence, but for the subgroup that exercised most, participants in the DP group reported significantly more exercise time (Paper I). Participants in both groups reported a boost in balance after the intervention, and in the DP group also improved leg strength. Significantly more participants continued to use the DP at 12 months. The self-managed exercise intervention (Paper II) resulted in improvements in functional leg strength, which positively correlated with exercise time, but no other performance-based outcomes showed any significant improvements. Performance-based measurements of balance as well as the self- reported balance confidence and fear of falling revealed ceiling effects. Pre- assessments of self-reported outcomes and performance-based measures showed significant but low correlations, no such correlations were seen in change scores. The deductive-inductive analysis of the co-creation process resulted in 17 subcategories within the seven facets of the Optimized Honeycomb model for

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user experience (Paper III). The main results were that participants desired clear and appropriate information to understand why things were done in a certain way, and their contributions enhanced the user experience of the self-test. The concurrent validity testing of the self-test prototype (IV) showed low to moderate correlations for the strength test but limited correlations for the balance test.

In conclusion the DP group showed comparable adherence to the programme as the PB group, as well as to previous studies, indicating it was feasible to use the new DP. DP participants also reported better exercise maintenance after 12 months. Positive self-reported effects were expressed in addition to leg strength improvement. Outcome measures for balance and falls efficacy revealed ceiling effects, consequently, these instruments might not be suitable for assessments in all community-dwelling older adults. In particular, for balance related outcomes there is a need for new more sensitive measurements. The co- creation of the smartphone self-test was feasible and valuable for user experience, but further validity and reliability testing are needed before it can serve as an independent assessment tool.

Enkel sammanfattning på svenska

När antalet äldre i världen ökar blir fallprevention en viktig insats, både för att minska behovet av vård och för att öka livskvalitén för individer så att de kan leva ett rikt liv upp i åldrarna. Forskning har visat att träningsprogram med balans- och styrkeövningar är effektiva för att förhindra fall, men genomförandet av olika träningsprogram är ofta svårt att upprätthålla på lång sikt. Den ökande användningen av digital teknik i samhället skapar däremot nya möjligheter att erbjuda seniorer stöd att genomföra träning för att förebygga fall. Syftet med denna avhandling var att undersöka möjligheten att använda digital teknik för fallpreventiva insatser, dels genom att testa ett nyutvecklat digitalt självtränings- program ”Säkra steg”, dels genom att utveckla och undersöka en mobilapp med självtest för att mäta balans och benmuskelstyrka med hjälp av en mobil där man kan ladda ner appar. I avhandlingens tre olika datainsamlingar har personer som var 70 år eller äldre deltagit. Alla var hemmaboende och kunde resa sig upp från en stol utan stöd.

Samma studie låg till grund för delarbete I och II, där deltagarna var en grupp relativt aktiva seniorer. Medelåldern var 77 år och 72 % var kvinnor. I delarbete I utvärderades deltagande och följsamhet under fyra månaders självträning, där ett digitalt träningsprogram jämfördes med ett träningsprogram som utfördes med hjälp av ett pappershäfte. I delarbete II undersöktes lämpligheten i de instrument som användes för att utvärdera träningsstudien. Deltagarna fick själva välja program innan start och 67 personer påbörjade studien. Av dessa valde 29 det digitala programmet och 38 valde det så kallade pappersprogrammet, där övningarna presenterades i ett häfte med text och bild. Båda programmen bestod av en samling övningar som skulle komponeras ihop till egna individuella program bestående av tio övningar. Rekommendationen var byta ut övningar under perioden för att anpassa programmet så att det upplevdes som tillräckligt utmanande under hela träningsperioden. Deltagarna rekommen- derades att träna minst 30 minuter 3 dagar i veckan. Data om deras träning inhämtades från deltagarna månadsvis genom en träningsdagbok. I början och slutet av träningsperioden svarade deltagarna på enkäter om sin funktion, fallrädsla och tillit till sin balansförmåga samt deltog i mätningar av gång- hastighet, balans och förmåga att resa sig från en stol.

Totalt 83 % av deltagarna som använde det digitala programmet fullföljde studien, motsvarande andel var 63 % för pappersprogrammet. Båda användes i liknande omfattning av de som fullföljde, dock användes det digitala programmet i större grad i den grupp av deltagare som tränade mest. Vidare visade gransk- ningen av utvärderingsinstrumenten på begränsning i att kunna mäta förbättring eftersom höga värden uppmättes redan vid start, en så kallad takeffekt. Detta var

fallet för 2 av 3 enkäter och för balansmätningen i de fysiska testerna. Det på- visades ett visst samband mellan de självrapporterade enkätsvaren och de fysiska testerna vid studiens start. Resultatet av fyra månaders träning visade på uppmätta förbättringar i benstyrka i båda grupperna och att deltagare som tränat med det digitala programmet i högre grad uppgav att de själva iakttagit en förbättring av styrkan i benen. Efter 12 månader svarade deltagarna på en uppföljningsenkät och signifikant fler deltagare som använde det digitala programmet rapporterade att de fortsatt att träna med programmet.

I delarbete III utvecklades en självtestapp som innehåller två olika test. Det första är ett balanstest där man testar sig i två positioner under 30 sekunder, att stå med fötterna ihop samt att stå i semitandem med ena foten lite framför den andra. Det andra testet innefattar att man reser sig upp från en stol upprepade gånger. Utvecklingsarbetet skedde tillsammans med tio seniorer som vid fem tillfällen bidrog till innehåll och utseende av appens information och användargränssnitt. Medelåldern var 76 år och 60 % var kvinnor. Deltagarnas förslag och åsikter analyserades med kvalitativ innehållsanalys med hjälp av en modell för användarupplevelse (Optimized Honeycomb model). Resultatet visade att deltagarna ville få enkla och tydliga instruktioner för att veta hur självtestet skulle göras samt motiveringar till varför. Med hjälp av deltagarnas synpunkter och idéer skapades en app med enkla symboler och tydliga kontraster för enkel navigering, samt likaledes konkreta och korta instruktioner för testgenomförandet. Deltagarna uttryckte förväntan att de och andra seniorer som behöver träna sin balans kan använda appen i framtiden.

I delarbete IV undersöktes om data som samlats in med prototypen av den nya självtestappen överensstämde med resultaten från kliniska tester. Studien genomfördes med 31 deltagare som hade en medelålder på 79 år och där 77 % var kvinnor. En fysioterapeut utförde flera kliniska tester för att mäta balans och benmuskelstyrka samt utförde mätningar med mobilen, vilka sedan jämfördes för att se om mätningarna överensstämde, en validitetstestning. Ett visst samband påvisades för styrketesterna men för balanstesterna visades endast ett mindre eller inget samband. Dock visades visst samband för balans med frekvensvariabler, vid positionen ”fötter ihop”.

Denna forskning visar att seniorer kan använda och har ett intresse för fallprevention med digital teknik och att självträning med båda programmen är genomförbart. Trots att både det digitala- och pappersprogrammet användes i liknande omfattning, uppgav deltagare med det digitala programmet i högre grad att de var nöjda och upplevde ett stöd av programmet. Dessutom var det en större andel som fortsatte att träna med det digitala programmet efter 12 månader jämfört med pappersprogrammet. Den brist på känslighet som utvärderings- instrumenten påvisade i delarbete II pekar på att det finns ett behov av nya, mer

känsliga mätinstrument för att utvärdera balansförmåga. Detta behov skulle kunna tillgodoses med ett självtest i mobilen, som också skulle kunna vara användbart för att följa utvecklingen av fallpreventionsträning. Självtestet som utvecklades och designades tillsammans med en grupp seniorer upplevdes som användbart och värdefullt för deltagarna. Självtestappens begränsade överens- stämmelse med de kliniska testerna förklaras till viss del av att olika aspekter av balans mäts i de olika testerna, framför allt i balanstesten. Prototypen behöver testas ytterligare innan den kan användas som ett självtest på den egna mobilen, både med hänseende till användarupplevelse samt validitet och reliabilitet.

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Abbreviations

1 RM One Repetition Maximum 30s CST 30 second Chair Stand Test 5TSTS Five Times Sit-To-Stand ABC The Activities-specific Balance Confidence Scale AccMax Max Vertical Acceleration CFREQ Centroidal Frequency DP Digital Exercise Programme FR Functional Reach hAREA Horizontal Sway Area HC Health Care Centre HCI Human-Computer Interaction hMEAN Horizontal Mean Acceleration hRMS Horizontal Root Mean Square Acceleration Icon-FES Iconographical Falls Efficacy Scale IMU Inertial Measurement Unit JerkMax Max Vertical Jerk LiFE Lifestyle-integrated Functional Exercise LLFDI Late-Life Function and Disability Instrument m4-stageBT Modified 4-stage Balance Test MaxStep Modified Maximal Stepping Test MCID Minimal Clinically Important Difference MDF Median Frequency MiniB Mini-BESTest; Mini-Balance Evaluation Systems Test NPL Normalized Path Length P2P Acceleration Peak to Peak PB Paper Booklet Exercise Programme PowerMax Max Vertical Power RMS Root Mean Square Acceleration SGPALS Saltin-Grimby Physical Activity Level Scale SO Senior Citizen Organisations SPPB Short Physical Performance Battery SUS System Usability Scale TUG Timed Up and Go UX User Experience VelMax Max Vertical Velocity WHO World Health Organization

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Definitions

Adherence To what degree the exercises are completed. In studies within this thesis the amount of self-reported exercise. Application (app) A program on a smartphone or tablet. Attrition rate The number of participants that left the feasibility study. Balance A complex postural control task, that requires a coordination between the central nervous system and the musculoskeletal system. Where functional balance contains elements of strength to be able to maintain equilibrium, retain and adjust posture during voluntary movements. Co-creation In this thesis it was collaboration with participants to provide suggestions and opinions during an iterative development process, and to give feedback on new developments, to improve user experience. Community-dwelling Living in ordinary housing, e.g., flat or house, as opposed to living in a residential care facility. Digital technology Electronic tools, systems, devices and resources that generate, store or process data, for example, devices like smartphones, tablets and computers. eHealth Short for electronic health, healthcare practice supported by electronic processes and communication, eHealth has been described in several different ways. Fall An unexpected event in which a person come to rest on the floor or ground. mHealth Short for mobile health, used for eHealth solutions utilizing mobile devices. Older adults In studies within this thesis persons ≥70 years old are regarded as older adults. Physical active Consider an older adult active in society and partaking in activities outside their home. Postural control Is used synonymous for balance, and is the complex sensory, motor, and cognitive system to maintain equilibrium. Self-management Management by oneself of, for example, a treatment or an exercise programme. Self-report Participants own perception of their function, as well as, their self-reported exercise in the exercise diary. User-test A test where the user tries the interface and functions of a website, app, etc. to identify issues that needs further development.

Original papers

The thesis is based on the following papers, which will be referred to in the text by their Roman numerals:

I. Older adults’ preferences for, adherence to and experiences of two self- management falls prevention home exercise programmes: a comparison between a digital programme and a paper booklet. Mansson L, Lundin- Olsson L, Skelton D, Janols R, Lindgren H, Rosendahl E, Sandlund M. BMC Geriatrics. 2020;20(1):209.

II. Self-managed fall prevention exercise guided by a digital programme or a paper booklet: effectiveness and feasibility of outcomes. Mansson L, Pettersson B, Lundin-Olsson L, Skelton D, Rosendahl E, Sandlund M. (Manuscript)

III. Co-Creation with Older Adults to Improve User Experience of a Smartphone Self-Test Application to Assess Balance Function. Mansson L, Wiklund M, Öhberg F, Danielsson K, Sandlund M. International Journal of Environmental Research and Public Health. 2020;17(11):3768.

IV. Evaluation of concurrent validity between a smartphone self-test application and clinical tests for balance and leg strength. Mansson L, Bäckman P, Öhberg F, Sandlund J, Selling J, Sandlund M. (Submitted)

The original papers were published as Open Access articles, reproduced with attributions according to Creative Commons Attribution (CC BY) license 4.0.

My contributions:

During the data collection phase of Papers I & II, I was responsible for the administrative and coordinating tasks for the feasibility study and entered data. I carried out the analyses and wrote manuscript I, and all co-authors contributed with reviewing and editing the manuscript. The research group shared responsibility for manuscript II. I composed and prepared the data file, wrote parts of the introduction and methods sections, and contributed to the rest of the manuscript.

I contributed to the conceptualization of the study in Papers III & IV. I also took part in developing the prototype, and in the planning for and participation in the investigation. I played a key role in leading the co-creation sessions, carried out the analyses, and wrote the manuscripts to which all authors contributed by reviewing and editing the manuscripts.

Preface

I have nourished the dream and vision to do a PhD since my final year as an undergraduate student in Physiotherapy in Umeå, while completing my Bachelor’s project in 1995, when I was introduced to research. A doctoral student position in the Safe Step project had been announced at the same time as my work situation in Spain changed in 2016. This was the perfect project for me, i.e. digital technology and older adults. My interest in technology and internet use in the physiotherapy profession started as early as 1998 when I took the university course “Information and media science 1”, at Linköpings University. The content was on how the internet could be used as a source of sharing information, and I wrote an essay titled: Benefit to the physiotherapist of electronic media in professional practice; the Internet, newsgroups and mailing lists. (in Swedish, Sjukgymnastens nytta av elektroniska media i sin yrkesutövning: Internet, newsgroups och e-postlistor). When we moved to Ireland in 2000, I was tempted to start working with on-line physiotherapy treatment, but the world was not ready for that then; and instead, I got a nice job working in neurology in an acute hospital and soon after it was mainly stroke rehabilitation. I continued to work in that field, and when this post as a doctoral student was offered to me, I felt that I was ready to continue with new challenges. This was my point of departure for this PhD journey.

I have learnt many things as a PhD student during the past years in Umeå; not just in academic training but also training in project management, personal development, developing listening skills, and so forth. I’m very grateful to have been given this opportunity. The work with digital fall prevention with older adult participants, meeting them, and seeing their enthusiasm about the research we were doing was gratifying. They expressed a need for this and couldn’t wait to have access to it. Knowledge of postural balance is an essential part of physiotherapy and I have broadened my understanding in this field during my doctoral studies. My previous 20-year experience as a clinician have facilitated and reinforced my research but there is still more to learn. Digital technology has been close to my heart for a long time, and I have felt that it could support physiotherapists in our work. Digital technology empowers patients by helping them take control of their own situation, and digital technology gives family members and caregivers the possibility to assist in rehabilitation. I can see the potential in using digital technology, regardless of condition, in fall prevention and other primary prevention, or as in my previous practice, in stroke rehabilitation, and many other areas.

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I had many rewarding experiences during my time as doctoral student, such as at the World Physiotherapy Congress WCPT in Geneva, May 2019, where I had the opportunity to give an oral presentation of my co-creation research. I had the pleasure to go to Trondheim for a 3-week research visit, in 2019, which gave new contacts for future research collaboration. I presented a poster and helped out as co-host during presentations at the EU Falls Festival in Umeå, 2019. I also presented my research on three occasions locally at our “Petite Nobel”, organised by Umeå Centre of Health Science (U-CHEC) at Umeå University where I was part of the organising committee for two years. At last, my first research trip went to Stuttgart, January 2017, to participate in a conference organised by MOBEX, an informal European research network for persons involved in falls research. This was the beginning of the journey of my thesis, Digital fall prevention for older adults.

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Introduction

This thesis explores the potential use of digital technology in fall prevention for older adults. Falls among the ageing population are a dilemma around the world with increasing suffering and costs. Preventive exercises with balance and strength elements are known to reduce falls in older adults, although one challenge is to support older adults to regularly perform this type of exercise. The increase in digital solutions provides an opportunity for self-managed exercise and assessment of fall prevention for a larger population. The independence and flexibility for older adults to self-manage their fall prevention with digital solutions could be beneficial for individuals and resource saving for the health care system. This thesis addresses fall prevention and eHealth from a physiotherapy perspective, with multifaceted research topics including participatory methods with older adults, to reach good user experience when introducing new digital solutions. The feasibility of a new digital fall prevention exercise programme was evaluated, and a self-test application was developed for smartphones.

The ageing population and falls This introduction gives a brief explanation of the ageing process and ageing’s implication for falls. Ageing is inescapable, every day we get older, but who is old? There is no general consensus on when one becomes old (1), but ageing is habitually associated with retirement. The individual’s feelings about being old must be considered. Peel (2) uses the following definitions in years of age: young- old 65 to 74 years, old-old 75 to 84 years, and oldest-old 85 years and older. Other terms frequently used are: young seniors 60-70 years old, and very old from 80 or 85 years. For global demographic purposes, people 60 years and older are referred to as elderly, but in developed countries with rising life expectancy, the entry point for "old age" is traditionally 65 years (2). The older adults in this thesis are represented by participants 70 years old and older.

Statistics from the United Nations (UN), 2019, state that 703 million people in the world are ≥ 65 years old. This demographic is expected to increase by 120% until 2050 when 16% (1.5 billion) of the world population will be ≥ 65 years old, with a projected increase of only 48% in Europe and North America (3). In Sweden 2019, 536 000 people were ≥ 80 years old, and by 2050, this age group is predicted to have reached over one million (4, 5). Figure 1 presents the predicted demographic change in Sweden (≥ 65 yrs.) the next 30 years. The number of older adults is relevant for accommodating the future need for prevention and health care of this growing group of older adults.

Predicted demographic change in Sweden (≥65 yrs), 2019 to 2050 Men 2019 Women 2019 Estimated 2050

100+

95-99

90-94

85-89

80-84 Age, years

75-79

70-74

65-69

400 000 300 000 200 000 100 000 100 000 200 000 300 000 400 000 Number of people

Figure 1. Population in Sweden 2019 showing women (orange) and men (blue), and the predicted demographic change in population for 2050 (grey), presented in five-year age groups for people ≥ 65 years old. Data from Statistics Sweden and Delmi (4, 5).

The normal ageing process can be described with the concepts primary ageing and secondary ageing (6, 7). Primary ageing refers to biological ageing, the deterioration of cellular structure and function, which is not yet possible to modify in humans. Secondary ageing refers to the processes of ageing, which can be altered through the environment, our behaviour, and lifestyle such as: food or exercise habits, and social interaction. Primary and secondary aging interact, and life expectancy is determined by both environmental and genetic factors (6). Due to primary ageing processes flexibility will be reduced, the ratio of body fat to muscles mass increased, and bone mass reduced (8). Muscle strength starts to decline in the mid-forties and the decline is greater at the age of 70, both muscle fibre size and neuromuscular connexions become reduced and cause a reduction in strength (9). Older adults are a very heterogenous group and the balance impairment due to reduction in muscle strength could vary considerably.

Falls in older adults Accidental falls are a major cause of injury in old age, and with the increasing size of the group of older adults and possibilities to impact secondary ageing, it is relevant to try to prevent falls. Accidental falls is the MeSH (Medical Subject Headings) term, referring to falls due to slipping or tripping that may result in injury. The term “fall” will be used hereinafter. A fall can have different characteristics; it could be a trip or slip, and some people may not even remember a fall afterwards or consider it as a fall because they did not injure themselves.

A fall can also be a major incident. A frequently used definition of a fall is; an unexpected event in which the participant come to rest on the ground, floor, or lower level (10).

Falls among older people are a major problem around the world, and the World Health Organization (WHO) has reported that the group, 65 years and older, suffers the greatest number of fatal falls (11). The frequency of falls increases with age and frailty level, but the incidence of falls varies among countries. Globally, approx. 28-35% of the group 65 years and older fall every year, and falls exponentially increase with age, leading to a substantial increase in falls among those over 80 years (12). 2018 statistics report 50 000 hospital admissions after a fall related injury for people ≥ 65 years in Sweden; a hip fracture was the most frequent injury of these falls (13). Every third Swede over the age of 65 suffers a fall yearly, and this increases to every second for persons over 80 years, where 9 of 10 injuries after a fall in the older group affect the lower extremities (14).

The cost of falls for society vary from country to country depending on different systems, how costs are calculated, and how care is organised. All costs have to be considered; direct costs includes health care and medication whereas indirect costs includes losses in productivity for individuals and family members (12). A meta-regression analysis of costs for hip fractures globally has reported a cost equivalent of almost US$ 44 000/case in expenses for health and social care during the first year (15). The economical expenses are high, but this is not the only reason to address this issue. The individual and society are affected by falls in many other ways. The consequences of falls can be physical injuries, and the fear of falling can lead to avoiding activities (16).

Known risk factors for falls can be categorized into four areas: biological, behavioural, environmental, and socioeconomic factors (12). Decline in physical or cognitive function and chronic illness are examples of biological risk factors, and multiple medication use or physical inactivity are examples of behavioural risk factors. Environmental risk factors are hazards in the home or public environment like poor lighting or slippery surfaces, and socioeconomic risk factors are related to for example the level of education. Risk factors were investigated in a meta-analysis which showed that the risk for another fall event after a reported fall, in community-dwelling older adults, has various degrees of association, the two highest odds ratio was 2.8 for previous fall history, and 2.1 for gait problems (17). Other common terms for fall risk factors are: intrinsic and extrinsic risk factors (18). Intrinsic risk factors refer to biological risk factors, and extrinsic risk factors refer to behavioural, environmental, and socioeconomic factors. This thesis focusses on the intrinsic factors, related to the human body

with a decline in physical function such as reduced balance, leg muscle strength, and dual-tasking, that can be addressed with exercise-based fall prevention interventions (19, 20).

Balance the opposite to falls During the process of ageing, as in other stages of life, maintaining postural balance can be viewed as the opposite to falling. Remaining in an upright position and not falling require balance, so balance is a major building block in fall prevention and in this thesis. Balance is usually understood as maintaining equilibrium. Pollock et al. (21) have addressed the need for a simple clinical and universally accepted description of human balance. They describe human balance as a multidimensional concept, referring to the ability of a person not to fall, emerging from mechanical balance for inanimate objects where balance is the state of an object when the resultant force acting upon it is zero. Balance and postural control are both terms referring to the act of maintaining, achieving or restoring the state of balance, during any posture or activity.

To balance, is a complex postural control task, that requires a coordination between the central nervous system and the musculoskeletal system, with components of stability and orientation. Stability refers to centre of mass (COM), or really, centre of gravity (COG) in relation to the base of support (BOS). Orientation refers to the postural alignment of body parts, as well as the body’s orientation to its surroundings. The brain receives input from the visual, sensorimotor, and vestibular systems for the body to maintain balance. Balance emerges as an interaction between the individual and the environment in the context of the postural task of maintaining equilibrium. The individual part contains motor, sensory, and cognitive components, and the environmental part consists of support surfaces, sensory cues, and cognitive demands. (22)

Functional balance is described as the ability to maintain positions and to adjust posture during voluntary movements (23). Several motor and sensory components are important for functional balance, e.g. latency to postural response, motor learning, and sense of stability limits (24). Diminishing balance is anticipated with increased age, but older adults are a heterogenous group, therefore, age is not the sole cause of increased risk of falls, however, it has to be considered. The primary aging processes affect mobility and balance, due to reduced leg muscle strength, for example. Postural control and balance are built on complex sensory, motor, and cognitive systems that also suffer decreased function with increased age, like vision, reaction time, dual-task performance, etc. (25, 26). Various aspects play a part in fall prevention where reduced balance plays a major role, and balance can get better with exercise by improving these sensory, motor, and cognitive systems.

The ability to maintain balance can be evaluated with clinical assessments, which are regularly used in physiotherapy practice. Instruments frequently used for balance assessment with older adults are: the Berg Balance Scale (27), the Balance Evaluation Systems Test (BESTest) (28), and the condensed version, the Mini-BESTest (29). Other instruments have more elements of mobility, like the well-established timed walking test Timed Up and Go (TUG) (30), the Tinetti Performance-Oriented Mobility Assessment Tool (POMA) (31), and the Short Physical Performance Battery (SPPB) (32). Functional leg strength assessments are likewise important for balance function, and common instruments are: the 30s Chair stand test (33), or the Five Times sit-to-stand (34, 35). These instruments are assessed by a subjective observation of the individual’s performance of different tasks. All instruments provide different grades of completeness for the functional balance assessment, and they measure different aspects of balance. However, functional balance can be elusive to assess as it is a complex task affected by intrinsic and extrinsic factors, which are challenging to measure in a clinical setting. It is essential to assess balance to identify change in function, for example, when creating a fall prevention exercise programme, and/or finding people that need fall preventive interventions.

Exercise as fall prevention Exercise has the potential to slow down the secondary ageing process through maintained or improved balance and mobility, and is therefore important in fall prevention. In addition, by improving mobility, older adults can become more socially active, and as an extension of this, may increase their quality of life. General physical activity and exercise are also favourable for general health benefits. Often the terms physical activity and exercise are used interchangeably. However, physical activity is any bodily movement produced by skeletal muscle that requires energy expenditure; and exercise is planned, structured, and repetitive movement to improve or maintain one or more components of physical fitness (36). Fall prevention exercise is considered as exercise herein, as it often entails balance and strength components and is often a structured programme.

There are age-group-specific global recommendations for physical activity. According to WHO, adults are recommended to do at least 150 min moderate intensity or 75 min vigorous aerobic physical activity per week(or an equivalent combination thereof), at a minimum of 10 min bouts, including muscle strength activities twice or more per week, and balance-boosting exercises three days per week for healthy older adults (≥ 65 years) (37). If guidelines cannot be followed for health reasons, the recommendation is to be as active as health condition and ability permit. WHO continues to promote physical activity for well-being as part of the 2030 Agenda for Sustainable Development, and it is intended for all people of all ages and abilities to increase their physical activity with the vision

“More active people for a healthier world” (38). A position stand addressing older adults is in line with the above recommendations, and states that regular physical activity can increase life expectancy by limiting development and progression of disabling and chronic health conditions, and recommend exercise prescription of aerobic exercise, resistance, flexibility, and balance training (39). Strong evidence in a comprehensive review support the notion that physical activity improves physical function and reduces the risk of fall-related injuries by 32-40% (40).

Even with these recommendations, specific exercise programmes for fall prevention are necessary. Fall prevention exercise interventions have been researched in numerous ways in recent decades, and a Cochrane review of interventions studies involving community-dwelling older adults in balance and strength exercises found that exercise reduces the rate of falls by 23% and the number of fallers by 15% (19). The fear of falling has also been reviewed in relation to fall prevention, where exercise interventions like Tai Chi, balance, strength, and resistance training, showed a small to moderate reduction of fear of falling (41). A variety of studies of exercise interventions have been conducted, and the diversity of programmes is extensive; most interventions do not use a specific programme but use their own different protocols with combinations of exercises (19). Some examples of recognised set programmes from New Zeeland, United Kingdom, Australia, and Canada are: The Otago Exercise Programme (42); Falls Management Exercise Programme (FaME) (43); LiFE programme (Lifestyle approach to reducing Falls through Exercise) (44, 45); Weight-bearing Exercise for Better Balance (WEBB) (46); and Osteofit (47, 48). Most of these programmes were developed as group exercises but also have elements of individual practise built in or as a continuation of the group programme. Even though research has come a long way, new fields still have to be explored to provide suitable exercise programmes for older adults. A problem with nearly all exercise interventions is poor adherence and especially long-term adherence (49).

Adherence to fall prevention interventions Adherence is important for fall prevention interventions to be successful. as high adherence is essential to achieve good effect of the exercises. Exercise interventions and interventions with home-based programmes have often poor adherence (49, 50). Adherence has often been reported in medical research as compliance. While compliance may be perceived as somewhat negative and passive, adherence represents a more active patient, or person, who participates in the intervention (51). The definition of adherence to long-term therapy is “the extent to which a person’s behaviour—taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider” (51, p. 3). Who adhered or not to an intervention is up to the researcher to decide, and without clear standards, adherence measures

vary from study to study. In exercise interventions, adherence could be the number of sessions completed, or the number of days per week, e.g., exercise at least two of recommended three days. King et al. (52) have stated that 2/3 of the exercise routine had to be completed for exercise interventions to be considered as adhered to. The cut-off point at 2/3 of accomplished exercise was slightly amplified to 75% in a review paper that focused on older adults’ participation in exercise classes (53). The authors aimed to create a definition of adherence to facilitate pooling of data, and four distinct definitions of adherence were suggested: (a) Completion, retained in the study until follow-up. (b) Attendance, the number of classes attended in percent. (c) Duration, the number of minutes per week. (d) Intensity according to the American College of Sports Medicine (ACSM) guidelines (53). Some of these characteristics of adherence might not be suitable for home exercise but can be modified or new ones agreed on. There is still need for more distinct measures and reporting for adherence.

Various review studies have in the last few years researched the area of adherence for home-based physiotherapy in general or physical activity (54–56), or specific- to-fall-prevention interventions for older adults (50, 57, 58). The overall conclusion was that more structured reporting of adherence is needed along with well-defined valid and reliable adherence measurements. Key factors for adherence for physiotherapy home-based treatments are: intention to engage in the therapy, self-motivation, self-efficacy, previous adherence behaviour, and social support (54). These are also central factors for fall prevention interventions. The heterogeneous group of older adults has different needs and desires. Participants also expressed a wish for more autonomy and responsibility for their fall prevention, and professionals should take more of an enabler than expert role (59). Exercise adherence improved for community-dwelling older adults participating in fall prevention interventions if multifactorial approaches were used, and integrated into activities of daily living as well as use of telecommunication (57). These multifactorial approaches appeared promising, although with limited evidence. The characteristics identified from fall preventive home-exercise programmes that affect the level of adherence positively were: types of exercises, if the programme was delivered by a physiotherapist, if home visits were included, etc. but a pooled estimate reported that only 21% of participants were fully adherent (50). Poor adherence is a problem in exercise interventions. Maybe, one way to encourage individuals to adhere to exercise programmes, would be to provide support mechanisms to the intervention like planning, feedback, and monitoring features.

Self-management of exercise to prevent falls Another challenge with fall-prevention exercise programmes is to reach older adults before the first fall occurs as preventive exercise is seldom available to the

general public. To handle this, self-managed pro-active fall prevention with exercises to improve balance and leg strength is needed. This could be a physiotherapy intervention or administered separately from the health care system. Self-managed exercise programmes are often a home-based programme, although the home-based programme does not have to be self-managed as it might be managed from the clinic by a physiotherapist or through home visits. A self-managed programme could be performed at the location of choice and would be managed independently.

Self-management origins from the care of chronic health conditions and might be applicable for home exercise for fall prevention interventions. Self-management facilitates handling of the individual’s perceived problems, and five skills are applied: problem solving, decision making, resource utilization, building a partnership between patient and the health care professional, and taking action (60). As fall prevention and other types of preventive interventions could start without a health care contact, the partnership between health care professional and the individual may be limited. A definition used in a fall prevention review about self-management interventions is "actions individuals take or behaviours they perform to prevent themselves from falling" (61, p. 748).

To take actions and independently manage one’s fall prevention could be done by using self-tailored exercise programmes by adjusting the choice of exercises to one’s abilities and needs. Self-tailoring has been described as using skills and knowledge to change behaviour and self-manage a situation by making changes according to ability (60). Giving control to the older adults themselves may help manage the challenge to facilitate service and utilize resources effectively for the health care sector as the older adult population increases. Home-exercise programmes would also be convenient for fall-prevention exercise for many older adults by enabling the person to own the situation and make decisions and act upon them (62). Gained knowledge and own control through a self-managed exercise programme might increase the individual’s motivation to follow through with the programme.

In the future, technology could provide enjoyable and cost-effective fall prevention interventions and maybe assess fall risk, but translation of evidence into practice about these new interventions is required to gain positive effects in the community (63). The advancing use of digital technology could offer opportunities for self-managed fall prevention interventions by introducing more interactive training sessions, greater flexibility of where and when to perform the exercises, and giving possibilities to self-monitor progress. Providing public access to digital self-managed fall-prevention interventions could, potentially, lead to a reduction in the need for health care services.

Digital technology, eHealth, and falls in older adults The emergent use of digital technology in society at large is also reflected in older adults’ extended usage. The relatively new area of electronic health (eHealth) has been described in multiple ways. WHO’s explanation of eHealth is the use of information and communication technologies (ICT) for health (64). This comprises everything from electronic record keeping to self-monitoring of physical activity, and could be applied on different levels of patient care. In an editorial, Eysenbach broadens the definition to include more aspects than just electronic ones for the e in eHealth, for example, efficiency, empowerment, education, enabling, and equity (65). This gives a perspective on the possibilities and the limitations of using digital solutions. Another term, mHealth, is an abbreviation for mobile health and is used for eHealth solutions with mobile devices like smartphones, tablets, or smart watches, etc. (66).

Solutions with eHealth and mHealth could be used by both health care professionals and by the general population. In a recent public health commentary, eHealth was considered an applicable and suitable solution for the general public (67). Smartphone use was identified for health promotion in four areas: nutrition, fitness and physical activity, lifestyle, and health in the elderly (68). Digital fall prevention is linked to at least three of these areas: fitness and physical activity, lifestyle, and health in the elderly.

A relatively new term, gerontechnology, is used for the combination of the two scientific disciplines, gerontology and technology (69). Gerontechnology aims to achieve good health, social participation, and independent living up to a high age by using assistive technology and inclusive design. A comprehensive overview presented developments in gerontechnology projects in different fields which contributed to a good life for the ageing population (70). The overview was structured by the following headings: understanding the ageing users, designing for ageing users, convincing the ageing users, prevention, compensating restrictions, and care support. Covering anything from experimental housing, to social robots, and self-medication applications. One example: design for aging users, presented four areas regarding individual differences, user participation, inclusive design, and standardisation. These four areas were incorporated in the physiology and psychology disciplines from gerontology, and ergonomics and design disciplines from technology (70). Gerontechnology is a significant field in eHealth developments in that it integrates these two essential scientific disciplines to improve life for older adults.

The gerontechnology is still relatively unknown outside related research fields, including physiotherapy, and the use of technology is in the bud for older adults. The general belief is that older adults do not have access to nor use digital

technology to a large extent. A report from 2017 including data from twelve countries around the world (representing all continents except South America) indicated that 3-71% of people > 65 years old use the internet. In this age group, a large disparity was presented between the countries with the lowest internet use, such as Taiwan and Tunisia, and the United States with the most use (71). However, data from the 2019 Swedish annual survey Swedes and the Internet (in Swedish, Svenskarna och internet) (72) showed that in the group ≥ 66 years old, 76% owned a smartphone, 84% used the internet, and 67% used a smartphone daily to connect to the internet. Further, 77% used the internet to search for health- and medical-related information. These statistics show that many older adults already use digital technology in some countries, like Sweden and the United States.

Fall prevention with digital technology As many older adults are users of new technology to some extent, smartphone applications could potentially be used to distribute primary prevention interventions such as exercise programmes. Treatment with exercise programmes are common in physiotherapy, both as prevention and rehabilitation, and some of these could be delivered as digital programmes. Research is fast emerging around the use of digital technology for fall prevention exercise interventions, and some of this research ongoing around the world is described below.

By using existing technology, the potential to engage people in fall prevention and give instructions without the requirement of a real-life encounter could be achieved. Some research projects with home-based digital fall prevention exercise programmes with motivation and support tools are: Standing Tall from Australia (73), a two-year study where 700 participants were randomised into either a home-based balance training tablet application programme or control. Both groups got weekly education information and one initial home visit, then the tablet group had a second home visit after about a month where the control group instead got a phone call. ActiveLifestyle from Switzerland (74, 75) included 44 participants to evaluate a tablet application, individual or social version, compared with a control group that received the same information in a printed version. The 12-week intervention supported active ageing by providing autonomous home-based physical workouts. Finally, the European PreventIT project (Norway, Germany, Netherlands) (76) is a feasibility study that evaluated Lifestyle-integrated Functional Exercise (LiFE) as integrated physical activity for 61-70-year olds, where 180 participants were randomised into eLiFE, aLiFE, or a control group. The intervention offered strategies to improve balance and strength and increase physical activity by adapting the previously evaluated LiFE programme (45) to young seniors. The PreventIT eLiFE programme was

delivered as an application on a smartphone and a smartwatch and had four home visits from a trainer during six months. The aLiFE was a paper manual and had six home visits. The PreventIT control group had one home visit and were supplied with written information about the WHO’s recommendations for physical activity (76).

Another example and important basis for this thesis, a digital self-managed exercise programme, the Safe Step application, was developed at Umeå University as an interdisciplinary co-creation project with older adults (77). Safe Step provides a self-managed and self-tailored exercise programme that is not prescribed by health or exercise professionals. It is to be used independently on a smartphone, tablet, or computer. The purpose of the programme is to motivate and educate older adults to prevent falls. The positive experiences of using the self-managed exercise programme have been reported in a qualitative study (62).

Regarding adherence in digital programmes for fall prevention, as previously mentioned in the section Adherence to fall prevention interventions, reporting is not compatible between studies. The Standing Tall study has not presented results as of this writing. The ActiveLifestyle study has two publications reporting on adherence. The App with social features had 73% adherence, and the App for individual use had 68% adherence (74). The second publication described adherence as active or inactive participants, and reported 85% as active and 15% as inactive for the App with social features, and the individual App group reported 57% as active and 43% as inactive (75). For both tablet App groups, significant improvements were reported for single and dual task walking after the intervention period (75). Finally, the PreventIT six-month intervention period reported 30% full adherence, 52% partial adherence, and 19% non-adherent participants for the eLiFE programme (76). No significant differences in change were reported for any of the outcome measures between groups (e.g., Late-Life Function and Disability Instrument, gait speed, sit-to-stand tests, balance assessments, EuroQol-5D, physical activity monitoring), and feasibility, usability, and acceptability were found successful for the eLiFE programme. A further six months unsupervised follow-up, showed continued application use with 22% full adherence, 57% partial adherence, and 22% non-adherent by participants remaining in the study.

In a review of technology-based exercise programmes for older adults high adherence was found, but most programmes were provided with gaming consoles and were supervised (78). Studies included mostly healthy older adults, and had short follow-up periods, and these studies were not specific to fall prevention. The use of digital technology for eHealth interventions for fall prevention exercise is expanding. Development and evaluation are currently under way, and this

thesis is part of the expansion. Digital technology could provide additional benefits by using smartphones for measurements.

Smartphones and sensor measurements The digital technology and especially the growth of smartphone use have opened up new grounds for mHealth. This is a recent development which gained popularity from 2006 when internet-connected smartphones became commercial availability, and only five years later, sales for smartphones were higher than for mobile phones without internet connectivity (79). Blackberry became popular in 2006, followed by iPhone 2007, and in 2008 the Samsung Instinct was introduced. The smartphone is a mobile device combining traditional phone functions with mobile computing functions in one portable unit. It also contains advanced sensors that gives the potential to objectively assess movements, including balance function, and provides easy access to tests separate from health care facilities and without expensive equipment. Smartphone sensors like accelerometer and gyroscope, are contained in the inertial measurement unit (IMU). The accelerometer measures change of speed and can detect a very small change in movement with high precision. The gyroscope measures rotational motion and registers the orientation of the device.

The evolution of monitoring human movement with smartphones was presented in an overview, where global positioning system (GPS) receivers, and sensing capabilities (i.e. IMU, magnetometer and barometer) were pointed out to create potential for health care applications to non-invasively monitor movement (80). Some challenges were also recognised in the overview, such as battery capacity for continuous monitoring, and the location and/or orientation of the device on the body. Smartphone use for monitoring in a variety of health areas have been reported, these cover areas like dermatology, cardiovascular conditions, eye conditions, respiratory conditions, physical activity, falls, and mental health (81). Various areas are expected to benefit from this new technology, and fall prevention for older adults is one part as both fall risk assessment, exercise, and monitoring balance could be handled with smartphone technology.

This is a fast-developing area with a growing interest for smartphone use for monitoring of health care and wellness which is promising but still faces some challenges (81). Although the potential benefits are promising, more rigorous trials must be performed, more organised ways of finding good quality apps are desired, the apps need to be more useable, and security and privacy matters must be addressed. A review about wearable sensors assessing standing balance included 47 high-quality studies and described the benefits of wearable sensors in posturography as accurate, reliable, and useful (82). Even though, only one third of the articles reviewed validated their instrument against a gold standard

measurement, due to early development testing. Another systematic review included only studies using embedded smartphone sensors and assessed the current state of 13 applications monitoring fall risk and balance and aimed at patients and health care professionals (83). Several of the 13 studies were still in development and/or in initial testing phase. Using smartphone applications have shown promising results according to this review, but further research is needed to establish the validity, reliability, and usability, of such applications. The development of smartphone technology has made the access to high accuracy sensor measurements available at a relatively low cost, which was not possible just a few years ago. However, these new developments have to be evaluated thoroughly as previous reviews also have highlighted. The ubiquitous use of smartphones can enable self-assessments and direct feedback to provide support in self-managed fall prevention.

Balance function self-assessment with smartphone The novel and expanding area of now available sensor measurements generates opportunities for self-assessments. When self-managed digital exercise programmes become more common and provided as smartphone applications, self-assessment could be possible with additional applications. Built-in sensors in smartphones are able to measure human movements accurately (80, 84), which could be applied to assess and monitor postural control tasks. This capacity to measure movements could create options for older adults to assess their balance and strength at home with a self-test on the smartphone without interaction with health care professionals (85). In fact, during the development process of the Safe Step application, participants asked for a tool to be able to assess balance and strength to follow their progress while following the exercise programme. Previous assessment of balance function with sensors in smartphones have been tried in a few studies. Smartphone assessments of mobility functions have been compared to clinical assessments with good correlations (86–91). Two studies measured only timed performance, and one of them counted repetitions of sit-to-stand but without other types of sensor measurements, both reported benefits of remote assessment and early detection of function decline in healthy adults and older adults (86, 87).

Research is evolving in the area of assessing balance with smartphones. One measure of balance is sway, which can be measured with embedded accelerometers in a smartphone or tablet. Studies with young healthy adults have shown that force plate measurements of sway have good correlation with smartphone accelerometer measurements (92–94). A study of movements during gait and balance tests on 60 healthy adults and older adults showed comparable results for sway measured with an iPod Touch and wearable accelerometers (DynaPort hybrid) (88). Testing of 48 healthy young adults with

three smartphones placed at ankle, knee, and waist, estimated the degree of difficulty of advancing balance positions in the same manner as experts (89).

Moreover, assessment of leg strength (that influences functional balance) can also be measured with smartphone sensors, with a simple sit-to-stand task. One study has shown similar results in an elderly population for maximal power and duration during sit-to-stand when comparing data from a smartphone and two force plates, one under the seat and the other under the feet (95). Clinical tests of balance and strength (Timed Up and Go test and 30s chair stand-test) have also been compared to measurements from a smartphone and portable motion sensor systems; both measurements could detect early decline in function in younger seniors (90).

The above research has shown reassuring results for smartphone sensor measurements compared with clinical instruments and lab equipment, but so far, research has mostly been conducted with healthy subjects and with a limited number of participants. This promising research quantifying balance and strength with sensor technology suggests that self-assessment with smartphones could be possible. Although better understanding about the sway measurements from balance assessments and how they can be interpreted in terms of postural stability is needed (96).

Self-testing independently with smartphone sensors is a novel field of research. Two recent publications have shown results from one research project in the PreventIT group. Only these two were found in searches for independently performed smartphone balance self-test applications. A development was carried out with iTUG to investigate the possibility to predict the Community Balance and Mobility Scale (CBMS) score with five repeated TUG tests (91). Additional results from the same project presented the development process of three applications for self-assessments of mobility, balance, and strength in young seniors, together with an early stage usability study (97). The results are early stage and have not yet been validated but show promising results both for prediction and usability, and more research is needed according to the authors.

As digital technology today can support objective measurements and supply exercise programmes, our ambition as researchers is to make valid and reliable tools accessible to the older adults. Validity describes how well an instrument measures what it is supposed to measure, and reliability indicates the stability of a measurement (98, 99). An instrument can be reliable without being valid, but validity requires a reliable instrument. With sensor measurements, the risk of poor inter-rater and intra-rater reliability can be cut out, and the sensor measurements can be free of bias. Useful guidelines for the development of new assessments, to secure valid and reliable outcome measures, has been presented

by the research initiative COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) (100, 101). It is essential to consider these guidelines when developing self-assessment applications like a smartphone self- test for functional balance. Also, it is crucial for a self-test to be easy to use, to ensure that the instructions can be followed, and the self-test to be safe to use (85, 102). Validity and reliability depend on a correct and similar test performance at each round of self-test measurements, which is a reason to have instructions well explained. Using new digital technology for self-tests could be both a societal benefit and an individual advantage, therefore is the user experience (UX) an important aspect so the application can be valuable to older adults.

User experience to facilitate eHealth User experience is central to eHealth. It is essential to simplify the usage since correct handling of the self-test application and test performance is crucial for a valid test result. A satisfying user experience is essential for digital exercise programmes. User experience has been described as the user’s subjective, situated, complex, and dynamic experience (103), and is central to web and application design. Hassenzahl & Tractinsky (103) have explained its background and future in relation to human-computer interaction (HCI) and interaction design, and have explained important user experience aspects, such as emotions and desires, that go beyond the more traditional instrumental HCI aspect for application design. The terms usability and user experience are interconnected, while user experience also represents the emotional aspect, usability alludes to efficiency and effectiveness when interacting with an application (104). Some guidelines have been produced with the goal to improve usability for older users (105–107). These guidelines highlight various characteristics like structure, how navigable the application is, and aspects influencing acceptance of technology.

A few cases illustrate examples of user experience in fall prevention and eHealth. A field deployment study, exploring the user perspective when using a wearable device for sensor-based fall risk assessment, found the potential to motivate behavioural changes, and it was perceived as usable (108). Participants communicated that it was easy to read and understand, it was effortlessly integrated into daily routines, and the feedback from risk data gave them the confidence to take action (108). A few fall prevention projects developing applications have performed user-test studies to evaluate usability. The PreventIT project has reported usability as average, participants expressed some frustration over immature technology but also showed promising results for use in a home setting (76, 97). An early implementation study with a small sample examined two combined smartphone applications to support patients in performing exercises at home, one for health professionals and another for

older adults (109). Patients found the application acceptable, while professionals highlighted various options for improvement to facilitate its use.

The user experience perspective is central to health-related application development. The UX Honeycomb model by Morville is commonly used when applying a user-experience perspective. It comprises seven design aspects of usability: useful, usable, desirable, findable, accessible, credible, and valuable (110). Although this model was not constructed scientifically, it is frequently used in applied design work (111) and, so far, is only occasionally used in scientific research. Nevertheless, some examples from different types of medical- associated application developments have been found (112–115). Modifications to the original UX Honeycomb model have been done at various occasions. One reorganisation emphasised how users interact with a product by Thinking, Feeling, and Using it, this was called the Optimized Honeycomb model (116). The Optimized Honeycomb model helps to focus development on user needs, such as when developing a self-test application, the emphasis must be on the older adult user and their user experience.

Co-creation for user experience To achieve good user experience, co-creation methods could be applied to engage the users during the development phase, to gain knowledge and bridge the gap between developers and end-users. This has evolved from user-centred design and follows human-centred design, which aims to design products based on user needs (104). The background and evolution of co-creation arose in the 1970’s and is described by Sanders & Stappers (117) it started in the Scandinavian countries where co-creation was used in industry to engage workers in new production systems, and in the UK as reorientation in design to avoid adverse effects.

The terminology participatory design, co-design or co-creation, all correspond to a process where the end-user is engaged in the development or design of a product (117, 118). Regardless of terminology, each involves the mutual learning and mixed roles of designer and user when working together in a creative way. The collaborative knowledge through co-creation may increase research impact (119). Examples of key principles for co-creation in public health interventions are: frame the aim, sample carefully, establish ownership, define the procedure, and structure the evaluation (120). When using co-creation, careful planning and accurate performance of the co-creation are vital to achieving a constructive result. By using co-creation in the development process, the user experience is expected to be strengthened, and the co-creation could advance development further.

Rationale

The increasing number of older adults in the world and the falls many of them suffer call for enhanced fall prevention, and here physiotherapists can add valuable knowledge. Both societal and individual benefits from a reduced number of falls are important. Evidence has shown that balance and strength exercises programmes can reduce falls, but adherence to such programmes is often poor. Therefore, it is desirable to provide appealing and effective fall preventive exercise programmes to increase adherence. The incentive for the smartphone self-test application emerged from the development of a new digital exercise programme where older adults wanted to monitor their progress as part of the programme. Sensors in smartphones offer possibilities to measure functional balance, but so far this is not done as self-assessment.

The recent and rapid advances of digital technology in society offer opportunities in fall prevention to encourage adherence and support self-management. Digital technology-based fall prevention may get older adults more involved through self-management. Fall prevention could be advanced by presenting flexible and tailored programmes, customised for older adults’ individual needs. Digital technology could provide information and exercise interventions as smartphone applications to the general public in environments separate from the health care setting, to reach a broader population. A smartphone self-test could offer long-term follow-up, remote monitoring, and for the older adult, it could establish independence and motivation for fall preventive interventions.

So far digital technology in fall prevention is a relatively new field and still under development. Digital technology is also a novel field within physiotherapy. Researchers around the world are investigating different fall prevention solutions, and various exercise interventions are currently studied. There is still no consensus on how the exercise programme should be designed and provided as a fall prevention intervention, or how self-assessments with sensor technology in smartphones could be used. Nevertheless, evidence from individual trials has shown positive results for fall prevention programmes and sensor technology measurements.

Currently, fall prevention studies using digital technology generally investigate early-stage interventions, published material also includes developments, and ongoing larger studies have still not published comprehensive results. Therefore, this thesis is relevant for testing the newly developed exercise programme before including a larger population in a randomized study. Only pre-stage results from

one independent self-test application with sensor-based assessment of balance have so far been published. With this limited knowledge in this new field of using digital technology in fall prevention interventions, there is a need for more information about the feasibility of using a self-managed exercise programme and developing a valuable self-test for older adults.

Aims

The overall aim of this thesis was to explore the use of digital technology in fall prevention for older adults. One part was to describe and evaluate the use of two self-managed fall prevention exercise programmes, a new digital programme (DP) and a paper booklet (PB) and to investigate the feasibility of outcome measures used in the study. The other part aimed to develop and investigate a smartphone self-test for balance and leg strength, which could be used to enrich fall prevention for older adults.

The specific aims of the research conducted for this thesis were:

To explore older adults’ participation in a fall prevention study that compared a digital with a paper booklet exercise programme (Paper I): • describe the participants’ characteristics and distribution in relation to the self-selected choice of programme • describe and compare attrition rates and adherence to the programmes • describe and compare experiences and self-rated effects after the intervention • compare exercise maintenance at one year after study start

To investigate the feasibility of the performance-based and self-reported outcome measures used in the above study (Paper II):

• explore ceiling effects and sensitivity to change of the outcomes • investigate associations between performance-based measurements and self-report scales • describe effect sizes of the outcomes • investigate if amount of exercise time was associated with the outcomes • compare results between the two programmes

To describe the older adult participants’ preferences for, and their contribution to, the design of instructions and user interface of a smartphone self-test application for balance and leg strength, in a user experience co-creation study (Paper III).

To investigate the concurrent validity between clinical instruments commonly used in physiotherapy and variables collected and calculated with the smartphone self-test application prototype for balance and leg strength test (Paper IV).

Materials and Methods

This thesis comprises data from two different fall prevention projects: Safe Step and MyBalance. The two projects are interlinked and concern the use of digital technology with a focus on the older adult users and their user experience. Both quantitative and qualitative research methods were used to get a deeper understanding of the use of digital technology as an approach to fall prevention. Table 1 presents an overview of the four papers. Papers I & II are part of the Safe Step feasibility study, a four month fall prevention exercise intervention study aimed at describing and evaluating two exercise programmes delivered in digital or paper format. Papers III & IV are part of the MyBalance project, where the development of a smartphone self-test application for balance and leg strength was commenced. The MyBalance project included a co-creation study with older adults, with five sessions during one year for application design developments of instructions and user interface. It included a concurrent validation study to explore correlations between the smartphone prototype measurements and clinical balance assessments.

Table 1. Summary of the papers in this thesis.

Paper I Paper II Paper III Paper IV Project Safe Step MyBalance Method Quantitative; Controlled participant preference trial Qualitative; Quantitative; Validity feasibility study Co-creation study study Study sample Community-dwelling participants ≥ 70 years Location Umeå Umeå Stockholm Period Sep 2016-Oct 2017 Jan-Nov 2018 Dec 2017-Jan 2018 Jan-Feb 2019 Participants n = 67 n = 10 n = 31 Outcome Adherence Assessments: Transcriptions: Assessments: measures/Data Attrition SPPB Observations Mini-BESTest Self-reported 30 s Chair Stand Test (audio and video) Functional reach background data and Questionnaires: Annotations: Max stepping test survey of experiences ABC Application 4-stage balance test Icon-FES development Five times STS LLFDI Reflections after 30 s Chair Stand Test Self-reported background workshop sessions 1 RM leg press data and self-reported Sensor variables for improvements balance and strength Data analysis Students t-Test Wilcoxon signed-rank test Qualitative Content Descriptive data Mann Whitney U-test Mann Whitney U-test Analysis Spearman’s rank Chi2/Fisher’s exact Rank biserial correlations correlation Spearman’s rank correlation MCID SPPB = Short Physical Performance Battery; ABC = The Activities-specific Balance Confidence Scale; Icon-FES = Iconographical Falls Efficacy Scale; LLFDI = Late Life Function and Disability Instrument; MCID = minimal clinically important difference; STS = sit-to-stand; 1 RM = One repetition maximum

Study design

Safe Step feasibility study (Papers I & II) The four-month Safe Step feasibility study was performed to explore and evaluate the use of two self-managed exercise programmes and the selected outcome measures. The purpose of a feasibility study is primarily preparation by trying out the pieces before a larger trial (121, 122). Pilot studies are small-scale studies testing the whole setup before a larger trial (123). None of these need to be adequately powered. This feasibility study compared two interventions, two different fall prevention exercise programmes, provided as a new digital programme (DP) or a paper booklet (PB). Pooled data for feasibility testing of the outcome measures was further analysed to study the self-managed exercise strategy regardless of whether the programme was provided in digital or paper format. This was also a participant preference trial where all participants chose their preferred type of programme (124) based on their personal preferences and access to technology, as no technical devices were provided for the study. An overview of the Safe Step feasibility study is shown in Figure 2.

Recruitment

• At HC and SO • Self-selected choice of programme DP or PB

Pre-assessment

• Introduction meeting • Informed consent • Questionnaires and assessment baseline data

Interventions 4 months

• Self-managed exercise programme DP or PB • Exercise diary • Phone-interview after a few weeks • Observation n=6 DP halfway • Monthly peer-mentor meetings (50% of SO participants DP)

Post-assessment

• Final meeting • Questionnaires and assessment

Follow-up at 12 months

• Postal survey

Figure 2. Overview of the Safe Step feasibility study.

Recruitment was done at senior citizen organisations (SO) by members of the research team and at one health care centre (HC) by health care professionals. The study was presented for the staff at the health care centre in a presentation aimed at providing them with more details about the two programmes. The physiotherapist at the health care centre had the overall responsibility of enrolling participants at the health care centre. Participants recruited at SO signed up on a list when presented with information about the study during a meeting at their organisation. The individuals on the list were soon after the presentation contacted by phone by a member of the research team for a screening interview and selection of programme. The study started with an introduction meeting with pre-assessments, followed by independent practice with the exercise programmes and self-reporting in an exercise diary for four months. The intervention finished with a similar meeting after four months to repeat the assessments. A postal survey was sent out as follow-up 12 months after study start.

The Safe Step feasibility study had from a recruitment perspective five groups, depending on the programme selected and recruitment strategy. The goal was to recruit at least 10 participants in each recruitment group. If a couple joined the study, they were kindly asked to choose the same programme. The five recruitment groups were used for descriptive purposes only. Participants from the SO recruitment with the digital programme were divided into two halves; one half had scheduled peer-mentor group meetings once a month. This was also by self-selected choice. An exercise prescription, Green Prescription (in Swedish, Fysisk aktivitet på recept (FaR)) was issued for all participants from the health centre.

Interventions in the Safe Step feasibility study (Papers I & II) The interventions compared in the Safe Step feasibility study were two self- managed fall prevention exercise programmes including a short introduction at the start of the intervention: the new Safe Step digital exercise programme (DP) and a paper booklet programme (PB). Both programmes are based on exercises from the Otago Exercise Programme (OEP) (125). The DP has additional exercises inspired by the Falls Management Exercise Programme (FaME) (43). The digital programme, the Safe Step application, was developed as a co-creation project with older adults and an interdisciplinary research group (77). The Safe Step application is currently used in a randomised controlled trial, but a limited version is accessible free even without participation in the trial at Google Play and App Store under its Swedish name Säkra steg (126). During the feasibility study, the DP was distributed as a web application managed on participants’ private computer, smartphone or tablet. No digital technology devices were provided to participants. The PB was an edited version of the Otago Home Exercise Booklet Swedish version (127). All participants were instructed to

self-tailor a programme with 10 different exercises, and were recommended to exercise 30 min at least 3 times per week over 4 months. They were encouraged to change exercises to, progress or regress, the programme over the intervention period to maintain a suitable level of difficulty according to their ability. The advice was that exercises should be challenging but not too hard. For balance exercises, participants should feel unstable but not risking a fall, and for strength exercises, they should feel strain in their muscles after completing the planned number of repetitions. Details about the feasibility study’s exercise programmes can be found in Table 2.

Table 2. A brief description of the two interventions.

Characteristics Safe Step digital programme (DP) Paper booklet programme (PB)

Exercises Video clips with verbal instructions. Drawings with written instructions. The repository of exercises was organised into The repository of exercises was organised 10 exercise clusters: 5 for lower-limb muscle into two sections of strength and balance strength, 2 for balance, and 3 for gait and step exercises. Each section was divided into exercises. Each cluster offered several three levels of difficulty. exercises with different levels of difficulty. Adaptation of exercises and reasons for Adaptation and reasons for the exercises were doing the exercises were not provided. provided for each exercise cluster. Tailoring In video clips verbal instructions guided The instruction page at the beginning of the participants to find the right level and know booklet guided participants to find the right when to progress. For example, when easily level and know when to progress. For managing 2 sets of 10 repetitions. example, when easily managing 2 sets of 10 repetitions. Exercise diary Integrated self-reported digital exercise diary. Self-reported paper-based exercise diary Data was saved on a secure server and monthly sent in monthly to the research group in a reports were sent to the research group by an pre-paid envelope. administrator. Behavioural Behavioural change support included weekly Behavioural change support was not change support activity planning, monitoring previous reported provided. exercise, and motivational messages from the virtual physiotherapist. Extras Examples of exercises integrated into everyday The booklet had additional exercises for activities and tips on how to do exercises warm-up and stretching, but no integrated outdoors. exercises. Safety Safety advice was provided in each exercise The first page in the booklet presented video and also for every exercise group. general safety instructions for doing the General safety information for everyday life was exercise programme. also provided.

Both the digital programme and the paper booklet exercise programme were introduced separately. During the introduction meeting, participants had the opportunity to ask questions, and they were also given a contact number to the research group in case they had any problem with the programme. All participants had a short phone interview a few weeks after starting the intervention from one of four researchers in the group. All four had varying experience of interviewing. A semi-structured interview guide with 12 questions

was used, and the aim was to get a first impression about how it was going, encourage the participants, and identify any problems getting started. To the final meetings, even participants that had withdrawn from the intervention were invited to attend, to give feedback on the programme.

MyBalance project (Papers III & IV)

The self-test application prototype A smartphone self-test application for balance and leg strength was developed as part of the project MyBalance (Figure 3). The development of the prototype started in the research group by experimenting with different positions and tests that could be possible to perform as a self-test procedure, while they also had to be compatible with the sensors’ ability to register movements. Thereafter, positions and tests were tried out using the smartphone in a human motion lab with a 3D motion analysis system. The systems used were: sensor analysis system (MoLab, AnyMo AB, Umeå, Sweden), kinematics 3D-motion analysis system (Oqus 300+, Qualisys AB, Gothenburg, Sweden), and kinetics with two portable force plates (Kistler 9260AA6, Kistler Holding AG, Winterthur, Switzerland). The smartphone’s position was explored both at the lower back, on the side of the body on the lateral side of the hip. This pre-research was performed with two men 44 and 49 years old without known injuries or balance impairments.

A first version of the self-test prototype was developed based on the outcome of this pre-research in the motion lab and with physiotherapy knowledge and experience about balance assessments and postural control. This prototype was used in the validity testing in Paper IV. The prototype for the co-creation study was further developed with an early user interface to start from when co-creating the instructions and user interface. The MyBalance application prototype contained two test procedures: a Three maximal chair stand test for functional leg strength test, and a 30s static standing balance test in two different foot positions (feet together and semi tandem). The two tests were predefined by the research team based on the pre-research and considering safety aspects when self-testing.

Prototype App Development

Pre- Validity Validity Preparation research testing testing

2017 2018 2019

Co-creation Session 1 to 5

Figure 3. MyBalance prototype development timeline.

Co-creation study for the self-test application development (Paper III) The co-creation study with older adults to develop a smartphone self-test application to assess balance function independently was performed with five co-creation sessions during 11 months. The objective was to design instructions for the self-test performance and the MyBalance application’s user interface. Co-creation was used involving older adults representing the end-users to gain knowledge about end-user needs and desires, to facilitate our understanding of how older adults perceived the self-test application MyBalance.

An iterative design process was applied, which is often used in software development and informatics. The iterative process builds on cyclic work with prototypes, using continuous evaluations and improvements (128). An outline for the five sessions was drawn up before the co-creation sessions started, and this was revised in relation to the issues addressed after each session according to the iterative process. Before the co-creation sessions commenced, early prototypes for the application’s sensor measurements, and for video instructions and user interface were developed to provide working material for the first session.

Content in co-creation sessions 1-5 The activities during the co-creation sessions were hands-on workshops followed by reflections on the different tasks. A summary of the aims and data collection methods for the five, 2.5 hour long, sessions can be found below in Table 3. At session 1, a short project introduction was done, followed by the presentation of all participants and researchers involved. The co-creation task was to watch the video instructions (v1), try to do the self-tests, and discuss the content of the instructions and experiences from performing the tests. At session 2, revised video instructions (v2) were explored in pairs. The participants had two different roles, to perform the self-test or be an active observer; and after the first self-test the roles switched. The researcher’s role was to be a facilitator at the user-test workshop, read the instructions out loud from a script and manage the video recording. Session 3 included three hands-on workshops: (a) navigate the app with the smartphone, (b) discuss preferences for the results presentations, (c) paper mock-up exploration of the graphical user interface. At session 4, a homework assignment evaluating self-test instructions (v3) was discussed and the results presentation was further elaborated. Session 5 included reflection on and discussion of the second homework assignment, a user-test of the most recent version of the MyBalance application, and a member check.

Table 3. Summary of the five co-creation sessions.

Session Month Participants Aim Data collection methods

1 January n = 9 Introduction, and feedback on Observations instructions (v1) Video and audio recordings

2 February n = 6 Explore navigation in the application and Think aloud the understanding of instructions (v2) PC mock-up Video and audio recordings

3 March n = 10 Explore navigation in the application on the Think aloud smartphone and discussion about user Paper mock-up interface and presentation of results Audio recordings

4 May n = 4 Gain information about how a novice Audio recordings perceived the self-test instructions (v3) and discussion about presentation of results

5 November n = 4 Gain information about how a novice Think aloud perceived the overall instructions and Video and audio recordings realise a user-test with final prototype

Each session finished with an audio recorded conversation involving the whole group, where thoughts and considerations from the hands-on workshop sessions were discussed. Every session followed an iterative design process, meaning that the content in the next session was a development of the application design from the previous co-creation session.

After the final session, a focus group was held to gain knowledge about how the participants had experienced their participation in the co-creation process. However, the focus group was not part of the analysis for the thesis but instead was used to evaluate the sessions and serve as information for future co-creation sessions.

Concurrent validity study (Paper IV) The validity study was realized in a physiotherapy outpatient setting. A prototype of the smartphone self-test application was compared with clinical instruments to explore concurrent validity during individual testing sessions. The associations between the prototype’s variables for balance and leg strength calculated from the MyBalance application and seven matching clinical physiotherapy instruments for balance and leg strength was analysed.

The MyBalance prototype was implemented as a custom Android application developed to sample built-in gyroscope and accelerometer data. Sensor data was collected with an Android smartphone (Sony Xperia X Compact F5321) connected via Bluetooth to a PC. The registered acceleration from body

movements was transmitted in real time to a custom application running MATLAB, version R2017a-9.2.0.556344 (The MathWorks Inc.) [computer software] on the computer for storage and later analysis. Once per day before starting data collection the smartphone was calibrated following a routine to minimize the risk of sensor measurement errors. The smartphone was fixed to the participants lower back (L4-5) in an upright position using a sports armband with an elastic Velcro band extension around the waist. The self-test sequence accommodated just one fitting of the smartphone, as the sensor leg strength test followed the balance tests.

When using this early development prototype, a manual procedure was necessary to prepare data from sensor measurements in MATLAB. The manual procedure amounted to identifying the start and stop of movement to stand up in the Three maximal chair stand test. The balance self-test also had manual identification for the start and stop of events. Variables for the balance self-test were derived from the horizontally transformed accelerometer data, and variables for the leg strength self-test were derived from the vertically transformed accelerometer data.

Setting Data collection for Papers I-III took place in Umeå, Sweden. The feasibility study was realized from September 2016 to February 2017, with a follow-up survey 12 months after study start. The co-creation study took part from January 2018 to November 2018. Both went on during the winter season when weather conditions, with snowy and slippery roads, could impact participation. The data collection for Paper IV took place in Stockholm, Sweden, in two separate data collection rounds, December 2017 to January 2018, and January to February 2019. All tests were managed by a master’s student in physiotherapy, and using the same MyBalance prototype. Participants in the validity study only attended one testing session. Fika was served at all sessions lasting more than 1.5 hours. This is a Swedish custom that means time for coffee and socialising. It included a selection of beverages; a healthy snack and/or biscuit and special dietary needs were catered for.

Participants All studies included community-dwelling older adults ≥ 70 years, who were able to rise from a chair and stand without support. All participants volunteered to participate after study information was presented. Three different studies are included in this thesis, and participant characteristics are presented in Table 4 for age, gender, living condition, previous falls, walking aids, physical activity level, access to smartphone, and education level.

Table 4. Characteristics of the participants in all studies.

Paper I & II III IV Project Safe Step MyBalance MyBalance Participants, n 67 10 31 Age (mean ± sd) 76 ± 4 76 ± 3 79 ± 5 Women, n (%) 48 (72) 6 (60) 24 (77) Lives alone, n (%) 26 (39) 3 (30)† 12 (39) Fall last year, n (%) 39 (58) 6 (60)† 10 (32) Uses a walking aid, n (%) 14 (21) 1 (10) 9 (29) SGPALS active* summer, n (%) 59 (88) 10 (100) 26 (84) SGPALS active* winter, n (%) 58 (87) 10 (100) 26 (84) Access to smartphone/tablet, n (%) 39 (60) 8 (80) — Education level, n (%) † Primary 34 (51) 4 (40) 13 (42) Secondary 19 (28) 2 (20) 6 (19) Tertiary 14 (21) 3 (30) 12 (39) * dichotomized into the groups inactive (level 1-2) or active (level 3-6); † 1 missing

Safe Step feasibility study (Paper I & II) The Safe Step feasibility study recruited 78 participants, and 67 participants started the study. The revoked participation was mainly due to medical conditions that emerged after signing up for the study as the recruitment at the senior citizen organisations took place a few months before the study start. These 67 participants had a mean age of 76 ± 4, and 48 were women (72%). The inclusion criteria were: ≥ 70 years old, lives independently, able to rise from a chair and stand without support, experiences of deterioration in balance and/or needs to be more careful not to lose balance and/or has experienced a fall the past year. Exclusion criteria were: does physical exercise more than 3 hours per week, self-reported progressive disease that was likely to influence mobility, and cognitive difficulties. The cognitive condition was estimated by the person who included the participants in the study.

MyBalance co-creation study (Paper III) Participants in the MyBalance co-creation study were a purposeful sample of participants from the feasibility study, ten older adults with a mean age of 76 ± 3, and six were women (60%). All had volunteered to participate in additional research projects. Inclusion criteria were: some previous experience of using either a computer, tablet, or smartphone and has shared opinions openly at group meetings in a previous study. A shortlist of 15 individuals was made, and they were contacted by e-mail. A suitable group size of 10-12 participants with a mix between men and women was desired. Number of participants attending each session can be found in Table 3.

MyBalance validity study (Paper IV) In the MyBalance validity study, a convenience sample of 31 participants were recruited from a primary health care setting, the mean age was 79 ± 5 years, and 24 were women (77%). Participants met health care rehab personnel and received verbal and written information about the study. If they were interested, the test leader contacted them with more detailed information. Recruitment took place October 2017 to January 2018, and January to February 2019. Inclusion criteria were: community-dwelling ≥ 70 years old, able to rise from a chair independently, and able to understand and read Swedish. Exclusion criteria were: self-reported progressive disease that was likely to influence cognitive function, or impaired cognitive function considered by the test leader.

Data collection

Safe Step feasibility study (Paper I & II)

Outcome measures feasibility study (Paper I & II) At the introduction meeting and final meeting, a battery of assessments and self- report instruments were completed. The assessments were performed by a blinded assessor, an experienced physiotherapist. Two different assessors were used, but the intention was to try to get all participants assessed by the same person on both occasions. An outline of outcome measures and timepoints for data collection are shown in Table 5.

Table 5. Outline of outcome measures and timepoints for data collection in the Safe Step feasibility study.

Outcome measures Start 2 months 4 months 12 months Questionnaire with self-reported health, fall incidence, activity P P level (SGPALS), etc. Short Physical Performance Battery (SPPB) P P 30s Chair stand test (30s CST) P P The Activities-specific Balance Confidence Scale (ABC) P P Late-Life Function and Disability Instrument (LLFDI) P P Iconographical Falls Efficacy Scale (Icon-FES) P P Attitudes to Fall-Related Interventions Scale (AFRIS) P P P Behavioural Regulation in Exercise Questionnaire (BREQ-2) P P P Self-reported effects and experience P Exercise diary Continuous for 4 months Follow-up survey P

Descriptive data was collected with a study-specific questionnaire regarding: sociodemographic characteristics, fall history, use of walking-aids, self-reported health, and access to technological devices. Activity level was assessed with the Saltin-Grimby Physical Activity Level Scale (SGPALS) (129), the version that also incorporates household activities. Balance and functional strength were assessed with two different instruments: the Short Physical Performance Battery (SPPB) (32), a responsive instrument, valid, and reliable (130), including components of standing balance, sit-to-stand test, and walking speed. Original scores from each component sum up to a score of 0-12, where a higher score indicates better functional performance. The SPPB components were in Paper II analysed as separate outcome measures (standing balance, gait speed over 4 meters, and Five times sit-to-stand test). The 30s Chair stand test (30s CST) was used to further assess leg muscle strength and enable comparisons of chair stand tests (33). The test evaluates the number of sit-to-stands that can be completed during 30 seconds. The test has shown moderate test-retest reliability and concurrent validity with 1 RM leg-press among community-dwelling older adults (33).

Five self-report instruments were included at the pre and post assessments. A Swedish translation of the Activities-specific Balance Confidence Scale (ABC) was used for measuring balance confidence (131). The scale uses a grading from 0 to 100% for 16 ambulatory activities, where a higher score means better confidence. The ABC-scale has been found both valid and reliable for assessing balance confidence in community-dwelling older adults (132), and has shown excellent correlation with performance-based measures as the Timed Up and Go Test and the SPPB (133). Fear of falling was assessed with the 30-item Iconographical Falls Efficacy Scale (Icon-FES) (134). Fall-related concern is rated on a 4-point scale, ranging from not at all concerned to very concerned, by looking at pictures illustrating activities in daily life situations. A lower score reflects less concerns about falling. The Icon-FES has shown excellent psychometric properties and has been suggested to be better suited for higher- functioning older adults living in the community than the Falls Efficacy Scale International (134). Icon-FES was translated into Swedish for the purpose of this study, according to published guidelines for translations (135). The last balance and function related instrument was the Late-Life Function and Disability Instrument (LLFDI), which measures functional limitations and disability in community-dwelling older adults (136, 137). The function component (LLFDI-FC) assesses 32 physical activities and includes a general scale of function. The scale’s raw scores are transformed into a standardised score of 0-100, where a higher score indicates better levels of functioning. The LLFDI-FC is frequently used in research on community-dwelling older adults and has been shown to have high construct validity, high test-retest reliability, and to be

sensitive to change (138, 139). The Swedish LLFDI has shown high reliability and validity when assessing older women with self-reported balance deficits and fear of falling (140).

Thoughts about the intervention were explored with Attitudes to Falls-Related Interventions (AFRIS) with six translated statements about the participants attitude to the programme, scored on a scale of 1-7, from disagree strongly to agree strongly with each statement (141). Higher scores indicate a more positive attitude. Motivation for physical activity was assessed with the Behavioural Regulation in Exercise Questionnaire (BREQ-2) with 10 statements graded on a 0-4 Likert scale, ranging from not true for me to very true for me (142). The last two instruments were not used for any further analysis in the papers of this thesis.

Other outcome measures were used in addition to the pre- and post-intervention assessments. These were: the exercise diary, post assessment self-reported effects and experiences, and the 12-month survey. Participants in both groups self- reported their exercises over the four-month intervention in exercise diaries. The exercise diary for the DP included self-reporting of: date, which of the predetermined exercises were done, time spent on the practice, how they felt in general, and how they felt about the exercise session. The digital diary allowed reports once daily, and it could be filled in afterwards but not in advance. The exercise diary for the PB involved a paper sheet for each month, with rows for daily exercise reports, and columns containing the same information as the DP exercise diary. An example of the exercise diary is presented in Appendix Figure A1. These were returned at the end of each month using a pre-paid envelope. All diaries were reviewed monthly by a member of the research team, and if data was uncertain or missing for an entire month, the participant was contacted by phone.

A survey was developed to evaluate the subjective effects and experiences of using the self-managed exercise programmes, and it was distributed to the participants at the final meeting. The survey had three parts evaluating experiences of using the programme and perceived effects. The first part consisted of eleven statements scored on a Likert-type scale from 1 (disagree strongly) to 5 (agree strongly), e.g., “I’m content with the programme I used”, or “The programme contains exercises that are challenging for me”. The second part had two multiple-choice questions about positive and negative effects of the intervention. The third part had additional questions about any falls that may have occurred whilst exercising, if they would recommend the programme to others, and if they planned to continue with the programme. Participants not attending the final meeting received the survey by post with a pre-paid envelope, as their opinions

were considered valuable. Even participants who had withdrawn from the intervention were invited to take part in this survey, as their opinions were also interesting in the evaluation of self-managed exercise programmes.

Finally, a short survey was sent out 12 months after the study start to participants who had finished the study or attended the final meeting (n = 45). The aim of this follow-up was to investigate if participants continued to exercise with the programme. If not, the participant was asked for the reasons why, and if they planned to restart. This was mailed out with a pre-paid envelope to return the survey.

Co-creation study for the self-test application development (Paper III) In the co-creation study, where the objective was the design of the self-test application’s instructions and user interface (Table 3), multiple data collection methods were used (Figure 4). To facilitate later analysis of the participants’ preferences and suggestions for the design, video and audio recordings were captured during sessions. These recordings, above of all the video recordings, were helpful to observe the handling of the application and the self-test performance during the sessions. The video recordings provided non-verbal communication as well as other reactions.

The Think aloud method (143) was used in the hands-on workshops for user tests at sessions 2-3 and 5. This method gives the observer insight into the participant's reasoning, and facilitates the design process, as the instructions to participants are to talk out loud and express what they as participants see, do, and think. The Think aloud method could give information on how participants handled the smartphone, what they were looking at, their reasoning for the next step, or if they experienced any difficulties in test performance or navigation. All the Think aloud sessions were video recorded.

Watching and discussing instructions were done in both structured and semi- structured ways. Often a set of discussion questions was provided, but it was optional to use them or not. Different forms of information were explored by participants, video instructions were the most common form of information along with printed material. All sessions finished with an open whole-group discussion about the content of that session, where anything could be brought up.

Mock-ups are models made from low-tech materials. In co-creation session 2, a PowerPoint mock-up was used to explore participants’ ability to navigate the user interface of the MyBalance application, and the self-test performance was observed. A paper mock-up was also used in session 3 to explore and find out participants’ preferences for the user interface layout.

Homework assignments were completed prior to the session with the aim to gather information from persons without previous experience of the project. This was done with a novice, a friend, or family member not previously involved in the design process. Observations were made by the participants to gain information about how novice users comprehended the information, and how the video instructions were perceived. For the homework task, the participants had access to a private YouTube channel where videos could be viewed unlimited. A guide was provided on how to conduct the observation, as well as a set of questions to discuss the content of the video with the novice.

Think aloud Watching and For user-tests, discussing participants are asked to Watching instructions talk out loud while and exploring in smaller performing the tasks, to groups followed by verbalise what they are discussion with or without thinking, doing, and facilitating questions feeling

Mock-ups Homework Use of models that Find out if instructions provides opportunity to were appropriate to test and remodel a perform the self-test and design use the application independently

Figure 4. These methods were used during the data collection in the co-creation sessions.

Concurrent validity study (Paper IV) The first MyBalance self-test application prototype was tested for concurrent validity in comparison to seven clinical instruments. Each test session was led by an experienced physiotherapist using the same sequence test protocol, starting with balance assessments followed by the leg strength instruments, for a summary see Figure 5. Participants were allowed to rest at any time during the testing session, but no one did.

Figure 5. The sequenced study test protocol in summary. This was used for each participant.

Outcome measures concurrent validity study (Paper IV) The following four clinical balance instruments were used in this study. The Mini- BESTest (MiniB) (29), a valid (144) and reliable test (145) that evaluates balance with 14 different tasks in four different dimensions. The score ranges from 0 to 28 points, and a higher score indicates better balance (29). Functional reach (FR) measures postural control during a reaching forward movement. It is a reliable (146) and valid test (147), and is measured in centimetres. The Modified 4-stage balance test (m4-stageBT) evaluates balance in four progressive balance positions: feet together, semi tandem stance, tandem stance, and one leg stand (148). The modification from the original was that each position was held for 30 seconds (in the original test 10 s), and that the total time in seconds was used in the analysis (max 120 seconds). To get into the start position, it was optional to hold on to a support, and a complete test was required to continue to the next position (148). The Modified maximal stepping test (MaxStep) (149) was modified from a more advanced stepping test (150) and evaluates how large a step one can take and return to the starting position. The result is measured in centimetres, and the use of either foot is permitted. Two practice trials are allowed before three test trials, and the outcome for the test is the longest of the three steps.

The following four clinical leg strength instruments were used in this study. The Modified maximal stepping test (MaxStep) was used both as a balance instrument and a leg-strength instrument, and was described above. Five times sit-to-stand (5TSTS) measures lower limb function during the stopwatch timing

of five chair stands from a normal-height chair with arms crossed over the chest (35). The result is measured in seconds and has shown to be both a reliable (151) and valid test (34). The 30s Chair stand test (30s CST) is a reliable and valid test (33) that measures muscle function in the lower limbs. The participants were instructed to stand up as many times as possible from a normal-height chair with arms crossed over the chest for a period of 30 seconds. Finally, the One repetition maximum in sitting leg press (1 RM) is a reliable and valid test for older adults when leg strength is to be measured (152, 153). A previous published protocol (152) was followed, with five repetitions to warm up at low resistance. The 1 RM test started with a weight as close to 10 kg below participant’s bodyweight as possible if the participant could rise from a chair (45 cm) with arms crossed over their chest. If this was not the possible, the test started at 10 kg below half the bodyweight. The starting position was seated with a 90-degree knee flexion, the participant then fully extended the knees. The weight was increased nine kilograms after a 45-second rest between each repetition, and this was repeated until a complete leg extension was not possible. The maximum weight was recorded as the person’s 1 RM. Life Fitness equipment was used at the seated leg press assessment in this study.

The two smartphone self-test procedures with the application prototype, the balance test, and the Three maximal chair stand test were completed during three separate sensor measurements. The 30s static standing balance was tested in two different foot positions. If the option to use a support to get into the start position the participant let go of the support before the timer started. Feet together, standing with feet close together and maintaining balance for 30 seconds. This position was repeated three times, and it was the same position as the first position in the clinical assessment Modified 4-stage balance test. Semi tandem stance (heel beside the big toe of the other foot), standing and maintaining balance in a semi-tandem position for 30 seconds. This position was performed at the same time as the second position in the clinical assessment Modified 4-stage balance test. Three maximal chair stand test, was used for test of functional leg strength. This was a modification of the traditional Five times sit- to-stand test that earlier has been used as sensor measurement to calculate muscle power (154). The Three maximal chair stand test was started sitting on a normal-height chair with arms crossed over the chest. The participant was instructed to rise from the chair as fast as possible on the command ‘stand up’, and sit down on the next command ‘sit down’, and remain still until the test leader gave the next command. A pause of at least three seconds was given between each movement before the next rising or sitting movement, as the sensors required these pauses for a correct measurement. The Three maximal chair stand test included two practice trials. These were followed by the three maximal trials, and the mean value of the three was used in the analyses. Variables derived from the sensor test data are listed in Table 6.

Table 6. Sensor measurement variables for the Standing balance test (balance) and Three maximal chair stand test (leg strength) from the MyBalance prototype.

Test Measure Unit Reference Balance (time domain) Normalized Path Length (NPL) mg/s (155) Horizontal sway area (hAREA) mg² (156) Horizontal Root Mean Square acceleration mg (156) (hRMS) Horizontal mean acceleration (hMEAN) mg (156) Root Mean Square acceleration (RMS) mg (155) Acceleration Peak to Peak (P2P) mg (155)

Balance (frequency domain) Median frequency (MDF) Hz (156) Centroidal frequency (CFREQ) Hz (156)

Leg strength Max vertical power (PowerMax) W (95, 157) Max vertical velocity (VelMax) m/s (95, 157) Max vertical acceleration (AccMax) m/s² (95, 157) Max vertical jerk (JerkMax) m/s³ (95, 157) mg/s = milli gravitational acceleration per second; mg² = milli gravitational acceleration square; mg = milli gravitational acceleration; Hz = Hertz; W = watt; m/s = metre per second; m/s2 = metres per second square; m/s3 = metres per second cubed.

Data analyses

Quantitative methods for data analysis (Papers I-II & IV) All significance tests were two-tailed, and the level of significance was set to p < .05 for the quantitative data analyses. The following levels were applied for interpreting the correlation analyses: very high correlation (0.90 to 1.00), high correlation (0.70 to 0.90), moderate correlation (0.50 to 0.70), low correlation (0.30 to 0.50), or poor correlation (less than 0.30) (158). The distribution of data was regarded as normal if the skewness value was below 1.0 for parametric data.

Adherence, attrition, and self-reported experiences (Paper I) The Safe Step feasibility study sample size was not calculated as it was not primarily designed to study effectiveness but rather to explore the feasibility of the interventions and outcomes used (123). Group differences for baseline characteristics and experience between the DP and PB groups were analysed using the Chi-square test (Fisher’s exact test was used if the expected count was < 5) for nominal data. The Student t-test was used for normally distributed data, and the Mann-Whitney U-test was used for non-normally distributed data and ordinal level data. Activity level estimated with the SGPALS was dichotomized into the groups inactive (level 1-2) or active (level 3-6) using a previously published method (159). The established procedure for withdrawal

was when participants explicitly let us know they had stopped exercising with the programme, this was noted. Then the attrition rate was defined by the proportion of participants that withdrew.

Adherence analyses used the first 16 weeks of self-reported exercise from the exercise diaries and was analysed with the Mann-Whitney U-test. Adherence was defined according to guidelines for older adults participating in exercise classes (53). The guidelines present four definitions of adherence: completion, attendance, duration, and intensity. Two of those were considered to be applicable to our self-management exercise programme intervention: completion and exercise duration. Completed study was defined as participants who took part in the intervention all four months. Exercise completion was determined by the number of weeks with any self-reported exercise. Exercise duration was defined as adherence to recommended exercise time determined by self-reported exercise time from the diary. The above definitions were used to create four subgroups aimed at comparing adherence for the DP and the PB groups. 1. Enrolled: everyone that started the intervention. 2. Completed study: all participants that did not explicitly withdraw from the exercise programme, regardless of the degree of participation. 3. Exercise completion ≥ 75% of the weeks: participants that self-reported exercise at least one session per week for at least 12 of the 16 weeks. 4. Exercise duration ≥ 75%: participants that self-reported at least 75% of the recommended 90 minutes of exercise per week (at least 1080 minutes over 16 weeks).

All data were analysed using IBM SPSS Statistics for Macintosh, version 24.0 (IBM Corp.) [computer software].

Feasibility of performance-based and self-reported outcomes (Paper II) The feasibility of using performance-based and self-reported outcomes was explored, after the four-months intervention. In these analyses data was pooled for the DP and PB interventions. The SPPB components: gait speed over 4 metres, standing balance, and the 5TSTS were analysed as separate outcomes as well as the total score of SPPB. Data were summarized for pre-post values using nonparametric measures of central tendency and variability (median and interquartile range). Possible ceiling effects were investigated by examining pre- assessment median values and the scores for the third or first quartile depending on if a high or low score was considered superior. The minimal clinically important difference (MCID) was used to examine outcomes without ceiling effects. The proportions of participants who improved or deteriorated according

to limit values based on MCID were calculated. The MCID applied to SPPB was 1 point (160), for gait speed, it was 0.1 m/s (161), for the 30s CST, it was 2 repetitions (162), and for LLFDI, it was 2 standard scores (163). Change after the intervention was analysed using the Wilcoxon signed-rank test, and effect sizes were calculated as Rank biserial correlations (164). The Rank biserial correlation gives a value (from -1 to 1) indicating the proportion between participants with improved scores and decreased scores, e.g., 0.2 indicates a 20% difference between the two compared groups. Differences between the DP and PB groups were compared with the Mann-Whitney U-test to analyse changes after the intervention. Associations between effects and exercise time and associations between performance-based and self-reported outcomes, were explored by calculating Spearman's rank correlations. All data were analysed using jamovi, version 1.6.3 (The jamovi project) [computer software].

Concurrent validity study (Paper IV) The following data analysis was applied to investigate the concurrent validity between clinical instruments and variables from the self-test application prototype. A mean value was calculated for variables with more than one attempt (Five times sit-to-stand, Feet together, and Three maximal chair stand test). The variables were normalized for the Functional reach and Modified maximal stepping test before further statistical analyses by dividing the measured value by the height of the individual. Relative strength was calculated from 1 RM leg press maximum weight divided by body weight. Correlations were calculated with Spearman’s rank correlations coefficient as some clinical instruments provide ordinal-scaled data. All data were analysed using jamovi, version 1.1.9.0 (The jamovi project) [computer software].

Qualitative methods for data analysis (Paper III) A deductive-inductive qualitative content analysis (165–167) was conducted to describe participant preferences for, and their contribution to, the design of the smartphone self-test application. The Optimized Honeycomb model for user experience was used for the deductive analysis. Data from the co-creation sessions were analysed using the following procedure. Audio recordings were transcribed verbatim, some by the author, and others by a professional transcriber. Both verbalized and non-verbalized activities were documented by the author as text from the video recordings. These observations of activities were documented and managed in the analytic software ATLAS.ti, version 8.4.4 (ATLAS.ti Scientific Software Development GmbH) [computer software]. This software was also used for the first steps of the analysis process for all transcribed data. All co-authors discussed the Optimized Honeycomb model and agreed on definitions of the facets (Table 7) prior to the start of analysis. These facets were subsequently used as categories in the deductive analysis. The analysis followed

these steps: (1) naïve reading, (2) identifying meaning units and video sequences according to the aim, (3) condensing text or verbalising video sequences, (4) deductively sorting the condensed meaning units according to facets of the Optimized Honeycomb model, (5) coding the condensed meaning units, and (6) sorting and inductively analysing the codes to outline and label the subcategories within each category. Content not germane to the aim was omitted. The initial coding and analysis were conducted by the author and thereafter triangulated together with all four co-authors with different competences and perspectives (e.g., eHealth, engineering, fall prevention, informatics, physiotherapy).

Optimized Honeycomb model for user experience The Optimized Honeycomb model for user experience (116) was used as categorisation matrix for the deductive analysis. To help understand the original UX Honeycomb model (110), a reorganisation and colour coding were done by Karagianni to illustrate the relation between the facets (116), and the parts USE, FEEL and THINK evolved where facets were grouped together. USE includes three facets (findable, accessible, and usable) and deals with practical aspects of the design. FEEL and THINK create an overlap for credibility, where the participant could be both logically and emotionally engaged and believe that something is reliable. Valuable, the facet in the middle, is an overall facet that incorporates the six surrounding categories into something valuable for the user- experience. The seven facets are described in Table 7 with our definitions for each facet that formed our categories.

Table 7. The seven facets as our categorisation matrix with definitions for each category.

Category Definition for deductive analysis Findable information in the application being easy to localise. Accessible the possibility to access information in the application regarding both physical and cognitive function, as well as accessibility to the actual device, in this case a smartphone. Usable how information is perceived, the feeling of being able to use the information given in the application and being able to perform the tests. Desirable the group’s needs and interests, how the application could be something to talk about and something everyone wants to use. Credible confidence in the application, if it feels safe to use, and if it is trustworthy. Useful the application as a whole must fulfil a need. Valuable the sum of all six categories surrounding it, offering value to the user.

Ethical considerations Ethical considerations were appraised with the Declaration of Helsinki as the foundation when planning and conducting these studies (168). All participants received written and verbal information about the study, and everyone signed a written consent form. Participants recruited at a health care setting were informed that participation or non-participation in the study would not influence their forthcoming treatment.

The Safe Step feasibility study obtained ethical approval in 2016 (Umeå Dnr 2016/106-31). The ethical consideration for the study was safety during practice, and this was addressed both at the introduction meeting and within the programmes. Participants were advised to perform balance exercises close to a wall, countertop, or sturdy piece of furniture for support, and to choose exercises wisely according to their functional level. For balance exercises, this was feeling a bit unsteady but without risk of falling. All information was tailored to the age group to ensure good understanding and reduce any possible risks.

The MyBalance project got ethical approval in 2017 (Umeå Dnr 2017/317-31). Participation in the co-creation study had limited risks that caused ethical concerns, but as the participants shared information about their opinions and personal experiences, it was important to ensure that they felt safe within the group and could openly express their ideas while maintaining mutual respect for each other. The validity study involved a potential risk for the participant of losing balance during the balance tests, which was prevented by having a sturdy object or the test leader nearby, for support if needed. Depending on the physical fitness level of the individual, the leg strength test posed the potential risk of muscle soreness, which would resolve in a few days if it occurred.

Results

The two projects, the Safe Step feasibility study and the development of MyBalance application, have each contributed to the results of this thesis. New knowledge was gained about digital technology as means to prevent falls by exploring the older adults use and experience in these projects.

The Safe Step feasibility study (Papers I & II)

Description of the participants In total 67 participants started the Safe Step feasibility study (48 women and 19 men) with a mean age of 76 years. The participant preference choice showed that 43% preferred the DP, and 57% preferred the PB intervention. Despite that this was a participant preference trial the groups were comparable, and the only significant differences were: the DP group had access to a smartphone to a higher degree (p < .004), and they had a more positive attitude to the intervention at start (p < .035). The PB group self-reported more heart and cardiovascular medical conditions (p < .036). The self-reported descriptive data collected at study start indicated that 93% of the participants experienced deterioration in balance during recent years, 58% reported at least one fall in the past year (1-6 falls), and only 21% reported using a walking aid.

Attrition to the study The attrition rate varied between 9-55% for the five recruitment groups, in Table 8 the participation throughout the study can be followed for these different recruitment strategies. The DP group from the senior organisations had the highest number of participants to remain in the study during the four-month intervention in contrast to the lowest number in the PB group, who were recruited from a health care centre.

Table 8. Number of participants in each step of the study divided into the five intervention arms.

DP SO DPPM SO DP HC PB SO PB HC

Recruitment 14 14 6 23 21 Pre-assessment 11 12 6 18 20 Post-assessment 10 9 5 15 9 12-month follow-up 9 9 5 13 5 Attrition rate* 9% 25% 17% 13% 55% *attrition rate, the proportion of participants that did not completed the intervention of whom started the same; DP = digital programme; DPPM = digital programme with peer-mentor meetings; PB = paper booklet; SO = senior citizen organisations; HC = health care centre

The result is from here on presented regarding the intervention programmes DP and PB. The number of participants that withdrew from the study were only five in the DP group (17%) and 14 from the PB intervention (37%), as shown in Figure 6. The difference was substantial although not statistically significant (p = .078). The distribution of withdrawals was as follows: for DP, one never started, and four withdrew after approximately two months; for PB, three never started, five informed that they would like to withdraw during the phone interview after a few weeks, and six withdrew after 1-2 months.

Digital programme Paper booklet (DP) (PB)

29 Pre-assessment 38

5 Medical reasons 3 Family reasons 2 Medical reasons 1 Other engagements 1 Family reasons Withdrawals 1 Study too demanding 1 Other engagements 2 Exercises too easy 1 Study too demanding 1 Deceased (not due to fall) 1 No reason given

24 24 Post-assessment (83%) (63%)

17 Completion 19 (59%) ≥75% of weeks (50%)

9 Completion 13 (31%) ≥75% of duration (34%)

Figure 6. Flow chart of participants’ participation in the study.

Adherence to the exercise intervention Adherence considered as completion and/or exercise duration was analysed using the four defined subgroups. The subgroups were: Enrolled, Completed the study, Exercise completion ≥ 75% of the weeks, and Exercise duration ≥ 75%, in Table 9 data for adherence for all subgroups are presented. Adherence as percentage of number of recommended sessions were not significantly different between the two programmes in any subgroup. In the subgroup that exercised ≥ 75% of recommended exercise duration, the DP group reached a significantly higher level of exercise time peer week (p < .001).

Table 9. Summary of adherence of recommended sessions (%) and exercise time in self-reported minutes in median (Q1-3).

Digital programme (DP) Paper booklet (PB) p-value Enrolled n = 29 n = 38 Adherence 63% 54% 0.183 Exercise time per week (min) 61 (0-110) 65 (0-84) 0.450 Completed study n = 24 n = 24 Adherence 74% 80% 0.893 Exercise time per week (min) 65 (44-117) 75 (61-88) 0.703 Exercise completion ≥ 75% of weeks n = 17 n = 19 Adherence 91% 91% 0.505 Exercise time per week (min) 86 (58-136) 81 (61-89) 0.375 Exercise duration ≥ 75% n = 9 n = 13 Adherence 108% 103% 0.094 Exercise time per week (min) 123 (110-156) 85 (75-94) <.001

Reported exercise time for the subgroup that completed the study showed that participants using the DP reported less exercise 65 (44-117) minutes per week compared to the PB group, which reported 75 (61-88) minutes (p < .703). The non-significant difference is illustrated in Figure 7. In summary the figure shows that only 37% of DP participants self-reported 75% or more than recommended time, while 55% of PB participants reported 75% or more. Participants in the peer-mentor group, the DPPM (n = 9), self-reported a median of 65 min exercise per week, which was equivalent to 64 min for the DP participants (n = 15) that did not attend peer-mentor meetings (p = .571).

Figure 7. The graph shows data from exercise diaries, for the subgroup that completed the study. Categories are divided into sections of 25%, where > 100% represents participants who reported more than the recommended 1440 min of completion.

Feasibility of performance-based and self-reported outcome measures

Ceiling and floor effects The feasibility of outcome measures was analysed with data combined from both programmes. The assessment revealed evident ceiling effects for the SPPB balance component and indicated ceiling effects for self-reported balance confidence or fear of falling according to the ABC scale and Icon-FES. At pre- assessment, 70% reached the maximum four points when performing the balance test in the SPPB, and 95% managed at least level 3 of 4. For ABC the total score median value was 84 of 100 points, and 25% reached scores over 89.5 points at baseline. A lower score on the Icon-FES indicates a great balance confidence, range from 30 to 120. The median score was 47, and 25% scored below 40.5 points. For the SPPB total scores no clear ceiling effect was demonstrated but scores were in the higher range with a median score of 9, and 25% of participants scored above 10 points of 12 at pre-assessment. The LLFDI-FC instrument did not indicate any ceiling or floor effects. As chair stand tests and gait speed are timed measures, ceiling effects were avoided, nor were floor effects seen for these measures.

Sensitivity to change Improvement or deterioration was analysed for outcome measures free from ceiling effects, and the limits of MCID were used to assess sensitivity to change. One additional point or more in the total SPPB score indicated an improvement, which was seen in 56% of participants, and 23% deteriorated in SPPB. An improvement at or above 0.1 m/s in gait speed was seen in 15% of the participants, and gait speed indicated deterioration in 29% of the participants. An additional two or more chair stands in the 30s CST indicated a change, where 45% had improved and 4% deteriorated at post-assessment. The MCID of 2 standard scores for change in LLFDI-FC indicated that 34% of the participants had improved and 26% had deteriorated.

Associations between performance-based measurements and self-report scales The self-reported ABC, Icon-FES and LLFDI-FC at the pre-assessment showed significant and low to moderate correlations with performance-based measures SPPB, gait speed, balance and 30s CST, but poor correlation between Icon-FES and SPPB balance component. For 5TSTS poor correlations were shown with all self-report scales. No significant correlations were shown for any instruments when analysing change scores.

Effect size The feasibility also included an investigation of effect sizes for all outcome measures. The analyses showed improved functional leg strength at

post-assessment for 30s CST (rrb = 0.76) and 5TSTS (rrb = -0.65) with substantial effect sizes. Improvements in total SPPB approached significance with a lower effect size (rrb = 0.34). No statistically significant improvements were found for balance, gait speed, self-reported falls efficacy, or self-reported functional ability. Table 10 presents the absolute values for the outcome measures and analyses.

Association between exercise time and outcomes A low but significant correlation was found for total exercise time and 30s CST (ϱ = 0.393; p = .006) and 5TSTS (ϱ = 0.415; p = .003), and for total SPPB (ϱ = 0.462; p < .001). No other outcomes were significantly correlated with exercise time.

Comparison between DP and PB The two interventions were also compared, but no significant differences were found for the performance-based and self-reported outcome measurements. Nevertheless, the survey assessing the participants’ own experiences indicated that balance had improved for both interventions, and the DP participants indicated to a higher degree that leg strength had improved (p < .033), see Figure 8.

Table 10. Pre- and post-assessment absolute values of pooled data for DP and PB.

n Median (Q1-Q3) Effect size Assessment Outcome measure pre pre p-valuea (rrb)b post post SPPB Total (0-12) 67 9 (8-10) 0.058 0.34 48 10 (9-11) Balance score (0-4) 67 4 (3-4) 0.842 -0.07 48 4 (4-4) Gait speed 4 m, m/s 67 0.8 (0.7-1.0) 0.204 0.21 48 0.9 (0.7-0.9) 5TSTS, s 64 15.4 (13.1-17.6) < .001 -0.65 48 14.1 (11.9-15.3) Chair stand test 30s CST, n 65 11 (10-13) < .001 0.76 47 12 (10-13) Self-reported ABC, total score (0-100) 67 84.0 (73.0-89.5) 0.520 0.10 questionnaires 53 86.0 (75.0-93.0) Icon-FES, total score (30-120) 67 47.0 (40.5-56.5) 0.391 -0.14 53 46.0 (38.0-54.0) LLFDI-FC, scaled score (0-100) 66 62.7 (54.9-70.4) 0.420 0.13 53 63.5 (56.9-71.3) SPPB = Short Physical Performance Battery; 5TSTS = Five times sit-to-stand; 30s CST = 30 second chair stand test; ABC = Activities-Specific Balance Confidence Scale; Icon-FES = Iconographical Falls Efficacy Scale; LLFDI-FC = Late-Life Function and Disability Instrument—function component; aWilcoxon signed-rank test; bRank biserial correlation

4-month survey After four months, the survey assessing the participants’ own experiences showed that participants were content with the programme, regardless of intervention. Although significant difference was indicated where DP participants expressed a higher degree of contentedness with the programme (p = .026) and feeling supported by the programme (p = .044). The statement “difficult to choose exercises at right level” was borderline significant (p = .050), where DP users disagreed to a higher degree that it was difficult to choose the right exercises. Below in Figure 8, the responses for the 11 statements in the survey are presented. More positive effects than negative effects were reported in the post-assessment survey for both interventions; the only significant difference (p = .010) was that 26% in the PB group stated that no positive effects had been noticed during the study period. The only adverse event during the study was one person in the PB group, who reported a fall on two occasions during exercise practice, although no injuries were sustained.

12-month survey One year after study start, the participants answered a short postal survey. This survey showed that significantly more DP participants (67%) still exercised regularly with the programme, and 35% of PB participants reported regular exercise (p = .036). Regardless of intervention, of participants (n = 21) that discontinued exercising with the programme 14% stated that they would take it up within the month, 43% might take it up again, and 33% did not answer. Only two participants (10%) stated that they would not restart again; both had been using the PB.

Figure 8. The responses for the 11 statements in the 4-month survey. Some statements have missing answers, the response rate was 27 participants for each group; * p < .05.

Co-creation application design with older adults (Paper III)

Result of the qualitative content analysis The user-experience-oriented co-creation study used a qualitative content analysis. Seventeen subcategories were generated for the seven predefined categories from the Honeycomb model from this analysis, see Figure 9. Features from the older adults’ perspective about design regarding instructions and user interface were extracted from the co-creation sessions and the three parts, USE, FEEL, and THINK. The categories findable, accessible, and usable were the areas where the majority of meaning units originated from, which also mirror the discussions during the first sessions which contained much content related to practical elements of the application. However, data from these three categories furthermore existed in all sessions.

Credible THINK • Important to understand why FEEL • Feeling safe with the app

Useful Desirable • Preferable with an uncompli- • Need for a self-test cated and familiar design • Motivation through monitoring • Not preferable with interrupting or slow elements • Do not want to share results

Valuable • Added value

Findable Accessible • Adapt for vision and hearing • Organise information logically impairment and simply • Facilitate for different levels of • Use details and visual effects cognition • Provide adequate information • Accommodate for appropriate Usable use of appliances and tools • Essential with clear instructions • Need practice to learn the test • Practical handling needs to be smooth

USE Figure 9. The results from the deductive-inductive analysis inserted in the Optimized Honeycomb model with the seven facets as categories and their respective subcategories, 17 in total.

USE The part USE forms the base of the model which contains the facets findable, accessible, and usable. These reflect practical and concrete suggestions for modifications of the prototype based on the co-creation sessions.

The category findable comprises participant views and discussions of how navigable the application was, how the information could be easy to navigate by organising it using a simple structure. Suggestions for using contrasts, colours, and symbols to guide navigation within the app were explored. This also included providing adequate information, expressed as “not too much nor too little”.

The category accessible refers to the users’ views of their ability to access the material in the application, considering cognitive and physical functions, as well as hearing and vision impairments. Cognitive limitations, like the capacity to process and retain information, are fundamental when performing a self-test. However, the co-creation process helped to reach an agreement on the level of information that was appropriate, e.g., instructions that were not too complex, a limit on the amount of information to be relevant to the actual test performance, and keeping other associated information in the overall instructions. The use of contrasts was desired as a visual aid. A combination of verbal short cues and audio signals, such as beeps, was suggested to improve the perception of instructions during test performance. Accessible also included the user’s access to the smartphone during the test, and the possession of a smartphone to be able to perform the self-test.

The last category was usable, which focussed on how the information was perceived and how to present the essential information. The need to practise with the application was highlighted at the practical user-test workshops. The importance of clear and audible instructions with short commands during the test procedure was also pointed out, for the test to be used independently. The manual handling of the smartphone during the test performance was also part of the category usable, for example starting the test and fasten the smartphone around the waist. These actions of handling the smartphone and the application had to be free from hassle because, otherwise, interest in using the application waned.

FEEL The part FEEL comprises the two facets of desirable and credible, where the older adults provided new and creative ideas to the application design.

The aspirations for using the app were conveyed in the category desirable, but this category also contained contrasting feelings of disapproval. At times the participants’ opinions varied, for example, regarding which features were desired in the app. To accommodate participant wishes, development combined their

suggestions in the best possible way. The solution was most often acceptable for all participants. They felt it was important that information was presented in a familiar way, like in a newspaper or online banking application, as they did not fancy new designs. They did not desire interrupting elements in the instructions or that information was given too slowly. Neither did they wish to share their own results, especially not on social media platforms.

The category credible refers to confidence in the application and pertains to both feeling and thinking, as it involves how you relate to the application emotionally and conceptually. Within this category, participants’ curiosity and eagerness of knowing why things were done in a certain way was expressed. This curiousness was evident throughout all the sessions. Participant trust in the measurements and the understanding of them, as well as the importance of performing the test according to instructions, was a large part of the discussions during the co-creation sessions, and the participants showed genuine interest. The safety aspect was also part of the category credible as the feeling of being safe when performing the test was often confirmed in the sessions, and this feeling positively influenced the development of the self-test application.

THINK The part THINK includes the facets useful, valuable, and also credible mentioned above.

The category useful refers to what is achieved by the application, and which needs are fulfilled by using the self-test application. The participants’ reaction was that the self-test could increase motivation to perform fall preventive exercises if one could monitor and measure one’s balance. The participants thought it was important for them, and they saw a need for this type of intervention also for other friends and family members. To monitor with self-testing and the possibility to set goals and be able to follow progress over time was appreciated.

Valuable is a facet that comprises overall user experience. It incorporates the six surrounding facets to generate the self-test application’s added value, and form the category valuable. Participants discussed their options about being able to compare their measures with a reference value, as that might give them more sense of importance from the self-test results. To note, one participant expressed an opposing view about the reference value, that it might not be a good idea because if you performed well on the test you might not continue with the balance exercises. Another topic brought up by the participants themselves, was the probability of older adults being able to use a smartphone self-test, and consensus was that maybe not all older adults have access to a smartphone, or interest in using one. However, the self-test application would be valuable for those who had interest in the test and access to a smartphone.

Enriching co-creation sessions Over time the co-creation sessions had broader discussions and added more general value from the participants which enriched the app development. During later sessions in the co-creation process, when the design had started to take shape and had become familiar to them, participants started to reflect and relate more to situations and experiences not directly related to the MyBalance development. Below in Figure 10 are some quotes from session 4. Quotes 1 and 2, represent the category findable, with reference to participants’ reflections about other comparable situations and/or previous experiences with digital technology. Quote 3 represents valuable, about the added value of the MyBalance application, and how the participants imagined the use of the self-test in a broader context. Paper III has more quotes that exemplify some subcategories; these are not repeated in the thesis.

Figure 10. Three examples of quotes from participants from co-creation session 4.

Application development To summarize the application development from the co-creation sessions it is important to understand the user. By understanding the user the information can be developed and presented so users can relate to it. Users want to know why things are done in a certain way, so it is important to explain why, and the application must be easy and interesting to use and avoid technical challenges to preserve user interest. These key factors are essential for a good user experience. Through the co-creation sessions we learned how to design the self-test application so that it is valued by older adult users.

The co-creation sessions resulted in the application MyBalance (v1). Table 11 lists the developments after each session. The developments were made after every session according to the iterative process for the design development of the video instructions and user interface.

Table 11. Developments made after each of the five co-creation sessions.

Session Aim Developments 1 Introduction and get feedback on instructions Shortened and adjusted instructions Use of contrasts 2 Explore navigation in the application and the Condensed information with more specific test understanding of instructions instructions 3 Explore navigation in the application on the Added voice to signals smartphone and discussion about user interface Small interface changes to keep design simple and results 4 Gain information about how a novice perceived the Simple graphs for results self-test instructions and discussion about Test instructions are perceived clear and presentation of results audible, and same style will be applied to overall instructions 5 Gain information about how a novice perceived the Additional user-testing and (eventual) further overall instructions and realise a user-test with final development prototype

MyBalance application The application MyBalance (v1) contains two test procedures: The Three maximal chair stand test, for the functional leg strength test; The 30s static standing balance test with two different foot positions (feet together and semi tandem). Following the co-creation process, the application now contains three short instruction videos: an introduction video with overall information about the self-test procedure, how to use MyBalance (approx. 5 min), and separate video instructions for each of the test procedures, i.e., leg strength and balance (approx. 2 min each). The videos can be watched as many times as desired, with or without performing the test. The user interface for both tests has only three options: (1) watch instruction video, (2) see results from earlier tests, and (3) start the test. The test is started on the smartphone before the device is rolled-up in a scarf and fastened to the lower-back, tied around the waist. Then instructions are followed during the test, when finished the smartphone is unfolded from the scarf, and the test is confirmed on the smartphone. The results of previous tests are accessible for each test procedure. In Appendix Figure A2, example from the MyBalance (v1) user interface is presented.

Concurrent validity testing of the MyBalance prototype (Paper IV)

Participants’ characteristics The 31 participants had a mean age of 78 ± 5 years, 32% had a fall the past year, 29% used a walking aid, and 84% rated themselves as physically active.

The results of the seven clinical instruments are presented in Table 12. These results indicate that the group was well able to perform the balance and leg strength tasks in the study. Sensor-test measurement values are presented in Table 13.

Table 12. Descriptive data for clinical instrument assessments, presented as median values (Q1-3), n = 31.

Clinical instruments Outcome Balance instruments Mini-BESTest (score 0-28) 21 (15-23) Functional reach (cm) 22 (18-28) 4-stage balance test (s) 99 (91-120) Maximal stepping test (cm)* 67 (58-80)† Leg strength instruments Five times sit to stand (s) 13.6 (11.6-16.4) 30s Chair stand test (n) 11 (9-13) Leg press sitting 1 RMn (1 RM/body weight) 0.98 (0.76–1.32)‡ * used in correlations for both balance and leg strength instruments; 1 RMn = One repetition maximum normalized value; † 1 missing; ‡ n = 21

Table 13. Descriptive data for smartphone sensor-test from MyBalance prototype, presented as median values (Q1-3), n = 31.

Three maximal Test Measure Feet together Semi tandem chair stand test Balance NPL (mg/s) 21.1 (17.9-34.4) 36.6 (27.3-60.4) (time domain) hAREA (mg²) 8.2 (6.8-14.4) 15.7 (11.4-23.9) hRMS (mg) 1.4 (1.2-2.6) 2.8 (2.0-4.2) hMEAN (mg) 1.1 (0.9-1.8) 2.3 (1.5-3.4) apRMS (mg) 1.1 (0.7-1.7) 1.8 (1.2-3.3) mlRMS (mg) 1.1 (0.8-1.8) 2.0 (1.3-2.9) apP2P (mg) 11.0 (7.7-17.3) 16.9 (12.6-32.0) mlP2P (mg) 12.1 (8.5-16.0) 20.1 (11.5-32.4) Balance apMDF (Hz) 2.3 (2.1-2.6) 2.2 (2.0-2.6) (frequency domain) mlMDF (Hz) 2.1 (2.0-2.5) 2.2 (1.9-2.4) apCFREQ (Hz) 2.6 (2.5-2.8) 2.6 (2.4-2.9) mlCFREQ (Hz) 2.7 (2.5-2.9) 2.7 (2.4-2.7) Leg strength PowerMax (W) 478 (384-531) VelMax (m/s) 0.6 (0.5-0.7) AccMax (m/s²) 1.8 (1.5-2.2) JerkMax (m/s³) 8.8 (8.1-14.3) NPL = Normalized Path Length; hAREA = Horizontal sway area; hRMS = Horizontal Root Mean Square acceleration; hMEAN = Horizontal mean acceleration; ap = anterior posterior direction; ml = medio lateral direction; RMS = Root Mean Square acceleration; P2P = Acceleration Peak to Peak; MDF = Median frequency; CFREQ = Centroidal frequency; PowerMax = Max vertical power; VelMax = Max vertical velocity; AccMax = Max vertical acceleration; JerkMax = Max vertical jerk;

Sensor measurement and clinical instrument correlation analyses

Balance tests The smartphone application measurements correlated moderately to three clinical balance instruments for measures of frequency domain variables in the medio lateral direction in the first position, feet together, standing balance test. While all the anterior posterior variables showed poor correlations. The only significant correlation for the time domain variables was a low correlation between the Mini-BESTest and mlP2P. All data for correlation analyses for the feet together position are presented in Table 14. No significant correlations were found for any of the clinical balance instruments for the second position, semi tandem stand, in the balance test.

Table 14. Correlation between clinical balance instruments and sensor tests for standing balance with the first position feet together (n = 31). Values are Spearman’s rho (ϱ).

Test Measure MiniB FRn m4-stageBT MaxStepn Balance NPL -0.169 -0.125 -0.125 -0.057 (time domain) apRMS -0.254 -0.221 -0.242 -0.115 mlRMS -0.264 -0.167 -0.146 -0.109 apP2P -0.264 -0.217 -0.258 -0.061 mlP2P -0.392* -0.168 -0.196 -0.119 hAREA -0.234 -0.190 -0.173 -0.092 hRMS -0.235 -0.182 -0.181 -0.091 hMEAN -0.232 -0.208 -0.201 -0.127 Balance mlMDF 0.607*** 0.351 0.557** 0.589*** (frequency domain) apMDF 0.156 -0.041 0.177 0.140 mlCFREQ 0.673*** 0.457* 0.615** 0.615*** apCFREQ 0.245 0.073 0.247 0.233 NPL = Normalized Path Length; ap = anterior posterior direction; ml = medio lateral direction; RMS = Root Mean Square acceleration; P2P = Acceleration Peak to Peak; hAREA = Horizontal sway area; hRMS = Horizontal Root Mean Square acceleration; hMEAN = Horizontal mean acceleration; MDF = Median frequency; CFREQ = Centroidal frequency; MiniB = Mini-BESTest; FRn = normalized Functional reach; m4-stageBT = Modified 4-stage balance test; MaxStepn = normalized Modified maximal stepping test; * correlation is significant at 0.05 level (2-tailed); ** correlation is significant at 0.01 level (2-tailed); *** correlation is significant at < 0.01 level (2-tailed)

Leg strength tests The variables for leg strength from the Three maximal chair stand test with the smartphone application showed stronger correlations with clinical instruments than the balance test. A moderate correlation was found for all variables in the 30s CST and sensor measurements. The MaxStep and 5TSTS had low to moderate correlations with varying degrees of significance. The sensor variables PowerMax and VelMax had moderate correlations for muscle power measured with 1 RM, but the other two sensor variables had poor correlation. All data from the correlation analyses are presented in Table 15.

Table 15. Correlation between clinical leg strength instruments and the sensor test for Three maximal chair stand test (n = 31). Values are Spearman’s rho (ϱ).

Measure MaxStepn 5TSTS 30sCST 1 RMn† PowerMax 0.340 -0.414* 0.561** 0.523* VelMax 0.398* -0.366* 0.639*** 0.559** AccMax 0.529** -0.526** 0.591*** 0.238 JerkMax 0.526** -0.533** 0.524** 0.289 PowerMax = Max vertical power; VelMax = Max vertical velocity; AccMax = Max vertical acceleration; JerkMax = Max vertical jerk; † n = 21; * correlation is significant at 0.05 level (2-tailed); ** correlation is significant at 0.01 level (2-tailed); *** correlation is significant at < 0.01 level (2-tailed)

Discussion

The results from this thesis show that older adults can manage and had an interest in the digital technology used for fall prevention interventions. The Safe Step feasibility study showed similar adherence for both DP and PB programmes. Although, for a limited number of participants, the subgroup that had exercised ≥ 75% of the recommended duration, significantly more exercise time for the DP was reported. Also, in the 12-month survey DP participants reported, to a significantly higher degree than the PB participants, that they had continued to exercise with the programme regularly. The feasibility of the outcome measures was analysed with data combined from both programmes. These analyses revealed ceiling effects in the balance test within SPPB, as well as in the ABC scale and Icon-FES. This indicates that new outcome measurements are needed to assess balance and balance concerns. Significant improvements in functional leg strength were found for performance-based outcome measures, which also correlated with more exercise time, but no differences between the two programmes were seen. The key results from the MyBalance co-creation process were that participants desired clear and appropriate information to understand why things were done in a certain way, and they expected the self-test to be a useful tool to measure balance. The concurrent validity testing of the first prototype of the self-test applications showed low to moderate correlations with clinical instruments for the leg strength test but limited correlations for the standing balance test.

Adherence to exercise programmes A major issue with comparing adherence is the different ways of registering and reporting exercise time (49, 50, 57, 169), level of adherence is determined by each author, and what is considered adherence to an intervention varies. Adherence is often reported as a proportion of the participation that is recommended or provided (57, 169). This adherence measure could be based on: number of days or hours per week, total number of exercise sessions over a set period, or may have a set criterion, as a minimum of two days per week for example. A consensus on how to report adherence is called for to facilitate further research. We used a 75% cut-off-point for analyses of the subgroups, and the two terms completion and duration were applied for adherence (53). Both minutes and number of sessions per week were analysed in the Safe Step feasibility study to gain more detailed and diverse information about adherence. At the same time, analysing more detailed variables made comparisons to other studies more difficult, and for that reason, adherence was also calculated in percent of completed sessions. In these terms the overall adherence was reported as 63% of the recommended 48 sessions during the four months for the DP group and 54% for the PB group.

These results are comparable to results from other studies where adherence to digital self-managed exercise interventions varied from 38 to 73% (74, 170, 171). Adherence in fall prevention exercise interventions that were supervised at home or had follow-up visits has been reported to be 25-77% (172–175).

The subgroup that completed the study, reported a median number of 65 minutes per week for the DP group, not significantly less than the PB group with 75 minutes per week. The DP participants on the other hand, reported significantly more time for participants that had exercised ≥ 75% of recommended duration. There are several possible explanations for this pattern. The higher exercise time among DP users in the subgroup that exercises most might be due to the added value of the DP, which may encourage adherence. This was reported in a previous qualitative study from this feasibility study, where DP users expressed more learning and reflection from the programme than the PB participants (62). Participants in both self-managed programmes reported that the programme offered flexibility and could be tailored to their needs, and that the self-managed programme gave them independence (62). Another explanation for the variation in adherence might be related to the self-reporting in the exercise diary. There are some known risks of bias using exercise diaries, such as over or under estimating, filling in data in advance or later on (176, 177). The digital diary might have been more complicated to fill in, with multiple entries (clicks), compared to the paper version for participants in the PB group. Another part of the self-reporting exercise in a study is the risk of bias when participants have recommendations to follow, in this case, 30 min 3 times per week. As participants may want to comply with a study, they may report the number of recommended minutes rather than actual exercised minutes, which results in over or under reporting (176). Some participants reported the exact number of minutes, such as 23, 25, 28, or 31 min while others reported 30 min every day. The individual participants varied in their reporting in both types of exercise diary. This was probably more related to the participant than the diary. The digital exercise diary had intervals of 5 min for reporting, and advance reporting was not possible. Nothing in the data collection indicates that our exercise diaries had incorrect reporting from the participants. However, a few participants mentioned at the final meeting that they did not open the DP as they had learnt the programme by heart, this indicates that not all exercise was reported, especially in the DP group.

Attrition is important in terms of adherence (178), and one reason to do the subgroup analyses for adherence in the Safe Step feasibility study. Attrition rate was significantly higher in the HC recruitment strategy, and more participants in the PB group came from HC. With more participants in the PB group from start the PB did not have fewer participants in any subgroup. The five recruitment groups were not analysed, as all analyses compared the interventions not the recruitment. The attrition rate for PB HC was as high as 55%, despite this share

of participants who did not register any exercise time, no significant differences were found for any measures of adherence between DP and PB for all enrolled. Previous studies have indicated that recommendation from primary health care professional about fall prevention exercises reinforces participation (179, 180). However, in our study, getting information about the study from a health care professional did not positively influence attrition. Maybe it was not seen as optional to participate as a health care professional presented the information, or perhaps the exercise programme did not meet expectations. While participants recruited at SO were more independent in their choice to participate in the study, and may have recognised their own need for exercise to prevent falls.

The attrition rate was 17% for the DP group and 37% for the PB group, similar to other studies. ActiveLifestyle had attrition rates of 8% (social) and 21% for the two tablet interventions and 41% for their paper intervention (74). A 20% attrition rate was reported from another 12-week digital technology intervention with pre-frail participants (170). The majority of participants that withdrew from the Safe Step feasibility study did so for medical reasons; either their own medical problems or a family member’s illness that affected their participation in the study, and seven of 19 withdrew for other reasons.

Feasibility of outcome measures Both self-managed programme interventions were combined to assess the feasibility of the outcome measures in the Safe Step feasibility study in order to inform a larger RCT. Even though this feasibility study was not appropriate powered, effects were still assessed to find out if the outcome measures were sensitive to change in this population under these conditions. Regardless of programme, DP or PB, the performance-based measures only showed a significant change in leg strength assessed with 30s CST and 5TSTS. These improvements had substantial effect sizes, and significant correlations with exercise time. No other outcomes revealed significant correlations between change scores and exercise time. As various outcomes had ceiling effects, especially in the performance-based balance test, the potential to detect changes in balance was limited. However, a majority of participants self-rated their balance as improved. ABC and Icon-FES are self-report instruments that measure the fear of falling and balance confidence. Both demonstrated ceiling effects at pre-assessment, which could indicate a limitation of the instruments for the group older adults with higher functional level. Similar findings for ABC have been described earlier (181–183). Icon-FES scores in the range of 30-40 describes a person with low fear of falling (184). The median Icon-FES score of 47 in this study might indicate that improvements would be difficult to detect using this scale during a four-month intervention. The SPPB is a well-researched instrument and is highly recommended for assessing community-dwelling older

adults (130). The SPPB total score were in the higher range but still more than half of the participants showed an improvement larger than the previously published MCID, 1 point (160). Thus, our results support the use of SPPB in this population. However, as the balance sub-component had severe ceiling effects which influenced the total SPPB score, the balance test is considered a weak link in SPPB for this population.

In comparison, the performance-based results in other studies have presented varied results of effect after interventions. To date, few studies have reported results from digital fall prevention interventions. No significant differences in change were seen in the performance measures at six or twelve months for the digital programme eLiFE in younger seniors with relatively high function (76). Significant improvement in SPPB was reported for all three interventions in the ActiveLifestyle study, and these improvements also included gait speed at preferred and fast speed for the digital programmes after 12 weeks (75).

To complement the outcome instruments, older participants also self-rated effects of improved balance and leg strength at the end of the intervention. Participants in both programmes reported improved balance, but only participants in the DP group self-rated improvements in leg strength. No other studies have been found that use a subjective measure of perceived balance and leg strength. It is interesting that the subjective impressions were not in accordance with result from the performance-based outcomes for balance and leg strength. However, the revealed ceiling effect in the balance test is probably one explanation for this inconsistency.

These ceiling effects in the functional balance test call for new more sensitive methods to measure balance in older adults with higher function. The clinical instruments used in physiotherapy today, like the ones in this thesis, are instruments for subjective observations and are graded according to a protocol. The number of repetitions and/or a stopwatch are sometimes used as more objective measurements in clinical assessment. Both subjective and objective measures are essential for the evaluation of self-managed interventions. This thesis is part of a development to find new sensitive instruments to measure balance performance in this group of relatively high functioning community- dwelling older adults.

Psychometric properties must be considered in the development of new instruments. Psychometric properties are essential for valid and reliable instruments and are crucial for research quality (98). To facilitate the use of valid and reliable outcome measures, the research initiative COSMIN has developed guidelines for methodological and practical tools for selecting the outcome measurement instrument (100, 101). Validity and reliability are probably not

a major concern for the actual sensor in the new sensor test, as sensors are very responsive and accurate, but the sensor must be handled correctly to attain good validity and reliability. These guidelines are informative and relevant when looking at developing a new assessment tool.

User experience and involvement User experience is a central feature when implementing new digital technology. To achieve good user experience for the self-test application, a co-creation process took part to design the instructions and user interface. Useful information was gathered, both expected and unexpected, from this study. The main results within the categories and subcategories generated from participant opinions were that older users must have a clear understanding of what to do, how to do it, and how to handle the smartphone. Analyses were made both after each session and deductively after the whole co-creation process. These analyses helped us to understand how the application design suited the users desires and needs. Recommendations for general application development for older adults have been reported (105–107), and these guidelines were considered in the prototype development, such as large text font and button size, high contrasts, and maintaining consistency in the application. In the MyBalance application, the test performance was the main point. While trying to achieve good test performance, the co-creation process added various improvements. For example, participants wanted information conveyed in a familiar way, but at the same time not too simple as they expressed “just because you are old you are not daft” in relation to slow and clear speech in the first video instructions. Other studies evaluating applications have described similar issues. Parents using a web-application expressed need for information to be provided as a mix of verbal, written and video content (115). Also the need for simplified and limited content has been expressed (112, 114).

In addition to this, the co-creation process gave additional insight into how the older adults wanted their application. The content in THINK and FEEL, where desirable, credible, and useful features were discussed during the co-creation sessions, and gave access to participant thoughts. This content gave unexpected information, which generated suggestions to enrich the design to make the application valuable. Participants expressed an interest in knowing why, for example the reason why the test positions should be done in a certain way. When they were informed about the motive, for example, about the standing in semi tandem, the test was more easily understood. This led to that some more information was added to the instructions. The users do not only need to know how to perform the test, but also why. That knowledge from an app could provide a support to users, have been described in previous developments of health applications (114, 115). Access to older adults’ thoughts, together with additional

information about their opinions of digital technology, made the development advantageous by using co-creation.

For the design and development, it was rewarding to understand the older participants’ thoughts. Previous research have indicated that social features in an application is positive (74, 106, 107, 185), and this was brought up at the co-creation sessions. However, the participants in our study had a strong opinion about social media and sharing results. They expressed they felt there was a difference between generations, i.e., that young people live their lives on the smartphone. The option to share their results from the self-test application on social media platforms was unanimously rejected. They wanted to keep their results to themselves and maybe share with someone close if they got a positive result that they were proud of. Opposing results were reported from the AcitveLifestyle study, where social features were used, such as social motivation, sharing results, and external monitoring (74, 185). Though, this was social interaction within the participant’s own network and other participants in the study. Other studies about aging in place have identified social influence as a central factor for a positive outcome for using digital technology, but even in these studies, the social influence was related to a social network of friends and family (106, 107).

Some aspects of user experience were also assessed in the feasibility study. Participants using the new DP were more content with and felt supported by the programme to a higher degree than PB users when asked about their experience in the self-rated effects survey at post-assessment. Previous published qualitative data from the Safe Step feasibility study have revealed that participants expressed feelings of being engaged in the programme, such as becoming friends with the speaker voice and the older persons demonstrating the exercises (62). They also expressed appreciation over clear instructions from the videos and being able to perform the exercises and listen to instructions at the same time. These benefits could be offered in a digital intervention and seem to have provided a good user experience for the DP.

Although no formal user-testing was performed during the Safe Step feasibility study, for the DP notes were kept regarding technical issues. Twenty items were recorded: exercise diary registration (n = 6), log-in issue (n = 6), problem navigating in the programme (n = 4), hardware issue (n = 3), Wi-Fi/data plan (n = 1). Six of the items were caused by that the server required rebooting, 12 problems were resolved with some guidance from the research team, and the last two had resolved before contact was re-established with the participant. Despite these few issues, a positive user-experience could be expected as significantly more older participants continued to use the DP at the time of the 12-month follow-up survey. Other digital fall prevention programmes have been

evaluating user experience, often using the System Usability Scale (SUS) (76, 170, 186). The SUS scale a usability score of 0-100, where a product is considered be acceptable from a score of approximately 70 (187). PreventIT reported SUS scores of approximately 60 for the eLiFE program where some matters of immature technology disturbed the user experience (76). The Standing Tall project have been evaluated in a pilot study, and reported SUS scores 68 for people with dementia, and 69 for caregivers (186). Moreover, 16 pre-frail older adults tried a web application exercise programme for three months and reported a usability score of 84 (170). These studies were all early developments, and it is necessary to consider user experience when developing for older adults.

Self-assessment with digital technology The validation of the MyBalance prototype against clinical instruments did not show great correlation between the balance variables from the sensor measurements and the clinical instruments. No studies were found that used sensor-measurements in the way that MyBalance assesses balance and by comparing with the same clinical instruments as in our validity study. Nonetheless, various studies have reported different set-ups with sensors for their validations (188–191). Results have been inconclusive as this is early research, and like our development, requires more validation. A study where the balance instrument Mini-BESTest was compared to the iTUG’s five subcomponents in a geriatric inpatient population, reported that the vertical angular velocity in the turning component could accurately predict the Mini-BESTest score (188). Other studies have compared sensor measurements for balance assessments with gold standard instruments, e.g., force plate and kinematic systems and not with clinical instruments, and have established that sensors and lab equipment can measure balance equally well (189, 190). Both centroidal frequency and RMS have been found to capture balance deficits in individuals with Parkinson’s disease (191). Results corresponding to ours for Feet together and Semi tandem were found in a study where eight positions of standing balance were tested for 30s. This study showed good sensitivity to variables where RMS values increased and frequency variables values decreased when the position became more challenging (189).

Balance can be assessed as dynamic balance or static balance (192). These have different characters and that is probably one reasons why the MyBalance self-test for balance showed modest correlations with clinical tests. Clinical instruments often include dynamic aspects of balance, while sensor measures, such as accelerometer measurements, are more sensitive when it comes to static balance assessment. The sensor measurements have better metric properties and are more specific, to quantify, for example, duration and angular velocity (188) than the human eye in a clinical assessment. In the case of static balance performance,

the clinical assessment measure duration, i.e., for how long a position can be held without losing balance. The sensor measurement, on the other hand, registers the ability to stay steady and assess the small movement that occurs in the body while maintaining balance in the test position. Considering that balance is complex and includes several different aspects and dimensions, it is not surprising to find poor correlations between sensor tests and clinical instruments. In addition, the clinical instruments for balance showed low to moderate correlations between each other in this study, an observation also presented in other studies (192).

The correlation for leg strength showed moderate correlations between the Three maximal chair stand variables and the 1 RM in the MyBalance validity study. Previous studies with smartphones have also shown that it is possible to quantify a sit-to-stand movement with smartphone measurements (95). Two studies with smartphone sensor-tests, although not self-tests, compared assessments with clinical instruments. First, high-level functioning older adults completed a 30s CST with a smartphone, which showed potential to discover functional decline (90). Second, a validity study with a smartphone kept in a pocket reported good correlations for TUG, 30s CST, and 5STST for measures of duration and number of repetitions (86). Two studies have found good correlations when using sensor technology in the development of tests for home-assessments, but these studies have only measured the duration with sensors for TUG and 5TSTS (87, 193). Further was leg strength assessments done using sensor-test, but not with smartphone, to assess leg strength and power after an exercise intervention, which concluded that sensors accurately can indicate sensitivity to change when compared to other standard clinical assessments (154). The use of sensor measurements with different variables makes comparisons difficult, even if the same technology is used. The leg strength sensor-test with MyBalance is more comparable to the performance of the clinical tests than the balance test, as it measures the change from siting to standing position. The sit-to-stand assessments are repeated movements that included rising and sitting down again various times where the person and/or muscles risk becoming fatigued in the clinical tests. The sit-to-stand also involves rapid change in the direction of the movement, which may cause discomfort like dizziness. A sensor-test could test the maximal performance of a sit-to-stand instead of a functional leg strength test without too many repetitions.

Many projects are still at the start line when it comes to using digital technology for functional assessments, and research concerning the use of sensors to assess human movements needs proper validation, which were the conclusions of two systematic reviews (82, 83). Various developments have tried to predict falls with smartphone applications (83), but our self-assessment application had no such intention; it only measures two aspects of functional balance i.e. static standing balance and sit-to-stand. The challenge with a self-test is the risk that the test

is not performed correctly, or that the device is applied incorrectly, and subsequently, untrue measurements will be recorded. Likewise, the same test procedure is desirable at every test session. Each individual may perform the test in the same way every time, and as long as measures are not compared with others an absolute standardised test performance is not required. While conducting the co-creation study, it was found that a learning period was needed, but after just a few times of practice, the participants felt the self-test was easy to perform. Publications about the use of sensor-measurements as self-tests are still scarce, but the PreventIT project self-test is one example. That smartphone self-test, still under development, contains three instrumented tests, performed with a smartphone in a front trousers pocket: the Self-TUG iTUG for walking 3m, the Self-Tandem stance for 15s, the Self-STS a 5TSTS (97). Similar to MyBalance, this application has video instructions and verbal cues while performing the test. It has shown promising results for use in a home setting, but further improvements and validation are needed.

A smartphone sensor-test can provide an objective instrument that could be used for self-assessment. This would provide low-cost alternatives to balance assessment for remote monitoring and in clinics. It would also provide possibilities for more precise, specific, and sensitive measurements, as well as more frequent assessments during longer follow-up periods.

Methodological reflections

Participants First, the Safe Step feasibility, was a participant preference trial, suitable for studies in a real-life context and chosen to evaluate the interest and choice of programme. The participant preference trial design has been described as an appealing method to try to improve adherence in intervention studies to consolidate interest for exercise and behavioural change programmes, and perform studies in real-life context (124). Although participants were not randomised, the two groups were comparable at study start, and only three characteristics were significantly different between DP and PB: access to a smartphone, attitude to the intervention, and heart and cardiovascular medical conditions. The greater access to a smartphone in the DP group was expected due to that mobile devises were not provided to participants. The more positive attitude to the intervention at the start of the intervention in the DP group was difficult to explain. A reason might be that when selecting the DP, they had a great interest in finding out what this new exercise program could offer and were curious about it. The more frequent self-reported heart and cardiovascular conditions in the PB group could have been caused by the higher proportion of participants recruited from the HC. One aim was to explore adherence in relation

to the choice of programme, but adherence was similar between groups and also found to be in accordance with comparable studies (74, 170, 171). Consequently, the individual choice of programme did not seem to affect adherence during the intervention period, but in the long-term follow-up, more participants reported that they continued with the DP. And regarding the real-life study context, where full control of conditions is not possible, the purpose was to study how people integrate an exercise programme into normal life. As little interaction as possible is desirable in this type of study, and a self-assessment tool would have been valuable to follow the progress with the participants’ own measurements.

Second, the recruitment in the Safe Step feasibility study had two different strategies, recruitment at a HC and at SO. We saw a lower attrition rate for participants recruited in the HC context, in addition more participants from the HC chose the PB intervention. The benefits and disadvantages of the two recruitment strategies have not been evaluated in this research, but would be an interesting area to explore. Only three features that were significantly different between participants, according to recruitment strategy, were that the HC recruits self-reported more lung conditions, reported to be less physically active during the summer, and had less access to a computer than participants recruited from SO. The significant difference for the lung conditions in the HC participants was probably because education classes for people with chronic obstructive pulmonary disease (COPD) were held at the HC. Maybe more active older adults, engaged in activities outside the home, were reached at the SO. The recruitment strategies need further investigations to better understand how the recruitment context influence intervention reach and subsequent effects.

One reflection regarding participants across the three studies was that 58% of the participants in the feasibility study, but only 32% in the validity study, reported having a fall in the past year. Even though the sample in the validity study was slightly older, descriptive data were otherwise comparable in the two different studies. Maybe the exercise intervention study attracted people that had had a previous fall and felt the need for this type of fall preventive intervention, which might not have been the case among average older persons in a primary health care facility. In a fall prevention study targeting a similar population of 70-90-year olds, physically active, community-dwelling seniors, 35% reported a fall in the previous six months (194). This number of participants with previous falls was comparable to the validity study in this thesis.

Third, the distribution between men and women was not even, from 60% women in the co-creation study with a purposeful sample to 77% in the validity study. This is a common distribution for studies of older adults. A Cochrane review of exercise interventions for fall prevention also reported that 77% of participants were women (19).

Finally, it has been very rewarding work in these studies, where participants show great interest in devoting their time to our research. The older adult participants also expressed that they were pleased that their opinions mattered, and their participation gave them a sense of being a valued member of society. They indicated that fall prevention was something important for them and others in their surroundings, such as friends and family and other older adults in general. It is promising that the fall prevention interventions are valuable for them. This attitude will help in the next implementation phase.

Data collection and data analysis

Safe step feasibility study The Safe Step feasibility study had a fairly long set of questionnaires because it was a feasibility study where one objective was to explore different self-reported outcome measures. The data collection during the pre-assessment and post- assessment was done in a group setting. This might have been stressful for some participants, for instance, one person had to sit in a separate room undisturbed to complete the questionnaires. But it was also beneficial as participants could ask for help if any questions arose. A verbal explanation was provided to the LLFDI questionnaire and the ABC self-reported falls efficacy questionnaire as the written instructions were extensive. The rest of the questions had written instructions. The Icon-FES had illustrations of situations and had a few items that a lot of participants were intrigued by, for example: How concerned are you about crossing the street on a pedestrian crossing against the light? How concerned are you when reaching for something above your head standing on a chair? How concerned are you when cleaning the gutter? These are all things that they have learnt they should not do or be careful with as getting older. It was good to have experienced these reactions when analysing the results, as they provided an explanation to some contradicting answers in the questionnaire.

The performance-based outcome measures were chosen as they are commonly used and results would be able to compare with other studies. A short assessment was desirable as up to eight persons could be tested during one meeting and SPPB is a short and well researched instrument. The subcomponents from the SPPB were used in analyses to get more detailed data where duration and velocity had not been transformed into a score. This was a feasibility study for an RCT that will not have any objectively assessed performance-based outcome measures. The next study with the Safe Step application will have all data collection done via online questionnaires, consequently only self-report instruments can be used. Hence, we wanted to investigate associations between such instruments and performance-based measurements. A smartphone self-test application may also assist in resolving these matters.

The role of the exercise diary in the data collection for adherence was discussed above. An additional topic worth a reflection is regarding the exercise diaries (Figure A1 in Appendix) and progression of the exercise programme. At the planning stage and when analyses started, the plan was to describe how the participants used the programme during the intervention period to progress or regress exercises over the intervention period. Also, to compare how adjustment of the programme was managed in the DP and PB groups. In the end, this was removed, as the structure of reporting data was too diverse and made the comparison between groups complicated and probably not fair. For example, the DP had two strength exercises with clear levels for progression, but the progression in the other exercises were less distinct and dependent on individual preferences. For example, how challenging a certain gait exercise is perceived may vary across individuals. In the PB levels were predefined in the programme, but even so, in the diary only number of exercises for each level were reported, which made it impossible to see the exact exercises performed. This made it troublesome to compare the two programmes.

Instead, this information was covered in a qualitative study (62), where the quantitative analysis could have strengthened findings about progression and how the self-tailored programme was utilised. To explore the self-managed programme for patterns of different strategies such as: how many changes were made in the programme during four months; if participants chose difficult exercises and had to go back to a previous level; and if there was a progression over the whole intervention period. This would have been interesting to follow, and to my knowledge, no such study has been done earlier.

Co-creation development of MyBalance The co-creation development was constructive and participants generously shared their views and opinions at the sessions. The choice of model for the qualitative content analysis was between the Model of user experience by Hassenzahl (195) and the UX Honeycomb model by Morville (110). The option fell on the Honeycomb model as it seemed closer to the expressive content in the co-creation sessions. Another advantage was its absence of being a process, and that all facets had equal weight in the model. Further on, the reorganisation of the UX Honeycomb model into the Optimized Honeycomb model (116) was appealing and useful for the deductive analysis. The aspects of USE, FEEL, and THINK were applicable to capture older adults’ perspective during the design process.

For the qualitative content analysis, the aspects of credibility, dependability, transferability, authenticity, and conformability (196), were addressed to strengthen the study’s trustworthiness. A group of ten participants was expected to provide a variety of opinions, and with ten people there was still room for

someone to be absent and still have a dynamic discussion and creative sessions. The different opinions from various individuals each with their own experiences, and the discussions within the group provided many suggestions on how to improve the development of the application. The participants’ opinions, together with a clear description of the researchers’ roles during the analysis, strengthened the credibility of the results. By documenting preparations before each session, and keeping notes during and after each session, the data were reliably complemented and enhanced dependability. To reinforce transferability the selection and characteristics of participants, along with the analysis process, was thoroughly described. The authenticity of the research was established by describing the whole process of this study. Conformability was established as all authors were involved in the analysis at different stages, contributing with different competences and perspectives. To describe the diversity in the material, by using quotes distinguishable to participant and session, credibility is expected to be emphasised (165).

My own participation in planning, realising the sessions, and analysing must be considered. It is not possible to stay completely objective to a project that you worked closely with, but the involvement from the other authors was of great assistance to maintain my neutrality to the data (165). In contrast, for me being engaged in the whole process helped understand moments in the session discussions, that was hard to understand for co-authors not being present at the sessions. To note, this was a development process, and the application will be further tested in additional settings.

Concurrent validity study In the validity study of the MyBalance prototype, two rounds of data collection were done, first with 21 participants and one year later an additional 10 participants were tested. The reason behind this was that the first data collection was done as part of a Master’s thesis, and about 20 participants were considered appropriate to test. However, when analysing the data, it did not have a normal distribution for the clinical instruments; the middle range was missing. The physiotherapist kindly agreed to continue data collection and added ten more participants. With this we expected to reach normal distribution with approximate 30 participants, and that was also achieved.

The validity of an instrument or measure is fundamental. This must be regarded in the development phase as well as in the planning phase for a proper evaluation. As a need to monitor balance function was identified in the development of Safe Step, the development of MyBalance was relevant to proceed with. To consider a thorough development the validity of the self-test application has been considered in this thesis assignment. Construct, content, and criterion validity are different types of validity, where concurrent validity is part of criterion

validity, which is to confirm the correlation of existing and new measurements (98). The objective of concurrent validity is to generate evidence that a new measure predicts the same outcome as previously validated instruments (197). Convergent validity, on the other hand, is part of construct validity, where the objective is to confirm that the new instrument measures similar or related outcomes as a previously existing instrument (198). In our concurrent validity study, the results showed poor correlation to the balance part of the new self-test application. This showed that the sensor-test and clinical tests do not measure the same aspects of balance. This does not mean that MyBalance is not adequate to measure balance, just that the sensor-test measures other aspects of balance than clinical tests. Further validity and reliability testing of the MyBalance application is needed.

Ethical reflections The potential benefits of these studies are considered greater than the risks and/or burdens for the participants. Before study start, both projects were approved by the regional ethical review board in Umeå, Sweden, (2016/106-31; 2017/317-31). The studies follow the principles outlined in the 1964 Helsinki Declaration and its later amendments, the latest version from 2013 (168). The reflection over ethical consideration in this discussion will focus on access to digital technology. It is important to be aware that not everyone has interest in and/or access to smartphones and/or other digital technology, but a majority of older adults in Sweden have such access (72). The benefits for the group that has the possibility to use digital technology for fall prevention should not be impeded by those without the same prerequisites. Access and interest are not only subject to age as a sole factor, but it can be speculated that the oldest in the population do not have access to smartphones to the same extent as their younger peers. Moreover, education level and socioeconomic status may influence access to digital technology. The possibility to use digital technology might also be subject to, if family members or friends facilitate and help to explore this new digital world. These new developments have to be seen as complements to and not replacements for existing, or non-existing, services in the health care system. Development takes time, so more and more older adults will have access to digital technology in the coming years. It is essential to conduct research on digital technology use for fall prevention to ensure that the quality of programmes and assessments is high, and that provided applications are feasible and used as intended.

Overall reflection Parallel with finalising this thesis, many rapid changes have come into place in many parts of the world due to the COVID-19 pandemic. The virus made digitalization take a giant leap, as social and physical distancing was introduced, and more contacts were done with digital technology. Digital health technology is one part of this acceleration of services. Even before the spring of 2020, when the pandemic started, technology had been used to provide rehabilitation, but it was not used to its full capacity. The benefits of digital technology for older adults, such as reduced travel, greater access to services, and now avoiding face-to-face contacts, are seen (199). Even though older adults were less likely to opt for these alternative ways of treatment, most patients were positive after using it, and at times also a hybrid approach could be applied where some face-to-face contact is offered to facilitate distance treatment with digital technology.

The older population is severely affected by COVID-19 and are therefore recommended to maintain quarantine-like conditions (200) to avoid being infected by the virus. This could have a negative impact on other aspects of health for this age group, such as social isolation, reduced time outdoors, which could lead to vitamin D deficiency, and reduced physical activity, etc. By providing exercise at home, such as fall prevention exercises, some of these negative effects can be diminished and the advantages of such programmes can be enhanced. A recently published review of applications and websites providing fall prevention exercise (publicly available and in the English language) was well timed for this COVID-19 pandemic (201). The authors write that 4 out of 13 applications were considered to provide evidence-based exercises and have good quality according to the Mobile Application Rating Scale (MARS) rating. Three websites provide video instruction of the Otago exercises and received an excellent quality rating (credibility and senior friendliness). These applications are examples of how balance and strength exercises can be provided digitally, in other words, we have great possibilities at our fingertips.

This pandemic might also change the predicted demographic development, and a drop in the rapidly increasing older population may be seen. Perhaps we will not reach the predicted 1.5 billion persons over 65 years in 2050, but the number of older adults will increase. Fall preventive work is very meaningful, so the way forward is to continue to develop digital fall prevention.

Implications for fall prevention and physiotherapy The results of this thesis focus on older adults and falls prevention, where physiotherapists play a major role. There is a need for the general public to gain access to evidence-based fall-prevention interventions and professional knowledge, such as physiotherapy, to prevent falls. It is vital to establish this

connection between older adults and physiotherapists in the development, implementation, and maintenance of the interventions. Also, the growing gerontechnology research is part of this evolution of using digital technology. Responsibility and execution may be taken over more by the individual by using digital technology for fall-preventive exercise, the older adult can empower themselves in this important area (62). The results showed improved leg strength in this study with just a brief introduction and four months of exercise, but older adults might require some guidance. By extending the physiotherapist’s role to more enabling instead of being an expert this self-managed exercise set-up could be a success. Digital platform with chat functions or personal video calls could be used for interaction where physiotherapists can have a vital role. This service could be provided independent of location and opening hours and deliver a flexible platform for both professionals and older adult users. Physiotherapists’ work could change entering a digital era, but the core of guiding and educating patients to self-manage conditions, consultation, and follow-up, could be done online. This does not imply that all work will be done online, but that some work could be done using digital technology.

The feasibility study showed that current instruments might not indicate improvements in balance function for persons with higher function, which may be solved with a smartphone self-test. The self-test could offer older adults control through monitoring balance remotely which may support the exercise adherence by increasing motivation through an auto-control mechanism. Results could be used for personal use or reported back to the physiotherapist as a follow-up. The smartphone application could also be a complement to assess balance in the clinical, a fast test procedure and low-cost alternative for the physiotherapist. The smartphone self-test could be a valuable addition to traditional balance tests in the clinic, and an alternative to balance assessments in a laboratory setting. The application could be useful not only in fall prevention, but also in other areas of physiotherapy where balance assessments in the clinic are common like stroke rehabilitation and other neurological conditions, orthopaedic rehabilitation for lower limb assessments etc.

To successfully implement digital fall prevention for older adults, users’ needs must be considered. The co-creation development showed the benefits of working together with the intended users, to reach a satisfactory user-experience. A certain level of knowledge is required to apply these new digital technology tools independently, both for older adults and physiotherapists. Digital technology can empower individuals by providing tools and techniques to create non-physical interaction and information exchange, but these tools and techniques can also cause frustration (202). The concept of digital health literacy, is explained as the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained

to address or solve a health problem (203). Digital health literacy requires a set of skills, regardless of age. Analytic skills that are not specific to digital technology and eHealth are needed, which include the ability to read and understand text, find and use information, and critically question the provided information. As well as context-specific skills are needed like health, computer, and scientific literacy, where the ability to understand, handle and act upon specific areas is necessary (203). The user requires these skills to use digital fall prevention. During the thesis work, this sample of participants acknowledged that they had these skills or some of them, by participating actively. They were willing to learn new skills to take advantage of this new digital fall prevention.

Future research The fast, digital development in society is spreading into the health care sector as well, and development and research are ongoing. Digital technology could have a large impact on the community for fall prevention, and there are opportunities and challenges ahead. As seen in the Safe Step feasibility study, new outcome measures are desired. Proper testing and evaluation are essential before launching new applications and interventions, as well as user-tests among a broader population. This is part of the advancing gerontechnology research.

A randomised controlled trial (RCT) on national level is ongoing for the Safe Step fall prevention application, to study its effectiveness regarding falls, cost-effectiveness, and the reach of the intervention to inform further implementation (204).

The MyBalance smartphone self-test is still in early development, where further validity testing and also reliability testing are needed to confirm and understand the sensor measurements. Reliability testing includes the aspect of user involvement in the test procedure, and therefore, user tests combined with performance-based tests in a lab-setting using gold-standard instruments with force plates are desirable. To assess older persons with different levels of balance function is necessary, as well as persons with different levels of digital technology experience.

Conclusions

This thesis demonstrates that digital technology to support fall prevention exercises is possible for older adults to use. Concluded from the two different projects, Safe Step feasibility study and MyBalance co-creation study, older adults have shown an interest in digital fall prevention.

The self-managed fall prevention interventions in the Safe Step feasibility study showed similar adherence for both DP and PB, adherence and attrition were also comparable to other previous studies. Users of the new DP were, to a higher degree, content with and felt supported by the programme. The DP participants reported more exercise time among those that fully followed the recommended exercise duration, which was about 1/3 of participants in both interventions. Both self-managed exercise programmes were feasible to use for older adults, and 12 months after study start participants in the DP group continued with the programme to a higher degree than the PB participants. The study indicates that the DP was feasible for fall prevention exercise in this group of community- dwelling older adults. At study start the measurements for balance and self-reported balance confidence and falls-efficacy showed ceiling effects. Leg strength was the only performance-based outcome showing significant improvement after four months, while self-rated perceived positive effects were reported for balance, and in the DP group also for leg strength. In particular the performance-based instrument for balance showed inadequacy for measuring change in balance function, and new more sensitive instruments suitable for high functioning older adults are needed. Also, more conformity on how to report adherence data is necessary, especially in self-managed exercise programmes to measure adherence in reliable ways in future studies.

The co-creation development of instructions and user-interface for the MyBalance application was advantageous to gain information about the older- adult user experience. The participants desired clear and distinct information to understand how to perform the self-test and handle the smartphone during testing. It was also desired to understand the reason why the test is done in a certain way. Through the co-creation sessions participants enhanced the design with ideas that improved the self-test application to become more findable, accessible, usable, desirable, credible, useful, and valuable—all criteria of user experience (UX). By involving the older adults in the development, valuable suggestions thus changed the application design according to the users’ experiences, preferences and needs. A test of concurrent validity between clinical assessments and sensor-tests with the MyBalance prototype showed limited correlation between the balance sensor-test and clinical instruments, but the leg strength sensor-test showed reasonable correlation with clinical instruments.

The smartphone self-test application requires further research, to validate the application against gold standard measurements, and conduct user tests. The MyBalance prototype leg strength variables, and static balance frequency variables for Feet together, showed potential for smartphone sensor measurements to be a complement to clinical instruments for balance assessments.

Acknowledgements

I would like to express my sincerest gratitude to all of you that have supported me over the past years, this dream had not come true without you. Many contributed, collaborated, and inspired me; this was not a one-woman job. Therefore, I would like to acknowledge the people who contributed to this work: To all participants in the studies, for your time and commitment, there would be no thesis without you. To Marlene Sandlund, my main supervisor, for believing in me, giving me the opportunity to complete my dream, and for the endless guidance. Thanks for your friendship and bringing me back to Umeå for a few years. To Maria Wiklund, my co-supervisor, for rigorous reading and offering great meticulous feedback, for being calm and attentive, and for now eating Spanish strawberries in the spring. Thanks for taking me under your wings. To Erik Rosendahl, my co-supervisor, for being methodological, thoughtful, and the opposite of a tech freak. It was a pleasure to have you onboard this journey. To Fredrik Öhberg, my co-supervisor, for contributing with your engineering knowledge, and encouragement during this work. Looking forward to future work. To Lillemor Lundin-Olsson, the grandmother of Safe Step, for welcoming me into the world of fall prevention research, and always being thorough and willing to share your knowledge. Hope to see you soon again. To Anncristine Fjellman-Wiklund, my examiner, for wisdom and guidance during my doctoral student period. You bear a resemblance to my late mother. To Charlotte Häger, for chairing the PhD defence, and being my academic grandmother, at all times approachable for a friendly chat. To Jonas Selling, research engineer, for your patience with my questions during the application development and for our talks about fishing; without you there would be no MyBalance app. To Pernilla Bäckman, for data collection in Stockholm, collaboration on Paper IV, companionship in Geneva at the WCPT, and continued friendship. To all co-authors Karin Danielsson, Jonas Sandlund, Dawn Skelton, Helena Lindgren, Rebecka Janols, and Beatrice Pettersson, for fruitful discussions and valuable contributions during the process, where good advice was never far away.

To all PhD students in the department, former, present, and future, for discussions, support, friendship, and joy. You are all very special friends, too many to mention all of you here by name, but you know who you are. Going through the doctoral student years is a once-in-a-lifetime experience, and we share that together. Special mention of PhD Anna Sondell, a former colleague who became a very good friend when I moved to Umeå to take up my PhD studies, for walks and talks, and dinners. Åsa Karlsson, my “thesis buddy”, we went through this together with our weekly phone and video calls during the COVID-19 pandemic, thanks for your support. And, Beatrice Pettersson appreciated roommate, and Saranda Bajraktari, both doctoral student companions in the Safe Step project. To all colleagues, present and former, in the Department of Community Medicine and Rehabilitation, for company during tea breaks and lunches, for advice and interesting discussions in a friendly environment where someone was always available. Working from home most of the time this last year, I missed you. To Jorunn, Kristin, and Ronny for welcoming me to Trondheim, Norway, for three fantastic weeks with the GeMS group for a research visit to the Norwegian University of Science and Technology (NTNU). To family and friends, especially my sister Marie, for sharing good times and challenging times together, you are all always there for me. To my late parents, Siv & Inge, because you always believed in me. To Johan, my dear husband, for practical, technical, and emotional support throughout this journey. We have been apart and together, here, there, and everywhere. I look forward to the next stopover. Thank you for being you! To everyone near and far away, mentioned or not in the list of acknowledgements, you all have a special place in my heart. Thank you! ©

This work was financially supported by: the Swedish Research Council (grant number 521–2011-3250 and grant number 2015–03481); the Strategic Research Programme in Care Sciences (SFO-V), Umeå University and Karolinska Institutet, Sweden; the Swedish Research Council for Health, Working Life and Welfare (FORTE); King Gustav V and Queen Victoria’s Freemasons’ Foundation; the Promobilia Foundation (ref nr 18118); the Foundation in Memory of Ragnhild & Einar Lundström; the JC Kempe Academic foundation; the Kempe Foundation; and Anna Cederbergs Stiftelse för medicinsk forskning.

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Appendix ted orted orted the date, the time spent on the practice, the exercises

s. rep - Figure Figure A1. Above is the paper diary, where completed, and participants how the participant self felt both in general and how they felt about the exercise session. The top row wasinto English for thetransla purpose of providing an example in the thesi

Figure A2. Screen dumps from MyBalance (v1): a) Home screen MyBalance b) Start page for the Three maximal chair stand test c) Information about essential equipment for the balance test (from instruction video) d) The two foot positions in the standing balance test (from instruction video)