Characterization of a wearable monitoring system of physical activity as a surrogate of brain structure and function in older populations

Universidade do Minho Escola de Medicina Célia Margarida Viegas Domingos julho de 2021 Characterization of a wearable monitoring system of physical activity as a surrogate of brain structure and function in older populations Célia Margarida Viegas Domingos Characterization of a wearable monitoring system of physical activity as a surrogate of brain structure and function in older populations UMinho|2021 FUNDING The work presented in this thesis was developed in the Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal, the iCognitus4ALL – IT Solutions, Lda, the Clinical Academic Center (2CA), Braga, Portugal, Hospital de Braga, Braga, Portugal, and local health centers. Financial support was provided by FEDER funds through the Operational Programme Competitiveness Factors – COMPETE and National Funds through FCT under the project POCI01-0145-FEDER-007038, UIDB/50026/2020 and UIDP/50026/2020, by the project MEDPERSYST [ POCI-01-0145-FEDER-016428; supported by the Operational Programme Competitiveness and Internationalization (COMPETE 2020) and the Regional Operational Program of Lisbon and National Funding through Portuguese Foundation for Science and Technology (FCT, Portugal)], and by the Portuguese North Regional Operational Programme . The work was also developed under the scope of the 2CA-Braga Grant of the 2017 Clinical Research Projects. CD was supported by a combined Ph.D. scholarship from FCT and the company iCognitus4ALL – IT Solutions, Lda, Braga, Portugal (grant number PD/BDE/127831/2016). Célia Margarida Viegas Domingos julho de 2021 Characterization of a wearable monitoring system of physical activity as a surrogate of brain structure and function in older populations Trabalho efetuado sob a orientação da Professora Doutora Nadine Correia Santos e do Professor Doutor José Miguel Gomes Moreira Pêgo Tese de Doutoramento Doutoramento em Ciências da Saúde Universidade do Minho Escola de Medicina ii DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ iii AGRADECIMENTOS Aos meus avós, meus pais do coração, pelo amor incondicional, pelo apoio, pelos valores e princípios transmitidos, que contribuíram para formação do meu carater e permitiram que eu me tornasse a mulher que hoje sou. Em especial ao meu avô, sem ele não teria sido possível ter chegado até aqui. Não existem palavras que expressem toda a gratidão de ter tido estes seres humanos fantásticos na minha vida. Aos orientadores Nadine Correia Santos e José Miguel Pêgo, por terem aceite o desafio de orientar a minha tese, pelas sugestões e críticas construtivas que permitiram melhorar o trabalho final. Agradeço também a confiança depositada no meu trabalho e a liberdade de criação. Ao Nuno Santos pela amizade e por todas as dicas informáticas. Ao Ricardo Magalhães e Maria Picó, pelas conversas científicas e ajuda no delineamento metodológico da Ressonância Magnética e análise de dados. À Mariana Moreira pelo companheirismo e pela equipa fantástica que formamos no primeiro momento de avaliação dos participantes. Ao Bruno Correia pela ajuda no script que permitiu a extração dos dados de atividade física. A todos os colegas por todas as trocas de ideias e científicas e não científicas e a todos que contribuíram para o desenvolvimento desta tese. Aos amigos que sempre tiveram e continuam a caminhar ao meu lado. Por último, mas não menos importante o meu agradecimento especial a todos os participantes que voluntariamente contribuíram para o sucesso deste projeto. iv STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. Assinado por : CÉLIA MARGARIDA VIEGAS DOMINGOS Num. de Identificação: BI121498689 Data: 2021.07.23 20:46:33+01'00' v CARACTERIZAÇÃO DE UM SISTEMA DE MONITORIZAÇÃO DE ATIVIDADE FÍSICA COMO SUBSTITUTO DA ESTRUTURA E FUNÇÃO DO CÉREBRO EM IDOSOS RESUMO O envelhecimento cerebral saudável é um fator determinante do estado de saúde e da qualidade de vida da população idosa. Estudos tem demonstrado que a atividade física (AF) pode melhorar a saúde cerebral. Contudo, o uso de medidas subjetivas de AF e a ausência de abordagens multimodais de neuroimagem têm limitado a compreensão dos mecanismos associados. Nesta tese, pretendemos realizar um estudo observacional transversal em idosos para (i) comparar estimativas objetivas de AF com dados obtidos a partir de questionários em idosos residentes na comunidade; (ii) avaliar o perfil de AF e suas associações com variáveis relativas a indicadores de saúde; (iii) testar a aceitação, usabilidade e satisfação de um sistema de monitorização de AF (Xiaomi Mi Band 2®); e (iv) explorar associações entre AF e a estrutura e função do cérebro através da avaliação objetiva da AF e aquisições de neuroimagem. Esta abordagem foi complementada por uma análise sistemática de estudos de neuroimagem observacionais que avaliaram a relação entre PA e a estrutura e função do cérebro em idosos sem doença cognitiva ou neuropatológica. Os resultados evidenciaram uma grande variação do tempo sedentário e AF entre as medidas subjetivas (auto-relato) e as correspondentes medidas objetivas (Xiaomi Mi Band 2®), verificando-se a maior diferença para a estimativa do tempo sedentário. Adicionalmente, verificámos que a AF auto-relatada e medida objetivamente associam-se de forma diferente com as variáveis relativas a indicadores de saúde. Neste estudo verificou-se também que o Xiaomi Mi Band® apresenta um excelente nível de aceitação, usabilidade e satisfação entre os idosos, sugerindo que este dispositivo é adequado para esta população. Além disso, verificámos que a usabilidade é um fator importante na determinação da satisfação do utilizador. Por fim, observou-se uma correlação positiva significativa entre a AF vigorosa e o volume do giro parahipocampal esquerdo e do hipocampo direito. Além disso, foi observada uma maior conectividade funcional (CF) entre o giro frontal, o giro cingulado, o lobo inferior occipital e a AF de intensidade leve, moderada e total e menor CF associada ao tempo sedentário para as mesmas redes. Concluindo, os resultados sugerem que dispositivos com características semelhantes ao Xiaomi Mi Band® podem ser sistemas de monitorização de AF viáveis, podendo ser implementados com sucesso em estratégias de promoção de AF para a adoção de um estilo de vida fisicamente ativo. Além disso, os benefícios de um estilo de vida fisicamente ativo podem resultar numa melhor saúde cerebral. Palavras-chave: Atividade física; dispositivos móveis; envelhecimento; experiência do utilizador; saúde cerebral. vi CHARACTERIZATION OF A WEARABLE MONITORING SYSTEM OF PHYSICAL ACTIVITY AS A SURROGATE OF BRAIN STRUCTURE AND FUNCTION IN OLDER POPULATIONS ABSTRACT Healthy brain aging is one of the most important determinants of health status and quality of life in the older population. There is increasing evidence that physical activity (PA) contributes to brain health. However, the lack of use of integrative approaches including PA objective measures and multimodal neuroimaging has limited the knowledge of the mechanisms underlying the association. In this thesis, we aimed to conduct an observational cross-sectional study with community-dwelling older adults to (i) compare PA objective estimates with data obtained from self-report questionnaires; (ii) assess PA profiles and their associations with health outcomes; (iii) test acceptability, usability, and user satisfaction of a commercially available wearable PA monitoring system (Xiaomi Mi Band 2®); and (iv) explore the associations between objectively measured PA and brain structure and function. This approach was complemented by a systematic review of observational neuroimaging studies that have examined the relationship between PA and brain structure and function in older adults without cognitive disease or neuropathology. Altogether, the findings highlighted the large variation between subjective (self-reported) and objectively measured PA (Xiaomi Mi Band 2®) and sedentary time parameters, with the highest difference found for the latter. Additionally, self-reported and objectively measured PA were differently associated with health outcomes (cognitive and mood profiles, anthropometric, body composition, and physical performance measures). Findings also demonstrated that the Xiaomi Mi Band® has an excellent level of acceptability, usability, and satisfaction among older adults, suggesting that this device is suitable for this population. Moreover, usability was noted to be an important factor influencing user satisfaction. Finally, a significant positive correlation was found between the time spent in vigorous PA and the left parahippocampal gyrus and right hippocampus volumes. Findings also revealed higher functional connectivity (FC) between the frontal gyrus, cingulate gyrus, and occipital inferior lobe for light, moderate, and total PA time, and sedentary time associated with lower FC in the same networks. In conclusion, these results suggest that wearables with characteristics similar to the Xiaomi Mi Band® may be a feasible monitoring system of PA. Thus, these types of devices may be used to create PA promotion strategies for the adoption of a physically active lifestyle in older populations. Moreover, the benefits of having a physically active lifestyle may translate into better brain health. Keywords: Aging; brain health; physical activity; user experience; wearable device. vii TABLE OF CONTENTS TABLE OF CONTENTS .......................................................................................................................VII LIST OF ABBREVIATIONS ................................................................................................................... XI LIST OF FIGURES ............................................................................................................................. XV LIST OF TABLES .............................................................................................................................. XVII INTRODUCTION ................................................................................................................................. 1 1. Aging & healthy aging ........................................................................................................ 2 2. Physical activity.................................................................................................................. 3 3. Physical activity and healthy aging ...................................................................................... 4 4. Physical activity assessment .............................................................................................. 6 5. Wearable technologies among older adults ......................................................................... 8 6. Aims .................................................................................................................................. 9 7. Chapters overview ............................................................................................................ 10 8. References ...................................................................................................................... 11 CHAPTER I ....................................................................................................................................... 15 ASSOCIATION BETWEEN SELF-REPORTED AND ACCELEROMETER-BASED ESTIMATES OF PHYSICAL ACTIVITY IN PORTUGUESE OLDER ADULTS .............................................................................................................. 15 1. Abstract ........................................................................................................................... 16 2. Introduction ..................................................................................................................... 18 3. Methods .......................................................................................................................... 19 4. Results ............................................................................................................................ 25 5. Discussion ....................................................................................................................... 37 6. Acknowledgments ............................................................................................................ 41 7. Author Contributions ........................................................................................................ 41 8. Funding ........................................................................................................................... 41 9. Institutional Review Board statement ................................................................................ 42 10. Informed consent Statement ............................................................................................ 42 11. Data Availability statement ............................................................................................... 42 12. Conflicts of interest .......................................................................................................... 42 xiv T T1w: T1-weighted TAM: Technology Acceptance Model TFCE: Threshold-free cluster enhancement TICS: The Telephone Interview for Cognitive Status TIL: Tucker–Lewis Index TMT: Trail Making Test TRA: Theory of Reasoned Action U USE: Satisfaction and Ease of Use questionnaire USEQ: User Satisfaction Evaluation Questionnaire V VBM: Voxel-based morphometry VLTM: Verbal Long-term memory VT: Vitality W W: Word WAIS: Wechsler Abbreviated Scale of Intelligence WAIS-R: Wechsler Adult Intelligence Scale-Revised WC: Waist circumference WHO: World Health Organization WHR: Waist-to-hip ratio WM: White matter WMH: White matter hyperintensities WTAR: Wechsler Test of Adult Reading WAIS – III Y YA: Young Adult YPAS: Yale Physical Activity Survey xv LIST OF FIGURES CHAPTER I Figure 1. Flow diagram of the assessments. Figure 2. Bland-Altman plot for vigorous PA assessed by IPAQ and Xiaomi Mi Band 2®. The mean difference (systematic error) and 95% limits of agreement (mean ± 1.96 SD) and confidence interval shadings are displayed in the figure. Figure 3. Bland-Altman plot for vigorous PA assessed by IPAQ and Xiaomi Mi Band 2®. The mean difference (systematic error) and 95% limits of agreement (mean ± 1.96 SD), proportional bias line, and proportional bias line confidence intervals are displayed in the figure. Figure 4. Cluster comparison. CHAPTER II Figure 1. Path diagram and standardized estimates for the one-factor model of the User Satisfaction Evaluation Questionnaire. CHAPTER III Figure 1. Research hypothesis framework. Figure 2. Moderating effect of user characteristics. Figure 3. Path diagram for the research model. Figure 4. Simple slope plot for moderating effect of GDS and usability in the prediction of user satisfaction. Figure 5. Simple slope plot for moderating effect of education and usability in the prediction of user satisfaction. xvi Figure 6. Simple slope plot for moderating effect of MMSE and usability in the prediction of user satisfaction. CHAPTER IV Figure 1. PRISMA Flow diagram describing the study selection process [adapted from Liberati, Altman et al. 2009, Moher, Liberati et al. 2009)]. Figure 2. Summary of the selected studies included in the systematic review. Figure 3. Distribution of brain regions showing increased volume associated with PA in crosssectional and longitudinal studies (n=26). Note that some of the studies reported effect in more than one brain region. CHAPTER V Figure 1. Flow diagram of the evaluation moments. Figure 2. Flow diagram of participant screening and enrollment. Figure 3. Sagittal, coronal, and axial view of the clusters and the scatter plot showing a significant positive correlation between vigorous time and: (A) left parahippocampal gyrus (-18, 2, -24) and (B) right hippocampus (40, -26, -14), after controlling for age, gender, age, MMSE, and GDS. All clusters illustrated were defined with a threshold at a p-value of 0.1. Figure 4. Networks of FC identified using NBS showing increased connectivity during rs-fMRI acquisition. Sagittal and axial view of the network with nodes and edges colored in red-yellow color scheme representing the statistical t-value and the scatter plot showing the relationships between the mean FC and: (A) light time; (B) moderate time; (C) total PA time; and (D) sedentary time; after controlling for age, gender, age, MMSE, and GDS. xvii LIST OF TABLES CHAPTER I Table 1. General characteristics of the study participants. Table 2. Descriptive PA data from International Physical Activity Questionnaire. Table 3. Descriptive PA data from Yale Physical Activity Survey. Table 4. Descriptive PA data from Xiaomi Mi Band 2®. Table 5. Spearman correlations between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS). Table 6. Mean difference in minutes per week between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS). Table 7. Associations between Xiaomi Mi Band 2®–measured PA and the mean difference in minutes per week between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS) with health outcomes. Table 8. Classification of older individuals according to the current public health PA recommendations. Table 9. Descriptive statistics for each cluster. CHAPTER II Table 1. Characteristics of the study participants (N=110). Table 2. Descriptive statistics for User Satisfaction Evaluation Questionnaire items. Table 3. Factor matrix containing obliquely unrotated factor loadings of principal axis factoring (forcing one-factor solution). The eigenvalue and the percentage of variance explained by the factor are also shown. xviii Table 4. Fit indices for confirmatory factor analysis model Table 5. Inter-Item Correlation Matrix. Table 6. Scores obtained on the User Satisfaction Evaluation Questionnaire for the original scale with 6 items and the newly proposed scale with 5 items. Table 7. Spearman bivariate correlations between User Satisfaction Evaluation Questionnaire scores and demographic, mood, and global cognitive characteristics Supplementary Table 1. Translation process of the USEQ items. Supplementary Table 2. Pre-final version tested in a pilot study with 20 participants and final European Portuguese version obtained after the pilot study. Supplementary Table 3. Original item vs. corresponding item in English and European Portuguese versions. CHAPTER III Table 1. Characteristics of the study participants (N=110). Table 2. Descriptive statistics for TAM 3 items. Table 3. Descriptive statistics for SUS items. Table 4. Descriptive statistics for USEQ items. Table 5. Confirmatory factor analysis for instruments. Table 6. User experience classification for usability and satisfaction. Table 7. Fit indices for the hypothesized model. Table 8. Results of hypothesis testing based on standardized path coefficients for the research model. xix Table 9. Estimates for the moderating effect of GDS and usability in the prediction of user satisfaction. Table 10. Effect of the usability on satisfaction at different levels of GDS. Table 11. Estimates for the moderating effect of education and usability in the prediction of user satisfaction. Table 12. Effect of the usability on satisfaction at different levels of education. Table 13. Estimates for the moderating effect of MMSE and usability in the prediction of user satisfaction. Table 14. Effect of the usability on satisfaction at different levels of MMSE. Supplementary Table 1. Measurement items of TAM 3. Supplementary Table 2. Measurement items of USEQ. Supplementary Table 3. Measurement items of SUS. CHAPTER IV Table 1. Electronic search strategy. Table 2. Summary of cross-sectional studies reporting the effects of physical activity measured by self-reported questionnaires on brain structure. Table 3. Summary of cross-sectional studies reporting the effects of physical activity measured by accelerometry on brain structure. Table 4. Summary of cross-sectional studies reporting the effects of physical activity measured by self-reported questionnaires on brain function. Table 5. Summary of cross-sectional studies reporting the effects of physical activity measured by accelerometry on brain function. Table 6. Summary of longitudinal studies reporting the effects of physical activity measured by self-reported questionnaires on brain structure. xx Table 7. Summary of longitudinal studies reporting the effects of physical activity measured by accelerometry on brain structure. Supplementary Table 1. Critical appraisal for cross-sectional studies evaluating brain structure. Supplementary Table 2. Critical appraisal for cross-sectional studies evaluating brain function. Supplementary Table 3. Critical appraisal for longitudinal studies evaluating brain structure. CHAPTER V Table 1. General characteristics of the study participants (n=104). Table 2. Neuropsychological characterization of the study participants (n=104). Table 3. Descriptive statistics for SF-36 dimensions (n=104). Table 4. Descriptive PA data obtained from Xiaomi Mi Band 2® (n=104). Table 5. Classification of older individuals according to PA Guidelines (n=104). Table 6. VBM results – Regression analysis of PA and brain volume (n=104). Table 7. Results of the functional connectomics analysis using NBS (n=104). xxi À memória do meu avô por tudo o que representou na minha vida. “A gratidão é a memória do coração.” Jean Baptiste Massieu 1 INTRODUCTION 2 1. Aging & healthy aging The number of individuals aged 60 or older will grow dramatically in the next three decades being estimated that globally it will surpass two billion by 2050. Notably, the decrease in mortality and lengthening of the average human lifespan has rapidly shifted the worldwide demographic structure towards the elderly. Nonetheless, with the increase in longevity there is a parallel increase in age-related conditions. Therefore, the aging population increases the need for improved social and economic measures necessary to deal with the physiological consequences of the aging process (S. M. Gregory, Parker, & Thompson, 2012; Wu, Wang, Burgess, & Wu, 2013). Age represents the primary risk factor for chronic diseases, including cardiovascular, malignant, and neurodegenerative conditions (Barnes, 2015; Khan, Singer, & Vaughan, 2017). Additionally, normal aging is associated with changes in brain structure and function, such as cognition, including executive functioning (i.e., abstract thinking, planning, coordination, cognitive flexibility, and working memory) (M. A. Gregory, Gill, & Petrella, 2013), attention, memory, and concentration, as well as physical-related functions such as walking and balance (Sofi et al., 2011). These are consequence of cerebral cortical tissue loss in the frontal, parietal, and temporal lobes (M. A. Gregory et al., 2013; S. M. Gregory et al., 2012; Sofi et al., 2011). Moreover, hippocampal volume loss in older adults has been associated with cognitive impairments (M. A. Gregory et al., 2013; S. M. Gregory et al., 2012). Despite this, age-related cognitive decline can occur as a part of the brain aging processes without leading to dementia, but potentially resulting in a poorer quality of life (Sofi et al., 2011). Still, many cognitive changes may become evident and cause mild disability, even without a state of dementia (S. M. Gregory et al., 2012; Sofi et al., 2011). In other dimensions, the incidence of age-related neurological pathologies has been associated with cardiovascular disease risk factors. For example, high blood pressure contributes to progressive vascular stiffening during aging and is recognized as a primary risk factor associated with age-related reductions in cerebral perfusion and the development of white matter lesions in older individuals (M. A. Gregory et al., 2013). Consequently, longevity has challenged researchers, clinicians, and societal players to develop strategies to promote successful aging (M. A. Gregory et al., 2013). Nowadays, it is imperative to identify feasible, effective, and scalable interventions to mitigate the burden of age-related chronic disease risk and cognitive decline (M. A. Gregory et al., 2013). The World Health Organization (WHO) defines healthy aging as the process of developing and maintaining the functional ability that enables wellbeing in older age (Beard et al., 2016). Functional ability is described as the ability to: meet their basic needs; learn, grow, and make decisions; move around; build and maintain relationships, and contribute to society (Beard et al., 2016). Although there 9 seniors may have anxiety and lower self-efficacy (Rupp et al., 2018). In fact, over 75% of the over-65 age group state that requiring assistance to use new technologies. The main reason for the need for technical assistance is because most technology systems are not developed for older users and frequently have small buttons, fiddly controls, complex interfaces, complex commands, and operating procedures (Steinert et al., 2018). Moreover, the colors, font sizes, and the display of critical information such as steps taken, and battery life that usually are hard for the elderly to interpret and are important aspects to consider to decreasing barriers and promote the usability and the acceptance of activity trackers (Preusse et al., 2017). In addition, factors associated with normal aging such as physical and cognitive decline could limit the ability to adopt the technology (Berkowsky, Sharit, & Czaja, 2018). Therefore, it is required a better understanding of the barriers and facilitators that older adults face when using technologies to understand whether they are satisfied or not. This is crucial to promote technology adoption and to ensure an appropriate implementation in clinical and research settings (Campelo & Katz, 2020; Farina & Lowry, 2017). Future research should consider design features of activity trackers before implementing them in older adults (Farina & Lowry, 2017). Finally, in this work, we intend to broaden the knowledge of the association between PA (measured with a wearable device) and brain structure and function, and at the same time provide new insights regarding wearable technologies among older adults. 6. Aims In this thesis, we performed an observational cross-sectional study in older individuals, using a multimodal imaging approach combined with an objective measurement of daily PA to explore brain health (structure and function) in aging populations. The study was divided into the following objectives:  Compare accelerometer-based estimates of PA from Xiaomi Mi Band 2® with commonly used self-report questionnaires and assess PA profile in community-dwelling older adults.  Evaluate the usability, feasibility, and performance of a wearable monitoring system (Xiaomi Mi Band 2®) in older populations.  Explore associations between PA and brain structure and function by objectively evaluating PA and conducting parallel neuroimaging studies. 10 7. Chapters overview This thesis is organized into five chapters. In Chapter I, we aimed to compare accelerometer-based estimates of PA from Xiaomi Mi Band 2® with PA data obtained from self-report questionnaires in older adults. In Chapter II, we conducted a transcultural adaptation and validation of a questionnaire to evaluate user satisfaction with technologies in Portuguese older adults. 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Journal of Sport and Health Science, 2(4), 193-203. doi:https://doi.org/10.1016/j.jshs.2013.09.001 15 CHAPTER I Association between self-reported and accelerometer-based estimates of physical activity in Portuguese older adults Domingos C, Santos NC Pêgo JM Sensors (Basel). DOI: 10.3390/s21072258. 16 Association between self-reported and accelerometer-based estimates of physical activity in Portuguese older adults Domingos C 1,2,3,4, Santos NC 1,2,4,5*, Pêgo JM 1,2,3,4* 1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal 2ICVS/3B’s, PT Government Associate Laboratory, 4710-057 Braga/Guimarães, Portugal 3iCognitus4ALL-IT Solutions, 4710-057 Braga, Portugal 4Clinical Academic Center-Braga (2CA-B), 4710-057 Braga, Portugal 5ACMP5-Associação Centro de Medicina P5 (P5), School of Medicine, University of Minho, 4710-057 Braga, Portugal *These authors contributed equally to the work. 1. Abstract Accurate assessment of physical activity (PA) is crucial in interventions promoting it and in studies exploring its association with health status. Currently, there is a wide range of assessment tools available, including subjective and objective measures. This study compared accelerometer-based estimates of PA with self-report PA data in older adults. Additionally, the associations between PA and health outcomes and PA profiles were analyzed. Participants (n = 110) wore a Xiaomi Mi Band 2® for fifteen consecutive days. Self-reported PA was assessed using the International Physical Activity Questionnaire (IPAQ) and the Yale Physical Activity Survey (YPAS). The Spearman correlation coefficient was used to compare self-reported and accelerometer-measured PA and associations between PA and 17 health. Bland–Altman plots were performed to assess the agreement between methods. Results highlight a large variation between self-reported and Xiaomi Mi Band 2® estimates, with poor general agreement. The highest difference was found for sedentary time. Low positive correlations were observed for IPAQ estimates (sedentary, vigorous, and total PA) and moderate for YPAS vigorous estimates. Finally, self-reported and objectively measured PA associated differently with health outcomes. Summarily, although accelerometry has the advantage of being an accurate method, selfreport questionnaires could provide valuable information about the context of the activity. Keywords: seniors; elderly; physical activity; accelerometry; fitness trackers; self-report physical activity; IPAQ; YPAS 18 2. Introduction Regular physical activity (PA) is essential for healthy aging and chronic disease prevention, with its benefits on general health and overall quality of life in older adults being well established (Lee, Jackson, & Richardson, 2017; Lohne-Seiler, Hansen, Kolle, & Anderssen, 2014). Specifically, PA contributes to maintaining physical function and performance (e.g., walking speed, handgrip strength) (Langhammer, Bergland, & Rydwik, 2018) and plays a preventing role in frailty and falls (Kononova et al., 2019; Mercer et al., 2016). Furthermore, it associates with more favorable body composition and anthropometric parameters such as body mass index (BMI), waist circumference, and weight (Dunsky et al., 2014). There is also increasing evidence of its beneficial effects on various neuropsychological outputs (Busse, Gil, Santarém, & Jacob Filho, 2009). Despite this, the quantification of the strength and nature of the relationship between PA and health outcomes should rely on the accurate measurement of PA behavior (Celis-Morales et al., 2012). In population-based studies, there are currently several objective and subjective self-report instruments available (Oyeyemi, Umar, Oguche, Aliyu, & Oyeyemi, 2014), but with accumulating scientific evidence being mainly based on the latter (e.g., self-report questionnaires, logs, recalls, and activity diaries) (Haskell, 2012; Lohne-Seiler et al., 2014). Nonetheless, recalling PA is a complex cognitive task, particularly for older adults who may have memory and recall limitations (Lohne-Seiler et al., 2014). Indeed, self-report questionnaires have limited validity in measuring daily activities due to issues with question interpretation, imprecise recall, judgment formation, and response editing (Celis-Morales et al., 2012; Kirk I. Erickson et al., 2014; Nicaise et al., 2014). On the other hand, self-report measures are more feasible for use in large-scale studies because of their low cost and ease of use (Celis-Morales et al., 2012; Nicaise, Crespo, & Marshall, 2014; Oyeyemi et al., 2014; Rääsk et al., 2017; Schmidt et al., 2020). Moreover, questionnaires can provide information on the type (e.g., leisure, household, work, transportation) and duration of PA (e.g., min.day−1, min.wk−1, h.wk−1) (Nicaise et al., 2014). Still, and complementing this type of methodology, more objective PA measurement is of concern. Across time and populations, accelerometers are reliable and valid objective instruments (Kirk I. Erickson et al., 2014; Lohne-Seiler et al., 2014; Schwenk et al., 2015), providing a precise assessment of everyday activities (frequency, duration, intensity, and type), and allowing for the identification of specific PA patterns (Kirk I. Erickson et al., 2014; O’Neill et al., 2017; Schwenk et al., 2015), without the need for self-report (Erickson, Leckie, & Weinstein, 2014). However, they are unable to capture all PA dimensions, such as leisure, household, work, and transportation activities (Nicaise et al., 2014). 25 4. Results Study Participants Table 1 summarizes the participants’ demographic, depressive mood, global cognitive, anthropometric, body composition, and physical performance characteristics. The mean age was 68.4 (SD ± 3.1), 60% were female, and the mean number of years of formal education was 8.0 (SD ± 5.4) (number of school years). Table 1. General characteristics of the study participants. Characteristics Total Sample n = 110 Male n = 50 Female n = 60 p -Value Gender, n (%) 50 (45.5) 60 (54.5) Age, years (mean ± SD) 68.4 ± 3.1 69.4 ± 2,9 67.5 ± 3.0 <0.000 Education, years (mean ± SD) 8.0 ± 5.4 9.4 ± 6.0 6.8 ± 4.6 0.008 MMSE, total score (mean ± SD) 27.0 ± 2.0 27.1 ± 1.8 26.8 ± 2.1 0.57 GDS, total score (mean ± SD) 6.1 ± 4.6 5.3 ± 3.9 6.7 ± 5.0 0.25 Anthropometric (mean ± SD) Weight, Kg 73.6 ± 13.0 79.0 ± 11.8 69.2 ± 12.4 <0.000 Height, m2 159.7 ± 8.36 166.3 ± 6.4 154.2 ± 5.3 <0.000 BMI, kg/m2 28.5 ± 4.3 27.8 ± 3.5 29.0 ± 4.9 0.65 WC, cm 99.4 ± 9.8 100.2 ± 9.7 98.7 ± 9.7 0.43 HP, cm 104.8 ± 9.03 103.3 ± 6.9 106.0 ± 10.4 0.29 WHR 0.95 ± 0.07 0.97 ± 0.08 0.93 ± 0.06 0.013 Body composition (mean ± SD) FAT, % 33.5 ± 7.8 27.8 ± 5.2 38.3 ± 6.3 <0.000 FAT, Kg 24.7 ± 8.6 21.5 ± 6.5 27.3 ± 9.2 <0.000 26 FFM, % 66.5 ± 7.8 72.2 ± 5.2 61.7 ± 6.3 <0.000 FFM, Kg 48.2 ± 9.2 54.8 ± 7.6 42.7 ± 6.4 <0.000 Mineral, Kg 4.0 ± 0.5 4.2 ± 0.5 3.8 ± 0.4 <0.000 Muscle, Kg 23.7 ± 4.2 26.5 ± 3.6 21.3 ± 3.0 <0.000 BCM, Kg 27.5 ± 4.9 30.9 ± 4.2 24.7 ± 3.4 <0.000 Malnutrition Index 0.66 ± 0.05 0.69 ± 0.05 0.64 ± 0.04 <0.000 Physical performance (mean ± SD) ADL score 5.7 ± 0.5 5.9 ± 0.4 5.5 ± 0.6 0.002 IADL score 8.0 ± 0.1 8.0 ± 0.1 7.9 ± 0.2 0.67 Balance score 15.6 ± 0.7 15.5 ± 0.7 15.6 ± 0.8 0.28 Gait score 12.0 ± 0.2 12.0 ± 0.1 12.0 ± 0.1 0.1 Max Right Grip, Kg 31.0 ± 10.1 39.4 ± 8.0 24.0 ± 4.8 <0.000 Max Left Grip, Kg 29.6 ± 9.6 37.2 ± 8.6 23. ± 4.4 <0.000 Speed, m/sec 1.7 ± 0.3 1.8 ± 0.3 1.6 ± 0.3 <0.000 Abbreviations: MMSE, Mini Mental State Examination; GDS, Geriatric Depression Scale; BMI, Body mass index; WC, waist circumference; HP, hip circumference; WHR, waist-to-hip ratio; FAT, Fat Mass; FFM, Free Fat Mass; Muscle, Muscle mass; Mineral, Mineral body density; BCM, Body Cell Mass; ADL, Activities of Daily Living; IADL, Instrumental Activities of Daily Living. According to cut-offs of the Portuguese version of MMSE all participants were classified as without cognitive impairment (27.0 ± 2.0). The GDS results indicated no presence of depressive symptoms (6.1 ± 4.6). The average BMI was 28.5 (SD ± 4.3), which is categorized as overweight, and the WHR ratio was 0.95 (SD ± 0.07), meaning that this sample of older adults had a substantially increased risk of metabolic complications. Results did not show statistical differences between genders for BMI or WHR. Women had significantly higher fat mass (%) 38.3 (SD ± 6.3) compared to men 27.8 (SD ± 5.2). The values are categorized within optimal values for both genders. The gait speed and handgrip strength were in the range of reference values, indicating good physical performance. The functional status evaluation confirmed the independence in activities of daily living and performance of instrumental activities of daily living. 27 The descriptive data from IPAQ, YPAS, and Xiaomi Mi Band 2® are presented in Table 2, Table 3, and Table 4, respectively. Overall, the activities self-reported were predominantly walking, household, leisure, and gardening activities. Furthermore, the results have shown a significant difference in PA measured by IPAQ between gender for domestic activities, sitting time, moderate activities, and total PA (Table 2), with higher values for women. Similarly, results from PA measured by YPAS also showed significant differences in domestic activities and total PA (Table 3). Table 2. Descriptive PA data from International Physical Activity Questionnaire. Variable Total Sample n = 110 Male n = 50 Female n = 60 p -Value Total Work (min.d−1) 218.4 ± 1272.8 209.6 ± 1201.7 225.7 ±1339.2 0.53 Total Transport (min.d−1) 698.5 ± 1013.0 903.5 ± 1107.2 527.7 ± 901.5 0.030 Total Domestic (min.d−1) 3789.9 ± 3948.5 1976.6 ± 3074.9 5301.0 ± 3980.2 <0.000 Total Leisure (min.d−1) 1248.6 ± 1577.4 1212.1 ± 1343.5 1279.0 ± 1759.4 0.99 Total Average Sitting (min.d−1) 247.4 ± 127.3 288.4 ± 149.0 213.4 ± 94.4 0.008 Total Walking (min.d−1) 1262.9 ± 1752.6 1313.4 ± 1292.3 1220.7 ± 2069.7 0.084 Total Vigorous (MET.min.d−1) 333.3 ± 828.4 432.7 ± 921.4 250.3 ± 739.8 0.22 Total Moderate (MET.min.d−1) 4359.3 ± 3884.9 2555.6 ± 3176.1 5862.3± 3801.4 <0.000 Total MET (MET.min.d−1) 5955.4 ± 3957.1 4301.8 ± 3380.0 7333.4 ± 3897.6 <0.000 Abbreviations: PA, physical activity; min.d−1; minutes per day, MET.min.d−1, metabolic equivalent of task minutes per day. 28 Table 3. Descriptive PA data from Yale Physical Activity Survey. Variable Total Sample n = 110 Male n = 50 Female n = 60 p -Value Household Total Time (min.wk−1) 1022.4 ± 805.5 476.6 ± 520.4 1477.2 ± 714.8 <0.000 Household Total EE (kcal.wk−1) 3097.9 ± 2406.4 1542.7 ± 1566.8 4394.0 ± 2214.0 <0.000 Yard work Total Time (min.wk−1) 209.7 ± 514.4 137.6 ± 253.4 269.8 ± 653.8 0.71 Yard work Total EE (kcal.wk−1) 965.6 ± 2338.9 637.6 ± 1172.4 1238.9 ± 2966.1 0.71 Caregiving Total Time (min.wk−1) 124.4 ± 396.6 118.8 ± 279.5 129.0 ± 432.9 0.54 Caregiving Total EE (kcal.wk−1) 578.1 ± 1645.8 566.1 ± 1382.3 588.0 ± 1848.6 0.54 Exercise Total Time (min.wk−1) 218.2 ± 230.0 216.9 ± 234.2 219.3 ± 228.4 0.88 Exercise Total EE (kcal.wk−1) 1268.1 ± 1510.8 1217.7 ± 1327.4 1310.0 ± 1658.2 0.90 Leisure Total Time (min.wk−1) 117.9 ± 252.0 136.3 ± 299.1 102.6 ± 206.1 0.99 Leisure Total EE (kcal.wk−1) 388.8 ± 859.9 512.8 ± 1144.6 285.5 ± 504.1 0.87 Total PA Time (min.wk−1) 1692.6 ± 1107.0 1086.2 ± 821.4 2197.9 ± 1064.0 <0.000 Total EE (kcal.wk−1) 6298.4 ± 4152.3 4476.8 ± 3265.3 7816.4 ± 4224.1 <0.000 Vigorous activity index (units.month−1) 4.7 ± 13.1 7.5 ± 17.1 2.3 ± 7.9 0.16 Leisurely walking index (units.month−1) 14.6 ± 14.8 15.7 ± 14.3 13.7 ± 15.3 0.33 Moving index (h.d−1) 12.1 ± 3.7 11.0 ± 3.6 13.0 ± 3.5 0.003 Standing index (h.d−1) 1.0 ± 1.3 1.1 ± 1.4 1.0 ± 1.3 0.81 Sitting index (h.d−1) 1.8 ± 0.7 1.9 ± 0.7 1.6 ± 0.6 0.015 Stair climbing (units.d−1) 9.5 ± 12.5 9.9 ± 14.2 9.1 ± 11.0 0.78 Summary index (total units) 34.1 ± 20.4 37.2 ± 23.7 31.6 ± 16.9 0.42 Seasonal Adjust 1.2 ± 2.0 1.4 ± 3.0 1.00 ± 0.10 0.76 Abbreviations: PA, physical activity; min.wk−1, minutes per week; EE, energy expenditure; kcal.wk−1, kilocalorie per week; h.d−1, hours per day. 29 Table 4. Descriptive PA data from Xiaomi Mi Band 2®. Variable Total Sample n = 110 Male n = 50 Female n = 60 p -Value Sedentary time (min.wk−1) 9088.9 ± 735.7 9084.6 ± 639.0 9092.5 ± 813.0 0.33 Light PA (min.wk−1) 275.4 ± 392.7 278.4 ± 316.3 272.9 ± 449.2 0.045 Moderate PA (min.wk−1) 120.5 ± 127.9 116.5 ± 91.4 123.8 ± 152.5 0.40 Vigorous PA (min.wk−1) 62.4 ± 113.5 63.5 ± 132.6 61.4 ± 96.0 0.14 Total PA Time (min.wk−1) 991.1 ± 735.7 995.4 ± 634.0 987.5 ± 813.0 0.33 Abbreviations: PA, physical activity; min.wk−1. Xiaomi Mi Band 2®-measured PA have shown that the participants spent more time in light or moderate activities (275.4 ± 392.7, 120.5 ± 127.9 min.wk−1, respectively), compared to vigorous activities (62.4 ± 113.5 min.wk−1) (Table 4). On the other hand, no significant differences were observed between men and women in total minutes per day spent in all PA intensities, as measured by the Xiaomi Mi Band 2®. Correlation Between Self-Reported and Accelerometer-Based Estimates of PA Correlations between Xiaomi Mi Band 2® and PA measured by IPAQ and are shown in Table 5. A significant low positive correlation was found for sedentary time, vigorous-intensity, and total PA. For moderate-intensity, the results indicate a negligible correlation between accelerometer and self-reported PA. Concerning the correlations between Xiaomi Mi Band 2® and PA measured by YPAS, a significant moderate positive correlation (r = 0.42, p = 0.001) for vigorous-intensity was found (Table 5). 30 Table 5. Spearman correlations between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS). Variable r p -Value International Physical Activity Questionnaire Sedentary time 0.38 <0.000 Moderate time 0.04 0.70 Vigorous time 0.30 0.002 Total PA time 0.27 0.004 Yale Physical Activity Survey Moderate time 0.003 0.97 Vigorous time 0.42 <0.000 Total PA time 0.23 0.017 Agreement Between Self-Reported and Accelerometer-Based Estimates of PA Analyses comparing the concordance of values between the Xiaomi Mi Band 2® and IPAQ showed that older adults reported both less sedentary time and less vigorous-intensity time compared to the accelerometer measured data. Moreover, there was a significant difference between the two methods for all intensities, except for vigorous-intensity time; meaning that the two methods only agree on the estimation of vigorous-intensity time (p = 0.60) (Table 6). Regarding the concordance of absolute values between the Xiaomi Mi Band 2® and YPAS, we observed higher self-reported PA for all intensities tested. Additionally, we observed that the two methods are significantly different from one another (p = 0.001) (Table 6). 31 Table 6. Mean difference in minutes per week between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS). Variable Mean Difference ± SD p -Value International Physical Activity Questionnaire Sedentary time 7357.8 ± 940.9 <0.000 Moderate time −1191.1 ± 1176.7 <0.000 Vigorous time 9.1 ± 180.9 0.60 Total PA time −767.9 ± 1234.4 <0.000 Yale Physical Activity Survey Moderate time −885.0 ± 869.0 <0.000 Vigorous time −190.9 ± 247.2 <0.000 Total PA time −701.5 ± 1076.6 <0.000 Note: Positive mean difference value: Xiaomi Mi Band 2® PA is higher than self-reported PA. Negative mean difference value: Xiaomi Mi Band 2® PA is lower than self-reported PA. Since a possible agreement between vigorous-intensity time obtained from the Xiaomi Mi Band 2® and IPAQ was found, the Bland–Altman plot was used to explore the strength of this agreement. Figure 2 and Figure 3 show the differences between Xiaomi Mi Band 2®–measured PA and IPAQ plotted against the mean of the accelerometer and IPAQ for vigorous PA minutes per week. Overall, the mean difference was 9.1 ± 180.9 min.wk−1, and the limits of agreement presented a higher variation, ranging from –345.4 to 363.6 min.wk−1. The mean of the data lies considerably above 0, indicating that the Xiaomi Mi Band 2® estimates are consistently greater than IPAQ estimates. Moreover, the cluster of points showed a trend or error proportional to the size of the measure. 32 Figure 2. Bland–Altman plot for vigorous PA assessed by IPAQ and Xiaomi Mi Band 2®. The mean difference (systematic error) and 95% limits of agreement (mean ± 1.96 SD) and confidence interval shadings are displayed in the figure. Figure 3. Bland–Altman plot for vigorous PA assessed by IPAQ and Xiaomi Mi Band 2®. The mean difference (systematic error) and 95% limits of agreement (mean ± 1.96 SD), proportional bias line, and proportional bias line confidence intervals are displayed in the figure. 33 Finally, the results from the regression analysis of the differences versus mean values confirms a significant systematic bias in the methods agreement (p < 0.000). Association Between PA Estimates and Health Outcomes The association between health outcomes variables with Xiaomi Mi Band 2®–measured and selfreported PA are presented in Table 7. IPAQ estimates show a moderate positive correlation with muscle mass and FFM, and a weak positive correlation with weight, WHR, and speed. Similarly, YPAS estimates also show a moderate positive correlation with muscle mass and FFM, and a weak positive correlation with weight. Concerning Xiaomi Mi Band 2®–measured PA, results reveal a weak positive correlation between a Xiaomi Mi Band 2®–measured PA and GDS, gait, and speed. Table 7. Associations between Xiaomi Mi Band 2®–measured PA and the mean difference in minutes per week between Xiaomi Mi Band 2® and self-reported PA (IPAQ and YPAS) with health outcomes. Weight WHR BMI FFM Muscle Education GDS Gait Speed International Physical Activity Questionnaire r −0.30 −0.24 −0.037 −0.39 −0.40 −0.092 −0.084 0.13 −0.22 p -value 0.001 0.014 0.07 <0.000 <.000 0.339 0.382 0.182 0.020 Yale Physical Activity Survey r −0.30 −0.20 −0.007 −0.48 −0.48 −0.26 0.040 0.039 −0.19 p -value 0.001 0.037 0.94 <0.000 <0.000 0.006 0.68 0.68 0.050 Xiaomi Mi Band 2® r −0.13 −0.071 −0.14 −0.010 −0.008 −0.076 −0.23 0.22 0.28 p -value 0.19 0.47 0.14 0.92 0.94 0.43 0.017 0.022 0.003 Abbreviations: WHR, waist-to-hip ratio; BMI, Body mass index; FFM, Free Fat Mass; GDS, Geriatric Depression Scale. 34 Physical Activity Profile Forty-eight percent of the participants (25.5% males and 22.7% females) were found to meet the guidelines of a minimum of 8000 steps/day (Table 8). In this sample of older adults, 45.5% had a sedentary behavior (17.3% sedentary, 28.2% low active) and 54.7% active behavior (25.5% somewhat active, 14.6% active, and 14.6% highly active). Table 8. Classification of older individuals according to the current public health PA recommendations. Characteristics Total Sample n = 110 Male n = 50 Female n = 60 Classification, n (%) Sedentary 19 (17.3) 7 (6.4) 12 (10.9) Low active 31 (28.2) 13 (11.8) 18 (16.4) Somewhat active 28 (25.5) 15 (13.6) 13 (11.8) Active 16 (14.6) 8 (7.3) 8 (7.3) Highly active 16 (14.6) 7 (6.4) 9 (8.2) PA recommendation, n (%) Meeting PA 53 (48.2) 28 (25.5) 25 (22.7) Not meeting PA 57 (51.8) 22 (20.0) 35 (31.8) Cluster Analysis of Physical Activity and Sedentary Behavior The analysis revealed three distinct clusters with a silhouette measure of cohesion and separation of 0.8. Kruskal–Wallis H test confirmed that individual clusters differed significantly from each other. The different clusters showed the following consistent rank order of PA levels: C1 > C2 > C3. Cluster 1 displayed higher moderate PA levels, lower sedentary behavior, and meeting PA recommendations. In cluster 2, participants also met PA recommendations but had less moderate PA levels, and higher sedentary behavior, compared to cluster 1. On other hand, cluster 3 displayed low moderate PA levels, higher sedentary behavior, and participants did not meet the PA recommendations (Figure 4). 41 comparison between the two methods provides valuable information about habitual PA levels in senior populations. 6. Acknowledgments The authors acknowledge the participation of Mariana Pinote Moreira and Rita Viera in psychological assessment. Finally, we would also acknowledge the participants for their voluntary contribution to the study. 7. Author Contributions Conceptualization, C.D., N.C.S., and J.M.P.; methodology, C.D.; formal analysis, C.D.; investigation, C.D.; data curation, C.D.; writing—original draft preparation, C.D.; writing—review and editing, C.D., N.C.S., and J.M.P.; supervision, N.C.S. and J.M.P.; funding acquisition, N.C.S. and J.M.P. All authors have read and agreed to the published version of the manuscript. 8. Funding This research was funded by FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the project POCI-01-0145-FEDER-007038. This article has been developed under the scope of the project NORTE-01-0145-FEDER000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). This work was also supported by the project grants MEDPERSYST (POCI-01-0145-FEDER-016428) and GIRO (RESEARCH4COVID, 1st edition, project n. 078) by FCT, and the 2CA-Braga Grant for the 2017 Clinical Research Projects. CD was supported by a combined Ph. D. scholarship from FCT and the company iCognitus4ALL-IT Solutions, Lda, Braga, Portugal (grant number PD/BDE/127831/2016). 42 9. Institutional Review Board statement The study was approved by the local ethical committees (Approval Number 42-2018), conducted in accordance with the Declaration of Helsinki (59th Amendment), developed in compliance with the European General Data Protection Regulation, and approved by the Portuguese Data Protection Authority (Approval Number 11286/2017). The study goals and assessments were explained to participants during screening procedures. 10. Informed consent Statement Informed consent was obtained from all subjects involved in the study. 11. 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DOI: 10.2196/19245. 49 European Portuguese Version of the User Satisfaction Evaluation Questionnaire (USEQ): Transcultural Adaptation and Validation Study Domingos C 1,2,3,4, Costa, Patrício 1,2,4, Santos NC 1,2,4,5*,Pêgo JM 1,2,3,4*, 1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal. 2ICVS-3Bs PT Government Associate Laboratory, Braga/Guimarães, Portugal 3iCognitus4ALL – IT Solutions, Braga, Portugal 4Clinical Academic Center – Braga (2CA-B), Braga, Portugal 5Associação Centro de Medicina Digital P5 (ACMP5) *These authors contributed equally to the work 1. Abstract Background: Wearable activity trackers have the potential to encourage users to adopt healthier lifestyles by tracking daily health information. However, usability is a critical factor in technology adoption. Older adults may be more resistant to accept novel technologies. Understanding the difficulties that older adults face when using activity trackers may be useful for implementing strategies to promote their use. Objective: The purpose of this study was to conduct a transcultural adaptation of the User Satisfaction Evaluation Questionnaire (USEQ) into European Portuguese and validate the adapted questionnaire. Additionally, we aimed to provide information about older adults’ satisfaction regarding the use of an activity tracker (Xiaomi Mi Band 2). Methods: The USEQ was translated following internationally accepted guidelines. The psychometric evaluation of the final version of the translated USEQ was assessed based on structural validity using 50 exploratory and confirmatory factor analyses. Construct validity was examined using divergent and discriminant validity analysis, and internal consistency was evaluated using Cronbach α and McDonald ω coefficients. Results: A total of 110 older adults completed the questionnaire. Confirmatory factor analysis supported the conceptual unidimensionality of the USEQ ( χ24=7.313, P=.12, comparative fit index=0.973, Tucker–Lewis index=0.931, goodness of fit index=0.977, root mean square error or approximation=0.087, standardized root mean square residual=0.038). The internal consistency showed acceptable reliability (Cronbach α=0.677; McDonald ω=0.722). Overall, 90% of the participants reported excellent satisfaction with the Xiaomi Mi Band 2. Conclusions: The findings support the use of this translated USEQ as a valid and reliable tool for measuring user satisfaction with wearable activity trackers in older adults, with psychometric properties consistent with the original version. Keywords: elderly; reliability; satisfaction; seniors; technology; usability; validity; wearables 57 Ethics Statement The study was conducted in accordance with the Declaration of Helsinki and was approved by the local and national ethics committees (approval number 42-2018). The study goals and assessments were explained to potential participants. All participants provided written informed consent before study enrollment. 4. Results Study Participants A total of 110 participants completed the USEQ questionnaire. Demographic, mood, and global cognitive characteristics of the sample are presented in Table 1. Participants had an average age of 68.41 (SD 3.11) years with a mean of 7.95 (SD 5.38) years of education. Table 1. Characteristics of the study participants (N=110). Characteristics Values Age, years, mean (SD) 68.41 (3.11) Gender, male, n (%) 50 (45.5) Education, years of formal schooling, mean (SD) 7.95 (5.38) Education, years of formal schooling, mean (SD) 26.95 (2.00) Geriatric Depression Scale, total score, mean (SD) 6.05 (4.58) Study participants responded to all items of the USEQ. Descriptive statistics for USEQ items are presented in Table 2. Given the ordinal nature of the variables assessed, item distribution demonstrates some degree of nonnormality. In fact, the majority of data collected in behavioral research does not follow univariate normal distributions (Curran, West et al. 1996, Norman 2010). For the USEQ total score, the results reveal acceptable values for both skewness (sk=–2.02) and kurtosis (k=3.96), 58 showing no severe violation of normality. Regarding the individual items, the kurtosis value was not acceptable for item 1; thus, it was excluded from further analysis. Table 2. Descriptive statistics for User Satisfaction Evaluation Questionnaire items. Items Minimum Maximum Media n Mean (S D) Skewnes s Kurtosis Item 1 1 5 5 4.80 (0.59) -3.81 17.67 Item 2 3 5 5 4.82 (0.47) -2.66 6.46 Item 3 2 5 5 4.65 (0.71) -2.21 4.50 Item 4 2 5 5 4.47 (0.75) -1.30 0.98 Item 5 1 5 5 4.65 (0.93) -2.71 6.15 Item 6 1 5 5 4.70 (0.69) 2.68 8.30 Structural validity An exploratory factor analysis was conducted on the 5 items of the USEQ. The Kaiser-Meyer-Olkin measure demonstrated adequacy for the analysis (KMO=0.629) as a value above 0.6 indicates an adequate sample size (Howard 2016). The Bartlett sphericity test (χ210=126, P<.001) indicated that the correlation between the items is sufficient to perform the analysis. The analysis showed that the one-factor solution explains 47% of the variance (Table 3) and comprises all items with factor loadings higher than 0.3. Our findings corroborated the decision of the original questionnaire authors, who considered a one-factor solution to be appropriate. 59 Table 3. Factor matrix containing obliquely unrotated factor loadings of principal axis factoring (forcing one-factor solution). The eigenvalue and the percentage of variance explained by the factor are also shown. Items Factor 1 Item 2 0.568 Item 3 0.870 Item 4 0.680 Item 5 0.403 Item 6 0.346 Eigenvalues 2.345 % of Variance 46.9 A one-factor solution CFA model was tested for the interference dimension, as hypothesized by the original authors (Gil-Gómez, Manzano-Hernández et al. 2017). All 5 items were loaded onto a single latent variable. Table 4 shows goodness-of-fit measures for the model, revealing acceptable measures for the following indices: χ24=7.313, P=.12; CFI=0.973, TLI=0.931, GFI=0.977, RMSEA=0.087, and SRMR=0.038 (Schneider 2012). The final model is presented in Figure 1. 60 Table 4. Fit indices for confirmatory factor analysis model. Indices Model Chi-square value (df) 7.313 (4) Chi-square value to df ratio 1.83 P value 0.120 CFI 0.973 Tucker–Lewis Index 0.931 Goodness of fit index 0.977 Root mean squared error of approximation 0.087 Standardized root mean squared residual 0.038 Figure 1. Path diagram and standardized estimates for the one-factor model of the User Satisfaction Evaluation Questionnaire. 61 Internal consistency Analysis of the internal consistency showed acceptable reliability (Cronbach α=0.677; McDonald ω =0.722), indicating a reliability homogeneity of the items for the one-factor solution. The corrected itemtotal correlation values ranged from 0.080 to0.654, showing an adequate correlation of each item and suggesting adequate scale homogeneity. The inter-item correlations were all below 0.70, indicating nonredundancy of items (Table 5). Table 5. Inter-Item Correlation Matrix. Item 2 Item 3 Item 4 Item 5 Item 3 0.495 —a — — Item 4 0.270 0.654 — — Item 5 0.251 0.317 0.327 — Item 6 0.397 0.219 0.207 0.080 aNot applicable. Convergent and divergent validity The association between the USEQ and SUS was moderate in magnitude (r=0.43, P<.001), and strong between TAM 3 factor “perceived ease of use” (r=0.690, P<.001) and TAM 3 factor “perceptions of external control” (r=0.571, P<.001). No significant correlation was observed between USEQ and MMSE (r=0.042, P=.661). USEQ scores To rate the USEQ score, the following classification was used: poor (0-5), fair (5-10), good (10-15), very good (15-20), or excellent (20-25) [62]. 62 The mean USEQ score was 23.30 (SD 2.40), indicating an excellent level of satisfaction with the activity tracker (Xiaomi Mi Band 2; Table 6). Table 6. Scores obtained on the User Satisfaction Evaluation Questionnaire for the original scale with 6 items and the newly proposed scale with 5 items. Items Minimum Maximum M (SD) Skewness Kurtosis USEQ (5 Items) 14 25 23.30 (2.40) -1.81 2.99 USEQ (origin al) 17 30 28.07 (2.84) -2.01 3.96 All participants reported user satisfaction experiences above “good,” with 90% of all participants reporting excellent satisfaction with the device. Furthermore, a significant correlation (r=–0.319, P=.001) between depressive mood and user satisfaction was noted; a higher score on the GDS (i.e., higher depressive mood) was negatively associated with satisfaction with the device (Table 7). Table 7. Spearman bivariate correlations between User Satisfaction Evaluation Questionnaire scores and demographic, mood, and global cognitive characteristics. Gender Age Years of educatio n Mini-Mental State Examination Geriatric Depression Scale rs –0.005 – 0.120 0.124 0.042 –0.319a p .96 .21 .20 .66 .001 aSignificant correlation. 63 5. Discussion Physical activity is associated with health benefits, a decreased burden of disease, and a decrease in allcause mortality in adults (Steinert, Haesner et al. 2018). Nowadays, wearable activity trackers provide the opportunity to increase physical activity levels through continuous monitoring (Kononova, Li et al. 2019), which may be especially beneficial for older adults. However, over 75% of the over-65 age group state that they require assistance to use new technologies (Steinert, Haesner et al. 2018). Usability studies on wearable activity trackers are needed to better understand the barriers that older adults face when using these technologies. In a cohort of older adults, this study aimed to provide a valid questionnaire to evaluate user satisfaction using an activity tracker (Xiaomi Mi Band 2). The results from the translation phase show that the items were easy to understand and that there were no semantic problems. Moreover, the translated items were considered equivalent to the original version. In particular, the USEQ was found to be a simple and easy-to-understand questionnaire with an appropriate number of questions to apply in older populations. Validity evidence was obtained with 5 questions, maintaining the original one-factor structure. Similar to the original study by Gil-Gómez et al [23], reporting that the one-factor solution explained about 43% of the variance, here an approximate 47% of the variance was explained by the one-factor solution. Confirmatory factor analysis showed acceptable fit indexes ( χ24=7.313, P=.12, CFI=0.973, TLI=0.931, GFI=0.977, RMSEA=0.087, and SRMR=0.038). However, it is recommended to evaluate structural validity in another independent study. The reliability results show that the European Portuguese version of the USEQ yielded an adequate internal consistency (Cronbach α=0.677; McDonald ω=0.722) in a sample of older individuals. Overall, results indicate that the psychometric properties of the European Portuguese version of the USEQ are comparable with those of the original version, and therefore may be used for the evaluation of satisfaction concerning other technologies, including wearable activity trackers. Convergent validity is one of the fundamental aspects of construct validity and it refers to how closely the new questionnaire is related to other variables and other measures of the same construct. Regarding convergent validity, as expected, the USEQ correlated with the SUS (r=0.43, P<.001). The SUS is a widely used standardized questionnaire for the assessment of the perceived usability of technology. Thus, a moderate positive correlation was expected, given that the two questionnaires are meant to measure similar constructs. TAM 3 has been one of the most influential models regarding technology acceptance. It distinguishes two concepts influencing an individual’s intention to use new 64 technology: perceived ease of use and perceived usefulness. Therefore, we also expected a correlation between some constructs of the TAM 3 and the USEQ. Indeed, results showed a positive moderate correlation of the USEQ with the TAM 3 factors “perceived ease of use” (r=0.690, P<.001) and “perceptions of external control” (r=0.571, P<.001). No demographic variables were found to be significantly correlated to user satisfaction with the device. However, a higher depressive mood, as evaluated by the GDS, was negatively associated with the satisfaction perceived by participants using the device. Similarly, a recent study investigating the impact of depressive symptoms on measures of web user experience found a significant association between depressive symptoms and subjective user experience (Thielsch and Thielsch 2018). These results indicate that mood may be a factor influencing technology usability and may warrant further guidance and/or targeted approaches in the use of these technologies by specific populations. Regarding user satisfaction with the Xiaomi Mi Band 2, the device achieved a score of 23.30 (SD 2.40) in the USEQ, demonstrating an excellent reported level of satisfaction and thus suggesting suitability for older adults. Furthermore, results showed that item 4—“Is the information provided by the system clear?”—which is related to the perceived ease of use, yielded the lowest score. This result may suggest that older adults could have difficulties in understanding the information provided by the activity tracker, which should be noted by manufacturers. The perceived ease of use refers to the degree to which a person perceives how easy it is to use the technology and is one of the primary factors that affect an individual’s intention to use new technology (Davis 1989, Preusse, Mitzner et al. 2017, Rupp, Michaelis et al. 2018). This kind of difficulty is especially interesting considering the age of the participants enrolled in the study. Older adults tend to perceive technologies as difficult to use due to usability problems related to poor memory, decreased vision, and poor literacy (Li and Luximon 2018), but this may not necessarily be the case for all older individuals. Thus, results should be interpreted with caution and future studies should include cohorts with different characteristics (for instance, higher school levels or those that have been [early] adopters of different types of technologies). Concerning limitations, the study was conducted using a convenience sample; therefore, the participants may not represent the entire older population. Moreover, if the sample used in this study is more homogenous than the wider population on the common factors, this can lead to attenuation in correlations and can influence the strength or bias of correlations among variables Fabrigar, Wegener et al. 1999). Future studies should have a larger sample, and it would be beneficial to the study to maximize variance on measured variables relevant to the constructs of interest Fabrigar, Wegener et al. 1999). It would also be of value to evaluate the long-term use of this device and motivations for long- 65 term use. Moreover, studies combining quantitative and qualitative methods, such as interviews, would also be valuable to explore older adults’ perceptions and experiences, and to provide details about user behaviors, user needs, and specific problems that quantitative measures cannot address. Finally, further user satisfaction studies of older adults using activity trackers should include other devices. This would ensure that such devices can be effectively implemented in clinical and research settings to promote physical activity. In conclusion, the European Portuguese version of the USEQ has adequate psychometric properties consistent with the original version, supporting its use as a valid and reliable tool for measuring user satisfaction in older adults. Furthermore, we adapted USEQ to a generic questionnaire for user satisfaction that can be used with several mHealth technologies, including smartphones, patient monitoring devices, tablets, mobile health apps, personal digital assistants, and other wireless devices. Finally, this study has contributed to the currently available and growing body of information on the usability of wearable technologies among older adults. 66 6. Acknowledgments Financial support was provided by the European Regional Development Fund (FEDER) through the Operational Programme Competitiveness Factors – COMPETE and National Funds through FCT under the projects POCI-01-0145-FEDER-007038, UIDB/50026/2020, and UIDP/50026/2020, by the projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-000023 (supported by the North Portugal Regional Operational Programme [NORTE 2020], under the Portugal 2020 [P2020] Partnership Agreement, through FEDER), by the project MEDPERSYST (POCI-01-0145-FEDER-016428; supported by the Operational Programme Competitiveness and Internationalization [COMPETE 2020] and the Regional Operational Program of Lisbon and National Funding through the Portuguese Foundation for Science and Technology [FCT, Portugal]), and by the Portuguese North Regional Operational Programme (ON.2 – O Novo Norte, under the National Strategic Reference Framework [QREN], through FEDER). The work was also developed under the scope of the 2CA-Braga Grant of the 2017 Clinical Research Projects. CD was supported by a combined PhD scholarship from FCT and the company iCognitus4ALL – IT Solutions, Lda (grant number PD/BDE/127831/2016). The authors acknowledge the participation of Mariana Pinote Moreira and Rita Vieira in the psychological assessments. Finally, we would also like to acknowledge the participants for their contribution to the study. 7. Authors' Contributions CD contributed to conceptualization, data curation, formal analysis, investigation, methodology, and writing (original draft, review, and editing). PSC contributed to formal analysis, methodology, and writing (review and editing). NCS contributed to funding acquisition, supervision, and writing (review and editing). JMP contributed to funding acquisition, supervision, and writing (review and editing). All authors reviewed and approved the final version of the manuscript. 8. Conflicts of Interest None declared. 73 Steinert, A., Haesner, M., & Steinhagen-Thiessen, E. (2018). Activity-tracking devices for older adults: comparison and preferences. Universal Access in the Information Society, 17(2), 411-419. doi:10.1007/s10209-017-0539-7 Thielsch, M. T., & Thielsch, C. (2018). Depressive symptoms and web user experience. PeerJ, 6, e4439. doi:10.7717/peerj.4439 Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best Alternatives to Cronbach's Alpha Reliability in Realistic Conditions: Congeneric and Asymmetrical Measurements. 7(769). doi:10.3389/fpsyg.2016.00769 Turner-McGrievy, G., Jake-Schoffman, D. E., Singletary, C., Wright, M., Crimarco, A., Wirth, M. D., . . . McGrievy, M. J. (2018). Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies. Health Promotion Practice, 20(3), 381-389. doi:10.1177/1524839918769559 Venkatesh, V., & Bala, H. J. D. s. (2008). Technology acceptance model 3 and a research agenda on interventions. 39(2), 273-315. When is a correlation matrix appropriate for factor analysis? Some decision rules, 81, American Psychological Association 358-361 (1974). World Health Organization. (2007). Process of translation and adaptation of instruments. URL: https://www.who.int/substance_abuse/research_tools/translation/en/ [accessed 2019-1216] Wullems, J.A., et al., (2016). A review of the assessment and prevalence of sedentarism in older adults, its physiology/health impact and non-exercise mobility counter-measures, Biogerontology, 17(3): p. 547-565. Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. J. J. o. p. r. (1982). Development and validation of a geriatric depression screening scale: a preliminary report. 17(1), 37-49. Zhou, L., Bao, J., Setiawan, I. M. A., Saptono, A., & Parmanto, B. (2019). The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study. JMIR Mhealth Uhealth, 7(4), e11500. doi:10.2196/11500 74 11. Supplementary material Supplementary Table 1. The translation process of the User Satisfaction Evaluation Questionnaire. English version Forward translation Back translation Did you enjoy your experience with the system? Foi agradável usar esta tecnologia? Was it enjoyable to use this technology? Were you successful using the system? Conseguiu usar com sucesso esta tecnologia? Were you able to successfully use this technology? Were you able to control the system? Foi capaz de controlar esta tecnologia? Were you able to control this technology? Is the information provided by the system clear? A informação fornecida por esta tecnologia foi clara? Was the information provided by this technology clear? Did you feel discomfort during your experience with the system? Sentiu-se desconfortável durante o uso desta tecnologia? Did you feel uncomfortable while using this technology? Do you think that this system will be helpful for your rehabilitation? Considera que esta tecnologia será útil na sua reabilitação? Do you think this technology will be useful in your rehabilitation? 75 Supplementary Table 2. Preliminary version tested in a pilot study with 20 participants and final European Portuguese version obtained after the pilot study. Preliminary version Final version Foi agradável usar esta tecnologia? Gostou de usar esta tecnologia? Conseguiu usar com sucesso esta tecnologia? Foi bem-sucedido a usar esta tecnologia? Foi capaz de controlar esta tecnologia? Foi capaz de controlar esta tecnologia? A informação fornecida por esta tecnologia foi clara? A informação fornecida por esta tecnologia foi clara? Sentiu-se desconfortável durante o uso desta tecnologia? Sentiu-se desconfortável durante o uso desta tecnologia? Considera que esta tecnologia será útil na sua reabilitação? Considera que esta tecnologia será útil na melhoria da sua saúde? 76 Supplementary Table 3. Original item vs. corresponding item in English and European Portuguese versions. Espanhol – Versão Original English version European Portuguese final version Te has divertido com el sistema? Did you enjoy your experience with the system? Gostou de usar esta tecnologia? Superaste com êxito lo planteado por el sistema? Were you successful using the system? Foi bem-sucedido a usar esta tecnologia? Has sentido que tenias el control de la situación com el sistema? Were you able to control the system? Foi capaz de controlar esta tecnologia? Te ha parecido clara la información que te ha dado el sistema? Is the information provided by the system clear? A informação fornecida por esta tecnologia foi clara? Te has sentido incomodo em algun momento durante el ejercicio? Did you feel discomfort during your experience with the system? Sentiu-se desconfortável durante o uso desta tecnologia? Crees que este tratamento resultará útil para tu rehabilitacion? Do you think that this system will be helpful for your rehabilitation? Considera que esta tecnologia será útil na melhoria da sua saúde? 77 CHAPTER III Usability, acceptability, and satisfaction of a wearable activity tracker in adults: an observational study in a real-life context. Domingos C, Costa, PS, Santos NC, Pêgo JM Journal of Medical Internet Research 10.2196/26652 (Preprint) 78 Usability, acceptability, and satisfaction of a wearable activity tracker in adults: an observational study in a real-life context. Domingos C 1,2,3,4, Costa, Patrício 1,2,4, Santos NC 1,2,4,5*, Pêgo JM 1,2,3,4*, 1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; 2ICVS-3Bs PT Government Associate Laboratory, Braga/Guimarães, Portugal 3iCognitus4ALL – IT Solutions, Braga, Portugal 4Clinical Academic Center – Braga (2CA-B), Braga, Portugal 5Associação Centro de Medicina Digital P5 (ACMP5) *These authors contributed equally to the work 1. Abstract Background: The use of activity trackers has significantly increased over the last few years. This technology has the potential to improve the levels of physical activity and health-related behaviors in older adults. However, despite the potential benefits, the rate of adoption is low among older adults. Therefore, understanding how technology is perceived may potentially offer insight to promote its use. Objective: This study aimed to (a) assess acceptability, usability, and user satisfaction with Xiaomi Mi Band 2® in Portuguese community-dwelling older adults in a real-world context; (b) explore the mediating effect of the usability on the relationship between user characteristics and satisfaction, and (c) examine the moderating effect of user characteristics on the relationship between usability and user satisfaction. Methods: Older adults used the Xiaomi Mi Band 2® over 15 days. The user experience was evaluated through the Technology Acceptance Model (TAM 3), System Usability Scale (SUS), and User Satisfaction Evaluation Questionnaire (USEQ). An integrated framework for usability and user 79 satisfaction was used to explore user experience. Statistical data analysis included descriptive data analysis, reliability analysis, confirmatory factor analysis, and mediation and moderation analysis. Results: A sample of 110 older adults with an average of 68.41 years old (SD = 3.11) completed the user experience questionnaires. User acceptance was very high [ PEOU= 6.45 (SD ± 0.78), PEC = 6.74 (SD ± 0.55), CANX= 6.85 (SD ± 0.47), 6.60 (SD ± 0.97)]. The usability was excellent with an average score of 92.70 (SD ± 10.73), and user satisfaction was classified as a good experience 23.30 (SD ± 2.40). The mediation analysis confirmed the direct positive effect of usability on satisfaction (β = 0.530; p < 0.01) and direct negative effect of depression on usability (β = -0.369; p < 0.01). Lastly, the indirect effect of usability on user satisfaction was higher in individuals with lower GDS levels. Conclusion: Findings demonstrate that the Xiaomi Mi Band 2® is suitable for older adults. Furthermore, the results confirmed usability as a determinant of older satisfaction with the technology and extended the existing knowledge in wearable activity trackers in older adults. Keywords: aging; fitness trackers; health monitoring; seniors; Technology Acceptance Model; user experience. . 80 2. Introduction Wearable devices are electronic devices that allow users to automatically track and monitor their physical fitness metrics, including the number of steps, level of activity, walking distance, calories burned, heart rate, and sleep patterns (Shih, Han et al. 2015, Rupp, Michaelis et al. 2018, Steinert, Haesner et al. 2018, Shin, Jarrahi et al. 2019). Over the last few years, these devices have also become increasingly popular among researchers interested in assessing and intervening on physical activity (PA)-related behaviors in real-world contexts. Wearable devices offer the opportunity to collect objective PA data in a less intrusive and inexpensive manner and provide tailored and personalized interventions in real-time (Rupp, Michaelis et al. 2018, Turner-McGrievy, Jake-Schoffman et al. 2018, Alley, van Uffelen et al. 2019). In fact, overall, academic and industry research has shown that their use can increase PA levels and promote a healthier lifestyle through real-time self-monitoring of healthrelated behaviors (Maher, Ryan et al. 2017, Seifert, Schlomann et al. 2017, Liang, Xian et al. 2018, Rupp, Michaelis et al. 2018, Turner-McGrievy, Jake-Schoffman et al. 2018, Kononova, Li et al. 2019). However, despite these potential benefits, older adults still show slow technology adoption rates (Berkowsky, Sharit et al. 2018, Kononova, Li et al. 2019), possibly because these technologies are mainly developed for a younger target group, without considering health psychology or gerontology theories (Seifert, Schlomann et al. 2017). Consequently, older adult users may have usability barriers to technology adoption (Preusse, Mitzner et al. 2017, Steinert, Haesner et al. 2018). Furthermore, factors associated with normal aging, such as physical and cognitive decline, could limit the ability to use the technology (Berkowsky, Sharit et al. 2018). A better understanding of older adults’ intentions to use activity trackers, and examining actual usage behavior, is becoming increasingly relevant; however, only a few studies have been conducted to determine older adults’ perceptions (Mercer, Giangregorio et al. 2016, Seifert, Schlomann et al. 2017, Kononova, Li et al. 2019, Schlomann, Seifert et al. 2019). Therefore, the present study aimed to understand the user experience and acceptability of an activity tracker (Xiaomi Mi Band 2®), throughout daily life activities, in a cohort of community-dwelling older adults. Theoretical framework After carrying out a literature search, three major key concepts were identified regarding user experience and technology adoption: technology acceptance, usability, and user satisfaction. Variables were also selected regarding user characteristics, such as cognitive function, mood, and education, 81 which may significantly influence user experience to develop our model. Thus, the theoretical framework was designed to explore older adults’ user experience with the Xiaomi Mi Band 2®, by combining different theories as next described, while also enabling the examination of the impact of usability and individual characteristics on user satisfaction with technology. Technology acceptance model Technology acceptance is an important factor in determining the long-term adoption of activity trackers (Rupp, Michaelis et al. 2018). The Technology Acceptance Model (TAM) is the most applied theoretical model for evaluating or predicting users’ acceptance of new technologies. TAM was adapted from the Theory of Reasoned Action (TRA) (LaCaille 2013) and was initially developed by Davis (1989) (Davis 1989). This model assumes that the perceived ease of use (PEOU) and perceived usefulness (PU) are the primary factors influencing an individual’s intention to use new technology (Davis 1989, Preusse, Mitzner et al. 2017, Rupp, Michaelis et al. 2018). PEOU refers to the degree to which a person perceives how easy it is to use the technology, and PU refers to how using the technology will improve performance (Davis 1989). Moreover, PEOU and PU can be influenced by various external factors, including both the device and user characteristics (Davis 1989, Rupp, Michaelis et al. 2018, Revythi and Tselios 2019). The usability seems to be predictive of acceptance regarding the device characteristics because they directly relate to the PEOU and PU and may moderate attitudes and behavioral intentions (BI) to use a system (Rupp, Michaelis et al. 2018). The original TAM model was extended to TAM 2 by Venkatesh and Davis (2000) to explain PU and usage intentions in terms of social influence and cognitive instrumental determinants (Venkatesh and Davis 2000). Later, Venkatesh and Bala (2008) updated the model, including other variables affecting PEOU, such as individual differences (computer self-efficacy, computer anxiety, and computer playfulness), perception of external control, and system characteristics-related adjustments (perceived enjoyment, and objective usability) (Venkatesh and Bala 2008). System Usability Scale Initially proposed by John Brooke in 1986, the System Usability Scale (SUS) is the most widely used standardized questionnaire to measure perceived usability (Brooke 1996, Lewis 2018, Liang, Xian et al. 2018, Revythi and Tselios 2019). Recent literature shows that several studies extend the TAM by 82 incorporating the SUS (Scholtz, Mahmud et al. 2016, Pande, Saravu et al. 2017, Revythi and Tselios 2019). Although the SUS has been assumed to be unidimensional, recent research reveals that the SUS has two sub-scales – usability and learnability–, with items 4 and 10 providing the learnability dimension and the other 8 items the usability dimension (Lewis and Sauro 2009, Martins, Rosa et al. 2015). According to ISO-9241-11, usability refers to the effectiveness, efficiency, and user satisfaction rating of a product in a specific environment by a particular user for a particular purpose. More precisely, effectiveness refers to which of the system's intended goals can be achieved; efficiency is the effort required for a user to achieve the goals; and satisfaction depends on how comfortable the user feels using the system (Brooke 1996, Bevan, Carter et al. 2016, Liang, Xian et al. 2018). Therefore, usability is a critical factor that directly affects the use and adoption of technology by older adults. User Satisfaction Evaluation Questionnaire The literature on technology acceptance has included many model variants and extensions, including user satisfaction as a key indicator of user acceptance (Mather, Caputi et al. 2002, Dalcher and Shine 2003, Wixom and Todd 2005, Ghobakhloo, Zulkifli et al. 2010, He, Kim et al. 2017, Chao 2019, Ho, Ho et al. 2019). Moreover, satisfaction has been described as a predictor of behavior intention (Chao 2019). The User Satisfaction Evaluation Questionnaire (USEQ) was initially designed by Gil-Gómez et al. (2017) to evaluate the satisfaction of the users with virtual rehabilitation systems (Gil-Gómez, ManzanoHernández et al. 2017). Recently, the USEQ was adapted and validated into European Portuguese by Domingos et al. to evaluate an activity tracker (Xiaomi Mi Band 2®) in older adults, showing psychometric properties consistent with the original version (Domingos C 2020). User characteristics In a theoretical framework developed by Venkatesh (2008), individual differences, such as personality and demographics (e.g., traits or individuals’ states, gender, and age), were suggested to influence individuals’ perceptions of PU and PEOU (Venkatesh and Bala 2008). Specifically, personality is related to individual differences in cognitive, emotional, and motivational aspects of mental states that result in stable behavioral action (Montag and Panksepp 2017). Moreover, personality has been found to affect technology perceptions and acceptance (Svendsen, Johnsen et al. 2013, Rupp, Michaelis et al. 2018). 89 Structural equation modeling (SEM) SEM was applied to check the hypothesis relationship between the proposed factors that directly and indirectly influence older adult’s user satisfaction (structural model) with technology. SEM allows to analyze the structural relationship between measured variables and latent variables. The derived scores for usability and user satisfaction were supported by CFA. Data were analyzed using IBM SPSS AMOS (version 25) and the parameters were estimated by the maximum likelihood method. The significance level of 5% was used as a threshold for the research proposition testing. To determine whether the model was reasonable and acceptable the following indices were considered: χ2, χ2/degrees of freedom ratio CFI, TLI, GFI, and RMSEA. The criteria for an acceptable model fit were the same as those reported for the CFA. To assess multicollinearity, the inspection of the correlation matrix of the predictor variables (education, MMSE, GDS, and usability), and the analysis of the variance inflation factor (VIF) and tolerance, was performed (IBM SPSS Statistics (version 26)). The tolerance values close to 1 were considered as an indicator of low multicollinearity, whereas a value close to zero potential indicator of collinearity problem (Senaviratna, Cooray et al. 2019, Shrestha 2020). Moreover, the VIF =1 was considered an indicator that the independent variables are not correlated, and 1< VIF < 5 an indicator that the variables are moderately correlated to each other (Shrestha 2020). Moderation analysis Moderation analysis was performed to examine whether the relationship between usability (predictor) and user satisfaction (outcome variable) depended on user characteristics (moderator). The analysis was performed using the MedMod package in Jamovi (version 1.2.27) software. The significance of the interaction term of usability on user satisfaction at specific values (-1 SD, mean, +1 SD) of GDS, education, and MMSE (moderators), was assessed, exploring when the effect of usability in user satisfaction depends on the level of the moderating test variable. 90 4. Results Study Participants A total of 110 participants completed the final assessment after the testing period. Table 1 summarizes the demographic, mood, and global cognitive characteristics of the sample. Participants have a mean age of 68.41 (SD±3.11) years old, and 46% were identified as males. The mean years of formal education were 7.95 (SD± 5.38). Table 1. Characteristics of the study participants (N=110). Characteristics n Percentage (%) M ± SD Gender, n (%) males 50 46 Age, years 68.41 ± 3.11 [64, 70[ 73 66 ≥ 70 37 34 Education, years 7.95 ± 5.38 [1, 4] 58 53 [5, 11] 24 22 ≥12 28 26 MMSE, total score 26.95 ± 2.00 [22, 27[ 41 63 ≥ 27 69 37 GDS, total score 6.05 ± 4.58 > 11 17 16 91 Abbreviations: MMSE, Mini Mental State Examination; GDS, Geriatric Depression Scale; BMI, Body mass index; WC, waist circumference; HP, hip circumference; WHR, waist-to-hip ratio; FAT, Fat Mass; FFM, Free Fat Mass; Muscle, Muscle mass; Mineral, Mineral body density; BCM, Body Cell Mass; ADL, Activities of Daily Living; IADL, Instrumental Activities of Daily Living. Instruments descriptive statistics The results of descriptive statistics for the instruments (TAM 3, SUS, and USEQ) are presented in Table 2, Table 3, and Table 4, respectively. The skewness and kurtosis values indicate some degree of nonnormality. In reality, most behavioral research data does not follow univariate normal distributions (Curran, West et al. 1996, Norman 2010). Moreover, the results reveal a severe violation of normality for the following items and constructs: USEQ1, SUS1, SUS3, SUS5, SUS9, PEOU3, and perceptions of external control (PEC) and computer anxiety (CANX). Thus, these were excluded for further path analysis. Table 2. Descriptive statistics for TAM 3 items. Items Minimum Maximum M SD Skewness Kurtosis Perceived Ease of Use PEOU 1 2 7 6.28 1.08 -1.62 2.25 PEOU 2 1 7 6.06 2.01 -1.97 2.22 PEOU 3 3 7 6.84 0.60 -4.37 20.92 PEOU 4 3 7 6.60 0.92 -2.42 5.11 PEOU score 3.50 7.00 6.45 0.78 -1.48 1.54 PEOU final 3.67 7.00 6.31 0.94 -1.25 0.24 Perceptions of External control 92 PEC1 3 7 6.55 0.97 -2.38 5.19 PEC2 4 7 6.94 0.41 -6.84 46.91 PEC score 4.00 7.00 6.74 0.55 -2.62 7.34 Computer Anxiety CANX1 6 7 6.99 0.10 -10.49 110.00 CANX2 1 7 6.86 0.83 -6.72 45.64 CANX3 2 7 6.71 0.97 -3.49 11.44 CANX score 4.33 7.00 6.85 0.47 -3.55 12.72 Behavioral intention BI 1 7 6.60 0.97 -3.00 10.84 USE (hours) 13 24 23.85 1.12 -8.99 85.16 Abbreviations: M, mean; SD, standard deviation. Analyses comparing the concordance of absolute values between the IPAQ and Xiaomi Mi Band 2® showed the older adults reported less sedentary time and less moderate-intensity time compared with the accelerometer data (Table 3). Table 3. Descriptive statistics for SUS items. Items Minimum Maximu m M SD Skewness Kurtosis Item 1 1 5 4.71 0.65 -2.82 9.87 Item 2 1 5 1.44 1.03 2.40 4.70 Item 3 2 5 4.92 0.36 -5.81 40.38 93 Item 4 1 5 1.38 1.04 2.51 4.75 Item 5 1 5 4.85 0.56 -4.47 23.01 Item 6 1 5 1.28 0.83 3.12 9.26 Item 7 1 5 3.58 0.78 -2.15 4.88 Item 8 1 5 1.30 0.92 3.06 8.16 Item 9 3 5 4.86 0.46 -3.42 10.71 Item 10 1 5 1.41 1.08 2.52 4.95 SUS Score 55 100 92.70 10.73 -1.61 1.77 Abbreviations: M, mean; SD, standard deviation. Table 4. Descriptive statistics for USEQ items. Items Minimu m Maximum M SD Skewnes s Kurtosis Item 2 3 5 4.82 0.47 -2.66 6.46 Item 3 2 5 4.65 0.71 -2.21 4.50 Item 4 2 5 4.47 0.75 -1.30 0.98 Item 5 1 5 4.65 0.93 -2.71 6.15 Item 6 1 5 1.28 0.83 3.12 9.26 USEQ Score 14 25 23.30 2.40 -1.81 2.99 Abbreviations: M, mean; SD, standard deviation. 94 Instruments psychometric proprieties As reported by Domingos et al. (2020) the CFA supported the conceptual unidimensionality of the USEQ [(χ2 (4) = 1.83, p = 0.120, χ2/df =1.83; CFI = 0.973, TLI = 0.931, GFI = 0.977, RMSEA= 0.087, SRMR = 0.038)] (Domingos C 2020). Furthermore, the CFA analysis for the SUS showed satisfactory values for the following indexes: CFI = 0.816; GFI= 0.928; SRMR = 0.074. The fit Indices for the model are presented in Table 5. Table 5. Confirmatory factor analysis for instruments. Fit Indices USEQ SUS χ2 7.313 30.074 Df 4 9 χ2 /Df 1.83 3.34 p 0.120 < .001 CFI 0.973 0.816 TLI 0.931 0.694 GFI 0.977 0.928 RMSEA 0.087 0.146 SRMR 0.038 0.074 Abbreviations: χ2, chi-square; df, degrees of freedom; CFI, Comparative Fit Index; TIL, Tucker–Lewis Index; GFI, Goodness of fit index; RMSEA, Root Mean Squared Error of Approximation; SRMR, Standardized Root Mean Squared Residual. Regarding internal consistency, for the SUS questionnaire's reliability was calculated only with items included in path analysis (SUS2, SUS4, SUS6, SUS7, SUS8, SUS10). Moreover, the USEQ showed acceptable reliability (Cronbach’s α = 0.677; McDonald's ω = 0.722), as reported by Domingos et al. 95 (2020) (Domingos C 2020). The McDonald's ω coefficients showed acceptable values for SUS and USEQ questionnaires ranging from 0.712 to 0.722, respectively. Users’ experience The high ratings of TAM 3 indicate excellent technology acceptance by the participants. Overall, the average ratings for user experience with Xiaomi Mi Band 2® were 6.45 (SD ± 0.78) for PEOU, 6.74 (SD ± 0.55) for PEC, 6.85 (SD ± 0.47) for CANX, and 6.60 (SD ± 0.97) for BI. Furthermore, the participants reported an average of 23.85 (SD ± 1.12) hours of use per day (Table 2). These results indicate that participants found that Xiaomi Mi Band 2® is an easy-to-use and easy-to-control device, potentially perceiving its usefulness regarding health benefits and having the intention to use it in the future. Regarding usability, the overall SUS score ranged from 55 to 100 (92.70 ± 10.73), with 96% of the participants reporting a score above the acceptability baseline of the SUS. Moreover, 46% of the participants classified the activity tracker achieved as best Imaginable (Table 6). Thus, these results suggest that the Xiaomi Mi Band 2® is a usable wearable activity tracker among older adults. Finally, all participants reported a user satisfaction experience above the USEQ baseline value defined as a good experience, with a mean USEQ score of 23.30 (SD ± 2.40) (Table 4). Moreover, 86% of the participants rated the satisfaction with the Xiaomi Mi Band 2® as excellent (Table 6). Still, despite older adults reporting good satisfaction with the device, concerns were noted regarding the clarity of the technology's information. 96 Table 6. User experience classification for usability and satisfaction. Classification n (%) Usability (SUS) Ok 8 (7.3) Good 16 (14.5) Excellent 36 (32.7) Best imaginable 50 (45.5) Satisfaction (USEQ) Good 2 (1.8) Very good 14 (12.7) Excellent 94 (85.5) Abbreviations: n, frequency; %, percentage. The structural equation modeling for user satisfaction Table 7 shows fit indexes for the structural model, showing acceptable values for the χ2/df= 1.67 and RMSEA= 0.079 indexes and values slightly less than the threshold for a good model fit for the following indexes: GFI= 0.880 TLI = 0.818, CFI= 0.868. Based on these indexes, the model has a moderate acceptable fit. 97 Table 7. Fit indices for the hypothesized model Model Fit Index χ2 110.475 Df 66 χ2 /Df 1.67 p < .000 GFI 0.880 TLI 0.818 CFI 0.868 RMSEA 0.079 Abbreviations: χ2, chi-square; df, degrees of freedom; CFI, Comparative Fit Index; TIL, Tucker–Lewis Index; GFI, Goodness of fit index; RMSEA, Root Mean Squared Error of Approximation; SRMR, Standardized Root Mean Squared Residual. 98 The path diagram of the model is presented in Figure 3. Coefficients within paths are standardized regression coefficients from regressions. Figure 3. Path diagram for the research model. Table 8 summarizes the results of hypothesis testing, including standardized coefficients and significance levels. Specifically, results show that usability was significantly and positively associated with user satisfaction (β = 0.530; p < 0.01), thereby supporting hypotheses 1. On the other hand, depression was significantly and negatively associated with usability (β = -0.369; p < 0.01), supporting hypotheses 7. Individual characteristics (education, cognition, and depression) collectively explained 16.8% of usability variance. Furthermore, individual characteristics and usability collectively explained 39.1% of the variance in satisfaction. Specifically, depression negatively impacted usability and satisfaction, with a significant effect on usability; while, regarding education, a positive, but not significant, usability and satisfaction effect was observed. Despite confirming the theoretical model, most research hypotheses were not statistically proven with adequate goodness of fit. Nonetheless, usability seems to be a strong predictor of user satisfaction. 105 SUS (92.70 ± 10.73) was surprising. In a study by Liang et al. (2018), and albeit the Xiaomi Mi Band 2® being one of the devices that achieved the highest score among several selected wearable devices with distinct market performance, its SUS score was 65.12 ± 14.73 (Liang, Xian et al. 2018). Possible explanations range from the intrinsic motivation to use the device and how the device is supplied; therefore, such aspects should be evaluated in future studies. This study also examined factors influencing user satisfaction with Xiaomi Mi Band 2®, based on the proposed theoretical framework. The hypothetical model was supported by moderate acceptable fit indices values ( χ2/Df= 1.67, GFI= 0.880 TLI = 0.818, CFI= 0.868, and RMSEA= 0.079). Furthermore, two of the testing hypotheses were proven. Overall, results indicate that usability is a significant predictor of user satisfaction (β = 0.530; p < 0.01), which, in turn, was negatively affected by depression symptoms (β = -0.369; p < 0.01). The model shows that individual characteristics explain 16.8% of the usability variance and 39.1% of the variance in satisfaction collectively with usability. Specifically, a significant negative effect of depression on usability was found. Additionally, user characteristics' potential moderating effect on the interaction between usability and user satisfaction were also examined. Results suggested that GDS moderates the usability effect on user satisfaction, and the effect is higher in individuals with lower GDS levels. However, we did not observe significant moderating effects for education and MMSE on the interaction between usability and user satisfaction, contrary to our expectations. Future research should explore additional moderating effects through the user characteristics, including personal traits, motivational and cultural aspects to enable a better understanding of the factors that may influence user satisfaction and consequently facilitate technology adoption. Overall, our results align with a recent study investigating the impact of depressive symptoms on web user experience measures, indicating that mood may be a factor influencing technology usability (Thielsch and Thielsch 2018). Additionally, recent research investigating the relationship between user perceptions and user characteristics has shown that older adults demonstrate positive attitudes toward mobile technologies and report technologies' complexity. User characteristics, such as age, processing speed, and attention, significantly influence older adults’ usage behavior. Furthermore, the education level was found to be positively correlated to the diversity of use. Probably, individuals with higher education levels are typically more motivated to accept new concepts. The authors also mentioned that the usability problems could be attributed to poor memory, decreased vision, and poor literacy, thus older adults tended to perceive the technologies as difficult to use (Li and Luximon 2018). 106 Beyond the proposed research framework of the study, we aimed to use an integrated technology acceptance model and user satisfaction, similar to other studies (Mather, Caputi et al. 2002, He, Kim et al. 2017, Chao 2019). However, due to the severe violation of normality observed in TAM 3 constructs, we cannot integrate the technology acceptance model in path analysis. Nonetheless, previous research has shown a significant influence of PEOU on user satisfaction, with the latter proposed to be a key predictor of BI (Ghobakhloo, Zulkifli et al. 2010, He, Kim et al. 2017, Ho, Ho et al. 2019). Additionally, Chao (2019) showed that perceived enjoyment, effort expectancy, and performance expectancy have a significantly positive effect on satisfaction (Chao 2019); thus, it would have been relevant to the inclusion of these variables to predict satisfaction. Regarding usability, Venkatesh (1996) theorizes that PEOU is affected by the objective usability of a specific system only after a direct experience with the system; where, perceptions about the PEOU are determined solely by usability features, which in turn form the basis for acceptance or rejection. Moreover, if the system has higher objective usability it means that system that is easy to use (Venkatesh 2000, Venkatesh and Bala 2008). Several studies suggested that usability is a determinant of PEOU (Venkatesh 2000, Venkatesh and Bala 2008, Scholtz, Mahmud et al. 2016). Regarding study limitations, our sample is not representative of the entire older population since we used a convenience sample. Therefore, findings cannot be widely generalizable. Moreover, the population sample is more homogenous than the wider population on the common factors, possibly leading to attenuation in correlations and/or erroneous correlations among variables (Fabrigar, Wegener et al. 1999, Wolf, Harrington et al. 2013). Although we have a minimum sample size adequate for the estimation method (>100 participants), the structural equation modeling is a large-sample technique (Kyriazos 2018). Therefore, future studies should have a larger and more heterogeneous sample to obtain sufficiently accurate estimates, even that the present study had a larger sample size compared to previous ones (Vooijs, Alpay et al. 2014, Mercer, Giangregorio et al. 2016, Farina and Lowry 2017, Puri, Kim et al. 2017). A further limitation is that the user experience was assessed for a specific wearable activity tracker (Xiaomi Mi Band 2®); therefore, it is not representative of the full range of devices currently available on the market. Moreover, the testing period was limited to 15 days. Shortterm technology acceptance may not be indicative of long-term acceptance as research indicates that activity trackers tend to drop after the first few weeks (Puri, Kim et al. 2017, Shin, Jarrahi et al. 2019), with also short-time frames making it difficult to determine the impact of the novelty effect (defined as a person’s subjective "first responses to a technology, not the patterns of usage that will persist over time as the product ceases to be new"(Sung, Christensen et al. 2009)). Moreover, research suggests that 107 the declining novelty effect could be a reason for many activity tracker users discontinuing their use. Recently, Shin et al. (2018) explored the effect of novelty in the early stages (<3 months) of activity tracker adoption, as well as the motivation factors for sustained activity tracker use for the long term (> 6 months). Findings reveal that the use beyond the novelty period is determined by intrinsic and extrinsic motivations (Shin, Feng et al. 2019). Finally, we selected the SUS for the usability evaluation since it is the most widely used questionnaire to measure perceived usability; however, this instrument does not comprise all of the concepts regarding usability. For instance, there are several different standards (e.g., ISO-9241-11, ISO/IEC 9126) and conceptual models to evaluate usability. Shackel (1991) reported on the four important characteristics of usability –namely, effectiveness, learnability, flexibility, and attitude–, and Nielsen Model (1993) gave five sub-attributes of usability –namely, learnability, efficiency, memorability, errors, and satisfaction [1, 2]. Therefore, there still a need for future studies comprising key dimensions of usability. Concluding, while there is a pressing need for studies to include other devices currently on the market and evaluate longer-term use, our study extended on the existing research providing valuable insight into the field of wearable activity trackers in older adults. First, a significant contribution of this work was to demonstrate the relevance of usability as an important factor influencing user satisfaction, which probably has an impact on technology acceptance and in the intention to use activity trackers. However, we were not able to predict BI in our structural model. Furthermore, our results emphasize the need to consider strategies to minimize the usability barriers to technology adoption in older adults. In addition, system designers should provide systems that address these concerns, and the researchers must ensure that selected systems adequately address the usability issues to be effectively implemented in clinical and research settings. Second, our study investigated the impact of user characteristics as moderating factors influencing the relationship between usability and user satisfaction and found that depression symptoms have a significant influence on older adults’ perception of using technology. However, other individual differences/personal user characteristics should be examined, and the identified moderating effects should be taken into consideration when implementing strategies trying to promote technology adoption. Finally, our results suggested that the Xiaomi Mi Band 2® is a suitable wearable activity tracker for older adults to use in a real-life context. 108 5. Acknowledgments Financial support was provided by FEDER funds through the Operational Programme Competitiveness Factors – COMPETE and National Funds through FCT under the project POCI01-0145-FEDER-007038, UIDB/50026/2020 and UIDP/50026/2020, by the project NORTE-01-0145FEDER-000013 and NORTE-01-0145-FEDER-000023 [supported by the North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 (P2020) Partnership Agreement, through the European Regional Development Fund (FEDER)], by POCI-01-0145FEDER-016428 [supported by the Operational Programme Competitiveness and Internationalization (COMPETE 2020) and the Regional Operational Program of Lisbon and National Funding through Portuguese Foundation for Science and Technology (FCT, Portugal)], by the Portuguese North Regional Operational Programme [ON.2 – O Novo Norte, under the National Strategic Reference Framework (QREN), through FEDER]. The work was also developed under the scope of the 2CA-Braga Grant of the 2017 Clinical Research Projects. CD was supported by a combined Ph.D. scholarship from FCT and the company iCognitus4ALL - IT Solutions, Lda, Braga, Portugal (grant number PD/BDE/127831/2016). 6. Authors' Contributions CD: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. PSC: Formal analysis, Methodology, Writing – review & editing. NCS: Funding acquisition, Supervision, Writing – review & editing. JMP: Funding acquisition, Supervision, Writing – review & editing. All authors reviewed and approved the final version of the manuscript. 7. Conflicts of Interest None declared. 109 8. Abbreviations CANX: computer anxiety CFA: Confirmatory factor analysis CFI: Comparative Fit Index GDS: Geriatric Depression Scale GFI: Goodness of Fit Index KMO: Kaiser-Meyer-Olkin Measure M: Mean MMSE: Mini-Mental State Examination PA: Physical activity PEC: Perceptions of external control PEOU: Perceived ease of use PU: Perceived usefulness RMSEA: Root Mean Squared Error of Approximation SD: Standard deviation SRMR: Standardized Root Mean Squared Residual SUS: System Usability Scale TAM 3: Technology Acceptance Model TLI: Tucker–Lewis Index TRA: Theory of Reasoned Action USEQ: User Satisfaction Evaluation Questionnaire 110 9. References Alley, S., van Uffelen, J. G., Schoeppe, S., Parkinson, L., Hunt, S., Power, D., ... & Vandelanotte, C. (2019). Efficacy of a computer-tailored web-based physical activity intervention using Fitbits for older adults: a randomised controlled trial protocol. BMJ open, 9(12), e033305. Bangor, A., Kortum, P., & Miller, J. (2009). 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Interact J Med Res 3(4): e14. Wixom, B. H. and P. A. Todd (2005). A Theoretical Integration of User Satisfaction and Technology 121 1. Abstract Despite the increasing evidence that physical activity (PA) contributes to brain health in older individuals, impacting on both brain structure and function, this relationship is not yet well established. To explore this association, a systematic literature search was performed using PubMed, Scopus, and Web of Science, adhering to PRISMA guidelines. A total of 32 studies met the eligibility criteria: 24 cross-sectional and 8 longitudinal. Results from structural MRI studies showed that PA was associated with larger brain volumes (less brain atrophy) specifically in brain regions vulnerable to dementia, comprising the hippocampus, temporal, and frontal regions. Furthermore, fMRI studies showed greater task-relevant activity in brain areas recruited in executive function and memory tasks. However, the dose-response relationship is unclear due to the high variability in PA measures. Further research using objective PA measures is needed to better understand which PA intensity, frequency, and duration, as well as PA type, has the greatest protective effect on brain health. Findings highlight the importance of PA in both cognitive decline and dementia prevention. Keywords Exercise; accelerometry; seniors; elderly; brain ageing; neuroimaging. 122 2. Introduction Normal aging typically leads to global brain atrophy, modifications in brain functional responses, and cognitive decline (C. J. Boraxbekk, Salami, Wahlin, & Nyberg, 2016). Nevertheless, it has high inter-individual variability and appears to be dependent, for instance, on lifestyle habits (Bittner, Jockwitz, & Muhleisen, 2019). Among these, physical activity (PA) represents a promising non-pharmacological interventional approach to maintain, delay and/or improve brain structure and function throughout life (Benedict et al., 2013; Bittner et al., 2019; Sexton et al., 2016). Moreover, it is easily accessible, safe, and potentially cost-effective (Haeger, Costa, Schulz, & Reetz, 2019; Voss et al., 2016). Structural and functional magnetic resonance imaging (MRI) have been used to assess the link between PA and brain structure and function in older adults (Halloway, Wilbur, Schoeny, & Arfanakis, 2017; Hayes, Hayes, Cadden, & Verfaellie, 2013). In structural MRI studies, higher levels have been associated with greater brain grey matter (GM) volumes (Kirk I. Erickson, Leckie, & Weinstein, 2014) in brain regions including the hippocampus (Kirk I. Erickson et al., 2014; Firth et al., 2018) and prefrontal cortex (Kirk I. Erickson et al., 2014), and preserved white matter (WM) integrity (Tian et al., 2015), reduced severity of WM lesions, and improvements in WM microstructure (Sexton et al., 2016; Tian et al., 2015). Notably, these structural changes are often associated with improved cognitive performance and reported to be severely affected in dementia Chieffi et al., 2017). Similarly, functional MRI (fMRI) research has shown that PA has a positive relationship with the functional connectivity (FC) of several cortical networks, with the strongest effect in the default mode network (DMN) (C.-J. Boraxbekk, Salami, Wåhlin, & Nyberg, 2016; Voss et al., 2016). Although fMRI is often considered the gold standard for the assessment of brain activity, other neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS), positron-emission tomography (PET), and electroencephalography (EEG), have also been used (Herold, Wiegel, Scholkmann, & Müller, 2018). For instance, acute exercise was associated with improved performance in executive tasks in fNIRS (Ji, Feng, Mei, Li, & Zhang, 2019) and improved fine motor control performance in an EEG study (Hübner, Godde, & Voelcker-Rehage, 2018). Nonetheless, most of the studies examining the effects of PA and brain health have, thus far, relied on subjective measures (Burzynska et al., 2014; Kirk I. Erickson et al., 2014), such as self-report questionnaires, whose validity in measuring the type, intensity, and duration of the activities of daily living is limited (Kirk I. Erickson et al., 2014; Tian et al., 2015). A precise measurement of PA is essential to advance our knowledge of PA effects in brain structure and 123 function. In fact, there are many different methods available to measure PA, including behavioral observations, questionnaires, diaries, direct/indirect calorimetry, and wearable sensors (Skender et al., 2016); with, for instance, the latter providing an objective and precise assessment of everyday activities including non-exercise lifestyle activities and sedentary behavior measures Burzynska et al., 2014; Kirk I. Erickson et al., 2014). In this context, recent systematic reviews have examined the relationship between PA and brain structure and function in older adults (Chen et al., 2020; Haeger et al., 2019; Sexton et al., 2016). Haeger et al. (2019) evaluated the effects of PA on brain structure and function in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients when compared to cognitively healthy individuals in randomized, non-randomized controlled, and cohort study designs. Results indicate that the effects of aerobic exercise and fitness seem to occur mainly in brain structures sensitive to neurodegeneration, including frontal, temporal, and parietal regions (Haeger et al., 2019). Chen et al. (2020) specifically examined the effects of randomized controlled exercise interventions on brain structure and function in cognitively intact older adults and explored the underlying relationships based on characteristics of exercise training (i.e., frequency, intensity, time, type, volume, and progression). The findings indicate that older adults involved in exercise training may have benefits in brain health in a dose-dependent manner (Chen et al., 2020). Still, despite a growing number of observational studies, no systematic review on the overall impact of PA on healthy brain aging has been conducted, with only a systematic review partially addressing the issue. Analyzing cross-sectional and longitudinal MRI studies, Sexton et al. (2016) explored the associations between PA and physical fitness in WM in brain aging. The authors found that higher levels of PA were associated with greater WM volumes, reduced volume or severity of WM lesions, or improved measures of WM microstructure. However, several negative findings were also found (Sexton et al., 2016). The present systematic review is, to the best of our knowledge, the first to explore observational cross-sectional and longitudinal neuroimaging MRI studies that have examined the relationship between PA and brain structure and function in older adults without cognitive or neuropathological disease. This study aims to answer the following questions: i) are PA levels associated with brain structure and function in older individuals without cognitive or neuropathological disease; ii) what structural and functional brain changes are associated with PA; and, if associated, iii) what are the PA recommendations for brain health improvement. 124 3. Methods The systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) recommendations (Liberati et al., 2009; Moher, Liberati, Tetzlaff, & Altman, 2009). The review protocol was not registered. Eligibility criteria Studies were included if met the following criteria: a. Studies with original data. Conference abstracts, reviews, and book chapters were excluded. No limits were applied to publication date; b. Cross-sectional or longitudinal observational studies. Experimental studies were excluded; c. Published in peer-reviewed and English language journals; d. Conducted in humans; e. Examined cognitively healthy older adults free from cognitive (dementia), psychiatric or neurological disorders, with population samples characterized as “older adults”, “elders” or “senior”, or age groups equal or above 60 years old. If studies comprised cohorts with disease, a “healthy control” group/sample must have been present. The same consideration was taken regarding age group, if younger adults it must also have comprised an “older” adult cohort; f. Assessed the level of PA by self-reported questionnaires or accelerometry. Due to the high variability in methods used to assess PA, studies with other methods were excluded to facilitate comparisons between results that are directly comparable; g. Assessed the brain structure and/or function as measured by MRI; h. Examined a direct association between PA and MRI measures; i. Comprised (at least) one cognitive screening/assessment test. Information sources and search The literature search was performed using Pubmed, Web of Science, and Scopus databases. The search was conducted on the 23rd of July 2019 by the author CD. The search strategy was 125 constructed based on the PICO framework (population (P), intervention (I), comparison (C), and outcome (O)). Table 1 provides a detailed description of the electronic search strategy in Pubmed. Similar terms were used for the other databases. Filters for Humans and English language were used in the Pubmed search. Table 1. Electronic search strategy. Boolean Builder Search terms Exercise OR "Physical Activity" OR "Physical Exercise" OR "Physical Training" OR "Exercise Training" OR Aerobic OR Fitness AND Brain OR "Gray Matter" OR "White Matter" OR Hippocampus OR Hippocamp* AND "Magnetic Resonance Imaging" OR MRI OR fMRI OR "Functional MRI" OR Neuroimaging AND Aged OR Older OR Old OR Elderly OR Age Study selection All studies were first screened based on title and abstract and, subsequently, the selected full-text publications were systematically reviewed. Uncertainty about eligibility assessment was resolved by discussion and consensus among all the authors. Data collection process All relevant information from eligible studies was collected using a data extraction sheet based on data items in a previous review article (Sexton et al., 2016). In case of uncertainty, the results were discussed among the authors. Duplicate publications and studies with overlapping samples were carefully analyzed to avoid double counting the same data. 126 Data items The following data were extracted from the eligible studies: (i) study design (cross-sectional or longitudinal); (ii) participant demographics (sample size, mean age, number of female participants); (iii) PA assessment (questionnaires or accelerometry); (iv) neuroimaging assessment (GM structure, WM structure, brain function, and functional connectivity); and (v) findings. Risk of bias in individual studies The risk of bias of the eligible studies was assessed through The Joanna Briggs Institute critical appraisal checklist (Joanna Briggs Institute, 2017). This checklist provides information about the methodological quality of a study regarding the possibility of bias in its design, conduct, and analysis. The following items were evaluated: Q1 “Were the criteria for inclusion in the sample clearly defined?” Q2 “Were the study subjects and the setting described in detail?” Q3 “Was the exposure measured in a valid and reliable way?” Q4 “Were objective, standard criteria used for measurement of the condition?” Q5 “Were confounding factors identified?” Q6 “Were strategies to deal with confounding factors stated?” Q7 “Were the outcomes measured in a valid and reliable way?” Q8 “Was an appropriate statistical analysis used?”. Possible responses for each question include: “Yes”,” No”, “Unclear”, and “Not Applicable”. An overall quality score was decided based on the number of answers scored as “No” and/or “Unclear”: Good: 0–1; Fair: 2– 3; Poor: 4–5; Very Poor: 6 – 8. 127 4. Results The literature search identified 1759 studies after duplicates removal. A total of 1583 studies were excluded based on title and abstract screening. Full texts of the remaining 176 studies were examined in detail accounting for the inclusion criteria. From those, 32 studies met the inclusion criteria. Details are outlined in the PRISMA flow diagram of the selection process (Figure 1). Figure 1. PRISMA Flow diagram describing the study selection process [adapted from Liberati, Altman et al. 2009, Moher, Liberati et al. 2009)]. 128 Study characteristics A total of 32 original studies met the eligibility criteria: 24 cross-sectional and 8 longitudinal studies (Figure 2). Figure 2. Summary of the selected studies included in the systematic review. The identified studies were published between 2007 and 2019. In 30 studies (94 % of the studies) the proportion of female participants was higher than males; with, a few studies having either more males than females (Corlier et al., 2018; Kharabian Masouleh et al., 2018; Lamont, Mortby, Anstey, Sachdev, & Cherbuin, 2014; Lee et al., 2016; Young, Dowell, Watt, Tabet, & Rusted, 2016) or equal distribution (Arenaza-Urquijo et al., 2017; McGregor et al., 2011; Thielen et al., 2016). Eighteen studies had a sample size inferior to 100 participants, with 6 having less than 50 participants (Kimura, Yasunaga, & Wang, 2013; McGregor et al., 2011; Siddarth et al., 2018; Thielen et al., 2016; Zlatar et al., 2015; Zlatar et al., 2013). Among those that used a self-report measure of PA (72 % of studies, n = 23), the Minnesota Leisure Time Physical Activity Questionnaire (MLTPAQ) was the most used questionnaire (26 % of studies, n = 6) followed by The International Physical Activity Questionnaire (IPAQ) (22 % of studies, n = 5). Of the included studies, only 28 % (n = 9) evaluated PA by accelerometry. In longitudinal studies, the mean follow-up time was 6.9 ± 4.6 years, with a minimum follow-up time of 12 months and a maximum of 13 years. Regarding global cognition assessment, the Mini Mental State Examination (MMSE) was used in most studies (53 % of studies, n = 17). Several 129 works used a set of neuropsychological tests, including assessments of memory, attention, language, executive function, and visuo-spatial functioning. The evaluation of depression or anxiety scores was also considered in several studies. Diffusion-tensor imaging (DTI) was used in 6 of the included studies (Best et al., 2017; Burzynska et al., 2014; Kim et al., 2016; Smith et al., 2016; Tian et al., 2014; Young et al., 2016). fMRI studies used semantic (Smith et al., 2011), episodic memory (Thielen et al., 2016), semantic fluency (Zlatar et al., 2015), motor task (McGregor et al., 2011), and task-switching reaction time (Kimura et al., 2013) tasks, and evaluated the FC of the resting state networks (Voss et al., 2016). Figure 3 shows the structural brain regions affected by PA in 26 of the included works. Effects of PA were reported in several brain regions, including hippocampus (n = 9), parietal lobe (n = 4), temporal lobe (n = 6), and frontal lobe (n = 9) (Figure 3). However, 4 reported no significant results (Cho et al., 2014; Kharabian Masouleh et al., 2018; Voss et al., 2016; Young et al., 2016) and 2 reported only significant results for women but not men (V. R. Varma, Chuang, Harris, Tan, & Carlson, 2015; Vijay R. Varma, Tang, & Carlson, 2016). Of note, some of the studies reported effects in more than one brain region. Figure. 3. Distribution of brain regions showing increased volume associated with PA in crosssectional and longitudinal studies (n = 26). Note that some of the studies reported effect in more than one brain region. 130 Cross-sectional studies Structural MRI studies A total of 18 cross-sectional studies examined the relationship between brain structure and PA. From those, 14 used self-reported questionnaires (Table 2) and 4 measured PA by accelerometry (Table 3). Table 2. Summary of cross-sectional studies reporting the effects of physical activity measured by self-reported questionnaires on brain structure. Study Participants groups Sample size Mean age ± SD Number of females Cognitive screening test PA assessment Neuroimaging assessment Findings Floel et al. (2010) No groups 75 60.5±6.9 47 MMSE AVLT Freiburg Questionnaire of physical activity GM volume Higher levels of PA were associated with increased cerebral GM volume in prefrontal and cingulate cortex. 233 Puri, A., Kim, B., Nguyen, O., Stolee, P., Tung, J., & Lee, J. (2017). User Acceptance of Wrist-Worn Activity Trackers Among Community-Dwelling Older Adults: Mixed Method Study. JMIR Mhealth Uhealth, 5(11), e173. doi:10.2196/mhealth.8211 Rolland, Y., Abellan van Kan, G., & Vellas, B. (2010). Healthy brain aging: role of exercise and physical activity. Clinics in geriatric medicine, 26(1), 75-87. doi:10.1016/j.cger.2009.11.002 Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A. (2018). The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Appl Ergon, 70, 77-87. doi:10.1016/j.apergo.2018.02.005 Tian, Q., Glynn, N. W., Erickson, K. I., Aizenstein, H. J., Simonsick, E. M., Yaffe, K., . . . Rosano, C. (2015). Objective measures of physical activity, white matter integrity and cognitive status in adults over age 80. Behav Brain Res, 284, 51-57. doi:10.1016/j.bbr.2015.01.045 Turner-McGrievy, G., Jake-Schoffman, D. E., Singletary, C., Wright, M., Crimarco, A., Wirth, M. D., . . . McGrievy, M. J. (2018). Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies. Health Promotion Practice, 20(3), 381-389. 234 APPENDIX A Ethics committee approval - University of Minho 235 236 237 238 239 240 241 242 249 250 251 252 APPENDIX D Ethical committee approval – Hospital de Braga 253 254 255 256 257 APPENDIX E CNPD approval 258