Unveiling the digital phenotype: A protocol for a prospective study on physical activity behavior in community-dwelling older adults

Unveiling the digital phenotype: A protocol for a prospective study on physical activity behavior in community-dwelling older adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol Unveiling the digital phenotype: A protocol for a prospective study on physical activity behavior in community-dwelling older adults Kim Daniels, Sharona Vonck, Jolien Robijns, Annemie Spooren, Dominique Hansen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3896647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Physical activity (PA) is a recognized boon for older adults, enhancing their overall well-being and mitigating health risks. Nevertheless, to encourage active lifestyles in this demographic, it is vital to understand the factors influencing PA. Conventional approaches predominantly rely on supervised cross-sectional evaluations, presuming both the stability of PA determinants over time and their isolated components. However, the complex nature of real-life dynamics often involves temporal variability in individual-level determinants. Digital phenotyping (DP), employing data recruited from personal digital devices, enables the continuous, unsupervised and real-time quantification of an individual's behavior within their natural context. This approach offers more ecological and dynamic assessments, revolutionizing our understanding of the intricacies underlying individual PA patterns in their environmental context. Objective. This paper aims to design a robust research protocol for the DP of PA behavior among healthy community-dwelling older adults aged 65 and above by employing a novel measurement approach. Methods. Observational data will be collected over a two-week period to assess various functions combining both cross-sectional and longitudinal data collection methods. Patterns of PA behavior and factors affecting PA outcomes will be detected in order to identify digital phenotypes related to PA. The measurements are based on the Behavior Change Wheel and include self-reporting and clinical assessments for cross-sectional data collection and ecological momentary assessment as well as time series collection for longitudinal data. The statistical analysis involves machine learning which will handle data complexity. Unsupervised learning will be used to uncover patterns, and supervised learning to identify variables. The analysis will be conducted in RStudio (v3.6.3) with significance set at 0.05. Discussion. A novel approach to understanding older adults' PA behavior will be used in this study. Challenges include varying technology adoption, usability, and unproven validity of health tech. Ethical considerations, representativeness, participant engagement, and machine learning expertise are also key aspects of the study's success. This study offers promise in bridging traditional and dynamic assessment methods for older adults' PA behavior to promote active lifestyles. Trial registration: Clinical Trials.gov: NCT06094374 Digital phenotype Physical activity Activity tracking older adults multidimensional assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The global increase in life span is accompanied by an important rise in age-related impairments ( 1 ), often referred as noncommunicable disease pandemic ( 2 ). However, studies have shown that regular physical activity (PA) not only enhances overall well-being but also significantly reduces the likelihood of experiencing adverse health outcomes, in particular - but not limited to - chronic or noncommunicable disorders (e.g., coronary heart disease, stroke, certain cancers, diabetes mellitus type 2, obesity, hypertension, osteoporosis, falls and mortality) ( 3 , 4 , 5 ). Physical activity is defined by the World Health Organization (WHO) as: “Any bodily movement produced by skeletal muscles that increases energy expenditure” and is recognized as a key component of a healthy lifestyle” ( 6 ). Although older adults (often categorized as those aged 65 years and above) are more susceptible to these noncommunicable diseases and thus truly benefit from preventive measures, they rank among the least physically active age groups. They devote a significant portion of their daily routine to sedentary behaviors, which contrasts with the increased risk they face for developing such health conditions ( 7 , 8 ). Current evidence indeed highlights the fact that a sedentary lifestyle correlates with the manifestation of up to 35 distinct chronic conditions, thereby imposing a significant decline in both the cumulative years of life and those lived in a state of high quality of life (QoL) ( 9 , 10 , 11 , 12 , 13 ). In light of the ever-increasing awareness of the significance of PA and the adverse outcomes associated with a sedentary way of life, it remains concerning that a substantial 58,2% of the global population of older adults is falling short of the recommended guideline of engaging in at least 30 minutes of moderate to vigorous PA per day for a minimum of 5 days a week ( 14 , 15 ). As the proportion of older adults continues to rise, understanding the factors that contribute to healthy aging becomes increasingly critical ( 16 ). Moreover, the knowledge of the barriers and facilitators of PA among older adults is fundamental to design interventions to promote an active healthy lifestyle ( 17 ). In recent years, the emergence of digital health technologies has ushered in a new era of monitoring, analysis, and intervention in the domain of PA ( 18 ). Mobile health technologies (e.g., wearable, portable, connected sensors) present a unique opportunity to capture and quantify a rich array of data concerning individuals' daily movements and exercise routines. This technological advancement allows for a comprehensive exploration of the intricacies of PA behavior, unveiling patterns that were previously difficult to discern using traditional self-report methods ( 19 ). The rise of these mobile health technologies equipped with sensors that measure motion, motor skills, and mobility for unsupervised, real-world scenarios, is becoming increasingly prominent as essential adjunctive tools in the conventional clinical evaluations ( 20 ). These technologies offer a breakthrough by addressing the limitations inherent in routine clinical tests ( 21 ). The data collected in ecologically valid and individual-relevant environments have the capacity to capture diverse and unforeseen events, potentially overcoming the constraints of conventional assessment methods ( 22 ). Furthermore, the continuous nature of data collection facilitates the identification of subtle changes in individuals' status, enhancing the precision and sensitivity of outcome information. In essence, these advancements in technology pave the way for a more nuanced understanding of individuals' PA patterns and contribute to more accurate and insightful clinical assessments ( 23 , 24 , 25 , 26 ). This evolution has given rise to a concept known as digital phenotyping (DP), which refers to the utilization of data sourced from personal digital devices to swiftly quantify the individual human phenotype ( 27 ). DP achieves real-time, continuous quantification in an unsupervised way of an individual within their natural environment through the automated aggregation of data. The aim of DP is to measure human behavior patterns and functioning in both health and disease on a moment-to-moment basis ( 28 ). In addition, other approaches such as ecological momentary assessment (EMA) can be used. EMA is designed to repeatedly and intensively sample individual’s behavior, cognition, affect, context and other experiences in real time and ecologically ( 29 ). It is a method that enables capturing time-dependent variations of behavior and its determinants ( 30 ). The combination of traditional data collection methods, such as self-reported assessment and clinical assessment – latter named as supervised assessment with more dynamic and time-sensitive methods, such as time series assessment – referred as unsupervised assessment - and EMA offer the promise to map and identify the crucial factors that influence PA behavior. This information can then be used as the basis for the optimization of, in this specific case, promoting PA in a precise, predictive, and personalized manner. The aim of this paper is to develop and present a comprehensive and exhaustive methodology dynamically capturing and characterizing PA behavior of community-dwelling older adults. The highly innovative approach presented is to combine different types of evaluation (i.e., supervised and unsupervised) as well as different timeframes (i.e., cross-sectional and continuous data collection) in one single protocol. This research project has three core objectives. These objectives collectively aim to enhance our understanding of PA behavior in older adults, refine digital phenotyping methodologies, and optimize the use of wearable technology in clinical trials. METHODS 3.1 Study setting and design An observational study will be conducted to gather data on multiple levels, by using a hybrid approach combining both supervised and unsupervised data collection methods. This integrated strategy will be complemented by four distinct measurement approaches, ensuring a comprehensive assessment of the research objectives, which are visualized in Figs. 1 and 2 . The complete methodology of data collection is presented and in Table 1 . This study was registered at Clinical Trials.gov (NCT06094374) and was approved by the Ethical Committee of Hasselt University (B1152023000011). An informed consent will be obtained from all subjects before participation. 3.2 Participants Participants include older adults aged 65 years and over with no severe illness introducing a loss of mobility or function or a reduction in cognitive functions preventing the proper understanding of instructions. They will be recruited via social media reach, newspaper advertisements and pitches at several senior citizen organizations, through the local community services. To be eligible for participation, individuals must meet the inclusion and exclusion criteria fully described in Table 2. Table 2 In- and exclusion criteria Inclusion criteria Participants are 65 years of older Participants are competent to give informed consent Participants are able to actively participate in the study Participants are community-dwelling (living independent at home or in a service apartment) Without a severe illness Dutch language proficiency as native speaker Exclusion criteria Current neurological disorder such as Parkinson’s disease, multiple sclerosis, cerebrovascular accident, … Current cardiovascular disorder such as stroke, acute myocard ial infarct, coronary artery bypass grafting, percutaneous coronary intervention less than 5 years ago Current respiratory disorder, such as chronic obstructive pulmonary disease, pneumonia, pulmonary fibrosis, asthma, … Current severe metabolic disorder, such as diabetes type 1 and 2, severe osteoporosis, … Current severe cognitive disorders, such as Alzheimer’s disease, vascular dementia, Lewy Body dementia, frontotemporal dementia, 3.2.1 Sample size Due to this study’s innovative and exploratory character involving the utilization of emerging technologies previously unexplored for this specific purpose, formal sample size calculations were deemed unattainable. Because of the lack of accessible prior studies that could provide foundational information, a sample size calculation was impossible. Therefore, a convenient sample of 200 healthy older adults was opted for this trial ( 31 ). 3.3 Supervised versus unsupervised data collection 3.3.1 Supervised data collection 3.3.1.1 Self-reported assessment At the baseline assessment (T0), participants will be asked to complete a comprehensive series of standardized and validated questionnaires in Dutch (English translations of these questionnaires are presented in Appendix 1). 3.3.1.2 Clinical assessment Additionally at baseline (T0), participants will undergo a thorough clinical assessment, consisting of balance testing, muscle strength evaluation, cardiovascular examination, and cognitive analysis. The complete psychometric characteristics of the clinical assessments are summarized in Table 3 . Table 3. Psychometric measurement instruments included in the test battery of the study for measuring psychosocial skills Variable Instrument Description Psychosocial skills Cognition Cognitive flexibility Inventory (CFI) (45) Brief self-report measure, designed to measure three aspects of cognitive flexibility: (a) the tendency to perceive difficult situations as controllable; (b) the ability to perceive multiple alternative explanations for life occurrences and human behavior; and (c) the ability to generate multiple alternative solutions to difficult situations. Reaction Time - App – Sway Medical Evaluation of fundamental sensory processing and neuromotor response speed by assessing an individual's capacity to swiftly detect a screen color change and promptly initiate a device movement. Simple reaction time is the duration required to accomplish this task. Impulse Control App – Sway Medical Quantification of inhibitory processing time by presenting the user with either a "go" stimulus necessitating a motion response or a "no-go" stimulus indicating the need to refrain from a motion response. Memory - App – Sway Medical Evaluation of working memory, the delayed recall test involves presenting the user with a sequence of three consonants. Subsequently, the user engages in a working memory task by tracking a sequence of illuminated squares. Upon completing the working memory task, the user is prompted to recall the initial three-letter sequence. Self Efficacy SCI Exercise Self-Efficacy Scale (ESES) (86) A self reporting scale which instructs respondents to indicate on the 4-point rating scale (1 = not at all true, 2 = rarely true, 3 = moderately true, 4 = always true) how confident they are with regard to carrying out regular physical activities and exercise. Self identification Exercise Identity Scale (85) A 9-item instrument measuring the salience of an individual's identification with exercise as an integral part of the concept of self. Stress Perceived Stress Scale (PSS) (44) Measurement of the degree to which situations in one’s life are appraised as stressful EMA question Stress is evaluated via EMA, through a series of questions: On a scale from 0 to 7, with 0 being no stress at all and 10 being extremely stressed, how stressed do you feel right now? How would you describe your current mood? (e.g., happy, anxious, sad, relaxed) Are you experiencing any physical symptoms of stress right now? (e.g., racing heart, tension in muscles, sweating) Social Interaction: Are you currently alone, with others, or in a social situation? How is this affecting your stress level? Location: Where are you right now? (e.g., at home, at work, in transit) Recent Activities: What were you doing just before you received this prompt? (e.g., working, watching TV, exercising Continuous registration via smartwatch The smartwatch continuously measure stress levels by monitoring heart rate, heart rate variability, physical activity, and other factors to provide users with a stress score and insights into their stress patterns. Depression Geriatric Depression Scale Short Form (GDS-SF) (43) It is a 15-item instrument used to diagnose depression in older adults. Anxiety Geriatric Anxiety Scale Short Form (GAS 10) (47) It is a 10-item self-report measure designed to assess, screen, and quantify severity of anxiety symptoms among older adults Subjective wellbeing Satisfaction with life scale (SWLS) (46) The scale is focused to assess global life satisfaction EMA Subjective wellbeing is evaluated via EMA, through a series of questions: Overall Well-Being: On a scale from 0 to 10, how would you rate your overall well-being right now, with 0 being extremely low and 10 being extremely high? Happiness: How happy do you feel right now, on a scale from 1 to 7, with 1 being not at all and 7 being very happy? Life Satisfaction: On a scale from 1 to 7, how satisfied are you with your life at this moment, with 1 being very dissatisfied and 7 being very satisfied? Positive Emotions: Please indicate which positive emotions you are currently experiencing (e.g., joy, gratitude, contentment). Negative Emotions: Please indicate which negative emotions you are currently experiencing (e.g., sadness, stress, anger). Engagement: How engaged or absorbed are you in your current activity or situation right now, on a scale from 1 to 5, with 1 being not at all and 5 being completely absorbed? Meaning and Purpose: Do you feel that what you are doing right now has meaning or purpose? (yes/no) Environmental Context: Where are you right now, and what are you doing? (e.g., at home, at work, in nature, reading a book) Social Interactions: Are you currently alone, with others, or in a social situation? How do these interactions make you feel? Physical Well-Being: How would you rate your physical well-being right now, on a scale from 1 to 5, with 1 being very poor and 5 being excellent? Emotional loneliness De Jong Gierveld Loneliness Scale (50) Instrument to measure loneliness Table 4. Psychometric measurement instruments included in the test battery of the study for measuring motor skills Motor skills Muscle strength Hand Grip strength measurement Measurement to assess overall upper body muscle strength. It is measured using a handheld dynameter. Quadriceps strength measurement Measurement to assess overall lower body muscle strength. It is measured using a handheld dynameter. Balance Kinvent Force Plate ® Balance system Kinvent™ utilizes a force plate technology to provide accurate and reliable balance assessment and rehabilitation for healthcare professionals. Flexibility Sit and Reach Test Measurement of lower back and hamstring flexibility. Height Stadiometer Weight Scale Blood Pressure BP monitor Systolic blood pressure measured with upper arm blood pressure monitor Cardiometabolic Outcome Six Minute Walking Test (6MWT) (105) Is used to assess the fitness level of healthy adults and of older adults including spatiotemporal data from DigitSole ® technology. Continuous registration via smartwatch pf aerobic capacity The smartwatch uses a combination of heart rate data, GPS tracking, and other sensor information to estimate and monitor aerobic capacity (such as heart rate monitoring, VO2 Max estimation, recovery advisor, training load and status and performance metrics. Physical Activity IPAQ (48) Self-reported assessment tool designed to measure an individual's physical activity levels and patterns Continuous registration via smartwatch The smartwatch measures continuously steps, calories, heart rate, number of floors, MVPA, Body Battery, Sleep EMA PA is evaluated via EMA, through a series of questions: What type of physical activity are you currently engaged in? Environmental Context: Where are you right now, and what are you doing? (e.g., at home, at work, in nature, reading a book) On a scale from 1 to 5, with 1 being very light and 5 being very intense, how would you rate the intensity of what you were currently doing? Are you alone, with others, or in a group? What is the weather like as you engage in this activity? (e.g., sunny, rainy, hot, cold) Are there any factors or obstacles that are making it difficult for you to be active right now? (e.g., lack of time, fatigue) How would you rate your current energy levels? (e.g., low, moderate, high) Sleep Geriatric Sleep Questionnaire (GSQ – 6) (49) Short questionnaire specifically designed to assess the subjective sleep quality in older people Continuous registration via smartwatch The smartwatch measures sleep using a combination of sensors and algorithms to monitor your movement patterns and heart rate throughout the night, with movement tracking, heart rate monitoring, light vs. deep sleep, heart rate variability (HRV), wake detection, sleep duration, quality, and stages. EMA Sleep is evaluated via EMA, through a series of questions: How many hours of sleep did you get last night? On a scale from 1 to 5, with 1 being very poor and 5 being excellent, how would you rate the quality of your sleep last night? Did you wake up during the night? If yes, how many times and for how long? How fatigued or refreshed do you feel this morning, on a scale from 1 to 7, with 1 being very fatigued and 7 being very refreshed? What time did you go to bed last night, and what time did you wake up this morning? Was your sleep environment comfortable and conducive to sleep last night? (yes/no) Have you experienced any episodes of excessive daytime sleepiness today? (yes/no) Have you taken any naps today? If yes, please indicate the duration. Have you consumed any caffeine or alcohol in the last few hours? (yes/no) Abdominal circumference Measuring tape Assessment of abdominal obesity or waist size. 3.3.2 Unsupervised data collection 3.3.2.1 Ecological momentary assessment (EMA) Participants will receive three random prompts daily (TR) over a two-week period on their mobile phones via auditory signal. The Smartphone Ecological Momentary Assessment 3 (SEMA 3 ) application ( 32 ) will be installed on the participant’s smartphone and will be used to trigger the EMA questionnaire. To ensure adequate spacing across the day, four timeframes, each of two hours, will be constructed between 8:00 AM and 8:00 PM, in which one trigger will be randomly given. They will be instructed to halt their ongoing activities and promptly complete the EMA questionnaire, which typically will take two to three minutes. In cases where participants are driving or engaged in activities incompatible with questionnaire completion, they are strictly advised to disregard the prompt. If a participant fails to complete the EMA questionnaire following the initial prompt, the phone will emit a maximum of three reminder signals at 5-minute intervals. After the third reminder, access to the EMA questionnaire will be temporarily suspended until the subsequent scheduled questionnaire. 3.3.3 Time Series data collection Finally, participants will be invited to wear a monitoring device continuously for a period of two weeks (24/7) to record their activity data (TC). The continuous data contains data derived from a GARMIN wearable, capturing participants' day-to-day activities through seamless, non-intrusive sensing. The GARMIN Vivosmart 5 was selected, hence it is a widely embraced smartwatch renowned for its popularity and high acceptance. Additionally, this type of wearable can be used specifically for research purposes, providing direct access to unprocessed raw data through the manufacturer’s research portal. 3.4 Measurements All measurements are based upon the Behavior Change Wheel (BCW) ( 33 ), which finds its theoretical foundation in Michie's COM-B framework ( 34 ). Briefly, it represents a comprehensive theoretical structure that dissects behavior into three essential components: Capability, opportunity, and motivation. This model, illustrated in Fig. 3 , provides a holistic perspective on the three main factors influencing behavior Opportunity ( context ) pertains to the external conditions enabling or hindering the behavior Capability ( skills ) refers to the individual's psychological and physical ability to engage in the behavior, Motivation ( drive ) encompasses the internal processes driving the inclination to perform the behavior. By adopting this framework, the study tends to embrace a multifaceted and dynamic approach for analyzing and understanding the complex interplay of these elements that shape observed behaviors in PA. A summary of all included measurements on the different levels can be found in detail in Table 1 . We are now going to discuss the three subcomponents of the BCW are elaborated in details. 3.4.1 Opportunity The opportunity of the BCW includes aspects of the physical, sociocultural, economic, and political environment that can influence behavior from a micro, meso or a macro level ( 35 ). Influences can arise from concrete settings in which the behavior occurs or from broader systems that influence behavior indirectly. To gauge these components, self-reporting measurements and EMA will be employed. 3.4.1.1 Self-reported assessment Self-reported information on age, gender, height, smoking status, marital status, level of education, living arrangement, urbanization level, participation status, self-rated health level ( 36 ), and pain level ( 37 ) will be collected. Participants will also be asked to indicate their retirement status, level of income, living status, and access to facilities in the community. Their QoL level will be evaluated using the WHOQOL-BREF ( 38 ). These items will be collected using the online survey tool Qualtrics ( 39 ). 3.4.1.2 EMA Participants will rate their self-rated health, five physical complaints (i.e., muscle stiffness, pain, dizziness, shortness of breath, fatigue), contextual factors and QoL using a 7-point Likert scale ( 40 ). The sequence of questions in the questionnaire will vary, with questions presented in a random order. 3.4.2 Capabilities Capabilities refers to an individual's capacity to effectively perform a specific behavior. It encompasses a range of skills and abilities required for the successful execution of that behavior. The significance of capabilities lies in its pivotal role; when individuals lack the necessary skills for a particular behavior, the likelihood of them adopting and sustaining behavior change diminishes. Capabilities can be deconstructed into two primary categories, which are psychosocial and physical capability. Psychosocial skills pertain to an individual's cognitive and emotional aptitude to engage in a given behavior. It encompasses a spectrum of factors, including knowledge, skills, memory, attention, and self-regulation. Physical Capability refers to the physical capacity to carry out a behavior. It includes factors such as physical strength, mobility, cardiovascular capacity, and balance ( 41 , 42 ). The measurement instruments included to map the respective psychosocial and motor skills are summarized in Tables 3 and 4 . They will be evaluated across the four distinct levels of measurement, encompassing self-reporting, clinical assessment, ecological momentary assessment, and time series analysis. 3.4.2.1 Self-Reporting assessment Self-reported information on driving status, mobility issues, depression ( 43 ), stress ( 44 ), cognitive functioning ( 45 ), subjective wellbeing ( 46 ), anxiety ( 47 ), physical activity ( 48 ), sleep pattern ( 49 ), and emotional loneliness ( 50 ) will be collected. 3.4.2.2 Clinical assessment Psychological and motor skills will undergo comprehensive evaluation through clinical assessments administered by experienced therapists. These assessments will encompass cognitive functioning, cardiometabolic health, muscle strength, and balance, providing a holistic understanding of an individual's overall health. a. Cognitive functions The assessment of cognitive functioning will be conducted using the SWAY ( SWAY Medical Inc. in Tulsa, OK, USA) ( 51 ). The cognitive performance segment of the app encompasses three modules grounded in sensory and neuromotor principles. These modules aim to assess stimulus recognition, cognitive processing speed, neuromotor response, working memory, and reaction time. The cognitive testing segment, focusing on reaction time, has undergone clinical evaluation and demonstrated reliability and validity, comparing favorably to the standard Computerized Test of Information Processing assessment. However, the capacity of SWAY to function consistently across various mobile devices and operating systems is yet to be validated ( 52 , 53 , 54 ), therefore it will be use to collect all the data. b. Physical functioning Walking performance Gait analysis will be performed using the Six Minute Walking Test (6MWT). The 6MWT serves as a robust tool for evaluating exercise capacity at levels reflective of typical efforts exerted by the elderly during daily activities, as established by Lipkin in 1986 ( 55 ). Additionally, it proves invaluable for assessing the progression of functional exercise capacity in diverse clinical intervention studies ( 56 , 57 , 58 , 59 ). The test demonstrates high reliability among healthy elderly individuals (Intra-Class Correlation = 0.93) ( 60 , 61 , 62 ). During this test, diverse data will be gathered using specialized instruments. Gait speed, proven to be a robust predictor of adverse health outcomes, remains significant irrespective of the presence of common medical conditions and risk factors for diseases ( 63 , 64 , 65 ). Many studies demonstrated a strong association with incident disability, cognitive decline and dementia, falls and related fractures, mortality, and healthcare utilization (e.g., hospitalization and institutionalization). Although tested in very different populations (e.g., inpatients and outpatients, independent, frail, and disabled subjects), different walking distances, and studied outcomes, the prognostic value is very consistent ( 66 ). Gait analysis will be performed using Digitsole® insoles (Nancy, France) to quantify various parameters during walking. PODOSmart® insoles, equipped with wireless sensors, can seamlessly fit into any shoe, enabling the measurement of spatial, temporal, and kinematic gait parameters. These intelligent insoles feature multiple sensors to detect and record foot movements, alongside a microprocessor that computes biomechanical data related to gait ( 67 ). Additionally, potential gait deviations can be discerned through Inertial Measurement Units (IMUs). These IMUs capture crucial gait parameters such as speed, cadence, and biomechanical angles of motion during walking, interfacing with dedicated software on a tablet. The software facilitates the generation of comprehensive data reports, encompassing kinematic variables specific to an individual's walking patterns ( 59 ). Notably, the validity and reliability of Digitsole® have been studied in samples of healthy individuals over brief walking periods ( 67 , 68 ). Muscle strength Muscle strength will be assessed using the Kinvent®2016 handheld dynamometer. The test protocol involves consecutively evaluating the strength of different muscle groups of the lower extremities: abductors (side lying), adductors (supine), extensors (prone), and flexors (sitting). Each muscle group will undergo three tests, and the final result will be based on the best value obtained from these tests, following the protocol established by Thorborg ( 69 ). Additionally, hand grip force will be measured using the K-Force Grip® (Kinvent, Montpellier, France). This measurement serves to evaluate overall strength, enabling comparisons of muscle function across populations and tracking the progression of conditions such as sarcopenia, while also identifying potential deficits ( 70 , 71 ). The dynamometer has been designed for assessing and rehabilitating handgrip strength. It provides real-time biofeedback on a Tablet or Smartphone. A study conducted by Nikodelis ( 72 ) comparing Jamar and K-Force Grip® found no fixed or proportional bias. Both groups exhibited high correlation coefficients, with the lowest correlation observed between the two instruments (r = 0.90, p < 0.05), indicating strong reliability. Balance Postural balance is crucial for maintaining a specific posture in response to external disturbances. Imbalances stemming from malfunctions in the visual, vestibular, or proprioceptive sensory systems can lead to issues such as falls, injuries, and instability in joints. To identify and address these concerns, clinical tests are essential ( 73 , 74 ). In this study, postural balance will be assessed using the Kinvent PLATES v3® (Kinvent, Montpellier, France). Participants will undergo the Single Leg Balance (SLB) test under various conditions: (i) three repetitions for each leg with open eyes on the PLATES, and (ii) three repetitions for each leg with eyes closed on the PLATES. The SLB test involves maintaining a stationary position on one leg for 10 seconds, focusing on a point 5 meters away, with hands on hips and the non-load-bearing leg slightly bent at the hip and knee ( 75 , 76 ). To facilitate a comprehensive comparison between open and closed eyes conditions, a 10-second test duration was chosen, aligning with norms established for the closed eyes condition during unipodal balance exercises (norm = 9.4 seconds) ( 77 ). Additionally, a second test, the Single Leg Landing (SLL), will be conducted with three repetitions for each leg on the PLATES. This dynamic unilateral balance exercise requires participants to descend from a step positioned 19 cm above the force platform with a bounce, ensuring both feet are suspended before landing. Subsequently, participants must stabilize on one leg for 15 seconds, with hands on hips and their gaze fixed at a point 5 meters away ( 78 , 79 ). Functional capability The Short Physical Performance Battery (SPPB) has emerged as one of the most promising tools to evaluate functional capability and provide a measure of the biological age of an older individual ( 80 ). It is an objective tool for measuring the lower extremity physical performance status. Three domains, which include balance, usual or self-selected gait speed, and lower limb strength, are assessed by a three-stage balance test (feet side-by-side, semi tandem, and tandem positions), a 3-m or 4-m gait speed test (time spent to walk the course), and a repetitive chair stand test (five times chair sit-to-stand test), respectively. A 0- to 12-point scale is used to score the sum of the three assessments with higher point values corresponding with greater levels of physical function and lower disability, whereas lower point values correspond with lower levels of physical function and higher disability, respectively ( 80 ). The timed results of each subtest are rescaled according to predefined cut points for obtaining a score ranging from 0 (worst performance) to 12 (best performance) ( 81 ). 3.4.2.3 Ecological momentary assessment Participants will rate their stress, physical activity, sleep, and using a 7-point Likert scale. The sequence of questions in the questionnaire will vary, with questions presented in a random order ( 40 ). 3.4.2.4 Time Series data assessment Throughout the two-week trial, a continuous monitoring process using Garmin Vivosmart 5® activity tracker will collect various parameters, including stress levels, physical activity, step count, calorie expenditure, heart rate, the number of floors climbed, moderate to vigorous activity (MVPA), cardiometabolic outcomes, body battery, and sleep patterns ( 82 , 83 ). Finally, participants will be invited to wear the GARMIN Vivosmart 5® continuously for a period of two weeks (24/7) to record their activity data (TC). The continuous data contains data derived from a GARMIN wearable, capturing participants' day-to-day activities through seamless, non-intrusive sensing. The GARMIN Vivosmart 5® was selected, hence it is a widely embraced smartwatch renowned for its popularity and high acceptance. Additionally, this type of wearable can be used specifically for research purposes, providing direct access to unprocessed raw data through the manufacturer’s research portal. 3.4.2 Motivation 3.4.3.1 Clinical assessment Motivators or drives are the factors that guide or motivate a person's behavior from reflective or rational considerations or from automatic processes or factors such as needs, emotions, and habits. Within the realm of motivation, two fundamental drives can be identified: automatic and reflective motivation. The first one is characterized by the emotional and instinctual triggers that shape our actions. This particular facet is gauged via self-reporting assessment by employing the Exercise Motivation Inventory (EMI-2) ( 84 ). Another aspect is the Reflective Motivation, rooted in the cognitive and thoughtful aspects that steer behavior transformation. This latter dimension encompasses factors such as beliefs, intentions, and goal-setting, all of which play pivotal roles in the journey towards change. To assess this aspect, both the Exercise Identity Scale ( 85 ) and the Exercise Self-Efficacy Scale ( 86 ) are being used. Procedure The assessments will be administered at different significant time points, as depicted in Fig. 4 . 3.4.3.2 EMA Participants will rate their motivation level and intention to be physically active using a 7-point Likert scale ( 40 ). The sequence of questions in the questionnaire will vary, with questions presented in a random order. 3.5 Data management plan A range of instruments will be employed to gather data for the study. These instruments include self-report questionnaires, clinical assessments, and EMA tools. The questionnaires and assessments have been selected based on their relevance to the Behavior Change Wheel framework, which will guide the analysis. In the course of this study, a fundamental component of our data management strategy is the development of a data integration platform. This platform is essential for seamlessly connecting and consolidating all collected data, ensuring a holistic view of the information gathered. To prioritize data privacy and confidentiality, all data within the integration platform will undergo a pseudonymization process. This critical step involves the replacement of personally identifiable information (PII) with unique pseudonyms, rendering the data anonymous while preserving its analytical value. Pseudonymization will be performed in accordance with applicable data protection regulations to safeguard the privacy of study participants. The data integration platform will serve as the central repository for all collected data, including self-report questionnaires, clinical assessments, EMA, and time series data. By housing these diverse data types within a single, organized framework, we aim to facilitate comprehensive data analysis. Within this integrated platform, standardized coding schemes for variables will be applied to maintain consistency and facilitate data analysis. Coding guidelines and dictionaries will be established to ensure that all team members involved in data management adhere to uniform data standards. Data storage will adhere to relevant data protection regulations, ensuring that data is retained and managed in compliance with legal requirements. To maintain data quality within the integrated platform, regular data quality checks will be implemented throughout the course of the study. These checks will identify and address discrepancies or outliers in the data. 3.6 Statistical analysis The assessment of data normality will be conducted using graphical techniques, including QQ-plots, histograms, and boxplots. Continuous data will be reported using the mean and standard deviation (sd) or the median and interquartile range (IQR), depending on the distribution. Categorical data will be presented as frequencies and percentages. To answer the different research question, different machine learning methods will be used and tested. We will first evaluate unsupervised learning with the use of hierarchical clustering, a well-established method in multivariate statistical analysis ( 87 ). The purpose of this approach is to reveal hidden patterns in how participants classify themselves, based on their self-reported adherence to the WHO’s recommendation of PA, and to identify participants with increased risk of falls. If the current strategy proves unsuccessful, alternative method involving a random decision forest will be tested or gradient boosting algorithms ( 88 ). Ensemble learning methods, such as gradient boosting techniques, involve the integration of multiple weak predictors to form a more accurate one. This approach iteratively introduces decision trees to the model, with each new tree aiming to rectify the errors of its predecessors. Gradient boosting algorithms demonstrate notable effectiveness when dealing with intricate data, often achieving high accuracy across a diverse range of problems compared to stepwise linear regression ( 89 ). Nevertheless, the preference is to maintain a straightforward model, prioritizing simplicity to facilitate clinical interpretation. We will then evaluate supervised learning using (recurrent) neural network (RNN) ( 90 ). Different models will be trained according to the research questions. The objective of this procedure is to identify distinctive variables that distinguish individuals who have experienced falls from those who have not and those who adhere to the WHO’s PA recommendations. The added value of RNN, in our context, is the ability to process time series input. Such kind of network possess the capability to retain an internal memory of past inputs, leveraging it for predicting future inputs. RNNs excel in modeling intricate temporal interactions, demonstrating superior flexibility and robustness when handling sequential data ( 91 ). In comparison to stepwise regression, RNNs are adept at capturing complex temporal relationships and exhibit lower susceptibility to overfitting. The significance threshold will be set at 0.05. Statistical analyses will be performed in R using RStudio (version 3.6.3). DISCUSSION This research presents a novel approach to enhance the use of DP through the utilization of a hybrid measuring methodology that combines supervised and unsupervised methodologies. The main aim of the protocol is to acquire a thorough comprehension of PA behavior in the older adult population, ascertain the significant factors that influence this behavior, and develop DPs associated with PA behavior. The innovative approach presented, which integrates supervised and unsupervised data collection methods and incorporates a diverse range of measurement techniques such as self-reporting, clinical assessments, EMA, and continuous data from wearable devices, appears commendable. Our intention is to further investigate its effectiveness in acknowledging the multifaceted nature of physical activity behavior among older adults. Clinical relevance Previous works demonstrate that data collected within an ecologically valid and individually relevant environment can surpass the limitations inherent in conventional clinical assessments or one-time self-reporting ( 19 , 21 , 92 ). The distinct advantage of unsupervised daily PA monitoring lies in its ability to detect more nuanced changes over time. Ultimately, through the utilization of this methodology, our goal is to identify distinct DPs, enabling the tailoring of interventions to meet the unique needs of older adults. This personalized approach holds the potential to yield more effective and engaging interventions, finally enhancing their overall well-being and health ( 93 ). This initiative directly addresses a substantial public health concern. Challenges However, there are potential pitfalls and challenges that warrant careful consideration in this context. Technological adoption rates among community-dwelling older adults can vary widely, and some individuals may lack the necessary technological literacy or access to digital devices ( 94 , 95 ). This may introduce selection bias into the study, and researchers must be mindful of the sample’s representativeness. Additionally, usability concerns surrounding digital tools such as wearables must be addressed. Older adults may struggle with complex interfaces or may have physical limitations that hinder their interaction with these devices ( 96 ). Furthermore, it is imperative to acknowledge that the use of mobile technology has the ability to elicit modifications in behavior, the Hawthorne effect, even in the absence of explicit feedback. Therefore, it is imperative to do research that investigates the circumstances in which user performance in unsupervised environments corresponds to that in supervised environments. Furthermore, it is imperative to investigate if the observed alterations in behavior have a direct impact on levels of PA ( 21 , 97 ). Additionally, only a small proportion of health and performance technologies, about 5%, have been proven effective through rigorous, independent validation. Consequently, the value of these technologies remains a topic open to debate and should be approached carefully ( 98 , 99 ). Ethical considerations are paramount in the context of digital health interventions and data collection in older adults ( 100 ). Issues such as data privacy and -security must be thoroughly examined to safeguard the rights and well-being of the study's participants. Moreover, the success of the research heavily relies on the translation of complex data streams from wearable devices into actionable insights. The challenge lies in making this information understandable and beneficial for both individuals and healthcare practitioners. Effective data interpretation and communication are critical components in this matter ( 101 ). The representativeness of the sample is another aspect that will require attention. The demographics and health status of the older adults participating in the study should be carefully considered. A non-representative sample could limit the generalizability of the findings. Therefore, efforts should be made to ensure a diverse and inclusive sample, capturing a broader spectrum of experiences and needs. In order to ensure a representative sample, diverse recruitment methods will be employed, tapping into various channels and establishing partnerships with relevant organizations. Stratification based on key demographics, including age, gender, ethnicity, and socioeconomic status, will be implemented, with a particular focus on oversampling underrepresented groups. Additionally, continuous monitoring of the demographic composition during recruitment will guide necessary adjustments based on feedback and observed trends. Longitudinal data collection, while beneficial, can be resource-intensive and may pose difficulties in participant retention and compliance over an extended period. Strategies to minimize attrition and maximize engagement are necessary to ensure the data's quality and completeness. An extensive training on how to use the wearable and the SEMA³ application is recommended to obtain a high response rate. Additional, regular check-ins, personalized feedback will be implemented to maintain participants motivation and compliance. Lastly, the use of machine learning and neural networks, while offering powerful tools for data analysis, can be complex and require expertise. Ensuring that the chosen algorithms are appropriate and well-tuned is crucial for the study's success. The algorithms used for define the DPs need to undergo thorough validation. To enhance the effectiveness of unsupervised measures, there is a need for standardized reporting of parameters, such as establishing a core dataset across studies. This reporting should also encompass metadata, which includes data that accompanies and describes the primary data. Standardizing the duration of unsupervised assessments and providing detailed are additional requirements ( 21 , 102 , 103 ). Despite these challenges, the insights gained could inform targeted interventions and public health policies, addressing the unique challenges of an aging global population. Integration of findings into clinical practices may lead to personalized strategies for promoting PA among older adults, positively impacting health outcomes and reducing healthcare costs. Ultimately, the research contributes to the broader fields of gerontology, public health, and data science, with potential implications for societal well-being and the promotion of active aging, it is therefore of the utmost importance to perform such kind of multidimensional assessment. CONCLUSION In conclusion, this study holds promise in bridging the gap between conventional assessment methods, innovative methods and the dynamic nature of older adults' PA behavior. By addressing the aforementioned potential and possible challenges, researchers can navigate the complexities of applying digital tools in this context effectively, ultimately contributing to the promotion of active lifestyles and the well-being of older adults. It is crucial to recognize the time- and context-specific variations when crafting dynamic health behavior interventions. By effectively inspiring and engaging older adults in the appropriate time and context by knowing their PA phenotype, they can be encouraged to adopt healthier habits, such as increased physical activity and reduced sedentary behavior ( 104 ). Declarations Author Contribution KD: Writing – original draft, Writing – review & editing, Conceptualization, Funding acquisition, Investigation, Resources, Visualization. SV: Writing – review & editing, Investigation. JR: Writing – review & editing, Investigation. AS: Writing – review & editing. DH: Writing – review & editing. BB: Writing – review & editing, Methodology, Supervision, Writing – original draft. References Suzman R, Beard J. Global health and aging. NIH Publ. 2011;1(4):273-7. Allen L. Are we facing a noncommunicable disease pandemic? J Epidemiol Glob Health. 2017;7(1):5-9. Badenhop DT, Cleary PA, Schaal SF, Fox EL, Bartels RL. Physiological adjustments to higher-or lower-intensity exercise in elders. Medicine and Science in Sports and Exercise. 1983;15(6):496-502. Colcombe S, Kramer AF. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychological science. 2003;14(2):125-30. Galle SA, Liu J, Bonnechère B, Amin N, Milders MM, Deijen JB, et al. The long-term relation between physical activity and executive function in the Rotterdam Study. European Journal of Epidemiology. 2023;38(1):71-81. Organization WH. WHO guidelines on physical activity and sedentary behaviour. 2020. Piercy KL, Troiano RP. Physical activity guidelines for Americans from the US department of health and human services: Cardiovascular benefits and recommendations. Circulation: Cardiovascular Quality and Outcomes. 2018;11(11):e005263. Harvey JA, Chastin SF, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. Journal of aging and physical activity. 2015;23(3):471-87. Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Comprehensive physiology. 2012;2(2):1143. Andersen LB, Schnohr P, Schroll M, Hein HO. All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Archives of internal medicine. 2000;160(11):1621-8. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The lancet. 2012;380(9838):219-29. Carter S, Hartman Y, Holder S, Thijssen DH, Hopkins ND. Sedentary behavior and cardiovascular disease risk: mediating mechanisms. Exercise and sport sciences reviews. 2017;45(2):80-6. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The lancet. 2016;388(10051):1302-10. Cunningham C, O’Sullivan R. Why physical activity matters for older adults in a time of pandemic. European Review of Aging and Physical Activity. 2020;17:1-4. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1· 9 million participants. The lancet global health. 2018;6(10):e1077-e86. Gilbert C, Hagerty D, Taggert HM. Exploring factors related to healthy ageing. Self-Care, Dependent-Care & Nursing. 2012;19(1):20-5. Jackman PC, Cooke S, George T, Blackwell J, Middleton G. Physical activity experiences of community-dwelling older adults with physical disabilities: a scoping review of qualitative research. Disability and Rehabilitation. 2023:1-13. Omberg L, Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Adams J, et al. Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nature biotechnology. 2022;40(4):480-7. Bonnechère B, Timmermans A, Michiels S. Current technology developments can improve the quality of research and level of evidence for rehabilitation interventions. Sensors. 2023;23(2):875. Bonnechere B, Sholukha V, Omelina L, Van Vooren M, Jansen B, Jan SVS. Suitability of functional evaluation embedded in serious game rehabilitation exercises to assess motor development across lifespan. Gait & posture. 2017;57:35-9. Warmerdam E, Hausdorff JM, Atrsaei A, Zhou Y, Mirelman A, Aminian K, et al. Long-term unsupervised mobility assessment in movement disorders. The Lancet Neurology. 2020;19(5):462-70. Joshi M, Ashrafian H, Aufegger L, Khan S, Arora S, Cooke G, et al. Wearable sensors to improve detection of patient deterioration. Expert review of medical devices. 2019;16(2):145-54. Dillenseger A, Weidemann ML, Trentzsch K, Inojosa H, Haase R, Schriefer D, et al. Digital biomarkers in multiple sclerosis. Brain Sciences. 2021;11(11):1519. Yang Y, Schumann M, Le S, Cheng S. Reliability and validity of a new accelerometer-based device for detecting physical activities and energy expenditure. PeerJ. 2018;6:e5775. Torous J, Rodriguez J, Powell A. The new digital divide for digital biomarkers. Digital biomarkers. 2017;1(1):87-91. Cosco TD, Firth J, Vahia I, Sixsmith A, Torous J. Mobilizing mHealth data collection in older adults: challenges and opportunities. JMIR aging. 2019;2(1):e10019. Delrieu L, Hamy A-S, Coussy F, Kassara A, Asselain B, Antero J, et al. Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial. BMC cancer. 2022;22(1):493. Husebo BS, Heintz HL, Berge LI, Owoyemi P, Rahman AT, Vahia IV. Sensing technology to monitor behavioral and psychological symptoms and to assess treatment response in people with dementia. A systematic review. Frontiers in pharmacology. 2020;10:1699. Stone AA, Shiffman S. Ecological momentary assessment (EMA) in behavorial medicine. Annals of behavioral medicine. 1994. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1-32. Torrado JC, Husebo BS, Allore HG, Erdal A, Fæø SE, Reithe H, et al. Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson’s disease: Protocol of the mixed method, cyclic ActiveAgeing study. PloS One. 2022;17(10):e0275747. Koval P HJ, Dozo N, Gleeson J, Alvarez M, Harrison A, Vu D, Sustano R, Jayaputera G, Sinnot R. SEMA3: smartphone ecological momentary assessment 2019 [Computer Software]. Available from: https://sema3.com/. Boen F, Seghers J, van Uffelen J. Motiveren tot verandering: Hoe schil je de gedragsui? Leefstijlpsychiatrie: Springer; 2022. p. 309-32. Michie S, Van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science. 2011;6(1):1-12. Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive medicine. 1999;29(6):563-70. Fayers PM, Sprangers MA. Understanding self-rated health. The Lancet. 2002;359(9302):187-8. Karcioglu O, Topacoglu H, Dikme O, Dikme O. A systematic review of the pain scales in adults: which to use? The American journal of emergency medicine. 2018;36(4):707-14. De Vries J, Van Heck G. WHOQOL-BREF. Tilburg: Universiteit van Tilburg. 1996. Ginn J. qualtRics: retrieve survey data using the Qualtrics API. J Open Source Softw. 2018;3(24):690. Maes I, Mertens L, Poppe L, Crombez G, Vetrovsky T, Van Dyck D. The variability of emotions, physical complaints, intention, and self-efficacy: an ecological momentary assessment study in older adults. PeerJ. 2022;10:e13234. Cooper R, Kuh D, Cooper C, Gale CR, Lawlor DA, Matthews F, et al. Objective measures of physical capability and subsequent health: a systematic review. Age and Ageing. 2010;40(1):14-23. Michie S, Atkins L, West R. The behaviour change wheel. A guide to designing interventions 1st ed Great Britain: Silverback Publishing. 2014;1003:1010. Lesher EL, Berryhill JS. Validation of the geriatric depression scale‐short form among inpatients. Journal of clinical psychology. 1994;50(2):256-60. Cohen S, Kamarck T, Mermelstein R. Perceived stress scale. Measuring stress: A guide for health and social scientists. 1994;10(2):1-2. Dennis JP, Vander Wal JS. The cognitive flexibility inventory: Instrument development and estimates of reliability and validity. Cognitive therapy and research. 2010;34:241-53. Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. Journal of personality assessment. 1985;49(1):71-5. Carlucci L, Balestrieri M, Maso E, Marini A, Conte N, Balsamo M. Psychometric properties and diagnostic accuracy of the short form of the geriatric anxiety scale (GAS-10). BMC geriatrics. 2021;21(1):1-12. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. International journal of behavioral nutrition and physical activity. 2011;8(1):1-11. Espirito-Santo H, Dias-Azedo D, Lemos L, Grasina A, Andrade D, Henriques S, et al. Validation of the geriatric sleep questionnaire. Sleep Medicine. 2021;88:162-8. De Jong Gierveld J, Van Tilburg T. The De Jong Gierveld short scales for emotional and social loneliness: tested on data from 7 countries in the UN generations and gender surveys. European journal of ageing. 2010;7:121-30. VanRavenhorst-Bell HA, Muzeau MA, Luinstra L, Goering J, Amick RZ. Accuracy of the SWAY mobile cognitive assessment application. International Journal of Sports Physical Therapy. 2021;16(4):991. Burghart M, Craig J, Radel J, Huisinga J. Reliability and validity of a motion-based reaction time assessment using a mobile device. Applied Neuropsychology: Adult. 2018. Brett BL, Zuckerman SL, Terry DP, Solomon GS, Iverson GL. Normative data for the SWAY balance system. Clinical journal of sport medicine. 2020;30(5):458-64. Bennett CC, Ross MK, Baek E, Kim D, Leow AD. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. NPJ Digital Medicine. 2022;5(1):181. Lipkin D, Scriven A, Crake T, Poole-Wilson P. Six minute walking test for assessing exercise capacity in chronic heart failure. Br Med J (Clin Res Ed). 1986;292(6521):653-5. Pu CT, Johnson MT, Forman DE, Hausdorff JM, Roubenoff R, Foldvari M, et al. Randomized trial of progressive resistance training to counteract the myopathy of chronic heart failure. Journal of Applied Physiology. 2001;90(6):2341-50. Rubenstein LZ, Josephson KR, Trueblood PR, Loy S, Harker JO, Pietruszka FM, et al. Effects of a group exercise program on strength, mobility, and falls among fall-prone elderly men. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2000;55(6):M317-M21. Wright D, Khan K, Gossage E, Saltissi S. Assessment of a low-intensity cardiac rehabilitation programme using the six-minute walk test. Clinical rehabilitation. 2001;15(2):119-24. Enright PL, McBurnie MA, Bittner V, Tracy RP, McNamara R, Arnold A, et al. The 6-min walk test: a quick measure of functional status in elderly adults. Chest. 2003;123(2):387-98. Mangan D, Judge J. Reliability and validation of the six minutes walk. J Am Geriatr Soc. 1994;42:SA73. Bautmans I, Lambert M, Mets T. The six-minute walk test in community dwelling elderly: influence of health status. BMC geriatrics. 2004;4(1):1-9. Rikli RE, Jones CJ. The reliability and validity of a 6-minute walk test as a measure of physical endurance in older adults. Journal of aging and physical activity. 1998;6(4):363-75. Abellan Van Kan G, Rolland Y, Andrieu S, Bauer J, Beauchet O, Bonnefoy M, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. The journal of nutrition, health & aging. 2009;13:881-9. Cesari M, Kritchevsky SB, Penninx BW, Nicklas BJ, Simonsick EM, Newman AB, et al. Prognostic value of usual gait speed in well‐functioning older people—results from the Health, Aging and Body Composition Study. Journal of the American Geriatrics Society. 2005;53(10):1675-80. Woo J, Ho SC, Yu AL. Walking speed and stride length predicts 36 months dependency, mortality, and institutionalization in Chinese aged 70 and older. Journal of the American Geriatrics Society. 1999;47(10):1257-60. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait speed and survival in older adults. Jama. 2011;305(1):50-8. Ziagkas E, Loukovitis A, Zekakos DX, Chau TD-P, Petrelis A, Grouios G. A novel tool for gait analysis: Validation study of the smart insole podosmart®. Sensors. 2021;21(17):5972. Loukovitis A, Ziagkas E, Zekakos DX, Petrelis A, Grouios G. Test-Retest Reliability of PODOSmart® Gait Analysis Insoles. Sensors. 2021;21(22):7532. Thorborg K, Petersen J, Magnusson SP, Hölmich P. Clinical assessment of hip strength using a hand‐held dynamometer is reliable. Scandinavian journal of medicine & science in sports. 2010;20(3):493-501. Sousa-Santos A, Amaral T. Differences in handgrip strength protocols to identify sarcopenia and frailty-a systematic review. BMC geriatrics. 2017;17:1-21. Bohannon RW. Muscle strength: clinical and prognostic value of hand-grip dynamometry. Current Opinion in Clinical Nutrition & Metabolic Care. 2015;18(5):465-70. Nikodelis T, Savvoulidis S, Athanasakis P, Chalitsios C, Loizidis T. Comparative study of validity and reliability of two handgrip dynamometers: K-force grip and Jamar. Biomechanics. 2021;1(1):73-82. Pollock AS, Durward BR, Rowe PJ, Paul JP. What is balance? Clinical rehabilitation. 2000;14(4):402-6. Muir JW, Kiel DP, Hannan M, Magaziner J, Rubin CT. Dynamic parameters of balance which correlate to elderly persons with a history of falls. Plos one. 2013;8(8):e70566. Ross SE, Guskiewicz KM, Gross MT, Yu B. Balance measures for discriminating between functionally unstable and stable ankles. Medicine and science in sports and exercise. 2009;41(2):399-407. Goldie PA, Evans OM, Bach TM. Postural control following inversion injuries of the ankle. Archives of physical medicine and rehabilitation. 1994;75(9):969-75. Springer BA, Marin R, Cyhan T, Roberts H, Gill NW. Normative values for the unipedal stance test with eyes open and closed. Journal of geriatric physical therapy. 2007;30(1):8-15. Wikstrom EA, Tillman MD, Borsa PA. Detection of dynamic stability deficits in subjects with functional ankle instability. Medicine and science in sports and exercise. 2005;37(2):169-75. Meras Serrano H, Mottet D, Caillaud K. Validity and Reliability of Kinvent Plates for Assessing Single Leg Static and Dynamic Balance in the Field. Sensors. 2023;23(4):2354. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. Journal of gerontology. 1994;49(2):M85-M94. Pavasini R, Guralnik J, Brown JC, di Bari M, Cesari M, Landi F, et al. Short physical performance battery and all-cause mortality: systematic review and meta-analysis. BMC medicine. 2016;14:1-9. Fuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth and uHealth. 2020;8(9):e18694. Evenson KR, Spade CL. Review of validity and reliability of Garmin activity trackers. Journal for the Measurement of Physical Behaviour. 2020;3(2):170-85. Kim S-H, Cho D. Validation of exercise motivations inventory–2 (EMI-2) scale for college students. Journal of American college health. 2022;70(1):114-21. Anderson DF, Cychosz CM. Development of an exercise identity scale. Perceptual and motor skills. 1994;78(3):747-51. Kroll T, Kehn M, Ho P-S, Groah S. The SCI exercise self-efficacy scale (ESES): development and psychometric properties. International Journal of Behavioral Nutrition and Physical Activity. 2007;4(1):1-6. Manta SW, Sandreschi PF, Matias TS, Tomicki C, Benedetti TR. Clustering of physical activity and sedentary behavior associated to risk for metabolic syndrome in older adults. Journal of aging and physical activity. 2019;27(6):781-6. Campagnini S, Liuzzi P, Mannini A, Basagni B, Macchi C, Carrozza MC, et al. Cross-validation of predictive models for functional recovery after post-stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation. 2022;19(1):96. Hsu Y-C, Weng H-H, Kuo C-Y, Chu T-P, Tsai Y-H. Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study. Scientific Reports. 2020;10(1):16777. Tang K, Luo R, Zhang S. An Artificial neural network algorithm for the evaluation of Postoperative Rehabilitation of Patients. Journal of Healthcare Engineering. 2021;2021:1-6. De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, et al. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput Methods Programs Biomed. 2021;208:106180. Majumder S, Mondal T, Deen MJ. Wearable sensors for remote health monitoring. Sensors. 2017;17(1):130. Mehra S, Visser B, Dadema T, van den Helder J, Engelbert RH, Weijs PJ, et al. Translating behavior change principles into a blended exercise intervention for older adults: design study. JMIR Research Protocols. 2018;7(5):e9244. Redfern J. Can older adults benefit from smart devices, wearables, and other digital health options to enhance cardiac rehabilitation? Clinics in geriatric medicine. 2019;35(4):489-97. Guu T-W, Muurling M, Khan Z, Kalafatis C, Aarsland D, Brem A-K. Wearable devices: underrepresentation in the ageing society. The Lancet Digital Health. 2023;5(6):e336-e7. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth and uHealth. 2016;4(1):e4225. Wickström G, Bendix T. The" Hawthorne effect"—what did the original Hawthorne studies actually show? Scandinavian journal of work, environment & health. 2000:363-7. Peake JM, Kerr G, Sullivan JP. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in physiology. 2018;9:743. Daniels K, Bonnechère B. Harnessing Digital Health Interventions to Bridge the Gap in Prevention for Older Adults. Frontiers in Public Health.11:1281923. Huckvale K, Prieto JT, Tilney M, Benghozi P-J, Car J. Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC medicine. 2015;13(1):1-13. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ digital medicine. 2019;2(1):1-11. Schloemer SA, Thompson JA, Silder A, Thelen DG, Siston RA. Age-related differences in gait kinematics, kinetics, and muscle function: a principal component analysis. Annals of biomedical engineering. 2017;45:695-710. Lindemann U, Zijlstra W, Aminian K, Chastin SF, De Bruin ED, Helbostad JL, et al. Recommendations for standardizing validation procedures assessing physical activity of older persons by monitoring body postures and movements. Sensors. 2014;14(1):1267-77. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine. 2018;52(6):446-62. Southard V, Gallagher R. The 6MWT: will different methods of instruction and measurement affect performance of healthy aging and older adults? Journal of Geriatric Physical Therapy. 2013;36(2):68-73. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SPIRITChecklistDigitalPhenotypingStudyDanielsBonnechre.doc SPIRITFigureDanielsBonnechre.doc.docx Appendix.docx Table1SPIRITOutcomeSchedule.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3896647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":269692653,"identity":"2f0b3693-8c91-49f5-bd34-2a6166fda9b4","order_by":0,"name":"Kim Daniels","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACCWYgwQNC7AyMDxgYLBgMiNfCzMAMVC1BhBYGiBYGoBY2CaK0SLZzJzC8qbkjw8/M/qy6sE2CwZy9Ab8WaWbeDYxzjj3jkWzmMbs944wEg2XPAfxa5IBamHnYDvMYHOZhu81TAXTYjQRitPw7zGN/mP1ZMY8BUMv9B4QdxszbBrSFmcGMGWILfh0Mks28Gw7O7TvMI3GYx1ga6Bceyx4CDpM4f3bjgzffDtvzt7c//FzYZiNnzn6AgDVAAFcCjVZSADNpykfBKBgFo2CkAAAvXDbkSWr5dAAAAABJRU5ErkJggg==","orcid":"","institution":"PXL University of Applied Sciences and Arts","correspondingAuthor":true,"prefix":"","firstName":"Kim","middleName":"","lastName":"Daniels","suffix":""},{"id":269692654,"identity":"e2423d0b-4b1a-428d-8744-5bd450c0fcf0","order_by":1,"name":"Sharona Vonck","email":"","orcid":"","institution":"PXL University of Applied Sciences and Arts","correspondingAuthor":false,"prefix":"","firstName":"Sharona","middleName":"","lastName":"Vonck","suffix":""},{"id":269692655,"identity":"29ba44db-aff6-4d2f-9b09-4da32788a7fe","order_by":2,"name":"Jolien Robijns","email":"","orcid":"","institution":"PXL University of Applied Sciences and Arts","correspondingAuthor":false,"prefix":"","firstName":"Jolien","middleName":"","lastName":"Robijns","suffix":""},{"id":269692656,"identity":"fd07dc7e-4046-40a0-b783-2bc698f2c2b9","order_by":3,"name":"Annemie Spooren","email":"","orcid":"","institution":"Hasselt University","correspondingAuthor":false,"prefix":"","firstName":"Annemie","middleName":"","lastName":"Spooren","suffix":""},{"id":269692657,"identity":"8e8b6423-ccf4-47bc-8bae-08dd29e633c7","order_by":4,"name":"Dominique Hansen","email":"","orcid":"","institution":"Hasselt University","correspondingAuthor":false,"prefix":"","firstName":"Dominique","middleName":"","lastName":"Hansen","suffix":""},{"id":269692658,"identity":"fc50aa52-9837-493c-b489-e82eb6ddc083","order_by":5,"name":"Bruno Bonnechère","email":"","orcid":"","institution":"Hasselt University","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Bonnechère","suffix":""}],"badges":[],"createdAt":"2024-01-25 09:20:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3896647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3896647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50388089,"identity":"dcb2782e-df58-489c-a31a-fd74235be5f1","added_by":"auto","created_at":"2024-01-30 18:00:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":515303,"visible":true,"origin":"","legend":"\u003cp\u003eThe spectrum of data collection explored for defining digital phenotypes in PA behavior\u003c/p\u003e","description":"","filename":"Figure1DatacollectionSpectrum.png","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/f3ce17f10ec38e1e8b16d282.png"},{"id":50388094,"identity":"13aff265-c536-48ce-949e-8e92f606eaad","added_by":"auto","created_at":"2024-01-30 18:00:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":847501,"visible":true,"origin":"","legend":"\u003cp\u003eMeta view of the integrated data collection\u003c/p\u003e","description":"","filename":"Figure2Metaviewdatacollection.png","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/1a41bc8cf467db2d0abffa55.png"},{"id":50388575,"identity":"b4ce516b-f010-46bf-9122-999862cc4476","added_by":"auto","created_at":"2024-01-30 18:08:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":205298,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework used to develop study protocol based upon the Behavior Change Wheel\u003c/p\u003e","description":"","filename":"Figure3Theoreticalframeworkusedtodevelopstudyprotcol.png","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/49d82e6608925ee51f29925b.png"},{"id":50388574,"identity":"cb7ff7aa-5408-48a3-9198-d417a2cb1f7c","added_by":"auto","created_at":"2024-01-30 18:08:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36479,"visible":true,"origin":"","legend":"\u003cp\u003eStudy outline\u003c/p\u003e","description":"","filename":"Figure4Studyoutline.png","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/a19ff4977cecc202327feacf.png"},{"id":55265576,"identity":"e343eb1b-964e-44df-b14b-f21144d4d09f","added_by":"auto","created_at":"2024-04-25 02:08:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1689803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/e8d6b57f-7b61-4624-bf6e-1e2e6d33a92a.pdf"},{"id":50388887,"identity":"796b0890-8f48-4e7c-81b9-bc02c5d7db68","added_by":"auto","created_at":"2024-01-30 18:16:42","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":147456,"visible":true,"origin":"","legend":"","description":"","filename":"SPIRITChecklistDigitalPhenotypingStudyDanielsBonnechre.doc","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/f6acecf35d37915feae11460.doc"},{"id":50388091,"identity":"18e811f8-d64e-44cb-9dc3-74562b789845","added_by":"auto","created_at":"2024-01-30 18:00:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27338,"visible":true,"origin":"","legend":"","description":"","filename":"SPIRITFigureDanielsBonnechre.doc.docx","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/5e20808bf131d338c3385ed9.docx"},{"id":50388577,"identity":"c02d2c3d-04ba-4999-8de5-e0044f92ae1a","added_by":"auto","created_at":"2024-01-30 18:08:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":360516,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/5dc806e462e81a96836814f7.docx"},{"id":50388096,"identity":"778527d4-d741-4d02-b1e1-5cdf217215ab","added_by":"auto","created_at":"2024-01-30 18:00:42","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":57169,"visible":true,"origin":"","legend":"","description":"","filename":"Table1SPIRITOutcomeSchedule.docx","url":"https://assets-eu.researchsquare.com/files/rs-3896647/v1/d44295be44a4ce2743a05e5d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the digital phenotype: A protocol for a prospective study on physical activity behavior in community-dwelling older adults","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global increase in life span is accompanied by an important rise in age-related impairments (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), often referred as noncommunicable disease pandemic (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, studies have shown that regular physical activity (PA) not only enhances overall well-being but also significantly reduces the likelihood of experiencing adverse health outcomes, in particular - but not limited to - chronic or noncommunicable disorders (e.g., coronary heart disease, stroke, certain cancers, diabetes mellitus type 2, obesity, hypertension, osteoporosis, falls and mortality) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Physical activity is defined by the World Health Organization (WHO) as: \u0026ldquo;Any bodily movement produced by skeletal muscles that increases energy expenditure\u0026rdquo; and is recognized as a key component of a healthy lifestyle\u0026rdquo; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although older adults (often categorized as those aged 65 years and above) are more susceptible to these noncommunicable diseases and thus truly benefit from preventive measures, they rank among the least physically active age groups. They devote a significant portion of their daily routine to sedentary behaviors, which contrasts with the increased risk they face for developing such health conditions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Current evidence indeed highlights the fact that a sedentary lifestyle correlates with the manifestation of up to 35 distinct chronic conditions, thereby imposing a significant decline in both the cumulative years of life and those lived in a state of high quality of life (QoL) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In light of the ever-increasing awareness of the significance of PA and the adverse outcomes associated with a sedentary way of life, it remains concerning that a substantial 58,2% of the global population of older adults is falling short of the recommended guideline of engaging in at least 30 minutes of moderate to vigorous PA per day for a minimum of 5 days a week (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs the proportion of older adults continues to rise, understanding the factors that contribute to healthy aging becomes increasingly critical (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover, the knowledge of the barriers and facilitators of PA among older adults is fundamental to design interventions to promote an active healthy lifestyle (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, the emergence of digital health technologies has ushered in a new era of monitoring, analysis, and intervention in the domain of PA (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMobile health technologies (e.g., wearable, portable, connected sensors) present a unique opportunity to capture and quantify a rich array of data concerning individuals' daily movements and exercise routines. This technological advancement allows for a comprehensive exploration of the intricacies of PA behavior, unveiling patterns that were previously difficult to discern using traditional self-report methods (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The rise of these mobile health technologies equipped with sensors that measure motion, motor skills, and mobility for unsupervised, real-world scenarios, is becoming increasingly prominent as essential adjunctive tools in the conventional clinical evaluations (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These technologies offer a breakthrough by addressing the limitations inherent in routine clinical tests (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The data collected in ecologically valid and individual-relevant environments have the capacity to capture diverse and unforeseen events, potentially overcoming the constraints of conventional assessment methods (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Furthermore, the continuous nature of data collection facilitates the identification of subtle changes in individuals' status, enhancing the precision and sensitivity of outcome information. In essence, these advancements in technology pave the way for a more nuanced understanding of individuals' PA patterns and contribute to more accurate and insightful clinical assessments (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis evolution has given rise to a concept known as digital phenotyping (DP), which refers to the utilization of data sourced from personal digital devices to swiftly quantify the individual human phenotype (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). DP achieves real-time, continuous quantification in an unsupervised way of an individual within their natural environment through the automated aggregation of data. The aim of DP is to measure human behavior patterns and functioning in both health and disease on a moment-to-moment basis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In addition, other approaches such as ecological momentary assessment (EMA) can be used. EMA is designed to repeatedly and intensively sample individual\u0026rsquo;s behavior, cognition, affect, context and other experiences in real time and ecologically (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). It is a method that enables capturing time-dependent variations of behavior and its determinants (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The combination of traditional data collection methods, such as self-reported assessment and clinical assessment \u0026ndash; latter named as supervised assessment with more dynamic and time-sensitive methods, such as time series assessment \u0026ndash; referred as unsupervised assessment - and EMA offer the promise to map and identify the crucial factors that influence PA behavior.\u003c/p\u003e \u003cp\u003eThis information can then be used as the basis for the optimization of, in this specific case, promoting PA in a precise, predictive, and personalized manner.\u003c/p\u003e \u003cp\u003eThe aim of this paper is to develop and present a comprehensive and exhaustive methodology dynamically capturing and characterizing PA behavior of community-dwelling older adults. The highly innovative approach presented is to combine different types of evaluation (i.e., supervised and unsupervised) as well as different timeframes (i.e., cross-sectional and continuous data collection) in one single protocol. This research project has three core objectives.\u003c/p\u003e \u003cp\u003eThese objectives collectively aim to enhance our understanding of PA behavior in older adults, refine digital phenotyping methodologies, and optimize the use of wearable technology in clinical trials.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e3.1 Study setting and design\u003c/h2\u003e\n \u003cp\u003eAn observational study will be conducted to gather data on multiple levels, by using a hybrid approach combining both supervised and unsupervised data collection methods. This integrated strategy will be complemented by four distinct measurement approaches, ensuring a comprehensive assessment of the research objectives, which are visualized in Figs. \u003cspan\u003e1\u003c/span\u003e and \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe complete methodology of data collection is presented and in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThis study was registered at Clinical Trials.gov (NCT06094374) and was approved by the Ethical Committee of Hasselt University (B1152023000011). An informed consent will be obtained from all subjects before participation.\u003c/p\u003e\n \u003ch2\u003e3.2 Participants\u003c/h2\u003e\n \u003cp\u003eParticipants include older adults aged 65 years and over with no severe illness introducing a loss of mobility or function or a reduction in cognitive functions preventing the proper understanding of instructions. They will be recruited via social media reach, newspaper advertisements and pitches at several senior citizen organizations, through the local community services. To be eligible for participation, individuals must meet the inclusion and exclusion criteria fully described in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e\u003cstrong\u003eTable 2 In- and exclusion criteria\u003c/strong\u003e\u003c/h2\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants are 65 years of older\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants are competent to give informed consent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants are able to actively participate in the study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants are community-dwelling\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(living independent at home or in a service apartment)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eWithout a severe illness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eDutch language proficiency as native speaker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent neurological disorder such as Parkinson\u0026rsquo;s disease, multiple sclerosis, cerebrovascular accident, \u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent cardiovascular disorder such as stroke, acute myocard\u003cstrong\u003eial\u003c/strong\u003e infarct, coronary artery bypass grafting, percutaneous coronary intervention less than 5 years ago\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent respiratory disorder, such as chronic obstructive pulmonary disease, pneumonia, pulmonary fibrosis, asthma, \u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent severe metabolic disorder, such as diabetes type 1 and 2, severe osteoporosis, \u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent severe cognitive disorders, such as Alzheimer\u0026rsquo;s disease, vascular dementia, Lewy Body dementia, frontotemporal dementia,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e3.2.1 Sample size\u003c/h2\u003e\n \u003cp\u003eDue to this study\u0026rsquo;s innovative and exploratory character involving the utilization of emerging technologies previously unexplored for this specific purpose, formal sample size calculations were deemed unattainable. Because of the lack of accessible prior studies that could provide foundational information, a sample size calculation was impossible. Therefore, a convenient sample of 200 healthy older adults was opted for this trial (\u003cspan\u003e31\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.3 Supervised versus unsupervised data collection\u003c/h2\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.3.1 Supervised data collection\u003c/h2\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.3.1.1 Self-reported assessment\u003c/h2\u003e\n \u003cp\u003eAt the baseline assessment (T0), participants will be asked to complete a comprehensive series of standardized and validated questionnaires in Dutch (English translations of these questionnaires are presented in \u003cspan\u003eAppendix\u003c/span\u003e 1).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.3.1.2 Clinical assessment\u003c/h2\u003e\n \u003cp\u003eAdditionally at baseline (T0), participants will undergo a thorough clinical assessment, consisting of balance testing, muscle strength evaluation, cardiovascular examination, and cognitive analysis. The complete psychometric characteristics of the clinical assessments are summarized in Table \u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003ch2\u003eTable 3. Psychometric measurement instruments included in the test battery of the study for measuring psychosocial skills\u003c/h2\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"945\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.4491525423728815%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.59322033898305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.911016949152541%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstrument\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.04661016949153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.4491525423728815%\" rowspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychosocial skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.59322033898305%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.911016949152541%\" valign=\"top\"\u003e\n \u003cp\u003eCognitive flexibility Inventory (CFI)\u0026nbsp;(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.04661016949153%\" valign=\"top\"\u003e\n \u003cp\u003eBrief self-report measure, designed to measure three aspects of cognitive flexibility: (a) the tendency to perceive difficult situations as controllable; (b) the ability to perceive multiple alternative explanations for life occurrences and human behavior; and (c) the ability to generate multiple alternative solutions to difficult situations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.8428927680798%\" valign=\"top\"\u003e\n \u003cp\u003eReaction Time - App \u0026ndash; Sway Medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"87.1571072319202%\" valign=\"top\"\u003e\n \u003cp\u003eEvaluation of fundamental sensory processing and neuromotor response speed by assessing an individual\u0026apos;s capacity to swiftly detect a screen color change and promptly initiate a device movement. Simple reaction time is the duration required to accomplish this task.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.8428927680798%\" valign=\"top\"\u003e\n \u003cp\u003eImpulse Control App \u0026ndash; Sway Medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"87.1571072319202%\" valign=\"top\"\u003e\n \u003cp\u003eQuantification of inhibitory processing time by presenting the user with either a \u0026quot;go\u0026quot; stimulus necessitating a motion response or a \u0026quot;no-go\u0026quot; stimulus indicating the need to refrain from a motion response.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.8428927680798%\" valign=\"top\"\u003e\n \u003cp\u003eMemory - App \u0026ndash; Sway Medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"87.1571072319202%\" valign=\"top\"\u003e\n \u003cp\u003eEvaluation of working memory, the delayed recall test involves presenting the user with a sequence of three consonants. Subsequently, the user engages in a working memory task by tracking a sequence of illuminated squares. Upon completing the working memory task, the user is prompted to recall the initial three-letter sequence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eSelf Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eSCI Exercise Self-Efficacy Scale (ESES)\u0026nbsp;(86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eA self reporting scale which instructs respondents to indicate on the 4-point rating scale (1 = not at all true, 2 = rarely true, 3 = moderately true, 4 = always true) how confident they are with regard to carrying out regular physical activities and exercise.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eSelf identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eExercise Identity Scale\u0026nbsp;(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eA 9-item instrument measuring the salience of an individual\u0026apos;s identification with exercise as an integral part of the concept of self.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003ePerceived Stress Scale (PSS)\u0026nbsp;(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eMeasurement of the degree to which situations in one\u0026rsquo;s life are appraised as stressful\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.8428927680798%\" valign=\"top\"\u003e\n \u003cp\u003eEMA question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"87.1571072319202%\" valign=\"top\"\u003e\n \u003cp\u003eStress is evaluated via EMA, through a series of questions:\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eOn a scale from 0 to 7, with 0 being no stress at all and 10 being extremely stressed, how stressed do you feel right now?\u003c/li\u003e\n \u003cli\u003eHow would you describe your current mood? (e.g., happy, anxious, sad, relaxed)\u003c/li\u003e\n \u003cli\u003eAre you experiencing any physical symptoms of stress right now? (e.g., racing heart, tension in muscles, sweating)\u003c/li\u003e\n \u003cli\u003eSocial Interaction: Are you currently alone, with others, or in a social situation? How is this affecting your stress level?\u003c/li\u003e\n \u003cli\u003eLocation: \u0026nbsp;Where are you right now? (e.g., at home, at work, in transit)\u003c/li\u003e\n \u003cli\u003eRecent Activities: What were you doing just before you received this prompt? (e.g., working, watching TV, exercising\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.8428927680798%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous registration via smartwatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"87.1571072319202%\" valign=\"top\"\u003e\n \u003cp\u003eThe smartwatch continuously measure stress levels by monitoring heart rate, heart rate variability, physical activity, and other factors to provide users with a stress score and insights into their stress patterns.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eGeriatric Depression Scale Short Form (GDS-SF)\u0026nbsp;(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eIt is a 15-item instrument used to diagnose depression in older adults.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eGeriatric Anxiety Scale \u0026nbsp;Short Form (GAS 10) (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eIt is a 10-item self-report measure designed to assess, screen, and quantify severity of anxiety symptoms among older adults\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eSubjective wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eSatisfaction with life scale (SWLS)\u0026nbsp;(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eThe scale is focused to assess global life satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eEMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eSubjective wellbeing is evaluated via EMA, through a series of questions:\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eOverall Well-Being: On a scale from 0 to 10, how would you rate your overall well-being right now, with 0 being extremely low and 10 being extremely high?\u003c/li\u003e\n \u003cli\u003eHappiness: How happy do you feel right now, on a scale from 1 to 7, with 1 being not at all and 7 being very happy?\u003c/li\u003e\n \u003cli\u003eLife Satisfaction: On a scale from 1 to 7, how satisfied are you with your life at this moment, with 1 being very dissatisfied and 7 being very satisfied?\u003c/li\u003e\n \u003cli\u003ePositive Emotions: Please indicate which positive emotions you are currently experiencing (e.g., joy, gratitude, contentment).\u003c/li\u003e\n \u003cli\u003eNegative Emotions: Please indicate which negative emotions you are currently experiencing (e.g., sadness, stress, anger).\u003c/li\u003e\n \u003cli\u003eEngagement: How engaged or absorbed are you in your current activity or situation right now, on a scale from 1 to 5, with 1 being not at all and 5 being completely absorbed?\u003c/li\u003e\n \u003cli\u003eMeaning and Purpose: Do you feel that what you are doing right now has meaning or purpose? (yes/no)\u003c/li\u003e\n \u003cli\u003eEnvironmental Context: Where are you right now, and what are you doing? (e.g., at home, at work, in nature, reading a book)\u003c/li\u003e\n \u003cli\u003eSocial Interactions: Are you currently alone, with others, or in a social situation? How do these interactions make you feel?\u003c/li\u003e\n \u003cli\u003ePhysical Well-Being: How would you rate your physical well-being right now, on a scale from 1 to 5, with 1 being very poor and 5 being excellent?\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.086474501108647%\" valign=\"top\"\u003e\n \u003cp\u003eEmotional loneliness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.419068736141908%\" valign=\"top\"\u003e\n \u003cp\u003eDe Jong Gierveld Loneliness Scale\u0026nbsp;(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.49445676274945%\" valign=\"top\"\u003e\n \u003cp\u003eInstrument to measure loneliness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ch2\u003eTable 4. Psychometric measurement instruments included in the test battery of the study for measuring motor skills\u003c/h2\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"945\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.4491525423728815%\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotor skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMuscle strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.652542372881356%\" valign=\"top\"\u003e\n \u003cp\u003eHand Grip strength measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.03389830508475%\" valign=\"top\"\u003e\n \u003cp\u003eMeasurement to assess overall upper body muscle strength. It is measured using a handheld dynameter.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"top\"\u003e\n \u003cp\u003eQuadriceps strength measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"86.07594936708861%\" valign=\"top\"\u003e\n \u003cp\u003eMeasurement to assess overall lower body muscle strength. It is measured using a handheld dynameter.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eBalance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eKinvent Force Plate \u0026reg; Balance system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eKinvent\u0026trade; utilizes a force plate technology to provide accurate and reliable balance assessment and rehabilitation for healthcare professionals.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eFlexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eSit and Reach Test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eMeasurement of lower back and hamstring flexibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eStadiometer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eBlood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eBP monitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eSystolic blood pressure measured with upper arm blood pressure monitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCardiometabolic Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eSix Minute Walking Test (6MWT)\u0026nbsp;(105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eIs used to assess the fitness level of healthy adults and of older adults including spatiotemporal data from DigitSole \u0026reg; technology.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous registration via smartwatch pf aerobic capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"86.07594936708861%\" valign=\"top\"\u003e\n \u003cp\u003eThe smartwatch uses a combination of heart rate data, GPS tracking, and other sensor information to estimate and monitor aerobic capacity (such as heart rate monitoring, VO2 Max estimation, recovery advisor, training load and status and performance metrics.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eIPAQ\u0026nbsp;(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-reported assessment tool designed to measure an individual\u0026apos;s physical activity levels and patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous registration via smartwatch\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"86.07594936708861%\" valign=\"top\"\u003e\n \u003cp\u003eThe smartwatch measures continuously steps, calories, heart rate, number of floors, MVPA, Body Battery, Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"top\"\u003e\n \u003cp\u003eEMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"86.07594936708861%\" valign=\"top\"\u003e\n \u003cp\u003ePA is evaluated via EMA, through a series of questions:\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eWhat type of physical activity are you currently engaged in?\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEnvironmental Context: Where are you right now, and what are you doing? (e.g., at home, at work, in nature, reading a book)\u003c/li\u003e\n \u003cli\u003eOn a scale from 1 to 5, with 1 being very light and 5 being very intense, how would you rate the intensity of what you were currently doing?\u003c/li\u003e\n \u003cli\u003eAre you alone, with others, or in a group?\u003c/li\u003e\n \u003cli\u003eWhat is the weather like as you engage in this activity? (e.g., sunny, rainy, hot, cold)\u003c/li\u003e\n \u003cli\u003eAre there any factors or obstacles that are making it difficult for you to be active right now? (e.g., lack of time, fatigue)\u003c/li\u003e\n \u003cli\u003eHow would you rate your current energy levels? (e.g., low, moderate, high)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eSleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eGeriatric Sleep Questionnaire (GSQ \u0026ndash; 6)\u0026nbsp;(49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eShort questionnaire specifically designed to assess the subjective sleep quality in older people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous registration via smartwatch\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eThe smartwatch measures sleep using a combination of sensors and algorithms to monitor your movement patterns and heart rate throughout the night, with movement tracking, heart rate monitoring, light vs. deep sleep, heart rate variability (HRV), wake detection, sleep duration, quality, and stages.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eEMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eSleep is evaluated via EMA, through a series of questions:\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eHow many hours of sleep did you get last night?\u003c/li\u003e\n \u003cli\u003eOn a scale from 1 to 5, with 1 being very poor and 5 being excellent, how would you rate the quality of your sleep last night?\u003c/li\u003e\n \u003cli\u003eDid you wake up during the night? If yes, how many times and for how long?\u003c/li\u003e\n \u003cli\u003eHow fatigued or refreshed do you feel this morning, on a scale from 1 to 7, with 1 being very fatigued and 7 being very refreshed?\u003c/li\u003e\n \u003cli\u003eWhat time did you go to bed last night, and what time did you wake up this morning?\u003c/li\u003e\n \u003cli\u003eWas your sleep environment comfortable and conducive to sleep last night? (yes/no)\u003c/li\u003e\n \u003cli\u003eHave you experienced any episodes of excessive daytime sleepiness today? (yes/no)\u003c/li\u003e\n \u003cli\u003eHave you taken any naps today? If yes, please indicate the duration.\u003c/li\u003e\n \u003cli\u003eHave you consumed any caffeine or alcohol in the last few hours? (yes/no)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.416851441241684%\" valign=\"top\"\u003e\n \u003cp\u003eAbdominal circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003eMeasuring tape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.3880266075388%\" valign=\"top\"\u003e\n \u003cp\u003eAssessment of abdominal obesity or waist size.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.3.2 Unsupervised data collection\u003c/h2\u003e\n \u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3.2.1 Ecological momentary assessment (EMA)\u003c/h2\u003e\n \u003cp\u003eParticipants will receive three random prompts daily (TR) over a two-week period on their mobile phones via auditory signal. The Smartphone Ecological Momentary Assessment\u003csup\u003e3\u003c/sup\u003e (SEMA\u003csup\u003e3\u003c/sup\u003e) application (\u003cspan\u003e32\u003c/span\u003e) will be installed on the participant\u0026rsquo;s smartphone and will be used to trigger the EMA questionnaire.\u003c/p\u003e\n \u003cp\u003eTo ensure adequate spacing across the day, four timeframes, each of two hours, will be constructed between 8:00 AM and 8:00 PM, in which one trigger will be randomly given. They will be instructed to halt their ongoing activities and promptly complete the EMA questionnaire, which typically will take two to three minutes. In cases where participants are driving or engaged in activities incompatible with questionnaire completion, they are strictly advised to disregard the prompt. If a participant fails to complete the EMA questionnaire following the initial prompt, the phone will emit a maximum of three reminder signals at 5-minute intervals. After the third reminder, access to the EMA questionnaire will be temporarily suspended until the subsequent scheduled questionnaire.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.3.3 Time Series data collection\u003c/h2\u003e\n \u003cp\u003eFinally, participants will be invited to wear a monitoring device continuously for a period of two weeks (24/7) to record their activity data (TC).\u003c/p\u003e\n \u003cp\u003eThe continuous data contains data derived from a GARMIN wearable, capturing participants\u0026apos; day-to-day activities through seamless, non-intrusive sensing. The GARMIN Vivosmart 5 was selected, hence it is a widely embraced smartwatch renowned for its popularity and high acceptance. Additionally, this type of wearable can be used specifically for research purposes, providing direct access to unprocessed raw data through the manufacturer\u0026rsquo;s research portal.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.4 Measurements\u003c/h2\u003e\n \u003cp\u003eAll measurements are based upon the Behavior Change Wheel (BCW) (\u003cspan\u003e33\u003c/span\u003e), which finds its theoretical foundation in Michie\u0026apos;s COM-B framework (\u003cspan\u003e34\u003c/span\u003e). Briefly, it represents a comprehensive theoretical structure that dissects behavior into three essential components: Capability, opportunity, and motivation. This model, illustrated in Fig. \u003cspan\u003e3\u003c/span\u003e, provides a holistic perspective on the three main factors influencing behavior\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eOpportunity (\u003cem\u003econtext\u003c/em\u003e) pertains to the external conditions enabling or hindering the behavior\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCapability (\u003cem\u003eskills\u003c/em\u003e) refers to the individual\u0026apos;s psychological and physical ability to engage in the behavior,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMotivation (\u003cem\u003edrive\u003c/em\u003e) encompasses the internal processes driving the inclination to perform the behavior.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eBy adopting this framework, the study tends to embrace a multifaceted and dynamic approach for analyzing and understanding the complex interplay of these elements that shape observed behaviors in PA. A summary of all included measurements on the different levels can be found in detail in Table \u003cspan\u003e1\u003c/span\u003e. We are now going to discuss the three subcomponents of the BCW are elaborated in details.\u003c/p\u003e\n \u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.4.1 Opportunity\u003c/h2\u003e\n \u003cp\u003eThe opportunity of the BCW includes aspects of the physical, sociocultural, economic, and political environment that can influence behavior from a micro, meso or a macro level (\u003cspan\u003e35\u003c/span\u003e). Influences can arise from concrete settings in which the behavior occurs or from broader systems that influence behavior indirectly. To gauge these components, self-reporting measurements and EMA will be employed.\u003c/p\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4.1.1 Self-reported assessment\u003c/h2\u003e\n \u003cp\u003eSelf-reported information on age, gender, height, smoking status, marital status, level of education, living arrangement, urbanization level, participation status, self-rated health level (\u003cspan\u003e36\u003c/span\u003e), and pain level (\u003cspan\u003e37\u003c/span\u003e) will be collected. Participants will also be asked to indicate their retirement status, level of income, living status, and access to facilities in the community. Their QoL level will be evaluated using the WHOQOL-BREF (\u003cspan\u003e38\u003c/span\u003e). These items will be collected using the online survey tool Qualtrics (\u003cspan\u003e39\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.4.1.2 EMA\u003c/h2\u003e\n \u003cp\u003eParticipants will rate their self-rated health, five physical complaints (i.e., muscle stiffness, pain, dizziness, shortness of breath, fatigue), contextual factors and QoL using a 7-point Likert scale (\u003cspan\u003e40\u003c/span\u003e). The sequence of questions in the questionnaire will vary, with questions presented in a random order.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.4.2 Capabilities\u003c/h2\u003e\n \u003cp\u003eCapabilities refers to an individual\u0026apos;s capacity to effectively perform a specific behavior. It encompasses a range of skills and abilities required for the successful execution of that behavior. The significance of capabilities lies in its pivotal role; when individuals lack the necessary skills for a particular behavior, the likelihood of them adopting and sustaining behavior change diminishes.\u003c/p\u003e\n \u003cp\u003eCapabilities can be deconstructed into two primary categories, which are \u003cem\u003epsychosocial\u003c/em\u003e and \u003cem\u003ephysical\u003c/em\u003e capability. \u003cem\u003ePsychosocial skills\u003c/em\u003e pertain to an individual\u0026apos;s cognitive and emotional aptitude to engage in a given behavior. It encompasses a spectrum of factors, including knowledge, skills, memory, attention, and self-regulation. \u003cem\u003ePhysical Capability\u003c/em\u003e refers to the physical capacity to carry out a behavior. It includes factors such as physical strength, mobility, cardiovascular capacity, and balance (\u003cspan\u003e41\u003c/span\u003e, \u003cspan\u003e42\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe measurement instruments included to map the respective psychosocial and motor skills are summarized in Tables \u003cspan\u003e3\u003c/span\u003e and \u003cspan\u003e4\u003c/span\u003e. They will be evaluated across the four distinct levels of measurement, encompassing self-reporting, clinical assessment, ecological momentary assessment, and time series analysis.\u003c/p\u003e\n \u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.4.2.1 Self-Reporting assessment\u003c/h2\u003e\n \u003cp\u003eSelf-reported information on driving status, mobility issues, depression (\u003cspan\u003e43\u003c/span\u003e), stress (\u003cspan\u003e44\u003c/span\u003e), cognitive functioning (\u003cspan\u003e45\u003c/span\u003e), subjective wellbeing (\u003cspan\u003e46\u003c/span\u003e), anxiety (\u003cspan\u003e47\u003c/span\u003e), physical activity (\u003cspan\u003e48\u003c/span\u003e), sleep pattern (\u003cspan\u003e49\u003c/span\u003e), and emotional loneliness (\u003cspan\u003e50\u003c/span\u003e) will be collected.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e3.4.2.2 Clinical assessment\u003c/h2\u003e\n \u003cp\u003ePsychological and motor skills will undergo comprehensive evaluation through clinical assessments administered by experienced therapists. These assessments will encompass cognitive functioning, cardiometabolic health, muscle strength, and balance, providing a holistic understanding of an individual\u0026apos;s overall health.\u003c/p\u003e\u003cbr\u003ea. Cognitive functions\u003cbr\u003e\u003cbr\u003e\n \u003cp\u003eThe assessment of cognitive functioning will be conducted using the SWAY ( SWAY Medical Inc. in Tulsa, OK, USA) (\u003cspan\u003e51\u003c/span\u003e). The cognitive performance segment of the app encompasses three modules grounded in sensory and neuromotor principles. These modules aim to assess stimulus recognition, cognitive processing speed, neuromotor response, working memory, and reaction time. The cognitive testing segment, focusing on reaction time, has undergone clinical evaluation and demonstrated reliability and validity, comparing favorably to the standard Computerized Test of Information Processing assessment. However, the capacity of SWAY to function consistently across various mobile devices and operating systems is yet to be validated (\u003cspan\u003e52\u003c/span\u003e, \u003cspan\u003e53\u003c/span\u003e, \u003cspan\u003e54\u003c/span\u003e), therefore it will be use to collect all the data.\u003c/p\u003e\u003cbr\u003eb. Physical functioning\u003cbr\u003e\u003cbr\u003e\n \u003cp\u003e\u003cem\u003eWalking performance\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eGait analysis will be performed using the Six Minute Walking Test (6MWT). The 6MWT serves as a robust tool for evaluating exercise capacity at levels reflective of typical efforts exerted by the elderly during daily activities, as established by Lipkin in 1986 (\u003cspan\u003e55\u003c/span\u003e). Additionally, it proves invaluable for assessing the progression of functional exercise capacity in diverse clinical intervention studies (\u003cspan\u003e56\u003c/span\u003e, \u003cspan\u003e57\u003c/span\u003e, \u003cspan\u003e58\u003c/span\u003e, \u003cspan\u003e59\u003c/span\u003e). The test demonstrates high reliability among healthy elderly individuals (Intra-Class Correlation\u0026thinsp;=\u0026thinsp;0.93) (\u003cspan\u003e60\u003c/span\u003e, \u003cspan\u003e61\u003c/span\u003e, \u003cspan\u003e62\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDuring this test, diverse data will be gathered using specialized instruments. Gait speed, proven to be a robust predictor of adverse health outcomes, remains significant irrespective of the presence of common medical conditions and risk factors for diseases (\u003cspan\u003e63\u003c/span\u003e, \u003cspan\u003e64\u003c/span\u003e, \u003cspan\u003e65\u003c/span\u003e). Many studies demonstrated a strong association with incident disability, cognitive decline and dementia, falls and related fractures, mortality, and healthcare utilization (e.g., hospitalization and institutionalization). Although tested in very different populations (e.g., inpatients and outpatients, independent, frail, and disabled subjects), different walking distances, and studied outcomes, the prognostic value is very consistent (\u003cspan\u003e66\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eGait analysis will be performed using Digitsole\u0026reg; insoles (Nancy, France) to quantify various parameters during walking. PODOSmart\u0026reg; insoles, equipped with wireless sensors, can seamlessly fit into any shoe, enabling the measurement of spatial, temporal, and kinematic gait parameters. These intelligent insoles feature multiple sensors to detect and record foot movements, alongside a microprocessor that computes biomechanical data related to gait (\u003cspan\u003e67\u003c/span\u003e). Additionally, potential gait deviations can be discerned through Inertial Measurement Units (IMUs). These IMUs capture crucial gait parameters such as speed, cadence, and biomechanical angles of motion during walking, interfacing with dedicated software on a tablet. The software facilitates the generation of comprehensive data reports, encompassing kinematic variables specific to an individual\u0026apos;s walking patterns (\u003cspan\u003e59\u003c/span\u003e). Notably, the validity and reliability of Digitsole\u0026reg; have been studied in samples of healthy individuals over brief walking periods (\u003cspan\u003e67\u003c/span\u003e, \u003cspan\u003e68\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMuscle strength\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMuscle strength will be assessed using the Kinvent\u0026reg;2016 handheld dynamometer. The test protocol involves consecutively evaluating the strength of different muscle groups of the lower extremities: abductors (side lying), adductors (supine), extensors (prone), and flexors (sitting). Each muscle group will undergo three tests, and the final result will be based on the best value obtained from these tests, following the protocol established by Thorborg (\u003cspan\u003e69\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAdditionally, hand grip force will be measured using the K-Force Grip\u0026reg; (Kinvent, Montpellier, France). This measurement serves to evaluate overall strength, enabling comparisons of muscle function across populations and tracking the progression of conditions such as sarcopenia, while also identifying potential deficits (\u003cspan\u003e70\u003c/span\u003e, \u003cspan\u003e71\u003c/span\u003e). The dynamometer has been designed for assessing and rehabilitating handgrip strength. It provides real-time biofeedback on a Tablet or Smartphone. A study conducted by Nikodelis (\u003cspan\u003e72\u003c/span\u003e) comparing Jamar and K-Force Grip\u0026reg; found no fixed or proportional bias. Both groups exhibited high correlation coefficients, with the lowest correlation observed between the two instruments (r\u0026thinsp;=\u0026thinsp;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating strong reliability.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBalance\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003ePostural balance is crucial for maintaining a specific posture in response to external disturbances. Imbalances stemming from malfunctions in the visual, vestibular, or proprioceptive sensory systems can lead to issues such as falls, injuries, and instability in joints. To identify and address these concerns, clinical tests are essential (\u003cspan\u003e73\u003c/span\u003e, \u003cspan\u003e74\u003c/span\u003e). In this study, postural balance will be assessed using the Kinvent PLATES v3\u0026reg; (Kinvent, Montpellier, France). Participants will undergo the Single Leg Balance (SLB) test under various conditions: (i) three repetitions for each leg with open eyes on the PLATES, and (ii) three repetitions for each leg with eyes closed on the PLATES. The SLB test involves maintaining a stationary position on one leg for 10 seconds, focusing on a point 5 meters away, with hands on hips and the non-load-bearing leg slightly bent at the hip and knee (\u003cspan\u003e75\u003c/span\u003e, \u003cspan\u003e76\u003c/span\u003e). To facilitate a comprehensive comparison between open and closed eyes conditions, a 10-second test duration was chosen, aligning with norms established for the closed eyes condition during unipodal balance exercises (norm\u0026thinsp;=\u0026thinsp;9.4 seconds) (\u003cspan\u003e77\u003c/span\u003e). Additionally, a second test, the Single Leg Landing (SLL), will be conducted with three repetitions for each leg on the PLATES. This dynamic unilateral balance exercise requires participants to descend from a step positioned 19 cm above the force platform with a bounce, ensuring both feet are suspended before landing. Subsequently, participants must stabilize on one leg for 15 seconds, with hands on hips and their gaze fixed at a point 5 meters away (\u003cspan\u003e78\u003c/span\u003e, \u003cspan\u003e79\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFunctional capability\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe Short Physical Performance Battery (SPPB) has emerged as one of the most promising tools to evaluate functional capability and provide a measure of the biological age of an older individual (\u003cspan\u003e80\u003c/span\u003e). It is an objective tool for measuring the lower extremity physical performance status. Three domains, which include balance, usual or self-selected gait speed, and lower limb strength, are assessed by a three-stage balance test (feet side-by-side, semi tandem, and tandem positions), a 3-m or 4-m gait speed test (time spent to walk the course), and a repetitive chair stand test (five times chair sit-to-stand test), respectively. A 0- to 12-point scale is used to score the sum of the three assessments with higher point values corresponding with greater levels of physical function and lower disability, whereas lower point values correspond with lower levels of physical function and higher disability, respectively (\u003cspan\u003e80\u003c/span\u003e). The timed results of each subtest are rescaled according to predefined cut points for obtaining a score ranging from 0 (worst performance) to 12 (best performance) (\u003cspan\u003e81\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e3.4.2.3 Ecological momentary assessment\u003c/h2\u003e\n \u003cp\u003eParticipants will rate their stress, physical activity, sleep, and using a 7-point Likert scale. The sequence of questions in the questionnaire will vary, with questions presented in a random order (\u003cspan\u003e40\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e3.4.2.4 Time Series data assessment\u003c/h2\u003e\n \u003cp\u003eThroughout the two-week trial, a continuous monitoring process using Garmin Vivosmart 5\u0026reg; activity tracker will collect various parameters, including stress levels, physical activity, step count, calorie expenditure, heart rate, the number of floors climbed, moderate to vigorous activity (MVPA), cardiometabolic outcomes, body battery, and sleep patterns (\u003cspan\u003e82\u003c/span\u003e, \u003cspan\u003e83\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFinally, participants will be invited to wear the GARMIN Vivosmart 5\u0026reg; continuously for a period of two weeks (24/7) to record their activity data (TC).\u003c/p\u003e\n \u003cp\u003eThe continuous data contains data derived from a GARMIN wearable, capturing participants\u0026apos; day-to-day activities through seamless, non-intrusive sensing. The GARMIN Vivosmart 5\u0026reg; was selected, hence it is a widely embraced smartwatch renowned for its popularity and high acceptance. Additionally, this type of wearable can be used specifically for research purposes, providing direct access to unprocessed raw data through the manufacturer\u0026rsquo;s research portal.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003e3.4.2 Motivation\u003c/h2\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e3.4.3.1 Clinical assessment\u003c/h2\u003e\n \u003cp\u003eMotivators or drives are the factors that guide or motivate a person\u0026apos;s behavior from reflective or rational considerations or from automatic processes or factors such as needs, emotions, and habits. Within the realm of motivation, two fundamental drives can be identified: automatic and reflective motivation. The first one is characterized by the emotional and instinctual triggers that shape our actions. This particular facet is gauged via \u003cem\u003eself-reporting assessment\u003c/em\u003e by employing the Exercise Motivation Inventory (EMI-2) (\u003cspan\u003e84\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAnother aspect is the Reflective Motivation, rooted in the cognitive and thoughtful aspects that steer behavior transformation. This latter dimension encompasses factors such as beliefs, intentions, and goal-setting, all of which play pivotal roles in the journey towards change. To assess this aspect, both the Exercise Identity Scale (\u003cspan\u003e85\u003c/span\u003e) and the Exercise Self-Efficacy Scale (\u003cspan\u003e86\u003c/span\u003e) are being used. Procedure\u003c/p\u003e\n \u003cp\u003eThe assessments will be administered at different significant time points, as depicted in Fig. \u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cbr\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e3.4.3.2 EMA\u003c/h2\u003e\n \u003cp\u003eParticipants will rate their motivation level and intention to be physically active using a 7-point Likert scale (\u003cspan\u003e40\u003c/span\u003e). The sequence of questions in the questionnaire will vary, with questions presented in a random order.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e3.5 Data management plan\u003c/h2\u003e\n \u003cp\u003eA range of instruments will be employed to gather data for the study. These instruments include self-report questionnaires, clinical assessments, and EMA tools. The questionnaires and assessments have been selected based on their relevance to the Behavior Change Wheel framework, which will guide the analysis.\u003c/p\u003e\n \u003cp\u003eIn the course of this study, a fundamental component of our data management strategy is the development of a data integration platform. This platform is essential for seamlessly connecting and consolidating all collected data, ensuring a holistic view of the information gathered.\u003c/p\u003e\n \u003cp\u003eTo prioritize data privacy and confidentiality, all data within the integration platform will undergo a pseudonymization process. This critical step involves the replacement of personally identifiable information (PII) with unique pseudonyms, rendering the data anonymous while preserving its analytical value. Pseudonymization will be performed in accordance with applicable data protection regulations to safeguard the privacy of study participants.\u003c/p\u003e\n \u003cp\u003eThe data integration platform will serve as the central repository for all collected data, including self-report questionnaires, clinical assessments, EMA, and time series data. By housing these diverse data types within a single, organized framework, we aim to facilitate comprehensive data analysis.\u003c/p\u003e\n \u003cp\u003eWithin this integrated platform, standardized coding schemes for variables will be applied to maintain consistency and facilitate data analysis. Coding guidelines and dictionaries will be established to ensure that all team members involved in data management adhere to uniform data standards.\u003c/p\u003e\n \u003cp\u003eData storage will adhere to relevant data protection regulations, ensuring that data is retained and managed in compliance with legal requirements.\u003c/p\u003e\n \u003cp\u003eTo maintain data quality within the integrated platform, regular data quality checks will be implemented throughout the course of the study. These checks will identify and address discrepancies or outliers in the data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003e3.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe assessment of data normality will be conducted using graphical techniques, including QQ-plots, histograms, and boxplots. Continuous data will be reported using the mean and standard deviation (sd) or the median and interquartile range (IQR), depending on the distribution. Categorical data will be presented as frequencies and percentages.\u003c/p\u003e\n \u003cp\u003eTo answer the different research question, different machine learning methods will be used and tested. We will first evaluate unsupervised learning with the use of hierarchical clustering, a well-established method in multivariate statistical analysis (\u003cspan\u003e87\u003c/span\u003e). The purpose of this approach is to reveal hidden patterns in how participants classify themselves, based on their self-reported adherence to the WHO\u0026rsquo;s recommendation of PA, and to identify participants with increased risk of falls. If the current strategy proves unsuccessful, alternative method involving a random decision forest will be tested or gradient boosting algorithms (\u003cspan\u003e88\u003c/span\u003e). Ensemble learning methods, such as gradient boosting techniques, involve the integration of multiple weak predictors to form a more accurate one. This approach iteratively introduces decision trees to the model, with each new tree aiming to rectify the errors of its predecessors. Gradient boosting algorithms demonstrate notable effectiveness when dealing with intricate data, often achieving high accuracy across a diverse range of problems compared to stepwise linear regression (\u003cspan\u003e89\u003c/span\u003e). Nevertheless, the preference is to maintain a straightforward model, prioritizing simplicity to facilitate clinical interpretation.\u003c/p\u003e\n \u003cp\u003eWe will then evaluate supervised learning using (recurrent) neural network (RNN) (\u003cspan\u003e90\u003c/span\u003e). Different models will be trained according to the research questions. The objective of this procedure is to identify distinctive variables that distinguish individuals who have experienced falls from those who have not and those who adhere to the WHO\u0026rsquo;s PA recommendations. The added value of RNN, in our context, is the ability to process time series input. Such kind of network possess the capability to retain an internal memory of past inputs, leveraging it for predicting future inputs. RNNs excel in modeling intricate temporal interactions, demonstrating superior flexibility and robustness when handling sequential data (\u003cspan\u003e91\u003c/span\u003e). In comparison to stepwise regression, RNNs are adept at capturing complex temporal relationships and exhibit lower susceptibility to overfitting.\u003c/p\u003e\n \u003cp\u003eThe significance threshold will be set at 0.05. Statistical analyses will be performed in R using RStudio (version 3.6.3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis research presents a novel approach to enhance the use of DP through the utilization of a hybrid measuring methodology that combines supervised and unsupervised methodologies. The main aim of the protocol is to acquire a thorough comprehension of PA behavior in the older adult population, ascertain the significant factors that influence this behavior, and develop DPs associated with PA behavior.\u003c/p\u003e \u003cp\u003eThe innovative approach presented, which integrates supervised and unsupervised data collection methods and incorporates a diverse range of measurement techniques such as self-reporting, clinical assessments, EMA, and continuous data from wearable devices, appears commendable. Our intention is to further investigate its effectiveness in acknowledging the multifaceted nature of physical activity behavior among older adults.\u003c/p\u003e \u003cp\u003e \u003cem\u003eClinical relevance\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePrevious works demonstrate that data collected within an ecologically valid and individually relevant environment can surpass the limitations inherent in conventional clinical assessments or one-time self-reporting (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). The distinct advantage of unsupervised daily PA monitoring lies in its ability to detect more nuanced changes over time. Ultimately, through the utilization of this methodology, our goal is to identify distinct DPs, enabling the tailoring of interventions to meet the unique needs of older adults. This personalized approach holds the potential to yield more effective and engaging interventions, finally enhancing their overall well-being and health (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). This initiative directly addresses a substantial public health concern.\u003c/p\u003e \u003cp\u003e \u003cem\u003eChallenges\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, there are potential pitfalls and challenges that warrant careful consideration in this context. Technological adoption rates among community-dwelling older adults can vary widely, and some individuals may lack the necessary technological literacy or access to digital devices (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). This may introduce selection bias into the study, and researchers must be mindful of the sample\u0026rsquo;s representativeness. Additionally, usability concerns surrounding digital tools such as wearables must be addressed. Older adults may struggle with complex interfaces or may have physical limitations that hinder their interaction with these devices (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). Furthermore, it is imperative to acknowledge that the use of mobile technology has the ability to elicit modifications in behavior, the Hawthorne effect, even in the absence of explicit feedback. Therefore, it is imperative to do research that investigates the circumstances in which user performance in unsupervised environments corresponds to that in supervised environments. Furthermore, it is imperative to investigate if the observed alterations in behavior have a direct impact on levels of PA (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, only a small proportion of health and performance technologies, about 5%, have been proven effective through rigorous, independent validation. Consequently, the value of these technologies remains a topic open to debate and should be approached carefully (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEthical considerations are paramount in the context of digital health interventions and data collection in older adults (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Issues such as data privacy and -security must be thoroughly examined to safeguard the rights and well-being of the study's participants. Moreover, the success of the research heavily relies on the translation of complex data streams from wearable devices into actionable insights. The challenge lies in making this information understandable and beneficial for both individuals and healthcare practitioners. Effective data interpretation and communication are critical components in this matter (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe representativeness of the sample is another aspect that will require attention. The demographics and health status of the older adults participating in the study should be carefully considered. A non-representative sample could limit the generalizability of the findings. Therefore, efforts should be made to ensure a diverse and inclusive sample, capturing a broader spectrum of experiences and needs. In order to ensure a representative sample, diverse recruitment methods will be employed, tapping into various channels and establishing partnerships with relevant organizations. Stratification based on key demographics, including age, gender, ethnicity, and socioeconomic status, will be implemented, with a particular focus on oversampling underrepresented groups. Additionally, continuous monitoring of the demographic composition during recruitment will guide necessary adjustments based on feedback and observed trends.\u003c/p\u003e \u003cp\u003eLongitudinal data collection, while beneficial, can be resource-intensive and may pose difficulties in participant retention and compliance over an extended period. Strategies to minimize attrition and maximize engagement are necessary to ensure the data's quality and completeness. An extensive training on how to use the wearable and the SEMA\u0026sup3; application is recommended to obtain a high response rate. Additional, regular check-ins, personalized feedback will be implemented to maintain participants motivation and compliance.\u003c/p\u003e \u003cp\u003eLastly, the use of machine learning and neural networks, while offering powerful tools for data analysis, can be complex and require expertise. Ensuring that the chosen algorithms are appropriate and well-tuned is crucial for the study's success. The algorithms used for define the DPs need to undergo thorough validation. To enhance the effectiveness of unsupervised measures, there is a need for standardized reporting of parameters, such as establishing a core dataset across studies. This reporting should also encompass metadata, which includes data that accompanies and describes the primary data. Standardizing the duration of unsupervised assessments and providing detailed are additional requirements (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these challenges, the insights gained could inform targeted interventions and public health policies, addressing the unique challenges of an aging global population. Integration of findings into clinical practices may lead to personalized strategies for promoting PA among older adults, positively impacting health outcomes and reducing healthcare costs. Ultimately, the research contributes to the broader fields of gerontology, public health, and data science, with potential implications for societal well-being and the promotion of active aging, it is therefore of the utmost importance to perform such kind of multidimensional assessment.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, this study holds promise in bridging the gap between conventional assessment methods, innovative methods and the dynamic nature of older adults' PA behavior. By addressing the aforementioned potential and possible challenges, researchers can navigate the complexities of applying digital tools in this context effectively, ultimately contributing to the promotion of active lifestyles and the well-being of older adults. It is crucial to recognize the time- and context-specific variations when crafting dynamic health behavior interventions. By effectively inspiring and engaging older adults in the appropriate time and context by knowing their PA phenotype, they can be encouraged to adopt healthier habits, such as increased physical activity and reduced sedentary behavior (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKD: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Funding acquisition, Investigation, Resources, Visualization. SV: Writing \u0026ndash; review \u0026amp; editing, Investigation. JR: Writing \u0026ndash; review \u0026amp; editing, Investigation. AS: Writing \u0026ndash; review \u0026amp; editing. DH: Writing \u0026ndash; review \u0026amp; editing. BB: Writing \u0026ndash; review \u0026amp; editing, Methodology, Supervision, Writing \u0026ndash; original draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSuzman R, Beard J. Global health and aging. NIH Publ. 2011;1(4):273-7.\u003c/li\u003e\n\u003cli\u003eAllen L. Are we facing a noncommunicable disease pandemic? J Epidemiol Glob Health. 2017;7(1):5-9.\u003c/li\u003e\n\u003cli\u003eBadenhop DT, Cleary PA, Schaal SF, Fox EL, Bartels RL. Physiological adjustments to higher-or lower-intensity exercise in elders. Medicine and Science in Sports and Exercise. 1983;15(6):496-502.\u003c/li\u003e\n\u003cli\u003eColcombe S, Kramer AF. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychological science. 2003;14(2):125-30.\u003c/li\u003e\n\u003cli\u003eGalle SA, Liu J, Bonnech\u0026egrave;re B, Amin N, Milders MM, Deijen JB, et al. The long-term relation between physical activity and executive function in the Rotterdam Study. European Journal of Epidemiology. 2023;38(1):71-81.\u003c/li\u003e\n\u003cli\u003eOrganization WH. WHO guidelines on physical activity and sedentary behaviour. 2020.\u003c/li\u003e\n\u003cli\u003ePiercy KL, Troiano RP. Physical activity guidelines for Americans from the US department of health and human services: Cardiovascular benefits and recommendations. Circulation: Cardiovascular Quality and Outcomes. 2018;11(11):e005263.\u003c/li\u003e\n\u003cli\u003eHarvey JA, Chastin SF, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. Journal of aging and physical activity. 2015;23(3):471-87.\u003c/li\u003e\n\u003cli\u003eBooth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Comprehensive physiology. 2012;2(2):1143.\u003c/li\u003e\n\u003cli\u003eAndersen LB, Schnohr P, Schroll M, Hein HO. All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Archives of internal medicine. 2000;160(11):1621-8.\u003c/li\u003e\n\u003cli\u003eLee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The lancet. 2012;380(9838):219-29.\u003c/li\u003e\n\u003cli\u003eCarter S, Hartman Y, Holder S, Thijssen DH, Hopkins ND. Sedentary behavior and cardiovascular disease risk: mediating mechanisms. Exercise and sport sciences reviews. 2017;45(2):80-6.\u003c/li\u003e\n\u003cli\u003eEkelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The lancet. 2016;388(10051):1302-10.\u003c/li\u003e\n\u003cli\u003eCunningham C, O\u0026rsquo;Sullivan R. Why physical activity matters for older adults in a time of pandemic. European Review of Aging and Physical Activity. 2020;17:1-4.\u003c/li\u003e\n\u003cli\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1\u0026middot; 9 million participants. The lancet global health. 2018;6(10):e1077-e86.\u003c/li\u003e\n\u003cli\u003eGilbert C, Hagerty D, Taggert HM. Exploring factors related to healthy ageing. Self-Care, Dependent-Care \u0026amp; Nursing. 2012;19(1):20-5.\u003c/li\u003e\n\u003cli\u003eJackman PC, Cooke S, George T, Blackwell J, Middleton G. Physical activity experiences of community-dwelling older adults with physical disabilities: a scoping review of qualitative research. Disability and Rehabilitation. 2023:1-13.\u003c/li\u003e\n\u003cli\u003eOmberg L, Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Adams J, et al. Remote smartphone monitoring of Parkinson\u0026rsquo;s disease and individual response to therapy. Nature biotechnology. 2022;40(4):480-7.\u003c/li\u003e\n\u003cli\u003eBonnech\u0026egrave;re B, Timmermans A, Michiels S. Current technology developments can improve the quality of research and level of evidence for rehabilitation interventions. Sensors. 2023;23(2):875.\u003c/li\u003e\n\u003cli\u003eBonnechere B, Sholukha V, Omelina L, Van Vooren M, Jansen B, Jan SVS. Suitability of functional evaluation embedded in serious game rehabilitation exercises to assess motor development across lifespan. Gait \u0026amp; posture. 2017;57:35-9.\u003c/li\u003e\n\u003cli\u003eWarmerdam E, Hausdorff JM, Atrsaei A, Zhou Y, Mirelman A, Aminian K, et al. Long-term unsupervised mobility assessment in movement disorders. The Lancet Neurology. 2020;19(5):462-70.\u003c/li\u003e\n\u003cli\u003eJoshi M, Ashrafian H, Aufegger L, Khan S, Arora S, Cooke G, et al. Wearable sensors to improve detection of patient deterioration. Expert review of medical devices. 2019;16(2):145-54.\u003c/li\u003e\n\u003cli\u003eDillenseger A, Weidemann ML, Trentzsch K, Inojosa H, Haase R, Schriefer D, et al. Digital biomarkers in multiple sclerosis. Brain Sciences. 2021;11(11):1519.\u003c/li\u003e\n\u003cli\u003eYang Y, Schumann M, Le S, Cheng S. Reliability and validity of a new accelerometer-based device for detecting physical activities and energy expenditure. PeerJ. 2018;6:e5775.\u003c/li\u003e\n\u003cli\u003eTorous J, Rodriguez J, Powell A. The new digital divide for digital biomarkers. Digital biomarkers. 2017;1(1):87-91.\u003c/li\u003e\n\u003cli\u003eCosco TD, Firth J, Vahia I, Sixsmith A, Torous J. Mobilizing mHealth data collection in older adults: challenges and opportunities. JMIR aging. 2019;2(1):e10019.\u003c/li\u003e\n\u003cli\u003eDelrieu L, Hamy A-S, Coussy F, Kassara A, Asselain B, Antero J, et al. Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial. BMC cancer. 2022;22(1):493.\u003c/li\u003e\n\u003cli\u003eHusebo BS, Heintz HL, Berge LI, Owoyemi P, Rahman AT, Vahia IV. Sensing technology to monitor behavioral and psychological symptoms and to assess treatment response in people with dementia. A systematic review. Frontiers in pharmacology. 2020;10:1699.\u003c/li\u003e\n\u003cli\u003eStone AA, Shiffman S. Ecological momentary assessment (EMA) in behavorial medicine. Annals of behavioral medicine. 1994.\u003c/li\u003e\n\u003cli\u003eShiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1-32.\u003c/li\u003e\n\u003cli\u003eTorrado JC, Husebo BS, Allore HG, Erdal A, F\u0026aelig;\u0026oslash; SE, Reithe H, et al. Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson\u0026rsquo;s disease: Protocol of the mixed method, cyclic ActiveAgeing study. PloS One. 2022;17(10):e0275747.\u003c/li\u003e\n\u003cli\u003eKoval P HJ, Dozo N, Gleeson J, Alvarez M, Harrison A, Vu D, Sustano R, Jayaputera G, Sinnot R. SEMA3: smartphone ecological momentary assessment 2019 [Computer Software]. Available from: https://sema3.com/.\u003c/li\u003e\n\u003cli\u003eBoen F, Seghers J, van Uffelen J. Motiveren tot verandering: Hoe schil je de gedragsui? Leefstijlpsychiatrie: Springer; 2022. p. 309-32.\u003c/li\u003e\n\u003cli\u003eMichie S, Van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science. 2011;6(1):1-12.\u003c/li\u003e\n\u003cli\u003eSwinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive medicine. 1999;29(6):563-70.\u003c/li\u003e\n\u003cli\u003eFayers PM, Sprangers MA. Understanding self-rated health. The Lancet. 2002;359(9302):187-8.\u003c/li\u003e\n\u003cli\u003eKarcioglu O, Topacoglu H, Dikme O, Dikme O. A systematic review of the pain scales in adults: which to use? The American journal of emergency medicine. 2018;36(4):707-14.\u003c/li\u003e\n\u003cli\u003eDe Vries J, Van Heck G. WHOQOL-BREF. Tilburg: Universiteit van Tilburg. 1996.\u003c/li\u003e\n\u003cli\u003eGinn J. qualtRics: retrieve survey data using the Qualtrics API. J Open Source Softw. 2018;3(24):690.\u003c/li\u003e\n\u003cli\u003eMaes I, Mertens L, Poppe L, Crombez G, Vetrovsky T, Van Dyck D. The variability of emotions, physical complaints, intention, and self-efficacy: an ecological momentary assessment study in older adults. PeerJ. 2022;10:e13234.\u003c/li\u003e\n\u003cli\u003eCooper R, Kuh D, Cooper C, Gale CR, Lawlor DA, Matthews F, et al. Objective measures of physical capability and subsequent health: a systematic review. Age and Ageing. 2010;40(1):14-23.\u003c/li\u003e\n\u003cli\u003eMichie S, Atkins L, West R. The behaviour change wheel. A guide to designing interventions 1st ed Great Britain: Silverback Publishing. 2014;1003:1010.\u003c/li\u003e\n\u003cli\u003eLesher EL, Berryhill JS. Validation of the geriatric depression scale‐short form among inpatients. Journal of clinical psychology. 1994;50(2):256-60.\u003c/li\u003e\n\u003cli\u003eCohen S, Kamarck T, Mermelstein R. Perceived stress scale. Measuring stress: A guide for health and social scientists. 1994;10(2):1-2.\u003c/li\u003e\n\u003cli\u003eDennis JP, Vander Wal JS. The cognitive flexibility inventory: Instrument development and estimates of reliability and validity. Cognitive therapy and research. 2010;34:241-53.\u003c/li\u003e\n\u003cli\u003eDiener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. Journal of personality assessment. 1985;49(1):71-5.\u003c/li\u003e\n\u003cli\u003eCarlucci L, Balestrieri M, Maso E, Marini A, Conte N, Balsamo M. Psychometric properties and diagnostic accuracy of the short form of the geriatric anxiety scale (GAS-10). BMC geriatrics. 2021;21(1):1-12.\u003c/li\u003e\n\u003cli\u003eLee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. International journal of behavioral nutrition and physical activity. 2011;8(1):1-11.\u003c/li\u003e\n\u003cli\u003eEspirito-Santo H, Dias-Azedo D, Lemos L, Grasina A, Andrade D, Henriques S, et al. Validation of the geriatric sleep questionnaire. Sleep Medicine. 2021;88:162-8.\u003c/li\u003e\n\u003cli\u003eDe Jong Gierveld J, Van Tilburg T. The De Jong Gierveld short scales for emotional and social loneliness: tested on data from 7 countries in the UN generations and gender surveys. European journal of ageing. 2010;7:121-30.\u003c/li\u003e\n\u003cli\u003eVanRavenhorst-Bell HA, Muzeau MA, Luinstra L, Goering J, Amick RZ. Accuracy of the SWAY mobile cognitive assessment application. International Journal of Sports Physical Therapy. 2021;16(4):991.\u003c/li\u003e\n\u003cli\u003eBurghart M, Craig J, Radel J, Huisinga J. Reliability and validity of a motion-based reaction time assessment using a mobile device. Applied Neuropsychology: Adult. 2018.\u003c/li\u003e\n\u003cli\u003eBrett BL, Zuckerman SL, Terry DP, Solomon GS, Iverson GL. Normative data for the SWAY balance system. Clinical journal of sport medicine. 2020;30(5):458-64.\u003c/li\u003e\n\u003cli\u003eBennett CC, Ross MK, Baek E, Kim D, Leow AD. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. NPJ Digital Medicine. 2022;5(1):181.\u003c/li\u003e\n\u003cli\u003eLipkin D, Scriven A, Crake T, Poole-Wilson P. Six minute walking test for assessing exercise capacity in chronic heart failure. Br Med J (Clin Res Ed). 1986;292(6521):653-5.\u003c/li\u003e\n\u003cli\u003ePu CT, Johnson MT, Forman DE, Hausdorff JM, Roubenoff R, Foldvari M, et al. Randomized trial of progressive resistance training to counteract the myopathy of chronic heart failure. Journal of Applied Physiology. 2001;90(6):2341-50.\u003c/li\u003e\n\u003cli\u003eRubenstein LZ, Josephson KR, Trueblood PR, Loy S, Harker JO, Pietruszka FM, et al. Effects of a group exercise program on strength, mobility, and falls among fall-prone elderly men. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2000;55(6):M317-M21.\u003c/li\u003e\n\u003cli\u003eWright D, Khan K, Gossage E, Saltissi S. Assessment of a low-intensity cardiac rehabilitation programme using the six-minute walk test. Clinical rehabilitation. 2001;15(2):119-24.\u003c/li\u003e\n\u003cli\u003eEnright PL, McBurnie MA, Bittner V, Tracy RP, McNamara R, Arnold A, et al. The 6-min walk test: a quick measure of functional status in elderly adults. Chest. 2003;123(2):387-98.\u003c/li\u003e\n\u003cli\u003eMangan D, Judge J. Reliability and validation of the six minutes walk. J Am Geriatr Soc. 1994;42:SA73.\u003c/li\u003e\n\u003cli\u003eBautmans I, Lambert M, Mets T. The six-minute walk test in community dwelling elderly: influence of health status. BMC geriatrics. 2004;4(1):1-9.\u003c/li\u003e\n\u003cli\u003eRikli RE, Jones CJ. The reliability and validity of a 6-minute walk test as a measure of physical endurance in older adults. Journal of aging and physical activity. 1998;6(4):363-75.\u003c/li\u003e\n\u003cli\u003eAbellan Van Kan G, Rolland Y, Andrieu S, Bauer J, Beauchet O, Bonnefoy M, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. The journal of nutrition, health \u0026amp; aging. 2009;13:881-9.\u003c/li\u003e\n\u003cli\u003eCesari M, Kritchevsky SB, Penninx BW, Nicklas BJ, Simonsick EM, Newman AB, et al. Prognostic value of usual gait speed in well‐functioning older people\u0026mdash;results from the Health, Aging and Body Composition Study. Journal of the American Geriatrics Society. 2005;53(10):1675-80.\u003c/li\u003e\n\u003cli\u003eWoo J, Ho SC, Yu AL. Walking speed and stride length predicts 36 months dependency, mortality, and institutionalization in Chinese aged 70 and older. Journal of the American Geriatrics Society. 1999;47(10):1257-60.\u003c/li\u003e\n\u003cli\u003eStudenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait speed and survival in older adults. Jama. 2011;305(1):50-8.\u003c/li\u003e\n\u003cli\u003eZiagkas E, Loukovitis A, Zekakos DX, Chau TD-P, Petrelis A, Grouios G. A novel tool for gait analysis: Validation study of the smart insole podosmart\u0026reg;. Sensors. 2021;21(17):5972.\u003c/li\u003e\n\u003cli\u003eLoukovitis A, Ziagkas E, Zekakos DX, Petrelis A, Grouios G. Test-Retest Reliability of PODOSmart\u0026reg; Gait Analysis Insoles. Sensors. 2021;21(22):7532.\u003c/li\u003e\n\u003cli\u003eThorborg K, Petersen J, Magnusson SP, H\u0026ouml;lmich P. Clinical assessment of hip strength using a hand‐held dynamometer is reliable. Scandinavian journal of medicine \u0026amp; science in sports. 2010;20(3):493-501.\u003c/li\u003e\n\u003cli\u003eSousa-Santos A, Amaral T. Differences in handgrip strength protocols to identify sarcopenia and frailty-a systematic review. BMC geriatrics. 2017;17:1-21.\u003c/li\u003e\n\u003cli\u003eBohannon RW. Muscle strength: clinical and prognostic value of hand-grip dynamometry. Current Opinion in Clinical Nutrition \u0026amp; Metabolic Care. 2015;18(5):465-70.\u003c/li\u003e\n\u003cli\u003eNikodelis T, Savvoulidis S, Athanasakis P, Chalitsios C, Loizidis T. Comparative study of validity and reliability of two handgrip dynamometers: K-force grip and Jamar. Biomechanics. 2021;1(1):73-82.\u003c/li\u003e\n\u003cli\u003ePollock AS, Durward BR, Rowe PJ, Paul JP. What is balance? Clinical rehabilitation. 2000;14(4):402-6.\u003c/li\u003e\n\u003cli\u003eMuir JW, Kiel DP, Hannan M, Magaziner J, Rubin CT. Dynamic parameters of balance which correlate to elderly persons with a history of falls. Plos one. 2013;8(8):e70566.\u003c/li\u003e\n\u003cli\u003eRoss SE, Guskiewicz KM, Gross MT, Yu B. Balance measures for discriminating between functionally unstable and stable ankles. Medicine and science in sports and exercise. 2009;41(2):399-407.\u003c/li\u003e\n\u003cli\u003eGoldie PA, Evans OM, Bach TM. Postural control following inversion injuries of the ankle. Archives of physical medicine and rehabilitation. 1994;75(9):969-75.\u003c/li\u003e\n\u003cli\u003eSpringer BA, Marin R, Cyhan T, Roberts H, Gill NW. Normative values for the unipedal stance test with eyes open and closed. Journal of geriatric physical therapy. 2007;30(1):8-15.\u003c/li\u003e\n\u003cli\u003eWikstrom EA, Tillman MD, Borsa PA. Detection of dynamic stability deficits in subjects with functional ankle instability. Medicine and science in sports and exercise. 2005;37(2):169-75.\u003c/li\u003e\n\u003cli\u003eMeras Serrano H, Mottet D, Caillaud K. Validity and Reliability of Kinvent Plates for Assessing Single Leg Static and Dynamic Balance in the Field. Sensors. 2023;23(4):2354.\u003c/li\u003e\n\u003cli\u003eGuralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. Journal of gerontology. 1994;49(2):M85-M94.\u003c/li\u003e\n\u003cli\u003ePavasini R, Guralnik J, Brown JC, di Bari M, Cesari M, Landi F, et al. Short physical performance battery and all-cause mortality: systematic review and meta-analysis. BMC medicine. 2016;14:1-9.\u003c/li\u003e\n\u003cli\u003eFuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth and uHealth. 2020;8(9):e18694.\u003c/li\u003e\n\u003cli\u003eEvenson KR, Spade CL. Review of validity and reliability of Garmin activity trackers. Journal for the Measurement of Physical Behaviour. 2020;3(2):170-85.\u003c/li\u003e\n\u003cli\u003eKim S-H, Cho D. Validation of exercise motivations inventory\u0026ndash;2 (EMI-2) scale for college students. Journal of American college health. 2022;70(1):114-21.\u003c/li\u003e\n\u003cli\u003eAnderson DF, Cychosz CM. Development of an exercise identity scale. Perceptual and motor skills. 1994;78(3):747-51.\u003c/li\u003e\n\u003cli\u003eKroll T, Kehn M, Ho P-S, Groah S. The SCI exercise self-efficacy scale (ESES): development and psychometric properties. International Journal of Behavioral Nutrition and Physical Activity. 2007;4(1):1-6.\u003c/li\u003e\n\u003cli\u003eManta SW, Sandreschi PF, Matias TS, Tomicki C, Benedetti TR. Clustering of physical activity and sedentary behavior associated to risk for metabolic syndrome in older adults. Journal of aging and physical activity. 2019;27(6):781-6.\u003c/li\u003e\n\u003cli\u003eCampagnini S, Liuzzi P, Mannini A, Basagni B, Macchi C, Carrozza MC, et al. Cross-validation of predictive models for functional recovery after post-stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation. 2022;19(1):96.\u003c/li\u003e\n\u003cli\u003eHsu Y-C, Weng H-H, Kuo C-Y, Chu T-P, Tsai Y-H. Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study. Scientific Reports. 2020;10(1):16777.\u003c/li\u003e\n\u003cli\u003eTang K, Luo R, Zhang S. An Artificial neural network algorithm for the evaluation of Postoperative Rehabilitation of Patients. Journal of Healthcare Engineering. 2021;2021:1-6.\u003c/li\u003e\n\u003cli\u003eDe Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, et al. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput Methods Programs Biomed. 2021;208:106180.\u003c/li\u003e\n\u003cli\u003eMajumder S, Mondal T, Deen MJ. Wearable sensors for remote health monitoring. Sensors. 2017;17(1):130.\u003c/li\u003e\n\u003cli\u003eMehra S, Visser B, Dadema T, van den Helder J, Engelbert RH, Weijs PJ, et al. Translating behavior change principles into a blended exercise intervention for older adults: design study. JMIR Research Protocols. 2018;7(5):e9244.\u003c/li\u003e\n\u003cli\u003eRedfern J. Can older adults benefit from smart devices, wearables, and other digital health options to enhance cardiac rehabilitation? Clinics in geriatric medicine. 2019;35(4):489-97.\u003c/li\u003e\n\u003cli\u003eGuu T-W, Muurling M, Khan Z, Kalafatis C, Aarsland D, Brem A-K. Wearable devices: underrepresentation in the ageing society. The Lancet Digital Health. 2023;5(6):e336-e7.\u003c/li\u003e\n\u003cli\u003eMercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth and uHealth. 2016;4(1):e4225.\u003c/li\u003e\n\u003cli\u003eWickstr\u0026ouml;m G, Bendix T. The\u0026quot; Hawthorne effect\u0026quot;\u0026mdash;what did the original Hawthorne studies actually show? Scandinavian journal of work, environment \u0026amp; health. 2000:363-7.\u003c/li\u003e\n\u003cli\u003ePeake JM, Kerr G, Sullivan JP. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in physiology. 2018;9:743.\u003c/li\u003e\n\u003cli\u003eDaniels K, Bonnech\u0026egrave;re B. Harnessing Digital Health Interventions to Bridge the Gap in Prevention for Older Adults. Frontiers in Public Health.11:1281923.\u003c/li\u003e\n\u003cli\u003eHuckvale K, Prieto JT, Tilney M, Benghozi P-J, Car J. Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC medicine. 2015;13(1):1-13.\u003c/li\u003e\n\u003cli\u003eHuckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ digital medicine. 2019;2(1):1-11.\u003c/li\u003e\n\u003cli\u003eSchloemer SA, Thompson JA, Silder A, Thelen DG, Siston RA. Age-related differences in gait kinematics, kinetics, and muscle function: a principal component analysis. Annals of biomedical engineering. 2017;45:695-710.\u003c/li\u003e\n\u003cli\u003eLindemann U, Zijlstra W, Aminian K, Chastin SF, De Bruin ED, Helbostad JL, et al. Recommendations for standardizing validation procedures assessing physical activity of older persons by monitoring body postures and movements. Sensors. 2014;14(1):1267-77.\u003c/li\u003e\n\u003cli\u003eNahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine. 2018;52(6):446-62.\u003c/li\u003e\n\u003cli\u003eSouthard V, Gallagher R. The 6MWT: will different methods of instruction and measurement affect performance of healthy aging and older adults? Journal of Geriatric Physical Therapy. 2013;36(2):68-73.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital phenotype, Physical activity, Activity tracking, older adults, multidimensional assessment","lastPublishedDoi":"10.21203/rs.3.rs-3896647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3896647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003ePhysical activity (PA) is a recognized boon for older adults, enhancing their overall well-being and mitigating health risks. Nevertheless, to encourage active lifestyles in this demographic, it is vital to understand the factors influencing PA. Conventional approaches predominantly rely on supervised cross-sectional evaluations, presuming both the stability of PA determinants over time and their isolated components. However, the complex nature of real-life dynamics often involves temporal variability in individual-level determinants. Digital phenotyping (DP), employing data recruited from personal digital devices, enables the continuous, unsupervised and real-time quantification of an individual's behavior within their natural context. This approach offers more ecological and dynamic assessments, revolutionizing our understanding of the intricacies underlying individual PA patterns in their environmental context.\u003c/p\u003e\u003ch2\u003eObjective.\u003c/h2\u003e \u003cp\u003eThis paper aims to design a robust research protocol for the DP of PA behavior among healthy community-dwelling older adults aged 65 and above by employing a novel measurement approach.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eObservational data will be collected over a two-week period to assess various functions combining both cross-sectional and longitudinal data collection methods. Patterns of PA behavior and factors affecting PA outcomes will be detected in order to identify digital phenotypes related to PA. The measurements are based on the Behavior Change Wheel and include self-reporting and clinical assessments for cross-sectional data collection and ecological momentary assessment as well as time series collection for longitudinal data. The statistical analysis involves machine learning which will handle data complexity. Unsupervised learning will be used to uncover patterns, and supervised learning to identify variables. The analysis will be conducted in RStudio (v3.6.3) with significance set at 0.05.\u003c/p\u003e\u003ch2\u003eDiscussion.\u003c/h2\u003e \u003cp\u003eA novel approach to understanding older adults' PA behavior will be used in this study. Challenges include varying technology adoption, usability, and unproven validity of health tech. Ethical considerations, representativeness, participant engagement, and machine learning expertise are also key aspects of the study's success. This study offers promise in bridging traditional and dynamic assessment methods for older adults' PA behavior to promote active lifestyles.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eClinical Trials.gov: NCT06094374\u003c/p\u003e","manuscriptTitle":"Unveiling the digital phenotype: A protocol for a prospective study on physical activity behavior in community-dwelling older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 18:00:37","doi":"10.21203/rs.3.rs-3896647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b3f9417-f9e6-4085-a7ea-d0ffd211e59e","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T10:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:00:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3896647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3896647","identity":"rs-3896647","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}