This paper is in the following e-collection/theme issue:

Reviews on Aging (61) Physical Activity for Older People (281) Innovations and Technology for Physical Activity Education (926) Usability and Technology Use Studies with Elder Subjects (271) Prevention and Health Promotion (1362) Innovations in Exercise Rehabilitation (284)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65746, first published .
Technology-Assisted Physical Activity Interventions for Older People in Their Home-Based Environment: Scoping Review

Technology-Assisted Physical Activity Interventions for Older People in Their Home-Based Environment: Scoping Review

Technology-Assisted Physical Activity Interventions for Older People in Their Home-Based Environment: Scoping Review

Authors of this article:

Rosemary Dubbeldam1 Author Orcid Image ;   Rafal Stemplewski2 Author Orcid Image ;   Iuliia Pavlova3 Author Orcid Image ;   Magdalena Cyma-Wejchenig2 Author Orcid Image ;   Sunwoo Lee4 Author Orcid Image ;   Patrick Esser5 Author Orcid Image ;   Ellen Bentlage1 Author Orcid Image ;   Veysel Alcan6 Author Orcid Image ;   Özge Selin Çevik7 Author Orcid Image ;   Eleni Epiphaniou8 Author Orcid Image ;   Francesca Gallè9 Author Orcid Image ;   Antoine Langeard10 Author Orcid Image ;   Simone Gafner11 Author Orcid Image ;   Mona Ahmed1 Author Orcid Image ;   Niharika Bandaru12 Author Orcid Image ;   Arzu Erden Güner13 Author Orcid Image ;   Evrim Göz14 Author Orcid Image ;   Ilke Kara15 Author Orcid Image ;   Ayşe Kabuk16 Author Orcid Image ;   Ilayda Türkoglu17 Author Orcid Image ;   Zada Pajalic18 Author Orcid Image ;   Jan Vindiš4 Author Orcid Image ;   Damjan Jaksic19 Author Orcid Image ;   Uǧur Verep15 Author Orcid Image ;   Ioanna Chouvarda20 Author Orcid Image ;   Vera Simovska21 Author Orcid Image ;   Yael Netz22 Author Orcid Image ;   Jana Pelclova4 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

    1Department of Movement Science, Institute for Sport and Exercise Sciences, University Münster, Horstmarer Landweg 62B, Münster, Germany

    2Normandy University, UNICAEN, INSERM, COMETE, CYCERON, Caen, France

    3School of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland

    4Institut of Sport Science, Otto-Von Guericke Universität of Magdeburg, Magdeburg, Germany

    5Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Karadeniz Technical University, Trabzon, Turkey

    6Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Tarsus University, Tarsus, Turkey

    7Department of Physical Therapy and Rehabilitation, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey

    8Faculty of Health Sciences, Zonguldak Bülent Ecevit University, Zonguldak, Turkey

    9Department of Fundamentals of Nursing, Hamidiye Faculty of Nursing, Health Sciences University, İstanbul, Turkey

    10Faculty of Health and Social Sciences, University of South-Eastern Norway, Kongsberg, Norway

    11Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia

    12Department of Digital Technologies in Physical Activity, University of Physical Education, Poznan, Poland

    13Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

    14HEPA Macedonia National Organisation for the Promotion of Health-Enhancing Physical Activity, Skopje, North Macedonia

    15The Levinsky-Wingate Academic Center, Wingate Institute, Netanya, Israel

    16Department of Theory and Methods of Physical Culture, Ivan Boberskyj Lviv State University of Physical Culture, Lviv, Ukraine

    17Faculty of Physical Culture, Palacký University Olomouc, Olomouc, Czech Republic

    18Centre for Movement, Occupation and Rehabilitation Sciences, Oxford Brookes University, Oxford, United Kingdom

    19Department of Electrical and Electronics Engineering, Faculty of Engineering, Tarsus University, Tarsus, Turkey

    20Department of Physiology, Faculty of Medicine, Mersin University, Mersin, Turkey

    21School of Humanities, Social and Education Sciences, European University Cyprus, Engomi, Cyprus

    22Department of Medical, Movement and Wellbeing Sciences, University of Naples Parthenope, Naples, Italy

    *these authors contributed equally

    Corresponding Author:

    Rosemary Dubbeldam, PhD


    Background: Technology-assisted physical activity interventions for older adults in their home-based environment have been used to promote physical activity. Previous research has reported that such interventions benefit body composition, aerobic fitness, cognitive abilities, and postural control, reducing the risk of falls and maintaining regular physical activity among the older population.

    Objective: While previous reviews on technology-assisted physical activity interventions focused on health-related outcomes, this scoping review explores the characteristics of the technology in relation to the characteristics of populations, purpose of the activity, and usability in terms of adverse events, drop-outs, adherence, and user experience.

    Methods: A full search was performed in Medline, Embase, CINAHL, SportDiscus, and Web of Science. Sources were considered for inclusion if the participants aged on average 60 years and older, if the physical activity intervention was assisted by technology, and if performed within home-based environments.

    Results: We identified 8496 sources. After title and abstract screening, 455 full texts were assessed, and 148 were included, representing 12,717 participants aged 74 (SD 6) years. In total, 63% (93/148) of the sources reported on the population’s health status. The main purpose of the interventions was balance (75/148, 51%), and strength and power (64/148, 43%) and the intervention purposes were not related to the embedded technology. In studies where the participant’s health status was reported as healthy, 53% (78/148) implemented exergames compared to only 27% (40/148) in studies with participants with a clinical condition. Mobile apps (30/148, 20%) and trackers (16/148, 11%) were implemented likewise in both groups. The technology was embedded to provide continuous exercise information (40/148, 27%) and exercise feedback (40/148, 27%) or to record real-time movement data (38/148, 26%). Adverse events were reported in 46% (68/148) of the sources with three quarters (49/68) reporting no adverse events. Only two mild events were related to technology. Dropout rates were reported in 68% (100/148) of the sources, with no differences between intervention (16 SD 16%) and control (14 SD 12%) groups. Dropout reasons related to technology are only 3% (3/100). Adherence was reported in 53% (79/148) sources and was slightly higher in the intervention group (80 SD 18%) compared to the control group (71 SD 25%). A significantly higher adherence was found between interventions that were tailored (83 SD 15%) versus those that were not (75 SD 21%). General enjoyment of the technology was captured in 37% (55/148) of the sources. Within those sources, 91% rated positive (91/100), 7% neutral (7/100), and 2% negative (2/100). Occasionally reported wishes were related to goal setting, feedback, technical support, exercise variation, and social setting.

    Conclusions: Various technologies were successfully used in healthy and clinical older populations, though evidence regarding their implementation in physical activity interventions in hospital settings remains limited. The embedded technology was not a reason for additional dropouts, led to slightly better adherence, and adverse events were rarely related to technology. When assessed, the technology was well accepted and positively enjoyed.

    Trial Registration: OSF Registries OSF.IO/NVECQ; https://doi.org/10.17605/OSF.IO/B2NGD

    JMIR Aging 2025;8:e65746

    doi:10.2196/65746

    Keywords

    technology-assisted interventions; technological interventions; physical activity; physical exercise; exercise; gerontology; geriatric; older adult; elderly; older people; aging; home-based; dropout; adherence; adverse effects; scoping review; review 


    The benefits of physical activity in advanced age are well-documented, yet paradoxically, sedentary behavior increases with age [1,2]. Even if individuals are motivated to engage in physical activity outside the home, environmental barriers such as weather conditions, infrastructure constraints, or specific constraints such as COVID-19 can pose challenges for older adults [3,4]. This paradox has prompted clinicians to seek technological solutions to encourage older adults to be more physically active within their home environment. It is important to distinguish between physical activity and exercise. Physical activity encompasses any bodily movement that expends energy, while exercise is a structured subset of physical activity specifically designed to improve fitness components [5].

    Various electronic devices, software, or wearable technology such as smartphones [6], fitness trackers [7], mobile phone applications [8], tablets [9], virtual reality [10], exergames [11], E-coaching [12], and Robotics [13] have been reported to support beneficial changes in sedentary time and physical activity in the older adult population [14,15]. Such technological interventions benefit body composition, aerobic fitness, cognitive abilities, and postural control and reduce the risk of falls among the older population [9,16,17]. However, while there are indications that older adults tend to show high levels of adherence to technology-assisted physical activity interventions [15], quite a few barriers limiting the use of this technology have been reported, such as functional barriers related to the design of e-health programs and their interface with older end users, or lack of technological support [18].

    With the continuously increasing number of technology-assisted healthcare tools, particularly those for home-based physical activity, and the difficulties older adults face in adapting to and operating these tools, it is not surprising that several reviews have been conducted in recent years to examine the usability of technology-assisted physical activity programs [15,19-25], more than half of them in recent years – 2021‐2024 [19,20,22,23,25]. Most of these reviews focused on a specific population, egfor example, healthy, frail, or people with cardiovascular disease, focused on specific aspects of interventions, for example, balance or falls, or focused on a specific type of technology, for example, exergames [15,19-25]. Considering the boost of assistive-technology provision of physical activity in the recent five years, some are relatively dated [15,24]. A recent review by Costa-Brito et al elaborated on the usage of technology to improve physical function among older community-dwelling adults [23] and focused on the purpose and volume of an activity, types of technology, aspects of the usage of technology (adherence, acceptance, feasibility), and health outcomes. The review, however, did not go into the specific characteristics of the study participants, the technology’s functionality, or its user interface. Furthermore, it did not explore or analyze key aspects of technology design, function, and interface in relation to specific populations or physical activity goals. Just as many other reviews [19-21,25], Costa-Brito et al also neglected to examine safety concerns or adverse events associated with the use of technology.

    Aligned with the recommendations of the PhysAgeNet, a European Cooperation in Science and Technology (COST) network devoted to evidence-based physical activity in old age [26] and with the proliferation of technology related to home-based physical activity, our aim in this study was to explore the characteristics of that technology in relation to the characteristics of older populations (eg, age group or health status) and the interventions. Furthermore, we aimed to examine the usability of that technology in terms of adverse events, drop-outs, adherence, and the user experience (acceptability and enjoyment). This may promote the optimal utilization of technology to provide efficient home-based physical activity programs for older adults. A scoping review was selected as the most suitable methodology due to its capacity to encompass a broad spectrum of study designs and methodologies. This approach facilitates the mapping of key concepts and the identification of research gaps, which is particularly critical in this emerging field characterized by diverse evidence sources, including randomized controlled trials, pre-post studies, feasibility studies, qualitative research, and mixed methods investigations.


    Review Process

    This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews [26] and the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews (PRISMA-ScR) checklist [27]. The protocol for the scoping review is available at the Open Science Framework Registries [28]. The review was conducted by an international interdisciplinary team of researchers with regular weekly meetings to discuss, distribute tasks, and ensure consistency during the different steps of the process.

    Eligibility

    Sources were considered for inclusion if they met the following criteria: (1) participants aged on average 60 years and older with and without health conditions; (2) studies that focused on technology-assisted physical activity interventions; and (3) the intervention was based within home-based environments. Table 1 gives a detailed definition of the participants, concept, and context.

    Table 1. Details of the participants, concept, and context (PCC framework) which form the basis for source inclusion consideration.
    PCCInclusionDefinitions and descriptions
    PopulationOlder peopleOlder adults aged 60 years and older (the participant’s group mean age should be ≥60 y), living independently or in residential facilities (aged care facilities), with or without health conditions.
    ConceptTechnology-assisted physical activity interventionsA physical intervention is a structured or unstructured measure taken to improve or maintain the user’s physical activity and practiced individually. It can be tailored to specific health or fitness goals (structured) or it can be general and not specifically prescribed by a health professional (unstructured) [29]. The intervention includes any form of physical activity such as human daily activities, exercise, training, or physical fitness training. The technologies used to deliver the intervention are digital systems and include, among others, mobile applications, telemedicine, wearable electronic devices, exergaming, and virtual reality. The delivery of the intervention can take place supervised, facilitated (partly supervised), or unsupervised.
    ContextHome-based environmentThe home-based location and environment, ie, the immediate vicinity in the community environment, ie, a public open-access setting [29]. The participants may be living in a community setting on their own (community dwelling) or in an institution such as a care home or a hospital.
    Health statusPeople with or without mental or physical impairments.

    Sources were excluded if (1) the study design was a review or study protocol, or if the publication type was an abstract or non-peer-reviewed conference proceedings, (2) participants were on average younger than 60 years of age, (3) no physical activity intervention was present or the intervention took place within clinical setting, (4) digital technology was not present or not an integral part of the intervention, (5) the intervention did not take place in home-based setting, or (6) the study language was other than English.

    Search Strategy

    According to JBI recommendation, an initial limited search of PubMed and Web of Science was conducted to identify potentially relevant key search terms for the PCC framework. In an iterative process, the keywords in the text of retrieved sources were analyzed. The search terms provided were consulted with experts within the team to capture all relevant keywords and then grouped into four main headings: population group, residence, technology, and intervention. The search string was further expanded by running searches with Medical Subject Heading (MeSH Terms) as well as with adequate non-MeSH terms for databases that do (eg, PubMed) or do not use a MeSH tree (eg, Web of Science), respectively and cross-checked by a subject librarian at Oxford Brookes University: (“aged” OR “elder*” OR “old” OR “older people” OR “geriatric” OR “senior#”) AND (“home*” OR “resident*” OR “care facilit*” OR “nursing home*”) AND (“digital technolog*” OR “mobile health” OR “telehealth” OR “eHealth” OR “mHealth” OR “exergam*” OR “video gam*” OR “serious gam*” OR “wearable technolog*” OR “wearable electronic device*” OR “application*” OR “mobile app*” OR “virtual reality”) AND (“exercise#” OR “training” OR “intervention*” OR “physical activit*” OR “physical fitness”) NOT (“review”[Title/Abstract] OR “rehabilitation” [Title/Abstract])

    The created search strings were the basis for developing a full search strategy in Medline (PubMed), Embase (Ovid), CINAHL (Ebsco), SportDiscus (Ebsco), and Web of Science and converted as necessary (Multimedia Appendix 1). The search in the different databases was performed between September 26 and October 6, 2022, and all sources published in previous years were included. As a third step of the search strategy, an additional search was performed in the reference list of the identified sources selected from the full text search.

    Source of Evidence Selection

    Following the search, all identified sources were collated and uploaded into Rayyan [30], and duplicates were removed. Two reviewers performed a pre-screening pilot on 100 titles and abstracts to develop screening instructions (total agreement=95%, Cohen’s Kappa=0.701). Any disagreements were resolved via consensus with the help of a third reviewer. After the whole team approved the final version of the screening instruction tool, the 2 reviewers screened the remaining titles and abstracts (total agreement=95.5%, Cohen’s Kappa=0.515). Again, disagreements were resolved by a third reviewer.

    Full texts of potentially eligible sources were uploaded into Rayyan. Ten different pairs of reviewers assessed these sources (total agreement =81%, Cohen’s Kappa=0.696) within Rayyan [30]. Disagreements were resolved via consensus in a team of five other (independent) reviewers. The results of the search, the sources’ inclusion process, and exclusion reasons have been reported in full and presented according to the PRISMA flow diagram [27]. All sources obtained after the search process were stored in Mendeley (Mendeley Ltd., Elsevier).

    Data Extraction

    A data extraction tool (Multimedia Appendix 2) was made in line with the JBI guidelines [26,31] and a template for intervention description and replication [32]. Five reviewers piloted the draft extraction tool, topic explanations were added to ensure the correct data was extracted into relevant columns and a final data extraction tool was prepared after which a group analysis and discussion was conducted, making sure that each extractor understood the tool. Finally, ten pairs of reviewers independently completed data extractions. When preparing the data for the analysis, a third reviewer cross-checked the extracted data and, where necessary, consulted the full text for details. Discrepancies and uncertainties were discussed within the team.

    Data Analysis and Presentation

    Four teams consisting of 3‐5 reviewers analyzed the extracted data regarding the following topics: study design, participants, interventions and outcome measures, and technologies and key findings. An inductive thematic analysis was conducted. Codes or labels were defined to categorize the data and a coding framework with definitions of the categories and subcategories was developed. This coding framework allowed us to characterize the participants (age, health status, mobility, country of residence, etc), intervention (purpose, tailoring, temporal data, etc), the used technologies (design, function, interface), participation (feasibility, effectiveness, safety, etc), and health outcomes (physical and mental capacities, fall risk, quality of life, etc). A detailed description of the coding framework is presented in Multimedia Appendix 3. During the extracted data categorization process, the reviewers came together regularly in their small teams to discuss discrepancies until consensus was achieved and all data categorized. For this review, the focus lies with the participants, intervention, and especially the technology categories.

    The quantitative and qualitative data were analyzed descriptively using absolute and relative frequency, percentage, average, and standard deviation. Pie charts and bar charts were made to visualize results.

    Crosstabulation statistical analysis was used to get a better understanding of what kind of digital technologies were used to integrate specific physical activity intervention programs and for which populations. Observations between 2 or more nominal variables were explored, whereby the frequency of observations between variables was counted and reported accordingly and expressed as a percentage against all observations. Specifically, the cross-tabulation analysis was conducted between the following categorical variables: Technology (design, function, interface) versus Population (age group, health status, residency) and Intervention (aim of physical activity, tailoring).

    Adherence (attendance and duration) and dropout rates were expressed as a percentage of the relevant study population and compared against population and intervention characteristics (2 or more categories) using differential tests. In the case of a binomial category, an independent t test was used. The following categories and linear outcomes were compared: Participation outcomes (dropouts, adherence) versus Population (age groups, health status) and Intervention (group, tailoring, supervision).

    Relationships between linear parameters were regressed to extract the coefficient of determination (R2) with associated P value to indicate its significance. These were tested for the dropout and adherence outcomes, against the total number of intervention sessions and session durations.


    Source Selection

    A total of 12,512 sources were identified during searching in Medline (PubMed), Embase (Ovid), CINAHL (EBSCO), SportDiscus (EBSCO), and Web of Science databases. After screening, 148 sources remained. Inclusion and exclusion details can be found in the PRISMA flowchart (Figure 1). An overview of all excluded sources with reasons and all included sources and corresponding extracted data can be found in the supplementary material. Detailed results from the data analysis and the references to the 148 included sources can be found in Multimedia Appendix 4 [33-175]. The full data extraction table and reasons for source exclusions from this review can be found in Multimedia Appendices 5 and 6, respectively.

    ‎
    Figure 1. Flowchart search process.

    Study Characteristics

    Publication years ranged from 2003 to 2022, with most of the included sources published in the last five years (84/148, 57%). An overview of the number of sources performed by region and publication year is presented in Figure 2.

    ‎
    Figure 2. (A) Number of included sources by world region. (B) and by publication year and health status.

    The sources included 51% (75/148) randomized controlled trials, 24% (35/148) pre-post-test, and 12% (17/148) feasibility studies and to a lesser extent qualitative studies (6/148, 4%), non-randomized controlled trials (5/148, 3%), experimental studies (5/148, 3%), mixed methods (4/148, 3%), and one case study (1/148, 1%).

    Participant Characteristics

    The included sources contained a total sample of 12,717 participants, aged 74 (SD 6) years on average, with a range of 60‐88 years. Most sources were devoted to the youngest-old group of 60 to 74 years (87/148, 59%), followed by the middle-old group of 75 to 84 years (51/148, 35%) and the oldest-old group of 85 years and older (6/148, 4%). A small sample of sources reported on mixed (2/148, 1%) age categories. In 2 studies, the age of the participants was not reported. With increasing age groups, the percentage of women increased from 53% (5160/9802) (youngest-old) to 70% (1750/2510) (middle-old) and 79% (119/150) (old-old).

    A larger focus on the clinical population is visible since 2017, although the health status is not always properly reported (Figure 2B). In total, 63% (93/148) of the sources reported on the population’s health status (Figure 3). In 37% (55/148) of the sources, information on comorbid conditions was indicated, the average number of comorbidities was 4.2 (SD 2.6; range 1‐10).

    ‎
    Figure 3. Number of included sources (N) by reported health status. With n being the total number of participants with cardiovascular disease (Cardio-vasc); cognitive impairments (Cognitive imp); chronic obstructive pulmonary disease (COPD) and musculoskeletal disease (Musc skel).

    Most sources (101/148, 68%) included participants living in community-dwelling residences, 26% of the sources included participants from care homes, while 13% (19/148) of the sources were not specific on participant living arrangements (Figure 3). No source reported an intervention in a hospital setting.

    Prior experience with technology was stated in 16% (24/148) of sources, and 15% (22/148) of sources stated that participants had access to technology at home before the implementation of the intervention. Five percent of the sources (7/148) highlight information about the prior frequency of use of these services or devices.

    Intervention Design Characteristics

    In 44% (65/148) of the sources, participants received supervised interventions (professional or non-professional) with a further 55% (82/148) of the sources including unsupervised interventions. Additionally, one source tested supervised versus unsupervised technology-based interventions.

    In most of the sources (93/148, 63%), the intervention was tailored to individuals and/or groups of participants. The other 37% (55/148) applied a generic intervention approach to all participants. In the tailored interventions, the physical activity was adapted to the participant’s specific needs, or the progression of the activity was personalized by adapting the difficulty, intensity, or frequency level of the activity. Furthermore, individual assessments and feedback systems allowed for continuous adjustment of activities based on performance.

    Intervention durations varied from one week up to 288 weeks (Figure 4A). A total of 2 interventions (2/148, 1%) did not report intervention duration. Session duration varied by study from 5 minutes up to 120 minutes (Figure 4B). Session frequency was reported in 119 studies and ranged between one to 21 per week. In 2 studies, the weekly session frequency could be chosen independently. Most sources (87/148, 59%) ranged from one to three sessions per week.

    ‎
    Figure 4. Number of sources stratified by (A). intervention duration (average and SD) and (B) session duration (average and SD). For intervention duration in minutes (min), the average duration and corresponding standard deviation per group is also presented.

    The activity intensity was described in only 14% (20/148) of the sources. Of these, 3% (5/148) used Rate of Perceived Exertion, 2% (3/148) the Borg scale, with a further 6% (9/148) of the sources reported intensity in a different manner, eg, the level of the game. Only one source (1/148, 1%) set the intensity at 60% of Heart Rate Reserve, with another 2 sources (2/148,1%) using self-pacing of intensity.

    In total, 2 sources (2/148, 1%) did not report on the physical activity used as part of the intervention. The other 146 sources (146/148, 97%) reported 2 to 5 different purposes for the interventions resulting in a total of 296 different purposes. In these interventions, the top 5 purposes were balance (73/148, 49%), muscular strength and power (64/148, 43%), cardiorespiratory fitness (30/148, 20%), functional mobility (28/148, 19%) and general physical activity (25/148, 17%). In 40% (58/148) of the sources, the focus was on one purpose, whereas the majority (88/148, 59%) had on average 3 (min 2, max 5) purposes.

    Technology Characteristics

    An overview of the technology designs, functions, and interfaces used in the sources is given in Table 2. Below results of the cross-tabulation between technology and population or intervention characteristics are described. All detailed results can be found in Multimedia Appendix 4.

    Table 2. Extracted counts for the Design, Function, and Interface categories. N equals the total reported for each category, and in brackets, the percentage of the total reported per Design, Function, and Interface, respectively.
    VariableCategoriesN (%)
    Design
    Computer-based20 (9)
    Sound and video recording systems8 (4)
    Mobile applications (web-based, tablet, mobile phone)48 (22)
    Web video/ phone call30 (13)
    Exergames (incl. screen)68 (30)
    Trackers21 (9)
    Virtual reality6 (3)
    Augmented reality1 (0)
    Wearable devices14 (6)
    Other7 (3)
    Function
    Assessment of outcomes21 (6)
    Providing exercise program information once28 (8)
    Providing exercise information continuously102 (27)
    Feedback of exercise102 (27)
    Real-time health metrics6 (2)
    Real-time movement data98 (26)
    Real-time functionality ability9 (2)
    Other5 (1)
    Interface
    Graphical interface94 (27)
    Command line with feedback24 (7)
    Command line without feedback13 (4)
    Menu-driven40 (11)
    (Haptic) touch6 (2)
    Auditory or visual61 (17)
    Body movement93 (27)
    Form-based1 (0)
    Natural language processing0 (0)
    Remote driven14 (4)
    Other3 (1)

    Cross-Tabulation Between Technology and Population

    The results of the cross-tabulation between participant characteristics and technology design are presented in Table 3. The cross-tabulation results between participant characteristics and technology function and interface can be found in Multimedia Appendix 4. Exergames were implemented in half of the sources with oldest-old adult participants who are aged 85 years and older. In the sources with middle-old age groups (75‐85 y), exergames (25/75, 33%) and mobile apps (16/75, 21%) were both frequently used, and alternative designs such as web/video phone call (10/75, 13%) were implemented as well. In sources with youngest-old age groups (60‐74 y), trackers (18/125, 14%) were more often implemented than in the middle-old or oldest-old age groups (2/75, 3% and 1/18, 6% respectively). In sources where the participant’s health status was reported as healthy, 53% (10/19) implemented exergames compared to only 27% (34/127) in sources with participants with a clinical condition. For both health status groups, mobile apps (20%, ie., 4/19 and 24/127) and trackers (11%, ie., 2/19 and 14/127) were likewise implemented. However, sources with participants with a clinical condition also made use of other technological designs such as computer-based (14/127, 11%), web video/phone call (19/127, 15%) or wearable devices (10/127, 8%). Age and health status did not influence the technological functions or user interface.

    Table 3. Cross-tabulation results for participant characteristic age, health status, and residency versus technology design.
    DesignSumaComputer basedSound/ video recording systemsMobile appsWeb/ video phone callExer- gamesTrackersVirtual realityAugmented realityWearable devicesOther
    AGE
    Youngest old (60‐74 y)1257%2%24%15%25%14%2%-6%4%
    Middle old (75‐85 y)7511%7%21%13%33%3%4%-7%1%
    Oldest old (>85 + y)1811%6%11%6%50%6%--6%6%
    Mixed3----67%--33%--
    HEALTH STATUS
    Healthy19--21%5%53%11%5%--5%
    Clinical condition12711%5%19%15%27%11%2%-8%3%
    RESIDENCY
    Care Homes4210%5%7%2%50%7%10%-2%7%
    Community Dwelling1539%3%24%18%26%10%1%1%5%3%
    Hospital1-----100%----
    Other336%3%24%6%21%3%18%-18%-

    aThe horizontal rows add up to 100% of the sum in the second column. The other columns represent the percentages of the sum, and the total of each row equals 100%. With abbreviations: Apps, applications; cmnd, command; record., recording; y, years.

    In community setting studies, exergames (40/154, 26%) and apps (37/154, 24%) were equally deployed. However, in care homes, half of the sources used exergames. Wearable technology was used frequently in settings where residency was unfortunately not specified, whereas the use of web video/phone call designs was highest (relatively) in community setting studies. Making use of specific functions and interfaces was similarly distributed for community dwelling and care home residents. Only the body movement interface was 34% (21/61) in care homes compared to 25% (59/236) in community settings, probably related to the implemented exergames.

    Cross-Tabulation Between Technology and Intervention

    The results of the cross-tabulation analyses for purpose of the physical activity and technology are presented in Multimedia Appendix 4. Exergame and mobile app designs were used to achieve improvements for a large range of physiological parameters such as balance, muscle strength, functional mobility, or cardiovascular health. Similar for web video/phone calls, but these technologies were incorporated into the sources to a much lesser amount. Exergames were predominantly used to improve cardiorespiratory fitness (17/41, 41%), neuromotor function (14/29, 48%), mental and cognitive function (16/32, 50%), and fall risk (8/14, 57%). Only enhancement of general physical activity was trained most using trackers (13/39, 33%, eg, for step counting). Regardless of the purpose, the technologies provided continuous information on the activity, feedback, and assessed real-time movement data. These technological functions were achieved through graphical (25%‐30%), body movement (20%‐36%), or auditory and visual (13%‐23%) interfaces with the users.

    Tailoring of the intervention’s physical activity was observed across all technologies. Hence, incorporating tailoring of the intervention’s physical activity did not seem to be related to the type of technology design, function of user interface, and vice-versa.

    Outcome Measurements

    Adverse Events

    In total, 64% (68/148) of the sources reported information on adverse events. Out of these, 72% (49/68) of the sources reported no adverse events, with the other 28% (19/68) reporting a total of 78 adverse events (n=78, 100%) categorized in grade 1 (mild, 40/78, 51%), grade 2 (moderate, 10/78, 13%), grade 3 (severe, 28/78, 36%) events, with none reported in grade 4 (life-threatening) or grade 5 (death).

    Of these 78 adverse events, 47 events could be related to the intervention and included 37 mild events, e.g., primarily musculoskeletal pain in joints, 8 moderate events, e.g., rib fracture or COPD-related, and 2 severe events, eg, severe injury due to (bicycle) fall accident. Only 2 adverse events were related to technology and included mild impairments from a skin rash due to Fitbit wearing and nausea due to the Virtual Reality environment.

    Dropout and Adherence

    Information on dropout and adherence for the intervention groups was reported in 100 sources (68%) and 79 sources (53%), respectively. Information on dropout and adherence for the control groups was reported more sparely in 45 sources (47%) and 16 sources (17%), respectively. While the number of participants for all sources is variable, dropout rates of control groups (14 SD 12%) and intervention groups (16 SD 16%) were similar (t34 = −0.648, P=.521). Average adherence tended to be lower in the control groups (71 SD 25%) compared to the intervention groups (80 SD 18%), though this could not be tested statistically due to underreporting in the control group.

    In total, the dropout reasons for 815 participants were quantitatively reported. Reasons for dropout were mostly health-related (28%), though these were unrelated to the intervention. Furthermore, dropout participants mentioned time-related issues (17%), such as lack of time or traveling and burden or motivation-related issues (15%) such as lost interest or feeling burdened. Often another reason (17%) was indicated for the dropout, or no reason was mentioned at all (15%). About 5% of the dropout participants were lost due to follow-up issues ascribed to e.g., loss of contact or moving. Only 3% of the dropouts were related to technology: for example, broken computer, internet malfunctioning, lack of device or space.

    No differences (t98=−1.209, P=.115, d=−0.252) were found in dropout when interventions were tailored (15 SD 16%) or not tailored (19 SD 14%). A significantly higher adherence (t77=1.995, P=0.025, d=0.455) was found between interventions that were tailored (83 SD 15%) versus those that were not (75 SD 21%). On the contrary, no differences were observed when comparing supervised versus unsupervised interventions, when assessing dropout (t98=-1.374, P=.086, d=−0.275) and adherence (t77=0.606, P=.273, d=0.136).

    Sources including participants with reported clinical conditions as health status (18 SD 11% dropout) reported a similar level of dropout rate compared to those who used healthy populations (17 SD 18% dropout), which was nonsignificant (t62=0.063, P=.475, d=0.022). There were no differences between adherence rates when comparing the sources with reported healthy participants versus participants with a reported clinical condition (t48=0.027, P=.489, d=0.010). Differential testing between age groups was not possible, due to the low reported dropout ratios across three age categories, although no trends were observed of dropout rate differences across age categories.

    No further relationships were found between dropout or adherence, when regressing against the total number of intervention sessions (R2=0.012, P=.924) or session duration (R2=0.001, P=.850).

    User Experience

    A total of 55 sources (37%) explored the general enjoyment of the technology part of their intervention. An overwhelming 91% reported a positive experience, with a vast minority reporting a neutral or negative experience (7% and 2% respectively).

    Only 21 sources (14%) investigated the system usability. The system usability was reported using the System Usability Scale (SUS, N=12, 8%) or other questionnaires, mostly using Likert scales or percentages (N=9, 6%). The reported SUS values ranged from 62 to 90 with an average of 77 (SD 9) indicating okay, good, and excellent overall user friendliness [176]. In several sources (N=2), it was reported that the SUS was assessed, but then the result was not presented.

    Regardless of the study design, wishes and needs of the users were seldom (systematically) assessed. When reported, the wishes were related to goal setting, feedback (regarding own or other participants' performance, messages), support (technical, medical), the physical activity (variation, choice of level), the technology (access, comfort, malfunctioning, touch screen), and social setting (in groups, partnering up, avatar look, timing).


    Principal Findings

    This scoping review explores the application of digital technologies in relation to the characteristics of populations (eg, health status), interventions and usability in home-based physical activity interventions for older adults. Analyzing 148 sources, the review finds that technologies like exergames and mobile apps are widely applicable and easy to use across diverse older adult populations, including healthy and those with clinical conditions. These technologies offer continuous features like information about the physical activity programs, feedback, and real-time movement tracking, addressing various goals such as balance, strength, functional mobility, and cardiovascular fitness. Interestingly, technology use did not lead to higher dropout rates, and adherence was significantly higher in tailored interventions. Adverse events directly related to the technology were rare. User evaluations indicated positive feedback regarding the usability and enjoyment of the technology, suggesting its potential to increase physical activity engagement among older adults.

    Participants

    Our research underscores the growing evidence of technology-based physical activity interventions in home-based environments (community dwelling and care homes) since 2010, with a notable increase over the past five years [177]. Our review revealed that technology-assisted physical activity interventions have been explored among older adults in various age groups and with various health conditions. Given the increasing prevalence of age-related diseases and chronic health conditions among older adults, technology holds promise in promoting physical activity engagement, particularly among those with health challenges [19,25].

    The use of technology-assisted physical activity interventions for older adults in hospital settings is limited [178-180]. Initially, this review identified no such studies (N=0), likely due to excluding abstracts mentioning “rehabilitation” in our search. While physical activity interventions in hospitals generally show positive effects on functional decline or hospital-associated disability [3,181,182], Sheerman et al report inconsistent findings [183]. Similarly, technology-assisted interventions in hospitals report mixed results: adherence was high for supervised interactive gaming [180] and activity trackers [178] but low for self-regulated exergames [179]. Furthermore, Laver et al reported improved Time-Up-and-Go test results with no serious adverse events, while Oesch et al noted no balance improvements and 2 adverse events related to pain [179,180].

    Studies failed to report participants’ education (only 40 %) and socio-economic status (only 9 %), which are critical factors as lower education and lower socioeconomic status often correlate with lower digital health literacy [184,185]. Lower education influences the level of health literacy in general as well [184], which may influence judgment on the relevance of engaging with health technology [186]. Furthermore, lower socio-economic status has been related to suboptimal access to internet and digital tools [184], e.g., due to costs [187]. Unfortunately, only limited information was reported by the included sources (15%) on access to technology. Also, technology familiarity or e-literacy was poorly reported (15%). Lower education and the lack of familiarization with technology may introduce barriers to the capacity of users to understand and attend a technology-supported intervention and may lead to increased anxiety and dropouts when attending [186-188]. Hence, the limited reporting on such participant characteristics raises concerns about sample bias and the generalizability of results.

    Interventions

    This scoping review reveals significant diversity in technology-assisted intervention designs, supervision levels, and tailoring. While many interventions are appropriately tailored to the specific needs of older adults and target populations, this tailoring naturally results in variability in intervention durations, session lengths, and frequencies. This does not necessarily indicate a lack of structure but reflects the necessary flexibility in designing interventions that meet individual or group needs. However, the lack of consistent reporting frameworks for these interventions poses a challenge for comparing results across studies. Our findings are consistent with other reviews in the field (eg, [177,189-191]), which also identified significant variability in intervention designs and reporting frameworks. Those reviews, like ours, emphasize the importance of individualization and tailoring as the key components in ensuring that interventions are effective, but the absence of standardized reporting makes it difficult to evaluate the relative success of these programs. Particularly, the underreporting of intervention intensity poses a significant challenge, with only 14% of sources detailing intervention intensity using varied methods. This is unexpected since technology can easily track and report intensity, providing both motivation and precise data for older adults. For instance, studies using Kinect systems and wearables like Fitbit have shown potential in accurately tracking activity intensity, enhancing engagement and usability [192-194].

    Technologies

    In the sources we reviewed, exergaming emerged as the most used approach, featuring 53% of interventions for healthy populations and 27% for clinical groups. Despite most exergaming applications targeting younger populations [195,196], our scoping review identifies a significant shift towards older users, with exergames implemented in half of the sources involving participants aged 85 and above. The findings support and build upon previous research suggesting the utility of exergames in promoting physical activity, cognitive enhancement, and overall well-being in the older population [11]. Furthermore, the diversity of exergame intervention contents highlights a comprehensive strategy for addressing age-related challenges such as cardiorespiratory fitness (41%), neuromotor functions (48%), mental and cognitive abilities (50%), and reducing falls (57%).

    Our findings also indicate that while exergames and mobile applications are the primary technologies used in these physical activity interventions, web video and phone call technologies or wearable devices also play a role, albeit less prominently. Especially in the clinical population, more often other designs than exergames were used compared to the interventions performed with healthy older adults. These other forms of technology design, offering a more passive interaction than the dynamic engagement found in exergames, may not be as prevalent but are nonetheless vital. Older, or more prone older adults may prefer and benefit from their easy set-up and simple usage [197-199]. Their integration highlights their significance within a comprehensive technological intervention framework, which aligns with effective telehealth components like text messaging for education and reminders, web-based content for goal-setting, and interactive platforms for health data exchange [200].

    In advocating for the integration of technology in physical activity interventions, our review highlights the pivotal role of technology functions and user interfaces, as supported by Bentlage et al. (2023), who stress their essential contribution to improve the activation factors skills, knowledge, and motivation, yielding effective behavioral change [20]. We pointed out the primary technological functions as the continuous provision of physical activity information (28%), performance feedback (28%), and the recording of real-time movement data (26%). Such continuous and synchronous activity information offers users an immersive, enjoyable, and engaging experience [201], fosters regular habits [202], can facilitate the visualization of their movements, and significantly increase their motivation [203]. Enhancing user engagement and overcoming engagement barriers can be achieved by using advanced technology to incorporate different user interfaces, preferably multiple types [20,204]. While graphical or body movement interfaces were prevalent in many sources (each 63%), alternative user interfaces were limited (ie, 41% visual/auditory, 27% menu driven, and 16% command line).

    Notably, the tailoring of intervention exercises or activities did not significantly influence the choice of technology design, function, or user interface. This might indicate that the deployed technological solutions are versatile and adaptable across various intervention needs without requiring significant modifications [205,206]. Moreover, our review emphasizes a shift towards personalized interventions tailored to individual or group needs, with 63% of interventions adopting this approach (versus 37% with a generic approach). This shift aligns with emerging evidence suggesting the effectiveness of personalized strategies in promoting adherence and achieving positive health outcomes among older adults [207].

    Technology-Related Outcomes

    Technology engagement and adherence to protocols are tightly linked to the usability, enjoyment, and effectiveness of physical activity-based interventions [208]. Yet only a slight majority of studies reported on dropout and adherence rates, whereas an even smaller number reported on reasons for dropout or (reduced) adherence. Despite the Consolidated Standard of Reporting Trials (CONSORT) [209] statement and TIDieR (Template for Intervention Description and Replication) [32] guidelines dating back to 2010 and 2014, and the relevant Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2022 extension [210], there is still a lack of consistency of reporting.

    Sources that reported the reasons for dropout among participants primarily cited health-related issues, with only a few instances attributed to technology usage, underscoring the older participants’ willingness and engagement with home-based technology. Such instances of technology-related dropout (3%) were predominantly due to technological failures, connectivity issues, or general disinterest. These findings are consistent with recent studies, which also identified a lack of motivation and low familiarity with technology as key barriers to its implementation in older adults [15,23,211]. Furthermore, tailored interventions resulted in significantly higher adherence than control interventions. This underscores that it is imperative to adapt the technology to the preferences and needs of the target aging population [22,23,197-199,212-214]. To achieve suitable technological approaches, a generative co-design framework, could be used to overcome technological barriers in an aging population, as suggested by Bird et al [215].

    Strengths and Limitations of This Scoping Review

    Adopting a common vocabulary and definition of terms is challenging in research on technology-assisted physical activity interventions, which can be the intersection of medicine, physiotherapy, engineering and informatics, sensor and gaming technology, and even psychology and social sciences. Though challenging, we discussed and agreed upon definitions and vocabulary usage at each phase of the review. Furthermore, multiple protocols and double-blind scoring were incorporated in most phases of the review process to ensure consistency [216]. The relatively large number of screeners and extractors involved may be considered a potential limitation. However, the screening, data extraction, and data analysis tools were carefully prepared and discussed within the group, and conflicts were solved with a third independent reviewer or group discussions.

    Regarding the included sources, most of the reviewed studies were from highly developed and advanced countries. In addition, the sources included in this scoping review often failed to describe the used technology in sufficient detail to allow for replication of the design, function, and/or user interface. For example, exergames rely on an often bilateral interaction between the user and the system. However, the inputs (eg, handheld devices, sensors, video cameras) are poorly described on how these were adapted for use. Valenzuela et al 217 explored older adults’ experiences using an interactive cognitive-motor step training program. Yet, there is no clear description of how the interface has been adapted for their specific user group. Other sources assumed prior or even in-depth knowledge of commercial exergaming setups used within the intervention, avoiding interpretation of the data or replicability due to unknown use of commercial games (Wii-Fit), body movement recognition through three-dimensional camera setups (Xbox Kinect), or not clearly described engagement elements of online video calling for physical activity class delivery (eg, Yoga via Zoom calls) [218,219].

    This review has been carried out in a systematic and transparent manner [220]. However, we have not conducted a quality assessment or risk of bias of the included sources. Therefore, we cannot use the results of the included sources to draw significant conclusions on intervention effects on health outcomes.

    Recommendations

    There is no compelling reason to exclude specific older populations from home-based, technology-assisted physical activity interventions, as factors such as age, health status, and residency do not appear to negatively impact dropout rates, adherence, or result in technology-related adverse events. However, evidence regarding the use of such technologies in hospital settings remains limited. Vulnerable older adults, in particular, could benefit significantly from these interventions, as reduced physical activity during hospitalization often leads to functional decline and a loss of independence in daily activities at discharge. Regardless of the setting, the design of technology-assisted interventions must account for the physical and cognitive impairments commonly observed among older adults, which may result in a mismatch between their abilities and the demands of the intervention or technology [186]. Careful tailoring of both the intervention and the technology is therefore essential. For example, participatory design processes that engage older adults directly can ensure better alignment with their needs and preferences [215]. Our review highlights that tailored approaches are associated with significantly higher adherence rates. Furthermore, feedback from older adults highlights their strong preference for tailored approaches, indicating that tailoring is not only beneficial but also actively desired by older adults. To improve the accessibility and impact of these interventions, training to enhance health literacy (knowledge of the intervention’s relevance) and digital literacy (competence in using the technology) should be incorporated during both the recruitment and intervention phases. Developing clear recruitment guidelines may further promote inclusivity. Digital literacy barriers, often linked to low education levels and socioeconomic status, remain significant challenges to technology adoption and engagement [184,186-188]. To address this, technical support—either private or as part of the intervention—should be provided, aligning with preferences expressed by older adults in user feedback. Lastly, behavioral change techniques that enhance skills, knowledge, and motivation should be integrated into the design of technology-assisted physical activity interventions [20]. Feedback mechanisms, such as performance-based metrics, functional ability assessments, and real-time health data (eg, movement intensity), are currently underutilized, appearing in less than one-third of interventions. Implementing such features could encourage behavioral change and fulfill the desires for actionable feedback, as reported by older adults.

    Furthermore, future research should prioritize transparent reporting. The participant’s health status, including co-morbidities, mental and physical impairments, and activity level, as well as technology experience, should be recorded. The intervention design, including purpose, duration, intensity, supervision, and tailoring, should be well described. The technology design, functions (including consideration of behavioral change techniques), and user interface should be described in detail. Lastly, intervention and technology-related outcomes such as drop-out, adherence, adverse events, and feedback on user experience should be reported. Guidelines for the development of technology-assisted physical activity interventions in older adults with various health conditions may lead to conformity and could contribute to better designs engaging older adults. Such guidelines may improve the effectiveness of the interventions and make studies better comparable by adequate reporting. The latter results in a deeper understanding of user preferences and advances the development of tailored technology-assisted interventions.

    Conclusions

    The review suggests that using technology in physical activity interventions is feasible for all older adults in community dwellings and care homes, without additional risks of adverse events or dropouts. Furthermore, generally higher adherence was reported in technology-assisted interventions, which was significantly higher where interventions were tailored. Interventions were delivered to all older age groups, as well as those with and without clinical conditions and in a wide range of physical activity topics. The technology approaches for home-based physical activity interventions were found to be safe and enjoyable for older adults with acceptable usability. Despite the large number of sources that have been identified in this article, the lack of standardized reporting limited a more in-depth analysis of the main research question. Therefore, there is a need to update reporting guidelines for technology-specific interventions in this area.

    Acknowledgments

    This publication is based upon work from EU COST Action CA20104 – Network on evidence-based physical activity in old age (PhysAgeNet), supported by COST (European Cooperation in Science and Technology). We acknowledge the following members of the COST action ‘PhysAgeNet’ Working group 3 for their support in the data acquisition: Bassam Bouaziz from University of Sfax, Juel Jarani from University of Tirana, and Bojan Masanovic from University of Montenegro.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Search string development.

    DOCX File, 40 KB
    Multimedia Appendix 2

    Data extraction topics.

    DOCX File, 37 KB
    Multimedia Appendix 3

    Coding framework for data extraction.

    DOCX File, 69 KB
    Multimedia Appendix 4

    Result tables and sources.

    DOCX File, 137 KB
    Multimedia Appendix 5

    Full data extraction.

    XLSX File, 257 KB
    Multimedia Appendix 6

    Excluded sources reasons.

    XLSX File, 105 KB
    Checklist 1

    PRISMA-ScR checklist.

    DOCX File, 110 KB
    1. Vogel T, Brechat PH, Leprêtre PM, Kaltenbach G, Berthel M, Lonsdorfer J. Health benefits of physical activity in older patients: a review. Int J Clin Pract. Feb 2009;63(2):303-320. [CrossRef] [Medline]
    2. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. Dec 2020;54(24):1451-1462. [CrossRef] [Medline]
    3. Aubertin-Leheudre M, Rolland Y. The importance of physical activity to care for frail older adults during the COVID-19 pandemic. J Am Med Dir Assoc. Jul 2020;21(7):973-976. [CrossRef] [Medline]
    4. Bosco A, McGarrigle L, Skelton DA, Laventure RME, Townley B, Todd C. Make movement your mission: evaluation of an online digital health initiative to increase physical activity in older people during the COVID-19 pandemic. Digit Health. 2022;8:20552076221084468. [CrossRef] [Medline]
    5. Bangsbo J, Blackwell J, Boraxbekk CJ, et al. Copenhagen consensus statement 2019: physical activity and ageing. Br J Sports Med. Jul 2019;53(14):856-858. [CrossRef] [Medline]
    6. Sohaib Aslam A, van Luenen S, Aslam S, van Bodegom D, Chavannes NH. A systematic review on the use of mHealth to increase physical activity in older people. Clinical eHealth. 2020;3:31-39. [CrossRef]
    7. Shin G, Jarrahi MH, Fei Y, et al. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform. May 2019;93(October 2018):103153. [CrossRef] [Medline]
    8. Yerrakalva D, Yerrakalva D, Hajna S, Griffin S. Effects of mobile health app interventions on sedentary time, physical activity, and fitness in older adults: systematic review and meta-analysis. J Med Internet Res. Nov 28, 2019;21(11):e14343. [CrossRef] [Medline]
    9. Delbaere K, Valenzuela T, Woodbury A, et al. Evaluating the effectiveness of a home-based exercise programme delivered through a tablet computer for preventing falls in older community-dwelling people over 2 years: study protocol for the Standing Tall randomised controlled trial. BMJ Open. Oct 22, 2015;5(10):e009173. [CrossRef] [Medline]
    10. Dermody G, Whitehead L, Wilson G, Glass C. The role of virtual reality in improving health outcomes for community-dwelling older adults: systematic review. J Med Internet Res. Jun 1, 2020;22(6):e17331. [CrossRef] [Medline]
    11. Kappen DL, Mirza-Babaei P, Nacke LE. Older adults’ physical activity and exergames: a systematic review. International Journal of Human–Computer Interaction. Jan 20, 2019;35(2):140-167. [CrossRef]
    12. Chatterjee A, Gerdes M, Prinz A, Martinez S. Human coaching methodologies for automatic electronic coaching (eCoaching) as behavioral interventions with information and communication technology: systematic review. J Med Internet Res. Mar 24, 2021;23(3):e23533. [CrossRef] [Medline]
    13. Lotfi A, Langensiepen C, Yahaya SW. Socially assistive robotics: robot exercise trainer for older adults. Technologies (Basel). 2018;6(1):32. [CrossRef]
    14. Stockwell S, Schofield P, Fisher A, et al. Digital behavior change interventions to promote physical activity and/or reduce sedentary behavior in older adults: A systematic review and meta-analysis. Exp Gerontol. Jun 2019;120(January):68-87. [CrossRef] [Medline]
    15. Valenzuela T, Okubo Y, Woodbury A, Lord SR, Delbaere K. Adherence to technology-based exercise programs in older adults: a systematic review. J Geriatr Phys Ther. 2018;41(1):49-61. [CrossRef]
    16. Langeard A, Bigot L, Maffiuletti NA, et al. Non-inferiority of a home-based videoconference physical training program in comparison with the same program administered face-to-face in healthy older adults: the MOTION randomised controlled trial. Age Ageing. Mar 1, 2022;51(3):afac059. [CrossRef]
    17. Lee J, Jung D, Byun J, Lee M. Effects of a combined exercise program using an iPad for older adults. Healthc Inform Res. Apr 2016;22(2):65-72. [CrossRef] [Medline]
    18. Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health. Aug 17, 2021;21(1):1556. [CrossRef] [Medline]
    19. Batalik L, Filakova K, Sladeckova M, Dosbaba F, Su J, Pepera G. The cost-effectiveness of exercise-based cardiac telerehabilitation intervention: a systematic review. Eur J Phys Rehabil Med. Apr 2023;59(2):248-258. [CrossRef] [Medline]
    20. Bentlage E, Nyamadi JJ, Dubbeldam R. The importance of activating factors in physical activity interventions for older adults using information and communication technologies: systematic review. JMIR Mhealth Uhealth. Oct 24, 2023;11:e42968. [CrossRef] [Medline]
    21. McGarrigle L, Boulton E, Todd C. Map the apps: a rapid review of digital approaches to support the engagement of older adults in strength and balance exercises. BMC Geriatr. Nov 18, 2020;20(1):483. [CrossRef] [Medline]
    22. Piech J, Czernicki K. Virtual reality rehabilitation and exergames—physical and psychological impact on fall prevention among the elderly—a literature review. Appl Sci (Basel). 2021;11(9):4098. [CrossRef]
    23. Costa-Brito AR, Bovolini A, Rúa-Alonso M, et al. Home-based exercise interventions delivered by technology in older adults: A scoping review of technological tools usage. Int J Med Inform. Jan 2024;181:105287. [CrossRef] [Medline]
    24. Skjæret N, Nawaz A, Morat T, Schoene D, Helbostad JL, Vereijken B. Exercise and rehabilitation delivered through exergames in older adults: An integrative review of technologies, safety and efficacy. Int J Med Inform. Jan 2016;85(1):1-16. [CrossRef] [Medline]
    25. Yang Y, Li S, Cai Y, et al. Effectiveness of telehealth-based exercise interventions on pain, physical function and quality of life in patients with knee osteoarthritis: A meta-analysis. J Clin Nurs. Jun 2023;32(11-12):2505-2520. [CrossRef] [Medline]
    26. Peters MDJ, Marnie C, Tricco AC, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis. 2020;18(10):2119-2126. [CrossRef]
    27. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 2, 2018;169(7):467-473. [CrossRef] [Medline]
    28. Dubbeldam R, Stemplewski R, Cyma-Wejchenig M, et al. Scoping review on technology-assisted physical interventions for older people in their home environment. OSF. 2023. URL: https://osf.io/b2ngd [Accessed 2025-08-28]
    29. Denton F, Power S, Waddell A, et al. Is It really home-based? A commentary on the necessity for accurate definitions across exercise and physical activity programmes. Int J Environ Res Public Health. Sep 1, 2021;18(17):9244. [CrossRef] [Medline]
    30. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. Dec 5, 2016;5(1):210. [CrossRef] [Medline]
    31. Pollock D, Peters MDJ, Khalil H, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth. Mar 1, 2023;21(3):520-532. [CrossRef] [Medline]
    32. Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. Mar 7, 2014;348(3):g1687. [CrossRef] [Medline]
    33. Sheehy L, Sveistrup H, Knoefel F, et al. The use of home-based nonimmersive virtual reality to encourage physical and cognitive exercise in people with mild cognitive impairment: a feasibility study. J Aging Phys Act. Apr 1, 2022;30(2):297-307. [CrossRef] [Medline]
    34. Lee BJ, Park YH, Lee JY, Kim SJ, Jang Y, Lee JI. Smartphone application versus pedometer to promote physical activity in prostate cancer patients. Telemed J E Health. Dec 2019;25(12):1231-1236. [CrossRef] [Medline]
    35. Taylor ME, Close JCT, Lord SR, et al. Pilot feasibility study of a home-based fall prevention exercise program (StandingTall) delivered through a tablet computer (iPad) in older people with dementia. Australas J Ageing. Sep 2020;39(3):e278-e287. [CrossRef] [Medline]
    36. Leskinen T, Suorsa K, Tuominen M, et al. The effect of consumer-based activity tracker intervention on physical activity among recent retirees-an RCT study. Med Sci Sports Exerc. Aug 1, 2021;53(8):1756-1765. [CrossRef] [Medline]
    37. Lauzé M, Martel DD, Agnoux A, et al. Feasibility, acceptability and effects of a home-based exercise program using a gerontechnology on physical capacities after a minor injury in community-living older adults: a pilot study. J Nutr Health Aging. 2018;22(1):16-25. [CrossRef] [Medline]
    38. Nikitina S, Didino D, Baez M, Casati F. Feasibility of virtual tablet-based group exercise among older adults in Siberia: findings from two pilot trials. JMIR Mhealth Uhealth. Feb 27, 2018;6(2):e40. [CrossRef] [Medline]
    39. Janssen S, Tange H, Arends R. A preliminary study on the effectiveness of Exergame Nintendo “Wii Fit Plus” on the balance of nursing home residents. Games Health J. Apr 2013;2(2):89-95. [CrossRef] [Medline]
    40. Talbot LA, Gaines JM, Huynh TN, Metter EJ. A home-based pedometer-driven walking program to increase physical activity in older adults with osteoarthritis of the knee: a preliminary study. J Am Geriatr Soc. Mar 2003;51(3):387-392. [CrossRef] [Medline]
    41. Albores J, Marolda C, Haggerty M, Gerstenhaber B, Zuwallack R. The use of a home exercise program based on a computer system in patients with chronic obstructive pulmonary disease. J Cardiopulm Rehabil Prev. 2013;33(1):47-52. [CrossRef] [Medline]
    42. Hawley-Hague H, Tacconi C, Mellone S, et al. Smartphone apps to support falls rehabilitation exercise: app development and usability and acceptability study. JMIR Mhealth Uhealth. Sep 28, 2020;8(9):e15460. [CrossRef] [Medline]
    43. Silveira P, van de Langenberg R, van Het Reve E, Daniel F, Casati F, de Bruin ED. Tablet-based strength-balance training to motivate and improve adherence to exercise in independently living older people: a phase II preclinical exploratory trial. J Med Internet Res. Aug 12, 2013;15(8):e159. [CrossRef] [Medline]
    44. Gschwind YJ, Schoene D, Lord SR, et al. The effect of sensor-based exercise at home on functional performance associated with fall risk in older people - a comparison of two exergame interventions. Eur Rev Aging Phys Act. 2015;12:11. [CrossRef] [Medline]
    45. Demeyer H, Louvaris Z, Frei A, et al. Physical activity is increased by a 12-week semiautomated telecoaching programme in patients with COPD: a multicentre randomised controlled trial. Thorax. May 2017;72(5):415-423. [CrossRef] [Medline]
    46. Pettersson B, Janols R, Wiklund M, Lundin-Olsson L, Sandlund M. Older adults’ experiences of behavior change support in a digital fall prevention exercise program: qualitative study framed by the self-determination theory. J Med Internet Res. Jul 30, 2021;23(7):e26235. [CrossRef] [Medline]
    47. Muñoz J, Mehrabi S, Li Y, et al. Immersive virtual reality exergames for persons living with dementia: user-centered design study as a multistakeholder team during the COVID-19 pandemic. JMIR Serious Games. Jan 19, 2022;10(1):e29987. [CrossRef] [Medline]
    48. Hong J, Kim J, Kim SW, Kong HJ. Effects of home-based tele-exercise on sarcopenia among community-dwelling elderly adults: Body composition and functional fitness. Exp Gerontol. Jan 2017;87(Pt A):33-39. [CrossRef] [Medline]
    49. Neumann S, Meidert U, Barberà-Guillem R, Poveda-Puente R, Becker H. Effects of an exergame software for older adults on fitness, activities of daily living performance, and quality of life. Games Health J. Oct 2018;7(5):341-346. [CrossRef] [Medline]
    50. Chao YY, Scherer YK, Wu YW, Lucke KT, Montgomery CA. The feasibility of an intervention combining self-efficacy theory and Wii Fit exergames in assisted living residents: a pilot study. Geriatr Nurs. 2013;34(5):377-382. [CrossRef] [Medline]
    51. Golla A, Müller T, Wohlfarth K, Jahn P, Mattukat K, Mau W. Home-based balance training using Wii Fit: a pilot randomised controlled trial with mobile older stroke survivors. Pilot Feasibility Stud. 2018;4:143. [CrossRef] [Medline]
    52. Espay AJ, Baram Y, Dwivedi AK, et al. At-home training with closed-loop augmented-reality cueing device for improving gait in patients with Parkinson disease. J Rehabil Res Dev. 2010;47(6):573-581. [CrossRef] [Medline]
    53. Jaarsma T, Klompstra L, Ben Gal T, et al. Effects of exergaming on exercise capacity in patients with heart failure: results of an international multicentre randomized controlled trial. Eur J Heart Fail. Jan 2021;23(1):114-124. [CrossRef] [Medline]
    54. Rosenberg D, Depp CA, Vahia IV, et al. Exergames for subsyndromal depression in older adults: a pilot study of a novel intervention. Am J Geriatr Psychiatry. Mar 2010;18(3):221-226. [CrossRef] [Medline]
    55. Vestergaard S, Kronborg C, Puggaard L. Home-based video exercise intervention for community-dwelling frail older women: a randomized controlled trial. Aging Clin Exp Res. Oct 2008;20(5):479-486. [CrossRef] [Medline]
    56. Li F, Harmer P, Fitzgerald K, Winters-Stone K. A cognitively enhanced online Tai Ji Quan training intervention for community-dwelling older adults with mild cognitive impairment: a feasibility trial. BMC Geriatr. Jan 25, 2022;22(1):76. [CrossRef] [Medline]
    57. Davis JC, Hsu CL, Cheung W, et al. Can the Otago falls prevention program be delivered by video? A feasibility study. BMJ Open Sport Exerc Med. 2016;2(1):e000059. [CrossRef] [Medline]
    58. Fillol F, Paris L, Pascal S, et al. Possible impact of a 12-month web- and smartphone-based program to improve long-term physical activity in patients attending spa therapy: randomized controlled trial. J Med Internet Res. Jun 16, 2022;24(6):e29640. [CrossRef] [Medline]
    59. Duarte-Rojo A, Bloomer PM, Rogers RJ, et al. Introducing EL-FIT (exercise and liver fitness): a smartphone app to prehabilitate and monitor liver transplant candidates. Liver Transpl. Apr 2021;27(4):502-512. [CrossRef] [Medline]
    60. Zadro JR, Shirley D, Nilsen TIL, Mork PJ, Ferreira PH. Family history influences the effectiveness of home exercise in older people with chronic low back pain: a secondary analysis of a randomized controlled trial. Arch Phys Med Rehabil. Aug 2020;101(8):1322-1331. [CrossRef] [Medline]
    61. Loh KP, Sanapala C, Watson EE, et al. A single-arm pilot study of a mobile health exercise intervention (GO-EXCAP) in older patients with myeloid neoplasms. Blood Adv. Jul 12, 2022;6(13):3850-3860. [CrossRef] [Medline]
    62. Wu YZ, Lin JY, Wu PL, Kuo YF. Effects of a hybrid intervention combining exergaming and physical therapy among older adults in a long-term care facility. Geriatr Gerontol Int. Feb 2019;19(2):147-152. [CrossRef] [Medline]
    63. Yuan RY, Chen SC, Peng CW, Lin YN, Chang YT, Lai CH. Effects of interactive video-game-based exercise on balance in older adults with mild-to-moderate Parkinson’s disease. J Neuroeng Rehabil. Jul 13, 2020;17(1):91. [CrossRef] [Medline]
    64. Duscha BD, Piner LW, Patel MP, et al. Effects of a 12-week mHealth program on functional capacity and physical activity in patients with peripheral artery disease. Am J Cardiol. Sep 1, 2018;122(5):879-884. [CrossRef] [Medline]
    65. Wang CH, Chou PC, Joa WC, et al. Mobile-phone-based home exercise training program decreases systemic inflammation in COPD: a pilot study. BMC Pulm Med. Aug 30, 2014;14:142. [CrossRef] [Medline]
    66. Auerswald T, Meyer J, von Holdt K, Voelcker-Rehage C. Application of activity trackers among nursing home residents-a pilot and feasibility study on physical activity behavior, usage behavior, acceptance, usability and motivational Impact. Int J Environ Res Public Health. Sep 14, 2020;17(18):6683. [CrossRef] [Medline]
    67. Goode AP, Taylor SS, Hastings SN, Stanwyck C, Coffman CJ, Allen KD. Effects of a home-based telephone-supported physical activity program for older adult veterans with chronic low back pain. Phys Ther. May 1, 2018;98(5):369-380. [CrossRef] [Medline]
    68. Schwartz H, Har-Nir I, Wenhoda T, Halperin I. Staying physically active during the COVID-19 quarantine: exploring the feasibility of live, online, group training sessions among older adults. Transl Behav Med. Mar 16, 2021;11(2):314-322. [CrossRef] [Medline]
    69. van den Helder J, Mehra S, van Dronkelaar C, et al. Blended home-based exercise and dietary protein in community-dwelling older adults: a cluster randomized controlled trial. J Cachexia Sarcopenia Muscle. Dec 2020;11(6):1590-1602. [CrossRef] [Medline]
    70. Barnason S, Zimmerman L, Schulz P, Tu C. Influence of an early recovery telehealth intervention on physical activity and functioning after coronary artery bypass surgery among older adults with high disease burden. Heart Lung. 2009;38(6):459-468. [CrossRef] [Medline]
    71. McAuley E, Wójcicki TR, Gothe NP, et al. Effects of a DVD-delivered exercise intervention on physical function in older adults. J Gerontol A Biol Sci Med Sci. Sep 2013;68(9):1076-1082. [CrossRef] [Medline]
    72. Bao T, Noohi F, Kinnaird C, et al. Retention effects of long-term balance training with vibrotactile sensory augmentation in healthy older adults. Sensors (Basel). Apr 14, 2022;22(8):3014. [CrossRef] [Medline]
    73. Agmon M, Perry CK, Phelan E, Demiris G, Nguyen HQ. A pilot study of Wii Fit exergames to improve balance in older adults. J Geriatr Phys Ther. 2011;34(4):161-167. [CrossRef] [Medline]
    74. Moy ML, Collins RJ, Martinez CH, et al. An internet-mediated pedometer-based program improves health-related quality-of-life domains and daily step counts in COPD: a randomized controlled trial. Chest. Jul 2015;148(1):128-137. [CrossRef] [Medline]
    75. Anderson-Hanley C, Arciero PJ, Brickman AM, et al. Exergaming and older adult cognition: a cluster randomized clinical trial. Am J Prev Med. Feb 2012;42(2):109-119. [CrossRef] [Medline]
    76. Coultas DB, Jackson BE, Russo R, et al. Home-based physical activity coaching, physical activity, and health care utilization in chronic obstructive pulmonary disease. Chronic obstructive pulmonary disease self-management activation research trial secondary outcomes. Ann Am Thorac Soc. Apr 2018;15(4):470-478. [CrossRef] [Medline]
    77. Schwenk M, Grewal GS, Honarvar B, et al. Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults. J Neuroeng Rehabil. Dec 13, 2014;11:164. [CrossRef] [Medline]
    78. Esculier JF, Vaudrin J, Bériault P, Gagnon K, Tremblay LE. Home-based balance training programme using Wii Fit with balance board for Parkinsons’s disease: a pilot study. J Rehabil Med. Feb 2012;44(2):144-150. [CrossRef] [Medline]
    79. Snyder A, Colvin B, Gammack JK. Pedometer use increases daily steps and functional status in older adults. J Am Med Dir Assoc. Oct 2011;12(8):590-594. [CrossRef] [Medline]
    80. Wu G, Keyes L, Callas P, Ren X, Bookchin B. Comparison of telecommunication, community, and home-based Tai Chi exercise programs on compliance and effectiveness in elders at risk for falls. Arch Phys Med Rehabil. Jun 2010;91(6):849-856. [CrossRef] [Medline]
    81. Swinnen N, Vandenbulcke M, de Bruin ED, Akkerman R, Stubbs B, Vancampfort D. Exergaming for people with major neurocognitive disorder: a qualitative study. Disabil Rehabil. May 2022;44(10):2044-2052. [CrossRef] [Medline]
    82. Garcia JA, Schoene D, Lord SR, Delbaere K, Valenzuela T, Navarro KF. A bespoke kinect stepping exergame for improving physical and cognitive function in older people: a pilot study. Games Health J. Dec 2016;5(6):382-388. [CrossRef] [Medline]
    83. Olafsdottir SA, Jonsdottir H, Bjartmarz I, et al. Feasibility of ActivABLES to promote home-based exercise and physical activity of community-dwelling stroke survivors with support from caregivers: a mixed methods study. BMC Health Serv Res. Jun 22, 2020;20(1):562. [CrossRef] [Medline]
    84. Villumsen BR, Jorgensen MG, Frystyk J, Hørdam B, Borre M. Home-based “exergaming” was safe and significantly improved 6-min walking distance in patients with prostate cancer: a single-blinded randomised controlled trial. BJU Int. Oct 2019;124(4):600-608. [CrossRef] [Medline]
    85. Zahedian-Nasab N, Jaberi A, Shirazi F, Kavousipor S. Effect of virtual reality exercises on balance and fall in elderly people with fall risk: a randomized controlled trial. BMC Geriatr. Sep 25, 2021;21(1):509. [CrossRef] [Medline]
    86. Klompstra L, Jaarsma T, Mårtensson J, Strömberg A. Exergaming through the eyes of patients with heart failure: a qualitative content analysis study. Games Health J. Jun 2017;6(3):152-158. [CrossRef] [Medline]
    87. Schoene D, Lord SR, Delbaere K, Severino C, Davies TA, Smith ST. A randomized controlled pilot study of home-based step training in older people using videogame technology. PLoS ONE. 2013;8(3):e57734. [CrossRef] [Medline]
    88. O’Brien T, Troutman-Jordan M, Hathaway D, Armstrong S, Moore M. Acceptability of wristband activity trackers among community dwelling older adults. Geriatr Nurs. 2015;36(2 Suppl):S21-S25. [CrossRef] [Medline]
    89. Wu G, Keyes LM. Group tele-exercise for improving balance in elders. Telemed J E Health. Oct 2006;12(5):561-570. [CrossRef] [Medline]
    90. Padala KP, Padala PR, Lensing SY, et al. Home-based exercise program improves balance and fear of falling in community-dwelling older adults with mild alzheimer’s disease: a pilot study. J Alzheimers Dis. 2017;59(2):565-574. [CrossRef] [Medline]
    91. Hosteng KR, Simmering JE, Polgreen LA, et al. Multilevel mHealth intervention increases physical activity of older adults living in retirement community. J Phys Act Health. Jul 1, 2021;18(7):851-857. [CrossRef] [Medline]
    92. Golsteijn RHJ, Bolman C, Volders E, Peels DA, de Vries H, Lechner L. Short-term efficacy of a computer-tailored physical activity intervention for prostate and colorectal cancer patients and survivors: a randomized controlled trial. Int J Behav Nutr Phys Act. Oct 30, 2018;15(1):106. [CrossRef] [Medline]
    93. Yousefi Babadi S, Daneshmandi H. Effects of virtual reality versus conventional balance training on balance of the elderly. Exp Gerontol. Oct 1, 2021;153:111498. [CrossRef] [Medline]
    94. Swinnen N, Vandenbulcke M, de Bruin ED, et al. The efficacy of exergaming in people with major neurocognitive disorder residing in long-term care facilities: a pilot randomized controlled trial. Alzheimers Res Ther. Mar 30, 2021;13(1):70. [CrossRef] [Medline]
    95. Jung DI, Ko DS, Jeong MA. Kinematic effect of Nintendo Wii(TM) sports program exercise on obstacle gait in elderly women with falling risk. J Phys Ther Sci. May 2015;27(5):1397-1400. [CrossRef] [Medline]
    96. Yeşilyaprak SS, Yıldırım M, Tomruk M, Ertekin Ö, Algun ZC. Comparison of the effects of virtual reality-based balance exercises and conventional exercises on balance and fall risk in older adults living in nursing homes in Turkey. Physiother Theory Pract. 2016;32(3):191-201. [CrossRef] [Medline]
    97. Padala KP, Padala PR, Malloy TR, et al. Wii-fit for improving gait and balance in an assisted living facility: a pilot study. J Aging Res. 2012;2012:597573. [CrossRef] [Medline]
    98. Maddison R, Pfaeffli L, Whittaker R, et al. A mobile phone intervention increases physical activity in people with cardiovascular disease: Results from the HEART randomized controlled trial. Eur J Prev Cardiol. Jun 2015;22(6):701-709. [CrossRef] [Medline]
    99. van Diest M, Stegenga J, Wörtche HJ, Verkerke GJ, Postema K, Lamoth CJC. Exergames for unsupervised balance training at home: A pilot study in healthy older adults. Gait Posture. Feb 2016;44:161-167. [CrossRef] [Medline]
    100. Silveira P, van het Reve E, Daniel F, Casati F, de Bruin ED. Motivating and assisting physical exercise in independently living older adults: a pilot study. Int J Med Inform. May 2013;82(5):325-334. [CrossRef] [Medline]
    101. Li F, Harmer P, Voit J, Chou LS. Implementing an online virtual falls prevention intervention during a public health pandemic for older adults with mild cognitive impairment: a feasibility trial. Clin Interv Aging. 2021;16:973-983. [CrossRef] [Medline]
    102. Khaghani Far I, Ferron M, Ibarra F, et al. The interplay of physical and social wellbeing in older adults: investigating the relationship between physical training and social interactions with virtual social environments. PeerJ Comput Sci. 1:e30. [CrossRef]
    103. Cinini A, Cutugno P, Ferraris C, et al. Final results of the NINFA project: impact of new technologies in the daily life of elderly people. Aging Clin Exp Res. May 2021;33(5):1213-1222. [CrossRef] [Medline]
    104. McDermott MM, Spring B, Berger JS, et al. Effect of a home-based exercise intervention of wearable technology and telephone coaching on walking performance in peripheral artery disease: the HONOR randomized clinical trial. JAMA. Apr 24, 2018;319(16):1665-1676. [CrossRef] [Medline]
    105. Song J, Paul SS, Caetano MJD, et al. Home-based step training using videogame technology in people with Parkinson’s disease: a single-blinded randomised controlled trial. Clin Rehabil. Mar 2018;32(3):299-311. [CrossRef] [Medline]
    106. Chao YY, Scherer YK, Montgomery CA, Wu YW, Lucke KT. Physical and psychosocial effects of Wii Fit exergames use in assisted living residents: a pilot study. Clin Nurs Res. Dec 2015;24(6):589-603. [CrossRef] [Medline]
    107. Geraedts HAE, Zijlstra W, Zhang W, et al. A home-based exercise program driven by tablet application and mobility monitoring for frail older adults: feasibility and practical implications. Prev Chronic Dis. Feb 2, 2017;14:E12. [CrossRef] [Medline]
    108. Pischke CR, Voelcker-Rehage C, Ratz T, et al. Web-based versus print-based physical activity intervention for community-dwelling older adults: crossover randomized trial. JMIR Mhealth Uhealth. Mar 23, 2022;10(3):e32212. [CrossRef] [Medline]
    109. Batsis JA, Petersen CL, Clark MM, et al. Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity. BMC Geriatr. Jan 12, 2021;21(1):44. [CrossRef] [Medline]
    110. Gschwind YJ, Eichberg S, Ejupi A, et al. ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial. Eur Rev Aging Phys Act. 2015;12:10. [CrossRef] [Medline]
    111. Jansons P, Dalla Via J, Daly RM, Fyfe JJ, Gvozdenko E, Scott D. Delivery of home-based exercise interventions in older adults facilitated by amazon alexa: a 12-week feasibility trial. J Nutr Health Aging. 2022;26(1):96-102. [CrossRef] [Medline]
    112. Rodrigues EV, Gallo LH, Guimarães ATB, Melo Filho J, Luna BC, Gomes ARS. Effects of dance exergaming on depressive symptoms, fear of falling, and musculoskeletal function in fallers and nonfallers community-dwelling older women. Rejuvenation Res. Dec 2018;21(6):518-526. [CrossRef] [Medline]
    113. Morrison S, Simmons R, Colberg SR, Parson HK, Vinik AI. Supervised balance training and Wii Fit-based exercises lower falls risk in older adults with type 2 diabetes. J Am Med Dir Assoc. Feb 2018;19(2):185. [CrossRef] [Medline]
    114. Muñoz GF, Cardenas RAM, Pla F. A kinect-based interactive system for home-assisted active aging. Sensors (Basel). Jan 8, 2021;21(2):417. [CrossRef] [Medline]
    115. Brickwood KJ, Ahuja KDK, Watson G, O’Brien JA, Williams AD. Effects of activity tracker use with health professional support or telephone counseling on maintenance of physical activity and health outcomes in older adults: randomized controlled trial. JMIR Mhealth Uhealth. Jan 5, 2021;9(1):e18686. [CrossRef] [Medline]
    116. Peng X, Su Y, Hu Z, et al. Home-based telehealth exercise training program in Chinese patients with heart failure: a randomized controlled trial. Medicine (Baltimore). Aug 2018;97(35):e12069. [CrossRef] [Medline]
    117. Mehra S, van den Helder J, Visser B, Engelbert RHH, Weijs PJM, Kröse BJA. Evaluation of a blended physical activity intervention for older adults: mixed methods study. J Med Internet Res. Jul 23, 2020;22(7):e16380. [CrossRef] [Medline]
    118. Lewis ZH, Ottenbacher KJ, Fisher SR, et al. The feasibility and RE-AIM evaluation of the TAME health pilot study. Int J Behav Nutr Phys Act. Aug 14, 2017;14(1):106. [CrossRef] [Medline]
    119. Yin Z, Martinez CE, Li S, et al. Adapting Chinese qigong mind-body exercise for healthy aging in older community-dwelling low-income Latino adults: pilot feasibility study. JMIR Aging. Nov 1, 2021;4(4):e29188. [CrossRef] [Medline]
    120. Klompstra L, Jaarsma T, Strömberg A. Exergaming to increase the exercise capacity and daily physical activity in heart failure patients: a pilot study. BMC Geriatr. Nov 18, 2014;14:119. [CrossRef] [Medline]
    121. Irvine AB, Gelatt VA, Seeley JR, Macfarlane P, Gau JM. Web-based intervention to promote physical activity by sedentary older adults: randomized controlled trial. J Med Internet Res. Feb 5, 2013;15(2):e19. [CrossRef] [Medline]
    122. Mansson L, Lundin-Olsson L, Skelton DA, et al. Older adults’ preferences for, adherence to and experiences of two self-management falls prevention home exercise programmes: a comparison between a digital programme and a paper booklet. BMC Geriatr. Jun 15, 2020;20(1):209. [CrossRef] [Medline]
    123. Anderson-Hanley C, Stark J, Wall KM, et al. ): effects of a 3-month in-home pilot clinical trial for mild cognitive impairment and caregivers. Clin Interv Aging. 2018;13:1565-1577. [CrossRef] [Medline]
    124. Martel D, Lauzé M, Agnoux A, et al. Comparing the effects of a home-based exercise program using a gerontechnology to a community-based group exercise program on functional capacities in older adults after a minor injury. Exp Gerontol. Jul 15, 2018;108:41-47. [CrossRef] [Medline]
    125. Chao YY, Scherer YK, Montgomery CA, Lucke KT, Wu YW. Exergames-based intervention for assisted living residents: a pilot study. J Gerontol Nurs. Nov 2014;40(11):36-43. [CrossRef] [Medline]
    126. Baez M, Khaghani Far I, Ibarra F, Ferron M, Didino D, Casati F. Effects of online group exercises for older adults on physical, psychological and social wellbeing: a randomized pilot trial. PeerJ. 2017;5:e3150. [CrossRef] [Medline]
    127. Auerswald T, Hendker A, Ratz T, et al. Impact of activity tracker usage in combination with a physical activity intervention on physical and cognitive parameters in healthy adults aged 60+: a randomized controlled trial. Int J Environ Res Public Health. Mar 22, 2022;19(7):3785. [CrossRef] [Medline]
    128. Netz Y, Yekutieli Z, Arnon M, et al. Personalized exercise programs based upon remote assessment of motor fitness: a pilot study among healthy people aged 65 years and older. Gerontology. 2022;68(4):465-479. [CrossRef] [Medline]
    129. Gandolfi M, Geroin C, Dimitrova E, et al. Virtual reality telerehabilitation for postural instability in parkinson’s disease: a multicenter, single-blind, randomized, controlled trial. Biomed Res Int. 2017;2017:7962826. [CrossRef] [Medline]
    130. Ambrens M, van Schooten KS, Lung T, et al. Economic evaluation of the e-Health StandingTall balance exercise programme for fall prevention in people aged 70 years and over. Age Ageing. Jun 1, 2022;51(6):afac130. [CrossRef] [Medline]
    131. Baez M, Ibarra F, Far IK, Ferron M, Casati F. 2016 international conference on collaboration technologies and systems (CTS). Online group-exercises for older adults of different physical abilities. [CrossRef]
    132. Monteblanco Cavalcante M, Fraga I, Dalbosco B, et al. Exergame training-induced neuroplasticity and cognitive improvement in institutionalized older adults: a preliminary investigation. Physiol Behav. Nov 1, 2021;241:113589. [CrossRef] [Medline]
    133. Wall K, Stark J, Schillaci A, et al. The enhanced interactive physical and cognitive exercise system (iPACESTM v2.0): pilot clinical trial of an in-home ipad-based neuro-exergame for mild cognitive impairment (MCI). J Clin Med. Aug 30, 2018;7(9):249. [CrossRef] [Medline]
    134. Shubert TE, Chokshi A, Mendes VM, et al. Stand tall-a virtual translation of the otago exercise program. J Geriatr Phys Ther. 2020;43(3):120-127. [CrossRef] [Medline]
    135. Lynch BM, Nguyen NH, Moore MM, et al. A randomized controlled trial of a wearable technology-based intervention for increasing moderate to vigorous physical activity and reducing sedentary behavior in breast cancer survivors: the ACTIVATE trial. Cancer. Aug 15, 2019;125(16):2846-2855. [CrossRef] [Medline]
    136. Yang WC, Wang HK, Wu RM, Lo CS, Lin KH. Home-based virtual reality balance training and conventional balance training in Parkinson’s disease: a randomized controlled trial. J Formos Med Assoc. Sep 2016;115(9):734-743. [CrossRef] [Medline]
    137. Kim T, Xiong S. Effectiveness and usability of a novel kinect-based tailored interactive fall intervention system for fall prevention in older people: a preliminary study. Front Public Health. 2022;10:884551. [CrossRef] [Medline]
    138. Callisaya ML, Jayakody O, Vaidya A, Srikanth V, Farrow M, Delbaere K. A novel cognitive-motor exercise program delivered via a tablet to improve mobility in older people with cognitive impairment - standingtall cognition and mobility. Exp Gerontol. Sep 2021;152:111434. [CrossRef] [Medline]
    139. Sparrow D, Gottlieb DJ, Demolles D, Fielding RA. Increases in muscle strength and balance using a resistance training program administered via a telecommunications system in older adults. J Gerontol A Biol Sci Med Sci. Nov 2011;66(11):1251-1257. [CrossRef] [Medline]
    140. Jennings SC, Manning KM, Bettger JP, et al. Rapid transition to telehealth group exercise and functional assessments in response to COVID-19. Gerontol Geriatr Med. 2020;6:2333721420980313. [CrossRef] [Medline]
    141. Flynn A, Preston E, Dennis S, Canning CG, Allen NE. Home-based exercise monitored with telehealth is feasible and acceptable compared to centre-based exercise in Parkinson’s disease: a randomised pilot study. Clin Rehabil. May 2021;35(5):728-739. [CrossRef] [Medline]
    142. Szanton SL, Walker RK, Lim JH, et al. Development of an exergame for urban-dwelling older adults with functional limitations: results and lessons learned. Prog Community Health Partnersh. 2016;10(1):73-81. [CrossRef] [Medline]
    143. Alagumoorthi G, D BJ, Thirunavukarasu S, V R, A K. Effectiveness of Wii sports- based strategy training in reducing risk of falling, falls and improving quality of life in adults with idiopathic Parkinson’s disease- a randomized comparative trial. Clin Rehabil. Aug 2022;36(8):1097-1109. [CrossRef] [Medline]
    144. Hong J, Kong HJ, Yoon HJ. Web-based telepresence exercise program for community-dwelling elderly women with a high risk of falling: randomized controlled trial. JMIR Mhealth Uhealth. May 28, 2018;6(5):e132. [CrossRef] [Medline]
    145. Muellmann S, Buck C, Voelcker-Rehage C, et al. Effects of two web-based interventions promoting physical activity among older adults compared to a delayed intervention control group in Northwestern Germany: Results of the PROMOTE community-based intervention trial. Prev Med Rep. Sep 2019;15:100958. [CrossRef] [Medline]
    146. Ellmers TJ, Paraskevopoulos IT, Williams AM, Young WR. Recalibrating disparities in perceived and actual balance abilities in older adults: a mixed-methods evaluation of a novel exergaming intervention. J Neuroeng Rehabil. Mar 22, 2018;15(1):26. [CrossRef] [Medline]
    147. Ogawa EF, Huang H, Yu LF, et al. Effects of exergaming on cognition and gait in older adults at risk for falling. Med Sci Sports Exerc. Mar 2020;52(3):754-761. [CrossRef] [Medline]
    148. Daly RM, Gianoudis J, Hall T, Mundell NL, Maddison R. Feasibility, usability, and enjoyment of a home-based exercise program delivered via an exercise app for musculoskeletal health in community-dwelling older adults: short-term prospective pilot study. JMIR Mhealth Uhealth. Jan 13, 2021;9(1):e21094. [CrossRef] [Medline]
    149. Soancatl Aguilar V, Lamoth CJC, Maurits NM, Roerdink J. Assessing dynamic postural control during exergaming in older adults: A probabilistic approach. Gait Posture. Feb 2018;60:235-240. [CrossRef] [Medline]
    150. Batsis JA, Naslund JA, Gill LE, Masutani RK, Agarwal N, Bartels SJ. Use of a wearable activity device in rural older obese adults: a pilot study. Gerontol Geriatr Med. 2016;2:2333721416678076. [CrossRef] [Medline]
    151. Keogh JWL, Power N, Wooller L, Lucas P, Whatman C. Physical and psychosocial function in residential aged-care elders: effect of Nintendo Wii Sports games. J Aging Phys Act. Apr 2014;22(2):235-244. [CrossRef] [Medline]
    152. Adcock M, Thalmann M, Schättin A, Gennaro F, de Bruin ED. A pilot study of an in-home multicomponent exergame training for older adults: feasibility, usability and pre-post evaluation. Front Aging Neurosci. 2019;11:304. [CrossRef] [Medline]
    153. Jessen JD, Lund HH. Playful home training for falls prevention. Institute of Electrical and Electronics Engineers Inc; 2015. Presented at: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM):311-317; Busan, South Korea. [CrossRef]
    154. Saenz-de-Urturi Z, Garcia-Zapirain Soto B. Kinect-based virtual game for the elderly that detects incorrect body postures in real time. Sensors (Basel). May 16, 2016;16(5):704. [CrossRef] [Medline]
    155. Vaziri DD, Aal K, Ogonowski C, et al. Exploring user experience and technology acceptance for a fall prevention system: results from a randomized clinical trial and a living lab. Eur Rev Aging Phys Act. 2016;13:6. [CrossRef] [Medline]
    156. Vallabhajosula S, McMillion AK, Freund JE. The effects of exergaming and treadmill training on gait, balance, and cognition in a person with Parkinson’s disease: A case study. Physiother Theory Pract. Dec 2017;33(12):920-931. [CrossRef] [Medline]
    157. Blair CK, Harding E, Wiggins C, et al. A home-based mobile health intervention to replace sedentary time with light physical activity in older cancer survivors: randomized controlled pilot trial. JMIR Cancer. Apr 13, 2021;7(2):e18819. [CrossRef] [Medline]
    158. Gooch HJ, Jarvis KA, Stockley RC. Behavior change approaches in digital technology-based physical rehabilitation interventions following stroke: scoping review. J Med Internet Res. Apr 24, 2024;26:e48725. [CrossRef] [Medline]
    159. Fu AS, Gao KL, Tung AK, Tsang WW, Kwan MM. Effectiveness of exergaming training in reducing risk and incidence of falls in frail older adults with a history of falls. Arch Phys Med Rehabil. Dec 2015;96(12):2096-2102. [CrossRef] [Medline]
    160. Barisch-Fritz B, Bezold J, Scharpf A, Trautwein S, Krell-Roesch J, Woll A. ICT-based individualized training of institutionalized individuals with dementia. evaluation of usability and trends toward the effectiveness of the InCoPE-app. Front Physiol. 2022;13:921105. [CrossRef] [Medline]
    161. Uzor S, Baillie L. Investigating the Long-Term Use of Exergames in the Home with Elderly Fallers. CHI ’14; 2014. URL: https://dl.acm.org/doi/proceedings/10.1145/2556288 [CrossRef] [Medline]
    162. Lauzé M, Martel DD, Aubertin-Leheudre M. Feasibility and effects of a physical activity program using gerontechnology in assisted living communities for older adults. J Am Med Dir Assoc. Dec 1, 2017;18(12):1069-1075. [CrossRef] [Medline]
    163. Bao T, Carender WJ, Kinnaird C, et al. Effects of long-term balance training with vibrotactile sensory augmentation among community-dwelling healthy older adults: a randomized preliminary study. J Neuroeng Rehabil. Jan 18, 2018;15(1):5. [CrossRef] [Medline]
    164. Hsu YI, Chen YC, Lee CL, Chang NJ. Effects of diet control and telemedicine-based resistance exercise intervention on patients with obesity and knee osteoarthritis: a randomized control trial. Int J Environ Res Public Health. Jul 21, 2021;18(15):7744. [CrossRef] [Medline]
    165. Middleton A, Simpson KN, Bettger JP, Bowden MG. COVID-19 pandemic and beyond: considerations and costs of telehealth exercise programs for older adults with functional impairments living at home-lessons learned from a pilot case study. Phys Ther. Aug 12, 2020;100(8):1278-1288. [CrossRef] [Medline]
    166. Studenski S, Perera S, Hile E, Keller V, Spadola-Bogard J, Garcia J. Interactive video dance games for healthy older adults. J Nutr Health Aging. Dec 2010;14(10):850-852. [CrossRef] [Medline]
    167. Radhakrishnan K, Julien C, O’Hair M, et al. Usability testing of a sensor-controlled digital game to engage older adults with heart failure in physical activity and weight monitoring. Appl Clin Inform. Oct 2020;11(5):873-881. [CrossRef] [Medline]
    168. Adcock M, Fankhauser M, Post J, et al. Effects of an in-home multicomponent exergame training on physical functions, cognition, and brain volume of older adults: a randomized controlled trial. Front Med (Lausanne). 2019;6:321. [CrossRef] [Medline]
    169. Jung Y, Li KJ, Janissa NS, Gladys WLC, Lee KM. Games for a better life. In: IE ’09. 2009. URL: https://dl.acm.org/doi/proceedings/10.1145/1746050 [CrossRef]
    170. Ibrahim F, Usman J, Hamzah N, Yazed Ahmad M, Teh SJ, editors. 2nd International Conference for Innovation in Biomedical Engineering and Life Sciences. 2018. URL: http://link.springer.com/10.1007/978-981-10-7554-4 [CrossRef]
    171. Smith ST, Davies TA, Lennox J. Step training system: an ICT solution to measure and reduce fall risk in older adults. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:7033-7035. [CrossRef] [Medline]
    172. Weinstock RS, Brooks G, Palmas W, et al. Lessened decline in physical activity and impairment of older adults with diabetes with telemedicine and pedometer use: results from the IDEATel study. Age Ageing. Jan 2011;40(1):98-105. [CrossRef] [Medline]
    173. Pettersson B, Wiklund M, Janols R, et al. “Managing pieces of a personal puzzle” - older people’s experiences of self-management falls prevention exercise guided by a digital program or a booklet. BMC Geriatr. Feb 18, 2019;19(1):43. [CrossRef] [Medline]
    174. Zalecki T, Gorecka-Mazur A, Pietraszko W, et al. Visual feedback training using WII Fit improves balance in Parkinson’s disease. Folia Med Cracov. 2013;53(1):65-78. [Medline]
    175. Dekker-van Weering M, Jansen-Kosterink S, Frazer S, Vollenbroek-Hutten M. User experience, actual use, and effectiveness of an information communication technology-supported home exercise program for pre-frail older adults. Front Med. 2017;4. [CrossRef]
    176. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact. Jul 29, 2008;24(6):574-594. [CrossRef]
    177. Pajalic Z, de Sousa DA, Strøm BS, et al. Welfare technology interventions among older people living at home-a systematic review of RCT studies. PLOS Digit Health. Jan 2023;2(1):e0000184. [CrossRef] [Medline]
    178. Kim B, Hunt M, Muscedere J, Maslove DM, Lee J. Using consumer-grade physical activity trackers to measure frailty transitions in older critical care survivors: exploratory observational study. JMIR Aging. Feb 23, 2021;4(1):e19859. [CrossRef] [Medline]
    179. Oesch P, Kool J, Fernandez-Luque L, et al. Exergames versus self-regulated exercises with instruction leaflets to improve adherence during geriatric rehabilitation: a randomized controlled trial. BMC Geriatr. Mar 23, 2017;17(1):77. [CrossRef] [Medline]
    180. Laver K, George S, Ratcliffe J, et al. Use of an interactive video gaming program compared with conventional physiotherapy for hospitalised older adults: a feasibility trial. Disabil Rehabil. 2012;34(21):1802-1808. [CrossRef] [Medline]
    181. Ortiz-Alonso J, Bustamante-Ara N, Valenzuela PL, et al. Effect of a simple exercise program on hospitalization-associated disability in older patients: a randomized controlled trial. J Am Med Dir Assoc. Apr 2020;21(4):531-537. [CrossRef] [Medline]
    182. Juneau A, Bolduc A, Nguyen P, et al. Feasibility of implementing an exercise program in a geriatric assessment unit: the SPRINT program. Can Geriatr J. Sep 2018;21(3):284-289. [CrossRef] [Medline]
    183. Scheerman K, Raaijmakers K, Otten RHJ, Meskers CGM, Maier AB. Effect of physical interventions on physical performance and physical activity in older patients during hospitalization: a systematic review. BMC Geriatr. Nov 23, 2018;18(1):288. [CrossRef] [Medline]
    184. Estrela M, Semedo G, Roque F, Ferreira PL, Herdeiro MT. Sociodemographic determinants of digital health literacy: a systematic review and meta-analysis. Int J Med Inform. Sep 2023;177:105124. [CrossRef] [Medline]
    185. Wang X, Luan W. Research progress on digital health literacy of older adults: a scoping review. Front Public Health. 2022;10:906089. [CrossRef]
    186. Kebede AS, Ozolins LL, Holst H, Galvin K. Digital engagement of older adults: scoping review. J Med Internet Res. Dec 7, 2022;24(12):e40192. [CrossRef] [Medline]
    187. Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: Internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. J Med Internet Res. May 2, 2013;15(5):e93. [CrossRef] [Medline]
    188. Borghouts J, Eikey E, Mark G, et al. Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. J Med Internet Res. Mar 24, 2021;23(3):e24387. [CrossRef] [Medline]
    189. Manser P, Herold F, de Bruin ED. Components of effective exergame-based training to improve cognitive functioning in middle-aged to older adults – a systematic review and meta-analysis. Ageing Res Rev. Aug 2024;99(102385):102385. [CrossRef]
    190. Torre MM, Temprado JJ. Effects of exergames on brain and cognition in older adults: a review based on a new categorization of combined training intervention. Front Aging Neurosci. Mar 30, 2022. [CrossRef]
    191. Wollesen B, Herden M, Lamberti N, Giannaki CD. Defining and reporting exercise intensity in interventions for older adults: a modified Delphi process. Eur Rev Aging Phys Act. Feb 2, 2024;21(1):3. [CrossRef] [Medline]
    192. VandeBunte A, Gontrum E, Goldberger L, et al. Physical activity measurement in older adults: wearables versus self-report. Front Digit Health. 2022;4:869790. [CrossRef] [Medline]
    193. Matsuura H, Mukaino M, Otaka Y, et al. Validity of simplified, calibration-less exercise intensity measurement using resting heart rate during sleep: a method-comparison study with respiratory gas analysis. BMC Sports Sci Med Rehabil. 2019;11(1):27. [CrossRef] [Medline]
    194. Knippenberg E, Timmermans A, Palmaers S, Spooren A. Use of a technology-based system to motivate older adults in performing physical activity: a feasibility study. BMC Geriatr. Jan 28, 2021;21(1):81. [CrossRef] [Medline]
    195. Calcaterra V, Vandoni M, Marin L, et al. Exergames to limit weight gain and to fight sedentarism in children and adolescents with obesity. Children (Basel). May 24, 2023;10(6):928. URL: https://doi.org/10.3390/children10060928 [CrossRef] [Medline]
    196. Benzing V, Schmidt M. Exergaming for children and adolescents: strengths, weaknesses, opportunities and threats. J Clin Med. Nov 8, 2018;7(11):422. [CrossRef] [Medline]
    197. Garcia Reyes EP, Kelly R, Buchanan G, Waycott J. Understanding older adults’ experiences with technologies for health self-management: interview study. JMIR Aging. Mar 21, 2023;6:e43197. [CrossRef] [Medline]
    198. Krishnan S, Mandala MA, Wolf SL, Howard A, Kesar TM. Perceptions of stroke survivors regarding factors affecting adoption of technology and exergames for rehabilitation. PM R. Nov 2023;15(11):1403-1410. [CrossRef]
    199. Yusif S, Soar J, Hafeez-Baig A. Older people, assistive technologies, and the barriers to adoption: a systematic review. Int J Med Inform. Oct 2016;94:112-116. [CrossRef] [Medline]
    200. CDC. Telehealth interventions to improve chronic disease. 2022. URL: https://www.cdc.gov/cardiovascular-resources/php/data-research/telehealth.html [Accessed 2025-08-28]
    201. Tanaka K, Sheehan G, Holash D JR, Katz L. A comparison of exergaming interfaces for use in rehabilitation programs and research. Loading – The Journal of the Canadian Game Studies Association. 2012;6(9). URL: oai:ojs.journals.sfu.ca.loading:article/107 [Accessed 2025-08-28]
    202. Oda N, Kim JH, Yamamoto Y. Information presentation methods for setting achievable and meaningful goals on fitness apps. 2022. Presented at: GoodIT 2022; Sep 7-9, 2022:61-67; Limassol Cyprus. URL: https://dl.acm.org/doi/proceedings/10.1145/3524458 [CrossRef]
    203. Munoz JE, Cao S, Boger J. Kinematically adaptive exergames: personalizing exercise therapy through closed-loop systems. Presented at: 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR); Dec 9-11, 2019:118-1187; San Diego, CA, USA. URL: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8938596 [CrossRef]
    204. Abedi A, Colella TJF, Pakosh M, Khan SS. Artificial intelligence-driven virtual rehabilitation for people living in the community: a scoping review. NPJ Digit Med. Feb 3, 2024;7(1):25. [CrossRef] [Medline]
    205. Delbaere K, Valenzuela T, Lord SR, et al. E-health standingtall balance exercise for fall prevention in older people: results of a two year randomised controlled trial. BMJ. 2021;373:n740. [CrossRef]
    206. Fischl C, Blusi M, Lindgren H, Nilsson I. Tailoring to support digital technology-mediated occupational engagement for older adults - a multiple case study. Scand J Occup Ther. Nov 2020;27(8):577-590. [CrossRef] [Medline]
    207. Collado-Mateo D, Lavín-Pérez AM, Peñacoba C, et al. Key factors associated with adherence to physical exercise in patients with chronic diseases and older adults: an umbrella review. Int J Environ Res Public Health. Feb 19, 2021;18(4):2023. [CrossRef] [Medline]
    208. Tuena C, Pedroli E, Trimarchi PD, et al. Usability issues of clinical and research applications of virtual reality in older people: a systematic review. Front Hum Neurosci. 2020;14:93. [CrossRef] [Medline]
    209. Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. J Pharmacol Pharmacother. Jul 2010;1(2):100-107. [CrossRef] [Medline]
    210. Butcher NJ, Monsour A, Mew EJ, et al. Guidelines for reporting outcomes in trial protocols. JAMA. Dec 20, 2022;328(23):2345. [CrossRef]
    211. Iancu I, Iancu B. Designing mobile technology for elderly. a theoretical overview. Technol Forecast Soc Change. Jun 2020;155:119977. [CrossRef]
    212. Borghese NA, Murray D, Paraschiv-Ionescu A, et al. Rehabilitation at Home: A Comprehensive Technological Approach. 2014:289-319. [CrossRef]
    213. LaMonica HM, Davenport TA, Roberts AE, Hickie IB. Understanding technology preferences and requirements for health information technologies designed to improve and maintain the mental health and well-being of older adults: participatory design study. JMIR Aging. Jan 6, 2021;4(1):e21461. [CrossRef] [Medline]
    214. Ringgenberg N, Mildner S, Hapig M, et al. ExerG: adapting an exergame training solution to the needs of older adults using focus group and expert interviews. J Neuroeng Rehabil. Aug 16, 2022;19(1):89. [CrossRef] [Medline]
    215. Bird M, McGillion M, Chambers EM, et al. A generative co-design framework for healthcare innovation: development and application of an end-user engagement framework. Res Involv Engagem. Mar 1, 2021;7(1):12. [CrossRef] [Medline]
    216. Peters MDJ, Marnie C, Colquhoun H, et al. Scoping reviews: reinforcing and advancing the methodology and application. Syst Rev. Oct 8, 2021;10(1):263. [CrossRef] [Medline]
    217. Valenzuela T, Razee H, Schoene D, Lord SR, Delbaere K. An interactive home-based cognitive-motor step training program to reduce fall risk in older adults: qualitative descriptive study of older adults’ experiences and requirements. JMIR Aging. Nov 30, 2018;1(2):e11975. [CrossRef] [Medline]
    218. Hong J, Kim SW, Joo H, Kong HJ. Effects of smartphone mirroring-based telepresence exercise on body composition and physical function in obese older women. Aging Clin Exp Res. May 2022;34(5):1113-1121. [CrossRef] [Medline]
    219. Park J, Heilman KJ, Sullivan M, et al. Remotely supervised home-based online chair yoga intervention for older adults with dementia: feasibility study. Complement Ther Clin Pract. Aug 2022;48:101617. [CrossRef]
    220. Pollock D, Davies EL, Peters MDJ, et al. Undertaking a scoping review: a practical guide for nursing and midwifery students, clinicians, researchers, and academics. J Adv Nurs. Apr 2021;77(4):2102-2113. [CrossRef] [Medline]

    ‎
    CONSORT: Consolidated Standard of Reporting Trials
    COST: Cooperation in Science and Technology
    JBI: Joanna Briggs Institute
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews
    SPIRIT: Standard Protocol Items: Recommendations for Interventional Trials

    Edited by Qiping Fan; submitted 25.08.24; peer-reviewed by Maha Gasmi, Patrick Manser, Randa Salah Gomaa Mahmoud; final revised version received 19.12.24; accepted 25.12.24; published 15.09.25.

    Copyright

    © Rosemary Dubbeldam, Rafal Stemplewski, Iuliia Pavlova, Magdalena Cyma-Wejchenig, Sunwoo Lee, Patrick Esser, Ellen Bentlage, Veysel Alcan, Özge Selin Çevik, Eleni Epiphaniou, Francesca Gallè, Antoine Langeard, Simone Gafner, Mona Ahmed, Niharika Bandaru, Arzu Erden Güner, Evrim Göz, Ilke Kara, Ayşe Kabuk, Ilayda Türkoglu, Zada Pajalic, Jan Vindiš, Damjan Jaksic, Uǧur Verep, Ioanna Chouvarda, Vera Simovska, Yael Netz, Jana Pelclova. Originally published in JMIR Aging (https://aging.jmir.org), 15.9.2025.

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