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

Digital Health Reviews (1326) Public (e)Health, Digital Epidemiology and Public Health Informatics (750) Internet Access and Digital Technology Use in an Elderly Population (226) Prevention and Health Promotion (1015)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43542, first published .
Digital Technologies for Health Promotion and Disease Prevention in Older People: Scoping Review

Digital Technologies for Health Promotion and Disease Prevention in Older People: Scoping Review

Digital Technologies for Health Promotion and Disease Prevention in Older People: Scoping Review

Authors of this article:

Karina Karolina De Santis1, 2 Author Orcid Image ;   Lea Mergenthal1 Author Orcid Image ;   Lara Christianson3 Author Orcid Image ;   Annalena Busskamp4 Author Orcid Image ;   Claudia Vonstein4 Author Orcid Image ;   Hajo Zeeb1, 2, 5 Author Orcid Image
Article Authors Cited by (33) Tweetations (6) Metrics

    Review

    1Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany

    2Leibniz-Science Campus Digital Public Health Bremen, Bremen, Germany

    3Department of Administration, Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany

    4Department Q6- Adults, Seniors, Women’s and Men’s Health, Health Equity, Federal Centre for Health Education, Cologne, Germany

    5Faculty 11 Human and Health Sciences, University of Bremen, Bremen, Germany

    Corresponding Author:

    Karina Karolina De Santis, PhD

    Department of Prevention and Evaluation

    Leibniz Institute for Prevention Research and Epidemiology

    Achterstr. 30

    Bremen, 28359

    Germany

    Phone: 49 42121856 ext 908

    Fax:49 42121856941

    Email: desantis@leibniz-bips.de


    Background: Digital technologies have the potential to contribute to health promotion and disease prevention in the aging world.

    Objective: This study aims to identify digital technologies for health promotion and disease prevention that could be used independently by older people in nonclinical settings using a scoping review.

    Methods: Through database (MEDLINE, PsycINFO, CINAHL, and SCOPUS; to March 3, 2022) and manual searches (to June 14, 2022), 90 primary studies and 8 systematic reviews were included in this scoping review. The eligibility was based on the PCC (Population, Concept, and Context) criteria: (1) people aged 50 years or older (population), (2) any digital (health) technology (eg, smartphone apps, websites, virtual reality; concept), and (3) health promotion and disease prevention in daily life in nonclinical and noninstitutional settings (context). Data items included study characteristics, PCC criteria, opportunities versus challenges, and evidence gaps. Data were synthesized using descriptive statistics or narratively described by identifying common themes.

    Results: The studies were published in 2005-2022 and originated predominantly from North America and Europe. Most primary studies were nonrandomized, reported quantitative data, and investigated effectiveness or feasibility (eg, acceptance or usability) of digital technologies in older people. The participants were aged 50 years to 99 years, predominantly female, affluent (ie, with high income, education, and digital competence), and intended to use or used digital technologies for a median of 3 months independently at home or in community settings. The digital technologies included mobile or nonmobile technologies or virtual reality. The studies used “modern devices” (eg, smartphones, wearables, or gaming consoles) or modern and “older devices” (eg, computers or mobile phones). The users interacted with digital technologies via websites, emails, text messages, apps, or virtual reality. Health targets of digital technologies were mobility, mental health, nutrition, or cognition. The opportunities versus challenges of digital technologies were (1) potential health benefits versus unclear or no benefits for some outcomes, (2) monitoring of health versus ethical issues with data collection and management, (3) implications for functioning in daily life (ie, potential to prolong independent living) versus unclear application for clinical management or care, (4) tailoring of technical properties and content toward older users versus general use, (5) importance of human support for feasibility versus other factors required to improve feasibility, (6) reduction of social isolation versus access to digital technologies, and (7) improvement in digital competence versus digital divide.

    Conclusions: Various digital technologies were independently used by people aged 50 years or older for health promotion and disease prevention. Future studies should focus on (1) more diverse populations of older people, (2) new digital technologies, (3) other (clinical and care) settings, and (4) outcome evaluation to identify factors that could enhance any health benefits of digital technologies.

    International Registered Report Identifier (IRRID): RR2-10.2196/37729

    J Med Internet Res 2023;25:e43542

    doi:10.2196/43542

    Keywords

    digital technology; health technology; digital public health; health promotion; disease prevention; healthy aging; elderly population; older adult; older population; scoping review 


    Digital technologies have the potential to contribute to health promotion and disease prevention [1]. However, the majority of commercially available digital platforms focus on management of existing diseases rather than on health promotion and disease prevention [2]. Furthermore, predominantly younger and more affluent people (ie, those with more education, higher income, and higher digital competence) tend to use and possibly benefit from digital technologies that support healthy behavior [3].

    Older people could especially benefit from interventions addressing health promotion and disease prevention (for review, see [4]). Although most interventions targeting this population are analog (ie, nondigital) [5], digital technologies to support specific health outcomes, such as physical activity in people older than 50 years have already been identified in other reviews (eg, [6,7]). One advantage of interventions supported by digital technologies is the potential for using such technologies independently at home. However, declining motor and cognitive functioning could contribute to various barriers associated with the use of digital technologies by older people [8]. Furthermore, the trust in digital competence of older people is low in that older people are less likely than younger people to receive access to any digital health services by their health care providers [9]. This is despite older people reporting that they are willing to engage with new technologies [9] and maintain such engagement for longer than the general population [10].

    This study aimed to identify digital technologies for health promotion and disease prevention that could be used independently by older people in nonclinical settings using a scoping review. A scoping review methodology was used due to the broad scope of the population (older people), the concept (any digital technologies), and the context (health promotion and disease prevention in nonclinical settings). This scoping review was guided by the Arksey and O’Malley [11] framework for scoping studies. The objectives of this scoping review were to describe (1) studies published in this field so far (designs and aims), (2) characteristics of older people who independently use digital technologies, (3) digital technology (ie, types, devices, and use purpose), (4) health targets (eg, mobility), (5) digital technology use pattern (eg, setting, use duration, adherence, and opportunities and challenges associated with use), and (6) evidence gaps in this field (Figure 1).

    ‎
    Figure 1. Objectives of this scoping review.

    Study Design

    This study used a scoping review design and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist [12] (Table S1 in Multimedia Appendix 1).

    Protocol and Registration

    A protocol for this scoping review was prospectively registered [13] and published [4]. There were no changes between the published protocol [4] and this scoping review.

    Eligibility Criteria

    The eligibility for our scoping review was based on the PCC (Population, Concept, and Context) criteria (Textbox 1; Textbox S1 in Multimedia Appendix 1).

    Inclusion criteria for this scoping review.

    Population

    • Older people (all participants aged 50 years or above)
    • Any health status (healthy, at risk for any disease, or with any disease)

    Concept

    • Digital (health) technologies: (1) eHealth (information and communication technology to support health) and (2) mobile health (mHealth; digital devices with mobile and wireless technologies to support health objectives) [14]
    • Digital devices: any “older technologies” (eg, computers or mobile phones) or “modern technologies” (eg, smartphones, wearables, or gaming consoles)

    Context

    • Health promotion and disease prevention defined as any measures used to maintain or improve the existing health status and prevent the onset of new diseases
    • Any health target in the context of healthy aging (eg, mobility, nutrition, cognition, or mental health)
    • Any nonclinical and noninstitutionalized setting (eg, recruited from a community or living independently at home)

    Study type

    • Primary studies with any designs (randomized or nonrandomized) or data type (quantitative, qualitative, or mixed)
    • Reviews with systematic methodology (eg, systematic reviews)
    • Studies published as papers in peer-reviewed journals in English, German, or French and available as full text (other languages may be included if assistance from native speakers at our institutions was available)
    Textbox 1. Inclusion criteria for this scoping review.

    The inclusion of other reviews in this scoping review was based on 2 reasons. First, we aimed to identify the relevant literature on the 3 broad topics (digital technologies, health promotion and disease prevention, and older people) either in our literature search or in other reviews. Second, we aimed to provide an overview of existing reviews to potentially reduce research waste that occurs when new reviews are produced despite the existence of other reviews on similar topics [15,16].

    According to our protocol [4], we planned to include studies with older people using any age range as defined by study authors. However, due to substantial heterogeneity in terminology used to define older people, the studies selected for this scoping review included people aged 50 years or above because such an age is considered as the onset of older age [17].

    Information Sources

    The information sources for this scoping review were bibliographic databases, bibliographies of any included systematic reviews, Google Scholar, and most relevant journals in the field of digital health [18]. The databases were chosen based on our institutional access and because they identified relevant literature in our other searches for digital health technologies. Due to potential financial interests in the field of digital health technologies, only peer-reviewed literature was included, and conflicts of interest statements were assessed per article. We assumed that such peer-reviewed literature may critically and objectively evaluate the health applications of digital technologies in older people.

    Search Strategy

    The search strategy was developed and calibrated by the team. The electronic search was performed by a librarian on our team (LC) in MEDLINE, PsycINFO, CINAHL, and SCOPUS from inception through to March 3, 2022. The search syntax included the terms “older adults” AND “digital technologies” AND (“health promotion” OR “disease prevention”) in titles, abstracts, or subject terms (Multimedia Appendix 2). Manual searches of bibliographies, Google Scholar, and other relevant journals were performed by 3 authors (KKDS, LC, LM) up to June 14, 2022 (Table S2 in Multimedia Appendix 1). All search results were exported and managed in EndNote X9 (Clarivate).

    Study Selection

    Screening based on title, abstract, and full text was performed in EndNote independently by any 2 authors, and consensus was reached by discussion (Figure S1 and Table S3 in Multimedia Appendix 1).

    Data Charting

    A data charting form was developed in Excel (version 10; Microsoft Corp; Multimedia Appendix 3) and calibrated within the team. Data charting was performed independently by any 2 researchers, and consensus was reached by discussion.

    We performed data charting by extracting quantitative data and qualitative author statements from studies. The quantitative data were directly coded into predefined categories (eg, participant characteristics). Charting of qualitative information in scoping reviews involves sorting such data into meaningful categories or themes [11]. According to recommendations for scoping reviews [19], we first coded the relevant statements from studies (eg, noted the study conclusion according to authors). We then classified such statements into themes based on semantic analysis (eg, we detected opportunities of digital technologies in author statements) or latent analysis (eg, we detected opportunities of digital technologies that were not explicitly stated by study authors but inductively emerged from the study conclusion).

    Data Items

    A list of data items (Textbox 2) was developed based on the objectives (Figure 1) for this scoping review, and data coding instructions were summarized in a coding manual (Table S4 in Multimedia Appendix 1). Since digital technologies are typically described using heterogeneous terminology [15], we used 3 items to capture the different aspects of such technologies (technology type, device type, and interaction between the user and technology).

    Data items in this scoping review.
    1. Bibliographic information: publication year, author region, funding sources
    2. Study designs and aims: randomized or nonrandomized, data type (eg, quantitative), overlap in primary studies in reviews, aims (eg, effectiveness or feasibility: ie, acceptance, usability, engagement, satisfaction, or adherence)
    3. Population (older people): sample size, data collection region, age, gender, health, employment status, socioeconomic status (based on income and education), digital competence
    4. Concept (digital technology): type (eg, mobile technology with internet access), device (eg, computer), interaction with digital technology (eg, via website)
    5. Context (health promotion and disease prevention): health target (eg, mobility), health purpose (eg, monitoring)
    6. Use pattern of digital technologies: setting (eg, community), use duration, adherence, opportunities, challenges
    7. Evidence gaps: ideas for future research
    Textbox 2. Data items in this scoping review.

    Critical Appraisal

    A critical appraisal of individual primary studies is typically not performed in scoping reviews. The quality of existing evidence was indirectly assessed based on study designs included in this scoping review. The critical appraisal of systematic reviews was performed with AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews, version 2) [20]. AMSTAR 2 generates the overall confidence rating in the results of a systematic review (critically low, low, moderate, or high) that indicates if systematic reviews have any weaknesses in their methodology and interpretation of results. The appraisals with AMSTAR 2 were performed independently by 2 authors, and consensus was reached by discussion. The number of critical weaknesses and the overall confidence rating for each systematic review were coded into a spreadsheet (Excel, version 10; Multimedia Appendix 4).

    Synthesis of Results

    The quantitative data items and AMSTAR 2 appraisal outcomes were synthesized using descriptive statistics (frequencies or means, SDs, or ranges) in SPSS 24 (IBM Corp). The qualitative data items were synthesized narratively into themes. Each theme was mentioned in at least one study. Since we aimed to scope the field, we did not weigh the importance of the themes (eg, by counting the number of times each theme was mentioned in all studies).


    Study Selection

    Based on our electronic and manual searches, 2188 sources were screened, and study selection was summarized in a flowchart (Figure S1 in Multimedia Appendix 1). A total of 90 primary studies (reported in 105 publications) [9,10,21-123] and 8 systematic reviews [124-131] were included in this scoping review (for the list of excluded studies, see Table S3 in Multimedia Appendix 1). All results are reported in Textbox S2 in Multimedia Appendix 1.

    Bibliographic Characteristics of Included Studies

    The primary studies were published from 2005 to 2022 (Table 1). There was an exponential increase in the number of published studies indicating a growing interest in this area of research over time (Table 1). Most studies originated from North America and Europe (74/90), followed by Asia and Australia (16/90). Conflict of interest due to funding was absent (76/90) or unclear (4/90), or funding was not reported (10/90).

    Table 1. Publication years of 105 publications reporting the results of 90 primary studies included in this scoping review.
    Publication yearNumber of publications, n
    20052
    20060
    20071
    20082
    20092
    20101
    20111
    20122
    20139
    20147
    201510
    201612
    20174
    201810
    201912
    202012
    202116
    20222

    Objective 1 (Studies): Study Designs and Aims

    Most primary studies were nonrandomized (50/90), reported quantitative data (75/90), and investigated effectiveness (61/90) or feasibility (53/90) of digital technologies in older people (Figure 2).

    ‎
    Figure 2. Study designs and aims.

    Objective 2 (Population): Older People as Users of Digital Technologies

    The primary studies included a median of 45 participants who were predominantly female and either healthy or at risk for or with existing diseases (Figure 3). The age of older people was highly variable in all studies (from 50 years to 99 years). The most common age cutoffs were (1) 65 years (23/90), (2) 50 years (18/90), (3) 60 years (16/90), and (4) 55 years (15/90).

    Most participants were recruited in North America and Europe (75/90). Among studies with information on the socioeconomic status, most studies reported high income (25/28) and high education level (55/65) for most study participants. Some participants were employed (7/90). Only 4 of 90 studies focused on ethnic minority groups.

    If reported, digital competence was high in most studies (62/67) based on self-reported use of the internet and digital devices, such as computers or mobile phones, in daily life. All participants lived independently (ie, were not institutionalized at clinical or care institutions) and were capable of using the digital technologies at home (with or without human support provided by study staff).

    ‎
    Figure 3. Older people as users of digital technologies.

    Objective 3 (Concept): Digital Technologies Used by Older People

    Digital technologies were described in all studies using heterogeneous terminology. We classified the digital technologies into (1) “mobile technologies” (with or without internet access; 39/90), (2) “nonmobile technologies” (with internet access; 33/90), and (3) “virtual reality” (17/90) or other unspecified technologies (2/90; Figure 4). We classified the devices used in studies as “modern devices” (eg, smartphones, tablets, wearables, or gaming consoles; 46/90) or a mix of “older devices” (eg, computers or mobile phones) and “modern devices” (42/90). The most commonly used devices were (1) computers (33/90), (2) smartphones or tablets (25/90), (3) wearables (21/90), (4) gaming consoles or other virtual reality devices (eg, camera, step pad, headset, or robot; 16/90), and (5) mobile phones or iPods (9/90). The interaction of older people with digital technologies occurred via (1) websites, emails, or text messages (38/90); (2) apps (37/90); or (3) exergaming or other virtual reality (18/90).

    ‎
    Figure 4. Digital technologies used by older people.

    Objective 4 (Context): Health Targets of Digital Technologies

    The most commonly reported health targets of digital technologies were (1) mobility (72/90), (2) mental health (17/90), (3) nutrition (15/90), (4) cognition (7/90), and (5) other unspecified measures to promote healthy aging (10/90; Figure 5). There were several health purposes of using digital technologies that were either reported in studies or inductively emerged based on study description. Digital technologies were used to provide feedback on performance; encourage or measure engagement (eg, based on the login data); monitor or track health; provide reminders; provide recommendations and educational information regarding healthy lifestyle; and encourage goal setting, motivation, and social networking or support.

    ‎
    Figure 5. Health targets of digital technologies used by older people.

    Objective 5 (Use Pattern): Opportunities and Challenges With Digital Technologies

    Digital technologies were used independently at home or in community settings for a median of 3 months (82/90; Figure 6). Intention to use digital technologies was investigated in 8 of 90 studies. If reported, adherence to use of digital technologies was high (ie, dropout rates per study were less than 50% in 61 of 67 studies).

    There were several opportunities and challenges associated with digital technology use that were either reported in studies or inductively emerged based on study description (Table 2). The opportunities versus challenges of digital technologies were (1) potential health benefits versus unclear or no benefits for some outcomes, (2) monitoring of health versus ethical issues with data collection and management, (3) implications for functioning in daily life (ie, potential to prolong independent living) versus unclear application for clinical management or care, (4) tailoring of technical properties and content toward older users versus general use, (5) importance of human support for feasibility versus other factors required to improve feasibility, (6) reduction of social isolation versus access to digital technologies, and (7) improvement in digital competence versus digital divide.

    ‎
    Figure 6. Use pattern of digital technologies.
    Table 2. Opportunities and challenges with digital technologies.
    TopicsOpportunitiesChallenges
    Potential health benefitsPotential (small) health benefits exist for objectively measured outcomes (eg, physical activity), relative to baseline or to no intervention and in the short term (ie, pre versus post-digital technology use); digital technologies can be included in complex interventions (eg, audiobooks to promote walking, apps with local walking trails or healthy food outlets).Unclear or no benefits for outcomes that are difficult to measure objectively (eg, well-being), relative to active interventions (eg, group exercise) and in the long term; complex interventions can be too time-consuming.
    Monitoring of healthMonitoring and tracking of own health usefulEthical issues in detecting own decline in functioning and in data management (eg, who has access to and how data will be used)
    Implications for functioning in daily lifeEven small benefits could improve functioning in daily life (eg, walking), prolong independent living, and provide access to previously enjoyed activities (eg, bowling via exergaming).Unclear application for clinical management or care
    Tailoring toward older usersDevices and their content should be developed with and for older people. Relevance to daily life and perception of health benefits need to be tailored toward the users.User experience depends on technical properties (eg, button size or interface complexity) and is reduced by inappropriate content (eg, too high goal settings for physical activity).
    Understanding that feasibility depends on human supportFeasibility (ie, acceptance, usability, engagement, satisfaction, or adherence) can be improved by human support from study staff (eg, technical support, reminders, and assistance with the health content) or other study participants (eg, via social networks, such as walking groups or online discussion boards).Feasibility under real-life conditions (ie, alone without any human support) decreases over time; potentially high costs of human support; feasibility depends on multiple factors (eg, age, gender, digital competence and interest, health status and competence, education, duration and frequency of use, and reminders).
    Reduction of social isolationReduced social isolation by improved assess for geographically or functionally isolated people (eg, people with low mobility)Devices may not be available for independent use at home (eg, gaming consoles or stable internet access).
    Improvement in digital competenceImprovement in digital competence in daily life (eg, computer operating skills, using videocalls)Digital divide: higher use and potential benefits for more affluent, educated, and digitally competent people

    Objective 6 (Evidence Gaps): Ideas for Future Research

    There were several evidence gaps that were either reported in studies or inductively emerged based on study description (Textbox 3). Future studies should focus on (1) more diverse populations of older people, (2) new digital technologies, (3) other (clinical and care) settings, and (4) outcome evaluation to identify factors that could enhance any health benefits of digital technologies.

    Evidence gaps.

    Population

    • Investigating other (more diverse) populations based on (1) socioeconomic status (less educated, less affluent), (2) health status (less healthy, more sedentary, with worse cognitive and mental functioning), (3) psychosocial status (less motivated, more socially isolated), (4) culture (ethnicity, cultural background, language), (5) digital competence (less competent)

    Concept

    • Development of new digital health technologies and investigating their mechanisms of action (ie, how they work)
    • Tailoring the digital technologies toward individual needs of participants
    • Assessing the cost-effectiveness of digital technologies

    Context

    • Investigating other contexts and settings (eg, application of data for clinical management and in care settings)

    Outcomes

    • Evaluation of effectiveness (health benefits in the short versus long term or among different digital technologies)
    • Identifying factors that improve the effectiveness and feasibility of digital technologies (eg, factors that positively influence user experience)
    Textbox 3. Evidence gaps.

    Overview of 8 Systematic Reviews

    In addition to the primary studies, we identified 8 relevant systematic reviews from electronic and manual searches (Table S3 in Multimedia Appendix 1). Despite a common focus on physical activity promotion with digital technologies in older people, the reviews included different primary studies (Textbox S3 in Multimedia Appendix 1). Among 61 primary studies included in all 8 systematic reviews, most primary studies (38/61) were included in only 1 systematic review, and only 17 of 61 primary studies were also included in this scoping review (either from electronic or manual searches). Further inspection revealed that some of the 61 primary studies did not focus on digital technologies or focused on other contexts or settings (eg, clinical patient management or participant recruitment from care facilities).

    The reviews reported that digital technologies tended to improve physical activity outcomes relative to baseline or to no intervention in older people. No changes or worsening in physical activity were reported relative to other active interventions (digital or nondigital).

    According to AMSTAR 2, the confidence in the results of the systematic reviews was either low (3/8) or critically low (5/8; Multimedia Appendix 4). There were 1 to 4 critical weaknesses in each systematic review. The most common weaknesses were (1) no review protocol, (2) no list of excluded studies, and (3) no report of sources of funding for primary studies included in reviews.


    Principal Findings

    This scoping review, based on data from 90 primary studies and 8 systematic reviews, showed that people aged 50 years or older can independently use various digital technologies designed to promote healthy behavior. The population of older users of digital technologies was highly heterogeneous in terms of age (ie, ranging from younger, regular users to older, first-time users of digital technologies) and included predominantly female and affluent people (ie, more educated and wealthier). Device types used in the studies reflect the enormous technological progress of the last 20 years. About one-half of all studies relied on older technologies, such as websites accessed via computers or text messages sent to mobile phones, while another one-half included modern mobile devices (eg, smartphones, tablets, or wearables) or gaming consoles. Human support was important for feasibility (ie, acceptance, usability, engagement, adherence, or satisfaction). Digital technology use declined under real-life conditions (ie, when used alone without human support), if the health relevance of digital technologies was not explicitly evident, and if devices or their content were not tailored toward the needs of older people. Most studies investigated mobility, while other health targets, such as mental health, nutrition, cognition, and a general healthy lifestyle, were less commonly investigated, possibly due to difficulties in objective measurement of such outcomes.

    Comparison With Prior Work

    The studies included in this scoping review show the remarkably fast technological advancement from 2005 until 2022. Some barriers related to digital technology use mentioned in the earlier studies are no longer relevant. For example, access to personal computers at home was low, especially in less affluent populations in the older studies. Meanwhile access to personal computers at home might be once again low due to the availability of mobile technologies, including smartphones and tablets that tended to be included among devices for accessing the internet in the newer studies. The pattern of internet use has also rapidly changed over this short period of time, from occasional use per week in the older studies to continuous use throughout the day by 2022. Furthermore, the need to manually enter data can be avoided because modern technologies, such as smartphones or smartwatches, can automatically detect and measure some functions, such as physical activity or cardiovascular fitness.

    Although older people are willing to use digital technologies for health promotion, various facilitators are required to further encourage digital technology use [8,132,133]. One important facilitator appears to be human support. According to our scoping review, such support includes (1) continuous technical support on the phone or onsite (ie, a visit at home), (2) human coaching (eg, reminder calls, text messages, or emails from study staff to motivate the users), and (3) social networks established for older people either in real life or virtually (eg, support groups via online discussion boards). Furthermore, digital technologies need to be designed for and tailored toward the needs of older people, as already suggested a decade ago [124]. For example, a user-centered participatory approach should be used to design, develop, and evaluate digital technologies for older people [21,22], because effectiveness of any health program depends on positive user experience [125]. Such a positive user experience could be enhanced via (1) manageable complexity and costs of digital devices [126], (2) improved motivation and high enjoyment among participants [127], (3) consideration of the age-related skills to use new digital technologies [126], and the general experience with digital technologies [128,129] that may require human support [130]. Digital technologies for older people should be developed around the goal setting theory to provide explicit information on potential health benefits by including educational content, reminders, and feedback [126].

    Studies included in this scoping review show that ownership of digital devices or intentions to use them do not guarantee their actual use in the health context. In general, health benefits of digital technologies are unclear based on small differences in outcomes before versus after digital technology use and heterogenous outcomes assessed in studies. The most common health target of digital technologies for promotion of healthy behavior in any age group is physical activity [134]. This health target was also the most common in studies included in this scoping review. This is not surprising, since older people identify physical activity as the main domain of health promotion [23]. Physical activity can be objectively measured and monitored using modern mobile devices (eg, smartphones with GPS technology) that can be easily carried around without the need to attach them, like wearables [130]. Various digital technologies can contribute to promotion of physical activity, especially in the short term and relative to no intervention groups [128]. Although possibly not clinically meaningful, small changes in healthy behavior can positively affect daily functioning and overall well-being in terms of the improved ability to perform daily tasks. Such improvements could empower older people by prolonging independent living and promoting freedom [24], but this topic requires further research.

    Evidence Gaps and Ideas for Future Research

    Future research is needed to determine the generalizability of the results in this scoping review. Future studies should focus on (1) more diverse populations of older people, (2) new digital technologies, (3) other (clinical and care) settings, and (4) outcome evaluation to identify factors that could enhance any health benefits of digital technologies. In general, evaluation of effectiveness of digital technologies is difficult due to the lack of standardized terminology, heterogeneous descriptions of devices and procedures involved in the implementation of digital technologies, and the need for new study designs that could be used in this rapidly evolving field [15]. These reasons and inadequate reporting contribute to a generally low confidence in the results of systematic reviews in the field of digital health [15]. The 90 primary studies in this scoping review also used heterogeneous designs and implementation strategies. Most studies were nonrandomized, while some did not include control groups or relied on self-reported data. Thus, future research should focus on the identifying factors that could enhance the effectiveness of digital technologies and promote their independent use in the longer term (ie, beyond the study duration). Such factors include the digital technology types (eg, those tailored toward older people that are easy to use, automatically collect data, and encourage use via feedback and reminders) and the nondigital elements in digital health interventions (eg, social networks among study participants that improve the motivation to continue using digital technologies in the longer term). Furthermore, future systematic reviews are needed to evaluate individual digital technologies or to compare the effects of different digital technologies on health outcomes in older people. Such systematic evaluation is needed for any stakeholder to provide advice and guidance or develop new technologies addressing health promotion and disease prevention that target the needs of older people.

    There is yet much to learn about the use of digital technologies in the field of public health and focusing on older adults. Digital technologies can positively enable and transform the way interventions targeting health promotion and disease prevention are designed and implemented [135]. Digital public health interventions for older people should address essential public health functions relevant for this population through digital means and include members of the target population in the development process to improve social acceptance and achieve health benefits [136]. Future research on digital technologies addressing public health functions for older people could focus on various aspects of the 10 e’s framework of eHealth [137]. The framework was developed to define eHealth in the context of health care and the 10 e’s address various aspects of “e” in the term “eHealth” beyond “electronic” health [137]. Adapting this framework to the field of public health would mean that digital technologies for older people need to (1) be efficient at reducing health care costs by promoting healthy behavior, (2) enhance health and prevent disease, (3) be evidence-based according to a rigorous scientific evaluation, (4) empower users by making health knowledge accessible, (5) encourage shared decision-making in the health context (eg, using one’s own data to support clinical decision-making), (6) educate the users, (7) enable data and information exchange, (8) extend the scope of health promotion beyond analog boundaries (eg, use the virtual environment to promote health), (9) be ethical in terms of data sharing and privacy, and (10) promote equity at improving access to health promotion measures for those at need (eg, less affluent, less healthy, or less digitally competent).

    Limitations

    This scoping review had several limitations. First, locating the relevant studies was surprisingly difficult despite carefully designed search syntax. Consequently, 38 of the 98 included studies were located from manual searches, and there was a low overlap in primary studies either among the relevant 8 systematic reviews or among the reviews and our search. It is likely that highly heterogeneous terminology used in the field of digital technologies for health promotion and disease prevention [15,134,138] contributed to difficulties in locating the relevant literature. Furthermore, it cannot be ruled out that some located studies were incorrectly excluded based on limited information regarding the setting. In general, studies were excluded if digital technologies were not used independently (eg, used by caregivers of older people or if we could not determine if the use was independent) or used in clinical treatment (eg, as part of disease management). Thus, the 98 studies included in this scoping review can be considered a meaningful but far from complete sample of the literature in this field.

    Second, due to the focus on independent use, we included studies with samples representing younger old age that often included more affluent people. Such samples are more likely to be digitized and own digital devices for personal use. People from older age groups may require assistance with daily living, and less affluent people may not use digital technologies in the health context. The focus on younger groups of older adults is important to prepare these groups for health challenges of old age by using digital technologies to promote physical activity, reduce loneliness, and keep mentally and cognitively fit [125]. Future research is needed to promote the use of digital health technologies into older age and for all groups along the sociodemographic spectrum. Especially important is also development of strategies necessary to recruit older people from lower socioeconomic and ethnically diverse backgrounds.

    Third, this scoping review did not investigate the effectiveness of digital technologies. For example, it is unclear if modern technologies (eg, smartphones) are better than older technologies (eg, computers) at promoting healthy behavior among older people. There are advantages of both technology types. Some older people may be more familiar and more likely to own and use older technologies (eg, mobile phones without internet access). Modern technologies (eg, smartphones) are useful for automatic data collection, but their operation may be difficult due to small screen size or buttons, the need to operate a touch screen, or difficulties in attaching the device (eg, a smartwatch). Although computers were the most technologically advanced devices available for personal use in the older studies, the newer studies incorporated various devices in their digital interventions (eg, any device with internet access).

    Finally, it was difficult to determine the commercial interests in the included studies. A common weakness of systematic reviews of digital technologies is that they do not report the sources of funding in primary studies [15]. Our scoping review shows that 14 of the 90 primary studies either failed to report funding or the reported conflict of interest due to funding was unclear. Any potential conflicts of interest arising from commercial interests in the field of digital health technologies need to be carefully reported in primary studies and assessed by review authors.

    Dissemination

    The results of this study were presented in a conference poster (15th European Public Health Conference, November 2022, Berlin, Germany [139]), will be disseminated in English through this article, and will be shared in German through a project report. Furthermore, we summarized the main results for the nonscientific audience using plain language summaries in form of infographics in English and in German (Multimedia Appendix 5 and Multimedia Appendix 6, respectively).

    Conclusions

    Various digital technologies were independently used by people aged 50 years or older for health promotion and disease prevention. The digital technologies were modern (eg, smartphones) or older (eg, computers), and the interaction with such technologies occurred via different methods (eg, websites, emails, text messages, apps, or virtual reality). Different health targets were addressed (ie, mobility, mental health, nutrition, and cognition). Although digital technologies could contribute to health benefits, the challenges associated with their use need to be considered. Future studies should focus on (1) more diverse populations of older people, (2) new digital technologies, (3) other (clinical and care) settings, and (4) outcome evaluations to identify factors that could enhance any health benefits of digital technologies.

    Acknowledgments

    We thank Louisa Sell, Mia Reimer, Sophia Brüssermann, and Alejandra Sotomayor Sainz for their assistance with data coding. This research was financed by the Federal Centre for Health Education (BZgA), Germany (grant number: 030-Q6-2021). The BZgA is an independent, neutral, not-for-profit research institution that does not pursue any commercial aims. As an authority belonging to the portfolio of the Federal Ministry of Health in Germany, the BZgA carries out the legislative and administrative tasks in the fields of health education, prevention, and promotion. In particular, it is responsible for conducting research and for making recommendations on the basis of research findings. We gratefully acknowledge the support of the Leibniz-Science Campus Bremen Digital Public Health (lsc-diph.de), which is jointly funded by the Leibniz Association (W4/2018), the Federal State of Bremen, and the Leibniz Institute for Prevention Research and Epidemiology – BIPS. The Leibniz-Science Campus Bremen Digital Public Health funded the publication costs of this article.

    Authors' Contributions

    KKDS conceptualized the study, developed the methodology, selected the studies, coded the data, processed and analyzed the data, visualized the results, wrote the first draft of the manuscript, and reviewed and edited the manuscript. LM developed the methodology, selected the studies, coded the data, and reviewed and edited the manuscript. LC developed the methodology and reviewed and edited the manuscript. AB, CV, and HZ conceptualized the study, developed the methodology, and reviewed and edited the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Supplementary information.

    DOCX File , 1664 KB
    ‎
    Multimedia Appendix 2

    Search strategy and results.

    XLSX File (Microsoft Excel File), 203 KB
    ‎
    Multimedia Appendix 3

    Data file.

    XLSX File (Microsoft Excel File), 89 KB
    ‎
    Multimedia Appendix 4

    A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2) ratings.

    XLSX File (Microsoft Excel File), 11 KB
    ‎
    Multimedia Appendix 5

    Plain language summary (English).

    PDF File (Adobe PDF File), 377 KB
    ‎
    Multimedia Appendix 6

    Plain language summary (German).

    PDF File (Adobe PDF File), 484 KB
    1. Zeeb H, Pigeot I, Schüz B, Leibniz-Wissenschafts Campus Digital Public Health Bremen. [Digital public health-an overview]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020 Feb 09;63(2):137-144. [CrossRef] [Medline]
    2. Cohen AB, Dorsey ER, Mathews SC, Bates DW, Safavi K. A digital health industry cohort across the health continuum. NPJ Digit Med 2020 May 12;3(1):68 [FREE Full text] [CrossRef] [Medline]
    3. De Santis KK, Jahnel T, Sina E, Wienert J, Zeeb H. Digitization and health in Germany: cross-sectional nationwide survey. JMIR Public Health Surveill 2021 Nov 22;7(11):e32951 [FREE Full text] [CrossRef] [Medline]
    4. De Santis KK, Mergenthal L, Christianson L, Zeeb H. Digital technologies for health promotion and disease prevention in older people: protocol for a scoping review. JMIR Res Protoc 2022 Jul 21;11(7):e37729 [FREE Full text] [CrossRef] [Medline]
    5. Chiu C, Hu J, Lo Y, Chang E. Health promotion and disease prevention interventions for the elderly: a scoping review from 2015-2019. Int J Environ Res Public Health 2020 Jul 24;17(15):5335 [FREE Full text] [CrossRef] [Medline]
    6. McGarrigle L, Todd C. Promotion of physical activity in older people using mHealth and eHealth technologies: rapid review of reviews. J Med Internet Res 2020 Dec 29;22(12):e22201 [FREE Full text] [CrossRef] [Medline]
    7. Taylor J, Walsh S, Kwok W, Pinheiro MB, de Oliveira JS, Hassett L, et al. A scoping review of physical activity interventions for older adults. Int J Behav Nutr Phys Act 2021 Jun 30;18(1):82 [FREE Full text] [CrossRef] [Medline]
    8. 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 2021 Aug 17;21(1):1556 [FREE Full text] [CrossRef] [Medline]
    9. Graham SA, Stein N, Shemaj F, Branch OH, Paruthi J, Kanick SC. Older adults engage with personalized digital coaching programs at rates that exceed those of younger adults. Front Digit Health 2021 Aug 6;3:642818 [FREE Full text] [CrossRef] [Medline]
    10. Jaana M, Paré G. Comparison of mobile health technology use for self-tracking between older adults and the general adult population in Canada: cross-sectional survey. JMIR Mhealth Uhealth 2020 Nov 27;8(11):e24718 [FREE Full text] [CrossRef] [Medline]
    11. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005 Feb;8(1):19-32. [CrossRef]
    12. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018 Oct 02;169(7):467-473 [FREE Full text] [CrossRef] [Medline]
    13. De Santis KK, Mergenthal L, Christianson L, Zeeb H. Digital technologies for health promotion and disease prevention for older people: Scoping review protocol summary. Open Science Framework. 2022 May 09.   URL: https://osf.io/7wfub/ [accessed 2022-05-09]
    14. Recommendations on digital interventions for health system strengthening. World Health Organization. 2019 Jun 06.   URL: https://www.who.int/publications/i/item/9789241550505 [accessed 2023-02-10]
    15. De Santis KK, Jahnel T, Matthias K, Mergenthal L, Al Khayyal H, Zeeb H. Evaluation of digital interventions for physical activity promotion: scoping review. JMIR Public Health Surveill 2022 May 23;8(5):e37820 [FREE Full text] [CrossRef] [Medline]
    16. De Santis KK, Lorenz RC, Lakeberg M, Matthias K. The application of AMSTAR2 in 32 overviews of systematic reviews of interventions for mental and behavioural disorders: A cross-sectional study. Res Synth Methods 2022 Jul 28;13(4):424-433. [CrossRef] [Medline]
    17. Buyl R, Beogo I, Fobelets M, Deletroz C, Van Landuyt P, Dequanter S, et al. e-Health interventions for healthy aging: a systematic review. Syst Rev 2020 Jun 03;9(1):128 [FREE Full text] [CrossRef] [Medline]
    18. Taj F, Klein MCA, van Halteren A. Digital health behavior change technology: bibliometric and scoping review of two decades of research. JMIR Mhealth Uhealth 2019 Dec 13;7(12):e13311 [FREE Full text] [CrossRef] [Medline]
    19. Pollock D, Peters M, Khalil H, McInerney P, Alexander L, Tricco A, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth 2022 Oct 14:1. [CrossRef] [Medline]
    20. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 2017 Sep 21;358:j4008 [FREE Full text] [CrossRef] [Medline]
    21. Mascret N, Delbes L, Voron A, Temprado J, Montagne G. Acceptance of a virtual reality headset designed for fall prevention in older adults: questionnaire study. J Med Internet Res 2020 Dec 14;22(12):e20691 [FREE Full text] [CrossRef] [Medline]
    22. Tam E, Boas PKV, Ruaro F, Flesch J, Wu J, Thomas A, et al. Feasibility and adoption of a focused digital wellness program in older adults. Geriatrics (Basel) 2021 May 19;6(2):54 [FREE Full text] [CrossRef] [Medline]
    23. Cabrita M, Tabak M, Vollenbroek-Hutten MM. Older adults' attitudes toward ambulatory technology to support monitoring and coaching of healthy behaviors: qualitative study. JMIR Aging 2019 Mar 12;2(1):e10476 [FREE Full text] [CrossRef] [Medline]
    24. Ienca M, Schneble C, Kressig RW, Wangmo T. Digital health interventions for healthy ageing: a qualitative user evaluation and ethical assessment. BMC Geriatr 2021 Jul 02;21(1):412 [FREE Full text] [CrossRef] [Medline]
    25. Alley SJ, Kolt GS, Duncan MJ, Caperchione CM, Savage TN, Maeder AJ, et al. The effectiveness of a web 2.0 physical activity intervention in older adults - a randomised controlled trial. Int J Behav Nutr Phys Act 2018 Jan 12;15(1):4 [FREE Full text] [CrossRef] [Medline]
    26. Ammann R, Vandelanotte C, de Vries H, Mummery WK. Can a website-delivered computer-tailored physical activity intervention be acceptable, usable, and effective for older people? Health Educ Behav 2013 Apr;40(2):160-170. [CrossRef] [Medline]
    27. Andrews JA, Brown LJ, Hawley MS, Astell AJ. Older adults' perspectives on using digital technology to maintain good mental health: interactive group study. J Med Internet Res 2019 Feb 13;21(2):e11694 [FREE Full text] [CrossRef] [Medline]
    28. Antoine Parker C, Ellis R. Effect of electronic messaging on physical activity participation among older adults. J Aging Res 2016;2016:6171028 [FREE Full text] [CrossRef] [Medline]
    29. Auster-Gussman LA, Lockwood KG, Graham SA, Pitter V, Branch OH. Engagement in digital health app-based prevention programs is associated with weight loss among adults age 65. Front Digit Health 2022;4:886783 [FREE Full text] [CrossRef] [Medline]
    30. Batsis JA, Dokko R, Naslund JA, Zagaria AB, Kotz D, Bartels SJ, et al. Opportunities to improve a mobile obesity wellness intervention for rural older adults with obesity. J Community Health 2020 Feb;45(1):194-200 [FREE Full text] [CrossRef] [Medline]
    31. 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 [FREE Full text] [CrossRef] [Medline]
    32. Batsis JA, Naslund JA, Zagaria AB, Kotz D, Dokko R, Bartels SJ, et al. Technology for behavioral change in rural older adults with obesity. J Nutr Gerontol Geriatr 2019;38(2):130-148 [FREE Full text] [CrossRef] [Medline]
    33. Batsis JA, Petersen CL, Clark MM, Cook SB, Kotz D, Gooding TL, et al. Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity. BMC Geriatr 2021 Jan 12;21(1):44 [FREE Full text] [CrossRef] [Medline]
    34. Batsis JA, Petersen CL, Clark MM, Cook SB, Lopez-Jimenez F, Al-Nimr RI, et al. A weight loss intervention augmented by a wearable device in rural older adults with obesity: a feasibility study. J Gerontol A Biol Sci Med Sci 2021 Jan 01;76(1):95-100 [FREE Full text] [CrossRef] [Medline]
    35. Batsis JA, Zagaria A, Kotz DF, Bartels SJ, Boateng GG, Proctor PO, et al. Usability evaluation for the amulet wearable device in rural older adults with obesity. Gerontechnology 2018 Sep;17(3):151-159 [FREE Full text] [CrossRef] [Medline]
    36. Bickmore T, Caruso L, Clough-Gorr K, Heeren T. ‘It's just like you talk to a friend’ relational agents for older adults. Interact Comput 2005 Dec;17(6):711-735. [CrossRef]
    37. Blair CK, Harding E, Wiggins C, Kang H, Schwartz M, Tarnower A, 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 2021 Apr 13;7(2):e18819 [FREE Full text] [CrossRef] [Medline]
    38. Botner E. Impact of a virtual learning program on social isolation for older adults. TRJ 2018;52(2):126-139. [CrossRef]
    39. Broekhuizen K, de Gelder J, Wijsman CA, Wijsman LW, Westendorp RGJ, Verhagen E, et al. An internet-based physical activity intervention to improve quality of life of inactive older adults: a randomized controlled trial. J Med Internet Res 2016 Apr 27;18(4):e74 [FREE Full text] [CrossRef] [Medline]
    40. Cadmus-Bertram LA, Marcus BH, Patterson RE, Parker BA, Morey BL. Randomized trial of a Fitbit-based physical activity intervention for women. Am J Prev Med 2015 Sep;49(3):414-418 [FREE Full text] [CrossRef] [Medline]
    41. Carrasco M, Ortiz-Maqués N, Martínez-Rodríguez S. Playing with Nintendo Wii Sports: impact on physical activity, perceived health and cognitive functioning of a group of community-dwelling older adults. Act Adapt Aging 2019 Apr 08;44(2):119-131. [CrossRef]
    42. Coley N, Andre L, Hoevenaar-Blom MP, Ngandu T, Beishuizen C, Barbera M, HATICE study group, PRODEMOS study group. Factors predicting engagement of older adults with a coach-supported eHealth intervention promoting lifestyle change and associations between engagement and changes in cardiovascular and dementia risk: secondary analysis of an 18-month multinational randomized controlled trial. J Med Internet Res 2022 May 09;24(5):e32006 [FREE Full text] [CrossRef] [Medline]
    43. Compernolle S, Cardon G, van der Ploeg HP, Van Nassau F, De Bourdeaudhuij I, Jelsma JJ, et al. Engagement, acceptability, usability, and preliminary efficacy of a self-monitoring mobile health intervention to reduce sedentary behavior in Belgian older adults: mixed methods study. JMIR Mhealth Uhealth 2020 Oct 29;8(10):e18653 [FREE Full text] [CrossRef] [Medline]
    44. Cook RF, Hersch RK, Schlossberg D, Leaf SL. A Web-based health promotion program for older workers: randomized controlled trial. J Med Internet Res 2015 Mar 25;17(3):e82 [FREE Full text] [CrossRef] [Medline]
    45. Cooper D, Kavanagh R, Bolton J, Myers C, O'Connor S. ‘Prime Time of Life’, a 12-week home-based online multimodal exercise training and health education programme for middle-aged and older adults in Laois. Phys Act Health 2021;5(1):178-194. [CrossRef]
    46. Corbett A, Owen A, Hampshire A, Grahn J, Stenton R, Dajani S, et al. The effect of an online cognitive training package in healthy older adults: an online randomized controlled trial. J Am Med Dir Assoc 2015 Nov 01;16(11):990-997. [CrossRef] [Medline]
    47. David P, Buckworth J, Pennell ML, Katz ML, DeGraffinreid CR, Paskett ED. A walking intervention for postmenopausal women using mobile phones and interactive voice response. J Telemed Telecare 2012 Jan;18(1):20-25 [FREE Full text] [CrossRef] [Medline]
    48. 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 (Lausanne) 2017;4:208 [FREE Full text] [CrossRef] [Medline]
    49. Delbaere K, Valenzuela T, Lord SR, Clemson L, Zijlstra GAR, Close JCT, et al. E-health StandingTall balance exercise for fall prevention in older people: results of a two year randomised controlled trial. BMJ 2021 Apr 06;373:n740 [FREE Full text] [CrossRef] [Medline]
    50. Frei A, Dalla Lana K, Radtke T, Stone E, Knöpfli N, Puhan MA. A novel approach to increase physical activity in older adults in the community using citizen science: a mixed-methods study. Int J Public Health 2019 Jun;64(5):669-678. [CrossRef] [Medline]
    51. Gschwind YJ, Eichberg S, Ejupi A, de Rosario H, Kroll M, Marston HR, 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 [FREE Full text] [CrossRef] [Medline]
    52. Haeger M, Bock O, Zijlstra W. [Smartphone-based health promotion in old age: an explorative multi-component approach to improving health in old age]. Z Gerontol Geriatr 2021 Mar;54(2):146-151. [CrossRef] [Medline]
    53. Hageman PA, Walker SN, Pullen CH. Tailored versus standard internet-delivered interventions to promote physical activity in older women. J Geriatr Phys Ther 2005;28(1):28-33. [CrossRef] [Medline]
    54. 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 2013 Feb 05;15(2):e19 [FREE Full text] [CrossRef] [Medline]
    55. Jacobson CL, Foster LC, Arul H, Rees A, Stafford RS. A digital health fall prevention program for older adults: feasibility study. JMIR Form Res 2021 Dec 23;5(12):e30558 [FREE Full text] [CrossRef] [Medline]
    56. Jang IY, Kim HR, Lee E, Jung HW, Park H, Cheon SH, et al. Impact of a wearable device-based walking programs in rural older adults on physical activity and health outcomes: cohort study. JMIR Mhealth Uhealth 2018 Nov 21;6(11):e11335 [FREE Full text] [CrossRef] [Medline]
    57. Jenaro C, Flores N, Cruz M, Moro L, Pérez C. Efficacy of text messaging for health care in the elderly (Eficacia de los mensajes de texto para el cuidado de la salud en población mayorn mayor). Gerokomos 2016;27(2):42-47 [FREE Full text]
    58. Johnson N, Bradley A, Klawitter L, Johnson J, Johnson L, Tomkinson GR, et al. The impact of a telehealth intervention on activity profiles in older adults during the COVID-19 pandemic: a pilot study. Geriatrics (Basel) 2021 Jun 30;6(3):331-344 [FREE Full text] [CrossRef] [Medline]
    59. Kahlbaugh P, Sperandio A, Carlson A, Hauselt J. Effects of playing Wii on well-being in the elderly: physical activity, loneliness, and mood. Act Adapt Aging 2011 Oct;35(4):331-344. [CrossRef]
    60. Kim BH, Glanz K. Text messaging to motivate walking in older African Americans: a randomized controlled trial. Am J Prev Med 2013 Jan;44(1):71-75. [CrossRef] [Medline]
    61. King AC, Bickmore TW, Campero MI, Pruitt LA, Yin JL. Employing virtual advisors in preventive care for underserved communities: results from the COMPASS study. J Health Commun 2013;18(12):1449-1464 [FREE Full text] [CrossRef] [Medline]
    62. Kirk A, MacMillan F, Rice M, Carmichael A. An Exploratory Study Examining the Appropriateness and Potential Benefit of the Nintendo Wii as a Physical Activity Tool in Adults Aged ≥ 55 Years. Interact Comput 2013;25(1):102-114. [CrossRef]
    63. Kumar S, Tran JLA, Moseson H, Tai C, Glenn JM, Madero EN, et al. The impact of the virtual cognitive health program on the cognition and mental health of older adults: pre-post 12-month pilot study. JMIR Aging 2018 Nov 09;1(2):e12031 [FREE Full text] [CrossRef] [Medline]
    64. Kurti AN, Dallery J. Internet-based contingency management increases walking in sedentary adults. J Appl Behav Anal 2013 Sep 01;46(3):568-581. [CrossRef] [Medline]
    65. Lee HY, Kim J, Kim KS. The effects of nursing interventions utilizing serious games that promote health activities on the health behaviors of seniors. Games Health J 2015 Jun;4(3):175-182. [CrossRef] [Medline]
    66. Lee WJ, Peng LN, Lin MH, Loh CH, Chen LK. Active wearable device utilization improved physical performance and IGF-1 among community-dwelling middle-aged and older adults: a 12-month prospective cohort study. Aging (Albany NY) 2021 Aug 03;13(15):19710-19721 [FREE Full text] [CrossRef] [Medline]
    67. Li J, Theng YL, Foo S. Exergames for older adults with subthreshold depression: does higher playfulness lead to better improvement in depression? Games Health J 2016 Jun;5(3):175-182. [CrossRef] [Medline]
    68. Liu Y, Lachman ME. A group-based walking study to enhance physical activity among older adults: the role of social engagement. Res Aging 2021;43(9-10):368-377 [FREE Full text] [CrossRef] [Medline]
    69. Lyons EJ, Swartz MC, Lewis ZH, Martinez E, Jennings K. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: a randomized controlled pilot trial. JMIR Mhealth Uhealth 2017 Mar 06;5(3):e28 [FREE Full text] [CrossRef] [Medline]
    70. Mansson L, Lundin-Olsson L, Skelton DA, Janols R, Lindgren H, Rosendahl E, 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 2020 Jun 15;20(1):209 [FREE Full text] [CrossRef] [Medline]
    71. McMahon SK, Lewis B, Oakes M, Guan W, Wyman JF, Rothman AJ. Older adults' experiences using a commercially available monitor to self-track their physical activity. JMIR Mhealth Uhealth 2016 Apr 13;4(2):e35 [FREE Full text] [CrossRef] [Medline]
    72. McMahon S, Vankipuram M, Hekler EB, Fleury J. Design and evaluation of theory-informed technology to augment a wellness motivation intervention. Transl Behav Med 2014 Mar;4(1):95-107 [FREE Full text] [CrossRef] [Medline]
    73. McMahon SK, Wyman JF, Belyea MJ, Shearer N, Hekler EB, Fleury J. Combining motivational and physical intervention components to promote fall-reducing physical activity among community-dwelling older adults: a feasibility study. Am J Health Promot 2016 Nov;30(8):638-644 [FREE Full text] [CrossRef] [Medline]
    74. Mehra S, Visser B, Cila N, van den Helder J, Engelbert RH, Weijs PJ, et al. Supporting older adults in exercising with a tablet: a usability study. JMIR Hum Factors 2019 Feb 01;6(1):e11598 [FREE Full text] [CrossRef] [Medline]
    75. Mouton A, Cloes M. Efficacy of a web-based, center-based or combined physical activity intervention among older adults. Health Educ Res 2015 Jun;30(3):422-435 [FREE Full text] [CrossRef] [Medline]
    76. Muellmann S, Buck C, Voelcker-Rehage C, Bragina I, Lippke S, Meyer J, 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 2019 Sep;15:100958 [FREE Full text] [CrossRef] [Medline]
    77. Müller AM, Khoo S, Morris T. Text messaging for exercise promotion in older adults from an upper-middle-income country: randomized controlled trial. J Med Internet Res 2016 Jan 07;18(1):e5 [FREE Full text] [CrossRef] [Medline]
    78. Nahm ES, Barker B, Resnick B, Covington B, Magaziner J, Brennan PF. Effects of a social cognitive theory-based hip fracture prevention web site for older adults. Comput Inform Nurs 2010;28(6):371-379 [FREE Full text] [CrossRef] [Medline]
    79. Nahm ES, Resnick B, Brown C, Zhu S, Magaziner J, Bellantoni M, et al. The effects of an online theory-based bone health program for older adults. J Appl Gerontol 2017 Sep;36(9):1117-1144 [FREE Full text] [CrossRef] [Medline]
    80. Nahm ES, Resnick B, DeGrezia M, Brotemarkle R. Use of discussion boards in a theory-based health web site for older adults. Nurs Res 2009;58(6):419-426. [CrossRef] [Medline]
    81. Nebeker C, Zlatar ZZ. Learning from older adults to promote independent physical activity using mobile health (mHealth). Front Public Health 2021;9:703910 [FREE Full text] [CrossRef] [Medline]
    82. OʼBrien T, Jenkins C, Amella E, Mueller M, Moore M, Hathaway D. An internet-assisted weight loss intervention for older overweight and obese rural women: a feasibility study. Comput Inform Nurs 2016 Nov;34(11):513-519. [CrossRef] [Medline]
    83. O'Brien TR, Jenkins C, Amella E, Mueller M, Moore M, Troutman- Jordan M, et al. Perceptions of older rural women using computerized programs for weight management. OJRNHC 2014 Dec;14(2):80-96. [CrossRef]
    84. Padala KP, Padala PR, Lensing SY, Dennis RA, Bopp MM, Parkes CM, et al. Efficacy of Wii-Fit on static and dynamic balance in community dwelling older veterans: a randomized controlled pilot trial. J Aging Res 2017;2017:4653635 [FREE Full text] [CrossRef] [Medline]
    85. Papi E, Chiou SY, McGregor AH. Feasibility and acceptability study on the use of a smartphone application to facilitate balance training in the ageing population. BMJ Open 2020 Dec 02;10(12):e039054 [FREE Full text] [CrossRef] [Medline]
    86. Peels DA, Bolman C, Golsteijn RHJ, de Vries H, Mudde AN, van Stralen MM, et al. Long-term efficacy of a printed or a web-based tailored physical activity intervention among older adults. Int J Behav Nutr Phys Act 2013 Sep 02;10:104 [FREE Full text] [CrossRef] [Medline]
    87. Peels DA, de Vries H, Bolman C, Golsteijn RHJ, van Stralen MM, Mudde AN, et al. Differences in the use and appreciation of a web-based or printed computer-tailored physical activity intervention for people aged over 50 years. Health Educ Res 2013 Aug;28(4):715-731. [CrossRef] [Medline]
    88. Peels DA, van Stralen MM, Bolman C, Golsteijn RHJ, de Vries H, Mudde AN, et al. The differentiated effectiveness of a printed versus a web-based tailored physical activity intervention among adults aged over 50. Health Educ Res 2014 Oct;29(5):870-882. [CrossRef] [Medline]
    89. 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 2021 Jul 30;23(7):e26235 [FREE Full text] [CrossRef] [Medline]
    90. Pinto BM, Kindred M, Franco R, Simmons V, Hardin J. A 'novel' multi-component approach to promote physical activity among older cancer survivors: a pilot randomized controlled trial. Acta Oncol 2021 Aug;60(8):968-975. [CrossRef] [Medline]
    91. Proyer RT, Gander F, Wellenzohn S, Ruch W. Positive psychology interventions in people aged 50-79 years: long-term effects of placebo-controlled online interventions on well-being and depression. Aging Ment Health 2014;18(8):997-1005 [FREE Full text] [CrossRef] [Medline]
    92. Pullen CH, Hageman PA, Boeckner L, Walker SN, Oberdorfer MK. Feasibility of Internet-delivered weight loss interventions among rural women ages 50-69. J Geriatr Phys Ther 2008;31(3):105-112. [CrossRef] [Medline]
    93. Radhakrishnan K, Julien C, O'Hair M, Baranowski T, Lee G, Allen C, 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 2020 Oct;11(5):873-881 [FREE Full text] [CrossRef] [Medline]
    94. Ratz T, Lippke S, Muellmann S, Peters M, Pischke CR, Meyer J, et al. Effects of two web-based interventions and mediating mechanisms on stage of change regarding physical activity in older adults. Appl Psychol Health Well Being 2020 Mar;12(1):77-100. [CrossRef] [Medline]
    95. Richard E, Moll van Charante EP, Hoevenaar-Blom MP, Coley N, Barbera M, van der Groep A, et al. Healthy ageing through internet counselling in the elderly (HATICE): a multinational, randomised controlled trial. Lancet Digit Health 2019 Dec;1(8):e424-e434 [FREE Full text] [CrossRef] [Medline]
    96. Robin N, Toussaint L, Coudevylle GR, Ruart S, Hue O, Sinnapah S. Text messages promoting mental imagery increase self-reported physical activity in older adults: a randomized controlled study. J Aging Phys Act 2018 Jul 01;26(3):462-470. [CrossRef] [Medline]
    97. Robin N, Toussaint L, Sinnapah S, Hue O, Coudevylle GR. Beneficial influence of mindfulness training promoted by text messages on self-reported aerobic physical activity in older adults: a randomized controlled study. J Aging Phys Act 2019 Nov 21:1-9. [CrossRef] [Medline]
    98. Sato K, Kuroki K, Saiki S, Nagatomi R. The effects of exercise intervention using KinectTM on healthy elderly individuals: A quasi-experimental study. OJTR 2014;02(01):38-44. [CrossRef]
    99. Sato K, Kuroki K, Saiki S, Nagatomi R. Improving walking, muscle strength, and balance in the elderly with an exergame using Kinect: a randomized controlled trial. Games Health J 2015 Jun;4(3):161-167. [CrossRef] [Medline]
    100. Shubert TE, Chokshi A, Mendes VM, Grier S, Buchanan H, Basnett J, et al. Stand tall-a virtual translation of the Otago Exercise Program. J Geriatr Phys Ther 2020;43(3):120-127. [CrossRef] [Medline]
    101. Sill J, Steenbock B, Helmer S, Zeeb H, Pischke C. Apps zur Förderung von körperlicher Aktivität – Nutzung und Einstellungen bei Erwachsenen im Alter von 50 Jahren und älter. Präv Gesundheitsf 2018 Oct 16;14(2):109-118. [CrossRef]
    102. 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 2013 Aug 12;15(8):e159 [FREE Full text] [CrossRef] [Medline]
    103. Similä H, Immonen M, Toska-Tervola J, Enwald H, Keränen N, Kangas M, et al. Feasibility of mobile mental wellness training for older adults. Geriatr Nurs 2018;39(5):499-505. [CrossRef] [Medline]
    104. Strand KA, Francis SL, Margrett JA, Franke WD, Peterson MJ. Community-based exergaming program increases physical activity and perceived wellness in older adults. J Aging Phys Act 2014 Jul;22(3):364-371. [CrossRef] [Medline]
    105. Tiedemann A, Hassett L, Sherrington C. A novel approach to the issue of physical inactivity in older age. Prev Med Rep 2015;2:595-597 [FREE Full text] [CrossRef] [Medline]
    106. Tse MMY, Choi KCY, Leung RSW. E-health for older people: the use of technology in health promotion. Cyberpsychol Behav 2008 Aug;11(4):475-479. [CrossRef] [Medline]
    107. Turunen M, Hokkanen L, Bäckman L, Stigsdotter-Neely A, Hänninen T, Paajanen T, et al. Computer-based cognitive training for older adults: determinants of adherence. PLoS One 2019;14(7):e0219541 [FREE Full text] [CrossRef] [Medline]
    108. Valdes EG, Sadeq NA, Harrison Bush AL, Morgan D, Andel R. Regular cognitive self-monitoring in community-dwelling older adults using an internet-based tool. J Clin Exp Neuropsychol 2016 Nov;38(9):1026-1037. [CrossRef] [Medline]
    109. 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 2018 Nov 30;1(2):e11975 [FREE Full text] [CrossRef] [Medline]
    110. van der Mark M, Jonasson J, Svensson M, Linné Y, Rossner S, Lagerros YT. Older members perform better in an internet-based behavioral weight loss program compared to younger members. Obes Facts 2009;2(2):74-79 [FREE Full text] [CrossRef] [Medline]
    111. Van Dyck D, Herman K, Poppe L, Crombez G, De Bourdeaudhuij I, Gheysen F. Results of MyPlan 2.0 on physical activity in older Belgian adults: randomized controlled trial. J Med Internet Res 2019 Oct 07;21(10):e13219 [FREE Full text] [CrossRef] [Medline]
    112. Van Dyck D, Plaete J, Cardon G, Crombez G, De Bourdeaudhuij I. Effectiveness of the self-regulation eHealth intervention 'MyPlan1.0.' on physical activity levels of recently retired Belgian adults: a randomized controlled trial. Health Educ Res 2016 Oct;31(5):653-664. [CrossRef] [Medline]
    113. van Het Reve E, Silveira P, Daniel F, Casati F, de Bruin ED. Tablet-based strength-balance training to motivate and improve adherence to exercise in independently living older people: part 2 of a phase II preclinical exploratory trial. J Med Internet Res 2014 Jun 25;16(6):e159 [FREE Full text] [CrossRef] [Medline]
    114. van Middelaar T, Beishuizen CRL, Guillemont J, Barbera M, Richard E, Moll van Charante EP, HATICE consortium. Engaging older people in an internet platform for cardiovascular risk self-management: a qualitative study among Dutch HATICE participants. BMJ Open 2018 Jan 21;8(1):e019683 [FREE Full text] [CrossRef] [Medline]
    115. VanRavenstein K, Davis BH. When more than exercise is needed to increase chances of aging in place: qualitative analysis of a telehealth physical activity program to improve mobility in low-income older adults. JMIR Aging 2018 Dec 21;1(2):e11955 [FREE Full text] [CrossRef] [Medline]
    116. Watkins I, Xie B. Older adults' perceptions of using iPads for improving fruit and vegetable intake: an exploratory study. Care Manag J 2015;16(1):2-13. [CrossRef] [Medline]
    117. Wichmann F, Pischke CR, Jürgens D, Darmann-Finck I, Koppelin F, Lippke S, et al. Requirements for (web-based) physical activity interventions targeting adults above the age of 65 years - qualitative results regarding acceptance and needs of participants and non-participants. BMC Public Health 2020 Jun 11;20(1):907 [FREE Full text] [CrossRef] [Medline]
    118. Wichmann F, Sill J, Hassenstein M, Zeeb H, Pischke C. Apps zur Förderung von körperlicher Aktivität. Präv Gesundheitsf 2018 Oct 31;14(2):93-101. [CrossRef]
    119. Wijsman CA, Westendorp RG, Verhagen EA, Catt M, Slagboom PE, de Craen AJ, et al. Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial. J Med Internet Res 2013 Nov 06;15(11):e233 [FREE Full text] [CrossRef] [Medline]
    120. Wu Z, Li J, Theng YL. Examining the influencing factors of exercise intention among older adults: a controlled study between exergame and traditional exercise. Cyberpsychol Behav Soc Netw 2015 Sep;18(9):521-527. [CrossRef] [Medline]
    121. Xu X, Li J, Pham TP, Salmon CT, Theng YL. Improving psychosocial well-being of older adults through exergaming: the moderation effects of intergenerational communication and age cohorts. Games Health J 2016 Dec;5(6):389-397. [CrossRef] [Medline]
    122. Yardley L, Nyman SR. Internet provision of tailored advice on falls prevention activities for older people: a randomized controlled evaluation. Health Promot Int 2007 Jun;22(2):122-128 [FREE Full text] [CrossRef] [Medline]
    123. Zaslavsky O, Thompson HJ, McCurry SM, Landis CA, Kitsiou S, Ward TM, et al. Use of a wearable technology and motivational interviews to improve sleep in older adults with osteoarthritis and sleep disturbance: a pilot study. Res Gerontol Nurs 2019 Jul 01;12(4):167-173 [FREE Full text] [CrossRef] [Medline]
    124. Larsen LH, Schou L, Lund HH, Langberg H. The physical effect of exergames in healthy elderly-a systematic review. Games Health J 2013 Aug;2(4):205-212. [CrossRef] [Medline]
    125. Stara V, Santini S, Kropf J, D'Amen B. Digital health coaching programs among older employees in transition to retirement: systematic literature review. J Med Internet Res 2020 Sep 24;22(9):e17809 [FREE Full text] [CrossRef] [Medline]
    126. Núñez de Arenas-Arroyo S, Cavero-Redondo I, Alvarez-Bueno C, Sequí-Domínguez I, Reina-Gutiérrez S, Martínez-Vizcaíno V. Effect of eHealth to increase physical activity in healthy adults over 55 years: A systematic review and meta-analysis. Scand J Med Sci Sports 2021 Apr 16;31(4):776-789. [CrossRef] [Medline]
    127. Pacheco TBF, de Medeiros CSP, de Oliveira VHB, Vieira ER, de Cavalcanti FAC. Effectiveness of exergames for improving mobility and balance in older adults: a systematic review and meta-analysis. Syst Rev 2020 Jul 18;9(1):163 [FREE Full text] [CrossRef] [Medline]
    128. Muellmann S, Forberger S, Möllers T, Bröring E, Zeeb H, Pischke CR. Effectiveness of eHealth interventions for the promotion of physical activity in older adults: A systematic review. Prev Med 2018 Mar;108:93-110. [CrossRef] [Medline]
    129. Song Y, Qu J, Zhang D, Zhang J. Feasibility and effectiveness of mobile phones in physical activity promotion for adults 50 years and older: a systematic review. Topics in Geriatric Rehabilitation 2018;34(3):213-222. [CrossRef]
    130. 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 2019 Nov 28;21(11):e14343 [FREE Full text] [CrossRef] [Medline]
    131. Donath L, Rössler R, Faude O. Effects of virtual reality training (exergaming) compared to alternative exercise training and passive control on standing balance and functional mobility in healthy community-dwelling seniors: a meta-analytical review. Sports Med 2016 Sep;46(9):1293-1309. [CrossRef] [Medline]
    132. Kampmeijer R, Pavlova M, Tambor M, Golinowska S, Groot W. The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Serv Res 2016 Sep 05;16 Suppl 5(Suppl 5):290 [FREE Full text] [CrossRef] [Medline]
    133. Kuerbis A, Mulliken A, Muench F, Moore AA, Gardner D. Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health. Ment Health Addict Res 2017;2(2):1. [CrossRef]
    134. Stark AL, Geukes C, Dockweiler C. Digital health promotion and prevention in settings: scoping review. J Med Internet Res 2022 Jan 28;24(1):e21063 [FREE Full text] [CrossRef] [Medline]
    135. Wienert J, Zeeb H. Implementing health apps for digital public health - an implementation science approach adopting the consolidated framework for implementation research. Front Public Health 2021;9:610237 [FREE Full text] [CrossRef] [Medline]
    136. Wienert J, Jahnel T, Maaß L. What are digital public health interventions? First steps toward a definition and an intervention classification framework. J Med Internet Res 2022 Jun 28;24(6):e31921 [FREE Full text] [CrossRef] [Medline]
    137. Eysenbach G. What is e-health? J Med Internet Res 2001;3(2):E20 [FREE Full text] [CrossRef] [Medline]
    138. Kappen DL, Mirza-Babaei P, Nacke LE. Older adults’ physical activity and exergames: a systematic review. International Journal of Human–Computer Interaction 2018 Feb 23;35(2):140-167. [CrossRef]
    139. De Santis KK, Mergenthal L, Christianson L, Zeeb H. Health promotion and disease prevention with digital technologies for older people: Scoping review. European Journal of Public Health 2022:32-80. [CrossRef]

    ‎
    AMSTAR 2: A Measurement Tool to Assess Systematic Reviews, version 2
    PCC: Population, Concept, and Context
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews

    Edited by G Eysenbach, T Leung; submitted 14.10.22; peer-reviewed by P Staccini, A Lavoie; comments to author 09.11.22; revised version received 30.11.22; accepted 31.01.23; published 23.03.23

    Copyright

    ©Karina Karolina De Santis, Lea Mergenthal, Lara Christianson, Annalena Busskamp, Claudia Vonstein, Hajo Zeeb. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.03.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.