A qualitative systematic review and meta-ethnography were carried out. Noblit and Hare [27] described this type of synthesis of qualitative research results, which is the most widely used qualitative synthesis approach in health care. The protocol of this review was registered in PROSPERO (CDR498374). This review was conducted following the recommendations of the Cochrane Qualitative and Implementation Methods Group Guidance [28], the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) statement, and the criteria of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement [29] (Checklist 1).

The following inclusion and exclusion criteria were used to select the articles.

The inclusion criteria were as follows: (1) published in English and Spanish from January 2013 to January 2024 to include the latest publications on the subject, (2) used any type of qualitative approach or qualitative data collection technique, and (3) analyzed the use of mHealth ecosystems, specifically wearable PA trackers and their associated mobile apps, for the promotion of PA in healthy or chronically ill adults aged 50 years and older who were community-dwelling or institutionalized, functionally autonomous, and not classified as frail. The exclusion criteria were as follows: (1) studies that included participants with cognitive impairment or any mental pathology, (2) protocol studies or studies not completed, (3) mixed designs if the qualitative data were not analyzed in a disaggregated manner, and (4) studies that included the use of the wearable mHealth technologies exclusively for dietary recommendations.

Two researchers independently conducted the search and selection of articles and resolved discrepancies through consensus, with a third researcher consulted when necessary, following the established criteria in the following databases: MEDLINE (PubMed), Scopus, Web of Science, ÍnDICEs-CSIC, ProQuest, PsycINFO, The Cochrane Library Plus, and CINAHL. In addition, a secondary search was conducted using the reference lists of the selected articles. In cases of disagreement, a third researcher approved the study. The search strategy included a combination of keywords adapted to each database. The complete search strategy is presented in Multimedia Appendix 1.

Article selection was performed independently by 2 researchers, with disagreements resolved by a third reviewer. First, the titles and abstracts of the articles were examined, and those that did not meet the inclusion criteria were discarded. The full texts of the selected articles were subsequently read, and the following information was extracted from each of them using an ad hoc Excel template: authors, year of publication, paradigmatic approach, objectives of the study, characteristics of the sample and sampling techniques, data collection techniques, main results, conclusions, and evaluation of methodological quality.

The quality of the studies was assessed with the Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI) [30], which includes 10 items with 3 response options, “yes,” “no,” and “unclear,” to evaluate the methodological quality of the qualitative studies. Studies with poor methodological quality (fewer than 5 items) were rated as “excluded” and removed from the review.

Following Noblit and Hare’s interpretive approach [27], the findings were synthesized through reciprocal translation, evolving into a line of argument that provides an original theoretical reconceptualization of mHealth apps to promote PA.

Data were systematically categorized using an abstraction form designed to distinguish between first-order constructs (participants’ quotations and metaphors), second-order constructs (interpretations based on patients’ quotations made by the original researchers), and third-order constructs (interpretations generated by our research team). By juxtaposing these layers, we moved beyond data aggregation toward a reciprocal translation of findings, synthesizing these accounts into a novel line of argument that reconceptualizes the phenomenon within the current literature (Multimedia Appendix 2).

The main themes, categories, subcategories, and codes were extracted. The codes were grouped by areas of similarity, resulting in the different themes, categories, and subcategories of facilitators, barriers, and areas for improvement for the promotion of PA among older adults. For the translation of the studies, the data were exported to a table containing the thematic tree, categories, subcategories, codes, and quotations to help us understand the relationships between the studies. No refutations or contradictions were found, as the data from all included studies were very similar. Finally, the researchers compared the translations to identify the lines of argument from which the synthesis would be developed.

The research team has previous experience in qualitative research, evidence synthesis, PA, and research involving older people. The team included registered nurses, physiotherapists, an anthropologist, and a graduate in PA. The findings were analyzed from an interprofessional perspective.

Following the database search, 141 references were identified, 7 of which were eliminated because they were duplicates across several databases. After the title and abstract were read, 15 articles met the inclusion criteria; 5 were excluded because of low methodological quality, specifically the use of the Joanna Briggs Institute tool for qualitative studies. Finally, 10 articles were included in the narrative synthesis and meta-ethnography (Figure 1).

The main characteristics of the studies analyzed are summarized in Table 1.

All the studies analyzed included adults older than 50 years, whose ages ranged broadly from 51 to 94 years. Most included mixed-gender samples, although one study exclusively analyzed the perspective of women [39]. With respect to comorbidities and functional status, several studies have moved beyond healthy populations to include individuals with chronic illnesses, older adults with obesity (BMI >30 kg/m²), and participants with mild walking disabilities using wheeled walkers. Notably, formal categorizations of socioeconomic status (SES), culture, and ethnicity were absent from the primary studies’ demographic reports. In terms of geographic scope, 5 studies were conducted in the United States [32-34,36,40], 2 in Canada [37,38], and the remaining in Sweden [35], Ireland [31], and Germany [39].

Concerning the objectives, 2 studies analyzed the barriers and facilitators perceived by older adults regarding the use of PA monitoring devices [33,39], another analyzed the experience of this population group using PA monitoring devices and their perceptions of factors influencing their long-term use [32], and the remaining analyzed the experiences, acceptability, effects, and preferences of older adults regarding the use of PA monitoring devices [34-38,40].

Most studies used the phenomenological approach [32-40], except for one study that used meta-ethnography [31]. The main data collection techniques used were focus groups [33,37], semistructured interviews [32,34,36,38,40], or a combination of both [31,35,39].

Seven of the analyzed studies reported the barriers, facilitators, and areas of improvement perceived by adults for the use of wearable mHealth technologies for PA promotion [31,33-35,37,38,41]; 1 synthesized the facilitators that promoted the use of wearable mHealth technologies among older adults [32], and 2 analyzed the facilitators and barriers that facilitated or hindered the use of these devices and apps [36,40].

Three major themes emerged from the narratives explaining older adults’ perceptions of wearable mHealth technologies promoting PA: (1) barriers to their use of wearable mHealth technologies to promote PA: personal (physical and perceptions about technology and tastes and preferences), technological (functionality, content, design, alarms, availability, and accessibility), and environmental (seasonal variations); (2) facilitators of their use of wearable mHealth technologies to promote PA: personal (consideration that it improves health, perceptions about technology and tastes and preferences), technological (functionality, content, and design), relational (technological support and social support), environmental (seasonal variations), and health sector (support and monitoring of health services); and (3) main areas of improvement for the use of wearable mHealth technologies to promote PA: personal (perceptions about technology, tastes, and preferences), technological (functionality, content, and design), and relational (technological support). These themes are presented below with the corresponding categories, subcategories, and codes (Tables 2–4).

Through the reciprocal translation of the primary literature, the synthesis demonstrates that mHealth ecosystem adoption among older adults is structurally hindered by an intersection of age-associated physiological decline and inadequate user-centered design. Personal barriers encompass sensory and physical deterioration, slower information processing speeds, and technology-induced anxiety, which frequently collide with an established reluctance to alter daily habits [31-35,37-40]. This vulnerability is severely compounded by technical deficits; users routinely experience frustration regarding cumbersome and fragile hardware, complex smartphone synchronization requirements, a lack of automated troubleshooting, and unreliable data visualization [31-39]. Furthermore, frequent notifications or alarms act as intrusive stressors [31,34], while high purchasing costs [33,34] and jargon-heavy instructions create severe access barriers [31,33,35,37-39]. These technical challenges are further compounded by environmental variables, as adverse seasonal weather (winter, rain, snow, and so on) restricts physical outdoor use [37,40]. These barriers are detailed in Table 2.

Conversely, a positive line of argument emerges when wearable technologies successfully bridge the gap between intrinsic health motivation and intuitive functionality and desire for functional independence [31-40]. At the individual level, older adults demonstrate high adherence when they perceive that tracking tools directly translate into stress reduction, pain management, and measurable physical wellness [32-40]. From a technological standpoint, sustained engagement is facilitated by seamless, unobtrusive hardware configurations—specifically watch-like, waterproof devices featuring long battery life, flexible bands, and large, legible displays [31-35,37-40]. Software features that offer automated tracking, personalized goals, gamified self-competition, and real-time positive feedback act as powerful behavioral catalysts [31-35,37-40]. Critically, the synthesis highlights that hardware and software fail in isolation; relational and systemic scaffolding is paramount. Sustained use is driven by robust social networks (family and peers) [31,33,34,38-40] and, fundamentally, by the formal involvement of health care professionals who integrate these digital metrics into active clinical treatment plans and continuous medical monitoring [31-33,35-38,40]. Table 3 maps the comprehensive structure of these facilitators.

To minimize early abandonment and maximize clinical utility, the synthesized data establishes explicit pathways for age-inclusive development. Participants universally demand a shift toward age-friendly engineering that prioritizes intuitive interfaces, native-language instruction manuals, enhanced data privacy, and the expansion of monitored metrics to include broader clinical parameters beyond basic pedometry [31,33-39]. Relatably, older adults highlight that closing the digital divide requires robust instructional scaffolding. Developers and health care networks must cooperate to provide accessible, nontechnical training and ongoing troubleshooting support to foster digital self-efficacy and prevent technology rejection [31,33,37]. These strategic directions for technological and clinical adaptation are synthesized in Table 4.

The methodological quality of the articles was analyzed with the JBI-QARI [30]. Table 5 shows the detailed results of the analysis of the methodological quality of the included studies after this tool was applied.

The objective of this qualitative systematic review was to analyze the point of view of older adults on the aspects that can influence the use of wearable mHealth technologies for the promotion of PA and to analyze their demands, contexts, and desires to develop more accessible, age-friendly, and acceptable technologies for this population group [19].

People aged 50 years and older perceive personal barriers (physical aspects, perceptions about technologies, and tastes or preferences), technological barriers (functionality, content, design, alarms, availability, and accessibility), and environmental barriers (seasonal variations) that hinder the use of wearable mHealth technologies for the promotion of PA [31-38,40,41]. On the other hand, this population includes personal facilitators (which consider that it improves health, perceptions about technologies, and tastes or preferences), technological facilitators (functionality, content, and design), relational facilitators (technological support and social support), environmental facilitators (seasonal variations), and health professionals (support and follow-up by health professionals) that favor the use of wearable mHealth technologies [31-38,40,41]. Likewise, people more than 50 years of age perceive personal factors (perceptions about technologies and tastes or preferences), technological factors (functionality, content, and design), and relational factors (technological support) as areas for improvement in mHealth devices and apps for the promotion of PA [31,33-38,40,41].

To illustrate the practical relevance and tangible impact of these technologies, specific mHealth interventions from synthesized studies demonstrate how targeted features can drive positive behavioral changes in vulnerable cohorts. For instance, the implementation of a commercial wearable device (Fitbit) among older adults with obesity residing in rural areas proved highly acceptable; it successfully motivated positive behavior change by providing useful real-time feedback and fostering social interaction [40]. Similarly, in a cohort of older adults living with chronic illnesses, wearable activity trackers functioned as powerful behavioral catalysts [37]. By using alert functions and setting daily step goals, these devices gamify PAs, significantly increasing users’ awareness of their daily movement and transforming exercise into self-competition [37]. Furthermore, devices that allowed users to set small, progressive goals, supported by reminders and structured routines—such as wearing the device first thing in the morning—were instrumental in helping older adults develop long-term PA habits [32]. The increased self-awareness facilitated by these trackers also motivated older adults to make broader lifestyle changes and proactively seek health-related information [38]. These empirical examples underscore that when wearable mHealth technologies successfully integrate user-friendly feedback, gamification, and social support, they effectively translate theoretical facilitators into measurable health impacts for aging populations [32,37,38,40].

In line with previous studies that have analyzed the use of wearable mHealth technologies among older adults [4,42], our results confirm that the following barriers hinder their use: physiological state, age, vision loss, hearing loss [31], high price [33,34], lack of interest in older adults, and inexperience and fear of using technologies and sharing data [33-35,37,38]. In addition, the results of this review coincide with those of previous studies showing the following facilitators for the use of devices and apps among older adults: previous experience with technology [32,34,38], being user-friendly [31,35], and favoring the independence and autonomy of older adults [33,35,38].

Another previous study performed on people with low back pain [43] confirmed, as in our review, that older adults must be supported by family members, friends, health professionals, and other users to be able to continuously use PA monitoring devices and apps [31,33,34,37,39,40]. In addition, older adults positively value the use of reminders and alarms [32-35,37] and receive feedback on whether they are using the device correctly [38,40]. Our results are in line with those of previous studies analyzing the use of mHealth ecosystems to promote PA in patients with myeloid neoplasms [25], revealing the need to establish specific objectives according to physiological conditions so that users can use PA monitoring devices appropriately [31,32,35].

In contrast to our findings [31,33,38,39], a study involving adolescent and young adult cancer survivors reported that PA monitoring devices were easy to use and synchronize, allowing for quick data retrieval [44]. Such ease of use is not reflected in the narratives of older adults in our review. Instead, older participants frequently expressed the belief that these technologies were not designed for them [31,35,39]. Moreover, in contrast to studies that claim that devices and apps are affordable and economically profitable [33,34], our results show that cost and access to mHealth technology are barriers for older adults [31,33-35,37,38,41]. These results highlight the need for mHealth technology to be friendlier to older adults, which has been termed age-friendly, and to be more flexible to potential users. One study reported that mHealth devices and apps should contain simple functions and simple navigation instructions to reduce the cognitive load of older adults, common and simple language, the option of zoom or large print text, and voice navigation (if they are visually impaired) [45]. These findings are consistent with the results of our studies [31-35,37-39].

For these reasons, governments, international organizations, health professionals, academic institutions, media, and the private sector must consider all these findings to eliminate the existing digital gap between older adults and the use of mHealth devices and apps. In this way, through learning and training, older adults can benefit from the use of wearable mHealth technologies by achieving better self-management of their health [46].

However, advocating for inclusive design is insufficient without addressing the economic realities of the digital health ecosystem. The technology sector generally recognizes the value of user-centered interfaces; rather, the primary barrier is economic. The high costs and lower profit margins associated with developing customized platforms often fail to support a compelling business case. To genuinely drive industry change, future research must actively develop this business case. By leveraging resources largely inaccessible to private developers—such as longitudinal electronic health records, clinical registries, and biobanks—the academic community must conduct robust Health Economics and Outcomes Research and budget impact analyses. Demonstrating a clear clinical and economic return on investment is essential for incentivizing the tech sector to prioritize and fund highly customizable, age-friendly mHealth technologies.

This review included only articles in English and Spanish in the selected databases and excluded relevant articles on the phenomenon studied. In addition, no articles have studied the long-term use of wearable mHealth technologies among older adults, nor have the views of health professionals been considered. On the other hand, our study was based solely on the use of mHealth ecosystems that promote PA, and future studies should continue to investigate the repercussions of these devices and apps over time and from the point of view of professionals.

Another limitation of this meta-ethnography is the restricted demographic reporting in the primary literature. Although some studies detailed clinical parameters and geographic contexts, critical sociodemographic determinants such as SES, culture, and ethnicity were systematically omitted. This paucity of granular data precludes a robust intersectional analysis, obscuring how overlapping social identities and structural inequalities influence mHealth engagement. Consequently, the current evidence may disproportionately reflect privileged cohorts and fail to capture the unique barriers faced by marginalized subpopulations. To advance health equity, future research must rigorously report diverse, intersectional demographic variables, ensuring that mHealth interventions mitigate rather than exacerbate the digital divide among aging populations.

This meta-ethnography demonstrates that older adults’ engagement with wearable mHealth technologies for PA is not solely dictated by technological proficiency but by a complex interplay of personal, technological, and environmental factors. While usability constraints, age-related physical limitations, and privacy concerns significantly hinder adoption, older adults clearly recognize the potential of these digital health tools to enhance their well-being. Evidence from the synthesized cohorts highlights that when mHealth interventions use user-centered, age-friendly designs and integrate continuous support from health care professionals, they effectively drive sustained behavioral change and long-term adherence. However, a critical methodological gap persists in the current literature: the systemic omission of granular demographic data, specifically SES, cultural background, and ethnicity. This lack of intersectional reporting precludes a comprehensive understanding of how compounding structural inequalities shape digital health access. If mHealth is to promote true health equity, future research must transition from homogeneous sampling to rigorous, inclusive methodologies. Tech developers and clinicians must collaboratively address the multifaceted needs of diverse, marginalized older adult subpopulations. Only through this intersectional approach can we ensure that the proliferation of wearable mHealth technologies bridges, rather than inadvertently exacerbates, the existing digital divide.