A systematic review assessing mobile health (mHealth) physical activity interventions in older adults with and without chronic health conditions

Introduction

Engaging in regular physical activity is a key protective factor for reducing the risk of developing chronic health conditions associated with ageing, including type II diabetes, cardiovascular disease, and dementia (1,2). The World Health Organisation (WHO) (3) recommends that older adults (≥65 years) should participate in a minimum of 150 to 300 minutes of moderate intensity aerobic physical activity (e.g., brisk walking or riding a bike) or 75 to 150 minutes of vigorous intensity aerobic physical activity (e.g., running or swimming) per week, accumulated in bouts of ≥10 minutes. In addition, muscle and bone strengthening activities are recommended at least twice a week (3). However, globally, older adults do not tend to meet these guidelines, with rates of insufficient physical activity increasing markedly from 60 years of age (4). The barriers to physical activity in older adults can be complex and multifaceted, often reflecting the increasing prevalence of co-existing chronic health conditions, misconceptions about the importance of physical activity and exercise, and limited social support (5,6). As such, there is a clear need to develop and evaluate clinically- and cost-effective interventions that can support older adults, particularly those with chronic health conditions, to be more physically active.

Traditional face-to-face (or in-person) interventions that aim to increase physical activity among older adults can be resource-intensive and require individuals to travel to specific locations, reducing accessibility, take-up, and adherence (7). In contrast, mobile health (mHealth) interventions, defined as the use of mobile and wireless devices (e.g., smartphones, tablet computers, wearable activity monitors) to promote health, present a potentially viable and scalable alternative. Importantly, smartphone and internet use among older adults has increased globally. In the United Kingdom (UK), for example, it was estimated that 80% of older adults aged ≥65 years owned a smartphone in 2024, a significant rise from 5% in 2012 (8). Similarly, in the United States of America (USA), smartphone ownership rates in older adults have shown an upward trend, increasing from 30% in 2015 to 76% in 2023 (9). Given this growing digital engagement, mHealth interventions offer a potentially cost-effective and accessible means of increasing physical activity among older adults.

In a recent systematic review by Daniels and colleagues (10), mHealth interventions (e.g., apps, websites, wearable devices, or a combination) were shown to be effective for increasing physical activity in healthy community-dwelling older adults. However, in a meta-analysis of nine randomised controlled trials (RCTs) the effect size was small and heterogeneity across studies was considerable, suggesting the pooled effect size estimate could change with the inclusion of further evidence. Similar results have also been reported in other systematic reviews evaluating mHealth or broader digital physical activity interventions, which consistently highlight the need high-quality evidence, larger samples and longer-term follow-up to determine intervention effectiveness (11-13). Additionally, many existing reviews exclude studies involving older adults with chronic health conditions, focusing instead on relatively healthy populations. This may limit the generalisability of the review findings especially given the high prevalence of chronic disease in older adults. In the USA, for instance, 95% of older adults are estimated to have at least one chronic condition, with almost 80% living with two or more (i.e., multimorbidity or multiple long-term conditions) (14).

Notably, evidence from a systematic review evaluating the long-term effectiveness of mHealth interventions for physical activity in adults (≥18 years of age) suggests that larger benefits may be observed among individuals with chronic or at-risk health conditions (15). This finding highlights the importance of considering health status when evaluating intervention effectiveness. Nevertheless, the current evidence base remains limited in its coverage of the complex and diverse health needs of older adults. Interventions designed to increase physical activity in older adults with chronic conditions may require modifications compared to those aimed at otherwise younger or healthier populations, such as tailoring exercises to accommodate functional limitations (16). Consequently, there is a need for a more comprehensive synthesis of the available evidence that includes older adults both with and without chronic health conditions.

In addition, while Daniels and colleagues (10) identified that many of the studies included in their review incorporated behaviour change techniques (BCTs), these were not formally coded. Research suggests that interventions targeting physical activity in older adults can be more effective when they employ suitable BCTs (17,18). BCTs are observable, replicable, and irreducible mechanisms of change that are essential components of effective behavioural interventions (19). A recent rapid review by Gilchrist and colleagues (18) has shown that the inclusion of specific BCTs such action planning, instructions on how to perform a behaviour, graded tasks, demonstration of behaviour, and behavioural practice can significantly enhance the effectiveness of physical activity interventions for older adults. Thus, the comparative effectiveness of specific BCTs is needed when assessing mHealth interventions that aim to increase physical activity in this population.

The aim of the current systematic review was to build on previous research by synthesising evidence from RCTs exploring the effectiveness of mHealth interventions and associated BCTs in increasing physical activity among older adults with and without chronic conditions. The findings from this review may help to inform the development of more targeted and effective mHealth interventions that aim to increase physical activity and support healthy ageing in this population. We present this article in accordance with the PRISMA reporting checklist (20) (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-67/rc).

Methods

The systematic review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO), CRD42022342016.

Eligibility criteria

The inclusion criteria were specified according to participants/population, intervention(s), comparator(s)/control, outcomes, and study designs (PICOS).

Participants/population

Community-dwelling older adults, defined as individuals aged ≥65 years living independently in their own private homes, with or without one or more chronic conditions. This population was selected because mHealth interventions are typically designed for independent use in non-institutional settings, where individuals ordinarily have greater autonomy, access to personal mobile devices, and fewer environmental or organisational constraints. Chronic conditions could include physical (e.g., type II diabetes, cardiovascular disease) or mental (e.g., depression, anxiety) health conditions. Studies including older adults living in residential care settings (e.g., care-homes), older adults receiving direct (within 12 months) post-operative rehabilitation interventions, or younger adults (<65 years) were not included.

Intervention(s)

Any behaviour change intervention delivered primarily through mobile devices (e.g., smartphones, tablet computers, wearable activity monitors, personal digital assistants) in which the aim is to increase the frequency (amount), intensity (level), time (duration) and/or type of physical activity (also known as FITT-principles), which are important factors in designing exercise programs that are safe and effective (21). Interventions were permitted to involve hybrid digital delivery, provided that a core component of the intervention (e.g., activity monitoring, feedback, goal setting, behaviour change content) was delivered via a mobile device, including smartphone- or tablet-based apps, and that these technologies played a central role in supporting physical activity behaviour change. Interventions delivered exclusively via telephone or non-mobile web-based platforms (e.g., desktop-only websites or email-only interventions) were excluded. A behaviour change intervention was defined as an intervention that aims to alter a target behaviour through at least one of the following: (I) capability (e.g., knowledge, skills); (II) opportunity (e.g., social influences, environmental context); and/or (III) motivation (e.g., beliefs about capabilities, reinforcement) (17). These can be targeted at the level of the individual (e.g., self-efficacy), the target population (e.g., education), or the environment (e.g., environmental restructuring).

Comparators

The comparisons of interest were either passive/inactive (e.g., standard/usual care, waiting list, no intervention) or active (e.g., another physical activity intervention).

Outcomes

Primary outcomes included one or more of the following: (I) level or amount of physical activity, either self-reported (e.g., daily diary) or behavioural (e.g., pedometer/accelerometer monitor); (II) functional fitness (e.g., gait speed, sit-to-stand test); and (III) adverse effects, such as pain or injury. Secondary outcomes included: (I) physiological measures (e.g., heart rate, blood pressure); (II) psychosocial wellbeing [e.g., Revised-University of California, Los Angeles (UCLA) Loneliness Scale]; and (III) cognitive measures (e.g., working memory).

Study designs

Only RCTs were included. Non-RCTs, before and after (or pre-post) studies, prospective or retrospective studies, articles with expert opinions, practice or procedure guidelines, case reports, case series, abstracts from conferences, and book chapters were not included.

Search strategy

M.V.G. completed searches in the following databases on 15^th August 2023: APA PsycINFO (via EBSCO host), ClinicalTrials.gov, Cochrane Library, Medline (via EBSCO host), PubMed, Scopus, SPORTDiscus (via EBSCO host), and Web of Science (Core Collection). The searches were last updated on 17 February 2025, to ensure that recently published papers were considered before the final analysis. Full electronic search strategies for all databases are provided in Appendix 1. All database searches were completed in 1 day with no restrictions on time, language, document type, or publishing status. However, although no language restrictions were applied at the search stage, non-English articles were excluded at the screening stage because their eligibility could not be assessed due to resource limitations. ClinicalTrials.gov was searched to establish whether there were any eligible trials that had been completed but not yet published. Only completed trials were considered eligible for this review. The search terms were gathered using free text, controlled vocabularies [e.g., Medical Subject Headings (MeSH)], professional judgment, literature reviews, and verification of test search results. Additional information was sought through manual searching of the reference lists of included studies and by screening related articles by shortlisted authors (i.e., snowballing). However, no further eligible studies were identified beyond those retrieved in the initial database searches. Contact with study authors was also not necessary to ascertain whether any studies were ongoing.

Study selection

M.V.G. and K.S. screened the identified references for eligibility according to the PICOS criteria by reading the title and abstract. The full text was obtained for articles that appeared to meet eligibility or where there was any uncertainty (i.e., insufficient information to make a clear decision). The number of articles screened, included, and excluded at each stage of the process is detailed in Figure 1. Full texts were independently screened by M.V.G. and K.S. using Covidence (www.covidence.org). Contact with study authors was not needed to resolve questions concerning eligibility. Discrepancies were resolved through discussion between both authors. Where an agreement could not be reached, a third author (D.W.M.) arbitrated.

Figure 1 Selection of studies for the systematic review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

Data collection process

A standardised, pre-populated data collection form created by Covidence was used, which included study details, author’s contact information, study design, population, interventions, comparators, and outcomes. The data extraction process involved M.V.G. and K.S. independently extracting data from each included study. To ensure consistency, a pilot test was conducted before commencing data extraction, where both authors independently extracted data from three randomly selected studies and compared their results. Any discrepancies were discussed and resolved before beginning the data extraction process.

Risk of bias and evidence quality

M.V.G. and D.W.M. independently assessed risk of bias for each included study using the Cochrane Risk of Bias tool version 2 (22), which rates the studies as high risk, low risk, or some concerns across six domains: (I) randomisation process; (II) deviations from the intended interventions—effect of assignment to intervention; (III) deviations from the intended interventions—effect of adherence to intervention; (IV) missing outcome data; (V) measurement of the outcome; and (VI) selection of the reported result. The overall certainty of the evidence for each outcome was also assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, which categorises the quality of evidence as high, moderate, low, and very low (23,24).

Data synthesis

Meta-analyses were to be only performed if the included studies were broadly comparable in terms of study design, interventions, and outcomes (25). In the absence of suitable meta-analysis, primary and secondary outcomes were assessed through narrative synthesis.

BCT coding process

Intervention content was coded using Behaviour Change Technique Taxonomy v1 (BCTTv1) (19). Coding was based on all the available intervention materials, including published manuscripts, supplementary files, and trial registry entries where available. To ensure the reliability of the BCT coding process, M.V.G. and D.W.M., both with formal training and prior experience in applying the BCTTv1, independently coded the interventions [BCTTv1 (19)]. Prior to full coding, a calibration exercise was conducted using two studies to ensure consistency. Any discrepancies in coding were resolved through discussion between both authors, with reference to the BCTTv1 guidance, and consensus was reached in all cases.

Deviations from the registered protocol

During the early stages of the review, which were initially undertaken by another researcher, errors in eligibility specification were identified. Consequently, the decision was made to restart the review prior to formal screening and data extraction, applying revised eligibility criteria consistently across all stages. The core aims, outcomes, and conceptual framework of the review remained unchanged, and only a small number of deviations were introduced to improve methodological rigour and relevance. First, the minimum age criterion was revised from ≥50 to ≥65 years to better align with commonly accepted definitions of older adulthood in ageing and public health research. Second, the scope was narrowed from digital health interventions more broadly to mHealth interventions, reflecting the distinct delivery characteristics and growing relevance of mobile and wearable technologies. Third, eligibility was restricted to RCTs only to allow for a more robust assessments of intervention effectiveness. These deviations were implemented prior to screening and data extraction, thereby minimising the risk of potential biases.

Results

A total of 9,539 records were identified for screening. Following the removal of 2,270 duplicate publications, 7,269 records underwent a three-stage screening process (Figure 1). The full texts of 502 articles that passed the initial title and abstract screen were retrieved, after the removal of articles that did not meet the criteria, nine RCTs remained.

A summary of the characteristics of the studies included in the review can be found in Table 1. Across the included studies, a total of 1,203 older adults participated, with sample sizes ranging from 21 to 529. Most studies had a higher proportion of female participants, ranging from 48% to 80%. The average age of participants (mean or median) ranged from 69.6 to 78.2 years. Five studies included participants with chronic health conditions, but this was defined and reported inconsistently across studies. One focused entirely on cancer survivors, while two included participants with a range of chronic conditions, including diabetes, hypertension, dyslipidaemia, anxiety, and depression. One study reported subjective health status and another unspecified long-term conditions or disabilities. Four studies did not report any information on participants’ chronic health status.

Seven studies randomised participants to an intervention or control group (two-arm RCT), with two randomising participants to a control group or one of two intervention groups (three-arm RCT). In seven studies, the mHealth intervention incorporated a wearable device (e.g., pedometer, activity tracker, smartwatch) so that participants could monitor their physical activity throughout the trial. For the remaining two studies that did not incorporate wearable devices, the aim was to assess changes in physical function, rather than objective physical activity. Additional intervention components included virtual coaches, smartphone apps, motivational interviewing, as well as in-person and/or remote support. The follow-up duration varied between studies, ranging from 4 weeks to 1 year.

Primary and secondary outcomes were assessed through narrative synthesis as meta-analyses were not possible due to substantial methodological heterogeneity in study design, interventions, outcome measures, and follow-up durations. Several included studies were feasibility or pilot RCTs, which aim to assess whether a full trial can be done rather than evaluating the effectiveness of an intervention. In addition, interventions varied considerably in both content and modes of delivery, further limiting the comparability of findings.

Primary outcome

Physical activity

Physical activity was assessed using self-reported and objective measures, which are reported separately.

Objective physical activity

Assessed across seven studies, data were collected using a range of activity trackers (e.g., Jawbone, Garmin Vivofit, Actiwatch, FITBIT^® Zip, Xiaomi Miband). Across studies, measures included daily step counts (k=6), as well as minutes of light-, moderate- and/or vigorous-intensity physical activity and sedentary behaviour (k=2). Of the six studies reporting daily step counts, no change in daily step counts attributable to the intervention were observed. moderate-to-vigorous physical activity (MVPA) was reported in two studies (33,34). No statistically significant changes in MVPA were found in either study, though Blair et al. (27) found an improvement in the second intervention group that incorporated additional health coaching for minutes of moderate activity only.

Self-reported physical activity

Four studies assessed physical activity using a range of self-report measures, including the International Physical Activity Questionnaire (IPAQ), Physical Activity Scale for the Elderly (PASE), and the Nordic Physical Activity Questionnaire (NPAQ). At an individual study level, results were mixed; two studies using the PASE found a greater improvement in self-reported physical activity in the intervention compared to the control group. However, Lim et al. (30) only found significant differences at one-month but not two-month follow up. By comparison, Li et al. (29) found significant improvements across all time points assessed. None of the other studies showed a significant difference in the mean change from baseline to follow-up between intervention and control groups.

Functional fitness

Three studies assessed functional fitness with objective measures. Two used the Short Physical Performance Battery (SPPB) (27,30) and one used both a time-up-and-go test and force plate balance assessment (31). No statistically significant overall differences were found between groups across studies or measures employed.

Adverse effects

Five studies assessed adverse effects, although reporting was variable and generally limited. In four studies, adverse effects were identified through researcher or self-report rather than systematic monitoring. Bickmore et al. (26) deemed ten (n=8 control; n=2 intervention) mild-to-moderate severity adverse effects likely attributable to participation in the study but specific details were not provided. Both Larsen et al. (28) and Li et al. (29) reported two adverse effects, each in the intervention groups. Larsen et al. (28) reported that one participant died, and one experienced increased anxiety due to wearing the physical activity monitor that triggered an existing mental illness. Li et al. (29) reported that two participants with osteoarthritis reported worsening knee and foot pain but were able to complete the study with a modified intervention (i.e., walking at a slower speed). One study used two self-reported questionnaires to assess adverse effects, the Pain Interference Short-Form and the Function Assessment of Chronic Illness Therapy - Fatigue Scale. Neither scale differed statistically between intervention and control groups (27). Lim et al. (30) stated that there were no adverse events recorded during the intervention period. Overall, the absence of serious adverse effects should be interpreted cautiously given the small number of studies that included this outcome measure and inconsistent reporting.

Secondary outcomes

Physiological health

Only one study assessed physiological outcomes. These included systolic and diastolic blood pressure, body composition (e.g., body mass index, waist/hip circumference, body fat mass, etc.), and lipid profiles [e.g., glucose, cholesterol, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR)]. No statistically significant differences between intervention and control groups were found for any measures.

Psychosocial wellbeing

Six studies assessed this outcome using a range of different measures. Three reported on Health-Related Quality of Life, using the Short Form 36-item survey (27), and the EuroQol Five-Dimension, Five-Level (EQ-5D-5L) questionnaire and EuroQol Visual Analogue Scale (EQ-VAS) (28,34). Two studies measured depression using the Geriatric Depression Scale (30,31). In addition, loneliness (28), social interactions (32), and general wellbeing (34) were assessed in one study each. Out of the eight measures of psychosocial wellbeing, only loneliness demonstrated a statistical difference between intervention groups, whereby the intervention group had a significant decrease in reported loneliness compared to the control group.

Cognition

Two studies assessed cognition. One measured ‘cognitive activity’ (i.e., the number of cognitively stimulating activities engaged in each day out of 14, such as reading, playing word games), and did not show a statistically significant difference in activities between intervention and control groups (32). Another study employed the mini-mental state examination (MMSE), clock test, and verbal (categorical) fluency tests to examine cognition. Only the clock test demonstrated a statistically significant difference between groups, showing improved scores in the intervention group (34).

BCTs

In addition to assessing the effectiveness of mHealth interventions, we also conducted a comprehensive coding process to examine the BCTs employed across studies. Overall, the number of BCTs included within an intervention ranged from 3 to 11, with an average of 6 (SD =2.6) BCTs employed (Table 2). A total of 18 out of 93 possible BCTs were identified as present in the interventions across all studies (Table 2 and Table S1). The most frequently used BCT implemented was self-monitoring of behaviour, employed in all nine studies, followed by information about antecedents and feedback on behaviour, both employed in six studies. The least frequently implemented BCTs were behavioural contract, social support (emotional), generalisation of target behaviour, material incentive (behaviour) and reward (outcome), which were each only employed in one study.

Risk of bias and study quality

Table 3 reports outcomes for each study across the six domains from the Cochrane Risk of Bias 2 (22) tool. For the randomisation process, seven studies were judged to be low risk, while two were rated as some concerns due to insufficient information concerning the randomisation procedures (e.g., reporting of sequence generation, allocation concealment procedures). For deviations from the intended interventions—assignment to interventions, only one study was judged as low risk and the remaining eight studies some concerns for due to insufficient information being reported in terms of adherence to intervention protocols. For deviations from the intended interventions—effect of adhering to intervention seven studies were judged as some concerns, largely due to a lack of information on participant adherence or engagement with the intervention. One study was rated as high risk due to lack of blinding and inadequate reporting of adherence, which could possibly influence outcome estimates. Missing outcome data was rated as low risk for seven studies and some concerns for two, as data for some of the outcome measures were not available for all or nearly all the participants and reasons for missing data were insufficiently described. The measurement of the outcome was judged as low risk for eight studies, reflecting the use of validated outcomes. One study was rated high risk due to inconsistent measurement of some outcome measures at baseline compared to follow-up. Lastly, selection of the reported result was judged as low risk for all nine studies, as outcomes were generally reported in line with study objectives. Overall, three studies were classified as high risk (i.e., at least one domain high risk or some concerns for multiple domains in a way that substantially lowers confidence in the result) and six studies were classified as some concerns (i.e., the study is judged to raise some concerns in at least one domain for this result, but not to be at high risk of bias for any domain).

Following guidance by Murad and colleagues (35) for applying GRADE when meta-analysis is not feasible, for all outcomes (i.e., physical activity, functional fitness, adverse effects, physiological health, psychosocial wellbeing, cognition), the certainty of the evidence was rated as very low, reflecting the limited evidence, heterogeneity in measures employed across studies, and inconsistent findings (Table 4).

Discussion

This systematic review identified and synthesised existing evidence from nine RCTs evaluating the effectiveness of mHealth physical activity interventions in older adults living with and without chronic health conditions. Only five studies included participants with chronic health conditions, but this was defined and reported inconsistently. Furthermore, given the limited number of studies, as well as methodological heterogeneity in terms intervention types, outcome measures, and follow-up durations, meta-analyses were not possible. A narrative synthesis indicated limited or inconsistent evidence for measures of physical activity (both objective and self-reported), functional fitness, adverse effects, physiological health, psychosocial wellbeing, and cognition. Risk of bias was also established, with no study rated as low risk across all six domains from the Cochrane Risk of Bias 2 tool (22). Notably, all studies were judged as having at least some concerns against deviations from the intended interventions in terms of effect of assignment or adhering to intervention. This was mostly due to lack of clarity or failure to report sufficiently detailed information in the published paper. In addition, study quality using GRADE was very low across all outcomes due to the limited number of studies, reducing certainty in the evidence. Therefore, the overall findings of this review may be limited due to the small number of RCTs included, as well as inconsistencies in intervention design, delivery, and evaluation, suggesting that further research in this area is warranted.

It is noteworthy that the findings of the current review differ from those reported in another recently published systematic review similarly assessing mHealth physical activity interventions in older adults. In their review, Daniels and colleagues (10) concluded that mHealth interventions were effective in increasing physical activity in older adults. This conclusion based on a meta-analysis of nine RCTs, as well as a broader range of study designs. A potential reason for the discrepant results between reviews likely stems from the differing studies included. Specifically, only one of the RCTs included in Daniels and colleagues’ (10) meta-analysis was also included in the present study [i.e., (34)], which may be explained by several methodological distinctions. First, we excluded studies that evaluated mHealth interventions delivered exclusively via telephone or internet/web-based platforms, opting to include those that could be delivered primarily via mobile or wearable devices. Second, our inclusion criteria were more stringent with regards to participant age, as we required both the average age and range to be ≥65 years, whereas Daniels and colleagues (10) included studies where only the average age met this criterion even if some participants in the included studies were <65 years. Third, we excluded interventions that did not explicitly target physical activity behaviour change [i.e., where the aim was to increase the frequency (amount), intensity (level), time (duration) and/or type of physical activity], while Daniels and colleagues (10) included some studies without this specific focus. Finally, and perhaps most importantly, Daniels and colleagues (10) explored healthy community-dwelling older adults (i.e., those without severe preexisting chronic medical conditions). However, our review included studies involving participants with and without chronic health conditions to better reflect the general ageing population, where chronic ill-health is highly prevalent.

The current study additionally used the BCTTv1 to systematically identify and categorise the BCTs employed in the included mHealth interventions. This offered potentially valuable insights into the behaviour change strategies commonly incorporated in previously evaluated mHealth interventions that aim to increase physical activity among older adults. It was found that the interventions incorporated a limited number of the 93 available BCTs. The most frequently used were self-monitoring of behaviour, information about antecedents, and feedback on behaviour. However, the number and combination of BCTs employed varied considerably between studies.

Importantly, several BCTs that have previously been identified as relevant for improving physical activity in older adults were infrequently or inconsistently applied. For example, in their rapid review of 70 physical activity programs and services from 56 RCTs targeting older adults, Gilchrist and colleagues (18) identified that the most used BCTs were action planning (97% of interventions), instructions on how to perform a behaviour (85%), graded tasks (76%), demonstration of behaviour (63%), and behavioural practice (61%). Furthermore, the most promising BCTs for improving physical activity outcomes in this population included goal setting, problem solving, and self-monitoring of behaviour, alongside broader BCT groupings such as goals and planning, feedback and monitoring, social support, and repetition and substitution. On this basis, the limited effectiveness of mHealth interventions identified in the current review may reflect, in part, the underuse or inconsistent application of suitable BCTs that are particularly well suited to meet the complex and heterogeneous needs of older adults. This highlights the need for more tailored and theoretically informed interventions that draw on a broader and more purposeful range of BCTs.

To address this, future research could adopt participatory design approaches, such as co-design methods, that actively involve key stakeholders, including older adults with and without chronic health conditions, as well as carers and healthcare professionals (36,37). Engaging end users in the development process could help identify appropriate BCTs, ultimately effectiveness in terms of increasing physical activity behaviour change. In support of such an approach, Gilchrist and colleagues (18) argue that, because older adults have distinct physical, emotional, and cognitive needs, BCTs that are effective for younger populations may require adaptation or substitution to support physical activity. Taken together, the findings of the current review and those by Gilchrist and colleagues (18) reinforce the potential for mHealth interventions to increase physical activity in older adults when suitable, theoretically informed BCTs are selected that align with the bespoke needs of this population.

Study strengths and limitations

This systematic review has several strengths. First, by including only RCTs, the review aimed to provide a rigorous synthesis of intervention effectiveness, reducing the risk of biases typically associated with other study designs, such as before-and after or uncontrolled trials. Second, we included multiple outcome measures to comprehensively assess the effectiveness of mHealth interventions across both proximal (e.g., physical activity, functional fitness, adverse effects) and more distal, yet related, outcomes (e.g., physiological health, psychosocial wellbeing, cognition). Third, a systematic analysis of BCTs can provide important insights into the mechanisms of action (or active ingredients) underpinning intervention effectiveness and can be used to inform future intervention development and evaluation.

Despite these strengths, several limitations should be acknowledged. Meta-analyses were not possible due to the limited number of studies and methodological heterogeneity. As a result, pooled effect size estimates could not be calculated, which limited definitive conclusions of intervention effectiveness. In addition, deviations from the original, prospectively registered review protocol occurred, although these were informed by pragmatic considerations during the review process. First, the age inclusion criterion was revised to focus on studies that included participants ≥65 years (rather than ≥55 years), to better align with that commonly used to define older adults in research and public health policy within developed countries (2,38). Second, while the original protocol aimed to evaluate the effectiveness of digital interventions more broadly, the scope of the review was revised to focus exclusively on mHealth interventions. This change was made due to the diversity of digital technologies and the growing application of mHealth globally, particularly in the management of chronic health conditions (39). Finally, due to the small number of eligible studies and inconsistent reporting of participants’ health status, it was not possible to conduct planned sub-group analyses according to specific chronic health conditions. This limitation highlights a clear gap in the current evidence base, as older adults with multimorbidity or more complex health needs (e.g., cognitive or sensory impairments) were underrepresented across studies. Future research is therefore needed to evaluate the effectiveness of mHealth physical activity interventions specifically in older adults living with chronic ill-health, including those with multiple long-term conditions.

Conclusions

This systematic review provides novel insights into the effectiveness of mHealth interventions for increasing physical activity among older adults ≥65 years of age, including those living with chronic health conditions. It also identifies the BCTs most commonly employed, which could guide the development of future mHealth interventions. Overall, the findings of this review highlight that, while some mHealth interventions show promise, their effectiveness for increasing physical activity in older adults remains inconclusive. Importantly, this uncertainly may reflect limitations is the current evidence base, such as methodological heterogeneity and variable use of BCTs, rather than a lack of intervention effectiveness. In addition, the available evidence remains limited in its representation of individuals with multimorbidity or more complex health needs, as several studies did not consistently report chronic condition status. Further research is therefore needed to rigorously evaluate the impact and behaviour change potential of mHealth interventions specifically designed to meet the complex and diverse physical activity needs of older adults, both with and without chronic conditions, in order to support healthy ageing.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-67/rc

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-67/prf

Funding: This work was supported by Sonova AG, Switzerland.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-67/coif). At the time the research was carried out, E.U. was an employee at Sonova AG but received no personal fees from the funder. Sonova AG had no input into the analysis or reporting of the research. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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