What type of study is this?

This is a Systematic Review study (also classified as: Meta-Analysis).

October 2, 2025Open Access

Digital technology empowers exercise health management in older adults: a systematic review and meta-analysis of the effects of mHealth-based interventions on physical activity and body composition in older adults

GWGuanbo WangSichuan University of Science and Engineering RXRong XiangTianjin University of Commerce XYXuemei YangYunnan University

Key Points

mHealth interventions led to a significant increase in physical activity and reduction in sedentary behavior in older adults.
The meta-analysis of 14 randomized controlled trials included over 2,500 participants, demonstrating mHealth effectiveness on physical activity levels.
Subgroup analyses showed commercial apps outperformed research-based apps, highlighting the importance of application type in intervention success.
Integrating multiple behavior change techniques in mHealth interventions was critical for enhancing physical activity and reducing sedentary behavior.

Abstract

Background Prolonged Sedentary behavior (SB) and lack of Physical Activity (PA) in the older population significantly increase the risk of chronic disease development. The use of mobile health (mHealth) apps may have a positive impact on older adults, helping to increase their physical activity levels and optimize body composition. However, the effectiveness of mHealth-based interventions and potential moderators in this population is not fully understood. Objective To assess the effectiveness of a mHealth-based intervention in promoting PA/moderate to vigorous physical activity (MVPA), reducing SB, and lowering body mass index (BMI) in older adults. The moderating effects of the mHealth intervention effects were also explored through subgroup analysis. Method This study searched Embase, PubMed, Web of Science, and Cochrane databases (as of June 2025) to include randomized controlled trials (RCT) evaluating the effects of mHealth on PA, MVPA, SB, and BMI in older adults. Standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated using random effects models. Results A total of 14 RCTs were included (sample size = 2,511). mHealth intervention significantly elevated PA (SMD = 0.18, 95%CI: 0.01 to 0.35) and MVPA (SMD = 0.48, 95%CI: 0.20 to 0.75) and reduced SB (SMD = -0.55, 95% CI: −0.79 to −0.32), but no significant improvement in BMI (SMD = −0.27, 95% CI: −0.79 to 0.25). Subgroup analyses showed that: Commercial applications were better than research-based applications (PA: SMD = 0.18 vs. 0.07; MVPA: SMD = 0.70 vs. 0.31); more than 3 behavior change techniques (BCTs) interventions were effective for MVPA enhancement (SMD = 0.49) and SB reduction (SMD = −0.77); and the use of a theory paradigm intervention was more effective on SB reduction (SMD = −0.77 vs. 0.38). Conclusion mHealth apps were effective in increasing PA/MVPA levels and reducing SB levels in older adults, but did not reach statistical significance in terms of BMI improvement. Through subgroup analyses, this study further found that commercial apps demonstrated greater strengths in promoting PA/MVPA; meanwhile, integrating more than 3 BCTs synergistically promoted MVPA levels and reduced SB.

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