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

Guanbo Wang^1*Ranran Xiang²Xuemei Yang¹Liang Tan³

• ¹Physical education institute，Sichuan University of Science & Engineering, zigong, China
• ²School of Physical Education, Hunan University of Science and Technology, xiangtan, China
• ³Gdansk University of Physical Education and Sport, Gdansk, Poland

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.