Effectiveness of mHealth-Based Gamified Interventions on Physical Activity in Older Adults: Systematic Review
Bibliographic record
Abstract
Background: Global aging presents significant socioeconomic and health challenges, particularly for older adults who face an increased risk of chronic diseases and reduced physical activity levels. Although physical activity is crucial for maintaining health, most older adults do not meet the recommended guidelines. Gamification and mobile health (mHealth) technologies offer innovative solutions to motivate physical activity; however, research focusing on older adults is limited, especially regarding the effectiveness and sustainability of such interventions. Objective: This study aims to synthesize evidence on the effectiveness of mHealth-based gamified interventions for improving physical activity in older adults. Methods: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Meta-Analysis of Observational Studies in Epidemiology guidelines and analyzed studies from PubMed, Embase, Web of Science, CINAHL, Scopus, and Wiley Online Library, covering relevant literature from their inception up to May 2025. The inclusion criteria focused on gamified mHealth interventions for adults aged 60+ years, excluding serious games. Quality assessment was conducted according to the Joanna Briggs Institute standards, with data extracted on study design, gamification elements, and outcomes such as step counts and moderate-to-vigorous physical activity. Results: Of 2944 studies identified from the database search, 1454 individuals from 8 trials were included. Gamified interventions significantly increased daily step counts and time spent in moderate-to-vigorous physical activity among older adults. Goal setting and rewards were the most frequently used components, and the combined use of mobile and wearable devices offered greater flexibility and accessibility. A classification framework indicated that interventions integrating multiple gamification elements with hybrid technology systems were most effective, particularly when guided by a theoretical basis. However, the heterogeneity in study designs, small sample sizes, and lack of long-term follow-up studies limited the generalizability of the findings. Conclusions: mHealth-based gamification interventions demonstrate potential for increasing physical activity in older adults. Future interventions should consider employing multifaceted designs combining advanced gamification with hybrid technology systems, while also prioritizing theoretical integration, long-term sustainability, and caregiver involvement to improve sustainability and inclusivity. This review highlights the need for theory-driven, technology-mediated strategies that address the unique health needs of older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".