Effectiveness of Mobile Health–Based Gamification Interventions for Improving Physical Activity in Individuals With Cardiovascular Diseases: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Gamification refers to using game design elements in nongame contexts. Promoting physical activity (PA) through gamification is a novel and promising avenue for improving lifestyles and mitigating the advancement of cardiovascular diseases (CVDs). However, evidence of its effectiveness remains mixed. OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the efficacy of gamification interventions in promoting PA during short-term and follow-up periods in individuals with CVDs and to explore the most effective game design elements. METHODS: A comprehensive search of 7 electronic databases was conducted for randomized controlled trials published in English from January 1, 2010, to February 3, 2024. Eligible studies used mobile health-based gamification interventions to promote PA or reduce sedentary behavior in individuals with CVDs. In total, 2 independent reviewers screened the retrieved records, extracted data, and evaluated the risk of bias using the RoB 2 tool. Discrepancies were resolved by a third reviewer. Meta-analyses were performed using a random-effects model with the Sidik-Jonkman method adjusted by the Knapp-Hartung method. Sensitivity analysis and influence analysis examined the robustness of results, while prediction intervals indicated heterogeneity. A meta-regression using a multimodel inference approach explored the most important game design elements. Statistical analyses were conducted using R (version 4.3.2; R Foundation for Statistical Computing). RESULTS: In total, 6 randomized controlled trials were included. Meta-analysis of 5 studies revealed a small effect of gamification interventions on short-term PA (after sensitivity analysis: Hedges g=0.32, 95% CI 0.19-0.45, 95% prediction interval [PI] 0.02-0.62). Meta-analysis of 3 studies found the maintenance effect (measured with follow-up averaging 2.5 months after the end of the intervention) was small (Hedges g=0.20, 95% CI 0.12-0.29, 95% PI -0.01 to 0.41). A meta-analysis of 3 studies found participants taking 696.96 more steps per day than the control group (95% CI 327.80 to 1066.12, 95% PI -121.39 to 1515.31). "Feedback" was the most important game design element, followed by "Avatar." CONCLUSIONS: This meta-analysis demonstrates that gamification interventions effectively promote PA in individuals with CVD, with effects persisting beyond the intervention period, indicating they are not merely novel effects caused by the game nature of gamification. The 95% PI suggests that implementing gamification interventions in similar populations in the future will lead to actual effects in promoting PA in the vast majority of cases. However, the limited number of included studies underscores the urgent need for more high-quality research in this emerging field. TRIAL REGISTRATION: PROSPERO CRD42024518795; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=518795.
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.042 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".