Effectiveness of Gamification on Enjoyment and Satisfaction in Older Adults: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Sedentary behavior is highly prevalent among older adults, with adherence to exercise being a major challenge. Exercise offers substantial physical, psychological, and social benefits, but enjoyment is a key factor influencing adherence. Technology-based interventions have shown promise in enhancing motivation and participation, demonstrating higher adherence rates than conventional treatments, although challenges such as motivation loss and technological barriers persist. This review evaluates the effectiveness of active video game interventions on enjoyment and satisfaction in older adults. Objective: This systematic review and meta-analysis aims to determine whether active video games are superior to other interventions in generating greater enjoyment or satisfaction in older adults. Methods: PubMed, Cochrane, PEDro, SPORTDiscus, CINAHL, Web of Science, and Scopus databases were searched from inception to September 30, 2024, to identify randomized clinical trials or crossover studies. The primary outcome was enjoyment or satisfaction, assessed using various scales, including the Physical Activity Enjoyment Scale, Intrinsic Motivation Inventory, User Satisfaction Questionnaire, and Likert-type scoring scales. Secondary outcomes included adherence rates and adverse effects. Cochrane Risk of Bias 2 tool was used to evaluate the risk of bias. Results: Five studies were included in the quantitative analysis. The results indicated a significant improvement in enjoyment or satisfaction compared to the control groups (standardized mean difference 0.34, 95% CI 0.05-0.64; P=.02; I2=24%), although the effect size was small. Secondary outcomes could not be analyzed due to insufficient data in the selected studies. Conclusions: Active video game interventions may improve enjoyment and satisfaction in older adults, but the evidence remains of low certainty.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".