Personalized Interactive Music Systems for Physical Activity and Exercise: Exploratory Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Personalized Interactive Music Systems (PIMSs) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMSs align musical features, such as tempo and genre, with users' physical activity patterns, including frequency and intensity, enhancing their overall experience. Objective: This exploratory systematic review and meta-analysis evaluates the effectiveness of PIMSs across physical, psychophysical, and affective domains. Methods: Searches across 9 databases identified 18 eligible studies, of which 6 (comprising 17 intervention arms) contained sufficient data for meta-analysis. Random-effects meta-analyses and meta-regression were performed to assess outcomes for physical activity levels, physical exertion, ratings of perceived exertion, and affective valence. Results: Results showed significant improvements in physical activity levels (g=0.49, CI 0.07 to 0.91, P=.02, k=4) and affective valence (g=1.65, CI 0.35 to 2.96, P=.01, k=4), with faster music tempo identified as a significant moderator (P=.03). No significant effects were observed for ratings of perceived exertion (g=0.72, CI -0.13 to 1.58, P=.10, k=3) or physical exertion (g=0.78, CI -0.55 to 2.11, P=.25, k=5). Conclusions: Substantial heterogeneity and limited study quality indicate the need for more robust, randomized controlled trials to establish the efficacy of PIMSs in diverse populations.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".