Persuasive Games for Physical Activity in App Stores: A Systematic Review
Bibliographic record
Abstract
Persuasive games (PGs) are effective at motivating desired behaviour change using various strategies. Over the years, increasing research attention has been focused on developing PGs targeted at motivating behaviour change in various domains, including physical activity. This paper presents a systematic review of 45 PGs from app stores with the aim to: (1) deconstruct and highlight trends with respect to the persuasive strategies employed and the various ways the strategies were implemented; (2) examine for relationships between the strategies employed and games rating - effectiveness, (3) understand the effective genres for physical activity PGs and (3) uncover pitfalls of existing PGs. Our review showed that the liking strategy, which deals with making games visually attractive, is the most implemented persuasive strategy in physical activity PGs existing on app stores. We revealed that the three most popular genres for physical activity PGs are casual, simulation, and adventure. We found a negative correlation between the number of persuasive strategies employed and game effectiveness (app average rating) for PGs reviewed. Based on our findings, we offer suggestions for developing physical activity PGs that can effectively promote behaviour change.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".