TreeCare: Development and Evaluation of a Persuasive Mobile Game for Promoting Physical Activity
Bibliographic record
Abstract
Increased physical activity has been shown to reduce morbidity and mortality among adults. Over the years, mobile apps have been developed to encourage people to engage in physical activity, such as walking or running, by employing various persuasive strategies. However, the choice of these strategies is often based on designers' intuition without knowing if the strategies will be effective for target audience and the target behaviour. To address this gap, we conduct a study with 103 adults to assess the perceived effectiveness of 12 widely used strategies in health games design. The strategies are based on the Persuasive Systems Design (PSD) framework. Our results reveal that the strategies are effective for promoting physical activity at varying degrees. These results inform the development of the game, called TreeCare. Next, we conduct a 3-week field study involving 23 target users to evaluate the game in terms of effectiveness and usability. Our results show that TreeCare significantly improved users' physical activity levels. In addition, the game is found to be easy to use, engaging, aesthetically pleasing, and enjoyable. We reflect on our findings and offer practical guidelines to inform the design of effective and usable persuasive applications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".