Utilizing Gamification Approaches in Pervasive Health: How Can We Motivate Physical Activity Effectively?
Bibliographic record
Abstract
Persuasive health systems such as wearable trackers and mobile applications can facilitate self-reflection on one’s physical activity. The gamification approach incorporates game design elements with persuasive systems to encourage more physical activity. However, some investigations have shown that using gamification to promote physical activity could have contradictory effects. To explore the conflicted findings in more detail, we designed and studied FitPet – an interactive virtual pet-keeping mobile game focused on encouraging physical activity. In a six-week field study, its effectiveness was evaluated and compared with two other gamification strategies, the goal-setting strategy and the use of social communities. Findings are that the social interaction strategy was the most effective intervention among these three. Contrary to prior research, goal-setting was not found to be as effective at providing motivation compared to social interaction. Although FitPet failed to promote significantly higher levels of physical activity, participants enjoyed this approach and provided design insights for future research: implementing social components and more challenging gameplay.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".