Gamification in Stress Management Apps: A Critical App Review
Bibliographic record
Abstract
BACKGROUND: In today's society, stress is more and more often a cause of disease. This makes stress management an important target of behavior change programs. Gamification has been suggested as one way to support health behavior change. However, it remains unclear to which extend available gamification techniques are integrated in stress management apps, and if their occurrence is linked to the use of elements from behavior change theory. OBJECTIVE: The aim of this study was to investigate the use of gamification techniques in stress management apps and the cooccurrence of these techniques with evidence-based stress management methods and behavior change techniques. METHODS: A total of 62 stress management apps from the Google Play Store were reviewed on their inclusion of 17 gamification techniques, 15 stress management methods, and 26 behavior change techniques. For this purpose, an extended taxonomy of gamification techniques was constructed and applied by 2 trained, independent raters. RESULTS: Interrater-reliability was high, with agreement coefficient (AC)=.97. Results show an average of 0.5 gamification techniques for the tested apps and reveal no correlations between the use of gamification techniques and behavior change techniques (r=.17, P=.20), or stress management methods (r=.14, P=.26). CONCLUSIONS: This leads to the conclusion that designers of stress management apps do not use gamification techniques to influence the user's behaviors and reactions. Moreover, app designers do not exploit the potential of combining gamification techniques with behavior change theory.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".