Design, development, and evaluation of an mHealth app to reduce stress and promote happiness through smiling
Bibliographic record
Abstract
The field of mental health application research is growing, yet comprehensive, long-term studies validating claims of stress reduction and mood enhancement are limited, with many apps lacking empirical evidence. The purpose of this study was to evaluate an mHealth application called SmileApp to promote positive mood as a means of reducing stress. The design of SmileApp is grounded in psychological theories and integrates artificial intelligence (AI) and persuasive technology (PT). To evaluate SmileApp, we conducted a two-week in-the-wild study involving 72 participants. This was followed by an optional semi-structured interview with 23 participants. Quantitative results suggest that SmileApp is usable, useful, and encourages users to smile more frequently. Furthermore, qualitative results suggest that SmileApp was a unique design to help users alleviate stress. These results offer valuable insights into innovative approaches for designing mHealth applications that promote positive mood. Moreover, the findings underscore the importance of utilising technology to support emotional well-being. We present a novel approach to promote desired behaviours by motivating users to read supportive messages and playing mobile games through the act of smiling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".