Motivation-based approach for tailoring persuasive mental health applications
Why this work is in the frame
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Bibliographic record
Abstract
The growing number of people with mental health issues and the worldwide shortage of professionals emphasise the need for tailored persuasive interventions. This paper explores the relationships between the types of motivation individuals experience and their preferences for various features that are widely used in persuasive apps for mental and emotional well-being. First, we reviewed 103 mental health apps from app stores and identified various persuasive features and then conducted focus-group studies of 32 participants. Finally, we implemented the common features in persuasive mental health app prototypes and conducted a large-scale study of 561 users to evaluate their perceived effectiveness. The results reveal that people’s motivation types significantly influence the perceived persuasiveness of different features. People high in intrinsic motivation are more motivated by apps that offer relaxation exercises while providing opportunities to track various mental health-related information. We offer design guidelines for tailoring persuasive mental health apps based on motivation types.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it