A Chatbot-Based Version of the World Health Organization–Validated Self-Help Plus Intervention for Stress Management: Co-Design and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Advancements in technology offer new opportunities to support vulnerable populations, such as pregnant women and women diagnosed with breast cancer, during physiologically and psychologically stressful periods. OBJECTIVE: This study aims to adapt and co-design the World Health Organization's Self-Help Plus intervention into a mobile health intervention for these target groups. METHODS: On the basis of the Obesity-Related Behavioral Intervention Trials and Center for eHealth Research and Disease Management models, low-fidelity and high-fidelity prototypes were developed. Prototypes were evaluated by 13 domain experts from diverse sectors and 15 participants from the target groups to assess usability, attractiveness, and functionality through semantic differential scales, the User Version of the Mobile Application Rating Scale questionnaire, and semistructured interviews. RESULTS: Feedback from participants indicated positive perceptions of the mobile health intervention, highlighting its ease of use, appropriate language, and attractive multimedia content. Areas identified for improvement included enhancing user engagement through reminders, monitoring features, and increased personalization. The quality of the content and adherence to initial protocols were positively evaluated. CONCLUSIONS: This research provides valuable insights for future studies aiming to enhance the usability, efficacy, and effectiveness of the app, suggesting the potential role of a chatbot-delivered Self-Help Plus intervention as a supportive tool for pregnant women and women with a breast cancer diagnosis.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".