Understanding People With Chronic Pain Who Use a Cognitive Behavioral Therapy–Based Artificial Intelligence Mental Health App (Wysa): Mixed Methods Retrospective Observational Study
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Bibliographic record
Abstract
BACKGROUND: Digital health interventions can bridge barriers in access to treatment among individuals with chronic pain. OBJECTIVE: This study aimed to evaluate the perceived needs, engagement, and effectiveness of the mental health app Wysa with regard to mental health outcomes among real-world users who reported chronic pain and engaged with the app for support. METHODS: Real-world data from users (N=2194) who reported chronic pain and associated health conditions in their conversations with the mental health app were examined using a mixed methods retrospective observational study. An inductive thematic analysis was used to analyze the conversational data of users with chronic pain to assess perceived needs, along with comparative macro-analyses of conversational flows to capture engagement within the app. Additionally, the scores from a subset of users who completed a set of pre-post assessment questionnaires, namely Patient Health Questionnaire-9 (PHQ-9) (n=69) and Generalized Anxiety Disorder Assessment-7 (GAD-7) (n=57), were examined to evaluate the effectiveness of Wysa in providing support for mental health concerns among those managing chronic pain. RESULTS: The themes emerging from the conversations of users with chronic pain included health concerns, socioeconomic concerns, and pain management concerns. Findings from the quantitative analysis indicated that users with chronic pain showed significantly greater app engagement (P<.001) than users without chronic pain, with a large effect size (Vargha and Delaney A=0.76-0.80). Furthermore, users with pre-post assessments during the study period were found to have significant improvements in group means for both PHQ-9 and GAD-7 symptom scores, with a medium effect size (Cohen d=0.60-0.61). CONCLUSIONS: The findings indicate that users look for tools that can help them address their concerns related to mental health, pain management, and sleep issues. The study findings also indicate the breadth of the needs of users with chronic pain and the lack of support structures, and suggest that Wysa can provide effective support to bridge the gap.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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