Exploring the Role of App Features in Providing Continuity of Care to Users on a Digital Mental Health Platform (Wysa): Retrospective Mixed Methods Observational Study
Bibliographic record
Abstract
Background: Despite digital mental health services growing at a rapid pace to address global mental health needs, there exist challenges of low engagement and attrition. Ensuring continuity of care in the digital context can positively impact mental health care delivery and adherence to treatment, helping to establish digital mental health interventions (DMHIs) as a viable option for mental health support. Objective: This study aimed to examine the impact of adjunct app features of the mental health app Wysa and their ability to promote engagement and adherence to the text-based coaching sessions. Methods: This retrospective mixed methods observational study was based on real-world app data from users (n=1213) who subscribed to text-based sessions with mental health coaches (MHCs) between February 1 and July 31, 2022. Their engagement with the adjunct app features, such as brief interventions with the conversational agent, self-management tools, and journaling, was analyzed quantitatively using descriptive statistics. Acceptability of the app features was also assessed using qualitative feedback data. Adherence to sessions with MHCs was compared between app feature users (n=1042, 85.9%) and nonfeature users (n=171, 14.1%) using inferential statistics. Subgroup analysis was not feasible in the absence of demographic and clinical user data, potentially limiting the generalizability of the findings. Results: Findings demonstrated high use of the adjunct app features, which allowed communication with the MHCs in between sessions. The thematic analysis captures user experiences of helpfulness within the app and with the MHCs. The Mann-Whitney U test indicated that users who accessed one or more features completed significantly more sessions compared with users who did not use any feature (Mann-Whitney U=154,085.0; P<.001; rB=0.73) with a large effect size. The odds ratio analysis indicated that users were almost thrice as likely to complete sessions after using the adjunct app features (odds ratio 2.91, 95% CI 2.24-3.38; P<.001). Conclusions: Inclusion of adjunct app features enhances continuity in care delivery between sessions with MHCs and is associated with improved engagement with DMHIs. Further efforts are needed to assess the impact of this approach in DMHIs on clinical mental health outcomes.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".