Understanding Patients’ Preferences for a Digital Intervention to Prevent Posttreatment Deterioration for Bulimia-Spectrum Eating Disorders: User-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Deterioration rates after enhanced cognitive behavioral therapy (CBT-E) for patients with bulimia-spectrum eating disorders (BN-EDs) remain high, and decreased posttreatment skill use might be a particularly relevant contributor. Digital interventions could be an ideal option to improve skill use after treatment ends but they have yet to be investigated for BN-EDs. OBJECTIVE: This study used a user-centered design approach to explore patients' interest in a digital intervention to prevent deterioration after CBT-E and their desired features. METHODS: A total of 12 participants who previously received CBT-E for BN-EDs and experienced at least a partial response to treatment completed a qualitative interview asking about their interests and needs for an app designed to prevent deterioration after treatment ended. Participants were also presented with features commonly used in digital interventions for EDs and were asked to provide feedback. RESULTS: All 12 participants expressed interest in using an app to prevent deterioration after treatment ended. In total, 11 participants thought the proposed feature of setting a goal focusing on skill use weekly would help improve self-accountability for skill use, and 6 participants supported the idea of setting goals related to specific triggers because they would know what skills to use in high-risk situations. A total of 10 participants supported the self-monitoring ED behaviors feature because it could increase their awareness levels. Participants also reported wanting to track mood (n=6) and food intake (n=5) besides the proposed tracking feature. A total of 10 participants reported wanting knowledge-based content in the app, including instructions on skill practice (n=6), general mental health strategies outside of EDs (n=4), guided mindfulness exercises (n=3), and nutrition recommendations (n=3). Eight participants reported a desire for the app to send targeted push notifications, including reminders of skill use (n=7) and inspirational quotes for encouragement (n=3). Finally, 8 participants reported wanting a human connection in the app, 6 participants wishing to interact with other users to support and learn from each other, and 4 participants wanting to connect with professionals as needed. Overall, participants thought that having an app targeting skill use could provide continued support and improve self-accountability, thus lowering the risk of decreased skill use after treatment ended. CONCLUSIONS: Insights from participants highlighted the perceived importance of continued support for continued skill use after treatment ended. This study also provided valuable design implications regarding potential features focusing on facilitating posttreatment skill use to include in digital deterioration prevention programs. Future research should examine the optimal approaches to deliver the core features identified in this study that could lead to higher continued skill use and a lower risk of deterioration in the long term.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 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".