Understanding Patients’ Preferences for a Digital Intervention to Prevent Posttreatment Deterioration for Bulimia-Spectrum Eating Disorders: User-Centered Design Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».