User Experiences of and Preferences for Self-Guided Digital Interventions for the Treatment of Mild to Moderate Eating Disorders: Systematic Review and Metasynthesis
Notice bibliographique
Résumé
BACKGROUND: Digital interventions typically involve using smartphones or PCs to access online or downloadable self-help and may offer a more accessible and convenient option than face-to-face interventions for some people with mild to moderate eating disorders. They have been shown to substantially reduce eating disorder symptoms, but treatment dropout rates are higher than for face-to-face interventions. We need to understand user experiences and preferences for digital interventions to support the design and development of user-centered digital interventions that are engaging and meet users' needs. OBJECTIVE: This study aims to understand user experiences and user preferences for digital interventions that aim to reduce mild to moderate eating disorder symptoms in adults. METHODS: We conducted a metasynthesis of qualitative studies. We searched 6 databases for published and unpublished literature from 2013 to 2024. We searched for studies conducted in naturalistic or outpatient settings, using primarily unguided digital self-help interventions designed to reduce eating disorder symptoms in adults with mild to moderate eating disorders. We conducted a thematic synthesis using line-by-line coding of the results and findings from each study to generate themes. RESULTS: A total of 8 studies were included after screening 3695 search results. Overall, 7 metathemes were identified. The identified metathemes included the appeal of digital interventions, role of digital interventions in treatment, value of support in treatment, communication at the right level, importance of engagement, shaping knowledge to improve eating disorder behaviors, and design of the digital intervention. Users had positive experiences with digital interventions and perceived them as helpful for self-reflection and mindfulness. Users found digital interventions to be convenient and flexible and that they fit with their lifestyle. Overall, users noticed reduced eating disorder thoughts and behaviors. However, digital interventions were not generally perceived as a sufficient treatment that could replace traditional face-to-face treatment. Users have individual needs, so an ideal intervention would offer personalized content and functions. CONCLUSIONS: Users found digital interventions for eating disorders practical and effective but stressed the need for interventions to address the full range of symptoms, severity, and individual needs. Future digital interventions should be cocreated with users and offer more personalization. Further research is needed to determine the appropriate balance of professional and peer support and whether these interventions should serve as the first step in the stepped care model. TRIAL REGISTRATION: PROSPERO CRD42023426932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=426932.
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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 | 0,019 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,015 |
| Bibliométrie | 0,015 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».