Behavior Change Techniques Within Digital Interventions for the Treatment of Eating Disorders: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Background Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design. Objective This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included. Methods A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes. Results Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results. Conclusions There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions. Trial Registration PROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,023 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,026 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».