Real-world patient outcomes for telehealth-delivered, remote eating disorder treatment: a scoping review
Notice bibliographique
Résumé
Only 30% of individuals with eating disorders receive specialized treatment. While preliminary evidence suggests that telehealth-delivered, remote eating disorder treatment may offer improved accessibility with similar effectiveness to in-person treatment, research on these services remains limited, particularly regarding the communities that are disproportionately affected by barriers to standard care. This scoping review sought to map the existing research on real-world patient outcomes in remote eating disorder treatment, identify knowledge gaps, and prioritize areas for future studies. This review followed the Joanna Briggs Institute methodology for scoping reviews. It comprises observational evaluations of telehealth-delivered, remote eating disorder treatment conducted in routine clinical settings. An electronic database search was performed in PsycINFO, PubMed, and ProQuest Dissertations & Theses Global in August 2024 and updated in September 2025. Following the search and screening process, 27 articles, comprising six case reports and 21 cohort/case series designs, were deemed eligible for inclusion. Remote treatments evaluated differed across level of care, therapeutic modalities, provider types, dosage, and adjunctive technologies used. Just under half of the studies compared outcomes from remote and in-person treatment, while the remainder examined remote treatment alone. Articles were published between 2011 and 2025 and, when excluding case reports, nearly 60% evaluated programs that rapidly transitioned to remote delivery due to COVID-19. While demographic reporting was limited and inconsistent, available information indicated that participants ranged from three to 75 years old and were predominantly White, cisgender women/females diagnosed with anorexia nervosa. Though preliminary, findings tentatively suggest that remote eating disorder treatment can yield improvements across core outcome domains, largely comparable to in-person settings. Less is known about how outcomes may differ across demographic groups. Overall, this body of literature remains small and characterized by limitations and inconsistencies, including differences in the treatment services evaluated as well as disparities in study design, methodology, and reporting. Utilization of remote treatment by historically excluded groups remains low, calling for further reflection around its accessibility for target communities. Additional studies with more rigorous, intentional designs are needed. The field would also benefit from standardization in relation to data collection and reporting to allow for better synthesis of findings. Remote eating disorder treatment (i.e., telehealth) may help improve access to care, especially for groups like racial and ethnic minorities who often face additional barriers, such as stigma. Research on patient outcomes in remote eating disorder services delivered in real-world clinical settings is limited, especially in relation to these historically underrepresented groups. This scoping review mapped the existing research to identify gaps and prioritize directions for future studies. Twenty-seven articles from 2011 to 2025 were included in the review. Many studies evaluated programs that quickly switched to remote care because of COVID-19. Overall, the treatment services evaluated were quite varied, studies had limitations related to design and methodology, and there were inconsistencies in how things were reported, making it difficult to combine findings and draw conclusions. Tentatively, results suggest that remote eating disorder treatment can be effective, however this conclusion should be interpreted with caution given the inconsistencies and limitations identified, including a lack of diversity in study participants which limits generalizability. Additional high-quality research is needed to confirm these findings. More consistency in what data are collected and how data are reported would allow for better interpretation of results across studies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».