Digitally Delivered Dietary Interventions for Patients with Eating Disorders Undergoing Family-Based Treatment: Protocol for a Randomized Feasibility Trial
Notice bibliographique
Résumé
BACKGROUND: Eating disorders (EDs) affect 9% of the United States population, and anorexia nervosa (AN), specifically, has the second highest mortality rate of all psychiatric disorders. Yet, only 20% are able to access treatment. Access to care issues include long waitlists, lack of trained specialists, financial, and geographic barriers, all of which highlight the need for effective telehealth interventions. Family-based therapy (FBT) is a first-line treatment for adolescents and young adults with EDs, and weight gain early in treatment is considered a primary predictor of success with FBT. However, nutrition requirements for patients with EDs are uniquely complex. A variety of dietary interventions for guiding the renourishment process are used in practice, but empirical data on the effectiveness and acceptability of the various interventions are sparse. The significance of nutritional restoration and issues with access to first-line treatments underscore the need for further research exploring virtually delivered dietary interventions. OBJECTIVE: Our objective is to compare the effectiveness and acceptability of 2 digitally delivered dietary interventions frequently used in eating disorder treatment settings: (1) calorie-based meal plans and (2) the Plate-by-Plate approach. Specifically, we will explore any potential differences in weight restoration achieved over 8 weeks of treatment as a primary measure of effectiveness, as well as additional treatment outcomes (ED symptoms, anxiety, depression, caregiver burden, and perceived effectiveness and acceptability for both caregivers and clinicians). METHODS: Patients (N=100) with either AN or avoidant restrictive food intake disorders (ARFID) aged 6-24 years seeking treatment at a nationwide virtual eating disorder treatment program, were enrolled between May and August 2022. Upon admission, patients were randomly assigned to receive either the calorie-based intervention or Plate-by-Plate approach from their registered dietitian, all of whom have received training as study interventionists. While we were primarily interested in responses during the first 8 weeks of treatment, patients will be followed for up to 12 months. Descriptive statistics were used to describe patient characteristics and demographics. Weight changes and other treatment outcomes between groups will be compared using generalized linear models. Semistructured caregiver and clinician interview transcripts will undergo qualitative analysis. RESULTS: Enrollment ran from March to August 2022, and we anticipate completion of data collection by November 2022. Analyses will be completed in January 2023. CONCLUSIONS: This study contributes to existing FBT literature by thoroughly exploring the acceptability of dietary interventions and their influence on weight restoration, an area in which research is sparse. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41837.
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 | 0,036 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,005 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,087 | 0,015 |
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 ».