An Image-Recognition Dietary Assessment App for Adolescents With Obesity: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Adolescence is a period of increased susceptibility to developing obesity-related health issues due to poor eating patterns and increased sedentary behaviors. Recommendations for pediatric obesity management include dietary assessments. However, adolescents often avoid food logging through traditional methods. The use of image-recognition dietary assessment apps in adolescents with obesity is not well studied. Eating for Wellness (E4W) is a mobile app that determines the nutritional content of meals from photos and incorporates nutritional goal setting. Nutritional data can be displayed for health care providers (HCPs) via the Clinician Portal, while the data are presented to the user in a manner that minimizes the focus on calorie counting. OBJECTIVE: This study aims to evaluate the usability and feasibility of E4W, a mobile health app designed to improve dietary intake in adolescents with obesity attending an obesity clinic, using a phased approach. METHODS: The overall study was conducted in 2 phases to refine and evaluate E4W. In Phase 1, usability was tested through 3 iterative cycles of patient interviews. A total of 14 patient participants, aged 12-18 years with a BMI≥97th percentile, were included. Participants performed standardized scenario-based tasks in E4W and provided feedback on the app. Two iterative cycles were conducted for HCPs (n=4). Refinements were made during each cycle based on issues encountered and feedback provided. In Phase 2, a pilot randomized controlled trial of 32 adolescents (16 adolescents enrolled in the experimental group for 1 month, and 16 controls enrolled for 1 month) was completed. Both groups met with their dietitian at baseline, midstudy, and 1 month following their baseline visit to discuss goals and eating patterns. The control group was instructed to take photos of all intake using their default phone camera, without access to E4W, while those in the experimental group received full access to E4W. The primary outcome was the feasibility of implementation. Secondary outcomes examined overall change in dietary intake and achievement of nutritional goals. RESULTS: Usability testing demonstrated that E4W and the Clinician Portal were easy to use, efficient, and well-liked by patients and HCPs. Feasibility testing revealed high patient acceptability scores. However, significant technical challenges were encountered. Although the use of E4W did not significantly impact patient engagement (control: mean 0.9, SD 0.7; experimental: mean 1.7, SD 1.9; P=.14), there were outliers in the experimental group with very high engagement and improved self-reported efficacy. Overall, there was no improvement in dietary intake, although assessment was hindered by poor adherence to traditional methods of food logging. CONCLUSIONS: E4W and the Clinician Portal were well-received by patients and HCPs. Further research is warranted and planned to determine if E4W can improve dietary intake and achievement of nutritional goals in adolescents with obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05548868; https://clinicaltrials.gov/study/NCT05548868.
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 | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».