Acceptability and Effectiveness of a Fully Web-Based Nutrition and Exercise Program for Individuals With Chronic Disease During COVID-19: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: In-person nutrition and exercise interventions improve physical function in chronic diseases, yet the acceptability and effectiveness of web-based delivery, especially with different levels of personnel support, require further investigation. OBJECTIVE: This study aims to evaluate a web-based nutrition and exercise intervention delivered entirely digitally from recruitment to trial completion. METHODS: A randomized controlled trial was conducted using the Heal-Me version 1 platform across 2 levels of personnel support (Light and Intensive). Eligible adults with a history of cancer, chronic lung disease, or liver or lung transplant; internet access; and prior participation in a rehabilitation program were enrolled in a fully web-based program to minimize barriers to exercise participation. Participants were randomly assigned (1:1:1) to 1 of 3 study groups. The control group received a detailed, self-directed digital nutrition and exercise guide. The Heal-Me Light group received the web-based intervention alongside dietitian and exercise specialist-led group classes. The Heal-Me Intensive group received web-based intervention, group classes, and one-to-one sessions with the dietitians and exercise specialists. All participants received a wearable activity tracker. The primary acceptability outcome was adherence to the intervention based on a priori targets. The primary effectiveness outcome was the change in Lower Extremity Functional Scale (LEFS) score. Secondary outcomes included physical function tests, which were performed and measured by videoconference. Questionnaires were used to assess well-being, quality of life, and food intake. Analyses adhered to the intention-to-treat principle. RESULTS: Of 216 participants, 202 (93.5%) completed the intervention (mean 61, SD 11 years; female: 130/202, 64.4%; cancer: 126/202, 62.4%). Adherence exceeded a priori targets, with 82% (105/128) attending >75% of the program elements including postintervention tests. Participants rated the program as "quite a bit" or "very" useful, with similar ratings between Heal-Me Light (56/64, 88%) and Heal-Me Intensive (51/58, 88%) groups (P=.69). No significant differences were found for changes in LEFS scores (control: mean 0.8, SD 7.7; Heal-Me: mean 0.3, SD 6.6; P=.53). Significant benefits were found in favor of the combined Heal-Me intervention groups versus controls for change in the 2-minute step test, World Health Organization-5 Well-Being Index, Short-Form-36 general, physical health role, energy or fatigue scales, and protein intake. While the change in physical function was similar between the 2 intervention arms, the more intensive one-to-one interaction (Heal-Me Intensive) led to greater improvements in perceived nutrition self-management. No serious adverse events occurred. CONCLUSIONS: The demonstrated satisfaction, adherence, and effectiveness highlight the high acceptability of a web-based, semisupervised nutrition and exercise intervention delivered entirely digitally in individuals with chronic disease. Future studies may benefit from having a baseline physical function inclusion threshold, the use of a more sensitive primary physical function measure, and a higher intensity digital exercise intervention in exercise-experienced participants. TRIAL REGISTRATION: Clinicaltrials.gov NCT04666558; https://clinicaltrials.gov/study/NCT04666558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2022.106791.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».