A Technology-Enhanced Medical Nutrition Therapy and Diabetes Self-Management Education for Adults With Disability and Type 2 Diabetes: Protocol for a Pilot and Feasibility Randomized Controlled Trial
Notice bibliographique
Résumé
Background Diabetes mellitus (DM) is a serious chronic disorder that affects many individuals globally, particularly persons with disabilities, and has long-term adverse effects on the health of individuals and society. Effective self-management education is therefore required. Diabetes management focused on medical nutrition therapy (MNT) and diabetes self-management education (DSME) combined with telehealth technology has the potential to increase the active performance of diabetes management behaviors among persons with disabilities and improve their overall quality of life and quality of self-care. Objective This study aims to evaluate the impact of different levels of technology on the delivery of MNT and DSME among persons with disabilities. Methods The study is a single-blinded, 3-arm, randomized controlled trial among adults living with both type 2 diabetes and a permanent physical disability. Web-based recruitment is done through partner organizations. The target sample size is 90 participants randomized into 3 arms: a high-technology, a low-technology, and an attention control arm. The high-technology arm receives diabetes-related materials weekly through mediums such as email, a telehealth platform, and text; the low-technology arm receives only 1 weekly email with diabetes-related material; and the attention control arm has no technology support. The intervention is provided by a certified diabetes care and education specialist. Using multivariate linear mixed models, the study examines the relationships between the level of technology intervention and DM self-management behaviors, self-efficacy, and reductions in glycated hemoglobin (HbA1c). The primary outcome is the proportion of participants in each group with improved self-management behaviors, as measured by several validated questionnaires. The secondary outcome is a better HbA1c reduction. Outcomes are measured at baseline and at 6 months. Questionnaires and HbA1c measures will be used to measure outcomes. Results Data collection began in June 2024 with a total of 90 recruited participants. The intervention was delivered. Make-up classes were delivered to participants in any of the 3 cohorts between November 2024 and December 2024. The final 3-month follow-up classes were held for each cohort 3 months after the first class. Data analysis is anticipated to be completed in fall 2025. Conclusions Effective self-management in DM is important to reduce complications. Using technology to deliver MNT and DSME could serve as an effective and convenient strategy for providing these interventions. However, intervention studies are required to determine the most effective level of technology for delivering MNT and DSME intervention to this target group. The YumABLE study is expected to provide new, meaningful, and detailed information about the effectiveness of technology and telehealth platforms for effectively delivering an MNT and DSME program for people living with a permanent physical disability and type 2 diabetes. The results will further improve web- and technology-based diabetes self-management interventions for people with disabilities. Trial Registration ClinicalTrials.gov NCT06049225; https://clinicaltrials.gov/study/NCT06049225 International Registered Report Identifier (IRRID) DERR1-10.2196/71495
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 | 0,030 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,005 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,010 |
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 ».