Grocery Delivery to Support Individuals With Type 2 Diabetes: Protocol for a Pilot Quality Improvement Program
Notice bibliographique
Résumé
Background People with low income are disproportionately affected by type 2 diabetes (T2D), and 17.6% of US adults with T2D experience food insecurity and low diet quality. Low-carbohydrate eating plans can improve glycemic control, promote weight loss, and are associated with improved cardiometabolic health and all-cause mortality. Little is known about supporting low-carbohydrate eating for people with T2D, although food-as-medicine interventions paired with nutrition education offer a promising solution. Objective This program aims to support the initiation of dietary changes by using grocery delivery and low-carbohydrate education to increase the quality of low-carbohydrate nutrition among people with T2D and food insecurity. Methods This program was a nonrandomized pilot conducted at 21 primary care practices in Michigan. Adults with T2D and food insecurity or low income were eligible to enroll. Patients were referred by primary care clinic staff. All participants received the 3-month program, which included monthly US $80 credits for healthy foods, free grocery delivery from Shipt, and low-carbohydrate nutrition education. Food credits were restricted to the purchase of healthy foods. Education materials, developed in collaboration with providers and patients, included print, digital, interactive web, and video formats. At enrollment, participants completed a survey including demographics, diabetes health, diet and physical activity, and diabetes management and knowledge. After the 3-month program, participants completed a survey with repeat assessments of diabetes health, diet and physical activity, and diabetes management and knowledge. Perspectives on participant experience and perceived program impact, food purchasing behaviors, and use of educational materials were also collected. Diabetes health information was supplemented with data from participant medical records. We plan to perform mixed methods analysis to assess program feasibility, acceptability, and impact. Primary quality improvement (QI) measures are the number of patients referred and enrolled, use of US $80 food credits, analysis of food purchasing behavior, participant experience with the program, and program costs. Secondary QI measures include changes in hemoglobin A1c, weight, medications, self-efficacy, diabetes and carbohydrate knowledge, and activity between baseline and follow-up. Results This program started in October 2022. Data collection is expected to be concluded in June 2024. A total of 151 patients were referred to the program, and 83 (55%) were enrolled. The average age was 57 (SD 13; range 18-86) years, 72% (57/79) were female, 90% (70/78) were White, and 96% (74/77) were of non-Hispanic ethnicity. All participants successfully ordered grocery delivery during the program. Conclusions This pilot QI program aimed to improve diet quality among people with T2D and food insecurity by using grocery delivery and low-carbohydrate nutrition education. Our findings may help inform the implementation of future QI programs and research studies on food-as-medicine interventions that include grocery delivery and education for people with T2D. International Registered Report Identifier (IRRID) DERR1-10.2196/54043
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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 | 0,033 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,008 |
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 ».