Grocery Delivery to Support Individuals With Type 2 Diabetes: Protocol for a Pilot Quality Improvement Program
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".