Grocery Delivery to Support Individuals With Type 2 Diabetes: Protocol for a Pilot Quality Improvement Program.

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.

Continuous Glucose Monitoring With Low-Carbohydrate Diet Coaching in Adults With Prediabetes: Mixed Methods Pilot Study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is preventable; however, few patients with prediabetes participate in prevention programs. The use of user-friendly continuous glucose monitors (CGMs) with low-carbohydrate diet coaching is a novel strategy to prevent T2DM. OBJECTIVE: This study aims to determine the patient satisfaction and feasibility of an intervention combining CGM use and low-carbohydrate diet coaching in patients with prediabetes to drive dietary behavior change. METHODS: We conducted a mixed methods, single-arm pilot and feasibility study at a suburban family medicine clinic. A total of 15 adults with prediabetes with hemoglobin A1c (HbA1c) levels between 5.7% and 6.4% and a BMI >30 kg/m2 were recruited to participate. The intervention and assessments took place during 3 in-person study visits and 2 qualitative phone interviews (3 weeks and 6 months after the intervention). During visit 1, participants were asked to wear a CGM and complete a food intake and craving log for 10 days. During visit 2, the food intake and craving log along with the CGM results of the participants were reviewed and the participants received low-carbohydrate diet coaching, including learning about carbohydrates and personalized feedback. A second CGM sensor, with the ability to scan and record glucose trends, was placed, and the participants logged their food intake and cravings as they attempted to reduce their total carbohydrate intake (<100 g/day). During visit 3, the participants reviewed their CGM and log data. The primary outcome was satisfaction with the use of CGM and low-carbohydrate diet. The secondary outcomes included feasibility, weight, and HbA1c change, and percentage of time spent in hyperglycemia. Changes in attitudes and risk perception of developing diabetes were also assessed. RESULTS: The overall satisfaction rate of our intervention was 93%. The intervention induced a weight reduction of 1.4 lb (P=.02) and a reduction of HbA1c levels by 0.71% (P<.001) since enrollment. Although not significantly, the percentage of time above glucose goal and average daily glucose levels decreased slightly during the study period. Qualitative interview themes indicated no major barriers to CGM use; the acceptance of a low-carbohydrate diet; and that CGMs helped to visualize the impact of carbohydrates on the body, driving dietary changes. CONCLUSIONS: The use of CGMs and low-carbohydrate diet coaching to drive dietary changes in patients with prediabetes is feasible and acceptable to patients. This novel method merits further exploration, as the preliminary data indicate that combining CGM use with low-carbohydrate diet coaching drives dietary changes, which may ultimately prevent T2DM.

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review.

OBJECTIVE: To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. DESIGN: Systematic review. DATA SOURCE: Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. OUTCOME MEASURES: Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. RESULTS: Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval -0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). CONCLUSIONS: On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

Youth Perspectives of Prescription Pain Medication in the Opioid Crisis.

OBJECTIVE: To characterize current youth perspectives of prescription pain medication. STUDY DESIGN: In total, 1047 youths aged 14-24 years were recruited by targeted social media advertisements to match national demographic benchmarks. Youths were queried by open-ended text message prompts about exposure and access to prescription pain medication, perceived safety of prescribed and nonprescribed medication, and associations with the word "opioid." Responses were analyzed inductively for emerging themes and frequencies. RESULTS: Among 745 respondents (71.2% response rate), 439 identified as female (59.3%), 561 as white (75.8%), and mean age was 18.3 ± 3.2 years. Previous exposure to prescription pain medication was reported by 377 respondents (52.0%), most commonly related to dentistry (32.8%), surgery (19.2%), and injury (12.0%). Nonmedical sources of access to prescription pain medication were identified by 256 respondents (36.9%) and medical sources other than their doctor by an additional 111 respondents (16.0%). Three additional themes emerged from youth responses: (1) prescribed medication was thought to be safer than nonprescribed medication, based on trust in doctors; (2) risks of addiction and overdose were thought to be greater for nonprescribed medication; (3) respondents had a widely ranging understanding of the word "opioid," from historical to current events, medical to illicit substances, and personal to public associations. CONCLUSIONS: Although youths are aware of the opioid crisis, they perceive less risk of prescription pain medication prescribed by a doctor, than from other sources. Policies should target education to youth in clinical and nonclinical settings, highlighting the risks of addiction and overdose with all opioids.