Implementation of a Technology-Enabled Diabetes Self-Management Peer Coaching Intervention for Patients With Poorly Controlled Diabetes: Quasi-Experimental Case Study
Bibliographic record
Abstract
Background Patients with diabetes experience worse health outcomes and greater health care expenditure. Improving diabetes outcomes requires involved self-management. Peer coaching programs can help patients engage in self-management while addressing individual and structural barriers. These peer coaching programs can be scaled with digital platforms to efficiently connect patients with peer supporters who can help with diabetes self-management. Objective This study aimed to evaluate the implementation of a technology-enabled peer coaching intervention to support diabetes self-management among patients with uncontrolled diabetes. Methods MetroPlusHealth, a predominant Medicaid health maintenance organization based in New York City, partnered with Pyx Health to enroll 300 Medicaid patients with uncontrolled diabetes into its 6-month peer coaching intervention. Pyx Health peer coaches conduct at least 2 evidence-based and goal-oriented coaching sessions per month with their assigned patients. These sessions are focused on addressing both behavioral and social determinants of health (SDoH) with the goal of helping patients increase their diabetes self-management literacy, implement self-management behaviors, and reduce barriers to ongoing self-care. Data analyzed in this study included patient demographic data, clinical data (patient’s hemoglobin A1c [HbA1c]), and program implementation data including types of behavioral determinants of health and SDoH reported by patients and types of interventions used by peer coaches. Results A total of 330 patients enrolled in the peer mentoring program and 2118 patients were considered to be on a waitlist group and used as a comparator. Patients who enrolled in the peer coaching program were older; more likely to be English speakers, female, and African American; and less likely to be White or Asian American or Pacific Islander than those in the waitlist condition, and had similar HbA1c laboratory results at baseline (intervention group 10.59 vs waitlist condition 10.62) Patients in the enrolled group had on average a –1.37 point reduction in the HbA1c score (n=70; pre: 10.99, post 9.62; P<.001), whereas patients in the waitlist group had a –0.16 reduction in the HbA1c score (n=207; pre 9.75, post 9.49; P<.001). Among a subsample of participants enrolled in the program with at least 2 HbA1c scores, we found that endorsement of emotional health issues (β=1.344; P=.04) and medication issues (β=1.36; P=.04) were significantly related to increases in HbA1c. Conclusions This analysis of a technology-enabled 1-on-1 peer coaching program showed improved HbA1c levels for program participants relative to nonprogram participants. Results suggested participants with emotional stressors and medication management issues had worse outcomes and many preferred to connect through phone calls versus an app. These findings support the effectiveness of digital programs with multimodal approaches that include human support for improving diabetes self-management in a typically marginalized population with significant SDoH barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".