Targeted Diabetes Education Text Messaging Program Increases Requests for Certified Diabetes Educator Coaching and Improves Blood Glucose Trends
Bibliographic record
Abstract
Background: Livongo Diabetes Program offers a cellularly enabled blood glucose monitoring system that measures blood glucose, captures contextual data (eg, relationship to food, exercise, illness), and stores this data in the cloud. Depending on the blood glucose value, personalized recommendations are delivered back through the glucose meter. Livongo members receive an unlimited supply of glucose test strips as well as access to a diabetes coaching team for questions, goal setting, and support for extreme glucose excursions. We have previously reported that members who establish contact with Livongo coaches experience an HbA1c reduction of 0.7% on average after 90 days with the program. We hypothesized that a targeted, text-message campaign designed to provide education about taking diabetes medications would encourage members to connect with Certified Diabetes Educator (CDE) coaches available to them by phone, email, or text. Objective: To determine whether diabetes medication education offered to a targeted diabetes population via text messages will increase requests for telephonic coaching. Methods: We examined a 4-week text message program offered to Livongo members with a calculated or self-reported HbA1c>7% to provide education about medication adherence. A total of 20 text messages were delivered during the weekdays over one month. Weekly topics included (1) Why are medications important? (2) Tips for remembering to take medications, (3) Medication myths, and (4) Overcoming barriers to taking medications. Results: Out of the 2017 members offered the program, 514 (25%) opted into the program and 21 (1%) members opted out. Text messaging content triggered 38 personalized CDE coaching session requests, a rate of 7.4% of participating members, which is 85% more than the rate for members who did not participate in text message program (4%). Conclusions: These preliminary findings suggest that engaging people with diabetes through a cellular-enabled blood glucose meter with real-time, personalized education in a targeted and personalized manner helps connect members with CDE coaches and may improve blood glucose control.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".