Text Message Responsiveness to BG Monitoring (BGM) Reminders Improves A1c in Teens with Type 1 Diabetes (T1D)
Bibliographic record
Abstract
Background: As teens with T1D become increasingly independent in self-care, adherence and glycemic control deteriorate. Teens need innovative ways to improve self-care and protect against glycemic decline. We evaluated a text messaging intervention in teens with T1D and assessed factors associated with text responsiveness and glycemic benefit over 18 months. Methods: Teens with T1D (N=147), ages 13-17 years, received 2-way text reminders at self-selected times to check BG levels and reply by text with BG results. Teens received increasing numbers of text reminders, beginning with 1 and increasing to a maximum of 4/day. Results: At baseline, teens (48% male, 78% white, 63% pump-treated) had mean±SD age 14.9±1.3 years, T1D duration 7.1±3.9 years, and A1c 8.5±1.1%. The mean proportion of days with 1+ BG response declined over time (months 0-6: 60±26% of days, months 6-12: 53±32%, months 12-18: 43±33%). Over the 18 month study, 50% of teens responded with 1+ BG result on ≥50% of days (“High Responders”). High Responders compared with “Low Responders” (<50% of days with 1+ BG response) were similar regarding age, T1D duration, and sex distribution but High Responders had lower baseline A1c (8.2±1.0 vs. 8.7±1.1%, p=.005) and higher daily BGM frequency (5.2±2.3 vs. 4.3±1.6, p=.01). Regression analysis controlling for baseline A1c revealed no significant change in A1c from baseline to 18 months in High Responders (p=.42) compared with a significant A1c increase in Low Responders (+0.3%, p=.009). In teens with baseline A1c ≥8%, High Responders (n=39) were 2.6 (95% CI 1.03, 6.6) times more likely than Low Responders (n=52) to improve A1c by ≥0.5% from baseline to 18 months (p=.04). Conclusions: Responding to text reminders on ≥50% of days over 18 months provided clinically significant glycemic benefit to teens with T1D, especially in those with high baseline A1c. There remains a need to tailor interventions for teens over time to maintain their engagement and optimize improvements. Disclosure D.E. McGill: None. L. Volkening: None. D.A. Butler: None. W. Levy: None. R.M. Wasserman: None. B. Anderson: Advisory Panel; Self; Sanofi-Aventis. L.M. Laffel: Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Sanofi US, MannKind Corporation, Roche Diagnostics Corporation, Dexcom, Inc., Insulet Corporation, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Johnson & Johnson Diabetes Institute, LLC..
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".