Text messaging interventions are associated with reductions in HbA1c among patients with diabetes: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Achieving optimal glycemic control remains challenging for many patients with diabetes. Text message-based interventions offer a scalable approach to enhance management. This systematic review and meta-analysis evaluated the impact of texting interventions on glycemic control in adults with diabetes. Research design and methods We searched EMBASE, PubMed, and Cochrane CENTRAL for randomized controlled trials comparing texting interventions to standard care in high-income countries. The primary outcome was the between-group difference in hemoglobin A1c (HbA1c) change from baseline. Risk of bias and overall quality of evidence were assessed using the Cochrane and Grading of Recommendations Assessment, Development, and Evaluation tools respectively. Results were pooled using an inverse variance random-effects model. Heterogeneity was evaluated using the I 2 statistic. Results Over 3 months of follow-up (14 trials, n=1,460 intervention, n=1,487 control), texting interventions were associated with a 0.29-unit greater reduction in percent HbA1c over control (95% CI 0.14 to 0.45, p=0.0001, I 2 =57%). At 6 months (20 trials, n=2,332 intervention, n=2,371 control), texting was associated with 0.19-unit greater HbA1c reduction (95% CI 0.07 to 0.30, p=0.001 I 2 =45%). At 12 months (seven trials, n=2,038), there was a non-significant benefit associated with texting. Among studies with a mean baseline HbA1c ≥8.6%, texting was associated with 0.48- and 0.36-unit greater HbA1c reductions at 3 (p=0.004) and 6 (p=0.004) months, respectively. Subgroups were not significantly different. Conclusion Text messaging interventions are associated with modest improvements in glycemic control over 3–6 months, particularly in patients with poorer baseline HbA1c. These effects may be meaningful at scale and support texting as a potential adjunct to routine diabetes care. Benefits appear to diminish by 12 months, underscoring the need for high-quality trials focused on long-term impact and intervention optimization. PROSPERO registration number CRD42023416462.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".