Effectiveness of text message-delivered health behaviour intervention on HbA1c change in adults with type 2 diabetes mellitus: a systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
This study aims to investigate the effectiveness of text message-delivered health behaviours intervention on HbA1c change among adults with T2DM, and to identify key moderators including intervention features, message characteristics, target behaviours, and the usage of behaviour change techniques (BCTs). We systematically reviewed 37 randomised controlled trials published between 2016 and 2025, involving 8,971 participants. Changes in HbA1c and health behaviours were analysed using the standardised mean difference. The meta-analysis revealed a significant reduction in HbA1c (g = −0.32, 95% CI = −0.46 to – 0.18). Meta-regression also found that the intervention significantly improved health behaviours, which in turn predicted a significant reduction in HbA1c levels. Subsequent subgroup analyses revealed that studies with a shorter duration (≤6 months) demonstrated a larger effect size in reducing HbA1c. Notably, interventions employing specific BCTs including ‘body changes’ (g = −0.643), ‘habit formation’ (g = −0.624), ‘credible source’ (g = −0.513), ‘self-monitoring of outcomes of behaviours’ (g = −0.377), and "instruction on how to perform the behaviour’ (g = −0.354) were significantly associated with greater HbA1c reductions. These effects were particularly pronounced in trials focused on physical activity, healthy eating, and medication adherence. Conclusions suggest that text message-delivered health behavior interventions should be tailored to specific target behavior and incorporate these high-impact BCTs to comprehensively improve diabetes management.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".