Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): systematic review and individual patient data meta-analysis
Bibliographic record
Abstract
Background: A variety of small mobile phone text-messaging interventions have indicated improvement in risk factors for cardiovascular disease (CVD). Yet the extent of this improvement and whether it impacts multiple risk factors together is uncertain. We aimed to conduct a systematic review and individual patient data (IPD) meta-analysis to investigate the effects of text-messaging interventions for CVD prevention. Methods: Electronic databases were searched to identify trials investigating a text-messaging intervention focusing on CVD prevention with the potential to modify at least two CVD risk factors in adults. The main outcome was blood pressure (BP). We conducted standard and IPD meta-analysis on pooled data. We accounted for clustering of patients within studies and the primary analysis used random-effects models. Sensitivity and subgroup analyses were performed. Results: Nine trials were included in the systematic review involving 3779 participants and 5 (n=2612) contributed data to the IPD meta-analysis. Standard meta-analysis showed that the weighted mean differences are as follows: systolic blood pressure (SBP), -4.13 mm Hg (95% CI -11.07 to 2.81, p<0.0001); diastolic blood pressure (DBP), -1.11 mm Hg (-1.91 to -0.31, p=0.002); and body mass index (BMI), -0.32 (-0.49 to -0.16, p=0.000). In the IPD meta-analysis, the mean difference are as follows: SBP, -1.3 mm Hg (-5.4 to 2.7, p=0.5236); DBP, -0.8 mm Hg (-2.5 to 1.0, p=0.3912); and BMI, -0.2 (-0.8 to 0.4, p=0.5200) in the random-effects model. The impact on other risk factors is described, but there were insufficient data to conduct meta-analyses. Conclusion: Mobile phone text-messaging interventions have modest impacts on BP and BMI. Simultaneous but small impacts on multiple risk factors are likely to be clinically relevant and improve outcome, but there are currently insufficient data in pooled analyses to examine the extent to which simultaneous reduction in multiple risk factors occurs. PROSPERO registration number: CRD42016033236.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".