Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to analyse the effects of text messaging versus usual care in improving adherence to antiretroviral therapy (ART) in people living with HIV using individual patient data meta-analysis. Adjusted, sensitivity and subgroup analyses were conducted. SETTING: 3 randomised controlled trials conducted between 2010 and 2012 in rural and urban centres in Cameroon and Kenya (two studies) were used. PARTICIPANTS: A total of 1166 participants were included in this analysis (Cameroon=200; Kenya=428 and 538). PRIMARY AND SECONDARY OUTCOMES: The primary outcome was adherence to ART >95%. The secondary outcomes were mortality, losses to follow-up, transfers and withdrawals. RESULTS: Text messaging improved adherence to ART (OR 1.38; 95% CIs 1.08 to 1.78; p=0.012), even after adjustment for baseline covariates (OR 1.46; 95% CI 1.13 to 1.88; p=0.004). Primary education (compared with no formal education) was associated with a greater intervention effect on adherence (OR 1.65; 95% CI 1.10 to 2.48; p=0.016) and also showed a significant subgroup effect (p=0.039). In sensitivity analysis, our findings were robust to a modified threshold of adherence, multiple imputation for missing data and aggregate level data pooling, but not to fixed-effects meta-analyses using generalised estimation equations. There was a significant subgroup effect for long weekly (p=0.037), short weekly text messages (p=0.014) and interactive messaging (p=0.010). Text messaging did not significantly affect any of the secondary outcomes. CONCLUSIONS: Text messaging has a significant effect on adherence to ART, and this effect is influenced by level of education, gender, timing (weekly vs daily) and interactivity. We recommend the use of interactive weekly text messaging to improve adherence to ART, which is most effective in those with at least a primary level of education.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.060 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".