Effectiveness of motivational interviewing interventions on medication adherence in adults with chronic diseases: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Medication adherence is frequently suboptimal in adults with chronic diseases, resulting in negative consequences. Motivational interviewing (MI) is a collaborative conversational style for strengthening a person's motivation and commitment to change. We aimed to assess whether MI interventions are effective to enhance medication adherence in adults with chronic diseases and to explore the effect of individual MI intervention characteristics. Methods: We searched electronic databases and reference lists of relevant articles to find randomized controlled trials (RCTs) that assessed MI intervention effectiveness on medication adherence in adults with chronic diseases. A random-effects model was used to estimate a pooled MI intervention effect size and its heterogeneity (I 2 ). We also explored the effects of individual MI characteristics on MI intervention effect size using a meta-regression with linear mixed model. Results: : Nineteen RCTs were identified, and 16 were included in the meta-analysis. The pooled MI intervention effect size was 0.12 [95% confidence interval (CI) = (0.05, 0.20), I 2 = 1%]. Interventions that were based on MI only [β = 0.183, 95% CI = (0.004, 0.362)] or those in which interventionists were coached during intervention implementation [β = 0.465, 95% CI = (0.028, 0.902)] were the most effective. MI interventions that were delivered solely face to face were more effective than those that were delivered solely by phone [β = 0.270, 95% CI = (0.041, 0.498)]. Conclusions: This synthesis of RCTs suggests that MI interventions might be effective at enhancing of medication adherence in adults treated for chronic diseases. Further research is however warranted, as the observed intervention effect size was small.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".