Interventions that improve adherence to antihypertensive medications in coronary heart disease patients: a systematic review
Bibliographic record
Abstract
A systematic review is conducted to identify effective interventions that improved adherence to antihypertensive drugs among patients with coronary heart diseases (CHDs). Primary studies designed to measure interventions to improve adherence on antihypertensive drugs in patients with CHD were included. Three online databases, COCHRANE, EMBASE and MEDLINE, were searched for primary studies published in English from 2005 to 2019. Studies were screened independently for eligibility. Cochrane risk-of-bias tool and the Newcastle-Ottawa Scale were used for quality assessment of randomised controlled trials (RCTs) and non-randomised studies, respectively. Of the 2000 entries identified, seven articles, including one cross-sectional study and six RCTs, met the inclusion criteria and were reviewed. One of the articles evaluated two interventions, so eight interventions were evaluated in total. Quality of the included studies was generally high, with the cross-sectional study rated as having good quality under Newcastle-Ottawa Scale, while four and two RCTs were rated as having low and some risk of bias under Cochrane risk-of-bias tool, respectively. Six of the intervention programmes were considered effective. An intervention was considered effective if it is associated with a significant (p≤0.05) and non-trivial (Cohen's d≥0.2) improvement in compliance-related outcomes such as in terms of the Morisky 8-item Medication Adherence Scale. Medication education, disease education, health education, constant reminders and medications dispensed using blister packs were identified to be effective in improving patients' compliance to medications. The importance of the continuity of interventions was also established. It is recommended that health service institutions should provide continuous education programmes, daily reminders and regular follow-ups for their patients who have CHD. It is recommended that further research ought to be carried out by using only one intervention in each trial with a standardised outcome measure, or using factorial designs, so that further cost-effectiveness evaluation of each intervention can be carried out independently, leading to the formulation of a comprehensive, optimised intervention programme for patients with CHD taking antihypertensives.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".