Enhancing Medication Adherence in Older Adults: A Systematic Review of Evidence‐Based Strategies
Bibliographic record
Abstract
BACKGROUND: Medication adherence is essential for achieving favorable health outcomes, particularly in older adults with multiple chronic conditions. OBJECTIVE: This systematic review critically appraised current evidence on interventions aimed at enhancing medication adherence in older adults. METHODS: Literature searches were performed in PubMed/MedLine, EMBASE, and Web of Science for articles published up to December 31, 2024. We identified peer-reviewed studies assessing interventions to improve medication adherence in older adults (≥ 60 years). The primary outcome was intervention effectiveness; secondary outcomes were clinical parameters, disease control, health-related quality of life, rehospitalization rates, event rates, mortality rates, feasibility, acceptability or satisfaction levels, and overall costs or cost-effectiveness. RESULTS: A total of 128 studies was included: 96 randomized controlled trials (RCTs), 16 pre-post studies, 9 non-RCTs, and 7 longitudinal evaluations. The majority (51.2%) was implemented in primary care. An educational component was present in 56.3% of interventions, a technical component in 47.6%, and an attitudinal component in 32.0%. Only 3.2% of interventions included rewards. Various healthcare professionals, such as pharmacists, nurses, and physicians, were involved in delivering interventions. Most studies reported improved adherence, though some factors, such as high baseline adherence, insufficient intervention intensity, and brief follow-up limited the effectiveness. Secondary outcomes often included improvements in disease knowledge, patient satisfaction, quality of life, and clinical indicators like blood pressure and HbA1c levels. CONCLUSIONS: Despite most studies showed a positive impact on adherence, a high heterogeneity was highlighted, and effectiveness was mainly observed in the short term.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".