Patients' perspectives on feedback interventions to support adherence to long-term medication: a systematic review with thematic synthesis of qualitative evidence
Bibliographic record
Abstract
Abstract Background Medication optimisation is a global issue with up to 50% of people not taking medicines as prescribed. Numerous interventions have been developed to address this, many of them including feedback on behaviour and outcomes. Understanding patients’ views on such interventions is essential for successful adoption and use. Purpose The purpose of this systematic review is to: (1) Understand patients’ perspectives and experiences of medication adherence feedback interventions and (2) Identify barriers and facilitators which influence their implementation within practice. Methods CINAHL, MEDLINE, EMBASE, PubMED, PsycINFO and Google Scholar were systematically searched to identify relevant studies. The inclusion criteria included; studies with qualitative or mixed method designs describing patients’ perspectives on medication adherence feedback interventions, primary studies with adult participants on long-term medications and studies involving interventions suitable for self-management in primary or community care. Quality assessment was completed using the Mixed Methods Appraisal Tool. The review was reported using the PRISMA and conducted using ENTREQ guidelines. Data were extracted and analysed using thematic synthesis (NVivo 20). Findings were presented narratively. Results From the 1,031 records screened, ten studies were included. Five studies were conducted in the United States, two in the United Kingdom, and one in the Netherlands, Canada, and Tanzania, respectively. Medication adherence interventions included the use of therapeutic drug monitoring methods and digital adherence technologies such as mHealth and eHealth for people living with asthma, HIV, coronary heart disease, hypertension, and type 2 diabetes. Patients found interventions acceptable if they were simple to use, allowed control over data sharing options, incorporated audio visual cues, and provided emotional and motivational support. Building trust between patients and healthcare providers and the resulting benefit of shared decision-making were also considered key factors for intervention success. However, developing interventions without user input was identified as a potential barrier to intervention implementation. Patients expressed an overall desire to have interventions tailored to meet their personal needs and preferences, highlighting the importance of placing user needs at the centre of intervention development. Conclusion Placing user needs at the centre of adherence intervention development is crucial for successful implementation. Further research which facilitates user involvement in co-designing interventions to compliment patients’ characteristics and preferences would be key for intervention implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".