The effectiveness of short mobile phone text message reminders compared to usual care on medication adherence in patients with hypertension: a systematic review protocol
Bibliographic record
Abstract
Abstract Background Poor adherence to long-term medication increases the risk of morbidity and mortality and decreases the quality of life of patients with hypertension. One strategy to improve treatment adherence is to use a short text message reminder. Although evidence indicates that such programs increase medication adherence, the extent of their effectiveness and translation into clinical practice needs to be better documented. Our systematic review will collect and analyze the available evidence for clinical practice implementation. This systematic review aimed to evaluate the effectiveness of short mobile phone text message reminders versus usual/standard care for medication adherence in patients with hypertension. Methods This review will include and summarize evidence from randomized controlled trials. Adults (age > 18 years) with hypertension. The comparator group received either the usual care or standard care. It encompasses standard medical care for patients not participating in a structured and supervised intervention program such as a telemedicine program. We will include studies that assess the effectiveness of short mobile phone text message reminders in improving medication adherence in patients with hypertension compared to usual care. We will search the following databases: PubMed, EMBASE, CINAHL, SCOPUS, Web of Science, Cochrane Library Central Register of Controlled Trials, and Cochrane Library. We will include studies published in English. Furthermore, we will consider studies published from the inception of the database until April 20, 2024. At least two reviewers will independently conduct study selection, data extraction, and quality assessment. A third reviewer will determine and resolve discrepancies. We will conduct a quality assessment using the ROBIS 2 critical appraisal checklist. At least two independent reviewers will crosscheck the data synthesis. Discussion We expect this review to provide current evidence for future studies and clinical practice concerning the impact of mobile phone text message reminders on medication adherence issues. We will publish our results in a peer-reviewed journal for publication. Systematic review registration PROSPERO CRD42023391236.
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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 | 0.074 | 0.099 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.029 | 0.021 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".