Evaluating the effectiveness of mobile applications on medication adherence for chronic conditions: Systematic Review and Meta-analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Medication adherence is crucial in managing chronic conditions, yet only 50% of chronically ill patients take medications as prescribed, leading to poor health outcomes. Mobile applications include a variety of possible features that have the potential to support and improve medication adherence. OBJECTIVE The purpose of this systematic review was to evaluate the effectiveness of mobile applications in promoting medication adherence for patients managing chronic conditions. METHODS MEDLINE (Ovid), Embase (Ovid) and Cochrane Central Register of Controlled Trials databases were searched for randomized controlled trails (RCTs) evaluating the effectiveness of mobile app interventions in improving medication adherence in patients with chronic conditions. Meta-analyses were performed on medication adherence scores, categorized by adherence measurement scale, and bias assessment was conducted using the Cochrane Risk of Bias tool. RESULTS This review included 14 RCTs published between 2014 to 2022, with sample sizes between 57 to 412 participants and the length of interventions ranging from 30 days to 12 months. A range of patient populations were evaluated in the included studies, including those with Parkinson’s disease, coronary heart disease, psoriasis, and hypertension, with the latter being the most common. All 14 studies reported that app interventions improved medication adherence and 10 RCTs demonstrated statistically significant improvement in medication adherence. Three separate sets of meta-analyses and difference in difference analyses were conducted on studies, categorized by the 3 scales used in individual studies: the 8-item Morisky Medication Adherence Scale, 4-item Morisky Medication Adherence Scale and percentage adherence scale. Each set of analyses demonstrated that app-based interventions improved medication adherence. CONCLUSIONS From the studies included in this review, mobile apps, designed for a range of conditions with a range of features, can improve medication adherence and may be a tool to successfully manage chronic conditions. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42023488188
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".