Evaluating the Effectiveness of Mobile Apps on Medication Adherence for Chronic Conditions: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Medication adherence is crucial for managing chronic conditions. Mobile apps may have the potential, through a wide variety of features, to support and improve medication adherence. Objective: The purpose of this systematic review was to evaluate the effectiveness of mobile apps 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 trials (RCTs) evaluating the effectiveness of mobile app interventions in improving medication adherence in patients with chronic conditions. Study design and app features were qualitatively described. Meta-analyses were performed on studies, grouped by medication adherence measurement scale, on the mean differences in medication adherence scores between intervention and control groups, using random effects models. If baseline medication adherence data were available, a difference in differences meta-analysis with a random effects model was also conducted. Bias assessment was conducted using the Cochrane Risk of Bias tool. Results: This review included 14 RCTs published between 2014 and 2022, with sample sizes between 57 and 412 participants and the length of interventions ranging from 30 days to 12 months. A range of patient populations was evaluated, including those with Parkinson disease, coronary heart disease, psoriasis, and hypertension, with hypertension being the most common condition. 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, categorized by the medication adherence measurement scales, were conducted on the mean difference between medication adherence scores between the control and intervention groups: the 8-item Morisky Medication Adherence Scale (MMAS-8; 0.57, 95% CI 0.33-0.80; P<.001, I2=0%, τ2=0, P value for heterogeneity test=.94), 4-item Morisky Medication Adherence Scale (MMAS-4; 0.15, 95% CI -0.12 to 0.42; P=.28, I2=0%, τ2=0, P value for heterogeneity test=.54) and a percentage medication adherence scale (18.85, 95% CI 2.17-35.53; P=.03, I2=63%, τ2=94.89, P value for heterogeneity test=.10). Additionally, with available baseline adherence scores, difference in differences meta-analyses were conducted for studies using the MMAS-8 scale (0.38, 95% CI 0.15-0.62; P=.001, I2=0%, τ2=0, P value for heterogeneity test=.51) and for studies using the MMAS-4 scale (0.55, 95% CI 0.17 to 0.93; P=.005, I2=33%, τ2=0.03, P value for heterogeneity test=.22). The meta-analysis of the MMAS-8 scale, percentage medication adherence scale, and both difference-in-differences meta-analyses demonstrated that app-based interventions improved medication adherence. Conclusions: From the studies included in this review, mobile apps, designed for a wide variety of chronic conditions with a range of features, were shown to improve medication adherence and may be a tool to successfully manage chronic conditions.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".