Effectiveness of Mobile Apps in Improving Medication Adherence Among Chronic Kidney Disease Patients: Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic kidney disease (CKD) is a serious condition affecting millions of individuals worldwide. Adherence to medication regimens among patients with CKD is often suboptimal, leading to poor health outcomes. In recent years, mobile apps have gained popularity as a promising tool to improve medication adherence and self-management in various chronic diseases. OBJECTIVE This study aimed to evaluate the effectiveness of mobile apps to improve medication adherence among patients with CKD (including end-stage and renal replacement therapy). METHODS A systematic search was conducted using Scopus, Cochrane, PubMed, and EBSCOhost to include eligible articles that studied mobile apps to improve medication adherence among patients with CKD. The quality of the selected studies was evaluated using the Newcastle‒Ottawa Scale and the Cochrane risk-of-bias tool. RESULTS Out of 231 relevant articles, only 9 studies were selected for this systematic review. Based on Newcastle‒Ottawa Scale, 7 were deemed to be of high quality, while others were of fair quality. The Cochrane risk-of-bias tool indicated a low to moderate risk of bias across the included studies. Most of the included studies had a randomized controlled design. Of the 9 selected studies, 3 papers represented medication adherence by a coefficient of 10 variability of tacrolimus, 3 papers used adherence measurement scales to calculate the score for assessing medication adherence, 2 papers represented medication adherence by self-reporting, 2 papers represented medication adherence using electronic monitoring, and 1 represented medication adherence by pill count. The mobile apps were identified as Transplant Hero (Transplant Hero LLC), Perx (Perx Health), Smartphone Medication Adherence Saves Kidneys (developed by John McGillicuddy), Adhere4U (developed by Ahram Han), My Dialysis (developed by Benyamin Saadatifar), Kidney Love (developed by National Kidney foundation), and iCKD (developed by Dr Vivek Kumar). Of these apps, 3 focused on evaluating Transplant Hero, while the remaining investigated each of the other mentioned apps individually. The apps use various strategies to promote medication adherence, including reminders, gamification, patient education, and medication monitoring. A majority, 5 out of 9 mobile apps, had a statistically significant (P<.05) effect on medication adherence. There was strong evidence for a positive effect of interventions focusing on games and reminders combined with electronic medication tray monitoring and patient education. CONCLUSIONS Mobile apps effectively improved medication adherence in patients with CKD, but low evidence and short intervention duration warrant caution. Future research should identify ideal features, provider costs, and user-friendly, secure apps.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".