Mobile apps to improve medication adherence in cardiovascular disease: systematic review and meta-analysis

Research output: Contribution to journalReview articlepeer-review

External organisations

  • Liverpool Heart and Chest Hospital NHS Foundation Trust
  • University of Liverpool

Abstract

Background: Adherence rates of preventative medication for cardiovascular disease (CVD) have been reported as 57%, and approximately 9% of all CVD events in Europe are attributable to poor medication adherence. Mobile health technologies, particularly mobile apps, have the potential to improve medication adherence and clinical outcomes. Objective: The objective of this study is to assess the effects of mobile health care apps on medication adherence and health-related outcomes in patients with CVD. This study also evaluates apps' functionality and usability and the involvement of health care professionals in their use. Methods: Electronic databases (MEDLINE [Ovid], PubMed Central, Cochrane Library, CINAHL Plus, PsycINFO [Ovid], Embase [Ovid], and Google Scholar) were searched for randomized controlled trials (RCTs) to investigate app-based interventions aimed at improving medication adherence in patients with CVD. RCTs published in English from inception to January 2020 were reviewed. The Cochrane risk of bias tool was used to assess the included studies. Meta-analysis was performed for clinical outcomes and medication adherence, with meta-regression analysis used to evaluate the impact of app intervention duration on medication adherence. Results: This study included 16 RCTs published within the last 6 years. In total, 12 RCTs reported medication adherence as the primary outcome, which is the most commonly self-reported adherence. The duration of the interventions ranged from 1 to 12 months, and sample sizes ranged from 24 to 412. Medication adherence rates showed statistically significant improvements in 9 RCTs when compared with the control, and meta-analysis of the 6 RCTs reporting continuous data showed a significant overall effect in favor of the app intervention (mean difference 0.90, 95% CI 0.03-1.78) with a high statistical heterogeneity (I2=93.32%). Moreover, 9 RCTs assessed clinical outcomes and reported an improvement in systolic blood pressure, diastolic blood pressure, total cholesterol, and low-density lipoprotein cholesterol levels in the intervention arm. Meta-analysis of these clinical outcomes from 6 RCTs favored app interventions, but none were significant. In the 7 trials evaluating app usability, all were found to be acceptable. There was a great variation in the app characteristics. A total of 10 RCTs involved health care professionals, mainly physicians and nurses, in the app-based interventions. The apps had mixed functionality: 2 used education, 7 delivered reminders, and 7 provided reminders in combination with educational support. Conclusions: Apps tended to increase medication adherence, but interventions varied widely in design, content, and delivery. Apps have an acceptable degree of usability; yet the app characteristics conferring usability and effectiveness are ill-defined. Future large-scale studies should focus on identifying the essential active components of successful apps.

Bibliographic note

Funding Information: DAL is a coauthor of the mAF app but reports no other conflicts of interest in relation to this manuscript. LF has received institutional research grants from the European Union (for the design of mobile health apps for patient education), the British Heart Foundation, Medical Research Council (UK), and German Research Foundation. The Institute of Cardiovascular Research, University of Birmingham, has received an Accelerator Award by the British Heart Foundation (AA/18/2/34218) during the conduct of the study. LF is listed as an inventor on 2 patents held by the University of Birmingham (Atrial Fibrillation Therapy [WO 2015140571] and Markers for Atrial Fibrillation [WO 2016012783], not related to medical adherence or apps). LF has received grants from EU Horizon 2020 (CATCH ME Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly (633196), and MAESTRIA Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation [(965286) to fund WC). No other disclosures were reported.

Details

Original languageEnglish
Article numbere24190
JournalJournal of Medical Internet Research
Volume23
Issue number5
Publication statusPublished - 25 May 2021

Keywords

  • Cardiovascular disease, Medication adherence, Mobile health care applications, Mobile phone, Systematic review

ASJC Scopus subject areas

Sustainable Development Goals