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评估移动应用程序对慢性病药物依从性的有效性:系统评价与荟萃分析。

Evaluating the Effectiveness of Mobile Apps on Medication Adherence for Chronic Conditions: Systematic Review and Meta-Analysis.

作者信息

Lanke Vaidehee, Trimm Kevin, Habib Bettina, Tamblyn Robyn

机构信息

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Ave, Montreal, QC, H3A 1Y7, Canada, 1 3062227521.

Department of Experimental Medicine, McGill University, Montreal, QC, Canada.

出版信息

J Med Internet Res. 2025 Jul 31;27:e60822. doi: 10.2196/60822.

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.

摘要

背景

药物依从性对于慢性病管理至关重要。移动应用程序可能通过各种各样的功能,具有支持和改善药物依从性的潜力。

目的

本系统评价的目的是评估移动应用程序对慢性病患者提高药物依从性的有效性。

方法

检索MEDLINE(Ovid)、Embase(Ovid)和Cochrane对照试验中央注册库数据库,查找评估移动应用程序干预对慢性病患者提高药物依从性有效性的随机对照试验(RCT)。对研究设计和应用程序功能进行定性描述。根据药物依从性测量量表对研究进行分组,使用随机效应模型对干预组和对照组之间药物依从性得分的平均差异进行荟萃分析。如果有基线药物依从性数据,还使用随机效应模型进行差异差值荟萃分析。使用Cochrane偏倚风险工具进行偏倚评估。

结果

本评价纳入了2014年至2022年发表的14项RCT,样本量在57至412名参与者之间,干预时间从30天到12个月不等。评估了一系列患者群体,包括帕金森病、冠心病、银屑病和高血压患者,其中高血压是最常见的疾病。所有14项研究均报告应用程序干预改善了药物依从性,10项RCT显示药物依从性有统计学意义的改善。根据药物依从性测量量表对对照组和干预组之间药物依从性得分的平均差异进行了三组独立的荟萃分析:8项Morisky药物依从性量表(MMAS - 8;0.57,95%CI 0.33 - 0.80;P <.001,I2 = 0%,τ2 = 0,异质性检验P值 =.94)、4项Morisky药物依从性量表(MMAS - 4;0.15,95%CI - 0.12至0.42;P =.28,I2 = 0%,τ2 = 0,异质性检验P值 =.54)和药物依从性百分比量表(18.85,95%CI 2.17 - 35.53;P =.03,I2 = 63%,τ2 = 94.89,异质性检验P值 =.10)。此外,对于有可用基线依从性得分的情况,对使用MMAS - 8量表的研究(0.38,95%CI 0.15 - 0.62;P =.001,I2 = 0%,τ2 = 0,异质性检验P值 =.51)和使用MMAS - 4量表的研究(0.55,95%CI 0.17至0.93;P =.005,I2 = 33%,τ2 = 0.03,异质性检验P值 =.22)进行了差异差值荟萃分析。MMAS - 8量表、药物依从性百分比量表以及两项差异差值荟萃分析均表明基于应用程序的干预改善了药物依从性。

结论

从本评价纳入的研究来看,针对多种慢性病设计、具有一系列功能的移动应用程序显示出可提高药物依从性,可能是成功管理慢性病的一种工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a63/12312993/a5ee5449c61b/jmir-v27-e60822-g001.jpg

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