The Impact of Digital Health Interventions on Quality of Life Outcomes in Heart Failure Patients: A Systematic Review and Meta-Analysis

数字健康干预对心力衰竭患者生活质量结局的影响:系统评价和荟萃分析

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Abstract

Despite routine management, heart failure (HF) patients often contend with a burdensome symptomatology negatively affecting their quality of life (QoL). The use of digital health interventions (DHIs) such as mobile health (mHealth) applications, wearable devices, and remote monitoring systems (RMS) is transforming the management of cardiovascular disease (CVD). However, evidence regarding their effectiveness in improving QoL remains limited. This systematic review and meta-analysis aimed to evaluate the impact of DHIs on QoL in patients with HF. A systematic search of randomised controlled trials (RCTs) and observational studies identified 14 studies evaluating QoL; nine of them reported statistically significant improvements associated with DHIs. Among the three studies assessing self-care outcomes, two demonstrated statistically significant improvements with DHI use, while one reported a non-significant reduction. Meta-analysis of six studies utilising the Minnesota Living with Heart Failure Questionnaire (MLHFQ) revealed a significant improvement in QoL (-8.06, 95% confidence interval (CI): -11.0 to -5.04), although this finding was limited by considerable heterogeneity (p=0.0025, I²=73%). Additionally, analysis of four studies employing the Kansas City Cardiomyopathy Questionnaire (KCCQ) indicated a statistically significant improvement in QoL (2.13, 95% CI: 1.34 to 2.92) with no significant heterogeneity (p=0.1719, I²=35.3%). Overall, this review demonstrates that DHIs generally have a positive impact on QoL in HF patients. However, the heterogeneity within the meta-analysis due to the varied intervention types used, study durations, and participant characteristics highlights the need for more focused, high-quality research to facilitate stronger meta-analyses. Future studies should aim to assess the most effective components of DHI types, evaluate longer-term outcomes, and explore implementation in real-world settings.

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