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Effectiveness of Mobile Health Technology Interventions for Patients With Heart Failure: Systematic Review and Meta-analysis

机译:Effectiveness of Mobile Health Technology Interventions for Patients With Heart Failure: Systematic Review and Meta-analysis

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Background: Heart failure (HF) is a complex and serious condition associated with substantial morbidity, mortality, and health care costs. We conducted a systematic review and meta-analysis to evaluate the effects of mobile health (mHealth) interventions compared with usual care in patients with HF. Methods: We searched MEDLINE, CENTRAL, CINAHL, and EMBASE databases to identify eligible randomized controlled trials (RCTs) of mHealth interventions. Primary outcomes included: all-cause mortality, cardiovascular mortality, HF-related hospitalizations, and all-cause hospitalizations. Meta-analyses using a random effects model were performed for all outcomes. Risk of bias and quality of evidence were evaluated using the Cochrane Tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Results: Sixteen RCTs involving 4389 patients were included. Compared with usual care, mHealth interventions reduced the risk of all-cause mortality (risk ratio [RR], 0.80; 95% confidence interval [CI], 0.65-0.97; absolute risk reduction [ARR], 2.1%; high-quality evidence), cardiovascular mortality (RR, 0.70; 95% CI, 0.53-0.91; ARR, 2.9%; high-quality evidence), and HF hospitalizations (RR, 0.77; 95% CI, 0.67-0.88; ARR, 5%; high-quality evidence), but had no effect on all cause hospitalizations. Results were driven by mHealth interventions with remote monitoring and clinical feedback, which were associated with larger reductions than stand-alone mHealth interventions. However, subgroup differences were not statistically significant. Conclusions: mHealth interventions with remote monitoring and clinical feedback reduce mortality and HF-related hospitalizations, but might not reduce all-cause hospitalizations in patients with HF. Additional studies are needed to determine the efficacy of stand-alone mHealth interventions as well as active features of mHealth that contribute to efficacy.

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