University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Westmead Hospital, Sydney, New South Wales, Australia; The George Institute for Global Health, Sydney, New South Wales, Australia.
University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Westmead Hospital, Sydney, New South Wales, Australia; The George Institute for Global Health, Sydney, New South Wales, Australia.
Can J Cardiol. 2018 Jul;34(7):905-913. doi: 10.1016/j.cjca.2018.02.012. Epub 2018 Jun 11.
Cardiovascular disease (CVD) is a leading global cause of death and morbidity and prevention needs to be strengthened to tackle this. Mobile health (mHealth) might present a novel and effective solution in CVD prevention, and interest in mHealth has grown dramatically since the advent of the smartphone. In this review, we discuss mHealth interventions that target multiple cardiovascular risk factors simultaneously in the context of primary as well as secondary prevention. There is some evidence that mHealth interventions improve a range of individual CVD risk factors, but a relative paucity of evidence on mHealth interventions improving multiple CVD risk factors simultaneously. The existing data suggest mHealth programs improve overall CVD risk, at least in the short term. Interpretation of the evidence is difficult in the context of poor methodology and mHealth modalities often being a part of large complex interventions. In this review we identify a number of unanswered questions including: which mode of mHealth (or combination of interventions) would be most effective, what is the durability of intervention effects, and what degree of personalization and interactivity is required.
心血管疾病(CVD)是全球主要的死亡和发病原因,需要加强预防措施来应对这一问题。移动医疗(mHealth)可能是 CVD 预防的一种新颖而有效的解决方案,自智能手机问世以来,人们对 mHealth 的兴趣大幅增长。在本文综述中,我们讨论了 mHealth 干预措施,这些措施针对一级和二级预防中的多种心血管危险因素。有一些证据表明 mHealth 干预措施可改善多种心血管危险因素,但关于同时改善多种 CVD 危险因素的 mHealth 干预措施的证据相对较少。现有数据表明,mHealth 方案至少在短期内可改善整体 CVD 风险。鉴于方法学较差以及 mHealth 模式通常是大型复杂干预措施的一部分,因此很难对证据进行解释。在本文综述中,我们确定了一些未解答的问题,包括:哪种 mHealth 模式(或干预措施的组合)最有效,干预效果的持久性如何,以及需要多大程度的个性化和交互性。