Department of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.
Highland Health Sciences Library, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.
JMIR Mhealth Uhealth. 2021 Mar 3;9(3):e21061. doi: 10.2196/21061.
Approximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level.
The aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD.
This study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance.
Digital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at -0.29 [-0.44, -0.15], P<.001; high-density lipoprotein SMD at -0.09 [-0.19, 0.00], P=.05; low-density lipoprotein SMD at -0.18 [-0.33, -0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at -0.37 [-1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at -0.06 [-0.20, 0.08], P=.43; systolic BP SMD at -0.03 [-0.18, 0.13], P=.74; Hemoglobin A blood sugar (HbA) RR at 1.04 [0.40, 2.70], P=.94; alcohol intake SMD at -0.16 [-1.43, 1.10], P=.80; smoking RR at 0.87 [0.67, 1.13], P=.30; and medication adherence RR at 1.10 [1.00, 1.22], P=.06.
Digital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA).
大约 50%的心血管疾病（CVD）病例归因于生活方式风险因素。尽管进行了广泛的教育，个人知识和功效，许多人仍然无法充分改变这些风险因素，即使在心血管事件发生后也是如此。数字技术干预措施已被提议作为传统心脏康复护理中心的可行替代品和潜在替代方案。然而，对于这些技术在个体水平上对 CVD 患者的行为改变的临床效果知之甚少。
本研究旨在确定和衡量数字技术（例如手机、互联网、软件应用程序、可穿戴设备等）干预措施在随机对照试验（RCT）中的有效性，并确定哪些行为改变构建体在 CVD 患者中有效实现风险因素的改变。
这是一项系统评价和荟萃分析，根据 PRISMA（系统评价和荟萃分析的首选报告项目）声明标准设计。从选定的研究数据库中提取的混合数据并仅过滤 RCT 进行分析，使用定量方法进行分析。假设检验的结果为 95%CI 和 P=.05 用于统计学意义。
数字干预措施使用手机、智能手机、个人计算机和可穿戴设备等设备以及互联网、短信、软件应用程序和移动传感器等技术进行了传输。使用的行为改变构建体包括认知、随访、目标设定、记录保存、感知益处、说服、社交、个性化、奖励和激励、支持和自我管理。对结果进行的荟萃分析效应估计值（均数差[MD]；标准均数差[SMD]；风险比[RR]）表明，总胆固醇 SMD 有获益，为-0.29[-0.44，-0.15]，P<.001；高密度脂蛋白 SMD 有获益，为-0.09[-0.19，0.00]，P=.05；低密度脂蛋白 SMD 有获益，为-0.18[-0.33，-0.04]，P=.01；身体活动（PA）SMD 有获益，为 0.23[0.11，0.36]，P<.001；身体不活动（久坐）RR 有获益，为 0.54[0.39，0.75]，P<.001；以及饮食（食物摄入）RR 有获益，为 0.79[0.66，0.94]，P=.007。初始效应估计表明，体重指数（BMI）MD 没有显著获益，为-0.37[-1.20，0.46]，P=.38；舒张压（BP）SMD 没有获益，为-0.06[-0.20，0.08]，P=.43；收缩压 SMD 没有获益，为-0.03[-0.18，0.13]，P=.74；糖化血红蛋白（HbA）RR 没有获益，为 1.04[0.40，2.70]，P=.94；饮酒量 SMD 没有获益，为-0.16[-1.43，1.10]，P=.80；吸烟 RR 没有获益，为 0.87[0.67，1.13]，P=.30；以及药物依从性 RR 没有获益，为 1.10[1.00，1.22]，P=.06。
数字干预措施可能会改善健康行为因素（PA、健康饮食和药物依从性），并且在治疗多种行为结果（例如药物依从性加）时更有效。然而，它们似乎并没有降低不健康的行为因素（吸烟、饮酒和不健康的饮食）和临床结果（BMI、甘油三酯、舒张压和收缩压以及 HbA）。
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