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A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic
Adewale Samuel Akinosun1, Rob Polson2, Yohanca Diaz-Skeete3
1Department of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.
Insights
Digital technology interventions show promise for improving cardiovascular disease (CVD) risk factors like cholesterol and physical activity. However, they did not significantly impact weight, blood pressure, or smoking cessation in this review.
Area of Science:
- Cardiology
- Digital Health
- Behavioral Science
Background:
- Cardiovascular disease (CVD) is significantly influenced by lifestyle risk factors, yet many patients struggle to modify them.
- Digital technology interventions are emerging as potential alternatives to traditional cardiac rehabilitation.
- The clinical effectiveness of digital interventions for individual behavior change in CVD patients remains largely unknown.
Purpose of the Study:
- To systematically review and meta-analyze randomized controlled trials (RCTs) evaluating digital technology interventions for CVD.
- To identify effective behavior change constructs within these interventions for risk factor modification.
Main Methods:
- A systematic review and meta-analysis adhering to PRISMA guidelines.
- Quantitative analysis of mixed data from selected research databases, focusing exclusively on RCTs.
- Statistical significance set at a 95% confidence interval and P=.05.
Main Results:
- Digital interventions significantly improved total cholesterol, HDL, LDL, physical activity, and reduced sedentary behavior and unhealthy food intake.
- No significant benefits were observed for body mass index (BMI), diastolic blood pressure (BP), systolic BP, HbA1c, alcohol intake, smoking, or medication adherence.
Conclusions:
- Digital interventions show potential for enhancing healthy behaviors such as physical activity and diet in CVD patients.
- These interventions may be more effective when targeting multiple behavioral outcomes simultaneously.
- Current evidence does not support the effectiveness of digital interventions in reducing unhealthy behaviors like smoking or improving key clinical outcomes such as BMI and blood pressure.
Background:
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.
Objective:
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.
Methods:
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.
Results:
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 A1C blood sugar (HbA1c) 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.
Conclusions:
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 HbA1c).
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