Health behavior and disease self-management indicators in patients with cardiovascular diseases using a health app: Findings from an RCT

使用健康应用程序的心血管疾病患者的健康行为和疾病自我管理指标:一项随机对照试验的结果

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Abstract

BACKGROUND: Prevention of acute cardiovascular events in patients with cardiovascular disease (CVD) requires promoting health-protective behaviors (e.g., physical activity) and reducing health-compromising behaviors (e.g., sitting). Digital interventions addressing health behavior offer great potential. Based on a multiple behavior change theory, an intervention in the form of a digital health application (app) was evaluated in a pilot trial, testing the following hypotheses (H): H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely related; H2: changes in health behaviors and disease self-management indicators over time (T0 to T1) are more pronounced in the intervention group (IG, app users) than in the control group (CG); H3: within the IG, changes in systolic and diastolic blood pressure indicate a positive trajectory. METHODS: A 12-week randomized controlled trial (RCT) was conducted with two measurement points. The IG received an app addressing self-management and health behavior change. A total of N = 40 CVD patients were randomized equally to the CG (45% women; mean age = 60.6 years) and the IG (35% women; mean age = 61.5 years). RESULTS: Findings support H1 with correlations between behaviors (r = -0.66-0.79) and disease self-management (r = -0.06-0.70). H2 was also partially supported, with significant improvements over time in self-management indicators, especially self-care maintenance, in the IG (Eta² = 0.35; p < 0.001). H3 could not be confirmed as no significant changes were found. CONCLUSIONS: This study provides evidence that an app addressing different behavior change techniques (BCTs) can help to manage CVD by promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account may increase the effectiveness of behavioral intervention, thereby improving individual and public health. Replications with larger samples and more objective measures are needed.

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