Eliza ML Wong
The Chinese University of Hong Kong, China
Title: The effectiveness of a home-based interactive e-health educational intervention for middle-aged coronary heart disease (CHD) adults in improving total exercise, adherence rate, exercise efficacy and outcomes: A randomized controlled trial
Biography
Biography: Eliza ML Wong
Abstract
Introduction:
Coronary heart disease (CHD) is the leading cause of death globally, and e-health educational programs have been proved to be an effective support to patients. Considering the advantages of e-health programs, as well as the growing number of young patients with CHD in Hong Kong, we plan to conduct a randomized controlled trial (RCT) to investigate the effectiveness of a home-based interactive e-health educational intervention for patients with CHD in terms of improvements in total physical exercise, exercise adherence and self-efficacy, risk factor profile, psychological outcomes, and quality of life.
Methods and analysis:
The RCT will be conducted in two government cardiac clinics in Hong Kong. Using a block randomization method, 438 eligible CHD clients will be randomly categorized to either the control group or the intervention group. All participants will receive usual care, but those in the intervention group will additionally receive the e-health educational intervention program. This program will consist of a one-hour educational session, one telephone follow up, and an e-health link on self-monitoring, which includes the recording of health measures and physical exercise across six months. Data will be collected at baseline, three-, and six-month intervals. The primary outcomes will be total physical exercise, which will be measured by the Godin–Shephard Leisure-Time Physical Activity Questionnaire. The secondary outcomes will consist of exercise efficacy and adherence rate, CVD risk profile, physical and psychological health outcomes (as measured by the Chinese version of the Health Survey Questionnaire and Hospital Anxiety and Depression Scale), and biological parameter. The data will be analyzed using mixed effect models and confirmatory factor analysis.
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