Key result
AI-HEALS trial tests AI-guided health education to improve post-PCI self-management.
Why the study?
Traditional interventions after PCI have shortcomings including dependence on medical institutions and lack of personalized treatment plans, motivating evaluation of an AI-based health education system.
RCT (n=86)
Single-blind
1:1 ratio
No
Answers are grounded in this paper and cite their sources.
Results of this planned AI-HEALS RCT are awaited before changing post-PCI education; leaves open impact on self-management in younger adults.
Background Coronary artery disease is a common heart disease worldwide, and the morbidity and mortality rates among Chinese residents continue to rise. Percutaneous coronary intervention (PCI) can improve survival rates, but long-term management is required after the procedure. Traditional interventions have shortcomings such as dependence on medical institutions and the lack of personalized treatment plans. This study aims to evaluate the effectiveness of the Artificial Intelligence-based Health Education Precision Linkage System (AI-HEALS) in improving self-health management among young and middle-aged patients after PCI. Methods and analysis This is a single-center, randomized, single-blind, parallel-group controlled trial. Eligible patients aged 18–59 years from Xuanwu Hospital, Capital Medical University, will be randomly allocated at a 1:1 ratio to receive either standard care plus the AI-HEALS intervention (intervention group) or standard care alone (control group). The primary outcome is the change in self-management ability from baseline to the 3-month intervention endpoint, assessed using the Coronary Heart Disease Self-Management Behavior Scale (CSMS). Secondary outcomes include medication adherence, dietary behavior, exercise frequency, and psychological indicators. Assuming an effect size of d = 0.69, 80% statistical power, and a two-sided significance level of α = 0.05, with an anticipated 20% dropout rate, the required sample size was calculated as 86 participants (43 per group). Primary analyses will follow the intention-to-treat principle. Generalized linear mixed models will be used for repeated measures outcomes, and multiple imputation by chained equations will handle missing data. Quantitative findings will be integrated with qualitative data from structured interviews conducted among intervention group participants. Ethics and dissemination This study has been approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval No.[2024] 409-002). Written informed consent will be obtained from all participants. Results will be disseminated through peer-reviewed publications and academic conferences. Clinical Trial Registration : http://www.chictr.org.cn/ , identifier ChiCTR2500100519.
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Li et al. (2026) conducted an RCT in Coronary artery disease post-percutaneous coronary intervention (n=86). Artificial Intelligence-based Health Education Precision Linkage System (AI-HEALS) vs. Standard care alone was evaluated on Change in self-management ability from baseline to the 3-month intervention endpoint, assessed using the Coronary Heart Disease Self-Management Behavior Scale (CSMS). The Artificial Intelligence-based Health Education Precision Linkage System (AI-HEALS) will be evaluated in a randomized controlled trial to determine its effect on self-management in young and middle-aged patients after percutaneous coronary intervention.
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