Analysis of Factors Influencing Exercise Benefits and Barriers in Cardiovascular Disease Patients Visiting the Emergency Department Based on Machine Learning

Yan Sun; Aiying Li; Jin Zhang

doi:10.62051/ijphmr.v2n2.01

Authors

Yan Sun
Aiying Li
Jin Zhang

DOI:

https://doi.org/10.62051/ijphmr.v2n2.01

Keywords:

Emergency care, Cardiovascular disease, Physical activity, Exercise benefits/barriers, Machine learning

Abstract

Objective: To investigate the exercise benefits/barriers and influencing factors among cardiovascular disease (CVD) patients seeking emergency care. Methods: A convenience sampling method was used to select CVD patients who visited the emergency department of a tertiary hospital in Chengdu from December 2023 to July 2024 as the study subjects. The investigation was conducted through medical record review and on-site questionnaire surveys. The research tools included a general information questionnaire, the International Physical Activity Questionnaire-Short Form (IPAQ-SF), the Exercise Benefits/Barriers Scale (EBBS), the Exercise Self-Efficacy Scale, the Patient Health Questionnaire-9 (PHQ-9), and the Social Support Rating Scale (SSRS). Data were entered into Excel and statistically analyzed using SPSS 26.0. Results: Among the 1080 patients, 62.9% did not meet physical activity standards. Depression and social support explained 48.0% of the total variance in exercise benefits/barriers. The random forest algorithm performed best in predicting influencing factors. Conclusion: CVD patients seeking emergency care are generally insufficiently active, with depression and social support significantly impacting exercise benefits/barriers.

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