← Back to Volume 2, Issue 2

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
Vol.2, No.2, pp.1-8, 2024 DOI: 10.62051/ijphmr.v2n2.01 October 14, 2024
Download PDF
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.
Keywords: Emergency care, Cardiovascular disease, Physical activity, Exercise benefits/barriers, Machine learning
APA Citation: Yan Sun, Aiying Li, Jin Zhang (2024). Analysis of Factors Influencing Exercise Benefits and Barriers in Cardiovascular Disease Patients Visiting the Emergency Department Based on Machine Learning. International Journal of Public Health and Medical Research, 2(2), 1-8. https://doi.org/10.62051/ijphmr.v2n2.01

References

  1. Mortensen K, Rudolph V, Willems S, Ventura R. New developments in antibradycardic devices. Expert Rev Med Devices. 2007; 4(3):321-333.
  2. Shen Jiaxin. Analysis of disease spectrum and characteristics of more than 240,000 adult emergency patients [D]. Tianjin Medical University, 2019.
  3. Chen HH, Hsieh PL. Applying the Pender's health promotion model to identify the factors related to older adults' participation in community-based health promotion activities [J]. Int J Environ Res Public Health, 2021, 18(19): 9985.
  4. Heydari A, Khorashadizadeh F. Pender's health promotion model in medical research [J]. J Pak Med Assoc, 2014, 64(9): 1067-1074.
  5. Bai Chenxiao. Study on physical activity and its influencing factors in COPD patients based on health promotion model [D]. Shandong University, 2020.
  6. Wen Z. Study on the influence of exercise on depression in the elderly -- the mediating role of mental resilience [J]. Journal of Guangzhou University of Physical Education, 2018, 38(05): 99-102.
  7. Moser O, Eckstein ML, West DJ, Goswami N, Sourij H, Hofmann P. Type 1 diabetes and physical exercise: moving (forward) as an adjuvant therapy. Curr Pharm Des. 2020; 26(9):946–57.
  8. Kennedy A, Narendran P, Andrews RC, Daley A, Greenfield SM. Attitudes and barriers to exercise in adults with a recent diagnosis of type 1 diabetes: a qualitative study of participants in the Exercise for Type 1 Diabetes (EXTOD) study. BMJ Open. 2018; 8(1).
  9. Roberts AJ, et al. Association between fear of hypoglycemia and physical activity in youth with type 1 diabetes: the SEARCH for diabetes in youth study. Pediatr Diabetes. 2020; 21(7):1277–84.
  10. Ward R, Thomas SB, Power LE, Taylor SE. Barriers and facilitators to exercise adherence in community-dwelling adults with diabetes: a systematic review. Diabetes Res Clin Pract. 2021; 179:109007.
  11. Zhang Y, et al. Fear of hypoglycemia in patients with type 1 and 2 diabetes: a systematic review. J Clin Nurs. 2020.
  12. Sechrist KR, Walker SN, Pender NJ. Development and psychometric evaluation of the exercise benefits/barriers scale [J]. Res Nurs Health, 1987, 10(6): 357-365.
  13. Zheng Jing. Physical activity and its influencing factors in maintenance hemodialysis patients [D]. Sun Yat-sen University, 2009.
  14. Hallal PC, Victora CG. Reliability and validity of the International Physical Activity Questionnaire (IPAQ) [J]. Med Sci Sports Exerc, 2004, 36(3): 556.
  15. Wang Y, Li X, Hu H. The impact of marital status on cardiovascular disease and mortality among Chinese adults [J]. JAMA Network Open, 2021, 4(5): e212083.
  16. Kim TJ, von dem Knesebeck O. Socioeconomic inequalities in overweight and obesity among adults in Europe: A systematic review and meta-analysis [J]. Obesity Reviews, 2018, 19(5): 615-628.
  17. Li X, Xie X, Shi Y. Association of dietary fat intake with cardiovascular and all-cause mortality among adults in China: A nationwide cohort study [J]. The Lancet Regional Health-Western Pacific, 2020, 10: 100132.
  18. Lavie CJ, Laddu D, Arena R. Healthy lifestyle interventions to combat noncommunicable disease—A novel strategy to reverse global trends in physical inactivity and obesity [J]. Progress in Cardiovascular Diseases, 2022, 70: 53-63.
  19. Zhai L, Zhang Y, Zhang D. The relationship between physical activity and mental health: A systematic review and meta-analysis [J]. PLoS One, 2015, 10(10): e0138916.
  20. Rebar AL, Stanton R, Geard D. A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations [J]. Health Psychology Review, 2015, 9(3): 366-378.
  21. McAuley E, Mullen SP, Szabo AN. Self-efficacy and functional limitations in older adults: The role of social support and social efficacy [J]. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 2011, 66(3): 363-370.
  22. Wang J, Mann F, Lloyd-Evans B. Associations between loneliness and perceived social support and outcomes of mental health problems: A systematic review [J]. BMC Psychiatry, 2018, 18(1): 156.
  23. Couronné R, Probst P, Boulesteix AL. Random forest versus logistic regression: A large-scale benchmark experiment [J]. BMC Bioinformatics, 2018, 19(1): 270.
  24. Deo RC. Machine learning in medicine [J]. Circulation, 2015, 132(20): 1920-1930.