A Causal Model of Physical Activity Among People with Acute Coronary Syndrome After Percutaneous Coronary Intervention: A Cross-sectional Study

Authors

  • Thayuta Inkaew PhD (Candidate), Faculty of Nursing, Chiang Mai University, Thailand.
  • Tipaporn Wonghongkul Faculty of Nursing, Chiang Mai University, Thailand.
  • Chiraporn Tachaudomdach Faculty of Nursing, Chiang Mai University, Thailand.
  • Chomphoonut Srirat Faculty of Nursing, Chiang Mai University, Thailand.

DOI:

https://doi.org/10.60099/prijnr.2024.267807

Keywords:

Acute coronary syndrome, Causal model, Health Action Process Approach, Percutaneous coronary intervention, Physical activity, Self-efficacy, Self-regulation

Abstract

Physical activity is considered a fundamental component of cardiac rehabilitation programs and is recommended to improve the secondary prevention outcomes of people with acute coronary syndrome after percutaneous coronary interventions. However, the physical activity levels of this population are often low. Most research studies have primarily focused on older populations and chronic diseases. This descriptive cross-sectional study aimed to test a causal model of physical activities among adults based on the Health Action Process Approach. Four hundred twenty-four people were recruited using multi-stage sampling from the outpatient departments of seven tertiary hospitals in Thailand. Data were collected using a demographic data form, the International Physical Activity Questionnaire-Long form, the Self-Efficacy Questionnaire, the Outcome Expectation Questionnaire, the Risk Perception Questionnaire, the Behavioral Intention Questionnaire, the Planning Questionnaire, and the Action Control Questionnaire. Data were analyzed using descriptive statistics and structural equation modeling with Mplus software.

The findings of our study hold significant promise for patient care. The model we tested, which accounted for 46% of the physical activity variance, underscores the crucial role of self-efficacy, planning, and action control in directly influencing physical activity. Notably, self-efficacy was found to exert the most profound effect. Outcome expectation was shown to influence physical activity indirectly through intention and planning. This highlights the potential for nurses to design interventions that foster self-efficacy, plan­ning, and self-regulatory strategies. These strategies can empower patients to overcome challenges in cardiac rehabilitation and maintain physical activities, thereby improving their health outcomes. However, it is important to stress that our proposed intervention should be rigorously tested for effectiveness before implementation.

References

Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avey CL, et al. Heart disease and stroke statistics-2023 update: a report from the American Heart Association. Circulation. 2023;147(8):e93-621. doi:10.1161/CIR.0000000000001123. DOI: https://doi.org/10.1161/CIR.0000000000001137

Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation. 2024;149(8):e347-913. doi:10.1161/CIR.0000000000001209. DOI: https://doi.org/10.1161/CIR.0000000000001247

Thai ACS Registry. Summary ACS registry 2022 [Internet]. 2022 [cited 2023 Oct 1]. Available from: http://ncvdt.org/document/THAIACSREGISTRY65.pdf (in Thai).

Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41(3):407-77. doi:10.1093/eurheartj/ehz425. DOI: https://doi.org/10.1093/eurheartj/ehz425

Chacko LP, Howard J, Rajkumar C, Nowbar AN, Kane C, Mahdi D, et al. Effects of percutaneous coronary intervention on death and myocardial infarction stratified by stable and unstable coronary artery disease: a meta-analysis of randomized controlled trials. Circ Cardiovasc Qual Outcomes. 2020; 13(2):e006363. doi:10.1161/CIRCOUTCOMES.119.006363. DOI: https://doi.org/10.1161/CIRCOUTCOMES.119.006363

Kronish IM, Diaz KM, Goldsmith J, Moise N, Schwartz JE. Objectively measured adherence to physical activity guidelines after acute coronary syndrome. J Am Coll Cardiol. 2017; 69(9):1205-7. doi:10.1016/j.jacc.2016.10.087. DOI: https://doi.org/10.1016/j.jacc.2016.10.087

Liu F, Han J, Wang Y, Jin Y. The later status and impact factors of physical activity among patients after percutaneous coronary intervention in China. Am J Health Behav. 2022;46(6):654-63. doi:10.5993/AJHB.46.6.8. DOI: https://doi.org/10.5993/AJHB.46.6.8

Wang J, Liu H, Chen C, Chang W, Ma Y, Zhao C, et al. Physical activity and factors affecting its maintenance among patients with coronary heart disease not undergoing cardiac rehabilitation in China. J Cardiovasc Nurs. 2020;35(6):558-67. doi:10.1097/JCN.0000000000000698. DOI: https://doi.org/10.1097/JCN.0000000000000698

Zhang H, Chang R. Effects of exercise after percutaneous coronary intervention on cardiac function and cardiovascular adverse events in patients with coronary heart disease: systematic review and meta-analysis. J Sports Sci Med. 2019;18(2):213-22.

Ek A, Ekblom Ö, Hambraeus K, Cider A, Kallings LV, Börjesson M. Physical inactivity and smoking after myocardial infarction as predictors for readmission and survival: results from the SWEDEHEART-registry. Clin Res Cardiol. 2019;108(3):324-32. doi:10.1007/s00392-018-1360-x. DOI: https://doi.org/10.1007/s00392-018-1360-x

Li S, Barywani S, Fu M. Relationship between physical inactivity and long-term outcome in patients aged ≥ 80 years with acute coronary syndrome. Curr Med Sci. 2018;38(1):64-9. doi:10.1007/s11596-018-1847-8. DOI: https://doi.org/10.1007/s11596-018-1847-8

Schwarzer R, Hamilton K. Changing behavior using the health action process approach. In: Hagger MS, Cameron LD, Hamilton K, Hankonen N, Lintunen T, editors. The handbook of behavior change [Internet]. 2020 [cited 2022 Mar 2]. Available from: https://www.cambridge.org/core/books/the-handbook-of-behavior-change/BD76D0E53DB1BC2EE8C53C1603D45E6A

Schwarzer R. Health action process approach (HAPA) as a theoretical framework to understand behavior change. Act Psic. 2016;30(121):119-30. doi:10.15517/ap.v30i121.23458. DOI: https://doi.org/10.15517/ap.v30i121.23458

Bandura A. Self-efficacy: the exercise of control. New York: W.H. Freeman and Company; 1997. 604 p. ISBN: 9780716728504.

Han NS, Won MH. Association between social support and physical activity in patients with coronary artery disease: multiple mediating roles of self-efficacy and autonomous motivation. Healthcare. 2022;10(3):425. doi:10.3390/healthcare10030425. DOI: https://doi.org/10.3390/healthcare10030425

Wannapo P, Kunsongkeit W, Duangpaeng S. Factors influencing physical activity among patients with acute myocardial infarction receiving percutaneous coronary intervention. J Health Sci Res [Internet]. 2018 [cited 2023 Mar 2];12(2):10-8. Available from: https://he01.tci-thaijo.org/index.php/JHR/article/view/164232/119011(in Thai).

Steca P, Pancani L, Cesana F, Fattirolli F, Giannattasio C, Greco A, et al. Changes in physical activity among coronary and hypertensive patients: a longitudinal study using the health action process approach. Psychol Health. 2017;32(3):361-80. doi:10.1080/08870446.2016.1273353. DOI: https://doi.org/10.1080/08870446.2016.1273353

Mohammadi Zeidi I, Morshedi H, Shokohi A. Predicting psychological factors affecting regular physical activity in hypertensive patients: application of health action process approach model. Nurs Open. 2021;8(1):442-52. doi:10.1002/nop2.645. DOI: https://doi.org/10.1002/nop2.645

Yi G, Xin S, Ze-ming L, Ping C, Min G, Xue-ying C, et al. Analysis of the status quo of exercise behavior in type 2 diabetic patients and the influencing factors analysis based on the health action process approach model. CJDCP. 2022;26(1):56-60, 98. doi:10.16462/j.cnki.zhjbkz.2022.01.010 (in Chinese).

Hardcastle SJ, Maxwell-Smith C, Hagger MS. Predicting physical activity change in cancer survivors: an application of the Health Action Process Approach. J Cancer Surviv. 2022;16(6):1176-83. doi:10.1007/s11764-021-01107-6. DOI: https://doi.org/10.1007/s11764-021-01107-6

Schwarzer R, Sniehotta FF, Lippke S, Luszczynska A, Scholz U, Schüz B, et al. On the assessment and analysis of variables in the health action process approach: conducting an investigation [Internet]. 2003 [cited 2022 Mar 2]. Available from: https://www.semanticscholar.org/paper/On-the-assessment-and-analysis-of-variables-in-the-Schwarzer-Sniehotta/52e4a98d0558eaf3e60dad1ead2cd647306673e9

Teleki S, Zsidó AN, Lénárd L, Komócsi A, Kiss EC, Tiringer I. Role of received social support in the physical activity of coronary heart patients: the health action process approach. Appl Psychol Health Well-Being. 2022;14(1): 44-63. doi:10.1111/aphw.12290. DOI: https://doi.org/10.1111/aphw.12290

Schwarzer R, Luszczynska A. Health action process approach. In: Conner M, Norman P, editors. Predicting and changing health behaviour: research and practice with social cognition models. Maidenhead: Open University Press; 2015. pp. 252-78.

Sequeira M, Pereira C, Alvarez M. Predicting physical activity in survivors of breast cancer: the Health Action Process Approach at the intrapersonal level. Int J Behav Med. 2023;30(6): 777-89. doi:10.1007/s12529-022-10140-3. DOI: https://doi.org/10.1007/s12529-022-10140-3

Hsu HJ, Chung DT, Lee LY, Lin IP, Chen SC. Beliefs, benefits and barriers associated with physical activity: impact of these factors on physical activity in patients with type II diabetes mellitus. Clin Nurs Res. 2021;30(3):302- 10. doi:10.1177/1054773820967699. DOI: https://doi.org/10.1177/1054773820967699

Kanejima Y, Kitamura M, Izawa KP. Self-monitoring to increase physical activity in patients with cardiovascular disease: a systematic review and meta-analysis. Aging Clin Exp Res. 2019;31(2):163-73. doi:10.1007/s40520-018-0960-7. DOI: https://doi.org/10.1007/s40520-018-0960-7

Sebastian AT, Rajkumar E, Tejaswini P, Lakshmi R, Romate J. Applying social cognitive theory to predict physical activity and dietary behavior among patients with type-2 diabetes. Health Psychol Res. 2021;9(1):24510. doi:10.52965/001c.24510. DOI: https://doi.org/10.52965/001c.24510

Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. New Jersey: Prentice Hall; 2009. 816 p. ISBN: 9780138132637.

Elfil M, Negida A. Sampling methods in clinical research: an educational review. Emerg (Tehran). 2017;5(1):e52. PMID: 28286859.

Berndt AE. Sampling methods. J Hum Lact. 2020;36(2): 224-6. doi:10.1177/0890334420906850. DOI: https://doi.org/10.1177/0890334420906850

Dolgin M. Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. 9th ed. New York: Little, Brown & Co; 1994. 352 p. ISBN: 9780316605380.

Cha ES, Kim KH, Erlen JA. Translation of scales in cross‐cultural research: issues and techniques. J Adv Nurs. 2007;58(4):386-95. doi:10.1111/j.1365-2648.2007.04242.x. DOI: https://doi.org/10.1111/j.1365-2648.2007.04242.x

Leethong-in M, Yunibhand J, Aungsuroch Y, Magiivy JK. Assessment of the environmental support for physical activity scale among Thai elderly. Chula Med J. 2011;55(5): 421-35. doi:10.58837/CHULA.CMJ.55.5.2 (in Thai). DOI: https://doi.org/10.58837/CHULA.CMJ.55.5.2

Scholz U, Sniehotta FF, Schwarzer R. Predicting physical exercise in cardiac rehabilitation: the role of phase-specific self-efficacy beliefs. J Sport Exerc Psychol. 2005;27(2): 135-51. doi:10.5167/uzh-102309. DOI: https://doi.org/10.1123/jsep.27.2.135

Sniehotta FF, Scholz U, Schwarzer R. Bridging the intention-behaviour gap: planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychol Health. 2005;20(2):143-60. doi: 10.1080./08870440512331317670. DOI: https://doi.org/10.1080/08870440512331317670

Sniehotta FF, Schwarzer R, Scholz U, Schüz B. Action planning and coping planning for long‐term lifestyle change: theory and assessment. Eur J Soc Psychol. 2005;35(4):565-76. doi:10.1002/ejsp.258. DOI: https://doi.org/10.1002/ejsp.258

Kline RB. Principle and practice of structural equation modeling. 4th ed. New York: The Guilford; 2016. p. 272-8. ISBN: 9781462523344.

Gómez de Mariscal E, Guerrero V, Sneider A, Jayatilaka H, Phillip JM, Wirtz D, et al. Use of the p values as a size dependent function to address practical differences when analyzing large datasets. Sci Rep. 2021;11(1):20942. doi:10.1038/s41598-021-00199-5. DOI: https://doi.org/10.1038/s41598-021-00199-5

Sukrueangkul A, Vannarit T, Tachaudomdach C. Factors predicting health promoting behaviors among people with coronary artery disease and undergone percutaneous coronary intervention. Nurs J CMU [Internet]. 2019 [cited 2023 Mar 2];43(6):118-29. Available from: https://he02.tci-thaijo.org/index.php/cmunursing/article/view/218544 (in Thai).

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Published

2024-06-01

How to Cite

1.
Inkaew T, Wonghongkul T, Tachaudomdach C, Srirat C. A Causal Model of Physical Activity Among People with Acute Coronary Syndrome After Percutaneous Coronary Intervention: A Cross-sectional Study. PRIJNR [Internet]. 2024 Jun. 1 [cited 2024 Jul. 24];28(3):567-82. Available from: https://he02.tci-thaijo.org/index.php/PRIJNR/article/view/267807