Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-05T02:29:11.873Z Has data issue: false hasContentIssue false
  • English
  • Français

The Impact of Socio-environmental Barriers on the Process of Engagement in Cardiac Rehabilitation Programs

Published online by Cambridge University Press:  11 August 2020

Sepideh Jahandideh Open the ORCID record for Sepideh Jahandideh [Opens in a new window] ,
Elizabeth Kendall ,
Samantha Low-Choy ,
Kenneth Donald ,
Rohan Jayasinghe  and
Ebrahim Barzegari
Sepideh Jahandideh*
Affiliation:
School of Human Services and Social Work, Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
Elizabeth Kendall
Affiliation:
School of Human Services and Social Work, Menzies Health Institute Queensland, Griffith University, Meadowbrook, Queensland, Australia
Samantha Low-Choy
Affiliation:
Griffith Social and Behavioral Research College, Griffith University, Gold Coast, Queensland, Australia
Kenneth Donald
Affiliation:
School of Medicine, Griffith University, Gold Coast, Queensland, Australia
Rohan Jayasinghe
Affiliation:
Medical Director, Cardiology Department, Gold Coast University Hospital, Gold Coast, Queensland, Australia
Ebrahim Barzegari
Affiliation:
Medical Biology Research Centre, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
*
*Corresponding author: Sepideh Jahandideh, School of Human Services and Social Work, Menzies Health Institute Queensland, Gold Coast Campus, Griffith University, QLD, Australia. E-mail: sepideh.jahandideh@alumni.griffithuni.edu.au
Get access

Abstract

Cardiac rehabilitation (CR) is a multi-disciplinary intervention designed to stabilise, slow, or reverse CVD, restore health following a cardiac event and facilitate the prevention of further events. The Model of Therapeutic Engagement (MTE) is a comprehensive conceptual model for explaining the process of engagement in rehabilitation. Of concern is that the role of socio-environmental factors is absent from explaining individual engagement in the MTE. There is also a lack of prospective studies investigating the impact of socio-environmental barriers on engagement in CR programs over time. This study aimed to expand the MTE, by illuminating the role of socio-environmental barriers in a three-stage process of engagement in CR programs. A prospective study was conducted, with 217 individuals recruited from the Cardiology Ward in the Gold Coast University Hospital (GCUH) and the Robina Cardiac Rehabilitation Centre. The collected data were examined using a structural equation model that added socio-environmental factors into the MTE, using multi-group analyses. In this study, we found that socio-environmental factors were not associated with intention to engage in the CR program, but were related to actual attendance and maintenance of participation in CR programs. Knowing how these socio-environmental barriers affect the process of engagement at different stages may help to tailor more accessible CR programs for the population.

Keywords

cardiac rehabilitationindividual engagementsocio-environmental barriersstructural equation modelling
Type
Standard Paper
Information
Behaviour Change , Volume 37 , Issue 3 , September 2020 , pp. 130 - 150
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al Quait, A, Doherty, P, Gutacker, N and Mills, J (2017). In the modern era of percutaneous coronary intervention: Is cardiac rehabilitation engagement purely a patient or a service level decision? European Journal of Preventive Cardiology, 24, 1351–1357.CrossRefGoogle ScholarPubMed
Anderson, JC and Gerbing, DW (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411.CrossRefGoogle Scholar
Arbuckle, JL (2012). IBM SPSS Amos 21 User's Guide. Chicago: IBM.Google Scholar
Arbuckle, JL and Wothke, W (1999). Amos 4.0 User's guide. Chicago, IL: SmallWaters Corporation.Google Scholar
Bains, B, Powell, T and Lorenc, L (2007). An exploratory study of mental representations for rehabilitation based upon the Theory of Planned Behaviour. Neuropsychological Rehabilitation, 17, 174–191.CrossRefGoogle ScholarPubMed
Banerjee, ATM, Grace, SLP, Thomas, SGP and Faulkner, GP (2010). Cultural factors facilitating cardiac rehabilitation participation among Canadian South Asians: A qualitative study. Heart & Lung: The Journal of Acute and Critical Care, 39, 494–503.CrossRefGoogle ScholarPubMed
Blasche, G and Marktl, W (2011). Recovery intention: Its association with fatigue in the working population. International Archives of Occupational and Environmental Health, 84, 859–865.CrossRefGoogle ScholarPubMed
Bollen, KA and Long, JS (1993) Testing structural equation models. Newbury Park: CA, USA: Sage.Google Scholar
Browne, MW and Cudeck, R (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136–136.Google Scholar
Cangur, S and Ercan, I (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14, 14.CrossRefGoogle Scholar
Chervinsky, AB, Ommaya, AK, deJonge, M, Spector, J, Schwab, K and Salazar, AM (1998). Motivation for traumatic brain injury rehabilitation questionnaire (MOT-Q): Reliability, factor analysis, and relationship to MMPI-2 variables. Archives of Clinical Neuropsychology, 13, 433–446.CrossRefGoogle ScholarPubMed
Clark, AM, Redfern, J and Briffa, T (2014). Cardiac rehabilitation: Fit to face the future? Heart, 100, 355–356.CrossRefGoogle ScholarPubMed
Cohen, J, Cohen, P, West, SG and Aiken, LS (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: erlbaum Associates.Google Scholar
Creswell, JW and Plano Clark, VL (2007). Designing and conducting mixed methods research. Thousand Oaks, Calif: SAGE Publications.Google Scholar
de Melo Ghisi, GL, Oh, P, Benetti, M and Grace, SL (2013). Barriers to cardiac rehabilitation use in Canada versus Brazil. Journal of Cardiopulmonary Rehabilitation and Prevention, 33, 173–179.CrossRefGoogle ScholarPubMed
de Melo Ghisi, GL, dos Santos, RZ, Aranha, EE, Nunes, AD, Oh, P, Benetti, M and Grace, SL (2013). Perceptions of barriers to cardiac rehabilitation use in Brazil. Vascular Health and Risk Management, 9, 485–491.CrossRefGoogle Scholar
Ding, L, Velicer, WF and Harlow, LL (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2, 119–143.CrossRefGoogle Scholar
Doig, E, Prescott, S, Fleming, J, Cornwell, P and Kuipers, P (2015). Development and construct validation of the Client-Centredness of Goal Setting (C-COGS) scale. Scandinavian Journal of Occupational Therapy, 22, 302–310.CrossRefGoogle ScholarPubMed
Dunlay, SM, Witt, BJ, Allison, TG, Hayes, SN, Weston, SA, Koepsell, E and Roger, VL (2009). Barriers to participation in cardiac rehabilitation. American Heart Journal, 158, 852–859.CrossRefGoogle ScholarPubMed
Evenson, KR and Fleury, J (2000). Barriers to outpatient cardiac rehabilitation participation and adherence. Journal of Cardiopulmonary Rehabilitation and Prevention, 20, 241–246.CrossRefGoogle ScholarPubMed
Fernandez, RS, Salamonson, Y, Juergens, C, Griffiths, R and Davidson, P (2008). Development and preliminary testing of the Cardiac Rehabilitation Enrolment Obstacles (CREO) Scale: Implications for Service Development. European Journal of Cardiovascular Nursing, 7, 96–102.CrossRefGoogle ScholarPubMed
Gardiner, FW, Nwose, EU, Regan, E, Park, BK, Bwititi, PT, Crockett, J and Wang, L (2018). Outpatient cardiac rehabilitation: Patient perceived benefits and reasons for non-attendance. Collegian.CrossRefGoogle Scholar
Gaskin, J. (2016). Excel StatTools. Stats tools package.Google Scholar
George, D and Mallery, P (2003). Reliability Analysis In SPSS for Windows, step by step: A simple guide and reference (14th ed., pp. 222–232). Boston: Pearson Education.Google Scholar
Giuliano, C, Cowie, K, Saliba, J, Scholes, E, Fisher, K, Cox, N and Neil, C (2015). Barriers to exercise rehabilitation in the older adult with heart failure. Heart, Lung and Circulation, 24, S450–S450.CrossRefGoogle Scholar
Grace, SL, Gravely-Witte, S, Kayaniyil, S, Brual, J, Suskin, N and Stewart, DE (2009). A multisite examination of sex differences in cardiac rehabilitation barriers by participation status. Journal of Women's Health, 18, 209–216.CrossRefGoogle ScholarPubMed
Hagan, NA, Botti, MA and Watts, RJ (2007). Financial, family, and social factors impacting on cardiac rehabilitation attendance. Heart & Lung, 36, 105–113.CrossRefGoogle ScholarPubMed
Hair, JF Jr, Black, W, Babin, B and Anderson, R (2010). Multivariate. Upper Saddle River, NJ: Pearson Education, Inc.Google Scholar
Hayduk, LA (1987). Structural equation modeling with LISREL: Essentials and advances. Johns Hopkins University Press.Google Scholar
Hukkelberg, SS and Dykstra, JL (2009). Using the Prototype/Willingness model to predict smoking behaviour among Norwegian adolescents. Addictive Behaviors, 34, 270–276.CrossRefGoogle ScholarPubMed
Jahandideh, S, Kendall, E, Low-Choy, S, Donald, K and Jayasinghe, R (2018). The process of patient engagement in cardiac rehabilitation: A model-centric systematic review. Behaviour Change, 35, 185–202.CrossRefGoogle Scholar
Jahandideh, S, Kendall, E, Low-Choy, S, Donald, K, Jayasinghe, R and Barzegari, E (2019). The process of patient engagement in outpatient cardiac rehabilitation programs. Behaviour Change, 36, 233–251.CrossRefGoogle Scholar
José Liébana-Cabanillas, F, Sánchez-Fernández, J and Muñoz-Leiva, F (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114, 220–240.CrossRefGoogle Scholar
Kline, T (2005). Psychological testing: A practical approach to design and evaluation. Thousand Oaks, California: Sage Publications.Google Scholar
Kortte, KB, Falk, LD, Castillo, RC, Johnson-Greene, D and Wegener, ST (2007). The Hopkins rehabilitation engagement rating scale: Development and psychometric properties. Archives of Physical Medicine and Rehabilitation, 88, 877–884.CrossRefGoogle ScholarPubMed
Law, M, Baptiste, S, Carswell, A, McColl, MA, Polatajko, H and Pollock, N (2014). Canadian occupational performance measure (5th ed.). Ottawa: CAOT Publications.Google Scholar
Lequerica, AH and Kortte, K (2010). Therapeutic engagement: A proposed model of engagement in medical rehabilitation. American Journal of Physical Medicine & Rehabilitation, 89, 415–422.CrossRefGoogle ScholarPubMed
Marcoulides, GA and Schumacker, RE (1996). Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: L. Erlbaum Associates.Google Scholar
Matterne, U, Diepgen, TL and Weisshaar, E (2011). A longitudinal application of three health behaviour models in the context of skin protection behaviour in individuals with occupational skin disease. Psychology & Health, 26, 1188–1207.CrossRefGoogle ScholarPubMed
McCarthy, MM, Vaughan Dickson, V and Chyun, D (2011). Barriers to cardiac rehabilitation in women with cardiovascular disease: An integrative review. The Journal of Cardiovascular Nursing, 26, E1–E10.CrossRefGoogle Scholar
McMurray, JP, McNeil, HMA, Lafortune, CB, Black, SB, Prorok, JM and Stolee, PP (2016). Measuring patients’ experience of rehabilitation services across the care continuum. Part II: Key dimensions. Archives of Physical Medicine and Rehabilitation, 97, 121–130.CrossRefGoogle ScholarPubMed
Norušis, MJ (2012). IBM SPSS statistics 19 statistical procedures companion. Upper Saddle River, NJ, USA: Prentice Hall.Google Scholar
Redfern, J, Hyun, K, Chew, DP, Astley, C, Chow, C, Aliprandi-Costa, B, … Briffa, T (2014). Prescription of secondary prevention medications, lifestyle advice, and referral to rehabilitation among acute coronary syndrome inpatients: Results from a large prospective audit in Australia and New Zealand. Heart, 100, 1281–1288.CrossRefGoogle ScholarPubMed
Resurrección, DM, Motrico, E, Rubio-Valera, M, Mora-Pardo, JA and Moreno-Peral, P (2018). Reasons for dropout from cardiac rehabilitation programs in women: A qualitative study. Public Library of Science one, 13, e0200636.Google ScholarPubMed
Rogerson, MC, Murphy, BM, Bird, S and Morris, T (2012). “I don't have the heart”: A qualitative study of barriers to and facilitators of physical activity for people with coronary heart disease and depressive symptoms. International Journal of Behavioral Nutrition and Physical Activity, 9, 140.CrossRefGoogle ScholarPubMed
Rouleau, CR, King-Shier, KM, Tomfohr-Madsen, LM, Aggarwal, SG, Arena, R and Campbell, TS (2018). A qualitative study exploring factors that influence enrollment in outpatient cardiac rehabilitation. Disability and Rehabilitation, 40, 469–478.CrossRefGoogle ScholarPubMed
Safarnia, H, Akbari, Z and Abbasi, A (2011). Review of competitive intelligence & competitive advantage in the industrial estates companies in the Kerman City: Appraisal and testing of model by Amos graphics. International Business and Management, 2, 47–61.Google Scholar
Schwarzer, R (2001). Social-cognitive factors in changing health-related behaviors. Current Directions in Psychological Science, 10, 47–51.CrossRefGoogle Scholar
Scott, IA, Lindsay, KA and Harden, HE (2003). Utilisation of outpatient cardiac rehabilitation in Queensland. Medical Journal of Australia, 179, 341–345.CrossRefGoogle ScholarPubMed
Sniehotta, FF, Schwarzer, R, Scholz, U and Schüz, B (2005). Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35, 565–576.CrossRefGoogle Scholar
Soper, DS (2018). A-priori Sample Size Calculator for Structural Equation Models. Retrieved from Stats Tools Package: http://www.danielsoper.com/statcalc.Google Scholar
Turner-Stokes, L, Vanderstay, R, Stevermuer, T, Simmonds, F, Khan, F and Eagar, K (2015). Comparison of rehabilitation outcomes for long-term neurological conditions: A cohort analysis of the Australian Rehabilitation Outcomes Centre Dataset for Adults of Working Age. Public Library of Science One, 10, e0132275.Google ScholarPubMed
Wasserstein, RL and Lazar, NA (2016). The ASA's statement on p-values: Context, process, and purpose. The American Statistician, 70, 129–133.CrossRefGoogle Scholar
Woodruffe, S, Neubeck, LP, Clark, RAP, Gray, K, Ferry, C, Finan, JMN, … Briffa, TGP (2015). Australian Cardiovascular Health and Rehabilitation Association (ACRA) Core Components of Cardiovascular Disease Secondary Prevention and Cardiac Rehabilitation 2014. Heart, Lung and Circulation, 24, 430–441.CrossRefGoogle ScholarPubMed
World Health Organization (1993). Action priorities in cardiac rehabilitation and secondary prevention in patients with CHD. Geneva: WHO.Google Scholar