A Prediction Model for Physical Activity Adherence for Secondary Prevention among Patients with Coronary Artery Disease

Mi Hwa Won; Oksoo Kim

doi:10.7475/kjan.2019.31.1.78

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Won and Kim: A Prediction Model for Physical Activity Adherence for Secondary Prevention among Patients with Coronary Artery Disease

Original Article

Korean J Adult Nurs 2019;31(1):78-88.

Published online: 21 February 2019

DOI: https://doi.org/10.7475/kjan.2019.31.1.78

A Prediction Model for Physical Activity Adherence for Secondary Prevention among Patients with Coronary Artery Disease

Mi Hwa Won^1,, Oksoo Kim²

¹Assistant Professor, Department of Nursing, Wonkwang University, Ilsan, Korea

²Professor, College of Nursing, Ewha Womans University, Seoul, Korea

Corresponding author: Won, Mi Hwa https://orcid.org/0000-0002-3452-7295 Department of Nursing, Wonkwang University, 460 Iksan-daero, Iksan 54538, Korea. Tel: +82-63-850-6045, Fax: +82-63-850-6666, E-mail: mihwon7729@wku.ac.kr

Received 26 December 201816 January 2019 Accepted 28 January 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

The purpose of this study was to construct and test a predictive model for physical activity adherence for secondary prevention among patients with coronary artery disease.

Methods

Two hundred and eighty-two patients with coronary artery disease were recruited at cardiology outpatient clinics in four general hospitals and the data collection was conducted from September 1 to October 19, 2015.

Results

The model fit indices for the final hypothetical model satisfied the recommended levels: x²/dF=0.77, adjusted goodness of fit index=.98, comparative fit index=1.00, normal fit index=1.00, incremental fit index=1.00, standardized root mean residual=.01, root mean square error of approximation=.03. Autonomy support (β=.50), competence (β=.27), and autonomous motivation (β=.31) had significant direct effects on physical activity adherence for secondary prevention among patients with coronary artery disease. This variable explained 35.1% of the variance in physical activity adherence.

Conclusion

This study showed that autonomy support from healthcare providers plays a key role in promoting physical activity adherence for secondary prevention among patients with coronary artery disease. The findings suggest that developing intervention programs to increase feelings of competence and autonomous motivation through autonomy support from healthcare providers are needed to promote physical activity adherence for secondary prevention among patients with coronary artery disease.

Keywords: Exercise, Compliance, Coronary artery disease, Models, Structural

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Figure 1.

Conceptual framework for physical activity adherence among patients with coronary artery disease.

Figure 2.

Path diagram of the final model.

Table 1.

Sociodemographic and Clinical Characteristic among Patients with Coronary artery Disease (N=28

Characteristics	Categories	n (%) or M±SD
Age (year)		62.27±9.3
	65	157 (55.7)
	≥65	125 (44.3)
Gender	Men	171 (60.6)
Gender	Women	111 (39.4)
Education	Below elementary school	44 (15.6)
	Middle school	75 (26.6)
	High school	107 (37.9)
	Above collage	56 (19.9)
Spouse	No	27 (9.6)
Spouse	Yes	255 (90.4)
Job	No	149 (52.8)
Job	Yes	133 (47.2)
Smoking	No	201 (71.3)
Smoking	Yes	81 (28.7)
Alcohol intake	No	96 (34.0)
Alcohol intake	Yes	186 (66.0)
BMI (kg/m²)		23.99±2.5
	≤22.9	98 (34.8)
	23.0~24.9	83 (29.4)
	≥25.0	103 (35.8)
CCSA classification	Grade I	199 (70.6)
CCSA classification	Grade II	83 (29.4)
Previous PCI	No	175 (62.1)
Previous PCI	Yes	107 (37.9)
Duration of		4.29±3.69
diagnosis (year)
CCI		1.44±0.87

BMI=body mass index; CCI=charlson comorbidity index;

CCSA=Canadian Cardiovascular Society Angina; PCI=percutaneous coronary intervention.

Table 2.

Descriptive Statistics of Study Variables among Patients with Coronary Artery Disease (N=282)

Variables	M± SD	Min	Max	Skewness	Kurtosis
Autonomy support	75.78±12.38	43	104	-0.18	-0.64
Autonomy	21.26±4.31	8	30	-0.13	-0.37
Relatedness	20.97±3.58	11	30	0.26	-0.03
Competence	20.93±4.52	8	30	-0.05	-0.48
Autonomous motivation	33.69±5.78	9	42	-0.46	0.26
Physical activity adherence (METs min/week)	1,892.74±1,455.38	66	7,092	1.23	1.38

MET=metabolic equivalent task.

Table 3.

Effects of Predictor Variables in the Final Model (N=282)

Endogenous variables	Exogenous variables	SE	CR	p	SMC	Direct effect		Indirect effect		Total effect
Endogenous variables	Exogenous variables	SE	CR	p	SMC	β	p^†	β	p^†	β	p^†
Autonomy	Autonomy support	0.02	9.66	.001	.249	.50	.002			.50	.002
Relatedness	Autonomy support	0.01	11.69	.001	.327	.57	.002			.57	.002
Competence	Autonomy support	0.02	9.09	.001	.678	.39	.002	.28	.003	.67	.002
	Autonomy	0.04	13.56	.001		.55	.003			.55	.003
	Relatedness	0.05	0.01	.966		.30	.996			.30	.996
Autonomous motivation	Autonomy support	0.03	4.61	.001	.376	.29	.002	.25	.001	.54	.001
	Autonomy							.21	.002	.21	.002
	Relatedness							.01	.995	.01	.995
	Competence	0.08	5.94	.001		.38	.002			.38	.002
Physical activity adherence	Autonomy support	7.90	3.41	.001	.351	.23	.004	.27	.002	.50	.008
	Autonomy	22.30	2.17	.030		.15	.068	.15	.001	.15	.001
	Relatedness	15.31	5.16	.001		.31	.002	.01	.992	.01	.992
	Competence							.12	.001	.27	.002
	Autonomous motivation									.31	.002

SE=standard estimate; CR=critical ratio; SMC=squared multiple correlation;

^† Bootstrapping method.

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