Journal List > Korean J Adult Nurs > v.31(1) > 1116799

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



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.


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.


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.


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.


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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)
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)
Yes 255 (90.4)
Job No 149 (52.8)
Yes 133 (47.2)
Smoking No 201 (71.3)
Yes 81 (28.7)
Alcohol intake No 96 (34.0)
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)
Grade II 83 (29.4)
Previous PCI No 175 (62.1)
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
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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