Associated factors of sedentary behavior among patients with coronary artery disease based on health belief model

doi:10.21203/rs.3.rs-3774465/v1

Background

People’s health belief has been an important factor affecting health behavior. However, there has been little use of the health belief model (HBM) in determining the pathway effect of patients’ health belief on sedentary behavior among patients with coronary artery disease (CHD). The goal of our study was to evaluate determinants of sedentary behavior among patients with CHD based on the HBM.

Methods

It was a cross-sectional study. A survey about health belief and sedentary behavior was completed by 379 adults with CHD from February to August 2023. The causal relationship between HBM-related factors and sedentary behavior was explored using a structural equation model.

Results

A total of 379 complete responses were included; 67.0% of participants were male. The mean sedentary time was (7.18 ± 2.64) h/d. The model fit the data from the study well. Perceived barriers (total effect 0.296, P < 0.01) had promoting effects on sedentary behavior among patients with CHD. Self-efficacy (total effect − 0.253, P < 0.01), the knowledge of sedentary behavior (total effect − 0.279, P < 0.01), perceived susceptibility (total effect − 0.084, P < 0.05), perceived severity (total effect − 0.317, P < 0.01), perceived benefits (total effect − 0.266, P < 0.01) and health motivation (total effect − 0.105, P < 0.05) had negative effects on sedentary behavior. The relationship between the knowledge of sedentary behavior, perceived severity, perceived barriers, and perceived benefits on sedentary behavior were moderated by self-efficacy. The health belief could explain 32.9% of sedentary behavior among patients with CHD (P < 0.05).

Conclusions

The HBM constructs can serve as good predictors of sedentary behavior. Community medical staff can develop targeted sedentary behavior interventions among patients with CHD based on the health belief model in the future.

Sedentary behavior

Coronary artery disease

Health belief model

Structural equation model

Coronary heart disease (CHD) is a clinical disease resulting from the narrowing or blockage of coronary arteries due to the buildup of atherosclerotic plaques ¹. CHD includes stable ischemic heart disease (SIHD), atherosclerotic heart disease (AHD), and acute myocardial infarction (AMI) as major types ¹. It stands as the foremost contributor to mortality in the United States (US) ². In the United States, the age-standardized CHD mortality rate is 66.8 and 156.7 per 100,000 in 2019 among females and males, respectively ³. From 2017 to 2018, the estimated annual average direct and indirect costs related to heart disease in the United States amounted to $228.7 billion ². The total inpatient expenditure for CHD in China reached 125.625 billion RMB. In 2019 ⁴. The onset of coronary heart disease is attributed to various risk factors, including inadequate physical activity, smoking, and sedentary behavior (SB) ². Notably, researchers found that prolonged sedentary behavior (SB) can result in various health consequences, including but not limited to insulin resistance, impaired vascular function, reduced cardiorespiratory fitness, muscle and bone mass loss, increased overall and visceral fat, and heightened inflammation ⁵. Sedentary behavior in this context is described as "any awake activity marked by an energy expenditure ≤ 1.5 METs [metabolic equivalent units] while in a seated or lying position." ⁶. Typically, these encompass activities such as working on a computer, viewing television, and driving a vehicle.

A 2019 sedentary behavior survey in the US reported that half of adults spend more than 9.5 hours of their day sitting ⁷. A scoping review of sedentary behavior among the Chinese population revealed that 10.7% of adults spend more than 6 hours a day engaged in sedentary behavior ⁸. Furthermore, research indicates that patients with coronary heart disease tend to engage in longer periods of sedentary behavior compared to those without CHD ^9–11. Previous studies showed cardiac rehabilitation patients have an average daily sedentary time of 8–10 hours ^11,12. In a study involving 131,558 American cardiovascular disease patients, 27.2% reported being entirely sedentary without any physical activity ¹³. Previous work has detailed the potential links between sedentary behavior and a range of health risks among patients with CHD, including associations with both mortality rates and psychological health ^14,15.

Gaining a profound comprehension of the factors related to sedentary behavior is essential when developing targeted intervention programs ¹⁶. For researchers in the field of CHD patients’ intervention, theoretical frameworks can be instrumental in systematically exploring the factors influencing sedentary behavior. The health belief model (HBM) has been extensively employed as one of the behavioral frameworks to evaluate the cognitive factors that influence health-related behaviors, including physical activity ¹⁷, the willingness of the COVID-19 vaccine booster shots ¹⁸, self-care behaviors ¹⁹, and breast cancer screening ²⁰. HBM is based on the principle that individuals perform healthy behavior if they feel that they are at risk (perceived susceptibility), the risks of unsafe behavior are serious (perceived severity), the healthy behavior is beneficial for them (perceived benefits), the barriers to healthy behavior can be removed (perceived barriers), and they are able to have healthy behavior (self-efficacy) ²¹. In the present study, we examined the possible relationships between the HBM constructs and sedentary behavior among CHD patients. We attempted to identify the factors associated with sedentary behavior among the patients based on the HBM.

Design and study population

A sample of 380 coronary heart disease (CHD) patients was selected through convenient sampling at a tertiary hospital's cardiology outpatient department in Shanghai from February to August 2023. All participants gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Ruijin Hospital Affiliated to Medical School of Shanghai Jiao Tong University.

The questionnaire was carried out anonymously in accordance with the voluntary principle. A total of 380 questionnaires were collected for this study. One questionnaire with obvious errors (all items were selected with the same option) was removed, and 379 valid questionnaires were finally recovered, with an effective recovery rate of 97.7%, and the sample size could meet the needs of our study.

Eligible adults were recruited from China using convenient sampling. The inclusion criteria were as follows: (1) age 18 years or older; (2) confirmed diagnosis of CHD by a senior physician or higher; (3) clear consciousness, effective communication skills, and the ability to complete the questionnaire; (4) willingness to provide informed consent and participate voluntarily in the study. The exclusion criteria were: (1) severe organic heart diseases, malignancies, renal or hepatic failure, or other serious medical conditions; (2) being unable to engage in independent physical activities; (3) having profound hearing or visual impairments, cognitive dysfunction, or psychiatric disorders.

Measures

Demographic characteristic questionnaire

The questionnaire covered the demographic characteristics of participants, including age, gender, marital status, education level, BMI, comorbidities, history of percutaneous coronary intervention, presence of family members requiring care, fear of discomfort after physical activity, and importance of reducing sedentary behavior.

Sedentary behavior knowledge questionnaire

The questionnaire was self-designed based on definition the of sedentary behavior ⁶ and guidelines by WHO ²² to assess the knowledge of sedentary behavior. A total of 13 questions were included in this questionnaire. These items concerned the definition of sedentary behavior and its effects on health. A pilot survey involving 20 coronary heart disease patients attending cardiology outpatient clinics was conducted. The Cronbach's α was 0.727.

Champion Health Belief Model Scale

The Champion Health Belief Model Scale (CHBMS), initially developed by Champion et al. ²³, was utilized by Wells ²⁴ and Su ²⁵ to assess the health beliefs of anal and colorectal cancer screening. In a previous study, the scale was translated into a Chinese version through forward and backward translation. In this study, the scale was adapted to measure perceptions of coronary artery disease and sedentary behavior. This scale encompasses six dimensions: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, health motivation, and self-efficacy, comprising a total of 28 items. The measurement of HBM variables were on 5-point Likert scales ranging from “strongly disagree” (scored 1 point) to “strongly agree” (scored 5 points). The dimension of perceived barriers is reverse-scored. The scores from each dimension are aggregated, and higher scores signify stronger health beliefs. The scale's content validity was is 1. A pilot survey involving 20 coronary heart disease patients attending cardiology outpatient clinics was conducted. The Cronbach's α coefficients for perceived susceptibility, perceived severity, perceived benefits, perceived barriers, health motivation, and self-efficacy were 0.866, 0.960, 0.979, 0.939, 0.780, and 0.992, respectively. The scale demonstrated robust reliability and validity.

The Chinese Adult Sedentary Behavior Questionnaire

Sedentary time was assessed in ten different settings using the Chinese Adult Sedentary Behavior Questionnaire, developed by Tian Tian and Gu Bowen ²⁶. The ten items include sitting during work or study, computer or internet use, eating, taking brief naps, reading books, newspapers, or magazines, engaging in hobbies, driving or using public transportation, sitting while chatting or talking on the phone, watching television, and other activities. Sedentary time is calculated by multiplying the number of days of a specific sedentary behavior performed within a week by the average daily time spent, divided by 7. The average amount of sedentary time per day was calculated by multiplying estimates and dividing this by 7 (excluding nap time). This questionnaire demonstrates a test-retest reliability of 0.82 and a criterion validity of 0.51 ²⁶.

Statistical analysis

Descriptive statistics and Spearman correlation coefficients were computed for all study variables by utilizing SPSS 23.0 (IBM Corp., New York, NY, USA, 2018). Based on previous studies, a proposed model was designed (Fig. 1). AMOS 26.0 (IBM Corp., New York, NY, USA, 2019) was used to evaluate the fitting index of the proposed model. The proposed model would be accepted only if test values satisfied the following standards: χ2/df < 3, GFI/AGFI/TFI > 0.9, RMSEA < 0.08. The statistical significance was defined as P < 0.05.

Demographic characteristics

This study enrolled 379 patients, comprising 115 patients (30.4%) under the age of 60, 137 patients (36.1%) between the ages of 60 and 69, and 127 patients (33.5%) aged 70 or older. Among the participants, 125 were female (33.0%), while 254 were male (67.0%). The mean sedentary time was 7.18±2.64 h/d. Detailed demographic information about CHD patients is presented in Table 1.

Table 1. Characteristics of coronary artery disease patients（n=379）.

Varibles	Categories	Total
Varibles	Categories	n (%)
Age group
	≤60	115(30.4)
	60-69	137(36.1)
	≥70	127(33.5)
Gender
	Male	254(67.0)
	Female	125(23.0)
Marital status
	Married	324(85.5)
	Unmarried /Widow(er)/Divorced	55(14.5)
BMI
	Normal	256(67.5)
	Overweight/Obesity	123(32.5)
Number of comorbidities
	0	109(28.8)
	1	143 (37.7)
	≥2	127(33.5)
History of percutaneous coronary intervention
	No	178(47.0)
	Yes	201(53.0)
Presence of family members requiring care
	No	270(71.2)
	Yes	109(28.8)
Fear of discomfort after physical activity
	No	252(66.5)
	Yes	127(33.5)
Importance of reducing sedentary behavior
	No	86(22.7)
	Yes	293(77.3)

Descriptive statistics regarding the characteristics of the measured variables

A normal distribution was indicated by the observed variables' ranges of skewness and kurtosis, which were 0.029—0.910 and 0.146—0.930, respectively (Table 2). The correlation between the study variables in the model, as shown in Table 3. All dimensions of health belief were significantly correlated with sedentary behavior. The majority of health belief's dimensions interacted with one another.

Table2. Descriptive statistics of measured variables(n=379).

Variable	Score(M±SD)	Skewness	kurtosis
perceived susceptibility	13.36±2.17	0.334	0.930
perceived severity	16.82±6.16	0.076	-0.651
perceived benefit	11.12±2.44	-.029	-0.330
perceived barriers	13.78±4.52	0.311	-0.381
health motivation	30.43±3.65	-0.592	-0.218
self-efficacy	11.82±3.60	-0.910	-0.146
sedentary behavior	7.18±2.64	0.289	-0.696

Table 3. Correlation of measured variables(n=379)

Variables	1	2	3	4	5	6
1. Perceived susceptibility	1
2. perceived severity	-0.174^*	1
3. perceived benefit	0.104^*	0.209^**	1
4. perceived barriers	-0.019	-0.454^**	-0.458^**	1
5.health motivation	0.012	0.072	0.122^*	-0.131^*	1
6.self-efficacy	0.021	0.402^**	0.472^**	-0.646^**	0.134^**	1
7.sedentary behavior	-0.117^*	-0.377^**	-0.370^**	0.507^**	-0.196^**	-0.556^**

* P＜0.01

** P＜0.05

Test of proposed model

The model-fit indices were as follows: χ2/df =2.334 (which was less than 3), CFI (Comparative Fit Index) = 0.977, GFI (Goodness-of-Fit Index) =0.980, TLI (Tucker–Lewis Index) = 0.950, and RMSEA (Root-Mean-Square Error of Approximation) = 0.059, which indicates that the research model fitted the collected data well (CFI > 0.9, GFI > 0.9, TLI > 0.9, RMSEA < 0.08) (Fig. 2).

Effect estimate

Table 4 presents significant paths for the study. Perceived susceptibility (β =-0.122), self-efficacy (β = -0.253), perceived barriers (β = 0.185), perceived severity (β = -0.175), perceived benefits (β = -0.121) , and health motivation (β = -0.105) were found to have significant direct effects on sedentary behavior, whereas the knowledge of sedentary behavior (β = -0.279) only had indirect effects. Perceived severity has the greatest negative impact on sedentary behavior (β =-0.317). In addition, the relationship between sedentary behavior knowledge, perceived severity, perceived barriers, perceived benefits and sedentary behavior were moderated by self-efficacy. Perceived barriers’ positive impact on sedentary behavior was attenuated by the presence of high self-efficacy. With high self-efficacy, the negative effect of perceived severity, sedentary behavior knowledge, and perceived benefits on sedentary behavior was strengthened.

Table 4. Standardized direct, indirect and total effect of model (n=379)

Paths	Standardized direct effect	Standardized indirect effect	Standardized total effect
Perceived susceptibility →sedentary behavior	-0.122^**	0.037^*	-0.084^*
Perceived severity →sedentary behavior	-0.175^**	-0.142^**	-0.317^**
Perceived benefits →sedentary behavior	-0.121^*	-0.145^**	-0.266^**
Perceived barriers →sedentary behavior	0.185^**	0.111^**	0.296^**
Self-efficacy →sedentary behavior	-0.253^**	-	-0.253^**
Health motivation →sedentary behavior	-0.105^*	-	-0.105^*
The knowledge of sedentary behavior →sedentary behavior	-	-0.279^**	-0.279^**

* P＜0.01

** P＜0.05

The study assessed the factors that influence sedentary behavior in patients with coronary artery disease based on the HBM. The findings show that the knowledge of sedentary behavior and health belief factors either directly or indirectly affect sedentary behavior in CHD patients. These factors include the knowledge of sedentary behavior, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and health motivation.

Perceived severity was a significant factor influencing the sedentary behavior of CHD patients. Perceived severity describes how people assess the importance of a given illness, taking into account both the clinical (such as pain or death) and social (such as effects on relationships with family and friends) ramifications ²⁷. In this study, it was observed that the higher the perceived severity, the less sedentary time patients with CHD engage in. In addition, perceived severity exerted an indirect influence on sedentary behavior through perceived barriers and self-efficacy. This implies that patients perceive more severe consequences, such as decreased exercise tolerance, associated with coronary heart disease, and consequently reduce their sedentary behavior. Enhancing self-efficacy or reducing perceived barriers can strengthen the adverse impact of perceived severity on sedentary behavior.

Perceived barriers refer to individuals' perceptions of the difficulties associated with adopting healthy behaviors ²⁸. In this research, we found perceived barriers to be one of the strongest predictors of sedentary behavior among the patients with CHD. Similarly, Li Hua et al. in a previous study found that reducing the barriers in taking action may promote the efforts of participants in maintaining a healthy lifestyle ²⁷. Nadrian et al. also reported perceived barriers as the strongest predictor of self-care behaviors among patients with HF ¹⁹. Patients with CHD are less likely to strive to alter their sedentary behavior when they perceive greater challenges and barriers. As a result, it's critical to recognize and remove barriers preventing CHD patients from becoming less sedentary. Providing standing tables ²⁹ in walking environment and expanding community activity spaces may be potential solutions.

Another predictor of sedentary behavior among patients with CHD included perceived susceptibility, perceived benefits, and health motivation. Patients with higher perceived susceptibility reported lower sedentary behavior. Previous research has indicated that perceived susceptibility has a positive impact on health behavior ^27,30. In addition, perceived benefits influence sedentary behavior among patients with CHD. Previous studies showed that health behavior compliance is influenced by patients' perceived benefits³¹. Therefore, health education on behavior can increase patients' awareness of the risks of prolonged unhealthy behavior and enhance their perception of the benefits of reducing sedentary behavior. Another determinant of sedentary behavior was health motivation. Health motivation has a direct negative effect on sedentary behavior among patients with CHD. This suggests that the more these patients focus on their overall health, the less time they spend on sedentary behavior. Although the influence of health motivation on sedentary behavior is relatively minor in this study, it remains statistically significant. Zhang Xiaoni et al. ³² and Li Hua et al. ²⁷ found that health motivation plays an important role in health behavior. Therefore, encouraging healthy behavior to accelerate overall health and raising patients' awareness of their overall health situation rather than only concentrating on certain diseases is likely one of the most efficient approaches to reducing inactive behavior.

The ability of CHD patients' perceptions of their ability to decrease sedentary behavior was our definition of self-efficacy³³. Self-efficacy was the most important directly negative factor for sedentary behavior. Additionally, the mediation role of self-efficacy may not be disregarded on this major path. Our study found a substantial correlation between perceived barriers and self-efficacy. This indicates that the primary focus of the intervention should be the patients' conviction in their ability to overcome the obstacles to decreasing sedentary behavior. Previous studies largely supported the idea that improving patients' self-efficacy can mitigate the positive impact of obstacles on sedentary behavior ³⁴. Moreover, based on our results, self-efficacy mediated the relationship between perceived severity, perceived benefits and sedentary behavior. Huang et al. also reported that self-efficacy played a mediating role between perceived benefits and the level of physical activity among elderly nursing home residents ³⁵. Therefore, strategies like health education to boost self-efficacy may effectively reduce sedentary time among these patients.

Considering the total effect of the independent variables on sedentary behavior in the present study, the knowledge of sedentary behavior had an important effect and was one of the most significant indirect predictors of sedentary behavior in CHD patients, mainly through perceived benefits, perceived severity, and self-efficacy. This finding shows the knowledge of sedentary behavior as the most influential element in enhancing the level of perceived benefits, perceived severity, and self-efficacy among the patients, which is similar to those reported in previous studies ^27,36. Therefore, in developing health promotion programs targeted at the sedentary behavior of CHD patients, increasing awareness of sedentary behavior should still be regarded as one of the fundamental categories.

Study Limitation

The study has several limitations. First, the study was cross-sectional. Second, the sample was based on convenience sampling. Future studies need to break through this limitation and make more representative national-level studies of cross-country studies.

In summary, we found that the knowledge of sedentary behavior, perceived severity, perceived susceptibility, perceived barriers, perceived benefits, health motivation, and self-efficacy were associated with sedentary behavior. HBM was found to be helpful for understanding the direct and indirect associations of determinants with sedentary behavior among patients with CHD. This study provides evidence about factors that are useful for interventions to decrease sedentary behavior among patients with CHD. To reduce the sedentary time of CHD patients, it is crucial to provide them with health education on sedentary behavior and enhance their level of health belief, such as by identifying and resolving the barriers hindering CHD patients from reducing their sedentary behavior.

Ethical Approval All procedures performed in studies involving human participants were in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Ruijin Hospital Affiliated to Medical School of Shanghai Jiao Tong University

Informed Consent Informed consent was obtained from all individual participants included in the study.

Conflict of Interest The authors declare no competing interests.

Funding

No funding

Funding

No funding

Conflict of interest statement

The authors declare that they have no conflict of interest.

Availability of Data and Materials

The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank the associate Xian-Hua Li for her instruction.

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No competing interests reported.

Associated factors of sedentary behavior among patients with coronary artery disease based on health belief model

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Design and study population

Measures

Demographic characteristic questionnaire

Sedentary behavior knowledge questionnaire

Champion Health Belief Model Scale

The Chinese Adult Sedentary Behavior Questionnaire

Statistical analysis

Results

Discussion

Study Limitation

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1