A structural model of health behavior modification among patients with cardiovascular disease

Goong, Hwasoo; Ryu, Seungmi; Xu, Lijuan

doi:10.1016/j.apnr.2015.06.005

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…Many studies examined CVD risk factors [16][17][18][19][20][21]. However, they cannot usually detect causal pathways between the associated factors and CVD.…”

Section: Introductionmentioning

confidence: 99%

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

et al. 2020

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Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socioeconomics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

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“…Many studies examined CVD risk factors [16][17][18][19][20][21]. However, they cannot usually detect causal pathways between the associated factors and CVD.…”

Section: Introductionmentioning

confidence: 99%

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, they cannot usually detect causal pathways between the associated factors and CVD. Some studies investigated cardiovascular risk factors using structural equation modelling (17)(18)(19)(20)(21). Among which, Azizi et al studied the effect of emotional distress, physical activity, and body mass index (BMI) on the link between CVD and short sleep duration (<7 hours per day) (17).…”

Section: Introductionmentioning

confidence: 99%

“…Kerkhof et al presented SEM with different pathways indicating fat mass is in uenced by atherosclerosis risk factors at the age of 21 years (20). Goong et al indicated that health behaviour modi cation performance could be described by health belief, social support, and changing the knowledge and attitude about health using the SEM in patients with CVDs (21).…”

Section: Introductionmentioning

confidence: 99%

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

Shakibaei

Hassannejad

Mohammadifard

et al. 2020

Preprint

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Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

show abstract

“…Many studies have examined cardiovascular risk factors (16)(17)(18)(19)(20)(21). They have relied on analysis that cannot detect causal pathways between the associated factors and CVD.…”

Section: Introductionmentioning

confidence: 99%

Pathways Leading to Prevention of Fatal and Non Fatal CVDs: An Interaction Model on 15 years Population-Based Cohort study

Shakibaei

Hassannejad

Mohammadifard

et al. 2020

Preprint

View full text Add to dashboard Cite

Background An extensive study on cardiovascular risk factors interaction seems to be of crucial importance in order to prevent cardiovascular (CVD) events. The main focus of this study is understanding direct and indirect relationships between different CVDs risk factors. Methods A longitudinal data on adults aged ≥ 35 years, who were free of CVD at baseline, were used to study. The endpoints were CVD events, while their measurements were demographic, socio-economics, life-style components, laboratory findings, anthropometric measures, psychological factors, and quality of life status. A Bayesian structural equation modeling (BSEM) was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results In this study a total of 3161 individuals with complete information were included in the study. Total 407 CVD events were occurred during follow-up. The causal associations between 6 latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile influences the occurrence of CVD events as the most important, but it did so indirectly mediate through the risky behaviors and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy life style components. Conclusions Analyzing a causal network on risk factors reveals the flow of information in direct and indirect paths, as well as determining predictors and demonstrate the utility of integrating multi-factor data in a complex framework to identify novel candidate preventable pathways to lower risk of CVDs.

show abstract

A structural model of health behavior modification among patients with cardiovascular disease

Cited by 14 publications

References 21 publications

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

Pathways Leading to Prevention of Fatal and Non Fatal CVDs: An Interaction Model on 15 years Population-Based Cohort study

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