Conscientiousness and Cardiometabolic Risk: A Test of the Health Behavior Model of Personality Using Structural Equation Modeling

2021 ◽  
Author(s):  
Mark C Thomas ◽  
Katherine A Duggan ◽  
Thomas W Kamarck ◽  
Aidan G C Wright ◽  
Matthew F Muldoon ◽  
...  
Keyword(s):  
Physical Activity ◽  
Substance Use ◽  
Health Behavior ◽  
Health Behaviors ◽  
Latent Variable ◽  
Structural Equation ◽  
Cardiometabolic Risk ◽  
Equation Modeling ◽  
Inverse Association ◽  
Subjective Sleep Quality

Abstract Background High trait conscientiousness is associated with lower cardiometabolic risk, and health behaviors are a putative but relatively untested pathway that may explain this association. Purpose To explore the role of key health behaviors (diet, physical activity, substance use, and sleep) as links between conscientiousness and cardiometabolic risk. Methods In a cross-sectional analysis of 494 healthy, middle-aged working adults (mean age = 42.7 years, 52.6% women, 81.0% White), participants provided self-reports of conscientiousness, physical activity, substance use, diet, and sleep, and wore monitors over a 7-day monitoring period to assess sleep (Actiwatch-16) and physical activity (SenseWear Pro3). Cardiometabolic risk was expressed as a second-order latent variable from a confirmatory factor analysis involving insulin resistance, dyslipidemia, obesity, and blood pressure. Direct, indirect, and specific indirect effect pathways linking conscientiousness to health behaviors and cardiometabolic risk were examined. Unstandardized indirect effects for each health behavior class were computed separately using bootstrapped samples. Results After controlling for demographics (sex, age, race, and education), conscientiousness showed the predicted, inverse association with cardiometabolic risk. Among the examined health behaviors, objectively-assessed sleep midpoint variability (b = −0.003, p = .04), subjective sleep quality (b = −0.003, p = .025), and objectively-assessed physical activity (b = −0.11, p = .04) linked conscientiousness to cardiometabolic risk. Conclusions Physical activity and sleep partially accounted for the relationship between conscientiousness and cardiometabolic risk.

scholarly journals Longitudinal stability and interrelations between health behavior and subjective well-being in a follow-up of nine years

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259280
Author(s):  
Säde Stenlund ◽  
Niina Junttila ◽  
Heli Koivumaa-Honkanen ◽  
Lauri Sillanmäki ◽  
David Stenlund ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Health Behaviors ◽  
Structural Equation ◽  
Well Being ◽  
Equation Modeling ◽  
Subjective Well Being ◽  
Cross Sectional ◽  
The Cross

Background The bidirectional relationship between health behavior and subjective well-being has previously been studied sparsely, and mainly for individual health behaviors and regression models. In the present study, we deepen this knowledge focusing on the four principal health behaviors and using structural equation modeling with selected covariates. Methods The follow-up data (n = 11,804) was derived from a population-based random sample of working-age Finns from two waves (2003 and 2012) of the Health and Social Support (HeSSup) postal survey. Structural equation modeling was used to study the cross-sectional, cross-lagged, and longitudinal relationships between the four principal health behaviors and subjective well-being at baseline and after the nine-year follow-up adjusted for age, gender, education, and self-reported diseases. The included health behaviors were physical activity, dietary habits, alcohol consumption, and smoking status. Subjective well-being was measured through four items comprising happiness, interest, and ease in life, and perceived loneliness. Results Bidirectionally, only health behavior in 2003 predicted subjective well-being in 2012, whereas subjective well-being in 2003 did not predict health behavior in 2012. In addition, the cross-sectional interactions in 2003 and in 2012 between health behavior and subjective well-being were statistically significant. The baseline levels predicted their respective follow-up levels, the effect being stronger in health behavior than in subjective well-being. Conclusion The four principal health behaviors together predict subsequent subjective well-being after an extensive follow-up. Although not particularly strong, the results could still be used for motivation for health behavior change, because of the beneficial effects of health behavior on subjective well-being.

scholarly journals Examining associations between COVID-19 stressors, intimate partner violence, health, and health behaviors

2021 ◽  
pp. 026540752110120
Author(s):  
Abriana M. Gresham ◽  
Brett J. Peters ◽  
Gery Karantzas ◽  
Linda D. Cameron ◽  
Jeffry A. Simpson
Keyword(s):  
Mental Health ◽  
Intimate Partner Violence ◽  
Substance Use ◽  
Health Behaviors ◽  
Partner Violence ◽  
Structural Equation ◽  
Online Survey ◽  
Intimate Partner ◽  
Equation Modeling ◽  
Physical And Mental Health

The economic, social, and health impacts of the COVID-19 pandemic are expected to increase the occurrence of intimate partner violence (IPV) victimization. IPV victimization may, in turn, contribute to physical and mental health, substance use, and social distancing behaviors during the COVID-19 pandemic. The primary objective of the current study was to understand the extent to which 1) COVID-19 stressors are associated with IPV victimization and 2) IPV victimization is associated with health and health behaviors. Participants ( N = 1,813) completed an online survey between May 15 and 28, 2020 that assessed COVID-19 stressors (financial anxiety, social disconnection, health anxiety, COVID-19-specific stress), IPV victimization, physical and mental health, substance use, and movement outside of the home. Structural equation modeling indicated that greater COVID-19-related stressors were associated with greater IPV victimization during the pandemic, even after controlling for enduring vulnerabilities associated with IPV victimization. Additionally, greater IPV victimization during the COVID-19 pandemic was associated with higher levels of substance use and movement outside of the home, but not poorer physical and mental health. COVID-19 stressors may have detrimental relationship effects and health implications, underscoring the need for increased IPV intervention and support services during the pandemic. Findings from the current work provide preliminary correlational evidence for a theoretical model centered on IPV victimization, rather than perpetration.

scholarly journals Role of DNA methylation on the association between physical activity and cardiovascular diseases: results from the longitudinal multi-ethnic study of atherosclerosis (MESA) cohort

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hangchuan Shi ◽  
Deborah J. Ossip ◽  
Nicole L. Mayo ◽  
Daniel A. Lopez ◽  
Robert C. Block ◽  
...  
Keyword(s):  
Physical Activity ◽  
Dna Methylation ◽  
Structural Equation ◽  
Final Analysis ◽  
Equation Modeling ◽  
Inverse Association ◽  
Ethnic Study ◽  
Activity Intensity ◽  
Multiple Variables

Abstract Background The complexity of physical activity (PA) and DNA methylation interaction in the development of cardiovascular disease (CVD) is rarely simultaneously investigated in one study. We examined the role of DNA methylation on the association between PA and CVD. Results The Multi-Ethnic Study of Atherosclerosis (MESA) cohort Exam 5 data with 1065 participants free of CVD were used for final analysis. The quartile categorical total PA variable was created by activity intensity (METs/week). During a median follow-up of 4.0 years, 69 participants developed CVD. Illumina HumanMethylation450 BeadChip was used to provide genome-wide DNA methylation profiles in purified human monocytes (CD14+). We identified 23 candidate DNA methylation loci to be associated with both PA and CVD. We used the structural equation modeling (SEM) approach to test the complex relationships among multiple variables and the roles of mediators. Three of the 23 identified loci (corresponding to genes VPS13D, PIK3CD and VPS45) remained as significant mediators in the final SEM model along with other covariates. Bridged by the three genes, the 2nd PA quartile (β = − 0.959; 95%CI: − 1.554 to − 0.449) and the 3rd PA quartile (β = − 0.944; 95%CI: − 1.628 to − 0.413) showed the greatest inverse associations with CVD development, while the 4th PA quartile had a relatively weaker inverse association (β = − 0.355; 95%CI: − 0.713 to − 0.124). Conclusions The current study is among the first to simultaneously examine the relationships among PA, DNA methylation, and CVD in a large cohort with long-term exposure. We identified three DNA methylation loci bridged the association between PA and CVD. The function of the identified genes warrants further investigation in the pathogenesis of CVD.

Personality, inflammation, and health status: Prospective associations from multiple perspectives

2021 ◽  
Author(s):  
Amanda Jo Wright ◽  
Sara J Weston ◽  
Sara Norton ◽  
Michaela Voss ◽  
Ryan Bogdan ◽  
...  
Keyword(s):  
Health Behavior ◽  
Health Behaviors ◽  
Personality Traits ◽  
Big Five ◽  
Indirect Effect ◽  
Structural Equation ◽  
Big Five Personality Traits ◽  
Inflammatory Biomarkers ◽  
Equation Modeling ◽  
Big Five Personality

Objective: Personality influences many aspects of the health process, including associations with possible mechanisms such as inflammation and health behaviors. It is currently unclear to what extent, if any, the Big Five personality traits uniquely impact later health through independent pathways of inflammatory biomarkers and health behaviors. Furthermore, it is unknown if this relationship varies for self- and informant-reports of personality. Methods: Using data from older adults (N = 1,630) enrolled in the St. Louis Personality and Aging Network study, we test whether self- and informant-reported personality (Big Five personality traits) show consistent associations with inflammation (i.e., IL-6, CRP, and TNF-α). Further, we tested whether inflammation and health behavior indirectly link personality to health outcomes through independent or shared pathways using longitudinal mediation in a structural equation modeling framework.Results: Self- and informant-reports of personality uniquely predicted future levels of inflammatory biomarkers (self bs range from -0.11 to 0.07; informant bs range from -0.15 to 0.11). Additionally, both reports of personality impacted health through biomarker and health behavior pathways. Effects were primarily found for conscientiousness (indirect effect bs range from 0.01 to 0.04) and neuroticism (indirect effect bs range from -0.01 to -0.02) and IL-6 and CRP were the biomarkers most repeatedly linked with the Big Five personality traits and health. Conclusions: Findings highlight the potential benefits of using of multiple assessments of personality and the importance of examining multiple, distinct pathways by which personality might influence later health in order to more fully understand the mechanisms underlying this relationship.

Abstract MP87: Major Life Events and Social Strain are Associated with Incident Coronary Heart Disease: Results from the Women’s Health Initiative (WHI)

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Kiarri N Kershaw ◽  
Gretchen Brenes ◽  
Luenda E Charles ◽  
Mace Coday ◽  
Martha L Daviglus ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
Life Events ◽  
Structural Equation ◽  
Equation Modeling ◽  
Major Life Events ◽  
Major Life ◽  
Current Smoking ◽  
Social Strain ◽  
Poor Diet

Background: Despite the longstanding notion that psychosocial stressors are associated with higher CHD risk, findings from epidemiologic studies are mixed. Recent research suggests positive associations may be driven by angina pectoris, a condition more susceptible to reporting bias, rather than MI. We assessed associations of major life events (MLE) and social strain with incident CHD defined as the first occurrence of clinical MI, definite silent MI, or death due to definite or possible CHD. We also examined whether these relationships were mediated by several health behaviors. Methods: WHI participants (baseline ages 50-79) with complete data on all covariates were included. Baseline MLE scores were calculated and broken into quartiles based on the occurrence of 11 different life events in the past year and the extent to which each event upset them. Baseline social strain was assessed with four questions and divided into tertiles. Mean follow-up time was 11.2 years. Cox proportional hazard models were used to separately estimate associations of MLE and social strain with incident CHD. Structural equation modeling was used to quantify mediation by health behaviors (current smoking, heavy alcohol use, poor diet, and physical activity) measured at baseline and Year 3. Results: Greater MLE and social strain were positively associated with incident CHD. Health behaviors accounted for 30.1% of the association between highest (vs. lowest) MLE category and incident CHD and 23.0% of the high (vs. low) strain-CHD relationship. Current smoking was the strongest mediator for both MLE (23.6%) and strain (16.3%), followed by physical activity (4.6% each) and poor diet (2.1% and 2.6%, respectively). Conclusions: We found MLE and social strain were both associated with incident CHD, and that these relationships were mediated by the same set of health behaviors. However, a substantial proportion of these associations were not explained by health behaviors alone, suggesting alternative pathways need to be explored.

scholarly journals Associations of Physical Activity, School Safety and Non-Prescription Steroid Use in Adolescents: A Structural Equation Modeling Approach

2021 ◽  
Vol 19 (1) ◽  
pp. 87
Author(s):  
Timothy Brusseau ◽  
Ryan Burns
Keyword(s):  
Physical Activity ◽  
Risk Behavior ◽  
Latent Variables ◽  
School Safety ◽  
Latent Variable ◽  
Structural Equation ◽  
Self Report ◽  
Equation Modeling ◽  
Physical And Mental Health ◽  
Steroid Use

Non-prescription steroid use can negatively impact adolescent physical and mental health and wellbeing. Determining correlates of this risk behavior is needed to help mitigate its prevalence. Two potential correlates are physical activity and school safety. The purpose of this study was to examine the associations of physical activity, school safety, and non-prescription steroid use within a sample of adolescents from the 2015–2019 US National Youth Risk Behavior Survey (YRBS). A multi-stage cluster sampling procedure yielded a representative sample of US adolescents from the 2015–2019 YRBS (n = 44,066; 49.6% female). Two latent variables indicating physical activity and unsafe schools were the independent variables. The dependent variable was a self-report of non-prescription steroid use. A weighted structural equation model examined the associations between physical activity and unsafe schools with non-prescription steroid use, controlling for age, sex, BMI percentile, race/ethnicity, and sexual minority status. The latent physical activity variable did not associate with non-prescription steroid use (β = 0.007, 95%CI: −0.01–0.02, p = 0.436); however, the unsafe schools latent variable did associate with non-prescription steroid use (β = 0.64, 95%CI: 0.59–0.69, p < 0.001). An unsafe school environment may be a determinant of non-prescription steroid use in adolescents. Physical activity behaviors did not associate with steroid use.

Associations between multiple positive health behaviours and cardiometabolic risk using three alternative measures of physical activity: NHANES 2005–2006

2020 ◽  
Author(s):  
Stina Oftedal ◽  
Elroy J Aguiar ◽  
Mitch J. Duncan
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Health Behaviors ◽  
Screen Time ◽  
Cardiometabolic Risk ◽  
Vigorous Physical Activity ◽  
Single Point ◽  
Index Score ◽  
Linear Regression Models ◽  
Positive Health

Purpose: The study aimed to investigate the association between clustered cardiometabolic risk (CCMR) and health-behavior indices comprising three different measures of physical activity, screen time, diet and sleep in NHANES 2005-2006. Methods: CCMR was calculated by standardizing and summarizing measures of blood pressure, fasting glucose, triglycerides, insulin, high-density lipoprotein and waist circumference to create a Z-score. Three health behavior indices were constructed with a single point allocated to each of the following lower risk behaviors: muscle strengthening activity, healthy eating score, sleep disorder/disruption, sleep duration, screen time and physical activity (self-reported moderate-to-vigorous physical activity [MVPA] (Index Score-SR), accelerometer-measured MVPA (Index Score-MVPA) or accelerometer-measured steps Index Score-Steps). Linear regression models explored associations between index scores and CCMR. Results: In the sample (n=1537, 52% male, aged 45.5 [SE:0.9] years), reporting 0-5 vs. 6 health behaviors using Index Score-SR and Index Score-MVPA, and 0-4 vs. 6 health behaviors using Index Score-Steps, were associated with a significantly higher CCMR. The beta (β [95%CI]) for zero vs. six behaviors were: Index Score-SR (2.86 [2.02, 3.69], Index Score-MVPA (2.41 [1.49, 3.33] and Index Score-Steps (2.41 [1.68, 3.15]). Conclusion: Irrespective of the measure of physical activity, engaging in fewer positive health behaviors was associated with greater CCMR. Novelty bullets • Physical activity, screen time, diet and sleep may exert synergistic/cumulative effects on clustered cardiometabolic risk. • A greater number of positive health behaviors was associated with a lower clustered cardiometabolic risk factor score. • The reduction in cardiometabolic risk was similar irrespective of which physical activity measure was used.

Stages of Change in Physical Activity: A Validation Study in Late Adolescence

2009 ◽  
Vol 37 (3) ◽  
pp. 318-329 ◽  
Author(s):  
Philip D. Parker ◽  
Andrew J. Martin ◽  
Carissa Martinez ◽  
Herbert W. Marsh ◽  
Susan A. Jackson
Keyword(s):  
Physical Activity ◽  
Stages Of Change ◽  
Latent Variable ◽  
Structural Equation ◽  
Activity Level ◽  
Maladaptive Behavior ◽  
Self Concept ◽  
Equation Modeling ◽  
Future Research ◽  
Physical Activity Motivation

The present study explores the validity of a recent stages of change (SoC) measure and algorithm among a sample of late adolescents. MANOVA and structural equation modeling are used to assess the relationship between five SoC groups (precontemplation, contemplation, preparation, action, and maintenance) and a set of dependent measures including physical activity level, physical activity motivation, physical self-concept, and flow. Findings are based on 705 Australian adolescents, using scale score and latent variable approaches, provided support for the construct validity of the SoC measure and algorithm. Specifically, findings reveal that participants in the upper SoC (action and maintenance) score significantly higher on positively geared dimensions (e.g., physical self-concept, flow, etc.) and significantly lower on negatively geared dimensions (e.g., maladaptive behavior). Implications for future research and practice with adolescent populations are discussed.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

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