Clustering of Health-Related Behavior Patterns and Demographics. Results From the Population-Based KORA S4/F4 Cohort Study

Rabel, Matthias; Laxy, Michael; Thorand, Barbara; Peters, Annette; Schwettmann, Lars; Mess, Filip

doi:10.3389/fpubh.2018.00387

Cited by 43 publications

(39 citation statements)

References 52 publications

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“…Life-style behaviours are the most crucial determinant [ 5 , 6 ] and lie at the root of many chronic diseases [ 7 – 14 ] causing higher disease burden [ 15 ] and premature mortality worldwide [ 16 , 17 ]. There is evidence that lifestyle risk factors like tobacco smoking and chewing, alcohol consumption, unhealthy diet are associated and tend to co-occur [ 8 , 18 , 19 ] as clusters [ 9 , 10 , 20 ] or in bundles [ 6 ]. Evidence suggests that population that indulge in multiple health risk factors tend to have significantly worse health outcomes than those engaging in one health risk behaviour [ 15 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Clustering of lifestyle risk factors among adult population in India: A cross-sectional analysis from 2005 to 2016

Shaikh¹,

Khan

2021

PLoS ONE

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Introduction Individual’s early life style and health behaviors are directly linked to chronic non-communicable diseases. Considering the increased burden of NCDs during the last two decades, the aim of this study is to assess co-occurrence/clustering of lifestyle risk factors and its association with different socio-demographic and economic characteristics among adult men and women in India from 2005–2016. Methods This study utilized the data from the National Family Health Survey 2005–06 and 2015–16 survey rounds. Multinomial logistic regression is employed to evaluate co-occurrence of multiple risk factors among adult men and women of different socio-economic and demographic characteristics to identify the subgroups with elevated risk of clustering of multiple unhealthy lifestyle risk factors. Results More adult men in India tend to exhibit clustering of multiple non-communicable disease risk factors than females. Individuals between 30–49 years of age, residing in urban areas, the population with no education, separated couples and those from poor economic strata are the specific population subgroups show higher prevalence of co-occurrence of multiple risk factors. The regional pattern of clustering of risk factors shows that the prevalence of co-occurrence of multiple risk factors is higher among men and women from the North-Eastern part of India compared to the other regions of the country. Conclusion The prevalence of clustering of multiple risk factors associated with chronic NCDs is substantially high and has increased between 2005–06 to 2015–16. India may therefore experience a significant increase in the burden of chronic non-communicable diseases in the coming years. We therefore conclude that appropriate strategies should be implemented by policy makers and the government to reduce the overall health burden of NCDs due to lifestyle habits.

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Section: Introductionmentioning

confidence: 99%

Clustering of lifestyle risk factors among adult population in India: A cross-sectional analysis from 2005 to 2016

Shaikh¹,

Khan

2021

PLoS ONE

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“…The only exception was physical activity; the higher percentage of men was active in comparison to women (17) . Although it has been well established that men are more likely to engage in unhealthy behaviours than women (18)(19)(20) , there is a gap in the understanding of which particular dietary behaviours tend to cluster with other lifestyle behaviours. Only a few studies reported clusters of dietary and lifestyle behaviours specific to the population of young men (21) , despite strong recommendations to analyse behavioural patterns using age and gender stratification (22) .…”

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confidence: 99%

Empirically derived dietary-lifestyle patterns and cardiometabolic health in young men: a review

Lonnie

Wądołowska

2020

Proc. Nutr. Soc.

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Sex and gender are important factors that impact cardiometabolic traits. Men have lower life expectancy and higher prevalence of fatal chronic conditions at younger age. Lifestyle risk profiles in young men have been rarely studied in the context of cardiometabolic health. This review aimed to summarise the evidence regarding the patterns of dietary-lifestyle behaviours in a population of young men (age <40years) and their associations with cardiometabolic health. Overall, unfavourable clusters of health behaviours are more prominent in men, when compared to women and more prevalent in younger adults, when compared to older age groups. Early signs of cardiometabolic health abnormalities have been reported among men with higher adherence to patterns which consistently shared poor dietary habits as a common denominator, combined with stimulant use, inadequate sleep or insufficient physical activity. In the majority of studies, dietary assessment was limited to the investigation of one or two behaviours, most frequently fruit and vegetable intake. Since young men may engage in a mixture of explicit behaviours, the examination of a singular dietary habit may not represent the overall diet quality. To conclude, the data regarding the synergistic effects of a broad spectrum of dietary and lifestyle behaviours in the context of cardiometabolic health remain scarce in this population. The inclusion of a broader range of dietary and lifestyle variables into the multicomponent pattern analysis might have a greater potential in explaining the association with cardiometabolic health. Defining behavioural clusters can help to develop interventions, tailored to the specific needs of the targeted group.

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“…We found that cumulative ETV was associated with latent profiles of multiple risk behaviors implicated in developing chronic diseases, premature mortality, and health disparities. 4,9,10 We found 4 distinct latent profiles: low overall risk profile , physically inactive profile, high substance use profile, and high overall risk profile . Notably, individuals with high ETV were more likely to be in the high overall risk profile with high levels of alcohol and cigarette use, unhealthy diet, and increased physical inactivity.…”

Section: Discussionmentioning

confidence: 78%

“…They found being male and having lower SES were associated with the prevalence of unhealthy behavioral clusters. 3,9 -11 Nevertheless, one cross-sectional study focusing on multiple risk behaviors among African Americans found that social-environmental factors (e.g., perceived neighborhood cohesion), compared to demographic or individual-level psychological factors (e.g., perceived cancer risk), plays a much more critical role in differentiating the healthiest group from groups engaging in less healthy behaviors. 8 In line with this finding, researchers have also found that exposure to violence (ETV) influences health outcomes.…”

Section: Purposementioning

confidence: 99%

The Longitudinal Association Between Exposure to Violence and Patterns of Health Risk Behaviors Among African American Youth

et al. 2021

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Purpose: We investigate whether exposure to violence (ETV) during adolescence and emerging adulthood predicts engagement in chronic disease-related health risk behaviors years later among African Americans. Design: A longitudinal study following youth from mid-adolescence (mean age = 14.8 years) to young adulthood (mean age = 32.0 years). Setting: Flint, Michigan. Sample: Four hundred forty-two African American (96.2%) and mixed African American and White (3.8%) participants. Measures: Outcomes were diet, smoking, drinking, and physical inactivity. Covariates were ETV, sex, mother’s educational attainment, and substance use by siblings, peers, and parents. Analysis: Latent profile analysis was conducted to identify distinct patterns of adult health risk behaviors and assess the association of youth ETV and identified patterns. Results: Four latent profiles were identified: high substance use (n = 46; 10.41%) , high overall risk (n = 71; 16.06%) , low overall risk ( n = 140; 31.67%) and inactive (n = 185, 41.86%). Relative to the low overall risk profile, ETV was associated with being in the high overall risk profile (b = 0.37, p = 0.04), but not other profiles. Female gender and higher maternal education were associated with being in the inactive profile compared to the low overall risk profile. Peer alcohol and tobacco use were associated with being in the high substance use profile. Conclusion: ETV during adolescence and emerging adulthood increased the risk of engagement in multiple health risk behaviors later in life.

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Clustering of Health-Related Behavior Patterns and Demographics. Results From the Population-Based KORA S4/F4 Cohort Study

Cited by 43 publications

References 52 publications

Clustering of lifestyle risk factors among adult population in India: A cross-sectional analysis from 2005 to 2016

Clustering of lifestyle risk factors among adult population in India: A cross-sectional analysis from 2005 to 2016

Empirically derived dietary-lifestyle patterns and cardiometabolic health in young men: a review

The Longitudinal Association Between Exposure to Violence and Patterns of Health Risk Behaviors Among African American Youth

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