Clustering Of Unhealthy Behaviors Research Articles

Is Family History for the Management of Cardiovascular Health in Youth Still Relevant in Clinical Practice?

Family history of premature cardiovascular disease is a strong predictor of individual cardiovascular risk. However, family history is not always available and not always reliable. Roughly 80% of health outcomes are influenced not by genetic risk but by societal factors, including adverse health behaviors and environment. Furthermore, in the present age of genetic testing, laboratory evaluations, and imaging, a key question remains: What is the contemporary relevance of family history screening in the management of cardiovascular disease in youth? Knowledge of an individual's family history can help clinicians identify not only inherited risk but also familial clustering of unhealthy behaviors and environmental adversity contributing to enhanced cardiovascular disease risk in youth. For those at greatest risk, prevention strategies can be applied sooner and more conservatively. Integrating family history into clinical practice is crucial for cardiovascular risk assessment and for optimizing outcomes, but, in some cases, is more reflective of social factors.

Latent classes of unhealthy behaviours and their associations with subsequent sickness absence: a prospective register-linkage study among Finnish young and early midlife employees

ObjectivesUnhealthy behaviours are associated with increased sickness absence (SA), but few studies have considered person-oriented approach in these associations. Using latent class analysis, we examined clustering of unhealthy behaviours among...

Association between clustering of unhealthy behaviors and depressive symptom among adolescents in Taiwan: A nationwide cross-sectional survey.

Among Taiwanese adolescents, how the clustering of unhealthy behaviors, including insufficient physical activity, screen-based sedentary behavior and frequent sugar-sweetened beverage consumption affecting depressive symptom remains unclear. This study aims to examine the cross-sectional association between clustering of unhealthy behaviors and depressive symptom. We analyzed 18,509 participants from the baseline survey of the Taiwan Adolescent to Adult Longitudinal Survey in 2015. The outcome was depressive symptoms, and the main exposures were insufficient physical activity, screen-based sedentary behaviors and frequent sugar-sweetened beverage consumption. Generalized linear mixed models were performed to find key factor associated with depressive symptom. Depressive symptoms were common among participants (31.4%), particularly in female and older adolescents. After adjustments for covariates including sex, school type, other lifestyle factors and social determinants, individuals exhibiting clustering of unhealthy behaviors were more likely (aOR = 1.53, 95% CI: 1.48-1.58) to exhibit depressive symptoms than those who have no or only one unhealthy behavior. Clustering of unhealthy behaviors is positively associated with depressive symptom among Taiwanese adolescents. The findings highlight the importance of strengthening public health interventions to improve physical activity and decrease sedentary behaviors.

Clustering of unhealthy behaviours and subsequent sickness absence among Finnish municipal employees

Abstract Background Unhealthy behaviours are associated with increased sickness absence (SA), but few studies have explored these associations using person-oriented approach. We aimed to identify latent classes of unhealthy behaviours among female and male employees and examined their associations with subsequent SA. Methods Health behaviours (leisure-time physical activity, sedentary behaviour, fruit and vegetable [F&V] consumption, sleep, binge drinking, and use of tobacco products) were derived from the Helsinki Health Study questionnaire survey, collected in 2017 among 19-39-year-old employees of the City of Helsinki, Finland. The questionnaire data were linked to employer's SA register. Latent class analysis was used to identify underlying profiles of unhealthy behaviours and negative binomial regression was used to examine their associations with subsequent SA (≤7 days, >7 days, and all lengths) among 3228 women and 771 men. The mean follow-up time was 2.1 years. Results Among women, we identified 3 latent classes: 1) healthy behaviours (81% of women), 2) binge drinking and tobacco use (12%), and 3) inadequate F&V consumption and insufficient sleep (7%). Classes 2 and 3 showed increased rates for subsequent SA compared to class 1, regardless of the length of SA spells (age-adjusted rate ratios [RR] 1.37-1.42 and 1.35-1.64, respectively). Among men, we identified 3 latent classes: 1) healthy behaviours (51% of men), 2) binge drinking and tobacco use (19%), and 3) inadequate F&V consumption, binge drinking and tobacco use (30%). While classes 1 and 2 were not different in terms of subsequent SA, class 3 had increased rates of subsequent, particularly short-term SA (RR 1.24, 95% CI 1.03-1.48). Conclusions Preventive actions should consider simultaneously several unhealthy behaviours while aiming to reduce employees’ SA. These actions might benefit from regarding potential gender differences in the clustering of unhealthy behaviours and their associations with SA. Key messages • Preventive actions to reduce sickness absence should consider clustering of unhealthy behaviours among employees. • Potential gender differences need to be regarded in these actions.

An examination of temporal trends in health behaviors among United States children, adolescents, and adults from 2005 to 2016

Introduction Lifestyle-related health behaviors are major contributors to preventable chronic diseases. We aimed to characterize temporal trends in health behaviors of US children, adolescents, and adults and examine the association of sociodemographic characteristics with clustering of unhealthy behaviors. Methods The study population included 46,793 participants from the 2005 to 2016 National Health and Nutrition Examination Survey from three age groups: children (aged 2–11), adolescents (aged 12–19), and adults (aged ≥20). We calculated weighted prevalences of unhealthy behaviors—poor diet quality, low physical activity, screentime, fast food consumption, smoking (adolescents and adults), alcohol use, and short sleep duration (adults)—for each survey wave to examine temporal trends across age groups. Multivariable logistic regression estimated associations of sociodemographic characteristics with unhealthy behavior clustering (≥2 behaviors), stratified by age. Results Results of the study demonstrated that between 2005 and 2016, the proportion of children and adolescents not meeting the physical activity guidelines increased and screentime increased among all ages. Dietary quality improved and smoking prevalence declined among adolescents and adults, while fast food consumption declined among adolescents. Unhealthy behavior clustering among children increased by 13%. The odds of unhealthy behavior clustering were higher among children and adolescents that were older, non-Hispanic Black, or lived in unmarried households, and among adults who were younger, non-Hispanic black, had lower educational attainment, were uninsured or had public insurance, and had lower poverty-to-income ratios. Conclusion The findings suggest that screentime and physical inactivity are growing areas of concern in the U.S. population, and that disparities in adherence to multiple healthy behaviors may contribute to disparities in chronic diseases.

Clustering of unhealthy lifestyle behaviors, self-rated health and disability

The main objective was to identify sociodemographic characteristics of the population at risk for a greater clustering of unhealthy behaviors and to evaluate the association of such clustering with self-rated health status and disability. Data come from the 2017 Spanish National Health Survey with a sample of 21,947 participants of 15 years of age or older. Based on tobacco consumption, risk drinking, unbalanced diet, sedentarism, and body mass index <18.5/≥25 we created two indicators of risk factor clustering: 1) Number of unhealthy behaviors (0–5); and 2) Unhealthy lifestyle index (score: 0–15). Self-rated health was dichotomized into “optimal” and “suboptimal,” and disability was classified as “no disability,” “mild,” and “severe” based on the Global Activity Limitation Index (GALI). We estimated prevalence ratios (PR) adjusted for covariates using generalized linear models using the clustering count variable, and dose-response curves using the unhealthy lifestyle index. Most participants (77.4%) reported 2 or more risk factors, with men, middle-age individuals, and those with low socioeconomic status being more likely to do so. Compared to those with 0–1 risk factors, the PR for suboptimal health was 1.26 (95% CI:1.18–1.34) for those reporting 2–3 factors, reaching 1.43 (95% CI:1.31–1.55) for 4–5 factors. The PR for severe activity limitation was 1.66 (95% CI:1.35–2.03) for those reporting 2–3 factors and 2.06 (95% CI:1.59–2.67) for 4–5 factors. The prevalence of both health indicators increased in a non-linear fashion as the unhealthy lifestyle index score increased, increasing rapidly up to 5 points, slowing down between 5 and 10 points, and plateauing afterwards.

Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging.

BackgroundHealth behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.ObjectiveThe aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes.MethodsUsing cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.ResultsBaseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).ConclusionsThis study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.International Registered Report Identifier (IRRID)DERR1-10.2196/24887

Factors Associated with Health Behaviors in Thyroid Cancer Survivors.

In this study, we compared health behaviors, including current smoking, drinking, and physical inactivity, in thyroid cancer survivors and non-cancer controls and investigated the factors associated with unhealthy behaviors among survivors. Baseline data from the Health Examinees study, collected from 2004 to 2013, were used. Thyroid cancer survivors (n = 942), defined as those who had received a clinical diagnosis of thyroid cancer, and 9,420 matched non-cancer controls without past history of any cancer were included in the analysis. The prevalence of smoking, alcohol consumption, and physical inactivity in thyroid cancer survivors were 2.3%, 26.6%, and 52.0%, respectively, with adjusted OR (aOR) and 95% CI between survivors and non-cancer controls of 0.35 (95% CI = 0.08-1.56), 0.46 (95% CI = 0.29-0.74), and 0.65 (95% CI = 0.44-0.96). The prevalence of unhealthy behaviors was higher among male thyroid cancer survivors than female thyroid cancer survivors. In thyroid cancer survivors, those who smoked were more likely to drink (aOR = 4.55 [95% CI = 1.61-12.85]) and those who were physically inactive were less likely to drink (aOR = 0.58 [95% CI = 0.42-0.82]). Current drinking and physical inactivity contributed to a higher likelihood of smoking (aOR = 4.31 [95% CI = 1.35-13.73] and 6.34 [95% CI = 1.65-24.34]). Thyroid cancer survivors had better health behaviors than the non-cancer controls. However, some survivors still had unhealthy lifestyles, especially displaying a clustering of unhealthy behaviors. Thus, health behavior promotion through medical intervention is important for thyroid cancer survivors.

Clustering of unhealthy behaviors in a nationally representative sample of U.S. children and adolescents

Poor diet, low physical activity, sedentary behavior and smoking are modifiable risk factors for chronic diseases that often develop early in life. An improved understanding of how unhealthy behaviors co-occur within individual children across childhood and adolescence could inform the development of more effective prevention approaches. Using data from the 2011–2016 National Health and Nutrition Examination Survey, we calculated weighted prevalence of five unhealthy behaviors – excessive screen time, poor diet quality, low physical activity, fast food consumption, and smoking (adolescents only) – alone and in combination among U.S. children and adolescents, stratified by age group (2–5, 6–11, 12–15, and 16–19 years). Multivariable logistic regression was used to estimate associations between health behavior clustering (≥2 unhealthy behaviors) and sociodemographic characteristics by age group. Among 7714 children and adolescents, the most prevalent behaviors were excessive screen time and poor diet quality. Unhealthy behavior clustering increased significantly with age (from 29.0% for ages 2–5 to 73.9% for ages 16–19 years, p-trend: <0.0001). The most common health behavior combination was excessive screen time and poor diet (from 14.4% prevalence for ages 2–5 to 45.3% for ages 16–19 years). Smoking prevalence was low, but 97% of smokers had ≥1 other unhealthy behavior. Unhealthy behavior clustering was significantly more prevalent among black than white children (ages 2–5 and 6–11) and less prevalent among Hispanic older adolescents (age 16–19). Associations with household characteristics varied by age group. These results provide a population-level understanding of the extent to which unhealthy behaviors co-occur in U.S. children and adolescents.

Intake of Sugar-Sweetened Beverages in Adolescents from Troms, Norway-The Tromsø Study: Fit Futures.

High intake of sugar-sweetened beverages (SSB) has been associated with weight gain and chronic disease. The objective of this paper was to study the intake of SSB and characteristics associated with SSB intake in adolescents from Troms, Norway. We present results from a cross-sectional analysis from the Tromsø Study: Fit Futures, with 426 female and 444 male students aged 15–17 years (93% participation rate). Descriptive statistics and logistic regression analyses were performed. Among females, 31.8% drank at least one glass of SSB per day on average, compared to 61.0% among males. The adjusted OR (odds ratio) of daily SSB drinking for males vs. females was 3.74 (95% CI (confidence interval) 2.68–5.22). Other dietary habits such as eating snacks, drinking artificially sweetened beverages, fruit juice, and seldom eating breakfast were associated with higher odds for daily SSB drinking, as was daily snuffing. Weight class was not associated with daily SSB drinking. Students in vocational studies, particularly males tended to be more likely to be daily SSB drinkers. The prevalence of participants who on average were daily drinkers was higher than in national studies. We have identified several possible targets for interventions. Clustering of unhealthy behaviours and tendencies to socioeconomic differences are of particular concern.

Health behavior changes in African American family members facing lung cancer: Tensions and compromises

Behavioral interventions targeting cancer survivors often fail to address the clustering of unhealthy behaviors among family members and friends, and the impact of close relationships on behavior change. The study's aim was to identify factors associated with receptivity and preferences for lifestyle behavior change among family members of African-American survivors of lung cancer. Principles of social cognitive theory guided the design. A descriptive, qualitative study recruited 26 African-American family members of lung cancer survivors from two teaching hospitals in the southeastern United States. A 20-item Information Form collected demographic, health status, and health behavior information. Family members participated in one of three semi-structured focus group discussions. Four major themes emerged: family members and survivors both resisted the caregiver role; dramatic changes evoked by the diagnosis of lung cancer were facilitators and barriers to lifestyle choices; leaning on faith was the primary source of support; and these families live with a constant threat of multiple cancers. Findings emphasize the importance of meaningful conversations among health-care providers, survivors, and family members during the time of diagnosis, treatment, and recovery, so that family members are better prepared to cope with anticipated changes. This study highlights the stressors that affect family members and sheds light on their unique needs. The stressors limit their ability to change health behaviors. Family members need basic education, skills training, and support related to the lung cancer diagnosis and other cancers. Current methods to provide these services are limited in their accessibility, availability, and effectiveness.

Clustering and correlates of screen-time and eating behaviours among young children

BackgroundScreen-time and unhealthy dietary behaviours are highly pervasive in young children and evidence suggests that these behaviours often co-occur and are associated. Identifying clusters of unhealthy behaviours, and their influences early in childhood, can assist in the development of targeted preventive interventions. The purpose of this study was to examine the sociodemographic, behavioural, and home physical environmental correlates of co-occurring screen-time and unhealthy eating behaviours and to assess the clustering of screen-time and unhealthy dietary behaviours in young children.MethodsParents of 126 children, from the UK, aged 5–6 years (49% boys) completed a questionnaire which assessed their child’s screen-time (ST), fruit and vegetable (FV), and energy-dense (ED) snack consumption. Categories of health behaviours were created based on frequencies of children meeting recommendations for FV and ST and median splits of frequencies for ED snacks. Parents reported on their own behaviours (ST, FV, and ED snack consumption), how often they ate meals and watched TV with their child, and on the availability and accessibility of foods within the home. An observed over expected ratio (O/E) was used to assess behavioural clustering. Multivariable multinomial logistic regression was used to examine correlates of behaviour patterns.ResultsApproximately 25% of children had two or three health risk behaviours. Correlates consistently associated with clusters included parental income, eating meals at the TV, parental ST and ED snack food consumption, and home availability of ED snack foods. Observed over expected ratios were close to 1 and ranged from 0.78 to 1.43. The three-risk behaviour combination of insufficient FV consumption, high ED snack consumption, and excessive ST occurred more frequently than expected (1.23 (95% CI 0.89, 1.58)).ConclusionsST and unhealthy dietary behaviours cluster in children as young as 5 years of age and parents’ own behaviours appear to be important influencing factors. Further research into the development of behavioural clustering in young children to identify and further understand the mechanisms underlying the synergy among health behaviours is needed. Feasibility interventions promoting reductions in both screen-time and unhealthy dietary behaviours reciprocally, while simultaneously focusing on changing parental behaviours, are warranted.

Balancing health literacy and information access

Gastrointestinal NursingVol. 13, No. 7 EditorialBalancing health literacy and information accessLiz WatsonLiz WatsonSearch for more papers by this authorLiz WatsonPublished Online:21 Sep 2015https://doi.org/10.12968/gasn.2015.13.7.5AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Benjamin RM (2010) Surgeon General's perspectives: improving health by improving health literacy. Public Health Reports 25(November-December): 784–5. http://bit.ly/1NjBItE (accessed 10 September 2015) Google ScholarBuck D, Frosini F (2012) Clustering of Unhealthy Behaviours Over Time. Implications for Policy and Practice. King's Fund, London. http://bit.ly/1Ka3dAy (accessed 10 September 2015) Google ScholarDunlop C (2015) Aspirin, obesity and inherited bowel cancer – what's the story? http://bit.ly/1LYOqfY (accessed 10 September 2015) Google ScholarEvans DG, Barewell J, Eccles DM et al. (2014) The Angelina Jolie effect: how high celebrity profile can have a major impact on provision of cancer related services. Breast Cancer Res 16(5): 442. doi: https://doi.org/10.1186/s13058-014-0442-6 Crossref, Google ScholarHealth Literacy (2015) Why is health literacy important? www.healthliteracy.org.uk/ (accessed 10 September 2015) Google ScholarRowlands G (2015) Health literacy and health information in England: how big is the problem? http://bit.ly/1Lm3XUa (accessed 10 September 2015) Google ScholarRoyal College of General Practicioners (2014) Health Literacy: Report from an RCGP-led health literacy workshop. http://bit.ly/1nCC1Rl (accessed 10 September 2015) Google Scholar FiguresReferencesRelatedDetails 2 September 2015Volume 13Issue 7ISSN (print): 1479-5248ISSN (online): 2052-2835 Metrics History Published online 21 September 2015 Published in print 2 September 2015 Information© MA Healthcare LimitedPDF download

Clustering of unhealthy behaviours over time: An analysis of health and wellbeing surveys in Southeast England, 2003 and 2012

Background Most chronic diseases are strongly associated with four modifiable behaviours: smoking, alcohol consumption, low fruit and vegetable consumption and physical inactivity. Most of the research is directed into understanding how a single behaviour affects health; and relatively little is known about how these behaviours cluster together in the population, and how the prevalence of multiple lifestyle risk behaviours have changed over time. The objective of this study was to examine the clustering …

Adolescent consumption of sports and energy drinks: linkages to higher physical activity, unhealthy beverage patterns, cigarette smoking, and screen media use.

To examine patterns of adolescent sports and energy drink (SED) consumption and identify behavioral correlates. Data were drawn from Eating and Activity in Teens, a population-based study. Adolescents from 20 middle and high schools in Minneapolis/St Paul, MN completed classroom-administered surveys. A total of 2,793 adolescents (53.2% girls) in grades 6-12. Beverage patterns; breakfast frequency; moderate to vigorous physical activity (MVPA); media use; sleep; and cigarette smoking. Linear and logistic regression models were used to estimate associations between health behaviors and SED consumption, adjusting for demographics. Over a third of adolescents consumed sports drinks and 14.7% consumed energy drinks at least once a week. Among boys and girls, both sports and energy drink consumption were related to higher video game use; sugar-sweetened beverage and fruit juice intake; and smoking (P < .05). Sports drink consumption was also significantly related to higher MVPA and organized sport participation for both genders (P < .01). Although sports drink consumption was associated with higher MVPA, adolescents should be reminded of recommendations to consume these beverages only after vigorous, prolonged activity. There is also a need for future interventions designed to reduce SED consumption, to address the clustering of unhealthy behaviors.

Clustering of unhealthy behaviors in a Brazilian population of industrial workers

ObjectiveTo analyze the clustering of four unhealthy behaviors (tobacco use, excessive alcohol consumption, physical inactivity and low consumption of fruits and vegetables) and their association with sociodemographic factors among adult industrial workers in Brazil. MethodsData from 23 Brazilian states and the Federal District were collected via questionnaire from 2006 to 2008. Clustering of risk behaviors was identified using the ratio between the observed and expected percentages of each combination of unhealthy behaviors. ResultsAmong the 44,477 workers surveyed, 18.0% of men and 11.4% of women accumulated three or four unhealthy behaviors. The most frequent combination was physical inactivity and low consumption of fruits and vegetables. The concurrent presence of all four behaviors was 86% higher in men and 148% higher in women than would be predicted if the frequency of these behaviors were independent. Presence of one or more negative behaviors was more frequent in men, younger individuals and in those with lower levels of education and family income than subjects without these characteristics. ConclusionsThere was a strong tendency for unhealthy behaviors to assume combinatorial patterns, mainly in men, people with less education, income, and age, suggesting important disparities.

Health Behaviours as Mediating Pathways between Socioeconomic Position and Body Mass Index

Low socioeconomic position is widely reported to associate with high body mass index (BMI). We, however, lack scientific evidence if health behaviours mediate the association between socioeconomic position and BMI. The aims of the study were to explore associations of education and income with BMI and to study the mediating pathways through health behaviours. Study population comprised 3,995 Finnish men and women aged 25 to 64 years who participated in a cross-sectional, population-based FINRISK 2002 Study. Participants' height and weight were measured to calculate BMI. Self-administered questionnaire assessed education, household income, leisure time physical activity, sitting behaviour, dietary habits, smoking, and alcohol consumption. Structural equation modelling with latent variables was applied to estimate age-adjusted direct and indirect associations between variables. Most health behaviours mediated the association between socioeconomic position and BMI. Strongest and most consistent mediators were diet and sitting in men and women, as well as leisure time physical activity in women. Health behaviours clustered strongly with each other. The strongest indirect associations between socioeconomic position and BMI were mediated through variables related to energy balance, i.e. diet and sedentariness. To reduce the socioeconomic variation in overweight and obesity, the main focus should be on food and sedentary behaviours while also taking into account the gender differences and clustering of unhealthy behaviours.

Clustering behaviours among 13-year-old Portuguese adolescents

AimThe aims of this study were to identify clustering of behaviours in young adolescents and to analyse differences in food intake according to clusters.Subject and methodsA cross-sectional study was carried out on adolescents’ behaviours comprising 2,160 adolescents aged 13 years attending schools of Porto, Portugal. We used data of 875 girls and 737 boys with complete information for all variables considered in the cluster definition (tobacco and alcohol use, sport activities, fruit intake, daily sleep hours). The average method was used to find the natural structure of clusters.ResultsWe identified three clusters: cluster 1 characterized by never smokers and never drinkers represented those with higher intake of fruits and lower intake of soft drinks. Cluster 2 comprised essentially tobacco and alcohol experimenters and was almost similar to cluster 1 regarding the other characteristics. Cluster 3 represented mostly regular smokers and drinkers, and those with the lowest daily sport activities, the lowest number of daily sleep hours, the lowest mean of fruit consumption and the highest intake of sweets and soft drinks.ConclusionOur findings show that even in the early stages of adolescence there is a cluster of smoking, alcohol use and food intake, providing further evidence of the clustering of unhealthy behaviours.

Dietary patterns and socioeconomic position

To test a socioeconomic hypothesis on three dietary patterns and to describe the relation between three commonly used methods to determine dietary patterns, namely Healthy Eating Index, Mediterranean Diet Score and principal component analysis. Cross-sectional design in 1852 military men. Using mailed questionnaires, the food consumption frequency was recorded. The correlation coefficients between the three dietary patterns varied between 0.43 and 0.62. The highest correlation was found between Healthy Eating Index and Healthy Dietary Pattern (principal components analysis). Cohen's kappa coefficient of agreement varied between 0.10 and 0.20. After age-adjustment, education and income remained associated with the most healthy dietary pattern. Even when both socioeconomic indicators were used together in one model, higher income and education were associated with higher scores for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern. The least healthy quintiles of dietary pattern as measured by the three methods were associated with a clustering of unhealthy behaviors, that is, smoking, low physical activity, highest intake of total fat and saturated fatty acids, and low intakes of fruits and vegetables. The three dietary patterns used indicated that the most healthy patterns were associated with a higher socioeconomic position, while lower patterns were associated with several unhealthy behaviors.

Do healthy and unhealthy behaviours cluster in New Zealand?

To describe the co-occurrence and clustering of healthy and unhealthy behaviours in New Zealand. Data were sourced from the 2002/03 New Zealand Health Survey. Behaviours selected for analysis were tobacco use, quantity and pattern of alcohol consumption, level of physical activity, and intake of fruit and vegetables. Clustering was defined as co-prevalence of behaviours greater than that expected based on the laws of probability. Co-occurrence was examined using multiple logistic regression modelling, while clustering was examined in a stratified analysis using age and (where appropriate) ethnic standardisation for confounding control. Approximately 29% of adults enjoyed a healthy lifestyle characterised by non-use of tobacco, non- or safe use of alcohol, sufficient physical activity and adequate fruit and vegetable intake. This is only slightly greater than the prevalence expected if all four behaviours were independently distributed through the population i.e. little clustering of healthy behaviours was found. By contrast, 1.5% of adults exhibited all four unhealthy behaviours and 13% exhibited any combination of three of the four unhealthy behaviours. Unhealthy behaviours were more clustered than healthy behaviours, yet Maori exhibited less clustering of unhealthy behaviours than other ethnic groups and no deprivation gradient was seen in clustering. The relative lack of clustering of healthy behaviours supports single issue universal health promotion strategies at the population level. Our results also support targeted interventions at the clinical level for the 15% with 'unhealthy lifestyles'. Our finding of only limited clustering of unhealthy behaviours among Maori and no deprivation gradient suggests that clustering does not contribute to the greater burden of disease experienced by these groups.