Clustering Of Health-related Behaviours Research Articles

Cross-sectional associations of gender identity and sexual orientation, with co-occurrence and clustering of health-related behaviours among British adolescents: Millennium cohort study

PurposeWe examined whether gender identity and sexual orientation were associated with seven health-related behaviours, and with co-occurrence and clustering of these behaviours among British adolescents. MethodsMillennium Cohort Study (age 17 wave) provided data on the exposures, gender identity (male, female, genderqueer) and sexual orientation (heterosexual, bisexual, gay or lesbian, or other), and seven self-reported health-related behaviours (binge drinking, drug use, no consumption of breakfast, no consumption of fruits or vegetables, physical inactivity, poor sleep, and smoking or vaping). Poisson regressions examined associations between the exposures and single behaviours (reporting prevalence ratios (PRs)); and multinomial logistic regressions were used for behavioural cumulative co-occurrence score (reporting PRs). Cluster patterns were identified using Ward's agglomerative hierarchical cluster analysis while associations with cluster membership were performed using logistic regressions (reporting odds ratios (ORs)). ResultsOur sample included 6022 adolescents (55.4% female, 1.5% genderqueer, 11.6% non-heterosexual). Adolescents who identified as genderqueer had the highest prevalence of not eating breakfast (PR: 60.5% [95%CI 48.4–71.4]) and poor sleep (68.7% [95%CI 55.6–79.3]). Those who identified as bisexual had a higher PR of co-occurring behaviours (2.46 [95%CI 1.39–4.27]). Among the three clusters identified (1: Multiple risk behaviours; 2: Physical inactivity and binge drinking; 3: Poor diet and physical inactivity), adolescents who identified as genderqueer or other sexual orientation showed the highest prevalence in cluster 3. ConclusionGender and sexual minority British adolescents showed a higher prevalence of risky health-related behaviours, and higher risk of co-occurring behaviours. Physical inactivity and poor diet behaviours commonly clustered together for these groups.

Changes in the clustering of health-related behaviors during the COVID-19 pandemic: examining predictors using latent transition analysis

The COVID-19 pandemic has had a significant impact on daily life, affecting both physical and mental health. Changes arising from the pandemic may longitudinally impact health-related behaviors (HRB). As different HRBs co-occur, in this study, we explore how six HRBs - alcohol (past-week and binge-drinking), tobacco, marijuana, benzodiazepine use, and unhealthy food consumption - were grouped and changed over time during the COVID-19 pandemic. A sample of 1038 university students and staff (18 to 73 years old) of two universities completed an online psychometrically adequate survey regarding their recalled HRB (T0, pre-COVID-19 pandemic) and the impact of COVID-19 on their behaviors during July (T1) and November (T2). Latent Transition Analysis (LTA) was used to identify HRB cluster membership and how clusters changed across T0, T1, and T2. Four clusters emerged, but remained mainly stable over time: ‘Lower risk’ (65.2–80%), ‘Smokers and drinkers’ (1.5–0.01%), ‘Binge-drinkers and marijuana users’ (27.6–13.9%), and ‘Smokers and binge-drinkers’ (5.6–5.8%). Participants who moved from one cluster to another lowered their HRB across time, migrating from the ‘Binge-drinkers and marijuana users’ cluster to ‘Lower risk’. Participants in this cluster were characterized as less affected economically by the COVID-19 pandemic, with lower reported stress levels, anxiety, depression, and loneliness than the other clusters. Our results provide evidence of how HRBs clustered together and transitioned longitudinally during the COVID-19 pandemic. HRB clustering across time offers a valuable piece of information for the tailoring of interventions to improve HRB.

Factors associated with clustering of health-related behaviors amongst emerging adults in Taiwan

ABSTRACT The objective of this study is to investigate factors associated with health-related behavior clusters amongst emerging adults in Taiwan. We employed data from the 2009 National Health Interview Survey in Taiwan and restricted the study sample to respondents aged 18–25 (n = 2,636). We analyzed the factors associated with three health-related behavior clusters, labeled ‘smoking-drinking’, ‘unbalanced diet’ and ‘health consciousness’ identified in a previous study. We performed multinomial logistic regression by controlling demographic, socioeconomics, disease knowledge, and medical history. Compared with the ‘health-consciousness’ cluster, the ‘smoking-drinking’ cluster was mostly comprised of men who were married, non-students, residents of low-income regions, and those earning a high income; by contrast, the ‘unbalanced diet’ cluster was mostly comprised of women in their late teens, residing in more affluent areas, and whose incomes ranged up to NT$20,000. Our study identified sociodemographic factors that are associated with unhealthy clusters, thus providing relevant evidence for designing tailor-made health promotion programs.

The Clustering of Health-Related Behaviors in the Adult Japanese Population.

BackgroundResearch findings indicate that four health-related behaviors (HRBs), smoking, alcohol, diet, and physical activity, do not co-occur within individuals by chance and therefore cluster. To date, there is a lack of research investigating the clustering of these HRBs in the Japanese population.MethodsThe Japanese National Health and Nutrition Survey 2010 was used, containing information on 8,015 community-dwelling adults. Latent profile analysis identified distinct cluster patterns of four HRBs: smoking status, alcohol consumption, calorie intake, and the number of steps per day.ResultsFor men, four distinct HRB clusters were identified. The largest cluster (54%) was characterized by drinking more than Japan’s recommended alcohol guidelines and walking an inadequate number of steps per day. A small cluster (4%) also emerged, characterized by smoking, high calorie intake, and exceeding alcohol guidelines. Members of these clusters had higher systolic blood pressure than those in the remaining clusters. For women, five distinct HRB clusters were identified. The largest cluster (57%) was characterized by not smoking or drinking and walking an inadequate number of steps per day. For both genders, there was a relationship between cluster membership and age. Cluster membership was associated with income and health status among men but not women.ConclusionDetecting distinct clusters of HRBs in a Japanese population-based survey provides a person-centered understanding of Japanese lifestyles. This approach can assist policy makers in Japan and overseas to identify new strategies for targeting behavioral risk factors and make health promotion policies more effective in their respective countries.

Clustering of health-related behaviours within children aged 11\u201316: a systematic review

ObjectiveWe aimed to systematically review and synthesise evidence on the clustering of a broad range of health-related behaviours amongst 11–16 year olds.MethodA literature search was conducted in September 2019. Studies were included if they used cluster analysis, latent class analysis, prevalence odds ratios, principal component analysis or factor analysis, and considered at least three health-related behaviours of interest among 11–16 year olds in high-income countries. Health-related behaviours of interest were substance use (alcohol, cigarettes and other drug use) and other behavioural risk indicators (diet, physical activity, gambling and sexual activity).ResultsThe review identified 41 studies, which reported 198 clusters of health-related behaviours of interest. The behaviours of interest reported within clusters were used to define eight behavioural archetypes. Some included studies only explored substance use, while others considered substance use and/or other health-related behaviours. Consequently, three archetypes were comprised by clusters reporting substance use behaviours alone. The archetypes were: (1) Poly-Substance Users, (2) Single Substance Users, (3) Substance Abstainers, (4) Substance Users with No/Low Behavioural Risk Indicators, (5) Substance Abstainers with Behavioural Risk Indicators, (6) Complex Configurations, (7) Overall Unhealthy and (8) Overall Healthy.ConclusionStudies of youth health behavioural clustering typically find both a ‘healthy’ cluster and an ‘unhealthy’ cluster. Unhealthy clusters are often characterised by poly-substance use. Our approach to synthesising cluster analyses may offer a means of navigating the heterogeneity of method, measures and behaviours of interest in this literature.

Clustering of multiple health risk factors among a sample of adolescents in Liberia: a latent class analysis

Non-communicable diseases (NCDs) are associated with modifiable health risk factors. There is a lack of evidence regarding clusters of health-related behaviours among school-going adolescents from sub-Saharan Africa. This study was conducted to identify clustering patterns of health risk factors (i.e. smoking tobacco, inadequate fruit intake, inadequate vegetable intake, physical inactivity, sedentary behaviour, anxiety and depression) and association with sociodemographic factors among school-going adolescents in Liberia. The 2017 Liberian Global School-based Student Health Survey on 2774 adolescents aged 11 years and above (52.5% females) sampled with a two-stage cluster sample design was used. Latent class analysis was used to generate the clusters and latent class regression assessed the associations between sociodemographic factors and the clusters. We identified three clusters labelled as (1) ‘low substance use, moderately active cluster’ (34.8%); (2) ‘inadequate fruit and vegetable cluster’ (48.9%) and (3) ‘risk taking cluster’ (16.3%)’. Compared to cluster 1, adolescent boys [AOR = 1.71, 1.29–2.27, p < 0.001], and those in grade 10–12 [AOR = 1.51, 1.13–2.02, p < 0.001] were more likely to belong to cluster 2. Participants aged 15 years and above [AOR = 0.60, 0.39–0.91, p = 0.018] were less likely to belong to cluster 2. Compared to cluster 1, adolescents aged 15 years and above [AOR = 3.58, 1.33–9.62, p = 0.011] and those with low socio-economic status [AOR = 1.83, 1.22–2.73, p = 0.003] were more likely to belong to cluster 3. These results underscore the need for interventions that address adolescent multiple health risk factors, especially considering sociodemographic differences.

Clustering of health-related behaviours and its relationship with individual and contextual factors in Portuguese adolescents: results from a cross-sectional study

BackgroundHealth behaviours are shaped early in life and tend to occur in complex specific patterns. We aimed to characterise these patterns among Portuguese adolescents and their association with individual and contextual factors.MethodsThis study was based in the Portuguese 2009/10 survey of Health Behaviour in School-Aged Children Study, comprising 4036 adolescents. Individuals were grouped using two-step cluster analysis based on 12 behaviours regarding diet, physical activity, screen use and substance use. The association between clusters and individual and contextual factors was analysed using multinomial regression.ResultsThe median age was 13,6, and 54% were female. Overweight and obesity were highly prevalent (25%). We identified four behavioural clusters: “Active screen users”, “Substance users”, “Healthy” and “Inactive low fruit and vegetable eaters”. Sociodemographics varied across clusters. The “Substance users” and “Active screen users” clusters were associated with poor family communication, academic performance and school attachment and violent behaviours, and the “Inactive low fruit and vegetable eaters” were associated with lower socioeconomic status.ConclusionThe understanding of these health-compromising patterns and their social determinants is of use to Public Health, allowing tailored health-promoting interventions. Further research is needed to understand how cluster membership evolves and its influence on nutritional status.

Healthy or Unhealthy? The Cocktail of Health-Related Behavior Profiles in Spanish Adolescents.

The aim of this study was to identify the prevalence and clustering of health-related behaviors in Spanish adolescents and to examine their association with sex, body mass index (BMI), different types of sedentary screen time, and adherence to 24-hour movement guidelines. A final sample of 173 students (M = 12.99 ± 0.51) participated in this study. Cluster analysis was conducted based on five health-related behaviors: PA and sedentary time derived from accelerometers, as well as healthy diet, sedentary screen time, and sleep duration derived from self-reported scales. Recommendations for 24-hour movement guidelines (i.e., physical activity (PA), screen time, and sleep duration) were analyzed both independently and combined. A total of 8.9% of the sample did not meet any of the guidelines, whereas 72.3%, 17.3%, and 1.7% of the sample met 1, 2, or all 3 guidelines, respectively. Six distinct profiles were identified, most of them showing the co-occurrence of healthy- and unhealthy-related behaviors. Given that most of the adolescents failed to meet the combination of PA, screen time, and sleep duration guidelines, these findings suggest the necessity to implement school-based interventions that target multiple health behaviors, especially because (un)healthy behaviors do not always cluster in the same direction.

Identification of health-related behavioural clusters and their association with demographic characteristics in Irish university students

BackgroundStudents engage in risky health-related behaviours that influence their current and future health status. Health-related behaviours cluster among adults and differently based on sub-populations characteristics but research is lacking for university populations. Examining the clustering of health- related behaviours can inform our initiatives and strategies, while examining cluster members’ characteristics can help target those who can prosper most from health promotion efforts. This study examines the clustering of health-related behaviours in Irish university students, and investigates the relationship with students’ sex, age, field of study and accommodation type.MethodsAn online survey was completed by 5672 Irish university students (51.3% male; 21.60 ± 5.65 years) during 2014. Two-step cluster analysis was used to understand how health-related behaviours (physical activity, smoking, alcohol intake, drug use and dietary habits) cluster among male and female students. Binary logistic regressions were conducted to examine the likelihood of students falling into certain clusters based on their characteristics.ResultsFive cluster groups were identified in males and four in females. A quarter of males were categorised as ideal healthy with older students and those from certain fields of study having a higher likelihood of being classified in a low physical activity and poor diet (OR = 1.06–2.89), alcohol consumption (OR = 1.03–3.04), or smoking and drug use (OR = 1.06–2.73) cluster. Forty-five percent of females were categorised as ideal healthy with older females more likely to be in a low active and smoking cluster (OR = 1.03), and less likely to be in a convenience food cluster (OR = 0.96). Females from certain fields of study were also more likely to be classified in these clusters (OR = 1.59–1.76). Students living away from their family home had in increased likelihood of being in a cluster related to a higher frequency of alcohol consumption (OR = 1.72–3.05).ConclusionHealth-related behaviours cluster among this population and need to be taken into account when designing multi-health interventions and policies. These findings can be used to target student groups at risk, leading to more efficient and successful health promotion efforts. The addition of modules providing information regarding health-related behaviours are advised in all fields of study.

Socioeconomic inequality in clusters of health-related behaviours in Europe: latent class analysis of a cross-sectional European survey

BackgroundModifiable health-related behaviours tend to cluster among most vulnerable sectors of the population, particularly those at the bottom of the social hierarchy. This study aimed to identify the clusters of health-related behaviours in 27 European countries and to examine the socioeconomic inequalities in these clusters.MethodsData were from Eurobarometer 72.3–2009, a cross-sectional survey of 27 European countries. The analyses were conducted in 2016. The main sections of the survey included questions pertaining to sociodemographic factors, health-related behaviours, and use of services. In this study, those aged 18 years and older were included. We selected five health-related behaviours, namely smoking, excessive alcohol consumption, frequent fresh fruit consumption, physical activity and dental check-ups. Socioeconomic position was indicated by education, subjective social status and difficulty in paying bills. Latent class analysis was conducted to explore the clusters of these five behaviours. Multinomial logistic regression model was used to examine the relationships between the clusters and socioeconomic positions adjusting for age, gender, marital status and urbanisation.ResultsThe eligible total population was 23,842. Latent class analysis identified three clusters; healthy, moderate and risky clusters in this European population. Individuals with the lowest socioeconomic position were more likely to have risky and moderate clusters than healthy cluster compared to those with the highest socioeconomic position.ConclusionsThere were clear socioeconomic gradients in clusters of health-related behaviours. The findings highlight the importance of adopting interventions that address multiple health risk behaviours and policies that tackle the social determinants of health-related behaviours.

Clustering of health-related behaviors among early and mid-adolescents in Tuscany: results from a representative cross-sectional study.

BackgroundA huge amount of literature suggests that adolescents’ health-related behaviors tend to occur in clusters, and the understanding of such behavioral clustering may have direct implications for the effective tailoring of health-promotion interventions. Despite the usefulness of analyzing clustering, Italian data on this topic are scant. This study aimed to evaluate the clustering patterns of health-related behaviors.MethodsThe present study is based on data from the Health Behaviors in School-aged Children (HBSC) study conducted in Tuscany in 2010, which involved 3291 11-, 13- and 15-year olds. To aggregate students’ data on 22 health-related behaviors, factor analysis and subsequent cluster analysis were performed.ResultsFactor analysis revealed eight factors, which were dubbed in accordance with their main traits: ‘Alcohol drinking’, ‘Smoking’, ‘Physical activity’, ‘Screen time’, ‘Signs & symptoms’, ‘Healthy eating’, ‘Violence’ and ‘Sweet tooth’. These factors explained 67% of variance and underwent cluster analysis. A six-cluster κ-means solution was established with a 93.8% level of classification validity. The between-cluster differences in both mean age and gender distribution were highly statistically significant.ConclusionsHealth-compromising behaviors are common among Tuscan teens and occur in distinct clusters. These results may be used by schools, health-promotion authorities and other stakeholders to design and implement tailored preventive interventions in Tuscany.

The clustering of health-related behaviours in a British population sample: Testing for cohort differences

Research findings indicate that health-related behaviours (HRBs) do not co-occur within individuals by chance and therefore cluster. This study uses Latent Profile Analysis (LPA), to identify the clustered patterns and prevalence of four HRBs: smoking, alcohol, diet, physical activity. We used data, collected from participants in their early 30s, from two British cohorts born in 1958 and 1970 (N=21,019). Multi-group LPA models were run separately by gender testing for cohort differences in HRB cluster patterns. For both genders three clusters emerged: ‘Risky’ (1–9%), ‘Moderate Smokers’ (20–30%) and ‘Mainstream’ (68–77%). HRBs amongst members of the ‘Mainstream’ cluster were more beneficial than HRBs amongst members of the other two clusters, characterised as not smoking, frequent fruit and vegetable consumption, less frequent consumption of chips and fried food and being more physically active. Nevertheless, frequent consumption of sweet foods was common in the ‘Mainstream’ cluster. There was a large shift in membership to the ‘Mainstream’ cluster for men and women born in 1970. Amongst women members of the ‘Mainstream’ cluster, a higher proportion of those born in 1970 appeared to have drunk alcohol above the contemporaneous UK recommended limits but consumed sweet foods less frequently, than those born in 1958. In summary our findings provide additional evidence of HRB clustering, identifying largely consistent HRBs cluster patterns across cohort and gender groups, with some differences in prevalence. This evidence of HRB clustering across time and by gender provides a person-centred understanding that can inform interventions to improve HRBs.

Cultural, social and intrapersonal factors associated with clusters of co-occurring health-related behaviours among adolescents.

Adverse health-related behaviours (HRBs) have been shown to co-occur in adolescents. Evidence lacks on factors associated with these co-occurring HRBs. The Theory of Triadic Influence (TTI) offers a route to categorize these determinants according to type (social, cultural and intrapersonal) and distance in the causal pathway (ultimate or distal). Our aims were to identify cultural, social and intrapersonal factors associated with co-occurring HRBs and to assess the relative importance of ultimate and distal factors for each cluster of co-occurring HRBs. Respondents concerned a random sample of 898 adolescents aged 12-18 years, stratified by age, sex and educational level of head of household. Data were collected via face-to-face computer-assisted interviewing and internet questionnaires. Analyses were performed for young (12-15 years) and late (16-18 years) adolescents regarding two and three clusters of HRB, respectively. For each cluster of HRBs (e.g. smoking, delinquency), associated factors were found. These accounted for 27 to 57% of the total variance per cluster. Factors came in particular from the intrapersonal stream of the TTI at the ultimate level and the social stream at the distal level. Associations were strongest for parenting practices, risk behaviours of friends and parents and self-control. Results of this study confirm that it is possible to identify a selection of cultural, social and intrapersonal factors associated with co-occurring HRBs among adolescents.

Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study

BackgroundRecent studies show several health-related behaviors to cluster in adolescents. This has important implications for public health. Interrelated behaviors have been shown to be most effectively targeted by multimodal interventions addressing wider-ranging improvements in lifestyle instead of via separate interventions targeting individual behaviors. However, few previous studies have taken into account a broad, multi-disciplinary range of health-related behaviors and connected these behavioral patterns to health-related outcomes. This paper presents an analysis of the clustering of a broad range of health-related behaviors with relevant demographic factors and several health-related outcomes in adolescents.MethodsSelf-report questionnaire data were collected from a sample of 2,690 Dutch high school adolescents. Behavioral patterns were deducted via Principal Components Analysis. Subsequently a Two-Step Cluster Analysis was used to identify groups of adolescents with similar behavioral patterns and health-related outcomes.ResultsFour distinct behavioral patterns describe the analyzed individual behaviors: 1- risk-prone behavior, 2- bully behavior, 3- problematic screen time use, and 4- sedentary behavior. Subsequent cluster analysis identified four clusters of adolescents. Multi-problem behavior was associated with problematic physical and psychosocial health outcomes, as opposed to those exerting relatively few unhealthy behaviors. These associations were relatively independent of demographics such as ethnicity, gender and socio-economic status.ConclusionsThe results show that health-related behaviors tend to cluster, indicating that specific behavioral patterns underlie individual health behaviors. In addition, specific patterns of health-related behaviors were associated with specific health outcomes and demographic factors. In general, unhealthy behavior on account of multiple health-related behaviors was associated with both poor psychosocial and physical health. These findings have significant meaning for future public health programs, which should be more tailored with use of such knowledge on behavioral clustering via e.g. Transfer Learning.

Clustering of Health-Related Behaviors and Their Determinants: Possible Consequences for School Health Interventions

Characterizing school health promotion is its category-by-category approach, in which each separate health-related behavior is addressed independently. Such an approach creates a risk that extra-curricular activities become overloaded, and that teaching staff are distracted by continuous innovations. Within the health promotion sector there are thus increasing calls for an integrative approach to health-related behaviors. However, a meaningful integrative approach to different lifestyles will be possible only if there is some clustering of individual health-related behaviors and if health-related behaviors have a minimum number of determinants in common. This systematic review aims to identify to what extent the four health-related behaviors smoking, alcohol abuse, safe sex and healthy nutrition cluster; and how their determinants are associated. Potentially modifiable determinants that offer clues for an integrative approach of school health-promotion programs are identified. Besides, the direction in which health educators should look for a more efficient instructional design is indicated.

Clustering of Health-Related Behaviors among 18-Year-Old Australians

Background.Few studies among young adults have examined clustering of health behaviors affecting risk for lifestyle diseases.Methods.Smoking, alcohol consumption, physical activity, and diet were examined among Australian 18-year-olds (301 males, 282 females) initially recruited at the age of 9 years from 26 schools. Association analysis was used to recognize behavior clustering.Results.Fat intake was greater among male smokers than nonsmokers (36% energy vs 34% energy). Women smokers ate less fiber (14.1 g/day) than did nonsmokers (17.8 g/day). Smoking was significantly related, among males, to unsafe drinking (odds ratio 2.38) and higher fat intake (odds ratio 1.06) and, among females, to unsafe drinking (odds ratio 1.59), lower dietary fiber (odds ratio 0.93), and less physical activity (odds ratio 0.36). Cluster analysis defined separate behavior clusters for men and women with smoking status identifying further subgroups. Smoking, drinking alcohol to excess, and adverse dietary choices clustered among men and women, with physical inactivity also clustering among women.Conclusion.Smoking among adolescents is an important indicator of behaviors influencing risk for later cardiovascular disease and other medical disorders. Multimodal approaches allowing for gender differences in health-related behaviors are likely to be more successful than targeting a single behavior in this age group.