Clustering Of Health Behaviors Research Articles

Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.

Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions. We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union. We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category. In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001). Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes.

Health lifestyles of six Zhiguo ethnic groups in China: a latent class analysis

BackgroundZhiguo ethnic groups, commonly known as “the directly-entering-socialism ethnic groups”, represent Chinese ethnic minorities who have undergone a unique social development trajectory by transforming directly from primitive societies to the socialist stage. In recent decades, significant lifestyle transformations have occurred among Zhiguo ethnic groups. Understanding their health lifestyles can play a strategic role in China’s pursuit of universal health coverage. This study aims to examine patterns of health-related lifestyle among Zhiguo ethnic groups and explore whether sociodemographic features and specific indicators related to health status are associated with particular classes.MethodsA cross-sectional study was conducted in Yunnan Province, China, from July to December 2022. Stratified random sampling method was employed to recruit residents belonging to six Zhiguo ethnic groups aged between 15 and 64. Latent class analysis was performed to identify clusters of health-related behaviors within each ethnic group. Logistic regression was utilized to determine the predictors of health lifestyles.ResultsA total of 1,588 individuals from the Zhiguo ethnic groups participated in this study. Three latent classes representing prevalent health lifestyles among the Zhiguo ethnic groups were identified: “unhealthy lifestyle” (31.80%), “mixed lifestyle” (57.37%), and “healthy lifestyle” (10.83%). In the overall population, individuals belonging to the “healthy lifestyle” group exhibited a higher likelihood of being non-farmers (OR: 2.300, 95% CI: 1.347–3.927), women (OR: 21.459, 95% CI: 13.678–33.667), married individuals (OR: 1.897, 95% CI: 1.146–3.138), and those residing within a walking distance of less than 15 min from the nearest health facility (OR: 2.133, 95% CI: 1.415–3.215). Conversely, individuals in the age cohorts of 30–39 years (OR: 0.277, 95% CI: 0.137–0.558) and 40–49 years (OR: 0.471, 95% CI: 0.232–0.958) showed a decreased likelihood of adopting a healthy lifestyle.ConclusionsA considerable proportion of the Zhiguo ethnic groups have not adopted healthy lifestyles. Targeted interventions aimed at improving health outcomes within these communities should prioritize addressing the clustering of unfavorable health behaviors, with particular emphasis on single male farmers aged 30–49, and expanding healthcare coverage for individuals residing more than 15 min away from accessible facilities.

Association between work-related factors and health behaviour clusters among Finnish private-sector service workers

PurposeWe examined how work-related factors associate with several health behaviours that appear together among the large, but less-studied, blue- and pink-collar worker group, which is characterized by low education and income levels.MethodsIn 2019, we conducted a cross-sectional survey among private sector service workers (n = 5256) in Finland. We applied two-step cluster analysis to identify groups on the basis of leisure-time physical activity, sleep adequacy, frequency of heavy drinking, smoking status, and frequency of fruit, vegetable and berry consumption. We examined the associations with work-related factors, using multinomial regression analyses and adjusting for confounding factors.ResultsWe identified six clusters labelled as Moderately Healthy (28% of the participants), Healthy – Vigorous Exercise (19%), Sedentary Lifestyle (16%), Inadequate Sleep (15%), Mixed Health Behaviours (15%), and Multiple Risk Behaviours (8%). Those who perceived their work to be mentally or physically strenuous more commonly belonged to the Inadequate Sleep and Multiple Risk Behaviours clusters. Time pressure made belonging to the Inadequate Sleep, Mixed Health Behaviours, and Multiple Risk Behaviours clusters more likely. Those who were dissatisfied with their work more often belonged to the Healthy – Vigorous Exercise, Inadequate Sleep, and Multiple Risk Behaviours clusters.ConclusionIn addition of finding several considerably differing health behaviour clusters, we also found that adverse working conditions were associated with clusters characterized by multiple risk behaviours, especially inadequate sleep. Private-sector service workers’ working conditions should be improved so that they support sufficient recovery, and occupational health services should better identify co-occurring multiple risk behaviours.

Clustering of health behaviors and their associations with cardiometabolic risk factors among adults at high risk for type 2 diabetes in India: A latent class analysis.

We aimed to identify clusters of health behaviors and study their associations with cardiometabolic risk factors in adults at high risk for type 2 diabetes in India. Baseline data from the Kerala Diabetes Prevention Program (n = 1000; age 30-60 years) were used for this study. Information on physical activity (PA), sedentary behavior, fruit and vegetable intake, sleep, and alcohol and tobacco use was collected using questionnaires. Blood pressure, waist circumference, 2-h plasma glucose, high-density lipoprotein and low-density lipoprotein cholesterol, and triglycerides were measured using standardized protocols. Latent class analysis was used to identify clusters of health behaviors, and multilevel mixed-effects linear regression was employed to examine their associations with cardiometabolic risk factors. Two classes were identified, with 87.4% of participants in class 1 and 12.6% in class 2. Participants in both classes had a high probability of not engaging in leisure-time PA (0.80 for class 1; 0.73 for class 2) and consuming <5 servings of fruit and vegetables per day (0.70 for class 1; 0.63 for class 2). However, participants in class 1 had a lower probability of sitting for >=3 h per day (0.26 vs 0.42), tobacco use (0.10 vs 0.75), and alcohol use (0.08 vs 1.00) compared to those in class 2. Class 1 had a significantly lower mean systolic blood pressure (β = -3.70 mm Hg, 95% confidence interval [CI] -7.05, -0.36), diastolic blood pressure (β = -2.45 mm Hg, 95% CI -4.74, -0.16), and triglycerides (β = -0.81 mg/dL, 95% CI -0.75, -0.89). Implementing intervention strategies, tailored to cluster-specific health behaviors, is requiredfor the effective prevention of cardiometabolic disorders among high-risk adults for type 2 diabetes.

Does the effect of adolescent health behaviours on adult cardiometabolic health differ by socioeconomic background? Protocol for a population-based cohort study

IntroductionAdolescence is a sensitive period for cardiometabolic health. Yet, it remains unknown if adolescent health behaviours, such as alcohol use, smoking, diet and physical activity, have differential effects across socioeconomic...

Health behavior profiles and association with mental health status among US active-duty service members.

This study investigated the clustering of health behaviors among US active duty servicemembers (ADSM) into risk profiles and explored the association between these profiles with ADSM sociodemographic characteristics and mental health status. This study utilized secondary data from the 2018 Health Related Behaviors Survey (HRBS), a Department of Defense (DoD) self-administered online survey. Health behaviors included physical activity, screen use, sleep habits, tobacco/substance use, alcohol drinking, preventive health care seeking and condom use at last sex/having multiple sexual partners. Past-year mental health status was measured using the Kessler Screening Scale for Psychological Distress (K6). Latent class analysis (LCA) on health behaviors was used to cluster ADSMs into risk profiles. Multivariable logistic model was used to examine whether ADSM characteristics and mental health status were associated with ADSMs' risk profiles. The LCA identified a four-class model that clustered ADSMs into the following sub-groups: (1) Risk Inclined (14.4%), (2) High Screen Users (51.1%), (3) Poor Sleepers (23.9%) and (4) Risk Averse (10.6). Over a tenth (16.4%) of ADSMs were categorized as having serious psychological distress. Being male, younger, less educated, in the Army, Marine Corps or Navy were associated with higher odds of being Risk Inclined (AOR ranging from 1.26 to 2.42). Compared to the reference group of Risk Adverse ADSMs, those categorized as Risk Inclined (AOR: 8.30; 95% CI: 5.16-13.36), High Screen Users (AOR: 2.44; 95% CI: 1.56-3.82) and Poor Sleepers (AOR: 5.26; 95% CI: 3.38-8.19) had significantly higher odds of having serious psychological distress. Study findings suggest opportunities to tailor behavioral and health promotion interventions for each of the distinct risk profiles. For example, ADSM described as Risk Inclined may benefit from preventive mental health services. Solutions for ADSM described as Poor Sleepers may include education on sleep hygiene; instituting duty schedules; and shifting military cultural norms to promote sleep hygiene as a pathway to optimal performance and thus military readiness. ADSM with low-risk behavior profiles such as those described as Risk Averse may prove beneficial in the roll-out of interventions as they act as peer-educators or mentors.

Longitudinal clustering of health behaviours and their association with multimorbidity in older adults in England: A latent class analysis.

Health-risk behaviours such as smoking, unhealthy nutrition, alcohol consumption, and physical inactivity (termed SNAP behaviours) are leading risk factors for multimorbidity and tend to cluster (i.e. occur in specific combinations within distinct subpopulations). However, little is known about how these clusters change with age in older adults, and whether and how cluster membership is associated with multimorbidity. Repeated measures latent class analysis using data from Waves 4-8 of the English Longitudinal Study of Ageing (ELSA; n = 4759) identified clusters of respondents with common patterns of SNAP behaviours over time. Disease status (from Wave 9) was used to assess disorders of eight body systems, multimorbidity, and complex multimorbidity. Multinomial and binomial logistic regressions were used to examine how clusters were associated with socio-demographic characteristics and disease status. Seven clusters were identified: Low-risk (13.4%), Low-risk yet inactive (16.8%), Low-risk yet heavy drinkers (11.4%), Abstainer yet inactive (20.0%), Poor diet and inactive (12.9%), Inactive, heavy drinkers (14.5%), and High-risk smokers (10.9%). There was little evidence that these clusters changed with age. People in the clusters characterised by physical inactivity (in combination with other risky behaviours) had lower levels of education and wealth. People in the heavy drinking clusters were predominantly male. Compared to other clusters, people in the Low-risk and Low-risk yet heavy drinkers had a lower prevalence of all health conditions studied. In contrast, the Abstainer but inactive cluster comprised mostly women and had the highest prevalence of multimorbidity, complex multimorbidity, and endocrine disorders. High-risk smokers were most likely to have respiratory disorders. Health-risk behaviours tend to be stable as people age and so ought to be addressed early. We identified seven clusters of older adults with distinct patterns of behaviour, socio-demographic characteristics and multimorbidity prevalence. Intervention developers could use this information to identify high-risk subpopulations and tailor interventions to their behaviour patterns and socio-demographic profiles.

The clustering of multiple health and lifestyle behaviors among Swedish adolescents: a person-oriented analysis.

Knowledge of the distribution, prevalence, and clustering of multiple health and lifestyle related behaviors (HLBs) among adolescents can inform the development of effective health-promoting policies and interventions. We assessed the clustering of multiple HLBs among 11, 13 and 15-year-old Swedish adolescents and examined the socioeconomic and demographic correlates for the identified clusters. We used data from the 2017/2018 Swedish Health Behaviour in School-aged children (HBSC) study to conduct sex and age-stratified latent class analysis (LCA). The LCA was based on five HLBs: eating behavior and habits (EBH), physical activity (PA), tobacco usage (TU), alcohol consumption (AC) and sleeping habits and patterns (SHPs). Multinomial logistic regression models were used to assess the associations between the identified clusters and the socioeconomic and demographic characteristics of adolescents and their parents. Health behaviors varied by sex and age. Four distinct clusters were identified based on sex: cluster 1 (Mixed eating behaviors and habits, physical activity and low alcohol consumption), cluster 2 (Healthy lifestyle behaviors), cluster 3 (Unhealthy lifestyle behaviors), and cluster 4 (Breakfast, low alcohol consumption and tobacco usage). In the age-stratified analyzes, three clusters were identified: cluster 1 (Unhealthy lifestyle behaviors), cluster 2 (Moderately healthy lifestyle behaviors) and cluster 3 (Healthy lifestyle behaviors). The multinomial analysis showed that sex, age, family situation and perceived family wealth were strong predictors of health behaviors. Unhealthy behaviors were most commonly associated with socioeconomic disadvantage, having a migrant background, and living in reconstructed families or single-parent households. Health behaviors vary significantly based on socioeconomic and demographic circumstances. Targeted policies and intervention programs are necessary to improve HLBs among vulnerable and at-risk adolescents.

Prevalence and clustering of health behaviours and the association with socio-demographics and mental well-being in Dutch university students

The college years represent a vulnerable period for developing health-risk behaviours (e.g., physical inactivity/unhealthy eating habits/substance use/problematic internet use/insufficient sleep). This study examined current health behaviour levels (RQ1), health behaviour classes (RQ2) and between-class differences in socio-demographics (RQ3) and mental well-being (RQ4) among Dutch university students (n = 3771). Participants (Mage = 22.7 (SD = 4.3); 71.2% female/27.3% male/1.5% other) completed an online survey (Oct-Nov 2021). Descriptive statistics (RQ1), Latent Class Analysis (RQ2), and Kruskal-Wallis/Chi-square tests (RQ3-4) were used. RQ1: Prevalence rates suggest that a subsequent proportion of the student sample engages in health-risk behaviours. RQ2: Four classes were identified: class 1 (n = 862) “Licit substance use health-risk group”, class 2 (n = 435) “Illicit and licit substance use health-risk group”, class 3 (n = 1876) “Health-protective group” and class 4 (n = 598) “Non-substance use health-risk group”. RQ3: Class 1 represents relatively more international students and students in a steady relationship. Class 2 represents relatively more older/male/(pre-)master students and students living with roommates/in a steady relationship/with more financial difficulty. Class 3 represents relatively more younger/female students and students living with family/with lower Body Mass Index (BMI)/less financial difficulty. Class 4 represents relatively more younger/non-Western/international/bachelor students and students living with children/single/part of LGBTIQ+ community/with higher BMI. RQ4: Class 3 has significantly higher mental well-being while class 4 has significantly lower mental well-being, relative to the other classes. Above findings provide new insights which can help educational institutes and governments better understand the clustering of students’ health behaviours and between-class differences in socio-demographics and mental well-being.

Clustering of lifestyle and health behaviours in Australian adolescents and associations with obesity, self-rated health and quality of life

ObjectiveThe primary aim of this study was to identify clusters of lifestyle and health behaviours and explore their associations with health outcomes in a nationally representative sample of Australian adolescents.MethodsThe study participants were 3127 adolescents aged 14–15 years who participated in the eighth wave of the birth cohort of the Longitudinal Study of Australian Children (LSAC). A latent class analysis (LCA) was performed to identify clusters based on the behaviours of physical activity, alcohol consumption, smoking, diet, eating disorders, sleep problems and weight consciousness. Multinomial logistic regression models were fitted to the following health outcome variables: obesity, self-rated general health and pediatric health-related quality of life, to investigate their associations with LCA clusters.ResultsBased on the prevalence of health behaviour related characteristics, LCA identified gender based distinct clusters of adolescents with certain outward characteristics. There were five clusters for male and four clusters for female participants which are named as: healthy lifestyle, temperate, mixed lifestyle, multiple risk factors, and physically inactive (male only). Adolescents in the healthy lifestyle and temperate clusters reported low and moderately active health risk behaviours, for example, low physical activity, inadequate sleep and so on, while these behaviours were prevailing higher among adolescents of other clusters. Compared to adolescents of healthy lifestyle clusters, male members of physically inactive (OR = 3.87, 95% CI: 1.12 – 13.33) or mixed lifestyle (OR = 5.57, 95% CI: 3.15 – 9.84) clusters were over three to five times more likely to have obesity; while for female adolescents, members of only multiple risk factors clusters (OR = 3.61, 95% CI: 2.00 – 6.51) were over three time more likely to have obesity compared to their counterpart of healthy lifestyle clusters. Adolescents of physically inactive (b = -9.00 for male only), mixed lifestyle (b = -2.77 for male; b = -6.72 for female) or multiple risk factors clusters (b = -6.49 for male; b = -6.59 for female) had a stronger negative association with health-related quality of life scores compared to adolescents of healthy lifestyle clusters.ConclusionThe study offers novel insights into latent class classification through the utilisation of different lifestyles and health-related behaviours of adolescents to identify characteristics of vulnerable groups concerning obesity, general health status and quality of life. This classification strategy may help health policy makers to target vulnerable groups and develop appropriate interventions.

Clustering of Health Behaviors in Canadians: A Multiple Behavior Analysis of Data from the Canadian Longitudinal Study on Aging.

Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviors tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behavior change. However, whether co-occurrence or co-variation-based approaches are better suited for this task remains relatively unknown. To compare the utility of co-occurrence vs. co-variation-based approaches for understanding the interconnectedness between multiple health-impacting behaviors. Using baseline and follow-up data (N = 40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviors. We used cluster analysis to group individuals based on their behavioral tendencies across multiple behaviors and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioral correlations and compared regression analyses of clusters and individual behaviors predicting future health outcomes. Seven clusters were identified, with clusters differentiated by six of the seven health behaviors included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviors were generally small. In regression analyses individual behaviors accounted for more variance in health outcomes than clusters. Co-occurrence-based approaches may be more suitable for identifying sub-groups for intervention targeting while co-variation approaches are more suitable for building an understanding of the relationships between health behaviors.

Longitudinal clustering of health behaviours and their association with health outcomes in older adults in England: a latent class analysis

Longitudinal clustering of health behaviours and their association with health outcomes in older adults in England: a latent class analysis

Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel in Central Peninsular Malaysia.

Health- and oral health-compromising behaviours (HOHCBs) impact the health readiness of military personnel, resulting in decreased fitness performance, thus affecting combat readiness. This study aimed to identify the clustering patterns and number of HOHCBs in army personnel in Central Peninsular Malaysia. Thus, a cross-sectional study using a multistage sampling technique and a validated 42-item online questionnaire was conducted to assess ten health (medical screening, physical activity, sedentary lifestyle, smoking status, alcohol consumption, substance abuse, aggressive behaviours, sleep, and road safety habits) and five oral health behaviour domains (tooth brushing, fluoridated toothpaste use, flossing, dental visits, and bruxism). Each HOHCB was dichotomised into healthy and health-compromising behaviour and analysed using hierarchical agglomerative cluster analysis (HACA). With the majority being males (92.5%), of other ranks (96.8%), and healthy (83.9%), 2435 army members of a mean age of 30.3 years (SD = 5.9) participated, with a response rate of 100%. HACA identified two clustering patterns: (i) 'high-risk behaviours' (30 HOHCBs) and (ii) 'most common risk behaviours' (12 HOHCBs) with a mean clustering number of 14.1 (SD = 4.1). In conclusion, army personnel in Central Peninsular Malaysia displayed 2 broad HOHCB clustering patterns, 'high-risk' and 'most common risk', with an average of 14 HOHCB clusters per person.

Association of lifestyle behaviors with health-related quality of life among patients with hematologic diseases.

Health-related quality of life (HRQoL) is a multi-dimensional construct used to assess the impact of health status on quality of life, and it is known to be affected by lifestyle behaviors. This study focused on multiple lifestyle behaviors among patients with hematologic diseases, including physical activity, dietary intake, sleep quality, occupational exposure, alcohol consumption and smoking. The main objective was to investigate the association of both individual and clustering of health behaviors with HRQoL among the population with hematologic diseases based on a comprehensive lifestyle survey. A total of 539 patients with hematologic diseases aged over 18years were enrolled in this cross-sectional study. Latent class analysis was used to identify homogeneous, mutually exclusive lifestyle classes, and multinomial logistic regression was then performed to explore the association of lifestyle classes membership with HRQoL. Meanwhile, multiple linear regression and quantile regression were used to identify the relationship between individual lifestyle behaviors and HRQoL. A three-class model was selected based on conceptual interpretation and model fit. We found no association between multiple lifestyle behaviors and HRQoL in the 3-class model, either in the whole patients or in subgroups stratified by hematological malignancies. Further research on each lifestyle found that physical activity, dietary intake, occupational exposure, alcohol consumption or smoking were independent of HRQoL. Sleep quality was positively associated with HRQoL. Our findings suggested that clustering of lifestyle behaviors may not be an indicator to reflect the health quality of patients with hematologic diseases. Sleep represents a viable intervention target that can confer health benefits on the hematologic patients.

Health behaviors and subsequent mental health problems during the COVID-19 pandemic: A longitudinal analysis of adults in the UK.

Public health mitigation policies aimed at slowing the spread of COVID-19 led to an increase in mental health problems (MHPs). This study examines the association between multiple pre-pandemic health behaviors and MHPs prior to, and during, the COVID-19 pandemic. We analyzed a representative population sample of 11,256 adults (aged 20-65 years) from Understanding Society-The UK Household Longitudinal Study. Baseline data from participants interviewed in 2017/2019 (wave 9) were linked to web surveys conducted during the COVID-19 pandemic. We used latent class analysis (LCA) to identify mutually exclusive health behavior (physical activity, alcohol consumption, eating habits and smoking tobacco) clusters by gender, and examined the sociodemographic correlates of each cluster. We assessed how pre-pandemic latent classes of health behaviors were associated with changes in MHPs during the pandemic using fixed effects regression models. Three health behavior clusters were identified: positive (33%), moderate (24%), and high risk (43%), where similar behaviors clustered within individuals and sociodemographic circumstances. In particular, gender, age, migrant status and ethnicity were found to have strong associations with each cluster. Our results also demonstrated a clear association in MHPs with health behaviors both prior to, and during the pandemic. There were significant increases in MHPs between 2017/2019 and January 2021, with fluctuations coinciding with changes in public health mitigation policies. Assessments across the three clusters showed about 25.2%, 16.9%, and 0.7% increases in MHPs in the positive, moderate and high risk health behavior clusters, respectively. This study shows that pre-pandemic health behaviors were significantly associated with mental health before and during the pandemic. Holistic policy interventions and promotions targeting multiple health behaviors may be an effective strategy to improve mental health in the pandemic recovery period.

A cluster analysis of health behaviours and their relationship to mental health difficulties, life satisfaction and functioning in adolescents

Research has documented relationships between individual health behaviours and mental health, but few studies have examined patterns of health behaviours (i.e., health lifestyles) and mental health outcomes. This study investigated the relationship between health lifestyles and mental health and psychological wellbeing in adolescents. Data from the My World Survey 2 - Second Level (MWS2-SL), a random national sample of 9493 adolescents (44.2% male) aged 12–18 years (M = 14.8, SD = 1.66) in second level education in Ireland were used. TwoStep cluster analysis identified clustering of health behaviours (sleep, alcohol use, cannabis use, social media use, sport and hobby participation). ANOVA and Chi Square tests identified potential differences in demographic characteristics (age, gender, socioeconomic status, ethnicity) and psychological outcomes (anxiety, depression, life satisfaction, daily functioning) across clusters. Three clusters were identified; 1. Low (22.0%), 2. Moderate-high (41.5%) and 3. High (36.5%), health promoting. The ‘Low’ health-promoting cluster, characterised by high alcohol, cannabis and social media use, moderate sport and hobby participation, and low sleep duration, demonstrated the highest levels of anxiety and depression and the lowest levels of life satisfaction, self-esteem and daily functioning. The ‘High’ health-promoting cluster reported the most favourable psychological outcomes. Cluster 1 were more likely to be older and male, Cluster 3 were more likely to be younger and female. Findings demonstrate the potential for health promotive lifestyles to mitigate mental health difficulties and promote life satisfaction and daily functioning in adolescents. Health lifestyles represent important indicators of mental health and targets for prevention/promotion efforts.

Clustering of health behaviors among Japanese adults and their association with socio-demographics and happiness.

Scholars have associated happiness with health behaviors, which co-occur and exert synergistic effects. Therefore, identifying clusters of health behaviors and their effect on happiness can contribute to the development of strategies for promoting happiness and improving health behaviors. This study aimed to examine clusters of health behaviors and their associations with socio-demographics and with happiness among Japanese adults. This study used data from the Japanese Household Panel Survey and the Keio Household Panel Survey. Questionnaires were distributed to 4,993 households out of which 1,554 responses were analyzed (participants aged 27-65 years). The survey included health behaviors (alcohol consumption, smoking, vegetable and fruit consumption, breakfast-eating habits, and physical activities), happiness, and socio-demographics. Latent class analysis was conducted to identify the clusters, whereas latent regression was employed to investigate socio-demographics related to the clusters. Logistic regression analysis was conducted to examine the relation between happiness and the clusters. Two health behavior clusters were identified: Cluster 1 (low substance use and good dietary habit; n = 817; 52.3%) and Cluster 2 (high alcohol, poor nutrition, and inactive; n = 737; 47.7%). Latent regression analysis indicated that all socio-demographics, barring socioeconomic status, were significantly associated with the clusters. The "low substance use and good dietary habit" cluster was significantly related with higher odds of happiness (odds ratio = 1.425, 95% confidence interval = 1.146-1.772, p = 0.001). This study identified health behavior clusters among Japanese adults and established the association between the "low substance use and good dietary habit" cluster and high levels of happiness. However, the causality of the relationship between health behavior and happiness remains unclear, which highlights the need for further research to elucidate the underlying mechanisms.

Subjective well-being and COVID-19 prevention practices among people living with HIV in Cape Town, South Africa

ABSTRACT COVID-19 poses significant threats to the health of people with underlying chronic conditions, including people living with HIV. The association between subjective well-being and practicing COVID-19 preventive behaviours among people living with HIV is yet to be empirically tested. The objective of the current study was to test the hypothesis that subjective well-being would be associated with engaging in greater COVID-19 preventive behaviours. A sample of 200 women and 72 men (mean age 38.1, SD = 10.4) receiving HIV treatment at a clinic in Cape Town, South Africa completed interview administered surveys of health behaviours and three dimensions of subjective well-being: emotional, social and psychological well-being. Hierarchical regression models were performed to test subjective well-being as predictors of COVID-19 preventive behaviours adjusting for age, sex, education, TB history, antiretroviral therapy adherence, and concern about contracting COVID-19. Results showed that psychological well-being, along with concerns over COVID-19, predicted greater COVID-19 preventive behaviours over and above the other variables in the model, accounting for 9.2% of the variance. Results support incorporating interventions to foster psychological well-being into existing clinical services for people living with HIV to improve clusters of health behaviours.

Exploring the role of social capital, self-efficacy and social contagion in shaping lifestyle and mental health among students representing the future healthcare workforce in Palestine: social cohort study protocol

IntroductionNon-communicable diseases (NCDs) and depression form an unhealthy mix. The project focuses on potentially effective psychosocial factors shaping health-related habits and mental health. The study is conducted among health domain...

Clustering of health behaviours and association with sociodemographic factors among adults in Japan

Abstract Background Health behaviours (HBs) commonly co-occur, acting synergistically. Little is known about the clustering of HBs in the Japanese population. Therefore, this study aimed to investigate the clusters of HB patterns and sociodemographic factors related to the clusters in the Japanese population. Methods Data used in this study was obtained from the Japanese and Keio Household Panel Surveys. The responses of 1,554 questionnaires (aged 27 − 65 years) were analysed. The surveys probed HBs (alcohol consumption, smoking, vegetable and fruit consumption, breakfast consumption habits, and physical activity) and sociodemographic characteristics. Latent class analysis was used to identify the clusters, and latent regression was used to investigate sociodemographic characteristics related to the clusters by gender. Results Two HB clusters were identified: ‘inactive, moderately healthy' and ‘inactive, high alcohol consumption, poor nutrition'. Age in the 60s indicated significantly higher odds ratios (ORs) of the ‘inactive, moderately healthy' cluster in men and women [OR = 1.820, 95% confidence interval (CI)=1.109 − 2.985, p = 0.018; OR = 3.865, CI = 1.830 − 8.163, p = 0.000]. In men, living with families and higher education levels indicated significantly higher ORs of the ‘inactive, moderately healthy' cluster (OR = 1.812, CI = 1.078 − 3.047, p = 0.025; OR = 2.236, CI = 1.698 − 2.943, p = 0.000). In women, being married, higher socioeconomic status, and higher education levels indicated significantly higher ORs of the ‘inactive, moderately healthy' cluster (OR = 2.097, CI = 1.309 − 3.359, p = 0.002; OR = 2.068, CI = 1.092 − 3.917, p = 0.026; OR = 2.516, CI = 1.563 − 4.051, p = 0.000). Conclusions Policymakers must recognise HB clusters unique to Japanese adults and have to build effective strategies based on understanding gender differences and similarities in sociodemographic factors associated with HB clusters. Key messages Interventions for multiple HB simultaneously should be designed and developed. Health behavior patterns have to be clarified for targeting population when interventions are built.