Stability and Change in Health Behavior Profiles of U.S. Adults

Life Chances, Subjective Perceptions, and Healthy Lifestyles in Older Adults: Longitudinal Evidence From China

OBJECTIVES

Previous studies and theories show that healthy lifestyles are affected by life chances and subjective perceptions. However, it remains unclear how older adults change between different lifestyle profiles as they age. We proposed the healthy lifestyle duality framework and tested it among older Chinese adults using a longitudinal design.

METHODS

Data were obtained from 4 Chinese Longitudinal Healthy Longevity Surveys conducted between 2008 and 2018 (N = 31,445). Latent transition analysis and hierarchical multinomial growth curve models were performed to investigate healthy lifestyle profiles and their associations with life chances and subjective perceptions over time.

RESULTS

Four distinct healthy lifestyle profiles were identified: healthy, risky, low-standard, and mixed groups, and their changes show path dependency. Across 10 years, the proportion of the healthy group ranged from 11.16% to 16.97%. Both life chances and subjective perceptions were longitudinally associated with healthy lifestyles, with age and cohort effects observed.

DISCUSSION

Our findings support the healthy lifestyle duality framework and reveal that life chances and subjective perceptions influence lifestyle changes over time. Public health policies and health intervention programs should adapt to the specific needs of different age groups and generations.

Longitudinal Patterns of Systolic Blood Pressure, Diastolic Blood Pressure, Cardiorespiratory Fitness, and Their Association With Dementia Risk: The HUNT Study

BACKGROUND

High blood pressure and poor cardiorespiratory fitness are independent risk factors for dementia. However, few studies have examined if combined longitudinal patterns of these modifiable risk factors are associated with dementia risk.

METHODS

In this prospective cohort study, we used data from the population-based Trøndelag Health (HUNT) Study, Norway. We applied group-based multidimensional trajectory modeling to identify age-specific multidimensional trajectories of SBP, DBP, and estimated cardiorespiratory fitness across 3 surveys (HUNT1, 1984-1986 to HUNT3, 2006-2008). Dementia was diagnosed in the HUNT4 70+ substudy in 2017-2019. We used multivariate logistic regression to estimate odds ratios (ORs) and risk differences (RDs) of dementia.

RESULTS

In total, 7 594 participants (54.9% women) were included, with a mean age of 44.7 (SD 6.3) years at HUNT1. Dementia was diagnosed in 1 062 (14.0%) participants. We identified 2 multidimensional trajectories throughout adulthood within 3 age groups: one with higher systolic blood pressure (SBP) and diastolic blood pressure (DBP), and lower estimated cardiorespiratory fitness (the poorer group), and one with lower SBP and DBP, and higher cardiorespiratory fitness (the better group). After adjustment for sex, apolipoprotein E ε4 status, education, marital status, and diabetes, the better group had consistently lower risk of dementia in all age groups with the lowest OR in the middle-aged group of 0.63 (95% confidence intervals [95% CI]: 0.51, 0.78) with corresponding RD of -0.07 (95% CI: -0.10, -0.04).

CONCLUSIONS

Having a beneficial multidimensional trajectory of SBP, DBP, and cardiorespiratory fitness in adulthood was associated with reduced dementia risk. Aiming for optimal SBP, DBP, and estimated cardiorespiratory fitness throughout adulthood may reduce dementia risk.

Stability in health behavior patterns in middle adulthood: a 19-year follow-up study

Objective: This study investigated subgroups of adults with particular health behavior patterns, their stability over 19 years, and the role of sociodemographic and personality characteristics in these.

Methods and Measures: Data on smoking, alcohol consumption, and physical activity were collected at ages 42, 50, and 61 in the Jyväskylä Longitudinal Study of Personality and Social Development (n = 205-302). Latent class, latent transition, and logistic regression analyses were used.

Results: Four similar classes of health behaviors were identified at each age. A class named low alcohol consumption (AC)-high physical activity (PA) included individuals with the lowest levels of alcohol consumption and the highest levels of physical activity, and a class named high AC-low PA vice versa. Classes between these extremes of alcohol consumption and physical activity levels were nonsmokers with the lowest proportion of smokers, and smokers vice versa. Although transitions emerged, class memberships were relatively stable. Women, those who were married, held a degree, had higher occupational status, and certain personality traits at age 42 were more likely to belong continuously to healthier classes compared to a stable membership in high AC-low PA.

Conclusion: Health behaviors exist in patterns, are relatively stable across adulthood, and associated with sociodemographic and personality characteristics.

Longitudinal health behaviour patterns among adults aged ≥50 years in China and their associations with trajectories of depressive symptoms

OBJECTIVES

Against the background of the growing recognition of the need for a holistic perspective on health behaviour, we aim to identify longitudinal patterns of multiple health behaviours, and to assess associations of such patterns with depressive symptoms among older people in China.

METHODS

Using three waves of China Health and Retirement Longitudinal Study data (n = 8439), we performed latent class growth analyses (LCGAs) to identify longitudinal patterns of multiple health behaviours. Random-effects models were estimated to assess associations between health behaviour patterns and depressive symptoms.

RESULTS

The best fitting LCGA model had seven classes: (1) connected active non-smokers (average posterior probability: 21.8%), (2) isolated active non-smokers (24.7%), (3) isolated inactive non-smokers (17.0%), (4) isolated active smokers (14.5%), (5) connected active smokers (12.2%), (6) increasingly connected and active non-smokers (5.4%), and (7) moderately connected inactive smokers (4.4%). Depressive symptoms were highest in the four classes with lower probabilities of social participation across waves. No evidence was found of change over time in depressive symptomatology gaps between people with different health behaviour trajectories.

CONCLUSION

Health behaviour patterns characterized by consistently low social participation were associated with raised depressive symptomatology, suggesting that focusing on social participation may benefit later-life mental health promotion strategies.

The role of combined modifiable lifestyle behaviors in the longitudinal association between stressful life events and allostatic load in Australian adults

Allostatic load is a model that is used to quantify the physiological damage from exposure to stressors. Stressful life events are chronic stressors that can lead to an elevated allostatic load through the physiological and behavioral stress responses. However, there is limited empirical studies that has tested the proposed behavioural pathway. Our study addresses this gap by examining the mediating role of combined modifiable lifestyle behaviors in the 12-years longitudinal association between stressful life events and allostatic load among participants from the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study cohort. A latent profile analysis was performed to identify latent subgroups with distinct behavioral clusters based on five modifiable lifestyle behaviors (smoking, sedentary behavior, physical activity, alcohol consumption, and diet quality). We then used a sequential mediation model design with path analysis to test the mediating effect of these latent subgroups in the associations between stressful life events and three measures of allostatic load. Indirect effects were estimated using the product of coefficient approach and the statistical significance was determined by the 95% bias-corrected bootstrap confidence intervals with 1000 replications. We identified three latent subgroups: "least healthy lifestyle" (12%; n = 396), "moderately healthy lifestyle" (78.7%; n = 2599), and "most healthy lifestyle" (9.2%; n = 306). Exposure to stressful life events was not associated with the allocation of participants in latent subgroups. Compared to the "moderately healthy lifestyle" subgroups, we found that the "least healthy lifestyle" behavioral cluster was not associated with allostatic load. However, there was a significant inverse association between the "most healthy lifestyle" behavioral cluster and allostatic load. Overall, we did not find significant indirect effects between stressful life events and three measures of allostatic load via the "least healthy lifestyle" and the "most healthy lifestyle" groups. In summary, the combinations of modifiable lifestyle behaviors did not explain the association between stressful life events and allostatic load. More longitudinal studies are needed to replicate our study to confirm this finding.

Longitudinal patterns and sociodemographic profiles of health-related behaviour clustering among middle-aged and older adults in China and Japan

Given inevitable age-related decreases in physical or mental capacity, studies on health-related behaviour (HRB) clustering in older people provide an opportunity to reduce health-care costs and promote healthy ageing. This study explores the clustering of HRBs and transition probabilities of cluster memberships over time, and compares sociodemographic characteristics of these clusters among Chinese and Japanese middle-aged and older adults. Using the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015 (N = 19614) and the Japanese Study of Ageing and Retirement (JSTAR) from 2007 to 2011 (N = 7,080), Latent Transition Analysis was applied to investigate the clustering and change in clustering memberships of smoking, alcohol consumption, physical activity and body mass index. Multivariate logistic regression was used to explore the sociodemographic characteristics of these longitudinal HRB cluster members. We identified four common clusters in CHARLS and JSTAR: ‘smoking’, ‘overweight or obese’, ‘healthy lifestyle’ and ‘current smoking with drinking’, and an additional cluster named ‘ex-smoking with drinking’ in JSTAR. Although HRB cluster members were largely stable in both cohorts, participants in China tended to move towards an unhealthy lifestyle, while participants in Japan did the opposite. We also found that participants who smoked and drank were more likely to be male, younger, less educated and unmarried in both cohorts, but the overweight or obese participants were female, urban and higher income in CHARLS but not JSTAR. Our study not only contributes to the knowledge of longitudinal changes in health-related behavioural clustering patterns in an Asian elderly population, but may also facilitate the design of targeted multi-behavioural interventions to promote healthy lifestyles among older people in both countries.

The development and validation of scales to measure the presence of a teachable moment following a cardiovascular disease event

Better conceptually-driven research is necessary to learn more about 1) the characteristics of life events as teachable moments (TMs) and 2) the potential of life events to evoke lifestyle change intention (LCI). This study aimed to develop and validate two scales for the purposes of TM research in the context of cardiovascular disease (CVD): the CardiacTM and CardiacLCI-scales. After the initial development of items based on a theoretical framework and literature search, six experts rated the content validity of both scales as sufficient. The item list was further adjusted after think-aloud sessions with two CVD patients. The resulting scales were presented online in a cross-sectional survey, which yielded 625 responses of Dutch CVD patients (June 2020). To test construct validity, we conducted Exploratory Factor Analysis (EFA) with Varimax rotation on a random split-half of the sample (n = 300) and evaluated the factor structure with Confirmatory Factor Analysis (CFA) on the holdout sample (n = 325). EFA and CFA on the CardiacTM-scale (α = 0.88) revealed a 28-item six-factor structure explaining 61.0% of the variance, with adequate goodness-of-fit statistics (CFI = 0.87; TLI = 0.85; SRMR = 0.07) and internally reliable factors (Affective impact, Risk CVD, Changed self-concept, CVD group identity, Risk non-communicable disease, Anticipated regret). The CardiacLCI-scale (α = 0.81) revealed an 11-item two-factor structure explaining 51.5% of the variance, with adequate model fit (CFI = 0.92; TLI = 0.90; SRMR = 0.08) and internally reliable factors (Event-related lifestyle change and General healthy lifestyle). The scales may be used to expand knowledge around life events as TMs and to support conversation regarding lifestyle after cardiac and other life events.

Theoretical Approaches to Research on the Social Determinants of Obesity

This article reviews selected theoretical approaches explaining the social determinants of obesity. The significance of this topic for medicine, public health, and other areas of obesity-related research is the growing body of evidence showing that the social environment is often key to understanding the risk of obesity. A review of relevant literature and analysis of empirical evidence linking theory to data in studies of obesity was performed. Several studies show that differences in social behavior and living conditions associated with SES, lifestyles, inequality, and other social variables have important roles in weight gain. Because the social determinants of obesity often begin in childhood, life course theory and its concepts of cumulative advantage/disadvantage and cumulative inequality are initially reviewed, followed by a discussion of how fundamental cause theory, health lifestyle theory, and cultural capital theory can be applied to obesity research. The stress process model and the concepts of social networks and neighborhood disadvantage concerning obesity are also included.

Health behaviour and its determinants in elderly patients with chronic diseases: evidence from Jiangsu Province, China

Background

Chronic disease is a major cause of mortality among elderly individuals in China, and treatment is a substantial public health burden. However, behavioural interventions may be more important than mere clinical treatment of these chronic diseases.

Objective

The paper aimed to assess the health behaviour of a sample of elderly individuals with chronic diseases in Jiangsu Province, China and to identify how demographic characteristics influence health behaviour. Furthermore, the group that would likely need the most health intervention was identified.

Design

A version of the Health Promoting Lifestyle Profile II (HPLP-II) was adapted to Chinese to evaluate health behaviours in six dimensions: nutrition, tobacco and alcohol use, physical activity, stress management, health responsibility, and spiritual growth. Multistage random sampling was conducted from October 2020 to May 2021. Questionnaires incorporating the adapted HPLP-II were distributed to 900 elderly patients (i.e., aged 60 and above) with chronic diseases in the three sampled prefectures of Jiangsu Province; of these questionnaires, 791 were completed. Univariate t tests, principal component analysis, and multivariate linear regressions were employed in the analysis.

Results

The average total score of respondents on health behaviour was 73.73. The dimensions (ordered from highest to lowest scores) are as follows: “nutrition”, “tobacco and alcohol use”, “health responsibility”, “spiritual growth”, “stress management”, and “physical activity”. The multivariate linear regression suggested that the determinants (P < 0.05) of health behaviour (total score) were income, sex, age, relationship status, residence, and education.

Conclusions

Elderly patients with chronic diseases in Jiangsu Province generally behaved in a healthy manner. “Physical activity”, “stress management”, and “spiritual growth” were the dimensions that would most benefit from health intervention, while elderly single/divorced/widowed patients with lower income and less education should be the target group for health intervention.

Contributions and Challenges in Health Lifestyles Research

The concept of health lifestyles is moving scholarship beyond individual health behaviors to integrated bundles of behaviors undergirded by group-based identities and norms. Health lifestyles research merges structure with agency, individual-level processes with group-level processes, and multifaceted behaviors with norms and identities, shedding light on why health behaviors persist or change and on the reproduction of health disparities and other social inequalities. Recent contributions have applied new methods and life course perspectives, articulating health lifestyles's dynamic relationships to social contexts and demonstrating their implications for health and development. Culturally focused work has shown how health lifestyles function as signals for status and identity and perpetuate inequalities. We synthesize literature to articulate recent advances and challenges and demonstrate how health lifestyles research can strengthen health policies and inform scholarship on inequalities. Future work emphasizing health lifestyles's collective nature and attending to upstream social structures will further elucidate complex social processes.

Early-Life Circumstances, Health Behavior Profiles, and Later-Life Health in Great Britain

Objectives: Drawing on UK Household Longitudinal Study data, this study assessed a pathway from early-life disadvantage to suboptimal later-life health via health behavior. Methods: Latent class analysis was used to identify distinct smoking, nutrition, alcohol, and physical activity health behavior profiles. Mediation analyses were performed to assess indirect effects of early-life disadvantage via health behavior on allostatic load, an objective measure of physiological wear and tear. Results: Four health behavior profiles were identified: (1) broadly healthy and high alcohol consumption, (2) low smoking and alcohol consumption, healthy nutrition, and physically inactive, (3) broadly unhealthy and low alcohol consumption, and (4) broadly moderately unhealthy and high alcohol consumption. Having grown up in a higher socioeconomic position family was associated with lower later-life allostatic load. This was partly attributable to health behavioral differences. Discussion: Growing up under disadvantageous socioeconomic circumstances may initiate a chain of risk by predisposing people to health behavior profiles associated with poorer later-life health.

Clustering of multiple health risk behaviors and its association with diabetes in a Southern Chinese adult population: a cross-sectional study

Background

Identifying the clustering patterns of health risk behaviors (HRBs) within individuals and their health impacts are essential to develop lifestyle promotion strategies. This study aimed to explore the clustering of a range of HRBs and the associations between such identified clusters and diabetes in Southern Chinese adults.

Methods

Data from 5,734 adults aged 35-75 years and underwent health examinations from November 2012 to December 2013 at a tertiary hospital in Guangzhou were analyzed. Behavioral characteristics, including smoking, alcohol use, physical activity, and sleep duration and quality, were measured by questionnaires. Latent class analysis was conducted by gender to identify HRBs clustering patterns, and logistic regression models were used to estimate the associations between behavioral patterns and diabetes.

Results

Three distinct behavioral clusters emerged in both genders. Male classes were defined as: (1) healthy lifestyle (Class 1, 62.9%); (2) cumulate harmful habits (Class 2, 27.1%); (3) poor sleep and risky habits (Class 3, 10.0%). Female classes were: (1) healthy lifestyle (Class 1, 83.0%); (2) inactive, daytime dysfunction (Class 2, 5.7%); (3) poor sleep habits (Class 3, 11.3%). Individuals of Class 2 and Class 3 showed a higher likelihood of diabetes across genders (multivariable-adjusted ORs [95% CIs], 2.03 [1.49-2.76] and 2.61 [1.78-3.81] among males, 2.64 [1.16-5.98] and 1.81 [1.07-3.06] among females) when compared with those of Class 1.

Conclusions

Our data provided additional evidence of HRBs clustering among adults, and such clustering was associated with an increased risk of diabetes. These findings have implications for identifying vulnerable subgroups and developing diabetes prevention programs.

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

Background: Health behaviors are of great importance for public health. Previous research shows that health behaviors are clustered and do not occur by chance. The main objective of this study was to investigate and describe the clustering of alcohol consumption, nutrition, physical activity and smoking while also considering the influence of sex, age and education. Methods: Using data from the population-based KORA S4/F4 cohort study, latent class regression analysis was undertaken to identify different clusters of health behavior patterns. The clusters were described according to demographics. Furthermore, the clusters were described regarding health-related quality of life at baseline and at a 7 year follow-up. Results: Based on a sample of 4,238 participants, three distinct classes were identified. One overall healthy class and two heterogeneous classes. Classes varied especially according to sex, indicating a healthier behavior pattern for females. No clear association between healthier classes and age, education or physical and mental health-related quality of life was found. Discussion: This study strengthens the literature on the clustering of health behaviors and additionally describes the identified clusters in association with health-related quality of life. More research on associations between clustering of health behaviors and important clinical outcomes is needed.