Longitudinal Health Behaviour Patterns Among Chinese Adults Aged ≥ 50 Years and Their Associations With Trajectories of Depressive Symptoms Over Time: A Latent Class Analysis

Background Whether different longitudinal patterns of multiple health behaviours are associated with different trajectories of depressive symptoms is not well understood.Purpose To identify distinct longitudinal patterns of multiple health behaviours and their associations with trajectories of depressive symptoms among people aged ≥ 50 years in China.Methods We used longitudinal data from the Harmonized China Health and Retirement Longitudinal Study (three waves, 2010–2015; n = 8439). We performed latent class analyses to identify distinct patterns of multiple health behaviours at three timepoints. We estimated longitudinal random-effects models to predict differences in depressive symptoms trajectories by health behaviour class.Results The best-fitting model had five latent classes, all of which showed strong behavioural stability over time: 1) socially active, moderately physically active non-smokers (29.4%); 2) socially inactive, physically active non-smokers (22.3%); 3) socially and physically inactive non-smokers (17.9%); 4) socially inactive, moderately physically active smokers (14.6%); and (5) socially active, moderately physically active smokers (14.2%). All classes characterized by low social participation (classes 2–4) were associated with significantly higher predicted depressive symptom scores compared with the other classes (1 and 5). Conclusions Longitudinal behavioural patterns involving low probabilities of social participation were associated with more depressive symptoms. This overshadowing effect suggests that the damage caused by socially inactivity may render the effects of co-existing (un)healthy behaviours meaningless. The stability of the patterns of multiple health behaviours across survey waves suggests that interventions are needed earlier in life.

Clusters of health behaviours in Queensland adults are associated with different socio-demographic characteristics

Background The co-occurrence of unhealthy lifestyles, calls for interventions that target multiple health behaviours. This study investigates the clustering of health behaviours and examines demographic differences between each cluster. Methods In total, 934 adults from Queensland, Australia completed a cross-sectional survey assessing multiple health behaviours. A two-step hierarchical cluster analysis using multiple iterations identified the optimal number of clusters and the subset of distinguishing health behaviour variables. Univariate analyses of variance and chi-squared tests assessed difference in health behaviours by socio-demographic factors and clusters. Results Three clusters were identified: the ‘lower risk’ cluster (n = 436) reported the healthiest profile and met all public health guidelines. The ‘elevated risk’ cluster (n = 105) reported a range of unhealthy behaviours such as excessive alcohol consumption, sitting time, fast-food consumption, smoking, inactivity and a lack of fruit and vegetables. The ‘moderate risk behaviour’ cluster (n = 393) demonstrated some unhealthy behaviours with low physical activity levels and poor dietary outcomes. The ‘elevated risk’ cluster were significantly younger and more socio-economically disadvantaged than both the ‘lower and moderate risk’ clusters. Discussion Younger people who live in more deprived areas were largely within the ‘elevated risk’ cluster and represent an important population for MHBC interventions given their wide range of unhealthy behaviours.