Longitudinal patterns of lifestyle behaviours in adolescence: a latent transition analysis

Change in clusters of lifestyle behaviours from childhood to adolescence: a longitudinal analysis

This study aimed to identify changes in clusters of lifestyle behaviours (physical activity, screen time and diet) between the ages of 7 and 14 years, and to examine socio-demographic determinants of changes. Longitudinal analyses were performed on a sample of 9339 children from the UK Millennium Cohort Study (MCS) who had complete data on behaviours of interest at age 7 (wave 4) and 14 years (wave 6). Joint Correspondence Analysis (JCA) and k-means cluster analysis were used to identify clusters of lifestyle behaviours at both time waves. Multinomial logistic regressions were used to examine the associations between socio-economic variables and changes in cluster membership. Analyses were conducted separately for boys and girls. Clusters of behaviours at age 7 and 14 years were identified as healthy, mixed or unhealthy respectively. Compared to girls, a higher proportion of boys remained in the healthier cluster over time (19.1% vs. 13.1%) or became healthier (26.4% vs. 9.36%). A higher proportion of girls changed to an unhealthier cluster (57.2% vs. 33.9%). Indicators of lower socio-economic status, such as low family income, low parental education, and not living with both parents at age 7 were associated with unhealthier changes in cluster membership. Conclusion Lifestyle behaviours cluster in children and are susceptible to change over a 7-year period, with a high proportion of boys becoming healthier and a higher proportion of girls became unhealthier. Indicators of socio-economic status appear to be important in determining changes in clusters. What is Known: • Poor lifestyle behaviours (i.e. unhealthy dietary habits, low physical activity, and sedentary behaviours) tend to cluster in children and adolescents. What is New: • Lifestyle behaviours cluster in children and are susceptible to changes between childhood and adolescence. Changes occur differently in boys and girls. Indicators of low socio-economic status are associated with unhealthier changes in behavioural clusters.

Environment profiles, social participation patterns, depressive symptoms and quality of life of disabled older adults: a longitudinal investigation

OBJECTIVES

This study used the World Health Organization's healthy ageing framework to explore longitudinal influences of environment profiles and social participation (SP) patterns on depressive symptoms (DSs) and on quality of life (QOL).

METHODS

Data were collected from a sample of community-dwelling older adults in Taiwan in 2018 (T1; N = 1,314) and a follow-up survey in 2020 (T2; N = 831). Latent class analysis was conducted to obtain environment and SP profiles. Multilevel modeling was carried out to explicate the hypothesized associations.

RESULTS

Three unique environment profiles, labeled as Highly- (Highly-FE), Moderately- (Moderately-FE) and Weakly-Facilitative Environment (Weakly-FE), were identified from T1 data. The three SP classes that were obtained from T1 and T2 data denoted High-, Moderate- and Low-SP. Participants in the 'Highly-FE' class were more likely to belong to the 'High-SP' and 'Moderate-SP' subgroups and exhibited significantly fewer DSs and better QOL. The associations were confirmed both cross-sectionally and longitudinally.

CONCLUSION

Interventions should be developed to promote or maintain preferred SP to maximize the current and future mental health and subjective well-being of disabled older adults.

Physical Activity and Eating Behavior Inequalities Mediating the Effects of Socioeconomic Status and Sex on Adolescent Quality of Life: Insights of the PRALIMAP Trial

BACKGROUND

This study aimed to quantify the 2-year mediating effects of physical activity (PA) and eating behavior (EB) on socioeconomic and sex inequalities in quality of life (QoL) among French adolescents.

METHODS

Adolescents from a 2-year school-based trial were included. PA and EB were assessed at baseline, 1 year, and 2 years using the International Physical Activity Questionnaire and an eating habits questionnaire, respectively. QoL was measured at 2 years using the Duke Health Profile questionnaire. The mediating effect of adherence to PA and EB guidelines in the associations between socioeconomic status or sex and QoL was analyzed with a counterfactual method.

RESULTS

Among the 3562 included adolescents (mean age, 15.2 [0.6] y), being a boy and having a high socioeconomic status was associated with high QoL. Differences in QoL by socioeconomic status were mediated by adherence to guidelines for PA (up to 10.2%) and EB (up to 8.8%), and differences in QoL by sex were predominantly mediated by adherence to guidelines for PA (up to 163.8%).

CONCLUSIONS

Policies, programs, and interventions designed to reduce socioeconomic and sex inequalities in QoL among adolescents could use PA and EB as leverage.

A latent transition analysis of physical activity and screen-based sedentary behavior from adolescence to young adulthood

BACKGROUND

Distinct typologies of physical activity and screen-based sedentary behaviors are common during adolescence, but it is unknown how these change over time. This longitudinal study examined the stability of activity-related behavioral typologies over the transition out of secondary school.

METHODS

Year 11 students (penultimate school year) completed a self-report survey (baseline), which was repeated 2 years later (follow-up) (75% female, mean baseline age: 16.9 ± 0.4 years). Latent transition analysis identified typologies of physical activity and screen time behaviors and explored changes in typology membership between baseline and follow-up among those with complete data and who were not attending secondary school at follow-up (n = 803).

RESULTS

Three unique typologies were identified and labelled as: 1) Sedentary gamers (baseline: 17%; follow-up: 15%: high levels of screen behaviors, particularly video gaming); 2) Inactives (baseline: 46%; follow-up: 48%: low physical activities, average levels of screen behaviors); and 3) Actives (baseline: 37%; follow-up: 37%: high physical activities, low screen behaviors). Most participants remained in the same typology (83.2%), 8.5% transitioned to a typology with a more health-enhancing profile and 8.3% transitioned to a typology with a more detrimental behavioral profile.

CONCLUSIONS

The high proportion within the 'inactive' typology and the stability of typologies over the transition period suggests that public health interventions are required to improve activity-related behavior typologies before adolescents leave secondary school.