Patterns of sedentary behaviours in Irish female adolescents

A prospective analysis of physical activity and mental health in children: the GECKO Drenthe cohort

BACKGROUND

Mental health problems in young people have become a global health burden. The positive effects of physical activity on mental health in adults are well known but still not clear in children. The aim of this study was to investigate to what extent physical activity in early childhood would affect mental health in middle childhood.

METHODS

From the Dutch GECKO Drenthe birth cohort, 850 children (51.5% boys) were enrolled in this analysis. Physical activity and sedentary time were measured at age 5-6 using ActiGraph GT3X. Mental health was assessed using the Strengths and Difficulties Questionnaire (SDQ) at age 5-6 and age 10-11. Multiple linear regression models were used to estimate the associations between physical activity, sedentary time and SDQ subscales, stratified by gender, adjusting for age, BMI, maternal education level, family size, accelerometer wear time and season, and additionally adjusting for SDQ scores at age 5-6 to take tracking of mental health over time into account.

RESULTS

Greater physical activity volume at age 5-6 was associated with lower peer problems scores at age 10-11 in boys and girls. An increase in MVPA was associated with lower peer problems scores in boys (b = -0.445, -0.713 to -0.176) and girls (b = -0.354, -0.601 to -0.107), however, increased sedentary time was linked to higher peer problems scores in boys (b = 1.18, 0.455 to 1.906) and girls (b = 0.870, 0.191 to 1.550). For hyperactivity, higher levels of physical activity volume and MVPA were associated with higher hyperactivity scores in boys. Increased sedentary time was related to lower hyperactivity scores in boys. Further adjustment for SDQ scores at age 5-6 attenuated associations between physical activity and hyperactivity in boys but hardly changed the relationships with peer problems. No significant associations between physical activity and other SDQ subscales or total difficulties scores were observed, neither in boys nor in girls.

CONCLUSIONS

Children who are more physically active at age 5-6 have fewer peer problems at age 10-11, and for boys, greater activity levels at age 5-6 could be an indicator of hyperactivity at age 10-11.

Does modality matter? A latent profile and transition analysis of sedentary behaviours among school-aged youth: The UP&DOWN study

The aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8-18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time.

Clusters of health behaviours and their relation to body mass index among adolescents in Northern Finland

AIMS

To identify clusters of health behaviours among Finnish adolescents and examine whether these clusters are associated with body mass index (BMI).

METHODS

The data, drawn from the prospective population-based Northern Finland Birth Cohort 1986, comprised a total of 7182 adolescents who replied to a follow-up postal questionnaire at the age 15 to 16. The adolescents were asked about multiple health behaviours, such as physical activity, screen time, cigarette smoking, alcohol use and diet. Adolescents who answered the questions concerning all of these variables were included in the study, yielding a final sample of 4305 subjects. K-means cluster analysis was performed to identify group of adolescents with similar health behaviours. Chi-squared tests were performed to analyse the association between the clusters and BMI.

RESULTS

Two clusters - Healthy Lifestyle and Unhealthy Lifestyle - were identified for both genders. The values of all the studied variables differed significantly between the clusters identified for both boys and girls. No significant relationship between the identified clusters and BMI was found for either boys or girls.

CONCLUSIONS

It is important to consider multiple lifestyle-related behaviours and trying to identify groups that present probability for developing lifestyle diseases.

A person-centred analysis of the time-use, daily activities and health-related quality of life of Irish school-going late adolescents

PURPOSE

The health, well-being and quality of life of the world's 1.2 billion adolescents are global priorities. A focus on their patterns or profiles of time-use and how these relate to health-related quality of life (HRQoL) may help to enhance their well-being and address the increasing burden of non-communicable diseases in adulthood. This study sought to establish whether distinct profiles of adolescent 24-h time-use exist and to examine the relationship of any identified profiles to self-reported HRQoL.

METHOD

This cross-sectional study gathered data from a random sample of 731 adolescents (response rate 52%) from 28 schools (response rate 76%) across Cork city and county. A person-centred approach, latent profile analysis, was used to examine adolescent 24-h time-use and relate the identified profiles to HRQoL.

RESULTS

Three male profiles emerged, namely productive, high leisure and all-rounder. Two female profiles, higher study/lower leisure and moderate study/higher leisure, were identified. The quantitative and qualitative differences in male and female profiles support the gendered nature of adolescent time-use. No unifying trends emerged in the analysis of probable responses in the HRQoL domains across profiles. Females in the moderate study/higher leisure group were twice as likely to have above-average global HRQoL.

CONCLUSION

Distinct time-use profiles can be identified amongst adolescents, but their relationship with HRQoL is complex. Rich mixed-method research is required to illuminate our understanding of how quantities and qualities of time-use shape lifestyle patterns and how these can enhance the HRQoL of adolescents in the twenty-first century.

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

Background

Recent 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.

Methods

Self-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.

Results

Four 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.

Conclusions

The 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.