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Background: Combinations of movement behaviors (i.e., physical activity, sedentary behavior, sleep) are associated with health and developmental outcomes in youth. Youth vary in how they accumulate these behaviors, both in volume and specific domains (e.g., sedentary time spent on recreational screen activities vs homework). The aim of this study was to examine how youth's combined general and domain-specific movement trajectories differ by socioeconomic position. Methods: We conducted a longitudinal, group-based multi-trajectory analysis to identify general and domain-specific movement trajectory profiles for 2457 youth from age 10 to 14 years from the Longitudinal Study of Australian Children from 2014–2018. We used multinomial logistic regression to test if socioeconomic position predicted profile membership. Results: We identified three general movement trajectory profiles for both sexes, four domain-specific profiles for males, and five for females. For general movement trajectories, females from lower socioeconomic positions were more likely to be a combination of less active and more sedentary than females from higher socioeconomic positions. Males across socioeconomic positions spend similar amounts of time in physical activity, sedentary time, and sleep. For domain-specific movement trajectories, youth from lower socioeconomic positions were likely to spend a combination of less time in education-based sedentary behavior and more time in recreational screen activities than their higher socioeconomic position peers. Conclusion: Our results indicate that socioeconomic position predicted in which domains youth accumulate their movements. Future observational research and interventions targeting different socioeconomic groups should therefore consider domain-specific movement trajectories. [ABSTRACT FROM AUTHOR]

Background: Reliable estimates of habitual sleep, physical activity, and sedentary time are essential to investigate the associations between these behaviours and health outcomes. While the number of days needed and hours/day for estimates of physical activity and sedentary time are generally known, the criteria for sleep estimates are more uncertain. The objective of this study was to identify the number of nights needed to obtain reliable estimates of habitual sleep behaviour using the GENEActiv wrist worn accelerometer. The number of days to obtain reliable estimate of physical activity was also examined. Methods: Data was used from a two-year longitudinal study. Children wore an accelerometer for up to 8 days 24 h/day across three timepoints. The sample included 2,745 children (51 % girls) between the ages of 7-12-years-old (mean = 9.8 years, SD = 1.1 year) with valid accelerometer data from any timepoint. Reliability estimates were calculated for sleep duration, sleep efficiency, sleep onset, wake time, time in bed, light physical activity, moderate physical activity, moderate-to-vigorous physical activity, vigorous physical activity, and sedentary time. Results: Intraclass correlations and the Spearman Brown prophecy formula were used to determine the nights and days needed for reliable estimates. We found that between 3 and 5 nights were needed to achieve acceptable reliability (ICC = 0.7) in sleep outcomes, while physical activity and sedentary time outcomes required between 3 and 4 days. Conclusions: To obtain reliable estimates, researchers should consider these minimum criteria when designing their studies and prepare strategies to ensure sufficient wear time compliance. [ABSTRACT FROM AUTHOR]

Objective: It is often hypothesised that neighbourhood green space may help prevent well-known declines in physical activity and increases in sedentary behaviour that occur across childhood. As most studies in this regard are cross-sectional, the purpose of our study was to use longitudinal data to examine whether green space promotes active lifestyles as children grow older. Methods: Data came from participants (n = 4983; age = 4-5) of the Longitudinal Study of Australian Children, a nationally representative study on health and child development. Physical activity and screen time were measured biennially (2004-2012) using questionnaires and time use diaries. Quantity of neighbourhood green space was objectively measured using Australian Bureau of Statistics mesh block data for each participant's statistical area level 2. Multilevel regression was used to test for associations between physical activity and screen time with green space quantity, adjusting for socio-economic confounders. Results: Boys living in areas with 10 % more neighbourhood green space had a: 7 % (95 % CI = 1.02,1.13) greater odds of choosing physically active pastimes; 8 % (95 % CI = 0.85, 1.00) lower odds of not enjoying physical activity; 2.3 min reduction in weekend television viewing (95 % CI = -4.00, -0.69); and 7 % (95 % CI = 1.02; 1.12) and 9 % (95 % CI = 1.03; 1.15) greater odds of meeting physical activity guidelines on weekdays and weekends, respectively. No statistically (or practically) significant results were observed for girls. Conclusion: Current provisions of neighbourhood green space may be more amenable to promoting active lifestyles among boys than girls. Research is needed to explore what types of green space promote active lifestyles in all children. [ABSTRACT FROM AUTHOR]

Background: Excessive engagement with digital screens is harmful to children's health. However, new evidence suggests that exposure at moderate levels may not be harmful and may even provide benefit. Therefore, our objective was to determine if there are curvilinear relationships between different types of screen time and a diverse set of outcomes, including health and education. Methods: We address our objective using a repeated measures design. Children (N = 4013), initially aged 10–11 were assessed every 2 years between 2010 and 2014. Children's screen time behavior was measured using time-use diaries, and categorized into five types: social, passive, interactive, educational, or other. We used measures of children's physical health, health-related quality of life, socio-emotional outcomes, and school achievement. The analysis plan was pre-registered. Models were adjusted for gender, socio-economic status, ethnicity, number of siblings, and housing factors. Results: There were linear associations between total screen time and all outcomes, such that more screen time was associated with worse outcomes. However, there was variability when examined by screen time type. Passive screen time (e.g., TV) was associated with worse outcomes, educational screen time (e.g., computer for homework) was associated with positive educational outcomes and had no negative relations with other outcomes. Interactive screen time (e.g., video games) had positive associations with educational outcomes but negative associations with other outcomes. In all instances, these significant associations were small or very small, with standardised effects < 0.07. We found little evidence of curvilinear relationships. Conclusions: The small effects of screen time on children's outcomes appear to be moderated by the type of screen time. Policy makers, educators, and parents should consider the type of screen time when considering the benefits and harms of use. [ABSTRACT FROM AUTHOR]

Background: Understanding the early roots of physical activity and sedentary behaviors is critical to developing intervention programs that promote healthy lifestyle habits in infants and children. There is, however, no evidence on how these behaviors cluster and develop together during early childhood. The aim of this study was to identify single and joint longitudinal trajectories in physical activity and screen time amongst children aged 0 to 9 years, their social-demographic predictors and their prospective health-related quality-of-life and socio-emotional outcomes. Methods: Three waves of data from The Longitudinal Study of Australian Children, a national study tracking two cohorts every 2 years (B-cohort, 0–5 years, n = 4,164; K-cohort, 4–9 years, n = 3,974) were analysed. Growth mixture modelling was applied to longitudinal time-use diary data to identify joint trajectories in children's physical activity and screen time over Waves 1–3. Key socio-demographic variables measured at Wave 1 were used to predict membership in different trajectories. The prospective consequences (at Wave 3) of time-use trajectories on health-related quality-of-life and socio-emotional outcomes were assessed. Results: Three physical-activity/screen-time trajectories were identified for both cohorts: Cluster-A—children who maintained low levels of physical activity and screen time (∽50% of the sample), Cluster-B—children who progressively increased physical activity and maintained low screen-time levels (∽25%), and Cluster-C—children who maintained low physical-activity levels and increased screen time (∽25%). Children in Cluster-B experienced the best health-related quality-of-life and socio-emotional outcomes, while those in Cluster-C experienced the worst. Children who were female, Indigenous, from non-English-speaking backgrounds, not living with two biological parents, in more affluent households and neighbourhoods, without siblings and with parents with poor mental health were at greater risk of falling into Cluster-A or Cluster-C. Conclusion: Our findings identified which children are most at-risk of falling into time-use trajectories that lead to poor health-related quality-of-life and socio-emotional outcomes later in life, increasing our ability to monitor, detect and prevent these suboptimal behaviours prior to their onset. [ABSTRACT FROM AUTHOR]