Purpose: There is emerging evidence that the way in which total physical activity is accumulated may be important for health. However, little is known about whether replacing sedentary time with time in physical activity bouts of varying intensities is beneficial for the cardiometabolic health of children with healthy weight or overweight. This study investigated the theoretical effects of reallocating a specific amount of sedentary time with an equal amount of time in total physical activity and physical activity bouts of varying intensities on children’s cardiometabolic risk biomarkers, and examined whether this varies by weight status.

Methods: Children aged 8-9 years (n=599) from 20 primary schools in Melbourne, Australia, participated in the Transform-Us! study (2010). Children were included if they had complete accelerometer and blood biomarker data (high-density lipoprotein cholesterol [HDL-C]; low-density lipoprotein cholesterol [LDL-C]; triglycerides [TG]; homeostatic model assessment of insulin resistance [HOMA-IR]). Isotemporal substitution models assessed the theoretical impact of replacing 10 minutes of sedentary time with 10 minutes of physical activity or physical activity in bouts of varying intensities on cardiometabolic health risk factors. Analyses were conducted for the whole sample and stratified by weight status.
Results: Overall, 169 children (age 8.7 [0.4] years; 55.6% girls; 66% healthy weight) were included in the analyses. Replacing sedentary time with 10 minutes of vigorous-intensity physical activity (VPA), regardless of manner of accumulation, was associated with lower TG levels in the whole sample (0.96; 95% CI: 0.93, 1.00). Replacing sedentary time with VPA accumulated in bouts was associated with better HDL-C (1.04; 95% CI: 1.00-1.09) and TG (0.95; 95% CI: 0.91-1.00), and worse HOMA-IR (1.17; 95% CI: 1.05-1.29) in in children with a healthy weight only.

Conclusion: Replacing sedentary time with MPA and VPA, regardless of accumulation pattern, may have some benefits on cardiometabolic health. This was mostly observed among children with healthy weight; few associations were observed for children with overweight/obesity. No associations were found for replacing sedentary time with LPA. The findings suggest interventions should focus on replacing sedentary time with higher intensity activities to benefit children’s cardiometabolic health. 

Purpose: Previous research has examined associations between individual activity behaviours (e.g. sleep, screen time and physical activity) and academic achievement. Yet, activity behaviours should be analysed together because they are co-dependent parts of the 24-h day. This study used compositional data analysis to examine associations between all daily activity behaviours and academic achievement.

Methods: Child Health CheckPoint was a cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children. Included participants were 963 children aged 11 to 12 years (mean age 12.0 years (SD=0.4); 50% male). Objective daily activity behaviour data (sleep, sedentary time, light and moderate-to-vigorous intensity physical activity) were collected using 8-day, 24-hour wrist worn accelerometry (GENEActiv). Daily time use was also assessed across 7 super domains (sleep, screen time, physical activity, school related, domestic/social, passive transport, quiet time) using the Multimedia Activity Recall for Children and Adolescents (MARCA). Recalls were completed on at least one school day and at least one non-school day. Academic achievement was assessed using a nationally administered standardised test in literacy (spelling, grammar and punctuation, writing and reading) and numeracy.  Compositional models (adjusted for age, sex, socioeconomic position and pubertal development) regressed isometric log ratios of activity behaviours against academic scores. We used the models to estimate academic achievement for every possible daily activity mix (in 10-minute increments) within the empirical range. Estimated outcomes were plotted against incrementally increasing min/d for each individual activity component and loess curves were fitted.

Results: Both accelerometry and MARCA activity behaviour compositions were associated with all academic outcomes (all models p≤0.02). Accelerometry showed less light intensity physical activity, longer sleep and more sedentary time to be beneficially associated across all academic outcomes. The MARCA showed more time spent in school-related behaviour, and less screen time and physical activity was consistently positively associated with all domains of academic achievement.

Conclusion: Results suggest that spending more time in sleep and school-related behaviours, whilst reducing screen time and light intensity physical activity may contribute positively to academic achievement. This finding warrants further investigation given the known physical and mental health benefits associated with physical activity.

Purpose:

Twenty-four hour movement behaviours represented by sleep, sedentary behaviour (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) may influence child mental health, which is under-researched relative to indicators of physical health. The study aims were (1) to examine the relationships between 24-hour movement behaviours and indicators of mental health in English primary school children, and (2) to investigate the predicted differences in mental health outcomes when time was reallocated between the movement behaviours.

Methods:

Wrist-mounted accelerometers were worn continuously for 7-days by 132 children (66 boys) aged 9-10 years. Following processing, estimates of time spent sleeping, and in SB, LPA, and MVPA were calculated. Questionnaires were used to assess social and emotional wellbeing (SEW), prosocial behaviour (PSB), depression symptoms (DEP), and self-esteem (SE). To account for the mutually exclusive and co-linear nature of the movement behaviours compositional data analysis was used to express the movement behaviours in relative terms as isometric log-ratio coordinates. For aim 1, regression models adjusted for sex, age, parental education level, and BMI examined the influence of each movement behaviour on the outcomes, relative to the other behaviours. For aim 2 differences in mental health outcomes between the baseline composition and new compositions when 15 minutes was added to each movement behaviour in turn was calculated.

Results:

Movement behaviours did not significantly predict SEW, DEP, and SE. PSB was significantly predicted by sleep (ß= 3.26 (1.36), p=.018) and SB (ß=-3.05 (1.12), p=0.007). The greatest predicted changes in mental health outcomes were observed when MVPA increased by 15-minutes with a proportional 15-minute decrease in the remaining behaviours. Specifically, there were favourable predicted changes in SEW (-0.25), DEP (-0.68), and SE (+0.51). Moreover, adding 15-minutes to SB reflected a predicted 0.12 decrease in PSB, while allocating this time to sleep resulted in a 0.12 predicted increase.

Conclusions:

The relationships between 24-hour movement behaviours and mental health of the children in this study were equivocal. The exception was prosocial behaviour which was significantly related to sleep and SB. Time reallocations suggested that increasing MVPA may have a favourable influence on some mental health outcomes.

Purpose: Physical activity (PA) tends to decline during late childhood and adolescence. In Australia, this decline has been shown to occur particularly in non-organized PA (e.g. active play and informal sport). Using a social marketing approach, market segments (segments of youth) may be identified and targeted based on their profile of alternative activities that compete with non-organised PA during the transition to adolescence. This study aimed to identify and describe segments of youth whose participation in non-organized PA declined between 11 and 13 years, based on changes in other potential competing activities during this period.

Methods: Data were sourced from Waves 4 and 5 of the Longitudinal Study of Australian Children. Participation in non-organized PA and thirteen alternative activities (e.g. video games, homework, sleep) were measured using 24-hour time-use diaries. Analyses were limited to participants whose non-organized PA had declined between 11 and 13 years (n=1043). Two-stage cluster analysis was conducted and segments were described using chi-square and t-tests.

Results: Among the analytic sample, non-organized PA declined by 87 minutes/day between 11y and 13y (p<0.001). Two segments were identified (κ = 0.66). The ‘Social Screens’ segment (n=143) had large increases in texting, emailing and social media use (+56 min/day, p<0.001) and other internet use (+32 min/day, p<0.001). Conversely, ‘the Mainstream’ segment (n=900) had smaller increases in a wider range of activities, including other PA (organised PA, active transport, active chores/work) (+16.0 min/day, p<0.001), homework/study (+9.5 min/day, p<0.001) and electronic gaming (+6.7 min/day, p<0.05). The ‘Social Screens’ segment were more likely to attend public school, live in urban areas and have more advanced pubertal development (girls only). ‘The Mainstream’ were more likely to participate in PA and out-of-school activities.

Conclusions: The ‘Social Screens’ segment had a much larger increase in texting, emailing, social media and other internet use, and lower participation in overall PA and out-of-school activities.  Future research may trial PA promotion strategies to replace benefits that this segment may seek in competing activities (e.g. social PA apps).

Purpose: The Structured Days Hypothesis (SDH; Int J Behav Nutr Phys Act 14:100, 2017) posits that children’s obesogenic behaviors – such as physical activity - are more favorable on days that contain more ‘structure’ (i.e., rules, routines, regulation) such as school weekdays (WD), compared to days with less structure, such as weekend days (WE). The purpose of this study is to test the SDH by using a large, multi-country, device-measured physical activity dataset to compare youth (age 6 to 18 years) moderate-to-vigorous physical activity (MVPA) on WD versus WE.

Methods: Data were obtained from the International Children’s Accelerometer Database (ICAD) during summer 2019. In addition to the ICAD inclusion criteria for a valid day of wear, only non-intervention data (e.g., baseline intervention data), children with at least 3 WD and 1 WE, and ICAD studies with data collected during school months, were included for analysis. Mixed effects models accounting for the nested nature of the data (i.e., days within kids) assessed MVPA differences between WD and WE. Separate models explored differences by country/region. All models controlled for sex, age, and total wear time.

Results: 9,460 children (56% female, 10.8 ±2.1 yrs) across 9 different countries from 15 studies provided 51,934 days of valid accelerometer data. Boys and girls attained +11.7 (95%CI: 10.5, 12.9) and +6.9 (95%CI: 6.2, 7.6) more MVPA minutes, respectively, on WD versus WE days. Children from the United Kingdom (+4.7, 95%CI: 3.7, 5.8), Continental Europe (+14.6, 95%CI: 13.2, 16.0), USA (+6.4, 95%CI: 5.3, 7.6), and Australia (+13.0, 95%CI: 11.6, 14.4) achieved more MVPA minutes on WD versus WE. No WD versus WE differences were observed for Brazilian children.

Conclusions: Children and adolescents from multiple countries display higher levels of MVPA on WD versus WE during school months, consistent with the SDH. Future intervention studies targeting children’s obesogenic behaviors should consider periods of time when children are not exposed to a structured environment such as extended holidays or summer breaks.

Background/Purpose: Research suggests that a positive feedback loop exists, in which children with greater levels of physical activity develop better motor competence, consequently further increasing engagement. However, very little to no evidence has been provided on the reallocation of time between physical activity and other movement, and the effect on motor competence. Therefore, the aim of this study was to investigate the motor competence predictions of time reallocations between 24-hour movement behaviours, using a compositional data analysis approach.

Methods: Cross-sectional data were captured on 210 children (51% boys; aged 10.4±0.7 years) as part of the Waterloo CoDA Project conducted in the West Lancashire region of northwest England. Accelerometer-derived 24h free-living activity (sedentary time (ST), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sleep) was assessed. Motor competence was measured using the Dragon Challenge (DC) which involves nine tasks, requiring the application of different combinations of fundamental, combined and complex motor skills. The DC is completed in a continuous timed circuit and yields DC technique, outcome, time, and total scores, with higher scores displaying better motor competence. Log-ratio multiple linear regression models were used to predict DC technique (process), outcome (product), time, and total scores for the mean movement behaviour composition. Predictions were also made for new compositions where fixed durations of time had been reallocated to one behaviour, while all other behaviours were proportionally decreased to maintain a total daily maximum of 24 hours.

 Results: Mean ± SD for DC technique, outcome, time, and total score were, 9.0 ± 3.1, 10.0 ± 3.5, 12.2 ± 2.2, 31.2 ± 7.4, respectively. The distribution of time spent in MVPA, relative to other behaviours, was significantly associated with DC total (p=0.003), time (p=0.004) and process (p=0.038) scores, but not product scores (p>0.05). A 15 minute increase in MVPA and a proportional total decrease in other movement behaviours predicted higher DC technique (0.41), outcome (0.20), time (0.44), and total (0.16) scores.

Conclusion: The findings from the present study reinforce the key role of MVPA for children’s motor competence. Reallocating time from sleep, ST and LPA to MVPA in children is supported.