Purpose: The purpose of this study was to examine the associations for parent’s movement behaviours and parent-child proximity with preschool-aged children’s movement behaviours. 

Methods: Bluetooth enabled ActiGraph wGT3X-BT accelerometers were used to classify a parent’s and a child’s movement behaviours as sleep, stationary time, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA). Parent-child proximity was assessed using the Bluetooth sensor,  and parent-child proximity behaviours were categorized as: no proximity (NP) detected, proximity detected and matching parent-child movement behaviours (Co), and proximity detected but mismatching parent-child movement behaviours (Close). Lastly, proximity movement behaviours were categorized specific to children’s movement behaviours (e.g., NP-MVPA, Co-MVPA, and Close-MVPA). Compositional linear regression models were created with pivot coordinates to determine the dominance of a single component of the composition in relation to the rest of the composition. When compositional regression models were significant, 1% one-for-all compositional substitutions were conducted.

Results:  Parent movement behaviours were not associated with children’s movement behaviours. For parent-child proximity behaviours, total Close proximity was positively associated with children’s LPA, whereas NP was negatively associated with LPA. Substituting 1% of the proximity behaviour composition to either Close or NP resulted in +2.45 minutes/day or -0.74 minutes/day of LPA. For parent-child proximity movement behaviours, NP-MVPA was positively associated with children’s MVPA. Reallocating 1% of the proximity-MVPA composition to NP-MVPA resulted in +1.61 minutes/day of MVPA.

Conclusions: Novel aspects of this study include the use of Bluetooth proximity sensing to determine the patterns of parent-child proximity and movement behaviours, as well as the use of compositional data analyses to appropriately examine the relationships between these compositions. No associations were found between the compositions of parent’s and children’s movement behaviours in this study. However, other patterns of parent-child proximity and movement behaviours were observed to be important for children’s movement behaviours. Parent-child proximity may be a modifiable correlate of children’s physical activity. However, future research should examine the findings in this study with more robust study designs (i.e., longitudinal and larger sample size), while measuring the whole family, and in other settings such as childcare.

Purpose: Understanding variability in day-to-day durations of movement behaviours can elucidate if particular day-types would benefit from targeted interventions. High variability in daily durations across a week may indicate lack of daily routine and be a marker for an unfavourable lifestyle. The overall aim of this study was to use a compositional approach to characterize patterns of day-to-day variability in duration of movement behaviours among children, and to compare daily durations among children with high and low day-to-day variability.

Methods: This study was based on 7-day, 24-hour accelerometry data from 1368 children (11-12 years old, 50% males) participating in the Child Health CheckPoint study, nested within the Longitudinal Study of Australian Children. A daily four-part movement behaviour composition consisting of moderate-to-vigorous physical activity (MVPA), light physical activity (LPA) sedentary behaviour and sleep was created for each participant. Day-to-day movement behaviours were described using compositional means and variability in each child’s week was described using a compositional variation matrix of log-ratio variances. Children were categorised as having a high or low day-to-day variability using a median split based on their total variance, calculated from the sum of all values in their variation matrix. 

Results: Children were less active and more sedentary on weekends compared to weekdays (e.g., 13 min MVPA, 145 min LPA and 706 min sedentary on Sunday vs 15 min MVPA, 152 min LPA and 694 sedentary on Monday). They slept less on weekends compared to weekdays (e.g., 576 min on Sunday vs. 581 min on Wednesday). Children with higher day-to-day variability consistently had lower MVPA (up to 15 min/d) and lower LPA (up to 146 min/d) and higher sedentary time (up to 722 min/d) than children with lower day-to-day variability.

Conclusions: Among this sample, weekdays had higher durations of physical activities and lower sedentary time compared to weekends, indicating a requirement for programs to encourage weekend physical activity. Our findings suggest that greater inconsistency in day-to-day durations of behaviours may accompany a less active, more sedentary lifestyle. Further research is warranted to understand how regularity in daily behaviours can be characterised and related to health. 

Purpose: This study aimed to describe children’s time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behaviour and physical activity, and their associations with cardiometabolic biomarkers.

Methods: Hip-worn ActiGraph accelerometer and cardiometabolic biomarker data from 7–13 year olds from two Australian studies were pooled (n=782 complete cases). A nine-component time-use composition was formed, including time in shorter and longer bouts of sedentary behaviour, light-, moderate- and vigorous-intensity physical activity and “other time” (i.e., non-wear and sleep). Sedentary shorter and longer bouts were defined as <5 and ≥5 min, respectively. Physical activity (including light-, moderate- and vigorous-intensity) was subdivided into time in bouts of <1 and ≥1 min. Observed zeros (n=9 participants for time in longer VPA bouts) were replaced using the multiplicative lognormal imputation method. Regression models examined associations between the nine-component movement behaviour composition and cardiometabolic biomarkers. Then, associations between ratios of longer relative to shorter activity patterns, and each intensity relative to more intense activities and “other time”, with cardiometabolic biomarkers (zBMI, waist circumference, lipids, blood pressure, and a summary z-score) were derived. 

Results: Confounder-adjusted models (clustering by school; age, sex, SES, dataset) showed that the overall movement behaviour composition was associated with adiposity, blood pressure, lipids and the summary score. Specifically, more time in longer relative to shorter bouts of light-intensity physical activity was significantly associated with greater zBMI (β=1.79, SE=0.68, p=0.009) and waist circumference (β=18.35, SE=4.78, p<0.001). With each activity considered relative to all higher intensities and “other time”, more time in light-intensity and vigorous-intensity physical activity and less time in sedentary behaviour and moderate-intensity physical activity, were associated with lower waist circumference.

Conclusions: The results suggest that accumulating physical activity, particularly light-intensity, in frequent short bursts may be beneficial for improving adiposity compared to engaging in the same amount of these intensities in longer bouts. These findings should be corroborated or refuted with evidence from other samples, including sleep data and longitudinal designs.