Dylan P. Cliff and Anthony D. Okely
Anja Groβek, Christiana van Loo, Gregory E. Peoples, Markus Hagenbuchner, Rachel Jones, and Dylan P. Cliff
This study reports energy expenditure (EE) data for lifestyle and ambulatory activities in young children.
Eleven children aged 3 to 6 years (mean age = 4.8 ± 0.9; 55% boys) completed 12 semistructured activities including sedentary behaviors (SB), light (LPA), and moderate-to-vigorous physical activities (MVPA) over 2 laboratory visits while wearing a portable metabolic system to measure EE.
Mean EE values for SB (TV, reading, tablet and toy play) were between 0.9 to 1.1 kcal/min. Standing art had an energy cost that was 1.5 times that of SB (mean = 1.4 kcal/min), whereas bike riding (mean = 2.5 kcal/min) was similar to LPA (cleaning-up, treasure hunt and walking) (mean = 2.3 to 2.5 kcal/min), which had EE that were 2.5 times SB. EE for MVPA (running, active games and obstacle course) was 4.2 times SB (mean = 3.8 to 3.9 kcal/min).
EE values reported in this study can contribute to the limited available data on the energy cost of lifestyle and ambulatory activities in young children.
Byron J. Kemp, Anne-Maree Parrish, Marijka Batterham, and Dylan P. Cliff
Background: Information about the domains of physical activity (PA) that are most prone to decline between late childhood (11 y), early adolescence (13 y), and mid-adolescence (15 y) may support more targeted health promotion strategies. This study explored longitudinal trends in nonorganized PA, organized PA, active transport and active chores/work between childhood and adolescence, and potential sociodemographic moderators of changes. Methods: Data were sourced from the Longitudinal Study of Australian Children (n = 4108). Participation in PA domains was extracted from youth time-use diaries. Potential moderators were sex, Indigenous status, language spoken at home, socioeconomic position, and geographical remoteness. Results: A large quadratic decline in nonorganized PA (−48 min/d, P < .001) was moderated by sex (β = 5.55, P = .047) and home language (β = 8.55, P = .047), with girls (−39 min/d) and those from a non-English speaking background (−46 min/d) declining more between 11 and 13 years. Active chores/work increased between 11 and 13 years (+4 min/d, P < .001) and then stabilized. Active transport increased among boys between 11 and 13 years (+6 min/d, P < .001) and then declined between 13 and 15 years (−4 min/d, P < .001). Organized PA remained stable. Conclusions: The longitudinal decline in PA participation may be lessened by targeting nonorganized PA between childhood and adolescence. Future interventions may target girls or those from non-English speaking backgrounds during this transition.
Kar Hau Chong, Dorothea Dumuid, Dylan P. Cliff, Anne-Maree Parrish, and Anthony D. Okely
Background: Little is known about the influence of 24-hour movement behaviors on children’s psychosocial health when transitioning from primary to secondary school. This study described changes in 24-hour domain-specific movement behavior composition and explored their associations with changes in psychosocial health during this transition. Methods: Data were drawn from the Longitudinal Study of Australian Children. The analytical sample (n = 909) included children who were enrolled in primary school at baseline (2010) and in secondary school at follow-up (2012). Time spent in 8 domains of movement behaviors was derived from the child-completed time-use diaries. Psychosocial health was examined using the self-report version of the Strengths and Difficulties Questionnaires. Analyses included repeated-measures multivariate analysis of variance and compositional regression. Results: Children reported engaging in more social activities and sleeping less over the transition period. Increased time spent in social activities (β ilr = −0.06, P = .014) and recreational screen use (β ilr = −0.17, P = .003) (relative to other domains) were associated with decreased prosocial behavior in boys. Changes in movement behavior composition were not associated with changes in girls’ psychosocial health. Conclusion: This study found considerable changes in children’s 24-hour movement behavior composition, but a lack of consistent association with changes in psychosocial health during the primary to secondary school transition.
Lyndel Hewitt, Anthony D. Okely, Rebecca M. Stanley, Marjika Batterham, and Dylan P. Cliff
Background: Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants. Methods: The analysis was conducted on accelerometer data from 32 healthy infants (4–25 wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3 min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland–Altman plots. Results: Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and −0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838–0.842) and specificity (0.948, 95% confidence interval, 0.944–0.948). Conclusions: Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than −0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.
Katherine L. Downing, Jo Salmon, Anna Timperio, Trina Hinkley, Dylan P. Cliff, Anthony D. Okely, and Kylie D. Hesketh
Background: Although there is increasing evidence regarding children’s screen time, little is known about children’s sitting. This study aimed to determine the correlates of screen time and sitting in 6- to 8-year-old children. Methods: In 2011–2012, parents in the Healthy Active Preschool and Primary Years (HAPPY) study (n = 498) reported their child’s week/weekend day recreational screen time and potential correlates. ActivPALs™ measured children’s nonschool sitting. In model 1, linear regression analyses were performed, stratified by sex and week/weekend day and controlling for age, clustered recruitment, and activPAL™ wear time (for sitting analyses). Correlates significantly associated with screen time or sitting (P < .05) were included in model 2. Results: Children (age 7.6 y) spent 99.6 and 119.3 minutes per day on week and weekend days engaging in screen time and sat for 119.3 and 374.6 minutes per day on week and weekend days, respectively. There were no common correlates for the 2 behaviors. Correlates largely differed by sex and week/weekend day. Modifiable correlates of screen time included television in the child’s bedroom and parental logistic support for, encouragement of, and coparticipation in screen time. Modifiable correlates of sitting included encouragement of and coparticipation in physical activity and provision of toys/equipment for physical activity. Conclusions: Interventions may benefit from including a range of strategies to ensure that all identified correlates are targeted.
João R. Pereira, Dylan P. Cliff, Eduarda Sousa-Sá, Zhiguang Zhang, Jade McNeill, Sanne L.C. Veldman, and Rute Santos
Background: This study aimed to understand whether a higher number of sedentary bouts (SED bouts) and higher levels of sedentary time (SED time) occur according to different day types (childcare days, nonchildcare weekdays, and weekends) in Australian toddlers (1–2.99 y) and preschoolers (3–5.99 y). Methods: The SED time and bouts were assessed using ActiGraph GT3X+ accelerometers. The sample was composed of 264 toddlers and 343 preschoolers. The SED bouts and time differences were calculated using linear mixed models. Results: The toddlers’ percentage of SED time was higher on nonchildcare days compared with childcare days (mean difference [MD] = 2.3; 95% confidence interval, 0.7 to 3.9). The toddlers had a higher number of 1- to 4-minute SED bouts on nonchildcare days compared with childcare days. The preschoolers presented higher percentages of SED time during nonchildcare days (MD = 3.1; 95% confidence interval, 1.6 to 4.5) and weekends (MD = 1.9; 95% confidence interval, 0.4 to 3.4) compared with childcare days. The preschoolers presented a higher number of SED bouts (1–4, 5–9, 10–19, and 20–30 min) during nonchildcare days and weekends compared with childcare days. No SED times or bout differences were found between nonchildcare days and weekends, neither SED bouts >30 minutes on toddlers nor on preschoolers. Conclusion: The SED time and bouts seem to be lower during childcare periods, which means that interventions to reduce sedentary time should consider targeting nonchildcare days and weekends.
Sanne L.C. Veldman, Rachel A. Jones, Rebecca M. Stanley, Dylan P. Cliff, Stewart A. Vella, Steven J. Howard, Anne-Maree Parrish, and Anthony D. Okely
Background: The aim of this study was to examine the efficacy of an embedded after-school intervention, on promoting physical activity and academic achievement in primary-school-aged children. Methods: This 6-month, 2-arm cluster randomized controlled trial involved 4 after-school centers. Two centers were randomly assigned to the intervention, which involved training the center staff on and implementing structured physical activity (team sports and physical activity sessions for 75 min) and academic enrichment activities (45 min). The activities were implemented 3 afternoons per week for 2.5 hours. The control centers continued their usual after-school care practice. After-school physical activity (accelerometry) and executive functions (working memory, inhibition, and cognitive flexibility) were assessed pre- and postintervention. Results: A total of 60 children were assessed (7.7 [1.8] y; 50% girls) preintervention and postintervention (77% retention rate). Children in the intervention centers spent significantly more time in moderate to vigorous physical activity (adjusted difference = 2.4%; 95% confidence interval, 0.6 to 4.2; P = .026) and scored higher on cognitive flexibility (adjusted difference = 1.9 units; 95% confidence interval, 0.9 to 3.0; P = .009). About 92% of the intervention sessions were implemented. The participation rates varied between 51% and 94%. Conclusion: This after-school intervention was successful at increasing moderate to vigorous physical activity and enhancing cognitive flexibility in children. As the intervention was implemented by the center staff and local university students, further testing for effectiveness and scalability in a larger trial is required.
Christiana M.T. van Loo, Anthony D. Okely, Marijka Batterham, Tina Hinkley, Ulf Ekelund, Soren Brage, John J. Reilly, Gregory E. Peoples, Rachel Jones, Xanne Janssen, and Dylan P. Cliff
To validate the activPAL3 algorithm for predicting metabolic equivalents (TAMETs) and classifying MVPA in 5- to 12-year-old children.
Fifty-seven children (9.2 ± 2.3y, 49.1% boys) completed 14 activities including sedentary behaviors (SB), light (LPA) and moderate-to-vigorous physical activities (MVPA). Indirect calorimetry (IC) was used as the criterion measure. Analyses included equivalence testing, Bland-Altman procedures and area under the receiver operating curve (ROC-AUC).
At the group level, TAMETs were significantly equivalent to IC for handheld e-game, writing/coloring, and standing class activity (P < .05). Overall, TAMETs were overestimated for SB (7.9 ± 6.7%) and LPA (1.9 ± 20.2%) and underestimated for MVPA (27.7 ± 26.6%); however, classification accuracy of MVPA was good (ROC-AUC = 0.86). Limits of agreement were wide for all activities, indicating large individual error (SB: −27.6% to 44.7%; LPA: −47.1% to 51.0%; MVPA: −88.8% to 33.9%).
TAMETs were accurate for some SB and standing, but were overestimated for overall SB and LPA, and underestimated for MVPA. Accuracy for classifying MVPA was, however, acceptable.