Dylan P. Cliff and Anthony D. Okely
Rachel A. Jones, Jacque Kelly, Dylan P. Cliff, Marijka Batterham and Anthony D. Okely
Single sex after-school physical activity programs show potential to prevent unhealthy weight gain. The aim of this study was to assess the acceptability and potential efficacy of single-sex after-school physical activity programs for overweight and at-risk children from low-income communities.
7-month, 2-arm parallel-group, RCT, conducted at an elementary school in a disadvantaged area in Wollongong, Australia (March-November 2010).
20 boys and 17 girls were randomized to intervention (PA) or active comparison groups (HL). Primary outcomes included implementation, acceptability, percentage body fat and BMI z-score.
The PA programs were acceptable with high implementation and enjoyment rates. At 7 months postintervention girls in the PA group displayed greater changes in percentage body fat (adjust diff. = -1.70, [95% CI -3.25, -0.14]; d = -0.83) and BMI z-score (-0.19 [-0.36, -0.03]; d= -1.00). At 7 months boys in the PA group showed greater changes in waist circumference (-3.87 cm [-7.80, 0.15]; d= -0.90) and waist circumference z-score (-0.33 [-0.64, -0.03]; d= -0.98). For both boys’ and girls’ PA groups, changes in adiposity were not maintained at 12-month follow-up.
Single-sex after-school physical activity programs are acceptable and potentially efficacious in preventing unhealthy weight gain among overweight and at-risk children. However improvements are hard to sustain once programs finish operating.
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.
Yvonne G. Ellis, Dylan P. Cliff, Steven J. Howard and Anthony D. Okely
Purpose: To examine the acute effects of a reduced sitting day on executive function (EF) and musculoskeletal health in preschoolers. Methods: A sample of 29 children (54% boys; 4–5 y) participated in a randomized cross-over trial. Each child completed 2 protocols, which simulate a day at childcare in random order for 2.5 hours; a typical preschool day (50% sitting) and a reduced preschool day (25% sitting) where most sitting activities were replaced with standing activities. Sitting, standing, and stepping time were objectively assessed using an activPAL accelerometer. EF was evaluated using tablet-based EF assessments (inhibition, working memory, and task shifting). Musculoskeletal health was assessed using a handheld dynamometer and goniometer. Results: Compared with the typical preschool day, the reduced sitting day showed no significant differences for EF scores. Effect sizes for inhibition (d = 0.04), working memory (d = 0.02), and shifting (d = 0.11) were all small. For musculoskeletal health, no significant differences were reported after the reduced preschool day. The effect sizes for the hip extension force, hamstring flexibility, gastrocnemius length, and balancing on 1 leg were all small (d = 0.21, d = 0.25, d = 0.28, and d = 0.28). Conclusions: This study suggests that reducing sitting time is unlikely to result in acute changes in EF and musculoskeletal health among preschoolers.
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.
Dylan P. Cliff, Anthony D. Okely, Leif M. Smith and Kim McKeen
Gender differences in cross-sectional relationships between fundamental movement skill (FMS) subdomains (locomotor skills, object-control skills) and physical activity were examined in preschool children. Forty-six 3- to 5-year-olds (25 boys) had their FMS video assessed (Test of Gross Motor Development II) and their physical activity objectively monitored (Actigraph 7164 accelerometers). Among boys, object-control skills were associated with physical activity and explained 16.9% (p = .024) and 13.7% (p = .049) of the variance in percent of time in moderate-to-vigorous physical activity (MVPA) and total physical activity, respectively, after controlling for age, SES and z-BMI. Locomotor skills were inversely associated with physical activity among girls, and explained 19.2% (p = .023) of the variance in percent of time in MVPA after controlling for confounders. Gender and FMS subdomain may influence the relationship between FMS and physical activity in preschool children.
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.
Xanne Janssen, Dylan P. Cliff, John J. Reilly, Trina Hinkley, Rachel A. Jones, Marijka Batterham, Ulf Ekelund, Soren Brage and Anthony D. Okely
This study examined the classification accuracy of the activPAL, including total time spent sedentary and total number of breaks in sedentary behavior (SB) in 4- to 6-year-old children. Forty children aged 4–6 years (5.3 ± 1.0 years) completed a ~150-min laboratory protocol involving sedentary, light, and moderate- to vigorous-intensity activities. Posture was coded as sit/lie, stand, walk, or other using direct observation. Posture was classified using the activPAL software. Classification accuracy was evaluated using sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC). Time spent in each posture and total number of breaks in SB were compared using paired sample t-tests. The activPAL showed good classification accuracy for sitting (ROC-AUC = 0.84) and fair classification accuracy for standing and walking (0.76 and 0.73, respectively). Time spent in sit/lie and stand was overestimated by 5.9% (95% CI = 0.6−11.1%) and 14.8% (11.6−17.9%), respectively; walking was underestimated by 10.0% (−12.9−7.0%). Total number of breaks in SB were significantly overestimated (55 ± 27 over the course of the protocol; p < .01). The activPAL performed well when classifying postures in young children. However, the activPAL has difficulty classifying other postures, such as kneeling. In addition, when predicting time spent in different postures and total number of breaks in SB the activPAL appeared not to be accurate.
Leon Straker, Erin Kaye Howie, Dylan Paul Cliff, Melanie T. Davern, Lina Engelen, Sjaan R. Gomersall, Jenny Ziviani, Natasha K. Schranz, Tim Olds and Grant Ryan Tomkinson
Australia has joined a growing number of nations that have evaluated the physical activity and sedentary behavior status of their children. Australia received a “D minus” in the first Active Healthy Kids Australia Physical Activity Report Card.
An expert subgroup of the Australian Report Card Research Working Group iteratively reviewed available evidence to answer 3 questions: (a) What are the main sedentary behaviors of children? (b) What are the potential mechanisms for sedentary behavior to impact child health and development? and (c) What are the effects of different types of sedentary behaviors on child health and development?
Neither sedentary time nor screen time is a homogeneous activity likely to result in homogenous effects. There are several mechanisms by which various sedentary behaviors may positively or negatively affect cardiometabolic, neuromusculoskeletal, and psychosocial health, though the strength of evidence varies. National surveillance systems and mechanistic, longitudinal, and experimental studies are needed for Australia and other nations to improve their grade.
Despite limitations, available evidence is sufficiently convincing that the total exposure and pattern of exposure to sedentary behaviors are critical to the healthy growth, development, and wellbeing of children. Nations therefore need strategies to address these common behaviors.
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.