The purpose of this review is to describe the amount of time children spend in sedentary behaviour and to determine if there are specific factors that associate with sedentary behaviour in children. The following search terms were used to identify relevant articles: sedentary behaviour, inactivity, television, computer, video games, small screen, sitting, prevalence, patterns, correlates, factors and determinants. The databases used to conduct the search included PubMed, PsycINFO, ERIC (Education Resources Information Center) and Academic Search Premier. The studies reviewed were limited to those that sampled children (2–18 years), were written in English and used a measure of sedentary behaviour as the dependent variable. Several studies reported the time spent watching television or the proportion of children at or above a threshold for television viewing (eg, ≥3 h/day). Among the accelerometer studies included, the National Health and Nutrition Examination Survey is the largest and reported ∼6.1, 7.5 and 8.0 h/day mean sedentary time in children 6–11, 12–15 and 16–19 years old, respectively. Taken together, the existing literature across the world indicates a slightly higher level of sedentary behaviour in older children. Higher levels of sedentary behaviour were also reported in non-white children, children from lower socioeconomic status background and children from households with more access to televisions/computers. Lower levels of sedentary behaviour were reported in children whose parents have rules/limitations on screen time.

Tracking physical activity and sleep patterns using wearable trackers has become a current trend. However, little information exists about the comparability of wearable trackers measuring sleep. This study examined the comparability of wearable trackers for estimating sleep measurement with a sleep diary (SD) for three full nights. A convenience sample of 78 adults were recruited in this research with a mean age of 27.6 ± 11.0 years. Comparisons between wearable trackers and sleep outcomes were analyzed using the mean absolute percentage errors, Pearson correlations, Bland–Altman Plots, and equivalent testing. Trackers that showed the greatest equivalence with the SD for total sleep time were the Jawbone UP3 and Fitbit Charge Heart Rate (effect size = 0.09 and 0.23, respectively). The greatest equivalence with the SD for time in bed was seen with the SenseWear Armband, Garmin Vivosmart, and Jawbone UP3 (effect size = 0.09, 0.16, and 0.07, respectively). Some of the wearable trackers resulted in closer approximations to self-reported sleep outcomes than a previously sleep research-grade device, these trackers offer a lower-cost alternative to tracking sleep in healthy populations.

ObjectiveTo determine, using a social-cognitive framework and structural equation modeling, if parent-reported family physical activity (PA) variables are related to PA of young children.DesignCross-sectional study.SettingChildren attending 23 preschools in and around Columbia, South Carolina.ParticipantsThree hundred sixty-nine children (48.0% male and 50.4% black) and their parents.Main ExposuresFamily variables were reported by parents and included parent PA, parent enjoyment of PA, importance to adults of child playing sports and being active, and family support.Main Outcome MeasuresModerate to vigorous physical activity (MVPA) of children was modeled as a latent variable using PA from direct observation, accelerometers, and parent's perception of the child's athletic coordination.ResultsA model of direct and indirect relations of family variables, preschool quality, home PA equipment, and child's enjoyment of PA had acceptable fit (root mean square error of approximation, 0.053; comparative fit index, 0.90). Parent PA, parent enjoyment of PA, and importance of child's PA were significantly related to family support. Family support, quality of preschool attended, home PA equipment, and child's enjoyment of PA were positively related to child's PA. However, there was no direct relationship between parent PA and the child's PA.ConclusionAlthough parent PA was not directly related to children's MVPA, results showed that parent PA indirectly affects preschool children's MVPA via its influence on family support for children's PA.

The triaxial GT3X+ accelerometer can measure activity counts in the vertical, horizontal right to left, horizontal front to back planes, and can generate a summative score of the three axes represented by vector magnitude (VM). Information on the reliability of the GT3X+ at the hip, wrist and ankle sites, over all axes and VM during activities of daily living (ADL) is lacking in the literature. Forty healthy adults (14 men and 26 women) were randomly assigned to perform 10 of 20 ADL (consisting of sedentary, housework, yard work, locomotive and recreational activities) while wearing two monitors on the hip, wrist and ankle. Subjects performed each ADL over 7 min and the mean activity counts during the last 4 min were used for analyses. Average intraclass correlations between monitors were high for the three sites for each axis and VM (hip: 0.943, 0.857, 0.864 and 0.966, respectively; wrist: 0.994, 0.963, 0.961 and 0.989, respectively; ankle: 0.977, 0.979, 0.927 and 0.986, respectively). These data suggest that GT3X+ accelerometers measurements made from the hip, wrist and ankle sites are reliable during ADL across all axes and VM.

Objectives: The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. Methods: Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. Results: The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. Conclusion: All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.