The GT3X+ worn at the wrist promotes greater compliance than at the hip. Minutes in SB and PA calculated from raw accelerations at the hip and wrist provide contrasting estimates and cannot be directly compared. Wear-time for the wrist (15.6 to 17.4 h.d⁻¹) was greater than the hip (15.2 to 16.8 h.d⁻¹) across several wear-time criteria (all P < 0.05). Moderate-strong associations were found between time spent in SB (r = 0.39), LPA (r = 0.33), MPA (r = 0.99), VPA (r = 0.82) and MVPA (r = 0.81) between the two device placements (All P < 0.001). The wrist device detected more minutes in LPA, MPA, VPA and MVPA whereas the hip detected more SB (all P = 0.001). Estimates of time in SB and all activity outcomes from the wrist and hip lacked equivalence. One hundred and eighty-eight 9–12-year-old children wore a wrist- and hip-mounted accelerometer for 7 days. Data were available for 160 (hip) and 161 (wrist) participants. Time spent in SB and PA was calculated using GGIR. This study examined the compliance of children wearing wrist- and hip-mounted ActiGraph GT3X+ accelerometers and compared estimates of sedentary behaviour (SB) and physical activity (PA) between devices.

Purpose: This study explored the feasibility of conducting a classroom-based active breaks intervention on sedentary behavior (SB), physical activity (PA) and attention in 8–12-year-old children. Methods: Eight schools were randomized on a 1:1 basis to the control or intervention. Teachers selected 10 cards detailing an activity break at random. Children then undertook each of the ten activity breaks for 30 s, three times per day for 6 weeks. School and participant recruitment, attrition rates, percentage of outcome measures collected, and acceptability were used to explore the feasibility of the study. Mixed effects models were undertaken to examine intervention effects upon measures of PA, SB and attention. Results: Two hundred and thirty-nine consent forms were issued and 153 were returned (64%). Of the 153 consents, 146 children (95%) were measured at baseline, and 117 participated in the follow-up measures (80%) 6 weeks later suggesting the intervention was acceptable for the participants. From teacher interviews, it was noted that the intervention was feasible to implement, and teachers observed positive classroom behavior changes in children. Inclusion rates for outcome measures ranged from 49% to 66%. Significant, intervention effects were observed for sitting time (B = −27.19; 95%CI: −36.84, −17.17), standing time (B = 23.51; 95%CI 14.1, 32.45) and the number of sit to stand transitions (B = 16.1; 95%CI 4.7, 26.79). Conclusion: Findings suggest that it was feasible and acceptable to implement an active breaks intervention within the classroom setting. Future work should consider the effectiveness of implementing this intervention across a full academic year.

Background: Limited studies have examined the diagnostic performance of body mass index (BMI), waist circumference (WC) or waist-to-height ratio (WHtR) for identifying cardiometabolic risk (increased clustered glucose, triglycerides, mean arterial pressure and inv-HDL-cholesterol) in pre-adolescent youth. Aim: To compare the utility of BMI, WC and WHtR as predictors of cardiometabolic risk (CMR) in Scottish pre-adolescent children. Subjects and methods: A cross-sectional analysis of 223 Scottish children (55.2% boys, mean age =8.4 years) was undertaken. BMI, WC and WHtR were used as exposure variables within multivariate logistic regression analysis and ROC analysis to examine the utility of these anthropometrical indices in identifying those at cardiometabolic risk. Results: Individuals with an elevated WHtR, WC and BMI were 3.51 (95% CI = 1.71–7.23; p < .001); 2.34 (95% CI = 1.35–4.06; p = .002) and 2.59 (95% CI = 1.42–4.73; p = .002) times more likely to be at cardiometabolic risk, respectively. The areas under the curves [AUC] to identify children with cardiometabolic risk were significant and similar among anthropometric indices (AUC’s = 0.60–0.65). When stratified by BMI, both WC and WHtR demonstrated a fair-to-good ability for identifying those at cardiometabolic risk (AUC = 0.75–0.81). Conclusions: Findings suggest that the combination of BMI with either WC or WHtR may provide an added benefit in the assessment of cardiometabolic risk amongst pre-adolescents.

Background: Raw acceleration data lend themselves to innovative metrics in which comparisons can be made across populations. This study examined whether the intensity gradient (IG) or average acceleration (AvAcc) was associated with body mass index (BMI) in children and adults and explored parent–child associations between time spent in physical activity intensities and BMI. Methods: Parent–child dyads (n = 90) wore a wrist-mounted ActiGraph GT3X+ monitor for ≥ 3 valid days (i.e., ≥ 16 hrs each day). Participants’ BMI was calculated. Results: Girls’ BMI-z scores were positively associated with parent BMI and inactive time. Parent BMI was negatively associated with boys’ inactive time. Parental inactive time predicted girls’ BMI-z scores. The metrics were not associated with parent BMI, and no independent effects were observed between the metrics. Associations between boys’ IG and BMI z-score were observed. No independent effects were observed between the metrics and no significant associations were observed between boys’ BMI z-score and AvAcc. Girls’ AvAcc and IG were not associated with BMI z-score and no independent effects were observed between the metrics. Conclusion: Findings support previous research which suggests that parent’s activity does not influence their child’s activity.