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Melitta A. McNarry

Pulmonary oxygen uptake (V˙O2) kinetics, which describes the aerobic response to near instantaneous changes in metabolic demand, provides a valuable insight into the control and coordination of oxidative phosphorylation during exercise. Despite their applicability to the highly sporadic habitual physical activity and exercise patterns of children, relatively little is known regarding the influence of internal and external stimuli on the dynamic V˙O2 response. Although insufficient evidence is available during moderate-intensity exercise, an age-related slowing of the phase 2 time constant (τ) and augmentation of the V˙O2 slow component appears to manifest during heavy-intensity exercise, which may be related to changes in the muscle phosphate controllers of oxidative phosphorylation, muscle oxygen delivery and utilization, and/or muscle fiber type recruitment patterns. Similar to findings in adults, aerobic training is associated with a faster phase 2 τ and smaller V˙O2 slow component in youth, independent of age or maturity, indicative of an enhanced oxidative metabolism. However, a lack of longitudinal or intervention-based training studies limits our ability to attribute these changes to training per se. Further, methodologically rigorous studies are required to fully resolve the interaction(s) between age, sex, biological maturity, and external stimuli, such as exercise training and exercise intensity and the dynamic V˙O2 response at the onset and offset of exercise.

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Melitta A. McNarry, Joanne R. Welsman, and Andrew M. Jones

The influence of training status on pulmonary VO2 recovery kinetics, and its interaction with maturity, has not been investigated in young girls. Sixteen prepubertal (Pre: trained (T, 11.4 ± 0.7 years), 8 untrained (UT, 11.5 ± 0.6 years)) and 8 pubertal (Pub: 8T, 14.2 ± 0.7 years; 8 UT, 14.5 ± 1.3 years) girls completed repeat transitions from heavy intensity exercise to a baseline of unloaded exercise, on both an upper and lower body ergometer. The VO2 recovery time constant was significantly shorter in the trained prepubertal and pubertal girls during both cycle (Pre: T, 26 ± 4 vs. UT, 32 ± 6; Pub: T, 28 ± 2 vs. UT, 35 ± 7 s; both p < .05) and upper body exercise (Pre: T, 26 ± 4 vs. UT, 35 ± 6; Pub: T, 30 ± 4 vs. UT, 42 ± 3 s; both p < .05). No interaction was evident between training status and maturity. These results demonstrate the sensitivity of VO2 recovery kinetics to training in young girls and challenge the notion of a “maturational threshold” in the influence of training status on the physiological responses to exercise and recovery.

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Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

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Kelly A. Mackintosh, Nicola D. Ridgers, Rachel E. Evans, and Melitta A. McNarry

Background: Regular physical activity (PA) is increasingly recognized as important in the care of patients with cystic fibrosis (CF), but there is a dearth of evidence regarding physical activity levels or how these are accrued in those with CF. Methods: PA was measured by a hip-worn accelerometer for 7 consecutive days in 18 children [10 boys; 12.4 (2.8) y] with mild to moderate CF and 18 age- and sex-matched controls [10 boys; 12.5 (2.7) y]. Results: Both children with CF and healthy children demonstrated similar physical activity levels and patterns of accumulation across the intensity spectrum, with higher levels of PA during weekdays in both groups. Forced expiratory volume in 1 second was predicted by high light PA in children with CF compared with low light PA in healthy children. Conclusion: These findings highlight weekends and light PA as areas warranting further research for the development of effective intervention strategies to increase PA in the youth CF population.

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Phillip J. Hill, Melitta A. McNarry, Leanne Lester, Lawrence Foweather, Lynne M. Boddy, Stuart J. Fairclough, and Kelly A. Mackintosh

This study aimed to assess whether sex moderates the association of fundamental movement skills and health and behavioral outcomes. In 170 children (10.6 ±0.3 years; 98 girls), path analysis was used to assess the associations of fundamental movement skills (Get Skilled, Get Active) with perceived sports competence (Children and Youth—Physical Self-Perception Profile), time spent in vigorous-intensity physical activity, sedentary time, and body mass index z score. For boys, object control skill competence had a direct association with perceived sports competence (β = 0.39; 95% confidence interval, CI [0.21, 0.57]) and an indirect association with sedentary time, through perceived sports competence (β = −0.19; 95% CI [−0.09, −0.32]). No significant association was observed between fundamental movement skills and perceived sports competence for girls, although locomotor skills were found to predict vigorous-intensity physical activity (β = 0.18; 95% CI [0.08, 0.27]). Perceived sports competence was associated with sedentary time, with this being stronger for boys (β = −0.48; 95% CI [−0.64, −0.31]) than girls (β = −0.29; 95% CI [−0.39, −0.19]). The study supports a holistic approach to health-related interventions and highlights a key association of perceived sports competence and the time children spend sedentary.

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Nils Swindell, Damon Berridge, Melitta A. McNarry, Kelly A. Mackintosh, Lynne M. Boddy, Stuart J. Fairclough, and Gareth Stratton

Purpose:To examine (1) associations between body fat percent (BF) and lifestyle behaviors in children aged 9–11 years and (2) the consistency of these associations over a 10-year period. Methods: In this repeat, cross-sectional study, 15,977 children aged 9–11 years completed an anthropometric assessment and the SportsLinx Lifestyle survey between 2004 and 2013. Body fat was estimated according to the sum of the triceps and subscapular skinfold measurements. Multilevel models were utilized to examine associations between BF and responses to the lifestyle survey while controlling for known covariates. Results: Lifestyle behaviors explained 8.6% of the total variance in body fat. Specifically, negative associations were found between BF and active transport to school ( β = −0.99 [0.19], P < .001), full-fat milk (−0.07 [0.15], P < .001), and sweetened beverage consumption (−0.40 [0.15], P = .007). Relative to the reference group of ≤8:00 PM, later bedtime was positively associated with BF: 8:00 to 8:59 PM ( β = 1.60 [0.26], P < .001); 9:00 to 10:00 PM ( β = 1.04 [0.24], P < .001); ≥10:00 PM ( β = 1.18 [0.30], P < .001). Two-way interactions revealed opposing associations between BF and the consumption of low-calorie beverages for boys ( β = 0.95 [0.25], P < .001) and girls ( β = −0.85 [0.37], P = .021). There was no significant change in these associations over a 10-year period. Conclusions: In this population-level study covering a decade of data collection, lifestyle behaviors were associated with BF. Policies and interventions targeting population-level behavior change, such as active transport to school, sleep time, and consumption of full-fat milk, may offer an opportunity for improvements in BF.

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Kimberly A. Clevenger, Jan Christian Brønd, Daniel Arvidsson, Alexander H.K. Montoye, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer

Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.