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Oxygen Uptake Kinetics in Youth: Characteristics, Interpretation, and Application

Melitta A. McNarry

Pulmonary oxygen uptake ( V ˙ O 2 ) 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 ˙ O 2 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 ˙ O 2 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 ˙ O 2 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 ˙ O 2 response at the onset and offset of exercise.

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Why Do Children Have Faster VO2 Kinetics?—A Response to Dotan (2019)

Melitta A. McNarry

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The Effect of Sex, Maturity, and Training Status on Maximal Sprint Performance Kinetics

Adam Runacres, Kelly A. Mackintosh, and Melitta A. McNarry

Purpose: The development of sprint running during youth has received renewed interest, but questions remain regarding the development of speed in youth, especially the influences of sex, training, and maturity status. Methods: One hundred and forty-seven team sport trained (69 girls; 14.3 [2.1] y) and 113 untrained (64 girls; 13.8 [2.7] y) youth completed two 30-m sprints separated by 2-minute active rest. Velocity was measured using a radar gun at >46 Hz, with power and force variables derived from a force–velocity–power profile. Results: Boys produced a significantly higher absolute peak power (741 [272] vs 645 [229] W; P < .01) and force (431 [124] vs 398 [125] N; P < .01) than girls, irrespective of maturity and training status. However, there was a greater sex difference in relative mean power and peak velocity in circa peak height velocity adolescents (46.9% and 19.8%, respectively) compared with prepeak height velocity (5.4% and 3.2%) or postpeak height velocity youth (11.6% and 5.6%). Conclusions: Sprint development in youth is sexually dimorphic which needs considering when devising long-term training plans. Further research is needed to explore the independent, and combined, effects of sex, training, and maturity status on sprint performance kinetics in youth.

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Influence of Training Status and Maturity on Pulmonary O2 Uptake Recovery Kinetics Following Cycle and Upper Body Exercise in Girls

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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Association of Recess Provision With Elementary School-Aged Children’s Physical Activity, Adiposity, and Cardiorespiratory and Muscular Fitness

Kimberly A. Clevenger, Melitta A. McNarry, Kelly A. Mackintosh, and David Berrigan

Purpose: To identify associations between amount of school recess provision and children’s physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. Method: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children’s PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey’s complex sampling design. Results: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. Conclusion: Current recess recommendations (20 min·d−1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed.

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National Policy Response to the United Nations Sustainable Development Goals: A Physical Activity Case Study of Wales

Catherine A. Sharp, Kelly A. Mackintosh, Rhi Willmot, Rachel Hughes, Melitta A. McNarry, and Karen Milton

Background: Population level changes in physical activity (PA) may benefit from policy intervention. In response to the United Nations Sustainable Development Goals, Wales introduced legislation to holistically improve health and well-being, including Public Service Boards, to improve the translation of national policy into practice. Method: An audit of policies published by national and subnational public bodies since 2015 was conducted. Content of the policies were extracted and synthesized to determine: (1) how many policies included a PA action, (2) what the drivers of those policies were, (3) the content of the PA actions, and (4) how the PA actions aligned with the Well-being of Future Generations (Wales) Act 2015. Results: Sixteen national-level documents with a PA action were published by 4 of 13 public bodies. The policies vary in terms of the clarity and specificity of the actions, the assignment of clear roles/responsibilities, and the setting of targets. Of the 19 subnational Public Service Boards well-being policies, 15 included PA actions. Conclusion: This audit provides a valuable example of how connections between national and subnational policy can be achieved. The appointment of Public Service Boards has supported the translation of policies into practice in Wales, and similar approaches could be utilized in other countries.

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Physical Activity and Sedentary Time Patterns in Children and Adolescents With Cystic Fibrosis and Age- and Sex-Matched Healthy Controls

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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Comparison of Child and Adolescent Physical Activity Levels From Open-Source Versus ActiGraph Counts

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

ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.

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Impact of ActiGraph Sampling Rate and Intermonitor Comparability on Measures of Physical Activity in Adults

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.

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Cross-Validation and Comparison of Energy Expenditure Prediction Models Using Count-Based and Raw Accelerometer Data in Youth

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.