Melitta A. McNarry
Melitta A. McNarry
Pulmonary oxygen uptake (
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.
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.
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.