relationships can inform intervention approaches. Therefore, we evaluated the association of PA intensity and type, using objective and self-reported measures of PA, with circulating sex hormones in premenopausal and postmenopausal women. Methods Study Population The Women’s Lifestyle Validation Study is an
Marquis Hawkins, Deirdre K. Tobias, Hala B. Alessa, Andrea K. Chomistek, Junaidah B. Barnett, Walter C. Willett and Susan E. Hankinson
Alba Reguant-Closa, Margaret M. Harris, Tim G. Lohman and Nanna L. Meyer
athletes ( Mettler et al., 2009 ). Athletes are a population with specific nutritional requirements to optimize health and performance ( Thomas et al., 2016 ). One of the more relevant studies is the validation of the Food Pyramid for Swiss Athletes (FPSA) by Mettler et al. ( 2009 ). The aim of the
Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale and Aki I.T. Salo
An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.
Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.
PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).
Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.
Katherine L. Hsieh, Yaejin Moon, Vignesh Ramkrishnan, Rama Ratnam and Jacob J. Sosnoff
such sensors to be a valid measure of gait and static postural control. 17 – 19 However, these investigations did not use a body-tracking depth sensor to quantify VTC, which may provide an alternative approach to assess postural stability. Consequently, the aim of this study was to validate VTC
Jennifer L. Huberty, Jeni L. Matthews, Meynard Toledo, Lindsay Smith, Catherine L. Jarrett, Benjamin Duncan and Matthew P. Buman
validated wrist-worn triaxial sensor used to objectively measure physical activity in the free-living setting. However, the validity of both devices in measuring energy expenditure has not been evaluated for yoga. The validation of portable, light-weight devices such as the Actigraph and GENEActiv is
Paul M. Wright, K. Andrew R. Richards, Jennifer M. Jacobs and Michael A. Hemphill
promote these actions in and beyond the classroom” ( 2017 , p. 10). Regarding the instrumentation required to support these lines of research, tools have been developed and validated to assess students’ personal and social responsibility in the physical education setting via self-report ( Li, Wright
John Goetschius, Mark A. Feger, Jay Hertel and Joseph M. Hart
-plates are considered the ‘gold-standard’ for postural control assessments, and have previously been utilized to validate novel postural control assessment devices. 3 – 5 Force-plates calculate COP excursions using load cells and measuring the 3-dimensional (x, y, z) forces and moments generated between
Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer
al., 2016 ; Staudenmayer et al., 2015 ) and one study in youth ( Hibbing, Ellingson, et al., 2018 ) have demonstrated that the accuracy of these models decreases when cross-validating in a new or independent sample, indicating a tendency for machine learning models to be over-fit to the data. Further
Stacy N. Scott, Cary M. Springer, Jennifer F. Oody, Michael S. McClanahan, Brittany D. Wiseman, Tyler J. Kybartas and Dawn P. Coe
estimated VO 2 peak is significantly underestimated in individuals with a high BMI, and researchers have suggested the inclusion of BMI as a predictor variable in the PACER equation ( 25 ). All currently validated equations are based on tests measuring VO 2 peak on a treadmill and not during the actual
Albert R. Mendoza, Kate Lyden, John Sirard, John Staudenmayer, Catrine Tudor-Locke and Patty S. Freedson
device battery life, and on board device or cloud-based memory capacity. However, growth of the market and advances in consumer device technology far outpace our understanding about the validity of such devices. The majority of consumer activity tracker validation studies currently available have been