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Britton W. Brewer, Christine M. Caldwell, Albert J. Petitpas, Judy L. Van Raalte, Miquel Pans and Allen E. Cornelius

) item pool generation, (b) expert review, (c) administration of items to a development sample, (d) item evaluation, and (e) administration of scales to validation samples. Item Generation and Expert Review To facilitate the process of item pool generation, descriptions based on the review of identity

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Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale and Aki I.T. Salo

Purpose:

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.

Methods:

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.

Results:

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).

Conclusions:

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.

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Kathryn J. DeShaw, Laura Ellingson, Yang Bai, Jeni Lansing, Maria Perez and Greg Welk

.g., Fitbit, Jawbone, Apple Watch, Polar). These devices are user-friendly, comfortable to wear, and offer potential to both assess and promote PA. Unfortunately, the literature on the validity of these devices is equivocal due to inherent differences in validation methods, including differences in outcome measures

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Clara Teixidor-Batlle, Carles Ventura Vall-llovera, Justine J. Reel and Ana Andrés

example, cheerleading, swimming, or synchronized skating ( Reel & Gill, 1996 ). The attempt to standardize a weight pressures measure across sports originated with the validation of the English-language version of the weight pressures in sport for female athletes (WPS-F; Reel, Petrie, SooHoo, & Anderson

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Lindsey Tulipani, Mark G. Boocock, Karen V. Lomond, Mahmoud El-Gohary, Duncan A. Reid and Sharon M. Henry

highly accurate systems (eg, electromagnetic motion tracking system, potentiometer); however, these studies did not attempt to collect data with human subjects performing functional tasks. 10 , 11 Clearly there exists a dearth of research that have attempted to validate inertial sensor data during

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Marquis Hawkins, Deirdre K. Tobias, Hala B. Alessa, Andrea K. Chomistek, Junaidah B. Barnett, Walter C. Willett and Susan E. Hankinson

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

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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

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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

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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

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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