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Patty Freedson

In this issue we feature the paper, “Methods for Activity Monitor Validation Studies: An Example With the Fitbit Charge,” by Kathryn DeShaw and colleagues ( 2018 ). Kathryn is a doctoral student working under the direction of Dr. Greg Welk at Iowa State University. This paper examines free

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Samantha L. Winter, Sarah M. Forrest, Joanne Wallace and John H. Challis

-specific BSIPs, however, a key problem is that no female-specific geometric models for estimating BSIPs have been validated, despite significant differences in the shapes of segments between males and females. 3 There are several methods of estimating BSIPs. Scanning techniques such as dual x-ray absorptiometry

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Ashley A. Hansen, Joanne E. Perry, John W. Lace, Zachary C. Merz, Taylor L. Montgomery and Michael J. Ross

treatment monitoring in sport psychological practice; therefore, the current study sought to create an instrument addressing this need. This research endeavor resulted in the development and initial psychometric validation of a 17-item self-report instrument that measures four dimensions of progress

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Christopher J. Nightingale, Sidney N. Mitchell and Stephen A. Butterfield

. Our goal was to evaluate the TUG test as an indicator of balance. To assess the TUG test, we selected the OptoGait gait test as a tool with which we could compare TUG performance. The OptoGait photoelectric system has been validated as an effective tool for precise measurement of spatiotemporal gait

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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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Franco M. Impellizzeri and Samuele M. Marcora

We propose that physiological and performance tests used in sport science research and professional practice should be developed following a rigorous validation process, as is done in other scientific fields, such as clinimetrics, an area of research that focuses on the quality of clinical measurement and uses methods derived from psychometrics. In this commentary, we briefly review some of the attributes that must be explored when validating a test: the conceptual model, validity, reliability, and responsiveness. Examples from the sport science literature are provided.

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Levi Frehlich, Christine Friedenreich, Alberto Nettel-Aguirre, Jasper Schipperijn and Gavin R. McCormack

( Aadland & Ylvisåker, 2015 ) and has been validated in adults using indirect calorimetry ( Santos-Lozano et al., 2013 ) and doubly labelled water ( Chomistek et al., 2017 ) as criterion measures. GPS Monitoring GPS monitors (model: Qstarz BT-Q1000XT ® ; Qstarz International Inc., Taiwan) captured the

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Lise Gauvin and W. Jack Rejeski

This research describes the development and validation of a measure designed to assess feeling states that occur in conjunction with acute bouts of physical activity—the Exercise-Induced Feeling Inventory (EFI). The EFI consists of 12 items that capture four distinct feeling states: revitalization, tranquility, positive engagement, and physical exhaustion. The multidimensional structure of the EFI is supported by confirmatory factor analysis. The subscales have good internal consistency, share expected variance with related constructs, are sensitive to exercise interventions, and appear responsive to the different social contexts in which activity may occur. After describing the psychometric properties of the EFI, several directions for theory-based research are proposed.

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Alan K. Bourke, Espen A. F. Ihlen and Jorunn L. Helbostad

The measurement of physical activity patterns has the potential to reveal underlying causes of changes in modifiable risk-factors associated with health and well-being. Accurate classification of physical activity (PA) in free-living situations requires the use of a validated measurement system to

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Ryan D. Burns, James C. Hannon, Timothy A. Brusseau, Patricia A. Eisenman, Pedro F. Saint-Maurice, Greg J. Welk and Matthew T. Mahar

Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13–16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74–0.78), and prediction error (RMSE) ranged from 5.95 ml·kg-1, min-1 to 8.27 ml·kg-1.min-1. Criterion-referenced agreement into FITNESSGRAM’s Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31–0.62; Agreement = 75.5–89.9%; F = 0.08–0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM’s Healthy Fitness Zones.