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Direct linkMerrigan, Justin J.; Stovall, J. Hannah; Stone, Jason D.; Stephenson, Mark; Finomore, Victor S.; Hagen, Joshua A. – Measurement in Physical Education and Exercise Science, 2023
Heart rate samples (n = 4500-8000) from wearables were compared to electrocardiography during a steady-state ruck (Ruck-S), maximal effort ruck (Ruck-M), submaximal cycle (Cycle), and Tabata Circuit. One device was worn at each location (wrist: Polar Grit-X, Garmin Fenix 6; chest-straps: Polar H10, Garmin HRM-Pro; armband: Polar Verity).…
Descriptors: Measurement Equipment, Exercise Physiology, Training, Metabolism
Agar-Newman, Dana J.; Tsai, Ming-Chang; Klimstra, Marc – Measurement in Physical Education and Exercise Science, 2020
To evaluate if takeoff speed can be predicted using peak-speed from a linear position transducer (LPT), 21 rowing athletes performed hexagonal-bar jumps in line with the National Team testing protocol. Predictive validity was assessed by comparing peak-speed from a LPT to the criterion measure takeoff speed collected from force plates. The…
Descriptors: Physical Activities, Athletes, Predictive Validity, Measurement Equipment
Kim, Kyungyeol; Lee, Senyung – Measurement in Physical Education and Exercise Science, 2023
The purpose of the study is to validate the 54-item Fitness Switching Costs Scale (FSCS). The FSCS consists of a third-order formative construct with four second-order formative constructs and 11 first-order reflective constructs. Although there was support for the reliability and validity of higher-order constructs, validity of the 11 first-order…
Descriptors: Physical Fitness, Measurement Techniques, Diagnostic Tests, Validity
O'Brien, Myles W.; Johns, Jarrett A.; Fowles, Jonathon R.; Kimmerly, Derek S. – Measurement in Physical Education and Exercise Science, 2020
The activPAL is a widely-used measure of sedentary time but few studies have evaluated its ability to estimate physical activity intensity. This study determined the accuracy of the algorithm used by the activPAL to predict metabolic equivalents (METs) from cadence and a curvilinear cadence-METs equation individualized for height. Thirty-six…
Descriptors: Validity, Physical Activity Level, Accuracy, Metabolism
Mahar, Matthew T.; Welk, Gregory J.; Rowe, David A. – Measurement in Physical Education and Exercise Science, 2018
Purpose: To develop models to estimate aerobic fitness (VO[subscript 2]max) from PACER performance in 10- to 18-year-old youth, with and without body mass index (BMI) as a predictor. Method: Youth (N = 280) completed the PACER and a maximal treadmill test to assess VO[subscript 2]max. Validation and cross-validation groups were randomly formed to…
Descriptors: Exercise, Physical Fitness, Preadolescents, Adolescents
Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A. – Measurement in Physical Education and Exercise Science, 2017
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a…
Descriptors: Validity, Electronic Equipment, Prediction, Physical Activity Level
Webb, Carrie; Vehrs, Pat R.; George, James D.; Hager, Ronald – Measurement in Physical Education and Exercise Science, 2014
The purpose of this study was to develop a step test with a personalized step rate and step height to predict cardiorespiratory fitness in 80 college-aged males and females using the self-reported perceived functional ability scale and data collected during the step test. Multiple linear regression analysis yielded a model (R = 0.90, SEE = 3.43…
Descriptors: Tests, Physical Fitness, College Students, Multiple Regression Analysis
