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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
Crotty, Nora May; Boland, Marie; Mahony, Nick; Donne, Bernard; Fleming, Neil – Measurement in Physical Education and Exercise Science, 2021
This study evaluated the reliability of the portable Lactate Pro 2 analyzer (LP2), and its validity compared to a laboratory-based analyzer, YSI 1500 Sport (YSI). Blood samples (n = 258) were collected during 44 graded incremental rowing tests, with data from 17 tests used to quantify load, heart rate, and oxygen consumption at lactate threshold.…
Descriptors: Reliability, Validity, Measurement Equipment, Exercise
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
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
Power, Allan; Faught, Brent E.; Przysucha, Eryk; McPherson, Moira; Montelpare, William – Measurement in Physical Education and Exercise Science, 2012
In this study the authors examine the test-retest reliability and concurrent validity of the Repeat Ice Skating Test (RIST). This was an on-ice field anaerobic test that measured average peak power and was validated with 3 anaerobic lab tests: (a) vertical jump, (b) the Margaria-Kalamen stair test, and (c) the Wingate Anaerobic Test. The…
Descriptors: Team Sports, Test Validity, Foreign Countries, Validity
Lee, C. Matthew; Gorelick, Mark – Measurement in Physical Education and Exercise Science, 2011
The purpose of this study was to examine the validity of the Smarthealth watch (Salutron, Inc., Fremont, California, USA), a heart rate monitor that includes a wristwatch without an accompanying chest strap. Twenty-five individuals participated in 3-min periods of standing, 2.0 mph walking, 3.5 mph walking, 4.5 mph jogging, and 6.0 mph running.…
Descriptors: Metabolism, Intervals, Physical Activities, Validity
