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Brendan T. O’ Keeffe, Ciaran MacDonncha, Kwok Ng and Alan E. Donnelly

Survey Development A survey designed specifically for the purposes of this study was developed. Survey items were also generated in part from the Teachers Uses of Fitness Tests survey developed by Keating and Silverman ( 2004 ) and a Health, Activity and Fitness Monitoring survey administered by Cale

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Mathieu Lacome, Ben Simpson, Nick Broad and Martin Buchheit

Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149 [46] GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.

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In the article O’Keefe, B., MacDonncha, C., Ng, K., and Donnelly, A. (2020) Health-related fitness monitoring practices in secondary school-based physical education programs. Journal of Teaching in Physical Education 39 (1), 59–68, https://doi.org/10.1123/jtpe.2018-0336 , an incorrect statistic

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Alireza Rabbani, Mehdi Kargarfard, Carlo Castagna, Filipe Manuel Clemente and Craig Twist

. Rabbani A , Kargarfard M , Twist C . Fitness monitoring in elite soccer players: group vs. individual analyses [published online ahead of print June 14, 2018]. J Strength Cond Res . doi:10.1519/JSC.0000000000002700 29912075 10.1519/JSC.0000000000002700 8. Cummins C , Orr R , O’Connor H

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Brendan T. O’Keeffe, Alan E. Donnelly and Ciaran MacDonncha

. 2010 ; 81 ( 3)(suppl ): S24 – 30 . PubMed ID: 21049835 doi:10.1080/02701367.2010.10599691 21049835 10.1080/02701367.2010.10599691 30. O’Keeffe BT , MacDonncha C , Ng K , Donnelly AE . Health-related fitness monitoring practices in secondary school-based physical education programs . J

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Grant R. Tomkinson, Justin J. Lang, Joel Blanchard, Luc A. Léger and Mark S. Tremblay

fitness” as an indicator in the harmonized process used to develop country report cards on the physical activity of children and youth ( 8 ). In this paper, we built the case for the 20mSRT to be widely used as a measure of CRF in a resurgence of fitness monitoring and surveillance among children and

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Dinesh John, Qu Tang, Fahd Albinali and Stephen Intille

, Sasaki, Hickey, Mavilia, & Freedson, 2014 ; Lee, Macfarlane, & Cerin, 2010 ; Sasaki, John, & Freedson, 2011 ), and the lack of uniformity among devices that are typically targeted at end-consumers (e.g., smartwatches/phones/fitness monitors), contribute to inter-device output variability. Proprietary