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      Assessing the External Load Associated With High-Intensity Activities Recorded During Official Basketball Games

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          Abstract

          Load monitoring in basketball is fundamental to develop training programs, maximizing performance while reducing injury risk. However, information regarding the load associated with specific activity patterns during competition is limited. This study aimed at assessing the external load associated with high-intensity activities recorded during official basketball games, with respect to different (1) activity patterns, (2) playing positions, and (3) activities performed with or without ball. Eleven male basketball players (six backcourt, five frontcourt, age: 20.5 ± 1.1 years, stature: 191.5 ± 8.7 cm, body mass: 86.5 ± 11.3 kg; experience: 8.5 ± 2.4 years) competing in the Lithuanian third division were recruited for this study. Three in-season games were assessed via time-motion analysis and microsensors. Specifically, the high-intensity activities including sprints, high-intensity specific movements (HSM) and jumps were identified and subsequently the external load [PlayerLoad™ (PL) and PlayerLoad™/min (PL/min)] of each activity was determined. Linear mixed models were used to examine differences in PL, PL/min and mean duration between activity pattern, playing positions and activities performed with or without ball. Results revealed PL was lower in jumps compared to sprints [ p < 0.001, effect size (ES) = 0.68] and HSMs (p < 0.001, ES = 0.58), while PL/min was greater in sprints compared to jumps ( p = 0.023, ES = 0.22). Jumps displayed shorter duration compared to sprints ( p < 0.001, ES = 1.10) and HSMs ( p < 0.001, ES = 0.81), with HSMs lasting longer than sprints ( p = 0.002, ES = 0.17). Jumps duration was longer in backcourt than frontcourt players ( p < 0.001, ES = 0.33). When considering activity patterns combined, PL ( p < 0.001, ES = 0.28) and duration ( p < 0.001, ES = 0.43) were greater without ball. Regarding HSMs, PL/min was higher with ball ( p = 0.036, ES = 0.14), while duration was longer without ball ( p < 0.001, ES = 0.34). The current findings suggest that external load differences in high-intensity activities exist among activity patterns and between activities performed with and without ball, while no differences were found between playing positions. Practitioners should consider these differences when designing training sessions.

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          Progressive statistics for studies in sports medicine and exercise science.

          Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
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            The reliability of MinimaxX accelerometers for measuring physical activity in Australian football.

            To assess the reliability of triaxial accelerometers as a measure of physical activity in team sports. Eight accelerometers (MinimaxX 2.0, Catapult, Australia) were attached to a hydraulic universal testing machine (Instron 8501) and oscillated over two protocols (0.5 g and 3.0 g) to assess within- and between device reliability. A static assessment was also conducted. Secondly, 10 players were instrumented with two accelerometers during Australian football matches. The vector magnitude was calculated, expressed as Player load and assessed for reliability using typical error (TE) ± 90% confidence intervals (CI), and expressed as a coefficient of variation (CV%). The smallest worthwhile difference (SWD) in Player load was calculated to determine if the device was capable of detecting differences in physical activity. Laboratory: Within- (Dynamic: CV 0.91 to 1.05%; Static: CV 1.01%) and between-device (Dynamic: CV 1.02 to 1.04%; Static: CV 1.10%) reliability was acceptable across each test. Field: The between-device reliability of accelerometers during Australian football matches was also acceptable (CV 1.9%). The SWD was 5.88%. The reliability of the MinimaxX accelerometer is acceptable both within and between devices under controlled laboratory conditions, and between devices during field testing. MinimaxX accelerometers can be confidently utilized as a reliable tool to measure physical activity in team sports across multiple players and repeated bouts of activity. The noise (CV%) of Player load was lower than the signal (SWD), suggesting that accelerometers can detect changes or differences in physical activity during Australian football.
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              The physiological load imposed on basketball players during competition.

              In this study, the intensities of activity and movement patterns during men's basketball were investigated by videoing the movements and monitoring the heart rate and blood lactate responses of eight elite players during competition. The results are expressed according to 'live time', which is actual playing time, and 'total time', which includes live time as well as all stoppages in play. The mean (+/- S.D.) frequency of all activities was 997 +/- 183, with a change in movement category every 2.0 s. A mean total of 105 +/- 52 high-intensity runs (mean duration 1.7 s) was recorded for each game, resulting in one high-intensity run every 21 s during live time. Sixty percent of live time was spent engaged in low-intensity activity, while 15% was spent in high-intensity activity. The mean heart rate (HR) during live time was 169 +/- 9 beats min-1 (89 +/- 2% peak HR attained during laboratory testing); 75% of live time was spent with a HR response of greater than 85% peak HR. The mean blood lactate concentration was 6.8 +/- 2.8 mM, indicating the involvement of glycolysis in the energy demands of basketball. It is concluded that the physiological requirements of men's basketball are high, placing considerable demands on the cardiovascular and metabolic capacities of players.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                13 April 2021
                2021
                : 12
                : 668194
                Affiliations
                [1] 1Department of Biomedical Sciences for Health, University of Milan , Milan, Italy
                [2] 2Department of Coaching Science, Lithuanian Sports University , Kaunas, Lithuania
                [3] 3CS Pallacanestro Trapani SSDARL , Trapani, Italy
                [4] 4IRCCS Istituto Ortopedico Galeazzi , Milan, Italy
                [5] 5Institute of Sport Science and Innovations, Lithuanian Sports University , Kaunas, Lithuania
                Author notes

                Edited by: Donatella Di Corrado, Kore University of Enna, Italy

                Reviewed by: Filipe Manuel Clemente, Polytechnic Institute of Viana do Castelo, Portugal; Alejandro Vaquera, Universidad de León, Spain

                *Correspondence: Davide Ferioli ferio89@ 123456hotmail.it

                This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2021.668194
                8076679
                a23994f5-9ff7-4973-9c3f-aef0c35f26de
                Copyright © 2021 Pernigoni, Ferioli, Butautas, La Torre and Conte.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 February 2021
                : 15 March 2021
                Page count
                Figures: 0, Tables: 4, Equations: 0, References: 37, Pages: 8, Words: 6931
                Funding
                Funded by: Università degli Studi di Milano 10.13039/100012352
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                time-motion analysis,physical demands,playerload,inertial measurement units,accelerometers,microsensors

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