The relationship between changing physical loads and cognitive performance over the entire period of exercise is a crucial question in sports. The maintenance of quick and accurate reactions for the whole exercise period is highly important in order to be successful. It is generally well
Thomas Finkenzeller, Sabine Würth, Michael Doppelmayr and Günter Amesberger
Karin Fischer-Sonderegger, Wolfgang Taube, Martin Rumo and Markus Tschopp
The primary purpose for monitoring physical load in soccer is to provide information to practitioners to optimize physical performance and to minimize the risk of injury. 1 – 3 To estimate the physical load, different performance indicators are used. Traditional indicators usually encompass the
Stephan Dutke, Thomas Jaitner, Timo Berse and Jonathan Barenberg
Research on effects of acute physical exercise on performance in a concurrent cognitive task has generated equivocal evidence. Processing efficiency theory predicts that concurrent physical exercise can increase resource requirements for sustaining cognitive performance even when the level of performance is unaffected. This hypothesis was tested in a dual-task experiment. Sixty young adults worked on a primary auditory attention task and a secondary interval production task while cycling on a bicycle ergometer. Physical load (cycling) and cognitive load of the primary task were manipulated. Neither physical nor cognitive load affected primary task performance, but both factors interacted on secondary task performance. Sustaining primary task performance under increased physical and/or cognitive load increased resource consumption as indicated by decreased secondary task performance. Results demonstrated that physical exercise effects on cognition might be underestimated when only single task performance is the focus.
Liam Anderson, Patrick Orme, Rocco Di Michele, Graeme L. Close, Jordan Milsom, Ryland Morgans, Barry Drust and James P. Morton
To quantify the accumulative training and match load during an annual season in English Premier League soccer players classified as starters (n = 8, started ≥60% of games), fringe players (n = 7, started 30–60% of games) and nonstarters (n = 4, started <30% of games).
Players were monitored during all training sessions and games completed in the 2013–14 season with load quantified using global positioning system and Prozone technology, respectively.
When including both training and matches, total duration of activity (10,678 ± 916, 9955 ± 947, 10,136 ± 847 min; P = .50) and distance covered (816.2 ± 92.5, 733.8 ± 99.4, 691.2 ± 71.5 km; P = .16) were not different between starters, fringe players, and nonstarters, respectively. However, starters completed more (all P < .01) distance running at 14.4–19.8 km/h (91.8 ± 16.3 vs 58.0 ± 3.9 km; effect size [ES] = 2.5), high-speed running at 19.9–25.1 km/h (35.0 ± 8.2 vs 18.6 ± 4.3 km; ES = 2.3), and sprinting at >25.2 km/h (11.2 ± 4.2 vs 2.9 ± 1.2 km; ES = 2.3) than nonstarters. In addition, starters also completed more sprinting (P < .01, ES = 2.0) than fringe players, who accumulated 4.5 ± 1.8 km. Such differences in total high-intensity physical work done were reflective of differences in actual game time between playing groups as opposed to differences in high-intensity loading patterns during training sessions.
Unlike total seasonal volume of training (ie, total distance and duration), seasonal high-intensity loading patterns are dependent on players’ match starting status, thereby having potential implications for training program design.
Liam Anderson, Graeme L. Close, Ryland Morgans, Catherine Hambly, John Roger Speakman, Barry Drust and James P. Morton
Quantification of Daily and Accumulatively Weekly Load An overview of the individual daily training and match load and the accumulative weekly load is presented in Table 1 . Table 1 An Overview of the Absolute and Accumulative Training and Match External Physical Loads of the Goalkeeper During the 7-Day Data
Ryland Morgans, Rocco Di Michele and Barry Drust
being associated with the highest physical load (in terms of both volume and intensity). 2 Longitudinal studies carried out on professional teams suggest that longer individual match playing time completed across the season favors the improvement and/or maintenance of physical capacities relevant to
Emmanuel Jacobs, Ann Hallemans, Jan Gielen, Luc Van den Dries, Annouk Van Moorsel, Jonas Rutgeerts and Nathalie A. Roussel
, 2008 ; Krasnow, Wilmerding, Stecyk, Wyon, & Koutedakis, 2011 ; Reynolds, Leduc, Kahnert, & Ludewig, 2014 ). A narrative review, about biomechanical evaluations of 14 typical dance movements, emphasized the high physical loading during dance performances ( Krasnow et al., 2011 ). For example, dancers
Paul G. Montgomery, David B. Pyne and Clare L. Minahan
To characterize the physical and physiological responses during different basketball practice drills and games.
Male basketball players (n = 11; 19.1 ± 2.1 y, 1.91 ± 0.09 m, 87.9 ± 15.1 kg; mean ± SD) completed offensive and defensive practice drills, half court 5on5 scrimmage play, and competitive games. Heart rate, VO2 and triaxial accelerometer data (physical demand) were normalized for individual participation time. Data were log-transformed and differences between drills and games standardized for interpretation of magnitudes and reported with the effect size (ES) statistic.
There was no substantial difference in the physical or physiological variables between offensive and defensive drills; physical load (9.5%; 90% confidence limits ±45); mean heart rate (-2.4%; ±4.2); peak heart rate (-0.9%; ±3.4); and VO2 (–5.7%; ±9.1). Physical load was moderately greater in game play compared with a 5on5 scrimmage (85.2%; ±40.5); with a higher mean heart rate (12.4%; ±5.4). The oxygen demand for live play was substantially larger than 5on5 (30.6%; ±15.6).
Defensive and offensive drills during basketball practice have similar physiological responses and physical demand. Live play is substantially more demanding than a 5on5 scrimmage in both physical and physiological attributes. Accelerometers and predicted oxygen cost from heart rate monitoring systems are useful for differentiating the practice and competition demands of basketball.
Gerold Sattlecker, Michael Buchecker, Christoph Gressenbauer, Erich Müller and Stefan J. Lindinger
To identify biomechanical predictors that distinguish between high- and low-score athletes in biathlon shooting and to determine the relationships among these variables in field testing.
Twenty-two biathletes (8 female, 14 male) from the World Cup, the European Cup, and a federal youth squad each fired 3 clips of 5 shots in prone and standing shooting positions without physical load, followed by 2 respective series in both disciplines during a simulated 12.5-km pursuit race on roller skis. Biomechanical variables describing triggering, rifle force in the back shoulder, and body and rifle sway were calculated over the last 0.5 second before firing. For computed linear discriminant analyses, subjects were divided into high- and low-level performers based on mean scores for each condition separately. In addition, correlations among all biomechanical factors were calculated.
Regarding prone shooting, shoulder force in the rest condition and vertical rifle sway in the race simulation were shown to be main discriminators. Several body- and rifle-sway variables were found to be predictors in standing rest shooting. Body sway across the shooting line discriminated the groups in the standing race situation tendentially. Thus, the main performance predictors changed due to fatigue. Correlations between triggering and rifle sway, shoulder force and rifle sway, and body sway and rifle sway were discovered.
Referring to the current results, athletes are recommended to focus on vertical rifle sway in prone position and on body sway across the shooting line during standing shooting when fatigued.
James J. Malone, Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G.P. Frencken and Michel S. Brink
The purpose of this investigation was to (1) quantify the training load practices of a professional soccer goalkeeper and (2) investigate the relationship between the training load observed and the subsequent self-reported wellness response. One male goalkeeper playing for a team in the top league of the Netherlands participated in this case study. Training load data were collected across a full season using a global positioning system device and session-RPE (rating of perceived exertion). Data were assessed in relation to the number of days to a match (MD− and MD+). In addition, self-reported wellness response was assessed using a questionnaire. Duration, total distance, average speed, PlayerLoad™, and load (derived from session-RPE) were highest on MD. The lowest values for duration, total distance, and PlayerLoad™ were observed on MD−1 and MD+1. Total wellness scores were highest on MD and MD−3 and were lowest on MD+1 and MD−4. Small to moderate correlations between training load measures (duration, total distance covered, high deceleration efforts, and load) and the self-reported wellness response scores were found. This exploratory case study provides novel data about the physical load undertaken by a goalkeeper during 1 competitive season. The data suggest that there are small to moderate relationships between training load indicators and self-reported wellness response. This weak relation indicates that the association is not meaningful. This may be due to the lack of position-specific training load parameters that practitioners can currently measure in the applied context.