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Jeremiah J. Peiffer, Chris R. Abbiss, Eric C. Haakonssen and Paolo Menaspà

variable nature of competition. 1 , 6 A greater understanding of this variability is important to more accurately quantify training and racing load, thereby informing the development of more efficient and effective training and racing strategies. Studies examining the physical demands of cycling have

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Jordan D. Philpott, Chris Donnelly, Ian H. Walshe, Elizabeth E. MacKinley, James Dick, Stuart D.R. Galloway, Kevin D. Tipton and Oliver C. Witard

). Reliability of the visual analog scale for measurement of acute pain . Academic Emergency Medicine, 8 ( 12 ), 1153 – 1157 . PubMed doi:10.1111/j.1553-2712.2001.tb01132.x 10.1111/j.1553-2712.2001.tb01132.x Bloomfield , J. , Polman , R. , & O’Donoghue , P. ( 2007 ). Physical demands of different

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F. Marcello Iaia, Rampinini Ermanno and Jens Bangsbo

This article reviews the major physiological and performance effects of aerobic high-intensity and speed-endurance training in football, and provides insight on implementation of individual game-related physical training. Analysis and physiological measurements have revealed that modern football is highly energetically demanding, and the ability to perform repeated high-intensity work is of importance for the players. Furthermore, the most successful teams perform more high-intensity activities during a game when in possession of the ball. Hence, footballers need a high fitness level to cope with the physical demands of the game. Studies on football players have shown that 8 to 12 wk of aerobic high-intensity running training (>85% HRmax) leads to VO2max enhancement (5% to 11%), increased running economy (3% to 7%), and lower blood lactate accumulation during submaximal exercise, as well as improvements in the yo-yo intermittent recovery (YYIR) test performance (13%). Similar adaptations are observed when performing aerobic high-intensity training with small-sided games. Speed-endurance training has a positive effect on football-specific endurance, as shown by the marked improvements in the YYIR test (22% to 28%) and the ability to perform repeated sprints (~2%). In conclusion, both aerobic and speed-endurance training can be used during the season to improve high-intensity intermittent exercise performance. The type and amount of training should be game related and specific to the technical, tactical, and physical demands imposed on each player.

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Paul G. Montgomery, David B. Pyne and Clare L. Minahan

Purpose:

To characterize the physical and physiological responses during different basketball practice drills and games.

Methods:

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.

Results:

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).

Conclusions:

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.

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Sebastian Altfeld, Paul Schaffran, Jens Kleinert and Michael Kellmann

Paid coaches have to regularly deal with a range of potential stressors in the workplace. These stressors may include emotional and physical demands caused by the complex nature of coaching work. Many coaches have developed useful strategies to cope with these demands. Nevertheless, unexpected changes within the dynamic environment in which they typically operate (e.g., injury, public scrutiny, social media), problems with members of the board or management, continuous negative performance results, or personal factors may challenge the adequacy of coaches’ coping mechanisms. This inability to cope with these stresses can lead to a state of chronic stress. If that state manifests permanently, it can result in a state of emotional exhaustion, ultimately leading to coach burnout. The aim of this article is to define the burnout phenomenon and to provide a clear description of the triggering factors. Furthermore, ideas are presented to guide how coaches can protect themselves and how officials (club or association management) can reduce coaches’ burnout.

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Chris R. Abbiss, Paolo Menaspà, Vincent Villerius and David T. Martin

A number of laboratory-based performance tests have been designed to mimic the dynamic and stochastic nature of road cycling. However, the distribution of power output and thus physical demands of high-intensity surges performed to establish a breakaway during actual competitive road cycling are unclear. Review of data from professional road-cycling events has indicated that numerous short-duration (5–15 s), high-intensity (~9.5–14 W/kg) surges are typically observed in the 5–10 min before athletes’ establishing a breakaway (ie, riding away from a group of cyclists). After this initial high-intensity effort, power output declined but remained high (~450–500 W) for a further 30 s to 5 min, depending on race dynamics (ie, the response of the chase group). Due to the significant influence competitors have on pacing strategies, it is difficult for laboratory-based performance tests to precisely replicate this aspect of mass-start competitive road cycling. Further research examining the distribution of power output during competitive road racing is needed to refine laboratory-based simulated stochastic performance trials and better understand the factors important to the success of a breakaway.

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Dean J. McNamara, Tim J. Gabbett, Geraldine Naughton, Patrick Farhart and Paul Chapman

Purpose:

This study investigated key fatigue and workload variables of cricket fast bowlers and nonfast bowlers during a 7-wk physical-preparation period and 10-d intensified competition period.

Methods:

Twenty-six elite junior cricketers (mean ± SD age 17.7 ± 1.1 y) were classified as fast bowlers (n = 9) or nonfast bowlers (n = 17). Individual workloads were measured via global positioning system technology, and neuromuscular function (countermovement jump [relative power and flight time]), endocrine (salivary testosterone and cortisol concentrations), and perceptual well-being (soreness, mood, stress, sleep quality, and fatigue) markers were recorded.

Results:

Fast bowlers performed greater competition total distance (median [interquartile range] 7049 [3962] m vs 5062 [3694] m), including greater distances at low and high speeds, and more accelerations (40 [32] vs 19 [21]) and had a higher player load (912 [481] arbitrary units vs 697 [424] arbitrary units) than nonfast bowlers. Cortisol concentrations were higher in the physical-preparation (mean ± 90% confidence intervals, % likelihood; d = –0.88 ± 0.39, 100%) and competition phases (d = –0.39 ± 0.30, 85%), and testosterone concentrations, lower (d = 0.56 ± 0.29, 98%), in the competition phase in fast bowlers. Perceptual well-being was poorer in nonfast bowlers during competition only (d = 0.36 ± 0.22, 88%). Differences in neuromuscular function between groups were unclear during physical preparation and competition.

Conclusions:

These findings demonstrate differences in the physical demands of cricket fast bowlers and nonfast bowlers and suggest that these external workloads differentially affect the neuromuscular, endocrine, and perceptual fatigue responses of these players.

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Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie and Ben J. Dascombe

Purpose:

To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.

Methods:

TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.

Results:

Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.

Conclusions:

Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

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Jason C. Tee, Mike I. Lambert and Yoga Coopoo

Purpose:

In team sports, fatigue is manifested by a self-regulated decrease in movement distance and intensity. There is currently limited information on the effect of fatigue on movement patterns in rugby union match play, particularly for players in different position groups (backs vs forwards). This study investigated the effect of different match periods on movement patterns of professional rugby union players.

Methods:

Global positioning system (GPS) data were collected from 46 professional match participations to determine temporal effects on movement patterns.

Results:

Total relative distance (m/min) was decreased in the 2nd half for both forwards (–13%, ±8%, ES = very likely large) and backs (–9%, ±7%, ES = very likely large). A larger reduction in high-intensity-running distance in the 2nd half was observed for forwards (–27%, ±16%, ES = very likely medium) than for backs (–10%, ±15%; ES = unclear). Similar patterns were observed for sprint (>6 m/s) frequency (forwards –29%, ±29%, ES = likely small vs backs –13% ±18%, ES = possibly small) and acceleration (>2.75 m/s2) frequency (forwards –27%, ±24%, ES = likely medium vs backs –5%, ±46%, ES = unclear). Analysis of 1st- and 2nd-half quartiles revealed differing pacing strategies for forwards and backs. Forwards display a “slow-positive” pacing strategy, while the pacing strategy of backs is “flat.”

Conclusions:

Forwards suffered progressively greater performance decrements over the course of the match, while backs were able to maintain performance intensity. These findings reflect differing physical demands, notably contact and running loads, of players in different positions.

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Carl Petersen, David B. Pyne, Marc R. Portus, Stuart Karppinen and Brian Dawson

Purpose:

The time-motion characteristics and the within-athlete variability in movement patterns were quantified for the same male fast bowler playing One Day International (ODI) cricket matches (n = 12).

Methods:

A number of different time motion characteristics were monitored using a portable 5-Hz global positioning system (GPS) unit (Catapult, Melbourne, Australia).

Results:

The bowler’s mean workload per ODI was 8 ± 2 overs (mean ± SD). He covered a total distance of 15.9 ± 2.5 km per game; 12 ± 3% or 1.9 ± 0.2 km was striding (0.8 ± 0.2 km) or sprinting (1.1 ± 0.2 km), whereas 10.9 ± 2.1 km was spent walking. One high-intensity (running, striding, or sprinting) repetition (HIR) occurred every 68 ± 12 s, and the average duration of a HI effort was 2.7 ± 0.1 s. The player also completed 66 ± 11 sprints per game; mean sprint distance was 18 ± 3 m and maximum sprinting speed 8.3 ± 0.9 m·s−1.

Conclusions:

The movement patterns of this fast bowler were a combination of highly intermittent activities of variable intensity on the base of ~16 km per game. This information provides insight for conditioning coaches to determine the physical demands and to adapt the training and recovery processes of ODI fast bowlers.