Search Results

You are looking at 81 - 90 of 339 items for :

Clear All
Restricted access

Nick B. Murray, Georgia M. Black, Rod J. Whiteley, Peter Gahan, Michael H. Cole, Andy Utting and Tim J. Gabbett

Purpose:

Throwing loads are known to be closely related to injury risk. However, for logistic reasons, typically only pitchers have their throws counted, and then only during innings. Accordingly, all other throws made are not counted, so estimates of throws made by players may be inaccurately recorded and underreported. A potential solution to this is the use of wearable microtechnology to automatically detect, quantify, and report pitch counts in baseball. This study investigated the accuracy of detection of baseball pitching and throwing in both practice and competition using a commercially available wearable microtechnology unit.

Methods:

Seventeen elite youth baseball players (mean ± SD age 16.5 ± 0.8 y, height 184.1 ± 5.5 cm, mass 78.3 ± 7.7 kg) participated in this study. Participants performed pitching, fielding, and throwing during practice and competition while wearing a microtechnology unit. Sensitivity and specificity of a pitching and throwing algorithm were determined by comparing automatic measures (ie, microtechnology unit) with direct measures (ie, manually recorded pitching counts).

Results:

The pitching and throwing algorithm was sensitive during both practice (100%) and competition (100%). Specificity was poorer during both practice (79.8%) and competition (74.4%).

Conclusions:

These findings demonstrate that the microtechnology unit is sensitive to detect pitching and throwing events, but further development of the pitching algorithm is required to accurately and consistently quantify throwing loads using microtechnology.

Restricted access

Liam Anderson, Patrick Orme, Robert J. Naughton, Graeme L. Close, Jordan Milsom, David Rydings, Andy O’Boyle, Rocco Di Michele, Julien Louis, Catherine Hambly, John Roger Speakman, Ryland Morgans, Barry Drust and James P. Morton

In an attempt to better identify and inform the energy requirements of elite soccer players, we quantified the energy expenditure (EE) of players from the English Premier League (n = 6) via the doubly labeled water method (DLW) over a 7-day in-season period. Energy intake (EI) was also assessed using food diaries, supported by the remote food photographic method and 24 hr recalls. The 7-day period consisted of 5 training days (TD) and 2 match days (MD). Although mean daily EI (3186 ± 367 kcals) was not different from (p > .05) daily EE (3566 ± 585 kcals), EI was greater (p < .05) on MD (3789 ± 532 kcal; 61.1 ± 11.4 kcal.kg-1 LBM) compared with TD (2956 ± 374 kcal; 45.2 ± 9.3 kcal.kg-1 LBM, respectively). Differences in EI were reflective of greater (p < .05) daily CHO intake on MD (6.4 ± 2.2 g.kg-1) compared with TD (4.2 ± 1.4 g.kg-1). Exogenous CHO intake was also different (p < .01) during training sessions (3.1 ± 4.4 g.h-1) versus matches (32.3 ± 21.9 g.h-1). In contrast, daily protein (205 ± 30 g.kg-1, p = .29) and fat intake (101 ± 20 g, p = .16) did not display any evidence of daily periodization as opposed to g.kg-1, Although players readily achieve current guidelines for daily protein and fat intake, data suggest that CHO intake on the day before and in recovery from match play was not in accordance with guidelines to promote muscle glycogen storage.

Restricted access

Matthew D. Portas, Jamie A. Harley, Christopher A. Barnes and Christopher J. Rush

Purpose:

The study aimed to analyze the validity and reliability of commercially available nondifferential Global Positioning System (NdGPS) devices for measures of total distance during linear, multidirectional and soccer-specific motion at 1-Hz and 5-Hz sampling frequencies.

Methods:

Linear (32 trials), multidirectional (192 trials) and soccer-specific courses (40 trials) were created to test the validity (mean ± 90% confidence intervals), reliability (mean ± 90% confidence intervals) and bias (mean ± 90% confidence intervals) of the NdGPS devices against measured distance. Standard error of the estimate established validity, reliability was determined using typical error and percentage bias was established.

Results:

The 1-Hz and 5-Hz data ranged from 1.3% ± 0.76 to 3.1% ± 1.37 for validity and 2.03% ± 1.31 to 5.31% ± 1.2 for reliability for measures of linear and soccer-specific motion. For multidirectional activity, data ranged from 1.8% ± 0.8 to 6.88% ± 2.99 for validity and from 3.08% ± 1.34 to 7.71% ± 1.65 for reliability. The 1-Hz underestimated some complex courses by up to 11%.

Conclusions:

1-Hz and 5-Hz NdGPS could be used to quantify distance in soccer and similar field-based team sports. Both 1-Hz and 5-Hz have a threshold beyond which reliability is compromised. 1-Hz also underestimates distance and is less valid in more complex courses.

Restricted access

Liam Anderson, Patrick Orme, Rocco Di Michele, Graeme L. Close, Jordan Milsom, Ryland Morgans, Barry Drust and James P. Morton

Purpose:

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).

Methods

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.

Results:

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.

Conclusions

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.

Restricted access

Thomas Kempton and Aaron J. Coutts

Purpose:

To describe the physical and technical demands of rugby league 9s (RL9s) match play for positional groups.

Methods:

Global positioning system data were collected during 4 games from 16 players from a team competing in the Auckland RL9s tournament. Players were classified into positional groups (pivots, outside backs, and forwards). Absolute and relative physical-performance data were classified as total high-speed running (HSR; >14.4 km/h), very-high-speed running (VHSR; >19.0 km/h), and sprint (>23.0 km/h) distances. Technical-performance data were obtained from a commercial statistics provider. Activity cycles were coded by an experienced video analyst.

Results:

Forwards (1088 m, 264 m) most likely completed less overall and high-speed distances than pivots (1529 m, 371 m) and outside backs (1328 m, 312 m). The number of sprint efforts likely varied between positions, although differences in accelerations were unclear. There were no clear differences in relative total (115.6−121.3 m/min) and HSR (27.8−29.8 m/min) intensities, but forwards likely performed less VHSR (7.7 m/min) and sprint distance (1.3 m/min) per minute than other positions (10.2−11.8 m/min, 3.7−4.8 m/min). The average activity and recovery cycle lengths were ~50 and ~27 s, respectively. The average longest activity cycle was ~133 s, while the average minimum recovery time was ~5 s. Technical involvements including tackles missed, runs, tackles received, total collisions, errors, off-loads, line breaks, and involvements differed between positions.

Conclusions:

Positional differences exist for both physical and technical measures, and preparation for RL9s play should incorporate these differences.

Restricted access

Nicola Furlan, Mark Waldron, Mark Osborne and Adrian J. Gray

Purpose:

To assess the ecological validity of the Rugby Sevens Simulation Protocol (R7SP) and to evaluate its interday reliability.

Methods:

Ten male participants (20 ± 2 y, 74 ± 11 kg) completed 2 trials of the R7SP, separated by 7 d. The R7SP comprised typical running and collision activities, based on data recorded during international rugby sevens match play. Heart rate (HR) was monitored continuously during the R7SP, and the participants’ movements were recorded through a 20-Hz global positioning system unit. Blood lactate and rating of perceived exertion were collected before and immediately after the 1st and 2nd halves of the R7SP.

Results:

The average activity profile was 117 ± 5 m/min, of which 27 ± 2 m/min was covered at high speed, with a calculated energetic demand of 1037 ± 581 J/kg, of which ~40% was expended at a rate above 19 W/kg. Mean HR was 88% ± 4% of maximal HR. Participants spent ~45% ± 27% of time above 90% of maximal HR (t >90%HRmax). There were no significant differences between trials, except for lactate between the halves of the R7SP. The majority of the measured variables demonstrated a between-trials coefficient of variation (CV%) lower than 5%. Blood lactate measurements (14–20% CV) and t >90%HRmax (26% CV) were less reliable variables. In most cases, the calculated moderate worthwhile change was higher than the CV%.

Conclusions:

The R7SP replicates the activity profile and HR responses of rugby sevens match play. It is a reliable simulation protocol that can be used in a research environment to detect systematic worthwhile changes in selected performance variables.

Restricted access

Paolo Menaspà, Franco M. Impellizzeri, Eric C. Haakonssen, David T. Martin and Chris R. Abbiss

Purpose:

To determine the consistency of commercially available devices used for measuring elevation gain in outdoor activities and sports.

Methods:

Two separate observational validation studies were conducted. Garmin (Forerunner 310XT, Edge 500, Edge 750, and Edge 800; with and without elevation correction) and SRM (Power Control 7) devices were used to measure total elevation gain (TEG) over a 15.7-km mountain climb performed on 6 separate occasions (6 devices; study 1) and during a 138-km cycling event (164 devices; study 2).

Results:

TEG was significantly different between the Garmin and SRM devices (P < .05). The between-devices variability in TEG was lower when measured with the SRM than with the Garmin devices (study 1: 0.2% and 1.5%, respectively). The use of the Garmin elevation-correction option resulted in a 5–10% increase in the TEG.

Conclusions:

While measurements of TEG were relatively consistent within each brand, the measurements differed between the SRM and Garmin devices by as much as 3%. Caution should be taken when comparing elevation-gain data recorded with different settings or with devices of different brands.

Restricted access

Matthias W. Hoppe, Christian Baumgart and Jürgen Freiwald

Purpose:

To investigate differences in running activities between adolescent and adult tennis players during match play. Differences between winning and losing players within each age group were also examined.

Methods:

Forty well-trained male players (20 adolescents, 13 ± 1 y; 20 adults, 25 ± 4 y) played a simulated singles match against an opponent of similar age and ability. Running activities were assessed using portable devices that sampled global positioning system (10 Hz) and inertial-sensor (accelerometer, gyroscope, and magnetometer; 100 Hz) data. Recorded data were examined in terms of velocity, acceleration, deceleration, metabolic power, PlayerLoad, and number of accelerations toward the net and the forehand and backhand corners.

Results:

Adult players spent more time at high velocity (≥4 m/s2), acceleration (≥4 m/s2), deceleration (≤–4 m/s2), and metabolic power (≥20 W/kg) (P ≤ .009, ES = 0.9–1.5) and performed more accelerations (≥2 m/s2) toward the backhand corner (P < .001, ES = 2.6–2.7). No differences between adolescent winning and losing players were evident overall (P ≥ .198, ES = 0.0–0.6). Adult winning players performed more accelerations (2 to <4 m/s2) toward the forehand corner (P = .026, ES = 1.2), whereas adult losing players completed more accelerations (≥2 m/s2) toward the backhand corner (P ≤ .042, ES = 0.9).

Conclusions:

This study shows that differences in running activities between adolescent and adult tennis players exist in high-intensity measures during simulated match play. Furthermore, differences between adolescent and adult players, and also between adult winning and losing players, are present in terms of movement directions. Our findings may be helpful for coaches to design different training drills for both age groups of players.

Restricted access

Ryu Nagahara, Jean-Benoit Morin and Masaaki Koido

Purpose:

To assess soccer-specific impairment of mechanical properties in accelerated sprinting and its relation with activity profiles during an actual match.

Methods:

Thirteen male field players completed 4 sprint measurements, wherein running speed was obtained using a laser distance-measurement system, before and after the 2 halves of 2 soccer matches. Macroscopic mechanical properties (theoretical maximal horizontal force [F0], maximal horizontal sprinting power [Pmax], and theoretical maximal sprinting velocity [V0]) during the 35-m sprint acceleration were calculated from speed–time data. Players’ activity profiles during the matches were collected using global positioning system units.

Results:

After the match, although F0 and Pmax did not significantly change, V0 was reduced (P = .038), and the magnitude of this reduction correlated with distance (positive) and number (negative) of high-speed running, number of running (negative), and other low-intensity activity distance (negative) during the match. Moreover, Pmax decreased immediately before the second half (P = .014).

Conclusions:

The results suggest that soccer-specific fatigue probably impairs players’ maximal velocity capabilities more than their maximal horizontal force-production abilities at initial acceleration. Furthermore, long-distance running, especially at high speed, during the match may induce relatively large impairment of maximal velocity capabilities. In addition, the capability of producing maximal horizontal power during sprinting is presumably impaired during halftime of a soccer match with passive recovery. These findings could be useful for players and coaches aiming to train effectively to maintain sprinting performance throughout a soccer match when planning a training program.

Restricted access

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

Purpose:

Bowling workload is linked to injury risk in cricket fast bowlers. This study investigated the validity of microtechnology in the automated detection of bowling counts and events, including run-up distance and velocity, in cricket fast bowlers.

Method:

Twelve highly skilled fast bowlers (mean ± SD age 23.5 ± 3.7 y) performed a series of bowling, throwing, and fielding activities in an outdoor environment during training and competition while wearing a microtechnology unit (MinimaxX). Sensitivity and specificity of a bowling-detection algorithm were determined by comparing the outputs from the device with manually recorded bowling counts. Run-up distance and run-up velocity were measured and compared with microtechnology outputs.

Results:

No significant differences were observed between direct measures of bowling and nonbowling events and true positive and true negative events recorded by the MinimaxX unit (P = .34, r = .99). The bowling-detection algorithm was shown to be sensitive in both training (99.0%) and competition (99.5%). Specificity was 98.1% during training and 74.0% during competition. Run-up distance was accurately recorded by the unit, with a percentage bias of 0.8% (r = .90). The final 10-m (–8.9%, r = .88) and 5-m (–7.3%, r = .90) run-up velocities were less accurate.

Conclusions:

The bowling-detection algorithm from the MinimaxX device is sensitive to detect bowling counts in both cricket training and competition. Although specificity is high during training, the number of false positive events increased during competition. Additional bowling workload measures require further development.