game-related physical demands of the sport, NBA athletes also travel and compete in 4 different time zones across the continental United States and Canada (with several teams also traveling to Mexico and Europe each season) to play approximately half of their games in the home cities of opposing teams
Blake D. McLean, Donald Strack, Jennifer Russell and Aaron J. Coutts
Adam Douglas, Michael A. Rotondi, Joseph Baker, Veronica K. Jamnik and Alison K. Macpherson
, Gabbett TJ . Physical demands of training and competition in collegiate netball players . J Strength Cond Res . 2014 ; 28 ( 10 ): 2732 – 2737 . PubMed ID: 24983848 doi:10.1519/JSC.0000000000000486 24983848 10.1519/JSC.0000000000000486 11. Hulin BT , Gabbett TJ , Johnston RD , Jenkins DG
Hugh H.K. Fullagar, Robert McCunn and Andrew Murray
load is becoming commonplace in AF (personal communication), presently there are limited studies which have quantified the physical demands of AF gameplay. Wellman and colleagues 14 monitored 33 DI players during 12 regular season games. The authors found significant differences between offensive and
Sam Coad, Bon Gray, George Wehbe and Christopher McLellan
To examine the response or pre- and postmatch salivary immunoglobulin A concentration ([s-IgA]) to Australian Football League (AFL) match play and investigate the acute and cumulative influence of player workload and postmatch [s-IgA] after repeated participation in AFL match play.
Eleven elite AFL athletes (21.8 ± 2.4 y, 186.9 ± 7.9 cm, 87.4 ± 7.5 kg) were monitored throughout 3 matches during the preseason that were separated by 7 d. Saliva samples were collected across each AFL match at 24 h and 1 h prematch and 1, 12, 36, and 60 h postmatch to determine [s-IgA]. Global positioning systems (GPS) with integrated triaxial accelerometers were used to determine total player workload during match play. Hypothesis testing was conducted for time-dependent changes in [s-IgA] and player load using a repeated-measures ANOVA.
Player load during match 3 (1266 ± 124.6 AU) was significantly (P < .01) greater than in match 1 (1096 ± 115.1 AU) and match 2 (1082 ± 90.4 AU). Across match 3, [s-IgA] was significantly (P < .01) suppressed at 2 postmatch measures (12 and 36 h) compared with prematch measures (24 and 1 h), which coincided with significantly (P < .01) elevated player load.
The findings indicate that an increase in player load during AFL preseason match play resulted in compromised postmatch mucosal immunological function. Longitudinal assessment of AFL-match player load and mucosal immunological function across the first 60 h of recovery may augment monitoring and preparedness strategies for athletes.
Sam Coad, Bon Gray and Christopher McLellan
To assess match-to-match variations in salivary immunoglobulin A concentration ([s-IgA]) measured at 36 h postmatch throughout an Australian Football League (AFL) premiership season and to assess the trends between 36-h-postmatch [s-IgA] and match-play exercise workloads throughout the same season.
Eighteen elite male AFL athletes (24 ± 4.2 y, 187.0 ± 7.1 cm, 87.0 ± 7.6 kg) were monitored on a weekly basis to determine total match-play exercise workloads and 36-h-postmatch [s-IgA] throughout 16 consecutive matches in an AFL premiership season. Global positioning systems (GPS) with integrated triaxial accelerometers were used to measure exercise workloads (PlayerLoad) during each AFL match. A linear mixed-model analyses was conducted for time-dependent changes in [s-IgA] and player load.
A significant main effect was found for longitudinal postmatch [s-IgA] data (F 16,240 = 3.78, P < .01) and PlayerLoad data (F 16,66 = 1.98, P = .03). For all matches after and including match 7, a substantial suppression trend in [s-IgA] 36-h-postmatch values was found compared with preseason baseline [s-IgA].
The current study provides novel data regarding longitudinal trends in 36-h-postmatch [s-IgA] for AFL athletes. Results demonstrate that weekly in-season AFL match-play exercise workloads may result in delayed mucosal immunological recovery beyond 36 h postmatch. The inclusion of individual athlete-monitoring strategies of [s-IgA] may be advantageous in the detection of compromised postmatch mucosal immunological function for AFL athletes.
Billy T. Hulin, Tim J. Gabbett, Simon Kearney and Alex Corvo
To quantify activity profiles in approximately 5-min periods to determine if the intensity of rugby league match play changes after the most intense period of play and to determine if the intensity of activity during predefined periods of match play differ between successful and less-successful teams playing at an elite standard.
Movement was recorded using a MinimaxX global positioning system (GPS) unit sampling at 10 Hz during 25 rugby league matches, equating to 200 GPS files. Data for each half of match play were separated into 8 equal periods. These periods represented the most intense phase of match play (peak period), the period after the most intense phase of match play (subsequent period), and the average demands of all other periods in a match (mean period). Two rugby league teams were split into a high-success and a low-success group based on their success rates throughout their season.
Compared with their less-successful counterparts, adjustables and hit-up forwards from the high-success team covered less total distance (P < .01) and less high-intensity-running distance (P < .01) and were involved in a greater number of collisions (P < .01) during the mean period of match play.
Although a greater number of collisions during match play is linked with a greater rate of success, greater amounts of high-intensity running and total distance are not related to competitive success in elite rugby league. These results suggest that technical and tactical differences, rather than activity profiles, may be the distinguishing factor between successful and less-successful rugby league teams.
Brian Cunniffe, Carissa Fallan, Adora Yau, Gethin H. Evans and Marco Cardinale
Little data exists on drinking behavior, sweat loss, and exercise intensity across a competitive handball tournament in elite female athletes. Heart rate (HR), fluid balance and sweat electrolyte content were assessed on 17 international players across a 6-day tournament involving 5 games and 2 training sessions played indoors (23 ± 2 °C, 30 ± 2% relative humidity). Active play (effective) mean HR was 155 ± 14 bpm (80 ± 7.5% HRmax) with the majority of time (64%) spent exercising at intensities >80% HRmax. Mean (SD) sweat rates during games were 1.02 ± 0.07 L · h-1 and on 56% of occasions fluid intake matched or exceeded sweat loss. A significant relationship was observed between estimated sweat loss and fluid intake during exercise (r 2 = .121, p = .001). Mean sweat sodium concentration was 38 ± 10 mmol · L-1, with significant associations observed between player sweat rates and time spent exercising at intensities >90% HRmax (r 2 = .181, p = .001). Fluid and electrolyte loss appear to be work rate dependent in elite female handball players, whom appear well capable of replacing fluids lost within a tournament environment. Due to large between-athlete variations, a targeted approach may be warranted for certain players only.
Chelsea L. Oxendale, Craig Twist, Matthew Daniels and Jamie Highton
While exercise-induced muscle damage (EIMD) after rugby league match play has been well documented, the specific match actions that contribute to EIMD are unclear. Accordingly, the purpose of this study was to investigate the positional demands of elite rugby league matches and examine their relationship with subsequent EIMD.
Twenty-eight performances (from 17 participants) were captured using 10-Hz global positioning systems over 4 competitive matches. Upper- and lower-body neuromuscular fatigue, creatine kinase (CK), and perceived muscle soreness were assessed 24 h before and at 12, 36, and 60 h after matches.
High-intensity running was moderately higher in backs (6.6 ± 2.6 m/min) than in forwards (5.1 ± 1.6 m/min), whereas total collisions were moderately lower (31.1 ± 13.1 vs 54.1 ± 37.0). Duration (r = .90, CI: .77–.96) and total (r = .86, CI: .70–.95) and high-intensity distance covered (r = .76, CI: .51–.91) were associated (P < .05) with increased CK concentration postmatch. Total collisions and repeated high-intensity efforts were associated (P < .05) with large decrements in upper-body neuromuscular performance (r = –.48, CI: –.74 to .02; r = –.49, CI: –.77 to .05, respectively), muscle soreness (r = –.68, CI: –.87 to –.10, r = –.66, CI: –.89 to .21, respectively), and CK concentration (r = .67, CI: .42–.85; r = .73, CI: .51–.87, respectively). All EIMD markers returned to baseline within 60 h.
Match duration, high-intensity running, and collisions were associated with variations in EIMD markers, suggesting that recovery is dependent on individual match demands.
Luke J. Boyd, Kevin Ball and Robert J. Aughey
To describe the external load of Australian football matches and training using accelerometers.
Nineteen elite and 21 subelite Australian footballers wore accelerometers during matches and training. Accelerometer data were expressed in 2 ways: from all 3 axes (player load; PL) and from all axes when velocity was below 2 m/s (PLSLOW). Differences were determined between 4 playing positions (midfielders, nomadics, deeps, and ruckmen), 2 playing levels (elite and subelite), and matches and training using percentage change and effect size with 90% confidence intervals.
In the elite group, midfielders recorded higher PL than nomadics and deeps did (8.8%, 0.59 ± 0.24; 34.2%, 1.83 ± 0.39 respectively), and ruckmen were higher than deeps (37.2%, 1.27 ± 0.51). Elite midfielders, nomadics, and ruckmen recorded higher PLSLOW than deeps (13.5%, 0.65 ± 0.37; 11.7%, 0.55 ± 0.36; and 19.5%, 0.83 ± 0.50, respectively). Subelite midfielders were higher than nomadics, deeps, and ruckmen (14.0%, 1.08 ± 0.30; 31.7%, 2.61 ± 0.42; and 19.9%, 0.81 ± 0.55, respectively), and nomadics and ruckmen were higher than deeps for PL (20.6%, 1.45 ± 0.38; and 17.4%, 0.57 ± 0.55, respectively). Elite midfielders, nomadics, and ruckmen recorded higher PL (7.8%, 0.59 ± 0.29; 12.9%, 0.89 ± 0.25; and 18.0%, 0.67 ± 0.59, respectively) and PLSLOW (9.4%, 0.52 ± 0.30; 11.3%, 0.68 ± 0.25; and 14.1%, 0.84 ± 0.61, respectively) than subelite players. Small-sided games recorded the highest PL and PLSLOW and were the only training drill to equal or exceed the load from matches across positions and playing levels.
PL differed between positions, with midfielders the highest, and between playing levels, with elite higher. Differences between matches and training were also evident, with PL from small-sided games equivalent to or higher than matches.
Jared A. Bailey, Paul B. Gastin, Luke Mackey and Dan B. Dwyer
Most previous investigations of player load in netball have used subjective methodologies, with few using objective methodologies. While all studies report differences in player activities or total load between playing positions, it is unclear how the differences in player activity explain differences in positional load.
To objectively quantify the load associated with typical activities for all positions in elite netball.
The player load of all playing positions in an elite netball team was measured during matches using wearable accelerometers. Video recordings of the matches were also analyzed to record the start time and duration of 13 commonly reported netball activities. The load associated with each activity was determined by time-aligning both data sets (load and activity).
Off-ball guarding produced the highest player load per instance, while jogging produced the greatest player load per match. Nonlocomotor activities contributed least to total match load for attacking positions (goal shooter [GS], goal attack [GA], and wing attack [WA]) and most for defending positions (goalkeeper [GK], goal defense [GD], and wing defense [WD]). Specifically, centers (Cs) produced the greatest jogging load, WA and WD accumulated the greatest running load, and GS and WA accumulated the greatest shuffling load. WD and Cs accumulated the greatest guarding load, while WD and GK accumulated the greatest off-ball guarding load.
All positions exhibited different contributions from locomotor and nonlocomotor activities toward total match load. In addition, the same activity can have different contributions toward total match load, depending on the position. This has implications for future design and implementation of position-specific training programs.