This study aimed to identify the minimum increment duration required to accurately assess 2 distinct lactate thresholds. A total of 21 elite rowers (12 women and 9 men) participated in this study, and each performed 8 or 9 rowing tests comprising 5 progressive incremental tests (3-, 4-, 5-, 7-, or 10-min steps) and at least three 30-min constant-intensity maximal lactate steady-state assessments. Power output (PO) at lactate threshold 1 was higher in the 3- and 4-min incremental tests. No other measures were different for lactate threshold 1. The PO at the second lactate threshold was different between most tests and was higher than the PO at maximal lactate steady state, except for the 10-min incremental test. Lactate threshold 2 oxygen consumption was higher in the 3-, 4-, and 5-min tests, but heart rate (HR) and rating of perceived exertion were not different between tests. Peak PO in the incremental tests was inversely related to the step durations (r 2 = .86, P ≤ .02). Peak oxygen consumption was higher in the shorter (≤5 min) than the longer (≥7 min) incremental tests, whereas peak HR was not different between tests. These data suggest that for the methods used in this study, incremental exercise tests with step durations ≤7 min overestimate maximal lactate steady-state exercise intensity, peak physiological values are best determined using incremental tests with step durations ≤4 min, and HR measures are not affected by step duration, and therefore, prescription of training HRs can be made using any of these tests.
Pitre C. Bourdon, Sarah M. Woolford and Jonathan D. Buckley
Pitre C. Bourdon, Marco Cardinale, Warren Gregson and N. Timothy Cable
Sebastien Racinais, Martin Buchheit, Johann Bilsborough, Pitre C. Bourdon, Justin Cordy and Aaron J. Coutts
To examine the physiological and performance responses to a heat-acclimatization camp in highly trained professional team-sport athletes.
Eighteen male Australian Rules Football players trained for 2 wk in hot ambient conditions (31–33°C, humidity 34–50%). Players performed a laboratory-based heat-response test (24-min walk + 24 min seated; 44°C), a YoYo Intermittent Recovery Level 2 Test (YoYoIR2; indoor, temperate environment, 23°C) and standardized training drills (STD; outdoor, hot environment, 32°C) at the beginning and end of the camp.
The heat-response test showed partial heat acclimatization (eg, a decrease in skin temperature, heart rate, and sweat sodium concentration, P < .05). In addition, plasma volume (PV, CO rebreathing, +2.68 [0.83; 4.53] mL/kg) and distance covered during both the YoYoIR2 (+311 [260; 361] m) and the STD (+45.6 [13.9; 77.4] m) increased postcamp (P < .01). None of the performance changes showed clear correlations with PV changes (r < .24), but the improvements in running STD distance in hot environment were correlated with changes in hematocrit during the heat-response test (r = –.52, 90%CI [–.77; –.12]). There was no clear correlation between the performance improvements in temperate and hot ambient conditions (r < .26).
Running performance in both hot and temperate environments was improved after a football training camp in hot ambient conditions that stimulated heat acclimatization. However, physiological and performance responses were highly individual, and the absence of correlations between physical-performance improvements in hot and temperate environments suggests that their physiological basis might differ.
Martin Buchheit, Hani Al Haddad, Ben M. Simpson, Dino Palazzi, Pitre C. Bourdon, Valter Di Salvo and Alberto Mendez-Villanueva
The aims of the current study were to examine the magnitude of between-GPS-models differences in commonly reported running-based measures in football, examine between-units variability, and assess the effect of software updates on these measures. Fifty identical-brand GPS units (15 SPI-proX and 35 SPIproX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 wk from 4 professional football players (N = 53 files) were also analyzed before and after 2 manufacturersupplied software updates. There were substantial differences between the different models (eg, standardized difference for the number of acceleration >4 m/s2 = 2.1; 90% confidence limits [1.4, 2.7], with 100% chance of a true difference). Between-units variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m/s2). Some GPS units measured 2–6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but 1 of the updates led to large and small decreases in the occurrence of accelerations (–1.24; –1.32, –1.15) and decelerations (–0.45; –0.48, –0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.
Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson and N. Timothy Cable
Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.