Purpose: To investigate the association between training and match loads and injury in elite junior Australian football players over 1 competitive season. Methods: Elite junior Australian football players (n = 290, age 17.7 [0.3] y, range 16–18 y) were recruited from the under-18 state league competition in Victoria to report load and injury information. One-week load (session rating of perceived exertion multiplied by duration) and all time-loss injuries were reported using an online sport-injury surveillance system. Absolute load measures (weekly sums) enabled the calculation of relative measures such as the acute:chronic workload ratio. Load measures were modeled against injury outcome (yes/no) using a generalized estimating equation approach, with a 1-wk lag for injury. Results: Low (<300 arbitrary units [au]) and high (>4650 au) 1-wk loads were associated with significantly higher risk of injury. Furthermore, low (<100 au) and high (>850 au) session loads were associated with a higher risk of injury. High strain values (>13,000) were associated with up to a 5-fold increase in the odds of injury. There was a relatively flat-line association between the acute:chronic workload ratio and injury. Conclusions: This study is the first investigation of elite junior athletes demonstrating linear and nonlinear relationships between absolute and relative load measures and injury. Coaches should focus player loads on, or at least close to, the point at which injury risk starts to increase again (2214 au for 1-wk load and 458 au for session load) and use evidence-based strategies across the week and month to help reduce the risk of injury.
Timothy J.H. Lathlean, Paul B. Gastin, Stuart V. Newstead, and Caroline F. Finch
Timothy J.H. Lathlean, Paul B. Gastin, Stuart V. Newstead, and Caroline F. Finch
Purpose: To investigate associations between load (training and competition) and wellness in elite junior Australian Football players across 1 competitive season. Methods: A prospective cohort study was conducted during the 2014 playing season in 562 players from 9 teams. Players recorded their training and match intensities according to the session-rating-of-perceived-exertion (sRPE) method. Based on sRPE player loads, a number of load variables were quantified, including cumulative load and the change in load across different periods of time (including the acute-to-chronic load ratio). Wellness was quantified using a wellness index including sleep, fatigue, soreness, stress, and mood on a Likert scale from 1 to 5. Results: Players spent an average of 85 (21) min in each match and 65 (31) min per training session. Average match loads were 637 (232) arbitrary units, and average training loads were 352 (233) arbitrary units. Over the 24 wk of the 2014 season, overall wellness had a significant linear negative association with 1-wk load (B = −0.152; 95% confidence interval, −0.261 to −0.043; P = .006) and an inverse U-curve relationship with session load (B = −0.078; 95% confidence interval, 0.143 to 0.014; P = .018). Mood, stress, and soreness were all found to have associations with load. Conclusions: This study demonstrates that load (within a session and across the week) is important in managing the wellness of elite junior Australian Football players. Quantifying loads and wellness at this level will help optimize player management and has the potential to reduce the risk of adverse events such as injury.
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.
Jacqueline Tran, Anthony J. Rice, Luana C. Main, and Paul B. Gastin
To investigate changes in physiology, performance, and training practices of elite Australian rowers over 6 mo.
Twenty-one elite rowers (14 male, 7 female) were monitored throughout 2 phases: phase 1 (specific preparation) and phase 2 (domestic competition). Incremental tests and rowing-ergometer time trials over 100, 500, 2000, and 6000 m were conducted at the start of the season, midseason, and late season. Weekly external (frequency, duration, distance rowed) and internal (T2minute method) loads are reported.
Heavyweight male rowers achieved moderate improvements in VO2max and power at VO2max. Most other changes in physiology and performance were small or unclear. External loads decreased from phase 1 to phase 2 (duration 19.3 to 18.0 h/wk, distance rowed 140 to 125 km/wk, respectively). Conversely, internal loads increased (phase 1 = 19.0 T2hours, phase 2 = 20.3 T2hours). Low-intensity training predominated (~80% of training hours at T1 and T2), and high-intensity training was greater in phase 2. Training was rowing-focused (68% of training duration), although 32% of training time was spent in nonspecific modes. The distribution of specificity was not different between phases.
Physiology and performance results were stable over the 6-mo period. Training-load patterns differed depending on the measure, highlighting the importance of monitoring both external and internal loads. The distribution of intensity was somewhat polarized, and substantial volumes of nonspecific training were undertaken. Experimental studies should investigate the effects of different distributions of intensity and specificity on rowing performance.
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.
Jad Adrian Washif, Øyvind Sandbakk, Stephen Seiler, Thomas Haugen, Abdulaziz Farooq, Ken Quarrie, Dina C. Janse van Rensburg, Isabel Krug, Evert Verhagen, Del P. Wong, Iñigo Mujika, Cristina Cortis, Monoem Haddad, Omid Ahmadian, Mahmood Al Jufaili, Ramzi A. Al-Horani, Abdulla Saeed Al-Mohannadi, Asma Aloui, Achraf Ammar, Fitim Arifi, Abdul Rashid Aziz, Mikhail Batuev, Christopher Martyn Beaven, Ralph Beneke, Arben Bici, Pallawi Bishnoi, Lone Bogwasi, Daniel Bok, Omar Boukhris, Daniel Boullosa, Nicola Bragazzi, Joao Brito, Roxana Paola Palacios Cartagena, Anis Chaouachi, Stephen S. Cheung, Hamdi Chtourou, Germina Cosma, Tadej Debevec, Matthew D. DeLang, Alexandre Dellal, Gürhan Dönmez, Tarak Driss, Juan David Peña Duque, Cristiano Eirale, Mohamed Elloumi, Carl Foster, Emerson Franchini, Andrea Fusco, Olivier Galy, Paul B. Gastin, Nicholas Gill, Olivier Girard, Cvita Gregov, Shona Halson, Omar Hammouda, Ivana Hanzlíková, Bahar Hassanmirzaei, Kim Hébert-Losier, Hussein Muñoz Helú, Tomás Herrera-Valenzuela, Florentina J. Hettinga, Louis Holtzhausen, Olivier Hue, Antonio Dello Iacono, Johanna K. Ihalainen, Carl James, Saju Joseph, Karim Kamoun, Mehdi Khaled, Karim Khalladi, Kwang Joon Kim, Lian-Yee Kok, Lewis MacMillan, Leonardo Jose Mataruna-Dos-Santos, Ryo Matsunaga, Shpresa Memishi, Grégoire P. Millet, Imen Moussa-Chamari, Danladi Ibrahim Musa, Hoang Minh Thuan Nguyen, Pantelis T. Nikolaidis, Adam Owen, Johnny Padulo, Jeffrey Cabayan Pagaduan, Nirmala Panagodage Perera, Jorge Pérez-Gómez, Lervasen Pillay, Arporn Popa, Avishkar Pudasaini, Alizera Rabbani, Tandiyo Rahayu, Mohamed Romdhani, Paul Salamh, Abu-Sufian Sarkar, Andy Schillinger, Heny Setyawati, Navina Shrestha, Fatona Suraya, Montassar Tabben, Khaled Trabelsi, Axel Urhausen, Maarit Valtonen, Johanna Weber, Rodney Whiteley, Adel Zrane, Yacine Zerguini, Piotr Zmijewski, Helmi Ben Saad, David B. Pyne, Lee Taylor, and Karim Chamari
Purpose: To investigate differences in athletes’ knowledge, beliefs, and training practices during COVID-19 lockdowns with reference to sport classification and sex. This work extends an initial descriptive evaluation focusing on athlete classification. Methods: Athletes (12,526; 66% male; 142 countries) completed an online survey (May–July 2020) assessing knowledge, beliefs, and practices toward training. Sports were classified as team sports (45%), endurance (20%), power/technical (10%), combat (9%), aquatic (6%), recreational (4%), racquet (3%), precision (2%), parasports (1%), and others (1%). Further analysis by sex was performed. Results: During lockdown, athletes practiced body-weight-based exercises routinely (67% females and 64% males), ranging from 50% (precision) to 78% (parasports). More sport-specific technical skills were performed in combat, parasports, and precision (∼50%) than other sports (∼35%). Most athletes (range: 50% [parasports] to 75% [endurance]) performed cardiorespiratory training (trivial sex differences). Compared to prelockdown, perceived training intensity was reduced by 29% to 41%, depending on sport (largest decline: ∼38% in team sports, unaffected by sex). Some athletes (range: 7%–49%) maintained their training intensity for strength, endurance, speed, plyometric, change-of-direction, and technical training. Athletes who previously trained ≥5 sessions per week reduced their volume (range: 18%–28%) during lockdown. The proportion of athletes (81%) training ≥60 min/session reduced by 31% to 43% during lockdown. Males and females had comparable moderate levels of training knowledge (56% vs 58%) and beliefs/attitudes (54% vs 56%). Conclusions: Changes in athletes’ training practices were sport-specific, with few or no sex differences. Team-based sports were generally more susceptible to changes than individual sports. Policy makers should provide athletes with specific training arrangements and educational resources to facilitate remote and/or home-based training during lockdown-type events.