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Profiling Professional Rugby Union Activity After Peak Match Periods

Samuel T. Howe, Robert J. Aughey, William G. Hopkins, and Andrew M. Stewart

The aim of this investigation was to quantify professional rugby union player activity profiles after the most intense (peak) passages of matches. Movement data were collected from 30 elite and 30 subelite professional rugby union athletes across respective competitive seasons. Accelerometer-derived PlayerLoad and global navigation satellite system–derived measures of mean speed and metabolic power were analyzed using a rolling-average method to identify the most intense 5- to 600-second passages (ie, worst-case scenarios) within matches. Player activity profiles immediately post their peak 5- to 600-second match intensity were identified using 5 epoch duration-matched intervals. Mean speed, metabolic power, and PlayerLoad declined sharply (∼29%–86%) after the most intense 5 to 600 seconds of matches. Following the most intense periods of rugby matches, exercise intensity declined below the average match-half intensity 81% of the time and seldom returned to or exceeded it, likely due to a host of individual physical and physiological characteristics, transient and/or accumulative fatigue, contextual factors, and pacing strategies. Typically, player exercise intensities after the most intense passages of matches were similar between match halves, positional groups, and levels of rugby competition. Accurate identification of the peak exercise intensities of matches and movement thereafter using novel methodologies has improved the limited understanding of professional rugby union player activity profiles following the worst-case scenarios of matches. Findings of the present study may inform match-representative training prescription, monitoring, and tactical match decisions (eg, substitutions and positional changes).

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Identification of Sensitive Measures of Recovery After External Load From Football Match Play

Amber E. Rowell, Robert J. Aughey, Will G. Hopkins, Andrew M. Stewart, and Stuart J. Cormack

Objective measures of recovery from football match play could be useful for assessing athletes’ readiness to train, if sensitive to preceding match load.

Purpose:

To identify the sensitivity of countermovement-jump (CMJ) performance and concentration of salivary testosterone and cortisol relative to elite football match load.

Methods:

CMJ performance and salivary hormones were measured in 18 elite football players before (27, 1 h) and after (0.5, 18, 42, 66, 90 h) 3 consecutive matches. Match load was determined via accelerometer-derived PlayerLoad and divided into tertiles. Sensitivity of CMJ performance and hormone concentrations to match load was quantified with t statistics and magnitude-based inferences (change in mean as % ± 90% confidence interval) derived with a linear mixed model.

Results:

Jump height was reduced in medium and high load at 0.5 h (10% ± 7% and 16% ± 8%) and 18 h (7% ± 4% and 9% ± 5%) postmatch. There was a 12% ± 7% reduction in ratio of flight time to contraction time (FT:CT) in high load at 0.5 h post, with reductions in medium and high load at 18 h. Reductions in FT:CT persisted at later postmatch time points than changes in jump height. Increased cortisol (range 55–165%) and testosterone (range 17–20%) were observed in all match loads at 0.5 h post, with individual variability thereafter.

Conclusions:

Measures of CMJ performance and hormonal concentrations were sensitive to levels of A League football match load. Although jump height was reduced immediately postmatch, FT:CT provided a more sensitive measure of recovery. Football match play induces an acute hormonal response with substantial individual variability thereafter.

Open access

Effects of Training Load and Leg Dominance on Achilles and Patellar Tendon Structure

Alireza Esmaeili, Andrew M. Stewart, William G. Hopkins, George P. Elias, and Robert J. Aughey

Purpose:

Detrimental changes in tendon structure increase the risk of tendinopathies. The aim of this study was to investigate the influence of individual internal and external training loads and leg dominance on changes in the Achilles and patellar tendon structure.

Methods:

The internal structure of the Achilles and patellar tendons of both limbs of 26 elite Australian footballers was assessed using ultrasound tissue characterization at the beginning and the end of an 18-wk preseason. Linear-regression analysis was used to estimate the effects of training load on changes in the proportion of aligned and intact tendon bundles for each side. Standardization and magnitude-based inferences were used to interpret the findings.

Results:

Possibly to very likely small increases in the proportion of aligned and intact tendon bundles occurred in the dominant Achilles (initial value 81.1%; change, ±90% confidence limits 1.6%, ±1.0%), nondominant Achilles (80.8%; 0.9%, ±1.0%), dominant patellar (75.8%; 1.5%, ±1.5%), and nondominant patellar (76.8%; 2.7%, ±1.4%) tendons. Measures of training load had inconsistent effects on changes in tendon structure; eg, there were possibly to likely small positive effects on the structure of the nondominant Achilles tendon, likely small negative effects on the dominant Achilles tendon, and predominantly no clear effects on the patellar tendons.

Conclusion:

The small and inconsistent effects of training load are indicative of the role of recovery between tendon-overloading (training) sessions and the multivariate nature of the tendon response to load, with leg dominance a possible influencing factor.

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Factors Affecting Match Outcome in Elite Australian Football: A 14-Year Analysis

Brendan H. Lazarus, William G. Hopkins, Andrew M. Stewart, and Robert J. Aughey

Effects of fixture and team characteristics on match outcome in elite Australian football were quantified using data accessed at AFLtables.com for 5109 matches for seasons 2000 to 2013. Aspects of each match included number of days’ break between matches (≤7 d vs ≥8 d), location (home vs away), travel status (travel vs no travel), and differences between opposing teams’ mean age, body mass, and height (expressed as quintiles). A logistic-regression version of the generalized mixed linear model estimated each effect, which was assessed with magnitude-based inference using 1 extra win or loss in every 10 matches as the smallest important change. For every 10 matches played, the effects were days’ break, 0.1 ± 0.3 (90% CL) wins; playing away, 1.5 ± 0.6 losses; traveling, 0.7 ± 0.6 losses; and being in the oldest, heaviest, or shortest, quintile, 1.9 ± 0.4, 1.3 ± 0.4, and 0.4 ± 0.4 wins, respectively. The effects of age and body-mass difference were not reduced substantially when adjusted for each other. All effects were clear, mostly at the 99% level. The effects of playing away, travel, and age difference were not unexpected, but the trivial effect of days’ break and the advantage of a heavier team will challenge current notions about balancing training with recovery and about team selection.

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Modeling Professional Rugby Union Peak Intensity–Duration Relationships Using a Power Law

Samuel T. Howe, Robert J. Aughey, William G. Hopkins, and Andrew M. Stewart

Purpose: Can power law models accurately predict the peak intensities of rugby competition as a function of time? Methods: Match movement data were collected from 30 elite and 30 subelite rugby union athletes across competitive seasons, using wearable Global Navigation Satellite Systems and accelerometers. Each athlete’s peak rolling mean value of each measure (mean speed, metabolic power, and PlayerLoad) for 8 durations between 5 seconds and 10 minutes was predicted by the duration with 4 power law (log–log) models, one for forwards and backs in each half of a typical match. Results: The log of peak exercise intensity and exercise duration (5–600 s) displayed strong linear relationships (R 2 = .967–.993) across all 3 measures. Rugby backs had greater predicted intensities for shorter durations than forwards, but their intensities declined at a steeper rate as duration increased. Random prediction errors for mean speed, metabolic power, and PlayerLoad were 5% to 6%, 7% to 9%, and 8% to 10% (moderate to large), respectively, for elite players. Systematic prediction errors across the range of durations were trivial to small for elite players, underestimating intensities for shorter (5–10 s) and longer (300–600 s) durations by 2% to 4% and overestimating 20- to 120-second intensities by 2% to 3%. Random and systematic errors were slightly greater for subelites compared to elites, with ranges of 4% to 12% and 2% to 5%, respectively. Conclusions: Peak intensities of professional rugby union matches can be predicted with adequate precision (trivial to small errors) for prescribing training drills of a given duration, irrespective of playing position, match half, level of competition, or measure of exercise intensity. However, practitioners should be aware of the substantial (moderate to large) prediction errors at the level of the individual player.

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Peak Running Intensity of International Rugby: Implications for Training Prescription

Jace A. Delaney, Heidi R. Thornton, John F. Pryor, Andrew M. Stewart, Ben J. Dascombe, and Grant M. Duthie

Purpose:

To quantify the duration and position-specific peak running intensities of international rugby union for the prescription and monitoring of specific training methodologies.

Methods:

Global positioning systems (GPS) were used to assess the activity profile of 67 elite-level rugby union players from 2 nations across 33 international matches. A moving-average approach was used to identify the peak relative distance (m/min), average acceleration/deceleration (AveAcc; m/s2), and average metabolic power (Pmet) for a range of durations (1–10 min). Differences between positions and durations were described using a magnitude-based network.

Results:

Peak running intensity increased as the length of the moving average decreased. There were likely small to moderate increases in relative distance and AveAcc for outside backs, halfbacks, and loose forwards compared with the tight 5 group across all moving-average durations (effect size [ES] = 0.27–1.00). Pmet demands were at least likely greater for outside backs and halfbacks than for the tight 5 (ES = 0.86–0.99). Halfbacks demonstrated the greatest relative distance and Pmet outputs but were similar to outside backs and loose forwards in AveAcc demands.

Conclusions:

The current study has presented a framework to describe the peak running intensities achieved during international rugby competition by position, which are considerably higher than previously reported whole-period averages. These data provide further knowledge of the peak activity profiles of international rugby competition, and this information can be used to assist coaches and practitioners in adequately preparing athletes for the most demanding periods of play.

Open access

Status and Trends of Physical Activity Surveillance, Policy, and Research in 164 Countries: Findings From the Global Observatory for Physical Activity—GoPA! 2015 and 2020 Surveys

Andrea Ramírez Varela, Pedro C. Hallal, Juliana Mejía Grueso, Željko Pedišić, Deborah Salvo, Anita Nguyen, Bojana Klepac, Adrian Bauman, Katja Siefken, Erica Hinckson, Adewale L. Oyeyemi, Justin Richards, Elena Daniela Salih Khidir, Shigeru Inoue, Shiho Amagasa, Alejandra Jauregui, Marcelo Cozzensa da Silva, I-Min Lee, Melody Ding, Harold W. Kohl III, Ulf Ekelund, Gregory W. Heath, Kenneth E. Powell, Charlie Foster, Aamir Raoof Memon, Abdoulaye Doumbia, Abdul Roof Rather, Abdur Razzaque, Adama Diouf, Adriano Akira Hino, Albertino Damasceno, Alem Deksisa Abebe, Alex Antonio Florindo, Alice Mannocci, Altyn Aringazina, Andrea Backović Juričan, Andrea Poffet, Andrew Decelis, Angela Carlin, Angelica Enescu, Angélica María Ochoa Avilés, Anna Kontsevaya, Annamaria Somhegyi, Anne Vuillemin, Asmaa El Hamdouchi, Asse Amangoua Théodore, Bojan Masanovic, Brigid M. Lynch, Catalina Medina, Cecilia del Campo, Chalchisa Abdeta, Changa Moreways, Chathuranga Ranasinghe, Christina Howitt, Christine Cameron, Danijel Jurakić, David Martinez-Gomez, Dawn Tladi, Debrework Tesfaye Diro, Deepti Adlakha, Dušan Mitić, Duško Bjelica, Elżbieta Biernat, Enock M. Chisati, Estelle Victoria Lambert, Ester Cerin, Eun-Young Lee, Eva-Maria Riso, Felicia Cañete Villalba, Felix Assah, Franjo Lovrić, Gerardo A. Araya-Vargas, Giuseppe La Torre, Gloria Isabel Niño Cruz, Gul Baltaci, Haleama Al Sabbah, Hanna Nalecz, Hilde Liisa Nashandi, Hyuntae Park, Inés Revuelta-Sánchez, Jackline Jema Nusurupia, Jaime Leppe Zamora, Jaroslava Kopcakova, Javier Brazo-Sayavera, Jean-Michel Oppert, Jinlei Nie, John C. Spence, John Stewart Bradley, Jorge Mota, Josef Mitáš, Junshi Chen, Kamilah S Hylton, Karel Fromel, Karen Milton, Katja Borodulin, Keita Amadou Moustapha, Kevin Martinez-Folgar, Lara Nasreddine, Lars Breum Christiansen, Laurent Malisoux, Leapetswe Malete, Lorelie C. Grepo-Jalao, Luciana Zaranza Monteiro, Lyutha K. Al Subhi, Maja Dakskobler, Majed Alnaji, Margarita Claramunt Garro, Maria Hagströmer, Marie H. Murphy, Matthew  Mclaughlin, Mercedes Rivera-Morales, Mickey Scheinowitz, Mimoza Shkodra, Monika Piątkowska, Moushumi Chaudhury, Naif Ziyad Alrashdi, Nanette Mutrie, Niamh Murphy, Norhayati Haji Ahmad, Nour A. Obeidat, Nubia Yaneth Ruiz Gómez, Nucharapon Liangruenrom, Oscar Díaz Arnesto, Oscar Flores-Flores, Oscar Incarbone, Oyun Chimeddamba, Pascal Bovet, Pedro Magalhães, Pekka Jousilahti, Piyawat Katewongsa, Rafael Alexander Leandro Gómez, Rawan Awni Shihab, Reginald Ocansey, Réka Veress, Richard Marine, Rolando Carrizales-Ramos, Saad Younis Saeed, Said El-Ashker, Samuel Green, Sandra Kasoma, Santiago Beretervide, Se-Sergio Baldew, Selby Nichols, Selina Khoo, Seyed Ali Hosseini, Shifalika Goenka, Shima Gholamalishahi, Soewarta Kosen, Sofie Compernolle, Stefan Paul Enescu, Stevo Popovic, Susan Paudel, Susana Andrade, Sylvia Titze, Tamu Davidson, Theogene Dusingizimana, Thomas E. Dorner, Tracy L. Kolbe-Alexander, Tran Thanh Huong, Vanphanom Sychareun, Vera Jarevska-Simovska, Viliami Kulikefu Puloka, Vincent Onywera, Wanda Wendel-Vos, Yannis Dionyssiotis, and Michael Pratt

Background: Physical activity (PA) surveillance, policy, and research efforts need to be periodically appraised to gain insight into national and global capacities for PA promotion. The aim of this paper was to assess the status and trends in PA surveillance, policy, and research in 164 countries. Methods: We used data from the Global Observatory for Physical Activity (GoPA!) 2015 and 2020 surveys. Comprehensive searches were performed for each country to determine the level of development of their PA surveillance, policy, and research, and the findings were verified by the GoPA! Country Contacts. Trends were analyzed based on the data available for both survey years. Results: The global 5-year progress in all 3 indicators was modest, with most countries either improving or staying at the same level. PA surveillance, policy, and research improved or remained at a high level in 48.1%, 40.6%, and 42.1% of the countries, respectively. PA surveillance, policy, and research scores decreased or remained at a low level in 8.3%, 15.8%, and 28.6% of the countries, respectively. The highest capacity for PA promotion was found in Europe, the lowest in Africa and low- and lower-middle-income countries. Although a large percentage of the world’s population benefit from at least some PA policy, surveillance, and research efforts in their countries, 49.6 million people are without PA surveillance, 629.4 million people are without PA policy, and 108.7 million live in countries without any PA research output. A total of 6.3 billion people or 88.2% of the world’s population live in countries where PA promotion capacity should be significantly improved. Conclusion: Despite PA is essential for health, there are large inequalities between countries and world regions in their capacity to promote PA. Coordinated efforts are needed to reduce the inequalities and improve the global capacity for PA promotion.