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Darren J. Burgess

Research describing load-monitoring techniques for team sport is plentiful. Much of this research is conducted retrospectively and typically involves recreational or semielite teams. Load-monitoring research conducted on professional team sports is largely observational. Challenges exist for the practitioner in implementing peer-reviewed research into the applied setting. These challenges include match scheduling, player adherence, manager/coach buy-in, sport traditions, and staff availability. External-load monitoring often attracts questions surrounding technology reliability and validity, while internal-load monitoring makes some assumptions about player adherence, as well as having some uncertainty around the impact these measures have on player performance This commentary outlines examples of load-monitoring research, discusses the issues associated with the application of this research in an elite team-sport setting, and suggests practical adjustments to the existing research where necessary.

Open access

Martin Buchheit and Ben Michael Simpson

With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).

Open access

Avish P. Sharma, Philo U. Saunders, Laura A. Garvican-Lewis, Brad Clark, Jamie Stanley, Eileen Y. Robertson and Kevin G. Thompson

Purpose:

To determine the effect of training at 2100-m natural altitude on running speed (RS) during training sessions over a range of intensities relevant to middle-distance running performance.

Methods:

In an observational study, 19 elite middle-distance runners (mean ± SD age 25 ± 5 y, VO2max, 71 ± 5 mL · kg–1 · min–1) completed either 4–6 wk of sea-level training (CON, n = 7) or a 4- to 5-wk natural altitude-training camp living at 2100 m and training at 1400–2700 m (ALT, n = 12) after a period of sea-level training. Each training session was recorded on a GPS watch, and athletes also provided a score for session rating of perceived exertion (sRPE). Training sessions were grouped according to duration and intensity. RS (km/h) and sRPE from matched training sessions completed at sea level and 2100 m were compared within ALT, with sessions completed at sea level in CON describing normal variation.

Results:

In ALT, RS was reduced at altitude compared with sea level, with the greatest decrements observed during threshold- and VO2max-intensity sessions (5.8% and 3.6%, respectively). Velocity of low-intensity and race-pace sessions completed at a lower altitude (1400 m) and/or with additional recovery was maintained in ALT, though at a significantly greater sRPE (P = .04 and .05, respectively). There was no change in velocity or sRPE at any intensity in CON.

Conclusion:

RS in elite middle-distance athletes is adversely affected at 2100-m natural altitude, with levels of impairment dependent on the intensity of training. Maintenance of RS at certain intensities while training at altitude can result in a higher perceived exertion.

Open access

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.

Open access

Andrew Murray

While historically adolescents were removed from their parents to prepare to become warriors, this process repeats itself in modern times but with the outcome being athletic performance. This review considers the process of developing athletes and managing load against the backdrop of differing approaches of conserving and maximizing the talent available. It acknowledges the typical training “dose” that adolescent athletes receive across a number of sports and the typical “response” when it is excessive or not managed appropriately. It also examines the best approaches to quantifying load and injury risk, acknowledging the relative strengths and weaknesses of subjective and objective approaches. Making evidence-based decisions is emphasized, while the appropriate monitoring techniques are determined by both the sporting context and individual situation. Ultimately a systematic approach to training-load monitoring is recommended for adolescent athletes to both maximize their athletic development and allow an opportunity for learning, reflection, and enhancement of performance knowledge of coaches and practitioners.

Open access

William A. Sands, Ashley A. Kavanaugh, Steven R. Murray, Jeni R. McNeal and Monèm Jemni

Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in “big data” analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.