The purpose of this 2-part commentary series is† to explain why we believe our ability to control injury risk by manipulating training load (TL) in its current state is an illusion and why the foundations of this illusion are weak and unreliable. In part 1, we introduce the training process framework and contextualize the role of TL monitoring in the injury-prevention paradigm. In part 2, we describe the conceptual and methodologic pitfalls of previous authors who associated TL and injury in ways that limited their suitability for the derivation of practical recommendations. The first important step in the training process is developing the training program: the practitioner develops a strategy based on available evidence, professional knowledge, and experience. For decades, exercise strategies have been based on the fundamental training principles of overload and progression. Training-load monitoring allows the practitioner to determine whether athletes have completed training as planned and how they have coped with the physical stress. Training load and its associated metrics cannot provide a quantitative indication of whether particular load progressions will increase or decrease the injury risk, given the nature of previous studies (descriptive and at best predictive) and their methodologic weaknesses. The overreliance on TL has moved the attention away from the multifactorial nature of injury and the roles of other important contextual factors. We argue that no evidence supports the quantitative use of TL data to manipulate future training with the purpose of preventing injury. Therefore, determining “how much is too much” and how to properly manipulate and progress TL are currently subjective decisions based on generic training principles and our experience of adjusting training according to an individual athlete's response. Our message to practitioners is to stop seeking overly simplistic solutions to complex problems and instead embrace the risks and uncertainty inherent in the training process and injury prevention.
The online athlete management system for assessing s-RPE was shown to be a valid indicator of internal training load and can be used in elite sport.
Background The original subsequent injury categorisation (SIC-1.0) model aimed to classify relationships between chronological injury sequences to provide insight into the complexity and causation of subsequent injury occurrence. An updated model has recently been published. Comparison of the data coded according to the original and revised subsequent injury categorisation (SIC-1.0 and SIC-2.0) models has yet been formally compared. Methods Medical attention injury data was prospectively collected for 42 elite water polo players over an 8 month surveillance period. The SIC-1.0 and SIC-2.0 models were retrospectively applied to the injury data. The injury categorisation from the two models was compared using descriptive statistics. Results Seventy-four injuries were sustained by the 42 players (median = 2, range = 0–5), of which 32 injuries (43.2%) occurred subsequent to a previous injury. The majority of subsequent injuries were coded as occurring at a different site and being of a different nature, while also being considered clinically unrelated to the previous injury (SIC-1.0 category 10 = 57.9%; SIC-2.0 clinical category 16 = 54.4%). Application of the SIC-2.0 model resulted in a greater distribution of category allocation compared to the SIC-1.0 model that reflects a greater precision in the SIC-2.0 model. Conclusions Subsequent injury categorisation of sport injury data can be undertaken using either the original (SIC-1.0) or the revised (SIC-2.0) model to obtain similar results. However, the SIC-2.0 model offers the ability to identify a larger number of mutually exclusive categories, while not relying on clinical adjudication for category allocation. The increased precision of SIC-2.0 is advantageous for clinical application and consideration of injury relationships. Electronic supplementary material The online version of this article (10.1186/s40621-019-0183-1) contains supplementary material, which is available to authorized users.
Background: In high-performance sport, athlete performance health encompasses a state of optimal physical, mental, and social wellbeing related to an athlete’s sporting success. The aim of this study was to identify the priority areas for achieving athlete performance health in Australia’s high-performance sport system (HPSS). Methods: Participants across five socioecological levels of Australia’s HPSS were invited to contribute to this study. Concept mapping, a mixed-methods approach incorporating qualitative and quantitative data collection, was used. Participants brainstormed ideas for what athlete performance health requires, sorted the ideas into groups based on similar meaning and rated the importance, and ease of achieving each idea on a scale from 1 (not important/easiest to overcome) to 5 (extremely important/hardest to overcome). Results: Forty-nine participants generated 97 unique statements that were grouped into 12 clusters following multidimensional scaling and hierarchical cluster analysis. The three clusters with highest mean importance rating were (mean importance rating (1–5), mean ease of overcoming (1–5)): ‘Behavioral competency’ (4.37, 2.30); ‘Collaboration and teamwork’ (4.19, 2.65); ‘Valuing athlete wellbeing’ (4.17, 2.77). The 12 clusters were grouped into five overarching domains: Domain one—Performance health culture; Domain two—Integrated strategy; Domain three—Operational effectiveness; Domain four—Skilled people; Domain five—Leadership. Conclusion: A diverse sample of key stakeholders from Australia’s HPSS identified five overarching domains that contribute to athlete performance health. The themes that need to be addressed in a strategy to achieve athlete performance health in Australia’s HPSS are ‘Leadership’, ‘Skilled people’, ‘Performance health culture’, ‘Operational effectiveness’, and ‘Integrated strategy’.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.