What Contributes to Athlete Performance Health? A Concept Mapping Approach.

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'.

Training Load and Its Role in Injury Prevention, Part I: Back to the Future.

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.

Comparison of subsequent injury categorisation (SIC) models and their application in a sporting population.

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.

Validity of the Online Athlete Management System to Assess Training Load.

PURPOSE: To validate the quantification of training load (session rating of perceived exertion [s-RPE]) in an Australian Olympic squad (women's water polo), assessed with the use of a modified RPE scale collected via a newly developed online system (athlete management system). METHODS: Sixteen elite women water polo players (age = 26 [3] y, height = 1.78 [0.05] m, and body mass = 75.5 [7.1] kg) participated in the study. Thirty training sessions were monitored for a total of 303 individual sessions. Heart rate was recorded during training sessions using continuous heart-rate telemetry. Participants were asked to rate the intensity of the training sessions on the athlete management system RPE scale, using an online application within 30 min of completion of the sessions. Individual relationships between s-RPE and both Banister training impulse (TRIMP) and Edwards' method were analyzed. RESULTS: Individual correlations with s-RPE ranged between r = .51 and .79 (Banister TRIMP) and r = .54 and .83 (Edwards' method). The percentages of moderate and large correlation were 81% and 19% between s-RPE method and Banister TRIMP, and 56% and 44% between s-RPE and Edwards' method. CONCLUSIONS: 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.