Futureproofing triathlon: expert suggestions to improve health and performance in triathletes
Bibliographic record
Abstract
BACKGROUND: Given the multi-modal nature of triathlon (swimming, cycling, running), training for a triathlon event has numerous potential health benefits including physical fitness. However, triathletes also have a high prevalence of health issues including overuse injury, illness, fatigue, and burnout. To address the ongoing prevalence of health issues, roundtable discussions were organized at the International Triathlon Union Science of Triathlon 2017 conference to develop strategic objectives deemed necessary to "futureproof triathlon". Futureproofing as a concept serves to design new approaches and ways of thinking to reduce consequences in the future. In this case, the futureproof process aimed to develop key recommendations for triathlon. METHODS: This qualitative study had 22 participants including athletes, coaches, practitioners, academics, and policy makers who participated in roundtable discussions at the Science of Triathlon conference. Seven of these participants completed follow-up semi-structured interviews on the same topics. The data collected from the roundtable discussions and the semi-structured interviews was analyzed using thematic analysis. RESULTS: Five main themes were produced: "Critical appraisal and application of knowledge"; "Integrated approaches to developing, disseminating, and using research and expertise"; "Appropriate development and use of measures for monitoring training and recovery"; "Knowing your athletes and adopting holistic approaches to athlete/person-development", and; "Challenging accepted cultural and sporting norms". Participants indicated the need to reduce the knowledge gap between research and practice as well as a more collaborative approach to triathlon research development amongst coaches/practitioners and academics. It was stated that current monitoring tools require more research to determine which are most useful to informed decision making for coaches/practitioners. It was cautioned that data driven assessments should be used judiciously and be athlete centered. Triathlon as a sport should also have a greater focus on healthy participation and development of youth athletes. CONCLUSIONS: A series of applied implications were developed based on these five themes as guiding principles for how to futureproof triathlon. Additionally, roundtable and interview participants who held varying positions and opinions within the sport of triathlon agreed that the unique challenge of training for and competing in a triathlon should not be forgotten in the futureproofing of the sport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".