Pacing strategy of a wheelchair athlete in a 5x and 10x Ironman ultra triathlon: a case study
Bibliographic record
Abstract
OBJECTIVE: For disabled athletes such as wheelchair athletes, there is no knowledge about competing and pacing during a long-distance triathlon such as an Ironman triathlon. This study aimed to investigate the pacing strategy of a paraplegic wheelchair athlete competing and finishing a Quintuple Iron ultra-triathlon (i.e., five times 3.8 km swimming, 180 km handbike cycling and 42.195 km wheelchair racing in five days) and a Deca Iron ultra-triathlon (10 times the same distance in 10 days). METHODS: Data from an ultra-distance triathlon race (Swissultra) covering 5x and 10x Ironman distance were collected. Official performance data were acquired from the race organizer's website and athlete's personal information from the athlete through online interviews. The athlete is a man born in 1962, the races analysed in this study were held in the summer of 2017 (5x) and 2019 (10x). The split times for swimming, cycling and running, the overall race times for each Ironman and the lap times in cycling (handbike) and running (wheelchair) were analysed. RESULTS: The athlete finished the Quintuple Iron ultra-triathlon in an overall race time of 66:28:31 h:min:s and the Deca Iron ultra-triathlon in 137:03:20 h:min:s. He adopted an even pacing in both races in split disciplines and for overall race time. CONCLUSION: The paraplegic wheelchair athlete was able to finish both a Quintuple and a Deca Iron ultra-triathlon by adopting an even pacing in all split disciplines and for overall race time.IMPLICATIONS FOR REHABILITATIONTriathlon is a growing sport among athletes with spinal cord injury.Ultra-triathlons are ultra-endurance events and pacing is a key aspect to a successful race regardless the athlete's category.An athlete with a spinal cord injury finished a 5x and 10x Ironman ultra-triathlon applying an even pacing strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".