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Animals : an open access journal from MDPI2024; 14(9); doi: 10.3390/ani14091342

Cardiovascular Fitness and Stride Acceleration in Race-Pace Workouts for the Prediction of Performance in Thoroughbreds.

Abstract: In-training racehorse physiological data can be leveraged to further explore race-day performance prediction. To date, no large retrospective, observational study has analysed whether in-training speed and heart rate recovery can predict racehorse success. Speed (categorised as 'slow' to 'fast' according to the time taken to cover the last 600 m from a virtual finish line) and heart rate recovery (from gallop to 1 min after exercise) of flat racehorses (n = 485) of varying age, sex and type according to distance (e.g., sprinter, miler and stayer) were obtained using a fitness tracker from a single racing yard in Australia. Race-pace training sessions on turf comprised 'fast gallop' (n = 3418 sessions) or 'jumpout' (n = 1419). A posteriori racing information (n = 3810 races) for all 485 racehorses was extracted and combined with training data. Race performance was categorised as win/not-win or podium or not, each analysed by logistic regression. Colts (p < 0.001), stayers (p < 0.001) and being relatively fast over the last 600 m of a benchmark test in training (p < 0.008) were all predictive of race performance. Heart rate recovery after exercise (p = 0.21) and speed recorded at 600 m of a 1 km benchmark test in training (p = 0.94) were not predictive. In-training physiological data analytics used along with subjective experience may help trainers identify promising horses and improve decision-making.
Publication Date: 2024-04-29 PubMed ID: 38731345PubMed Central: PMC11083884DOI: 10.3390/ani14091342Google Scholar: Lookup
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  • Journal Article

Summary

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This study investigates the effectiveness of using in-training physiological data such as speed and heart rate recovery from racehorses in predicting their race-day performance. The findings show certain factors such as type of horse (colts, stayers) and speed over the last 600m of a benchmark test in training correlate with race performance, but factors like heart rate recovery and speed at certain distances were not predictive.

Article Summary and Scope

  • This research is a large retrospective, observational study examining how in-training heart rate and speed data from racehorses can help predict their race performance.
  • The relevant data was collected from a variety of flat racehorses using a fitness tracker at a single racing yard in Australia. This included horses of different age, sex, and type in terms of distances they race (sprinter, miler, and stayer).
  • A total of 485 racehorses were monitored during ‘fast gallop’ or ‘jumpout’ race-pace training sessions on turf. The performance of these racehorses in 3810 races was also tracked and the two types of data were combined for analysis.

Methodology

  • The researchers divided their speed measurements into ‘slow’ to ‘fast’ categories. Speed was measured by time taken to cover the last 600m from a virtual finish line.
  • Heart rate recovery was measured as the change from the gallop heart rate to the heart rate after 1 minute of rest.
  • Race performance was then categorised as win/not-win or podium/not podium standings.
  • The correlation between these in-training measurements and actual race performance was analysed by logistic regression.

Findings

  • The study found that being a colt, being a stayer, and having a faster speed over the last 600m of a benchmark test in training were predictive of winning a race.
  • However, recovery heart rate after exercise and the speed recorded at 600m of a 1km benchmark test during training did not predict race performance.
  • The study concludes that the use of objective in-training physiological data, paired with the trainers’ subjective experience, could help in identifying promising racehorses and assist in decision-making.

Cite This Article

APA
Schrurs C, Dubois G, Van Erck-Westergren E, Gardner DS. (2024). Cardiovascular Fitness and Stride Acceleration in Race-Pace Workouts for the Prediction of Performance in Thoroughbreds. Animals (Basel), 14(9). https://doi.org/10.3390/ani14091342

Publication

ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 14
Issue: 9

Researcher Affiliations

Schrurs, Charlotte
  • School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK.
Dubois, Guillaume
  • Arioneo, Rue Claude Farrère, 6, 75016 Paris, France.
Van Erck-Westergren, Emmanuelle
  • Equine Sports Medicine Practice, 83 Avenue Beau Séjour, 1410 Waterloo, Belgium.
Gardner, David S
  • School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK.

Conflict of Interest Statement

Charlotte Schrurs is self-funding her Ph.D. All data were collected by Arioneo Ltd. David S. Gardner is funded by the School of Veterinary Medicine and Science, University of Nottingham. Guillaume Dubois is an employee of Arioneo Ltd. and contributed to the design of this study but had no influence on the reporting of the results as presented. Emmanuelle Van Erck-Westergren is an equine sports medicine specialist and consultant for Arioneo Ltd.

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