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Equine veterinary journal2022; 55(2); 194-204; doi: 10.1111/evj.13581

Changes in Thoroughbred speed and stride characteristics over successive race starts and their association with musculoskeletal injury.

Abstract: Certain stride characteristics have been shown to affect changes in biomechanical factors that are associated with injuries in human athletes. Determining the relationship between stride characteristics and musculoskeletal injury (MSI) may be key in limiting injury occurrence in the racehorse. Objective: This study aimed to determine whether changes in race day speed and stride characteristics over career race starts are associated with an increased risk of MSI in racehorses. Methods: Case-control study. Methods: Speed, stride length, and stride frequency data were obtained from the final 200 m sectional of n = 5660 race starts by n = 584 horses (case n = 146, control n = 438). Multivariable joint models, combining longitudinal and survival (time to injury) analysis, were generated. Hazard ratios and their 95% confidence intervals (CI) are presented. Results: The risk of MSI increased by 1.18 (95% CI 1.09, 1.28; P < 0.001) for each 0.1 m/s decrease in speed and by 1.11 (95% CI 1.02, 1.21; P = 0.01) for each 10 cm decrease in stride length over time (career race starts). A more marked rate of decline in speed and stride length was observed approximately 6 races prior to injury. Risk of MSI was highest early in the horse's racing career. Conclusions: Only final sectional stride characteristics were assessed in the model. The model did not account for time between race starts. Conclusions: Decreasing speed and stride length over multiple races is associated with MSI in racehorses. Monitoring stride characteristics over time may be beneficial for the early detection of MSI.
© 2022 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2022-06-09 PubMed ID: 35477925PubMed Central: PMC10084173DOI: 10.1111/evj.13581Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research focused on determining how changes in race day speed and stride characteristics over a racehorse’s career relate to musculoskeletal injuries. It was found that decreases in speed and stride length over several races indicate an increased risk of such injuries.

Methodology

  • The researchers followed a case-control study approach.
  • Data on speed, stride length, and stride frequency were gathered from the final 200 meter section of 5660 race starts involving 584 horses. This included 146 case horses and 438 control horses.
  • The study made use of multivariable joint models, which integrate longitudinal and survival (time to injury) analysis, to generate comprehensive results.
  • The generated hazard ratios and their 95% confidence intervals (CI) were also presented as part of the study’s findings.

Results

  • A decrease in speed by 0.1 m/s increased the risk of musculoskeletal injury (MSI) by 1.18. Similarly, a decrease in stride length by 10 cm corresponded to a 1.11 increase in the risk.
  • An abrupt decline in the speed and stride length was noted approximately six races before an injury occurred.
  • The risk of musculoskeletal injury was found to be highest in the early stages of a horse’s racing career.

Conclusions

  • The analysis only considered the final sectional stride characteristics. It did not take into account the time between the different race starts.
  • Overall, the study concluded that a decrease in stride length and speed over several races is associated with musculoskeletal injuries in racehorses.
  • The results suggest that monitoring stride characteristics over time could present a beneficial method for the early detection of musculoskeletal injuries in racehorses.

Cite This Article

APA
Wong ASM, Morrice-West AV, Whitton RC, Hitchens PL. (2022). Changes in Thoroughbred speed and stride characteristics over successive race starts and their association with musculoskeletal injury. Equine Vet J, 55(2), 194-204. https://doi.org/10.1111/evj.13581

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 55
Issue: 2
Pages: 194-204

Researcher Affiliations

Wong, Adelene S M
  • Equine Centre, Melbourne Veterinary School, University of Melbourne, Werribee Victoria, Australia.
Morrice-West, Ashleigh V
  • Equine Centre, Melbourne Veterinary School, University of Melbourne, Werribee Victoria, Australia.
Whitton, R Chris
  • Equine Centre, Melbourne Veterinary School, University of Melbourne, Werribee Victoria, Australia.
Hitchens, Peta L
  • Equine Centre, Melbourne Veterinary School, University of Melbourne, Werribee Victoria, Australia.

MeSH Terms

  • Animals
  • Case-Control Studies
  • Horses
  • Risk Factors
  • Running / injuries
  • Musculoskeletal System / injuries
  • Wounds and Injuries / diagnosis
  • Wounds and Injuries / veterinary
  • Walking Speed

Grant Funding

  • Grayson-Jockey Club Research Foundation
  • Tasracing
  • Racing Victoria
  • Victorian Racing Industry Fund
  • University of Melbourne

Conflict of Interest Statement

No competing interests have been declared.

References

This article includes 47 references
  1. Verheyen KL. Reducing injuries in racehorses: mission impossible?. Equine Vet J 2013 Jan;45(1):6-7.
    pubmed: 23231383doi: 10.1111/evj.12009google scholar: lookup
  2. Riggs CM, Whitehouse GH, Boyde A. Pathology of the distal condyles of the third metacarpal and third metatarsal bones of the horse.. Equine Vet J 1999 Mar;31(2):140-8.
    pubmed: 10213426doi: 10.1111/j.2042-3306.1999.tb03807.xgoogle scholar: lookup
  3. Martig S, Chen W, Lee PV, Whitton RC. Bone fatigue and its implications for injuries in racehorses.. Equine Vet J 2014 Jul;46(4):408-15.
    pubmed: 24528139doi: 10.1111/evj.12241google scholar: lookup
  4. Whitton RC, Trope GD, Ghasem-Zadeh A, Anderson GA, Parkin TD, Mackie EJ, Seeman E. Third metacarpal condylar fatigue fractures in equine athletes occur within previously modelled subchondral bone.. Bone 2010 Oct;47(4):826-31.
    pubmed: 20659599doi: 10.1016/j.bone.2010.07.019google scholar: lookup
  5. Stover SM, Johnson BJ, Daft BM, Read DH, Anderson M, Barr BC, Kinde H, Moore J, Stoltz J, Ardans AA. An association between complete and incomplete stress fractures of the humerus in racehorses.. Equine Vet J 1992 Jul;24(4):260-3.
    pubmed: 1499531doi: 10.1111/j.2042-3306.1992.tb02831.xgoogle scholar: lookup
  6. Colgate VA, Marr CM. Science-in-brief: Risk assessment for reducing injuries of the fetlock bones in Thoroughbred racehorses.. Equine Vet J 2020 Jul;52(4):482-488.
    pubmed: 32525619doi: 10.1111/evj.13273google scholar: lookup
  7. Turlo AJ, Cywinska A, Frisbie DD. Revisiting predictive biomarkers of musculoskeletal injury in thoroughbred racehorses: longitudinal study in polish population.. BMC Vet Res 2019 Feb 26;15(1):66.
    pmc: PMC6390350pubmed: 30808359doi: 10.1186/s12917-019-1799-7google scholar: lookup
  8. Jackson BF, Lonnell C, Verheyen KL, Dyson P, Pfeiffer DU, Price JS. Biochemical markers of bone metabolism and risk of dorsal metacarpal disease in 2-year-old Thoroughbreds.. Equine Vet J 2005 Jan;37(1):87-91.
    pubmed: 15651741doi: 10.2746/0425164054406775google scholar: lookup
  9. Jackson BF, Dyson PK, Lonnell C, Verheyen KL, Pfeiffer DU, Price JS. Bone biomarkers and risk of fracture in two- and three-year-old Thoroughbreds.. Equine Vet J 2009 Apr;41(4):410-3.
    pubmed: 19562906doi: 10.2746/042516409x416206google scholar: lookup
  10. Frisbie DD, Mc Ilwraith CW, Arthur RM, Blea J, Baker VA, Billinghurst RC. Serum biomarker levels for musculoskeletal disease in two- and three-year-old racing Thoroughbred horses: A prospective study of 130 horses.. Equine Vet J 2010 Oct;42(7):643-51.
    pubmed: 20840580doi: 10.1111/j.2042-3306.2010.00123.xgoogle scholar: lookup
  11. Rosanowski SM, Chang YM, Stirk AJ, Verheyen KLP. Risk factors for race-day fatality, distal limb fracture and epistaxis in Thoroughbreds racing on all-weather surfaces in Great Britain (2000 to 2013).. Prev Vet Med 2017 Dec 1;148:58-65.
    pubmed: 29157375doi: 10.1016/j.prevetmed.2017.10.003google scholar: lookup
  12. Georgopoulos SP, Parkin TD. Risk factors for equine fractures in Thoroughbred flat racing in North America.. Prev Vet Med 2017 Apr 1;139(Pt B):99-104.
    pubmed: 28017453doi: 10.1016/j.prevetmed.2016.12.006google scholar: lookup
  13. Stewart BD, Lam KH. Racing injury risk assessment at official pre‐race veterinary inspections in Hong Kong Jockey Club. Proceedings of the 18th international conference of racing analysts and veterinarians New Zealand: 2010. p. 296–7.
  14. Lopes MA, Dearo AC, Lee A, Reed SK, Kramer J, Pai PF, Yonezawa Y, Maki H, Morgan TL, Wilson DA, Keegan KG. An attempt to detect lameness in galloping horses by use of body-mounted inertial sensors.. Am J Vet Res 2016 Oct;77(10):1121-31.
    pubmed: 27668584doi: 10.2460/ajvr.77.10.1121google scholar: lookup
  15. Morrice-West AV, Hitchens PL, Walmsley EA, Stevenson MA, Wong ASM, Whitton RC. Variation in GPS and accelerometer recorded velocity and stride parameters of galloping Thoroughbred horses.. Equine Vet J 2021 Sep;53(5):1063-1074.
    pubmed: 33098592doi: 10.1111/evj.13370google scholar: lookup
  16. Morrice-West AV, Hitchens PL, Walmsley EA, Stevenson MA, Whitton RC. Training practices, speed and distances undertaken by Thoroughbred racehorses in Victoria, Australia.. Equine Vet J 2020 Mar;52(2):273-280.
    pubmed: 31386764doi: 10.1111/evj.13156google scholar: lookup
  17. Crowther MJ, Andersson TM, Lambert PC, Abrams KR, Humphreys K. Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification.. Stat Med 2016 Mar 30;35(7):1193-209.
    pmc: PMC5019272pubmed: 26514596doi: 10.1002/sim.6779google scholar: lookup
  18. Dupuy JF, Mesbah M. Joint modeling of event time and nonignorable missing longitudinal data.. Lifetime Data Anal 2002 Jun;8(2):99-115.
    pubmed: 12048867doi: 10.1023/a:1014871806118google scholar: lookup
  19. Crowther MJ. Stjm: Stata module for joint modelling of longitudinal and survival data. .
  20. Li S. Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes.. Lifetime Data Anal 2016 Jan;22(1):145-60.
    pubmed: 25573223doi: 10.1007/s10985-014-9316-6google scholar: lookup
  21. Cole SR, Hudgens MG. Survival analysis in infectious disease research: describing events in time.. AIDS 2010 Oct 23;24(16):2423-31.
    pmc: PMC2954271pubmed: 20827167doi: 10.1097/qad.0b013e32833dd0ecgoogle scholar: lookup
  22. Pu W, Niu XF. Selecting mixed‐effects models based on a generalized information criterion. J Multivar Anal 2006;97:733–58.
  23. Brewer MJ, Butler A, Cooksley SL. The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods Ecol Evol 2016;7:679–92.
  24. StataCorp. Stata multilevel mixed‐effects reference manual. Statistical software Stata: release 16. Colege Station, TX: StataCorp LLC. 2019;52:743–9.
  25. Crowther MJ, Lambert PC. Stgenreg: a stata package for general parametric survival analysis. J Stat Softw 2013;53:1–17.
  26. Crowther MJ, Lambert PC. A general framework for parametric survival analysis.. Stat Med 2014 Dec 30;33(30):5280-97.
    pubmed: 25220693doi: 10.1002/sim.6300google scholar: lookup
  27. Crowther MJ, Abrams KR, Lambert PC. Joint modeling of longitudinal and survival data. Stata J 2013;13:165–84.
  28. van Oudenhoven FM, Swinkels SHN, Ibrahim JG, Rizopoulos D. A marginal estimate for the overall treatment effect on a survival outcome within the joint modeling framework.. Stat Med 2020 Dec 10;39(28):4120-4132.
    pmc: PMC7674249pubmed: 32838484doi: 10.1002/sim.8713google scholar: lookup
  29. Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data.. J Clin Oncol 2010 Jun 1;28(16):2796-801.
    pmc: PMC4503792pubmed: 20439643doi: 10.1200/jco.2009.25.0654google scholar: lookup
  30. Kliethermes SA, Stiffler-Joachim MR, Wille CM, Sanfilippo JL, Zavala P, Heiderscheit BC. Lower step rate is associated with a higher risk of bone stress injury: a prospective study of collegiate cross country runners.. Br J Sports Med 2021 Aug;55(15):851-856.
    pmc: PMC8336536pubmed: 33990294doi: 10.1136/bjsports-2020-103833google scholar: lookup
  31. Schubert AG, Kempf J, Heiderscheit BC. Influence of stride frequency and length on running mechanics: a systematic review.. Sports Health 2014 May;6(3):210-7.
    pmc: PMC4000471pubmed: 24790690doi: 10.1177/1941738113508544google scholar: lookup
  32. Boyer ER, Derrick TR. Select injury-related variables are affected by stride length and foot strike style during running.. Am J Sports Med 2015 Sep;43(9):2310-7.
    pubmed: 26243741doi: 10.1177/0363546515592837google scholar: lookup
  33. Chumanov ES, Wille CM, Michalski MP, Heiderscheit BC. Changes in muscle activation patterns when running step rate is increased.. Gait Posture 2012 Jun;36(2):231-5.
    pmc: PMC3387288pubmed: 22424758doi: 10.1016/j.gaitpost.2012.02.023google scholar: lookup
  34. Morin JB, Samozino P, Zameziati K, Belli A. Effects of altered stride frequency and contact time on leg-spring behavior in human running.. J Biomech 2007;40(15):3341-8.
    pubmed: 17602692doi: 10.1016/j.jbiomech.2007.05.001google scholar: lookup
  35. Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, Ryan MB. Effects of step rate manipulation on joint mechanics during running.. Med Sci Sports Exerc 2011 Feb;43(2):296-302.
    pmc: PMC3022995pubmed: 20581720doi: 10.1249/mss.0b013e3181ebedf4google scholar: lookup
  36. Parkin TD, Clegg PD, French NP, Proudman CJ, Riggs CM, Singer ER, Webbon PM, Morgan KL. Risk factors for fatal lateral condylar fracture of the third metacarpus/metatarsus in UK racing.. Equine Vet J 2005 May;37(3):192-9.
    pubmed: 15892225doi: 10.2746/0425164054530641google scholar: lookup
  37. Parkin TD, Clegg PD, French NP, Proudman CJ, Riggs CM, Singer ER, Webbon PM, Morgan KL. Horse-level risk factors for fatal distal limb fracture in racing Thoroughbreds in the UK.. Equine Vet J 2004 Sep;36(6):513-9.
    pubmed: 15460076doi: 10.2746/0425164044877387google scholar: lookup
  38. Georgopoulos SP, Parkin TD. Risk factors associated with fatal injuries in Thoroughbred racehorses competing in flat racing in the United States and Canada.. J Am Vet Med Assoc 2016 Oct 15;249(8):931-939.
    pubmed: 27700272doi: 10.2460/javma.249.8.931google scholar: lookup
  39. Peloso JG, Vogler JB 3rd, Cohen ND, Marquis P, Hilt L. Association of catastrophic biaxial fracture of the proximal sesamoid bones with bony changes of the metacarpophalangeal joint identified by standing magnetic resonance imaging in cadaveric forelimbs of Thoroughbred racehorses.. J Am Vet Med Assoc 2015 Mar 15;246(6):661-73.
    pubmed: 25719849doi: 10.2460/javma.246.6.661google scholar: lookup
  40. Bailey CJ, Reid SW, Hodgson DR, Bourke JM, Rose RJ. Flat, hurdle and steeple racing: risk factors for musculoskeletal injury.. Equine Vet J 1998 Nov;30(6):498-503.
    pubmed: 9844968doi: 10.1111/j.2042-3306.1998.tb04525.xgoogle scholar: lookup
  41. Hitchens PL, Morrice-West AV, Stevenson MA, Whitton RC. Meta-analysis of risk factors for racehorse catastrophic musculoskeletal injury in flat racing.. Vet J 2019 Mar;245:29-40.
    pubmed: 30819423doi: 10.1016/j.tvjl.2018.11.014google scholar: lookup
  42. Rajeswaran J, Blackstone EH, Barnard J. Joint modeling of multivariate longitudinal data and competing risks using multiphase sub‐models. Stat Biosci 2018;10:651–85.
  43. Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data.. Am J Epidemiol 2009 Jul 15;170(2):244-56.
    pmc: PMC2732996pubmed: 19494242doi: 10.1093/aje/kwp107google scholar: lookup
  44. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94:496–509.
  45. Sweeting MJ, Thompson SG. Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture.. Biom J 2011 Sep;53(5):750-63.
    pmc: PMC3443386pubmed: 21834127doi: 10.1002/bimj.201100052google scholar: lookup
  46. Ashra NB, Marriott L, Johnson S, Abrams KR, Crowther MJ. Jointly modelling longitudinally measured urinary human chorionic gonadotrophin and early pregnancy outcomes.. Sci Rep 2020 Mar 12;10(1):4589.
    pmc: PMC7067806pubmed: 32165717doi: 10.1038/s41598-020-61461-wgoogle scholar: lookup
  47. Billingham LJ, Abrams KR. Simultaneous analysis of quality of life and survival data.. Stat Methods Med Res 2002 Feb;11(1):25-48.
    pubmed: 11923992doi: 10.1191/0962280202sm269ragoogle scholar: lookup

Citations

This article has been cited 15 times.
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