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Risk Factors for Injury in Border Collies Competing in Agility Competitions.

Arielle Pechette MarkleyAbigail B ShobenNina R Kieves
Published in: Animals : an open access journal from MDPI (2024)
Border Collies are the most common breed in agility and their reported injury rate is much higher than that of other breeds. We aimed to identify demographic, training, and competition variables associated with the injury risk for this breed. We hypothesized that higher jump heights and competition at national/international levels would increase the injury risk. Data were collected from an internet-based survey. A logistic regression model was built using backward selection. There were 934 Border Collies in the sample, with 488 reporting an injury. The jump height relative to the shoulder height was associated with injury, with dogs jumping noticeably above or below shoulder height more likely to report a history of injury. Other identified risk factors included the number of weekends spent competing/year, the number of competitions at the national level, the age when starting elbow height jumps and backside jumps, the acquisition of the dog from a breeder, and the age of the handler. Factors associated with prolonged injury (>3-month duration) were the age when starting elbow height jumps and having a veterinary assistant as a handler. Border Collies jumping above shoulder height had an increased risk of injury. However, those jumping below shoulder height were also at a higher risk, which could have been due to reverse causality. Similarly, the observed associations regarding differences based on the number of trial weekends/year may have been impacted by reverse causality as well. The increased risk of injury with elbow height jump training at <10 months of age may indicate that the repetitive impact of jump training prior to skeletal maturity negatively influences musculoskeletal development. These data provide valuable information for further prospective studies.
Keyphrases
  • body mass index
  • risk factors
  • clinical trial
  • big data
  • deep learning
  • high frequency
  • health information
  • phase iii
  • artificial intelligence
  • drug induced
  • data analysis