Interday variation in vertical ground reaction force in clinically normal Greyhounds at the trot
P F Rumph1, J E Steiss, M S West
1Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, AL 36849, USA.
Interday variation significantly impacts vertical ground reaction force variables in dogs, affecting peak vertical force (PFz) and impulse (IFz). Accounting for this day-to-day variability is crucial for accurate statistical analysis in canine biomechanics research.
Area of Science:
- Canine biomechanics
- Veterinary orthopedics
- Animal locomotion analysis
Background:
- Vertical ground reaction force (vGRF) variables are critical for assessing canine locomotion and lameness.
- Interday variation in vGRF measurements can affect the reliability of research findings.
- Understanding day-to-day variability is essential for accurate interpretation of canine gait data.
Purpose of the Study:
- To quantify the extent of interday variation in vGRF variables in clinically normal dogs.
- To determine the impact of day-to-day changes on peak vertical force (PFz) and impulse (IFz) in canine hind limbs.
- To assess the significance of within-dog variation across consecutive measurement days.
Main Methods:
- 52 clinically normal Greyhounds were analyzed.
- vGRF variables (PFz and IFz) were measured from hind limbs during trotting over a force platform.
- Data were collected on three consecutive days, with variance components estimated using maximal likelihood and ANOVA.
Main Results:
- Significant interday variation in PFz, IFz, or both was observed in 29 out of 52 dogs.
- ANOVA confirmed that the day-within-dog interaction was a significant factor for PFz and IFz.
- A substantial portion of dogs exhibited day-to-day differences in vGRF measurements.
Conclusions:
- Interday variation is a significant factor that must be considered in statistical models analyzing canine vGRF data.
- A statistical model incorporating accommodation for interday variation is proposed.
- Researchers should identify and account for factors contributing to variation, including interday effects, in their data analysis.
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