The objective of this study was to explore the mathematical relationships between independent variables (patient morphometrics and treadmill speed) and dependent variables (accelerometer or pedometer output) when evaluating data from accelerometers and pedometers in dogs. Twenty dogs took part in 3 randomized activities, consisting of exercise on a treadmill at 1.0, 1.5, and 2.0 m/s for a total distance of 1 km at each speed. Dogs simultaneously wore both an accelerometer and a pedometer. Statistical analysis used multiple regression models to discover the relationships between independent and dependent variables. A formula was developed to predict the distance traveled by a dog based on its morphometrics and activity monitor output. Shoulder height had stronger correlations to accelerometer and pedometer outputs than other morphometric variables. As shoulder height increased, all accelerometer and pedometer outputs decreased. As treadmill speed increased, both accelerometer and pedometer step counts decreased, while accelerometer activity counts increased. According to a formula derived to predict the total distance traveled using patient shoulder height and accelerometer or pedometer output, pedometer steps were the most accurate predictor of distance traveled. Accelerometer steps were less accurate when using the same model. Accelerometer activity counts did not reveal a meaningful predictive formula. The results of this study indicate that patient morphometrics and treadmill speed (as a measure of intensity) influenced accelerometer and pedometer data. The pedometer data more precisely and accurately estimated the distance traveled based on step counts and patient shoulder height. In normal dogs, accelerometer and pedometer steps may reasonably estimate distance traveled.
|Original language||English (US)|
|Number of pages||8|
|Journal||Canadian Journal of Veterinary Research|
|State||Published - Jan 2020|