Objective The aim of this study was to identify risk factors for an agility dog becoming injured during its career. We hypothesized that certain factors involved with the training, competition, age, sex, age of neuter, body condition, and management could be associated with the risk for injury. Study Design The outcome of interest in this cross-sectional survey design was injury versus no injury, and an initial univariable analysis screening was performed. All variables with a p-value of less than 0.20 in univariable analysis were entered into a multivariable logistic regression model. Manual backward stepwise removal was performed until remaining variables had a p-value of less than 0.05. Results Five-hundred responses were included in the analysis. In the final multivariable model adjusting for all other variables, breed, age, age at neuter, and level of competition remained associated with injury in the study population. Conclusion These findings support existing literature on the predispositions for injury with certain breeds and competition level. Our study further suggests, however, that there is a need to better understand how health decisions earlier in life may affect the prevalence for injury in the agility competitor, particularly regarding age at neutering and age of the competitor.

A survey was designed and administered at eighteen agility competitions across the Northeast and Midwest USA in 2015 to obtain information regarding competition level, training, feeding practices, owner-reported weight, body condition score (BCS) and supplement use. Average energy intake per d from reported consumption was assessed for all dogs in ideal body condition based on manufacturers’ or US Department of Agriculture database information. To assess the respective parameters across competition levels (novice, open, master/elite), non-parametric or parametric ANOVA or χ2 was used to determine significance. There were 494 respondents with usable data. Results showed that approximately 99 % of respondents used treats and 62 % utilised supplements. Of the respondents, 61 % fed primarily commercial dry food. Approximately 25 % of owners fed foods other than commercial dry (i.e. raw/home-prepared or freeze-dried). This 25 % of non-traditional diets included: 11 % home-prepared raw/cooked diets, 11 % commercial raw/cooked diets, and the remaining 3 % were fed commercial freeze-dried raw products. The remaining 14 % fed a mix of commercial dry food and raw/home-cooked blend. Average BCS was 4·7 (sd 1·1). Mean energy consumption of 238 dogs (BCS 4–5/9) was 444 (sd 138) kJ/kg body weight0·75 per d (106 (sd 33) kcal/kg body weight0·75 per d), with no significant differences observed between dogs at different levels of competition. The mean percentage of energy from treats was 15·1 (sd 12·7) % of overall energy consumption.

Post-exercise carbohydrate repletion of skeletal muscle glycogen utilising maltodextrin, with or without highly digestible protein, can improve performance in humans which has been extrapolated to dogs. There is limited metabolic evidence regarding substrate utilisation during exercise with and without supplementation other than serum hormone status and serum amino acid responses. The objectives of this study were 2-fold; (1) to examine the metabolomic changes associated with a weight-pulling exercise; and (2) to examine the effects of maltodextrin/protein supplementation on serum metabolomics during recovery. Serum was collected from 12 dogs (6 control and 6 treatment) at different time points (pre-exercise, 0 min post-exercise, 30 min post, 3 h post) and liquid chromatography-mass spectrometry (LC-MS) was performed identifying 242 metabolites. A two-way analysis of variance for time and treatment with false discovery rate correction was performed using MetaboAnalyst 3.0. There were 9 metabolites found to be significantly increased or decreased immediately after exercise from baseline representing primarily citric acid cycle metabolites. Treatment differences at 30 min post-exercise showed increases in 8 metabolites including amino acids and carbohydrate intermediates with supplementation. Thirty-seven metabolites were significantly different at 3 h post-exercise, with most metabolites being related to amino acid increases, as well as decreases in fatty acid metabolites with supplementation. Definite alterations in metabolites suggesting that post-exercise supplementation with maltodextrin and protein supports glucose metabolism and alters fatty acid metabolism or clearance during recovery from a weight-pulling exercise.