O'Rourke Abigail, Haydock Richard, Butterwick Richard F, German Alexander J, Carson Aletha, Lyle Scott, O'Flynn Ciaran
Waltham Petcare Science Institute, Waltham on the Wolds, Leicestershire, United Kingdom.
Institute of Life Course and Medical Sciences, University of Liverpool, Neston, United Kingdom.
Front Vet Sci. 2025 Jul 10;12:1572794. doi: 10.3389/fvets.2025.1572794. eCollection 2025.
The aim of this study was to determine patterns of physical activity in pet dogs using real-world data at a population scale aided by the use of accelerometers and electronic health records (EHRs).
A directed acyclic graph (DAG) was created to capture background knowledge and causal assumptions related to dog activity, and this was used to identify relevant data sources, which included activity data from commercially available accelerometers, and health and patient metadata from the EHRs. Linear mixed models (LMM) were fitted to the number of active minutes following log-transformation with the fixed effects tested based on the variables of interest and the adjustment sets indicated by the DAG.
Activity was recorded on 8,726,606 days for 28,562 dogs with 136,876 associated EHRs, with the median number of activity records per dog being 162 [interquartile range (IQR) 60-390]. The average recorded activity per day of 51 min was much lower than previous estimates of physical activity, and there was wide variation in activity levels from less than 10 to over 600 min per day. Physical activity decreased with age, an effect that was dependent on breed size, whereby there was a greater decline in activity for age as breed size increased. Activity increased with breed size and owner age independently. Activity also varied independently with sex, location, climate, season and day of the week: males were more active than females, and dogs were more active in rural areas, in hot dry or marine climates, in spring, and on weekends.
Accelerometer-derived activity data gathered from pet dogs living in North America was used to determine associations with both dog and environmental characteristics. Knowledge of these associations could be used to inform daily exercise and caloric requirements for dogs, and how they should be adapted according to individual circumstances.
本研究的目的是利用加速度计和电子健康记录（EHR），在人群规模上通过实际数据确定宠物狗的身体活动模式。
创建了一个有向无环图（DAG），以捕捉与狗的活动相关的背景知识和因果假设，并用于识别相关数据源，其中包括来自市售加速度计的活动数据，以及来自EHR的健康和患者元数据。对经过对数转换后的活跃分钟数拟合线性混合模型（LMM），根据感兴趣的变量和DAG指示的调整集对固定效应进行检验。
记录了28,562只狗8,726,606天的活动情况，有136,876份相关的EHR，每只狗的活动记录中位数为162[四分位间距（IQR）60 - 390]。每天平均记录的活动时间为51分钟，远低于先前对身体活动的估计，并且活动水平差异很大，从每天不到10分钟到超过600分钟不等。身体活动随年龄下降，这种影响取决于品种大小，即随着品种大小增加，年龄增长导致的活动下降幅度更大。活动随品种大小和主人年龄独立增加。活动还随性别、位置、气候、季节和星期几独立变化：雄性比雌性更活跃，狗在农村地区、炎热干燥或海洋性气候地区、春季和周末更活跃。
从生活在北美的宠物狗收集的加速度计衍生活动数据用于确定与狗和环境特征的关联。了解这些关联可用于为狗的日常运动和热量需求提供信息，以及如何根据个体情况进行调整。
