Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64–76% and 70–86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.

Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active (p < 0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p < 0.05), but no differences were observed between seasons for indoor-only (n = 10) cats. Cats living in rural areas (n = 7) spent more time eating than cats in urban areas (n = 21; p < 0.05). Cats living in single-cat households (n = 12) spent more time lying but less time sitting than cats living in multi-cat households (n = 16; p < 0.05). Cats in households with at least one child (n = 20) spent more time standing in winter (p < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children (n = 8; p < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.

Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph^® accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R² = 0.91, p < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire.

To date, little is known about seasonal changes in specific cat behaviours, and how these are affected by weather patterns. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for seven research cats for a total of 13 weeks spread over one year, with weather data being collected simultaneously. Generalised linear mixed models were used to statistically test for seasonal differences in proportional behavioural data and how behaviour was affected by weather variables. Seasonal differences were found for time spent eating (p < 0.001), grooming (p < 0.001), littering (p = 0.037), lying (p < 0.001), scratching (p < 0.001), and sitting (p < 0.001) but not for active behaviours and standing (p > 0.05). A positive interaction effect of the temperature humidity wind index and daylength was found for time spent eating, lying, and standing (all p < 0.001), while it was negative for active (p < 0.001), grooming (p < 0.001), littering (p = 0.004), and sitting (p < 0.001). Rainfall negatively affected grooming (p = 0.023) and scratching (p = 0.037). These findings highlight seasonality in cat behaviours, influenced by weather conditions.

Cats are among the most popular pets globa lly, yet little is known about how the home environment influences their behaviour. Most studies have focused on cats in shelters or research facilities, potentially limiting applicability to pet cats. This study combined behavioural data from cats in three housing conditions: indoor pet (n = 10), free-roaming pet (n = 18), and research (n = 8), collected in summer and winter. Eight behaviours were classified from collar-mounted accelerometer data using a validated machine learning model and analysed using generalised linear mixed models. Free-roaming pet cats were more active in summer than winter (3.9 ± 0.39% vs. 2.7 ± 0.33%; p < 0.001) and more active than both research (2.0 ± 0.36%; p = 0.004) and indoor pet cats (2.0 ± 0.36%; p < 0.001) in summer. Research cats spent more time lying (52.9 ± 2.03% vs. 36.9 ± 2.89%; p = 0.009) and eating (7.8 ± 0.41% vs. 2.4 ± 0.39%; p = 0.003) in winter than summer, whereas no seasonal differences in these behaviours were observed for pet cats. A bimodal daily activity pattern, with peaks around sunrise and sunset, was observed across housing conditions and seasons. These findings demonstrate that both housing and seasonal conditions influence domestic cat behaviour and should be considered when interpreting behavioural studies.

New Zealand working farm dogs can struggle to maintain their body weight and condition during periods of high workload, despite substantial energy intake, highlighting the need to optimise energy balance. One potential strategy for reducing overall energy demands is to reduce energy expenditure for thermoregulation, particularly during colder conditions, which may improve both recovery and energy utilisation. This study investigated whether wearing coats influenced the sleep behaviour, activity, and apparent nutrient digestibility of outdoor-kennelled dogs. Eight adult working-breed dogs (n = 8) were studied using a randomised cross-over design, in which dogs wore a coat or no coat during overnight periods (15:00–09:00 h). Behaviour was monitored using triaxial accelerometers and classified using a validated machine learning model, while apparent nutrient digestibility was determined from pooled faecal samples. Dogs spent more time sleeping when wearing a coat compared to no coat (48.1% vs. 40.0%, p = 0.008), with the effect being most evident during cooler evening hours. Time spent resting and being active was reduced when coats were worn (p < 0.05), while overall activity did not differ (p = 0.856). No differences were observed in apparent digestibility of energy or nutrients (p > 0.05), although protein digestibility tended to be higher when coats were worn (p = 0.079). These findings suggest that coats can improve sleep behaviour, likely through improved thermal comfort. While this study was conducted in research dogs under relatively mild temperature conditions, the results indicate the potential welfare and functional benefits for working farm dogs.