Evaluation of the FitBark Activity Monitor for Measuring Physical Activity in Dogs

Beacon-based sleep-wake monitoring in dogs

The sleep-wake cycle represents a crucial physiological process essential for maintaining homeostasis and promoting individual growth. In dogs, alterations in sleep patterns associated with age and dog's correlation with temperament factors, such as nervousness, have been reported, and there is an increasing demand for precise monitoring of sleep and physical activity in dogs. The present study aims to develop an analysis method for measuring sleep-wake patterns and physical activity in dogs by utilizing an accelerometer and a smartphone. By analyzing time series data collected from the accelerometer attached to the dog's collar, a comprehensive sleep and activity analysis model was constructed. This model classified the activity level into seven classes and effectively highlighted the variations in sleep-activity patterns. Two classes with lower activity levels were considered as sleep, while other five levels were regarded as wake based on the rate of occurrence. This protocol of data acquisition and analysis provides a methodology that enables accurate and extended evaluation of both sleep and physical activity in dogs.

Mobility, functionality and functional mobility: A review and application for canine veterinary patients

Mobility is an essential aspect of a dog's daily life. It is defined as the ability to move freely and easily and deviations from an animals' normal mobility capabilities are often an indicator of disease, injury or pain. When a dog's mobility is compromised, often functionality (ability to perform activities of daily living [ADL]), is also impeded, which can diminish an animal's quality of life. Given this, it is necessary to understand the extent to which conditions impact a dog's physiological ability to move around their environment to carry out ADL, a concept termed functional mobility. In contrast to human medicine, validated measures of canine functional mobility are currently limited. The aim of this review is to summarise the extent to which canine mobility and functionality are associated with various diseases and how mobility and functional mobility are currently assessed within veterinary medicine. Future work should focus on developing a standardised method of assessing functional mobility in dogs, which can contextualise how a wide range of conditions impact a dog's daily life. However, for a true functional mobility assessment to be developed, a greater understanding of what activities dogs do on a daily basis and movements underpinning these activities must first be established.

Here Puppy, Chew on This: Short-Term Provision of Toys Does Not Improve Welfare in Companion Dogs

Retail dog toys are often provided to companion animals to provide cognitive and physical stimu- lation and improve the animals' quality of life. These toys, sometimes known as "enrichment toys", have been shown to play a role in increasing appetite and activity levels and decreasing undesirable behaviors (e.g., barking, self-isolating behaviors) in some domestic dog (Canis familiaris) populations. In this study, we evaluate the effect of toys on appetite, activity levels, and positive affective states as measures of well- being in companion dogs. Behaviors were compared before and after regular interactions with different types of toys over twelve days. We found that provisioning companion dogs with toys did not significantly alter their activity level, rate of food consumption, or cognitive bias. While dogs who received more complex toys showed a slightly improved cognitive bias, there were no significant differences in behaviors be- tween the subjects who received "less complex" toys (e.g., a bone, ball) and "more complex" toys (e.g., puzzle toys). We conclude with reflections on the relevance of our subject population to the result seen, and on the different forms of the cognitive bias test.

Research Note: Validation of a low-cost accelerometer to measure physical activity in 30 to 32-d-old male Ross 708 broilers

Poultry activity measurements are often associated with expensive equipment or time-consuming behavior observations. Since low-cost accelerometers are available, the current study validated the FitBark (FitBark 2, FitBark Inc., Kansas City, MO) accelerometer for use on 30 to 32-d-old male Ross 708 broilers. The FitBark provides aggregated activity levels based on tri-axial accelerometer technology. Broilers were housed in 5 rooms, each divided into 12 2 × 2.3 m pens (60 birds per pen, 31 kg m-2 final density). From 30 to 32 d, 1 broiler per room (n = 5) was randomly selected and equipped with a 13 g FitBark. Elastic loops were placed around the wings to secure the FitBark medially on the back. During the same time, validity was assessed via ceiling-mounted video cameras. The video recordings were analyzed using 20-min continuous sampling during the photo phase at 8 time periods per bird. Behavior was assessed every second using an ethogram (9,600 data points per bird). In the first step, the FitBark data were matched and correlated with the corresponding video-based observed activity (OA) data. The FitBark and OA data were not normally distributed (1-sample KS test, all n = 800, ZFitBark = 0.21, ZOA = 0.24, all P < 0.001). Therefore, data were transformed, and a repeated measures correlation was performed for each bird, showing a positive correlation between the FitBark and OA data (rrm = 0.76, 95% CI = 0.72-0.78, df = 794, P < 0.001). In the second step, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The FitBark correctly identified 91% (sensitivity) of the active and 74% (specificity) of the inactive birds. When the FitBark detected an active or inactive bird, there was a probability of 89% (PPV) and 78% (NPV) that the bird was observed to be active or inactive based on the OA data. Accuracy was at 86%. Overall, FitBark are useful for 1-min interval activity measurements in 30 to 32-d-old male Ross 708 broilers. Further research should focus on validating the FitBark at other ages and in different poultry species.

Mild to moderate increases in activity are associated with increased seizure incidence in dogs with idiopathic epilepsy receiving anti-epileptic drugs

OBJECTIVE

Epilepsy is a chronic disorder, and approximately 25% to 30% of dogs with epilepsy are refractory to anti-epileptic drugs. As increased activity has been shown to reduce seizure frequency in people with epilepsy, the goal of this study was to evaluate the relationship between deviation from baseline activity and seizure incidence in dogs with epilepsy.

MATERIALS AND METHODS

Activity and seizure data were obtained using a canine activity monitoring device and owner observed seizure logs in 53 dogs with idiopathic epilepsy receiving anti-epileptic drugs. Each dog's activity was individually measured, and 14-day baseline averages were calculated. Logistic regression was performed to evaluate how an observed increase in activity, ranging from 0% to 50%, above baseline activity, affects the incidence of a seizure in the following 24 hours.

RESULTS

A total of 8540 activity days and 365 seizure days were used in the final analysis with an average of 11 seizures per dog (range 0 to 30 seizures). Seizure incidence was significantly more likely when activity was 10%, 20%, or 30% above baseline activity in the 24 hours before the day of a documented seizure [95% confidence interval (1.02 to 1.60), P=0.033; 95% confidence interval (1.08 to 1.80), P=0.010; 95% confidence interval (1.13 to 2.07), P=0.005, respectively]. However, when activity levels were 40% and 50% above baseline, the effect diminished (95% confidence interval (0.74 to 1.70), P=0.532; or 95% confidence interval (0.56 to 1.66), P=0.988, respectively).

CLINICAL SIGNIFICANCE

Differently than in humans, this study demonstrated that a mild to moderate increase in activity resulted in a higher seizure incidence within 24 hours in dogs with epilepsy.

Physical activity and sport-specific training patterns in Swedish sporting and working trial dogs-A questionnaire survey

OBJECTIVE

To explore physical activity patterns, including conditioning exercise and sport-specific training, and management routines utilized by handlers of Swedish sporting and working dogs participating in agility, obedience, rally obedience and working trial disciplines.

PROCEDURES

Dog handlers provided information on competition-level dogs through an internet-based cross-sectional and descriptive survey on physical activity, sport-specific training and management. Results are reported overall and stratified by participation in specific disciplines.

RESULTS

We received 1615 replies to the questionnaire. After data cleaning, 1582 dogs (98%) remained for the analysis. Of these, 430 participated in agility, 790 in obedience, 596 in rally obedience, and 847 dogs had competed in a working trial, i.e., messenger, protection, search or tracking. Number of disciplines performed by each dog varied between one and five. Most common was participation in one (n = 767, 48%) or two (n = 541, 34%) disciplines. Out of the dogs competing in one discipline, 38% (n = 294) were considered to be specialized as they actively trained only that discipline for ≥10 months per year. The vast majority of the dogs (n = 1129, 71%) received more than 1 h of daily physical activity, e.g., walks, and only n = 51 (3%) were never exercised off-leash. Preferred self-selected gait was trot (n = 907, 57%) and gallop (n = 499, 32%). A fifth (n = 319, 20%) never played with other dogs. The majority (n = 1328, 84%) received more than 1 h of vigorous physical conditioning exercise per week. Almost three quarters (n = 1119, 71%) participated in physical conditioning exercise. Two thirds (n = 953, 60%) participated in at least 3 h of sport-specific training per week and only a very small portion (n = 35, 2%) trained their specific discipline less than once per week. Median total work load, i.e., all daily physical activity, vigorous physical conditioning exercise and sport-specific training, was 16.5 h per week.

CONCLUSION AND CLINICAL RELEVANCE

We observe physical activity at moderate to high durations and moderate to vigorous intensities among Swedish sporting and working trial dogs. Most dogs received physical conditioning exercise, but not all dogs were warmed up before training and competition. Our study provides veterinary professionals and dog trainers with valuable insights on the physical exposures and management routines of sporting and working trial dogs.

Dog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device

Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video data represent the moving area of dogs to detect the dogs. The sensor data represent the movement of the dogs and extract features that affect dog behavior recognition. Seven types of behavior recognition were conducted, and the results of the two data types were used to recognize the dog’s behavior through a fusion model based on deep learning. Experimentation determined that, among FasterRCNN, YOLOv3, and YOLOv4, the object detection rate and behavior recognition accuracy were the highest when YOLOv4 was used. In addition, the sensor data showed the best performance when all statistical features were selected. Finally, it was confirmed that the performance of multimodal data-based fusion models was improved over that of single data-based models and that the CNN-LSTM-based model had the best performance. The method presented in this study can be applied for dog treatment or health monitoring, and it is expected to provide a simple way to estimate the amount of activity.