Use of the Polar V800 and Actiheart 5 heart rate monitors for the assessment of heart rate variability (HRV) in horses

Assessing the impact of draught load pulling on welfare in equids

About 112 million working equids are the source of income for 600 million people globally. Many equids are used for pulling loads (up to 15,000 kg per day) to transport goods. Most of them are associated with brick kilns, mining, and agriculture industries in developing countries. They may suffer from welfare issues such as overloading, being beaten, and being forced to work for long periods. These issues may occur due to a poor understanding of load-pulling equids. Understanding their capabilities and the elements that influence them is critical for efficient performance and welfare. The measurement of stride characteristics and gait kinematics can reveal loading adaptations and help identify loading limitations. It is known that both loading and fatigue change the locomotor patterns of load-pulling horses. Heart rate is a stress quantifying metric and an important representative of the speed of work and draught force. Heart rate variability is a regularly used statistic to quantify a physiological response to stresses, but it has never been used for load-pulling equids. Changes in blood lactate, nitrogen, oxygen, and carbon dioxide contents are reliable biochemical indicators of the effects of load pulling. Changes in plasma cortisol levels reflect the intensity of exercise and stress levels in horses while pulling a load. However, eye blink rate is a cheap, simple, and immediate indicator of acute equine stress, and we suggest it may be used to aid in load-pulling equine welfare assessment. However, further research is needed for a standardized and evidence-based draught load pulling capacity of working horses, mules, and donkeys.

A Pilot Study on Behavioural and Physiological Indicators of Emotions in Donkeys

Recognizing animal emotions is critical to their welfare and can lead to a better relationship with humans and the environment, especially in a widespread species like the donkey, which is often prone to welfare issues. This study aims to assess the emotional response of donkeys through an operant conditioning task with two presumed different emotional contents. Specifically, a within-subject design including positive and negative conditions was conducted, collecting behavioural and physiological (heart rate variability and HRV) parameters. Facial expressions, postures, and movements were analysed by principal component analysis and behavioural diversity indexes (frequencies, activity budgets, richness, Shannon and Gini-Simpson). During the positive condition, both ears were held high and sideways (left: r = -0.793, p < 0.0001; right: r = -0.585, p = 0.011), while the ears were frontally erected (left: r = 0.924, p < 0.0001; right: r = 0.946, p < 0.0001) during the negative one. The latter was also associated with an increased tendency to walk (r = 0.709, p = 0.001), walk away (r = 0.578, p = 0.012), more frequent changes in the body position (V_{Body position} = 0, p = 0.022), and greater behavioural complexity (V_{Gini-Simpson Index} = 4, p = 0.027). As for HRV analysis, the root mean square of successive beat-to-beat differences (rMSSD) was significantly lower after the negative condition. These non-invasive parameters could be considered as possible indicators of donkeys' emotional state.

Donkey Heart Rate and Heart Rate Variability: A Scoping Review

Heart rate (HR) and heart rate variability (HRV) are commonly used physiological measures in animals. While several studies exist on horse HRV, less information is available for donkeys. This scoping review aims to understand the extent and type of published evidence on donkey HR and HRV, their clinical and research applications, the devices used, and the analysis performed. Only quantitative primary studies published in English were considered. Four different databases were queried through the Web of Science platform, with additional evidence identified by citation chasing. After a two-stage screening phase, data were extracted considering study and population characteristics, information on HR/HRV analysis, and applications. The majority of the 87 included articles (about 80%) concerned a sample size of up to 20 individuals and were published since 2011 (about 65%). Forty-one articles employed an electronic device for signal acquisition (mainly electrocardiographs and heart rate monitors), yet only two articles reported HRV parameters. The literature on donkey HRV is lacking, and this gap can be filled by gaining knowledge on donkey characteristics and finding useful tools for welfare assessment. Comparison with what is known about the horse allows a discussion of the technical and interpretative difficulties that can be encountered with donkeys.

Are infrared thermography, feeding behavior, and heart rate variability measures capable of characterizing group-housed sow social hierarchies?

Group gestation housing is quickly becoming standard practice in commercial swine production. However, poor performance and welfare in group housed sows may result from the formation and maintenance of the social hierarchy within the pen. In the future, the ability to quickly characterize the social hierarchy via precision technologies could be beneficial to producers for identifying animals at risk of poor welfare outcomes. Therefore, the objective of this study was to investigate the use of infrared thermography (IRT), automated electronic sow feeding systems, and heart rate monitors as potential technologies for detecting the social hierarchy within five groups of sows. Behavioral data collection occurred for 12 h after introducing five sow groups (1-5; n = 14, 12, 15, 15, and 17, respectively) to group gestation housing to determine the social hierarchy and allocate individual sows to 1 of 4 rank quartiles (RQ 1-4). Sows within RQ1 were ranked highest while RQ4 sows were ranked lowest within the hierarchy. Infrared thermal images were taken behind the neck at the base of the ear of each sow on days 3, 15, 30, 45, 60, 75, 90, and 105 of the experiment. Two electronic sow feeders tracked feeding behavior throughout the gestation period. Heart rate monitors were worn by 10 randomly selected sows per repetition for 1 h prior to and 4 h after reintroduction to group gestation housing to collect heart rate variability (HRV). No differences were found between RQ for any IRT characteristic. Sows within RQ3 and RQ4 had the greatest number of visits to the electronic sow feeders overall (P < 0.04) but spent shorter time per visit in feeders (P < 0.05) than RQ1 and RQ2 sows. There was an interaction of RQ with hour for feed offered (P = 0.0003), with differences between RQ occurring in hour 0, 1, 2, and 8. Higher-ranked sows (RQ1 and RQ2) occupied the feeder for longer during the first hour than lower ranking sows (RQ3 and RQ4; P < 0.04), while RQ3 sows occupied the feeder longer than RQ1 sows during hour 6, 7, and 8 (P < 0.02). Heart beat interval (RR) collected prior to group housing introduction differed between RQ (P < 0.02 for all), with RQ3 sows exhibiting the lowest RR, followed by RQ4, RQ1, and RQ2. Rank quartile also affected standard deviation of RR (P = 0.0043), with RQ4 sows having the lowest, followed by RQ1, RQ3, and RQ2 sows. Overall, these results indicate that feeding behavior and HRV measures may be capable of characterizing social hierarchy in a group housing system.

Risk Factors for Exercise-Associated Sudden Cardiac Death in Thoroughbred Racehorses

Cardiac arrhythmias resulting in sudden cardiac death (SCD) are an important cause of racehorse fatalities. The objective of this study was to determine risk factors for SCD in Thoroughbreds by evaluating a sample with a policy of mandatory post-mortem following racing or training fatalities. Risk factors were compared between case horses with SCD (n = 57) and control horses with other fatal injury (OFI, n = 188) by univariable and multivariable logistic regression. Survival in years for horses with SCD was compared to OFI using the Kaplan−Meier method with log rank test. The following variables were most important in the multiple logistic model: Horses with SCD were more likely to die during training than during racing, SCD (42/57, 74%) vs. OFI (82/188, 44%; odds ratio [OR], 95% confidence interval [CI], 2.5, 1.2−5.4; p = 0.01), had fewer lifetime starts, median (interquartile range [IQR]), SCD (3.0 [0.0−9.0]) vs. OFI (9.0 [0.0−22.8]; OR, 95% CI, 0.96, 0.9−1.0; p = 0.02 and were less likely to be entire (uncastrated) males, SCD 9/57 (16%) vs. OFI (46/188, 25%; OR, 95% CI, 0.47, 0.1−0.9; p = 0.03). Survival in years (median (IQR)) for horses with SCD was 3.6 (3.1−4.4), which was shorter than OFI (4.5 [3.1−6.0], hazard ratio, 95%CI, 1.6,1.2−2.3; p < 0.001). SCD occurs more commonly in training than racing, which suggests exercise intensity is less important in precipitating this fatality. In this study, SCD occurred early in the careers of affected horses.