Resting heart and respiratory rates in dogs in their natural environment: new insights from a long-term, international, prospective study in a cohort of 703 dogs using a biometric device for longitudinal non-invasive cardiorespiratory monitoring (the AI-COLLAR study) - Chetboul, Valérie; Humbert, Eric; Dougoud, Louis; Lorre, Guillaume (Frontiers in Veterinary Science, 2025)
BackgroundWearable devices are increasingly used in human medicine to monitor various cardiovascular parameters and support heart health. Similar tools have recently emerged in veterinary medicine. However, their current limitation is the lack of large-scale data in healthy animals, a prerequisite for identifying potentially pathological variations using artificial intelligence (AI)-based algorithms.ObjectivesTo establish a large database of resting heart rate (HR) and respiratory rate (RR) recorded over extended periods using a commercially available biometric health-monitoring device in a large international cohort of apparently healthy (AH) dogs.Animals703 AH dogs (median age [interquartile range] = 3.8 years [2.2–7.2]; body weight = 23.0 kg [14.8–31.8]).MethodsProspective observational study (2022–2025) including AH dogs of any age, breed, and sex, provided owners confirmed their dog’s apparent good health via a dedicated questionnaire.ResultsMedian device wear time was 189.0 days [51.0–433.0]. Both HR and RR significantly decreased early in life, then stabilized, with a slight increase in older dogs. Both were also lower at night than during the day (p < 0.0001). In dogs living in the Northern Hemisphere, HR and RR showed opposite significant seasonal patterns. Effects of sex, weight and breed were also analyzed.Conclusion and clinical importanceThis unique large-scale biometric study provides real-world reference data on resting HR and RR in dogs and the influence of intrinsic (age, sex, weight, breed) and extrinsic (circadian rhythm, season) factors on these vital signs, thus offering new insights into canine cardiorespiratory physiology under natural conditions, and laying the foundation for future AI-based detection of abnormal patterns.