By Asaf Dagan, DVM
Originally Posted on Pet Vet Magazine
Veterinary research is changing rapidly as wearable and mobile health technologies become more practical and widely adopted. These tools allow researchers and clinicians to gather continuous, objective data from animals living in their everyday environments, improving study quality and clinical decision-making. The shift toward remote monitoring, cloud-based data management, and machine learning transforms how we detect disease, assess recovery, and understand animal behavior.
Wearable Technology: Advances in Remote Measurement and Monitoring
Wearables such as smart collars and sensor-equipped harnesses provide a practical way to collect physiological and behavioral data without confining animals to a clinic. By measuring animals in familiar surroundings, researchers reduce stress-related artifacts and obtain a truer picture of baseline health and day-to-day variation. This approach minimizes the “white coat effect” and other measurement biases that can influence short, in-clinic assessments.
Miniaturized sensors now measure multiple parameters simultaneously, and modern smart collars can discreetly house sensors for heart rate, heart rate variability (HRV), respiration rate, temperature proxies, activity, posture, and more. These integrated devices are designed for continuous wear and can scale studies by monitoring many animals at once, enabling larger sample sizes and richer longitudinal datasets.
Big Data: High-Frequency, Longitudinal Data Capture
Continuous monitoring generates high-frequency data streams, often producing thousands of data points per subject each day. These datasets are uploaded securely to cloud-based platforms where they can be stored, organized, and accessed for analysis. The volume and resolution of these data make it possible to observe subtle trends and temporal patterns that would be missed with occasional spot checks.
For researchers, this means more precise characterization of normal ranges, better detection of early deviations from baseline, and stronger statistical power for clinical trials. For clinicians, ongoing data can support individualized care plans and more informed decisions about diagnostics, treatment adjustments, and post-operative monitoring.
Artificial Intelligence and Machine Learning: Extracting Insight from Complexity
Machine learning algorithms are well suited to interpret the complex, multi-dimensional data produced by wearable devices. AI models can detect patterns across diverse inputs—vital signs, activity, and behavior—and translate these into meaningful indicators of health, pain, sleep quality, and overall welfare. When models are trained on longitudinal data from the same individuals, they learn personalized baselines and become more adept at identifying clinically relevant changes.
Importantly, the effectiveness of these models improves with the quantity and quality of labeled data. Long-term datasets enable algorithms to distinguish normal individual variability from pathological signals, assisting early detection of disease and allowing interventions before clinical signs become obvious. Careful validation and transparent model evaluation remain essential to ensure accuracy and to avoid overfitting or misinterpretation.
Use Cases: Where Wearables Add Value in Veterinary Research and Practice
Wearable technology supports a wide range of applications across veterinary medicine and animal science. In pharmaceutical and clinical research, smart collars allow remote monitoring of subjects during trials, providing continuous safety and efficacy data without repeated clinic visits. In surgical practice, wearables assist in post-operative care by tracking recovery, pain-related behaviors, and activity levels to guide pain management and rehabilitation.
Behaviorists and trainers use these tools to identify stress markers and quantify responses to interventions, improving welfare-focused training methods. Telemedicine is another growing application: in the U.S., the TeleVet market is currently estimated at $137M annually and is projected to expand substantially in the coming years. Wearable devices can enrich teleconsultations by providing objective measurements that inform triage and treatment decisions, improving the quality of virtual care.
Considerations: Data Quality, Ethics, and Integration
While the potential is substantial, implementing wearables in research and clinical settings requires attention to data integrity, privacy, and animal welfare. Proper sensor calibration, consistent wearing protocols, and robust data handling practices are essential. Ethical oversight and transparent consent by owners remain central to responsible use. Integration with clinical workflows and electronic medical records can maximize utility but must be implemented with clear standards for interoperability and data governance.
Validation studies comparing wearable-derived metrics to gold-standard measurements are important to establish reliability and to define appropriate clinical thresholds. Collaboration among device manufacturers, researchers, clinicians, and regulators will help set standards and promote responsible adoption.
Wearable and mobile health technologies are reshaping how we study and care for companion animals. By enabling continuous, real-world monitoring paired with advanced analytics, these tools enhance our ability to detect disease earlier, tailor treatments to individual patients, and improve outcomes across research and practice.
Asaf Dagan is the Chief Veterinary Scientist & Co-Founder at PetPace™. Dr. Dagan is a Diplomate of the American Board of Veterinary Practitioners (ABVP), specializing in canine and feline medicine. He is certified in veterinary acupuncture by the International Veterinary Acupuncture Society (IVAS) and has completed advanced training in diagnostic ultrasound, laser surgery techniques, and orthopedics. A graduate of Tufts University School of Veterinary Medicine with honors, Dr. Dagan was admitted to Phi Zeta, the veterinary honor society, and has practiced small animal medicine in veterinary hospitals across Massachusetts, Connecticut, and Israel.