How Pet Health Trackers are Changing Veterinary Clinical Trials and Pet Care
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- Maven’s integrated team of engineers, data scientists, and clinical experts are responsible for building the tracking system.
- The proprietary validation dataset contains thousands of hours of labeled video recorded across hundreds of pets.
- Global brands and contract research organizations currently use the health tracker to generate real-world evidence for their own products.
- Veterinarians use the automated data to adjust diuretic therapy to the minimum effective dose for pets with heart conditions.
- The alert algorithms incorporate environmental data alongside animal biometrics rather than reacting to isolated data points.
Table of Contents
- The Shift From Subjective Questionnaires to Continuous Labeled Data
- Objective Endpoints in Veterinary Clinical Trials
- Tracking Osteoarthritis Pain Through Individualized Baselines
- Automated Respiratory Monitoring for Canine Heart Management
- Establishing Context-Aware Thresholds for Health Alerts
- Future Destinations for Pet Nutrition Research
Continuous wearable monitoring provides veterinarians with objective data to track chronic pet illnesses and measure clinical trial outcomes. Historically, veterinary diagnostics depended on periodic clinic visits and subjective journals kept by pet owners.
Automated tracking tools resolve these data gaps by recording physical movements and physiological metrics around the clock.
The Shift From Subjective Questionnaires to Continuous Labeled Data
Veterinary medicine requires precise data to differentiate normal animal behaviors from pathological itching, a severe medical urge to scratch. Dr. Carolina Domingues, Clinical Research Team Lead at Maven Pet, explains that the organization builds its own evidence base through specific validation strategies.
“What truly sets us apart is the combination of rigorous clinical validation and proprietary datasets at scale,” states Dr. Domingues. The research team collaborates with academic institutions, including the University of Florida, Tufts University, and UTAD, to verify data against gold-standard clinical frameworks.
The development process relies on a dataset containing thousands of hours of labeled video across hundreds of pets. A combined team of engineers, data scientists, and clinical experts, led by Dr. Joana Babo, manages the technology design.
Dr. Domingues notes, “The result is a system that does not just process data, but understands it in a clinically meaningful way and continuously improves with every pet we monitor”.
Objective Endpoints in Veterinary Clinical Trials
Wearable technology provides direct measurements for research organizations evaluating new animal pharmaceuticals and supplements. A recent proof-of-concept study used the Maven Pet health tracker to gather objective data on sleep efficiency and nighttime rest interruptions during a trial for a mushroom-derived nutraceutical targeting Canine Cognitive Decline, a form of cognitive impairment in aging dogs.
This technology alters the methodology of veterinary research. “For decades, veterinary clinical trials have relied heavily on owner-reported questionnaires, which are valuable but inherently subjective and low resolution,” states Dr. Domingues.
The Maven Pet health tracker collects up to 25 million data points per pet, per day. This volume improves the quality and reliability of the evidence collected during field studies.
Contract research organizations and global brands use this system to generate real-world evidence for new pet products. Dr. Domingues notes that this transition is already occurring within the industry.
The structured data allows the research team to support external partners with novel datasets.
Tracking Osteoarthritis Pain Through Individualized Baselines
Identifying pain events related to osteoarthritis, a degenerative joint disease, is difficult due to behavioral variations among individual animals. A pain event may cause a single dog to pace restlessly, while causing a different dog to withdraw and decrease its activity.
Standard population averages often fail to detect these subtle changes. The monitoring system resolves this issue by establishing a dynamic, individualized baseline for each animal.
The algorithm tracks how a pet deviates from its own normal patterns over time. This method avoids comparing the animal to general species averages.
Early detection of deviations allows veterinarians to adjust treatments earlier. Dr. Domingues states that pets do not communicate pain directly, and even attentive owners can miss early signals.
The individualized data gives veterinarians a tool to personalize medication choices based on real-world activity levels rather than general assumptions.
Automated Respiratory Monitoring for Canine Heart Management
Tracking resting respiratory rate, the number of breaths a pet takes per minute while resting, helps manage dogs at risk of left-sided congestive heart failure, a condition where the heart struggles to pump blood efficiently. Manual tracking by owners is often inconsistent due to the difficulty of counting breaths accurately over time.
Automating the measurement removes the primary barrier to consistent tracking for families managing sick pets. Thousands of owners use the tracker to manage pets with diagnosed heart conditions.
The continuous data helps veterinarians adjust diuretic therapy to maintain the minimum effective dose while keeping the animal stable. Automated tracking leads to earlier detection of clinical decline and helps owners avoid emergency medical situations.
Establishing Context-Aware Thresholds for Health Alerts
Setting alert thresholds requires a balance between early disease detection and the prevention of alert fatigue, a state of exhaustion caused by a high volume of frequent notifications. The system anchors its alerts in established clinical guidelines for heart rate and respiratory rate.
Veterinarians and owners can personalize these settings to match the specific needs of an individual pet. For signals without established clinical definitions, the team uses continuous real-world data and feedback from users.
The software uses context-aware algorithms that combine multiple signals and environmental trends rather than reacting to an isolated data point. Dr. Domingues states, “Every alert needs to be meaningful, actionable, and trustworthy”.
Future Destinations for Pet Nutrition Research
The research team is expanding its focus to address other aspects of domestic animal health. Future projects include increasing clinical research regarding feline health conditions.
The primary near-term focus centers on nutrition and energy expenditure, the amount of calories an animal burns daily. The team plans to combine continuous monitoring with metabolic insights to individualize daily feeding plans.
This approach uses real-world data to transition from generic dietary recommendations to individualized preventive care.
For more information about Maven, click here.
References
[1] Domingues, C. (2026). Clinical validation and continuous monitoring in veterinary medicine [Interview]. Maven Pet.
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