Anxiety disorders in dogs are ever‐growing and represent an important concern in the veterinary behavior field. These disorders are often disregarded in veterinary clinical practice, negatively impacting the animal’s and owner’s quality of life. Moreover, these anxiety disorders can potentially result in the abandonment or euthanasia of dogs. Growing evidence shows that the gut microbiota is a central player in the gut‐brain axis. A variety of microorganisms inhabit the intestines of dogs, which are essential in maintaining intestinal homeostasis. These microbes can impact mental health through several mechanisms, including metabolic, neural, endocrine, and immune‐mediated pathways. The disruption of a balanced composition of resident commensal communities, or dysbiosis, is implicated in several pathological conditions, including mental disorders such as anxiety. Studies carried out in rodent models and humans demonstrate that the intestinal microbiota can influence mental health through these mechanisms, including anxiety disorders. Furthermore, novel therapeutic strategies using prebiotics and probiotics have been shown to ameliorate anxiety‐related symptoms. However, regarding the canine veterinary behavior field, there is still a lack of insightful research on this topic. In this review, we explore the few but relevant studies performed on canine anxiety disorders. We agree that innovative bacterial therapeutical approaches for canine anxiety disorders will become a promising field of investigation and certainly pave the way for new approaches to these behavioral conditions.

Canine osteoarthritis (OA) is a chronic, progressive disease that impacts mobility and welfare, often with subtle clinical signs that fluctuate over time. This exploratory case series evaluated the potential of a wearable sensor system (Maven Pet AI System) to detect real-time deviations in activity and rest patterns in dogs with OA under home-based conditions. Five client-owned dogs were monitored over periods ranging from 56 to 126 days, generating longitudinal data on activity and rest patterns. Nine clinically relevant events were identified: seven OA-related flare-ups and two non-orthopedic health issues. In eight of these events, deviations in activity profiles were temporally aligned with symptom onset, therapeutic response, or recovery. Statistically significant changes were observed in six out of nine events, particularly in the Active and Excited categories, while visual trend analysis revealed clinically relevant deviations even in the absence of statistical significance. In one case, decreased activity preceded owner recognition, suggesting potential for early detection. Sensor data also contextualized episodes of overexertion and non-orthopedic conditions, such as pruritus and gastroenteritis. Owner and clinician feedback indicated high usability and perceived clinical value. Despite the small sample, these findings suggest that continuous sensor-based monitoring may complement conventional evaluations and support earlier, more individualized OA management in real-world settings. Further studies are needed to validate and expand these preliminary observations.

Standard predictive equations for maintenance energy requirements often do not account for individual variability in dogs, potentially leading to nutritional inaccuracies. This prospective proofofconcept study evaluated a dynamic energy prescription protocol integrating wearable activity data in adult shelter dogs. Twenty-six dogs were enrolled, of which 19 had valid longitudinal data for analysis. The 10-week protocol comprised a fixed energy prescription phase (R1), based on perceived activity level, followed by a dynamic phase (R2), in which energy allowance was recalculated weekly incorporating accelerometer-derived activity data and other individual parameters. During R2, BCS remained unchanged in 12/19 dogs and varied one unit in 7/19. Of these, two dogs with above-ideal baseline BCS decreased, and one dog with below-ideal BCS increased. Body weight (mean ± SD: 24.2 ± 7.0 vs. 23.6 ± 6.7 kg; p < 0.001) and body fat percentage (19.9 ± 5.4 vs. 18.3 ± 4.6%; p = 0.010) decreased, while muscle condition score remained stable. Feeding adherence improved significantly from 8/19 during R1 to 18/19 during R2 (p = 0.004). These findings support the feasibility of integrating activity monitoring into a dynamic energy prescription and its potential to improve alignment between prescribed and consumed energy. Longer-term studies are warranted to confirm these observations.