Proof of Concept of a Dynamic Energy Prescription Protocol Integrating Wearable Activity Data in 19 Adult Dogs: A Prospective Longitudinal Study
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Simple Summary Feeding dogs with the right amount of energy is important for their health, but standard feeding guidelines may not suit every individual. This study tested a new approach in 19 shelter dogs, where the amount of food each dog received was adjusted weekly based on data from a wearable sensor that tracked their activity levels, combined with other individual characteristics. Over 10 weeks, most dogs maintained a stable body condition, while a minority varied by one unit, both toward and away from the ideal range. Compared to a standard fixed feeding plan, this dynamic approach resulted in dogs eating more consistently close to their prescribed amount. These findings suggest that using wearable technology to personalize feeding plans could help improve nutritional management in dogs. plain-language-summary Abstract 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 individua
Abstract
Simple Summary Feeding dogs with the right amount of energy is important for their health, but standard feeding guidelines may not suit every individual. This study tested a new approach in 19 shelter dogs, where the amount of food each dog received was adjusted weekly based on data from a wearable sensor that tracked their activity levels, combined with other individual characteristics. Over 10 weeks, most dogs maintained a stable body condition, while a minority varied by one unit, both toward and away from the ideal range. Compared to a standard fixed feeding plan, this dynamic approach resulted in dogs eating more consistently close to their prescribed amount. These findings suggest that using wearable technology to personalize feeding plans could help improve nutritional management in dogs. plain-language-summary Abstract 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;< 0.001) and body fat percentage (19.9 ± 5.4 vs. 18.3 ± 4.6%;= 0.010) decreased, while muscle condition score remained stable. Feeding adherence improved significantly from 8/19 during R1 to 18/19 during R2 (= 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.