Pet ownership is associated with greater cognitive and brain health in a cross-sectional sample across the adult lifespan

Abstract

Human-animal interactions that stem from pet ownership have a wide range of benefits for social, emotional, and physical health. These factors also tend to improve cognition. Following this logic, owning a pet could indirectly enhance cognitive and brain health through mechanisms like improvements in well-being, socialization, and decreased stress. In the present study, cross-sectional data were drawn from the Alabama Brain Study on Risk for Dementia in which 95 participants aged 20–74 were recruited. Specifically, 56 adults were pet-owners and 39 adults were not pet-owners. Multivariate analyses revealed that pet ownership was related to higher levels of cognition and larger brain structures, and these effects were largest in dog owners. The most consistent cognitive relationships were found with better processing speed, attentional orienting, and episodic memory for stories, and with dorsal attention, limbic, and default mode networks. Moreover, we show that owning a pet can reduce one’s brain age by up to 15 years. Pet ownership was not related to indirect factors including social, emotional, and physical health. We found also that older adults’ brain health benefited from owning more than one pet versus owning one or fewer pets. These findings indicate that pet ownership, especially dog ownership, may play a role in enhancing cognitive performance across the adult lifespan, which could in turn influence protection against age-related cognitive decline.

Pet Ownership, Living Alone, and Cognitive Decline Among Adults 50 Years and Older

“…However, in addition to verbal memory and executive function, cognitive function also includes attention, reasoning, processing speed, accuracy, and so on. Another cross-sectional study has reported that pet ownership is associated with better processing speed and orientation. Therefore, a comprehensive cognition assessment is needed to explore the longitudinal correlation between pet ownership and global cognitive function.…”

“…Contrary to living alone, pet ownership (eg, raising dogs and cats) is related to reduced loneliness, an important risk factor for dementia and cognitive decline . However, the association between pet ownership and the rate of cognitive decline has not been fully explored, and the existing findings remain controversial . Several cross-sectional studies have found that pet ownership is associated with better verbal memory, story memory, executive function (ie, serial sevens subtraction and clock-drawing tests), processing speed, and orientation function .…”

“… 11 , 12 , 16 However, the association between pet ownership and the rate of cognitive decline has not been fully explored, and the existing findings remain controversial. 17 , 18 , 19 , 20 , 21 Several cross-sectional studies have found that pet ownership is associated with better verbal memory, 17 story memory, 17 executive function (ie, serial sevens subtraction and clock-drawing tests), 18 , 19 processing speed, 20 and orientation function. 20 However, some cross-sectional studies 19 , 21 have reported that pet ownership is not associated with verbal memory or executive function (ie, backward number counting tests).…”

“… 17 , 18 , 19 , 20 , 21 Several cross-sectional studies have found that pet ownership is associated with better verbal memory, 17 story memory, 17 executive function (ie, serial sevens subtraction and clock-drawing tests), 18 , 19 processing speed, 20 and orientation function. 20 However, some cross-sectional studies 19 , 21 have reported that pet ownership is not associated with verbal memory or executive function (ie, backward number counting tests). To date, prospective longitudinal studies to elucidate the association between pet ownership and the rate of cognitive decline are lacking.…”

Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale

“…Gaussian blur filter was also applied. The values of the parameters linked to these transformations were randomly applied in the following ranges using predefined probability distributions: rotation [ (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19), (341-357)]; shearing (− 0.16, 0.18); flips (left-right flip, rotation − 90, rotation − 270); contrast (0.6-2); sharpness (0.4-8); brightness (0.7-1.6); color balance (0.2-3.5) and Gaussian blur (1.05-2.9) 36,37 . As part of geometric transformations, face cropping and resizing predicted the boundaries of faces using the Haar Cascade method implemented in OpenCV 37,38 .…”