A therapy dog's impact on daytime activity and night-time sleep for older persons with Alzheimer's disease : a case study

Background: Animal-Assisted Therapy using dogs have been described as having a calming effect, decrease sundowning and blood-pressure in persons with Alzheimer’s disease. The aim was to investigate how continuous and scheduled visits by a prescribed therapy dog affected daytime and night-time sleep for persons with Alzheimer’s disease. Methods: In this case study, registration of activity and sleep curves was conducted from five persons with moderate to severe Alzheimer’s disease living at a nursing home, over a period of 16 weeks using an Actiwatch. Data was analysed with descriptive statistics. Result: The study shows no clear pattern of effect on individual persons daytime activity and sleep when encounter with a therapy dog, but instead points to a great variety of possible different effects that brings an increased activity at different time points, for example during night-time sleep. Conclusions: Effects from the use of a Animal-Assisted Therapy with a dog in the care of persons with Alzheimer’s disease needs to be further investigated and analysed from a personcentred view including both daytime and nightime activities.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.