Chelsea Sanders

Dementia severity and the longitudinal costs of informal care in the Cache County population

Dementia costs are critical for influencing healthcare policy, but limited longitudinal information exists. We examined longitudinal informal care costs of dementia in a population‐based sample.

A smartphone application to evaluate technology adoption and usage in persons with dementia

Dementia affects a proportionally large number of the older population, presenting a set of symptoms that cause cognitive decline and negatively affect quality of life. Technology offers an assistive role for some of these symptoms, specifically in addressing forgetfulness. Current works have explored the benefits of reminding technology, which whilst useful is only effective for those who adopt the technology. Therefore it is of merit to establish the individual parameters that characterize an adopter and non-adopter, to better target future interventions and their deployment. To aid the collection of this data a smartphone app was developed for persons with dementia. It has been designed as both a reminder application to help those with dementia accommodate their forgetfulness and a data collection tool to log usage and compliance with reminders. The app has been evaluated by a pre-pilot cohort (n=9) and was found to have a mean reminder acknowledgement of 73.09%.

Nutritional Status is Associated with Faster Cognitive Decline and Worse Functional Impairment in the Progression of Dementia: The Cache County Dementia Progression Study1.

Nutritional status may be a modifiable factor in the progression of dementia. We examined the association of nutritional status and rate of cognitive and functional decline in a U.S. population-based sample. Study design was an observational longitudinal study with annual follow-ups up to 6 years of 292 persons with dementia (72% Alzheimers disease, 56% female) in Cache County, UT using the Mini-Mental State Exam (MMSE), Clinical Dementia Rating Sum of Boxes (CDR-sb), and modified Mini Nutritional Assessment (mMNA). mMNA scores declined by approximately 0.50 points/year, suggesting increasing risk for malnutrition. Lower mMNA score predicted faster rate of decline on the MMSE at earlier follow-up times, but slower decline at later follow-up times, whereas higher mMNA scores had the opposite pattern (mMNA by time β= 0.22, p = 0.017; mMNA by time2 β= -0.04, p = 0.04). Lower mMNA score was associated with greater impairment on the CDR-sb over the course of dementia (β= 0.35, p <  0.001). Assessment of malnutrition may be useful in predicting rates of progression in dementia and may provide a target for clinical intervention.

Technology adoption and prediction tools for everyday technologies aimed at people with dementia

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.

Modelling assistive technology adoption for people with dementia

PURPOSE Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. MATERIALS AND METHODS The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a users background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. FINDINGS With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. CONCLUSIONS We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

Closer caregiver and care-recipient relationships predict lower informal costs of dementia care: The Cache County Dementia Progression Study

Identifying factors associated with lower dementia care costs is essential. We examined whether two caregiver factors were associated with lower costs of informal care.

Sex Differences in Risk for Alzheimer’s Disease Related to Neurotrophin Gene Polymorphisms: The Cache County Memory Study

Neurotrophins, including nerve-growth factor and brain-derived neurotrophic factor, have been implicated in Alzheimers disease (AD). Associations between AD and neurotrophin signaling genes have been inconsistent, with few studies examining sex differences in risk. We examined four single-nucleotide polymorphisms (SNPs) involved in neurotrophin signaling (rs6265, rs56164415, rs2289656, rs2072446) and risk for AD by sex in a population-based sample of older adults. Three thousand four hundred and ninety-nine individuals without dementia at baseline [mean (standard deviation) age = 74.64 (6.84), 58% female] underwent dementia screening and assessment over four triennial waves. Cox regression was used to examine time to AD or right censoring for each SNP. Female carriers of the minor T allele for rs2072446 and rs56164415 had a 60% (hazard ratio [HR] = 1.60, 95% confidence interval [CI] = 1.02-2.51) and 93% (HR = 1.93, 95% CI = 1.30-2.84) higher hazard for AD, respectively, than male noncarriers of the T allele. Furthermore, male carriers of the T allele of rs2072446 had a 61% lower hazard (HR = 0.39, 95% CI = 0.14-1.06) than male noncarriers at trend-level significance (p = .07). The association between certain neurotrophin gene polymorphisms and AD differs by sex and may explain inconsistent findings in the literature.

Predicting Technology Adoption in People with Dementia; Initial Results from the TAUT Project

The acceptance of technology is a crucial factor in successfully deploying technology solutions in healthcare. Our previous research has highlighted the potential of modelling user adoption from a range of environmental, social and physical parameters. This current work aims to build on the notion of predicting technology adoption through a study investigating the usage of a reminding application deployed through a mobile phone. The TAUT project is currently recruiting participants from the Cache County Study on Memory in Aging (CCSMA) and will monitor participants over a period of 12 months. Information relating to participants’ compliance with usage of the reminding application, details of cognitive assessments from the CCSMA and medical and genealogical related details from the Utah Population Database (UPDB) will be used as inputs to the development of a new adoption model. Initial results show, that with an unscreened dataset, it is possible to predict refusers and adopters with an F-measure of 0.79.

Impact of Medical History on Technology Adoption in Utah Population Database

In this paper we study the use of medical history information extracted from the Utah Population Database (UPDB) to predict adoption of a reminder solution for people with dementia. The adoption model was built using 24 categorised features. The kNN classification algorithm gave the best performance with 85.8 % accuracy. Whilst data from the UPDB is more readily available than that in our previous work, the results highlight the benefit of including psychosocial and background information within an adoption model.

Neurotrophin single nucleotide polymorphisms and cognitive functioning in older adults: The cache county study

cognitive performance only among current BDNFMED users (p1⁄40.011). Current estrogen use was associated with higher 3MS scores across all BDNF genotypes [betas1⁄4(0.61,0.76); SE1⁄4(0.22 all)]. Conclusions:In older adult males, BDNFMED use was associated with lower cognitive scores regardless of BDNF genotype; it is likely that BDNFMED use is a marker of underlying conditions associated with lower cognition. In females, the effect of BDNFMED use varied by rs6265 (BDNF Val66Met) genotype. These results emphasize the importance of considering sex when examining the effects of genes and medications that alter neurotrophin expression or signaling.