Alyssa Weakley

Naturalistic Assessment of Everyday Functioning in Individuals with Mild Cognitive Impairment: The Day Out Task

OBJECTIVE The day-out task (DOT), a naturalistic task that requires multitasking in a real-world setting, was used to examine everyday functioning in individuals with mild cognitive impairment (MCI). METHOD Thirty-eight participants with MCI and 38 cognitively healthy older adult controls prioritized, organized, initiated, and completed a number of subtasks in a campus apartment to prepare for a day out (e.g., determine and gather change for bus, bring a magazine). Participants also completed tests assessing cognitive constructs important in multitasking (i.e., retrospective memory, prospective memory, planning). RESULTS As compared with controls, the MCI group required more time to complete the DOT and demonstrated poorer task accuracy, performing more subtasks incompletely and inaccurately. Despite poorer DOT task accuracy, the MCI and control groups approached completion of the DOT in a similar manner. For the MCI group, retrospective memory was a unique predictor of the number of subtasks left incomplete and inaccurate, while prospective memory was a unique predictor of DOT sequencing. The DOT measures, but not the cognitive tests, were predictive of knowledgeable informant report of everyday functioning. CONCLUSIONS THESE findings suggest that difficulty remembering and keeping track of multiple goals and subgoals may contribute to the poorer performance of individuals with MCI in complex everyday situations.

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

Introduction: Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI), or dementia using a suite of classification techniques. Method: Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis; Clinical Dementia Rating, CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals with CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection. Results: No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0–99.1%), geometric mean (60.9–98.1%), sensitivity (44.2–100%), and specificity (52.7–100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2–9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions: The current study results reveal that machine learning techniques can accurately classify cognitive impairment and reduce the number of measures required for diagnosis.

Naturalistic assessment of task interruption in individuals with mild cognitive impairment.

Objective: Interruptions are ubiquitous in everyday life, and recovering from interruptions requires several cognitive processes working in tandem. In this study, we assessed the effects of an interruption on the performance of older individuals with and without mild cognitive impairment (MCI) completing everyday tasks in a naturalistic apartment setting. Method: Thirty-two persons with MCI and 64 cognitively healthy older adults (HOA) completed two different sets of everyday activities, of which one received an interruption. Participants also completed tests assessing cognitive constructs thought to be important in interruption recovery including retrospective memory, prospective memory, planning, working memory, and executive function. Results: As a consequence of an interruption, participants with MCI took longer to complete primary task demands and made more substitution errors, but did not make more omission errors. In contrast, an interruption led HOAs to make more omission errors, but their time on task was not affected. Results from a hierarchical linear regression suggest that the ecologically valid interruption task time was more predictive of everyday functional status than the traditional neuropsychological measures. Conclusions: Results suggest that a brief task interruption taxed cognitive resources of both MCI and HOA groups, but was more detrimental to MCI in terms of time on task and total errors committed. Participant groups appeared to use a speed–accuracy trade-off to mitigate negative effects, where HOAs emphasized speed and MCI participants focused on accuracy. Amount of cognitive engagement/disengagement was also theorized to have played a role, where MCI may have maintained information online throughout the interruption, and HOAs disengaged and reengaged resulting in worse reactivation of goals. Although MCI held onto task goals, their execution of details was imperfect over the interruption delay resulting in substitution errors likely due to further taxed executive abilities.

Automatic assessment of functional health decline in older adults based on smart home data

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of >2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy.

Compensation Strategies in Older Adults: Association With Cognition and Everyday Function:

Background/Rationale: Compensation strategies may contribute to greater resilience among older adults, even in the face of cognitive decline. This study sought to better understand how compensation strategy use among older adults with varying degrees of cognitive impairment impacts everyday functioning. Methods: In all, 125 older adults (normal cognition, mild cognitive impairment, dementia) underwent neuropsychological testing, and their informants completed questionnaires regarding everyday compensation and cognitive and functional abilities. Results: Cognitively normal and mild cognitive impairment older adults had greater levels of compensation use than those with dementia. Higher levels of neuropsychological functioning were associated with more frequent compensation use. Most importantly, greater frequency of compensation strategy use was associated with higher levels of independence in everyday function, even after accounting for cognition. Conclusion: Use of compensation strategies is associated with higher levels of functioning in daily life among older adults. Findings provide strong rational for development of interventions that directly target such strategies.

Effectiveness of a Video-based Aging Services Technology Education Program for Health Care Professionals.

ABSTRACT Health care professionals (HCPs) are a critical source of recommendations for older adults. Aging services technologies (ASTs), which include devices to support the health-care needs of older adults, are underutilized despite evidence for improving functional outcomes and safety and reducing caregiver burden and health costs. This study evaluated a video-based educational program aimed at improving HCP awareness of ASTs. Sixty-five HCPs viewed AST videos related to medication management, daily living, and memory. Following the program, participants’ objective and perceived AST knowledge improved, as did self-efficacy and anticipated AST engagement. About 95% of participants stated they were more likely to recommend ASTs postprogram. Participants benefitted equally regardless of years of experience or previous AST familiarity. Furthermore, change in self-efficacy and perceived knowledge were significant predictors of engagement change. Overall, the educational program was effective in improving HCPs’ awareness of ASTs and appeared to benefit all participants regardless of experience and prior knowledge.