Assessment of functional change and cognitive correlates in the progression from healthy cognitive aging to dementia

Functional Disability in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

Accumulating evidence suggests that the pre-dementia syndrome mild cognitive impairment (MCI) is characterized by decrements in instrumental activities of daily living (IADL). The current review was a quantitative synthesis of the available literature to objectively characterize IADL disability in MCI while clarifying inconsistencies in findings across studies. It was hypothesized that individuals with MCI would display significantly greater functional impairment relative to cognitively intact controls. Candidate moderators specified a priori included functional assessment approach, MCI subtype, depressive symptoms, and language conducted. Online databases (PubMed/MEDLINE and PsycINFO) and reference lists were searched to identify peer-reviewed publications assessing IADL in MCI compared to normal aging. A total of 151 effect sizes derived from 106 studies met inclusionary criteria (N = 62,260). Random effects models yielded a large overall summary effect size (Hedges' g = 0.76, 95 % confidence interval: 0.68 - 0.83, p < .001) confirmed in multi-level analyses adjusted for nesting of effect sizes within studies (g = 0.78, 95 % confidence interval: 0.69 - 0.87). Functional assessment strategy and MCI subtype were significant moderators of effect size, whereas depressive symptoms and language were not. Results convincingly demonstrate that MCI is associated with significant difficulties in the performance of complex everyday tasks. It appears that functional decline, like cognitive decline, exists on a continuum from healthy aging to dementia onset. Implications for clinical practice and research priorities are discussed.

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

Modeling Patterns of Activities using Activity Curves

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

Everyday functioning and cognitive correlates in healthy older adults with subjective cognitive concerns

OBJECTIVE

Few studies have examined functional abilities and complaints in healthy older adults (HOAs) with subjective cognitive concerns (SCC). The aims of this study were to assess everyday functioning in HOAs reporting high and low amounts of SCC and examine cognitive correlates of functional abilities.

METHOD

Twenty-six HOAs with high SCC and 25 HOAs with low SCC, as well as their knowledgeable informants, completed the Instrumental Activities of Daily Living-Compensation (IADL-C), a questionnaire measure of everyday functioning.

RESULTS

After controlling for depression, the high-SCC group self-reported significantly more everyday difficulties on the IADL-C, including all subdomains. Compared to the low-SCC group, informants for the high-SCC group endorsed more difficulties on the IADL-C and specifically the social skills subdomain. For the high-SCC group, poorer self-report of everyday functioning was related to poorer executive functioning and temporal order memory.

CONCLUSIONS

These findings indicate that there may be subtle functional changes that occur early in the spectrum of cognitive decline in individuals with high SCC, and these functional changes are evident to informants. Further work is needed to investigate whether individuals with both SCC and functional difficulties are at an even higher risk for progression to mild cognitive impairment.

Executive function subcomponents and their relations to everyday functioning in healthy older adults

Everyday functioning and its executive functioning cognitive correlates (i.e., switching, inhibition, and updating) were investigated in healthy older adults (HOAs) using multiple methods of functional status. In addition to whether computerized experimental tasks would better dissociate these subcomponents than neuropsychological measures of executive functioning, we were also interested in the contributions of both experimental and neuropsychological measures of executive function subcomponents to functional abilities. Seventy HOAs (45 young-old and 25 old-old) and 70 younger adults completed executive function and neuropsychological tests. In addition to self- and informant questionnaires of functional abilities, HOAs completed two performance-based measures. An aging effect was found on all executive function measures. Old-old older adults and their informants did not report more functional difficulties but demonstrated more difficulties on performance-based measures than did young-old participants. For the HOAs, after controlling for age and education, the neuropsychological measures of executive functioning, but not experimental measures, explained a significant amount of variance in the informant-report and both performance-based measures. Updating measures differentially predicted performance-based measures, while switching was important for questionnaire and performance-based measures. The contribution of executive functioning to functional status when measured with experimental measures specifically designed to isolate the executive subcomponent was not as strong as hypothesized. Further research examining the value of isolating executive function subcomponents in neuropsychological assessment and the prediction of functional abilities in older adults is warranted.

Examination of Variables That May Affect the Relationship Between Cognition and Functional Status in Individuals with Mild Cognitive Impairment: A Meta-Analysis

The objective of this meta-analysis was to improve understanding of the heterogeneity in the relationship between cognition and functional status in individuals with mild cognitive impairment (MCI). Demographic, clinical, and methodological moderators were examined. Cognition explained an average of 23% of the variance in functional outcomes. Executive function measures explained the largest amount of variance (37%), whereas global cognitive status and processing speed measures explained the least (20%). Short- and long-delayed memory measures accounted for more variance (35% and 31%) than immediate memory measures (18%), and the relationship between cognition and functional outcomes was stronger when assessed with informant-report (28%) compared with self-report (21%). Demographics, sample characteristics, and type of everyday functioning measures (i.e., questionnaire, performance-based) explained relatively little variance compared with cognition. Executive functioning, particularly measured by Trails B, was a strong predictor of everyday functioning in individuals with MCI. A large proportion of variance remained unexplained by cognition.

Computer mouse movement patterns: A potential marker of mild cognitive impairment

INTRODUCTION

Subtle changes in cognitively demanding activities occur in MCI but are difficult to assess with conventional methods. In an exploratory study, we examined whether patterns of computer mouse movements obtained from routine home computer use discriminated between older adults with and without MCI.

METHODS

Participants were 42 cognitively intact and 20 older adults with MCI enrolled in a longitudinal study of in-home monitoring technologies. Mouse pointer movement variables were computed during one week of routine home computer use using algorithms that identified and characterized mouse movements within each computer use session.

RESULTS

MCI was associated with making significantly fewer total mouse moves (p<.01), and making mouse movements that were more variable, less efficient, and with longer pauses between movements (p<.05). Mouse movement measures were significantly associated with several cognitive domains (p's<.01-.05).

DISCUSSION

Remotely monitored computer mouse movement patterns are a potential early marker of real-world cognitive changes in MCI.

Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy

Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

Assessment of planning abilities in individuals with mild cognitive impairment using an open-ended problem-solving task

INTRODUCTION

There is currently limited research evaluating planning abilities, a core subcomponent of executive functioning, in individuals with mild cognitive impairment (MCI). In the present study, we utilized the "Amap Task," an open-ended problem-solving task, to separately evaluate the formulation and execution components of planning ability in individuals with MCI.

METHOD

Thirty-seven cognitively healthy older adults and 37 individuals with MCI used a map layout of a university apartment to develop and write out a strategy (formulation stage) to successfully complete a list of tasks (e.g., retrieve and fill a water pitcher before placing it in the refrigerator). Subsequently, participants carried out the tasks in the apartment with the aid of their formulated plan (execution stage).

RESULTS

MCI participants performed more poorly than older adult (OA) controls during both the formulation and execution stages on measures of task accuracy and task efficiency. However, both groups were able to adjust and improve task accuracy and efficiency from formulation to task execution. Finally, MCI participants took significantly longer to complete the task and adhered less to their formulated plans during task completion.

CONCLUSIONS

Using an open-ended problem-solving task, the findings revealed that individuals with MCI experienced difficulties with both the formulation and execution components of planning. Like controls, participants with MCI were able to successfully modify their plan online, improving their performance from task formulation to task execution.

Development and psychometric properties of the instrumental activities of daily living: compensation scale

The Instrumental Activities of Daily Living - Compensation (IADL-C) scale was developed to capture early functional difficulties and to quantify compensatory strategy use that may mitigate functional decline in the aging population. The IADL-C was validated in a sample of cognitively healthy older adults (N=184) and individuals with mild cognitive impairment (MCI; N=92) and dementia (N=24). Factor analysis and Rasch item analysis led to the 27-item IADL-C informant questionnaire with four functional domain subscales (money and self-management, home daily living, travel and event memory, and social skills). The subscales demonstrated good internal consistency (Rasch reliability 0.80 to 0.93) and test-retest reliability (Spearman coefficients 0.70 to 0.91). The IADL-C total score and subscales showed convergent validity with other IADL measures, discriminant validity with psychosocial measures, and the ability to discriminate between diagnostic groups. The money and self management subscale showed notable difficulties for individuals with MCI, whereas difficulties with home daily living became more prominent for dementia participants. Compensatory strategy use increased in the MCI group and decreased in the dementia group.