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This study evaluated frontal behavioural symptoms, via the FrSBe self-report, in military personnel with and without a history of blast-related mild traumatic brain injury (mild TBI). Prospective observational cohort study of combat-deployed service members leveraging 1-year and 5-year demographic and follow up clinical outcome data. The blast mild TBI group (n = 164) showed greater frontal behavioural symptoms, including clinically elevated apathy, disinhibition, and executive dysfunction, during a 5-year follow-up, compared to a group of combat-deployed controls (n = 107) without mild TBI history or history of blast exposure. We also explored changes inbehaviourall symptoms over a 4-year span, which showed clinically significant increases in disinhibition in the blast mild TBI group, whereas the control group did not show significant increases in symptoms over time. Our findings add to the growing evidence that a proportion of individuals who sustain mild TBI experience persistent behavioural symptoms. We also offer a demonstration of a novel use of the FrSBe as a tool for longitudinal symptom monitoring in a military mild TBI population.

The COVID-19 pandemic caused near immediate service delivery adaptation among social service and healthcare sectors. Findings from in-depth interviews with 45 senior leaders of social services and healthcare organizations serving older adults in Washington State elucidate the role of trust in service provision at the onset of the pandemic. First, a history of trust facilitated service adaptation. Intentional outreach, programs, and culturally responsive services sustained trust with service recipients. Providing services and information in an older adult's preferred language built trust. Community networks facilitated and reflected trust between organizations and older adults. Finally, mistrust was prevalent for clients who perceived a high risk of experiencing negative consequences from accessing services. Our findings support the need for culturally and linguistically diverse services. To improve trust, the aging network should strengthen and expand partnerships with community-based organizations who have established trust through history, intentionality, and relevance to their service populations..

Background To demonstrate feasibility and utility of the iPad version of the NIH Toolbox Cognition Battery (NIHTB-CB) in a clinical trial of older adults. Methods Fifty-one adults, aged 55 and older without dementia were tested twice on NIHTB-CB and more traditional paper-and-pencil neuropsychological measures after meal ingestion, with approximately a 4-week interval. We also compared performances at Time 1 and Time 2 for significant change. We also extracted the response times and errors for available NIHTB-CB subtests to determine subtle changes in performance. Results Over the interval, improvement in fluid cognitive measures was noted at Time 2 (t = -3.07, p = 0.004), whereas crystallized measures were unchanged. Tests of fluid cognition negatively correlated with age, particularly for the second visit. Analysis of the average speed per item showed that, for two of the tests, speed increased at Time 2. Traditional neuropsychological tests correlated with many of the NIHTB-CB measures. Response times for all five timed tests decreased at Time 2, although only statistically significant for Picture Sequence and Picture Vocabulary. Conclusions The iPad version of the NIH Toolbox Cognition Battery appears to be an adequate measure to assess cognitive functioning in a clinical trial of older adults. Psychometric analyses suggest stability in measures of crystallized functioning, whereas measures of fluid abilities revealed improvements over the short time frame of the study. Response times and errors for individual tests revealed intriguing relationships that should be further evaluated to determine the utility in clinical sample analysis, as this could aid identification of subtle cognitive change over short periods. Additional studies with larger sample sizes will be helpful to understanding the reliability, sensitivity, and specificity of the NIHTB-CB sub-scores in older adults. In addition, further evaluations with clinical populations, including individuals with cognitive impairment, are warranted.

BACKGROUND Sleep quality has been associated with cognitive and mood outcomes in otherwise healthy older adults. However, most studies have evaluated sleep quality as aggregate and mean measures, rather than addressing the impact of previous night’s sleep on next-day functioning. OBJECTIVE This study aims to evaluate the ability of previous night’s sleep parameters on self-reported mood, cognition, and fatigue to understand short-term impacts of sleep quality on next-day functioning. METHODS In total, 73 cognitively healthy older adults (19 males, 54 females) completed 7 days of phone-based self-report questions, along with 24-hour actigraph data collection. We evaluated a model of previous night’s sleep parameters as predictors of mood, fatigue, and perceived thinking abilities the following day. RESULTS Previous night’s sleep predicted fatigue in the morning and midday, as well as sleepiness or drowsiness in the morning; however, sleep measures did not predict subjective report of mood or perceived thinking abilities the following day. CONCLUSIONS This study suggests that objectively measured sleep quality from the previous night may not have a direct or substantial relationship with subjective reporting of cognition or mood the following day, despite frequent patient reports. Continued efforts to examine the relationship among cognition, sleep, and everyday functioning are encouraged.

Background: Sleep quality has been associated with cognitive and mood outcomes in otherwise healthy older adults. However, most studies have evaluated sleep quality as aggregate and mean measures, rather than addressing the impact of previous night’s sleep on next-day functioning. Objective: This study aims to evaluate the ability of previous night’s sleep parameters on self-reported mood, cognition, and fatigue to understand short-term impacts of sleep quality on next-day functioning. Methods: In total, 73 cognitively healthy older adults (19 males, 54 females) completed 7 days of phone-based self-report questions, along with 24-hour actigraph data collection. We evaluated a model of previous night’s sleep parameters as predictors of mood, fatigue, and perceived thinking abilities the following day. Results: Previous night’s sleep predicted fatigue in the morning and midday, as well as sleepiness or drowsiness in the morning; however, sleep measures did not predict subjective report of mood or perceived thinking abilities the following day. Conclusions: This study suggests that objectively measured sleep quality from the previous night may not have a direct or substantial relationship with subjective reporting of cognition or mood the following day, despite frequent patient reports. Continued efforts to examine the relationship among cognition, sleep, and everyday functioning are encouraged.

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.

BACKGROUND: The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments.OBJECTIVE: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS: This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS: Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS: The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Neuropsychologists are often asked to answer questions about the effects of cognitive deficits on everyday functioning. This study examined the relationship between and the cognitive correlates of self-report, performance-based, and direct observation measures commonly used as proxy measures for everyday functioning. Participants were 88 community-dwelling, cognitively healthy older adults (age 50-86 years). Participants completed standardized neuropsychological tests and questionnaires, and performed eight activities of daily living (e.g., water plants, fill a medication dispenser) while under direct observation in a campus apartment. All proxy measures of everyday function were sensitive to the effects of healthy cognitive aging. After controlling for age, cognitive predictors explained a unique amount of the variance for only the performance-based behavioral simulation measure (i.e., Revised Observed Tasks of Daily Living). The self-report instrumental activities of daily living (IADL) and the performance-based everyday problem-solving test (i.e., EPT) did not correlate with each other; however, both were unique predictors of the direct observation measure. These findings suggest that neuropsychologists must be cautious in making predictions about the quality of everyday activity completion in cognitively healthy older adults from specific cognitive functions. The findings further suggest that a self-report of IADLs and the performance-based EPT may be useful measures for assessing everyday functional status in cognitively healthy older adults.

As individuals age they report increasing numbers of sleep problems (e.g., increased nighttime wakings) and this poorer sleep quality has been associated with increased risk for various medical conditions; however limited research has focused on the implications of sleep quality on everyday functioning in older adulthood. We compared three methods of sleep data collection (wrist actigraphy, self-report questionnaires, and sleep diary) and evaluated their relationships with three approaches to assessing everyday functioning (direct observation, self-report, and paper-and-pencil-based problem-solving tasks) in cognitively healthy older adults. Consistent with previous research, subjective sleep measures correlated significantly with each other but did not correlate with objective sleep measures. Multiple regression analyses revealed neither objective nor subjective sleep measures predicted everyday functioning. Individual variability in sleep may affect prediction of everyday functioning using a cross-sectional sample. Future research should investigate the combined influence of sleep and cognitive factors on everyday functioning in older adults.

There is currently limited understanding of the course of change in everyday functioning that occurs with normal aging and dementia. To better characterize the nature of this change, we evaluated the types of errors made by participants as they performed everyday tasks in a naturalistic environment.Participants included cognitively healthy younger adults (YA; n = 55) and older adults (OA; n = 88), and individuals with mild cognitive impairment (MCI: n = 55) and dementia (n = 18). Participants performed 8 scripted everyday activities (e.g., filling a medication dispenser) while under direct observation in a campus apartment. Task performances were coded for the following errors: inefficient actions, omissions, substitutions, and irrelevant actions.Performance accuracy decreased with age and level of cognitive impairment. Relative to the YAs, the OA group exhibited more inefficient actions which were linked to performance on neuropsychological measures of executive functioning. Relative to the OAs, the MCI group committed significantly more omission errors which were strongly linked to performance on memory measures. All error types were significantly more prominent in individuals with dementia. Omission errors uniquely predicted everyday functional status as measured by both informant-report and a performance-based measure.These findings suggest that in the progression from healthy aging to MCI, everyday task difficulties may evolve from task inefficiencies to task omission errors, leading to inaccuracies in task completion that are recognized by knowledgeable informants. Continued decline in cognitive functioning then leads to more substantial everyday errors, which compromise ability to live independently.

Most neuropsychological assessments include at least one measure that is administered, scored, or interpreted by computers or other technologies. Despite supportive findings for these technology-based assessments, there is resistance in the field of neuropsychology to adopt additional measures that incorporate technology components. This literature review addresses the research findings of technology-based neuropsychological assessments, including computer-, and virtual reality-based measures of cognitive and functional abilities. We evaluate the strengths and limitations of each approach, and examine the utility of technology-based assessments to obtain supplemental cognitive and behavioral information that may be otherwise undetected by traditional paper and pencil measures. We argue that the potential of technology use in neuropsychological assessment has not yet been realized, and continued adoption of new technologies could result in more comprehensive assessment of cognitive dysfunction and in turn, better informed diagnosis and treatments. Recommendations for future research are also provided.

In this paper, we describe an approach to developing an ecologically valid framework for performing automated cognitive assessment. To automate assessment, we use a machine learning approach that builds a model of cognitive health based on observations of activity performance and uses lab-based assessment to provide ground truth for training and testing the learning algorithm. To evaluate our approach, we recruited older adults to perform a set of activities in our smart home test-bed. While participants perform activities, sensors placed in the smart home unobtrusively capture the progress of the activity. During analysis, we extract features that indicate how well participants perform the activities. Our machine-learning algorithm accepts these features as input and outputs the cognitive status of the participants as belonging to one of two groups: Cognitively healthy or Dementia. We conclude that machine-learning algorithms can distinguish between cognitively healthy older adults and older adults with dementia given adequate features that represent how well they have performed the activity.

The clock drawing test (CDT) has been used as a screening tool for identifying cognitive decline in individuals with Alzheimer disease (AD) and other dementias but has not been extensively evaluated for categorizing individuals with mild cognitive impairment (MCI). This study used both an established quantitative scoring system and a revised scoring method based on qualitative error criteria developed by Rouleau et al to demonstrate the sensitivity of the CDT to MCI. Participants were 66 cognitively healthy older adult, 33 individuals with MCI, and 33 individuals with AD. Sensitivity analyses of the 2 clock drawing methods revealed that the Modified Rouleau scoring method was more sensitive to MCI than the original Rouleau scoring system. Conceptual, graphic, and spatial-planning difficulties were the most commonly committed errors on the CDT across all groups, and conceptual errors along with graphic difficulties were committed more often in the MCI group than the cognitively healthy control group. Participants in the AD group exhibited greater deficits in all error categories and significantly lower total CDT scores compared to both the MCI and older adult controls. Findings observed in this study suggest that qualitative observations of clock drawing errors can help increase sensitivity of the CDT to MCI and that use of a more detailed scoring system is necessary to differentiate individuals with MCI from cognitively healthy older adults.