A Comparison of Functional Abilities in Individuals with Mild Cognitive Impairment and Parkinson's Disease with Mild Cognitive Impairment Using Multiple Assessment Methods.

OBJECTIVE: This study used multiple assessment methods to examine instrumental activities of daily living (IADLs) performance in individuals with Parkinson's disease with mild cognitive impairment (PD-MCI) compared to individuals with mild cognitive impairment (MCI) and cognitively healthy older adults (HOA). Associations between functional performance and cognition were also examined. METHODS: Eighteen individuals with PD-MCI, 48 individuals with MCI, and 66 HOAs were assessed with multiple IADL measures, including direct observation, a performance-based measure, and self- and informant-report questionnaires. Performance on the direct-observation measure was further characterized by coding for four error types: omissions, substitutions, and inefficient and irrelevant/off-task actions. RESULTS: Both the PD-MCI and MCI groups performed more poorly on the overall score for all IADL measures relative to HOAs. Although the PD-MCI and MCI groups did not differ in overall performance, on the direct-observation measure, the PD-MCI group took longer and made more inefficient and irrelevant/off-task errors relative to the HOA and MCI groups, whereas the MCI group made more omission and substitution errors relative to HOAs. Further, the pattern of cognitive correlates that associated most strongly with the functional measures varied across groups and functional assessment methods. CONCLUSION: Compared to HOAs, PD-MCI and MCI groups demonstrated increased difficulties performing everyday activities, and cognitive and motor abilities differentially contributed to the everyday task difficulties of these two groups.

The Everyday Compensation (EComp) Questionnaire: Construct Validity and Associations with Diagnosis and Longitudinal Change in Cognition and Everyday Function in Older Adults.

OBJECTIVE: The Everyday Compensation scale (EComp) is an informant-rated questionnaire designed to measure cognitively based compensatory strategies that support both everyday memory and executive function in the context of completing instrumental activities of daily living (IADLs). Although previous findings provided early support for the usefulness of the initial version of EComp, the current paper further describes the development, refinement, and validation of EComp as a new assessment tool of compensation for IADLs. METHOD: Confirmatory factor analysis (CFA) was used to examine its factor structure. Convergent and predictive validity was evaluated by examining the relationship between EComp and markers of disease, including diagnosis, cognitive change, and trajectories of functional abilities. RESULTS: CFA supported a general compensation factor after accounting for variance attributable to IADL domain-specific engagement. The clinical groups differed in compensatory strategy use, with those with dementia using significantly fewer compensatory strategies as compared to individuals with normal cognition or mild cognitive impairment. Greater levels of compensation were related to better cognitive functions (memory and executive function) and functional abilities, as well as slower rates of cognitive and functional decline over time. Importantly, higher levels of compensation were associated with less functional difficulties and subsequently slower rate of functional decline independent of the level of cognitive impairment. CONCLUSIONS: Engagement in compensatory strategies among older adults has important implications for prolonging functional independence, even in those with declining cognitive functioning. Results suggest that the revised EComp is likely to be useful in measuring cognitively based compensation in older adults.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. OBJECTIVE: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. METHODS: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. CONCLUSIONS: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

The development of a manual-based digital memory notebook intervention with case study illustrations.

There is currently a need to identify feasible and effective interventions to help older individuals suffering from memory loss maintain functional independence and quality of life. To improve upon paper and pencil memory notebook interventions, the Digital Memory Notebook (DMN) application (app) was developed iteratively with persons with cognitive impairment. In this paper we detail a manual-based intervention for training use of the DMN app. A series of three case studies are described to illustrate the clinical process of the DMN intervention, the key components of the intervention and participants' perceptions of the intervention. The Reliable Change Index was applied to pre/post intervention scores that examined everyday memory lapses, daily functioning, coping self-efficacy, satisfaction with life, and quality of life with standardized measures. Following the intervention, two of three participants self-reported a clinically significant reduction in everyday memory lapses and improved everyday functioning. One participant reported clinically significant change in quality of life. All participants demonstrated clinically significant changes in their ability to cope with problems and build self-efficacy. Furthermore, all participants scored in the normative range post-intervention on the measure of satisfaction with life. Clinical observations and participant feedback were used for refinement of the DMN intervention (ClinicalTrials.gov NCT03453554).

Development and psychometric properties of the Healthy Aging Activity Engagement Scale (HAAE).

OBJECTIVES: Accumulating research indicates that engaging in healthy lifestyle behaviors (e.g. exercise, cognitive and social engagement, stress reduction) can prevent illness and disability as people age and improve mental health. The Healthy Aging Activity Engagement (HAAE) scale was developed to holistically assess an individual's level of engagement in healthy aging behaviors across multiple health domains. METHODS: Participants were 275 healthy younger, midlife and older adults. Participants rated engagement in 38 healthy aging lifestyle behaviors over the past 7 days on a scale from 1 (strongly disagree) to 5 (strongly agree). RESULTS: Traditional measurement analysis and Rasch modelling techniques resulted in a 32-item scale with three subscales representing biologic health, social and cognitive strategies, and health safeguard behaviors. With the exception of the health safeguard subscale, the HAAE total score and subscales demonstrated good internal consistency, test-retest reliability, convergent and discriminant validity, and responsiveness to a program designed to promote engagement in healthy brain aging behaviors. DISCUSSION: With further validation, the HAAE may be useful for holistic clinical assessment of behaviors that promote healthy aging, support brain and mental health, and for treatment planning. Furthermore, the HAAE can be used to measure the outcome of multi-domain lifestyle interventions.

Characterising omission errors in everyday task completion and cognitive correlates in individuals with mild cognitive impairment and dementia.

OBJECTIVE: Functional ability declines with age and cognitive impairment. This study investigated errors of omission made by community-dwelling older adults completing everyday tasks in a naturalistic setting. METHOD: Sixty-five cognitively healthy older adults (HOA), 19 individuals with single domain mild cognitive impairment (sdMCI), 33 individuals with multi-domain MCI (mdMCI), and 13 individuals with dementia completed measures of memory, processing speed, working memory, and executive functioning, as well as eight different activities of daily living in a naturalistic environment. Task steps were divided into preparatory, action-oriented, and concluding steps. RESULTS: For action-oriented steps, the number of omission errors increased with level of cognitive impairment beyond sdMCI (i.e., HOA = sdMCI < mdMCI < dementia). In contrast, for preparatory and concluding steps, the dementia group committed more omission errors than the HOA, sdMCI, and mdMCI groups, which did not differ. CONCLUSIONS: The results suggest that the more complex and integrative action-oriented steps may be the first type of everyday task step to be affected in the process of cognitive decline, with preparatory and concluding steps being preserved longer and only showing decline in later stages of impairment (i.e., dementia). Individuals with sdMCI may use other intact abilities to compensate for task omission errors.

A caregiver educational program: A video program to promote aging services technologies awareness.

Research demonstrated that aging services technologies (ASTs) can reduce caregiver burden. However, ASTs are underutilized by caregivers due to poor awareness. This study evaluated the effectiveness of a video-based educational program to increase caregiver AST knowledge among 43 caregivers. Paired sample t-tests showed positive change in AST knowledge, stigma, and intention to engage in AST activities post-program. Caregivers endorsed highly positive AST views pre- and post-program without statistically significant change. Group by time repeated measures ANOVAs showed younger caregivers (<65 years old) had better knowledge and were more open to ASTs (improved attitude and stigma scores) than older caregivers (>65 years old). A significant interaction revealed caregivers of individuals who had fewer, but not more, domains of functional limitation reported a positive change in AST attitude post-program. The video program appeared to be beneficial to all caregivers, who provided vastly positive program feedback.

Robot-Enabled Support of Daily Activities in Smart Home Environments.

Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity errors that may occur in everyday settings. We describe the components of the RAS system and demonstrate its use in a smart home testbed. To evaluate the usability of RAS, we also collected and analyzed feedback from participants who received assistance from RAS in a smart home setting as they performed routine activities.

Effectiveness of a video-based aging services technology education program for health care professionals.

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.

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.

Focused and divided attention abilities in the acute phase of recovery from moderate to severe traumatic brain injury.

PRIMARY OBJECTIVE: Impairments in attention following traumatic brain injury (TBI) can significantly impact recovery and rehabilitation effectiveness. This study investigated the multi-faceted construct of selective attention following TBI, highlighting the differences on visual nonsearch (focused attention) and search (divided attention) tasks. METHODS AND PROCEDURES: Participants were 30 individuals with moderate to severe TBI who were tested acutely (i.e. following emergence from PTA) and 30 age- and education-matched controls. Participants were presented with visual displays that contained either two or eight items. In the focused attention, nonsearch condition, the location of the target (if present) was cued with a peripheral arrow prior to presentation of the visual displays. In the divided attention, search condition, no spatial cue was provided prior to presentation of the visual displays. MAIN OUTCOMES AND RESULTS: The results revealed intact focused, nonsearch, attention abilities in the acute phase of TBI recovery. In contrast, when no spatial cue was provided (divided attention condition), participants with TBI demonstrated slower visual search compared to the control group. CONCLUSIONS: The results of this study suggest that capitalizing on intact focused attention abilities by allocating attention during cognitively demanding tasks may help to reduce mental workload and improve rehabilitation effectiveness.

Examining the impact of formal planning on performance in older adults using a naturalistic task paradigm.

Thirty-two younger adults (YAs) and 64 older adults (OAs) completed the Amap Task, a naturalistic measure designed to evaluate the formulation and execution stages of planning. Participants read a map layout of a university apartment and planned an efficient strategy to complete several tasks. To determine whether execution abilities are augmented by formulating a plan, we compared formal (i.e., written strategy) and informal (i.e., initiate task when ready without writing out a strategy) planning conditions. Participants did not have access to their plan during task execution. Amap scores were also compared with informant-report of everyday functioning. For the formal planning condition, OAs were less accurate and less efficient than YAs during the formulation stage, while there were no group differences in total formulation time for the informal planning condition. Across conditions, OAs obtained poorer execution accuracy and efficiency scores, took longer during execution, and omitted more task parts. Formal planning improved execution efficiency but not accuracy, suggesting one's approach to the task (i.e., efficiency, finesse) may improve with preplanning even if overall accuracy does not. Significant relationships between Amap scores and informant-report of everyday functioning highlight the ecological validity of the Amap as a measure sensitive to everyday planning abilities.

Unsupervised detection and analysis of changes in everyday physical activity data.

Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach. PACD is a framework that detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. We compare the abilities of three change detection algorithms from the literature and one proposed algorithm to capture different types of changes as part of PACD. We illustrate and evaluate PACD on synthetic data and using Fitbit data collected from older adults who participated in a health intervention study. Results indicate PACD detects several changes in both datasets. The proposed change algorithms and analysis methods are useful data mining techniques for unsupervised, window-based change detection with potential to track users' physical activity and motivate progress toward their health goals.

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.

Self-awareness and traumatic brain injury outcome.

PRIMARY OBJECTIVE: Impaired self-awareness following a traumatic brain injury (TBI) can reduce the effectiveness of rehabilitation, resulting in poorer outcomes. However, little is understood about how the multi-dimensional aspects of self-awareness may differentially change with recovery and impact outcome. Thus, this study examined four self-awareness variables represented in the Dynamic Comprehensive Model of Awareness: metacognitive awareness, anticipatory awareness, error-monitoring and self-regulation. RESEARCH DESIGN: This study evaluated change of the self-awareness measures with recovery from TBI and whether the self-awareness measures predicted community re-integration at follow-up. METHODS AND PROCEDURES: Participants were 90 individuals with moderate-to-severe TBI who were tested acutely following injury and 90 age-matched controls. Forty-nine of the TBI participants and 49 controls were re-tested after 6 months. MAIN OUTCOME AND RESULTS: Results revealed that the TBI group's error-monitoring performance was significantly poorer than controls at both baseline and follow-up. Regression analyses revealed that the self-awareness variables at follow-up were predictive of community re-integration, with error-monitoring being a unique predictor. CONCLUSIONS: The results highlight the importance of error-monitoring and suggest that interventions targeted at improving error-monitoring may be particularly beneficial. Understanding the multi-dimensional nature of self-awareness will further improve rehabilitation efforts and understanding of the theoretical basis of self-awareness.

Cognitive rehabilitation multi-family group intervention for individuals with mild cognitive impairment and their care-partners.

There is increasing need for early, pro-active programs that can delay dementia diagnosis and enhance well-being of individuals with mild cognitive impairment (MCI) and their care-partners (i.e., care-dyads). This randomized controlled trial evaluated the efficacy of a combined cognitive rehabilitation and multi-family group treatment (CR-MFG) that was designed to facilitate adoption of newly learned cognitive strategies into the care-dyads everyday lives. Analyzed data included 23 care-dyads who participated in CR-MFG treatment and 23 care-dyads in standard care (SC). The 3-month intervention consisted of individual joining sessions, an educational workshop, and 20/twice weekly multifamily memory strategy training and problem-solving sessions. Everyday functioning, memory, and psychological functioning (i.e., quality-of-life, depression, coping) were assessed. The CR-MFG intervention was associated with significant post-test group differences and improved post-test performances by the MCI participants on performance-based measures of everyday functioning and neuropsychological tests of memory. There was also some suggestion that CR-MFG care-partners perceived positive change in the everyday functioning of the MCI participants. In contrast, no post-test group differences were found for either care-dyad member on the self-report psychological measures; care-partners in the treatment group did self-report improved coping behaviors at post-test. These 3-month results are preliminary but suggestive that CR-MFG may produce modest, practical everyday functional benefits for persons with MCI.

A Clinic-Based Measure of Everyday Planning Ability: The Overnight Trip Task.

OBJECTIVE: Traditional measures of cognitive abilities most used by neuropsychologists are generally low in ecological validity and only capture a small proportion of the variance of a person's true functional capacity. This study evaluates the association between clinic-based performance and parallel real-world completion of an everyday planning test, the Overnight Trip Task (OTT). METHOD: A cross-sectional cohort of 65 community-dwelling older adults completed a battery of cognitive assessments and two formats of the OTT: the paper-and-pencil clinic OTT, which was completed remotely through video conferencing (Zoom), and the parallel real-world OTT (RW-OTT), which was completed at home between sessions. Both formats required participants to plan for what they would pack and prepare for a hypothetical overnight trip based on a provided story that included rules and embedded contingencies. RESULTS: The clinic OTT demonstrated a small-to-moderate relationship with the RW-OTT (r = 0.35) and no relationship with measures of learning, long delay recall, an executive function composite, and a measure of everyday planning. Hierarchical regressions indicated that the clinic OTT demonstrated incremental validity above an executive function composite measure and global cognition when predicting self-reported everyday functioning and RW-OTT performance. CONCLUSIONS: The clinic OTT showed only modest association with the RW-OTT and discriminate but not convergent validity was demonstrated. The clinic OTT showed incremental validity when predicting self-reported everyday functioning and RW-OTT performance above more traditional measures. Before the clinic OTT could be considered a reliable and valid clinic-based measure for predicting real-world behavior, additional research would be needed.

Health literacy in older adults: The newest vital sign and its relation to cognition and healthy lifestyle behaviors.

Health literacy tends to decrease with age, and lower health literacy has been associated with lower levels of physical function, mental health, and medication adherence. The present study examined health literacy in relation to cognition in a sample of community-dwelling older adults. The study also examined the impact of health literacy on engagement in healthy aging lifestyle behaviors. Participants included 128 older adults (age: M = 72.07, SD = 6.71; education: M = 16.34, SD = 2.56; 74% female) who completed a health literacy measure (Newest Vital Sign; NVS), a lifestyle behavior questionnaire (Healthy Aging Activity Engagement scale; HAAE), and several neuropsychological tests. The cognitive domains assessed included memory, executive function, and attention/working memory. Two variables were computed from the NVS to represent the health literacy factors of document and numeracy literacy; these factors demonstrated a small correlation (r = .18). Results revealed that attention/working memory, executive function, and memory were all significantly related to numeracy literacy and overall health literacy. Only memory was significantly related to document literacy. After accounting for age, education, and cognition, a hierarchical regression revealed that health literacy significantly predicted engagement in healthy aging lifestyle behaviors. Multiple cognitive abilities are necessary for searching, finding, and processing information to make health-related decisions. Health literacy accounted for a significant amount of variance in older adults' engagement in everyday lifestyle behaviors. Health literacy skills may be an area of focus for intervention efforts to improve brain health in older adults.

Real-world compensatory strategy use in community-dwelling mid-life and older adults: An evaluation of quality.

Objective: Older adults often spontaneously engage in compensatory strategies (CS) to support everyday task completion, but factors that influence success of chosen CS remain unclear. This study examines whether real-world prospective memory (PM) task completion is better predicted by CS count or a CS quality rating. Method: Seventy mid-life and older adult participants were presented four novel, real-world PM tasks via remote assessment and encouraged to use their typical CS. The examiner captured detailed information about planned CS at task presentation (T1) and utilized CS at follow-up testing (T2). From this information, count (CS Count; quantity of CS) and quality (CS Quality; rating of CS thoroughness and utility) scores were coded separately for the planned and utilized CS. PM task performance accuracy was also coded (PM Accuracy). Results: Hierarchical regressions revealed planned CS Count and Quality did not predict PM Accuracy. In contrast, the utilized CS Quality predicted a significant amount of PM Accuracy variance over and above CS Count, global cognition, and age (R2 = .47, ΔR2 = .24, ΔF = 29.36, p < .001, f2 = .45). Furthermore, utilized CS Quality accounted for a similar amount of variance in PM Accuracy when utilized CS Count was removed from the model. Conclusions: This study's CS coding system can capture and quantify the quality of strategies, which uniquely predicts real-world PM performance. This coding system may provide researchers with a nuanced CS measure and lead to improved CS interventions designed to support everyday PM performance, such as targeted CS trainings.

Efficacy of Digital and Non-Digital Compensatory Strategies in Supporting Prospective Memory Task Completion among Community-Dwelling Mid-Life and Older Adults.

OBJECTIVE: Compensatory strategies can be used to help mid-life and older adults successfully manage instrumental activities of daily living that rely upon prospective memory. This study compared the quality of digital and non-digital compensatory strategies in supporting accurate completion of naturalistic, real-world prospective memory tasks. METHOD: Participants included 70 community-dwelling mid-life and older adults. In this cross-sectional study, participants were tested remotely via Zoom in their homes. They were tasked with completing four real-world prospective memory tasks and encouraged to use their typical compensatory strategies. Utilized strategies were categorized, counted, and assigned quality scores (rating of thoroughness and utility), and prospective memory performance was coded. RESULTS: Participants who used any digital strategies utilized significantly more (ηp2 = .17) and better quality (ηp2 = .12) strategies than participants who did not use any digital strategies. However, the groups demonstrated equivalent prospective memory performance (ηp2 = .006). Within the digital group, participants' digital and non-digital strategies were of similar quality (d = .14) and resulted in similar prospective memory task accuracy (d = .01). CONCLUSIONS: Digital and non-digital strategies led to similar performance on naturalistic prospective memory tasks. Findings suggest that many different types of strategies can provide adequate prospective memory support to healthy mid-life and older adults. To inform development of compensatory strategy interventions, future studies should explore other factors that lead to successful prospective memory, such as the specific strategy type and task type match, across the continuum of cognitive impairment.