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BackgroundPoorly 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. ObjectiveThis 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. MethodsA 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. ResultsWe 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

ObjectiveIndividuals with Parkinson's disease (PD) are at risk for increased medication mismanagement, which can lead to worse clinical outcomes. However, the nature of the errors (i.e., undertaking or overtaking medications) contributing to mismanagement and their relationship to cognition in PD is unknown. Therefore, this study sought to examine errors committed on the Medication Management Ability Assessment (MMAA) between PD participants with normal cognition (PD-NC) or mild cognitive impairment (PD-MCI) relative to healthy adults (HA).MethodHA (n = 74), PD-NC (n = 102), and PD-MCI (n = 45) participants were administered the MMAA to assess undertaking, overtaking, and overall errors as well as overall performance (total score). Additionally, participants were administered a comprehensive neuropsychological battery from which cognitive composites of Attention, Learning, Memory, Language, Visuospatial, and Executive Functioning were derived.ResultsSeparate negative binomial regression analyses indicated the PD-MCI group performed significantly worse overall on the MMAA (total score) and committed more undertaking and overall errors relative to HA and PD-NC. In the PD-MCI group, poorer MMAA performance was associated with worse delayed memory performance, whereas cognitive performance was not related to MMAA in HA or PC-NC.ConclusionCompared to PD and healthy adults with normal cognition, PD-MCI patients exhibited greater difficulty with medication management, particularly with undertaking medications. Poorer medication management in PD-MCI was associated with worse delayed recall. Thus, PD-MCI patients experiencing memory problems may require additional assistance with their medications. Findings have clinical relevance suggesting that objective measures of medication errors may assist clinicians in identifying PD patients needing adherence strategies.

OBJECTIVE: The night out task (NOT) was developed as a naturalistic, open-ended, multitasking measure that requires individuals to complete eight subtasks comparable to those encountered during real-world functioning (e.g., pack travel bag, prepare tea). We examined psychometric properties and administration feasibility of this direct observation measure within a clinic-like setting using a tablet-based coding application. METHOD: A sample of 148 community-dwelling older adults (82% cognitively healthy; 18% mild cognitive impairment) and 57 younger adults completed the NOT along with other neurocognitive tests and questionnaires. RESULTS: Inter-rater reliability across NOT primary (i.e., time, accuracy, efficiency) and process-related (e.g., error-types, self-corrections) variables was mostly excellent. NOT primary measures showed expected patterns of convergent and discriminant validity with measures of cognition, demographics, and well-being. External validity was established by the NOT ability to distinguish between age and diagnostic (cognitively healthy vs. mild cognitive impairment) groups. Demonstrating incremental validity, the NOT primary variables (execution time in particular) were predictive of self-reported functional abilities and completion quality of in-home everyday tasks over and earlier variables such as demographics, cognition, and mobility. CONCLUSIONS: These findings suggest that the NOT and its app interface, which allows for continuous logging of observations, are a feasible in-clinic measure to assess cognitive capacities important for real-world functioning. With further validation, the NOT may allow for earlier detection of functional difficulties. Understanding errors and strategies used during NOT performance could also have implications for individualized interventions.

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.

Background/rationaleCompensation 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.MethodsIn 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.ResultsCognitively 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.ConclusionUse 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.

To date, few studies have assessed the ecological validity of current measures of memory and executive functioning, particularly the California Verbal Learning Test (CVLT) and the Wisconsin Card Sorting Test (WCST). A battery of tests, including the CVLT and the WCST, was administered to 28 severely head-injured adults who were 1 year or more postinjury. Hierarchical linear regression was performed to assess the relationship between these cognitive tests and work functioning while statistically controlling for physical disability, emotional distress, and memory compensation strategies utilized by the participants. Results suggest that the CVLT was best at predicting performance on the job; however, the CVLT and the WCST were about equally predictive of the type of position held by a participant. Hence, this study demonstrates the ecological validity of measures of memory and executive functioning for predicting work-related skills. [ABSTRACT FROM PUBLISHER]

Background: As older adults age, they may require assistance completing activities of daily living (ADLs). Robotic assistance can offset healthcare costs and allow older adults to preserve their autonomy. Younger adults are often involved in the design and purchase of these robotic technologies, and must take into account the needs and expectations of the target population (i.e., older adults) to create a robot that the end-user will adopt., Research Aim/questions: This study evaluated the opinions of both younger and older adults regarding the design and performance of the Robot Activity Support (RAS) system. It is important to understand points of agreement and divergence between these populations' perspectives so that effective robotic aids are created for older adults., Methods: Fifty-two younger and older adults completed three scripted tasks with the RAS robot in a smart home environment. Each participant made task errors to cue the robot to offer help via three prompt modalities (guide to the object, video of forgotten step, and video of the full task). After interacting with the robot, participants completed questionnaires to evaluate their opinions of and satisfaction with the robot., Results: There were no differences between younger and older adults' perceptions of the robot across a variety of factors (e.g., likability, cognitive demand), with both age groups expressing generally neutral opinions. Both groups rated the Full Video prompt as least helpful, effective, and liked. Participants recommended the robot's response accuracy, movement speed, alerting style and system flexibility be improved. Younger adults overestimated how much older adults would want a robot like this., Conclusions: This study underscores the importance of testing technology with the end-user, as older adults were less interested in having a similar robot in their home than younger counterparts expected. Future studies will show if older adults' opinions can be improved after making the recommended changes.

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.