Unobtrusive, in-home assessment of older adults’ everyday activities and health events: associations with cognitive performance over a brief observation period

Bernstein, John P K; Dorociak, Katherine E.; Mattek, Nora; Leese, Mira I.; Trapp, Chelsea; Beattie, Zachary; Kaye, Jeffrey; Hughes, Adriana

doi:10.1080/13825585.2021.1917503

Cited by 6 publications

(7 citation statements)

References 56 publications

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“…Of these activities, driving and computer use have both demonstrated promise in detecting conversion to MCI. Research has found that computer use is a cognitively demanding activity and may be a useful indicator of cognitive functioning (Bernstein et al, 2021;Kaye et al, 2014). Using in-home computer monitoring software, past work suggests that in comparison to cognitively intact older adults, those with cognitive impairments demonstrate less frequent at-home computer use, more day-to-day use variability, spend less time using e-mail and word processing applications, and endorse lower confidence in their computer use abilities (Bernstein et al, 2021;Kaye et al, 2014).…”

Section: Background and Objectivesmentioning

confidence: 99%

“…Research has found that computer use is a cognitively demanding activity and may be a useful indicator of cognitive functioning (Bernstein et al, 2021;Kaye et al, 2014). Using in-home computer monitoring software, past work suggests that in comparison to cognitively intact older adults, those with cognitive impairments demonstrate less frequent at-home computer use, more day-to-day use variability, spend less time using e-mail and word processing applications, and endorse lower confidence in their computer use abilities (Bernstein et al, 2021;Kaye et al, 2014). Further, researchers have found that less daily computer use is associated with smaller hippocampal brain volumes, which may serve as an early predictor of AD pathology (Silbert et al, 2016).…”

Section: Background and Objectivesmentioning

confidence: 99%

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Older Adults’ Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Leese

Bernstein

Dorociak

et al. 2021

Innovation in Aging

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Background and Objectives The coronavirus disease 2019 (COVID-19) pandemic has limited older adults’ access to in-person medical care, including screenings for cognitive and functional decline. Remote, technology-based tools have shown recent promise in assessing changes in older adults’ daily activities and mood, which may serve as indicators of underlying health-related changes (e.g., cognitive decline). This study examined changes in older adults’ driving, computer use, mood, and travel events prior to and following the COVID-19 emergency declaration using unobtrusive monitoring technologies and remote online surveys. As an exploratory aim, the impact of mild cognitive impairment (MCI) on these changes was assessed. Research Design and Methods Participants were 59 older adults (41 cognitively intact and 18 MCI) enrolled in a longitudinal aging study. Participants had their driving and computer use behaviors recorded over a 5-month period (75 days pre- and 76 days post-COVID emergency declaration) using unobtrusive technologies. Measures of mood, overnight guests, and frequency of overnight travel were also collected weekly via remote online survey. Results After adjusting for age, gender, and education, participants showed a significant decrease in daily driving distance, number of driving trips, highway driving, and nighttime driving, post-COVID-19 as compared to pre-COVID-19 (p < .001) based on generalized estimating equation models. Further, participants spent more time on the computer per day post-COVID-19 (p = .03). Participants endorsed increases in blue mood (p < .01) and loneliness (p < .001) and decreases in travel away from home and overnight visitors (p < .001) from pre- to post-COVID-19. Cognitive status did not impact these relationships. Discussion and Implications From pre- to post-COVID-19 emergency declaration, participants drove and traveled less, used their computer more, had fewer overnight visitors, and reported greater psychological distress. These results highlight the behavioral and psychological effects of stay-at-home orders on older adults who are cognitively intact and those with MCI.

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Section: Background and Objectivesmentioning

confidence: 99%

Section: Background and Objectivesmentioning

confidence: 99%

Older Adults’ Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Leese

Bernstein

Dorociak

et al. 2021

Innovation in Aging

Self Cite

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“…Determining the minimum necessary sampling frequency for smartphone data is directly tied to feasibility and is critical to inform the design of future studies, as greater frequencies come with greater costs (ie, increasingly expensive sensors, decreased battery life, increased storage needs). This also applies to the optimal length of the data collection period and the study sample size, which may differ depending on the population of interest and the study design [ 120 ], and are not appropriately determined using traditional power calculation methods. Barnett and colleagues [ 127 ] recommend the use of generalized linear mixed models and change point detection methods to inform the sample size and study duration necessary to achieve adequate power in such studies.…”

Section: Methodological Considerations Of the Digital Phenotyping App...mentioning

confidence: 99%

“…The VIBE model proposes a theoretical foundation from which to evaluate metrics of everyday behavior and cognition captured by the digital phenotyping approach, both in studies examining cross-sectional differences in individuals with different levels of cognitive impairment, and over time in individuals with progressive neurodegenerative disease in longitudinal designs. For example, decreasing cognitive abilities may be indexed by decreases in social activity [ 117 , 118 ], technology usage [ 119 , 120 ], positive mood (ie, increased depressive symptoms [ 121 ]), and range of movement/physical activity [ 122 ], which can all be inferred from passive sensor metrics. These activity metrics tend to remain stable in earlier stages and begin to decline more notably in the transition from MCI to dementia.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools

Hackett¹,

Giovannetti²

2022

JMIR Aging

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As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach—digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults.

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“…Briefly, the technology front end includes passive infrared sensors that capture home exits and activity within/transitions between rooms, an actigraphy watch detects step count and sleep duration, an electronic pillbox tracks medication-taking behavior, a scale records daily weight, and an under-the-mattress bedmat captures a variety of sleep metrics (e.g., bed exits at night, total sleep time) and physiologic data such as heart rate and respirations. The READyR Program uses an initial 3 months of sensor data to establish in-home activity patterns ( Bernstein et al, 2021 ). Activity patterns are then matched with ratings from both dyad members on the importance of four care values to the dyad member who is living with dementia: autonomy, safety, social relations, and avoiding burden ( Figure 2 ).…”

Section: Methodsmentioning

confidence: 99%

The Remote Assessment and Dynamic Response Program: Development of an In-Home Dementia-Related Care Needs Assessment to Improve Well-Being

Miller

Solomon

Whitlatch

et al. 2022

Innovation in Aging

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Background and Objectives The Remote Assessment and Dynamic Response (READyR) Program was developed in order to address the current lack of early-stage dementia care planning programs that assess the care needs of persons with dementia. The goal was to create a program informed by care values and ongoing ecologically-valid data. The objectives of this study are to describe the development and design process of the READyR Program, and to evaluate the utility of the READyR Program for identifying dementia-related care needs. Research Design and Methods A prototype of the web-based READyR program tool was first created using digital activity data that was collected by previous studies using a platform of multimodal sensors installed in the homes of older adult couples with and without dementia. Digital activity data were then mapped onto potential care values (e.g., safety & autonomy) to create a values-based needs assessment that is tailored to the individual care dyad. Next, evaluation of the READyR Program by 11 professional dementia care coordinators and case managers (across three semi-structured focus groups) was used to explore the utility of READyR for assessing dementia-related needs. Qualitative description using conventional content analysis was used to iteratively code focus group data and to describe prevalent themes. Results Prevalent focus groups themes included barriers to (e.g., family relationship strain) and facilitators of (e.g., tailored assessments) the optimal process for assessing dementia-related care needs by care coordinators, as well as advantages to (e.g., providing new objective insights into function, and routines) and disadvantages of (e.g., bringing up new questions about care) incorporating the remote monitoring data into a values-based needs assessment. Discussion and Implications READyR has the potential to help family members, as well as care coordinators and providers, gain insight into the values-based care needs of persons with early-stage dementia.

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Unobtrusive, in-home assessment of older adults’ everyday activities and health events: associations with cognitive performance over a brief observation period

Cited by 6 publications

References 56 publications

Older Adults’ Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Older Adults’ Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools

The Remote Assessment and Dynamic Response Program: Development of an In-Home Dementia-Related Care Needs Assessment to Improve Well-Being

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