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

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study

Introduction

Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.

Methods

A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.

Results

The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.

Discussion

This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

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

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.

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

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 were 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 3 semistructured 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. Clinical Trials Registration Number: NCT04542109.

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

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.

Use of in-home activity monitoring technologies in older adult veterans with mild cognitive impairment: The impact of attitudes and cognition

BACKGROUND

As researchers incorporate in-home technologies to identify and track changes in older adults' cognitive and daily functioning that could lead to early interventions, the attitudes of older adults across the continuum from normal cognitive aging to mild cognitive impairment (MCI) must be assessed to ensure technology adoption and adherence in each unique group.

OBJECTIVE

This exploratory pilot study incorporated both quantitative and qualitative approaches to examine mild cognitive impairment (MCI) and cognitively intact older adults' attitudes (i.e., usability, acceptability, digital readiness, barriers) and adherence to in-home technologies after undergoing 7 months of in-home activity monitoring.

METHOD

Participants were 30 older adult veterans who were classified as cognitively intact (n = 15) or having mild cognitive impairment (MCI) (n = 15) and participated in a longitudinal aging and technology study that monitored their physical activity and computer use.

RESULTS

While MCI older adults endorsed reduced digital readiness (p =.041) and required more in-home technology maintenance visits (p =.041) from staff as compared to cognitively intact older adults, there was no difference in adherence to the study technology (p >.05). Usability and acceptability attitudes in the entire sample predicted adherence to the physical activity monitoring technology employed in the study (p =.008).

CONCLUSION

Findings highlight the potential gap between technology developers and older adult end users, and technologies designed specifically for older adults with MCI should be developed with direct input from older adults with MCI to promote usability and long-term adoption in this clinical population. Larger studies are needed to replicate and increase the generalizability of the current findings.