Extended, continuous measures of functional status in community dwelling persons with Alzheimer's and related dementia: Infrastructure, performance, tradeoffs, preliminary data, and promise

Constricting Life Space and Likelihood of Neurodegenerative Disease in Community-Dwelling Older Men

Importance

Life space is a measure of the frequency, range, and independence of movement through the environment. There is increasing interest in life space as a holistic measure of function in older adults, but the association between change in life space and incident neurodegenerative disease is unknown.

Objective

To evaluate the association between change in life space and cognitive decline or incident neurodegenerative disease over 7 years among community-dwelling older men.

Design, Setting, and Participants

In this cohort study, logistic regression analyses were used to examine the association of baseline and change in life space with change in cognition unadjusted and adjusted for demographics, cardiovascular risk factors, depression, gait speed, and physical activity. Mixed linear effects models were used to evaluate the association between change in life space and change in cognition. Men were recruited from 6 US sites to participate in a prospective, community-based cohort study of aging and followed-up from 2007 to 2014. Individuals with prevalent dementia or Parkinson disease (PD) at baseline were excluded. Data were analyzed from May 2022 to September 2023.

Exposure

Life space, assessed using the University of Alabama at Birmingham Life Space Assessment and divided into tertiles.

Main Outcomes and Measures

Participants completed the Modified Mini-Mental State (3MS) Test, and Trail-Making Test Part B at baseline and 7 years later. At follow-up, participants were asked about a new physician diagnosis of dementia and PD.

Results

A total of 1684 men (mean [SD] age, 77.1 [4.2] years) were recruited and over 7 years of follow-up, 80 men (4.8%) developed dementia and 23 men (1.4%) developed PD. Mean (SD) life space score was 92.9 (18.7) points and mean (SD) change was -9.9 (22.3) points over follow up. In the adjusted model, each 1-SD decrement in life space was associated with increased odds of dementia (odds ratio [OR], 1.59; 95% CI, 1.28-1.98) but not PD (OR, 1.48; 95% CI, 0.97-2.25). For each 1-SD decrement in life space, men worsened by 20.6 (95% CI, 19.8-21.1) seconds in their Trails B score (P < .001) and declined by 1.2 (95% CI, 1.0-1.3) points in their 3MS score (P < .001) over 7 years.

Conclusions and Relevance

In this study of 1684 men followed up over 7 years, change in life space was associated with faster cognitive decline and increased likelihood of neurodegenerative illness. Future studies should examine the role of clinician assessments or wearable electronics in tracking life space in older adults at risk of cognitive decline and neurodegenerative illness.

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.

Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults

Assessments of Life-space Mobility (LSM) evaluate the locations of movement and their frequency over a period of time to understand mobility patterns. Advancements in and miniaturization of GPS sensors in mobile devices like smartwatches could facilitate objective and high-resolution assessment of life-space mobility. The purpose of this study was to compare self-reported measures to GPS-based LSM extracted from 27 participants (44.4% female, aged 65+ years) who wore a smartwatch for 1-2 weeks at two different site locations (Connecticut and Florida). GPS features (e.g., excursion size/span) were compared to self-reported LSM with and without an indicator for needing assistance. Although correlations between self-reported measures and GPS-based LSM were positive, none were statistically significant. The correlations improved slightly when needing assistance was included, but statistical significance was achieved only for excursion size (r=0.40, P=0.04). The poor correlations between GPS-based and self-reported indicators suggest that they capture different dimensions of life-space mobility.

Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development

Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.

Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment

BACKGROUND

The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI).

METHODS

One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory.

RESULTS

Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03).

CONCLUSIONS

The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

Smart-Home Concept for Remote Monitoring of Instrumental Activities of Daily Living (IADL) in Older Adults with Cognitive Impairment: A Proof of Concept and Feasibility Study

Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of three major components: (1) wireless physical tags (pTAG) attached to objects of interest, (2) a pendant-sensor to monitor physical activities and detect interaction with pTAGs, and (3) an interactive tablet as a gateway to transfer data to a secured cloud. Four studies, including an exploratory clinical study with five older adults with clinically confirmed cognitive impairment, who used IADLSys for 24 h/7 days, were performed to confirm IADLSys feasibility, acceptability, adherence, and validity of detecting IADLs of interest and physical activity. Exploratory tests in two cases with severe and mild cognitive impairment, respectively, revealed that a case with severe cognitive impairment either overestimated or underestimated the frequency of performed IADLs, whereas self-reporting and objective IADL were comparable for the case with mild cognitive impairment. This feasibility and acceptability study may pave the way to implement the smart-home concept to remotely monitor IADL, which in turn may assist in providing personalized support to people with cognitive impairment, while tracking the decline in both physical and cognitive function.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Peace of mind: A community-industry-academic partnership to adapt dementia technology for Anishinaabe communities on Manitoulin Island

Introduction

Aging Technologies for Indigenous Communities in Ontario (ATICON) explores the technology needs of Anishinaabe older adults in the Manitoulin region of Northern Ontario. Our program of research addresses inequitable access to supportive technologies that may allow Indigenous older adults to successfully age in place.

Methods

Using Indigenous research methodologies (IRM) and community-based participatory research (CBPR) we explored the acceptability of CareBand - a wearable location and activity monitoring device for people living with dementia using a LoRaWAN, a low-power wide-area network technology. We conducted key informant consultations and focus groups with Anishinaabe Elders, formal and informal caregivers, and health care providers (n = 29) in four geographically distinct regions.

Results

Overall, participants agreed that CareBand would improve caregivers' peace of mind. Our results suggest refinement of the technology is necessary to address the challenges of the rural geography and winter weather; to reconsider aesthetics; address privacy and access; and to consider the unique characteristics of Anishinaabe culture and reserve life.

Conclusion

All three partners in this research, including the Indigenous communities, industry partner, and academic researchers, benefited from the use of CBPR and IRM. As CareBand is further developed, community input will be crucial for shaping a useful and valued device.

Long-term digital device-enabled monitoring of functional status: Implications for management of persons with Alzheimer's disease

INTRODUCTION

Informal caregiving is an essential element of health-care delivery. Little data describes how caregivers structure care recipients' lives and impact their functional status.

METHODS

We performed observational studies of community dwelling persons with dementia (PWD) to measure functional status by simultaneous assessment of physical activity (PA) and lifespace (LS). We present data from two caregiver/care-recipient dyads representing higher and average degrees of caregiver involvement.

RESULTS

We acquired >42,800 (subject 1); >41,300 (subject 2) PA data points and >154,500 (subject 1); >119,700 (subject 2) LS data points over 15 months of near continuous observation. PA and LS patterns provided insights into the caregiver's role in structuring the PWD's day-to-day function and change in function over time.

DISCUSSION

We show that device-enabled functional monitoring (FM) can successfully gather and display data at resolutions required for dementia care studies. Objective quantification of individual caregiver/care-recipient dyads provides opportunities to implement patient-centered care.

Acceptability and Feasibility of a Multicomponent Telehealth Care Management Program in Older Adults With Advanced Dementia in a Residential Memory Care Unit

The aim of the current study was to examine the acceptability and feasibility of a multicomponent care management program in older adults with advanced dementia in a long-term memory care unit. Eighteen older adults with moderate to severe dementia were asked to wear an activity monitor (Fitbit Charge 2 HR) and participate in a once monthly telehealth intervention via iPads over a 6-month period. Activity monitor data were used to assess compliance. Acceptability was assessed through qualitative interviews conducted with the caregiving staff on the memory unit. The care management program was acceptable to residents and their caregivers. Results indicated that the care management program is feasible in older adults with advanced dementia although activity monitor adherence was better during the day than at night. Telehealth session compliance was excellent throughout the study. A long-term multicomponent dementia care program is acceptable and feasible in individuals with advanced dementia. Future studies should aim to evaluate whether data received from activity monitors can be used in a dementia care intervention program.

Examining Mobile Technologies to Support Older Adults With Dementia Through the Lens of Personhood and Human Needs: Scoping Review

Background

With the world’s rapidly growing older adult population, there is an increase in the number of people living with dementia. This growth leads to a strain on their caregivers and our health care system and to an increased attention on mitigating strain by using mobile technology to sustain the independence of people with dementia. However, less attention is given to whether these technologies meet the stated and unstated needs of people with dementia.

Objective

The aim of this study was to provide an overview of the current research on mobile technologies for people with dementia, considering the current research through the lens of personhood and human needs, and to identify any gaps that represent research opportunities.

Methods

We performed a systematic search in Medical Literature Analysis and Retrieval System Online (MEDLINE), Web of Science, PsycINFO, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Excerpta Medica dataBASE (EMBASE), and the Cochrane Central Register of Controlled Trials (CENTRAL) in October 2018. We screened 5560 articles and identified 24 that met our inclusion and exclusion criteria. We then performed thematic analysis to organize the articles by the types of support mobile technologies provide and mapped those types of support to human needs to identify the gaps in support.

Results

Articles described research on mobile technologies that support people with dementia to (1) perform daily activities, (2) maintain social interaction, (3) aid memory, (4) engage in leisure activities, (5) track location, and (6) monitor health. At least one type of support mapped to each human need, with most supporting lower-level needs such as physiological and safety needs. Little attention seems to be paid to personhood.

Conclusions

Mobile technologies that support daily activities, relationships, memory, leisure activities, health, and safety can partially compensate for decreased function owing to dementia, but the human needs of people with dementia are often not adequately considered. Most technologies support basic physiological and safety needs, whereas many pay little attention to higher-level needs such as self-esteem and agency. Important research opportunities include using person-centered methods to develop technology to meet higher-level needs and to preserve personhood by incorporating human and psychological needs of people with dementia along with ethical considerations.

Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia: Feasibility Case Series

BACKGROUND

Mobile and wearable devices are increasingly being used to support our everyday lives and track our behavior. Since daily support and behavior tracking are two core components of cognitive rehabilitation, such personal devices could be employed in rehabilitation approaches aimed at improving independence and engagement among people with dementia.

OBJECTIVE

The aim of this work was to investigate the feasibility of using smartphones and smartwatches to augment rehabilitation by providing adaptable, personalized support and objective, continuous measures of mobility and activity behavior.

METHODS

A feasibility study comprising 6 in-depth case studies was carried out among people with early-stage dementia and their caregivers. Participants used a smartphone and smartwatch for 8 weeks for personalized support and followed goals for quality of life. Data were collected from device sensors and logs, mobile self-reports, assessments, weekly phone calls, and interviews. This data were analyzed to evaluate the utility of sensor data generated by devices used by people with dementia in an everyday life context; this was done to compare objective measures with subjective reports of mobility and activity and to examine technology acceptance focusing on usefulness and health efficacy.

RESULTS

Adequate sensor data was generated to reveal behavioral patterns, even for minimal device use. Objective mobility and activity measures reflecting fluctuations in participants' self-reported behavior, especially when combined, may be advantageous in revealing gradual trends and could provide detailed insights regarding goal attainment ratings. Personalized support benefited all participants to varying degrees by addressing functional, memory, safety, and psychosocial needs. A total of 4 of 6 (67%) participants felt motivated to be active by tracking their step count. One participant described a highly positive impact on mobility, anxiety, mood, and caregiver burden, mainly as a result of navigation support and location-tracking tools.

CONCLUSIONS

Smartphones and wearables could provide beneficial and pervasive support and monitoring for rehabilitation among people with dementia. These results substantiate the need for further investigation on a larger scale, especially considering the inevitable presence of mobile and wearable technology in our everyday lives for years to come.

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background

Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others.

Objective

The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices.

Methods

A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week.

Results

The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones.

Conclusions

This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant.