Feasibility of Deploying Home-Based Digital Technology, Environmental Sensors, and Web-Based Surveys for Assessing Behavioral Symptoms and Identifying Their Precipitants in Older Adults: Longitudinal, Observational Study.

BACKGROUND: Apathy, depression, and anxiety are prevalent neuropsychiatric symptoms experienced by older adults. Early detection, prevention, and intervention may improve outcomes. OBJECTIVE: We aim to demonstrate the feasibility of deploying web-based weekly questionnaires inquiring about the behavioral symptoms of older adults with normal cognition, mild cognitive impairment, or early-stage dementia and to demonstrate the feasibility of deploying an in-home technology platform for measuring participant behaviors and their environment. METHODS: The target population of this study is older adults with normal cognition, mild cognitive impairment, or early-stage dementia. This is an observational, longitudinal study with a study period of up to 9 months. The severity of participant behavioral symptoms (apathy, depression, and anxiety) was self-reported weekly through web-based surveys. Participants' digital biomarkers were continuously collected at their personal residences and through wearables throughout the duration of the study. The indoor physical environment at each residence, such as light level, noise level, temperature, humidity, or air quality, was also measured using indoor environmental sensors. Feasibility was examined, and preliminary correlation analysis between the level of symptoms and the digital biomarkers and between the level of symptoms and the indoor environment was performed. RESULTS: At 13 months after recruitment began, a total of 9 participants had enrolled into this study. The participants showed high adherence rates in completing the weekly questionnaires (response rate: 275/278, 98.9%), and data collection using the digital technology appeared feasible and acceptable to the participants with few exceptions. Participants' severity of behavioral symptoms fluctuated from week to week. Preliminary results show that the duration of sleep onset and noise level are positively correlated with the anxiety level in a subset of our participants. CONCLUSIONS: This study is a step toward more frequent assessment of older adults' behavioral symptoms and holistic in situ monitoring of older adults' behaviors and their living environment. The goal of this study is to facilitate the development of objective digital biomarkers of neuropsychiatric symptoms and to identify in-home environmental factors that contribute to these symptoms.

Differences in Life Space Activity Patterns Between Older Adults With Mild Cognitive Impairment Living Alone or as a Couple: Cohort Study Using Passive Activity Sensing.

BACKGROUND: Measuring function with passive in-home sensors has the advantages of real-world, objective, continuous, and unobtrusive measurement. However, previous studies have focused on 1-person homes only, which limits their generalizability. OBJECTIVE: This study aimed to compare the life space activity patterns of participants living alone with those of participants living as a couple and to compare people with mild cognitive impairment (MCI) with cognitively normal participants in both 1- and 2-person homes. METHODS: Passive infrared motion sensors and door contact sensors were installed in 1- and 2-person homes with cognitively normal residents or residents with MCI. A home was classified as an MCI home if at least 1 person in the home had MCI. Time out of home (TOOH), independent life space activity (ILSA), and use of the living room, kitchen, bathroom, and bedroom were calculated. Data were analyzed using the following methods: (1) daily averages over 4 weeks, (2) hourly averages (time of day) over 4 weeks, or (3) longitudinal day-to-day changes. RESULTS: In total, 129 homes with people living alone (n=27, 20.9%, MCI and n=102, 79.1%, no-MCI homes) and 52 homes with people living as a couple (n=24, 46.2%, MCI and n=28, 53.8%, no-MCI homes) were included with a mean follow-up of 719 (SD 308) days. Using all 3 analysis methods, we found that 2-person homes showed a shorter TOOH, a longer ILSA, and shorter living room and kitchen use. In MCI homes, ILSA was higher in 2-person homes but lower in 1-person homes. The effects of MCI status on other outcomes were only found when using the hourly averages or longitudinal day-to-day changes over time, and they depended on the household type (alone vs residing as a couple). CONCLUSIONS: This study shows that in-home behavior is different when a participant is living alone compared to when they are living as a couple, meaning that the household type should be considered when studying in-home behavior. The effects of MCI status can be detected with in-home sensors, even in 2-person homes, but data should be analyzed on an hour-to-hour basis or longitudinally.

Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study.

BACKGROUND: Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults' physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter. OBJECTIVE: In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis. METHODS: Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi-enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys. RESULTS: Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre- to post-cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22). CONCLUSIONS: A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer.

Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology.

BACKGROUND: Outcome measures available for use in Alzheimer's disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated "digital biomarkers" (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology. OBJECTIVES: The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort. METHODS: Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the "ABC" assessment of AD-associated changes. RESULTS: The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed. DISCUSSION: This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.

Mood and activity changes during the COVID-19 pandemic in rural and urban Veterans and their cohabitants.

OBJECTIVES: We aimed to examine the impact of COVID-19 pandemic-related stay-at-home orders on weekly reports of mood and activity before and during COVID-19 in a sample of older Veterans and their cohabitants. METHODS: Urban and rural Veterans and their cohabitants living in the Pacific Northwest ≥62 years old were enrolled as part of the Collaborative Aging Research Using Technology initiative (n = 100, age = 71.2 ± 6.5, 41% women). Participants reported frequency of social activities (e.g., travel away), physical illness, and mood (blue mood and loneliness) via weekly online health forms. RESULTS: A total of 2,441 weekly online health forms (OHFs) were collected from 100 participants. During the COVID-19 pandemic, blue mood (OR = 4.4, p < .0001) and loneliness (OR = 7.2, p < .0001) were significantly higher than before the pandemic, and travel away from home was significantly lower (OR = 0.5, p < .0001). Prevalence of blue mood and loneliness were not associated with rurality. CONCLUSIONS: The current study established that blue mood and loneliness were significantly more prevalent in older Veterans following COVID-19 stay-at-home orders regardless of rurality. CLINICAL IMPLICATIONS: The COVID-19 pandemic associated health precautions, while necessary to curb acute health risks, have created a unique situation that places vulnerable populations at increased risk of low mood.

Preferences of NIA Alzheimer's Disease Research Center participants regarding remote assessment during the COVID-19 pandemic.

Introduction: During the COVID-19 pandemic, in-person research study visits were moved to an online format using a variety of communication platforms (e.g., Webex and Zoom). Increased technology use among older adults allowed for greater insight regarding the remote research study visit format. Methods: A survey developed by the National Alzheimer's Coordinating Center (NACC) was distributed among 12 Alzheimer's Disease Research Centers (ADRCs). The COVID-19 Technology Accessibility Survey aimed to understand preferences of older adults regarding their research study visits and how they accessed the internet. Results: Among 12 ADRCs, 2070 responses were received (mean age: 72.8 years [standard deviation (SD) = 10.4], mean education: 16.6 years [SD = 2.6], race/ethnicity: 85% White/non-Hispanic). Among respondents, those with some form of cognitive impairment were more likely to prefer remote research study visits (mild cognitive impairment [MCI] vs. normal [odds ratio (OR) = 1.40, P = 0.02] and dementia vs. normal [OR = 1.48, P < 0.01]). Respondents with cognitive impairment were also less likely to have interest in smartphone use during at-home study visits (MCI vs. normal [OR = 0.71, P = 0.02] and dementia vs. normal [OR = 0.63, P < 0.001]). Results were similar regarding tablet use (MCI vs. normal [OR = 0.73, P = 0.04] and dementia vs. normal [OR = 0.72, P = 0.01]). Geographical location was analyzed in terms of the percentage of respondents in each region who preferred remote research study visits: West, 51%; Midwest, 34%; South, 41%; and Northeast, 57% (P < 0.0001). Discussion: Results from the study suggest that there is a growing interest in the remote research study visit format. Further studies will allow for greater understanding and development of this research format.

Association Between Mild Cognitive Impairment and Seasonal Rest-Activity Patterns of Older Adults.

Seasonal variation in rest-activity patterns has been observed in healthy adult populations. This study examined seasonal variation in total time spent overnight in the bedroom by cognitively intact older adults and older adults with mild cognitive impairment (MCI). We hypothesize that seasonal variation in rest-activity patterns is observed in the cognitively intact group and that this variation is disturbed in those with MCI. Study participants were 128 older adults; mean age 85.2 years. Ninety-eight were cognitively intact, and 30 had been diagnosed with MCI. All were enrolled in an ongoing longitudinal study using in-home passive monitoring technology. Infrared presence sensors were placed throughout each participant's home to monitor movement and presence in each room of the home. Activity data was collected from the sensors over a period of up to 527 days. Overnight time in bedroom was found to vary seasonally for the cognitively intact group, with longer times spent overnight in the bedroom during the winter months. This seasonal variation was not observed for those with non-amnestic MCI. MCI is associated with an attenuation of seasonal variation in total time spent in the bedroom at night. Detection of changes in infradian sleep patterns may be an early marker of cognitive decline. Which key determinants are driving these disturbed rhythms, such as features intrinsic to changes in the brain or to environmental factors or external cues, remains an important question for ongoing and future studies.

Monitoring Behaviors of Patients With Late-Stage Dementia Using Passive Environmental Sensing Approaches: A Case Series.

OBJECTIVE: To show the feasibility of using different unobtrusive activity-sensing technologies to provide objective behavioral markers of persons with dementia (PwD). DESIGN: Monitored the behaviors of two PwD living in memory care unit using the Oregon Center for Aging & Technology (ORCATECH) platform, and the behaviors of two PwD living in assisted living facility using the Emerald device. SETTING: A memory care unit in Portland, Oregon and an assisted living facility in Framingham, Massachusetts. PARTICIPANTS: A 63-year-old male with Alzheimer's disease (AD), and an 80-year-old female with frontotemporal dementia, both lived in a memory care unit in Portland, Oregon. An 89-year-old woman with a diagnosis of AD, and an 85-year-old woman with a diagnosis of major neurocognitive disorder, Alzheimer's type with behavioral symptoms, both resided at an assisted living facility in Framingham, Massachusetts. MEASUREMENTS: These include: sleep quality measured by the bed pressure mat; number of transitions between spaces and dwell times in different spaces measured by the motion sensors; activity levels measured by the wearable actigraphy device; and couch usage and limb movements measured by the Emerald device. RESULTS: Number of transitions between spaces can identify the patient's episodes of agitation; activity levels correlate well with the patient's excessive level of agitation and lack of movement when the patient received potentially inappropriate medication and neared the end of life; couch usage can detect the patient's increased level of apathy; and periodic limb movements can help detect risperidone-induced side effects. This is the first demonstration that the ORCATECH platform and the Emerald device can measure such activities. CONCLUSION: The use of technologies for monitoring behaviors of PwD can provide more objective and intensive measurements of PwD behaviors.

Age Predicts Older Adults' Driving Self-Regulation but Not Dangerous Driving Behaviors after Controlling for Executive Function.

OBJECTIVES: The aim of the study was to examine the unique contributions of age to objectively measure driving frequency and dangerous driving behaviors in healthy older adults after adjusting for executive function (EF). METHOD: A total of 28 community-dwelling older adults (mean age = 82.0 years, standard deviation [SD] = 7.5) without dementia who were in good physical health and enrolled in a longitudinal aging study completed several EF and clinical self-report measures at baseline. Participants subsequently had a sensor installed in their vehicle for a mean of 208 (SD = 38, range = 127-257) days. RESULTS: Participants drove for an average of 54 min per day. Mixed-effects models indicated that after controlling for EF, older age was associated with less time driving per day, decreased number of trips, and less nighttime driving. Age was not associated with hard brakes or hard accelerations. DISCUSSION: After accounting for EF, greater age is associated with higher driving self-regulation but not dangerous driving behaviors in healthy older adults. Future studies should recruit larger samples and collect sensor-measured driving data over a more extended time frame to better determine how and why these self-regulation changes take place.

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

In-home assessment of everyday activities over many months to years may be useful in predicting cognitive decline in older adulthood. This study examined whether a comparatively brief data collection period (3 months) may yield similar diagnostic information. A total of 91 community-dwelling older adults without dementia underwent baseline neuropsychological testing and completed weekly computer-based surveys assessing health-related events/activities. A subset of participants wore fitness tracker watches assessing daily sleep and physical activity patterns, used a sensor-instrumented pillbox, and had their computer use frequency recorded on a daily basis. Similar patterns in computer use, sleep and medication use were noted in comparison to prior literature with more extensive data collection periods. Greater computer use and sleep, as well as self-reported pain and independence, were also linked to better cognition. These activities and symptoms may be useful correlates of cognitive function even when assessed over a relatively brief monitoring period.

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.

Corrigendum to: Prolonged Physical Inactivity in Older Adult Couples: A Dyadic Analysis Using Actigraphy.

[This corrects the article DOI: 10.1093/geroni/igaa066.].

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics.

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88-0.94]; 0.59[0.48-0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (ß = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (ß = -0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

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.

Passively-Measured Routine Home Computer Activity and Application Use Can Detect Mild Cognitive Impairment and Correlate with Important Cognitive Functions in Older Adulthood.

BACKGROUND: Computer use is a cognitively complex instrumental activity of daily living (IADL) that has been linked to cognitive functioning in older adulthood, yet little work has explored its capacity to detect incident mild cognitive impairment (MCI). OBJECTIVE: To examine whether routine home computer use (general computer use as well as use of specific applications) could effectively discriminate between older adults with and without MCI, as well as explore associations between use of common computer applications and cognitive domains known to be important for IADL performance. METHODS: A total of 60 community-dwelling older adults (39 cognitively healthy, 21 with MCI) completed a neuropsychological evaluation at study baseline and subsequently had their routine home computer use behaviors passively recorded for three months. RESULTS: Compared to those with MCI, cognitively healthy participants spent more time using the computer, had a greater number of computer sessions, and had an earlier mean time of first daily computer session. They also spent more time using email and word processing applications, and used email, search, and word processing applications on a greater number of days. Better performance in several cognitive domains, but in particular memory and language, was associated with greater frequency of browser, word processing, search, and game application use. CONCLUSION: Computer and application use are useful in identifying older adults with MCI. Longitudinal studies are needed to determine whether decreases in overall computer use and specific computer application use are predictors of incident cognitive decline.

Prolonged Physical Inactivity in Older Adult Couples: A Dyadic Analysis Using Actigraphy.

BACKGROUND AND OBJECTIVES: Many older adults remain inactive despite the known positive health implications of physical activity (improved mood, reduced mortality risk). Physical inactivity is an interdependent phenomenon in couples, but most research examines physical inactivity at the individual level. We estimated the average amount of prolonged physical inactivity for older adult couples and, using dyadic analysis, identified physical and mental health determinants thereof. RESEARCH DESIGN AND METHODS: Forty-six heterosexual older adult couples (age = 70.61 ± 6.56) from the Veterans Integrated Service Network 20 cohort of the Collaborative Aging Research using Technology (CART) initiative were included. The average number per day of prolonged inactive periods (no step counts or sleep activity for ≥30 min) was estimated using actigraphy data collected over a month. RESULTS: Multilevel modeling revealed that, within couples, there was no significant difference between partners in the average amount of inactive periods (p = .28). On average across couples, males and females had an average of 6.90 ± 2.02 and 6.56 ± 1.93 inactive periods per day, respectively. For males, older age was the only variable associated with more inactive periods (ß = 0.15, p = .002). For females, having more depressive symptoms in both dyad members was associated with fewer inactive periods (female: ß = -0.30, p = .03; male: ß = -0.41, p < .001), and more dependence in completing their own instrumental activities of daily living predicted more inactive periods (ß = 2.58, p < .001). DISCUSSION AND IMPLICATIONS: Viewing couples' activity as an interdependent phenomenon, rather than individual, provides a novel approach to identifying pathways to reduce inactivity in older adults, especially when focusing on the mental health and level of independence within the couple.

Subtle Changes in Medication-taking Are Associated With Incident Mild Cognitive Impairment.

INTRODUCTION: Medication-taking is a routine instrumental activity of daily living affected by mild cognitive impairment (MCI) but difficult to measure with clinical tools. This prospective longitudinal study examined in-home medication-taking and transition from normative aging to MCI. METHODS: Daily, weekly, and monthly medication-taking metrics derived from an instrumented pillbox were examined in 64 healthy cognitively intact older adults (Mage=85.5 y) followed for a mean of 2.3 years; 9 transitioned to MCI during study follow-up. RESULTS: In the time up to and after MCI diagnosis, incident MCI participants opened their pillbox later in the day (by 19 min/mo; ß=0.46, P<0.001) and had increased day-to-day variability in the first pillbox opening over time (by 4 min/mo) as compared with stable cognitively intact participants (ß=4.0, P=0.003). DISCUSSION: Individuals who transitioned to MCI opened their pillboxes later in the day and were more variable in their medication-taking habits. These differences increased in the time up to and after diagnosis of MCI. Unobtrusive medication-taking monitoring is an ecologically valid approach for identifying early activity of daily living changes that signal transition to MCI.

Reproducibility and replicability of high-frequency, in-home digital biomarkers in reducing sample sizes for clinical trials.

INTRODUCTION: Reproducibility and replicability of results are rarely achieved for digital biomarkers analyses. We reproduced and replicated previously reported sample size estimates based on digital biomarker and neuropsychological test outcomes in a hypothetical 4-year early-phase Alzheimer's disease trial. METHODS: Original data and newly collected data (using a different motion sensor) came from the Oregon Center for Aging & Technology (ORCATECH). Given trajectories of those with incident mild cognitive impairment and normal cognition would represent trajectories of the control and experimental groups in a hypothetical trial, sample sizes to provide 80% power to detect effect sizes ranging from 20% to 50% were calculated. RESULTS: For the reproducibility, identical P-values and slope estimates were found with both digital biomarkers and neuropsychological test measures between the previous and current studies. As for the replicability, a greater correlation was found between original and replicated sample size estimates for digital biomarkers (r = 0.87, P < .001) than neuropsychological test outcomes (r = 0.75, P < .001). DISCUSSION: Reproducibility and replicability of digital biomarker analyses are feasible and encouraged to establish the reliability of findings.

Step Count Standardization: Validation of Step Counts from the Withings Activite using PiezoRxD and wGT3X-BT.

Daily step counts from the Withings Activite were validated against those collected concurrently from the PiezoRxD Pedometer and the wGT3X-BT Actigraph worn on the waist and on the wrist in free-living conditions from 10 older adult volunteers. The Withings Activite underestimated step counts but showed good correlations with the other devices (Pearson correlation coefficient: 0.850 - 0.891).Clinical Relevance - Although the Withings Activite underestimated steps, they may be used in studies to estimate relative level of physical activity in free-living conditions since they have good correlations with other well-validated devices. Underestimation of steps may be corrected using linear transformation.

Sensing a problem: Proof of concept for characterizing and predicting agitation.

INTRODUCTION: Agitation, experienced by patients with dementia, is difficult to manage and stressful for caregivers. Currently, agitation is primarily assessed by caregivers or clinicians based on self-report or very brief periods of observation. This limits availability of comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of this article is to provide proof of concept for characterizing and predicting agitation using a system that continuously monitors patients' activities and living environment within memory care facilities. METHODS: Continuous and unobtrusive monitoring of a participant is achieved using behavioral sensors, which include passive infrared motion sensors, door contact sensors, a wearable actigraphy device, and a bed pressure mat sensor installed in the living quarters of the participant. Environmental sensors are also used to continuously assess temperature, light, sound, and humidity. Episodes of agitation are reported by nursing staff. Data collected for 138 days were divided by 8-hour nursing shifts. Features from agitated shifts were compared to those from non-agitated shifts using t-tests. RESULTS: A total of 37 episodes of agitation were reported for a male participant, aged 64 with Alzheimer's disease, living in a memory care unit. Participant activity metrics (eg, transitions within the living room, sleep scores from the bedmat, and total activity counts from the actigraph) significantly correlated with occurrences of agitation at night (P < 0.05). Environmental variables (eg, humidity) also correlated with the occurrences of agitation at night (P < 0.05). Higher activity levels were also observed in the evenings before agitated nights. DISCUSSION: A platform of sensors used for unobtrusive and continuous monitoring of participants with dementia and their living space seems feasible and shows promise for characterization of episodes of agitation and identification of behavioral and environmental precipitants of agitation.