An Ecologically Valid, Longitudinal, and Unbiased Assessment of Treatment Efficacy in Alzheimer Disease (the EVALUATE-AD Trial): Proof-of-Concept Study

Background: The current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests


Background
The current clinical trial methodology for testing dementia treatments relies on the time-honored approach of assessing enrolled individuals with a combination of self-report measures (eg, function, mood, adverse events), cognitive and physical function tests (eg, psychometric batteries, timed walks), and biomarkers (eg, neuroimaging-, cerebrospinal fluid-, plasma-based).These measures are typically collected at a baseline visit, followed by randomization of patients to a placebo or treatment arm.Patients are sent home until their next appointment, which may occur at varying time intervals depending on the phase and design of the study.In cases where follow-up is frequent (eg, every 2 weeks), the protocol needs to be modified to cover information carry-over, including practice effects, especially with regard to cognitive tests.Recency effects are also a particular concern, considering that people tend to report what they most recently experienced in the last few days as opposed to the overall quality of change for the entire period or may forget events which occurred during the period closest to the last visit.Across a wide range of behaviors and activities, self-report assessments have been shown to have weak correlations with objective measures [1][2][3][4].In general, the amount of information that can be obtained is restricted by limits on how much testing a patient may be reasonably asked to complete at a single appointment, and by the frequency of appointments as the accuracy of information gained decreases as the testing intervals become more widely dispersed.In all cases, key data related to cognition and functions are rarely ecologically valid.Patients are asked to perform tasks that they typically never do in real life (eg, memorize a list of words, copy figures) or to describe how well they perform a task at home, although it may vary from the reported actual daily performance on those tasks.
The limitations of such an assessment paradigm result in data that is inherently variable, episodic, and proxy based.The cardinal features of change in patients with mild cognitive impairment (MCI) and early Alzheimer disease (AD) are a slow decline in cognition and function punctuated with acute, unpredictable events.This trajectory is challenging to assess with conventional tools and methods that lack sensitivity to subtle changes.Thus, for definitive efficacy trials, large samples followed for long periods of time are needed to determine if there is a meaningful change in cognition or function.In earlier phase trials, it is generally not possible to detect a clinical signal of change in these patients unless the treatment has a substantial effect size.This state of affairs may be transformed by fundamentally changing the assessment paradigm [5][6][7][8][9].If data can be collected continuously as opposed to episodically and infrequently, then the data lends itself to improving the precision of the estimate of the trajectory of change (ie, the slope of a line composed of only a few points is less certain than a line composed of hundreds or thousands of points) as well as intraindividual estimates of change (as opposed to the conventional group change dichotomy) [10].High-dimensional, high-frequency data capture can be achieved by taking advantage of advances in in-home remote sensing, pervasive computing, and high dimensional data analytics.The objective sensed data also provides outcomes that are ecologically valid with immediate tangible clinical meaning.These outcome metrics collectively referred to as digital biomarkers include precise, time-stamped measures of physical activity, medication-taking behavior, sleep, socialization, and everyday cognitive function (eg, using a computer, driving).In addition, the approach employs relatively frequent (weekly) direct queries via email regarding internal states that inherently require direct reporting (eg, pain, mood states) as well as the opportunity to capture adverse events and health economic data (eg, falls, emergency department visits, clinic appointments).

Objectives
Over the past decade, these digital biomarkers have been studied in relevant populations (healthy elderly and those with early MCI), demonstrating that they are sensitive to change and that the technology to capture these changes is feasible to deploy in older adults' homes [7,8].However, the specific use of this multisensor methodology in dementia-specific clinical trials is yet to be evaluated.To begin to understand how these technologies and digital biomarkers may be best employed in dementia clinical trials, we established a longitudinal research study to examine the relative feasibility and sensitivity of this approach in patients taking typical symptomatic treatments for AD (eg, cholinesterase inhibitors and other central nervous system active medications).This study, EVALUATE-AD (Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer's Disease), is currently underway to determine the feasibility of capturing these more continuous and objective everyday measures at home, to assess the comparability of these novel measures to conventional outcome metrics, and to develop a composite model from these functional measures that can detect changes related to initiating and discontinuing common treatments employed in AD-related care.This paper describes the methodology behind the assessment protocol, presents preliminary results of feasibility measures, and provides examples of preliminary data from home-based system sensors.

Study Design
EVALUATE-AD is a longitudinal, naturalistic observational cohort study.Thirty patients and 30 spouse coresident care partners (a total of 60 participants in 30 households) will be enrolled and monitored continuously for up to 24 months with the home-based computing and sensor system.The participants are recruited from an existing cohort of patients followed at the National Institute on Aging (NIA)−Layton Oregon Aging and Alzheimer's Disease Center (OADC).Additionally, new patients seen at the Aging and Alzheimer's clinic and participants referred from community physicians are enrolled if they meet the inclusion criteria.All participants sign informed consent forms (Oregon Health and Science University, OHSU Institutional Review Board number 16515).
Participants with MCI or AD living in the Portland metropolitan and surrounding areas, together with a coresident considered as a care partner are invited to participate in the study.The inclusion criteria for the participants with cognitive impairment and their coresidents include the following: NIA and the Alzheimer's Association clinical criteria for MCI [11] or probable AD [12] and have a Mini-Mental State Examination (MMSE) [13] score of 15 to 30, inclusive; the coresident care partner is functionally independent and has an MMSE of 24 to 30, inclusive; any gender; aged 50 to 90 years; consents to enrollment in the protocol; The coresident care partner is computer literate, defined as being able to send and receive an email; the household owns and uses a desktop or laptop computer; households have a reliable, broadband internet connection; and live in a larger than 1-room apartment.
The exclusion criteria are as follows: Significant neurologic diseases other than MCI or early AD, such as multi-infarct dementia or vascular cognitive impairment, Parkinson's disease, normal pressure hydrocephalus, brain tumor, or a history of significant head trauma with subsequent persistent neurologic deficits; major psychiatric disorders such as major depression, bipolar disorder (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition; DSM-IV criteria) within the past year, or history of schizophrenia (DSM-IV); psychotic features, agitation, or behavioral problems within the last 3 months, which could lead to difficulty complying with the protocol; history of alcohol or substance abuse or dependence within the past 2 years (DSM-IV criteria); any uncontrolled medical condition that is expected to preclude completion of the study, such as late-stage cancers; and more than 2 people live in the participant's residence (overnight visitors are acceptable).
Participants have dementia screening laboratory studies (complete blood count, chemistry panel, thyroid function, vitamin B-12), and brain imaging (magnetic resonance imaging or computed tomography) as part of their initial diagnostic work-up.An in-home screening visit is conducted by a research coordinator where consent is obtained, self-report questionnaires are completed, and neurocognitive tests are administered.A baseline assessment is then performed by a clinician at the participants' residence with a physical and neurological exam and neurocognitive tests.At 12 months and at the end of the study, the self-report questionnaires, physical and neurological exam, and neurocognitive tests are repeated during separate home visits by the research coordinator and clinician.The full assessment protocol, including baseline and follow-up assessments are shown in Table 1.

Components of the Assessment System
After the screening and baseline clinician visits are complete, the sensor system is deployed at the participants' residence by a technology deployment field team according to the established Oregon Center for Aging and Technology (ORCATECH) Life Laboratory protocols [7][8][9] and the Collaborative Aging Research using Technology (CART) initiative [23].Initial data are recorded with regard to the layout of the home to label the use of various spaces (eg, kitchen, bathroom, bedroom, etc).To facilitate deployment of the system in the community, where each home typically has a unique layout, a tablet-based graphing tool is used to automatically record where various sensors are located and their physical adjacencies to other sensors.A schematic of the overall home-based setup is shown in Figure 1; specific details of each component are described in Multimedia Appendix 1 [24][25][26][27][28][29][30][31][32][33][34] and are available on the CART initiative website [35].The components are described briefly as follows: 1. Hub computer: A monitorless computer (Raspberry Pi) functions as a data hub for all the sensors.Data are collected via standard wireless communications protocol (eg, Bluetooth, Zigbee, Wi-Fi) and transferred securely to servers at OHSU.An electronic pillbox (TimerCap iSort) records the times when specific lids (marked by the days of the week) are opened and closed.The electronic pillbox is provided to the participants with cognitive impairment to track their medication usage.Care partners do not use the pillbox.However, care partners can assist or remind the patient to take medications if this is part of their normal routine. 4. Physiological monitoring: Participants are asked to weigh themselves daily using a digital bioimpedance scale (Withings Body Cardio). 5. Driving assessment: An on-board telematic device (Automatic Pro) records data on multiple aspects of driving behavior and connects to the on-board diagnostic (OBD-II) port in each participant's vehicle.

Medication Changes
To provide a conventional measure of changes in cognition that occur when patients transition on or off AD-related medications (cholinesterase inhibitors, memantine, antidepressants, hypnotics), the Telephone Interview for Cognitive Status (TICS) [36] is administered to participants within 1 week of a change in these medications and then subsequently at 6 and 12 weeks.Scores from the TICS are highly correlated with the MMSE [36].Prior studies of cholinesterase inhibitors in individuals with AD administered the MMSE at baseline, 6, and 12 weeks and found a significant difference in MMSE scores at 12 weeks [37,38].Changes in medication are identified using the weekly self-report survey, and an alert is sent from the ORCATECH home-participant management system to a research coordinator when participants indicate a medication change.

Analytic Considerations
This study is a proof of concept designed to construct a composite model of sensor-derived outcome measures that correlate with changes in conventional cognitive test scores seen when individuals start or stop cholinesterase inhibitors, memantine, or other medications, such as antidepressants, that are commonly used for managing AD.As this is an observational study, participants with MCI and AD are followed longitudinally, but medication changes are not dictated or restricted by the study; the participants' primary clinician prescribes these medications according to their practice.Therefore, participants may start, increase the dose, discontinue, or never be on AD-related medications.The Alzheimer Disease Assessment Scale-Cognitive Subscale (ADAS-Cog 11) was chosen for comparison to previous trials that found significant improvements in cognitive function with cholinesterase inhibitors [37][38][39][40] and memantine [41,42] relative to placebo.The ADAS-Cog is performed at baseline, 1 year, and 24 months (study end).The continuous sensor-based measures will be compared with the ADAS-Cog test scores.The effect of changes in dementia-related medications will be analyzed in a subset of participants where those changes occur.Our hypothesis is that changes in medications can be detected by high frequency, in-home monitored data with higher sensitivity (ie, high signal-to-noise ratio) than cognitive test scores, based on a previous study where we could reduce intraindividual variability and thereby reduce the required sample size [10].

Feasibility Measures (Adherence and Dropout)
The first objective of this study is to assess the feasibility of using home-based pervasive computing systems to identify changes in meaningful outcomes in patients across the spectrum of MCI through early AD.Accordingly, the focus of analysis is on measures of adherence, retention, and report of experience with the technologies and protocol.Primary measures are the percentages of completed weekly web-based health and activity forms and dropout at 24 weeks and at the end of the study.Criterion measures are >80% adherence to completion of the weekly web-based survey and 0 dropout (for nonmedical reasons).In addition, information on each participant's experience with respect to the home sensor will be collected using a modified home monitoring technology attitudes and beliefs survey administered at the study end or early discontinuation.

Description of Sensor-Based Measures
The measures from nine individual functional/health domains evaluated are summarized in Table 2.The sensors collect data on a daily or continuous basis that provides information on the core functions and measures.Sensor-derived outcome measures from each domain will be compared with the corresponding conventional assessment measures in subsequent analyses at the completion of study data collection.Self-report of adherence to medication-taking regimen (visual analog scale: ranging from 0% to 100%) Electronic pillbox Medication adherence: Percentage of doses missed in a 7-day period, relative to the prescribed schedule.
Self-report of eight social activities from the OADC Personal and Family History Questionnaire (eg, how often do you have visitors: rarely/never, daily, weekly, monthly, yearly) PIR motion sensors, contact sensors; wearable activity tracking wristwatch; personal computer Socialization and engagement: Time out of home, time alone or with spouse, and computer activity ADAS-Cog c 11 score [14] , MMSE d score [13], NCSE e scores [20], TICS f [36] (completed if participant has an AD g -related medication change) Personal computer or tablet; electronic pillbox; biofunction scale.
Cognitive function: Time to complete online tasks (eg, weekly web-based online health forms), mouse movements, prospective memory for medication, and AM weighing protocol.
FAQ h [16] rating of ability: traveling out of neighborhood, driving, arranging to take buses Home sensors (exit door contact sensors); automobile data port telematic sensor Community mobility: Driving time and distance driving, hard braking, hard accelerations, and most frequent locations out of home Mood: Geriatric Depression Scale (15-item) [15] and Neuropsychiatric Inventory [17]; self-report of health events from the OADC Personal and Family History Questionnaire Personal computer or tablet (online reporting) Health and life events: online self-report (ie, ER i , doctor, or hospital visits, home visitors, mood, pain, loneliness, falls, injuries, change in home space, home assistance received, change in medications) Zarit Caregiver Burden Scale [18] PIR motion sensors; door contact sensors; wearable activity tracking wristwatch Care partner engagement: Time alone or time with cognitively impaired partner, time in bathroom together a PIR: passive infrared.

Participant Characteristics
Thirty homes have been enrolled and had the home assessment system installed (Figure 1), as of February 2020.Here, we present the preliminary data from the first 10 dyads with over first 6 months of monitoring after enrollment, composed of 5 participants with AD and 5 participants with MCI and their respective care partners (20 participants total).Participants with cognitive impairment were, on average, 74.7 years old with 17.7 years of education (Table 3).Mean scores on the MMSE were 24.9 and 13.7 on the ADAS-Cog.Care partners were, on average, 71.1 years old with a mean MMSE score of 29.7.The mean total duration of monitoring for the first 10 homes was 14 months.

Recruitment
The screen failure rate was approximately 68.04% (132/194 individuals) for eligible participants (Figure 2).A total of 274 participants were assessed for eligibility, with 46 not meeting criteria and 34 not responding to messages left about participation in the trial.Other individuals who were contacted declined participation for a variety of reasons.The majority of individuals that declined indicated they were not interested in participating in a clinical trial at the time of contact.Some individuals were more interested in participation in an interventional trial, and others declined because their study partner did not agree to be involved in the trial.The installation of a home assessment system or having to wear an activity monitoring wristwatch was offered as another reason for declining participation in the study.

Acceptance of the Home Assessment System
The home-based pervasive computing system is well tolerated by participants.There have been no withdrawals from the study after the system has been deployed in the home.Exit survey responses were available from the care partners of the two homes that completed the study due to the individual with cognitive impairment transitioning to long-term care.The exit surveys are shown in Multimedia Appendices 2 and 3.Both care partners strongly agreed with the statements I do not mind being monitored unobtrusively in my home, and I did not find the sensor system was an extra source of stress.

Adherence
Adherence to completion of the weekly web-based health survey was 75% for participants with cognitive impairment (n=6, independently completing on the web) and 84% for care partners (n=10; Table 4), with the longest enrollment in the study being 396 days.The completion rate was good for the care partners; however, the completion rate for participants with cognitive impairment was slightly lower than the criterion rate.A total of 4 of the 5 participants with AD required assistance with the completion of the survey each week from their care partner.16) 75 (27) Weekly health form compliance (%), mean (SD) a N/A: not applicable.

Instances of Missing Data
A few technical issues were encountered during the enrollment and data collection of the first few participants.This was mainly due to a major upgrade in the home monitoring system that included, in part, the addition of new devices (eg, activity monitoring wristwatch and new electronic pillbox).These issues were quickly identified and resolved using a series of software and firmware updates.

Sensor-Based Outcome Measures
Table 4 shows a summary of a sample of sensor-based outcome measures comparing care partners with participants with cognitive impairment.

Medication-Taking Behavior
Of the 10 participants with cognitive impairment, 6 (5 with AD, 1 with MCI) were taking AD-related medications (cholinesterase inhibitors, memantine, antidepressants, or sleep aids) and using the electronic pillbox.Overall compliance for the group was 77% (Table 4).Figure 3 shows adherence for a single participant over 7 months for a once-daily medication (venlafaxine).

Activity Sensing and Sleep Behavior
Preliminary data collected from the activity monitoring are presented from a mean of 14.6 months of monitoring in participants with cognitive impairment.In this sample, participants with cognitive impairment (n=10) had a mean step count of 3709 and a mean total sleep time of 7.2 hours per night.
Care partners (n=10) had a mean step count of 4089 and a mean total sleep time of 7.8 hours per night.Compliance to wearing the watch ([number of days with watch data]/[total number of days]×100) for both groups is shown in Table 4.

Changes in Medications
Changes in AD-related medications occurred in 3 participants.The changes were all related to antidepressant medications used to treat behavioral symptoms associated with AD.Two participants had the dose of their medication increased and 1 was started on a new antidepressant medication.Figure 4 shows the results of TICS at the time of medication change (TICS 1), at 6 weeks (TICS 2), and at 12 weeks (TICS 3).In addition to the cognitive testing performed after medication changes, the weekly health report form also collects information that may be relevant to medications treating behavioral and psychiatric symptoms of dementia.Participants are asked if they have felt blue or lonely in the past week.In 1 participant, reports of feeling blue decreased from 33% (7/21) of weekly responses before the medication change to 10% (3/30) afterward, and reports of feeling lonely decreased from 24% (5/21) to 3% (1/30).In the other 2 participants, reports of feeling blue or lonely did not change significantly.In the second participant, there were no reports of feeling blue and only one report of feeling lonely after the medication change.In the third participant, there was one report of feeling blue before the medication change, with none afterward, and only one report of feeling lonely after the medication change.

Initial Findings
The EVALUATE-AD trial aims to determine the feasibility of detecting changes in everyday health and functional domains that are related to cognitive impairment in individuals with MCI and AD.In order to properly utilize remote sensing approaches in clinical trials, potentially more sensitive, objective, and ecologically valid measures digital biomarkers need to be longitudinally acquired and analyzed in real-world environments.Although individuals with MCI have been studied with home-based sensing systems for extended periods of time [44,45], people with early AD and their care partners have not.The collection of digital biomarkers in more natural settings provides the opportunity to collect data on novel outcomes related to daily functioning that cannot be ascertained with conventional clinic-based methods.Additionally, the data collection occurs unobtrusively and with little involvement of the participants, thereby avoiding the addition of potential stress and burden to individuals with cognitive impairment and their care partners.
Preliminary results from this study demonstrate that the deployment of the home-based computing and sensing system is well received by participants.There has been no dropout after study enrollment.Adherence to completion of the weekly health survey is above the expected criterion value for care partners, but slightly below the criterion for individuals with cognitive impairment.The difference between groups may be in part related to the need for assistance in completion of the form in some individuals with AD.Outcome metrics comprising multiple functional and health-related domains are being collected and analyzed from multiperson homes.Examples from preliminary data show how medication adherence, activity levels, and sleep behavior can be collected longitudinally by the home-based system.The use of an electronic pillbox has potential limitations, as the opening and closing of a daily compartment does not guarantee that the medication was ingested.However, daily monitoring of medication-taking behavior with this sensor should provide greater accuracy than the current practice of relying on study participants to bring unused medication to study visits for tabulation.Compliance with wearing the activity-monitoring wristwatch was higher during the day than at night and was collected for 60% of the nights in participants with cognitive impairment.This demonstrates the potential shortcoming of wearable technologies in everyday long-term use.Participants may not feel comfortable wearing the watch during sleep.Additionally, if the device is removed during the day, individuals may forget to put it back on.The activity watch provides the advantage of detecting activity levels even when the participant is outside of the home, but for monitoring sleep, unobtrusive sensors (eg, PIR sensors and movement-sensitive bed mats) may provide more reliable methods for longitudinal monitoring.
Technical issues that arose initially during the study demonstrated problems that can arise as new sensors are integrated into a platform.To ensure that all sensors were functioning, modifications to the alert system in the home monitoring platform were designed.An automated program was created to summarize the data from each sensor in each home on a weekly basis.Sensors that may not have collected data on a specific day still generate a regular check-in signal to ensure that they are functioning properly.This system also provides frequent data reviews to identify issues that arise with data collection as early as possible.Any issues that were detected by the program were identified by the study coordinator and the technology field team for the study, and a solution to the problem was provided either remotely or with a home visit if necessary.The technical solutions to these issues can be applied as new sensors continue to be integrated into research platforms and will help improve the reliability of data collection and prevent loss of data.

Future Analysis
The second objective of the project is to compare the outcome measures of the automated system in different functional and health domains with conventional clinical outcome measures in AD.As part of the evaluation of these novel approaches, comparison to current standards need to be conducted, and three approaches will be applied.Data from the continuous sensor-based measures will be aggregated from 2-month periods anchored on the date of conventional measure acquisition.This is done because the frame of reference of the conventional measure comparator is restricted to a single day and is a method used in previous studies [24].For these comparisons, simple correlations will be calculated between the objective, continuous sensor-derived variables, and the conventional test domains in the total cognitively impaired sample regardless of diagnosis and then in a secondary analysis dividing the group into MCI and early AD.The second approach examines the trajectories of change in continuously collected sensor-based measures, using a previously established procedure to determine these trajectories [10].A subject-specific distribution is calculated for each metric using the data collected during the first month, and an individual-specific threshold of low and high activity is created.The change (or shift) in individual-specific distributions over time can then be examined by tracking how often individuals move below or above their own threshold determined at baseline (ie, during the first 3 months).This approach, which utilizes individual-specific distributions instead of group means, was found to be sensitive to changes even among those with presymptomatic MCI, where detection of change is often quite difficult.Finally, using generalized mixed effects models, the likelihood of having low functional days that differs by diagnostic group (MCI or early AD) and medication status (eg, taking anticholinesterase medication vs not taking them) is determined.Before applying the above approach, we ensure that the trajectories for each metric are reasonable in terms of ranges, direction, and the amount of change using conventional approaches, such as examination of spaghetti plots, linear mixed effects models with or without nonlinear terms, and latent trajectory models (an approach successfully employed in previous work [25]).
The third goal of the project is to develop an objective behavioral-functional signature of patients on cholinesterase inhibitors and related therapies.This measure will be derived from a composite model composed of sensor-based outcome measures that are found to be significant in detecting differences in trajectories by cognitive impairment group as well as those on or off symptomatic AD treatments.The ultimate goal is to examine whether those initially without treatment or adjustment to treatment show changes (ie, improvement) in the derived digital composite score over time when they are on the medication.High-frequency, multidomain data afforded by the pervasive computing environment deployed affords the ability to identify contrasting dynamic changes in relevant functions between different pharmacologic agents.Those relevant to current, approved therapy form a baseline of activities and behaviors to contrast for future trials.This pharmacologic behavioral fingerprinting and, ultimately, the generation of more meaningful composite measures can be generalized to future randomized control trials using new agents.This objective is not the focus of this preliminary report and will be reported in a subsequent publication once data collection for the trial is complete.
Although a focus of this research is to detect treatment-specific changes, the sample size is small, and not all participants in the study will transition on or off a cholinesterase inhibitor, memantine, or a symptom-management medication.Nevertheless, we anticipate that a composite digital biomarker composed of multiple outcome measures derived from the home monitoring system will detect sensitive changes in the digital biomarker signal with increased statistical power.Unlike the presymptomatic subjects enrolled in prior studies [7], the MCI and AD patients recruited in this study are anticipated to experience greater cognitive decline (ie, MMSE declines by 0.02 points per year among presymptomatic subjects or over 5 years of change≈1 MMSE point), with MMSE declines of 2 or 3 points per year observed for AD patients (ie, a >10-fold faster decline) [46].Given that we would see an approximately 8-fold steeper decline in outcomes than previously shown, using this intraindividual approach, we would achieve 80% power to detect a 30% treatment effect size with 30 subjects (20 subjects with medication and 10 subjects without) over 2 years (alpha=.05,2-tailed).The automated sensor-based measures collected in EVALUATE-AD for up to 24 months will provide important measures of variance and trajectory of change data needed for future power estimates.

Conclusion
The use of high-frequency, longitudinal data acquisition appears more sensitive to change than conventional, episodic in-clinic testing.The measures lend themselves to more direct translation to meaningful outcomes for patients and care partners (eg, improved mobility, computer use, better sleep, better medication adherence).These digital biomarkers can be used in combination with conventional clinical assessment methods.A behavioral-pharmacologic signature composed of multiple digital biomarkers could be used to detect changes in cognition and functional status in individuals with cognitive impairment initiating or discontinuing symptomatic treatments.This methodology has the potential to reduce the size and/or length of clinical trials by more precisely estimating the true trajectory of change in participants with high-frequency in-home data and individual-specific distributions.The ultimate goal will be to use these longitudinal and person-specific measures to more effectively test new therapeutics and guide individual responses to therapies in patients.

Figure 1 .
Figure 1.Schematic of the home-based sensor system.EVALUATE-AD: Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer's Disease.

2 .
Activity sensing: Passive infrared (PIR) motion sensors using the Zigbee wireless communication protocol (NYCE Control) are placed in each room in the home and sense participants' motion at home and transitions between rooms.A line of four PIR sensors with more restricted fields of view are placed on the ceiling in an area where the participant walks regularly to detect walking speed.Each participant will also wear an activity-monitoring wristwatch (Withings Steel) to measure individual mobility and sleep measures. 3. Medication-taking behavior: b OADC: Oregon Aging and Alzheimer's Disease Center.c ADAS-Cog: Alzheimer Disease Assessment Scale-Cognitive Subscale.d MMSE: Mini-Mental State Examination.e NCSE: Neurobehavioral Cognitive Status Examination.f TICS: Telephone Interview for Cognitive Status.g AD: Alzheimer disease.h FAQ: Functional Assessment Questionnaire.i ER: emergency room.

Figure 2 .
Figure 2. Participant enrollment and follow-up summary.Two homes were enrolled with a third additional study partner in the home, who also wore an activity monitoring wristwatch.AD: Alzheimer disease.

Figure 3 .
Figure 3.Time of day that medication was taken for each day over 7 months of monitoring by a participant with mild Alzheimer disease.The dots indicate the times at which the pill was taken, and an X indicates when a pill was missed.Overall, participant adherence was 94% over 9 months.

Figure 4 .
Figure 4. Cognitive test scores in the 3 participants with medication changes.The MMSE was completed at the baseline study visit.The Telephone Interview for Cognitive Status were completed over the phone after a change in medication and subsequently at 6 and 12 weeks.MMSE: Mini-Mental State Examination; TICS: Telephone Interview for Cognitive Status.

Table 1 .
Study schedule of assessments.

Table 2 .
Core functions and measures collected and types of sensors used to collect data.Metrics may be event driven (eg, medication taking) or unscheduled (eg, minutes to days of total activity).

Table 4 .
Summary of sensor-based measures in patient participants and care partners.