Accepted for/Published in: JMIR Research Protocols
Date Submitted: Dec 27, 2019
Date Accepted: Feb 26, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
EVALUATE-AD: Ecologically Valid, Longitudinal, and Unbiased Assessment of Treatment Efficacy in Alzheimer’s Disease
ABSTRACT
Background:
Current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests, and biomarkers. This methodology is limited by recall bias and recency effects in self-report and by assessments that are brief, episodic, and clinic-based. Continuous monitoring of ecologically valid measures of cognition and daily functioning in the community may provide a more sensitive method to detect subtle, progressive changes in patients with cognitive impairment and dementia.
Objective:
To present an alternative trial approach using a home-based sensing and computing system to detect changes related to common treatments employed in Alzheimer’s disease (AD). In the current paper we introduce an ongoing study which aims to determine the feasibility of capturing sensor-based data in the home and to compare the sensor-based outcomes to conventional outcomes. We describe the methodology behind the assessment protocol and present preliminary results of feasibility measures and examples of data related to medication taking behavior, activity levels, and sleep.
Methods:
EVALUATE-AD is a longitudinal naturalistic observational cohort study recruiting 30 patients and 30 spouse co-resident care partners. Participants are being monitored continuously using a home based-sensing and computing system for up to 24 months. Outcome measures of the automated system are to be compared to conventional clinical outcome measures in AD. Acceptance of the home system and protocol are to be assessed by rates of drop-out and protocol adherence. After completion of the study monitoring period, a composite model using multiple functional outcome measures will be created that represents a behavioral-activity signature of initiating or discontinuing AD-related medications, such as cholinesterase inhibitors, memantine or anti-depressants.
Results:
The sensing and computing system has been well accepted by individuals with cognitive impairment and their care partners. Participants showed good adherence to completion of a weekly online health survey. Daily activity, medication adherence, and total time in bed were able to be derived from algorithms using data from the sensing and computing system. The mean monitoring time for current participants was 14.6 months. Medication adherence, as measured with an electronic pillbox was 77% for participants taking AD-related medications.
Conclusions:
Continuous, home-based assessment provides a novel approach to test the impact of new or existing dementia treatments generating objective, clinically meaningful measures related to cognition and everyday functioning. This approach may ultimately reduce trial durations, sample size needs, and reliance on clinic-based assessment.
Citation
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