Baseline characterization of the ARMADA (Assessing Reliable Measurement in Alzheimer's Disease) study cohorts
Tatiana Karpouzian-Rogers1,2, Emily Ho3, Miriam Novack3
1Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Introduction:
The National Institutes of Health (NIH) Toolbox (NIHTB) provides computerized measures of cognition, emotion, sensation, and motor abilities across the lifespan. The ARMADA (Assessing Reliable Measurement in Alzheimer's Disease and Cognitive Aging) study validated the NIHTB in individuals across the cognitive aging spectrum. This article reports the characteristics of our sample of participants.
Methods:
Participants were recruited across nine sites and classified clinically as cognitively normal (NC), with mild cognitive impairment (MCI), or with dementia of the Alzheimer's type (DAT.) They completed the NIHTB at multiple time points and many had at least one Alzheimer's biomarker previously obtained.
Results:
Groups differed with respect to dementia severity levels, as anticipated, but were well-matched across many demographic characteristics.
Discussion:
The ARMADA study demographics and baseline characteristics provide a suitable sample for validating the NIHTB across the cognitive aging spectrum. Other enriched samples (African American participants, Spanish NIHTB, 85+ years of age) will be reported elsewhere.
Highlights:
There is a need for assessments that can detect the early stages of cognitive decline in older adults. The ARMADA (Assessing Reliable Measurement in Alzheimer's Disease and Cognitive Aging) study will validate the National Institutes of Health (NIH) Toolbox across the aging spectrum, including mild cognitive impairment (MCI) and dementia of the Alzheimer's type (DAT). Here we report the characteristics of participants. Groups were well-matched across most demographic characteristics, and clinical characteristics differed as expected. ARMADA study cohorts reflect their respective clinical syndromes for validating the NIH Toolbox.
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