Objectives: The goal of this study was to evaluate the ability of semantic (animal naming) and phonemic (FAS) fluency in their ability to discriminate between normal aging, amnestic-Mild Cognitive Impairment (a-MCI), and Alzheimer’s disease (AD). Design: We used binary logistic regressions, multinomial regressions, and discriminant analysis to evaluate the predictive value of semantic and phonemic fluency in regards to specific diagnostic classifications. Setting: Outpatient geriatric neuropsychology clinic. Participants: 232 participants (normal aging = 99, a-MCI = 90, AD = 43; mean age = 65.75 years). Measurements: Mini-mental State Examination (MMSE), Controlled Oral Word Association Test Results: Results indicate that semantic and phonemic fluency were significant predictors of diagnostic classification, and semantic fluency explained a greater amount of the discriminant ability of the model. Conclusions: These results suggest that verbal fluency, particularly semantic fluency, may be an accurate and efficient tool in screening for early dementia in time-limited medical settings.
Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi-domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a twoarm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations.The other arm receives standard of care and personalized data-driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data-driven health coachingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
BackgroundMany clinical trials use performance‐based cognitive or functional test results as primary outcome measures. As few as two measurements, typically pre‐ and post‐intervention, can be used to define a cognitive trajectory. Increasing the number and frequency of assessments can increase power to test the primary hypothesis. However, increased assessment frequency may be expensive and burdensome to participants and staff. Frequent neuropsychological testing may also introduce a practice effect or learning effect. Trials should be designed with attention to the costs and benefits of more frequent assessments.MethodWe conducted two clinical trials and performed Monte Carlo simulations. The Montreal Cognitive Assessment (MoCA) and the MCI Screen (MCIS) were used to assess cognition; the Functional Assessment Staging Tool (FAST) was used to assess function. In addition to simulated data, we employed data from the Coaching for Cognition in Alzheimer’s (COCOA) and Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trials.ResultEffect sizes and measurement variability were similar between the two cognitive screens. More frequent assessments increased power to reject a null hypothesis The major factor driving this improved power was the influence of data from individuals withdrawing from or dropping out of the trial prior to trial end. More frequent testing captured more of the effect of the intervention over time in those individuals, and therefore, increased power for the entire study. The design of the MCIS with rotating word lists facilitated frequent testing without a learning effect. Hypothetically, a more frequently administered assessment might have less precision than a different assessment administered less frequently, and the unit costs of these assessments might differ. Under a wide range of parameters modeled in simulations, the advantages of more frequent testing outweighed decreases in the precision of individual assessments.ConclusionMore frequent testing should be preferred in clinical trials. Use of shorter and/or remote tests may facilitate such frequency, reduce costs, and increase safety during pandemics. In addition to gains in statistical power, dense measurements have other benefits. They may also reveal non‐linear trajectories and better enable longitudinal analyses such as time dependencies and dose‐response relationships.
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