Abstract
Detecting early signs of cognitive decline is crucial for early detection and treatment of Alzheimer's Disease. Most of the current screening tools for Alzheimer's Disease represent a significant burden, requiring invasive procedures, or intensive and costly clinical testing. Recent findings have highlighted changes to speech and language patterns that occur in Alzheimer's Disease, and may be detectable prior to diagnosis. Automated tools to assess speech have been developed that can be used on a smartphone or tablet, from one's home, in under 10 min. In this study, we present the results of a study of older adults who completed a digital speech assessment task over a 6-month period. Participants were grouped according to those who scored above (N = 18) or below (N = 18) the recommended threshold for detecting cognitive impairment on the Montreal Cognitive Assessment (MoCA) and those with diagnoses of mild cognitive impairment (MCI) or early Alzheimer's Disease (AD) (N = 14). Older adults who scored above the MoCA threshold had better performance on speech composites reflecting language coherence, information richness, syntactic complexity, and word finding abilities. Those with MCI and AD showed more rapid decline in the coherence of language from baseline to 6-month follow-up, suggesting that this score may be useful both for detecting cognitive decline and monitoring change over time. This study demonstrates that automated speech assessments have potential as sensitive tools to detect early signs of cognitive impairment and monitor progression over time.
Highlights
There is a clear and pressing need for sensitive tools to aid in the detection and monitoring of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) [1,2,3,4,5]
Word finding difficulty [r(46) = 0.83, p < 0.001] and Information units [r(46) = 0.69, p < 0.001] had the highest associations between scores at baseline and 1month, while Global coherence [r(46) = 0.38, p = 0.007] and Syntactic complexity [r(46) = 0.53, p < 0.001] had moderate, but still significant, correlations between the first two visits. This proof-of-concept study showed that a digital speech assessment can be used to derive measures of linguistic abilities which are sensitive to early signs of cognitive impairment in older adults
The word-finding difficulty and information content scores were significantly correlated with Montreal Cognitive Assessment (MoCA) scores, and had the highest test-retest reliability between baseline and 1-month testing sessions
Summary
There is a clear and pressing need for sensitive tools to aid in the detection and monitoring of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) [1,2,3,4,5]. Given the prevalence of Alzheimer’s disease and the aging populations in many countries [6], it will be essential to have tools to help identify the presence of cognitive impairment relating to MCI and AD that can be deployed frequently, and at scale This need will only increase as effective interventions are developed, requiring the ability to identify patients early in order to facilitate prevention or treatment of disease [7]. While classification models are well-suited to potentially aid with disease diagnosis or screening, they can be challenging to interpret based on their multivariate nature which may make it difficult to determine which aspects of speech and language contribute to the classification Such models are not ideal for longitudinal tracking to determine if symptoms are worsening over time or improving with treatment. The results from this study serve as a proof-ofconcept of how digital speech assessments can be used as quick, naturalistic, remote assessments of language abilities and cognitive status
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