Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment

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.

Keywords: Alzheimer's disease; digital biomarker; language; mild cognitive impairment; speech.

Figure 1

Schematic of the picture description task, part of the Winterlight Assessment (WLA).

Figure 2

Scores on speech aggregates that showed significant differences between groups. (A) Word-finding difficulty scores, reflecting increased pauses, and slower speech, were highest in the MCI/AD group and lowest in the High MoCA group. (B) Information units scores, reflecting how much content was accurately described in the pictures, were highest in the High MoCA group and lowest in the MCI/AD group. (C) Global coherence scores, reflecting the semantic relatedness of descriptions to the key words in the picture, were highest in the High MoCA group and had the greatest decline over 6-months in the MCI/AD group. (D) Syntactic complexity scores, reflecting the complexity of the sentences used, were highest in the High MoCA group.

Figure 3

Correlations between the baseline speech aggregates scores and baseline MoCA scores, including participants in all three groups. Word-finding difficulty (A) and Information units (B) had significant correlations with baseline MoCA scores, indicating that those with higher MoCA scores demonstrated less word-finding difficulty and increased accurate information content in their descriptions. Global coherence (C) and Syntactic complexity (D) were not significantly correlated with baseline MoCA scores.