Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment
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 findin...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2021.749758/full |
| _version_ | 1818444172985630720 |
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| author | Jessica Robin Mengdan Xu Liam D. Kaufman William Simpson William Simpson |
| author_facet | Jessica Robin Mengdan Xu Liam D. Kaufman William Simpson William Simpson |
| author_sort | Jessica Robin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T19:11:43Z |
| format | Article |
| id | doaj.art-e12b1f15f5fa4bd7993aded3e26700e8 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-12-14T19:11:43Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-e12b1f15f5fa4bd7993aded3e26700e82022-12-21T22:50:42ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2021-10-01310.3389/fdgth.2021.749758749758Using Digital Speech Assessments to Detect Early Signs of Cognitive ImpairmentJessica Robin0Mengdan Xu1Liam D. Kaufman2William Simpson3William Simpson4Winterlight Labs, Toronto, ON, CanadaWinterlight Labs, Toronto, ON, CanadaWinterlight Labs, Toronto, ON, CanadaWinterlight Labs, Toronto, ON, CanadaDepartment of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, CanadaDetecting 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.https://www.frontiersin.org/articles/10.3389/fdgth.2021.749758/fullspeechdigital biomarkerlanguagemild cognitive impairmentAlzheimer's disease |
| spellingShingle | Jessica Robin Mengdan Xu Liam D. Kaufman William Simpson William Simpson Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment Frontiers in Digital Health speech digital biomarker language mild cognitive impairment Alzheimer's disease |
| title | Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment |
| title_full | Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment |
| title_fullStr | Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment |
| title_full_unstemmed | Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment |
| title_short | Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment |
| title_sort | using digital speech assessments to detect early signs of cognitive impairment |
| topic | speech digital biomarker language mild cognitive impairment Alzheimer's disease |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2021.749758/full |
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