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dc.contributor.authorCay, Gozde
dc.contributor.authorPfeifer, Valeria A
dc.contributor.authorLee, Myeounggon
dc.contributor.authorRouzi, Mohammad Dehghan
dc.contributor.authorNunes, Adonay S
dc.contributor.authorEl-Refaei, Nesreen
dc.contributor.authorMomin, Anmol Salim
dc.contributor.authorAtique, Md Moin Uddin
dc.contributor.authorMehl, Matthias R
dc.contributor.authorVaziri, Ashkan
dc.contributor.authorNajafi, Bijan
dc.date.accessioned2024-05-08T21:18:08Z
dc.date.available2024-05-08T21:18:08Z
dc.date.issued2024-01-12
dc.identifier.citationGozde Cay, Valeria A. Pfeifer, Myeounggon Lee, Mohammad Dehghan Rouzi, Adonay S. Nunes, Nesreen El-Refaei, Anmol Salim Momin, Md Moin Uddin Atique, Matthias R. Mehl, Ashkan Vaziri, Bijan Najafi; Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults. Gerontology 8 April 2024; 70 (4): 429–438. https://doi.org/10.1159/000536250en_US
dc.identifier.pmid38219728
dc.identifier.doi10.1159/000536250
dc.identifier.urihttp://hdl.handle.net/10150/672325
dc.description.abstractIntroduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = -0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively. Discussion: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.en_US
dc.language.isoenen_US
dc.publisherS. Karger AGen_US
dc.rights© 2024 S. Karger AG, Basel.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectCognitive Declineen_US
dc.subjectDementiaen_US
dc.subjectDigital healthen_US
dc.subjectMachine learningen_US
dc.subjectSpeechen_US
dc.subjectwearablesen_US
dc.titleHarnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adultsen_US
dc.typeArticleen_US
dc.identifier.eissn1423-0003
dc.contributor.departmentDepartment of Psychology, University of Arizonaen_US
dc.identifier.journalGerontologyen_US
dc.description.note12 month embargo; first published 12 January 2024en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleGerontology
dc.source.volume70
dc.source.issue4
dc.source.beginpage429
dc.source.endpage438
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countrySwitzerland


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