Story Recall in Peer Conflict Resolution Discourse Task to Identify Older Adults Testing Within Range of Cognitive Impairment.
Purpose: The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy old...
| Publicado en: | American Journal of Speech-Language Pathology Vol. 33; no. 5; pp. 2582 - 2599 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179722762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179722762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10580360 5E7 jtl: American Journal of Speech-Language Pathology issn: 10580360 maglogo: N pubinfo: dt: Sep2024 vid: 33 iid: 5 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 179722762 179722762 179722762 10.1044/2024_AJSLP-24-00005 179722762 ppf: 2582 ppct: 17 formats: tig: atl: Story Recall in Peer Conflict Resolution Discourse Task to Identify Older Adults Testing Within Range of Cognitive Impairment. aug: au: Wilson, Sarah C. Teghipco, Alex Sayers, Sara Newman-Norlund, Roger Newman-Norlund, Sarah Fridriksson, Julius affil: Linguistics Program, University of South Carolina, Columbia sug: subj: Memory Evaluation Conflict (Psychology) Peer Group Cognition Disorders Diagnosis Health Screening Methods Human Female Male Middle Age Aged Machine Learning Algorithms Stability Evaluation Validity Evaluation Intuition Models, Statistical Prediction Models Descriptive Statistics Scales Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Purpose: The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy older adults. Method: Seventy-three older adults who self-identified as healthy completed an expository discourse task and neuropsychological screener. Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. We hypothesized that a higher rate of pronoun use and lower scores on story recall would best classify older adults testing within range of cognitive impairment. Results: The highest classification accuracy exploited a single variable in a remarkably intuitive way: using 66% story recall as a cutoff for cognitive impairment. Forcing this decision tree model to use more features or increasing its complexity did not improve accuracy. Permutation testing confirmed that the 77% accuracy and 0.18 Brier skill score achieved by the model were statistically significant (p < .00001). Conclusions: These results suggest that expository discourse tasks that place demands on executive functions, such as working memory, can be used to identify aging adults who test within range of cognitive impairment. Accurate representation of story elements in working memory is critical for coherent discourse. Our simple yet highly accurate predictive model of expository discourse provides a promising assessment for easy identification of cognitive impairment in older adults. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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