{"id":24618,"date":"2023-02-15T09:56:43","date_gmt":"2023-02-15T14:56:43","guid":{"rendered":"https:\/\/marymount.edu\/academics\/?page_id=24618"},"modified":"2026-03-13T12:01:17","modified_gmt":"2026-03-13T16:01:17","slug":"arlington-longitudinal-optimal-health-aging-study","status":"publish","type":"page","link":"https:\/\/marymount.edu\/academics\/center-for-optimal-aging\/bilingual-telephone-screening-study","title":{"rendered":"About the Bilingual Telephone Screening Study"},"content":{"rendered":"
The Bilingual Telephone Screening Study<\/strong>, approved by the Institutional Review Board (IRB) (MUIRB #1482) and led by Catherine Diaz-Asper<\/a><\/em> at Marymount University, seeks to improve early detection of Alzheimer\u2019s disease and related dementias (ADRD) in older adults who are bilingual or primarily speak a non-English language. The study focuses on developing a linguistically appropriate artificial intelligence (AI)-based tool for telephone-based screening tailored to bilingual older adults.<\/p>\n This study will assess whether the accuracy of speech-based ML models differs depending on the language used during testing, specifically comparing English and Spanish. We will recruit adults aged 60 and older who are conversationally fluent in both English and Spanish to participate in the study. The findings are expected to inform the development of linguistically appropriate AI-based tools that are low-cost, accessible, and effective for the early identification of cognitive impairment.<\/span><\/p>\n
Accurate early detection of cognitive impairment remains a significant challenge, particularly among older adults who speak languages other than English. Although machine learning (ML) models capable of detecting subtle changes in spontaneous speech have shown promise as screening tools for native English speakers, few have been developed, tested, or validated for individuals who are more fluent in other languages. <\/span><\/p>\n