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Past Webinar – How to Develop and Test Mechanism-Driven, Technology-Enabled Interventions in Response to the Scientific Foci of ASU Roybal Center for Older Living Alone with Cognitive Decline

http://youtube.com/watch?v=6n6meYGI_Ug Abstract: Purpose: 1) Explain the scientific foci of ASU Roybal Center for Older Adults Living Alone with Cognitive Decline to delay the onset and progression of Alzheimer’s disease and related dementia (AD/ADRD), and 2) Guide investigators to develop strong research proposals, focusing on the significance of a proposed solution, mechanism-driven and technology-enabled interventions, and health disparity factors.  Rationale: Poor lifestyle behaviors such as physical inactivity, unhealthy diet, and stress contribute to up to 40-50% of AD/ADRD cases; however, few intervention successes have been translated into real-world impacts. Most interventions are not mechanism-driven, precise, accessible, cost-effective, and/or scalable, which could potentially be addressed by technology through integrating Artificial Intelligence, real-time analytics and feedback, enhancing user autonomy and person-centeredness, and personalizing intervention prescription and delivery. In addition, the population of older adults living alone with cognitive…

Continue ReadingPast Webinar – How to Develop and Test Mechanism-Driven, Technology-Enabled Interventions in Response to the Scientific Foci of ASU Roybal Center for Older Living Alone with Cognitive Decline

Preprint: Detecting Preclinical Alzheimer’s Disease Risk in Cognitively Normal Adults Using Speech Acoustics: Validation with Plasma p-Tau217 and APOE-ε4 Status

Authors: Mehrdad Dadgostar, Lindsay C. Hanford, Maryam Tavakoli, Steven E. Arnold, David H. Salat, Tatiana Sitnikova, Pia Kivisakk Webb, Jordan R. Green, Hengru Liu, Brian D. Richburg, Mariam Tkeshelashvili, Marziye Eshghi Abstract INTRODUCTION We tested whether spontaneous speech acoustics provide a scalable digital marker of biologically defined Alzheimer’s disease (AD) risk. METHODS Forty-nine cognitively unimpaired older adults were stratified within APOE genotype into Low-, Moderate-, and High-Risk groups based on log₁₀-transformed plasma p-tau217. Acoustic features were extracted from spontaneous speech and entered into multiclass SVM classifiers with leave-one-out cross-validation, with and without genetic-algorithm feature selection and age. Parallel models using neuropsychological measures were evaluated for comparison. Feature contributions were interpreted using SHAP. RESULTS Speech-based models substantially outperformed cognition-only models and exceeded chance performance for three-group classification (33.3%), achieving up to 77% accuracy compared with 47%…

Continue ReadingPreprint: Detecting Preclinical Alzheimer’s Disease Risk in Cognitively Normal Adults Using Speech Acoustics: Validation with Plasma p-Tau217 and APOE-ε4 Status