#4 – Accelerometer-Measured Physical Activity Improves Predictive Validity of Fried Frailty Phenotype for All-Cause and Cardiovascular Disease Mortality
Lingsong Kong, PhD, Postdoctoral Scholar, Stanford University.
#2 – A Self-Administered, Smartphone App-Based Gait Assessment for Older Adults: From Validity and Reliability to Application
On-Yee (Amy) Lo, PhD, Assistant Scientist II, Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School.
#1 – Detecting Pre-Frailty and Frailty Using Free-Living Activity Monitoring from a Thigh-Worn Sensor
Andrew Song, Clinical Research Associate, Marcus Institute for Aging Research, Hebrew SeniorLife.
Past Webinar – Advancing Fair & Effective AI for Older Adults
https://www.youtube.com/watch?v=67St-zDEzjk Abstract: Artificial intelligence holds promise to transform care for older adults, yet today’s AI systems routinely underperform for this population due to poor data representation, limited validation, and weak alignment with lived experience. Drawing on a six-month collaboration between the SCAN Foundation, CHAI will be synthesizing evidence from literature review, expert interviews, and multi-stakeholder roundtables to surface why AI fails older adults—and what must change. They will outline practical pathways for building equitable AI, including multimodal data integration, standardized validation, local testing, and patient-centered deployment. The talk concludes with a roadmap for developing trustworthy AI that meaningfully improves outcomes for aging populations. Biography: Lucy Orr-Ewing, Head of Policy & Strategy, Coalition for Health AI (CHAI) Lucy Orr-Ewing leads Policy and Research for CHAI, where she leads policy engagement at both the state and federal…
Publication: Kinematic correlates of early speech motor changes in cognitively intact APOE-ε4 carriers: a preliminary study using a color-word interference task
Authors: Mehrdad Dadgostar, Lindsay C Hanford, Jordan R Green, Brian D Richburg, Averi Taylor Cannon, Nelson V Barnett, David H Salat, Steven E Arnold, Marziye Eshghi Abstract Introduction: Alzheimer's disease (AD) is the most prevalent form of dementia and a major public health challenge. In the absence of a cure, accurate and innovative early diagnostic methods are essential for proactive life and healthcare planning. Speech metrics have shown promising potential for identifying individuals with mild cognitive impairment (MCI) and AD, prompting investigation into whether speech motor features can detect elevated risk even prior to cognitive decline. This preliminary study examined whether speech kinematic features measured during a color-word interference task could distinguish cognitively normal APOE-ε4 carriers (ε4+) from non-carriers (ε4-). Methods: Sixteen cognitively normal older adults (n = 9 ε4+, n = 7 ε4-) completed…
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