Wan-Tai Au-Yeung

Assistant Professor

Oregon Health & Science University

United States

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Dr. Wan-Tai Au-Yeung is an Assistant Professor at Oregon Health & Science University in the United States. His research primarily focuses on the intersection of environmental factors and health, particularly concerning older adults and dementia. Recent articles include studies on air quality exposure in aging populations, behavioral interventions in home settings, and the use of digital technology for assessing behavioral symptoms in older adults.

Articles (14)

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2025

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2020

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2020

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Oregon Health & Science University

UNITED STATES

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Wan-Tai Au-Yeung

Research Interests

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About

Articles (14)

Estimating daily air quality exposure of the aging and dementia population

Examining the Relationships Between Indoor Environmental Quality Parameters Pertaining to Light, Noise, Temperature, and Humidity and the Behavioral and Psychological Symptoms of People Living With Dementia: Scoping Review

Feasibility of Deploying Home-Based Digital Technology, Environmental Sensors, and Web-Based Surveys for Assessing Behavioral Symptoms and Identifying Their Precipitants in Older Adults: Longitudinal, Observational Study

Remote Passive Sensing of Older Adults’ Activities and Function: User-Centered Design Considerations for Behavioral Interventions Conducted in the Home Setting (Preprint)

Differences in Life Space Activity Patterns Between Older Adults With Mild Cognitive Impairment Living Alone or as a Couple: Cohort Study Using Passive Activity Sensing

The Remote Assessment and Dynamic Response Program: Development of an In-Home Dementia-Related Care Needs Assessment to Improve Well-Being

Predicting Major Decline in Older Adults: The Role of Time Spent Out of Home.

Monitoring Behaviors of Patients With Late-Stage Dementia Using Passive Environmental Sensing Approaches: A Case Series

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm

The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community.

Sensing a problem: Proof of concept for characterizing and predicting agitation

State‐of‐the‐Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System

State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System

Collaborators (5)

Lyndsey Miller

Sara Czaja

Jeffrey Kaye

Hiroko Dodge

Lisa Silbert

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