Publication: Predicting Orthostatic Symptoms Using a Multiparameter Wearable Sensor

Authors: Ziad A Elhajjaji, Amar S Basu Abstract Orthostatic disorders affect 30% of older adults and increase the risk for falls. The current diagnostic standard, the blood pressure cuff, cannot capture the rapid, multifaceted dynamics of orthostasis physiology, resulting in frequent underdiagnosis. This paper demonstrates multiparameter, real-time measurement of orthostasis using TRACE, an earlobe mounted wearable developed in our group. In prior work, we demonstrated a novel metric called orthostatic hypovolemia (OHV1), the initial loss in cephalic (head) blood volume immediately upon standing. This study significantly advances our prior work by introducing an additional 2 metrics: OHV2, the cephalic blood volume deficit after the body achieves homeostasis after standing; and postural orthostatic tachycardia (POT), the increase in heart rate. The 3 metrics were evaluated in 101 older adults who wore the TRACE device during postural…

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Past Webinar – Practical Cognitive Assessment Methods for AD/ADRD Research Projects

https://youtu.be/4weOZ9Tf-Og?si=lzfkLGsUZVZSseRz Abstract:  This webinar will examine cognitive assessment methods in Alzheimer’s Disease (AD) and Alzheimer’s Disease-Related Dementias (ADRD) research, featuring insights from neuropsychology, geriatric psychiatry, and digital health technology. Panelists will highlight the challenges of accurate screening across diverse populations and settings, from community-based studies to clinical trials. Presentations will explore traditional tools like the MoCA and CDR alongside emerging digital approaches using speech AI, wearable sensors, and web-based platforms. Discussions will emphasize demographic influences, functional measures, and resource considerations in selecting appropriate screening strategies. Case studies will illustrate lessons learned in real-world pilot projects, including practice effects, scalability barriers, and the integration of cognitive and functional assessments.Attendees will gain a nuanced understanding of current and future cognitive screening tools and how to apply them effectively in research and clinical contexts. About the Speakers:  Kathryn…

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Oral Presentation: Inference for Longitudinal Data After Adaptive Sampling

This presentation was given on November 20, 2024 by Dr. Susan Murphy as part of the L. Brown Distinguished Lecture series at University of Pennsylvania Abstract: Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects. However, current methods for statistical inference on such data (a) make strong assumptions regarding the environment dynamics, e.g., assume the longitudinal data follows a Markovian process, or (b) require data to be collected with one adaptive sampling algorithm per user, which excludes algorithms that learn to select actions using data…

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Grant Funding: NIH Trailblazer Award

This is an R21 award for $650,000 over a period of 3 years. Their smartphone app prototype capitalizes on the handheld nature of a mobile phone and uses its built-in sensors to gauge grip strength to enhance preoperative screening for potential risks of complications post cardiac surgery. This effort will contribute to the growing portfolio of smartphone-based health monitoring solutions actively being developed by Wang and his research team. Source: https://today.ucsd.edu/story/uc-san-diego-researcher-receives-nih-trailblazer-award

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Oral Presentation: American Speech Language Hearing Association Conference – 2024

Presented " Quality Management Framework for an Automated, Phone-Based Telehealth Platform for Cognitive Rehabilitation Therapy" at the ASHA Conference in Seattle, WA on November 11, 2024 Source: https://plan.core-apps.com/asha2024/event/6a3d638ef5896a352b49fbfd8b5f1248

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