Year 2 Pilots

Publication: AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights from an Apple Watch Study

Authors: Tzu-An Song, Yubo Zhang, Ziyuan Zhou, Luke Hou, Masoud Malekzadeh, Aida Behzad, Joyita Dutta Abstract Polysomnography, the gold standard for sleep evaluations, involves complex setup and data acquisition protocols and requires manual scoring of sleep data. Smartwatches and other multi-sensor consumer wearable devices with automated sleep staging capabilities offer a promising and scalable alternative for routine and long-term sleep evaluations in individuals. We conducted a multi-night study using a smartwatch for sleep assessment and created an AI-driven automated sleep staging framework based on instantaneous heart rate (IHR) and accelerometry data using sleep stage labels based on electroencephalography (EEG) as the reference. 47 healthy adults were recruited to record their sleep for up to seven consecutive nights using an Apple Watch Series 6 and a Dreem 2 Headband. Our sleep staging framework relies on a…

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New Product Launch: BIDSleep iPhone and Apple Watch App

BIDSleep helps you access wellness data from your Apple Watch, including heart rate, motion, blood oxygen, and sleep stages, all securely stored on your device. App Purpose: BIDSleep is a wellness app that helps users collect heart rate, blood oxygen (SpO₂), motion, and optional sleep stage data during rest or overnight sessions. It is designed solely for personal data logging and self-monitoring purposes. Source: https://apps.apple.com/us/app/bidsleep/id6747012248?platform=iphone

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Open Source AI-model Released: SLAMSS-IFS

To the study team's knowledge, this is the first open-source four-class sleep staging model developed from a multi-night Apple Watch sleepstudy. SLAMSS-IFS, an advanced version of our previous SLAMSS model, for four-class sleep staging using IHR and accelerometry signals fromthese wearable devices. Key innovations in the model, including an intra-epoch learning LSTM, frequency information incorporation, andskip connections, contribute to substantial performance improvements over other SLAMSS variants and other state-of-the-art models. Ourresults show that SLAMSS-IFS outperforms competing models in overall accuracy, sensitivity, specificity, precision, weighted F1 score, weighted MCC, and most clinical sleep metrics. SLAMSS-IFS: The SLAMSS-IFS model builds on the original SLAMSS model with three additional components: an intraepoch learning sequence-to-sequence long short-term memory (LSTM (“I”), a frequency variable (“F”), and a skip connection (“S”). The intra-epoch learning LSTM processes temporal dependencies within individual epochs.…

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Grant Funding: Novel orthostatic vital signs measured by an earlobe wearable device (1R21AG088945)

Principal investigator, Amar Basu, professor of electrical and computer engineering at Wayne State University and CEO of TRACE Biometrics LLC, has received a National Institute on Aging R21 award to further validate the TRACE sensor against gold standard clinical orthostatic measures, which will build upon the pilot award's work towards securing FDA clearance. Project Summary: Orthostatic disorders, including orthostatic hypotension (OH), disproportionately affect older adults, presenting in 30% of older adults and up to 70% of nursing home residents. As OH is a major risk factor for syncope, falls, and cognitive decline, medical agencies stress the public health need for monitoring orthostatic vital signs (OVS) in at-risk individuals. This proposal investigates an NIA award-winning wearable device called TRACE, which addresses fundamental limitations of the current clinical standard, the blood pressure (BP) cuff: 1) The BP…

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Publication: Passive Measures of Physical Activity and Cadence as Early Indicators of Cognitive Impairment: Observational Study

Authors: Huitong Ding, Stefaniya Brown, David R Paquette, Taylor A Orwig, Nicole Spartano, Honghuang Lin Abstract Background: Emerging research shows regular physical activity reduces cognitive decline risk, but most studies rely on self-reported measures, which are limited by recall bias, subjectivity, and a lack of continuous monitoring capability. Objective: This study aimed to explore passive physical activity measures as early indicators of cognitive impairment by examining their association with cognitive impairment incidence and neuropsychological (NP) test performance. Methods: We included participants from the Framingham Heart Study (FHS), a community-based cohort with longitudinal cognitive impairment surveillance. Participants wore an Actical accelerometer for at least 3 days, excluding bathing. Thirty physical activity measures were grouped into intensity-specific durations, step and cadence summaries, and peak cadence. Cox proportional hazard models were applied to assess their associations with incident…

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Grant Funding: R41 AG092119

Continuation of VR technology development focused on the caregiver side of the dyad. Public Health Relevance Statement: The VR-CARES project is an innovative, collaborative effort that invites home health dementia caregivers into the design process of a virtual reality platform seeking to mitigate their work-related burden and social isolation by cultivating a virtual community of support. The co-created, caregiver-specific VR platform will serve as a safe, communal space where caregivers can remotely connect with their peers, share fun experiences together, access support, learn self-care and build resilience within a supportive virtual network to enhance their social and mental health and job satisfaction. Central to VR-CARES is the principle of user-led innovation, ensuring that the technology not only serves but is informed and successfully adopted by the very individuals it intends to benefit, an important standard…

Continue ReadingGrant Funding: R41 AG092119