Grant Funding

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

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

Continue ReadingGrant Funding: NIH Trailblazer Award

Grant Funding: NIA R01 (R01AG089169)

Title: Neural mechanisms of gait disturbances as individualized digital biomarker trajectories in preclinical dementia Public Health Relevance Statement: In this project, the research team uncovers the neural mechanisms of gait and mobility disturbances in preclinical dementia and identifies trackable individualized digital biomarkers (from videos). They evaluate the specificity and sensitivity of these gait-based biomarkers and relate those to neural mechanisms and clinical phenotypes. By leveraging these identified markers, they can monitor the disease's progression, potentially minimizing or even replacing the demand for expensive neuropsychological or neuroimaging evaluations. Source: R01AG089169 (NIH RePORTER)

Continue ReadingGrant Funding: NIA R01 (R01AG089169)

Grant Funding: NIA SBIR Phase I (R43AG090129)

Title: AVA AI Video-Based Mobile Application for Reliable, Accessible, and Low-Cost Fall Risk Assessments of Older Adults Public Health Relevance Statement: This project presents AVA, a video-based mobile app for at-home fall risk assessment of older adults, only using a smartphone to enable a much higher access, low-cost solution with full privacy protection. AVA empowers caregivers to assess the gait, balance, and strength of their older adults independently without the direct supervision of healthcare professionals. The Phase I study focuses on validating AVA's AI-based assessment technology and its usability in diverse home and independent living settings which can lead to revolutionizing current fall risk assessment practices. Source: R43AG090129 (NIH RePORTER)

Continue ReadingGrant Funding: NIA SBIR Phase I (R43AG090129)

Grant Funding: U01: Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the most common form of dementia characterized by progressive loss of cognitive function. Unfortunately, currently there is no effective treatment for AD and clinical interventions of AD have largely failed despite enormous efforts. For the current application, we seek to develop multimodal machine learning models by leveraging the rich collection of AD-related omics data and phenotypical data recently generated from large-scale collaborative projects such as Alzheimer Disease Neuroimaging Initiative (ADNI), Accelerating Medicines Partnership-AD (AMP-AD) and the Alzheimer's Disease Sequencing Project (ADSP). Three aims will be pursued in the current application. Aim 1. We will build an expandable multimodal unsupervised machine learning framework to investigate AD heterogeneity. Given the multifactorial nature of AD, we will perform AD subtyping by harnessing the rich information across multiple spectrum of data. Aim 2.…

Continue ReadingGrant Funding: U01: Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

Grant Funding: R21 AG088872

Title: Characterizing autonomic impairments in Frontotemporal Dementia This R21 builds upon the tech ready cohort that was established by the pilot project funding. Public Health Relevance Statement: This proposal will test the accuracy and reliability of autonomic measurements in bvFTD patients. Measurements will be collected both with established equipment and via at-home devices to assess the validity of the latter. Finally, autonomic measurements will be correlated to socioemotional dysfunction in patients. Source: R21 AG088872 (NIH RePORTER)

Continue ReadingGrant Funding: R21 AG088872