# Data Science and Applied Technology Core

> **NIH NIH P30** · UNIVERSITY OF FLORIDA · 2024 · $90,888

## Abstract

ABSTRACT
Data Science and Applied Technology (DSAT) Core (RC4), the most recent addition to the University of Florida
(UF) Older Americans Independence Center (OAIC), provides an interactive data and technology ecosystem
for promote mobility and independence. Big data initiatives, applied technologies, and new methodological
approaches for data science have exploded in many various environments, and the world is moving toward a
connected system of computing and sensing components. The broadly used phrase “connected health” refers
to an environment in which detailed data are collected on health, activity, location, and other aspects of the
participating entities. Flexible control of the different interconnected and frequently communicating components
can provide a rich set of applications that learn dynamically from data streams. Artificial intelligence (AI)
promises to transform our ability to harness these data streams to advance science and improve health. The
core uses techniques such as machine learning and deep learning to transform data utility through computer
models that use data to learn, predict and – potentially – infer causation. Additionally, DSAT is on the forefront
of mobile health (mHealth, smartphones and smartwatches) technologies that are changing the landscape for
how patients and research participants communicate about their health in real time. Importantly, the core
efforts are specifically targeted to older adults — a population that is often forgotten in these regards. DSAT
provides a central hub of expertise in gerontology/geriatrics, computer engineering, mHealth, applied
technology, and epidemiology. As a result, DSAT provides many unique attributes to the UF OAIC and
nationally that:
 • Support OAIC cores, train and provide resources to REC Scholars, researchers and practitioners;
 • Advance interactive monitoring and remote health using mobile devices designed for older adults;
 • Use and develop data repositories and repurpose existing data to support new research on mobility;
 • Conduct machine learning, artificial intelligence, and pattern discovery analyses;
 • Collaborate with partnering OAIC cores to develop new research endeavors on promoting mobility;
 • Enhance externally supported projects.
DSAT addresses many aspects of major national initiatives outlined in "Advancing Artificial Intelligence R&D"
and "Emerging technologies to support an aging population" and is poised to meet these new initiatives. Given
is interdisciplinary approach, it is well-suited to lead the UF OAIC into the future of connected health, mobile
technology, advanced sensing, artificial intelligence and machine learning specifically geared toward promoting
mobility and independence in older adults.

## Key facts

- **NIH application ID:** 10836518
- **Project number:** 5P30AG028740-18
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Todd Manini
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $90,888
- **Award type:** 5
- **Project period:** 2007-06-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10836518

## Citation

> US National Institutes of Health, RePORTER application 10836518, Data Science and Applied Technology Core (5P30AG028740-18). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10836518. Licensed CC0.

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