# Using passively collected person-generated health data to explore population-specific relationships between social determinants of health, sleep patterns, and cognitive outcomes

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2021 · $332,087

## Abstract

Project Summary
 Alzheimer’s disease and related dementias (ADRDs) impose disproportionate burden in underserved
populations (e.g., African Americans and other minorities, low-income individuals), which experience higher rates
and earlier onset of cognitive decline. Social and structural determinants of health--including economic and
educational disparities, healthcare access and quality, systemic racism, and lifetime stress--account for ~80%
of modifiable risk factors and profoundly contribute to such disparities. However, social determinants are
understudied in the context of ADRDs in the underserved, and there is limited understanding of how differential
lifetime experience of social determinants across various populations may influence heterogeneity in cognitive
outcomes. In addition, the ability to detect subtle cognitive changes in everyday life--often years prior to the onset
of discernible ADRD symptoms--is a methodological challenge in gold-standard approaches. Such barriers
impede development of effective screening, treatment, and preventive interventions that are appropriately
tailored to underserved populations. Consumer digital technologies offer non-invasive tools for measuring
cognitive change and experience of social determinants in everyday life. Today, individual lived-experiences and
social determinants can be precisely characterized by person-generated health data (PGHD) (spanning
environment, geolocation, diet, exercise, social interactions, and other activities) generated from nearly-
ubiquitous smartphones and wearable devices. Applied to cognition, our team members at Evidation Health and
others have demonstrated that several objective indicators passively collected from digital technologies may be
associated with cognitive decline and may precede clinical ADRD manifestation by 10-15 years. In our NLM-
funded parent R01LM013237, we propose a sustainable infrastructure to use continuous PGHD to explore the
multi-level influence of social determinants on sleep health. Poor sleep health is an emerging risk factor for
ADRDs that may be an important and understudied biobehavioral pathway linking individual social determinants
with poor cognitive outcomes. Our objective here, in response to NOT-AG-20-034, is to (1) add clinically validated
measures of cognition to our in-scope data collection and sleep study to (2) explore the dynamic and multilevel
relationships between individual-level social determinants, physical activity, sleep health, cognitive function, and
ADRD risk. We will collaborate with Dr. Sliwinski (U2CAG060408), whose group has developed a battery of
clinically validated smartphone-based assessments of cognitive function. Collectively, our multidisciplinary
approach will overlay (1) monthly self-reported health metrics; (2) periodic clinical measures of cognition; and,
(3) continuous PGHD on a 12-month time series from each individual. This work may significantly advance
cognitive science and transform public hea...

## Key facts

- **NIH application ID:** 10287279
- **Project number:** 3R01LM013237-02S1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Ritika Ratnam Chaturvedi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $332,087
- **Award type:** 3
- **Project period:** 2020-07-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10287279

## Citation

> US National Institutes of Health, RePORTER application 10287279, Using passively collected person-generated health data to explore population-specific relationships between social determinants of health, sleep patterns, and cognitive outcomes (3R01LM013237-02S1). Retrieved via AI Analytics 2026-07-19 from https://api.ai-analytics.org/grant/nih/10287279. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*