# Customized Health Alerts and Consumer-Centered Interfaces Using In-Home and Wearable Sensors

> **NIH NIH R01** · UNIVERSITY OF MISSOURI-COLUMBIA · 2021 · $474,296

## Abstract

In-home sensing technologies hold enormous potential for early detection of health changes that can
dramatically affect the experiences of aging: enabling functional independence, improving self-management
of chronic or acute conditions, and improving quality of life. Chronic diseases especially affect older adults.
Problems in chronic disease management are often the cause of losing independence for aging Americans. In
2012, 1 in 2 American adults (117 million) had at least one chronic condition, and 26% of the population had
multiple chronic conditions, accounting for 84% of US health care costs. Early illness recognition and early
treatment is key to improving health status with rapid recovery after an exacerbation of a chronic illness or
acute illness, and also key to reducing morbidity and mortality in older adults and controlling health care costs.
 In previous work, the team developed a health alert system that captures and analyzes data from sensors
embedded in the home. Sensor data are captured passively and continuously in the home. In a pilot NIH R21
study, significant differences in health outcomes were shown with health alerts from motion and bed sensor
data, based on bed restlessness and low, normal, and high pulse and respiration rates. The system actually
detected changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual
assessment methods or self-reports of illness.
For this project, the team will expand from the clinician-focused system to a consumer-focused system by
incorporating more finely grained sensing (gait and quantitative pulse and respiration), with new improved
algorithms that integrate individual health status and medication use, and track trajectories of health changes,
for more sensitive, and more personalized health alerts with fewer false alarms. A recently developed bed
sensor will be incorporated to passively capture quantitative pulse, respiration, and restlessness while the
subject is resting. Gait parameters (e.g., in-home walking speed, stride time and stride length) will also be
captured using depth images that show shadowy silhouettes. In addition, the team will solicit the consumer
perspective on customized health alerts and a user interface for displaying sensor and alert information. The
views of seniors and their family members will be used to inform the development of the new customized alert
algorithms and drive the development of a consumer-focused interface that will provide empowering tools for
self-management of chronic illnesses. In addition, the use of commercially available wrist-worn sensors will be
explored for the purpose of recognizing health changes. The study will include a retrospective analysis of
sensor data collected in 13 senior housing sites in Missouri. New participants will be recruited in 5 senior
housing sites in Columbia, MO to investigate the consumer perspective. The important process of engaging
consumers in this work is the next step in tr...

## Key facts

- **NIH application ID:** 10075985
- **Project number:** 5R01NR016423-04
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Marjorie Skubic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $474,296
- **Award type:** 5
- **Project period:** 2018-01-08 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10075985

## Citation

> US National Institutes of Health, RePORTER application 10075985, Customized Health Alerts and Consumer-Centered Interfaces Using In-Home and Wearable Sensors (5R01NR016423-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10075985. Licensed CC0.

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