# CareBand: A Collaborative Pilot Study to Optimize a Life-Space Performance Metric for Monitoring and Early Detection of Alzheimer's Disease and Related Dementias in Rural and Indigenous Communities

> **NIH NIH R21** · UNIVERSITY OF MINNESOTA · 2021 · $438,699

## Abstract

Project Summary/Abstract
Despite having higher rates of ADRD, increased caregiver stress and a lack of accessible healthcare services
compared to urban counterparts, rural and Indigenous populations are grossly underrepresented in dementia
research, especially in technology development. This R21 project, titled CareBand: A Collaborative Pilot Study
to Optimize a Life-Space Performance Metric for Monitoring and Early Detection of ADRD in Rural and
Indigenous Communities addresses urgent dementia disparities by developing aging-in-place technology to
monitor dementia-related behaviours. This project brings together the University of Minnesota’s Memory
Keepers - Medical Discover Team (MK-MDT) with an Industry partner, CareBand Inc., to digitize and expand
existing metrics for the recording and analysis of movement through personal (in-home) and community
environments, called life-space, using CareBand’s already available and proven, unobtrusive wearable
wristband device. CareBand uses LoRa technology making it accessible to those without Internet or cellular
services. This research will develop a new metric, a life-space performance (LSP) score, that will capture
mobility through the exact calculation of distance based on amount, frequency and speed and as well as
quality of movements (stability, falls, speed) across any clinically relevant period of time. Our long-term goal is
to use LSP metrics to track cognitive decline from pre-clinical subjective cognitive complaints through all
stages of ADRD among geographically and culturally diverse populations. Our overall hypothesis is that culture
and geography are significant factors in determining one’s life-space and that the accurate measure of LSP in
Indigenous and rural populations can be achieved by developing measures that account for their unique
contexts. Using a community-based participatory research approach, we will partner with Indigenous and rural
communities to pilot CareBand allowing us to collect data on rural and Indigenous life-space utilization to
produce a LSP score that will benefit PWD and their caregivers. Our research has three specific aims. First, we
will conduct depth interviews to generate qualitative data to inform the LSP data model by identifying and
prioritizing the most critical life-space behaviors for directing care decisions. Second, the CareBand will be
piloted for 12 weeks in rural and Indigenous with 20 People with Dementia caregivers, to capture location,
movement and speed of movement inside and outside the home. Third, we will develop a set of algorithms by
analyzing the qualitative and quantitative results captured in Aims 1 and 2 to create an LSP Score. This
research will digitize life-space measures into a LSP score which includes indicators appropriate to rural and
Indigenous populations and will lay the groundwork for a subsequent R01 application to validate the context
specific LSP metrics against existing gold standards. The culturally appropriate LSP me...

## Key facts

- **NIH application ID:** 10212745
- **Project number:** 1R21AG072566-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Wayne Richard Warry
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $438,699
- **Award type:** 1
- **Project period:** 2021-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212745

## Citation

> US National Institutes of Health, RePORTER application 10212745, CareBand: A Collaborative Pilot Study to Optimize a Life-Space Performance Metric for Monitoring and Early Detection of Alzheimer's Disease and Related Dementias in Rural and Indigenous Communities (1R21AG072566-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10212745. Licensed CC0.

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