# Using instrumented everyday gait to predict falls in older adults using the WHS cohort

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $614,420

## Abstract

Among community-living older adults, falls are a leading cause of injury, disability, injury-related death, and high
medical costs. Despite decades of research, the proportion of older adults who fall has not declined. Identifying
older adults at risk of falls remains a major public health priority. Exercise and other interventions can lower fall
risk; however, new tools are needed to determine who is most likely to benefit from early interventions.
 Early research linking fall risk to gait measures obtained in the clinic (e.g., average speed, stride variability)
contributed significantly to the understanding of the prediction of fall risk. Studies have also shown that older
adults who are more active have reduced risks of falls and fall-related injury. However, critical gaps
remain. Exciting advances in digital medicine and remote monitoring using wearable devices have afforded new
and more widely accessible opportunities for evaluating the relationships between Daily Living Gait (DLG) and
Daily Living Physical Activity (DLPA) to injurious falls in older adults. Measures of DLG (e.g., gait speed, cadence,
variability, and how these vary throughout the week) and measures of DLPA (e.g., activity levels and activity
fragmentation) can all be derived from a single accelerometer worn for 1 week. While growing evidence suggests
that DLG and DLPA do a better job at predicting falls than conventional in-clinic measures, studies to date have
been relatively small and have not focused on the prediction of injurious falls. Moreover, little is known about the
utility of combining DLG and DLPA measures to predict injurious falls.
 To address these gaps, we will leverage: 1) an existing large dataset of older women enrolled in the Women’s
Health Study (WHS) and 2) advances in wearable technology and machine learning. From 2011 to 2015, 17,466
WHS women wore a tri-axial accelerometer during waking hours for a week; they also regularly self-reported
their physical activity levels and health history. We propose to evaluate, for the first time, if and how DLG and
DLPA measures predict fall-related injuries in this aging cohort (average age=72 years at the time of
accelerometer wear) using records of injurious falls from the Centers for Medicare & Medicaid Services (CMS).
Primary Aims 1 and 2 will evaluate which specific measures of DLG and DLPA are associated with the risk of
injurious falls in the subsequent year after assessment, using statistical and machine learning approaches that
use time-to-event analyses (with and without adjustments for covariates). Primary Aim 3 will evaluate whether
utilizing measures of both DLG and DLPA is more strongly associated with the risk of injurious falls than utilizing
each of these measures alone. We will also determine if self-reported exercise history is associated with DLG
and DLPA, and explore whether markers of DLG and DLPA are associated with risks of injurious falls over more
extended periods of 5 and 10 years, as se...

## Key facts

- **NIH application ID:** 10851031
- **Project number:** 5R01AG078256-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** JEFFREY M HAUSDORFF
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $614,420
- **Award type:** 5
- **Project period:** 2023-06-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851031

## Citation

> US National Institutes of Health, RePORTER application 10851031, Using instrumented everyday gait to predict falls in older adults using the WHS cohort (5R01AG078256-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10851031. Licensed CC0.

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