# Backward Walking as a Novel Fall Prediction Tool for Multiple Sclerosis

> **NIH NIH R21** · WAYNE STATE UNIVERSITY · 2022 · $225,399

## Abstract

PROJECT SUMMARY
Falls are a common public health concern among persons with Multiple Sclerosis (MS), and can
substantially decrease quality of life. Current fall detection measures in MS rely upon forward walking
speed and balance; however, these measures exhibit limited predictive accuracy for falls. Backward
walking (BW) velocity is a promising clinical measure with known relationships to fall history in the elderly
and persons with MS. Retrospective reporting of fall is subject to inaccuracy, given the high prevalence
of cognitive dysfunction in MS; yet, the predictive accuracy of BW for prospective fall risk remains
unknown. There are many factors that may contribute to fall risk, but prior studies have failed to examine
mechanisms that may drive the relationship of BW to fall risk. Identification of a clinical marker of fall risk
that is related to both underlying neuropathology (i.e., myelin degradation) of key motor white matter
tracts and cognitive function is critically needed. Without such a measure, fall rates for MS and targeted
rehabilitation for fall prevention is not likely to be realized. The specific aims of this proposal are to 1)
Establish the reliability and minimal detectible change for BW as an outcome tool and determine the
predictive capacity of BW measures for fall risk over 6 months; 2) Examine the unique contributions of
cognitive domains to BW performance; and 3) Determine the contribution of myelin degradation in key
tracts to BW performance. Our central hypothesis is that BW velocity is a reliable and valid predictor of
future falls that is related to underlying myelin degradation and cognitive dysfunction. The proposed
project will use innovative techniques to measure myelin content using myelin water imaging, advanced
technologies and approaches for quantifying both laboratory and clinical assessments of walking and
cognition and prospective fall monitoring using wearable sensors and survey measurement. Identifying
a clinical marker of fall risk that is supported by underlying pathology and cognitive dysfunction addresses
a major gap of prior research and has the potential of improving targeted rehabilitation therapies for fall
prevention, clinical outcomes and quality of life for persons with MS.

## Key facts

- **NIH application ID:** 10450397
- **Project number:** 1R21HD106133-01A1
- **Recipient organization:** WAYNE STATE UNIVERSITY
- **Principal Investigator:** Nora E. Fritz
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $225,399
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10450397

## Citation

> US National Institutes of Health, RePORTER application 10450397, Backward Walking as a Novel Fall Prediction Tool for Multiple Sclerosis (1R21HD106133-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10450397. Licensed CC0.

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