# Monitoring Lower Limb Movement to Predict Ambulatory Ability after Spinal Cord Injury

> **NIH NIH F30** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $32,101

## Abstract

After a spinal cord injury (SCI), clinicians must quickly decide where to focus therapy time to maximize
an individual's functional mobility by discharge: either towards gait training or wheeled mobility interventions.
Clinical prediction rules (CPRs) can assist clinicians in making those difficult decisions, but literature has
shown that for individuals with moderate impairments, current CPRs that use age, strength, and sensation are
not sufficient in predicting independent ambulation. Further, existing CPRs do not provide insight into clinically
important descriptive measures of gait quality, efficiency, and endurance that contribute to functional
ambulation. Our recent work demonstrated individuals who received gait training, but primarily used a
wheelchair one year after SCI received less transfer and wheeled mobility training and had lower measures of
participation than non-ambulatory individuals who never received gait training. In the context of decreasing
inpatient rehabilitation length of stays, it is crucial that time in therapy be used efficiently to maximize function
at discharge and avoid those long-term consequences.
 Lower limb movement (LLM) captured using activity monitors may provide a more sensitive measure of
strength and sensation than traditional methods such as manual muscle and light touch sensation testing. This
technique is novel in that LLM has not yet been reported in literature for individuals with SCI. Our preliminary
analysis has shown promise for the association between LLM, strength, and ambulatory ability (as defined by
measures of gait quality, efficiency, and endurance). Using machine learning techniques, we are able to
determine which factors have the strongest association with ambulatory ability, among LLM, subject
demographics, clinical characteristics, and other covariates.
 Our long-term goal is to improve CPRs that predict ambulation after SCI, thus enabling appropriately
targeted functional mobility training. As a first step towards this goal, we will build a foundational knowledge of
LLM and its relationship as a potential biomarker for ambulatory ability cross-sectionally among individuals with
chronic SCI and known, diverse functional abilities (Aim 1). We will also explore longitudinal LLM data and
ambulatory ability for a population with acute SCI (Aim 2) to evaluate changes in LLM over time and create a
preliminary prediction model.
 Achieving the proposed aims will provide new insights into factors that predict mobility in individuals
with SCI and provide understanding as to how these factors change acutely following injury. Further, we will
gain insight to guide a future multisite longitudinal study that will assess a new, more effective CPR. This CPR
will aid clinical decision-making for individuals with SCI by allowing for optimally targeted therapies to be
employed throughout the rehabilitation continuum, thus improving long-term functional outcomes.

## Key facts

- **NIH application ID:** 10049966
- **Project number:** 5F30HD096828-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Stephanie Rigot
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $32,101
- **Award type:** 5
- **Project period:** 2019-11-01 → 2021-07-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10049966

## Citation

> US National Institutes of Health, RePORTER application 10049966, Monitoring Lower Limb Movement to Predict Ambulatory Ability after Spinal Cord Injury (5F30HD096828-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10049966. Licensed CC0.

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