# Characterizing Activity Patterns in Functional Mobility After Spinal Cord Injury

> **NIH NIH K23** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $132,314

## Abstract

Abstract
 My career and research interests have centered on the science of movement and factors that maximize
mobility. Whether this is through injury prevention, assistive technology, or biomechanical optimization, it is
critical to clinical practice that these processes be well understood so that we can provide the most informed
patient treatments. In order to carry out more effective clinically-based studies that inform patient care, it is my
desire to continue my training through practical experiences with both formal coursework and a oversight by a
strong mentoring team in the following domains: (1) activity-based data collection and analysis and (2) use of
advanced statistical methods to investigate multiple factors. Through the K23, I will also gain experience
specifically focused on my transition to independence; this will include grantsmanship and lab management,
leading the design and implementation of clinical and translational studies, management of personnel and
meetings, and pursuit of tenure and an R01. This continued training will be completed in the context of a
research study that characterizes activity patterns in functional mobility after spinal cord injury (SCI).
 Aim 1 of this study is to predict mobility at discharge and at 1-year post-discharge, based upon patient
characteristics and activity during IPR. Mobility outcomes can be challenging to predict, particularly for
individuals with moderate strength and sensory impairments. Selecting appropriate training is increasingly
important with shrinking lengths of stay and there are potential opportunity costs and adverse consequences
on quality of life and participation for individuals who do not receive appropriate interventions. Additional
activity measures that we can collect early in the IPR stay, by utilizing low-cost sensors, have the potential to
provide rich data sets that we can examine to garner insight into outcomes with little administrative burden.
Using a machine learning approach, we will investigate patient characteristics and activity-monitoring data to
improve predictive models of patient mobility based on data acquired early in the rehab stay. Achieving these
aims will improve patient and clinician understanding of anticipated changes in mobility in the year following
SCI to appropriately target expectations and interventions to maximize functional outcomes.
 Aim 2 of this proposal is to quantitatively evaluate functional mobility changes (i.e., wheeling walking
or changes in activity within mode) in the first year post injury and their impact on quality of life and
participation. There are factors following discharge that challenge or enhance the sustainability of walking for
functional mobility including energy costs, neurologic recovery and biopsychosocial factors such as resilience,
self-efficacy, environment, and caregiver support. The association between these factors and post-discharge
changes in mobility are not well understood. Using wearable sensor...

## Key facts

- **NIH application ID:** 10003374
- **Project number:** 5K23HD096134-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Lynn A Worobey
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $132,314
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003374

## Citation

> US National Institutes of Health, RePORTER application 10003374, Characterizing Activity Patterns in Functional Mobility After Spinal Cord Injury (5K23HD096134-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10003374. Licensed CC0.

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