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Updated: Jul 9, 2026

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
Enhanced Gait Variability Index in Neurological and Geriatric Rehabilitation: A Scoping Review
Mengdi Wang1, Priya Karakkattil1
1School of Physical Therapy, Texas Woman's University, Dallas, TX, USA.
Neurorehabilitation
|July 8, 2026
Summary
The Enhanced Gait Variability Index (eGVI) shows promise for assessing neuromotor function in neurological and geriatric populations. However, inconsistent protocols and lack of normative data hinder its widespread clinical adoption.
Area of Science:
- Neurology
- Geriatrics
- Rehabilitation Science
- Biomechanics
Background:
- Gait variability is a key indicator of neuromotor function, fall risk, and mobility impairment.
- The Enhanced Gait Variability Index (eGVI) offers a comprehensive measure of gait irregularity.
- Standardized implementation evidence for eGVI is currently limited.
Purpose of the Study:
- To map the application of eGVI across different populations and clinical contexts.
- To identify heterogeneity in eGVI assessment protocols.
- To pinpoint methodological gaps for precision medicine implementation in rehabilitation.
Main Methods:
- A scoping review following PRISMA-ScR guidelines.
- Searched major scientific databases (PubMed, CINAHL, Scopus, Emcare, Web of Science).
- Included 18 peer-reviewed studies with 1,915 participants reporting eGVI outcomes.
Main Results:
- Studies predominantly focused on Parkinson's disease (39%) and older adults (22%).
- eGVI showed sensitivity to neuromotor impairment, aging, and dual-task challenges, particularly in Parkinson's disease.
- Significant heterogeneity observed in walkways, conditions, assistive devices, and reference populations limited comparability.
Conclusions:
- The eGVI demonstrates potential as a clinical outcome measure for gait.
- Protocol variability and unvalidated normative data restrict current clinical utility.
- Population-specific protocols and validated normative data are crucial for reliable, precision medicine-aligned adoption.

