Background: Gait indices were developed to represent the magnitude of impairment extracted from a gait analysis with a single value. The Gillette Gait Index (GGI), and the Gait Deviation Index (GDI) are 2 widely used indices that represent gait impairment differently based on their statistical properties. Our purpose was to (1) report on the results of gait analysis for a broad spectrum of pediatric conditions using the GGI and GDI, and (2) identify the parameters that dominate impairment.
Methods: A total of 1439 children with 13 different diagnoses with a complete, baseline gait analysis were identified. The GGI and its 16 parameters were calculated in all cases, and the GDI was calculated from a smaller subset. T tests, and z-scores were used to compare each of these values to typically developing children for each diagnosis. A separate linear regression controlling for age, sex, and use of an orthosis, or assistive device was performed for the GGI.
Results: In our series, there were 71 typically developing children with a GGI of 31. We qualify relative gait impairment as severe, mild, or moderate as based on the GGI, and propose that values <100 represent mild, 100 to 200 represent moderate, and >200 represents severe impairment. On the basis of strong correlation between the GGI and GDI, we suggest that GDI values >80 represent mild, and values <70 represent severe impairment. T tests and z-scores demonstrated that both the number and magnitude of abnormal parameters increase the GGI. These tests also identified the most clinically relevant parameters contributing to functional impairment for each diagnosis. Multivariate linear regression showed that all diagnoses except flatfoot and scoliosis demonstrated statistically significant differences in GGI scores.
Conclusions: This is the first study to apply these gait indices to a large population of diverse pediatric conditions. We propose GGI and GDI values to qualify gait impairment among these conditions as severe, moderate, or mild. Furthermore, impairment in gait reflects both the number and magnitude of abnormal parameters within each condition.
Level Of Evidence: Level III-retrospective comparative study.
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http://dx.doi.org/10.1097/BPO.0000000000000823 | DOI Listing |
Front Bioeng Biotechnol
December 2024
Department of Rehabilitation Medicine, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Introduction: Parkinson's disease (PD) is characterized by muscle stiffness, bradykinesia, and balance disorders, significantly impairing the quality of life for affected patients. While motion pose estimation and gait analysis can aid in early diagnosis and timely intervention, clinical practice currently lacks objective and accurate tools for gait analysis.
Methods: This study proposes a multi-level 3D pose estimation framework for PD patients, integrating monocular video with Transformer and Graph Convolutional Network (GCN) techniques.
Biomechanical gait impairments, such as reduced paretic propulsion, are common post-stroke. Studies have used biofeedback to increase paretic propulsion and reduce propulsion asymmetry, but it is unclear if these changes impact overall gait asymmetry. There is an implicit assumption that reducing propulsion asymmetry will improve overall gait symmetry, as paretic propulsion has been related to numerous biomechanical impairments.
View Article and Find Full Text PDFMusculoskeletal Care
March 2025
School of Physiotherapy, Faculty of Health, Dalhousie University, Halifax, Canada.
Introduction: Osteoarthritis is a progressive joint disease that causes pain and disability, impairing physical function. Moderate-to-vigorous physical activity (MVPA) is recommended for knee osteoarthritis, while stationary time, independent of activity, may negatively impact health outcomes. We hypothesised that individuals with the highest MVPA and lowest stationary time would have better long-term function compared to those with the lowest MVPA and highest stationary time, as well as those with high levels of both MVPA and stationary time.
View Article and Find Full Text PDFJAMA Neurol
January 2025
Takeda Development Center Americas, Inc, Cambridge, Massachusetts.
Front Aging Neurosci
December 2024
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Background: The neural mechanisms underlying freezing of gait (FOG) in Parkinson's disease (PD) have not been completely comprehended. Sensory-motor integration dysfunction was proposed as one of the contributing factors. Here, we investigated short-latency afferent inhibition (SAI) and long-latency afferent inhibition (LAI), and analyzed their association with gait performance in FOG PD patients, to further validate the role of sensorimotor integration in the occurrence of FOG in PD.
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