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Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Related Experiment Video

Updated: Mar 20, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Applying different mathematical variability methods to identify older fallers and non-fallers using gait variability

Nise Ribeiro Marques^1,2, Camilla Zamfolini Hallal³, Deborah Hebling Spinoso⁴

¹Department of Physical Therapy and Occupational Therapy, São Paulo State University, UNESP, Marília, Brazil. nisermarques@yahoo.com.br.

Aging Clinical and Experimental Research

|June 4, 2016

Summary

The standard deviation of gait timing is the most effective method for identifying older adults at risk of falling. This analysis accurately distinguishes between fallers and non-fallers, improving fall prevention strategies.

Keywords:

Aging Falls risk Kinematics

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

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Published on: May 12, 2016

Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Gerontology
Biomechanics
Clinical Gait Analysis

Background:

Gait variability assessment is crucial for identifying older adults at risk of falls.
Existing gait timing variability metrics require further investigation for optimal fall risk screening.

Purpose of the Study:

To determine the most effective mathematical method for analyzing gait variability to differentiate between older fallers and non-fallers.
To identify the optimal temporal-kinematic gait parameter for distinguishing between older fallers and non-fallers.

Main Methods:

Thirty-five physically active older women (16 fallers, 19 non-fallers) participated.
Temporal kinematic gait parameters (stance, swing, stride time) were recorded using footswitch sensors during treadmill walking.
Six statistical methods were employed to analyze gait variability over 40 consecutive gait cycles.

Main Results:

The standard deviation of 40 consecutive gait cycles demonstrated 100% sensitivity and 100% specificity in discriminating between older fallers and non-fallers.
This method proved superior to other statistical approaches in fall risk assessment.

Conclusions:

The standard deviation of stance time is the most effective kinematic gait variability parameter for distinguishing older fallers from non-fallers.
This finding supports the use of standard deviation of stance time in clinical fall risk assessment for older adults.