Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values

Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is...

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Published inPloS one Vol. 13; no. 6; p. e0199215
Main Authors Mikos, Val, Yen, Shih-Cheng, Tay, Arthur, Heng, Chun-Huat, Chung, Chloe Lau Ha, Liew, Sylvia Hui Xin, Tan, Dawn May Leng, Au, Wing Lok
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 18.06.2018
Public Library of Science (PLoS)
Subjects
Adult
Age
Aged
Aged, 80 and over
Analysis
Ankle
Ankle - physiology
Anthropometry
Biology and Life Sciences
Biomechanics
Body mass
Body measurements
Computer and Information Sciences
Computer engineering
Correlation analysis
Data reduction
Dispersion
Female
Gait
Gait - physiology
Gait recognition
Gender
Gerontology
Healthy Volunteers
Humans
Independent variables
Linear Models
Male
Medicine and Health Sciences
Middle Aged
Mobility
Monitoring systems
Motion
Older people
Parameters
Parkinson's disease
Patients
Physical Sciences
Physical therapy
Posture
Regression analysis
Regression models
Research and Analysis Methods
Studies
Walking
Walking - physiology
Walking Speed - physiology
Singapore
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0199215

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Abstract Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
AbstractList Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. Methods 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization’s capability in de-correlating data and reducing data dispersion were evaluated. Results Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Conclusion Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |r.sub.s | < 0.74 to |r.sub.s | < 0.23, and reduced data dispersion by up to 69%. Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. Methods 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. Results Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |r.sub.s | < 0.74 to |r.sub.s | < 0.23, and reduced data dispersion by up to 69%. Conclusion Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable.BACKGROUNDDeviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable.87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated.METHODS87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated.Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%.RESULTSGait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%.Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.CONCLUSIONNormalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. Methods 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization’s capability in de-correlating data and reducing data dispersion were evaluated. Results Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Conclusion Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization's capability in de-correlating data and reducing data dispersion were evaluated. Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements.
Audience Academic
Author Heng, Chun-Huat
Liew, Sylvia Hui Xin
Chung, Chloe Lau Ha
Mikos, Val
Yen, Shih-Cheng
Au, Wing Lok
Tay, Arthur
Tan, Dawn May Leng
AuthorAffiliation 1 Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
The Ohio State University, UNITED STATES
3 Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore
2 Department of Physiotherapy, Tan Tock Seng Hospital, Singapore, Singapore
4 Department of Neurology, National Neuroscience Institute, Singapore, Singapore
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/29912992$$D View this record in MEDLINE/PubMed
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Competing Interests: The authors have declared that no competing interests exist.
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Snippet Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry...
Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by...
BACKGROUND:Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by...
Background Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by...
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SubjectTerms Adult
Age
Aged
Aged, 80 and over
Analysis
Ankle
Ankle - physiology
Anthropometry
Biology and Life Sciences
Biomechanics
Body mass
Body measurements
Computer and Information Sciences
Computer engineering
Correlation analysis
Data reduction
Dispersion
Female
Gait
Gait - physiology
Gait recognition
Gender
Gerontology
Healthy Volunteers
Humans
Independent variables
Linear Models
Male
Medicine and Health Sciences
Middle Aged
Mobility
Monitoring systems
Motion
Older people
Parameters
Parkinson's disease
Patients
Physical Sciences
Physical therapy
Posture
Regression analysis
Regression models
Research and Analysis Methods
Studies
Walking
Walking - physiology
Walking Speed - physiology
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Title Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values
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