scholarly journals FRAILTY ASSESSMENT BASED ON THE QUALITY OF DAILY WALKING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S85-S85 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Kaveh Laksari
Keyword(s):  
Physical Activity ◽  
Gait Analysis ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Age Related ◽  
Power Spectral ◽  
Gait Parameters ◽  
Clinical Gait Analysis

Abstract Frailty is an increasingly recognized geriatric syndrome resulting in age-related decline in reserve across multiple physiologic systems. An impaired physical function is a prime indicator of frailty. In this study, we aim to implement a body-worn sensor to characterize the quantity and quality of everyday walking, and establish associations between gait impairment and frailty. Daily physical activity was acquired for 48 hours from 125 older adults (≥65 years; 44 non-frail, 60 pre-frail, and 21 frail based on the Fried gold standard) using a tri-axial accelerometer motion-sensor. Continuous purposeful walks (≥60s) without pauses were identified from time-domain acceleration data. Power spectral density (PSD) analysis was performed to define higher gait variability, which was identified by a shorter and wider PSD peak. Association between frailty and gait parameters was assessed using multivariable nominal logistic models with frailty as the dependent variable, and demographic parameters along with the gait parameters as the independent variables. Stride times, PSD gait variability, and total and maximum continuous purposeful walking duration were significantly different between non-frail and pre-frail/frail groups (p<0.05). Using a step-wise model with the above qualitative and quantitative gait parameters as predictors, the pre-frail/frail group (vs. non-frail) was identified with 71.4% sensitivity and 75.4% specificity. Everyday walking characteristics were found to be accurate determinants of frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the stages of frailty. In-home gait analysis is advantageous over clinical gait analysis as it enables cost- and space-effective continuous monitoring.

scholarly journals Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment

2020 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Hossein Ehsani ◽  
Cassidy Mannier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Performance Parameters ◽  
New Paradigm ◽  
Gait Performance ◽  
Critical Elements ◽  
Frailty Assessment

Abstract Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods: DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results: 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p<0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.Trial registration: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

Sensor-based Characterization of Daily Walking: A New Paradigm in Frailty Assessment

2019 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Hossein Ehsani ◽  
Kaveh Laksari
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Performance Parameters ◽  
New Paradigm ◽  
Gait Performance ◽  
Critical Elements ◽  
Acceleration Data

Abstract Background Frailty is an increasingly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is a prime indicator of frailty.Objective The goal of this study was to develop an algorithm that discriminates between frailty groups (non-frail, pre-frail, and frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA).Methods DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Purposeful continuous bouts of walking (≥60s) without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables.Results 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and purposeful walking quantitative measures were significantly different between non-frail and pre-frail as well as non-frail and frail groups ( p <0.05). Using the logistic model pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity.Discussion Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

scholarly journals Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment

2020 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Hossein Ehsani ◽  
Cassidy Mannier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Performance Parameters ◽  
New Paradigm ◽  
Gait Performance ◽  
Critical Elements ◽  
Frailty Assessment

Abstract Background : Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods : DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results : 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups ( p <0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion : Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

scholarly journals Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment

2020 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Hossein Ehsani ◽  
Cassidy Mannier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Performance Parameters ◽  
New Paradigm ◽  
Gait Performance ◽  
Critical Elements ◽  
Frailty Assessment

Abstract Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. Objective: The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods: DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results: 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p<0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

Agreement and consistency of five different clinical gait analysis systems in the assessment of spatiotemporal gait parameters

2021 ◽  
Vol 85 ◽  
pp. 55-64
Author(s):  
Julian Rudisch ◽  
Thomas Jöllenbeck ◽  
Lutz Vogt ◽  
Thomas Cordes ◽  
Thomas Jürgen Klotzbier ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Gait Parameters ◽  
Clinical Gait Analysis

scholarly journals Synchronized Sensor Insoles for Clinical Gait Analysis in Home-Monitoring Applications

2018 ◽  
Vol 4 (1) ◽  
pp. 433-437 ◽  
Author(s):  
Nils Roth ◽  
Christine F. Martindale ◽  
Bjoern M. Eskofier ◽  
Heiko Gaßner ◽  
Zacharias Kohl ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Pressure Sensors ◽  
Home Monitoring ◽  
Fully Integrated ◽  
Double Support ◽  
Timing Offset ◽  
System A ◽  
Gait Parameters ◽  
Double Support Time ◽  
Clinical Gait Analysis

AbstractWearable sensor systems are of increasing interest in clinical gait analysis. However, little information about gait dynamics of patients under free living conditions is available, due to the challenges of integrating such systems unobtrusively into a patient’s everyday live. To address this limitation, new, fully integrated low power sensor insoles are proposed, to target applications particularly in home-monitoring scenarios. The insoles combine inertial as well as pressure sensors and feature wireless synchronization to acquire biomechanical data of both feet with a mean timing offset of 15.0 μs. The proposed system was evaluated on 15 patients with mild to severe gait disorders against the GAITRite® system as reference. Gait events based on the insoles’ pressure sensors were manually extracted to calculate temporal gait features such as double support time and double support. Compared to the reference system a mean error of 0.06 s ±0.06 s and 3.89 % ±2.61 % was achieved, respectively. The proposed insoles proved their ability to acquire synchronized gait parameters and address the requirements for home-monitoring scenarios, pushing the boundaries of clinical gait analysis.

Mitochondria, Muscle Health, and Exercise with Advancing Age

Physiology ◽  
2015 ◽  
Vol 30 (3) ◽  
pp. 208-223 ◽  
Author(s):  
Heather N. Carter ◽  
Chris C. W. Chen ◽  
David A. Hood
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Muscle Function ◽  
Older Individuals ◽  
Age Related ◽  
Optimal Function ◽  
Reduced Physical Activity ◽  
Muscle Health ◽  
Synthesis And Degradation

Skeletal muscle health is dependent on the optimal function of its mitochondria. With advancing age, decrements in numerous mitochondrial variables are evident in muscle. Part of this decline is due to reduced physical activity, whereas the remainder appears to be attributed to age-related alterations in mitochondrial synthesis and degradation. Exercise is an important strategy to stimulate mitochondrial adaptations in older individuals to foster improvements in muscle function and quality of life.

scholarly journals Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sungmoon Jeong ◽  
Hosang Yu ◽  
Jaechan Park ◽  
Kyunghun Kang
Keyword(s):  
Deep Learning ◽  
Gait Analysis ◽  
Normal Pressure Hydrocephalus ◽  
Learning Algorithms ◽  
Normal Pressure ◽  
Gait Variability ◽  
Stride Time ◽  
Analysis Method ◽  
Gait Parameters ◽  
Analysis System

AbstractA vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system. There was a strong correlation in 11 gait parameters between our vision-based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision-based gait analysis system were correlated with FAB scores. Vision-based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision-based gait analysis for INPH patients.

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