Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

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 h 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 One hundred twenty-six 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Pradeep Kumar, Danya [verfasserIn] Toosizadeh, Nima [verfasserIn] Mohler, Jane [verfasserIn] Ehsani, Hossein [verfasserIn] Mannier, Cassidy [verfasserIn] Laksari, Kaveh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Frailty Daily physical activity Wearable sensors Continuous walking Performance parameters

Übergeordnetes Werk:	Enthalten in: BMC geriatrics - London : BioMed Central, 2001, 20(2020), 1 vom: 06. Mai
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:1 ; day:06 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s12877-020-01572-1

Katalog-ID:	SPR039623653

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520			\|a 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 h 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 One hundred twenty-six 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.
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allfields	10.1186/s12877-020-01572-1 doi (DE-627)SPR039623653 (SPR)s12877-020-01572-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Pradeep Kumar, Danya verfasserin aut Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 h 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 One hundred twenty-six 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. Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213 Toosizadeh, Nima verfasserin aut Mohler, Jane verfasserin aut Ehsani, Hossein verfasserin aut Mannier, Cassidy verfasserin aut Laksari, Kaveh verfasserin aut Enthalten in BMC geriatrics London : BioMed Central, 2001 20(2020), 1 vom: 06. Mai (DE-627)335488994 (DE-600)2059865-8 1471-2318 nnns volume:20 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12877-020-01572-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 06 05
spelling	10.1186/s12877-020-01572-1 doi (DE-627)SPR039623653 (SPR)s12877-020-01572-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Pradeep Kumar, Danya verfasserin aut Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 h 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 One hundred twenty-six 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. Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213 Toosizadeh, Nima verfasserin aut Mohler, Jane verfasserin aut Ehsani, Hossein verfasserin aut Mannier, Cassidy verfasserin aut Laksari, Kaveh verfasserin aut Enthalten in BMC geriatrics London : BioMed Central, 2001 20(2020), 1 vom: 06. Mai (DE-627)335488994 (DE-600)2059865-8 1471-2318 nnns volume:20 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12877-020-01572-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 06 05
allfields_unstemmed	10.1186/s12877-020-01572-1 doi (DE-627)SPR039623653 (SPR)s12877-020-01572-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Pradeep Kumar, Danya verfasserin aut Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 h 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 One hundred twenty-six 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. Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213 Toosizadeh, Nima verfasserin aut Mohler, Jane verfasserin aut Ehsani, Hossein verfasserin aut Mannier, Cassidy verfasserin aut Laksari, Kaveh verfasserin aut Enthalten in BMC geriatrics London : BioMed Central, 2001 20(2020), 1 vom: 06. Mai (DE-627)335488994 (DE-600)2059865-8 1471-2318 nnns volume:20 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12877-020-01572-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 06 05
allfieldsGer	10.1186/s12877-020-01572-1 doi (DE-627)SPR039623653 (SPR)s12877-020-01572-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Pradeep Kumar, Danya verfasserin aut Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 h 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 One hundred twenty-six 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. Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213 Toosizadeh, Nima verfasserin aut Mohler, Jane verfasserin aut Ehsani, Hossein verfasserin aut Mannier, Cassidy verfasserin aut Laksari, Kaveh verfasserin aut Enthalten in BMC geriatrics London : BioMed Central, 2001 20(2020), 1 vom: 06. Mai (DE-627)335488994 (DE-600)2059865-8 1471-2318 nnns volume:20 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12877-020-01572-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 06 05
allfieldsSound	10.1186/s12877-020-01572-1 doi (DE-627)SPR039623653 (SPR)s12877-020-01572-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Pradeep Kumar, Danya verfasserin aut Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 h 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 One hundred twenty-six 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. Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213 Toosizadeh, Nima verfasserin aut Mohler, Jane verfasserin aut Ehsani, Hossein verfasserin aut Mannier, Cassidy verfasserin aut Laksari, Kaveh verfasserin aut Enthalten in BMC geriatrics London : BioMed Central, 2001 20(2020), 1 vom: 06. Mai (DE-627)335488994 (DE-600)2059865-8 1471-2318 nnns volume:20 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12877-020-01572-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 06 05
language	English
source	Enthalten in BMC geriatrics 20(2020), 1 vom: 06. Mai volume:20 year:2020 number:1 day:06 month:05
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authorswithroles_txt_mv	Pradeep Kumar, Danya @@aut@@ Toosizadeh, Nima @@aut@@ Mohler, Jane @@aut@@ Ehsani, Hossein @@aut@@ Mannier, Cassidy @@aut@@ Laksari, Kaveh @@aut@@
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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 h 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. 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author	Pradeep Kumar, Danya
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topic_title	610 ASE 44.00 bkl Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment Frailty (dpeaa)DE-He213 Daily physical activity (dpeaa)DE-He213 Wearable sensors (dpeaa)DE-He213 Continuous walking (dpeaa)DE-He213 Performance parameters (dpeaa)DE-He213
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title	Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
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title_full	Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
author_sort	Pradeep Kumar, Danya
journal	BMC geriatrics
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author_browse	Pradeep Kumar, Danya Toosizadeh, Nima Mohler, Jane Ehsani, Hossein Mannier, Cassidy Laksari, Kaveh
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title_sort	sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
title_auth	Sensor-based characterization of daily walking: a new paradigm in pre-frailty/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 h 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 One hundred twenty-six 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.
abstractGer	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 h 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 One hundred twenty-six 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.
abstract_unstemmed	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 h 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 One hundred twenty-six 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.
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title_short	Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment
url	https://dx.doi.org/10.1186/s12877-020-01572-1
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author2	Toosizadeh, Nima Mohler, Jane Ehsani, Hossein Mannier, Cassidy Laksari, Kaveh
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