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Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

BACKGROUND: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent...

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Autores principales: Marschollek, Michael, Rehwald, Anja, Wolf, Klaus-Hendrik, Gietzelt, Matthias, Nemitz, Gerhard, zu Schwabedissen, Hubertus Meyer, Schulze, Mareike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141375/
https://www.ncbi.nlm.nih.gov/pubmed/21711504
http://dx.doi.org/10.1186/1472-6947-11-48
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author Marschollek, Michael
Rehwald, Anja
Wolf, Klaus-Hendrik
Gietzelt, Matthias
Nemitz, Gerhard
zu Schwabedissen, Hubertus Meyer
Schulze, Mareike
author_facet Marschollek, Michael
Rehwald, Anja
Wolf, Klaus-Hendrik
Gietzelt, Matthias
Nemitz, Gerhard
zu Schwabedissen, Hubertus Meyer
Schulze, Mareike
author_sort Marschollek, Michael
collection PubMed
description BACKGROUND: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. METHODS: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. RESULTS: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. CONCLUSIONS: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.
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spelling pubmed-31413752011-07-23 Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients Marschollek, Michael Rehwald, Anja Wolf, Klaus-Hendrik Gietzelt, Matthias Nemitz, Gerhard zu Schwabedissen, Hubertus Meyer Schulze, Mareike BMC Med Inform Decis Mak Research Article BACKGROUND: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. METHODS: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. RESULTS: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. CONCLUSIONS: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach. BioMed Central 2011-06-28 /pmc/articles/PMC3141375/ /pubmed/21711504 http://dx.doi.org/10.1186/1472-6947-11-48 Text en Copyright ©2011 Marschollek et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Marschollek, Michael
Rehwald, Anja
Wolf, Klaus-Hendrik
Gietzelt, Matthias
Nemitz, Gerhard
zu Schwabedissen, Hubertus Meyer
Schulze, Mareike
Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title_full Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title_fullStr Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title_full_unstemmed Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title_short Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
title_sort sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141375/
https://www.ncbi.nlm.nih.gov/pubmed/21711504
http://dx.doi.org/10.1186/1472-6947-11-48
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