Cargando…

The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment

Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This...

Descripción completa

Detalles Bibliográficos
Autores principales: Pedrero-Sánchez, José-Francisco, De-Rosario-Martínez, Helios, Medina-Ripoll, Enrique, Garrido-Jaén, David, Serra-Añó, Pilar, Mollà-Casanova, Sara, López-Pascual, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385364/
https://www.ncbi.nlm.nih.gov/pubmed/37514860
http://dx.doi.org/10.3390/s23146567
_version_ 1785081388103368704
author Pedrero-Sánchez, José-Francisco
De-Rosario-Martínez, Helios
Medina-Ripoll, Enrique
Garrido-Jaén, David
Serra-Añó, Pilar
Mollà-Casanova, Sara
López-Pascual, Juan
author_facet Pedrero-Sánchez, José-Francisco
De-Rosario-Martínez, Helios
Medina-Ripoll, Enrique
Garrido-Jaén, David
Serra-Añó, Pilar
Mollà-Casanova, Sara
López-Pascual, Juan
author_sort Pedrero-Sánchez, José-Francisco
collection PubMed
description Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This study aimed to evaluate and validate the reliability and accuracy of an easy-to-use smartphone fall risk assessment by comparing it with the Physiological Profile Assessment (PPA) results. Sixty-five participants older than 55 performed a variation of the Timed Up and Go test using smartphone sensors. Balance and gait parameters were calculated, and their reliability was assessed by the (ICC) and compared with the PPAs. Since the PPA allows classification into six levels of fall risk, the data obtained from the smartphone assessment were categorised into six equivalent levels using different parametric and nonparametric classifier models with neural networks. The F1 score and geometric mean of each model were also calculated. All selected parameters showed ICCs around 0.9. The best classifier, in terms of accuracy, was the nonparametric mixed input data model with a 100% success rate in the classification category. In conclusion, fall risk can be reliably assessed using a simple, fast smartphone protocol that allows accurate fall risk classification among older people and can be a useful screening tool in clinical settings.
format Online
Article
Text
id pubmed-10385364
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-103853642023-07-30 The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment Pedrero-Sánchez, José-Francisco De-Rosario-Martínez, Helios Medina-Ripoll, Enrique Garrido-Jaén, David Serra-Añó, Pilar Mollà-Casanova, Sara López-Pascual, Juan Sensors (Basel) Article Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This study aimed to evaluate and validate the reliability and accuracy of an easy-to-use smartphone fall risk assessment by comparing it with the Physiological Profile Assessment (PPA) results. Sixty-five participants older than 55 performed a variation of the Timed Up and Go test using smartphone sensors. Balance and gait parameters were calculated, and their reliability was assessed by the (ICC) and compared with the PPAs. Since the PPA allows classification into six levels of fall risk, the data obtained from the smartphone assessment were categorised into six equivalent levels using different parametric and nonparametric classifier models with neural networks. The F1 score and geometric mean of each model were also calculated. All selected parameters showed ICCs around 0.9. The best classifier, in terms of accuracy, was the nonparametric mixed input data model with a 100% success rate in the classification category. In conclusion, fall risk can be reliably assessed using a simple, fast smartphone protocol that allows accurate fall risk classification among older people and can be a useful screening tool in clinical settings. MDPI 2023-07-20 /pmc/articles/PMC10385364/ /pubmed/37514860 http://dx.doi.org/10.3390/s23146567 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pedrero-Sánchez, José-Francisco
De-Rosario-Martínez, Helios
Medina-Ripoll, Enrique
Garrido-Jaén, David
Serra-Añó, Pilar
Mollà-Casanova, Sara
López-Pascual, Juan
The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title_full The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title_fullStr The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title_full_unstemmed The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title_short The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment
title_sort reliability and accuracy of a fall risk assessment procedure using mobile smartphone sensors compared with a physiological profile assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385364/
https://www.ncbi.nlm.nih.gov/pubmed/37514860
http://dx.doi.org/10.3390/s23146567
work_keys_str_mv AT pedrerosanchezjosefrancisco thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT derosariomartinezhelios thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT medinaripollenrique thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT garridojaendavid thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT serraanopilar thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT mollacasanovasara thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT lopezpascualjuan thereliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT pedrerosanchezjosefrancisco reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT derosariomartinezhelios reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT medinaripollenrique reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT garridojaendavid reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT serraanopilar reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT mollacasanovasara reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment
AT lopezpascualjuan reliabilityandaccuracyofafallriskassessmentprocedureusingmobilesmartphonesensorscomparedwithaphysiologicalprofileassessment