Callum M W Betteridge1,2,3,4, Pragadesh Natarajan1,2,3,4, R Dineth Fonseka1,2,3,4, Daniel Ho1,2,3,4, Ralph Mobbs1,2,3,4, Wen Jie Choy1,2,3,4. 1. Department of Medicine, University of New South Wales, Sydney, Australia. 2. NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia. 3. NeuroSpine Surgery Research Group, Sydney, Australia. 4. Wearables and Gait Assessment Group, Sydney, Australia.
Abstract
OBJECTIVES: The present narrative review aims to collate the literature regarding the current use of wearable gait measurement devices for falls-risk assessment in neurological and non-neurological populations. Thereby, this review seeks to determine the extent to which the aforementioned barriers inhibit clinical use. BACKGROUND: Falls contribute a significant disease burden in most western countries, resulting in increased morbidity and mortality with substantial therapeutic costs. The recent development of gait analysis sensor technologies has enabled quantitative measurement of several gait features related to falls risk. However, three main barriers to implementation exist: accurately measuring gait-features associated with falls, differentiating between fallers and non-fallers using these gait features, and the accuracy of falls predictive algorithms developed using these gait measurements. METHODS: Searches of Medline, PubMed, Embase and Scopus were screened to identify 46 articles relevant to the present study. Studies performing gait assessment using any wearable gait assessment device and analysing correlation with the occurrence of falls during a retrospective or prospective study period were included. Risk of Bias was assessed using the Centre for Evidence Based Medicine (CEBM) Criteria. CONCLUSIONS: Falls prediction algorithms based entirely, or in-part, on gait data have shown comparable or greater success of predicting falls than existing stratification scoring systems such as the 10-meter walk test or timed-up-and-go. However, data is lacking regarding their accuracy in neurological patient populations. Inertial measurement units (IMU) have displayed competency in obtaining and interpreting gait metrics relevant to falls risk. They have the potential to enhance the accuracy and efficiency of falls risk assessment in inpatient and outpatient setting. 2021 mHealth. All rights reserved.
OBJECTIVES: The present narrative review aims to collate the literature regarding the current use of wearable gait measurement devices for falls-risk assessment in neurological and non-neurological populations. Thereby, this review seeks to determine the extent to which the aforementioned barriers inhibit clinical use. BACKGROUND: Falls contribute a significant disease burden in most western countries, resulting in increased morbidity and mortality with substantial therapeutic costs. The recent development of gait analysis sensor technologies has enabled quantitative measurement of several gait features related to falls risk. However, three main barriers to implementation exist: accurately measuring gait-features associated with falls, differentiating between fallers and non-fallers using these gait features, and the accuracy of falls predictive algorithms developed using these gait measurements. METHODS: Searches of Medline, PubMed, Embase and Scopus were screened to identify 46 articles relevant to the present study. Studies performing gait assessment using any wearable gait assessment device and analysing correlation with the occurrence of falls during a retrospective or prospective study period were included. Risk of Bias was assessed using the Centre for Evidence Based Medicine (CEBM) Criteria. CONCLUSIONS: Falls prediction algorithms based entirely, or in-part, on gait data have shown comparable or greater success of predicting falls than existing stratification scoring systems such as the 10-meter walk test or timed-up-and-go. However, data is lacking regarding their accuracy in neurological patient populations. Inertial measurement units (IMU) have displayed competency in obtaining and interpreting gait metrics relevant to falls risk. They have the potential to enhance the accuracy and efficiency of falls risk assessment in inpatient and outpatient setting. 2021 mHealth. All rights reserved.
Authors: Matthew A D Brodie; Milou J M Coppens; Stephen R Lord; Nigel H Lovell; Yves J Gschwind; Stephen J Redmond; Michael Benjamin Del Rosario; Kejia Wang; Daina L Sturnieks; Michela Persiani; Kim Delbaere Journal: Med Biol Eng Comput Date: 2015-08-06 Impact factor: 2.602
Authors: René Schwesig; David Fischer; Andreas Lauenroth; Stephan Becker; Siegfried Leuchte Journal: Clin Rehabil Date: 2012-07-27 Impact factor: 3.477
Authors: Matthew A Brodie; Milou J Coppens; Andreas Ejupi; Yves J Gschwind; Janneke Annegarn; Daniel Schoene; Rainer Wieching; Stephen R Lord; Kim Delbaere Journal: Geriatr Gerontol Int Date: 2017-02-08 Impact factor: 2.730