Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using w...

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Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 16; p. 5940
Main Authors:	Arpan, Ishu, Shah, Vrutangkumar V., McNames, James, Harker, Graham, Carlson-Kuhta, Patricia, Spain, Rebecca, El-Gohary, Mahmoud, Mancini, Martina, Horak, Fay B.
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.08.2022 MDPI
Subjects:	Algorithms Balance Clinics Falls Falls (Accidents) Gait Gait - physiology home monitoring Humans instrumented gait and turning analysis Investigations Multiple Sclerosis Neurophysiology Pilot Projects pitch at toe-off Postural Balance prospective falls retrospective fall history Retrospective Studies Sensors Walking Walking - physiology Mississippi instrumented gait and turning analysis home monitoring retrospective fall history pitch at toe-off multiple sclerosis prospective falls
ISSN:	1424-8220, 1424-8220
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Abstract	This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.
AbstractList	This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk. This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.
Audience	Academic
Author	Shah, Vrutangkumar V. Mancini, Martina Carlson-Kuhta, Patricia Spain, Rebecca Harker, Graham Horak, Fay B. Arpan, Ishu El-Gohary, Mahmoud McNames, James
AuthorAffiliation	3 APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA 2 Advanced Imaging Research Center, Oregon Health & Science University Portland, OR 97239, USA 4 Department of Electrical and Computer Engineering, Portland State University, 1825 SW Broadway, Portland, OR 97201, USA 1 Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
AuthorAffiliation_xml	– name: 4 Department of Electrical and Computer Engineering, Portland State University, 1825 SW Broadway, Portland, OR 97201, USA – name: 3 APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA – name: 1 Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA – name: 2 Advanced Imaging Research Center, Oregon Health & Science University Portland, OR 97239, USA
Author_xml	– sequence: 1 givenname: Ishu surname: Arpan fullname: Arpan, Ishu – sequence: 2 givenname: Vrutangkumar V. orcidid: 0000-0002-8626-1089 surname: Shah fullname: Shah, Vrutangkumar V. – sequence: 3 givenname: James surname: McNames fullname: McNames, James – sequence: 4 givenname: Graham surname: Harker fullname: Harker, Graham – sequence: 5 givenname: Patricia orcidid: 0000-0002-5794-4155 surname: Carlson-Kuhta fullname: Carlson-Kuhta, Patricia – sequence: 6 givenname: Rebecca surname: Spain fullname: Spain, Rebecca – sequence: 7 givenname: Mahmoud surname: El-Gohary fullname: El-Gohary, Mahmoud – sequence: 8 givenname: Martina surname: Mancini fullname: Mancini, Martina – sequence: 9 givenname: Fay B. orcidid: 0000-0001-7704-5459 surname: Horak fullname: Horak, Fay B.
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Cites_doi	10.2196/mhealth.8815 10.1038/s41598-021-01694-5 10.1177/0269215517748714 10.1007/s11910-021-01101-6 10.1016/j.apmr.2012.11.027 10.1186/s12883-018-1111-7 10.1002/lio2.252 10.3389/fneur.2018.00216 10.1093/geront/46.3.367 10.1177/1352458514538884 10.1002/pri.1743 10.1371/journal.pone.0107620 10.1212/CPJ.0000000000000382 10.1016/j.neuron.2006.09.011 10.2522/ptj.20120231 10.1101/597294 10.1159/000369095 10.3233/THC-174736 10.1016/j.gaitpost.2020.11.024 10.1016/j.gaitpost.2011.11.026 10.3390/s140100356 10.1016/j.msard.2018.01.027 10.1146/annurev.neuro.30.051606.094313 10.1007/s00415-020-09696-5 10.1186/s12984-020-00781-4 10.1177/0269215508095087 10.1371/journal.pone.0028021 10.2147/IJGM.S32651
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Med. doi: 10.2147/IJGM.S32651
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Snippet	This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at...
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StartPage	5940
SubjectTerms	Algorithms Balance Clinics Falls Falls (Accidents) Gait Gait - physiology home monitoring Humans instrumented gait and turning analysis Investigations Multiple Sclerosis Neurophysiology Pilot Projects pitch at toe-off Postural Balance prospective falls retrospective fall history Retrospective Studies Sensors Walking Walking - physiology
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Title	Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis
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Volume	22
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