Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?
Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear. To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fal...
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| Vydáno v: | Parkinsonism & related disorders Ročník 106; s. 105235 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Elsevier Ltd
01.01.2023
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| ISSN: | 1353-8020, 1873-5126, 1873-5126 |
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| Abstract | Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.
To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.
People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a ‘best subsets selection strategy’ with a 5-fold cross validation, and calculated the area under the curve (AUC).
The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).
Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.
•Three models were explored to investigate which balance and gait measures that best discriminate PD fallers from non-fallers.•Logistic regression was used with a ‘best subsets selection strategy’ and 5-fold cross validation to test the model performance.•The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively.•Findings highlight the importance of considering both clinica and objective balance/gait measures for the classification of fallers in PD. |
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| AbstractList | Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.
To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.
People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a ‘best subsets selection strategy’ with a 5-fold cross validation, and calculated the area under the curve (AUC).
The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).
Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.
•Three models were explored to investigate which balance and gait measures that best discriminate PD fallers from non-fallers.•Logistic regression was used with a ‘best subsets selection strategy’ and 5-fold cross validation to test the model performance.•The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively.•Findings highlight the importance of considering both clinica and objective balance/gait measures for the classification of fallers in PD. Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear. To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD. People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC). The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x). Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD. Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.BACKGROUNDAlthough much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.OBJECTIVETo identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).METHODSPeople with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).RESULTSThe highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.CONCLUSIONSFindings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD. |
| ArticleNumber | 105235 |
| Author | Mancini, Martina Shah, Vrutangkumar V. Carlson-Kuhta, Patricia Horak, Fay B. Vitorio, Rodrigo |
| AuthorAffiliation | 2 Department of Sport, Exercise & Rehabilitation, Northumbria University, UK 3 APDM Wearable Technologies, a Clario company, Portland, OR, USA 1 Department of Neurology, Oregon Health & Science University, Portland, OR, USA |
| AuthorAffiliation_xml | – name: 2 Department of Sport, Exercise & Rehabilitation, Northumbria University, UK – name: 1 Department of Neurology, Oregon Health & Science University, Portland, OR, USA – name: 3 APDM Wearable Technologies, a Clario company, Portland, OR, USA |
| Author_xml | – sequence: 1 givenname: Rodrigo orcidid: 0000-0001-7128-9452 surname: Vitorio fullname: Vitorio, Rodrigo organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 2 givenname: Martina surname: Mancini fullname: Mancini, Martina organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 3 givenname: Patricia orcidid: 0000-0002-5794-4155 surname: Carlson-Kuhta fullname: Carlson-Kuhta, Patricia organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 4 givenname: Fay B. surname: Horak fullname: Horak, Fay B. organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 5 givenname: Vrutangkumar V. orcidid: 0000-0002-8626-1089 surname: Shah fullname: Shah, Vrutangkumar V. email: shahvr@ohsu.edu organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA |
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| Cites_doi | 10.1111/j.1445-5994.2006.01123.x 10.1136/bmjopen-2014-006434 10.1016/j.gaitpost.2017.04.013 10.1088/1361-6579/ab4023 10.1016/j.humov.2019.03.010 10.1080/09638280701828930 10.3233/JPD-202289 10.1002/mds.27195 10.1212/01.wnl.0000299085.18976.20 10.1152/jn.01260.2003 10.1111/ejn.15143 10.1093/ageing/26.5.353 10.1002/mds.22340 10.1212/WNL.0b013e3181fc29c9 10.1586/17434440.2016.1153421 10.1111/jgs.15304 10.1371/journal.pone.0096675 10.1111/j.1469-7793.1999.0931s.x 10.1002/mds.21667 10.1212/01.WNL.0000091864.39702.1C 10.1002/mds.20115 10.3390/ijerph16122216 10.3390/s140100356 10.1007/s00415-020-09696-5 10.1093/gerona/50A.1.M28 10.1007/s00221-016-4642-4 10.1093/gerona/glx254 10.1016/B978-0-444-63916-5.00002-1 10.2217/nmt.14.22 10.3390/s19153320 10.1016/j.gaitpost.2009.07.108 10.1002/1531-8257(199911)14:6<947::AID-MDS1006>3.0.CO;2-O 10.1002/mds.10396 10.1111/ene.13238 10.1212/WNL.0b013e3181e7b688 10.2340/16501977-0537 10.1016/j.clinph.2020.11.027 10.1212/WNL.17.5.427 |
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| Snippet | Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.
To identify... Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains... |
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| SubjectTerms | Balance Fall Gait Gait Disorders, Neurologic - diagnosis Gait Disorders, Neurologic - etiology Humans Parkinson Disease - complications Parkinson Disease - psychology Parkinson's disease Postural Balance Walking |
| Title | Should we use both clinical and mobility measures to identify fallers in Parkinson's disease? |
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