Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.
Identifieur interne : 000516 ( Main/Curation ); précédent : 000515; suivant : 000517Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.
Auteurs : Renee Beach Sample [États-Unis] ; Allison L. Kinney [États-Unis] ; Kurt Jackson [États-Unis] ; Wiebke Diestelkamp [États-Unis] ; Kimberly Edginton Bigelow [États-Unis]Source :
- Gait & posture [ 1879-2219 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Mouvement, Posture.
- prévention et contrôle : Chutes accidentelles.
- statistiques et données numériques : Chutes accidentelles.
- Facteurs de risque, Humains, Techniques de physiothérapie.
English descriptors
- KwdEn :
- MESH :
- physiology : Movement, Posture.
- prevention & control : Accidental Falls.
- statistics & numerical data : Accidental Falls.
- Humans, Outcome Assessment, Health Care, Physical Therapy Modalities, Risk Factors.
Abstract
The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.'s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R2 value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool.
DOI: 10.1016/j.gaitpost.2017.06.007
PubMed: 28645093
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Electronic address: beachr1@udayton.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238</wicri:regionArea></affiliation></author><author><name sortKey="Kinney, Allison L" sort="Kinney, Allison L" uniqKey="Kinney A" first="Allison L" last="Kinney">Allison L. Kinney</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA. Electronic address: akinney2@udayton.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238</wicri:regionArea></affiliation></author><author><name sortKey="Jackson, Kurt" sort="Jackson, Kurt" uniqKey="Jackson K" first="Kurt" last="Jackson">Kurt Jackson</name><affiliation wicri:level="1"><nlm:affiliation>Department of Physical Therapy, University of Dayton,300 College Park, Fitz Hall 207, Dayton, OH, 45469-2925, USA. Electronic address: kjackson1@udayton.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Physical Therapy, University of Dayton,300 College Park, Fitz Hall 207, Dayton, OH, 45469-2925</wicri:regionArea></affiliation></author><author><name sortKey="Diestelkamp, Wiebke" sort="Diestelkamp, Wiebke" uniqKey="Diestelkamp W" first="Wiebke" last="Diestelkamp">Wiebke Diestelkamp</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics, University of Dayton, 300 College Park, Dayton, OH, 45469-2316, USA. Electronic address: wdiestelkamp1@udayton.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mathematics, University of Dayton, 300 College Park, Dayton, OH, 45469-2316</wicri:regionArea></affiliation></author><author><name sortKey="Bigelow, Kimberly Edginton" sort="Bigelow, Kimberly Edginton" uniqKey="Bigelow K" first="Kimberly Edginton" last="Bigelow">Kimberly Edginton Bigelow</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA. Electronic address: kbigelow1@udayton.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238</wicri:regionArea></affiliation></author></analytic><series><title level="j">Gait & posture</title><idno type="eISSN">1879-2219</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accidental Falls (prevention & control)</term><term>Accidental Falls (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Movement (physiology)</term><term>Outcome Assessment, Health Care (MeSH)</term><term>Physical Therapy Modalities (MeSH)</term><term>Posture (physiology)</term><term>Risk Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Chutes accidentelles (prévention et contrôle)</term><term>Chutes accidentelles (statistiques et données numériques)</term><term>Facteurs de risque (MeSH)</term><term>Humains (MeSH)</term><term>Mouvement (physiologie)</term><term>Posture (physiologie)</term><term>Techniques de physiothérapie (MeSH)</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Mouvement</term><term>Posture</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Movement</term><term>Posture</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Accidental Falls</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Chutes accidentelles</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Accidental Falls</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Chutes accidentelles</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Outcome Assessment, Health Care</term><term>Physical Therapy Modalities</term><term>Risk Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs de risque</term><term>Humains</term><term>Techniques de physiothérapie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.'s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R<sup>2</sup> value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28645093</PMID><DateCompleted><Year>2018</Year><Month>03</Month><Day>21</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-2219</ISSN><JournalIssue CitedMedium="Internet"><Volume>57</Volume><PubDate><Year>2017</Year><Month>09</Month></PubDate></JournalIssue><Title>Gait & posture</Title><ISOAbbreviation>Gait Posture</ISOAbbreviation></Journal><ArticleTitle>Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.</ArticleTitle><Pagination><MedlinePgn>168-171</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0966-6362(17)30230-8</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.gaitpost.2017.06.007</ELocationID><Abstract><AbstractText>The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.'s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R<sup>2</sup> value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool.</AbstractText><CopyrightInformation>Copyright © 2017 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sample</LastName><ForeName>Renee Beach</ForeName><Initials>RB</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA. Electronic address: beachr1@udayton.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kinney</LastName><ForeName>Allison L</ForeName><Initials>AL</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA. Electronic address: akinney2@udayton.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jackson</LastName><ForeName>Kurt</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Physical Therapy, University of Dayton,300 College Park, Fitz Hall 207, Dayton, OH, 45469-2925, USA. Electronic address: kjackson1@udayton.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Diestelkamp</LastName><ForeName>Wiebke</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of Dayton, 300 College Park, Dayton, OH, 45469-2316, USA. Electronic address: wdiestelkamp1@udayton.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bigelow</LastName><ForeName>Kimberly Edginton</ForeName><Initials>KE</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Kettering Labs Room 363F, Dayton, OH, 45469-0238, USA. Electronic address: kbigelow1@udayton.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>06</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Gait Posture</MedlineTA><NlmUniqueID>9416830</NlmUniqueID><ISSNLinking>0966-6362</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000058" MajorTopicYN="N">Accidental Falls</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009068" MajorTopicYN="N">Movement</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017063" MajorTopicYN="N">Outcome Assessment, Health Care</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D026741" MajorTopicYN="Y">Physical Therapy Modalities</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011187" MajorTopicYN="N">Posture</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Fall prevention</Keyword><Keyword MajorTopicYN="Y">Fall risk</Keyword><Keyword MajorTopicYN="Y">Instrumented timed up and go (iTUG)</Keyword><Keyword MajorTopicYN="Y">Logistic regression</Keyword><Keyword MajorTopicYN="Y">Older adults</Keyword><Keyword MajorTopicYN="Y">Posturography</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>07</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>05</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>06</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>6</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>3</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>6</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28645093</ArticleId><ArticleId IdType="pii">S0966-6362(17)30230-8</ArticleId><ArticleId IdType="doi">10.1016/j.gaitpost.2017.06.007</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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