Loading…
Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults
Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine...
Saved in:
Published in: | PloS one 2021-02, Vol.16 (2), p.e0246397-e0246397 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3 |
---|---|
cites | cdi_FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3 |
container_end_page | e0246397 |
container_issue | 2 |
container_start_page | e0246397 |
container_title | PloS one |
container_volume | 16 |
creator | Hirata, Keisuke Suzuki, Makoto Iso, Naoki Okabe, Takuhiro Goto, Hiroshi Cho, Kilchoon Shimizu, Junichi |
description | Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2-3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility. |
doi_str_mv | 10.1371/journal.pone.0246397 |
format | article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2488535062</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A651503096</galeid><doaj_id>oai_doaj_org_article_364bb2ae91cf45e3b12282d0201dd2f0</doaj_id><sourcerecordid>A651503096</sourcerecordid><originalsourceid>FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3</originalsourceid><addsrcrecordid>eNqNk01v1DAQhiMEoqXwDxBEQkJw2MUfcZxckKqKj5UqVQLK1XKcSdYrx97GTqEH_jvObrraoB5QDolnnvd1PONJkpcYLTHl-MPGDb2VZrl1FpaIZDkt-aPkFJeULHKC6OOj75PkmfcbhBgt8vxpckIp45jQ8jT5c-21bdNOqrW2kBqQvR0DwaXa3oIPupUB0rCGtAcjg3bWr_U2rSD8ArBp7TqprU9dkzaDVWN-lEtbH9bSpJ2rtNHhLnqmztTQp7IeTPDPkyeNNB5eTO-z5Przpx8XXxeXV19WF-eXC8VZERaFVAVDvAJMGC8yTjhGvKYUM-BMxWQFUDZlzQjNMCbAZYUlJRXNCkUpSHqWvN77bo3zYqqcFyQrCkYZykkkVnuidnIjtr3uZH8nnNRiF3B9K2QftDIgaJ5VFZFQYtVkDGiFCSlIjQjCdU0aFL0-TrsNVQe1Aht6aWam84zVa9G6W8ELzmNjosG7yaB3N0Nsgui0V2CMtOCG3X-XhMf-8Yi--Qd9-HQT1cp4AG0bF_dVo6k4zxlmiKIyj9TyASo-NXRaxWvW6BifCd7PBJEJ8Du0cvBerL5_-3_26uecfXvErkGasPbODLvbNwezPah6530PzaHIGIlxSu6rIcYpEdOURNmr4wYdRPdjQf8Cee4OUQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2488535062</pqid></control><display><type>article</type><title>Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Hirata, Keisuke ; Suzuki, Makoto ; Iso, Naoki ; Okabe, Takuhiro ; Goto, Hiroshi ; Cho, Kilchoon ; Shimizu, Junichi</creator><contributor>Sakakibara, Manabu</contributor><creatorcontrib>Hirata, Keisuke ; Suzuki, Makoto ; Iso, Naoki ; Okabe, Takuhiro ; Goto, Hiroshi ; Cho, Kilchoon ; Shimizu, Junichi ; Sakakibara, Manabu</creatorcontrib><description>Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2-3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0246397</identifier><identifier>PMID: 33571239</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adults ; Age ; Artificial neural networks ; Balance ; Biology and Life Sciences ; Body composition ; Body mass ; Chronic illnesses ; Classification ; Computer and Information Sciences ; Disability evaluation ; Domains ; Geriatric assessment ; Health sciences ; Lean body mass ; Learning algorithms ; Machine learning ; Medicine and Health Sciences ; Methods ; Mobility ; Muscle strength ; Neural networks ; Older people ; People and Places ; Physical Sciences ; Respiratory function ; Support vector machines ; Walking</subject><ispartof>PloS one, 2021-02, Vol.16 (2), p.e0246397-e0246397</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Hirata et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Hirata et al 2021 Hirata et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3</citedby><cites>FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3</cites><orcidid>0000-0002-6623-5203</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2488535062/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2488535062?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33571239$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Sakakibara, Manabu</contributor><creatorcontrib>Hirata, Keisuke</creatorcontrib><creatorcontrib>Suzuki, Makoto</creatorcontrib><creatorcontrib>Iso, Naoki</creatorcontrib><creatorcontrib>Okabe, Takuhiro</creatorcontrib><creatorcontrib>Goto, Hiroshi</creatorcontrib><creatorcontrib>Cho, Kilchoon</creatorcontrib><creatorcontrib>Shimizu, Junichi</creatorcontrib><title>Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2-3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility.</description><subject>Accuracy</subject><subject>Adults</subject><subject>Age</subject><subject>Artificial neural networks</subject><subject>Balance</subject><subject>Biology and Life Sciences</subject><subject>Body composition</subject><subject>Body mass</subject><subject>Chronic illnesses</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>Disability evaluation</subject><subject>Domains</subject><subject>Geriatric assessment</subject><subject>Health sciences</subject><subject>Lean body mass</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Mobility</subject><subject>Muscle strength</subject><subject>Neural networks</subject><subject>Older people</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Respiratory function</subject><subject>Support vector machines</subject><subject>Walking</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNk01v1DAQhiMEoqXwDxBEQkJw2MUfcZxckKqKj5UqVQLK1XKcSdYrx97GTqEH_jvObrraoB5QDolnnvd1PONJkpcYLTHl-MPGDb2VZrl1FpaIZDkt-aPkFJeULHKC6OOj75PkmfcbhBgt8vxpckIp45jQ8jT5c-21bdNOqrW2kBqQvR0DwaXa3oIPupUB0rCGtAcjg3bWr_U2rSD8ArBp7TqprU9dkzaDVWN-lEtbH9bSpJ2rtNHhLnqmztTQp7IeTPDPkyeNNB5eTO-z5Przpx8XXxeXV19WF-eXC8VZERaFVAVDvAJMGC8yTjhGvKYUM-BMxWQFUDZlzQjNMCbAZYUlJRXNCkUpSHqWvN77bo3zYqqcFyQrCkYZykkkVnuidnIjtr3uZH8nnNRiF3B9K2QftDIgaJ5VFZFQYtVkDGiFCSlIjQjCdU0aFL0-TrsNVQe1Aht6aWam84zVa9G6W8ELzmNjosG7yaB3N0Nsgui0V2CMtOCG3X-XhMf-8Yi--Qd9-HQT1cp4AG0bF_dVo6k4zxlmiKIyj9TyASo-NXRaxWvW6BifCd7PBJEJ8Du0cvBerL5_-3_26uecfXvErkGasPbODLvbNwezPah6530PzaHIGIlxSu6rIcYpEdOURNmr4wYdRPdjQf8Cee4OUQ</recordid><startdate>20210211</startdate><enddate>20210211</enddate><creator>Hirata, Keisuke</creator><creator>Suzuki, Makoto</creator><creator>Iso, Naoki</creator><creator>Okabe, Takuhiro</creator><creator>Goto, Hiroshi</creator><creator>Cho, Kilchoon</creator><creator>Shimizu, Junichi</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6623-5203</orcidid></search><sort><creationdate>20210211</creationdate><title>Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults</title><author>Hirata, Keisuke ; Suzuki, Makoto ; Iso, Naoki ; Okabe, Takuhiro ; Goto, Hiroshi ; Cho, Kilchoon ; Shimizu, Junichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Adults</topic><topic>Age</topic><topic>Artificial neural networks</topic><topic>Balance</topic><topic>Biology and Life Sciences</topic><topic>Body composition</topic><topic>Body mass</topic><topic>Chronic illnesses</topic><topic>Classification</topic><topic>Computer and Information Sciences</topic><topic>Disability evaluation</topic><topic>Domains</topic><topic>Geriatric assessment</topic><topic>Health sciences</topic><topic>Lean body mass</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Mobility</topic><topic>Muscle strength</topic><topic>Neural networks</topic><topic>Older people</topic><topic>People and Places</topic><topic>Physical Sciences</topic><topic>Respiratory function</topic><topic>Support vector machines</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hirata, Keisuke</creatorcontrib><creatorcontrib>Suzuki, Makoto</creatorcontrib><creatorcontrib>Iso, Naoki</creatorcontrib><creatorcontrib>Okabe, Takuhiro</creatorcontrib><creatorcontrib>Goto, Hiroshi</creatorcontrib><creatorcontrib>Cho, Kilchoon</creatorcontrib><creatorcontrib>Shimizu, Junichi</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hirata, Keisuke</au><au>Suzuki, Makoto</au><au>Iso, Naoki</au><au>Okabe, Takuhiro</au><au>Goto, Hiroshi</au><au>Cho, Kilchoon</au><au>Shimizu, Junichi</au><au>Sakakibara, Manabu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-02-11</date><risdate>2021</risdate><volume>16</volume><issue>2</issue><spage>e0246397</spage><epage>e0246397</epage><pages>e0246397-e0246397</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2-3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33571239</pmid><doi>10.1371/journal.pone.0246397</doi><tpages>e0246397</tpages><orcidid>https://orcid.org/0000-0002-6623-5203</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2021-02, Vol.16 (2), p.e0246397-e0246397 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_2488535062 |
source | Publicly Available Content Database; PubMed Central |
subjects | Accuracy Adults Age Artificial neural networks Balance Biology and Life Sciences Body composition Body mass Chronic illnesses Classification Computer and Information Sciences Disability evaluation Domains Geriatric assessment Health sciences Lean body mass Learning algorithms Machine learning Medicine and Health Sciences Methods Mobility Muscle strength Neural networks Older people People and Places Physical Sciences Respiratory function Support vector machines Walking |
title | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T04%3A23%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20machine%20learning%20to%20investigate%20the%20relationship%20between%20domains%20of%20functioning%20and%20functional%20mobility%20in%20older%20adults&rft.jtitle=PloS%20one&rft.au=Hirata,%20Keisuke&rft.date=2021-02-11&rft.volume=16&rft.issue=2&rft.spage=e0246397&rft.epage=e0246397&rft.pages=e0246397-e0246397&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0246397&rft_dat=%3Cgale_plos_%3EA651503096%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c758t-8ac8507be125784727107d3315e75cac8bee9f9d5234112e7ab1a32b348c33ea3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2488535062&rft_id=info:pmid/33571239&rft_galeid=A651503096&rfr_iscdi=true |