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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0246397 |
| _version_ | 1818364195270295552 |
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| author | Keisuke Hirata Makoto Suzuki Naoki Iso Takuhiro Okabe Hiroshi Goto Kilchoon Cho Junichi Shimizu |
| author_facet | Keisuke Hirata Makoto Suzuki Naoki Iso Takuhiro Okabe Hiroshi Goto Kilchoon Cho Junichi Shimizu |
| author_sort | Keisuke Hirata |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T22:00:30Z |
| format | Article |
| id | doaj.art-364bb2ae91cf45e3b12282d0201dd2f0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T22:00:30Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-364bb2ae91cf45e3b12282d0201dd2f02022-12-21T23:30:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01162e024639710.1371/journal.pone.0246397Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults.Keisuke HirataMakoto SuzukiNaoki IsoTakuhiro OkabeHiroshi GotoKilchoon ChoJunichi ShimizuPrevious 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.https://doi.org/10.1371/journal.pone.0246397 |
| spellingShingle | Keisuke Hirata Makoto Suzuki Naoki Iso Takuhiro Okabe Hiroshi Goto Kilchoon Cho Junichi Shimizu Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. PLoS ONE |
| title | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. |
| title_full | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. |
| title_fullStr | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. |
| title_full_unstemmed | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. |
| title_short | Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults. |
| title_sort | using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults |
| url | https://doi.org/10.1371/journal.pone.0246397 |
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