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Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods
PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data. METHODS: Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activ...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023581/ https://www.ncbi.nlm.nih.gov/pubmed/29443824 http://dx.doi.org/10.1249/MSS.0000000000001578 |
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author | KERR, JACQUELINE CARLSON, JORDAN GODBOLE, SUNEETA CADMUS-BERTRAM, LISA BELLETTIERE, JOHN HARTMAN, SHERI |
author_facet | KERR, JACQUELINE CARLSON, JORDAN GODBOLE, SUNEETA CADMUS-BERTRAM, LISA BELLETTIERE, JOHN HARTMAN, SHERI |
author_sort | KERR, JACQUELINE |
collection | PubMed |
description | PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data. METHODS: Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit–stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models. RESULTS: Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts. CONCLUSIONS: This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted. |
format | Online Article Text |
id | pubmed-6023581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-60235812018-07-11 Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods KERR, JACQUELINE CARLSON, JORDAN GODBOLE, SUNEETA CADMUS-BERTRAM, LISA BELLETTIERE, JOHN HARTMAN, SHERI Med Sci Sports Exerc SPECIAL COMMUNICATIONS: Methodological Advances PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data. METHODS: Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit–stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models. RESULTS: Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts. CONCLUSIONS: This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted. Lippincott Williams & Wilkins 2018-07 2018-01-26 /pmc/articles/PMC6023581/ /pubmed/29443824 http://dx.doi.org/10.1249/MSS.0000000000001578 Text en Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | SPECIAL COMMUNICATIONS: Methodological Advances KERR, JACQUELINE CARLSON, JORDAN GODBOLE, SUNEETA CADMUS-BERTRAM, LISA BELLETTIERE, JOHN HARTMAN, SHERI Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title | Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title_full | Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title_fullStr | Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title_full_unstemmed | Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title_short | Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods |
title_sort | improving hip-worn accelerometer estimates of sitting using machine learning methods |
topic | SPECIAL COMMUNICATIONS: Methodological Advances |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023581/ https://www.ncbi.nlm.nih.gov/pubmed/29443824 http://dx.doi.org/10.1249/MSS.0000000000001578 |
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