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Using wearable technology to predict health outcomes: a literature review
OBJECTIVE: To review and analyze the literature to determine whether wearable technologies can predict health outcomes. MATERIALS AND METHODS: We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7263786/ https://www.ncbi.nlm.nih.gov/pubmed/29982520 http://dx.doi.org/10.1093/jamia/ocy082 |
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author | Burnham, Jason P Lu, Chenyang Yaeger, Lauren H Bailey, Thomas C Kollef, Marin H |
author_facet | Burnham, Jason P Lu, Chenyang Yaeger, Lauren H Bailey, Thomas C Kollef, Marin H |
author_sort | Burnham, Jason P |
collection | PubMed |
description | OBJECTIVE: To review and analyze the literature to determine whether wearable technologies can predict health outcomes. MATERIALS AND METHODS: We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits. RESULTS: Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome. DISCUSSION: Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes. CONCLUSION: Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology. |
format | Online Article Text |
id | pubmed-7263786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72637862020-06-09 Using wearable technology to predict health outcomes: a literature review Burnham, Jason P Lu, Chenyang Yaeger, Lauren H Bailey, Thomas C Kollef, Marin H J Am Med Inform Assoc Reviews OBJECTIVE: To review and analyze the literature to determine whether wearable technologies can predict health outcomes. MATERIALS AND METHODS: We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits. RESULTS: Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome. DISCUSSION: Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes. CONCLUSION: Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology. Oxford University Press 2018-06-29 /pmc/articles/PMC7263786/ /pubmed/29982520 http://dx.doi.org/10.1093/jamia/ocy082 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Reviews Burnham, Jason P Lu, Chenyang Yaeger, Lauren H Bailey, Thomas C Kollef, Marin H Using wearable technology to predict health outcomes: a literature review |
title | Using wearable technology to predict health outcomes: a literature review |
title_full | Using wearable technology to predict health outcomes: a literature review |
title_fullStr | Using wearable technology to predict health outcomes: a literature review |
title_full_unstemmed | Using wearable technology to predict health outcomes: a literature review |
title_short | Using wearable technology to predict health outcomes: a literature review |
title_sort | using wearable technology to predict health outcomes: a literature review |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7263786/ https://www.ncbi.nlm.nih.gov/pubmed/29982520 http://dx.doi.org/10.1093/jamia/ocy082 |
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