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TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit
The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a de...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436730/ https://www.ncbi.nlm.nih.gov/pubmed/36050329 http://dx.doi.org/10.1038/s41597-022-01636-4 |
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author | Yau, Joanna C. Girault, Benjamin Feng, Tiantian Mundnich, Karel Nadarajan, Amrutha Booth, Brandon M. Ferrara, Emilio Lerman, Kristina Hsieh, Eric Narayanan, Shrikanth |
author_facet | Yau, Joanna C. Girault, Benjamin Feng, Tiantian Mundnich, Karel Nadarajan, Amrutha Booth, Brandon M. Ferrara, Emilio Lerman, Kristina Hsieh, Eric Narayanan, Shrikanth |
author_sort | Yau, Joanna C. |
collection | PubMed |
description | The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a demanding environment, as residents worked in the ICU for three weeks. Residents wore a Fitbit, a Bluetooth-based proximity sensor, and an audio-feature recorder. They completed daily surveys and interviews at the beginning and end of their rotation. In addition, we collected data from environmental sensors (i.e., Internet-of-Things Bluetooth data hubs) and obtained hospital records (e.g., patient census) and residents’ job evaluations. This data set may be may be of interest to researchers interested in workplace stress, group dynamics, social support, the physical and psychological effects of witnessing patient deaths, predicting survey data from sensors, and privacy-aware and privacy-preserving machine learning. Notably, a small subset of the data was collected during the first wave of the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-9436730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94367302022-09-02 TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit Yau, Joanna C. Girault, Benjamin Feng, Tiantian Mundnich, Karel Nadarajan, Amrutha Booth, Brandon M. Ferrara, Emilio Lerman, Kristina Hsieh, Eric Narayanan, Shrikanth Sci Data Data Descriptor The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a demanding environment, as residents worked in the ICU for three weeks. Residents wore a Fitbit, a Bluetooth-based proximity sensor, and an audio-feature recorder. They completed daily surveys and interviews at the beginning and end of their rotation. In addition, we collected data from environmental sensors (i.e., Internet-of-Things Bluetooth data hubs) and obtained hospital records (e.g., patient census) and residents’ job evaluations. This data set may be may be of interest to researchers interested in workplace stress, group dynamics, social support, the physical and psychological effects of witnessing patient deaths, predicting survey data from sensors, and privacy-aware and privacy-preserving machine learning. Notably, a small subset of the data was collected during the first wave of the COVID-19 pandemic. Nature Publishing Group UK 2022-09-01 /pmc/articles/PMC9436730/ /pubmed/36050329 http://dx.doi.org/10.1038/s41597-022-01636-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Yau, Joanna C. Girault, Benjamin Feng, Tiantian Mundnich, Karel Nadarajan, Amrutha Booth, Brandon M. Ferrara, Emilio Lerman, Kristina Hsieh, Eric Narayanan, Shrikanth TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title | TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title_full | TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title_fullStr | TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title_full_unstemmed | TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title_short | TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
title_sort | tiles-2019: a longitudinal physiologic and behavioral data set of medical residents in an intensive care unit |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436730/ https://www.ncbi.nlm.nih.gov/pubmed/36050329 http://dx.doi.org/10.1038/s41597-022-01636-4 |
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