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Validating and Extending the Three Process Model of Alertness in Airline Operations
Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to val...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203690/ https://www.ncbi.nlm.nih.gov/pubmed/25329575 http://dx.doi.org/10.1371/journal.pone.0108679 |
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author | Ingre, Michael Van Leeuwen, Wessel Klemets, Tomas Ullvetter, Christer Hough, Stephen Kecklund, Göran Karlsson, David Åkerstedt, Torbjörn |
author_facet | Ingre, Michael Van Leeuwen, Wessel Klemets, Tomas Ullvetter, Christer Hough, Stephen Kecklund, Göran Karlsson, David Åkerstedt, Torbjörn |
author_sort | Ingre, Michael |
collection | PubMed |
description | Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three Process Model (TPM), on aircrews and extend the model with functions to model jetlag and to directly assess the risk of any sleepiness level in any shift schedule or roster with and without knowledge of sleep timings. We collected sleep and sleepiness data from 136 aircrews in a real life situation by means of an application running on a handheld touch screen computer device (iPhone, iPod or iPad) and used the TPM to predict sleepiness with varying level of complexity of model equations and data. The results based on multilevel linear and non-linear mixed effects models showed that the TPM predictions correlated with observed ratings of sleepiness, but explorative analyses suggest that the default model can be improved and reduced to include only two-processes (S+C), with adjusted phases of the circadian process based on a single question of circadian type. We also extended the model with a function to model jetlag acclimatization and with estimates of individual differences including reference limits accounting for 50%, 75% and 90% of the population as well as functions for predicting the probability of any level of sleepiness for ecological assessment of absolute and relative risk of sleepiness in shift systems for safety applications. |
format | Online Article Text |
id | pubmed-4203690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-42036902014-10-27 Validating and Extending the Three Process Model of Alertness in Airline Operations Ingre, Michael Van Leeuwen, Wessel Klemets, Tomas Ullvetter, Christer Hough, Stephen Kecklund, Göran Karlsson, David Åkerstedt, Torbjörn PLoS One Research Article Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three Process Model (TPM), on aircrews and extend the model with functions to model jetlag and to directly assess the risk of any sleepiness level in any shift schedule or roster with and without knowledge of sleep timings. We collected sleep and sleepiness data from 136 aircrews in a real life situation by means of an application running on a handheld touch screen computer device (iPhone, iPod or iPad) and used the TPM to predict sleepiness with varying level of complexity of model equations and data. The results based on multilevel linear and non-linear mixed effects models showed that the TPM predictions correlated with observed ratings of sleepiness, but explorative analyses suggest that the default model can be improved and reduced to include only two-processes (S+C), with adjusted phases of the circadian process based on a single question of circadian type. We also extended the model with a function to model jetlag acclimatization and with estimates of individual differences including reference limits accounting for 50%, 75% and 90% of the population as well as functions for predicting the probability of any level of sleepiness for ecological assessment of absolute and relative risk of sleepiness in shift systems for safety applications. Public Library of Science 2014-10-20 /pmc/articles/PMC4203690/ /pubmed/25329575 http://dx.doi.org/10.1371/journal.pone.0108679 Text en © 2014 Ingre et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ingre, Michael Van Leeuwen, Wessel Klemets, Tomas Ullvetter, Christer Hough, Stephen Kecklund, Göran Karlsson, David Åkerstedt, Torbjörn Validating and Extending the Three Process Model of Alertness in Airline Operations |
title | Validating and Extending the Three Process Model of Alertness in Airline Operations |
title_full | Validating and Extending the Three Process Model of Alertness in Airline Operations |
title_fullStr | Validating and Extending the Three Process Model of Alertness in Airline Operations |
title_full_unstemmed | Validating and Extending the Three Process Model of Alertness in Airline Operations |
title_short | Validating and Extending the Three Process Model of Alertness in Airline Operations |
title_sort | validating and extending the three process model of alertness in airline operations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203690/ https://www.ncbi.nlm.nih.gov/pubmed/25329575 http://dx.doi.org/10.1371/journal.pone.0108679 |
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