Literature DB >> 17517914

Developing mathematical models of neurobehavioral performance for the "real world".

Dennis A Dean1, Adam Fletcher, Steven R Hursh, Elizabeth B Klerman.   

Abstract

Work-related operations requiring extended wake durations, night, or rotating shifts negatively affect worker neurobehavioral performance and health. These types of work schedules are required in many industries, including the military, transportation, and health care. These industries are increasingly using or considering the use of mathematical models of neurobehavioral performance as a means to predict the neurobehavioral deficits due to these operational demands, to develop interventions that decrease these deficits, and to provide additional information to augment existing decision-making processes. Recent advances in mathematical modeling have allowed its application to real-world problems. Developing application-specific expertise is necessary to successfully apply mathematical models, in part because development of new algorithms and methods linking the models to the applications may be required. During a symposium, "Modeling Human Neurobehavioral Performance II: Towards Operational Readiness," at the 2006 SIAM-SMB Conference on the Life Sciences, examples of the process of applying mathematical models, including model construction, model validation, or developing model-based interventions, were presented. The specific applications considered included refining a mathematical model of sleep/wake patterns of airline flight crew, validating a mathematical model using railroad operations data, and adapting a mathematical model to develop appropriate countermeasure recommendations based on known constraints. As mathematical models and their associated analytical methods continue to transition into operational settings, such additional development will be required. However, major progress has been made in using mathematical model outputs to inform those individuals making schedule decisions for their workers.

Entities:  

Mesh:

Year:  2007        PMID: 17517914     DOI: 10.1177/0748730407301376

Source DB:  PubMed          Journal:  J Biol Rhythms        ISSN: 0748-7304            Impact factor:   3.182


  7 in total

1.  Learning to live on a Mars day: fatigue countermeasures during the Phoenix Mars Lander mission.

Authors:  Laura K Barger; Jason P Sullivan; Andrea S Vincent; Edna R Fiedler; Laurence M McKenna; Erin E Flynn-Evans; Kirby Gilliland; Walter E Sipes; Peter H Smith; George C Brainard; Steven W Lockley
Journal:  Sleep       Date:  2012-10-01       Impact factor: 5.849

2.  Generalizability of a biomathematical model of fatigue's sleep predictions.

Authors:  Samantha M Riedy; Desta Fekedulegn; Michael Andrew; Bryan Vila; Drew Dawson; John Violanti
Journal:  Chronobiol Int       Date:  2020-04-02       Impact factor: 2.877

3.  Neurobehavioral performance in young adults living on a 28-h day for 6 weeks.

Authors:  Jung H Lee; Wei Wang; Edward J Silva; Anne-Marie Chang; Karine D Scheuermaier; Sean W Cain; Jeanne F Duffy
Journal:  Sleep       Date:  2009-07       Impact factor: 5.849

4.  Flight crew fatigue risk assessment for international flights under the COVID-19 outbreak response exemption policy.

Authors:  Junya Sun; Ruishan Sun; Jingqiang Li; Ping Wang; Nan Zhang
Journal:  BMC Public Health       Date:  2022-10-01       Impact factor: 4.135

5.  Taking the lag out of jet lag through model-based schedule design.

Authors:  Dennis A Dean; Daniel B Forger; Elizabeth B Klerman
Journal:  PLoS Comput Biol       Date:  2009-06-19       Impact factor: 4.475

6.  Optimal schedules of light exposure for rapidly correcting circadian misalignment.

Authors:  Kirill Serkh; Daniel B Forger
Journal:  PLoS Comput Biol       Date:  2014-04-10       Impact factor: 4.475

7.  Applying mathematical models to predict resident physician performance and alertness on traditional and novel work schedules.

Authors:  Elizabeth B Klerman; Scott A Beckett; Christopher P Landrigan
Journal:  BMC Med Educ       Date:  2016-09-13       Impact factor: 2.463

  7 in total

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