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Crew fatigue safety performance indicators for fatigue risk management systems.
Gander, Philippa H; Mangie, Jim; Van Den Berg, Margo J; Smith, A Alexander T; Mulrine, Hannah M; Signal, T Leigh.
Afiliação
  • Gander PH; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA. p.h.gander@massey.ac.nz
  • Mangie J; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA.
  • Van Den Berg MJ; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA.
  • Smith AA; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA.
  • Mulrine HM; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA.
  • Signal TL; Sleep/Wake Research Centre, Massey University, Wellington, New Zealand, USA.
Aviat Space Environ Med ; 85(2): 139-47, 2014 Feb.
Article em En | MEDLINE | ID: mdl-24597158
ABSTRACT

INTRODUCTION:

Implementation of Fatigue Risk Management Systems (FRMS) is gaining momentum; however, agreed safety performance indicators (SPIs) are lacking. This paper proposes an initial set of SPIs based on measures of crewmember sleep, performance, and subjective fatigue and sleepiness, together with methods for interpreting them.

METHODS:

Data were included from 133 landing crewmembers on 2 long-range and 3 ultra-long-range trips (4-person crews, 3 airlines, 220 flights). Studies had airline, labor, and regulatory support, and underwent independent ethical review. SPIs evaluated preflight and at top of descent (TOD) were total sleep in the prior 24 h and time awake at duty start and at TOD (actigraphy); subjective sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli scale); and psychomotor vigilance task (PVT) performance. Kruskal-Wallis nonparametric ANOVA with post hoc tests was used to identify significant differences between flights for each SPI.

RESULTS:

Visual and preliminary quantitative comparisons of SPIs between flights were made using box plots and bar graphs. Statistical analyses identified significant differences between flights across a range of SPls.

DISCUSSION:

In an FRMS, crew fatigue SPIs are envisaged as a decision aid alongside operational SPIs, which need to reflect the relevant causes of fatigue in different operations. We advocate comparing multiple SPIs between flights rather than defining safe/unsafe thresholds on individual SPIs. More comprehensive data sets are needed to identify the operational and biological factors contributing to the differences between flights reported here. Global sharing of an agreed core set of SPIs would greatly facilitate implementation and improvement of FRMS.
Assuntos
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Segurança / Aviação / Medicina Aeroespacial / Fadiga Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans / Middle aged Idioma: En Ano de publicação: 2014 Tipo de documento: Article
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Segurança / Aviação / Medicina Aeroespacial / Fadiga Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans / Middle aged Idioma: En Ano de publicação: 2014 Tipo de documento: Article