Fatigued pilots are prone to experience cognitive disorders that degrade their performance and adherence to high safety standards. In light of the current challenging context in aviation, we report the early phase of our ongoing project on the re-evaluation of human factors research for flight crew. Our motivation stems from the need for aviation organisations to develop decision support systems for operational aviation settings, able to feed-in in the organisations’ fatigue risk management efforts. Key criteria to this end are the need for the least possible intrusiveness and the added information value for a safety system. Departing from the problems in compliance-focused fatigue risk management and the intrusive nature of clinical studies, we report a neuroscientific methodology able to yield markers that can be easily integrated in a decision support system at the operational level. Reporting the preliminary phase of our live project, we evaluate the tools suitable for the development of a system that tracks subtle pilot states, such as drowsiness and micro-sleep episodes.
Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.
