A significant safety challenge airline pilots contend with is the possibility of experiencing startle and surprise. These are cognitive-emotional responses that may temporarily impair performance and that have contributed to multiple fatal loss of control events. Several self-management methods exist that are intended to facilitate recovery from startle and surprise, but these have only been tested in simulator experiments. The current study addresses this research gap by surveying the perceptions of 239 airline pilots on the utility and benefit of a method which they use in operational practice– the “Reset Method”. Overall, the survey results revealed that pilots felt the method improved mental preparedness, and reduced stress. A reported reason for not applying the method was the urge to act quickly. In addition, not all steps of the method were applied equally, and some pilots found the method difficult to fit into the existing procedures of several time-critical scenarios (e.g., aircraft upsets and emergency landings). We recommend training self-management methods in scenarios which carry the most risk of negative effects of startle and surprise. We also recommend instilling awareness of the ‘startle paradox': self-management techniques are most difficult to apply in situations where they are most beneficial. Method shortening and simplification may facilitate application. Future research should focus on refining the method's implementation, addressing the startle paradox, and understanding the transferability of startle and surprise management methods to other safety critical industries defined by complex sociotechnical interactions.

Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics.

Startle and surprise can impair pilot performance and jeopardize flight safety. Self-management methods have been developed by the industry to address this acute source of stress, however, qualitative insights from pilots describing the quality of these methods are lacking. Ten semi-structured interviews with airline pilots, who had been taught a self-management method, were analyzed using thematic analysis). Pilots considered the method useful and reported positive effects (e.g., decrease in stress) when applying the method during operations. Pilots reported that the method was not often performed in full; specific steps were employed based on perceived benefit. Establishing fellow pilot status and situation awareness was considered most important, addressing own physical startle symptoms (e.g., muscle tension) were deemed less important. Pilots reported an urge to “act” rather than use the method, which is expected as the method aims to induce a pause and mitigate erroneous impulsi ve decisions. Barriers to applying the method included the difficult recognition of startle and surprise, and situational context. Suggested improvements for training dealt with recognition and sharing experiences from peers. The findings of the research provide directions for pilot training for startle and surprise. Future research will explore these pilot perceptions in a larger representative sample.