ArticleIndustrial health2025
Workload predictions from a biomathematical model compared to top-of-descent NASA Task Load Index scores in commercial pilots.
Article in Industrial health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Single-Port Robotics and the Changing Landscape of Thoracic Surgery.Interdisciplinary cardiovascular and thoracic surgery · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomathematical models of fatigue (BMMFs) are commonly used to predict cognitive alertness in commercial aviation. Accounting for workload in association with routine job tasks may help BMMFs to more accurately predict fatigue in real world operations. This study compared the accuracy of BMMF workload predictions (SF Workload) against pilot self-report of workload during normal flight operations. Ninety-nine (N=99) pilots from a major Asia-based airline completed the NASA Task Load Index (TLX) at top of descent (TOD) during a multiple-flight three-day roster that consisted of daytime flying. SF Workload predictions and TLX scores were normalized to a 100-point scale and compared using equivalence testing. SF Workload predictions were statistically non-different from pilot TLX scores at the same TOD (64 ± 7 vs. 65 ± 15; both t=1.56, p=0.06) using the two one-sided t-test (TOST) approach, indicating high workload and that BMMF predictions are non-inferior to pilot self-report as a means of estimating workload. Establishing the accuracy of workload predictions against real-world reports in a commercial pilot population is an important step towards risk management in situations where high workload may create a safety risk.
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