Peter Catcheside

Summary Predicting vigilance impairment in high‐risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi‐night, often manually entered, sleep data. This study developed a machine learning model for predicting vigilance errors based on a single prior sleep period, derived from an under‐mattress sensor. Twenty‐four healthy volunteers (mean [SD] age = 27.6 [9.5] years, 12 male) attended the laboratory on two separate occasions, 1 month apart, to compare wake performance and sleep under two different lighting conditions. Each condition occurred over an 8 day protocol comprising a baseline sleep opportunity from 10 p.m. to 7 a.m., a 27 h wake period, then daytime sleep opportunities from 10 a.m. to 7 p.m. on days 3–7. From 12 a.m. to 8 a.m. on each of days 4–7, participants completed simulated night shifts that included six 10 min psychomotor vigilance task (PVT) trials per shift. Sleep was assessed using an under‐mattress sensor. Using extra‐trees machine learning models, PVT performance (reaction times <500 ms, reaction, and lapses) during each night shift was predicted based on the preceding daytime sleep. The final extra‐trees model demonstrated moderate accuracy for predicting PVT performance, with standard errors (RMSE) of 19.9 ms (reaction time, 359 [41.6]ms), 0.42 reactions/s (reaction speed, 2.5 [0.6] reactions/s), and 7.2 (lapses, 10.5 [12.3]). The model also correctly classified 84% of trials containing ≥5 lapses (Matthews correlation coefficient = 0.59, F1 = 0.83). Model performance is comparable to current fatigue prediction models that rely upon self‐report or manually entered data. This efficient approach may help to manage fatigue and safety in non‐standard work schedules.