Hannah Scott

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.

Study Objectives This study was designed to test the utility of cardiovascular responses as markers of potentially different environmental noise disruption effects of wind farm compared to traffic noise exposure during sleep. Methods Twenty participants underwent polysomnography. In random order, and at six sound pressure levels from 33 dBA to 48 dBA in 3 dB increments, three types of wind farm and two types of road traffic noise recordings of 20-s duration were played during established N2 or deeper sleep, each separated by 20 s without noise. Each noise sequence also included a no-noise control. Electrocardiogram and finger pulse oximeter recorded pulse wave amplitude changes from the pre-noise onset baseline following each noise exposure and were assessed algorithmically to quantify the magnitude of heart rate and finger vasoconstriction responses to noise exposure. Results Higher sound pressure levels were more likely to induce drops in pulse wave amplitude. Sound pressure levels as low as 39 dBA evoked a pulse wave amplitude response (Odds ratio [95% confidence interval]; 1.52 [1.15, 2.02]). Wind farm noise with amplitude modulation was less likely to evoke a pulse wave amplitude response than the other noise types, but warrants cautious interpretation given low numbers of replications within each noise type. Conclusions These preliminary data support that drops in pulse wave amplitude are a particularly sensitive marker of noise-induced cardiovascular responses during. Larger trials are clearly warranted to further assess relationships between recurrent cardiovascular activation responses to environmental noise and potential long-term health effects.