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• Bouts of light-intensity walking may improve speed maintenance ability. • Drivers may favour lane maintenance over speed maintenance during driving. • Crash likelihood does not decrease after breaking up sitting during the day. • Breaking up sitting during the day does not decrease subjective sleepiness when sleep is restricted. Sleep restriction and prolonged sitting are both associated with reduced cognitive performance which is detrimental to safe driving. Regular bouts of walking to break up prolonged sitting have been shown to improve cognitive performance. It is unknown if breaking up sitting with walking may improve cognitive performance related to driving, particularly when drivers are sleep restricted. Healthy participants (n = 81, age 23.6 ± 4.5 years, 49% female) participated in a 7-day laboratory study (1 Adaptation day, 5 Experimental days, 1 Recovery day). Participants were randomly allocated to a condition: sitting 9 h sleep (Sit9), breaking up sitting 9 h sleep (Break9), sitting 5 h sleep (Sit5) and breaking up sitting 5 h sleep (Break5). Participants allocated to the Break9 and Break5 conditions completed 3-min of light-intensity walking on a treadmill every 30 min between 09:00–17:00 h, while participants in Sit9 and Sit5 groups remained seated during these times. Each participant completed a 20-min monotonous simulated driving commute at 08:10 h and 17:30 h on each of the 5 Experimental days. There was a significant main effect of condition with decreased speed variability for Break9 compared to Sit9, Sit5 and Break5 (p < 0.0001) for both commutes. Break5 had significantly higher lane variability compared to Sit9 and Break9, and Sit5 (p = <0.05) for both commutes. Break5 reported greater subjective sleepiness before each commute compared to Sit9, Break9 and Sit5 (p < 0.001). Cox Proportional Hazards model showed a significant increase in time to first crash for Sit5 and Break5 compared to Sit9 (p= <0.005). There were no significant two- or three-way interaction effects by condition, day or commute time for any of the outcome measures. The findings from this study highlight that breaking up sitting with light-intensity walking may improve driving performance in areas such as speed maintenance, but may not extend to lane keeping performance. The findings also show that the benefits of breaking up sitting with light-intensity walking may not be sufficient to overcome detrimental impacts of sleep restriction for all areas of driving performance. Future research should focus on increased intensity of activity as a possible means to extend the benefits more comprehensively across additional areas of driving performance. [ABSTRACT FROM AUTHOR]

• Breaking up sitting during the day may reduce calibration errors in subjective assessments of driving performance for key areas of performance. • Drivers were poorly calibrated for the areas of speed maintenance regardless of prior sleep history and levels of physical activity during the day. • Drivers with 9 h of lower had lower levels of subjective workload compared to drivers with 5 h of sleep. • Single component and 3-componenet Brier score provided a holistic approach to exploring subjective assessments of driving performance. Self-assessed driving ability may differ from actual driving performance, leading to poor calibration (i.e., differences between self-assessed driving ability and actual performance), increased risk of accidents and unsafe driving behaviour. Factors such as sleep restriction and sedentary behaviour can impact driver workload, which influences driver calibration. This study aims to investigate how sleep restriction and prolonged sitting impact driver workload and driver calibration to identify strategies that can lead to safer and better calibrated drivers. Participants (n = 84, mean age = 23.5 ± 4.8, 49 % female) undertook a 7-day laboratory study and were randomly allocated to a condition: sitting 9-h sleep opportunity (Sit9), breaking up sitting 9-h sleep opportunity (Break9), sitting 5-h sleep opportunity (Sit5) and breaking up sitting 5-h sleep opportunity (Break5). Break9 and Break5 conditions completed 3-min of light-intensity walking on a treadmill every 30 min between 09:00–17:00 h, while participants in Sit9 and Sit5 conditions remained seated. Each participant completed a 20-min simulated commute in the morning and afternoon each day and completed subjective assessments of driving ability and perceived workload before and after each commute. Objective driving performance was assessed using a driving simulator measuring speed and lane performance metrics. Driver calibration was analysed using a single component and 3-component Brier Score. Correlational matrices were conducted as an exploratory analysis to understand the strength and direction of the relationship between subjective and objective driving outcomes. Analyses revealed participants in Sit9 and Break9 were significantly better calibrated for lane variability, lane position and safe zone-lane parameters at both time points (p < 0.0001) compared to Sit5 and Break5. Break5 participants were better calibrated for safe zone-speed and combined safe zone parameters (p < 0.0001) and speed variability at both time points (p = 0.005) compared to all other conditions. Analyses revealed lower perceived workload scores at both time points for Sit9 and Break9 participants compared to Sit5 and Break5 (p = <0.001). Breaking up sitting during the day may reduce calibration errors compared to sitting during the day for speed keeping parameters. Future studies should investigate if different physical activity frequency and intensity can reduce calibration errors, and better align a driver's self-assessment with their actual performance. [ABSTRACT FROM AUTHOR]

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment. [ABSTRACT FROM AUTHOR]