Calibrated to drive: Measuring self-assessed driving ability and perceived workload after prolonged sitting and sleep restriction.

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.

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.

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.

Does breaking up sitting in office-based settings result in cognitive performance improvements which last throughout the day? A review of the evidence.

Sedentary behavior at work contributes to detrimental cognitive outcomes (e.g., decreases in attention). The length of time that cognitive performance benefits are sustained following bouts of breaking up sitting (e.g., using sit-stand desks or walking) is not known. A narrative review of the literature was conducted using a systematic search strategy, with keywords related to breaking up sitting interventions in office-based environments and cognitive performance outcomes in the period immediately post the cessation of the breaking up sitting intervention. Three types of office-based breaking up sitting interventions were identified; 1) sit-stand desks, 2) walking desks and 3) cycling desks. From the eight studies which met the criteria, the impacts of these interventions on cognitive performance outcomes were mixed, with significant benefits in some studies and others reporting no benefit. Of the cognitive domains assessed, working memory, attention, and psychomotor function showed significant sustained improvement for up to 30 minutes post intervention. While there are benefits to a key set of cognitive performance domains following breaking up sitting interventions in office-based settings, no studies have evaluated whether benefits to cognitive performance persist for longer than 30 minutes after the breaking up sitting intervention. Furthermore, specific applications of these cognitive benefits to tasks outside of work (e.g., driving home from work) are unknown.

Sleep and physical activity in university students: A systematic review and meta-analysis.

University students have low levels of physical activity and report disturbances to sleep, which are independently associated with poor health outcomes. Some research suggests that there is a bi-directional relationship between sleep and physical activity in adults. However, the relationship between sleep and physical activity in university students has not yet been evaluated. Therefore, the aim of this systematic review and meta-analysis was to qualitatively synthesise and quantitatively evaluate the evidence for the association between sleep and physical activity in university students. Twenty-nine eligible studies were included, with a total of 141,035 participants (43% men and 57% women). Only four studies used device-based measures of sleep and/or physical activity, with the remainder including self-report measures. Qualitative synthesis found that the majority of studies did not find any association between sleep and physical activity in university students. However, random-effects meta-analysis showed that moderate-to-high intensity physical activity was associated with lower PSQI scores (e.g., better sleep quality) [r = -0.18, 95% CI (-0.37, 0.03), p = 0.100]. Further, a weak negative association between moderate-to-vigorous physical activity level and sleep duration was also found [r = -0.02, 95% CI (-0.16, 0.12), p = 0.760]. As the findings of this review are predominantly derived from cross-sectional investigations, with limited use of device-based measurement tools, further research is needed to investigate the relationship between sleep and physical activity in university students. Future studies should employ longitudinal designs, with self-report and device-based measures, and consider the intensity and time of physical activity as well as records of napping behaviour.

The impact of a meal, snack, or not eating during the night shift on simulated driving performance post-shift.

Objective The commute home following a night shift is associated with an increased risk for accidents. This study investigated the relationship between food intake during the night shift and simulated driving performance post-shift. Methods Healthy non-shift working males (N=23) and females (N=16), aged 18-39 years (mean 24.5, standard deviation 5.0, years) participated in a seven-day laboratory study and underwent four simulated night shifts. Participants were randomly allocated to one of three conditions: meal at night (N=12; 7 males), snack at night (N=13; 7 males) or no eating at night (N=14; 9 males). During the night shift at 00:30 hours, participants either ate a large meal (meal at night condition), a snack (snack at night condition), or did not eat during the night shift (no eating at night condition). During the second simulated night shift, participants performed a 40-minute York driving simulation at 20:00, 22:30, 01:30, 04:00, and 07:30 hours (similar time to a commute from work). Results The effects of eating condition, drive time, and time-on-task, on driving performance were examined using mixed model analyses. Significant condition×time interactions were found, where at 07:30 hours, those in the meal at night condition displayed significant increases in time spent outside of the safe zone (percentage of time spent outside 10 km/hour of the speed limit and 0.8 meters of the lane center; P<0.05), and greater lane and speed variability (both P<0.01) compared to the snack and no eating conditions. There were no differences between the snack and no eating conditions. Conclusion Driver safety during the simulated commute home is greater following the night shift if a snack, rather than a meal, is consumed during the shift.

Are prolonged sitting and sleep restriction a dual curse for the modern workforce? a randomised controlled trial protocol.

INTRODUCTION: Prolonged sitting and inadequate sleep are a growing concern in society and are associated with impairments to cardiometabolic health and cognitive performance. However, the combined effect of prolonged sitting and inadequate sleep on measures of health and cognitive performance are unknown. In addition, the circadian disruption caused by shiftwork may further impact workers' cardiometabolic health and cognitive performance. This protocol paper outlines the methodology for exploring the impact of simultaneous exposure to prolonged sitting, sleep restriction and circadian disruption on cardiometabolic and cognitive performance outcomes. METHODS AND ANALYSIS: This between-subjects study will recruit 208 males and females to complete a 7-day in-laboratory experimental protocol (1 Adaptation Day, 5 Experimental Days and 1 Recovery Day). Participants will be allocated to one of eight conditions that include all possible combinations of the following: dayshift or nightshift, sitting or breaking up sitting and 5 hour or 9 hour sleep opportunity. On arrival to the laboratory, participants will be provided with a 9 hour baseline sleep opportunity (22:00 to 07:00) and complete five simulated work shifts (09:00 to 17:30 in the dayshift condition and 22:00 to 06:30 in the nightshift condition) followed by a 9 hour recovery sleep opportunity (22:00 to 07:00). During the work shifts participants in the sitting condition will remain seated, while participants in the breaking up sitting condition will complete 3-min bouts of light-intensity walking every 30 mins on a motorised treadmill. Sleep opportunities will be 9 hour or 5 hour. Primary outcome measures include continuously measured interstitial blood glucose, heart rate and blood pressure, and a cognitive performance and self-perceived capacity testing battery completed five times per shift. Analyses will be conducted using linear mixed models. ETHICS AND DISSEMINATION: The CQUniversity Human Ethics Committee has approved this study (0000021914). All participants who have already completed the protocol have provided informed consent. Study findings will be disseminated via scientific publications and conference presentations. TRIAL REGISTRATION DETAILS: This study has been registered on Australian New Zealand Clinical Trials Registry (12619001516178) and is currently in the pre-results stage.