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Shift work, involving night work, leads to impaired sleep, cognition, health and wellbeing, and an increased risk of occupational incidents. Current countermeasures include circadian adaptation to phase shift circadian biomarkers. However, evidence of real-world circadian adaptation is found primarily in occupations where light exposure is readily controlled. Despite this, non-photic adaptation to shift work remains under researched. Other markers of shift work adaptation exist (e.g., improvements in cognition and wellbeing outcomes) but are relatively unexplored. Timeframes for shift work adaptation involve changes which occur over a block of shifts, or over a shift working career. We propose an additional shift work adaptation timeframe exists which encompasses acute within shift changes in markers of adaptation. We also propose that physical activity might be an accessible and cost-effective countermeasure that could influence multiple markers of adaptation across three timeframes (Within Shift, Within Block, Within Work-span). Finally, practical considerations for shift workers, shift work industries and future research are identified. This Perspective proposes that shift work adaptation occurs within-shift, over a block of shifts, or over a shift working career and offers physical activity as an accessible countermeasure to support adaptation across these timeframes. [ABSTRACT FROM AUTHOR]

Background: Chronic disease is the leading cause of death globally. Sleep, diet, and physical activity are modifiable health behaviours that are key for reducing the burden of chronic disease. These health behaviours are collectively termed 'The 3 Pillars of Health' and are critical for populations who are at risk of poor health. Shiftworkers are one such at-risk population. To target behavioural change it is critical to first understand which of these health behaviours Australians currently prioritise. Therefore, this study aimed to investigate how Australians (including shift workers) prioritise sleep, physical activity, and diet, and examine the associations with correlates of health behaviours. Methods: Two cohorts of Australian adults were sampled. A cohort of 1151 Australian adults (54% female, aged 18–65 years) including employed (in any work schedule), unemployed, studying, and retired completed a phone interview. A cohort of 533 Australian shiftwork-only adults (76% female, 18–72) completed an online survey. All participants were asked which health behaviour (sleep, physical activity, or diet) they prioritised in their own life. Behavioural correlates of sleep, diet, and physical activity (sleep duration, frequency of moderate to physical activity, healthy dietary behaviour), and years of shiftwork experience were also collected. Multinomial logistic regressions were used to investigate the association between the highest prioritised pillar of health and the behavioural correlates. Results: Diet was prioritised by the Australian adults (49%), whereas sleep was prioritised by the shiftwork-only sample (68%). Australian adults who prioritised diet were significantly more likely to report diets with less fast-food consumption (p < 0.002) and more fruit consumption (p < 0.002) compared to those that prioritised sleep. For the shiftwork-only sample, those with 16–30 years of shiftwork experience were significantly more likely to prioritise sleep compared to diet (p < 0.05). However, prioritising sleep was not associated with meeting the sleep duration recommendations in the shiftwork-only sample. Conclusions: Across two cohorts of Australians, prioritisation of health behaviour was only associated with actual behaviour for diet. This may reflect different motivations for prioritising different health behaviours, in addition to different capabilities to change different health behaviours. Future research should include longitudinal methodologies to understand how behaviour prioritisation changes over work- and life-span, and any associations with actual health behaviour. [ABSTRACT FROM AUTHOR]

• 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]

Introduction: Inadequate sleep is a major public health concern, with large economic, health, and operational costs to Australia. Despite the implementation of public sleep health campaigns, approximately 40% of Australian adults do not obtain the recommended 7–9 hours of sleep. Thus, while people may know how much sleep is required, this knowledge may not be adequately translated to actual sleep behavior. Consequently, this study aims to examine the discrepancy between knowledge of sleep recommendations and self-reported sleep behaviors. Methods: A sample of 1265 Australian adults (54% female, aged 18–65) completed a phone interview as part of the 2017 National Social Survey and were asked questions about their knowledge of sleep guidelines and their actual sleep behavior. Binary logistic regression was used to determine the factors associated with awareness of sleep recommendations and whether this corresponded with reported sleep duration. Results: The final sample size was 998. Although 94% of the sample were aware of current sleep recommendations, 23% of participants did not self-report regularly obtaining 7–9 h sleep per night. These participants were less likely to want to obtain more sleep, less likely to view sleep as a priority before stressful events, and less likely to self-report good health. Conclusion: Although a majority of the sample were aware of sleep recommendations, almost a quarter of the participants' behavior did not align with their knowledge. Future sleep health campaigns should consider options beyond education, including emphasis on practical strategies and modifiable lifestyle factors to assist individuals to obtain the recommended amount of sleep. [ABSTRACT FROM AUTHOR]

Objective: Shift work is associated with circadian misalignment, sleep loss, and suboptimal health behaviors, which may contribute to longer term negative health outcomes. To inform future interventions for shift workers, the present study aimed to develop and evaluate the Health Belief Model for Shift Workers (HBM-SW) scale. Methods: The HBM-SW development involved a seven-step process, including a literature review, expert panel analysis, cognitive interviews with shift workers, and a trial with a pilot sample of shift workers (n = 153). Utilizing exploratory factor analysis for factor identification and item reduction, the developed scale loaded on seven factors in line with the theoretical framework of the Health Belief Model: Perceived Threat, Perceived Severity, Perceived Benefits, Perceived Barriers, Cues to Action, Self-efficacy, and Health Motivation. Validation of the scale was conducted utilizing Pittsburgh Sleep Quality Index, International Physical Activity Questionnaire, and Food Frequency Questionnaire. Results: The pilot sample had an average age of 34.0 (18.0) years, was majority female (54.2%), with an average of 8.0 (11.0) years shift work experience. The HBM-SW showed good - excellent (a = 0.74-0.93) internal consistency and moderate - good (ICC = 0.64-0.89) test re-test reliability. Using health behavior outcome measures, the HBM-SW scale showed meaningful correlations with sleep quality, sleep duration, diet quality and leisure time physical activity, and acceptable validity and reliability. Further testing should be conducted in a larger sample to facilitate confirmatory factor analysis. Conclusions: The developed Health Belief Model for Shift Workers scale is likely beneficial for use in future studies of interventions for shift workers. [ABSTRACT FROM AUTHOR]

Evidence suggests that providing out-of-home care to children is associated with high levels of compassion fatigue, possibly due to various work-related factors. This review examined the existing literature to determine the extent to which out of home care work results in compassion fatigue. To do so, it established which out of home care settings compassion fatigue has been measured in, how, and what factors contribute to developing compassion fatigue in this work. The study conducted a comprehensive search of five electronic databases (CINAHL, MEDLINE, PsycINFO, PubMED, and CENTRAL) for full-text articles examining compassion fatigue in out-of-home care workers caring for children aged 0–18 years. Out of the 2,759 articles initially identified, 14 articles were included. Studies were assessed against the Joanna Briggs Institute (JBI) Critical Appraisal Checklists for risk of bias. The findings suggest that compassion fatigue is prevalent in various out-of-home care settings and has been measured using validated quantitative and qualitative measures. Out of home care workers caring for children reported symptoms consistent with compassion fatigue, but a range of protective factors were also identified that may reduce its negative impact. This review highlights the need for further research in this area, using larger sample sizes and including a more comprehensive range of out-of-home care workers, settings, and countries. [ABSTRACT FROM AUTHOR]

Altering meal timing could improve cognition, alertness, and thus safety during the nightshift. This study investigated the differential impact of consuming a meal, snack, or not eating during the nightshift on cognitive performance (ANZCTR12615001107516). 39 healthy participants (59% male, age mean±SD: 24.5 ± 5.0y) completed a 7-day laboratory study and underwent four simulated nightshifts. Participants were randomly allocated to: Meal at Night (MN; n= 12), Snack at Night (SN; n = 13) or No Eating at Night (NE; n = 14). At 00:30 h, MN consumed a meal and SN consumed a snack (30% and 10% of 24 h energy intake respectively). NE did not eat during the nightshift. Macronutrient intake was constant across conditions. At 20:00 h, 22:30 h, 01:30 h, and 04:00 h, participants completed the 3-min Psychomotor Vigilance Task (PVT-B), 40-min driving simulator, post-drive PVT-B, subjective sleepiness scale, 2-choice Reaction Time task, and Running Memory task. Objective sleep was recorded for each of the day sleeps using Actigraphy and for the third day sleep, Polysomnography was used. Performance was compared between conditions using mixed model analyses. Significant two-way interactions were found. At 04:00 h, SN displayed increased time spent in the safe zone (p <.001; percentage of time spent within 10 km/h of the speed limit and 0.8 m of lane center), and decreases in speed variability (p <.001), lane variability (p <.001), post-drive PVT-B lapses (defined as RT > 355 ms; p <.001), and reaction time on the 2-choice reaction time task (p <.001) and running memory task (p <.001) compared to MN and NE. MN reported greater subjective sleepiness at 04:00 h (p <.001) compared to SN and NE. There was no difference in objective sleep between eating conditions. Eating a large meal during the nightshift impairs cognitive performance and sleepiness above the effects of time of night alone. For improved performance, shiftworkers should opt for a snack at night. [ABSTRACT FROM AUTHOR]

New mothers generally experience poor and/or disrupted sleep. A range of infant care and mental health factors may impact new mothers' sleep quality. A cross-sectional online survey was completed by a sample of 101 Australian new mothers with children under 12 months (M = 5.52 months, SD = 3.29 months) to examine the relationship between infant feeding method, infant sleeping location, and postpartum depression with maternal sleep quality. Subjective maternal sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), and postpartum depression was measured using the Edinburgh Postpartum Depression Scale (EPDS). Overall, new mothers experienced poor subjective sleep quality, with high average PSQI scores, above the cut-off of 5 (M = 9.63, SD = 4.07). The majority of new mothers did not experience postpartum depression, with an average EPDS score below the cut-off of 11 (8.66, SD = 5.20). Mothers who breastfed their infants experienced significantly better subjective sleep quality than mothers who bottle-fed, with a medium effect size (ηp2 = 0.458). Subjective maternal sleep quality did not differ based on infant sleeping location. Poor subjective maternal sleep quality was a significant predictor of postpartum depression. While poor sleep was common in this sample of Australian new mothers, this study demonstrated that new mothers who breastfeed may experience slightly better subjective sleep quality than other feeding methods. Further research into, and better services for the education and advocation of, new mothers' sleep quality will be beneficial to both new mothers and clinicians. • New mothers typically experience poor and/or disrupted sleep. • Their sleep can be impacted by personal factors and factors relating to infant care. • Breastfeeding was associated with better maternal sleep quality. • Maternal sleep quality was associated with infant sleeping location. • Poor maternal sleep quality predicted postpartum depression. [ABSTRACT FROM AUTHOR]

Shiftworkers have impaired performance when driving at night and they also alter their eating patterns during nightshifts. However, it is unknown whether driving at night is influenced by the timing of eating. This study aims to explore the effects of timing of eating on simulated driving performance across four simulated nightshifts. Healthy, non-shiftworking males aged 18–35 years (n= 10) were allocated to either an eating at night (n= 5) or no eating at night (n= 5) condition. During the simulated nightshifts at 1730, 2030 and 0300 h, participants performed a 40-min driving simulation, 3-min Psychomotor Vigilance Task (PVT-B), and recorded their ratings of sleepiness on a subjective scale. Participants had a 6-h sleep opportunity during the day (1000–1600 h). Total 24-h food intake was consistent across groups; however, those in the eating at night condition ate a large meal (30% of 24-h intake) during the nightshift at 0130 h. It was found that participants in both conditions experienced increased sleepiness and PVT-B impairments at 0300 h compared to 1730 and 2030 h (p< 0.001). Further, at 0300 h, those in the eating condition displayed a significant decrease in time spent in the safe zone (p< 0.05; percentage of time within 10 km/h of the speed limit and 0.8 m of the centre of the lane) and significant increases in speed variability (p< 0.001), subjective sleepiness (p< 0.01) and number of crashes (p< 0.01) compared to those in the no eating condition. Results suggest that, for optimal performance, shiftworkers should consider restricting food intake during the night. [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]

• >50% of new mothers who regularly drove did so while fatigued at least a few times each month. • 8.4% of new mothers reported having a near miss while driving every month. • New mothers should be categorised as an at-risk group for fatigued driving. During the first year of their child's life, mothers typically experience disrupted sleep and high levels of daytime fatigue. Other populations who experience sleep disruption and/or fatigue are considered to be at risk for fatigued driving. However, the degree of fatigue-related risk experienced by new mothers in Australia is unclear. This study aimed to investigate the prevalence of fatigued driving, fatigue-related near misses, and vehicle crashes in this population. A cross-sectional survey including the Pittsburgh Sleep Quality Index and a range of fatigue and driving questions was completed by 187 Australian new mothers. Over 50% of participants who regularly drove did so while fatigued at least a few times per month. Furthermore, most participants who reported near misses indicated that either some or more than some of these incidents were attributable to fatigue (>70%). Similarly, >55% of participants who reported experiencing a motor vehicle crash since the birth of their child indicated that this crash was caused by fatigue. These findings suggest that new mothers may be considered an at-risk group for fatigued driving. To address this issue, it may be appropriate to provide education around driver safety, and to establish strategies to help minimise fatigue-related driving risk in this population. Furthermore, future research should include objectively measured sleep to establish the link more accurately between prior sleep and driver safety in new mothers in Australia. [ABSTRACT FROM AUTHOR]

• New parents are likely to experience sleep loss due to childcare responsibilities. • A systematic review was performed to understand the relationship between sleep and driver fatigue in new parents. • Synthesis of available evidence suggests that new parents are at risk of fatigued driving. • A research agenda is proposed to address the limitations of this field of research. Fatigue-related motor vehicle crashes are common worldwide and have been addressed by a range of road safety campaigns. These campaigns are typically directed towards at-risk groups (e.g., heavy vehicle drivers), who may be likely to experience fatigue resulting from reduced or disrupted sleep opportunities. Another population likely to experience sleep loss and disruption is new parents. The sleep of new parents is likely to be significantly disrupted by childcare responsibilities. As such, new parents may also be likely to experience fatigue while driving. A systematic review of five databases (PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, EMBASE, and the Cochrane Central Register of Controlled Trials) was performed to identify what research is currently available on sleep, fatigue, and driving in new parents. A total of twelve documents were included in this review. A synthesis of findings suggests new parents are at risk of fatigued driving – though the amount and quality of evidence available is limited. A research agenda is proposed to address the limitations of this field of research. [ABSTRACT FROM AUTHOR]

Eating during the night may increase the risk for obesity and type 2 diabetes in shift workers. This study examined the impact of either eating or not eating a meal at night on glucose metabolism. Participants underwent four nights of simulated night work (SW1–4, 16:00–10:00 h, <50 lux) with a daytime sleep opportunity each day (10:00–16:00 h, <3 lux). Healthy males were assigned to an eating at night (NE;n = 4, meals; 07:00, 19:00 and 01:30 h) or not eating at night (NEN;n = 7, meals; 07:00 h, 09:30, 16:10 and 19:00 h) condition. Meal tolerance tests were conducted post breakfast on pre-night shift (PRE), SW4 and following return to day shift (RTDS), and glucose and insulin area under the curve (AUC) were calculated. Mixed-effects ANOVAs were used with fixed effects of condition and day, and their interactions, and a random effect of subject identifier on the intercept. Fasting glucose and insulin were not altered by day or condition. There were significant effects of day and condition × day (bothp < 0.001) for glucose AUC, with increased glucose AUC observed solely in the NE condition from PRE to SW4 (p = 0.05) and PRE to RTDS (p < 0.001). There was also a significant effect of day (p = 0.007) but not condition × day (p = 0.825) for insulin AUC, with increased insulin from PRE to RTDS in both eating at night (p = 0.040) and not eating at night (p = 0.006) conditions. Results in this small, healthy sample suggest that not eating at night may limit the metabolic consequences of simulated night work. Further study is needed to explore whether matching food intake to the biological clock could reduce the burden of type 2 diabetes in shift workers. [ABSTRACT FROM PUBLISHER]