Look before you (s)leep : evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry

Fatigue is a significant risk factor in workplace accidents and fatalities. Several technologies have been developed for organisations seeking to identify and reduce fatigue-related risk. These devices purportedly monitor behavioural correlates of fatigue and/or task performance and are understandably appealing as a visible risk control. This paper critically reviews evidence supporting fatigue detection technologies and identifies criteria for assessing evidence supporting these technologies. Fatigue detection devices, and relevant reliability and validation data, were identified by systematically searching the scientific, grey and marketing literature. Identified devices typically assessed correlates of fatigue using either psychophy siological measures or embedded performance measures drawn from the equipment being operated. Critically, the majority of the ‘validation’ data were not found within the scientific peer-reviewed literature, but within the quasi-scientific, grey or marketing literature. Based on the validation evidence available, none of the current technologies met all the proposed regulatory criteria for a legally and scientifically defensible device. Further, none were sufficiently well validated to provide a comprehensive solution to managing fatigue-related risk at the individual level in real time. Nevertheless, several of the technologies may be considered a potentially useful element of a broader fatigue risk management system. To aid organisations and regulators contemplating their use, we propose a set of evaluative and operational criteria that would likely meet the legal requirements for exercising due diligence in the selection and use of these technologies in workplace settings

Interindividual and intraindividual variability in adolescent sleep patterns across an entire school term: A pilot study

Objectives: This study aimed to investigate sleep patterns in adolescent males over a 12-week period (a 10-week school term and pre and post term holidays). Design: Intensive longitudinal design, with sleep data collected daily via actigraphy for 81 consecutive days. Setting: Five Secondary Schools in Adelaide, South Australia. Participants: Convenience sample of 47 adolescent males aged 14 to 17 years. Measurements: Daily sleep duration, bedtimes, rise times, and sleep efficiency were collected via actigraphy with all (except sleep efficiency) also measured by sleep diary. Mood was measured weekly with Depression Anxiety Stress Scale-21 (DASS-21) and weekly wellbeing with the Satisfaction with Life Scale (SWLS). Age, body mass index, self-reported mood, life satisfaction, and chronotype preference assessed at baseline (pre-term holiday week) were included as covariates. Results: Dynamic Structural Equation Modeling indicated significant but small fixed-effect and random-effect auto-regressions for all sleep variables. Collectively, these findings demonstrate day-to-day fluctuations in sleep patterns, the magnitude of which varied between individuals. Age, morningness, and mood predicted some of the temporal dynamics in sleep over time but other factors (BMI, life satisfaction) were not associated with sleep dynamics. Conclusions: Using intensive longitudinal data, this study demonstrated inter-individual and intra-individual variation in sleep patterns over 81 consecutive days. These findings provide important and novel insights into the nature of adolescent sleep and require further examination in future studies

Sleepy schoolboy blues? : Sleep and depression across the school term

Aims: Adolescents experience a change in sleep patterns, together with a later sleep onset time compared to children in younger age groups. This, in combination with early school starting times can restrict the time available for sleep. As a consequence, sleep loss can accumulate across the school week. If sleep loss is not recovered on weekends, a cumulative sleep debt may develop across weeks of the school term. Cumulative sleep loss has been linked to depression. We sought to determine whether adolescent males accumulated a sleep debt across the term, and if so, whether the loss was associated with the onset of depressive symptoms. Methods: Eleven healthy adolescent males, with a mean age of 15.29 (±0.83) years, participated in an 11-week field study. Baseline testing occurred in the pre-term holiday, prior to the term commencement. Participants wore an activity-monitoring device (Actiwatch) at all times and completed sleep diaries daily. The Depression Anxiety and Stress Scale (DASS-21) was completed weekly. Mixed-effects models examined differences in weekly sleep across the term and any associations with depressive symptoms. Results: On average, daily sleep was 18 minutes longer in the pre-term period than during the term period. Participants spent less time in bed on school nights than on weekends during the school term (p.05) because participants advanced weekly bed times and increased sleep duration on the weekends. The changes observed in sleep were not associated with depressive symptoms. Discussion: This pilot study provides insight into sleeping habits during a school term. Whilst a relationship with depression was not significant, future studies could investigate a clinical population rather than the normal population of adolescents sampled here