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The effects on dehydration and cognitive performance from heat and/or physical activity are well established in the laboratory, although have not yet been studied for personnel working in occupations such as wildland firefighting regularly exposed to these types of conditions. This study aimed to investigate the effects of temperature and dehydration on seventy-three volunteer firefighters (35.7 ± 13.7 years, mean ± standard deviation) during a simulation of wildfire suppression under either control or hot (18–20; or 33–35 °C) temperature conditions. Results showed cognitive performance on the psychomotor vigilance task declined when participants were dehydrated in the heat and Stroop task performance was impaired when dehydrated late in the afternoon.Firefighters may be at risk of deteriorations in simple cognitive functions in the heat whilst dehydrated, although may also experience impairments in complex cognitive functions if dehydrated late in the day, irrespective of the environmental temperature. Refereed/Peer-reviewed

The human core body temperature (CBT) rhythm is tightly coupled to an endogenous circadian pacemaker located in the suprachiasmatic nucleus of the anterior hypothalamus. The standard method for assessing the status of this pacemaker is by continuous sampling of CBT using rectal thermometry. This research sought to validate the use of ingestible, temperature-sensitive capsules to measure CBT as an alternative to rectal thermometry. Participants were 11 young adult males who had volunteered to complete a laboratory protocol that extended across 12 consecutive days. A total of 87 functional capsules were ingested and eliminated by participants during the laboratory internment. Core body temperature samples were collected in 1-min epochs and compared to paired samples collected concurrently via rectal thermistors. Agreement between samples that were collected using ingestible sensors and rectal thermistors was assessed using the gold-standard limits of agreement method. Across all valid paired samples collected during the study (n = 120,126), the mean difference was 0.06 degrees C, whereas the 95% CI (confidence interval) for differences was less than +/- 0.35 degrees C. Despite the overall acceptable limits of agreement, systematic measurement bias was noted across the initial 5 h of sensor-transit periods and attributed to temperature gradations across the alimentary canal. Refereed/Peer-reviewed

Shiftworkers are often required to sleep at inappropriate phases of their circadian timekeeping system, with implications for the dynamics of ultradian sleep stages. The independent effects of these changes on cognitive throughput performance are not well understood. This is because the effects of sleep on performance are usually confounded with circadian factors that cannot be controlled under normal day/night conditions. The aim of this study was to assess the contribution of prior wake, core body temperature, and sleep stages to cognitive throughput performance under conditions of forced desynchrony (FD). A total of 11 healthy young adult males resided in a sleep laboratory in which day/night zeitgebers were eliminated and ambient room temperature, lighting levels, and behavior were controlled. The protocol included 2 training days, a baseline day, and 7 × 28-h FD periods. Each FD period consisted of an 18.7-h wake period followed by a 9.3-h rest period. Sleep was assessed using standard polysomnography. Core body temperature and physical activity were assessed continuously in 1-min epochs. Cognitive throughput was measured by a 5-min serial addition and subtraction (SAS) task and a 90-s digit symbol substitution (DSS) task. These were administered in test sessions scheduled every 2.5 h across the wake periods of each FD period. On average, sleep periods had a mean (± standard deviation) duration of 8.5 (±1.2) h in which participants obtained 7.6 (±1.4) h of total sleep time. This included 4.2 (±1.2) h of stage 1 and stage 2 sleep (S1-S2 sleep), 1.6 (±0.6) h of slow-wave sleep (SWS), and 1.8 (±0.6) h of rapid eye movement (REM) sleep. A mixed-model analysis with five covariates indicated significant fixed effects on cognitive throughput for circadian phase, prior wake time, and amount of REM sleep. Significant effects for S1-S2 sleep and SWS were not found. The results demonstrate that variations in core body temperature, time awake, and amount of REM sleep are associated with changes in cognitive throughput performance. The absence of significant effect for SWS may be attributable to the truncated range of sleep period durations sampled in this study. However, because the mean and variance for SWS were similar to REM sleep, these results suggest that cognitive throughput may be more sensitive to variations in REM sleep than SWS. Read More: http://informahealthcare.com/doi/abs/10.3109/07420528.2010.488621 Refereed/Peer-reviewed

Study Objectives: To parameterize and validate a model to estimate average sleep times for long-haul aviation pilots during layovers following travel across multiple time zones. The model equations were based on a weighted distribution of domicile- and local-time sleepers, and included algorithms to account for sleep loss and circadian re-synchronization. Design: Sleep times were collected from participants under normal commercial operating conditions using diaries and wrist activity monitors. Participants: Participants included a total of 306 long-haul pilots (113 captains, 120 first officers, and 73 second officers). Measurement and Results: The model was parameterized based on the average sleep/wake times observed during international flight patterns from Australia to London and Los Angeles (global R2 = 0.72). The parameterized model was validated against the average sleep/wake times observed during flight patterns from Australia to London (r2 = 0.85), Los Angeles (r2 = 0.79), New York (r2 = 0.80), and Johannesburg (r2 = 0.73).Goodness-of-fit was poorer when the parameterized model equations were used to predict the variance across the sleep/wake cycles of individual pilots (R2 = 0.42, 0.35, 0.31, and 0.28 for the validation flight patterns, respectively), in part because of substantial inter-individual variability in sleep timing and duration. Conclusions: It is possible to estimate average sleep times during layovers in international patterns with a reasonable degree of accuracy. Models of this type could form the basis of a stand-alone application to estimate the likelihood that a given duty schedule provides pilots, on average, with an adequate opportunity to sleep. Refereed/Peer-reviewed