DAILY SLEEP,WEEKLY WORKING HOURS,AND RISK OF WORK-RELATED INJURY: US NATIONAL HEALTH INTERVIEW SURVEY (2004–2008)

The impact on health and safety of the combination of chronic sleep deficits and extended working hours has received worldwide attention. Using the National Health Interview Survey (NHIS), an in-person household survey using a multistage, stratified, clustered sample design representing the US civilian, non-institutionalized population, the authors estimated the effect of total daily self-reported sleep time and weekly working hours on the risk of a work-related injury. During the survey period 2004–2008, 177,576 persons (ages 18–74) sampled within households reported that they worked at a paid job the previous week and reported their total weekly work hours. A randomly selected adult in each household (n?=?75,718) was asked to report his/her usual (average) total daily sleep hours the prior week; complete responses were obtained for 74,415 (98.3%) workers. Weighted annualized work-related injury rates were then estimated across a priori defined categories of both average total daily sleep hours and weekly working hours. To account for the complex sampling design, weighted multiple logistic regression was used to independently estimate the risk of a work-related injury for categories of usual daily sleep duration and weekly working hours, controlling for important covariates and potential confounders of age, sex, race/ethnicity, education, type of pay, industry, occupation (proxy for job risk), body mass index, and the interaction between sleep duration and work hours. Based on the inclusion criteria, there were an estimated 129,950,376 workers annually at risk and 3,634,446 work-related medically treated injury episodes (overall injury rate 2.80/100 workers). Unadjusted annualized injury rates/100 workers across weekly work hours were 2.03 (≤20 h), 3.01 (20–30 h), 2.45 (31–40 h), 3.45 (40–50 h), 3.71 (50–60 h), and 4.34 (>60 h). With regards to self-reported daily sleep time, the estimated annualized injury rates/100 workers were 7.89 (<5 h sleep), 5.21 (5–5.9 h), 3.62 (6–6.9 h), 2.27 (7–7.9 h), 2.50 (8–8.9 h), 2.22 (9–9.9 h), and 4.72 (>10 h). After controlling for weekly work hours, and aforementioned covariates, significant increases in risk/1 h decrease were observed for several sleep categories. Using 7–7.9 h sleep as reference, the adjusted injury risk (odds ratio [OR] for a worker sleeping a total of <5 h/day was 2.65 (95% confidence interval [CI]: 1.57–4.47), for 5–5.9 h 1.79 (95% CI: 1.22–2.62), and for 6–6.9 h 1.40 (95% CI: 1.10–1.79). No other usual sleep duration categories were significantly different than the reference; however, for >10 h of usual daily sleep, the OR was marginally significantly elevated, 1.82 (95% CI: 0.96–3.47). These results suggest significant increases in work-related injury risk with decreasing usual daily self-reported sleep hours and increasing weekly work hours, independent of industry, occupation, type of pay, sex, age, education, and body mass (Author correspondence: david.lombardi@libertymutual.com)     

Daily Rhythms of Toxicity and Effectiveness of Anesthetics (MS222 and Eugenol) in Zebrafish (Danio Rerio)

Sleep inertia is a brief period of inferior task performance and/or disori-entation immediately after sudden awakening from sleep. Normally sleep inertia lasts <5 min and has no serious impact on conducting routine jobs. This preliminary study examined whether there are best and worst times to wake up stemming from circadian effects on sleep inertia. Since the process of falling asleep is strongly influenced by circadian time, the reverse process of awakening could be similarly affected. A group of nine subjects stayed awake for a 64-h continuous work period, except for 20-min sleep periods (naps) every 6 h. Another group of 10 subjects stayed awake for 64 h without any sleep. The differences between these two groups in performance degradation are expected to show sleep inertia on the background of sleep deprivation. Sleep inertia was measured with Baddeley's logical reasoning task, which started within 1 min of awakening and lasted for 5 min. There appeared to be no specific circadian time when sleep inertia is either maximal or minimal. An extreme form of sleep inertia was observed, when the process of waking up during the period of the circadian body temperature trough became so traumatic that it created “sleep (nap) aversion.” The findings lead to the conclusion that there are no advantages realized on sleep inertia by waking up from sleep at specific times of day.     

WORK HOURS AND SLEEP/WAKE BEHAVIOR OF AUSTRALIAN HOSPITAL DOCTORS

The objective of the study was to describe the work and sleep patterns of doctors working in Australian hospitals. Specifically, the aim was to examine the influence of work-related factors, such as hospital type, seniority, and specialty on work hours and their impact on sleep. A total of 635 work periods from 78 doctors were analyzed together with associated sleep history. Work and sleep diary information was validated against an objective measure of sleep/wake activity to provide the first comprehensive database linking work and sleep for individual hospital doctors in Australia. Doctors in large and small facilities had fewer days without work than those doctors working in medium-sized facilities. There were no significant differences in the total hours worked across these three categories of seniority; however, mid-career and senior doctors worked more overnight and weekend on-call periods than junior doctors. With respect to sleep, although higher work hours were related to less sleep, short sleeps (< 5 h in the 24 h prior to starting work) were observed at all levels of prior work history (including no work). In this population of Australian hospital doctors, total hours worked do impact sleep, but the pattern of work, together with other nonwork factors are also important mediators. (Author correspondence: sally.ferguson@unisa.edu.au)     

Time Course of Neurobehavioral Alertness During Extended Wakefulness in Morning- and Evening-Type Healthy Sleepers

The aim of the study was to evaluate the influence of chronotype (morning-type versus evening-type) living in a fixed sleep-wake schedule different from one's preferred sleep schedules on the time course of neurobehavioral performance during controlled extended wakefulness. The authors studied 9 morning-type and 9 evening-type healthy male subjects (21.4?±?1.9 yrs). Before the experiment, all participants underwent a fixed sleep-wake schedule mimicking a regular working day (bedtime: 23:30?h; wake time: 07:30?h). Then, following two nights in the laboratory, both chronotypes underwent a 36-h constant routine, performing a cognitive test of sustained attention every hour. Core body temperature, salivary melatonin secretion, objective alertness (maintenance of wakefulness test), and subjective sleepiness (visual analog scale) were also assessed. Evening-types expressed a higher level of subjective sleepiness than morning types, whereas their objective levels of alertness were not different. Cognitive performance in the lapse domain remained stable during the normal waking day and then declined during the biological night, with a similar time course for both chronotypes. Evening types maintained optimal alertness (i.e., 10% fastest reaction time) throughout the night, whereas morning types did not. For both chronotypes, the circadian performance profile was correlated with the circadian subjective somnolence profile and was slightly phase-delayed with melatonin secretion. Circadian performance was less correlated with circadian core body temperature. Lapse domain was phase-delayed with body temperature (2–4?h), whereas optimal alertness was slightly phase-delayed with body temperature (1?h). These results indicate evening types living in a fixed sleep-wake schedule mimicking a regular working day (different from their preferred sleep schedules) express higher subjective sleepiness but can maintain the same level of objective alertness during a normal waking day as morning types. Furthermore, evening types were found to maintain optimal alertness throughout their nighttime, whereas morning types could not. The authors suggest that evening-type subjects have a higher voluntary engagement of wake-maintenance mechanisms during extended wakefulness due to adaptation of their sleep-wake schedule to social constraints. (Author correspondence: jack.taillard@gmail.com)     

The relationship between assessments of jet lag and some of its symptoms

The power of the symptoms of jet lag in predicting the amount of jet lag measured at the same and different times of the day has been investigated. A total of 85 subjects was studied for 6 days after a flight from the UK to Australia (10 time zones to the east). At 08:00, 12:00, 16:00, 20:00, and 24:00h, the subjects recorded their jet lag and fatigue. At 08:00h, they also assessed their sleep. At 12:00 and 16:00h, they assessed their attitude to a meal, as well as their motivation, commitment, and irritability. On retiring, they recorded bowel activity. Assessments were by visual analog scales. Jet lag was treated as the dependent variable and the symptoms as covariates in ANCOVAs. Fatigue was a powerful predictor of jet lag, provided it was measured at the same time, and some aspects of sleep predicted jet lag measured on retiring or rising. The other symptoms predicted jet lag less powerfully and/or at a wider range of times. It is concluded that, even though jet lag at any time of the day can be predicted from contemporaneous assessments of fatigue and that it can be predicted on retiring or rising from some aspects of changed sleep, jet lag is predicted less reliably from other symptoms, including aspects of mental performance. These findings question exactly what causes jet lag at a particular time of day, and so are relevant to studies which use this measurement to investigate the problems associated with time-zone transitions, and ways to ameliorate them.     

Independent Effects of Sleep Duration and Body Mass Index on the Risk of a Work-Related Injury: Evidence From the US National Health Interview Survey (2004–2010)

Fatigue has been linked to adverse safety outcomes, and poor quality or decreased sleep has been associated with obesity (higher body mass index, BMI). Additionally, higher BMI is related to an increased risk for injury; however, it is unclear whether BMI modifies the effect of short sleep or has an independent effect on work-related injury risk. To answer this question, the authors examined the risk of a work-related injury as a function of total daily sleep time and BMI using the US National Health Interview Survey (NHIS). The NHIS is an in-person household survey using a multistage, stratified, clustered sample design representing the US civilian population. Data were pooled for the 7-yr survey period from 2004 to 2010 for 101 891 “employed” adult subjects (51.7%; 41.1?±?yrs of age [mean?±?SEM]) with data on both sleep and BMI. Weighted annualized work-related injury rates were estimated across a priori defined categories of BMI: healthy weight (BMI: <25), overweight (BMI: 25–29.99), and obese (BMI: ≥30) and also categories of usual daily sleep duration: <6, 6–6.99, 7–7.99, 8–8.99, and ≥9?h. To account for the complex sampling design, including stratification, clustering, and unequal weighting, weighted multiple logistic regression was used to estimate the risk of a work-related injury. The initial model examined the interaction among daily sleep duration and BMI, controlling for weekly working hours, age, sex, race/ethnicity, education, type of pay, industry, and occupation. No significant interaction was found between usual daily sleep duration and BMI (p?=?.72); thus, the interaction term of the final logistic model included these two variables as independent predictors of injury, along with the aforementioned covariates. Statistically significant covariates (p?≤?.05) included age, sex, weekly work hours, occupation, and if the worker was paid hourly. The lowest categories of usual sleep duration (<6 and 6–6.9?h) showed significantly (p?≤?.05) elevated injury risks than the referent category (7–8?h sleep), whereas sleeping >7–8?h did not significantly elevate risk. The adjusted injury risk odds ratio (OR) for a worker with a usual daily sleep of <6?h was 1.86 (95% confidence interval [CI]: 1.37–2.52), and for 6–6.9?h it was 1.46 (95% CI: 1.18–1.80). With regards to BMI, the adjusted injury risk OR comparing workers who were obese (BMI: ≥30) to healthy weight workers (BMI: <25) was 1.34 (95% CI: 1.09–1.66), whereas the risk in comparing overweight workers (BMI: 25–29.99) to healthy weight risk was elevated, but not statistically significant (OR?=?1.08; 95% CI: .88–1.33). These results from a large representative sample of US workers suggest increase in work-related injury risk for reduced sleep regardless of worker's body mass. However, being an overweight worker also increases work-injury risk regardless of usual daily sleep duration. The independent additive risk of these factors on work-related injury suggests a substantial, but at least partially preventable, risk. (Author correspondence: david.lombardi@libertymutual.com)     

Incorporation of caffeine into a quantitative model of fatigue and sleep

A recent physiologically based model of human sleep is extended to incorporate the effects of caffeine on sleep-wake timing and fatigue. The model includes the sleep-active neurons of the hypothalamic ventrolateral preoptic area (VLPO), the wake-active monoaminergic brainstem populations (MA), their interactions with cholinergic/orexinergic (ACh/Orx) input to MA, and circadian and homeostatic drives. We model two effects of caffeine on the brain due to competitive antagonism of adenosine (Ad): (i) a reduction in the homestatic drive and (ii) an increase in cholinergic activity. By comparing the model output to experimental data, constraints are determined on the parameters that describe the action of caffeine on the brain. In accord with experiment, the ranges of these parameters imply significant variability in caffeine sensitivity between individuals, with caffeine's effectiveness in reducing fatigue being highly dependent on an individual's tolerance, and past caffeine and sleep history. Although there are wide individual differences in caffeine sensitivity and thus in parameter values, once the model is calibrated for an individual it can be used to make quantitative predictions for that individual. A number of applications of the model are examined, using exemplar parameter values, including: (i) quantitative estimation of the sleep loss and the delay to sleep onset after taking caffeine for various doses and times; (ii) an analysis of the system's stable states showing that the wake state during sleep deprivation is stabilized after taking caffeine; and (iii) comparing model output successfully to experimental values of subjective fatigue reported in a total sleep deprivation study examining the reduction of fatigue with caffeine. This model provides a framework for quantitatively assessing optimal strategies for using caffeine, on an individual basis, to maintain performance during sleep deprivation.     

The Relationship Between Assessments of Jet Lag and Some of Its Symptoms

The power of the symptoms of jet lag in predicting the amount of jet lag measured at the same and different times of the day has been investigated. A total of 85 subjects was studied for 6 days after a flight from the UK to Australia (10 time zones to the east). At 08:00, 12:00, 16:00, 20:00, and 24:00h, the subjects recorded their jet lag and fatigue. At 08:00h, they also assessed their sleep. At 12:00 and 16:00h, they assessed their attitude to a meal, as well as their motivation, commitment, and irritability. On retiring, they recorded bowel activity. Assessments were by visual analog scales. Jet lag was treated as the dependent variable and the symptoms as covariates in ANCOVAs. Fatigue was a powerful predictor of jet lag, provided it was measured at the same time, and some aspects of sleep predicted jet lag measured on retiring or rising. The other symptoms predicted jet lag less powerfully and/or at a wider range of times. It is concluded that, even though jet lag at any time of the day can be predicted from contemporaneous assessments of fatigue and that it can be predicted on retiring or rising from some aspects of changed sleep, jet lag is predicted less reliably from other symptoms, including aspects of mental performance. These findings question exactly what causes jet lag at a particular time of day, and so are relevant to studies which use this measurement to investigate the problems associated with time‐zone transitions, and ways to ameliorate them.     

Sleep,Sleepiness, Fatigue,and Performance of 12-Hour-Shift Nurses

Nurses working 12-h shifts complain of fatigue and insufficient/poor-quality sleep. Objectively measured sleep times have not been often reported. This study describes sleep, sleepiness, fatigue, and neurobehavioral performance over three consecutive 12-h (day and night) shifts for hospital registered nurses. Sleep (actigraphy), sleepiness (Karolinska Sleepiness Scale [KSS]), and vigilance (Performance Vigilance Task [PVT]), were measured serially in 80 registered nurses (RNs). Occupational fatigue (Occupational Fatigue Exhaustion Recovery Scale [OFER]) was assessed at baseline. Sleep was short (mean 5.5?h) between shifts, with little difference between day shift (5.7?h) and night shift (5.4?h). Sleepiness scores were low overall (3 on a 1–9 scale, with higher score indicating greater sleepiness), with 45% of nurses having high level of sleepiness (score ?>?7) on at least one shift. Nurses were progressively sleepier each shift, and night nurses were sleepier toward the end of the shift compared to the beginning. There was extensive caffeine use, presumably to preserve or improve alertness. Fatigue was high in one-third of nurses, with intershift fatigue (not feeling recovered from previous shift at the start of the next shift) being most prominent. There were no statistically significant differences in mean reaction time between day/night shift, consecutive work shift, and time into shift. Lapsing was traitlike, with rare (39% of sample), moderate (53%), and frequent (8%) lapsers. Nurses accrue a considerable sleep debt while working successive 12-h shifts with accompanying fatigue and sleepiness. Certain nurses appear more vulnerable to sleep loss than others, as measured by attention lapses. (Author correspondence: jgeiger@son.umaryland.edu)     

“知信行”模式对老年糖尿病患者睡眠质量的改善作用

目的:调查老年糖尿病患者的睡眠质量,探讨\"知信行\"管理模式对糖尿病患者睡眠障碍的改善作用。方法:选取100例糖尿病患者,随机分为管理组和对照组,每组50例。管理组患者采用\"知信行\"管理模式进行护理,对照组患者采用常规基础护理模式。采用匹兹堡睡眠质量指数量评估量表(PSQI)评价患者的睡眠质量,Piper疲劳量表(PFS)评估患者的疲劳程度。结果:护理干预前,两组患者的睡眠质量评分与疲劳程度评分无显著性差异(P0.05)。接受不同护理干预后,两组患者的睡眠质量评分与疲劳程度评分均降低,且管理组评分低于对照组,差异具有统计学意义(P0.05)。结论:\"知信行\"护理管理模式对老年糖尿病患者的睡眠障碍具有一定的改善作用,有助于降低患者的疲劳程度,值得临床护理推广。     

24-Hour Pattern of Work-Related Injury Risk of French Firemen: Nocturnal Peak Time

The first aim of the study was to assess clock-time patterning of work-related injuries (WRIs) of firemen (FM) of Saône et Loire-71 (France) during the 4-yr span of 1 January 2004 to 31 December 2007. FM of this service are legally required to log every WRI and seek its evaluation by the medical service, whether the WRI was the result of worksite duties or exercise/sport activities at the station. WRI was defined specifically as a (nonexercise, nonsport, and nonemotional/stress) work-associated trauma, verified both by log book and medical records. For the corresponding years, the 24-h pattern of emergency calls (Calls) plus road traffic (Traffic) on the main roads of the service area was also assessed. Relative risk (R) of WRI was calculated as the quantity of WRIs/h divided by the quantity of Call responses/h?×?1000, which takes into account the number of at-risk FM/unit time, since each dispatched emergency vehicle is staffed with 4 FM. Comparably trained regular (RFM) and volunteer (VFM) FM experienced a total of 187 WRIs. The 24-h WRI curve patterns of RFM and VFM were correlated (r?=?0.4, p?<?.05), with no histogram difference (p?>?.05). Analysis of variance (ANOVA) validated comparable clock-time patterns in WRIs of RFM and VFM each year and each season (all p?<?.0001). Thus, time series of the RFM and VFM were pooled, revealing a statistical significant 24-h variation in WRIs (ANOVA, p?>?.0006; Cosinor analysis, p?<?.0001), with peak at 16:00?h and trough at 04:00?h. The 24-h pattern in Traffic, which mirrors that of human activity, with peak ～18:00?h and trough ～03:00?h, was also verified (ANOVA, p <?.0001; Cosinor, p?<?.0001). Calls (n?=?112,059) resulting in FM responses also exhibited statistically significant 24-h variation, with peak at ～20:00?h and trough at ～06:00?h. The 24-h pattern of R showed a nocturnal peak at 02:00?h (R?=?2.87?±?0.46; mean?±?SEM) and diurnal trough 14:00?h (R?=?1.30?±?0.05) (t test, p?<?.02); clock-time-related changes in R were further validated by ANOVA (p?=?.0001) and Cosinor (p?<?.0001), with acrophase (peak time, Ø) of 02:43?h?±?68?min (SD). The second aim of the study was to evaluate the relationship between the 24-h patterns of WRIs and lag-time (LT) response (used as a measure of work performance) of FM of the same service to urgent medical calls for out-of-hospital cardiac arrests. Highest R of WRI at 02:00?h corresponded closely to longest LT (raw data at ～02:00?h and Cosinor derived Ø of 02.54?h?±?71?min [SD]), thereby supporting the hypothesis of a common mechanism underlying the two 24-h profiles. A third aim was to determine the relevance of a new concept in work safety, “chronoprevention,” for future FM training programs. (Author correspondence: bergarein@yahoo.fr)     

Explaining Sleep Duration in Adolescents: The Impact of Socio‐Demographic and Lifestyle Factors and Working Status

Previous studies found students who both work and attend school undergo a partial sleep deprivation that accumulates across the week. The aim of the present study was to obtain information using a questionnaire on a number of variables (e.g., socio‐demographics, lifestyle, work timing, and sleep‐wake habits) considered to impact on sleep duration of working (n=51) and non‐working (n=41) high‐school students aged 14–21 yrs old attending evening classes (19:00–22:30 h) at a public school in the city of São Paulo, Brazil. Data were collected for working days and days off. Multiple linear regression analyses were performed to assess the factors associated with sleep duration on weekdays and weekends. Work, sex, age, smoking, consumption of alcohol and caffeine, and physical activity were considered control variables. Significant predictors of sleep duration were: work (p < 0.01), daily work duration (8–10 h/day; p < 0.01), sex (p=0.04), age 18–21 yrs (0.01), smoking (p=0.02) and drinking habits (p=0.03), irregular physical exercise (p < 0.01), ease of falling asleep (p=0.04), and the sleep‐wake cycle variables of napping (p < 0.01), nocturnal awakenings (p < 0.01), and mid‐sleep regularity (p < 0.01). The results confirm the hypotheses that young students who work and attend school showed a reduction in night‐time sleep duration. Sleep deprivation across the week, particularly in students working 8–10 h/day, is manifested through a sleep rebound (i.e., extended sleep duration) on Saturdays. However, the different roles played by socio‐demographic and lifestyle variables have proven to be factors that intervene with nocturnal sleep duration. The variables related to the sleep‐wake cycle—naps and night awakenings—proved to be associated with a slight reduction in night‐time sleep, while regularity in sleep and wake‐up schedules was shown to be associated with more extended sleep duration, with a distinct expression along the week and the weekend. Having to attend school and work, coupled with other socio‐demographic and lifestyle factors, creates an unfavorable scenario for satisfactory sleep duration.     

糖尿病足患者睡眠质量与疲劳程度的调查研究

目的:调查糖尿病足(diabetic foot,DF)患者的睡眠质量与疲劳程度,并分析其相关性。方法:选取105例糖尿病足患者,采用人口数据统计表调查患者的一般情况,匹兹堡睡眠质量指数量(PSQI)评估患者的睡眠质量,Piper疲劳量表(PFS)评估患者的疲劳程度,并通过pearson检测分析其相关性。结果:人口统计数据显示,56%的DF患者年龄在41岁以上,64.8%女性,82.9%已婚,48.6%小学学历,39%病程3年或更长的时间。PSQI的平均总得分为(8.17±3.02),PFS量表总分为(6.38±2.18),睡眠质量与疲劳度总得分之间呈显著正相关,相关系数r=0.622(P0.05)。结论:DF患者的睡眠质量较差,大多出现中度疲劳,二者之间呈正相关,应加强对DF患者的睡眠质量与疲劳的护理。     

Impact of Layover Length on Sleep,Subjective Fatigue Levels,and Sustained Attention of Long-Haul Airline Pilots

Long-haul airline pilots often experience elevated levels of fatigue due to extended work hours and circadian misalignment of sleep and wake periods. During long-haul trips, pilots are typically given 1–3 d off between flights (i.e., layover) to recover from, and prepare for, duty. Anecdotally, some pilots prefer long layovers because it maximizes the time available for recovery and preparation, but others prefer short layovers because it minimizes both the length of the trip, and the degree to which the body clock changes from “home time” to the layover time zone. The aim of this study was to examine the impact of layover length on the sleep, subjective fatigue levels, and capacity to sustain attention of long-haul pilots. Participants were 19 male pilots (10 Captains, 9 First Officers) working for an international airline. Data were collected during an 11- or 12-d international trip. The trips involved (i) 4 d at home prior to the trip; (ii) an eastward flight of 13.5?h across seven time zones; (iii) a layover of either 39?h (i.e., short, n?=?9) or 62?h (i.e., long, n?=?10); (iv) a return westward flight of 14.3?h across seven time zones; and (v) 4 d off at home after the trip. Sleep was recorded using a self-report sleep diary and wrist activity monitor; subjective fatigue level was measured using the Samn-Perelli Fatigue Checklist; and sustained attention was assessed using the psychomotor vigilance task for a personal digital assistant (PalmPVT). Mixed-model regression analyses were used to determine the effects of layover length (short, long) on the amount of sleep that pilots obtained during the trip, and on the pilots' subjective fatigue levels and capacity to sustain attention. There was no main effect of layover length on ground-based sleep or in-flight sleep, but pilots who had a short layover at the midpoint of their trip had higher subjective fatigue levels and poorer sustained attention than pilots who had a long layover. The results of this study indicate that a short layover during a long-haul trip does not substantially disrupt pilots' sleep, but it may result in elevated levels of fatigue during and after the trip. If short layovers are used, pilots should have a minimum of 4 d off to recover prior to their next long-haul trip. (Author correspondence: greg.roach@cqu.edu.au)