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Sleep and errors in a group of Australian hospital nurses at work and during the commute
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Applied Ergonomics 39 (2008) 605–613 www.elsevier.com/locate/apergo
Sleep and errors in a group of Australian hospital nurses at work and during the commute Jillian Dorriana,, Carolyn Tolleya, Nicole Lamonda, Cameron van den Heuvela,b, Jan Pincombec, Ann E. Rogersd, Dawson Drewa a
The School of Psychology and The Centre for Sleep Research, The University of South Australia, 7th Floor Playford Building (P7-35), City East Campus, Frome Rd., Adelaide, SA 5000, Australia b School of Paediatrics and Reproductive Health, University of Adelaide, Australia c The School of Nursing and Midwifery, The University of South Australia, Australia d School of Nursing, University of Pennsylvania, USA Received 1 November 2007; accepted 25 January 2008
Abstract There is a paucity of information regarding Australian nurses’ sleep and fatigue levels, and whether they result in impairment. Fortyone Australian hospital nurses completed daily logbooks for one month recording work hours, sleep, sleepiness, stress, errors, near errors and observed errors (made by others). Nurses reported exhaustion, stress and struggling to remain (STR) awake at work during one in three shifts. Sleep was significantly reduced on workdays in general, and workdays when an error was reported relative to days off. The primary predictor of error was STR, followed by stress. The primary predictor of extreme drowsiness during the commute was also STR awake, followed by exhaustion, and consecutive shifts. In turn, STR awake was predicted by exhaustion, prior sleep and shift length. Findings highlight the need for further attention to these issues to optimise the safety of nurses and patients in our hospitals, and the community at large on our roads. r 2008 Elsevier Ltd. All rights reserved. Keywords: Sleep loss; Work hours; Nursing
1. Introduction Due to a worldwide nursing workforce shortage (Preston, 2002; AHWAC, 2003; Senate Committee Report, 2002) and an aging population, nursing workload is becoming more demanding and complex (AHWAC, 2003; Johnson and Preston, 2001; Government of South Australia, 2003). Nurses are working extended and unpredictable hours with a lack of regular breaks (AHWAC, 2003; Johnson and Preston, 2001; Government of South Australia, 2003) and are therefore likely to experience elevated sleepiness and fatigue. The potential safety consequences of a fatigue-impaired nurse are serious. Nurses play a pivotal role in the health care team, performing a wide range of safety-critical tasks. Fatigued Corresponding author. Tel.: +61 8 8302 6624; fax: +61 8 8302 6623.
E-mail address: [email protected] (J. Dorrian). 0003-6870/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2008.01.012
nurses may make errors in clinical judgment or medication administration (Leape et al., 1995; International Council of Nurses, 2003; Dorrian et al., 2006), or fail to intercept errors made by others (Dorrian et al., 2006). Adverse events in Australian Health Care have received recent attention from the government and the general community (Wilson et al., 1999; Patient Safety and Clinical Quality Program, 2005). The economic cost of Adverse Medical Events in Australia has been estimated at $1–4 billion per annum (Wilson et al., 1996). The link between fatigue and error, and the resulting costs, has clearly been demonstrated and recognised in other industries. A case in point in Australia is the transportation industry (House of Representatives Standing Committee, 2000). More recently, studies in health care around the world are beginning to assess in more detail the impact of work hours on the fatigue level of the health care professionals involved, and consequences for the safe performance of their duties.
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These studies have typically focused on junior doctors. For example, the Harvard Work Health and Safety Group found that a reduction in interns’ work hours resulted in increased sleep, and a concomitant reduction in attentional failures (Lockley et al., 2004), serious adverse medical events and non-intercepted serious errors (Landrigan et al., 2004). While these findings suggest a link between fatigue and medical errors, further research is clearly needed to directly examine the relationship between work hours, sleep duration and errors, not only in junior doctors but also in other health care roles. A series of published studies from the US indicates nurses are working long hours and unscheduled overtime. They frequently experience reduced sleep durations, and subsequently feel drowsy at work and report committing more errors and near errors. One study in a group of 393 hospital nurses found that error likelihood increased with increasing overtime, shifts longer than 12 h, or weekly work hours in excess of 40 h (Rogers et al., 2004). A further study by the same group, involving 502 critical care nurses, supported this relationship between extended work hours, reduced vigilance and error likelihood (Scott et al., 2006). Not only are long work hours, reduced sleep and increased fatigue potentially a problem in the workplace, the negative impact can extend to the trip home. In Australia, it is estimated that between 20% and 30% of fatal road accidents have fatigue as a contributing factor (Department of Transport and Regional Services, 2000). In the Federal Office of Road Safety database (1990, 1992, 1994, 1996), fatigue was documented as a contributing factor in 7% of all motor vehicle accidents (MVA). The total economic cost of fatigue-related road accidents in Australia has been conservatively estimated at $850 million (Department of Transport and Regional Services, 2000), and more recently at $3 billion per year (Bureau of Transport Economics, 2000). Investigations suggest that drowsy driving increases crash/near crash likelihood by more than 400% (NHTSA, 2006), and that sleep-related driving accidents are significantly more common among those who have long (Stutts and Vaughn, 1999) or irregular (Brown, 1994) work hours or who work at night (Horne and Reyner, 1999). Therefore, it could reasonably be expected that health care professionals would be vulnerable to more sleep-related crashes while driving home from work. Indeed, studies have indicated that medical residents have high rates of fatigue-related motor vehicle crashes (Kowalenko et al., 2000; Geer et al., 1997; Barger et al., 2005). There is also an emerging literature examining work hours, drowsy driving and MVA in nurses. In a focus group study, 43 out of 45 night shiftworking nurses reported driving-related injuries and near accidents while travelling to and from work (Novak and Auvil-Novak, 1996). Gold et al. (1992), found that nurses working night shift were more likely to fall asleep at work than those working day, evening or rotating shifts. There was a clear risk for sleep-related driving crashes among all participat-
ing nurses, with up to 41% reporting near miss crashes. In a recent study of 895 US nurses, two-thirds reported at least one drowsy driving episode and 30 nurses reported drowsy driving after every shift. An increased likelihood of drowsy driving was predicted by reduced sleep, working night shifts and reports of struggling to remain (STR) awake at work (Scott et al., 2007). Therefore, in the face of current shortages and increasing work demands, research into fatigue, medical errors and drowsy driving is critical. As alluded to above, this fortunately is being recognised by the research community with an emerging literature investigating these factors, particularly among junior doctors. To date, there are few studies in nurses, and those have predominantly been conducted in the US. There is a paucity of relevant information for Australian nurses. To address this, a recent Australian pilot study (N ¼ 23) found that, in a month, nurses reported STR awake during more than a third of shifts, and extreme drowsiness while driving or cycling home on approximately one in 10 shifts. The results also were consistent with fatigued nurses being more likely to commit errors and less likely to catch someone else’s errors (Dorrian et al., 2006). The current study aims to extend these findings with a larger sample, and with more detailed analyses that specifically investigate the relationships between work hours, sleep, safety at work and while travelling home. 2. Methods 2.1. Participants Forty-one full-time nurses (mean age ¼ 36 y, range ¼ 21–57 y, 34 female, 7 male) in an Australian metropolitan hospital completed daily recordings over a 1-month period of their scheduled and actual work hours, sleep length and quality, sleepiness and fatigue levels. Information sessions were held in six wards (1–2 per ward). These sessions were organized and scheduled in conjunction with the Director of Nursing and Nurse Managers. Following these sessions, 45 nurses indicated their interest and were given an information package. Forty-one chose to participate in the study. Of the participants, 33 were registered and eight were enrolled nurses, working in coronary care, haematology/oncology, surgery, cardiology/medical and mental health areas. On average, they had 12.1 y (sd710.0) years of nursing experience. 2.2. Procedure Ethical clearance for this study was granted by The University of South Australia Human Research Ethics Committee and the ethics committee of the participating hospital. Participants completed a demographic questionnaire (age, training level, current position, nursing role, marital status, living arrangements, dependants and cultural demographics) and general health and sleep
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questionnaires. They also completed two 14-page logbooks (10 15 cm spiral bound with one page per day), covering 28 consecutive days of data. Each page contained 27 questions relating to work hours, sleep and error rates, and took approximately 5 min to complete. Participants were instructed to ‘‘fill out your diary at approximately the same time each evening (around 6 pm). If you forget to fill in the logbook on a particular day, skip that page and go on to the next page. Do not try to complete a page from memory.’’ Participants were compensated for their time at a rate of $5 per day. The logbooks were designed to parallel a comparable study conducted in the USA (Rogers et al., 2004), in order to directly compare and contrast Australian data. Questions related to: 1. Work hours: Participants recorded both scheduled and actual hours worked in each 24-h period. 2. Estimated sleep length and quality: For all main sleep periods and naps participants indicated the time at which they fell asleep and the time at which they awakened. This was used to calculate estimated sleep length and the sleep occurring in the 24 h prior to each shift start time. For each main sleep period, participants recorded whether they had disrupted sleep, reasons for the disruption (noise, work-related concerns, non-workrelated concerns, others), problems falling asleep or waking too early. 3. Fatigue/sleepiness/stress: Participants recorded ratings of fatigue, stress and physical and mental exhaustion. They were asked to ‘‘circle a number from 1 to 5, indicating how strongly each word describes how you felt overall today’’ (1: very, 2: moderately, 3: a little, 4: slightly, 5: not at all). Exact meanings of fatigue, sleepiness, stress and exhaustion were left open to the interpretation of the participant. Participants also indicated whether they were STR awake during each shift. 4. Errors: Frequency, type and severity of nursing errors and near errors, made or observed by the participant, were recorded, along with the time that these occurred and a short written narrative of the error and circumstances surrounding the error. As participants were required to respond yes/no to questions regarding the occurrence of error, near error or observed error, incomplete error logs could be distinguished from days where no error was recorded. There were six categories whereby nurses coded the type of errors that they recorded: (1) medical, including incorrect administration in terms of dosage, timing, delivery or patient; (2) transcription, including errors of transcribing between orders/charts, etc.; (3) charting, including incorrect entry in terms of information, timing or patient; (4) procedural, including any deviations from approved procedures; (5) slip or fall, including any physical injuries; and (6) others, including errors not falling into any of the other categories. It must be noted that no specific studyrelated training was given to participating nurses regard-
607
ing observation of other’s errors. Observed errors made by others were recorded in the logbook in the same way that own errors and near errors were recorded. 5. Drowsiness while travelling home: Following each shift, participants recorded whether they experienced drowsiness (mild, moderate or extreme), and any accidents or near accidents while driving or cycling home. 2.3. Statistical analysis A total of 1148 days of data were collected, including 694 work shifts. Principally, participating nurses worked sequences of shifts of 8, 10 or 12 h, which rotated between morning/day shifts (M, start time: 0600–0900 h), evening shifts (E, start time: 1300–1600 h), and night shifts (N, start time: 2100–2200 h). Mixed effects ANOVA (taking into account repeated observations by the same individuals, Van Dongen et al., 2004) were used to investigate differences in estimated sleep length (dependent variable) according to shift type (morning/day, evening, night) and according to error reporting (comparing days off, workdays, shifts when an error, near error or someone else’s error was reported) with Games–Howell post hoc comparisons for unequal variances. It should be noted that reported error rates are not corrected for exposure. Generalised estimating equation (GEE) binary logistic regression models were used to identify predictors of error occurrence (yes/no), extreme drowsiness/near accident occurrence during the commute (yes/no) and STR awake during shifts (yes/no). The GEE approach can account for the correlated responses between multiple shifts worked by the same nurse (Pan and Connett, 2002) and has been used in a study with a similar methodology (Scott et al., 2007). Independent variables included stress ratings, physical and mental exhaustion ratings, STR awake at work (for models of error and commute drowsiness only), sleep in the prior 24 and 48 h, total wake time, shift duration and number of consecutive shifts. Correlation analyses found high correlations between physical and mental exhaustion (r ¼ 0.78, po0.05) and sleep in the prior 24 and 48 h (r ¼ 0.75, po0.05). As such, mental exhaustion ratings and sleep in the prior 48 h were not subsequently included in the models. All other correlations between predictor variables were low (ro0.4) and non-significant. Final models (reported in the Section 3) include significant predictors (po0.05) only. It should also be noted that while data for all nurses (n ¼ 41) was included in all analyses (including figures), ANOVA and GEE analyses only included shifts with a valid sleep history (at least 24 h), leaving 637/694, or 92% of shifts (morning/ day shifts: 304, evening shifts: 211, night shifts: 392). 3. Results Nurses in this sample worked between 32 and 46 h per week. While nurses worked significantly longer hours than
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scheduled (t622 ¼ 2.70, po0.01), it was only for an average of 8.4 min per shift. There was minimal overtime reported (4% of shifts). As previously outlined, these nurses worked combinations of day/morning (33.5%), evening (23.3%) and night shifts (43.2%, definitions above). Of the night shifts, 28.7% were the first night shift in a sequence. Of the morning shifts, 38% were late/early shifts (i.e. the morning shift followed an evening shift). The mean number of consecutive shifts (7standard deviation) was 3.2071.75 shifts (mode ¼ 2 shifts). Mean shift duration (7standard deviation) was 8.9271.31 min (mode ¼ 8.5 h). Total days off was 454 out of 1148 days of data, which equates to an average of 11.1 days off per nurse during the month of data collection. See Table 1 for rosters of three example participants. The participating nurses reported disrupted sleep on 25.9% of days (including workdays and days off). Of these disruptions, work-related concerns were responsible for this sleep disruption on 14.8% of occasions. Problems falling asleep and waking too early were reported on approximately one-third of workdays (30% and 29%, respectively). Moderate to high levels of stress, physical exhaustion and mental exhaustion were reported on 27%, 42% and 39% of shifts, respectively. Struggling to remain awake was reported during 32% of shifts.
Table 1 Rosters of three example participants Days into study
Participant A
Participant B
Participant C
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Off Off Off M E M Off Off N N N N Off Off M M M E Off M Off Off E M M Off Off Off
Off Off Off E M M Off Off E E M Off Off E M Off Off N N N Off Off Off E E M E E
Off Off Off E M N N Off Off Off E E E M M M Off Off Off Off E M N N Off Off Off E
Off: day off, M: morning, E: evening, N: night shift.
Thirty-four participants usually drove to work, three cycled, one drove or cycled and the remainder walked or used public transport. Mean travel time (7standard deviation) was 19.3711.8 min. On 70 occasions, extreme drowsiness when driving or cycling home was reported, with seven near accidents. Nearly half of extreme drowsiness and near accident reports occurred between 0700 and 0900 h. This coincides with the end of the night shift (indeed, 26% of night shifts were associated with commute drowsiness Fig. 1). A further 40% of extreme drowsiness and near accident reports occurred between 1400 and 1900 h, with a peak at 1500 h (24%), coincident with the end of the morning shift. Less than 10% of such incidents followed the evening shift (2200 h). Table 2 shows error breakdown by type and severity. Overall, 38 errors, 38 near errors and 65 observed errors were recorded. While the majority of errors were perceived to have minor consequences, for a third of errors, the perceived consequences were moderate or extreme. The majority of errors occurred during morning/day shifts. Near errors were relatively consistently distributed across morning/day, evening and night shifts. In contrast, the majority reports of someone else’s error occurred during evening shifts (Figs. 1 and 2). Significant differences in estimated sleep length were found between morning/day, evening and night shifts (F2634 ¼ 22.63, po0.01) such that nurses had significantly more sleep prior to evening shifts compared to morning/ day or night shifts (po0.05, Fig. 1). There was no significant difference in prior sleep duration between late/ early shifts and other morning shifts (F1300.73 ¼ 0.11, p40.05). Sleep was significantly increased prior to the first night shift in a sequence (9.3073.09) relative to subsequent night shifts (7.31372.66, F1,108.78 ¼ 15.817, po0.001). Significant differences in sleep duration were also found between days off, workdays and shifts when an error, near error or someone else’s error was recorded (F4267.4 ¼ 6.67, po0.01). In comparison to days off, total sleep length was significantly shorter on workdays (excluding workdays where an error was reported, po0.05) and shifts where an error was reported (po0.05). In contrast, total sleep duration was not significantly different to days off on shifts where a near error, or when someone else’s error was recorded. The difference between sleep prior to shifts where an error was recorded was not significantly different to that on other workdays (Fig. 3). Results of logistic regression analyses are presented in Table 3 (OR, 95% CI and Wald Statistics). Stress ratings (OR ¼ 1.5) and STR awake during the shift (OR ¼ 2.4) were significant predictors of error (po0.05). Exhaustion ratings (OR ¼ 1.4), STR awake at work (OR ¼ 5.2) and number of consecutive shifts (OR ¼ 1.2) were significant predictors of extreme drowsiness and near accidents while driving or cycling home (po0.05). Exhaustion ratings (OR ¼ 1.6), hours of sleep in the preceding 24 h (OR ¼ 0.9) and shift length (OR ¼ 1.5) were significant predictors of STR awake during work (po0.05).
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%shift error (stacked)
Table 2 Summary of errors, near errors and else errors (made by others) broken down by type and perceived severity
Else Error Near Error Error
25.0 20.0 15.0 10.0 5.0 0.0 30.0
%shift error (stacked)
25.0 Ext Drowsy Near Accident
20.0
Error
Near error
Else error
Total (%)
Type Medical Transcription Charting Procedural Slip or fall Others
24.3 5.4 16.2 16.2 2.7 35.1
75.0 2.8 8.3 5.6 2.8 5.6
48.6 8.1 5.4 24.3 0.0 13.5
56.4 7.0 9.3 11.6 1.7 14.0
Severity Mild Moderate Extreme
81.8 9.1 9.1
79.3 20.7 0.0
44.8 48.3 6.9
67.4 28.3 4.3
Total N
38
38
65
141
15.0 10.0 5.0 0.0 9.5 9
Sleep Duration (hrs)
609
*
8.5 8 7.5 7 6.5 6 5.5 5 Day/Morning
Evening
Night
Shift Type Fig. 1. Distribution of errors (top panel) and incidences of extreme drowsiness and near accidents while driving or cycling home (middle panel). Error distribution is expressed as the percentage of day/morning, evening and night shifts where an error was recorded and bars are stacked (i.e. the contribution of each %value is compared across error categories). Lower panel displays sleep duration (hours+std error bars) as a function of shift type. *Indicates a significant difference (po0.05) compared to day/ morning and night shifts. Figures are not corrected for exposure.
4. Discussion This study aimed to investigate the relationship between work hours, sleep, errors and drowsiness at work and while travelling home in a group of Australian metropolitan hospital nurses. Participating nurses reported minimal overtime and working an average of less than 10 min longer than scheduled per shift. Nurses reported exhaustion and STR awake at work during one in three shifts. A substantial number of errors, near errors, errors made by
others were reported. The majority of errors occurred during morning shifts, while most reports of errors made by others occurred during evening shifts. Night shifts were primarily associated with reports of extreme drowsiness or near accidents while travelling home. This occurred following one in four night shifts. Estimated sleep durations were significantly reduced during morning and night shifts relative to evening shifts. Sleep durations were also significantly reduced on workdays in general, and workdays when an error was reported relative to days off. This was not the case for shifts when a near error or someone else’s error was recorded. The primary predictor of error was STR awake at work (OR ¼ 2.4), followed by stress (OR ¼ 1.5). The primary predictor of extreme drowsiness and near accidents while travelling home was also STR awake at work (OR ¼ 5.2), followed by exhaustion ratings (OR ¼ 1.4), and number of consecutive shifts (OR ¼ 1.2). In turn, predictors of STR awake at work were exhaustion ratings (OR ¼ 1.6), hours of sleep in the preceding 24 h (OR ¼ 0.9) and shift length (OR ¼ 1.5). Taken together, results indicate that the shift schedules worked by participating nurses were associated with reductions in sleep. This is consistent with studies conducted in other shiftworking populations (e.g. Foret and Latin, 1972; Ha¨rma¨ et al., 2002; Kecklund and A˚kerstedt, 1993; Sasaki et al., 1986). On average, nurses in this study had nearly 50 min less sleep on workdays compared to days off. Since research has demonstrated that sleep reductions of as little as 30 min per night can result in significant performance decrements over time (e.g. Belenky et al., 2003), it is likely that nurses were accruing a sleep debt, which may have impacted on their performance and safety. Notably, however, sleep on days off does not necessarily reflect basic sleep need. The increase in sleep on days off could reflect a compensatory response to accumulated sleep loss associated with workdays. Nevertheless, arguably, the nurses in this study were sleep-deprived.
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610 70
shift transition
60
Else Error Near Error Error
50 %error (stacked)
shift transition
40 30 20 10
2200-2359h
2000-2159h
1800-1959h
1600-1759h
1400-1559h
1200-1359h
1000-1159h
0800-0959h
0600-0759h
0400-0559h
0200-0359h
0000-0159h
0
Time (2h bins)
Fig. 2. Distribution of errors by time-of-day (stacked percentage). Transitions between day/morning, evening and night shifts are indicated by the dotted lines. Figures are not corrected for exposure.
9
Sleep Duration (hrs)
8.5 *
8
*
7.5 7 6.5 6 day off
work day
Error
Near Error
Else Error
Fig. 3. Reported sleep duration (hours+std error bars) on days off, and in the 24 h preceding workdays and shifts where an error, near error and observed error (made by others was recorded. *Indicates a significant difference (po0.05) compared to days off.
This is interesting given that nurses did not report significant amounts of overtime or shifts that extended greatly beyond scheduled durations. This suggests that, for this cohort, it may be the shift schedule per se that is responsible for the sleep loss, rather than particularly long work hours. This may be due to the rotating nature of the schedule. Indeed, while Gold et al. (1992), found a clear risk for sleep-related MVA among all participating nurses in their study, those on rotating schedules were more likely to report nodding off at work and to be involved in MVA than those working day/evening or night shifts. Not only did nurses report sleep reductions, they also reported disturbances. This is also consistent with the general shiftwork literature (Costa, 1997). One in three workdays were associated with sleep disruption and
problems initiating and maintaining sleep. One in three workdays were also associated with moderate to high levels of stress, exhaustion and STR awake at work. Therefore, nurses in this study frequently experienced sleep loss, sleep disturbance and stress, sleepiness and exhaustion at work. These findings are of operational concern given the plethora of studies that demonstrate the negative impact of sleep loss and fatigue on performance and safety (reviewed in Dinges and Kribbs, 1991; Harrison and Horne, 2000). Consistent with this body of research, a link between work hours, sleep loss and error was apparent in this study. Relative to days off, sleep was reduced prior to shifts were error was recorded. Interestingly, sleep was not significantly reduced prior to shifts where nurses recorded a near miss or caught someone else’s error. In addition, consistent with previous studies (Ha¨rma¨ et al., 2002), sleep was reduced prior to morning and night shifts relative to evening shifts. Morning shifts were most heavily associated with errors and night shifts with drowsy driving. In contrast, reports of someone else’s error were most frequent during evening shifts. Thus, findings are consistent with the pilot study (Dorrian et al., 2006), indicating that less sleep may lead to increased likelihood of making an error (and of drowsy driving), and importantly, decreased likelihood of catching your own, or someone else’s error. Arguably, this is particularly important for nurses, who are often responsible for intercepting errors (Leape et al., 1995). The relationship between work hours, sleep loss and errors was further investigated using regression analyses. The main predictors of errors were STR awake during the shift and stress ratings. Individuals who reported STR
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Table 3 For each binary logistic regression model, this table summarises the dependent variable (DV), chi-square, significant predictors (po0.05) and odds ratios (795% confidence intervals) DV
w2
p
Predictors
OR
95% CI
+95% CI
Error
12.40
o0.01
Stress Struggling to remain awake
1.48 2.40
1.09 1.17
2.02 4.910
Commute
50.61
o0.01
Exhaustion Struggling to remain awake Consecutive shifts
1.42 5.25 1.23
1.12 2.91 1.04
1.80 9.46 1.450
Struggling to remain awake
64.73
o0.01
Exhaustion
1.63
1.38
1.91
Sleep prior 24 h Shift length
0.92 1.46
0.85 1.24
0.98 1.71
awake at work were nearly two and a half times more likely to make an error. Every unit increase in stress rating resulted in a 50% increase in error likelihood. Interestingly, work variables (shift type, shift length, number of consecutive shifts) were not directly predictive of error. As discussed earlier, this may be due to the lack of overtime and extended shifts worked by this group of nurses. There was also no apparent relationship between error and late/early shifts or error and the first night shift in a sequence. This is possibly explainable by the fact that there was no significant difference in sleep associated with late/early shifts compared to other morning shifts. Moreover, the first night shift of a sequence was associated with more sleep compared to subsequent night shifts. This is likely to be indicative of prophylactic napping or sleeping in prior to night shift one. Also interesting was the fact that prior sleep duration did not directly predict error. This could be explained by the fact that sleep history was relatively consistent over time, that is (as described in the Section 2.3), the amount of sleep a nurse achieved in the 24 h prior to a shift was highly predictive of the amount achieved in the prior 48 h (r ¼ 0.75). This may suggest that accumulated, consistent sleep loss over time, rather than acute sleep loss prior to specific shifts may contribute to sleepiness and error for this group. However, it is important to note that the primary predictor of error, STR awake at work, was predicted by work and sleep-related factors. Specifically, every hour of sleep in the prior 24 h resulted in a 10% reduction and every hour on shift resulted in a 50% increase in STR awake. Additionally, every unit increase in exhaustion ratings produced a 60% increase in STR awake. STR awake at work was also the biggest predictor of extreme drowsiness or near accident while travelling home, increasing the likelihood by more than five times. This is slightly higher than Scott et al. (2007), (OR ¼ 3.37). Other predictors included exhaustion ratings (40% increase) and number of consecutive shifts (20% increase). Therefore, it appears that work hours and sleep loss reduced the ability to remain awake at work, which in turn increased error likelihood and drowsy driving.
There are several, clear limitations to the current study. First, while the repeated measures element of the study was designed to boost power (resulting in 1148 data points, 694 workdays), it is still a relatively small group (N ¼ 41) in a single metropolitan hospital. Therefore, generalisability is limited. However, the overall consistency of the results with studies in nurses (e.g. Niedhammer et al., 1995; Novak and Auvil-Novak, 1996; Rogers et al., 2004) and other shiftworkers (Foret and Latin, 1972; Ha¨rma¨ et al., 2002; Kecklund and A˚kerstedt, 1993; Sasaki et al., 1986) lends support to the current findings. Second, sleep reports were subjective. Use of actigraphy would certainly boost reliability of sleep measurement. Third, differences in error rates relative to shift type did not control for exposure (number of patients, degree and severity of patient need, number of staff on the ward at the time, etc.). Indeed, the increase in errors on the morning shift may be the result of increased workload during this time. Further, commuterelated near accidents may be related as much to traffic density as any other factor. This study did not control for traffic exposure. Fourth, it is likely that the error rates reported in the current study are under or over representative of actual error rates. Observational methodologies would provide a more objective measure of error and performance. However, self-reporting methodology offers significant advantages with respect to lack of intrusiveness and maximising sample size. Future studies should investigate both techniques. Current study findings highlight the need for further investigation into the effects of work schedules and related sleep loss on nurse and patient safety, at work and while travelling home. Future research investigating differences in job role characteristics, shift designs and geographic area (e.g. rural versus metropolitan) would be of benefit, as would more detailed coding and analysis of errors. Ultimately, such future studies will contribute to evidence-based fatigue management strategies in health care. For example, a recent review suggested ways in which knowledge of prior sleep durations could be used to target fatigue-proofing strategies at work. Such strategies (e.g. transfer to non-safety-critical duties, extra support from colleagues, earlier nap/break time
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arrangements) are often used informally by teams to safetymanage sleep loss and fatigue. Based on prior research, the review estimated that individuals need at least 5 h sleep in the 24 h prior to commencing work in order to remain safe (Dawson and McCulloch, 2005). In the current study, there were 9.1% of shifts associated with less than 5 h sleep. Interestingly, 11.8% of errors occurred on shifts associated with less than 5 h sleep. It is therefore possible that targeted fatigue proofing for these 9.1% of shifts may have lead to an 11.8% reduction in errors. This is an interesting area for future research. Taken together, findings suggest that Australian nurses are experiencing disrupted and reduced sleep and exhaustion, stress and problems remaining awake at work. Findings also suggest that this is related to increased error likelihood, and reduced likelihood of identifying others’ errors, and elevated likelihood of extreme drowsiness during the commute home. Clearly, attention to these issues is required to optimise nurse and patient safety in our hospitals, and the safety of the community at large on our roads. References Australian Health Workforce Advisory Committee, 2003. Annual Report 2002–2003. AHWAC Report 2003.1, Sydney. Barger, L.K., Cade, B.E., Ayas, N.T., Cronin, J.W., Rosner, B., Speizer, F.E., Czeisler, C.A., 2005. Harvard work hours, health, and safety group. Extended work shifts and the risk of motor vehicle crashes among interns. N. Engl. J. Med. 352, 125–135. Belenky, G., Wesensten, N.J., Thorne, D.R., Thomas, M.L., Sing, H.C., Redmond, D.P., Russo, M.B., Balkin, T.J., 2003. Patterns of performance degradation & restoration during sleep restriction & subsequent recovery: a sleep dose–response study. J. Sleep Res. 12, 1–12. Brown, I.D., 1994. Driver fatigue. Hum. Factors 36, 298–314. Bureau of Transport Economics, 2000. Road crash costs in Australia. Report 102, Department of Transport and Regional Services, Canberra. Costa, G., 1997. The problem: shiftwork. Chronobiol. Int. 14 (2), 89–98. Dawson, D., McCulloch, K., 2005. Managing fatigue: it is about sleep. Sleep Med. Rev. 9, 365–380. Department of Transport and Regional Services, 2000. In: Beyond the Midnight Oil: An Inquiry into Managing Fatigue in Transport. House of Representatives Standing Committee on Communication, Transport and the Arts, Canberra, vol. 6, October, pp. 1225–1282 (Submission No. 83). Dinges, D.F., Kribbs, N.B., 1991. Performing while sleepy: effects of experimentally induced sleepiness. In: Monk, T.H. (Ed.), Sleep, Sleepiness and Performance. Wiley, New York, pp. 97–128. Dorrian, J., Lamond, N., van den Heuvel, C., Pincombe, J., Dawson, D., 2006. A Pilot Study of the safety implications of Australian nurses’ sleep and work hours. Chronobiol. Int. 23 (6), 1149–1163. Foret, J., Latin, G., 1972. The sleep of train drivers: an example of the effects of irregular work hours on sleep. In: Coquhoun, W.P. (Ed.), Aspects of Human Efficiency. English Universities Press, London. Geer, R.T., Jobes, D.R., Tew, J.D., Stepsis, H., 1997. Incidence of automobile accidents involving anesthesia residents after on-call duty cycles. Anesthesiology 87, A938. Gold, D.R., Rogacz, S., Bock, N., Tosteson, T.D., Speizer, F.E., Czeisler, C., 1992. Rotating shift work, sleep, and accidents related to sleepiness in hospital nurses. Am. J. Public Health 82, 1011–1014. Government of South Australia, 2003. Better choices, better health. Final Report of the Generational Health Review, Australia.
Ha¨rma¨, M., Sallinen, M., Ranta, R., Mutanen, P., Muller, K., 2002. The effect of an irregular shift system on sleepiness at work in train drivers and railway traffic controllers. J. Sleep Res. 11 (2), 141–151. Harrison, Y., Horne, J.A., 2000. The impact of sleep deprivation on decision making: a review. J. Exp. Psychol. Appl. 6 (3), 236–249. Horne, J., Reyner, L., 1999. Vehicle accidents related to sleep: a review. Occup. Med. 56, 289–294. House of Representatives Standing Committee on Communication, 2000. Transport and the Arts (2000). Beyond the Midnight Oil: An Inquiry into Managing Fatigue in Transport, October, Canberra. International Council of Nurses. 2003. Nurses and Overtime Fact Sheet, vol. 2003. Johnson, D., Preston, B., 2001. An Overview of Issues in Nursing Education. Commonwealth Department of Education, Science and Training, Australia. Kecklund, G., A˚kerstedt, T., 1993. Sleepiness in long distance truck driving: an ambulatory EEG study of night driving. Ergonomics 36 (9), 1007–1017. Kowalenko, T., Haas-Kowalenko, J., Rabinovich, A., Grzybowski, M., 2000. Emergency medicine related MVCs—Is sleep deprivation a risk factor? Acad. Emerg. Med. 7, 451. Landrigan, C.P., Rothschild, J.M., Cronin, J.W., Kaushal, R., Burdick, E., Katz, J.T., Lilly, C.M., Stone, P.H., Lockley, S.W., Bates, D.W., Czeisler, C.A., 2004. Effect of reducing interns’ work hours on serious medical errors in intensive care units. N. Eng. J. Med. 351 (18), 1838–1848. Leape, L.L., Bates, D.W., Cullen, D.J., Cooper, J., Demonaco, H.J., Gallivan, T., Hallisey, R., Ives, J., Laird, N., Laffel, G., et al., 1995. Systems analysis of adverse drug events. ADE prevention study group. J. Am. Med. Assoc. 274, 35–43. Lockley, S.W., Cronin, J.W., Evans, E.E., Cade, B.E., Lee, C.J., Landrigan, C.P., Rothschild, J.M., Katz, J.T., Lilly, C.M., Stone, P.H., Aeschbach, D., Czeisler, C.A., 2004. Harvard work hours, health and safety group. Effect of reducing interns’ weekly work hours on sleep and attentional failures. N. Engl. J. Med. 351 (18), 1829–1837. National Highway Traffic Safety Administration (NHTSA), 2006. The Impact of Driver Inattention on Near-crash/Crash Risk. US Department of Transportation, Washington (DOT HS 810 594). Niedhammer, I., Lert, F., Marne, M.J., 1995. Psychotropic drug use and shift work among French nurses (1980–1990). Psychol. Med. 25 (2), 329–338. Novak, R.D., Auvil-Novak, S.E., 1996. Focus group evaluation of night nurse shiftwork difficulties and coping strategies. Chronobiol. Int. 13, 457–463. Pan, W., Connett, J.E., 2002. Selecting the working correlation structure in generalized estimating equations with application to the lung health study. Stat. Sin. 12, 475–490. Patient Safety and Clinical Quality Program, 2005. First Report on Incident Management in the NSW Public Health System 2003–2004, NSW Department of Health, North Sydney, NSW. Preston, B., 2002. Australian nurse supply and demand to 2006. A projections model and its application. A Report Prepared for the Australian Council of Deans of Nursing, Melbourne. Rogers, A.E., Hwang, W.T., Scott, L.D., Aiken, L.H., Dinges, D.F., 2004. The working hours of hospital staff nurses and patient safety. Health Aff. 23 (4), 202–212. Sasaki, M., Kurosaki, Y., Spinweber, C.L., Graeber, R.C., Takahashi, T., 1986. Patterns of sleep-wakefulness before and after transmeridian flight in commercial airline pilots. Aviat. Space Environ. Med. 57 (12 pt2), B29–B42. Scott, L.D., Rogers, A.E., Hwang, W.T., Zhang, Y., 2006. Effect of critical care nurses’ work hours on vigilance and patients’ safety. Am. J. Crit. Care 15 (1), 1–8. Scott, L.D., Hwang, W.T., Rogers, A.E., Nysse, T., Dean, G.E., Dinges, D.F., 2007. The relationship between nurse work schedules, sleep duration and drowsy driving. Sleep (in press). Senate Committee Report, 2002. The patient profession: time for action. Report on the Enquiry into Nursing, Australia.
ARTICLE IN PRESS J. Dorrian et al. / Applied Ergonomics 39 (2008) 605–613 Stutts, J.C., Vaughn, B.V., 1999. Why do people have drowsy driving crashes? Input from drivers who just did. AAA Foundation for Traffic Safety, Washington. Van Dongen, H.P.A., Olfsen, E., Dinges, D.F., Maislin, G., 2004. Mixed model regression analysis and dealing with interindividual differences. Meth. Enzymol. 384, 139–171.
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Wilson, R.M., Runciman, W.B., Gibberd, R.W., Hamilton, J.D., 1996. Quality in Australian health care study. Med. J. Aust. 164, 754. Wilson, R., Harrison, B., Gibberd, R., Hamilton, J.D., 1999. An analysis of the causes of adverse events from the quality in Australian health care study. Med. J. Aust. 170, 411–415.
Applied Ergonomics 39 (2008) 605–613 www.elsevier.com/locate/apergo
Sleep and errors in a group of Australian hospital nurses at work and during the commute Jillian Dorriana,, Carolyn Tolleya, Nicole Lamonda, Cameron van den Heuvela,b, Jan Pincombec, Ann E. Rogersd, Dawson Drewa a
The School of Psychology and The Centre for Sleep Research, The University of South Australia, 7th Floor Playford Building (P7-35), City East Campus, Frome Rd., Adelaide, SA 5000, Australia b School of Paediatrics and Reproductive Health, University of Adelaide, Australia c The School of Nursing and Midwifery, The University of South Australia, Australia d School of Nursing, University of Pennsylvania, USA Received 1 November 2007; accepted 25 January 2008
Abstract There is a paucity of information regarding Australian nurses’ sleep and fatigue levels, and whether they result in impairment. Fortyone Australian hospital nurses completed daily logbooks for one month recording work hours, sleep, sleepiness, stress, errors, near errors and observed errors (made by others). Nurses reported exhaustion, stress and struggling to remain (STR) awake at work during one in three shifts. Sleep was significantly reduced on workdays in general, and workdays when an error was reported relative to days off. The primary predictor of error was STR, followed by stress. The primary predictor of extreme drowsiness during the commute was also STR awake, followed by exhaustion, and consecutive shifts. In turn, STR awake was predicted by exhaustion, prior sleep and shift length. Findings highlight the need for further attention to these issues to optimise the safety of nurses and patients in our hospitals, and the community at large on our roads. r 2008 Elsevier Ltd. All rights reserved. Keywords: Sleep loss; Work hours; Nursing
1. Introduction Due to a worldwide nursing workforce shortage (Preston, 2002; AHWAC, 2003; Senate Committee Report, 2002) and an aging population, nursing workload is becoming more demanding and complex (AHWAC, 2003; Johnson and Preston, 2001; Government of South Australia, 2003). Nurses are working extended and unpredictable hours with a lack of regular breaks (AHWAC, 2003; Johnson and Preston, 2001; Government of South Australia, 2003) and are therefore likely to experience elevated sleepiness and fatigue. The potential safety consequences of a fatigue-impaired nurse are serious. Nurses play a pivotal role in the health care team, performing a wide range of safety-critical tasks. Fatigued Corresponding author. Tel.: +61 8 8302 6624; fax: +61 8 8302 6623.
E-mail address: [email protected] (J. Dorrian). 0003-6870/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2008.01.012
nurses may make errors in clinical judgment or medication administration (Leape et al., 1995; International Council of Nurses, 2003; Dorrian et al., 2006), or fail to intercept errors made by others (Dorrian et al., 2006). Adverse events in Australian Health Care have received recent attention from the government and the general community (Wilson et al., 1999; Patient Safety and Clinical Quality Program, 2005). The economic cost of Adverse Medical Events in Australia has been estimated at $1–4 billion per annum (Wilson et al., 1996). The link between fatigue and error, and the resulting costs, has clearly been demonstrated and recognised in other industries. A case in point in Australia is the transportation industry (House of Representatives Standing Committee, 2000). More recently, studies in health care around the world are beginning to assess in more detail the impact of work hours on the fatigue level of the health care professionals involved, and consequences for the safe performance of their duties.
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These studies have typically focused on junior doctors. For example, the Harvard Work Health and Safety Group found that a reduction in interns’ work hours resulted in increased sleep, and a concomitant reduction in attentional failures (Lockley et al., 2004), serious adverse medical events and non-intercepted serious errors (Landrigan et al., 2004). While these findings suggest a link between fatigue and medical errors, further research is clearly needed to directly examine the relationship between work hours, sleep duration and errors, not only in junior doctors but also in other health care roles. A series of published studies from the US indicates nurses are working long hours and unscheduled overtime. They frequently experience reduced sleep durations, and subsequently feel drowsy at work and report committing more errors and near errors. One study in a group of 393 hospital nurses found that error likelihood increased with increasing overtime, shifts longer than 12 h, or weekly work hours in excess of 40 h (Rogers et al., 2004). A further study by the same group, involving 502 critical care nurses, supported this relationship between extended work hours, reduced vigilance and error likelihood (Scott et al., 2006). Not only are long work hours, reduced sleep and increased fatigue potentially a problem in the workplace, the negative impact can extend to the trip home. In Australia, it is estimated that between 20% and 30% of fatal road accidents have fatigue as a contributing factor (Department of Transport and Regional Services, 2000). In the Federal Office of Road Safety database (1990, 1992, 1994, 1996), fatigue was documented as a contributing factor in 7% of all motor vehicle accidents (MVA). The total economic cost of fatigue-related road accidents in Australia has been conservatively estimated at $850 million (Department of Transport and Regional Services, 2000), and more recently at $3 billion per year (Bureau of Transport Economics, 2000). Investigations suggest that drowsy driving increases crash/near crash likelihood by more than 400% (NHTSA, 2006), and that sleep-related driving accidents are significantly more common among those who have long (Stutts and Vaughn, 1999) or irregular (Brown, 1994) work hours or who work at night (Horne and Reyner, 1999). Therefore, it could reasonably be expected that health care professionals would be vulnerable to more sleep-related crashes while driving home from work. Indeed, studies have indicated that medical residents have high rates of fatigue-related motor vehicle crashes (Kowalenko et al., 2000; Geer et al., 1997; Barger et al., 2005). There is also an emerging literature examining work hours, drowsy driving and MVA in nurses. In a focus group study, 43 out of 45 night shiftworking nurses reported driving-related injuries and near accidents while travelling to and from work (Novak and Auvil-Novak, 1996). Gold et al. (1992), found that nurses working night shift were more likely to fall asleep at work than those working day, evening or rotating shifts. There was a clear risk for sleep-related driving crashes among all participat-
ing nurses, with up to 41% reporting near miss crashes. In a recent study of 895 US nurses, two-thirds reported at least one drowsy driving episode and 30 nurses reported drowsy driving after every shift. An increased likelihood of drowsy driving was predicted by reduced sleep, working night shifts and reports of struggling to remain (STR) awake at work (Scott et al., 2007). Therefore, in the face of current shortages and increasing work demands, research into fatigue, medical errors and drowsy driving is critical. As alluded to above, this fortunately is being recognised by the research community with an emerging literature investigating these factors, particularly among junior doctors. To date, there are few studies in nurses, and those have predominantly been conducted in the US. There is a paucity of relevant information for Australian nurses. To address this, a recent Australian pilot study (N ¼ 23) found that, in a month, nurses reported STR awake during more than a third of shifts, and extreme drowsiness while driving or cycling home on approximately one in 10 shifts. The results also were consistent with fatigued nurses being more likely to commit errors and less likely to catch someone else’s errors (Dorrian et al., 2006). The current study aims to extend these findings with a larger sample, and with more detailed analyses that specifically investigate the relationships between work hours, sleep, safety at work and while travelling home. 2. Methods 2.1. Participants Forty-one full-time nurses (mean age ¼ 36 y, range ¼ 21–57 y, 34 female, 7 male) in an Australian metropolitan hospital completed daily recordings over a 1-month period of their scheduled and actual work hours, sleep length and quality, sleepiness and fatigue levels. Information sessions were held in six wards (1–2 per ward). These sessions were organized and scheduled in conjunction with the Director of Nursing and Nurse Managers. Following these sessions, 45 nurses indicated their interest and were given an information package. Forty-one chose to participate in the study. Of the participants, 33 were registered and eight were enrolled nurses, working in coronary care, haematology/oncology, surgery, cardiology/medical and mental health areas. On average, they had 12.1 y (sd710.0) years of nursing experience. 2.2. Procedure Ethical clearance for this study was granted by The University of South Australia Human Research Ethics Committee and the ethics committee of the participating hospital. Participants completed a demographic questionnaire (age, training level, current position, nursing role, marital status, living arrangements, dependants and cultural demographics) and general health and sleep
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questionnaires. They also completed two 14-page logbooks (10 15 cm spiral bound with one page per day), covering 28 consecutive days of data. Each page contained 27 questions relating to work hours, sleep and error rates, and took approximately 5 min to complete. Participants were instructed to ‘‘fill out your diary at approximately the same time each evening (around 6 pm). If you forget to fill in the logbook on a particular day, skip that page and go on to the next page. Do not try to complete a page from memory.’’ Participants were compensated for their time at a rate of $5 per day. The logbooks were designed to parallel a comparable study conducted in the USA (Rogers et al., 2004), in order to directly compare and contrast Australian data. Questions related to: 1. Work hours: Participants recorded both scheduled and actual hours worked in each 24-h period. 2. Estimated sleep length and quality: For all main sleep periods and naps participants indicated the time at which they fell asleep and the time at which they awakened. This was used to calculate estimated sleep length and the sleep occurring in the 24 h prior to each shift start time. For each main sleep period, participants recorded whether they had disrupted sleep, reasons for the disruption (noise, work-related concerns, non-workrelated concerns, others), problems falling asleep or waking too early. 3. Fatigue/sleepiness/stress: Participants recorded ratings of fatigue, stress and physical and mental exhaustion. They were asked to ‘‘circle a number from 1 to 5, indicating how strongly each word describes how you felt overall today’’ (1: very, 2: moderately, 3: a little, 4: slightly, 5: not at all). Exact meanings of fatigue, sleepiness, stress and exhaustion were left open to the interpretation of the participant. Participants also indicated whether they were STR awake during each shift. 4. Errors: Frequency, type and severity of nursing errors and near errors, made or observed by the participant, were recorded, along with the time that these occurred and a short written narrative of the error and circumstances surrounding the error. As participants were required to respond yes/no to questions regarding the occurrence of error, near error or observed error, incomplete error logs could be distinguished from days where no error was recorded. There were six categories whereby nurses coded the type of errors that they recorded: (1) medical, including incorrect administration in terms of dosage, timing, delivery or patient; (2) transcription, including errors of transcribing between orders/charts, etc.; (3) charting, including incorrect entry in terms of information, timing or patient; (4) procedural, including any deviations from approved procedures; (5) slip or fall, including any physical injuries; and (6) others, including errors not falling into any of the other categories. It must be noted that no specific studyrelated training was given to participating nurses regard-
607
ing observation of other’s errors. Observed errors made by others were recorded in the logbook in the same way that own errors and near errors were recorded. 5. Drowsiness while travelling home: Following each shift, participants recorded whether they experienced drowsiness (mild, moderate or extreme), and any accidents or near accidents while driving or cycling home. 2.3. Statistical analysis A total of 1148 days of data were collected, including 694 work shifts. Principally, participating nurses worked sequences of shifts of 8, 10 or 12 h, which rotated between morning/day shifts (M, start time: 0600–0900 h), evening shifts (E, start time: 1300–1600 h), and night shifts (N, start time: 2100–2200 h). Mixed effects ANOVA (taking into account repeated observations by the same individuals, Van Dongen et al., 2004) were used to investigate differences in estimated sleep length (dependent variable) according to shift type (morning/day, evening, night) and according to error reporting (comparing days off, workdays, shifts when an error, near error or someone else’s error was reported) with Games–Howell post hoc comparisons for unequal variances. It should be noted that reported error rates are not corrected for exposure. Generalised estimating equation (GEE) binary logistic regression models were used to identify predictors of error occurrence (yes/no), extreme drowsiness/near accident occurrence during the commute (yes/no) and STR awake during shifts (yes/no). The GEE approach can account for the correlated responses between multiple shifts worked by the same nurse (Pan and Connett, 2002) and has been used in a study with a similar methodology (Scott et al., 2007). Independent variables included stress ratings, physical and mental exhaustion ratings, STR awake at work (for models of error and commute drowsiness only), sleep in the prior 24 and 48 h, total wake time, shift duration and number of consecutive shifts. Correlation analyses found high correlations between physical and mental exhaustion (r ¼ 0.78, po0.05) and sleep in the prior 24 and 48 h (r ¼ 0.75, po0.05). As such, mental exhaustion ratings and sleep in the prior 48 h were not subsequently included in the models. All other correlations between predictor variables were low (ro0.4) and non-significant. Final models (reported in the Section 3) include significant predictors (po0.05) only. It should also be noted that while data for all nurses (n ¼ 41) was included in all analyses (including figures), ANOVA and GEE analyses only included shifts with a valid sleep history (at least 24 h), leaving 637/694, or 92% of shifts (morning/ day shifts: 304, evening shifts: 211, night shifts: 392). 3. Results Nurses in this sample worked between 32 and 46 h per week. While nurses worked significantly longer hours than
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scheduled (t622 ¼ 2.70, po0.01), it was only for an average of 8.4 min per shift. There was minimal overtime reported (4% of shifts). As previously outlined, these nurses worked combinations of day/morning (33.5%), evening (23.3%) and night shifts (43.2%, definitions above). Of the night shifts, 28.7% were the first night shift in a sequence. Of the morning shifts, 38% were late/early shifts (i.e. the morning shift followed an evening shift). The mean number of consecutive shifts (7standard deviation) was 3.2071.75 shifts (mode ¼ 2 shifts). Mean shift duration (7standard deviation) was 8.9271.31 min (mode ¼ 8.5 h). Total days off was 454 out of 1148 days of data, which equates to an average of 11.1 days off per nurse during the month of data collection. See Table 1 for rosters of three example participants. The participating nurses reported disrupted sleep on 25.9% of days (including workdays and days off). Of these disruptions, work-related concerns were responsible for this sleep disruption on 14.8% of occasions. Problems falling asleep and waking too early were reported on approximately one-third of workdays (30% and 29%, respectively). Moderate to high levels of stress, physical exhaustion and mental exhaustion were reported on 27%, 42% and 39% of shifts, respectively. Struggling to remain awake was reported during 32% of shifts.
Table 1 Rosters of three example participants Days into study
Participant A
Participant B
Participant C
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Off Off Off M E M Off Off N N N N Off Off M M M E Off M Off Off E M M Off Off Off
Off Off Off E M M Off Off E E M Off Off E M Off Off N N N Off Off Off E E M E E
Off Off Off E M N N Off Off Off E E E M M M Off Off Off Off E M N N Off Off Off E
Off: day off, M: morning, E: evening, N: night shift.
Thirty-four participants usually drove to work, three cycled, one drove or cycled and the remainder walked or used public transport. Mean travel time (7standard deviation) was 19.3711.8 min. On 70 occasions, extreme drowsiness when driving or cycling home was reported, with seven near accidents. Nearly half of extreme drowsiness and near accident reports occurred between 0700 and 0900 h. This coincides with the end of the night shift (indeed, 26% of night shifts were associated with commute drowsiness Fig. 1). A further 40% of extreme drowsiness and near accident reports occurred between 1400 and 1900 h, with a peak at 1500 h (24%), coincident with the end of the morning shift. Less than 10% of such incidents followed the evening shift (2200 h). Table 2 shows error breakdown by type and severity. Overall, 38 errors, 38 near errors and 65 observed errors were recorded. While the majority of errors were perceived to have minor consequences, for a third of errors, the perceived consequences were moderate or extreme. The majority of errors occurred during morning/day shifts. Near errors were relatively consistently distributed across morning/day, evening and night shifts. In contrast, the majority reports of someone else’s error occurred during evening shifts (Figs. 1 and 2). Significant differences in estimated sleep length were found between morning/day, evening and night shifts (F2634 ¼ 22.63, po0.01) such that nurses had significantly more sleep prior to evening shifts compared to morning/ day or night shifts (po0.05, Fig. 1). There was no significant difference in prior sleep duration between late/ early shifts and other morning shifts (F1300.73 ¼ 0.11, p40.05). Sleep was significantly increased prior to the first night shift in a sequence (9.3073.09) relative to subsequent night shifts (7.31372.66, F1,108.78 ¼ 15.817, po0.001). Significant differences in sleep duration were also found between days off, workdays and shifts when an error, near error or someone else’s error was recorded (F4267.4 ¼ 6.67, po0.01). In comparison to days off, total sleep length was significantly shorter on workdays (excluding workdays where an error was reported, po0.05) and shifts where an error was reported (po0.05). In contrast, total sleep duration was not significantly different to days off on shifts where a near error, or when someone else’s error was recorded. The difference between sleep prior to shifts where an error was recorded was not significantly different to that on other workdays (Fig. 3). Results of logistic regression analyses are presented in Table 3 (OR, 95% CI and Wald Statistics). Stress ratings (OR ¼ 1.5) and STR awake during the shift (OR ¼ 2.4) were significant predictors of error (po0.05). Exhaustion ratings (OR ¼ 1.4), STR awake at work (OR ¼ 5.2) and number of consecutive shifts (OR ¼ 1.2) were significant predictors of extreme drowsiness and near accidents while driving or cycling home (po0.05). Exhaustion ratings (OR ¼ 1.6), hours of sleep in the preceding 24 h (OR ¼ 0.9) and shift length (OR ¼ 1.5) were significant predictors of STR awake during work (po0.05).
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%shift error (stacked)
Table 2 Summary of errors, near errors and else errors (made by others) broken down by type and perceived severity
Else Error Near Error Error
25.0 20.0 15.0 10.0 5.0 0.0 30.0
%shift error (stacked)
25.0 Ext Drowsy Near Accident
20.0
Error
Near error
Else error
Total (%)
Type Medical Transcription Charting Procedural Slip or fall Others
24.3 5.4 16.2 16.2 2.7 35.1
75.0 2.8 8.3 5.6 2.8 5.6
48.6 8.1 5.4 24.3 0.0 13.5
56.4 7.0 9.3 11.6 1.7 14.0
Severity Mild Moderate Extreme
81.8 9.1 9.1
79.3 20.7 0.0
44.8 48.3 6.9
67.4 28.3 4.3
Total N
38
38
65
141
15.0 10.0 5.0 0.0 9.5 9
Sleep Duration (hrs)
609
*
8.5 8 7.5 7 6.5 6 5.5 5 Day/Morning
Evening
Night
Shift Type Fig. 1. Distribution of errors (top panel) and incidences of extreme drowsiness and near accidents while driving or cycling home (middle panel). Error distribution is expressed as the percentage of day/morning, evening and night shifts where an error was recorded and bars are stacked (i.e. the contribution of each %value is compared across error categories). Lower panel displays sleep duration (hours+std error bars) as a function of shift type. *Indicates a significant difference (po0.05) compared to day/ morning and night shifts. Figures are not corrected for exposure.
4. Discussion This study aimed to investigate the relationship between work hours, sleep, errors and drowsiness at work and while travelling home in a group of Australian metropolitan hospital nurses. Participating nurses reported minimal overtime and working an average of less than 10 min longer than scheduled per shift. Nurses reported exhaustion and STR awake at work during one in three shifts. A substantial number of errors, near errors, errors made by
others were reported. The majority of errors occurred during morning shifts, while most reports of errors made by others occurred during evening shifts. Night shifts were primarily associated with reports of extreme drowsiness or near accidents while travelling home. This occurred following one in four night shifts. Estimated sleep durations were significantly reduced during morning and night shifts relative to evening shifts. Sleep durations were also significantly reduced on workdays in general, and workdays when an error was reported relative to days off. This was not the case for shifts when a near error or someone else’s error was recorded. The primary predictor of error was STR awake at work (OR ¼ 2.4), followed by stress (OR ¼ 1.5). The primary predictor of extreme drowsiness and near accidents while travelling home was also STR awake at work (OR ¼ 5.2), followed by exhaustion ratings (OR ¼ 1.4), and number of consecutive shifts (OR ¼ 1.2). In turn, predictors of STR awake at work were exhaustion ratings (OR ¼ 1.6), hours of sleep in the preceding 24 h (OR ¼ 0.9) and shift length (OR ¼ 1.5). Taken together, results indicate that the shift schedules worked by participating nurses were associated with reductions in sleep. This is consistent with studies conducted in other shiftworking populations (e.g. Foret and Latin, 1972; Ha¨rma¨ et al., 2002; Kecklund and A˚kerstedt, 1993; Sasaki et al., 1986). On average, nurses in this study had nearly 50 min less sleep on workdays compared to days off. Since research has demonstrated that sleep reductions of as little as 30 min per night can result in significant performance decrements over time (e.g. Belenky et al., 2003), it is likely that nurses were accruing a sleep debt, which may have impacted on their performance and safety. Notably, however, sleep on days off does not necessarily reflect basic sleep need. The increase in sleep on days off could reflect a compensatory response to accumulated sleep loss associated with workdays. Nevertheless, arguably, the nurses in this study were sleep-deprived.
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610 70
shift transition
60
Else Error Near Error Error
50 %error (stacked)
shift transition
40 30 20 10
2200-2359h
2000-2159h
1800-1959h
1600-1759h
1400-1559h
1200-1359h
1000-1159h
0800-0959h
0600-0759h
0400-0559h
0200-0359h
0000-0159h
0
Time (2h bins)
Fig. 2. Distribution of errors by time-of-day (stacked percentage). Transitions between day/morning, evening and night shifts are indicated by the dotted lines. Figures are not corrected for exposure.
9
Sleep Duration (hrs)
8.5 *
8
*
7.5 7 6.5 6 day off
work day
Error
Near Error
Else Error
Fig. 3. Reported sleep duration (hours+std error bars) on days off, and in the 24 h preceding workdays and shifts where an error, near error and observed error (made by others was recorded. *Indicates a significant difference (po0.05) compared to days off.
This is interesting given that nurses did not report significant amounts of overtime or shifts that extended greatly beyond scheduled durations. This suggests that, for this cohort, it may be the shift schedule per se that is responsible for the sleep loss, rather than particularly long work hours. This may be due to the rotating nature of the schedule. Indeed, while Gold et al. (1992), found a clear risk for sleep-related MVA among all participating nurses in their study, those on rotating schedules were more likely to report nodding off at work and to be involved in MVA than those working day/evening or night shifts. Not only did nurses report sleep reductions, they also reported disturbances. This is also consistent with the general shiftwork literature (Costa, 1997). One in three workdays were associated with sleep disruption and
problems initiating and maintaining sleep. One in three workdays were also associated with moderate to high levels of stress, exhaustion and STR awake at work. Therefore, nurses in this study frequently experienced sleep loss, sleep disturbance and stress, sleepiness and exhaustion at work. These findings are of operational concern given the plethora of studies that demonstrate the negative impact of sleep loss and fatigue on performance and safety (reviewed in Dinges and Kribbs, 1991; Harrison and Horne, 2000). Consistent with this body of research, a link between work hours, sleep loss and error was apparent in this study. Relative to days off, sleep was reduced prior to shifts were error was recorded. Interestingly, sleep was not significantly reduced prior to shifts where nurses recorded a near miss or caught someone else’s error. In addition, consistent with previous studies (Ha¨rma¨ et al., 2002), sleep was reduced prior to morning and night shifts relative to evening shifts. Morning shifts were most heavily associated with errors and night shifts with drowsy driving. In contrast, reports of someone else’s error were most frequent during evening shifts. Thus, findings are consistent with the pilot study (Dorrian et al., 2006), indicating that less sleep may lead to increased likelihood of making an error (and of drowsy driving), and importantly, decreased likelihood of catching your own, or someone else’s error. Arguably, this is particularly important for nurses, who are often responsible for intercepting errors (Leape et al., 1995). The relationship between work hours, sleep loss and errors was further investigated using regression analyses. The main predictors of errors were STR awake during the shift and stress ratings. Individuals who reported STR
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Table 3 For each binary logistic regression model, this table summarises the dependent variable (DV), chi-square, significant predictors (po0.05) and odds ratios (795% confidence intervals) DV
w2
p
Predictors
OR
95% CI
+95% CI
Error
12.40
o0.01
Stress Struggling to remain awake
1.48 2.40
1.09 1.17
2.02 4.910
Commute
50.61
o0.01
Exhaustion Struggling to remain awake Consecutive shifts
1.42 5.25 1.23
1.12 2.91 1.04
1.80 9.46 1.450
Struggling to remain awake
64.73
o0.01
Exhaustion
1.63
1.38
1.91
Sleep prior 24 h Shift length
0.92 1.46
0.85 1.24
0.98 1.71
awake at work were nearly two and a half times more likely to make an error. Every unit increase in stress rating resulted in a 50% increase in error likelihood. Interestingly, work variables (shift type, shift length, number of consecutive shifts) were not directly predictive of error. As discussed earlier, this may be due to the lack of overtime and extended shifts worked by this group of nurses. There was also no apparent relationship between error and late/early shifts or error and the first night shift in a sequence. This is possibly explainable by the fact that there was no significant difference in sleep associated with late/early shifts compared to other morning shifts. Moreover, the first night shift of a sequence was associated with more sleep compared to subsequent night shifts. This is likely to be indicative of prophylactic napping or sleeping in prior to night shift one. Also interesting was the fact that prior sleep duration did not directly predict error. This could be explained by the fact that sleep history was relatively consistent over time, that is (as described in the Section 2.3), the amount of sleep a nurse achieved in the 24 h prior to a shift was highly predictive of the amount achieved in the prior 48 h (r ¼ 0.75). This may suggest that accumulated, consistent sleep loss over time, rather than acute sleep loss prior to specific shifts may contribute to sleepiness and error for this group. However, it is important to note that the primary predictor of error, STR awake at work, was predicted by work and sleep-related factors. Specifically, every hour of sleep in the prior 24 h resulted in a 10% reduction and every hour on shift resulted in a 50% increase in STR awake. Additionally, every unit increase in exhaustion ratings produced a 60% increase in STR awake. STR awake at work was also the biggest predictor of extreme drowsiness or near accident while travelling home, increasing the likelihood by more than five times. This is slightly higher than Scott et al. (2007), (OR ¼ 3.37). Other predictors included exhaustion ratings (40% increase) and number of consecutive shifts (20% increase). Therefore, it appears that work hours and sleep loss reduced the ability to remain awake at work, which in turn increased error likelihood and drowsy driving.
There are several, clear limitations to the current study. First, while the repeated measures element of the study was designed to boost power (resulting in 1148 data points, 694 workdays), it is still a relatively small group (N ¼ 41) in a single metropolitan hospital. Therefore, generalisability is limited. However, the overall consistency of the results with studies in nurses (e.g. Niedhammer et al., 1995; Novak and Auvil-Novak, 1996; Rogers et al., 2004) and other shiftworkers (Foret and Latin, 1972; Ha¨rma¨ et al., 2002; Kecklund and A˚kerstedt, 1993; Sasaki et al., 1986) lends support to the current findings. Second, sleep reports were subjective. Use of actigraphy would certainly boost reliability of sleep measurement. Third, differences in error rates relative to shift type did not control for exposure (number of patients, degree and severity of patient need, number of staff on the ward at the time, etc.). Indeed, the increase in errors on the morning shift may be the result of increased workload during this time. Further, commuterelated near accidents may be related as much to traffic density as any other factor. This study did not control for traffic exposure. Fourth, it is likely that the error rates reported in the current study are under or over representative of actual error rates. Observational methodologies would provide a more objective measure of error and performance. However, self-reporting methodology offers significant advantages with respect to lack of intrusiveness and maximising sample size. Future studies should investigate both techniques. Current study findings highlight the need for further investigation into the effects of work schedules and related sleep loss on nurse and patient safety, at work and while travelling home. Future research investigating differences in job role characteristics, shift designs and geographic area (e.g. rural versus metropolitan) would be of benefit, as would more detailed coding and analysis of errors. Ultimately, such future studies will contribute to evidence-based fatigue management strategies in health care. For example, a recent review suggested ways in which knowledge of prior sleep durations could be used to target fatigue-proofing strategies at work. Such strategies (e.g. transfer to non-safety-critical duties, extra support from colleagues, earlier nap/break time
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arrangements) are often used informally by teams to safetymanage sleep loss and fatigue. Based on prior research, the review estimated that individuals need at least 5 h sleep in the 24 h prior to commencing work in order to remain safe (Dawson and McCulloch, 2005). In the current study, there were 9.1% of shifts associated with less than 5 h sleep. Interestingly, 11.8% of errors occurred on shifts associated with less than 5 h sleep. It is therefore possible that targeted fatigue proofing for these 9.1% of shifts may have lead to an 11.8% reduction in errors. This is an interesting area for future research. Taken together, findings suggest that Australian nurses are experiencing disrupted and reduced sleep and exhaustion, stress and problems remaining awake at work. Findings also suggest that this is related to increased error likelihood, and reduced likelihood of identifying others’ errors, and elevated likelihood of extreme drowsiness during the commute home. Clearly, attention to these issues is required to optimise nurse and patient safety in our hospitals, and the safety of the community at large on our roads. References Australian Health Workforce Advisory Committee, 2003. Annual Report 2002–2003. AHWAC Report 2003.1, Sydney. Barger, L.K., Cade, B.E., Ayas, N.T., Cronin, J.W., Rosner, B., Speizer, F.E., Czeisler, C.A., 2005. Harvard work hours, health, and safety group. Extended work shifts and the risk of motor vehicle crashes among interns. N. Engl. J. Med. 352, 125–135. Belenky, G., Wesensten, N.J., Thorne, D.R., Thomas, M.L., Sing, H.C., Redmond, D.P., Russo, M.B., Balkin, T.J., 2003. Patterns of performance degradation & restoration during sleep restriction & subsequent recovery: a sleep dose–response study. J. Sleep Res. 12, 1–12. Brown, I.D., 1994. Driver fatigue. Hum. Factors 36, 298–314. Bureau of Transport Economics, 2000. Road crash costs in Australia. Report 102, Department of Transport and Regional Services, Canberra. Costa, G., 1997. The problem: shiftwork. Chronobiol. Int. 14 (2), 89–98. Dawson, D., McCulloch, K., 2005. Managing fatigue: it is about sleep. Sleep Med. Rev. 9, 365–380. Department of Transport and Regional Services, 2000. In: Beyond the Midnight Oil: An Inquiry into Managing Fatigue in Transport. House of Representatives Standing Committee on Communication, Transport and the Arts, Canberra, vol. 6, October, pp. 1225–1282 (Submission No. 83). Dinges, D.F., Kribbs, N.B., 1991. Performing while sleepy: effects of experimentally induced sleepiness. In: Monk, T.H. (Ed.), Sleep, Sleepiness and Performance. Wiley, New York, pp. 97–128. Dorrian, J., Lamond, N., van den Heuvel, C., Pincombe, J., Dawson, D., 2006. A Pilot Study of the safety implications of Australian nurses’ sleep and work hours. Chronobiol. Int. 23 (6), 1149–1163. Foret, J., Latin, G., 1972. The sleep of train drivers: an example of the effects of irregular work hours on sleep. In: Coquhoun, W.P. (Ed.), Aspects of Human Efficiency. English Universities Press, London. Geer, R.T., Jobes, D.R., Tew, J.D., Stepsis, H., 1997. Incidence of automobile accidents involving anesthesia residents after on-call duty cycles. Anesthesiology 87, A938. Gold, D.R., Rogacz, S., Bock, N., Tosteson, T.D., Speizer, F.E., Czeisler, C., 1992. Rotating shift work, sleep, and accidents related to sleepiness in hospital nurses. Am. J. Public Health 82, 1011–1014. Government of South Australia, 2003. Better choices, better health. Final Report of the Generational Health Review, Australia.
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