Next Article in Journal
Analysis of the Elderly’s Preferences for Choosing Medical Service Facilities from the Perspective of Accessibility: A Case Study of Tertiary General Hospitals in Hefei, China
Next Article in Special Issue
Long Working Hours and the Risk of Glucose Intolerance: A Cohort Study
Previous Article in Journal
Professional Development for Teachers of Gifted Education in Hong Kong: Instrument Validation and Training Effectiveness
Previous Article in Special Issue
Shiftwork Is Associated with Higher Food Insecurity in U.S. Workers: Findings from a Cross-Sectional Study (NHANES)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 15
10.3390/ijerph19159429
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of 90 Min Napping on Fatigue and Associated Environmental Factors among Nurses Working Long Night Shifts: A Longitudinal Observational Study

by
Kazuhiro Watanabe
1,
Naotaka Sugimura
1,
Inaho Shishido
2,
Issei Konya
1,3,
Shinya Yamaguchi
1 and
Rika Yano
2,*
1
Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan
2
Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan
3
Research Fellow of Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(15), 9429; https://doi.org/10.3390/ijerph19159429
Submission received: 4 June 2022 / Revised: 25 July 2022 / Accepted: 29 July 2022 / Published: 1 August 2022
(This article belongs to the Special Issue Health Effect of Shift Work and Long Working Hours)
Browse Figures
Versions Notes

Abstract

:
For nurses working long night shifts, it is imperative that they have the ability to take naps to reduce fatigue, and that an appropriate environment is prepared where such naps can be taken. We verified the effects of 90 min napping on fatigue and the associated factors among nurses working 16-h night shifts. We investigated 196-night shifts among 49 nurses for one month. Wearable devices, data logging devices, and questionnaires were used to assess nap parameters, fatigue, and environmental factors such as the napping environment, ways of spending breaks, and working environment. Nurses who nap at least 90 min on most night shifts had more nursing experience. Multivariable logistic regression analysis showed that the environmental factors significantly associated with total nap duration (TND) ≥ 90 min were noise, time spent on electronic devices such as cellphones and tablets during breaks, and nap break duration. The night shifts with TND ≥ 90 min showed lower drowsiness after nap breaks and less fatigue at the end of night shift compared to those with TND < 90 min. Nurses and nursing managers should recognize the importance of napping and make adjustments to nap for at least 90 min during long night shifts.

1. Introduction

In nursing, shift work is vital for providing round-the-clock patient care [1,2]. In recent years, the traditional three-shift system has been replaced with a two-shift system, which entails longer working hours for nurses [3,4]. The two-shift system with long night shifts is the most common work pattern among Japanese nurses, and almost two-thirds of Japanese hospitals (64.4%) adopt night shifts of over 16 h [5]. Long night shifts are associated with intense fatigue in nurses [6,7,8]. Fatigue causes medical errors by impairing nurses’ cognitive function and performance [9,10], which can compromise patient safety [11]. Therefore, implementing countermeasures to reduce fatigue is important to patient safety when nurses engage in long night shifts.
The greatest cause of fatigue is insufficient or disrupted sleep [12]. Napping is a countermeasure to fatigue recommended by the American Nurses Association and Japanese Nursing Association [13,14]. Although systematic reviews and meta-analyses of napping during night shifts have also shown that taking naps can reduce fatigue, the optimal duration and timing have not been identified [15,16,17]. Nap duration ranging from 15 to 180 min has been widely reported for nurses [15], but it is important to note that nap breaks and sleep duration are discussed together as nap duration. This is because some previous studies have focused on the implementation of napping and ensuring the duration of nap breaks during night shifts [18,19]. For example, although Smith-Coggins et al. examined the effects of a 40-min nap on fatigue [19], this length represented only nap break duration rather than total sleep duration. Moreover, sleep duration can be further divided into subjective aspects such as maintaining a sleep diary [18,20], and objective aspects such as an actigraphy [21,22]. We must distinguish between these durations to establish quantitative evidence without discussing the effects of napping by comparing the nap vs no-nap groups alone. Moreover, to measure the outcomes of effects on napping during night shifts, the outcomes must be integrated across multiple time points. This is considered the effect of sleep inertia, defined as the brief period of decreased cognitive function or performance immediately after a period of sleep that can temporarily obscure the recuperative effects of sleep [23]. In a study that investigated 196 night shifts of 16 nurses, the nap group showed higher sleepiness, one sign of fatigue, at 4:00, 5:00 and 6:00 than did the no-nap group; however, sleepiness was reversed at the end of the night shifts, so the nap group had lower sleepiness [20]. In other words, evaluating only a single point outcome during the night shift is insufficient. Furthermore, the literature has provided minimal justification for selected nap durations [17]. The Japanese Nursing Association suggests that two-hour nap breaks are required for nurses to be able to take a 90 min nap [14], which corresponds with the duration of one non-rapid eye movement (NREM)/rapid eye movement sleep cycle based on sleep physiology [24]. Slow wave sleep, the deepest of NREM, is considered to play an important role in energy recovery [25]. However, since the effects of 90 min napping during night shifts on fatigue among nurses have not been identified, we must measure napping quantitatively and measure fatigue at multiple time points.
In addition, the environments in which nurses nap are inadequate [18,26,27]. Environments refer to sleep hygiene [28] and the working environment. Nurses’ napping places are reported to be their restrooms with mattresses placed in an isolated section [29,30], storage rooms with reclining armchairs [20], stretchers, and bedsheets on the floor [31]. However, the environmental factors related to sleep, such as light, noise, temperature, and humidity [32,33,34], have not been investigated in nurses’ napping places. Sleep hygiene behaviors, such as caffeine intake [28], the use of electronic devices such as cellphones and tablets [35], and diet [36], may also be associated with napping during night shifts. However, there are no reports on how nurses spend their nap breaks aside from napping. Additionally, the working environment is another important factor associated with nurses’ napping. One review identified a high prevalence of missed, interrupted, or delayed rest breaks due to job demands and resources [37]. Despite these factors, the relationship between the environment and napping during night shifts has not been verified [23].
Conducting field studies is also important to expand the evidence of the effects of napping during night shifts on fatigue [17]. Nurses’ working environments, including nap breaks, differ day-to-day depending on job demands [20,31]. Hence, a longitudinal study including these environmental factors should be conducted. We aimed to verify the effects of 90 min napping on fatigue and the associated factors among nurses working 16-h night shifts. Initially, we explored which nurses were able to nap at least 90 min during four night shifts. We also explored factors related to taking a 90 min nap in terms of the environmental factors such as napping environment, ways of spending breaks, and working environment. Finally, we verified the effects of 90 min napping during night shifts on reducing fatigue. These findings could serve not only as evidence for a better environment for napping and developing effective fatigue countermeasures for nurses, but also as data for nurses’ managers and researchers to consider while developing strategies to tackle these issues.

2. Materials and Methods

2.1. Operational Definition

Napping: A nap is a short duration of sleep taken during a night shift. In this study, total nap duration (TND) was calculated using a wearable device.

2.2. Study Design

The longitudinal investigations of night shifts were conducted over the course of one month for each nurse. As the average number of night shifts per month for nurses in Japan is more than four to less than five [38], we investigated four night shifts per nurse between July and September 2019. To control the working environment, the investigation included only one facility that had adopted night shifts of over 16 h.

2.3. Participants

The participants comprised nurses from six wards in a general hospital with more than 200 beds located in northern Japan. The quantity and quality of sleep differ with age, for example, one of the reasons for this is hormonal imbalance associated with menopause occurring in women who are in their 40s [39,40,41]. Therefore, we recruited nurses aged 20–39 years to control for age-related factors as a priority in this study. The exclusion criteria were as follows: first year of clinical experience; regular use of sleeping pills; diseases under treatment; and pregnancy. The sample size was determined by nurses who fulfilled the criteria. Of the 55 nurses in the hospital who were eligible, 50 consented to participate. Ultimately, 49 nurses from all six wards were analyzed (one nurse withdrew from the study). Table 1 shows the participants’ basic attributes.

2.4. Work Condition

The hospital adopted a two-shift system with a night shift of over 16 h (from 16:30 to 9:00 the following day). Nurses are permitted to have both a supper break and at least two hours of nap break during the night shift. Nurses take a nap break after discussing the order of nap breaks among staff during the night shift. Nap break duration was basically evenly distributed among the staff, except when an event occurred.

2.5. Measures

2.5.1. Nurses’ Characteristics

Demographic data on age, years of nursing experience, years of nursing experience in the current ward, sex, marital status, and child-rearing were obtained as described above. Regarding usual sleep, we asked about the subjective mean daily sleep duration and whether or not they usually take a nap before the night shift. A self-assessment version of the Morningness–Eveningness Questionnaire [42,43], including 19 multiple-choice questions regarding sleep habits and preferences with a total score ranging from 16 to 86, was used to assess chronotype. The nurses were classified into five categories based on the total score: “definite morning type (total score: 70–86),” “moderate morning type (59–69),” “intermediate type (42–58),” “moderate evening type (31–41),” and “definite evening type (16–30).” Data on sleep-related habits were obtained using a questionnaire comprising frequency of caffeine intake, daily time spent on electronic devices (e.g., cellular phone, smartphone, tablet) during the day and before bedtime.

2.5.2. Sleep/Activity

The MTN-220 (ACOS CO., LTD., Iida, Japan) was used to assess sleep/activity. The reliability of this device has been verified based on polysomnography [44]. This device (diameter, 27 mm; thickness, 9.1 mm; weight, 9 g) records the amount of activity using an internal three-axis accelerometer. Posture can also be determined from six directions. The nurses were required to clip it to their uniform on the front side of the trunk only during the night shift. The amount of activity and posture were recorded every two mins.
Data were analyzed using the Sleep Sign Act ver.2.0 (SSA) software (KISSEI COMTEC CO., LTD., Matsumoto, Japan). To detect sleeping and awaking duration from the MTN-220 data, the in-bed and out-of-bed times were manually determined with reference to the subjective napping time reported by the nurses. The default settings in the SSA were used, with the parameters detected following a previously reported algorithm [45].
Optimizing the definitions of napping parameters:
  • TIB (min): Time in bed. Duration with a lying posture.
  • SL (min): Sleep latency. The interval duration between the time of changing posture from standing to lying and the first sleep-onset time.
  • WASO (min): Wake after sleep onset. The total duration one stays awake during the sleep onset to sleep offset interval.
  • BOT (min): Bed-out time. The interval duration between the last awakening time and the time of changing posture from lying to standing.
  • TND (min): Total nap duration. The TIB from which SL, WASO, and BOT were subtracted; sleeping duration.
  • SE (%): Sleep efficiency. The ratio of TND to TIB. This represented napping quality.
To calculate the awakening duration until nap breaks as an indicator of the homeostatic sleep pressure, the last sleep duration and awaking time before the night shift were recorded using a questionnaire. For the subjective evaluation of quality, the satisfaction level of napping was assessed using a questionnaire with the usual main sleep as 100%.
Steps between 17:00 and 9:00 the following day were calculated using the same software as an index reflecting workload [46]. The steps for each night shift were divided into two categories: steps before and after napping. The steps before napping were calculated as the steps taken every hour from 17:00 to time spent in bed, while the steps after napping were from time spent outside bed to 9:00.

2.5.3. Napping Environment

We measured illuminance, temperature, humidity, noise, unpleasant smells, bedding, and the napping places as a napping environment. Illuminance, temperature, humidity, and noise were defined as the mean values corresponding to the napping (from the start to end time of the napping) or time in bed if the nurses were unable to nap detected by the wearable device during each night shift. In order to measure illuminance, temperature, humidity, and noise, we prepared data logger devices on a wagon (65 cm above the floor) for the number of nurses working the night shift. Nurses were required to carry them to their napping place and place them as close to their heads as possible during their nap breaks.
Light levels in napping places were measured using a wireless thermo-hygrometric illuminance meter (LogMiru® BT HLT-100BT, CUSTOM Corporation, Tokyo, Japan). This sensor includes an environmental light integrated circuit, which measures light intensity with a range of 0–30,000 lx with a resolution of 1 lx and an accuracy of ±20% reading. In this study, measurements were recorded every minute and stored in the data logger’s internal memory. Temperature and humidity were measured using the same device as the illuminance meter (LogMiru® BT HLT-100BT, CUSTOM). The sensor included a thermistor measuring temperature within a range of −10–50 °C with a resolution of 0.1 °C and an accuracy of ±1 °C. The ceramic resistance humidity sensor had a range of 20–95% relative humidity, a resolution of 1%, and an accuracy of ±5%. Noise was measured using a digital sound level meter with a data logger (GM1356, Suzhou SUTEKI Information Technology Co., LTD, Suzhou, Jiangsu Province, China) for every 15 sec period during night shifts. Microphones were unified in an upward direction and placed on the wagon. Based on the above data of fast, A-weighted sound level, equivalent sound level (dB LAeq) over a given period of time (napping) was calculated as noise level. Unpleasant smells were judged subjectively and recorded after the night shift. Details regarding bedding and napping places were also asked in the original questionnaire.

2.5.4. Ways of Spending Breaks

At the end of the night shift, the original questionnaire was used for the nurses to describe how they spent their nap breaks, including naps. To consider the lasting effects of caffeine, caffeine consumption before nap breaks was checked. The questionnaire also asked about the amount of time spent using electronic devices. Additionally, the subjective time of falling asleep, subjective time of awakening, carrying a personal handy-phone system (PHS) with the nurse call system, changing clothes, using a sleep mask, using earplugs, watching television, listening to music, doing remaining work, and eating were also confirmed.

2.5.5. Working Environment

After the night shift, the nurses were asked about the working environment during the night shift using the original questionnaire. Data regarding the ward, including the number of nurses, care workers, hospitalized patients, and patients each nurse was responsible for, and nap break duration (discussed among staff during night shift), were collected. As well as data regarding individuals, such as supper breaks and the start and end times of nap breaks, were also obtained.

2.5.6. Fatigue

Based on previous study [21], the following two questionnaires are used to assess work-related fatigue: Cumulative fatigue was measured using the Cumulative Fatigue Symptoms Index (CFSI) [47]. The CFSI comprises 81 yes/no questions in eight categories: “general fatigue,” “chronic fatigue sign,” and “physical disorders” for physical aspects; “depressive feelings,” “anxiety,” and “decreased vitality” for mental aspects; and “irritability” and “unwillingness to work” for social aspects. The complaint rate was calculated as follows:
Complaint rate in each category (%) = (number of positive items in each category/[number of items in each category × number of participants]) × 100.
The nurses were asked to complete the CFSI at the beginning of the fourth night shift.
Feelings of fatigue during night shifts were measured using the Jikaku-sho shirabe questionnaire developed by the Research Group of Industrial Fatigue, a part of the Japan Society for Occupational Health [48]. This questionnaire includes 25 subjective fatigue symptoms categorized into five factors: (i) drowsiness; (ii) instability; (iii) uneasiness; (iv) local pain or dullness; and (v) eyestrain. All items are based on a 5-point rating scale (1 = disagree at all to 5 = agree strongly). The total score of each factor and the overall total score were used. The nurses completed the questionnaire four times per shift: at the beginning of the night shift; before a nap break; after a nap break; and at the end of the night shift.

2.6. Statistical Analysis

Descriptive statistics were computed for each variable. Continuous variables were presented as means and standard deviations or medians and interquartile ranges, while categorical variables were described as frequencies and percentages. Since the data in this study were measured four times, there is a data hierarchy based on the nurses (Level-2) and the night shift (Level-1). Intra-class correlations (ICC) were checked during the analysis.
First, to explore the characteristics of at least 90 min napping, 196-night shifts of Level-1 for the study period were divided into napping with TND ≥ 90 min and TND < 90 min groups using the calculated TND values. When the two values of TND ≥ 90 min and TND < 90 min group were used as dependent variables, the ICC was close to zero (<0.001) under the assumption that the logistic distribution [49]. So, we assumed the TND group to be independent between night shifts. We explored the factors associated with TND ≥ 90 min separately for nurse-dependent (Level-2) and night shift-dependent (Level-1) variables, respectively.
Regarding the nurse-dependent (Level-2) variables, we classified nurses into five groups (none, one, two, three, and four night shifts) according to the number of times they were able to take a nap of at least 90 min during four night shifts. Kruskal-Wallis and Fisher’s exact tests were conducted to compare the characteristics and cumulative fatigue among these groups. Post hoc tests were conducted using the Steel-Dwass test.
In terms of night shift-dependent (Level-1) variables, the napping parameters of the TND ≥ 90 min and TND < 90 min groups were compared using student’s t-tests. To identify the environmental factors associated with TND ≥ 90 min, student’s t-tests or Fisher’s exact tests were performed for each of the napping environments, ways of spending breaks, and working environments between groups. Variables with p < 0.20 were included in a multivariable logistic regression analysis to identify significantly associated factors. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were then calculated. Awakening duration until nap breaks, the interval from the last awakening time before the night shift to the start time of nap breaks, was used as a covariate to adjust the homeostatic sleep pressure [50,51].
To examine the effect of 90 min napping on fatigue during night shifts, mixed linear models were used based on a previous study [20]. The ICCs of all feelings of fatigue from nurses were from 0.08 to 0.15. The measures that were repeated during the night and on different nights during the study were included in the model as two random hierarchical effects, to take into account the correlation between these measures. One random effect was added for the night shift and another random effect for the nurses. To compare changes in each feeling of fatigue between the TND groups, we added TND groups, time (before nap breaks, after nap breaks, and at the end of the night shift), and their interactions as a fixed effect. Furthermore, to compare feelings of fatigue between the groups each measure point, post hoc t-tests were conducted using estimates of mixed linear models.
All other analyses were conducted using JMP® Pro software, version 16.0 (SAS Institute Inc., Cary, NC, USA) and R version 4.2.0 [52], and statistical significance was set at α = 0.05.

3. Results

3.1. Nurses’ Characteristics

The characteristics of the nurses are shown in Table 2, excluding basic attributes. The subjective mean daily main sleep duration was 6.3 h, and the most common chronotype was intermediate. The most common duration for the daily use of electronic devices was more than 2 h (61.2%) and that before bedtime was between 30 and 44 min (34.7%).

3.2. Characteristics and Cumulative Fatigue of Nurses Based on the Number of Naps with TND ≥ 90 Min during Four Night Shifts

The distribution of TND for each nurse’s four night shifts is shown in Figure 1. When all nurses were categorized by the number of naps with TND ≥ 90 min, 10.2% napped for TND ≥ 90 min all four night shifts (n = 5), while the proportions of the nurses who napped during three, two, and one night shift(s) were 4.1% (n = 2), 20.4% (n = 10), and 26.5% (n = 13), respectively. In contrast, 38.8% were unable to nap for at least 90 min during any of their night shifts (n = 19). Because few nurses napped during three night shifts, they were reclassified into three groups (none, one/two night shifts, and three/four night shifts). The three/four night shifts group had significantly more years of clinical experience than the none group (Table 3).
Next, we compared the cumulative fatigue in three nurses’ groups. The results showed no significant difference in CFSI (Figure S1).

3.3. Characteristics of Napping with TND ≥ 90 Min

During all 196-night shifts analyzed, the nurses managed to take breaks to lie down. The number of night shifts with TND ≥ 90 min was 59 (30.1%) compared with 137 for those with TND < 90 min (69.9%). Table 4 lists the napping parameters for both groups. Compared with the TND < 90 min group, the TND ≥ 90 min group showed significantly longer TIB, shorter SL, higher SE, and a later end time of napping. The TND ≥ 90 min group also had a longer WASO and a higher frequency of awakenings and postural changes. Subjective evaluation was significantly higher in the TND ≥ 90 min group. There was no significant difference in the start time of napping and awakening duration until nap breaks between the two groups.

3.4. Napping Environment, Ways of Spending Breaks, and Working Environment during Night Shifts

Table 5 lists the napping environment, ways of spending breaks, and working environments in both groups. For the napping environment, the nap area (restrooms, conference rooms, informed consent rooms, and examination rooms) was fixed in each ward; however, some nurses used beds in vacant hospital rooms. Bedding was also fixed in the nap area. For most of the nurses, the average illuminance during napping was less than 10 lx; in contrast, only one nurse had an average of more than 500 lx on all night shifts. The lighting equipment comprised either a downlight with low color temperature (warm color) or a fluorescent light with high color temperature (cold color); none of these were LEDs. Noise level in the TND ≥ 90 min group was significantly lower than that in the TND < 90 min group (p < 0.001). In this study, the higher noise level was not related to the longer WASO, higher frequency of awakenings, and postural changes (Figure S2). In contrast, there were no significant differences in temperature and humidity.
Regarding the ways of spending breaks, excluding napping, the use of electronic devices was the most common (mean use = 27.9 min). The reasons for use were listed as setting alarms, using social networking sites, watching videos, and using the Internet. In 48 night shifts (24.5%), the nurses ate something in addition to supper; most of these were light meals such as rice balls, bread, and sweets. Although the scheduled nap breaks were not interrupted, some completed the remaining work at their own discretion, even during nap breaks.
For the working environment, all of the nurses could take nap breaks for over 90 min. The nap break duration of the TND ≥ 90 min group was significantly longer than the TND < 90 min group (p < 0.001). For 24 night shifts, a part of the activity data could not be recorded due to a bug. Hence, we calculated the steps excluding the bug part. Even under these conditions, the number of steps before napping per hour of the TND ≥ 90 min group was significantly lower than the TND < 90 min group (p = 0.014).

3.5. Environmental Factors Associated with a Night Shift of TND ≥ 90 Min

The univariate analysis showed that the factors associated with TND ≥ 90 min were noise level, humidity, Japanese futons, folding beds, stretchers, examination tables, use of toweling blankets, carrying a PHS with the nurse call system, time spent on electronic devices, doing remaining work, eating, number of care workers, number of hospitalized patients, start time of nap breaks, nap break duration, and steps before napping per hour (Table 5). Multivariable logistic regression analysis was performed using these variables. Stretchers were excluded from this analysis due to making the model unstable (no night shifts using stretchers corresponded to TND ≥ 90 min). As all the variance inflation factors were below three, there were no signs of multicollinearity. The factors between night shifts significantly related to TND ≥ 90 min were noise level from the napping environment (aOR: 0.88 [95% CIs: 0.78, 0.98]), time spent on electronic devices from the ways of spending breaks (0.97 [0.94, 0.99]), and nap break duration from the working environment (1.04 [1.02, 1.07]) (Table 6).

3.6. Relationship between Naps with TND ≥ 90 Min and Feelings of Fatigue

The mixed linear models showed that significant interactions (TND group × times) were observed for drowsiness (F [2, 387.7] = 4.35, p = 0.013), uneasiness (F [2, 387.5] = 7.15, p < 0.001), local pain or dullness (F [2, 387.4] = 3.29, p = 0.038), eyestrain (F [2, 387.6] = 4.36, p = 0.013), and total score (F [2, 387.5] = 6.90, p = 0.001), except instability (F [2, 387.5] = 2.98, p = 0.052). Post hoc tests showed that the drowsiness of the TND ≥ 90 min was significantly lower than that of the TND < 90 min at the after nap breaks. At the end of night shifts, the drowsiness, instability, uneasiness, and total score of TND ≥ 90 min were significantly lower. On the contrary, all scores before nap breaks were not significantly different. Figure 2 shows the fatigue change and comparison after nap breaks and at the end of the night shift between the TND groups based on the least square means.

4. Discussion

4.1. Napping with TND ≥ 90 Min and Associated Factors

Longitudinally and repeatedly investigating the nurses’ night shifts for a month, we verified the factors associated with 90 min napping both in terms of nurses’ characteristics and the environment during the night shift.
Regarding napping during the night shift, although the nap breaks for all nurses were longer than 90 min, only 59 night shifts (30.1%) had TND ≥ 90 min. Only five nurses took naps of at least 90 min on all four night shifts. These results show the difficulty in sleeping long during working hours, even at night. There was no significant difference between the groups for the awakening duration until nap breaks, start time of napping, and drowsiness before nap breaks, suggesting that the homeostatic sleep pressure before the nap was probably at the same level in this study. In other words, other factors may affect TND.
One of the characteristics of nurses who slept for TND ≥ 90 min during most night shifts was more years of nursing experience, even when they were aged 20–39 years. This result is probably “the healthy shift worker effect,” which has been reported to be related to age and other individual attributes [53]. This effect has been explained as older workers representing a selection of workers who may be healthier, can cope well with shift work, and wish to stay in shift work [54]. The results of this study suggest that nurses are able to take enough naps every night shift, which may be one of the factors contributing to this effect. However, we are unable to conclude it in this study, since only five nurses slept TND ≥ 90 min on all night shifts. On the other hand, based on the distribution of TND in this study, it was difficult to explain TND ≥ 90 min by individual factors alone.
We focused on night shift-dependent variables, which suggested that TND ≥ 90 min was associated with three environmental factors: noise, time spent on electronic devices during nap breaks, and nap break duration. Noise was associated with TND ≥ 90 min, which is with the fact that many aircraft maintenance engineers report noise as preventing them from napping during night shifts [55]. Hence, noise may be a common issue when napping in the workplace. Hospitals are prone to noisiness, which comes from diverse noise sources such as conversations, the operation of medical equipment, and the coughing and snoring of patients [56]. In this study, each napping place was located in a public room used for other purposes. Therefore, since no positive correlation was found between noise level and indicators of body movement such as, WASO, the frequency of awakenings, and postural changes, those noise levels were considered to be more reflective of the napping place than the nurses’ self-generated sound. According to the Night Noise Guidelines for Europe, if negative effects on sleep are to be avoided, the equivalent sound pressure level should not exceed 30 dBA indoors for continuous noise [57]. Although both TND groups in this study exceeded this criterion, the noise level 39.6 dB LAeq of TND ≥ 90 min was lower than that of TND < 90 min. The results objectively support that nurses desire quiet nap rooms [26], and they may nap longer in such rooms. Providing nurses with a quiet room will be beneficial to allot time for effective naps during their shifts. Incidentally, we found no evidence that illuminance was associated with nurses’ napping. Light is an important factor that synchronizes circadian rhythms and influences sleep. Another nap room attribute desired by nurses is low lighting [26], because light exposure at night suppresses melatonin and reduces sleepiness [58,59]. Because the nurses in this study could control the lighting in the napping places, most set up an appropriate environment for themselves by turning off the lights during napping. Similarly, we found no evidence that temperature and humidity were associated with napping because they were properly maintained by the air conditioning in the hospital. Hence, we cannot conclude that illumination, temperature, or humidity did not affect napping based on the results of this study.
Regarding ways of spending breaks, time spent on electronic devices was related to TND ≥ 90 min. The use of electronic devices in the TND ≥ 90 min group was relatively short. The use of electronic devices during limited nap breaks reduces nap duration. To nap for a sufficient duration, time spent on electronic devices should be as short as possible. In this study, we could not determine the timing of the use of electronic devices. In the TND ≥ 90 min group, the mean time spent on electronic devices was 23.6 min, while in the TND < 90 min group it was 29.8 min. We confirmed that the mean interval time between the last awakening time of napping and the end time of nap breaks for the TND ≥ 90 min group was 15 min, while for the TND < 90 min group it was 14 min. Therefore, we assume that most of them used mobile devices before napping. In addition, as most of the nurses answered that they always use their electronic devices before bedtime daily, they may use them before napping as well. The use of light-emitting electronic devices before bedtime prolongs the time it takes to fall asleep and increases alertness, even in low-light conditions [32,60]. In addition, sleep disorders such as insomnia can be induced by the increased use of electronic devices at bedtime [61,62], which may have affected nurses’ napping during night shifts. However, considering that the nurses themselves chose to spend time on electronic devices, the decision regarding their use should be discussed after a multifaceted evaluation of their psychological and other effects. Therefore, the relationship between electronic device use and napping during the night shift requires further investigation, including time and timing of use as well as content viewed on the device.
Nap break duration was a related factor in the working environment. Even the TND ≥ 90 min group, which had better sleep quality than the TND < 90 min group, took an average of 17.2 min to fall asleep. If the napping place is not located in a room dedicated only to napping, bedding is necessary to nap, and it takes time to prepare. Thus, to take at least a 90 min nap, a sufficient nap break duration is required. However, staffing levels are lower during night shifts [18,27], making it difficult for nurses to improve this situation. Nursing managers must be aware of these issues and consider countermeasures.
When the factors for both nurses and night shift are considered together, nurses with longer nursing experience may be better able to manage the environment.

4.2. Effects of TND ≥ 90 Min on Fatigue

Our study showed that the changes in fatigue between the TND ≥ 90 min and TND < 90 min groups were different, as shown in Figure 2. Night shift nurses who were able to nap for at least 90 min were less drowsy after nap breaks and less fatigued at the end of the night shift compared with the TND < 90 min group.
Regarding after nap breaks, previous studies have reported that sleep inertia is likely to occur after nighttime napping, and nurses require attention [19,63]. Sleep inertia normally lasts about 30 min, but napping during the night shift may exacerbate the effects in terms of circadian rhythm [64]. In addition, while previous studies support short naps during the night shift [65,66], there is little evidence to support long naps. Nonetheless, it is interesting that the TND ≥ 90 min group had lower drowsiness after napping in this study. Moreover, the study contrasted with those of Takahashi et al., who reported that post-nap fatigue was higher with an increased nap length in nurses who were allowed two-hour nap breaks [67]. The reason may be that the previous study discussed the TIB, which was determined from the posture as well as the nap length, whereas we validated it with the nap duration. In the previous study, TIB was longer, but TND could have been shorter. Therefore, sleep efficiency may have been lower. When the same analysis as in the previous study was performed in this study, there was no correlation between TIB and fatigue (Figure S3), but a negative correlation was observed with TND (Figure S3). Thus, the results also suggest that the TND, actual sleep duration, is more important than TIB to discuss reducing fatigue.
At the end of the night shift, the result that the TND ≥ 90 min group showed lower fatigue concurred with a previous study that found less fatigue in the morning [19,20]. The reason for the lower fatigue was that naps of at least 90 min had good quality as well as quantity. The TND ≥ 90 min group fell asleep more easily and had higher sleep efficiency and subjective satisfaction than the TND < 90 min group. Moreover, drowsiness in the TND ≥ 90 min group was also lower at both time points. The highest level of sleepiness is recorded in the early morning hours for most individuals [68]. Napping for at least 90 min may also be effective at reducing or suppressing sleepiness in the morning. Higher fatigue and sleepiness cause cognitive impairment and performance deficits [9,10]. Thus, napping for at least 90 min may help prevent accidents and ensure patient safety [69].
On the other hand, there was no significant difference in the cumulative fatigue be-tween three nurses’ groups categorized by the number of TND ≥ 90 min during four night shifts. The result suggests that TND ≥ 90 min is effective in reducing or suppressing fatigue during the night shift, but is not sufficient on its own. The sleep quality at home was associated with recovery from the night shift [70]. Although this study was not able to evaluate sleep other than napping during the night shift, the quality of any sleep, whether main sleep or nap, is important for reducing nurses’ fatigue when both findings are integrated.
The implication for nurses’ occupational health of this study is that napping during night shifts as a countermeasure to fatigue is not only to ensure nap breaks but also for both nurses and managers to recognize the importance of TND for nurses. On 16-h night shifts, the environment should be adjusted to allow for TND ≥ 90 min during every night shift. Intervention studies are also needed to develop policy and programs for nurses’ napping based on evidence.

4.3. Limitation

There are several limitations to this study. First, considerable selection bias may be present, including with regard to age and season. The sample size was small because the nurses were restricted to one hospital, and there were differences among the wards. Second, napping was only measured through wearable devices. Sleep depth is not considered. Third, regarding the environment, we could not measure the amount of light to which nurses were exposed, and the noise sources were unclear. In the sound level measurement, possible effects of sound reflection on measured values were not considered, and peak levels of noise were not analyzed. We could not confirm exactly whether nurses used electronic devices before napping. The content and luminance of them were also unclear. Fourth, the effects of 90 min napping were limited to fatigue, and the effects on other outcomes such as performance and accidents are not known. Fifth, only five nurses were able to nap for at least 90 min on all night shifts. Hence, the characteristics of nurses such as, behavioral factors related to napping and fatigue have not been fully verified. For these reasons, it is not possible to generalize our results to all shift workers. In the future, further research is needed on variables that were not covered in this study, such as age, years of nursing experience, sleep depth, and light exposure among large samples. Moreover, it is necessary to verify the effects of nighttime napping on other outcomes.

5. Conclusions

The results suggested that nurses experienced less drowsiness after nap breaks, and less fatigue at the end of night shift if they took a nap of at least 90 min when working 16-h night shifts. In addition, the present results suggested that environmental factors such as noise, time spent on electronic devices during nap breaks, and nap break duration were the factors associated with taking a nap of at least 90 min. However, only five nurses took naps for at least 90 min on all four night shifts. The results showed the difficulty of nurses in sleeping for long period during working hours, even at night. To achieve this duration of napping, both individual nurses and nurses’ managers should consider countermeasures for the abovementioned factors.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19159429/s1, Figure S1: Comparison of the mean complaint rate (%) of the Cumulative Fatigue Symptoms Index by number of naps with TND ≥ 90 min during four night shifts; Figure S2. Correlation between indicators of body movement and noise level during napping; Figure S3. Correlation of TIB and TND with change in fatigue before and after the nap breaks.

Author Contributions

Conceptualization, K.W., N.S., I.S., I.K., S.Y., and R.Y.; methodology, K.W., N.S., I.S., I.K., S.Y., and R.Y.; formal analysis, K.W., S.Y., and R.Y.; investigation, K.W., N.S., I.S., and I.K.; data curation, K.W., and S.Y.; writing—original draft preparation, K.W.; writing—review and editing, N.S., I.S., I.K., S.Y., and R.Y.; visualization, K.W.; supervision, R.Y.; project administration, R.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by NEC Solution Innovators, Ltd. and supported by JST SPRING, Grant Number JPMJSP2119.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Faculty of Health Sciences, Hokkaido University and the investigated facility (reference No. 19–1, 23 May 2019).

Informed Consent Statement

The study aims and methods were explained orally and in writing to those who expressed an interest in participating. The nurses were also informed that they would not be penalized if they refused to participate, that they could withdraw from the study at any time, and that their anonymity would be protected. Written informed consent was obtained from all participating nurses to participate in the study and publish this paper.

Data Availability Statement

Due to the nature of this research, nurses participating in this study did not agree for their data to be shared publicly, so data sharing is not applicable.

Acknowledgments

The authors would like to express our appreciation to the nurses at the hospital in Japan, for their cooperation in this study. We also would like to thank T. Masamoto and Y. Matsushita for their assistance in this investigation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bae, S.-H.; Fabry, D. Assessing the relationships between nurse work hours/overtime and nurse and patient outcomes: Systematic literature review. Nurs. Outlook 2014, 62, 138–156. [Google Scholar] [CrossRef] [PubMed]
  2. Trinkoff, A.M.; Johantgen, M.; Storr, C.L.; Gurses, A.P.; Liang, Y.; Han, K. Nurses’ Work Schedule Characteristics, Nurse Staffing, and Patient Mortality. Nurs. Res. 2011, 60, 1–8. [Google Scholar] [CrossRef]
  3. Dall’Ora, C.; Griffiths, P.; Emmanuel, T.; Rafferty, A.M.; Ewings, S.; Sermeus, W.; Heede, K.V.D.; Bruyneel, L.; Lesaffre, E.; Aiken, L.; et al. 12-hr shifts in nursing: Do they remove unproductive time and information loss or do they reduce education and discussion opportunities for nurses? A cross-sectional study in 12 European countries. J. Clin. Nurs. 2020, 29, 53–59. [Google Scholar] [CrossRef] [PubMed]
  4. Stimpfel, A.W.; Aiken, L.H. Hospital Staff Nurses’ Shift Length Associated with Safety and Quality of Care. J. Nurs. Care Qual. 2013, 28, 122–129. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Japanese Nursing Association. Survey on Hospital Nursing 2020. Available online: https://www.nurse.or.jp/home/publication/pdf/research/96.pdf (accessed on 4 June 2022). (In Japanese).
  6. Barker, L.M.; Nussbaum, M.A. Fatigue, performance and the work environment: A survey of registered nurses. J. Adv. Nurs. 2011, 67, 1370–1382. [Google Scholar] [CrossRef] [PubMed]
  7. Smith-Miller, C.A.; Shaw-Kokot, J.; Curro, B.; Jones, C.B. An Integrative Review: Fatigue among Nurses in Acute Care Settings. JONA J. Nurs. Adm. 2014, 44, 487–494. [Google Scholar] [CrossRef] [PubMed]
  8. Wilson, M.; Permito, R.; English, A.; Albritton, S.; Coogle, C.; Van Dongen, H.P.A. Performance and sleepiness in nurses working 12-h day shifts or night shifts in a community hospital. Accid. Anal. Prev. 2019, 126, 43–46. [Google Scholar] [CrossRef] [PubMed]
  9. Geiger-Brown, J.; Rogers, V.E.; Trinkoff, A.M.; Kane, R.L.; Bausell, R.B.; Scharf, S.M. Sleep, Sleepiness, Fatigue, and Performance of 12-Hour-Shift Nurses. Chrono. Int. 2012, 29, 211–219. [Google Scholar] [CrossRef] [PubMed]
  10. Lowe, C.J.; Safati, A.; Hall, P.A. The neurocognitive consequences of sleep restriction: A meta-analytic review. Neurosci. Biobehav. Rev. 2017, 80, 586–604. [Google Scholar] [CrossRef] [PubMed]
  11. Rogers, A.E. Chapter 40: The Effects of Fatigue and Sleepiness on Nurse Performance and Patient Safety. In Patient Safety and Quality: An Evidence-Based Handbook for Nurses; Hughes, R.G., Ed.; Agency for Healthcare Research and Quality: Rockville, MD, USA, 2008; pp. 2-509–2-545. ISBN 9781587633539. Available online: https://www.ncbi.nlm.nih.gov/books/NBK2645/ (accessed on 4 June 2022).
  12. Caldwell, J.A.; Caldwell, J.L.; Thompson, L.A.; Lieberman, H.R. Fatigue and its management in the workplace. Neurosci. Biobehav. Rev. 2019, 96, 272–289. [Google Scholar] [CrossRef] [PubMed]
  13. American Nurses Association. Position Statement: Addressing Nurse Fatigue to Promote Safety and Health: Joint Responsibilities of Registered Nurses and Employers to Reduce Risks. Available online: https://www.nursingworld.org/~49de63/globalassets/practiceandpolicy/health-and-safety/nurse-fatigue-position-statement-final.pdf (accessed on 4 June 2022).
  14. Japanese Nursing Association. Guidelines on Night Shift and Shift Work for Nurses. Available online: https://www.nurse.or.jp/home/publication/pdf/guideline/yakin_guideline.pdf (accessed on 4 June 2022). (In Japanese).
  15. Li, H.; Shao, Y.; Xing, Z.; Li, Y.; Wang, S.; Zhang, M.; Ying, J.; Shi, Y.; Sun, J. Napping on night-shifts among nursing staff: A mixed-methods systematic review. J. Adv. Nurs. 2019, 75, 291–312. [Google Scholar] [CrossRef] [PubMed]
  16. Martin-Gill, C.; Barger, L.K.; Moore, C.G.; Higgins, J.S.; Teasley, E.M.; Weiss, P.M.; Condle, J.P.; Flickinger, K.L.; Coppler, P.J.; Sequeira, D.J.; et al. Effects of Napping During Shift Work on Sleepiness and Performance in Emergency Medical Services Personnel and Similar Shift Workers: A Systematic Review and Meta-Analysis. Prehosp. Emerg. Care 2018, 22, 47–57. [Google Scholar] [CrossRef] [PubMed]
  17. Patterson, P.D.; Liszka, M.K.; Mcilvaine, Q.S.; Nong, L.; Weaver, M.D.; Turner, R.L.; Platt, T.E.; Opitz, S.E.; Guyette, F.X.; Martin-Gill, C.; et al. Does the evidence support brief (≤30-mins), moderate (31–60-mins), or long duration naps (61+ mins) on the night shift? A systematic review. Sleep Med. Rev. 2021, 59, 101509. [Google Scholar] [CrossRef] [PubMed]
  18. Geiger-Brown, J.; Sagherian, K.; Zhu, S.; Wieroniey, M.A.; Blair, L.; Warren, J.; Hinds, P.S.; Szeles, R. CE: Original Research: Napping on the Night Shift: A Two-Hospital Implementation Project. Am. J. Nurs. 2016, 116, 26–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Smith-Coggins, R.; Howard, S.K.; Mac, D.T.; Wang, C.; Kwan, S.; Rosekind, M.R.; Sowb, Y.; Balise, R.; Levis, J.; Gaba, D.M. Improving Alertness and Performance in Emergency Department Physicians and Nurses: The Use of Planned Naps. Ann. Emerg. Med. 2006, 48, 596–604.e3. [Google Scholar] [CrossRef]
  20. Barthe, B.; Tirilly, G.; Gentil, C.; Toupin, C. Job demands and resting and napping opportunities for nurses during night shifts: Impact on sleepiness and self-evaluated quality of healthcare. Ind. Health 2016, 54, 157–162. [Google Scholar] [CrossRef] [Green Version]
  21. Kagamiyama, H.; Sumi, N.; Yoshida, Y.; Sugimura, N.; Nemoto, F.; Yano, R. Association between sleep and fatigue in nurses who are engaged in 16 h night shifts in Japan: Assessment using actigraphy. Jpn. J. Nurs. Sci. 2019, 16, 373–384. [Google Scholar] [CrossRef]
  22. Da Borges, F.N.S.; Fischer, F.M.; Rotenberg, L.; Soares, N.S.; Fonseca, M.B.; Smolensky, M.H.; Sackettlundeen, L.; Haus, E.; de Moreno, C.R.C. Effects of Naps at Work on the Sleepiness of 12-Hour Night Shift Nursing Personnel. Sleep Sci. 2009, 2, 24–29. [Google Scholar]
  23. Ruggiero, J.S.; Redeker, N.S. Effects of Napping on Sleepiness and Sleep-Related Performance Deficits in Night-Shift Workers: A Systematic Review. Biol. Res. Nurs. 2014, 16, 134–142. [Google Scholar] [CrossRef]
  24. Carskadon, M.A.; Dement, W.C. Chapter 2: Normal Human Sleep: An Overview. In Principles and Practice of Sleep Medicine, 6th ed.; Kryger, M., Roth, T., Dement, W.C., Eds.; Elsevier: Amsterdam, The Netherlands, 2017; pp. 15–24. ISBN 9780323242882. [Google Scholar]
  25. Léger, D.; Debellemaniere, E.; Rabat, A.; Bayon, V.; Benchenane, K.; Chennaoui, M. Slow-wave sleep: From the cell to the clinic. Sleep Med. Rev. 2018, 41, 113–132. [Google Scholar] [CrossRef]
  26. Fallis, W.M.; McMillan, D.E.; Edwards, M.P. Napping During Night Shift: Practices, Preferences, and Perceptions of Critical Care and Emergency Department Nurses. Crit. Care Nurse 2011, 31, e1–e11. [Google Scholar] [CrossRef] [PubMed]
  27. Edwards, M.P.; McMillan, D.E.; Fallis, W.M. Napping during breaks on night shift: Critical care nurse managers’ perceptions. Dynamics 2013, 24, 30–35. [Google Scholar] [PubMed]
  28. Irish, L.A.; Kline, C.E.; Gunn, H.E.; Buysse, D.J.; Hall, M.H. The role of sleep hygiene in promoting public health: A review of empirical evidence. Sleep Med. Rev. 2015, 22, 23–36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Chang, Y.-S.; Wu, Y.-H.; Lu, M.R.; Hsu, C.-Y.; Liu, C.-K.; Hsu, C. Did a brief nap break have positive benefits on information processing among nurses working on the first 8-h night shift? Appl. Ergon. 2015, 48, 104–108. [Google Scholar] [CrossRef] [PubMed]
  30. Oriyama, S.; Miyakoshi, Y.; Kobayashi, T. Effects of Two 15-min Naps on the Subjective Sleepiness, Fatigue and Heart Rate Variability of Night Shift Nurses. Ind. Health 2014, 52, 25–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Silva-Costa, A.; de Araú, M.M.; Nagai, R.; Fischer, F.M. Environmental and Organizational Conditions for Napping during Night Work: A Qualitative Study among Nursing Professionals. Sleep Sci. 2010, 3, 11–15. [Google Scholar]
  32. Touitou, Y.; Reinberg, A.; Touitou, D. Association between light at night, melatonin secretion, sleep deprivation, and the internal clock: Health impacts and mechanisms of circadian disruption. Life Sci. 2017, 173, 94–106. [Google Scholar] [CrossRef]
  33. Basner, M.; Müller, U.; Elmenhorst, E.-M. Single and Combined Effects of Air, Road, and Rail Traffic Noise on Sleep and Recuperation. Sleep 2011, 34, 11–23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Troynikov, O.; Watson, C.G.; Nawaz, N. Sleep environments and sleep physiology: A review. J. Therm. Biol. 2018, 78, 192–203. [Google Scholar] [CrossRef]
  35. Faraut, B.; Andrillon, T.; Vecchierini, M.-F.; Leger, D. Napping: A public health issue. From epidemiological to laboratory studies. Sleep Med. Rev. 2017, 35, 85–100. [Google Scholar] [CrossRef]
  36. Caruso, C.C.; Hitchcock, E.M. Strategies for Nurses to Prevent Sleep-Related Injuries and Errors. Rehabilitation Nurs. 2010, 35, 192–197. [Google Scholar] [CrossRef]
  37. Wendsche, J.; Ghadiri, A.; Bengsch, A.; Wegge, J. Antecedents and outcomes of nurses’ rest break organization: A scoping review. Int. J. Nurs. Stud. 2017, 75, 65–80. [Google Scholar] [CrossRef] [PubMed]
  38. Japanese Nursing Association. Survey on Hospital Nursing 2018. Available online: https://www.nurse.or.jp/home/publication/pdf/research/94.pdf (accessed on 4 June 2022). (In Japanese).
  39. Hirshkowitz, M.; Whiton, K.; Albert, S.M.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Katz, E.S.; Kheirandish-Gozal, L.; et al. National sleep foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health 2015, 1, 40–43. [Google Scholar] [CrossRef] [PubMed]
  40. Stock, D.; Knight, J.A.; Raboud, J.; Cotterchio, M.; Strohmaier, S.; Willett, W.; Eliassen, A.H.; Rosner, B.; Hankinson, S.E.; Schernhammer, E. Rotating night shift work and menopausal age. Hum. Reprod. 2019, 34, 539–548. [Google Scholar] [CrossRef] [PubMed]
  41. Gava, G.; Orsili, I.; Alvisi, S.; Mancini, I.; Seracchioli, R.; Meriggiola, M.C. Cognition, Mood and Sleep in Menopausal Transition: The Role of Menopause Hormone Therapy. Medicina 2019, 55, 668. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Horne, J.A.; Ostberg, O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int. J. Chronobiol. 1976, 4, 97–110. [Google Scholar] [PubMed]
  43. Center for Environmental Therapeutics. Morningness-Eveningness Questionnaire Self-Assessment Version (MEQ-SA). Available online: https://cet.org/wp-content/uploads/2019/11/MEQ-SA-EN.pdf (accessed on 4 June 2022).
  44. Matsuo, M.; Masuda, F.; Sumi, Y.; Takahashi, M.; Yamada, N.; Ohira, M.H.; Fujiwara, K.; Kanemura, T.; Kadotani, H. Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders. Front. Neurol. 2016, 7, 110. [Google Scholar] [CrossRef] [PubMed]
  45. Nakazaki, K.; Kitamura, S.; Motomura, Y.; Hida, A.; Kamei, Y.; Miura, N.; Mishima, K. Validity of an algorithm for determining sleep/wake states using a new actigraph. J. Physiol. Anthropol. 2014, 33, 31. [Google Scholar] [CrossRef] [Green Version]
  46. Brzozowski, S.L.; Cho, H.; Arsenault Knudsen, É.N.; Steege, L.M. Predicting nurse fatigue from measures of work demands. Appl. Ergon. 2021, 92, 103337. [Google Scholar] [CrossRef]
  47. Kosugoh, R.; Fujii, H.; Hirata, A. Subjective Assessment of Workload (1): Revision of the Cumulative Fatigue Symptoms Index. J. Sci. Labour 1992, 68, 489–502. (In Japanese) [Google Scholar]
  48. Sakai, K. Revision of the Jikaku-sho shirabe in 2002. Dig Sci. Labour 2002, 57, 295–298. (In Japanese) [Google Scholar]
  49. Snijders, T.A.B.; Bosker, R.J. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed.; SAGE: Thousand Oaks, CA, USA, 2011; ISBN 9781446254332. [Google Scholar]
  50. Ficca, G.; Axelsson, J.; Mollicone, D.J.; Muto, V.; Vitiello, M.V. Naps, cognition and performance. Sleep Med. Rev. 2010, 14, 249–258. [Google Scholar] [CrossRef] [PubMed]
  51. Zion, N.; Shochat, T. Let them sleep: The effects of a scheduled nap during the night shift on sleepiness and cognition in hospital nurses. J. Adv. Nurs. 2019, 75, 2603–2615. [Google Scholar] [CrossRef] [PubMed]
  52. R Core Team. R: A Language and Environment for Statistical; R Foundation for Statistical Computing: Vienna, Austria, 2022; Available online: https://www.R-project.org/ (accessed on 15 July 2022).
  53. Saksvik, I.B.; Bjorvatn, B.; Hetland, H.; Sandal, G.M.; Pallesen, S. Individual differences in tolerance to shift work–A systematic review. Sleep Med. Rev. 2011, 15, 221–235. [Google Scholar] [CrossRef] [PubMed]
  54. Knutsson, A. Methodological Aspects of Shift-Work Research. Chrono-Int. 2004, 21, 1037–1047. [Google Scholar] [CrossRef]
  55. Purnell, M.T.; Feyer, A.-M.; Herbison, G.P. The impact of a nap opportunity during the night shift on the performance and alertness of 12-h shift workers. J. Sleep Res. 2002, 11, 219–227. [Google Scholar] [CrossRef] [PubMed]
  56. Loupa, G.; Katikaridis, A.; Karali, D.; Rapsomanikis, S. Mapping the noise in a Greek general hospital. Sci. Total Environ. 2019, 646, 923–929. [Google Scholar] [CrossRef]
  57. World Health Organization. Night Noise Guidelines for Europe; World Health Organization Regional Office for Europe: Copenhagen, Denmark, 2009; ISBN 9789289041737. [Google Scholar]
  58. Gooley, J.J.; Chamberlain, K.; Smith, K.A.; Khalsa, S.B.S.; Rajaratnam, S.M.W.; Van Reen, E.; Zeitzer, J.M.; Czeisler, C.A.; Lockley, S.W. Exposure to room light before bedtime suppresses melatonin onset and shortens melatonin duration in humans. J. Clin. Endocrinol. Metab. 2011, 96, E463–E472. [Google Scholar] [CrossRef] [Green Version]
  59. Santhi, N.; Thorne, H.C.; van der Veen, D.R.; Johnsen, S.; Mills, S.L.; Hommes, V.; Schlangen, L.J.M.; Archer, S.N.; Dijk, D.-J. The spectral composition of evening light and individual differences in the suppression of melatonin and delay of sleep in humans. J. Pineal Res. 2012, 53, 47–59. [Google Scholar] [CrossRef]
  60. Chang, A.-M.; Aeschbach, D.; Duffy, J.F.; Czeisler, C.A. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc. Natl. Acad. Sci. USA 2015, 112, 1232–1237. [Google Scholar] [CrossRef] [Green Version]
  61. Randjelović, P.; Stojiljković, N.; Radulović, N.; Ilić, I.; Stojanović, N.; Ilić, S. The association of smartphone usage with subjective sleep quality and daytime sleepiness among medical students. Biol. Rhythm Res. 2019, 50, 857–865. [Google Scholar] [CrossRef]
  62. Xie, X.; Dong, Y.; Wang, J. Sleep quality as a mediator of problematic smartphone use and clinical health symptoms. J. Behav. Addict. 2018, 7, 466–472. [Google Scholar] [CrossRef] [PubMed]
  63. Kubo, T.; Takahashi, M.; Takeyama, H.; Matsumoto, S.; Ebara, T.; Murata, K.; Tachi, N.; Itani, T. How do the Timing and Length of a Night-Shift Nap Affect Sleep Inertia? Chronobiol. Int. 2010, 27, 1031–1044. [Google Scholar] [CrossRef] [PubMed]
  64. Hilditch, C.J.; McHill, A.W. Sleep inertia: Current insights. Nat. Sci. Sleep 2019, 11, 155–165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Rhéaume, A.; Mullen, J. The impact of long work hours and shift work on cognitive errors in nurses. J. Nurs. Manag. 2018, 26, 26–32. [Google Scholar] [CrossRef] [PubMed]
  66. Sun, Q.; Ji, X.; Zhou, W.; Liu, J. Sleep problems in shift nurses: A brief review and recommendations at both individual and institutional levels. J. Nurs. Manag. 2019, 27, 10–18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Takahashi, M.; Arito, H.; Fukuda, H. Nurses’ workload associated with 16-h night shifts. II: Effects of a nap taken during the shifts. Psychiatry Clin. Neurosci. 1999, 53, 223–225. [Google Scholar] [CrossRef] [PubMed]
  68. Boivin, D.B.; Boudreau, P. Impacts of shift work on sleep and circadian rhythms. Pathol. Biol. 2014, 62, 292–301. [Google Scholar] [CrossRef] [PubMed]
  69. Di Muzio, M.; Dionisi, S.; Di Simone, E.; Cianfrocca, C.; Di Muzio, F.; Fabbian, F.; Barbiero, G.; Tartaglini, D.; Giannetta, N. Can nurses’ shift work jeopardize the patient safety? A systematic review. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 4507–4519. [Google Scholar] [CrossRef] [PubMed]
  70. Konya, I.; Watanabe, K.; Shishido, I.; Sugimura, N.; Matsushita, Y.; Yamaguchi, S.; Yano, R. Post-Work Recovery from Fatigue and Sleep Episodes among Nurses Who Are Engaged in 16-Hour Night Shifts: A Prospective Observational Study. Healthcare 2022, 10, 1089. [Google Scholar] [CrossRef]
Ijerph 19 09429 g001 550
Figure 1. The distribution of total nap duration for each nurse’s during four night shifts. Notes. The vertical axis indicates total nap duration. The horizontal axis indicates the nurses’ ID. The auxiliary line show that the total nap duration is 90 min. Nurses were classified into five groups according to the number of times they were able to take a nap of at least 90 min during their four night shifts. Red crosses: None group (n = 19), Purple pluses: One night shift group (n = 13), Green squares: Two night shifts group (n = 10), Orange Triangles: Three night shifts group (n = 2), Blue circles: Four night shifts group (n = 5), respectively. Abbreviations. ID, identification.
Figure 1. The distribution of total nap duration for each nurse’s during four night shifts. Notes. The vertical axis indicates total nap duration. The horizontal axis indicates the nurses’ ID. The auxiliary line show that the total nap duration is 90 min. Nurses were classified into five groups according to the number of times they were able to take a nap of at least 90 min during their four night shifts. Red crosses: None group (n = 19), Purple pluses: One night shift group (n = 13), Green squares: Two night shifts group (n = 10), Orange Triangles: Three night shifts group (n = 2), Blue circles: Four night shifts group (n = 5), respectively. Abbreviations. ID, identification.
Ijerph 19 09429 g001
Ijerph 19 09429 g002 550
Figure 2. Comparison of change in feelings of fatigue between night shifts with TND ≥ 90 min (n = 59) and TND < 90 min (n = 137) groups. Notes. The sample units in this figure are the number of night shifts. The vertical axis indicates the least squares means in feelings of fatigue (Jikaku-sho shirabe), with higher scores showing higher fatigue. The horizontal axis indicates the measurement points. The blue line is TND ≥ 90 min group, while the red line is TND < 90 min group. Error bars represent 95% confidence interval. (A): total score, (B): drowsiness, (C): instability, (D): uneasiness, (E): local pain or dullness, (F): eyestrain, respectively. The least squares means were analyzed using the mixed linear model, while post hoc t-tests were conducted using their estimates to compare between groups each measurement point. Abbreviations. TND, total nap duration. * p < 0.05.
Figure 2. Comparison of change in feelings of fatigue between night shifts with TND ≥ 90 min (n = 59) and TND < 90 min (n = 137) groups. Notes. The sample units in this figure are the number of night shifts. The vertical axis indicates the least squares means in feelings of fatigue (Jikaku-sho shirabe), with higher scores showing higher fatigue. The horizontal axis indicates the measurement points. The blue line is TND ≥ 90 min group, while the red line is TND < 90 min group. Error bars represent 95% confidence interval. (A): total score, (B): drowsiness, (C): instability, (D): uneasiness, (E): local pain or dullness, (F): eyestrain, respectively. The least squares means were analyzed using the mixed linear model, while post hoc t-tests were conducted using their estimates to compare between groups each measurement point. Abbreviations. TND, total nap duration. * p < 0.05.
Ijerph 19 09429 g002
Table
Table 1. Participants’ basic attributes (n = 49).
Table 1. Participants’ basic attributes (n = 49).
VariablesMean (SD)
Age (years)28.8 (4.2)
Nursing experience (years)7.0 (4.2)
Nursing experience in the current ward (years)3.4 (2.2)
n (%)
Sex
 Male3 (6.1)
 Female46 (93.9)
Marital status (Yes)8 (16.3)
Child-rearing (Yes)5 (10.2)
Ward
 A (medical)12 (24.5)
 B (medical)13 (26.5)
 C (medical)5 (10.2)
 D (surgical)5 (10.2)
 E (medical)4 (8.2)
 F (surgical)10 (20.4)
Notes. The sample units in this table are persons. Abbreviations. SD, standard deviation.
Table
Table 2. Nurses’ characteristics (n = 49).
Table 2. Nurses’ characteristics (n = 49).
VariablesMean (SD)
Sleep
 Subjective mean daily sleep duration (hour)6.3 (0.9)
n (%)
 Take napping before night shift (Yes)13 (26.5)
 Chronotype
  Definite morning type0 (0.0)
  Moderate morning type6 (12.2)
  Intermediate type34 (69.4)
  Moderate evening type 8 (16.3)
  Definite evening type1 (2.1)
Sleep-related habits
 Frequency of caffeine intake
  Rarely drink (Can’t drink)18 (36.7)
  <2 times/week10 (20.4)
  3–4 times/week6 (12.2)
  5–6 times/week4 (8.2)
  Every day11 (22.5)
 Daily time spent on electronic devices
  30–59 min1 (2.1)
  1–2 h18 (36.7)
  ≥2 h30 (61.2)
 Time spent on electronic devices before bedtime
  None1 (2.1)
  <15 min7 (14.3)
  15–29 min8 (16.3)
  30–44 min17 (34.7)
  45–59 min9 (18.3)
  ≥60 min7 (14.3)
Notes. The sample units in this table are persons. Abbreviations. SD, standard deviation.
Table
Table 3. Comparison of nurses’ characteristics based on the number of night shifts with TND ≥ 90 min during four night shifts (n = 49).
Table 3. Comparison of nurses’ characteristics based on the number of night shifts with TND ≥ 90 min during four night shifts (n = 49).
VariablesNoneOne/Two
Night Shifts		Three/Four
Night Shifts		pPost Hoc
(n = 19)(n = 23)(n = 7)
Median (IQR)
Age (years)29.0 (26.0, 30.0)27.0 (24.0, 31.0)33.0 (28.0, 33.0)0.097
Nursing experience (years)6.2 (3.3, 8.3)5.3 (3.2, 9.0)10.2 (7.2, 12.2)0.038None < Three/Four *
Nursing experience in the current ward (years)2.7 (1.3, 3.8)3.3 (2.2, 5.3)4.2 (2.2, 5.0)0.497
Subjective mean daily sleep duration (hour)6.0 (5.5, 7.0)6.0 (5.5, 7.0)6.0 (6.0, 7.5)0.964
n (%)
Gender
 Male1 (5.3)2 (8.7)0 (0.0)0.999
 Female18 (94.7)21 (91.3)7 (100.0)
Marital status
 Yes5 (26.3)3 (13.0)0 (0.0)0.372
 No14 (73.7)20 (87.0)7 (100.0)
Child-rearing
 Yes3 (15.8)2 (8.7)0 (0.0)0.682
 No16 (84.2)21 (91.3)7 (100.0)
Take napping before night shift (Yes)
 Yes3 (15.8)7 (30.4)3 (42.9)0.302
 No16 (84.2)16 (69.6)4 (57.1)
Chronotype
 Definite morning type0 (0.0)0 (0.0)0 (0.0)0.370
 Moderate morning type2 (10.5)3 (13.0)1 (14.3)
 Intermediate type14 (73.7)17 (73.9)3 (42.9)
 Moderate evening type3 (15.8)2 (8.7)3 (42.9)
 Definite evening type0 (0.0)1 (4.4)0 (0.0)
Frequency of caffeine intake
 Rarely drink (Can’t drink)5 (26.3)9 (39.1)4 (57.1)0.328
 <2 times/week3 (15.8)7 (30.4)0 (0.0)
 3–4 times/week3 (15.8)2 (8.7)1 (14.3)
 5–6 times/week3 (15.8)0 (0.0)1 (14.3)
 Every day5 (26.3)5 (21.8)1 (14.3)
Daily time spent on electronic devices
 30–59 min0 (0.0)1 (4.4)0 (0.0)0.999
 1–2 h7 (36.8)8 (34.8)3 (42.9)
 ≥2 h12 (63.2)14 (60.8)4 (57.1)
Time spent on electronic devices before bedtime
 None0 (0.0)1 (4.4)0 (0.0)0.721
 <15 min2 (10.5)3 (13.0)2 (28.6)
 15–29 min4 (21.1)2 (8.7)2 (28.6)
 30–44 min6 (31.6)10 (43.5)1 (14.3)
 45–59 min5 (26.3)3 (13.0)1 (14.3)
 ≥60 min2 (10.5)4 (17.4)1 (14.3)
Notes: The sample units in this table are persons. p values were calculated using Kruskal-Wallis test for continuous variables, while Fisher’s exact test for categorical variables. Post hoc test was conducted using Steel-Dwass test. Abbreviations. IQR, interquartile range; SD, standard deviation; TND, total nap duration. * p < 0.05.
Table
Table 4. Comparison of sleep and napping-related parameters between TND ≥ 90 min and TND < 90 min groups (n = 196).
Table 4. Comparison of sleep and napping-related parameters between TND ≥ 90 min and TND < 90 min groups (n = 196).
VariablesTND < 90 min
(n = 137)		TND ≥ 90 min
(n = 59)		tp
Sleep or Nap before the night shifts
 Last awakening time (h:m)10:52 (2:10)11:39 (1:57)2.420.016
 Last sleep or nap duration (hour)7.3 (3.2)6.8 (3.6)−0.930.355
 Awakening duration until nap breaks (hour)13.9 (3.0)13.5 (2.5)−0.760.450
Nap breaks and napping during night shifts
 Start time of nap breaks (h:m)0:45 (1:53)1:12 (1:58)1.530.128
 TIB (min)104.6 (27.7)160.2 (32.3)12.24<0.001
 Start time of lying (h:m)1:02 (1:53)1:23 (1:57)1.190.235
 Start time of napping (h:m)1:30 (1:50) a1:40 (1:54)0.580.563
 SL (min)23.2 (18.9) a17.2 (12.3)−2.230.027
 TND (min)60.5 (20.4)120.9 (27.1)17.15<0.001
 SE (%)59.6 (18.2)75.9 (9.7)6.48<0.001
 WASO (min)11.8 (13.6) a16.3 (14.4)2.070.040
 Frequency of awakenings during napping (Times)1.7 (1.4) a2.9 (1.8)5.04<0.001
 Frequency of postural changes (Times)2.5 (2.4) a4.3 (2.9)4.32<0.001
 End time of napping (h:m)2:44 (1:46) a3:58 (1:50)4.36<0.001
 BOT (min)6.4 (4.6) a5.9 (2.8)−0.800.426
 End time of lying (h:m)2:47 (1:48)4:03 (1:50)4.54<0.001
 End time of nap breaks (h:m)2:58 (1:46)4:13 (1:50)4.45<0.001
 Subjective evaluation (%)47.1 (21.5) b65.9 (22.0)5.46<0.001
Notes. The sample units in this table are the number of night shifts. The data are presented as mean (standard deviation). p values were calculated using the student’s t-test. There were four night shifts which were not as determined getting sleep. 17 night shifts of subjective evaluation were excluded because they were not completed. Abbreviations. BOT, bed-out time; TIB, time in bed; TND, total nap duration; SE, sleep efficiency; SL, sleep latency; WASO, wake after sleep onset. a n = 133. b n = 120.
Table
Table 5. Environment during night shifts between TND ≥ 90 min and TND < 90 min groups (n = 196).
Table 5. Environment during night shifts between TND ≥ 90 min and TND < 90 min groups (n = 196).
VariablesMean (SD) or n (%)tp
TND < 90 min
(n = 137)		TND ≥ 90 min
(n = 59)
Napping environment
 Illuminance (lux)18.6 (89.3)11.0 (58.5) a−0.590.556
 Noise level (dB LAeq, napping)42.3 (4.7)39.3 (4.8)−4.11<0.001
 Temperature (°C)25.1 (1.3)24.9 (1.0) a−1.050.294
 Humidity (%)66.2 (7.5)64.3 (6.3) a−1.700.091
 Unpleasant smells (Yes)2 (1.5)0 (0.0) 0.999
 Japanese futons (Yes)40 (29.2)31 (52.5) 0.002
 Folding beds (Yes)36 (26.3)1 (1.7) <0.001
 Beds in vacant hospital room (Yes)23 (16.8)14 (23.7) 0.320
 Sofa beds (Yes)14 (10.2)7 (11.9) 0.802
 Stretchers (Yes)20 (14.6)0 (0.0) 0.001
 Examination tables (Yes)4 (2.9)6 (10.2) 0.069
 Bed sheets (Yes)124 (90.5)54 (91.5) 0.999
 Quilt (Yes)125 (91.2)55 (93.2) 0.781
 Toweling blankets (Yes)9 (6.6)10 (17.0) 0.034
 Pillow (Yes)117 (85.4)52 (88.1) 0.822
Ways of spending breaks
 Caffeine consumption before nap breaks (Yes)67 (48.9)23 (40.4) b 0.343
 Carrying a PHS with the nurse call system (Yes)8 (5.8)10 (17.0) 0.028
 Time spent on electronic devices (min)29.8 (26.8)23.6 (26.5)−1.500.136
 Changing clothes (Yes)8 (5.8)3 (5.1) 0.999
 Using a sleep mask (Yes)6 (4.4)4 (6.8) 0.492
 Using earplugs (Yes)2 (1.5)0 (0.0) 0.999
 Watching television (Yes)4 (2.9)4 (6.8) 0.245
 Listening to music (Yes)9 (6.6)1 (1.7) 0.287
 Doing remaining work (Yes)17 (12.4)3 (5.1) 0.196
 Eating (Yes)27 (19.7)21 (35.6) 0.029
Working Environment
 Number of nurses3.0 (0.6)3.1 (0.7)0.140.886
 Number of care workers0.2 (0.4)0.4 (0.5)1.540.125
 Number of hospitalized patients22.1 (4.3)21.0 (4.1)−1.680.095
 Number of patients each nurse responsible for8.2 (4.4)7.4 (3.0)−1.250.213
 Supper breaks (Yes)137 (100.0)59 (100.0) 0.999
 Nap break duration (min)138.3 (27.7)182.2 (39.6)8.88<0.001
 Steps10,367.2 (2652.9) c8918.6 (2199.9) d−3.230.002
  Steps before napping per hour736.5 (204.3) c648.6 (193.7) d−2.470.014
  Steps after napping per hour752.7 (229.7) e769.5 (238.6) b0.460.648
Notes. The sample units in this table are the number of night shifts. p values were calculated using student’s t-tests for continuous variables, while Fisher’s exact tests were used for categorical variables. Equipment bugs and incomplete questionnaires were excluded. Abbreviations. dB LAeq, napping, decibel equivalent A-weighted sound pressure level during napping; PHS, personal handy-phone system; TND, total nap duration. a n = 58. b n = 57. c n = 129. d n = 43. e n = 136.
Table
Table 6. Environmental factors associated with night shift of TND ≥ 90 min (n = 172).
Table 6. Environmental factors associated with night shift of TND ≥ 90 min (n = 172).
VariablesaOR [95% CIs]p
Napping Environment
 Noise level (dB LAeq, napping)0.88 [0.78, 0.98]0.022
 Humidity (%)0.94 [0.87, 1.02]0.152
 Japanese futons
  Yes1.19 [0.34, 4.14]0.787
  No1.00 [Ref]
 Folding beds
  Yes0.73 [0.06, 9.48]0.807
  No1.00 [Ref]
 Examination tables
  Yes15.02 [0.80, 283.19]0.071
  No1.00 [Ref]
 Toweling blankets
  Yes1.44 [0.29, 7.07]0.655
  No1.00 [Ref]
Ways of spending breaks
 Carrying a PHS with nurse call system
  Yes1.71 [0.13, 22.86]0.686
  No1.00 [Ref]
 Time spent on electronic devices (min)0.97 [0.94, 0.99]0.009
 Doing remaining work
  Yes0.24 [0.03, 2.01]0.188
  No1.00 [Ref]
 Eating
  Yes1.78 [0.57, 5.54]0.319
  No1.00 [Ref]
Working Environment
 Number of care workers (person)1.17 [0.22, 6.30]0.854
 Number of hospitalized patients (person)0.87 [0.74, 1.04]0.124
 Nap break duration (min)1.04 [1.02, 1.07]<0.001
 Steps before napping (100 steps/hour)0.75 [0.55, 1.03]0.078
Notes. Multivariable logistic regression analysis with awaking duration until nap break set was as a covariate. The sample units in this table are the number of night shifts. The 24 night shifts were excluded because the wearable devices could not record it due to a bugs. Abbreviations. CI, confidence interval; dB LAeq, napping, decibel equivalent A-weighted sound pressure level during napping; aOR, adjusted odds ratio; PHS, personal handy-phone system; TND, total nap duration.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Watanabe, K.; Sugimura, N.; Shishido, I.; Konya, I.; Yamaguchi, S.; Yano, R. Effects of 90 Min Napping on Fatigue and Associated Environmental Factors among Nurses Working Long Night Shifts: A Longitudinal Observational Study. Int. J. Environ. Res. Public Health 2022, 19, 9429. https://doi.org/10.3390/ijerph19159429

AMA Style

Watanabe K, Sugimura N, Shishido I, Konya I, Yamaguchi S, Yano R. Effects of 90 Min Napping on Fatigue and Associated Environmental Factors among Nurses Working Long Night Shifts: A Longitudinal Observational Study. International Journal of Environmental Research and Public Health. 2022; 19(15):9429. https://doi.org/10.3390/ijerph19159429

Chicago/Turabian Style

Watanabe, Kazuhiro, Naotaka Sugimura, Inaho Shishido, Issei Konya, Shinya Yamaguchi, and Rika Yano. 2022. "Effects of 90 Min Napping on Fatigue and Associated Environmental Factors among Nurses Working Long Night Shifts: A Longitudinal Observational Study" International Journal of Environmental Research and Public Health 19, no. 15: 9429. https://doi.org/10.3390/ijerph19159429

APA Style

Watanabe, K., Sugimura, N., Shishido, I., Konya, I., Yamaguchi, S., & Yano, R. (2022). Effects of 90 Min Napping on Fatigue and Associated Environmental Factors among Nurses Working Long Night Shifts: A Longitudinal Observational Study. International Journal of Environmental Research and Public Health, 19(15), 9429. https://doi.org/10.3390/ijerph19159429

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop