Poor sleep quality could affect as many as 95% of the nursing workforce (Sepehrmanesh et al, 2017). A systematic review and meta-analysis of 53 studies of 57 210 nursing staff from Australia, China, Germany, India, Italy, Iran, Korea, Malaysia, Norway, Saudi Arabia, Turkey, the UK and the USA reported that the pooled prevalence of poor sleep quality was 61.0% (95% CI (55.8–66.1)) (Zeng et al, 2020).
Another systematic review and meta-analysis of 52 studies with 31 749 Chinese health professionals revealed that the pooled prevalence of poor sleep quality was 39.2% (95% CI (36.0–42.7%)) (Qiu et al, 2020). In Iran, the estimated prevalence of poor sleep quality among nurses was 95.5% (191/200 cases) (Sepehrmanesh et al, 2017). In another study, 145 out of 159 (91.2%) Iranian hospital nurses at a teaching hospital had poor sleep quality (Roodbandi et al, 2016). Within the Middle East, a recent study showed that 64.0% of hospital nurses in United Arab Emirates (UAE) experienced sleep disturbance (Bani Issa et al, 2020).
Meanwhile, in Taiwan, a cross-sectional study of 156 staff nurses at a regional teaching hospital reported 75.8% (117 nurses) had a Pittsburgh Sleep Quality Index (PSQI) score of 5 or more, indicating poor sleep quality (Chien et al, 2013). In Japan, approximately 40% of rotating shift workers experienced difficulty in getting to sleep, exhibited an evening chronotype, delayed sleep phase and high social jet lag (Uekata et al, 2019). In a New Zealand study including 2813 nurses, 37.7% reported experiencing chronic sleep problems and 33.8% excessive sleepiness (Gander et al, 2019).
The lowest estimate of sleep problems among nurses was in Jordan, where only 7.9% of emergency department nurses were poor sleepers (Suleiman et al, 2020a).
Overall, these findings indicate that nurses' experiences of poor sleep vary greatly worldwide. These variations could be attributed to different factors that contribute towards good-quality sleep.
About sleep
Essentially, sleep is a neurobehavioural state and a fundamental part of human physiology that allows the brain and body to recover, improve physical and mental performance, and to prepare for another day (Hasson and Gustavsson, 2010; Medic et al, 2017). During sleep, the human body undergoes a series of biochemical processes that are vital to overall health, including neurological activity, cardiac function and the endocrine and immune systems, which affect thinking, concentration, energy levels and mood (Vyazovskiy, 2015).
Sleep is an important factor in an individual's psychological and physiological wellbeing and quality of life (Worley, 2018). A healthy, normal sleep is characterised by sufficient duration, good quality, appropriate timing and regularity, and the absence of sleep disturbances and disorders (Watson et al, 2015). An adult should sleep at least 7 hours per night whereas young adults should sleep for at least 9 hours per night to achieve ideal health (Watson et al, 2015). Concerns about both sleep problems and poor mental health are on the rise since they are highly prevalent in modern society.
Sleep-related issues could be a factor, cause or contributor to the rising prevalence of psychiatric problems, whether major or minor. Higher levels of depression, anxiety and stress are reported in individuals who take a long time to fall asleep, have fewer hours of sleep, have a higher level of sleep disturbances, take more sleep medications and experience daytime dysfunction (Del Rio João et al, 2018).
Literature review
Evidence from extant literature shows that doing shift work was among the highest contributors to poor sleep. In China, a cross-sectional study of 513 nurses in a hospital in Shanghai showed that those who had or were currently working shifts were three times more likely to have poor sleep quality (PSQI≥5) than those who had never done shift work (Zhang et al, 2016).
In addition, a cross-sectional study from UAE reported that nurses working fixed day shifts reported significantly poorer sleep quality than those working rotating night shifts (Bani Issa et al, 2020). Similar findings can be found in studies from Iran and Taiwan, where nurses working night shifts had shorter total sleep times and lower sleep efficacy than those working day shifts (Niu et al, 2013; Sepehrmanesh et al, 2017).
Empirical evidence has shown that shift work interrupts regular sleep patterns and is aggravated by endogenous and environmental factors such as a lack of exposure to light (De Martino, et al, 2013); this is because the circadian rhythm that regulates the human sleep-wake cycle is influenced by daylight (De Martino et al, 2013). Nurses working on shifts rarely achieve the optimal amount of sleep and this increases daily dysfunction and decreases sleep efficacy and sleep quality significantly (Zhang et al, 2016).
Another important factor is long hours related to shift work where higher weekly longer working hour burdens has led to sleep deprivation because of fewer opportunities to get sufficient sleep hours and poor sleep quality because of circadian misalignment (Trinkoff et al, 2011). Nurses who worked more than 13 hours had double the risk of burnout and job satisfaction of those working 8-hour shifts (Stimpfel et al, 2012). Furthermore, nurses who worked more than 40 hours per week made 46% more errors in patient care, including serious medical errors, and therefore had higher patient mortality rates (Trinkoff et al, 2011).
Additional signficant factors reported for sleep problems include demographic factors.
First, women experience higher sleep disturbance rates (Qiu et al, 2020). Being female more than tripled the chance of severe disturbance in at least one component of sleep, and night-shift work was associated with severe worsening of quality of life including sleep quality (Palhares et al, 2014). Women are more sensitive to stress, so are more likely to experience sleep problems (Sepehrmanesh et al, 2017). Frequent stress, including work stress, could cause more sleep problems in women than men (Sepehrmanesh et al, 2017).
Second, older age was significantly related to being a poor sleeper (age 50.81±7.8 years) and short-duration sleep (age 51.82±7.67 years) (Zamanian et al, 2016). Poor sleepers had significantly lower scores in all components of quality of life (except bodily pain) than younger people. They also had the highest scores in the bodily pain domain and the lowest scores in the vitality domain (Zamanian et al, 2016).
Finally, there are other factors including smoking. Tarhan et al (2018) found that 61.9% (105/152 cases) of Turkish nurses had poor sleep quality, and this was significantly related to smoking. They also found that those in the higher income brackets had significantly better sleep quality than staff in lower income bracket (Suleiman et al, 2020b).
Consequently, poor sleep quality impacts nurses organisationally, physically and psychologically. In terms of organisational impact, literature has highlighted a significant negative correlations of nurses' poor sleep quality with job satisfaction, where sleep deprivation related to shift work reduces job satisfaction (Chang and Chang, 2019) and the ability to provide optimal patient care (Gómez-García et al, 2016). In the same vein, it has led to poorer patient outcomes with an increased risk of mortality (Trinkoff et al, 2011) and of preventable errors, such as omitting information during handover (Gómez-García et al, 2016) and fatigue-related errors (Gander et al, 2019).
Nurses with poor sleep quality are more likely to leave the profession, exacerbating problems of nurse shortages (Brossoit et al, 2020). Nevertheless, favourable organisational and managerial support appear to mitigate poor sleep quality to a certain extent (Gómez-García et al, 2016).
In terms of the physical impact on nurses, poor sleep quality increases rates of acute fatigue (Han et al, 2014), and susceptibility to diseases such as hypertension (Liu et al, 2016) and diabetes (Khalil et al, 2020) because of low-quality diets and irregular eating patterns (Nea et al, 2015). In addition, restless legs syndrome (Willis-Ekbom disease) is also prevalent among nurses with poor sleep (Uekata et al, 2019).
Overall, quality of life and quality of sleep are closely correlated (Palhares et al, 2014).
In terms of psychological impact, sleep deprivation and poor-quality sleep are significantly associated with raised stress levels in nurses (Deng et al, 2020), higher depression and anxiety levels (Tarhan et al, 2018) as well as increased irritability, poor mood, reduced social communication and emotional instability (Gómez-García et al, 2016), also poor cognitive function, with a lack of alertness, poor response time, lack of energy and difficulty in maintaining attention (Cooke and Ancoli-Israel, 2011); they significantly affect human functioning. Consequently, poor sleep quality disrupts work-life balance and leads to a poor quality of life (Palhares et al, 2014; Gómez-García, 2016).
Students
Increased sleep disturbances have been reported among students at higher education institutions (Hershner and O'Brien, 2018), which increased symptoms of anxiety and depression as well as sleep medication use (Becker et al, 2018). This, in turn, can adversely affect students' academic and physical performance and mood (Quan et al, 2018).
Nursing students are no exception and their poor sleeping habits may continue as they transition into the nursing workforce. However, evidence on this is lacking. Studying the association between sleep quality and mental health among university students, and comparing them with hospital nurses, could increase awareness of the issue and enable student nurses to seek help and support early from different levels in the higher education and healthcare organisations.
Methods
This study aimed to explore sleep quality among hospital nurses and student nurses in Brunei. The objectives were:
- To compare seven key sleep indicators between hospital nurses and student nurses—subjective sleep quality, latent sleep, sleep duration, habitual sleep efficiently, sleep disturbances, use of sleeping medication and daytime dysfunction—over the previous month
- To compare these sleep quality domains between hospital nurses and student nurses.
Study design, population and sample
This cross-sectional study used a self-administered online survey.
The target student population was all nursing students at the PAPRSB Institute of Health Sciences (IHS). Under the eligibility criteria, participants had to be undertaking bachelor degrees in nursing; graduate programmes were excluded because they cater for experienced nurses.
For hospital nurses, the target population was all nurses working in the general medical and surgical wards in the largest referral hospital in Brunei, the Raja Isteri Pengiran Anak Saleha (RIPAS) hospital. They had to be: registered nurses working on shift duty; and involved in direct patient care.
Statistics obtained from the respective administrations showed there were an estimated 150 nursing students in IHS and 250 general nurses in RIPAS hospital. All these students and general nurses were eligible to be recruited to the study.
Ethical consideration
This study protocol was approved by the Ministry of Health and Universiti Brunei Darussalam joint ethics committee. The eligible participants who agreed to join the study clicked ‘I agree’ on the informed consent form before completing the questionnaire. Participants were free to withdraw at any time without penalty.
No participant-identifying information (such as name and identification card number) was collected. Data collected were stored in a password-locked computer, accessible only by the researchers working on the study.
Data collection and procedure
After ethical approval had been obtained, the principle researcher approached and briefed the programme leader of IHS and nurse managers of general wards in RIPAS hospital, regarding details of the study and the dissemination plan.
These ‘gatekeepers’ disseminated the online link, which included the questionnaire, the participant information sheet and a consent form. Participants were given ample time to read and understand the information sheet and, if there were questions, could email the researcher directly.
Research instrument
Sleep quality was measured using the PSQI. This is an open-source, widely used, validated questionnaire that consists of 10 items measured on a 0–3 Likert scale. A PSQI score of 5 or more indicates poor sleep quality, whereas a score of less than 5 indicates good sleep quality (https://eprovide.mapi-trust.org/instruments/pittsburgh-sleep-quality-index).
The questionnaire was pre-tested among a purposive sample of five student nurses and five hospital nurses selected by the gatekeepers. No major changes were made following this.
Statistical analysis
Descriptive statistics for sample characteristics of the two comparative groups were computed using frequency and percentages. Univariate analysis, including one-way ANOVA, chi-square test, Fisher's exact test and simple linear regression, was used to compare the two groups in terms of sleep quality domains and sample characteristics.
Factors associated with poor sleep were identified using multiple logistic regression. All hypothesis tests presented were two-sided tests and P<0.05 was considered statistically significant. All analyses were conducted using R Studio v1.2.1355 for Windows.
Results
Characteristics of the two groups
Table 1 presents the characteristics of the two groups being compared. There were 260 participants, divided into groups of 130 hospital nurses (52.0% response rate) and 130 student nurses (86.7% response rate).
Table 1. Characteristics of the two study samples (n=260)
| Characteristics | Hospital nurses (n=130) | Student nurses (n=130) | Total (n=260) | P value | |||
|---|---|---|---|---|---|---|---|
| n | (%) | n | (%) | n | (%) | ||
| Mean age (SD) | 36.2 (7.8) | 21.1 (2.6) | 28.6 (9.6) | <0.001* | |||
| Sex | |||||||
| Female | 101 | (77.7) | 100 | (76.9) | 201 | (77.3) | 1.000† |
| Male | 29 | (22.3) | 30 | (23.1) | 59 | (22.7) | |
| Body mass index | |||||||
| Underweight | 4 | (3.1) | 21 | (16.2) | 25 | (9.6) | <0.001† |
| Normal | 34 | (26.2) | 61 | (46.9) | 95 | (36.5) | |
| Overweight | 51 | (39.2) | 34 | (26.2) | 85 | (32.7) | |
| Obese | 41 | (31.5) | 14 | (10.8) | 55 | (21.1) | |
Chi-square test for independence
SD=standard deviation
The students were significantly younger with an average age of 21.1 (SD=2.6) years. Around one-quarter of the sample were male and the proportion of women and men was roughly equal in both groups. Student nurses had significantly better body mass index (BMI) with about 50% in the healthy BMI range. There were significantly more overweight and obese nurses than student nurses.
Comparison of perceived sleep quality
Perceived sleep quality in terms of seven PSQI components in the two study groups was compared (Table 2). The hospital nurses had a significantly poorer sleep quality index than the students (P<0.001). More than 99% of hospital nurses reported poor sleep compared with 88.5% of student nurses.
Table 2. Comparison of PSQI components between hospital nurses and student nurses
| PSQI | Hospital nurses | Student nurses | Chi sq (df) (P value)* |
|---|---|---|---|
| n (%) | n (%) | ||
| PSQI score | |||
| Good sleep (<5) | 1 (0.8) | 15 (11.5) | 11.3 (1) |
| Poor sleep (≥5) | 129 (99.2) | 115 (88.5) | (<0.001) |
| C1 subjective sleep quality | |||
| Very good | 5 (3.8) | 15 (11.5) | 7.9 (3) |
| Fairly good | 89 (68.5) | 83 (63.8) | (0.049) |
| Fairly bad | 33 (25.4) | 25 (19.2) | |
| Very bad | 3 (2.3) | 7 (5.4) | |
| C2 sleep latency index | |||
| 0 | 0 (0.0) | 17 (13.1) | 34.6 (3) |
| 1 | 72 (55.4) | 47 (36.2) | (<0.001)† |
| 2 | 54 (41.5) | 45 (34.6) | |
| 3 | 4 (3.1) | 21 (16.2) | |
| C3 sleep duration | |||
| <5 hours | 4 (3.1) | 28 (21.5) | 23.5 (3) |
| 5–6 hours | 30 (23.1) | 25 (19.2) | (<0.001) |
| 6–7 hours | 29 (22.3) | 33 (25.4) | |
| >7 hours | 67 (51.5) | 44 (33.8) | |
| C4 sleep efficiency | |||
| <65% | 57 (43.8) | 65 (50.0) | 3.1 (3) |
| 65–74% | 17 (13.1) | 19 (14.6) | [0.370] |
| 75–84% | 8 (6.2) | 11 (8.5) | |
| >85% | 48 (36.9) | 35 (26.9) | |
| C5 sleep disturbances index | |||
| 0 | 1 (0.8) | 7 (5.4) | 21.4 (3) |
| 1 | 48 (36.9) | 76 (58.5) | (<0.001)† |
| 2 | 76 (58.5) | 41 (31.5) | |
| 3 | 5 (3.8) | 6 (4.6) | |
| C6 sleep medication | |||
| 0 past month | 123 (94.6) | 123 (94.6) | 2.1 (3) |
| <1 a week | 0 (0.0) | 1 (0.8) | (0.550)† |
| 1–2 a week | 3 (2.3) | 1 (0.8) | |
| >2 a week | 4 (3.1) | 5 (3.8) | |
| C7 daytime dysfunction | |||
| No problem | 16 (12.3) | 33 (25.4) | 10.1 (3) |
| Slight problem | 75 (57.7) | 53 (40.8) | (0.018) |
| Somewhat problem | 30 (23.1) | 35 (26.9) | |
| Big problem | 9 (6.9) | 9 (6.9) | |
Fisher's exact test
Good sleep = PSQI score 5 or less; Poor sleep = PSQI score more than 5
Although students had significantly good sleep latency (P<0.001)—they fell asleep sooner—they were observed to have significantly more sleep disturbances as well as significantly lower sleep duration and reduced sleep efficiency.
There was no significant difference in sleep medication use. About 3% of each study group reported to have taken sleep medication more than twice a week.
Factors associated with PSQI score
Univariate one-way ANOVA analysis showed that PSQI global scores were significantly associated by both study group and sex (Table 3). Age had a low but positive significant relationship with PSQI global score (B=0.04; 95% CI (0.01–0.07); P=0.034) (Figure 1).
Table 3. Factors associated with PSQI total global score (n=260)
| Variables | Mean PSQI global score (SD) | F-statistics (df) | P value* |
|---|---|---|---|
| Nurses | |||
| Hospital | 9.4 (2.2) | 12.0 (1) | <0.001† |
| Student | 8.2 (3.1) | ||
| Sex | |||
| Female | 9.0 (2.8) | 5.4 (1) | 0.022† |
| Male | 8.1 (2.3) | ||
| Body mass index | |||
| Underweight | 8.6 (3.3) | 0.1 (3) | 0.974 |
| Normal | 8.8 (2.8) | ||
| Overweight | 8.8 (2.4) | ||
| Obese | 8.9 (2.9) | ||
Significance at 0.05
SD=standard deviation
Furthermore, multivariate analysis revealed hospital nurses were 4.29 times more likely to experience poor sleep than student nurses. In addition, those who were overweight were 2.35 times more likely to have poor sleep quality compared with those who were underweight (Table 4).
Table 4. Factors associated with poor sleep quality as per PSQI
| Simple logistic regression | Multiple logistic regression | ||||||
|---|---|---|---|---|---|---|---|
| b* | 95% CI | p | Adj b† | 95% CI | z | P | |
| Age | 0.11 | (1.03–1.23) | 0.019 | - | - | - | |
| Nurses | |||||||
| Hospital | 1.00 | 1.00 | |||||
| Student | -2.82 | (0.00–0.30) | 0.007 | -4.29 | (0.00–3.46) | -2.15 | 0.032 |
| Sex | |||||||
| Female | 1.00 | - | - | - | |||
| Male | -0.47 | (0.22–2.05) | 0.403 | - | - | - | |
| Body mass index | |||||||
| Underweight | 1.00 | 1.00 | |||||
| Normal | 1.04 | (0.67–10.80) | 0.132 | 0.89 | (0.56–9.97) | 1.25 | 0.213 |
| Overweight | 2.78 | (2.23–32.19) | 0.015 | 2.35 | (1.37–2.21) | 1.99 | 0.046 |
| Obese | 0.64 | (0.43–7.90) | 0.370 | –0.43 | (0.13–3.24) | –0.53 | 0.560 |
Adjusted regression coefficient
Discussion
To the best of the authors' knowledge, this is the first study to explore and compare sleep quality between hospital and student nurses in Brunei. There were several significant findings.
Prevalence of poor sleep quality
The present study revealed that 99.2% of hospital nurses and 88.5% of student nurses experienced poor-quality sleep (PSQI score ≥5), which is much higher than the global estimate of 61.0% for nurses (Zeng et al, 2020). The high proportion of student nurses experiencing poor sleep indicates a possibility that sleep deprivation may extend into nursing work life.
These figures were on a par with those from Iran where the prevalence of poor sleep quality in nurses has been found to be between 91.2% and 95.5% (Roodbandi et al, 2016; Sepehrmanesh et al, 2017). It was clearly higher than the 75.8% figure in Taiwan (Chien et al, 2013), 64.0% in UAE (Bani Issa et al, 2020), 40% in Japan (Uekata et al, 2019), 39.2% in China (Qiu et al, 2020), 33.8% in New Zealand (Gander et al, 2019) and 7.9% in Jordan (Suleiman et al, 2020b). Given the wide range of figures regardless of location, geographical variation may not be a major contribution towards this disparity.
However, when study design is taken into consideration (most studies were cross-sectional in nature), prevalence might have been over-or underestimated (Naing et al, 2006), with the exception of the global figures and those for China, which are systematic reviews and meta-analysis—the highest level of evidence. The lower prevalence of poor sleep quality among nurses from other countries could be attributed to differences in study settings.
Nurses working in more specialised areas such as emergency and intensive care units had better sleep quality than general nurses. The reason could be associated with fatigue-related outcomes that are extended from sleepiness at the end of the shift (Gander et al, 2019). Another suggestion could be better organisational and family support, which have been shown in empirical evidence to improve sleep quality among nurses (Gómez-García et al, 2016).
Factors related to poor sleep quality
Multivariable analysis of the present study revealed that hospital nurses were 4.29 times more likely to experience poor sleep than student nurses. This could be owing to a combination of factors such as age and work designation. Hospital nurses were older than student nurses so required fewer hours of sleep latency and duration (Stimpfel et al, 2012).
In addition, greater work experience was significantly associated with poorer sleep quality as shown in studies from China (Niu et al, 2016; Dong et al, 2017), Iran (Khatony et al, 2020), Nigeria (Aliyu et al, 2017) and Taiwan (Chien et al, 2013). Nurses with more work experience are usually in a higher position where they undertake less patient care or have less patient contact and more administrative tasks. In addition, they are more familiar with daily routines (Stimpfel et al, 2012). Previous studies have reported that those with more years of working experience also have greater work pressure and may be involved in academic research and teaching, which put them at risk of sleep problems (Niu et al, 2016; Dong et al, 2017).
Although other factors were not statistically significant in the present study, the small sample size might introduce a type 2 error, with inadequate power to detect significance (Naing et al, 2006). Previous studies have found that poor sleep quality was significantly associated with older age, working shifts and smoking (Tarhan et al, 2018), being female (Sepehrmanesh et al, 2017), cognitive factors such as insomnia (Khatony et al, 2020), work setting, with paediatrics and internal medicine being associated with poorer sleep quality (Aliyu et al, 2017) and salary with those with earning more having better sleep quality (Suleiman et al, 2020b).
Strategies to reduce sleep problems among nurses
Adequate shift assignment and stress reduction prevent pathological disruptions to the circadian cycle (Chien et al, 2013).
Sleep-related problems impose financial, health and safety costs on society, not only because of sickness-related absence but also in terms of reduced work performance (Pilcher and Morris, 2020). Sleep deprivation and subsequent excessive sleepiness may increase risks of errors and work-related injuries and accidents (Zhang et al, 2016; Gander et al, 2019). Therefore, the primary aim of treatment should be to prevent rather than treat these symptoms by encouraging improved sleep and/or circadian adaptation. Several strategies are proposed in this article.
People working night shifts are more likely to have disturbed circadian rhythms and a less robust activity rhythm. Therefore, they could be encouraged to sleep longer on their first day after starting night duty (Gómez-García et al, 2016; Bani Issa et al, 2020). It is important to keep the number of shifts less than 11 hours apart to a minimum, with time spent at work not exceeding 40 hours per week to ensure the time off between shifts is at least 11 hours to help the body to recover (Vyazovskiy, 2015). This is to protect both nurses and patients in their care, so individuals and organisational leadership must promote schedules that limit circadian disruption and develop policies and practices to monitor workers for signs of sleep disorders and functional decline. In addition, providing a positive work environment, such as one with conflict resolution and peer support groups, may decrease occupational stress, reduce the development of sleep disturbances and promote health, both physiological and psychosocial (Gómez-García et al, 2016; Dong et al, 2017). This can also reduce the likelihood of nurses burning out, becoming dissatisfied with their jobs and intending to quit.
Furthermore, a 3-month pilot study was performed at six nursing units in two American hospitals where rotation napping was implemented on the night shift (Geiger-Brown et al, 2016). Several barriers were identified to the successful implementation of this, which occurred primarily at the point of seeking unit-based nursing leadership approval. Only one out of the six units had excellent uptake of napping, where nurses who napped reported being less drowsy while driving home after their shift. Short naps during night shifts have been found to improve work performance and alertness (Geiger-Brown et al, 2016). Napping opportunities during night work have demonstrated improved work performance; however, less is known about the possible beneficial effects of napping before work (Flo, 2013).
Lastly, non-pharmaceutical interventions could help mitigate problems for poor sleepers. A systematic review and meta-analysis of non-pharmaceutical research showed that aromatherapy significantly improved the quality of sleep of nurses working shifts (Kang et al, 2020). Such provision could be conducted in tandem with physical activity, cognitive behavioural or shift-rotation interventions.
For organisations, nurse managers could organise a sleep hygiene seminar that emphasises adhering to good sleep hygiene advice and adequate exposure to bright light to balance circadian rhythms and sleep hormones (Jehan et al, 2017). In addition, for daytime sleepers, shift workers may benefit from using sunglasses on their commute home from night shifts, proper blinds in the bedroom and earplugs during sleep to reduce interruptive noise (Flo, 2013).
Raising awareness of the importance of these steps may also encourage shift workers to inform friends and family of their need to get sufficient sleep during the daytime (Vitale et al, 2015; Shiffer et al, 2018). Phones should be turned off, and other family members should refrain from loud, disruptive activities if possible. If family and friends are aware of the difficulties with shift work and are considerate in this respect, they may be a resource and not cause additional stress for shift workers (Vitale et al, 2015; Shiffer et al, 2018).
Limitations
The results of this study should be interpreted with the following limitations. Non-response bias from a low response rate could mean conditions were underreported or underestimated. Small sample sizes could mean there are type 2 errors, which limit generalisability.
Factors such as a history of shift work, total hours worked per week or breaks between shifts were not assessed before participants were recruited. Including these could have provided a more accurate estimate of sleeping problems in nurses, however, they were not measured in this study, which is a limitation.
It is difficult to assess all possible factors in one single study so further research on this issue is warranted. Future studies should evaluate the effects of independent variables on sleep disorders and quality of sleep in a longitudinal manner and examine the effects of poor quality sleep on nurses' lives.
Conclusion
The findings show that the prevalence of poor sleep quality among nurses in Brunei is higher than in the rest of the world. Stakeholders such as nursing leaders, nursing educators and healthcare policymakers should prepare student nurses and help current nurses by formulating strategies that promote working schedules and rosters that minimise circadian disruption.
KEY POINTS
- Poor sleep quality is common among nurses. This negatively affects patient care, increases medical errors and reduces nurses' quality of life
- Hospital nurses are four times more likely to have poor sleep quality than student nurses. Student nurses need to be prepared early to manage this challenge at the workplace
- Both organisational and family support are important to ensure strategies to improve sleep are effective
- Nurse leaders and managers need to incorporate nurses' sleep efficacy into workplace policy to help retain nurses, improve nurses' quality of life, and promote the overall quality of patient care
CPD reflective questions
- What sleep practices do you think student nurses should adopt to minimise the negative effects of sleep problems later as a registered nurse?
- Nurses with poor sleep quality cause 46% more errors in patient care. What preventive measures are or should be in place to address sleep problems among rotating shift nurses in hospitals?
- Is sleep quality being considered as part of your hospital policy to improve patient care and nurses' quality of life?