Reported sleep health and viral respiratory illness in nurses.
In addition to patients' risks, insufficient sleep may be harmful to health care workers themselves. Findings from numerous studies agree fatigue resulting from fragmented sleep or poor-quality sleep, which can occur during night and rotating shifts, is associated with a variety of health complaints (Chung, Wolf, & Shapiro, 2009; Sofianopoulos, Williams, Archer, & Thompson, 2011). Researchers determined disturbed sleep and fatigue are predictors of long-term work absence (Akerstedt, Kecklund, Alfredsson, & Selen, 2007; Akerstedt, Kecklund, & Selen, 2010). Perceived levels of reduced productivity, social isolation, increased accidents and injuries, and other detrimental health effects have been reported for workers on night shift (Isah et al., 2008). Evidence supports a link between poor sleep and chronic health effects (Akerstedt et al., 2007; Bjorvatn et al., 2012; Chan, 2009; Luyster, Strollo, Zee, & Walsh, 2012), but less is known about acute health effects. Findings of one study suggested insufficient sleep increases the risk of acquiring a viral respiratory infection (common cold) (Cohen, Doyle, Alper, Janicki-Deverts, & Turner, 2009). As this hazard could have implications for nurse staffing and workforce issues, an additional aim of this study was to add to literature linking respiratory viral illness and poor sleep health.
A literature review through CINAHL and PubMed databases using nurses and shift work for all years revealed 711 articles from CINAHL. Narrowing the search to 2007-2013 reduced the results from 312 to 181 studies on this topic. Focusing the search on nurses, shift work and health found fewer (n=94 articles); adding sleep or sleep disruption to nurses and shift work resulted in 14 research-related articles. Ten of these studies were conducted outside the United States.
As hospitals have adopted predominantly 12-hour work shifts versus the traditional 8-hour shifts, nurses are at increased risk for insufficient sleep. The risks of making an error increase when work shifts go beyond 12 hours, when nurses work overtime, or when they work more than 40 hours per week (Dorrian et al., 2008; Rogers, Hwang, Scott, Aiken, & Dinges, 2004). Nurses who work rotating shifts are most likely to become drowsy during work, struggle to stay awake while driving, or be involved in motor vehicle accidents (Dorrian et al., 2008). Night time sleep is most restful and restorative (Luyster et al., 2012; Muecke, 2005); therefore, night shift workers who must sleep during the day obtain fewer hours of quality sleep and accrue increased amounts of sleep debt.
An increase in the incidence of metabolic disorders, including obesity (Buss, 2012; Smith, Fritschi, Reid, & Mustard, 2013), heart disease, insulin resistance (Biggi, Consonni, Galluzzo, Sogliani, & Costa, 2008), gastrointestinal disorders (Ananthakrishnan, Long, Martin, Sandler, & Kappelman, 2013), and spontaneous abortion (Quansah & Jaakkola, 2010) also has been found among night shift workers. Cancers of the colon (Davis & Mirick, 2006; Schemhammer et al., 2003), endomentrium (Viswanathan, Hankinson, & Schernhammer, 2007), and breast (Hansen, 2006; Knutsson et al., 2013) have been associated with shift work and rotating shifts. With the aging nursing workforce, conversations about nurses' ability to manage long and rotating shifts have begun. Admi, Tzischinsky, Epstein, Herer, and Lavie (2008) determined increased age and body mass index were predictors of health symptoms and sleep disturbance, while increased age may be associated with some improvements in sleep quality despite shorter sleep periods (Chung, Chang, Yang, Kuo, & Hsu, 2008).
The specific aims of this study were to (a ) describe and compare sleep quality, reported hours of sleep, and viral illness experienced (retrospectively) in a 1-year period by nurses who practice on day shift and those who practice on shifts other than days; (b) determine if significant differences exist in sleep quality among age groups; and (c) determine if relationships exist between shift of e mployment and specific variables, such as number of sleep and waking aids used or drowsiness while driving.
Design and Sample
This study was conducted using a descriptive, cross-sectional, correlational design. The study inclusion criteria were direct-care nurses from all units that employ nurses in shift work in a 246-bed community-owned hospital in the northwestern United States. This Magnet[R]-designated facility serves a population of approximately 45,000 and covers an extensive, largely rural geographic area. The sample included nurses from four general medical-surgical units, three critical care units, one obstetric unit, one pediatric unit, the emergency department, heart center, and rehabilitation units. Excluded from the study were nurses who did not work in direct patient care positions. A sample of 134 participants was determined a priori to provide a power of 0.95 to detect a medium effect size (0.03-0.05) with a Type I error rate of 0.05. A convenience sample of 175 nurses was targeted for recruitment to assume a 70% response rate.
A self-reported questionnaire included two parts. Subjective sleep quality data were collected using the Karolinska Sleep Questionnaire (KSQ) (Akerstedt et al., 2002; Kecklund & Akerstedt, 1992). The KSQ is a validated instrument that correlates well with the Karolinska Sleep Diary and polysomnographic data (Axelsson, Kecklund, Akerstedt, Ekstedt, & Menenga, 2002; Eriksen & Kecklund, 2007). It consists of 10 items in English that assess habitual sleep quality on three dimensions: sleep status, sleep difficulties, and daytime distress of waking up and not feeling refreshed. Sleep status is evaluated on a 5-point scale ranging from very poor to very good. Sleep difficulties on issues of falling asleep and disturbed sleep are evaluated on a 5-point scale ranging from always everyday to never. Daytime distress concerning issues of difficulty waking, not feeling refreshed, and exhaustion is evaluated on a 5-point scale ranging from always everyday to never. The KSQ had good reliability and validity in previous studies, with Cronbach's alpha of 0.86 and 0.85, respectively (Chan, 2009), and items were not altered for use in this study.
The next part of the questionnaire included 20 items to capture demographic information, work and shift data, pre-sleep and awake/alert activities, and self-reports of recent illness, absenteeism, and drowsiness driving home after work. Average sleep was determined by asking participants to write the actual hours of sleep for a typical sleep schedule. This was verified later in the survey with a question that asked the typical clock time they sleep (e.g., 10:00 p.m.-5:00 a.m.). Participants were asked to record the number of days for each 3-month period in the previous 12 months (2008) they experienced an upper respiratory viral infection (URVI) and the number of days they were absent from work with the URVI. Viral illness was defined as an upper respiratory illness (cough, fever, chills, and congestion) that did not result in treatment with antibiotics. This variable was chosen to determine if findings would support those of Cohen and co-authors (2009), and because most nurses would understand the difference between acute respiratory viral illness (common cold) and other illnesses, such as influenza, bacterial pneumonia, bronchitis, or conditions requiring antibiotics.
The final instrument was reviewed for content and face validity by the hospital's nursing research council. This group includes advanced practice nurses, research faculty from nearby academic institutions with experience in survey development, and direct-care nurses. Feedback was used to clarify items on the final questionnaire.
The study proposal and survey were reviewed and approved by the institutional review boards from the university leading the study and the participating hospital. Research assistants distributed 17S paper self-completion questionnaires to each identified unit's classroom with signs inviting participation and indicating the timeline for completion. Participants had 4 weeks to complete the survey at their convenience. Instructions on the surveys identified the study purpose as well as the location to deposit completed surveys. These were collected in envelopes stored in locked drop boxes on nursing units and in the nursing administration office. Research assistants made frequent rounds to units to remind and invite nurses to participate.
Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 17. Responses were analyzed using descriptive and inferential statistics. Univariate analysis was used to test the difference in mean scores between day shift and non-day shift nurses for the KSQ and variables of interest. Chi-square analyses were used for categorical items. Pearson's r was used to examine the relationship between day and non-day shift nurses' reported viral illness and absences due specifically to viral respiratory illness over a 12-month period for the previous calendar year. Regression analysis was used to determine if any independent variables predicted nurse drowsiness when driving home.
Because the study site employs nurses on a wide variety of shift hours, most data analysis was completed by comparing day shift (7:00 a.m. to 3:00 p.m., or 7:00 a.m. to 7:00 p.m.) to all other shifts (3:00 p.m. to 11:00 p.m., 11:00 a.m. to 11:00 p.m., 7:00 p.m. to 7:00 a.m., 11:00 p.m. to 7:00 a.m. as well as variations that included nurses waking up at 2:00-3:00 a.m. to report to work by 4:00 or 5:00 a.m.). Almost all nurses at this facility work established shifts and generally do not rotate between shifts. Results were reviewed in aggregate and no responses were linked to individual participants to maintain confidentiality.
Nurses returned 131 surveys out of 175 distributed (74.8% response rate). The percentage of male (n=21, 16%) and female (n=110, 84%) nurses and the mean age of respondents (44.4 years, SD=11.3, range 23-68) (see Table 1) closely matched the proportion in the study site's overall nursing pool. Few nurses on either shift reported working a second job or attending school, but day shift nurses responded "yes" to this question more than non-day shift nurses (n=14, n=5, respectively). On average, day shift nurses had been employed at the facility 3 years longer than non-day shift nurses and also had been licensed as registered nurses 3-4 years longer.
Reported Viral Illness
Overall, as noted by shift, no significant differences existed in reported viral illness by quarter (3-month time frames), total days ill with viral illness, or days ill that resulted in missed work (see Table 1). Nurses averaged 2.9 days of absence with reported viral illness over a 12-month period. The majority of nurses (n=110, 57%) reported having had a viral illness, but having no work absences related to the episodes. For the nurses who reported they were more likely to call in sick when experiencing a viral respiratory illness (n=52, 39%), no significant difference was found between day shift and night shift. Approximately 60% (n=79) of the sample reported they never or rarely call in sick when they have a viral illness.
Sleep and Sleep Quality by Shift
Significant differences were found between day and non-day shift nurses in evaluating results of the KSQ. Participants responded to the 10 domain items using a Likert scale (l=never to 5=always). Non-day shift nurses responded they experienced "falling asleep during their awake period" more often than day shift nurses (p=0.02). No significant differences existed between shifts among the other nine items of the KSQ. When the scale was merged for a global sleep quality score (summation of all 10 domains), t-test analysis noted a significant difference (p=0.01) between shifts, with non-day shift nurses having a higher mean score (27.47) compared to day shift (26.54) (see Table 1). A higher global sleep quality score reflects poorer quality of sleep.
On the additional self-reported variables, non-day shift nurses had significantly fewer routine sleep schedules (p=0.02) as measured by their actual times and quality of hours slept each night (see Table 1). Non-day shift nurses reported using a greater number of self-help (pharmaceutical and non-pharmaceutical) activities to aid in "getting to sleep" (p=0.02). Non-day shift nurses also reported a significant increase in the number of activities used to "stay awake" during work (p=0.05).
Day shift nurses differed in their report of sufficiency of sleep or their perceptions that they slept enough quality hours compared to non-day shift nurses. More day shift nurses responded in the Occasionally enough and Definitely enough categories than non-day shift nurses, and more nonday shift nurses responded in the Definitely too little, Occasionally too little, and Sometimes enough categories. Non-day shift nurses were more likely to report extreme drowsiness or falling asleep while driving home than day shift nurses (p=0.005). Overall, nurses on all shifts reported at least occasionally feeling exhausted (n=86, 66%), having difficulty sleeping (n=81, 62%), or experiencing insufficient sleep (n=99, 76%).
Sleep Quality by Age Group
For younger (ages 20-39) and older (age 40 or older) nurses, global sleep quality score (GSQS) was poorer in younger nurses on day or non-day shifts. Younger nurses on day shift reported waking more often during their sleep periods and being sleepier, and rated their overall global sleep health higher (GSQS=31.9), indicating poorer quality of sleep health than older nurses on day shift (GSQS=28.3; p=0.0002). The pattern was repeated in examination of night shift GSQS by age, with younger night shift nurses indicating poorer quality of sleep (GSQS=32.0) than older night shift nurses (GSQS=29.8; p=0.01). Younger nurses reported feeling more exhausted when awake (p=0.01), and utilized more activities to aid in sleep (p=0.02) and to stay awake when working (p=0.0001).
Correlation of Variables
The correlation between non-day shift and the actual number of activities used by those nurses to assist with sleep was significant (r=0.21, p=0.02). A moderate correlation also existed between nurses who used activities to get to sleep and the number of activities reported to stay awake (r=0.44, p=0.01). Nurses who reported using more activities to aid in sleep also reported less hours of actual sleep (r=0.27, p =0.003).
Components that were correlated significantly (p<0.05) were entered into a regression equation. Working non-day shift, not having a routine sleep schedule, waking up during sleep, global sleep quality score, and sleepiness during wake periods were predictors of experiencing (occasionally-always) drowsiness driving home ([R.sup.2]=0.42).
Similar to nurses in previous studies, the majority of nurses (n=93, 71%) in this study reported sleeping 7-8 hours on average each night (Garde, Hansen, & Hansen, 2009); as many as 72% (n=117) reported insufficient sleep (Chan, 2009). Results of this study were consistent with past studies that detected significant differences between day and non-day shift nurses in both hours of sleep and sleeping patterns. Non-day shift nurses had poorer sleep quality and were more likely to report falling asleep during their wake period, not getting sufficient sleep, and drowsiness driving home. This was consistent with other studies that found nightshift nurses had the poorest reported sleep quality (Garde et al., 2009; Kunert et al., 2007). Research that found night shift workers experience the poorest sleep quality on days of work, and not on days off, suggested the work schedule rather than personal traits cause this discrepancy (Garde et al., 2009).
Night shift nurses reported automobile-related injuries or near-misses while driving to and from their workplace (Novak & Auvil-Novak, 1996). The AAA Foundation (2013) reported drowsy driving is a direct cause of over 100,000 accidents annually, with 71,000 injuries and 1,550 fatalities. Fatigued and drowsy drivers are twice as likely to make a performance (driving) error as drivers who are not fatigued (National Highway Traffic Safety Administration, 2008). Because long, irregular work hours can contribute to sleep-related driving accidents, nurses driving home after a 12-hour shift also can impact the safety of their communities (Dorrian et al., 2008).
The finding of more maladaptive sleep by younger nurses in this study differed from Chung and co-authors (2008), who found night rotations were especially difficult for nurses above age 40 and could have adverse psychological and physiological effects. Sleep disturbances vary throughout the lifespan, with women being at higher risk as they age (Orff et al., 2012). Aging is associated with decreased ability to achieve adequate sleep, and as nurses age, they find shift work less desirable (Blok & Looze, 2011; Chan, 2009; Muecke, 2005). More important than age, however, may be the understanding that adjustments to shift work differ on an individual basis. Admi and co-authors (2008) discussed the non-adaptive shift worker as one who complains of difficulties falling asleep after evening, morning, or night shift, as well as having multiple awakenings during day sleep after a night shift. Exploring differences among individuals in adapting to shift work could assist organizations in hiring nurses to shifts that will be best suited for their age, circadian rhythm, and sleep pattern (Chung et al., 2008). Cohen and co-authors (2009) found less than 7 hours of sleep per night was associated with a substantial risk of acquiring the common cold. However, this study did not find any positive correlations between hours of sleep and reported viral illness or differences between shifts. Nurses in the current study did not differ in their reported hours of sleep, but self-reports can be influenced by sociocultural expectations that 7-8 hours of sleep is considered a healthy or expected response. This study differed from Cohen and co-authors in that nurses used recall for the previous 12 months. Cohen and associates subjected participants to inoculation of viral culture drops while monitoring sleep and collecting nasal lavage samples for 5 days. Sample size for the current study (N=131) and the study by Cohen and co-authors (N=153) were similar. This study focused on nurses who may be more health conscious and knowledgeable about symptom self-treatment.
As safety has become a top priority for many health care organizations, addressing workforce issues of sleep and illness should be part of strategic planning to eliminate harm. Napping is considered a practical approach to improving alertness and performance, yet more study is needed to determine ideal nap schedules and quantitative effects. Authors informally surveyed eight Magnet facilities in 2009 about specific policies related to napping at work. Six facilities reported napping, even during authorized lunch or break periods, was considered cause for termination, discipline, or counseling. Eight facilities had no specific policy, and three facilities had specific policies that permitted napping. This result suggests a lack of consensus among health care organizations on how to handle employees' innate needs for rest. Inadequate work break periods and insufficient sleep compound the effects of 12-hour shift work with potential negative health effects (Chen, Davis, Davis, & Pan, 2011). Unpublished data (Arroyo, Smart, Theodorson, & Krikac, 2012) supported the idea nurses may be reluctant to take breaks due to workload demands and expectations that taking breaks is counter to the profession of nursing.
The National Sleep Foundation (2013) supports organizations providing sleep-friendly spaces to improve safety and alertness on duty in the belief napping can reduce injuries, accidents, and legal liabilities. Short naps of up to 20 minutes have been found to be beneficial in reducing fatigue symptoms in night shift nurses (Smith-Coggins et al., 2006). Innovations in fatigue countermeasure programs and staffing practices should be explored to address the inevitable fatigue associated with nurses' shift work.
Because many nurses in this study reported having had a viral illness but having no work absences related to the episodes, the conclusion is these nurses either were ill on their normal days off or they were reporting to work ill. This second possibility was confirmed by the 79 nurses who reported they never or rarely call in sick when they have a viral illness. Employers might examine whether organizational practices support reporting to work while ill.
Limitations of this study include convenience sampling and the self-reporting instrument. Confounding variables, such as stress and depression, were not measured; they might explain findings as they also are known to influence sleep quality. Nurses wrote detailed accounts of their absences and illnesses on the survey, yet it is possible their recall was not completely accurate.
Additional studies that include the use of actigraph monitors would offer more objective measures of sleep. However, as noted by Girschik, Fritschi, Heyworth, and Waters (2012), the cost of such measures often precludes their use. Because the current study was completed in an ethnically homogenous (96% Caucasian) community hospital, findings may not translate to a more diverse population. However, Chan (2009) found similar percentages of insufficient sleep in Hong Kong, suggesting the phenomenon of nurses and sleepiness is relatively constant.
Recommendations for Future Research
Further studies are needed to examine work health characteristics, such as sick leave history and reporting to work when ill to minimize use of sick leave. Reluctance to use sick leave for legitimate illness may impact nurses' overall health (Schell, Theorell, Nilsson, & Saraste, 2013) and may have patient safety implications, as well as economic implications for patients and hospitals if patients' length of hospital stay is increased due to nurses-patient cross-infection.
This study was not able to add evidence to the study by Cohen and colleagues (2009) in regard to poor sleep quality and viral respiratory illness risks, but it did confirm night shift nurses have poorer quality of sleep, utilize more self-aid activities to promote sleep and enhance wake states, and are more likely to be drowsy as they drive home from work. Health care leaders should align themselves with other industries, such as aviation, railroads, and mining, that previously recognized the impact fatigue may have on workplace safety and productivity. Innovations could be explored in the areas of napping programs, educational interventions to improve sleep habits and increase awareness of risks, and scheduling practices that support a balanced lifestyle.
Acknowledgment: The authors would like to thank the BSN students of Washington State University; Bobbi Woodward, BSN, RN; Kootenai Medical Center nurses; and Nursing Research Roundtable members who contributed to the design, implementation, and review of this project and manuscript.
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Denise Smart, DrPH, BSN, RN, is Assistant Professor, Washington State University, College of Nursing, Spokane, WA.
Marian Wilson, MPH, RN-BC, is Clinical Research Coordinator, Kootenai Medical Center, Coeur d'Alene, ID.
Note: Dr. Smart and Ms. Wilson presented a poster on this study at the ANCC Magnet Conference in Phoenix, AZ, in October 2010.
TABLE 1. Demographic and Sleep Characteristics of Subjects by Shift (N = 131) Non-Day Shift Day Shift (n = 72) (n = 59) Variable n Mean SD n Mean SD Age (years) 45.2 11.7 43.6 10.7 Sex * Male 9 12 Female 62 47 Routine sleep schedule Yes 62 40 No 10 18 Years employed 11.9 9.2 8.7 7.1 Years as RN 16.1 12.6 12.9 9.8 # days viral illness 2.9 2.6 2.9 2.8 # days ill and off work 2.2 2.5 2.1 2.1 Hours of sleep 7.5 0.86 7.48 1.1 Global sleep quality 26.5 5.3 27.5 5.9 Number of self-help 3.1 2.1 (0-13) 4.1 2.4 activities used to aid sleep Number of self-help 2.1 1.5 (0-13) 2.6 1.6 activities used to aid wakefulness Total Variable p Value Mean SD Age (years) 44.4 11.3 Sex * Male Female Routine sleep schedule Yes 0.02 (b) No Years employed 0.03 (a) * 10.4 8'.4 Years as RN 0.12 14.7 11.5 # days viral illness 0.93 2.9 2.7 # days ill and off work 0.86 2.1 2.3 Hours of sleep 0.82 7.5 0:96 Global sleep quality 0.01 (a) ** Number of self-help 0.02 (a) * activities used to aid sleep Number of self-help 0.05 (a) activities used to aid wakefulness (a) t-test. (b) Chi-square test * Significant at 0.05. ** Significant at 0.01. Note: Range in parentheses. * indicates missing data.
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|Title Annotation:||Research for Practice|
|Author:||Smart, Denise; Wilson, Marian|
|Date:||Jul 1, 2013|
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