Applied Nursing Research xxx (2014) xxx–xxx
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Original Article
Contributors to shift work tolerance in South Korean nurses working rotating shift Hye-Sun Jung, RN, MPH, PhD a,1, Bokim Lee, RN, MPH, PhD b,⁎ a b
School of Medicine, The Catholic University of Korea, 505 Banpo Dong, Seocho Gu, Seoul, Republic of Korea, 137-701 Department of Nursing, University of Ulsan, P.O. Box 18, Ulsan, Republic of Korea, 680-749
a r t i c l e
i n f o
Article history: Received 3 March 2014 Revised 17 September 2014 Accepted 19 September 2014 Available online xxxx Keywords: Shift work Depression Fatigue Insomnia Nurses
a b s t r a c t Shift workers have rapidly increased in South Korea; however, there is no published research exploring shift work tolerance among South Korean workers. This study aimed to investigate factors related to shift work tolerance in South Korean nurses. The sample comprised of 660 nurses who worked shifts in a large hospital in South Korea. A structured questionnaire included following comprehensive variables: demographic (age and number of children), individual (morningness and self-esteem), psychosocial (social support and job stress), lifestyle (alcohol consumption, physical activity, and BMI), and working condition factors (number of night shifts and working hours). Shift work tolerance was measured in terms of insomnia, fatigue, and depression. The results of hierarchical regressions indicate that all variables, except for three, number of children, BMI, and working hours, were related to at least one of the symptoms associated with shift work tolerance. Based on these results, we offer some practical implications to help improve shift work tolerance of workers. © 2014 Elsevier Inc. All rights reserved.
1. Background and purpose Shift work has been widely discussed in scientific literature and it generally includes any arrangement of daily working hours other than the standard daylight hours (7/8 A.M. to 5/6 P.M.). Increasingly, shift work is becoming more common in our so-called “24/7” society. Prior studies have reported that shift work can have major negative effects on individuals’ health, safety, quality of life, and work productivity (Costa, 2010). Because of the complex interaction among personal traits, social conditions, and working conditions, however, not all shift workers experienced adverse effects (Costa, 2010; Saksvik, Bjorvatn, Hetland, Sandal, & Pallesen, 2011). Shift work tolerance (SWT) is defined as: the ability to adapt to shift work without adverse consequences (Andlauer, Reinberg, Fourre, Battle, & Duverneuil, 1979), such as digestive troubles, fatigue, sleep problems, depression, etc. (Saksvik-Lehouillier et al., 2013). (See Table 1.) From the previous literature on the subject, factors related to SWT can be divided into five dimensions: demographic, individual, psychosocial, lifestyle, and working condition factors. Demographic factors shown to be associated with SWT include age, gender, and number of children (Blok & de Looze, 2011). Most studies found that individuals of a higher age had a lower tolerance for the demands of shift work (Bonnefond et al., 2006). With regards to ⁎ Corresponding author at: Department of Nursing, University of Ulsan, P.O. Box 18, Ulsan, South Korea, 680-749. Tel: +82 52 259 1283; fax: +82 52 259 1236. E-mail addresses: [email protected] (H.-S. Jung), [email protected] (B. Lee). 1 Tel.: +82 2 2258 7368; fax: +82 2 532 3820.
gender, the reduced SWT of women is posited to associate with family burden (i.e., number of children) (Costa, 2010). Individual factors such as morningness, languidity, flexibility, and self-esteem have also been linked with SWT. Most studies have found that flexibility and self-esteem are positively related to SWT; low scores on morningness and languidity are related to high SWT (SaksvikLehouillier et al., 2013). Regarding psychosocial factors such as job stress and social support, research indicates that sleepiness during the night shift was positively associated with job stress, but inversely associated with support from colleagues (Kageyama, Kobayashi, & Abe-Cotoh, 2011). Lifestyle factors, especially BMI (body mass index), smoking, alcohol consumption, and physical activity have also been studied. Smoking among shift workers is associated with adverse health outcomes (Zhao & Turner, 2008). Shift workers that have a high number of nights worked are prone to have high BMI (Zhao & Turner, 2008). Alcohol is known to disturb sleep patterns among shift workers (Kageyama et al., 2011). Physical activity can reduce sleep problems related to shift work (Thorpy, 2010). Working condition factors such as work hours and the number of nights worked likely have an impact on SWT. Extended work hours of shift workers can cause negative health outcomes (SaksvikLehouillier et al., 2013). Also, studies have found that working night shifts is an important cause of stress and fatigue among nurses (Winwood, Winefield, & Lushington, 2006). Starting from 2014, South Korean nurses who work shifts will be required by law to undergo an occupational medical examination at least once a year. One of the occupational medical examination’s aims is to evaluate work suitability. To evaluate the shift work suitability of potential workers, more information is needed regarding factors influencing
http://dx.doi.org/10.1016/j.apnr.2014.09.007 0897-1897/© 2014 Elsevier Inc. All rights reserved.
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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H-S. Jung, B. Lee / Applied Nursing Research xxx (2014) xxx–xxx
Table 1 Hierarchical regression analysis for variables predicting insomnia, fatigue, and depression. Variable
Insomnia β
Step 1 Age No. of children Step 2 Age No. of children Morningness Self-esteem Step 3 Age No. of children Morningness Self-esteem Social support Job stress Step 4 Age No. of children Morningness Self-esteem Social support Job stress BMI Heavy drinking Physical activity Step 5 Age No. of children Morningness Self-esteem Social support Job stress BMI Heavy drinking Physical activity No. of night shifts Working hours
§
Fatigue ΔR
2
β
0.010*
0.139***
0.046
0.005***
0.108***
0.045***
0.009***
0.145*** 0.07 0.09* 0.08* −0.24*** 0.03 −0.39***
0.009*** −0.19*** −0.49 −0.18* 0.34*** −0.02 0.22*** −0.08 1.12 −0.16
0.002*** −0.01 −0.03 −0.06*** 0.02* −0.01* 0.02*** −0.01 −0.29* −0.08*** −0.00 −0.00
0.131*** 0.03 0.10* 0.14*** −0.32***
−0.18*** −0.54 −0.17* 0.35*** −0.02 0.22***
−0.01 −0.03 −0.06*** 0.02* −0.01* 0.02*** −0.01 −0.29* −0.08***
0.04 −1.23* −0.09 0.16* −0.03 0.21*** −0.15 1.18* −0.27*
0.018** 0.04 0.11*
−0.14* −0.65 −0.28*** 0.45***
−0.01 −0.05 −0.06*** 0.02* −0.01 0.02***
ΔR2
β
0.026***
0.063***
0.092***
ΔR
2
−0.16** −0.74
−0.00 −0.06 −0.06** 0.02***
0.02 −1.40* −0.09 0.20** −0.02 0.21***
β
0.003
0.054*** 0.07 −1.52** −0.19* 0.30***
0.06 −1.10 −0.11 0.16* −0.03 0.20*** −0.13 1.76 −0.27* 0.24* −0.03
ΔR
−0.01 −0.05
0.05 −1.57**
SWT§§
Depression 2
0.006*** 0.06 0.08* 0.09* −0.22*** 0.04 −0.38*** 0.06 −0.07 0.09*
0.002*** −0.17** −0.47 −0.18* 0.34*** −0.02 0.21*** −0.08 1.06 −0.16 0.07 0.02
0.004*** 0.05 0.08 0.09* −0.22*** 0.04 −0.37*** 0.06 −0.06 0.08* −0.07 0.00
Note: Insomnia: Step1 F = 3.67, p = .026; Step2 F = 11.04, p b .000; Step3 F = 19.54, p b .000; Step4 F = 12.54, p b .000; Step5 F = 10.92, p b .000; Fatigue: Step1 F = 0.90, p = .407; Step2 F = 10.23, p b .000; Step3 F = 12.57, p b .000; Step4 F = 11.69, p b .000; Step5 F = 9.54, p b .000; Depression: Step1 F = 8.55, p = .000; Step2 F = 30.84, p b .000; Step3 F = 39.00, p b .000; Step4 F = 25.27, p b .000; Step5 F = 20.77, p b .000; SWT: Step1 F = 6.01, p = .003; Step2 F = 27.32, p = b.000; Step3 F = 43.15, p = b.000; Step4 F = 27.57, p = b.000; Step5 F = 22.92, p = b.000. ⁎ p b .05 ⁎⁎ p b .01 ⁎⁎⁎ p b .001. § Standardized coefficient. §§ High SWT indicated low scores on insomnia, fatigue, and depression.
the SWT of nurses, as well as results of the medical examination. Identifying factors related to SWT is a prerequisite to determine the work suitability of workers and to explore the suitable shift system and working condition for workers (Saksvik et al., 2011). However, currently there is no internationally published research on the significant predictors of SWT among South Korean nurses. Also, most of the previous research surveyed White or European individuals; thus, they have limited generalizability to potential South Korean shift workers. Nurses in South Korea tend to work long hours and are relatively young. According to the OECD data, actual working hours in South Korea in 2012 were the 3rd longest among the Organization for Economic Cooperation and Development (OECD) countries (OECD, 2013). In addition, while the average age of employed RNs in the U.S. was 45.5 in 2008 (American Nurses Association, 2011), the average age of employed RNs in South Korea was 34.6 in 2006 (Korean Nurses Association, 2006). Due to these differences in working conditions and demographic characteristics, there would be differences in determining factors of SWT as well. This substantial research lacuna on shift working for Korean nurses means that potential variables related to the SWT of South Korean nurses should be explored. Against this backdrop, the main purpose of
this study was to investigate demographic, individual, psychosocial, lifestyle, and working condition factors related to SWT in South Korean nurses. 2. Methods 2.1. Study design The study design was an exploratory-descriptive (cross-sectional), correlational study to investigate relationships among demographic, individual, psychosocial, lifestyle, and working condition factors and insomnia, fatigue, and depression in South Korean nurses. 2.2. Sample and setting Participants were registered nurses from multiple patient care areas of a large hospital in Seoul, South Korea. All registered nurses with at least six months of working experience were invited to participate in this study. Of the total of 1678 nurses working in the hospital, 1022 nurses participated in this study (response rate: 60.9%). Of the total participants,
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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660 nurses who worked shifts were included in this analysis. Nurses working rotating shifts accounted for 70% of the hospital’s total nursing population and of the respondents, nurses working rotating shifts accounted for 64.6%. Most participants worked a rotating three-shift schedule. The rotating shifts were comprised of day work (07:00 to 15:00), evening work (15:00 to 23:00), and night work (23:00 to 7:00). The number of night shifts varied from individual to individual. 2.3. Instruments For this study, a structured questionnaire was used. The questionnaire included demographic questions about age, gender, marital status, number of children, working department, and working position. Also, numerous variables were included in the questionnaire as follows: 2.3.1. Individual variables Individual variables, referring to factors indicating propensities inherent in a person apart from demographic variables, were measured using the Diurnal Scale (DS) (Torsvall & Åkerstedt, 1980) for “morningness” and the Rosenberg Self-Esteem Scale (RSE) (Rosenberg, 1965) for selfesteem. Since self-esteem is a person’s overall emotional evaluation of his or her own worth, it is regarded a propensity inherent in a person, being categorized as an individual factor. The DS is comprised of seven items about preferences and habits regarding sleep/wake time; each item is rated on four-point scale. Higher scores indicate higher levels of morningness. The internal consistency (Cronbach’s alpha) for this study was .51. The RSE consists of ten items Likert-scale related to overall feelings of self-worth, and are answered on a four-point scale rating from strongly agree to strongly disagree. The level of self-esteem was calculated by summing the ten items. Higher scores on the RSE reflect a higher level of self-esteem. The Cronbach’s alpha for the RSE was .63 for this study. 2.3.2. Psychosocial variables Psychosocial variables refer to the mentality and awareness a person develops from the relationships with others. The following psychosocial variables were measured: social support and job stress. To measure social support, the Multidimensional Scale of Perceived Social Support (MSPSS) was used (Zimet, Dahlem, Zimet, & Farley, 1988). The MSPSS is composed of 12 items divided into the three sources of support: family support, friends support, and significant others support. It is graded on a 5-point scale from ‘1’ (strongly disagree) to ‘5’ (strongly agree). The MSPSS is a brief measure, with good psychometric properties (López & Cooper, 2011). The reliability of the questions in this study was high (Cronbach’s alpha = .96). The job stress questionnaire consists of 27 items from the Korean Occupational Stress Scale (KOSS) (Chang et al., 2005). It comprises of eight subscales: job demand (4 items), job control (4 items), interpersonal conflict (3 items), job insecurity (2 items), organizational system (4 items), lack of reward (3 items), occupational climate (4 items), and physical environment (3 items). Higher scores indicate higher levels of job stress. The reliability of the KOSS was supported in previous studies (Chang et al., 2005). Cronbach’s alpha range in this study was from .33 to .72: job demand (.72), job control (.47), interpersonal conflict (.57), job insecurity (.66), organizational system (.71), lack of reward (.68), occupational climate (.33), and physical environment (.47). 2.3.3. Lifestyle variables Lifestyle variables consist of factors that indicate a way of life or style of living that reflects the attitudes and values of a person and its outcome. Lifestyle variables included body mass index (BMI), smoking status, heavy alcohol drinking, and physical activity. BMI was calculated by the kg/m2 base on self-reported heights and weights, and was categorized for analysis as underweight (18.4 or less), normal (from 18.5 to 22.9), overweight (from 23 to 24.9), and obese (25 or more), in accordance with the classification of World Health Organization (WHO) for Asian
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adults (WHO Expert Consultation, 2004). Self-reported smoking status was assessed using the following question: “Do you smoke cigarettes now?” Participants were classified into two groups: current smoker, and never or former smoker. Heavy alcohol drinking was determined using two questions: “How often did you drink alcohol in previous month?” and “How much do you drink in one day?” If a male (female) drinks more than 7 (5) glasses alcohol on any one day, and during more than 2 days in last week, he (she) is defined as a heavy alcohol drinker. The study also assessed the average number of days per week when participants do physical activity hard enough to go out of breath. 2.3.4. Working condition variables Total working hours per week and the number of night shifts per month were estimated using the following question: “How many hours do you work in a week on average, including overtime?” and “How many night shift did you work during the last month?” 2.3.5. Shift work tolerance (SWT) To measure SWT, Insomnia Severity Index (ISI), a question concerning fatigue, and Patient Health Questionnaire (PHQ-9) were used. The ISI includes a 7-item self-report questionnaire measuring the symptoms and consequences of insomnia over the past month (Morin, 1993). Each item is rated on a 5-point Likert scale (‘0’ = not at all; ‘4’ = very severe), with total scores ranging from 0 to 28. A total score of 8 or greater indicates insomnia: 8–14 indicates subthreshold insomnia, 15–21 indicates clinical insomnia with moderate severity, and 22–28 indicates severe clinical insomnia. Adequate psychometric property has been reported in previous studies (Gagnon, Bélanger, Ivers, & Morin, 2013). The reliability of the ISI in this study was high (Cronbach’s alpha = .87). Daily fatigue was measured using a single item measure (‘1’ = not at all to ‘5’ = extremely). Recent studies have indicated that single item fatigue is a useful and valid measure to assess daily fatigue (van Hooff, Geurts, Kompier, & Taris, 2007). The PHQ-9 is the depression module; it consists of nine criteria upon which the diagnosis of DSM- IV depressive disorder is based (Kroenke, Spitzer, & Williams, 2001). Each item is scored from ‘0’ (not at all) to ‘3’ (nearly every day). The PHQ-9 scores are calculated by summing the nine items, and are interpreted by using the following guide: 0–4 (normal range or full remission), 5–9 (minimal depressive symptoms), 10–14 (major depression, mild severity), 15–19 (major depression, moderate severity), and 20 or higher (major depression, severe severity). The reliability and validity of the PHQ-9 was supported in previous studies (Kroenke et al., 2001). The reliability of the questions in this study was high (Cronbach’s alpha = .88). In this study, the scores of insomnia, fatigue, and depression were added to create a composite SWT variable. The composite scores were then reversed, high SWT indicating low scores of insomnia, fatigue, and depression. 2.4. Data collection We obtained official acceptance for undertaking the study from the hospital’s top management. After institutional review board (IRB) approval was obtained, posters announcing the study were placed in several nursing stations. Data collection was not restricted to any particular unit. Following oral and written explanations about the selfdetermined, anonymity and confidentiality of the study, nurses who wanted to participate in this study signed an agreement of informed consent. Data were collected using self-reported questionnaires under the instruction of trained investigators in June and July of 2013. 2.5. Data analysis Data analysis was performed using the statistical program PASW, version 20 (SPSS/PASW Inc., Chicago, IL, USA). Five dimensions (demographic, individual, psychosocial, lifestyle, and working condition characteristics) and SWT status (insomnia, fatigue, and depression) of the
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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participants were described using descriptive statistics. Hierarchical multiple regressions were performed to investigate the relationship between five dimensions and SWT. Hierarchical multiple regression is an appropriate method to investigate the individual effects of each dimension on SWT and to compare the amount of impact each dimension has on SWT. In the first step of hierarchical multiple regression, demographic factors were entered: age and the number of children. In the second step, individual factors (morningness and self-esteem) were entered into the step 1 model. Psychosocial factors (social support and job stress) were entered into the third model; lifestyle factors (BMI, heavy drinking, and physical activity) were then entered into the fourth model; and working condition factors (working hours and number of night shifts) were entered last. To determine how much predictive power was added to the model via the addition of variables, the change in the coefficient of determination (ΔR 2) was computed. Also, standard coefficients were used to decide which dependent variables are most important for determining SWT. To verify the presence of autocorrelation between the variables used in the model, the Durbin-Watson test was performed. DW value of two or nearly two indicates no autocorrelation. There were no signs of autocorrelation in any of the regression models.
and job stress were significantly, positively related to fatigue in the final model. However, morningness, heavy drinking, and physical activity were significantly, negatively related to fatigue. The most influencing factor on fatigue was heavy drinking. 3.4. Depression The mean PHQ-9 score was 9.3 (SD = 5.47, range 0–27), which indicates having minimal depressive symptoms according to the established PHQ-9 definition. The hierarchical regression analysis for depression showed that the final model explained 28% of the variance in depression (F = 20.77, p b .000). Demographic factors explained 3% of the variance in depression. Step 2 explained 14% of variance, while step 3 (including psychosocial factors) explained an additional 11% of the variance in depression. When entering lifestyle factors in the model, it explained an additional 0.9% of the variance in depression. The working condition factors explained 0.2% of the variance in depression. The model’s explanatory power increased most when individual factors were included. In the final model, self-esteem and job stress showed a significant positive relation to depression, whereas age and morningness showed a significant negative relation to depression. The most influencing factor on depression was self-esteem.
3. Results 3.5. Composite SWT 3.1. Participants’ characteristics Most participants were women (98%), not married (85%), and a staff nurse (98%). The mean age of participants was 27.5 years; fifty-four percent of total participants were working in the inpatient part. The mean amount of working experience of participants as a nurse was 4.4 years. The mean score of morningness, self-esteem, social support, and job stress was 18.5, 11.8, 47.6, and 49.9, respectively. The mean BMI was 20.1 and the mean number of days engaging in intense physical activities (until you are out of breath) was 1.2 per week. The subjects worked 4.5 nights per month in average and the mean working hours were 48.2 hours per week. 3.2. Insomnia Among the participants, 21% had no insomnia, 46% had sub-threshold insomnia (ISI score, 8–14), and 33% had clinically significant insomnia (ISI score, N14). The results of the hierarchical regression analyses for insomnia indicate that 17% of the variance in insomnia was explained by the final model which includes all variables (F = 10.92, p b .000). The percentage of explained variance increased significantly when entering demographic factors (ΔR 2 = 0.010, p = .026), individual factors (ΔR2 = 0.054, p b .000), psychosocial factors (ΔR2 = 0.092, p b .000), lifestyle factors (ΔR 2 = 0.005, p b .000), and working condition factors (ΔR2 = 0.009, p b .000) into the model. The model’s explanatory power increased most when psychosocial factors were included. In the final model, self-esteem, job stress, and the number of night shifts were positively related to insomnia, whereas physical activity was negatively related to insomnia. The most influencing factor on insomnia was physical activity. 3.3. Fatigue The mean score of the participants’ fatigue was 4.3 (SD = 0.78, range 2 ~ 5). Regarding the results of the hierarchical regression analyses for fatigue, 16% of the variance in fatigue was explained by the final model considering all variables (F = 9.54, p b .000). When entering individual factors, lifestyle factors, and working condition factors in the model, the model explained an additional 6% (p b .000), 5% (p b .000), and 0.2% (p b .000) of the variance in fatigue. The model’s explanatory power increased most when individual factors were included. Entering demographic factors into the model did not result in a statistically significant increase in the explained variance of fatigue, neither did entering the psychosocial factors into the model. Self-esteem, social support
The scores of insomnia, fatigue, and depression were added to create a composite SWT variable. The final model explained 30% of the variance in depression (F = 22.92, p b .000). The percentage of explained variance increased significantly in all models, especially in the model where psychosocial factors were entered. In the final model, morningness, self-esteem, job stress, and physical activity had a statistically significant influence on SWT. Among those, job stress was the most influencing factor. 4. Discussion This research, an exploratory study examining the factors influencing insomnia, fatigue, and depression which shift-working nurses experience, is the groundwork for developing measures to improve Korean nurses’ SWT. The study’s significant results are as follows. 4.1. Demographic factors and SWT In the present study, depression of shift workers was negatively related to age of shift workers. In contrast to many earlier findings, however, older workers had a lower tolerance for shift work (Saksvik et al., 2011; Saksvik-Lehouillier et al., 2013). The reason why younger workers reported more depression in this study compared to older workers could be explained by initial work stresses. Young workers could be under a lot of stress due to the start of shift work and necessary physiological and social adaptations (Blok & de Looze, 2011). Also, research has posited that less problems related to night shifts in older compared with younger workers, in relation to sleepiness and accidents (Bonnefond et al., 2006). 4.2. Individual factors and SWT There has been an argument on the relationship between morningness and SWT (Saksvik et al., 2011): Some studies reported that low scores on morningness are related to high SWT (Saksvik et al., 2011), but the opposite result is also reported in some studies (Härmä, Partinen, Repo, Sorsa, & Siivonen, 2008). In our study, morningness was negatively correlated with fatigue and depression. The various shifts included in a three shift rotating schedule could be one reason for the discordant relationship between morningness and SWT (Forkard & Hunt, 2000). The association between self-esteem
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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and SWT was rarely reported. Saijo, Ueno, and Hashimoto (2008) found that low self-esteem is positively associated with depressive symptoms among shift workers. However, the findings of the current study showed that a high score on self-esteem linked high levels of insomnia, fatigue, and depression. The reason for this result could be explained as follows: People with high self-esteem tend to be assertive (Reid & Hammersley, 2000), and may report a sense of discomfort more aggressively. Further research should be done to confirm the direction of the relationship between self-esteem and SWT.
4.3. Psychosocial factors and SWT A paucity of research exists on the relationship between job stress, social support, and SWT. Some research was reported that lower social support and higher job stress among shift workers was associated with higher prevalence of insomnia (Nakata et al., 2001) and sleepiness during night shift (Kageyama et al., 2011). In this study, we found that shift workers with low social support and high job stress tended to reported high fatigue. Also, job stress of shift workers was positively associated with insomnia and depression. In the studies on daytime workers, job stress has been linked to adverse health outcomes such as depressive symptoms, insomnia, and fatigue (Nakao, 2010). Social support is known as a key modifier of response to job stress (Kageyama et al., 2011). Namely, low social support has been associated with the development of stress-related symptoms.
4.4. Lifestyle factors and SWT Alcohol consumption and physical activity have been reported as factors predicting sleep complains such as insomnia, sleep deprivation, and daytime sleepiness (Kageyama et al., 2011; Thorpy, 2010). This study confirmed that physical activity was negatively associated with insomnia. Additionally, we found that physical activity reduced the fatigue of shift workers; heavy drinking increased the fatigue of shift workers. Shift work generally decreases opportunities for physical activity. A lack of opportunity for exercise increases general fatigue, as reasons for a less active lifestyle (Atkinson, Fullick, Grindey, & Maclaren, 2008). However, physical activity can improve the tolerance for an individual to shift work. Atkinson et al. (2008) reported that general fatigue decreased significantly in the training shift worker group. Shift workers have reported increased use of alcohol as a sleep aid (Morikawa et al., 2013). But, as mentioned in our results, alcohol consumption can affect the fatigue of shift workers. Thus, it was needed to promote good sleep behavior, such as restricting alcohol intake before bedtime.
4.5. Working condition factors and SWT Concerning working condition factors, the number of night shifts per month was positively related to insomnia in current study. This finding is in agreement with Flo et al. (2012) findings that showed the numbers of nights worked was an important risk factor of shift work disorder, including insomnia. But Saksvik-Lehouillier et al. (2013) did not find a relationship between the number of night shifts and insomnia. As a result, there are no clear indications that number of night shifts causes increased risks of insomnia. However, night work is characterized by increased sleepiness (Drake, Roehrs, Richardson, Walsh, & Roth, 2004). To verify the relationship between the number of nights worked and insomnia, further research should be done. We did not find a relationship between working hours and SWT. This may be because the majority of the subjects were working 48 hours a week alike (mean working hours 48.2 h/wk). Only about 12% of our sample had worked 40 hours or below, which was defined as the legal working hour limit according a Korea Labor Act.
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4.6. Implication for nursing practice This study may help to understand determinants of SWT among Korean shift nurses. Based on these results from the analysis, several recommendations for improving SWT can be offered. First, encouragement for shift nurses to engage in physical activity and to reduce the alcohol consumption may be beneficial. Also, reducing the number of nights worked could be fruitful. Before nurses were arranged for shift work, checking out the individual characteristics of nurses, especially measuring morningness, may also be useful. Working conditions including working hours, physical aspects, legal rights and responsibilities should be designed to increase social support and reduce job stress of shift nurses. 4.7. Limitation of the study This study has several notable limitations. First, the study measured SWT by the scores of insomnia, fatigue, and depression. However, we cannot affirm that these symptoms are caused by shift work. Even though, understanding the factors influencing insomnia, fatigue, and depression occurring in shift-working nurses can serve as basic research for developing intervention measures to increase nurses’ SWT. Second, due to a cross-sectional survey design, we could not determine causality of any relationships. Additionally, we did not control the variables such as years of shift work experience, years of night shift work experience, income, job satisfaction or caffeine consumption, which also may be related to insomnia, fatigue or depression. Further research, which takes these variables into account, will need to be undertaken. The internal consistency of DS and some KOSS subscales (physical environment, job control, interpersonal conflict, job insecurity, and occupational climate), used in this study, was below 0.7, which is probably because of the small number of items. Such poor internal consistency may have been disruptive to drawing accurate results. Despite these noted limitations, this investigation fills in an existing lacuna on SWT of Korean nurses. Furthermore, this study includes comprehensive variables that may be related to SWT. Acknowledgments This study was supported by the 2013 Research Fund from the University of Ulsan in South Korea (2013–0187). References American Nurses Association (2011). Fact sheet. Retrieved from. http://nursingworld. org/nursingbythenumbersfactsheet.aspx. Andlauer, P., Reinberg, A., Fourre, L., Battle, W., & Duverneuil, G. (1979). Amplitude of the oral-temperature circadian-rhythm and the tolerance to shift work. Journal de Physiologie, 75, 507–512. Atkinson, G., Fullick, S., Grindey, C., & Maclaren, D. (2008). Exercise, energy balance and the shift worker. Sports Medicine, 38(8), 671–685. Blok, M. M., & de Looze, M. P. (2011). What is the evidence for less shift work tolerance in older workers? Ergonomics, 54(3), 221–232, http://dx.doi.org/10.1080/00140139. 2010.548876. Bonnefond, A., Härmä, M., Hakola, T., Sallinen, M., Kandolin, I., & Virkkala, J. (2006). Interaction of age with shift-related sleep-wakefulness, sleepiness, performance, and social life. Experimental Aging Research, 32(2), 185–208, http://dx.doi.org/10.1080/ 03610730600553968. Chang, S. J., Koh, S. B., Kang, D., Kim, S. A., Kang, M. G., Lee, C. G., et al. (2005). Developing an occupational stress scale for Korean employees. Korean Journal of Occupational Environmental Medicine, 17(4), 297–317 (in Korean). Costa, G. (2010). Shift work and health: Current problem and preventive actions. Safety and Health at Work, 1, 112–123, http://dx.doi.org/10.5491/SHAW.2010.1.2.112. Drake, C. L., Roehrs, T., Richardson, G., Walsh, J. K., & Roth, T. (2004). Shift work sleep disorder: Prevalence and consequences beyond that of symptomatic day workers. Sleep, 27(8), 1453–1462. Flo, E., Pallesen, S., Magerøy, N., Moen, B. E., Grønli, J., Hilde Nordhus, I., et al. (2012). Shift work disorder in nurses-assessment, prevalence and related health problems. PloS one, 7(4), e33981, http://dx.doi.org/10.1371/journal.pone.0033981. Forkard, S., & Hunt, L. J. (2000). Morningness-eveningness and long-term shift work tolerance. In S. Hornberger, P. Knauth, G. Gosta, & S. Folkard (Eds.), Shift work in the 21st century, Arbeitswissinshaft in der betrieblichen praxis. vol. 17. (pp. 311–316). Frankfurt: Peter Lang.
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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Gagnon, C., Bélanger, L., Ivers, H., & Morin, C. M. (2013). Validation of the insomnia severity index in primary care. Journal of the American Board of Family Medicine, 26(6), 701–710, http://dx.doi.org/10.3122/jabfm.2013.06.130064. Härmä, M., Partinen, M., Repo, R., Sorsa, M., & Siivonen, M. (2008). Effects of 6/6 and 4/8 watch systems on sleepiness among bridge officers. Chronobiology International, 25, 413–423, http://dx.doi.org/10.1080/07420520802106769. Kageyama, T., Kobayashi, T., & Abe-Cotoh, A. (2011). Correlates to sleepiness on night shift among male workers engaged in three-shift work in a chemical plant: Its association with sleep practice and job stress. Industrial Health, 49, 634–641, http://dx.doi.org/10. 2486/indhealth.MS1239. Korean Nurses Association (2006). Study on the employed RNs in South Korea. Seoul: Korean Nurses Association (in Korean). Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–6013, http:// dx.doi.org/10.1046/j.1525-1497.2001.016009606.x. López, M. L., & Cooper, L. (2011). Social support measures review. Los Angeles: First 5 LA. Morikawa, Y., Sakurai, M., Nakamura, K., Nagasawa, S. Y., Ishizaki, M., Kido, T., et al. (2013). Correlation between shift-work-related sleep problems and heavy drinking in Japanese male factory workers. Alcohol and Alcoholism, 48(2), 202–206, http://dx. doi.org/10.1093/alcalc/ags128. Morin, C. M. (1993). Insomnia: Psychological assessment and management. New York: Guilford Press. Nakao, M. (2010). Work-related stress and psychosomatic medicine. BioPsychoSocial Medicine, 4(1), 4, http://dx.doi.org/10.1186/1751-0759-4-4. Nakata, A., Haratani, T., Takahashi, M., Kawakami, N., Arito, H., Fujioka, Y., et al. (2001). Job stress, social support at work, and insomnia in Japanese shift workers. Journal of Human Ergology, 30(1–2), 203–209. Organisation for Economic Co-operation and Development (2013). OECD Employment Outlook 2013. Paris: OECD Publishing. Reid, M., & Hammersley, R. (2000). Communicating successfully in groups. New York: Psychology Press.
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton: Princeton University Press. Saijo, Y., Ueno, T., & Hashimoto, Y. (2008). Twenty-four-hour shift work, depressive symptoms, and job dissatisfaction among Japanese firefighters. American Journal of Industrial Medicine, 51(5), 380–391, http://dx.doi.org/10.1002/ajim.20571. Saksvik, I. B., Bjorvatn, B., Hetland, H., Sandal, G. M., & Pallesen, S. (2011). Individual differences in tolerance to shift work – A systematic review. Sleep Medicine Reviews, 15(4), 221–235, http://dx.doi.org/10.1016/j.smrv.2010.07.002. Saksvik-Lehouillier, I., Bjorvatn, B., Hetland, H., Sandal, G. M., Moen, B. E., Magerøy, N., et al. (2013). Individual, situational and lifestyle factors related to shift work tolerance among nurses who are new to and experienced in night work. Journal of Advanced Nursing, 69(5), 1136–1146, http://dx.doi.org/10.1111/j.1365-2648.2012. 06105. Thorpy, M. J. (2010). Managing the patient with shift-work disorder. The Journal of Family Practice, 59(Suppl 1), S24–S31. Torsvall, L., & Åkerstedt, T. (1980). A diurnal type scale. Construction, consistency and validation in shift work. Scandinavian Journal of Work, Environment & Health, 6(4), 283–290, http://dx.doi.org/10.5271/sjweh.2608. van Hooff, M. L., Geurts, S. A., Kompier, M. A., & Taris, T. W. (2007). “How fatigued do you currently feel?” Convergent and discriminant validity of a single-item fatigue measure. Journal of Occupational Health, 49(3), 224–234, http://dx.doi.org/10.1539/joh.49.224. WHO Expert Consultation (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet, 363, 157–163, http://dx.doi.org/10.1016/S0140-6736(03)15268-3. Winwood, P. C., Winefield, A. H., & Lushington, K. (2006). Work-related fatigue and recovery: The contribution of age, domestic responsibilities and shiftwork. Journal of Advanced Nursing, 56, 438–449, http://dx.doi.org/10.1111/j.1365-2648.2006.04011.x. Zhao, I., & Turner, C. (2008). The impact of shift work on people’s daily health habits and adverse health outcomes. Australian Journal of Advanced Nursing, 25, 8–22. Zimet, G., Dahlem, N., Zimet, S., & Farley, G. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41.
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Original Article
Contributors to shift work tolerance in South Korean nurses working rotating shift Hye-Sun Jung, RN, MPH, PhD a,1, Bokim Lee, RN, MPH, PhD b,⁎ a b
School of Medicine, The Catholic University of Korea, 505 Banpo Dong, Seocho Gu, Seoul, Republic of Korea, 137-701 Department of Nursing, University of Ulsan, P.O. Box 18, Ulsan, Republic of Korea, 680-749
a r t i c l e
i n f o
Article history: Received 3 March 2014 Revised 17 September 2014 Accepted 19 September 2014 Available online xxxx Keywords: Shift work Depression Fatigue Insomnia Nurses
a b s t r a c t Shift workers have rapidly increased in South Korea; however, there is no published research exploring shift work tolerance among South Korean workers. This study aimed to investigate factors related to shift work tolerance in South Korean nurses. The sample comprised of 660 nurses who worked shifts in a large hospital in South Korea. A structured questionnaire included following comprehensive variables: demographic (age and number of children), individual (morningness and self-esteem), psychosocial (social support and job stress), lifestyle (alcohol consumption, physical activity, and BMI), and working condition factors (number of night shifts and working hours). Shift work tolerance was measured in terms of insomnia, fatigue, and depression. The results of hierarchical regressions indicate that all variables, except for three, number of children, BMI, and working hours, were related to at least one of the symptoms associated with shift work tolerance. Based on these results, we offer some practical implications to help improve shift work tolerance of workers. © 2014 Elsevier Inc. All rights reserved.
1. Background and purpose Shift work has been widely discussed in scientific literature and it generally includes any arrangement of daily working hours other than the standard daylight hours (7/8 A.M. to 5/6 P.M.). Increasingly, shift work is becoming more common in our so-called “24/7” society. Prior studies have reported that shift work can have major negative effects on individuals’ health, safety, quality of life, and work productivity (Costa, 2010). Because of the complex interaction among personal traits, social conditions, and working conditions, however, not all shift workers experienced adverse effects (Costa, 2010; Saksvik, Bjorvatn, Hetland, Sandal, & Pallesen, 2011). Shift work tolerance (SWT) is defined as: the ability to adapt to shift work without adverse consequences (Andlauer, Reinberg, Fourre, Battle, & Duverneuil, 1979), such as digestive troubles, fatigue, sleep problems, depression, etc. (Saksvik-Lehouillier et al., 2013). (See Table 1.) From the previous literature on the subject, factors related to SWT can be divided into five dimensions: demographic, individual, psychosocial, lifestyle, and working condition factors. Demographic factors shown to be associated with SWT include age, gender, and number of children (Blok & de Looze, 2011). Most studies found that individuals of a higher age had a lower tolerance for the demands of shift work (Bonnefond et al., 2006). With regards to ⁎ Corresponding author at: Department of Nursing, University of Ulsan, P.O. Box 18, Ulsan, South Korea, 680-749. Tel: +82 52 259 1283; fax: +82 52 259 1236. E-mail addresses: [email protected] (H.-S. Jung), [email protected] (B. Lee). 1 Tel.: +82 2 2258 7368; fax: +82 2 532 3820.
gender, the reduced SWT of women is posited to associate with family burden (i.e., number of children) (Costa, 2010). Individual factors such as morningness, languidity, flexibility, and self-esteem have also been linked with SWT. Most studies have found that flexibility and self-esteem are positively related to SWT; low scores on morningness and languidity are related to high SWT (SaksvikLehouillier et al., 2013). Regarding psychosocial factors such as job stress and social support, research indicates that sleepiness during the night shift was positively associated with job stress, but inversely associated with support from colleagues (Kageyama, Kobayashi, & Abe-Cotoh, 2011). Lifestyle factors, especially BMI (body mass index), smoking, alcohol consumption, and physical activity have also been studied. Smoking among shift workers is associated with adverse health outcomes (Zhao & Turner, 2008). Shift workers that have a high number of nights worked are prone to have high BMI (Zhao & Turner, 2008). Alcohol is known to disturb sleep patterns among shift workers (Kageyama et al., 2011). Physical activity can reduce sleep problems related to shift work (Thorpy, 2010). Working condition factors such as work hours and the number of nights worked likely have an impact on SWT. Extended work hours of shift workers can cause negative health outcomes (SaksvikLehouillier et al., 2013). Also, studies have found that working night shifts is an important cause of stress and fatigue among nurses (Winwood, Winefield, & Lushington, 2006). Starting from 2014, South Korean nurses who work shifts will be required by law to undergo an occupational medical examination at least once a year. One of the occupational medical examination’s aims is to evaluate work suitability. To evaluate the shift work suitability of potential workers, more information is needed regarding factors influencing
http://dx.doi.org/10.1016/j.apnr.2014.09.007 0897-1897/© 2014 Elsevier Inc. All rights reserved.
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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Table 1 Hierarchical regression analysis for variables predicting insomnia, fatigue, and depression. Variable
Insomnia β
Step 1 Age No. of children Step 2 Age No. of children Morningness Self-esteem Step 3 Age No. of children Morningness Self-esteem Social support Job stress Step 4 Age No. of children Morningness Self-esteem Social support Job stress BMI Heavy drinking Physical activity Step 5 Age No. of children Morningness Self-esteem Social support Job stress BMI Heavy drinking Physical activity No. of night shifts Working hours
§
Fatigue ΔR
2
β
0.010*
0.139***
0.046
0.005***
0.108***
0.045***
0.009***
0.145*** 0.07 0.09* 0.08* −0.24*** 0.03 −0.39***
0.009*** −0.19*** −0.49 −0.18* 0.34*** −0.02 0.22*** −0.08 1.12 −0.16
0.002*** −0.01 −0.03 −0.06*** 0.02* −0.01* 0.02*** −0.01 −0.29* −0.08*** −0.00 −0.00
0.131*** 0.03 0.10* 0.14*** −0.32***
−0.18*** −0.54 −0.17* 0.35*** −0.02 0.22***
−0.01 −0.03 −0.06*** 0.02* −0.01* 0.02*** −0.01 −0.29* −0.08***
0.04 −1.23* −0.09 0.16* −0.03 0.21*** −0.15 1.18* −0.27*
0.018** 0.04 0.11*
−0.14* −0.65 −0.28*** 0.45***
−0.01 −0.05 −0.06*** 0.02* −0.01 0.02***
ΔR2
β
0.026***
0.063***
0.092***
ΔR
2
−0.16** −0.74
−0.00 −0.06 −0.06** 0.02***
0.02 −1.40* −0.09 0.20** −0.02 0.21***
β
0.003
0.054*** 0.07 −1.52** −0.19* 0.30***
0.06 −1.10 −0.11 0.16* −0.03 0.20*** −0.13 1.76 −0.27* 0.24* −0.03
ΔR
−0.01 −0.05
0.05 −1.57**
SWT§§
Depression 2
0.006*** 0.06 0.08* 0.09* −0.22*** 0.04 −0.38*** 0.06 −0.07 0.09*
0.002*** −0.17** −0.47 −0.18* 0.34*** −0.02 0.21*** −0.08 1.06 −0.16 0.07 0.02
0.004*** 0.05 0.08 0.09* −0.22*** 0.04 −0.37*** 0.06 −0.06 0.08* −0.07 0.00
Note: Insomnia: Step1 F = 3.67, p = .026; Step2 F = 11.04, p b .000; Step3 F = 19.54, p b .000; Step4 F = 12.54, p b .000; Step5 F = 10.92, p b .000; Fatigue: Step1 F = 0.90, p = .407; Step2 F = 10.23, p b .000; Step3 F = 12.57, p b .000; Step4 F = 11.69, p b .000; Step5 F = 9.54, p b .000; Depression: Step1 F = 8.55, p = .000; Step2 F = 30.84, p b .000; Step3 F = 39.00, p b .000; Step4 F = 25.27, p b .000; Step5 F = 20.77, p b .000; SWT: Step1 F = 6.01, p = .003; Step2 F = 27.32, p = b.000; Step3 F = 43.15, p = b.000; Step4 F = 27.57, p = b.000; Step5 F = 22.92, p = b.000. ⁎ p b .05 ⁎⁎ p b .01 ⁎⁎⁎ p b .001. § Standardized coefficient. §§ High SWT indicated low scores on insomnia, fatigue, and depression.
the SWT of nurses, as well as results of the medical examination. Identifying factors related to SWT is a prerequisite to determine the work suitability of workers and to explore the suitable shift system and working condition for workers (Saksvik et al., 2011). However, currently there is no internationally published research on the significant predictors of SWT among South Korean nurses. Also, most of the previous research surveyed White or European individuals; thus, they have limited generalizability to potential South Korean shift workers. Nurses in South Korea tend to work long hours and are relatively young. According to the OECD data, actual working hours in South Korea in 2012 were the 3rd longest among the Organization for Economic Cooperation and Development (OECD) countries (OECD, 2013). In addition, while the average age of employed RNs in the U.S. was 45.5 in 2008 (American Nurses Association, 2011), the average age of employed RNs in South Korea was 34.6 in 2006 (Korean Nurses Association, 2006). Due to these differences in working conditions and demographic characteristics, there would be differences in determining factors of SWT as well. This substantial research lacuna on shift working for Korean nurses means that potential variables related to the SWT of South Korean nurses should be explored. Against this backdrop, the main purpose of
this study was to investigate demographic, individual, psychosocial, lifestyle, and working condition factors related to SWT in South Korean nurses. 2. Methods 2.1. Study design The study design was an exploratory-descriptive (cross-sectional), correlational study to investigate relationships among demographic, individual, psychosocial, lifestyle, and working condition factors and insomnia, fatigue, and depression in South Korean nurses. 2.2. Sample and setting Participants were registered nurses from multiple patient care areas of a large hospital in Seoul, South Korea. All registered nurses with at least six months of working experience were invited to participate in this study. Of the total of 1678 nurses working in the hospital, 1022 nurses participated in this study (response rate: 60.9%). Of the total participants,
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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660 nurses who worked shifts were included in this analysis. Nurses working rotating shifts accounted for 70% of the hospital’s total nursing population and of the respondents, nurses working rotating shifts accounted for 64.6%. Most participants worked a rotating three-shift schedule. The rotating shifts were comprised of day work (07:00 to 15:00), evening work (15:00 to 23:00), and night work (23:00 to 7:00). The number of night shifts varied from individual to individual. 2.3. Instruments For this study, a structured questionnaire was used. The questionnaire included demographic questions about age, gender, marital status, number of children, working department, and working position. Also, numerous variables were included in the questionnaire as follows: 2.3.1. Individual variables Individual variables, referring to factors indicating propensities inherent in a person apart from demographic variables, were measured using the Diurnal Scale (DS) (Torsvall & Åkerstedt, 1980) for “morningness” and the Rosenberg Self-Esteem Scale (RSE) (Rosenberg, 1965) for selfesteem. Since self-esteem is a person’s overall emotional evaluation of his or her own worth, it is regarded a propensity inherent in a person, being categorized as an individual factor. The DS is comprised of seven items about preferences and habits regarding sleep/wake time; each item is rated on four-point scale. Higher scores indicate higher levels of morningness. The internal consistency (Cronbach’s alpha) for this study was .51. The RSE consists of ten items Likert-scale related to overall feelings of self-worth, and are answered on a four-point scale rating from strongly agree to strongly disagree. The level of self-esteem was calculated by summing the ten items. Higher scores on the RSE reflect a higher level of self-esteem. The Cronbach’s alpha for the RSE was .63 for this study. 2.3.2. Psychosocial variables Psychosocial variables refer to the mentality and awareness a person develops from the relationships with others. The following psychosocial variables were measured: social support and job stress. To measure social support, the Multidimensional Scale of Perceived Social Support (MSPSS) was used (Zimet, Dahlem, Zimet, & Farley, 1988). The MSPSS is composed of 12 items divided into the three sources of support: family support, friends support, and significant others support. It is graded on a 5-point scale from ‘1’ (strongly disagree) to ‘5’ (strongly agree). The MSPSS is a brief measure, with good psychometric properties (López & Cooper, 2011). The reliability of the questions in this study was high (Cronbach’s alpha = .96). The job stress questionnaire consists of 27 items from the Korean Occupational Stress Scale (KOSS) (Chang et al., 2005). It comprises of eight subscales: job demand (4 items), job control (4 items), interpersonal conflict (3 items), job insecurity (2 items), organizational system (4 items), lack of reward (3 items), occupational climate (4 items), and physical environment (3 items). Higher scores indicate higher levels of job stress. The reliability of the KOSS was supported in previous studies (Chang et al., 2005). Cronbach’s alpha range in this study was from .33 to .72: job demand (.72), job control (.47), interpersonal conflict (.57), job insecurity (.66), organizational system (.71), lack of reward (.68), occupational climate (.33), and physical environment (.47). 2.3.3. Lifestyle variables Lifestyle variables consist of factors that indicate a way of life or style of living that reflects the attitudes and values of a person and its outcome. Lifestyle variables included body mass index (BMI), smoking status, heavy alcohol drinking, and physical activity. BMI was calculated by the kg/m2 base on self-reported heights and weights, and was categorized for analysis as underweight (18.4 or less), normal (from 18.5 to 22.9), overweight (from 23 to 24.9), and obese (25 or more), in accordance with the classification of World Health Organization (WHO) for Asian
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adults (WHO Expert Consultation, 2004). Self-reported smoking status was assessed using the following question: “Do you smoke cigarettes now?” Participants were classified into two groups: current smoker, and never or former smoker. Heavy alcohol drinking was determined using two questions: “How often did you drink alcohol in previous month?” and “How much do you drink in one day?” If a male (female) drinks more than 7 (5) glasses alcohol on any one day, and during more than 2 days in last week, he (she) is defined as a heavy alcohol drinker. The study also assessed the average number of days per week when participants do physical activity hard enough to go out of breath. 2.3.4. Working condition variables Total working hours per week and the number of night shifts per month were estimated using the following question: “How many hours do you work in a week on average, including overtime?” and “How many night shift did you work during the last month?” 2.3.5. Shift work tolerance (SWT) To measure SWT, Insomnia Severity Index (ISI), a question concerning fatigue, and Patient Health Questionnaire (PHQ-9) were used. The ISI includes a 7-item self-report questionnaire measuring the symptoms and consequences of insomnia over the past month (Morin, 1993). Each item is rated on a 5-point Likert scale (‘0’ = not at all; ‘4’ = very severe), with total scores ranging from 0 to 28. A total score of 8 or greater indicates insomnia: 8–14 indicates subthreshold insomnia, 15–21 indicates clinical insomnia with moderate severity, and 22–28 indicates severe clinical insomnia. Adequate psychometric property has been reported in previous studies (Gagnon, Bélanger, Ivers, & Morin, 2013). The reliability of the ISI in this study was high (Cronbach’s alpha = .87). Daily fatigue was measured using a single item measure (‘1’ = not at all to ‘5’ = extremely). Recent studies have indicated that single item fatigue is a useful and valid measure to assess daily fatigue (van Hooff, Geurts, Kompier, & Taris, 2007). The PHQ-9 is the depression module; it consists of nine criteria upon which the diagnosis of DSM- IV depressive disorder is based (Kroenke, Spitzer, & Williams, 2001). Each item is scored from ‘0’ (not at all) to ‘3’ (nearly every day). The PHQ-9 scores are calculated by summing the nine items, and are interpreted by using the following guide: 0–4 (normal range or full remission), 5–9 (minimal depressive symptoms), 10–14 (major depression, mild severity), 15–19 (major depression, moderate severity), and 20 or higher (major depression, severe severity). The reliability and validity of the PHQ-9 was supported in previous studies (Kroenke et al., 2001). The reliability of the questions in this study was high (Cronbach’s alpha = .88). In this study, the scores of insomnia, fatigue, and depression were added to create a composite SWT variable. The composite scores were then reversed, high SWT indicating low scores of insomnia, fatigue, and depression. 2.4. Data collection We obtained official acceptance for undertaking the study from the hospital’s top management. After institutional review board (IRB) approval was obtained, posters announcing the study were placed in several nursing stations. Data collection was not restricted to any particular unit. Following oral and written explanations about the selfdetermined, anonymity and confidentiality of the study, nurses who wanted to participate in this study signed an agreement of informed consent. Data were collected using self-reported questionnaires under the instruction of trained investigators in June and July of 2013. 2.5. Data analysis Data analysis was performed using the statistical program PASW, version 20 (SPSS/PASW Inc., Chicago, IL, USA). Five dimensions (demographic, individual, psychosocial, lifestyle, and working condition characteristics) and SWT status (insomnia, fatigue, and depression) of the
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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participants were described using descriptive statistics. Hierarchical multiple regressions were performed to investigate the relationship between five dimensions and SWT. Hierarchical multiple regression is an appropriate method to investigate the individual effects of each dimension on SWT and to compare the amount of impact each dimension has on SWT. In the first step of hierarchical multiple regression, demographic factors were entered: age and the number of children. In the second step, individual factors (morningness and self-esteem) were entered into the step 1 model. Psychosocial factors (social support and job stress) were entered into the third model; lifestyle factors (BMI, heavy drinking, and physical activity) were then entered into the fourth model; and working condition factors (working hours and number of night shifts) were entered last. To determine how much predictive power was added to the model via the addition of variables, the change in the coefficient of determination (ΔR 2) was computed. Also, standard coefficients were used to decide which dependent variables are most important for determining SWT. To verify the presence of autocorrelation between the variables used in the model, the Durbin-Watson test was performed. DW value of two or nearly two indicates no autocorrelation. There were no signs of autocorrelation in any of the regression models.
and job stress were significantly, positively related to fatigue in the final model. However, morningness, heavy drinking, and physical activity were significantly, negatively related to fatigue. The most influencing factor on fatigue was heavy drinking. 3.4. Depression The mean PHQ-9 score was 9.3 (SD = 5.47, range 0–27), which indicates having minimal depressive symptoms according to the established PHQ-9 definition. The hierarchical regression analysis for depression showed that the final model explained 28% of the variance in depression (F = 20.77, p b .000). Demographic factors explained 3% of the variance in depression. Step 2 explained 14% of variance, while step 3 (including psychosocial factors) explained an additional 11% of the variance in depression. When entering lifestyle factors in the model, it explained an additional 0.9% of the variance in depression. The working condition factors explained 0.2% of the variance in depression. The model’s explanatory power increased most when individual factors were included. In the final model, self-esteem and job stress showed a significant positive relation to depression, whereas age and morningness showed a significant negative relation to depression. The most influencing factor on depression was self-esteem.
3. Results 3.5. Composite SWT 3.1. Participants’ characteristics Most participants were women (98%), not married (85%), and a staff nurse (98%). The mean age of participants was 27.5 years; fifty-four percent of total participants were working in the inpatient part. The mean amount of working experience of participants as a nurse was 4.4 years. The mean score of morningness, self-esteem, social support, and job stress was 18.5, 11.8, 47.6, and 49.9, respectively. The mean BMI was 20.1 and the mean number of days engaging in intense physical activities (until you are out of breath) was 1.2 per week. The subjects worked 4.5 nights per month in average and the mean working hours were 48.2 hours per week. 3.2. Insomnia Among the participants, 21% had no insomnia, 46% had sub-threshold insomnia (ISI score, 8–14), and 33% had clinically significant insomnia (ISI score, N14). The results of the hierarchical regression analyses for insomnia indicate that 17% of the variance in insomnia was explained by the final model which includes all variables (F = 10.92, p b .000). The percentage of explained variance increased significantly when entering demographic factors (ΔR 2 = 0.010, p = .026), individual factors (ΔR2 = 0.054, p b .000), psychosocial factors (ΔR2 = 0.092, p b .000), lifestyle factors (ΔR 2 = 0.005, p b .000), and working condition factors (ΔR2 = 0.009, p b .000) into the model. The model’s explanatory power increased most when psychosocial factors were included. In the final model, self-esteem, job stress, and the number of night shifts were positively related to insomnia, whereas physical activity was negatively related to insomnia. The most influencing factor on insomnia was physical activity. 3.3. Fatigue The mean score of the participants’ fatigue was 4.3 (SD = 0.78, range 2 ~ 5). Regarding the results of the hierarchical regression analyses for fatigue, 16% of the variance in fatigue was explained by the final model considering all variables (F = 9.54, p b .000). When entering individual factors, lifestyle factors, and working condition factors in the model, the model explained an additional 6% (p b .000), 5% (p b .000), and 0.2% (p b .000) of the variance in fatigue. The model’s explanatory power increased most when individual factors were included. Entering demographic factors into the model did not result in a statistically significant increase in the explained variance of fatigue, neither did entering the psychosocial factors into the model. Self-esteem, social support
The scores of insomnia, fatigue, and depression were added to create a composite SWT variable. The final model explained 30% of the variance in depression (F = 22.92, p b .000). The percentage of explained variance increased significantly in all models, especially in the model where psychosocial factors were entered. In the final model, morningness, self-esteem, job stress, and physical activity had a statistically significant influence on SWT. Among those, job stress was the most influencing factor. 4. Discussion This research, an exploratory study examining the factors influencing insomnia, fatigue, and depression which shift-working nurses experience, is the groundwork for developing measures to improve Korean nurses’ SWT. The study’s significant results are as follows. 4.1. Demographic factors and SWT In the present study, depression of shift workers was negatively related to age of shift workers. In contrast to many earlier findings, however, older workers had a lower tolerance for shift work (Saksvik et al., 2011; Saksvik-Lehouillier et al., 2013). The reason why younger workers reported more depression in this study compared to older workers could be explained by initial work stresses. Young workers could be under a lot of stress due to the start of shift work and necessary physiological and social adaptations (Blok & de Looze, 2011). Also, research has posited that less problems related to night shifts in older compared with younger workers, in relation to sleepiness and accidents (Bonnefond et al., 2006). 4.2. Individual factors and SWT There has been an argument on the relationship between morningness and SWT (Saksvik et al., 2011): Some studies reported that low scores on morningness are related to high SWT (Saksvik et al., 2011), but the opposite result is also reported in some studies (Härmä, Partinen, Repo, Sorsa, & Siivonen, 2008). In our study, morningness was negatively correlated with fatigue and depression. The various shifts included in a three shift rotating schedule could be one reason for the discordant relationship between morningness and SWT (Forkard & Hunt, 2000). The association between self-esteem
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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and SWT was rarely reported. Saijo, Ueno, and Hashimoto (2008) found that low self-esteem is positively associated with depressive symptoms among shift workers. However, the findings of the current study showed that a high score on self-esteem linked high levels of insomnia, fatigue, and depression. The reason for this result could be explained as follows: People with high self-esteem tend to be assertive (Reid & Hammersley, 2000), and may report a sense of discomfort more aggressively. Further research should be done to confirm the direction of the relationship between self-esteem and SWT.
4.3. Psychosocial factors and SWT A paucity of research exists on the relationship between job stress, social support, and SWT. Some research was reported that lower social support and higher job stress among shift workers was associated with higher prevalence of insomnia (Nakata et al., 2001) and sleepiness during night shift (Kageyama et al., 2011). In this study, we found that shift workers with low social support and high job stress tended to reported high fatigue. Also, job stress of shift workers was positively associated with insomnia and depression. In the studies on daytime workers, job stress has been linked to adverse health outcomes such as depressive symptoms, insomnia, and fatigue (Nakao, 2010). Social support is known as a key modifier of response to job stress (Kageyama et al., 2011). Namely, low social support has been associated with the development of stress-related symptoms.
4.4. Lifestyle factors and SWT Alcohol consumption and physical activity have been reported as factors predicting sleep complains such as insomnia, sleep deprivation, and daytime sleepiness (Kageyama et al., 2011; Thorpy, 2010). This study confirmed that physical activity was negatively associated with insomnia. Additionally, we found that physical activity reduced the fatigue of shift workers; heavy drinking increased the fatigue of shift workers. Shift work generally decreases opportunities for physical activity. A lack of opportunity for exercise increases general fatigue, as reasons for a less active lifestyle (Atkinson, Fullick, Grindey, & Maclaren, 2008). However, physical activity can improve the tolerance for an individual to shift work. Atkinson et al. (2008) reported that general fatigue decreased significantly in the training shift worker group. Shift workers have reported increased use of alcohol as a sleep aid (Morikawa et al., 2013). But, as mentioned in our results, alcohol consumption can affect the fatigue of shift workers. Thus, it was needed to promote good sleep behavior, such as restricting alcohol intake before bedtime.
4.5. Working condition factors and SWT Concerning working condition factors, the number of night shifts per month was positively related to insomnia in current study. This finding is in agreement with Flo et al. (2012) findings that showed the numbers of nights worked was an important risk factor of shift work disorder, including insomnia. But Saksvik-Lehouillier et al. (2013) did not find a relationship between the number of night shifts and insomnia. As a result, there are no clear indications that number of night shifts causes increased risks of insomnia. However, night work is characterized by increased sleepiness (Drake, Roehrs, Richardson, Walsh, & Roth, 2004). To verify the relationship between the number of nights worked and insomnia, further research should be done. We did not find a relationship between working hours and SWT. This may be because the majority of the subjects were working 48 hours a week alike (mean working hours 48.2 h/wk). Only about 12% of our sample had worked 40 hours or below, which was defined as the legal working hour limit according a Korea Labor Act.
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4.6. Implication for nursing practice This study may help to understand determinants of SWT among Korean shift nurses. Based on these results from the analysis, several recommendations for improving SWT can be offered. First, encouragement for shift nurses to engage in physical activity and to reduce the alcohol consumption may be beneficial. Also, reducing the number of nights worked could be fruitful. Before nurses were arranged for shift work, checking out the individual characteristics of nurses, especially measuring morningness, may also be useful. Working conditions including working hours, physical aspects, legal rights and responsibilities should be designed to increase social support and reduce job stress of shift nurses. 4.7. Limitation of the study This study has several notable limitations. First, the study measured SWT by the scores of insomnia, fatigue, and depression. However, we cannot affirm that these symptoms are caused by shift work. Even though, understanding the factors influencing insomnia, fatigue, and depression occurring in shift-working nurses can serve as basic research for developing intervention measures to increase nurses’ SWT. Second, due to a cross-sectional survey design, we could not determine causality of any relationships. Additionally, we did not control the variables such as years of shift work experience, years of night shift work experience, income, job satisfaction or caffeine consumption, which also may be related to insomnia, fatigue or depression. Further research, which takes these variables into account, will need to be undertaken. The internal consistency of DS and some KOSS subscales (physical environment, job control, interpersonal conflict, job insecurity, and occupational climate), used in this study, was below 0.7, which is probably because of the small number of items. Such poor internal consistency may have been disruptive to drawing accurate results. Despite these noted limitations, this investigation fills in an existing lacuna on SWT of Korean nurses. Furthermore, this study includes comprehensive variables that may be related to SWT. Acknowledgments This study was supported by the 2013 Research Fund from the University of Ulsan in South Korea (2013–0187). References American Nurses Association (2011). Fact sheet. Retrieved from. http://nursingworld. org/nursingbythenumbersfactsheet.aspx. Andlauer, P., Reinberg, A., Fourre, L., Battle, W., & Duverneuil, G. (1979). Amplitude of the oral-temperature circadian-rhythm and the tolerance to shift work. Journal de Physiologie, 75, 507–512. Atkinson, G., Fullick, S., Grindey, C., & Maclaren, D. (2008). Exercise, energy balance and the shift worker. Sports Medicine, 38(8), 671–685. Blok, M. M., & de Looze, M. P. (2011). What is the evidence for less shift work tolerance in older workers? Ergonomics, 54(3), 221–232, http://dx.doi.org/10.1080/00140139. 2010.548876. Bonnefond, A., Härmä, M., Hakola, T., Sallinen, M., Kandolin, I., & Virkkala, J. (2006). Interaction of age with shift-related sleep-wakefulness, sleepiness, performance, and social life. Experimental Aging Research, 32(2), 185–208, http://dx.doi.org/10.1080/ 03610730600553968. Chang, S. J., Koh, S. B., Kang, D., Kim, S. A., Kang, M. G., Lee, C. G., et al. (2005). Developing an occupational stress scale for Korean employees. Korean Journal of Occupational Environmental Medicine, 17(4), 297–317 (in Korean). Costa, G. (2010). Shift work and health: Current problem and preventive actions. Safety and Health at Work, 1, 112–123, http://dx.doi.org/10.5491/SHAW.2010.1.2.112. Drake, C. L., Roehrs, T., Richardson, G., Walsh, J. K., & Roth, T. (2004). Shift work sleep disorder: Prevalence and consequences beyond that of symptomatic day workers. Sleep, 27(8), 1453–1462. Flo, E., Pallesen, S., Magerøy, N., Moen, B. E., Grønli, J., Hilde Nordhus, I., et al. (2012). Shift work disorder in nurses-assessment, prevalence and related health problems. PloS one, 7(4), e33981, http://dx.doi.org/10.1371/journal.pone.0033981. Forkard, S., & Hunt, L. J. (2000). Morningness-eveningness and long-term shift work tolerance. In S. Hornberger, P. Knauth, G. Gosta, & S. Folkard (Eds.), Shift work in the 21st century, Arbeitswissinshaft in der betrieblichen praxis. vol. 17. (pp. 311–316). Frankfurt: Peter Lang.
Please cite this article as: Jung, H-S., & Lee, B., Contributors to shift work tolerance in South Korean nurses working rotating shift, Applied Nursing Research (2014), http://dx.doi.org/10.1016/j.apnr.2014.09.007
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Gagnon, C., Bélanger, L., Ivers, H., & Morin, C. M. (2013). Validation of the insomnia severity index in primary care. Journal of the American Board of Family Medicine, 26(6), 701–710, http://dx.doi.org/10.3122/jabfm.2013.06.130064. Härmä, M., Partinen, M., Repo, R., Sorsa, M., & Siivonen, M. (2008). Effects of 6/6 and 4/8 watch systems on sleepiness among bridge officers. Chronobiology International, 25, 413–423, http://dx.doi.org/10.1080/07420520802106769. Kageyama, T., Kobayashi, T., & Abe-Cotoh, A. (2011). Correlates to sleepiness on night shift among male workers engaged in three-shift work in a chemical plant: Its association with sleep practice and job stress. Industrial Health, 49, 634–641, http://dx.doi.org/10. 2486/indhealth.MS1239. Korean Nurses Association (2006). Study on the employed RNs in South Korea. Seoul: Korean Nurses Association (in Korean). Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–6013, http:// dx.doi.org/10.1046/j.1525-1497.2001.016009606.x. López, M. L., & Cooper, L. (2011). Social support measures review. Los Angeles: First 5 LA. Morikawa, Y., Sakurai, M., Nakamura, K., Nagasawa, S. Y., Ishizaki, M., Kido, T., et al. (2013). Correlation between shift-work-related sleep problems and heavy drinking in Japanese male factory workers. Alcohol and Alcoholism, 48(2), 202–206, http://dx. doi.org/10.1093/alcalc/ags128. Morin, C. M. (1993). Insomnia: Psychological assessment and management. New York: Guilford Press. Nakao, M. (2010). Work-related stress and psychosomatic medicine. BioPsychoSocial Medicine, 4(1), 4, http://dx.doi.org/10.1186/1751-0759-4-4. Nakata, A., Haratani, T., Takahashi, M., Kawakami, N., Arito, H., Fujioka, Y., et al. (2001). Job stress, social support at work, and insomnia in Japanese shift workers. Journal of Human Ergology, 30(1–2), 203–209. Organisation for Economic Co-operation and Development (2013). OECD Employment Outlook 2013. Paris: OECD Publishing. Reid, M., & Hammersley, R. (2000). Communicating successfully in groups. New York: Psychology Press.
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton: Princeton University Press. Saijo, Y., Ueno, T., & Hashimoto, Y. (2008). Twenty-four-hour shift work, depressive symptoms, and job dissatisfaction among Japanese firefighters. American Journal of Industrial Medicine, 51(5), 380–391, http://dx.doi.org/10.1002/ajim.20571. Saksvik, I. B., Bjorvatn, B., Hetland, H., Sandal, G. M., & Pallesen, S. (2011). Individual differences in tolerance to shift work – A systematic review. Sleep Medicine Reviews, 15(4), 221–235, http://dx.doi.org/10.1016/j.smrv.2010.07.002. Saksvik-Lehouillier, I., Bjorvatn, B., Hetland, H., Sandal, G. M., Moen, B. E., Magerøy, N., et al. (2013). Individual, situational and lifestyle factors related to shift work tolerance among nurses who are new to and experienced in night work. Journal of Advanced Nursing, 69(5), 1136–1146, http://dx.doi.org/10.1111/j.1365-2648.2012. 06105. Thorpy, M. J. (2010). Managing the patient with shift-work disorder. The Journal of Family Practice, 59(Suppl 1), S24–S31. Torsvall, L., & Åkerstedt, T. (1980). A diurnal type scale. Construction, consistency and validation in shift work. Scandinavian Journal of Work, Environment & Health, 6(4), 283–290, http://dx.doi.org/10.5271/sjweh.2608. van Hooff, M. L., Geurts, S. A., Kompier, M. A., & Taris, T. W. (2007). “How fatigued do you currently feel?” Convergent and discriminant validity of a single-item fatigue measure. Journal of Occupational Health, 49(3), 224–234, http://dx.doi.org/10.1539/joh.49.224. WHO Expert Consultation (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet, 363, 157–163, http://dx.doi.org/10.1016/S0140-6736(03)15268-3. Winwood, P. C., Winefield, A. H., & Lushington, K. (2006). Work-related fatigue and recovery: The contribution of age, domestic responsibilities and shiftwork. Journal of Advanced Nursing, 56, 438–449, http://dx.doi.org/10.1111/j.1365-2648.2006.04011.x. Zhao, I., & Turner, C. (2008). The impact of shift work on people’s daily health habits and adverse health outcomes. Australian Journal of Advanced Nursing, 25, 8–22. Zimet, G., Dahlem, N., Zimet, S., & Farley, G. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41.
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