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

Poor recovery from fatigue among shift-working nurses can cause a transition from acute to chronic fatigue. We aimed to clarify the relationship between nurses’ recovery from fatigue and sleep episodes after 16 h night shifts while considering age. This prospective study included 62 nurses who worked 16 h night shifts. Fatigue was assessed by a questionnaire before, during, and after the night shift, and the morning following the night shift. Sleep episodes were continuously measured using a wearable device. We performed a hierarchical cluster analysis of multivariate sleep parameters in first and main sleep episodes after night shifts. A linear mixed model was used to estimate the difference between clusters in recovery from fatigue after the night shift, considering age. The participants were classified into a high sleep quality group (HSQG) and low sleep quality group (LSQG) in sleep episodes after the night shift. There was a significant main effect of clusters, and HSQG was significantly more effective than LSQG in recovering from fatigue. However, no main effects of age or interaction were observed. The quality of first and main sleep episodes at home was associated with recovery from the night shift to the next day, regardless of age.

Objectives: Shift-work nurses are at a higher risk of inadequate recovery from fatigue and developing maladaptive fatigue with significant health consequences. Therefore, it is necessary to monitor fatigue and recovery levels with a reliable scale. We investigated psychometric properties of the Japanese version of the Occupational Fatigue Exhaustion Recovery scale (OFER-J) for shift-work nurses. Methods: Japanese shift-work nurses responded to self-administered questionnaires at baseline (n = 942) and one month later (n = 334). The confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were conducted to verify the structural validity and the correlation analysis and one-way analysis of variance were conducted to test the construct and discriminative validity. Cronbach's alpha coefficient, intra-class correlation coefficient (ICC), and smallest detectable change (SDC) were calculated to assess reliability. Results: The CFA showed high correlations between the factors and whilst the goodness-of-fit of the three-factor model was suboptimal, it was in an acceptable range. Most modifications included the error covariance of the Acute Fatigue (AF) and Intershift Recovery (IR) items. The EFA showed that Chronic Fatigue (CF) and AF were not clearly separated, indicating that the two AF items dropped out. Construct and discriminative validity were also well indicated. Cronbach's alpha coefficients were 0.75–0.85. Only CF showed sufficient reproductivity (ICC = 0.74). The SDC for CF, AF, and IR was 14.0, 17.1, and 18.7, respectively. Conclusions: The validity and reliability of the OFER-J were verified as acceptable for shift-work nurses. The OFER-J could contribute to a data-based approach to fatigue management in nursing management practice.

Background: Predicting nurse turnover risk is crucial due to the global nursing shortage; however, existing predictors, such as fatigue and burnout, lack objectivity. Salivary cortisol is a non-invasive marker of stress and fatigue, but its utility in predicting nurse turnover risk is unknown. We examined whether salivary cortisol profiles across three different day shifts in a month are predictors of the extent of nurses’ reluctance to stay in their current jobs. Methods: This preliminary longitudinal study followed forty female nurses who engaged in shift work at a university hospital for 3 months. Data at enrollment were collected including demographics, working conditions, chronic fatigue (the Japanese version of the Occupational Fatigue/Exhaustion Recovery Scale), and burnout (Japanese Burnout scale). Salivary cortisol was measured before the three different day shifts (after awakening) during the first month, and the means of these measurements were used as the cortisol profile. The extent of reluctance to stay was assessed using the numerical rating scale at 3 months. Results: Among the forty female nurses (mean [SD] age, 28.3 [5.1]), all completed follow-up and were included in the analysis. The cortisol profile was associated with the extent of reluctance to stay (P = 0.017), and this association was significant despite adjustments for chronic fatigue and burnout (P = 0.005). A multiple regression model with chronic fatigue, burnout, and job tenure explained 41.5% of the variation in reluctance to stay. When the cortisol profile was added to this model, the association of the cortisol profile was significant (P = 0.006) with an R² of 0.529 (ΔR² = 0.114). Conclusions: This preliminary study conducted in an actual clinical setting indicated the potential of the salivary cortisol profile across three different day shifts in a month to predict nurses’ reluctance to stay in their current jobs. The combination of subjective indicators and the cortisol profile would be useful in predicting nurses' turnover risk.

Although nurses’ fatigue affects their well-being and patient safety, no effective objective measurements exist. We explored the profiles of salivary biomarkers associated with nurses’ chronic fatigue across several shifts. This longitudinal study involved 45 shiftwork nurses and collected their saliva samples before two night and two day shifts for a month. Chronic fatigue was measured using the Cumulative Fatigue Symptom Index before the first night shift. Biomarker profiles were analyzed using hierarchical cluster analysis, and chronic fatigue levels were compared between the profiles. Cortisol profiles were classified into high- and low-level groups across two day shifts; the low-level group presented significantly higher irritability and unwillingness to work. Secretory immunoglobulin A (s-IgA) profiles across the four shifts were classified into high- and low-level groups; the high-level group had significantly higher depressive feelings, decreased vitality, irritability, and unwillingness to work. Cortisol (two day shifts) and s-IgA (four shifts) profiles were combined, and (i) cortisol low-level and s-IgA high-level and (ii) cortisol high-level and s-IgA low-level groups were identified. The former group had significantly higher chronic fatigue sign and irritability than the latter group. The profiles of salivary cortisol and s-IgA across several shifts were associated with nurses’ chronic fatigue.