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International Journal of Nursing Studies Advances logoLink to International Journal of Nursing Studies Advances
. 2022 Mar 21;4:100076. doi: 10.1016/j.ijnsa.2022.100076

Examining the impact of 12-hour day and night shifts on nurses’ fatigue: A prospective cohort study

Roberto M Benzo a,b,⁎, Amany Farag c, Kara M Whitaker b,d, Qian Xiao e, Lucas J Carr b
PMCID: PMC11080398  PMID: 38745643

Abstract

Background: Fatigue is prevalent among hospital nurses and has been linked to medical errors and decreased patient safety. However, little is known regarding the relationship between occupational physical activity, sedentary behavior, and fatigue.

Objective: To assess the impact of 12-hour shifts on nurses’ fatigue and its relationship to occupational physical activity and sedentary behavior.

Design: Prospective-cohort study design

Setting(s): Midwestern trauma one academic medical center

Participants: A total of 80 registered nurses working 12-hour day and night shifts participated in this study and completed momentary measures of fatigue (texting, aim one). Only 52 participants were included in aim two analyses (included activity monitoring, aim two).

Methods: Occupational patterns of momentary fatigue was measured via ecological momentary assessments. Occupational physical activity and sedentary behaviors (e.g., step count, time spent sitting, standing, and walking) were measured for 14 continuous days using the ActivPAL3 micro activity monitor. Mixed models were used to examine the effects of shift type and time within a shift on occupational fatigue. General estimation equations were used to examine the relationship between time spent sitting, standing, and walking on fatigue.

Results: Regardless of shift type, nurses exhibited a significant rise in fatigue; however, the rise was greater during night shifts compared to day shifts. Walking was positively associated with fatigue during day shifts, and negatively associated with fatigue during night shifts.

Conclusions: The rise in fatigue was greater among nurses working night shifts compared to day shifts, which could place them at greater risk for fatigue-related consequences. The relationship between walking and fatigue was moderated by shift-type.

Tweetable abstract: Nursing fatigue rises during 12-hour shifts, but the rise is greater for those working night shifts @DrRobertoBenzo

Keywords: Accelerometry, Ecological momentary assessments, Exercise, Fatigue, Nurses, Occupational health nursing, Sedentary behavior, Shift work schedule

What is already known about the topic?

  • •

    Previous studies have reported hospital nurses experience moderate to high levels of fatigue.

  • •

    Research suggests both personal (e.g., age, exercise, and sleep) and work schedule factors (e.g., shift length, shift type) can exacerbate nurses’ fatigue levels.

  • •

    Little is known regarding the impact of occupational physical activity and/or sedentary behaviors on nursing fatigue.

What this paper adds

  • •

    Nurses exhibit significantly higher levels of fatigue at the end of a 12-hour night shift compared to the end of a 12-hour day shift.

  • •

    Regardless of shift type (12-hour day vs. 12-hour night) nurses exhibited a significant rise in fatigue; however, the rise in fatigue was significantly greater during the night shift vs. the day shift.

  • •

    During 12-hour day shifts, an increase in walking was associated with a greater rise in fatigue among nurses; however, more walking during 12-hour night shifts mitigated the rise in fatigue.

1. Background

Shift work has been linked to disruptions of circadian rhythms, sleep debt, and increased fatigue among nurses; all of which could negatively impact nurses’ health and performance (Barker and Nussbaum, 2011, Saksvik-Lehouillier et al., 2013). To better meet the demands of 24-hour patient care, most healthcare organizations have adopted 12-hour shift work schedules (Banakhar, 2017). However, extended work hours can place nurses at risk of developing mental and physical fatigue (Estryn-Behar et al., 1990, Matheson et al., 2014, Hopcia et al., 2012).

Fatigue is defined as an excessive sense of tiredness, lack of energy, and a feeling of exhaustion associated with impairments in physical or cognitive functioning (Rogers, 2008). Among nurses fatigue has been associated with lower work satisfaction (Josten et al., 2003, Taylor and Barling, 2004), desire to leave the nursing profession (Liu et al., 2016), impaired work performance (Barker and Nussbaum, 2011, Barker and Nussbaum, 2011, Wilson et al., 2017), reduced patient safety (Rheaume and Mullen, 2017, Rogers et al., 2004), as well as work-related injuries (Geiger-Brown et al., 2004, Lipscomb et al., 2004, Yip, 2001, Caruso, 2014). Hospital nurses in the US experience moderate to high levels of acute fatigue, and moderate levels of chronic fatigue (Barker and Nussbaum, 2011, Blouin et al., 2016, Chen et al., 2014). Evidence suggests both personal (e.g., age, exercise, and sleep) and work schedule factors (e.g., shift length, rotating shifts, and time of day) can exacerbate nurses’ fatigue levels (Barker and Nussbaum, 2011, Chen et al., 2014).

Few studies have examined how nurses’ fatigue changes throughout a work shift (Wilson et al., 2017). This is important as it may elucidate when occupational fatigue is highest and when interventions are most appropriate. Wilson et al. examined the impact of nursing shift work on performance and sleepiness of nurses (n = 22, 91% female) at a community hospital (Wilson et al., 2017). Performance and sleepiness remained stable over multiple shifts among day shift workers, but performance decreased while sleepiness increased overtime among night shift workers. These findings suggest that nurses working night shifts may be at increased risk for impaired performance and increased sleepiness compared to nurse's working day shifts.

Past interventions designed to reduce occupational fatigue have typically focused on replacing occupational sedentary behavior with physical activity (Puetz et al., 2008, Bergouignan et al., 2016, Wennberg et al., 2016, Thorp et al., 2014). For example, Thorp et al. examined the impact of interrupting prolonged bouts of sitting with 30-minute standing bouts on fatigue among sedentary office workers (Thorp et al., 2014). Office workers reported significantly lower fatigue in the standing condition compared to a sitting condition. Similarly, Bergouignan et al. examined the effects of 30-minute walking bouts on vigor, energy, and fatigue among sedentary adults (Bergouignan et al., 2016). Breaking up sitting with five-minute walks each hour was effective for increasing energy and vigor and reducing fatigue compared to a six-hour sitting condition.

In the previous parent study, where we described and compared occupational levels of physical activity and sedentary behavior throughout a 12-hour shift among US hospital nurses, we found that US hospital nurses spend most (65%) of their 12-hour shifts physically active (standing or walking) (Benzo et al., 2021). Also, nurses working day shifts, sit less (35% vs 44% of shift), stand more (49% vs 42% of shift) and walk more (15% vs 13% of shift) at work compared to nurses working night shifts (Benzo et al., 2021). However, few studies have examined the relationships between occupational physical activity, sedentary behavior, and fatigue among nurses. This study advances our previous publication by comparing fatigue levels by shift type, and examining the relationship between occupational physical activity, sedentary behavior, and fatigue among nurses working 12-hour shifts. Thus, the first aim of this study was to examine the impact of shift type (12-hour day and night) on nurses’ fatigue levels, and the second aim was to assess the impact of physical activity and sedentary behavior on fatigue levels. We hypothesized that the change in fatigue would be significantly greater among nurses working night shifts compared to those working day shifts. We also hypothesized that both high levels of physical activity and sedentary behavior would be positively associated with high levels of fatigue.

2. Methods

2.1. Design

This study was approved by the Human Subjects Office and all participants signed an informed consent. This study was part of a larger study (n=2,025) focused on understanding predictors of nurse fatigue and its impact on medication errors. For this study, we recruited a subsample of participants from the larger study.

A prospective cohort design that included repeated measures was used for the current study. The inclusion criteria for this study were (1) registered nurse, (2) working 12-hour shifts, (3) owned a smartphone capable of receiving and sending text messages, and (4) currently working in critical care or in-patient units at the study site. Nurse managers, agency nurses, and travel nurses were excluded from the study.

2.2. Sample

A total of 116 participants were contacted to participate in the study. Of those 116 participants, 80 participants agreed to participate and completed momentary measures of fatigue (texting) and were included in the analyses testing our first hypothesis. Out of those 80 participants, only 52 wore activity monitors for the duration of the study and were included in the analyses testing the second hypothesis.

2.3. Measures

Participants’ demographic information was collected with an online survey administered by Qualtrics. Momentary fatigue levels were assessed via an ecological momentary assessment (EMA). Specifically, an EMA platform called Boomerang was used to send automated text messages to participants four-times per shift, over 14-consecutive days including work and non-workdays. The Boomerang platform has been successfully used in this capacity in previous studies (Anthony et al., 2015). During workdays, text messages were sent fifteen minutes before the start of the 12-hour work shift (T1), four hours after the start of the 12-hour work shift (T2), four hours prior to the end of the 12-hour work shift (T3), and fifteen minutes after the end of the 12-hour shift (T4). In the text message, participants were asked “How fatigued are you right now?” and asked to respond with a single number ranging from 0 “not at all” to 10 “very”. Responses were only considered valid and included in final analyses if the response was received within 30-minutes from the sent time. Participants were also sent a text message before each shift as a quality check to ensure participants worked that day and to verify their shift type (day vs. night). For example, if a participant was working a day shift the text message they received would read as follows “Will you work your scheduled shift from 07:00 PM to 07:00 AM today? (Y/N)”. If participants responded “No,” the platform automatically responded, “Thank you, this concludes your participation for today.” Using their response, we were able to include only those days that nurse reported working.

Measures of sedentary behavior and physical activity obtained objectively using an ActivPAL activity monitor (activPAL3 and activPAL3-micro; PAL Technologies Ltd., Glasgow, UK). The activPAL3 and activPAL3-micro, are small (7 mm and 5 mm thick, respectively) and light (20 g and 9 g, respectively) activity monitors worn on the anterior portion of the thigh (Calabro et al., 2014, Powell et al., 2016). The activPAL uses proprietary algorithms that take into account accelerometry and inclinometer-related information to estimate physical activity (steps, time spent walking) and body posture (i.e., sitting/lying vs. upright) (Edwardson et al., 2017). Participants were asked to wear the activPAL monitor 24-hours a day, for 14 consecutive days.

We classified days as invalid and periods of non-wear/sleep using a validated algorithm developed by Winkler et al. (Winkler et al., 2016). Waking wear time was defined as any period not labeled as non-wear/sleep by the algorithm (Winkler et al., 2016). The algorithm has been evaluated in previous studies which reported an ‘almost perfect’ (kappa > 0.8) agreement compared to the diary method in a great majority of participants (88%) and ‘substantial or better’ agreement (kappa > 0.6) for almost the entire sample (97%) (Winkler et al., 2016). All periods identified as sleep/non-wear were removed from our analyses.

Work shifts that included at least 10 hours of valid data were included in the final analysis. Individual hours with at least 50 minutes of valid data were included in the final analysis. Several studies have used similar thresholds of waking wear time to determine the validity of observation periods (Edwardson et al., 2017).

2.4. Analyses

Stata software version 14.2 software (StataCorp LP; College Station, Texas) was used for all analyses. Repeated measures mixed-effects regression models were used to examine the effects of shift type (day vs. night), and time (T1-T4) on momentary fatigue (Fitzmaurice et al., 2011). For all outcome measures between-subjects’ effects were estimated by the two main factors: shift-type and fatigue time point (T1-T4). Within-group differences were evaluated using tests of simple effects and pairwise comparisons. Between-group comparisons include differences of the main effect and simple effects. Interactions of shift-type and fatigue time points during work shifts were examined for statistically significant differences for all outcome measures. The trend over time was fitted over coefficients of orthogonal polynomials to test for difference in slopes. Post-hoc analyses of interactions and partial interactions were performed to identify interactions. Linear growth models were used to estimate the slope of fatigue over time (as a continuous variable); for both day and night 12-hour shifts. We used linear growth models to test for differences between group slopes. We considered a p value of < 0.05 as a statistically significant difference.

The unit of analysis for testing the second hypothesis was a single 12-hour shift, and we analyzed this dataset using Generalized Estimation Equations (Wang, 2014). GEE is considered a general statistical approach used to fit a marginal model for longitudinal/clustered data analysis (Wang et al., 2016). Data included in the models were limited to those shifts that had a minimum of 10-hours of valid activPAL data, as well as a non-missing observation for starting and ending fatigue during a 12-hour nursing shift. The independent or predictor variables included: percent of shift spent sitting (%Sitting), percent spent standing (%Standing), and percent spent walking (%Walking). The dependent or outcome variable ‘Change in shift fatigue,’ was operationalized as: [end of shift fatigue (T4)] – [start of shift fatigue (T1)]. While fatigue was reported as a whole number on a scale of 0 to 10, it was treated as a continuous variable given that the concept of momentary fatigue is continuous over time.

The following covariates were selected a priori: age in years, sex, average non-occupational step count, average hours of self-reported sleep per night, work status (full- vs. part-time), average hours of work per week, shift-type (12-hour day vs. 12-hour night shift), high start of shift fatigue level (above median starting fatigue), and consecutive shift. The median start in shift fatigue for all observations included in the dataset was 3.0 (Median T1).

Because of the multicollinearity observed between predictor variables (%Sitting, %Standing, %Walking), the GEE analyses tested each predictor variable separately. In order to examine the fit of the model, we analyzed and visually compared a total of four models for each predictor (%Sitting, %Standing, %Walking). The four general estimating equations for each predictor test include: (1) covariates only; (2) covariates + predictor; (3) covariates + predictor including participants equal to or below the median starting fatigue (≤ Median T1); and (4) covariates + predictor including participants above the median starting fatigue (> Median T1).

The interactions between shift type (Night Shift) and predictor variables were created to examine if the type of shift worked (night vs. day) had a moderating effect on the relationship between the predictor (%Sitting, %Standing, or %Walking) and the outcome (change in shift fatigue). Given a significant interaction between Night Shift and %Walking among participants starting their shift with a fatigue level below the median, we present two additional models group by shift type (see Supplemental Table 4). Statistically significant differences were established at p < 0.05. Stata software version 14.2 software (StataCorp LP; College Station, Texas) was used for all statistical analyses. A waiver of the signed consent was granted by the academic institution Human Subjects Office (IRB# 201703758).

3. Results

Demographic characteristics of participants included in the study are presented in Table 1. Participants were mostly young to middle-aged, mostly female, and self-reported meeting the physical activity guidelines (3.4 days per week * 48.4 minutes = 165 minutes of exercise per week). Participants reported an average of six-years of nursing experience, worked 36-hours per week, and most worked either in the medical surgical unit, the critical care unit, or the mother baby unit.

Table 1.

Demographics of Study Participants

Survey Variables Aim One Sample
Observations 80
Individual Level Factors
Average Age (years) 30.8 (9.0)
Percent Female (n) 91% (71)
Regular Exercisers (n) 60% (48)
Average Days of Weekly Exercise (SD) 3.4 (1.2)
Minutes/day of Exercise (SD) 48.4 (19.3)
Occupational Level Factors
Years of Nursing Experience (SD) 5.8 (7.5)
Typical Hours Worked per Week (SD) 36.1 (6.8)
Work Status (n)
Part-Time 22% (19)
Full-Time 79% (61)
Medical Unit (n)
Medical Surgical 47% (37)
Critical Care 30% (24)
Pediatrics 1% (1)
Mother and Baby 15% (12)
Other (n) 6% (5)
Abbreviations: SD, standard deviation; PRN, “pro re nata” (Latin term, i.e., as needed); n, count.
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3.1. Development of Fatigue During Day and Night Shifts

The main effects for fatigue, and the interaction of the two independent variables (time and shift-type), are reported in Table 2. The main effect for shift-type on fatigue was not significant. A significant interaction, however, was observed between the effects of shift type and time on participant's fatigue (p < 0.001).

Table 2.

Momentary fatigue during 12-hour day and night shift work.

df chi2 p > chi2
Shift Type 1 0.76 0.384
Time-Points (T1-T4) 3 127.62 0.000***
Interaction: Shift Type x Time-Point 3 21.47 0.000***
Predicted Marginal Averages of Momentary Fatigue by Shift Type
Timepoint Day Shift Fatigue (SE) Night Shift Fatigue (SE) p-value (between)
T1 3.5 (0.3) 2.8 (0.4) 0.091
T2 2.9 (0.3) 3.1 (0.4) 0.736
T3 3.7 (0.3) 4.4 (0.4) 0.088
T4 4.5 (0.3) 5.5 (0.4) 0.014
Abbreviations: *** p<0.001, ** p<0.01, * p<0.05
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Participants working 12-hour day shifts reported lower fatigue at time point four (T4) compared to those working 12-hour night shifts (see Fig. 1). Among participants working day shifts, fatigue declined from T1 to T2 (p = 0.012), increased from T2 to T3 (p < 0.001), and increased from T3 to T4 (p < 0.001). No other significant differences were observed in momentary fatigue when comparing neighboring hours among participants working 12-hour day shifts.

Fig. 1.

Figure 1

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Momentary Fatigue Levels for Nurses by Shift-Type

Participants working day and night shifts exhibited a significant increasing trend (i.e., slope of straight fitted line) in momentary fatigue over the course of a 12-hour nursing shift (day: m = 0.37, p < 0.001; night: m = 0.97, p < 0.001). The slope of the trend line was significantly greater for participants working night shifts than for those working day shifts (p < 0.001).

3.2. Effects of Physical Activity and Sedentary Behavior on Momentary Fatigue

A total of 52 participants were included in the analyses testing the second hypothesis. A summary of those participants demographic characteristics, predictors, and outcomes can be found in Table 3. We observed a total of 179 work shifts which included 108-day shifts (60%) and 71-night shifts (40%).

Table 3.

Summary of Variables Included in Analyses Examining the Impact of Physical Activity and Sedentary Behavior on Fatigue

Total 12-Hour
Day 		12-Hour Night p-value
Participants 52 34 21 -
Observations 179 108 71 -
Age 30.0 (8.6) 30 (8.1) 28.4 (8.8) 0.213
Work Hours 36.6 (5.6) 36.5 (5.5) 37.0 (6.3) 0.741
Sleep Hours 6.5 (1.10) 6.5 (1.1) 6.39 (1.2) 0.710
Female 94% 91% (29) 100 (0) 0.080
Part-Time 16% 16% (37%) 16% (37%) 0.988
Avg. Step Count (Non-Occupational) 6,831 (2,426) 6,757 (2,347) 6,970 (2,562) 0.780
T1 3.3 (2.1) 3.6 (2.3) 3.0 (1.9) 0.069
T4 4.9 (2.3) 4.7 (2.3) 5.4 (2.2) 0.040*
Change in shift fatigue 1.6 (2.5) 1.1 (2.5) 2.4 (2.3) 0.001**
Step Count (steps) 8,250 (2,048) 8,764 (2,044) 7,467 (1,789) 0.000***
%Sitting 38.4% (13.9%) 34.7% (13.2%) 44.1 (13.0%) 0.000***
%Standing 47.3% (12.0%) 50.1% (11.4%) 43.0 (11.6%) 0.000***
%Walking 14.3% (3.6%) 15.2% (3.6%) 12.9% (3.1%) 0.000***
High_T1 44.1% (49.8%) 47.2 (50.1) 39.4 (49.0) 0.306
Abbreviations: Part-Time (Percent of participants who reported working a part-time work schedule as opposed to a full-time work schedule), T1 (starting fatigue), T4 (ending fatigue), Change in Fatigue, and Mean Fatigue.
All personal and work schedule data (age, work hours, sleep hours, female, and part-time) were obtained during initial phases of larger study.
a All 12-hour nursing shift data presented correspond to a valid 12-hour nursing shift, obtained from the dataset included in testing the second hypothesis.
b Momentary fatigue was assessed via EMA using a single item 11-point scale (0- ‘no fatigue’ to 10- ‘extremely severe fatigue’).
High_T1 refers to participants who started their shift with a fatigue score above the median starting fatigue (3.0)
Standard errors in parentheses and p values are testing for differences between day and night shifts using unpaired t tests.
*** p<0.001, ** p<0.01, * p<0.05
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3.2.1. Percent Time Spent Sitting as a Predictor of Rising Shift Fatigue

No significant associations were observed between the percent of time spent sitting at work and change in fatigue (see Supplemental Table 1). However, we did observe a negative relationship between time spent sitting and fatigue among participants starting with low shift fatigue.

3.2.2. Percent Time Spent Standing as a Predictor of Rising Shift Fatigue

No significant associations were observed between the percentage of time spent standing and change in fatigue while controlling for covariates (see Supplemental Table 2).

3.2.2. Percent Time Spent Walking as a Predictor of Rising Shift Fatigue

We found an inverse relationship between time spent walking and change in fatigue which was dependent on shift type (see Supplemental Tables 3 and 4). Specifically, we observed a significant positive association between ‘%Walking’ and change in shift fatigue (p = 0.025) was observed during day shifts. And during night shifts, we observed a significant negative association was observed between walking (%Walking) and change in fatigue (p = 0.047). Also, during night shifts, we saw a significant negative association between consecutive shifts and change in fatigue (p = 0.010).

4. Discussion

The aims of the present study were to assess the impact of 12-hour shifts on nurses’ fatigue and its relationship to occupational physical activity and sedentary behavior, and we hypothesized that the rise in fatigue would be greater among nurses working night compared to day shifts, and that physical activity and sedentary behavior would be positively associated with fatigue. We found that during night shifts participants reported significantly higher fatigue levels at the end of shift (T4) than those working the day shift (day = 4.9 vs. night = 5.5; p = 0.014). Consistent with our hypothesis, we observed a significant increasing linear trend in fatigue among both day and night shifts, but the rise in fatigue was greater during night shifts compared to day shifts. Specifically, the increase in fatigue was 143% greater for participants working night shifts compared to participants working day shifts (mean change in fatigue by shift type: day = 1.02 vs. night = 2.45). This finding is consistent with those reported by Wilson et al., who reported a significant increase in sleepiness in nurses working 12-hour night shifts (Wilson et al., 2017).

Considering that nurses working the night shift exhibited a 25% increase (2.5-unit change) in momentary fatigue in the present study, and others have reported that a 32% increase (1.9-unit change in Fatigue Severity Score) is considered clinically meaningful (Learmonth et al., 2013), suggests that nurses working 12-hour night shifts could be at risk for fatigue-related consequences. This finding is of great importance given the rising concern of medical errors (Hipskind et al., 2020), and warrants research for interventions aimed at ameliorating the rise in fatigue among nurses working 12-hour night shifts.

The second aim of this study was to examine the effect of assess the impact of physical activity and sedentary behavior on fatigue levels in fatigue, while controlling for covariates [age, sex, average non-occupational daily step count, average hours of sleep per night, work status, average hours of work per week, shift-type, start of shift fatigue level, and consecutive shift]. Our findings suggest a significant relationship between occupational walking and change in fatigue, which is dependent on shift type. Specifically, more walking was associated with a greater change in fatigue during day shifts, and more walking was associated with an attenuated (lesser) change in fatigue during night shifts. Several factors could explain the moderating effect of shift type on the relationship between walking and the rise in fatigue. First, nurses were more active during day shifts compared to night shifts. During the day shift, nurses spent more time walking (15.2%), and standing (50.1%), compared to nurses working night shifts (12.9% and 43%, respectively). It is possible that the increased physical activity observed during day shift work may result in increased fatigue. Previous studies have shown that nurses working day shifts are predominantly involved in indirect patient tasks that require sustained attention such as walking, chart work, preparing medication, and transcription (Chappel et al., 2017). Increased occupational walking might exacerbate fatigue among nurses during the day shift. Previous work has shown that rest breaks have been shown to positively influence nurses’ occupational well-being and behavior; however, most of the literature stems from studies that do not allow for causal relationships to be explored (Wendsche et al., 2017, Querstret et al., 2020). Thus, more research is needed to explore the mechanisms and moderating effects of break-, occupational-, and person-related factors on fatigue among shift nurses.

Also, during night shifts, we found that nurses were significantly more sedentary (44.1%) compared to day shifts (34.7%). We hypothesize the sedentary nature related to working night shifts is due to the differing work duties that accompany night shift work which include helping patients rest and recover. Previous research has shown that prolonged sedentary time is associated with fatigue in an office setting. Specifically, previous work has shown that alertness was impaired with long continuous bouts of sitting among a sample of sedentary office workers (Benzo et al., 2018), suggesting that sitting might directly result in increased mental fatigue. However, these findings may not apply in a healthcare setting, which is quite different than that found in an office setting. Future studies should examine the effect of periodic walking break interventions as a method to prevent physical and mental fatigue among nurses working night shifts.

Third, reverse causality is another possible explanation for the relationship between walking and the rise in fatigue during night shifts; specifically, it is possible nurses who enter the shift feeling less fatigued end up walking more during their 12-hour nursing shift because they simply feel better. Conversely, those who arrive feeling fatigued might be more inclined to sit and rest while at work. The present study was not able to test for this possibility, but it should be an aim of future research.

Inconsistent with our hypothesis, a negative relationship was observed between time spent sitting and the rise in fatigue among nurses. This finding suggests that regardless of shift type, a 10% increase in sitting results in a 0.4 unit decrease in the rise of nursing fatigue during a 12-hour shift. Given that nursing work is associated with physical and mental fatigue, increasing periods of sitting or taking breaks could provide opportunities for needed recovery during a 12-hour nursing shift. This finding also supports studies that explore the effect of interventions promoting resting breaks to reduce fatigue among nurses at risk for fatigue.

This study has several strengths that are worth mentioning. First, conducting 24- hour observation of physical activity and sedentary behavior over 14-consecutive days with an objective monitor is a major strength. The use of the inclinometer allowed us to include posture in our assessment of sedentary behavior, which is a key component of sedentary behavior. Second, using an EMA approach to measure fatigue in real time allowed us to capture fluctuations in fatigue both within shifts and over multiple shifts. There were also several limitations worth noting. First, given most nurses in our samples were female, we cannot generalize to US registered nurses that are male. This is important given that women have physiological and social differences compared to men, such as social roles, family roles, as well as hormonal changes related to menstruation or menopause, all of which could influence physical activity, sedentary behavior, and fatigue. Second, our sample was younger than the overall US registered nurse population (average age 31.8 vs. 43.8 for females, respectively), less likely to be married (sample: 48% vs. 74%), and less likely to be providing child or elderly dependent care at home (38%-41% vs. 74%). Third, the cross-sectional study design limited our ability to infer causality. Fourth, we relied on a single-item measure of fatigue, which does not allow us to delineate what dimension (physical or mental) or state (acute vs. chronic) of fatigue was being captured. Lastly, we had to calculate the total time spent engaged in each behavior to examine the effects of activity (standing, walking) and sedentary behaviors (sitting) on the change in fatigue (sitting, standing, walking). This approach excludes information related to how those behaviors were accumulated. For example, there are many ways an individual could accumulate 10 minutes of standing and 10 minutes of walking (e.g., 10 min of walking followed by 10 min of standing, vs. 5 min of walking followed by 10 min of standing and then five minutes of walking). This is important as previous work has shown that higher task variation (frequent changes in activities) might counteract fatigue development (Luger et al., 2014, Mlekus and Maier, 2021). Future studies should implement advanced quantitative analyses such as latent profile analyses or deep learning to uncover different combinations of physical activity and sedentary behavior that predict fatigue.

4.1. Recommendations and Implications for Hospitals and Nurses

Based on our findings, there are several strategies that could be implemented to reduce physical and mental determinants of fatigue among nurses working 12-hour shifts. We found nurses are quite active while at work spending, on average, 65% or approximately 7.8 hours on their feet (standing or walking) during a 12-hour shift. Moreover, we observed a positive relationship between occupational walking and the rise in fatigue among nurses working day shifts. It is quite possible that this level of physical activity-induced fatigue in a single shift could negatively impact nurses’ job performance. Collectively, this work supports future research that tests whether providing more opportunities for recovery (i.e., breaks) is effective for reducing within shift rises in fatigue among nurses working 12-hour day shifts.

Second, we found nurses working 12-hour night shifts are spending 44% of a shift, or approximately 5.3 hours, sitting during a shift. We also found walking served as a protective factor against rising fatigue among night shift nurses. When combined with evidence that prolonged sitting increases the risk for several chronic diseases, these data support the need for strategies aimed at reducing prolonged bouts of sedentary behavior among nurses working night shifts. Future interventions could test whether introducing activity breaks during 12-hour night shifts attenuates rises in fatigue and sedentary related risk factors. Activity breaks could include, but are not limited to, walking breaks, active workstations, or providing access to elliptical machines or stationary bikes.

There are several other fatigue-reducing interventions that could be explored among nurses working 12-hour shifts including (1) providing opportunities for napping during the shift, (2) sleep-hygiene interventions to reduce sleep deprivation among nurses, (3) promoting leisure time physical activity to build strength and cardiorespiratory fitness, (4) reducing non-occupational sedentary behaviors associated with negative health outcomes among nurses, (5) improving the quality of food available to nurses at work to limit foods that might contribute to increased fatigue, and (6) exploring mindfulness-based approaches to reduce mental fatigue during a 12-hour nursing shift.

5. Conclusions

In conclusion, we found that nurses working both day and night shifts experience an increase in fatigue over the course of a given shift. We found nurses working night shifts experience a greater increase in fatigue. We found a positive association between time spent walking and change in fatigue during day shifts, and a negative association during night shifts. More research is needed to understand the different contributing factors of nursing fatigue by shift type. Given the current shortage of nurses across the nation, and considering that approximately one million nurses will retire by 2030 (Buerhaus et al., 2017), there is a need for fatigue management interventions that account known determinants (e.g., shift-type, nurses age, etc.).

Funding sources

Funding was obtained from the National Council State Board of Nursing.

Conflict of interest

None

Acknowledgements

We would like to thank all participants who volunteered in our study.

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