Research Article

Sources of work-related acute fatigue in United Stateshospital nurses

Jie Chen, PhD, RN,1 Nancy M. Daraiseh, PhD,2 Kermit G. Davis, PhD3 and Wei Pan, PhD4

1College of Health and Human Sciences, Northern Illinois University, DeKalb, Illinois, 2Center for ProfessionalExcellence, Cincinnati Children’s Hospital Medical Center, 3College of Medicine, University of Cincinnati, Cincinnati,Ohio and 4School of Nursing, Duke University, Durham, North Carolina, USA

Abstract This study identified the nursing work activities that could be the primary sources of work-related acutefatigue in US hospital nurses. Continuous recording of working heart rate and random observations of nursingactivities were applied to collect data from eight nurses during two consecutive 12 h day shifts. Usingdescriptive statistics and random-effect analysis of variance, the contributions of individual nursing workactivities to acute fatigue were compared based on the activity frequencies and nurses’ corresponding heartrate elevations. Of 860 observed nursing-related work activities, manual patient-handling, bedside-care, care-coordinating, and walking/standing activities accounted for 5%, 16%, 38%, and 41%, respectively. Aftercontrolling for the differences of participant and shift, the percentage of working heart rate to maximal heartrate of manual patient-handling (64.3%), bedside-care (59.7%), and walking/standing (57.4%) activities weresignificantly higher than that of care-coordinating activities (52.3%, F[3, 38.0] = 7.5, P < 0.001). These findingssuggest that bedside care and walking/standing, other than manual patient handling, contributed most to thelevel of acute fatigue.

Key words fatigue, heart rate, nursing work activities, stress, work demand.

INTRODUCTION

Hospital nurses’ fatigue has been subject to broad attentionin the literature because of its immediate impact on patientsafety. Historically, nurses’ fatigue has been considered aschronic in nature as a result of irregular shift schedules andsleep disturbances (Ruggiero, 2003). However, recent evi-dence suggests that acute fatigue tends to be more severethan chronic fatigue in hospital nurses (Winwood et al., 2006;Fang et al., 2008; Barker & Nussbaum, 2011a; Geiger-Brownet al., 2012; Chen et al., 2013). Work-related acute fatiguerefers to a feeling of lack of energy as a direct consequenceof previous work activities, and can be considered as a pro-tective human response to work demands (Ruggiero, 2003;Winwood et al., 2006). A high level of work-related acutefatigue might lead to declined vigilance and deteriorated per-formance (Barker & Nussbaum, 2011b; Geiger-Brown et al.,2012), which increase the risks of medical errors and work-place injuries (Caruso et al., 2004; Geiger-Brown & Trinkoff,2010). Research evidence suggests that work-related acutefatigue in hospital nurses is primarily associated with exces-sive workloads (Winwood & Lushington, 2006; Fang et al.,2008). However, given a large variety of nursing work per-

formed in a shift (Fiedler et al., 2012), little is known aboutwhich specific nursing activities could be the primary sourcesof acute fatigue. Without systemically evaluating the relativecontributions of different work activities to nurses’ acutefatigue, it would be difficult to identify the priority areas inneed of interventions and develop well-suited fatigue-reduction strategies for hospital nurses.

There is evidence indicating that nurses’ physiologicalstatus at work (e.g. heart rate) might provide a connectionbetween nursing work activities and acute fatigue level. Huiet al. (2001) reported that the nurses rated patient lifting,transfer, and turning as most strenuous work, which was con-sistent with the elevations of heart rate when these activitieswere performed. Chen et al. (2011) continuously recordedthe heart rates of 145 hospital nurses over a 12 h day shift,and reported a persistent, moderate level of cardiac stressthroughout the shift hours. Although the study did notexamine the relationship between nurses’ heart rates andspecific nursing work activities, a moderate-to-high level ofacute fatigue estimated in this group of nurses was consistentwith their heart rate findings (Chen et al., 2013).

The nature of nursing work determines that nurses beinvolved in a large variety of work activities (Pelletier &Duffield, 2003). Some researchers have attempted toexamine the relationship between specific nursing workactivities and working heart rate (heart rate at work). Nuikkaet al. (2001) divided direct patient care nursing activities into

Correspondence address: Jie Chen, School of Nursing and Health Studies, NorthernIllinois University, 1240 Normal Road, DeKalb, IL 60115, USA. Email: jchen2@niu.eduReceived 28 August 2013; revision received 2 October 2013; accepted 4 October 2013

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Nursing and Health Sciences (2014), 16, 19–25

© 2014 Wiley Publishing Asia Pty Ltd. doi: 10.1111/nhs.12104

four categories to compare the differences of working heartrates. Based on the heart rate data, transporting the patientand primary care situation (e.g. assistance in mobility) wereclassified as medium–heavy strenuous activity, whereas prac-tical nursing procedures (e.g. wound care), care situations(e.g. discussion with an aggressive patient), and care planning(e.g. admission interview) were classified as moderate or lightstrenuous activity. In an experimental study simulatingnursing tasks in a laboratory setting, heart rate responseswere used as a measure of fatigue to examine the impacts ofmental and physical work demands (Barker & Nussbaum,2011b). While heart rate increased significantly with the levelof physical demand, no significant effect on heart rate vari-ability was found for mental demand and mixed physical–mental demands. The researchers suspected that theinsensitivity of heart rate response to mental demand couldbe related to the inadequate exposure levels of simulatedmental demands compared with actual nursing work (Barker& Nussbaum, 2011b). Furthermore, some evidence suggeststhat heart rate elevation is more sensitive than heart ratevariability to mental work demands (Wilson, 2002). Regard-less, measuring nurses’ heart rate responses to mental workdemands needs to be further explored in real work situations.

In this study, we identified the nursing work activities thatcould be considered the primary sources of work-relatedacute fatigue in hospital nurses. A work sampling techniquewas applied to identify and classify nursing work activitiesover a 12 h day shift. Elevation of working heart rate wasused as a physiological measure of acute fatigue evoked bythe corresponding work activity. The contributions of differ-ent nursing work activities to acute fatigue were comparedbased on the frequencies and corresponding heart rate eleva-tions of individual activity categories. The specific researchquestions were: (i) how are nursing work activities distrib-uted over a 12 h day shift?; and (ii) how does heart rate leveldiffer across nursing work activities?

METHODS

Design

This was a non-experimental, observational study. Continu-ous recording of nurses’ working heart rates and randomobservations of nursing activities using a work sampling tech-nique were used to collect data from eight staff nurses on twoconsecutive 12 h day shifts (7.00 hours – 19.30 hours) in ahospital setting.

Setting

The study was conducted at the St Elizabeth Medical Center(Edgewood, KY, USA), which is a large, general medical andsurgical hospital where nurses from a 42-bed telemetry unitwere observed. The most common patients admitted to theunit are those with chest pain, congestive heart failure, myo-cardial infarction, stroke, renal failure, diabetes mellitus,gastrointestinal hemorrhage, and pneumonia. The nurse-to-patient ratio in this unit was 1:3–4 for day shifts.

Sample

After obtaining the approvals from the institutional reviewboards at the University of Cincinnati and St ElizabethMedical Center, flyers regarding general information aboutthe study were distributed on the unit.The sample was femalefor homogeneous purposes, as it was a sample of convenienceand few male nurses worked in this unit. The inclusion crite-ria for participation were: (i) female registered nurse (RN);(ii) over one-year RN experience; (iii) being able to work twoconsecutive 12 h day shifts; and (iv) free of chronic cardio-vascular conditions. The principal investigator met with thenurses who volunteered to participate in the study to explainthe study and ensure participation eligibility. Nurses wereinformed that collected data would be coded with identifica-tion numbers on the questionnaires, observation checklist,and computerized database, and that no identifiable personalinformation would be used or released in any research mate-rials or shared with their employer. Eight staff nurses volun-teered to participate in the study, and signed consent formsbefore the data collection.

Data collection

The data were collected over four weeks during June and July2006. The shifts were scheduled by the unit manager, basedon staffing need and nurses’ preferences. To eliminate poten-tial effects from previous working days, the participants wererequired not to work the previous 24 h prior to their firstmonitored shifts.

The participants’ ages, years of working as an RN, andworking hours per week were collected in a short question-naire.Weight was measured (in lbs) using a calibrated, digitalweighing scale, and height was measured (in inches) using astandard stadiometer at the beginning of the first monitoredshift.

Nursing work activities

A work sampling technique (Pelletier & Duffield, 2003) wasapplied to collect information regarding nursing work activi-ties. Each participant was assigned randomly to one of twoobservers, and was observed at a randomly selected time withan average 10 min interval, for a total of 72 observations ineach 12 h shift. Once the observers identified the nursingactivities at the selected observation time, they recorded theactivities immediately onto a time log describing the specificobservation time (e.g. 14.36 hours), exactly what a participantwas doing, working position (i.e. sitting, standing, or walking),and the location of the observed activity.

Two staff RN working on the unit were trained to be theobservers, as their familiarity with the work of the unit mighthelp with interpreting the observed tasks. Prior to the datacollection, two 4 h trial sessions were conducted on the unitand reached a satisfactory 90% inter-rater reliability.

As the observations might involve the presence of apatient, the observer asked for permission for observationfrom the involved patient. No patient refused the request forobservation during the data-collection period.

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Given a large variety of nursing activities observed, therecorded activities were divided into 16 categories.Accordingto a common classification method used in previous observa-tional studies of nursing work (Nuikka et al., 2001; Pelletier &Duffield, 2003; Upenieks et al., 2007; Williams et al., 2009),the 16 categories were classified as direct care (tasks per-formed directly on the patient, seven categories), indirectcare (patient-related tasks performed away from the patient,six categories), unit-related activities (tasks performed formaintenance of the nursing unit, one category), and workbreak (non-work activities, two categories).When nurses per-formed multiple activities simultaneously, the main task per-formed at that moment was assigned to the correspondingcategory.

Heart rate

Nurses’ working heart rates were used as a physiologicalmeasure of acute fatigue. Actiheart (Cambridge Neuro-technology, Cambridge, UK) is a technically reliable andvalid instrument used to measure heart rate in a wide rangeof free-living activities (Assah et al., 2010). The lightweight(12 g) monitor was attached to the participant’s chestthrough two electrodes to record heart rate continuouslythroughout a 12 h shift. The participants’ heart rates weresampled by the device once every 15 s. Every four readingswere averaged and registered as heart rate per min. At theend of the shift, the heart rate data stored in the Actiheartwere downloaded into a database, and later paired with cor-responding activities recorded in the observation time log.

Data analysis

Statistical analyses were performed using IBM SPSS Statis-tics version 21.0 (IBM, Armonk, NY, USA). Descriptive sta-tistics were used to calculate the average age, years of RNexperience, body mass index (BMI) (BMI = lb/in2 × 703)(Centers for Disease Control and Prevention, 2013), and fre-quencies of each category of work activities. The percentageof individual work activity category to the total observedactivities was calculated to estimate the distributions of dif-ferent nursing work activities over a 12 h day shift.

Nurse’s heart rates at the minute when the activityoccurred was selected as the working heart rate for thatspecific activity. The %HRmax, which is the percentage ofworking heart rate to maximal heart rate (maximal heartrate = 220 – age) (Panter-Brick, 2003), was calculated foreach observed activity and for each participant to justify theeffect of age on heart rate. The average %HRmax was calcu-lated for each activity category as the corresponding acutefatigue level.

Based on the %HRmax estimates and nature of individualwork activity categories, 13 of 16 activity categories related tonursing work were selected and classified into four activitylevels. Given the small number of participants and the largenumber of repeated measures of nursing work activities,random-effect ANOVA, with participant effect specified asa random effect rather than repeated-measures ANOVA(McCulloch, 2005), was performed to identify the significant

differences of %HRmax across activity levels or selected activ-ity categories. More specifically, in the general linear model,%HRmax was entered as the dependent variable, activitylevel or activity category and shift were entered as the fixedfactors, and participant was entered as the random factor.Themain effects of activity level or activity category, participant,and shift, and their interactions were examined and consid-ered statistically significant when P < 0.05. Bonferroni post-hoc tests were conducted to identify the pairs of activitylevels with significant difference of %HRmax.

RESULTS

The mean ± standard deviation for the ages of the eight par-ticipants was 43.4 ± 8.8 years.The average length of RN expe-rience was 10.6 ± 6.1 years. The average BMI was 28.9 ± 6.9.

During one shift, one nurse was assigned to an intensivecare unit for 1.5 h, resulting in nine missing data points. Attimes, the observers were unable to locate their participantsimmediately at the scheduled sampling time, resulting inanother 38 missed observations that occurred for all eightparticipants during one or two of their shifts. Thus, sixteen12 h day shifts (a total of 192 h) provided 1105 observationsof nursing activities.

The percentages of individual activity categories to totalobserved activities are shown in Figure 1. Indirect careaccounted for more than half (53.7%) of total activities,whereas direct care and work breaks accounted for nearlyone-quarter (24.1%) and one-fifth (20.7%), respectively. Themost frequent direct-care activity was nursing tasks, includingassessment, treatment, and collecting specimens, followed bycommunication with patients and families.The most frequentindirect-care activity was charting, followed by walking inhallways. Two-fifths of work-break activities were unofficialbreaks, which occurred during the transition time betweentasks, that is, sitting to rest, personal conversations, andphone calls.

Nurses’ average working heart rates for a 12 h shiftwas 98.3 ± 23.1 beats per min. The average %HRmax was55.3 ± 5.3%. The mean %HRmax of individual nursingwork activity categories, where all the activities reached amoderate cardiac stress level (low: < 49%HRmax, moderate:50–69%HRmax, high: > 70%HRmax) is shown in Figure 2 (U.S.Department of Health and Human Services, 1996). It isinteresting to note, however, that the %HRmax graduallydecreased with physical activities in the activity categories,and that there was consistency in the nature of the activitycategories with similar %HRmax levels. Thirteen nursing-related activity categories (housekeeping and work-breakactivities were excluded) were chosen and classified into fouractivity levels: manual patient handling, bedside care,walking/standing, and care coordinating. The distribution ofthese four activity levels over a 12 h day shift and their cor-responding %HRmax levels is shown in Figure 3.

There were significant differences of %HRmax across fouractivity levels (F[3, 38.0] = 7.5, P < 0.001), after the effects ofparticipant and shift were controlled for. The mean %HRmax

of care-coordinating level was significantly lower thanthose of manual patient handling (mean difference = 12%,

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P < 0.001, 95% confidence interval [CI]: 7.0–17.0), bedsidecare (mean difference = 7.3%, P < 0.001, 95% CI: 4.2–10.5),and walking/standing (mean difference = 5.1%, P < 0.001,95% CI: 2.7–7.4). The mean %HRmax of manual patient han-dling was significantly higher than that of walking/standing(mean difference = 6.9%, P = 0.001, 95% CI: 2.0–11.9). Themean %HRmax of bedside care did not significantly differfrom either manual patient handling or walking/standing. Itwas also noted that the %HRmax of charting in a standingposition was significantly higher than that of charting in asitting position (mean difference = 6.3%, P = 0.004, 95% CI:1.0–11.6), after the effects of participant and shift were con-trolled for.

There were significant effects of participant (F[7, 10.3] = 4.9,P = 0.011) and shift (F[1, 11.5] = 5.7, P = 0.035) on %HRmax,after the effect of activity level was controlled for.Nurses’ %HRmax was significantly higher in the secondshift (mean = 57.7 ± 15.1%) than in the first shift (mean =

53.7 ± 9.5%). However, no significant interaction was foundamong the effects of activity level, participant, and shift.

DISCUSSION

Combining heart rate monitoring and a work sampling tech-nique, this study evaluated the relative contributions of dif-ferent nursing work activities to work-related acute fatiguein hospital nurses. While all 16 categories of nursing workactivities reached a moderate cardiac stress level, basedon the %HRmax estimates, manual patient-handling, bedside-care, and walking/standing activities resulted in signifi-cantly higher levels of acute fatigue than care-coordinatingactivities.

As expected, the most strenuous nursing work activitieswere manual patient-handling activities, leading to a notablyhigher %HRmax level than all other activities. This result isconsistent with the findings of Hui et al. (2001) and Nuikka

15.1%

12.6%

10.9% 10.4%

8.1%7.6% 7.2%

6.2% 5.8%5.2%

3.9%

2.7%1.5% 1.2% 1.0% 0.6%

Tota

l obs

erve

d ac

vies

(%)

Ac vity categories

Figure 1. Percentages of individual activitycategories to total observed activities(n = 1105 observations). , Direct care;

, Indirect care; , Unit related; ,Work break.

64.7% 64.1%59.9% 59.8% 59.6% 58.5% 57.5% 57.3% 57.0% 56.8%

54.4% 53.4% 53.3% 53.0% 52.6% 50.6%

Mea

n %

HRm

ax

Nursing work ac vi es

Figure 2. Mean percentage of workingheart rate to maximal heart rate (%HRmax)of individual nursing work activitycategories.

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et al. (2001), confirming a clear association between manualpatient handling and working heart rate elevation. However,manual patient-handling activities only accounted for 5% ofnursing-related work activities over a 12 h day shift. Thisfinding was similar to the estimate in a video-based, observa-tional study in which 10 female hospital nurses acrossdifferent units and shifts spent less than 7% of their timeon average on patient-moving and transferring activities(Fiedler et al., 2012). Freitag et al. estimated that hospitalnurses performed 6.5 patient transfers per shift (2012), andthe average time spent on pure lifting processes was only2 min per shift (2007). Therefore, although manual patient-handling activities are the most strenuous activities, theymight not be mainly responsible for hospital nurses’ acutefatigue due to their low frequency.

The level of %HRmax of bedside care was next to that ofmanual patient handling. Bedside-care activities were morefrequent than manual patient-handling activities, accountingfor 16% of nursing-related work activities. When nursesperform non-patient-handling activities at bedside, twofactors in combination might contribute to the elevated heartrates. The first factor is muscular exhaustion resulting fromworking in an awkward, static posture to adapt to bedriddenpatients and crowded spaces (Hui et al., 2001; Nuikka et al.,2001; Freitag et al., 2007).An awkward, static posture, definedas a bending-forward position that lasts for longer than 4 s(Freitag et al., 2012), has been frequently observed in nurseswhen performing bedside care (Hui et al., 2001; Nuikka et al.,2001; Freitag et al., 2007; 2012). This hazardous postureresults in prolonged, excessive muscle contraction that even-tually leads to muscular exhaustion and injury over time (Huiet al., 2001). The second factor is mental stress from multi-tasking during bedside care. For example, when nursesperform bedside care, they need to concurrently monitor apatient’s condition, solve unpredictable problems arisingfrom a patient or equipment, address a patient’s and family’sconcerns, and deal with phone calls or other interruptions(Cornell et al., 2011). These multitasking situations increasedcognitive demands, which in turn might elevate nurses’ heart

rates (Wilson, 2002). Therefore, as it involves a mixture ofphysical and mental work, bedside care seems to play a sig-nificant role in determining acute fatigue levels in hospitalnurses.

The %HRmax of walking/standing activities was lower thanthat of bedside care, but did not differ significantly. Thewalking/standing activities mainly included indirect-careactivities, and were most frequently observed, accountingfor two-fifths of nursing-related work activities. Extensivewalking could be a result of expanded unit layouts and inef-ficient routes between functional areas, which require nursesto walk long distances to reach their patients or supplies(Hendrich et al., 2008; Yi & Seo, 2012). Chen et al. (2011)estimated that hospital nurses travel 5.5–8 km during a 12 hday shift. Hendrich et al. (2008) estimated that hospitalnurses travel 4–5.5 km per 10 h day shift, and 2–5.3 km per10 h night shift. Besides extensive walking, nurses performjobs in a standing position frequently during a shift (Freitaget al., 2007; Fiedler et al., 2012). The finding that nursesperform charting in a standing position with significantlyhigher %HRmax than charting in a sitting position suggests asignificant contribution of standing position to the elevationof heart rate. Therefore, given an elevated heart rate levelsimilar to bedside care and a high frequency of exposure, theindirect-care activities that involve walking and standingmight deplete a considerable amount of energy from nursesover a 12 h day shift.

While the %HRmax level of care-coordinating activities wassignificantly lower than those of manual patient handling,bedside care, and walking/standing, it was still above a mod-erate cardiac stress level. This finding does not reflect thenature of care-coordinating activities (i.e. documentation,communication), which was primarily administrative workinvolving minimum physical activities. One possible explana-tion for the elevated heart rates could be that the short dura-tion of care-coordinating tasks left insufficient time for heartrate recovery. Working in a dynamic and fast-paced hospitalenvironment, nurses switch tasks frequently because ofunpredictable interruptions (Cornell et al., 2011). Some

5.0%

15.9%

38.0%41.0%

Manual pa ent handling Bedside care Care coordina ng Walking/standing

Tota

l nur

sing

-rel

ated

wor

k ac

vies

(%)

Ac vity levels

Figure 3. Mean percentage of working heart rate to maximal heart rate (%HRmax) and percentages to total nursing-related work activities(unit-related and break activities were excluded) for manual patient-handling, bedside-care, walking/standing, and care-coordinating activities(n = 860 observations). ■, Manual patient handling (%HRmax: 64.3%): assisting with mobility, changing bed/positioning; ■, bedside care(%HRmax: 59.7%): assessment, treatment, and collecting specimens; ■, walking/standing (%HRmax: 57.4%): escorting patient out of unit,walking in hallways, charting while standing, and preparing medications; ■, care coordinating (%HRmax: 52.3%): communication with patientand family, communication with care team, charting while sitting, and transcribing orders.

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studies have reported that hospital nurses switch tasks every55 s, and average durations of documentation and profes-sional communication rang from 33 to 92 s, and 33–59 s,respectively (Cornell et al., 2011; Westbrook et al., 2011).

The %HRmax data also indicate a consistent, moderatecardiac stress level across all the activities, including workbreaks. This result is consistent with a report of persistent,elevated heart rates throughout the shift hours in hospitalnurses in a previous study (Chen et al., 2011). Furthermore,our study showed that the mean %HRmax of the second shiftwas significantly higher than that of the first shift after con-trolling for activity and participant effects. This finding mightpartially explain a previous finding that hospital nurses findit difficult to work two or more consecutive 12 h shifts(McGettrick & O’Neill, 2006; Richardson et al., 2007), andquestions if nurses’ acute fatigue be relieved by regular workbreaks or overnight sleep between consecutive shifts. Toanswer this, future research will need to explore the effects ofwork breaks and inter-shift recovery on nurses’ workingheart rates.

Limitations

The major strength of this study was using a working heartrate to quantify the acute fatigue levels in relation to specificnursing work activities over a 12 h shift. Some major limita-tions, however, should to be acknowledged when interpretingthe findings.

First, a very small number of participants cannot be repre-sentative of the general hospital nurse population, especiallygiven that all of the participants were female and worked thesame type of shifts in the same unit. As nursing work variessubstantially across job titles, shifts, and work settings, thestudy findings might not be generalizable to other nursepopulations (e.g. male nurses), shifts (e.g. night shifts), units(e.g. intensive care units), or hospitals (e.g. psychiatric hospi-tals). In order to draw a firm conclusion concerning thesources of acute fatigue in hospital nurses, future researchshould replicate this method using larger samples in differentnursing groups, shifts, and locations.

Second, staff nurses from the unit as observers might intro-duce bias into the data-collection process because of theco-worker relationships between the observers and partici-pants.For example,being observed by co-workers might affecta participant’s behavior; meanwhile, observing co-workersmight cause ethical difficulties and prejudices impeding theobjectivity of the observers. Using carefully-trained third-party research assistants as independent observers has beenrecommended to minimize the observer bias in observationalstudies of nursing activities (Pelletier & Duffield, 2003).

Nursing implications

The study revealed that although manual patient handlingwas the most strenuous nursing activity, bedside care andwalking/standing were more common and might play greaterroles in contributing to the overall level of acute fatigue innurses. Staff nurses should be aware of the relative contribu-tions of different work activities to their acute fatigue, and

adopt ergonomic techniques to their daily nursing practice.While ergonomic equipment and a lift team are essential toassist with direct patient care activities (Nelson et al., 2006),educational programs should be incorporated to promotesafe work behaviors on the unit. Nurses should be taught howto identify hazardous work situations (e.g. bedridden orbariatric patients, awkward postures, deviated walking path,prolonged standing) and seek practical solutions (e.g. arrangeenough space and use proper body mechanics when provid-ing bedside care, utilize assistive devices and lift team, teamwork approach) (Chao & Henshaw, 2009).

While an ergonomically designed unit layout is recom-mended for long-term benefits in improving work efficiencyand reducing fatigue, many inexpensive and simple solutionscan be implemented. For example, providing more foldingchairs in the unit can give nurses the convenience of sittingdown at any time, placing antifatigue floor mats in frequentstanding areas (e.g. medication room) can relieve nurses’ legand back strain, and creating a small supply storage space inpatient rooms can prevent unnecessary trips for nurses.

The study findings raise the concern that a fast-changingwork pace might interfere with heart rate recovery. A priorstudy reported that older nurses work at a slower pace thanyounger nurses during a 12 h shift (Chen et al., 2011). Ashospital nurses continue to age (U.S. Department of Healthand Human Services, Health Resources and ServicesAdministration, 2010), flexible shift scheduling (e.g. 4, 6, or8 h/shift), job sharing, and alternative roles (e.g. intellectual,rather than physical, work) can be potential solutions forreducing acute fatigue in older nurses (Fitzgerald, 2007).

Conclusions

In this study, we used continuous recording of working heartrate and random sampling of nursing activities to evaluatethe relative contributions of different nursing work activitiesto work-related acute fatigue in hospital nurses. The heartrate findings suggest that bedside care and walking/standingactivities, other than manual patient handling, contributedmost to the level of acute fatigue. Furthermore, a fast-changing work pace and consecutive shifts might have inter-fered with heart rate recovery, and consequently exacerbatedacute fatigue. Future studies with larger samples are neededto further examine the development process of acute fatigueover a shift, so that it will be possible to develop effectiveinterventions to reduce acute fatigue and optimize work per-formance in hospital nurses.

ACKNOWLEDGMENTS

This work was supported by the National Institute of Occu-pational Safety and Health Pilot Research Project TrainingProgram at the University of Cincinnati Education andResearch Center, Grant #T42/OH008432-01.

We sincerely thank the staff at St Elizabeth Medical Centerand all the study participants for their contributions to thestudy. We wish to acknowledge Professor Linda Sue Davis, atthe College of Nursing, University of Cincinnati, for her earlyguidance on the study design.

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CONTRIBUTIONS

Study design: JC, NMD, KGD.Data collection: JC.Data analysis: JC, WP.Manuscript writing: JC, NMD, KGD, WP.

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