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The impact of supplementary short rest breaks on task performance – A meta-analysis

  • Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Berlin, Germany

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Within-shift rest breaks are important to prevent an accumulation of impairing short-term effects of strain over working time. In this meta-analysis (k = 11, N = 705), we investigated how supplementary, frequent short rest breaks affect task performance and strain. We found positive effects on quality (g = 0.23) and quantity (g = 0.12) measures of task performance. The mean reduction of working time due to rest breaks was 9.3%. Performance improvements occurred not at costs of higher strain. Thus, our study shows that both employees' performance and well-being benefits from scheduled within-shift breaks. We found no further effects of potentially moderating variables. Future research should examine the boundary conditions and underlying mechanisms of these effects.
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The impact of supplementary short rest breaks on task performance –
A meta-analysis1
Within-shift rest breaks are important to prevent an accumulation of impairing
short-term effects of strain over working time. In this meta-analysis (k = 11,
N = 705), we investigated how supplementary, frequent short rest breaks affect task
performance and strain. We found positive effects on quality (g = 0.23) and quantity
(g = 0.12) measures of task performance. The mean reduction of working time due
to rest breaks was 9.3%. Performance improvements occurred not at costs of higher
strain. Thus, our study shows that both employees’ performance and well-being
benefits from scheduled within-shift breaks. We found no further effects of poten-
tially moderating variables. Future research should examine the boundary condi-
tions and underlying mechanisms of these effects.
Keywords: meta-analysis, performance, recovery, rest breaks, strain.
1 Author Note: The authors have no actual or potential financial and other conflicts of interest related to the submitted
manuscript. This study was part of the project “Mental health in the working world - current state of scientific evidence”
(F2353) granted by the German Federal Institute of Occupational Health and Safety and the first authors dissertation thesis.
We would like to thank Beate Beermann, Martina Morschhäuser, Martin Schütte, Anne Marit Wöhrmann, and two anony-
mous reviewers for their helpful comments on earlier drafts of this paper.
Correspondence concerning this article should be addressed to Johannes Wendsche, Federal Institute for Occupational
Safety and Health (Germany). Date of submission: 30.05.2016
2 Dipl.-Psych. Johannes Wendsche is a research fellow at the Federal Institute for Occupational Safety and Health (Fab-
ricestr. 8, D-01099 Dresden), Germany. He received a Diploma (2007) in psychology from TU Dresden (Germany). His
research focuses on recovery from work stress, job analysis, job design, and turnover behavior.
3 Dipl.-Psych. Andrea Lohmann-Haislah is a research fellow at the Federal Institute for Occupational Safety and Health
(Nöldnerstr. 40-42, D-10317 Berlin), Germany. She received a Diploma (1997) in psychology from Freie Universität Berlin.
Her research focuses on recovery from work stress, job analysis, and job design. She is the author of the German Stressreport
2012. E-mail:
4 Prof. Dr. Jürgen Wegge is currently a full professor of work and organizational psychology at the Technical University
of Dresden (Department of Psychology, TU Dresden. D-01062 Dresden), Germany. He earned his Ph.D. (1994) in industrial-
organizational psychology from the Technical University of Dortmund. His research interests are in the field of work motiva-
tion, leadership, demographic change, and occupational health. E-mail:
Rest breaks are within-shift interruptions between periods of planned work aiming to provide
time for recovery from impairing consequences of mental and physical strain and to prevent
their development and accumulation over the working day (Graf et al. 1970). This is a central
ergonomic standard in the design of work systems (DIN EN ISO 6385 2004; DIN EN ISO
10075-2 2000). During a rest break working is formally not required or expected (Graf et al.
1970; Trougakos/Hideg 2009).
After one century of research, there is wide evidence that rest breaks can improve employ-
ees’ well-being, health, and work safety (Nachreiner et al. 2010; Tucker 2003). Therefore, in
most countries national legislation requires to provide employees at least one longer manda-
tory and mainly unpaid rest break after a certain amount of working hours (McCann 2005).
Several studies found that employees might benefit from more frequently scheduled, short rest
breaks over the working day (Tucker 2003). Notably, it is argued that positive effects of such
rest break schedules on task performance can even compensate a loss of productive working
time due to more time for recovery (Graf et al. 1970). Therefore, these rest breaks could as
well be scheduled as paid working time. However, a quantitative review concerning this as-
sumption is still missing. To fill this gap, we conducted a meta-analysis of studies investigating
the impact of such rest breaks on task performance and strain.
Theoretical Background
Intuitively, after a period of demanding work, especially if endurance performance limits are
exceeded, rest is necessary to recover from impairing short-term consequences of strain such
as physical and mental fatigue or fatigue-related states such as monotony or mental satiation
(DIN EN ISO 10075-1 2000; Scholz 1970). Recovery is a process wherein “the psychophysio-
logical systems that were activated during work will return to and stabilize at a baseline level,
that is, a level that appears in a situation in which no special demands are made on the indi-
vidual.“ (Geurts/Sonnentag 2006: 483) or in more general terms “the continuous process of
harmonizing the ‘actual state’ with the ‘required state’” (Zijlstra et al. 2014: 250).
Managers and supervisors should think carefully about how they organize sufficient re-
covery periods for their employees as enhanced strain levels have been meta-analytically asso-
ciated with lower motivation, lower job satisfaction, lower work performance, lower work
safety, and higher withdrawal behavior and turnover rates (LePine et al. 2005; Nahrgang et al.
2011; Podsakoff et al. 2007).
Recovery periods can be organized within work shifts (internal recovery) such as rest
breaks or between work shifts (external recovery) such as the daily rest after work at evenings,
the weekend, or a vacation (Geurts/Sonnentag 2006). In most countries over the world, na-
tional legislation regulates at least some standards for internal and external recovery periods
(Linder/Nygaard 1998; McCann 2005).
Rest break organization in practice represents a complex system where several variables
and their interplay have to be considered (Wegge et al. 2014). For instance, this concerns the
distribution of time for work and recovery within a shift (i.e., the length, the frequency, and
the timing of breaks), the predictability of rest breaks, employees’ control over the rest break
schedule, the formalization of rest breaks, and the payment of rest break time (Müller-Seitz
1996; Tucker 2003; Wegge et al. 2014).
First experimental investigations to identify organizational principles of so called “worth-
while breaks” (Kraepelin 1902) for physical (Manzer 1927) and mental work (Amberg 1895;
Rivers/Kraepelin 1896) date back to end of the 19th century. This research aimed to find rest
break schedules at which a potential quantitative loss of task performance due to paid rest
break time is fully compensated by beneficial effects of rest breaks (Graf 1922, 1927; Graf et al.
1970; Rohmert 1973a,b).
Findings of this as well as subsequent research (Bokranz 1985; DIN EN ISO 10075-2 2000;
Konz 1998; Rohmert 1973a,b; Tucker 2003) suggested the following characteristics of worth-
while rest breaks: (a) they are included in the agreed (paid) working time, (b) they are short in
duration, i.e., shorter than 15 minutes from a legislative perspective (Schmidtke 1993) or,
from a more functional perspective, between three and ten minutes (Richter/Hacker 2014),
and (c) they are scheduled frequently, i.e., several times during the working day. Moreover
such rest breaks should be (d) scheduled in advance by the management meaning that they
are on the one hand authorized and, on the other hand, timing (i.e., start, duration, and end)
of breaks is pre-determined and predictable.
In the following, we will discuss some theoretical arguments and findings that underline
these principles of rest break organization. Moreover, we will explain how it is possible to im-
prove employees’ task performance by changing the balance of work and recovery.
Factors affecting task performance
Work performance is a multi-dimensional construct relating to task and contextual perfor-
mance. According to Campbell et al. (1993) psychophysical (i.e., declarative knowledge, pro-
cedural knowledge, and skills) and motivational factors (i.e., effort investment and persis-
tence) determine work performance. Results of a meta-analysis (LePine et al. 2005) support
this assumption and found that impairing mental strain symptoms (rc = -.21) and motivation
(rc = .44) both predicted work performance, even when adjusting single effects for each other.
In our review, we only focus on rest break mechanisms affecting task performance, thus, on
in-role behavior to reach goals that are formally expected from the employee (Sonnentag et al.
2008). Building upon Graf’s (1922, 1927) theory of worthwhile rest breaks, we propose
that rest breaks can have both beneficial and inhibiting effects on task performance
(Wendsche/Wegge 2014). A rest break is worthwhile under conditions at which a loss of po-
tentially productive working time due to time for resting is fully compensated by positive di-
rect and indirect effects of rest breaks on task performance. In such cases beneficial effects of
rest breaks on task performance outweigh their impairing effects.
Beneficial effects of supplementary short rest breaks
Rest breaks can improve task performance through beneficial strain-related, cognitive, affec-
tive, and motivational mechanisms.
Strain-related mechanisms. Impairing consequences of strain are determined by the level
of work demands (DIN EN ISO 10075-1 2000; Meijman/Mulder 1998) and the temporal char-
acteristics of the exposure to these demands (Schmidtke 1993). Such effects depend on the
configuration of the quality, the quantity, the duration, and temporal dynamics of work de-
mands (Bakker/Demerouti 2007; Schmidtke 1993; Sonnentag/Frese 2012). Supplementary,
frequent short rest breaks prevent the development and accumulation of impairing strain out-
comes in several ways. They reduce total working time and, thus, the duration of work de-
mands as they are included in the agreed (paid) working time. This should decrease physical
and mental strain outcomes. For instance, several meta-analyses found that shorter total
working hours relate to lower physical and mental strain outcomes (Ng/Feldman 2008; Nixon
et al. 2011; Sparks et al. 1997). This is in line with assumptions from the Effort-Recovery
Model (Meijman/Mulder 1998) suggesting that impairing strain outcomes increase with time
on duty as mental and physical resources that are needed for task execution are drained. Im-
portantly, increases in impairing strain outcomes accelerate with time on task as individuals
might have to invest compensatory effort to reach their goals which further increases resource
depletion (Meijman/Mulder 1998; Rohmert 1973a,b). Accordingly, rest breaks should be
scheduled early and frequently. Moreover, recovery from strain outcomes follows regressive
curves (Lehmann 1962). Thus, relative recovery is higher for shorter than longer rest breaks.
Moreover, with increasing work demands time for total recovery from impairing strain out-
comes increases (Rohmert 1973a,b). This supports in addition the idea of preferable early and
frequently scheduled rest breaks.
Cognitive mechanisms. It has been found that rest breaks have positive cognitive effects.
More specifically, results from two meta-analyses suggest that short rest breaks improve criti-
cal performance outcomes as learning, skill acquisition (Donovan/Radosevich 1999), and
problem-solving (Sio/Ormerod 2009) which all relate to task performance.
Affective mechanisms. Graf (1922, 1927) suggested that scheduled rest breaks might have a
positive ‘emotional value’ meaning that individuals positively anticipate the pre-planned re-
laxing break incorporating a relief from work demands. Research on the Broaden-and-Built-
Theory of Positive Emotions (Fredrickson 2013) suggests that positive emotions improve
well-being, attention, recovery from strain, and task performance. Thus, frequently scheduled
rest breaks will accumulate this increase in positive affect which, in turn, will improve task
Motivational mechanisms. Early research showed that (sub-)task allocation over total
working time is important for performance. According to Motivational Intensity Theory
(Brehm/Self 1989) individuals’ effort investment for goal attainment follows a conservation
principle. Thus, they do not mobilize more energy than necessary to reach the anticipated
goals. Accordingly, some studies found an inverse relationship between expected time on task
and task performance (Barmack 1939; Ross/Bricker 1951). More frequent scheduled rest
breaks will divide the total working time into shorter bouts of work. This increases individu-
als’ relative effort investment and, in turn, their task performance. Studies on the ‘curve of
work’ found that individuals increase the speed of work at the end of a work period (Kraepelin
1902). Thus, task performance will increase under frequently scheduled rest breaks where
these end-spurt effects could accumulate. Another motivational mechanism concerns the ac-
tual task-related use of working time. Several studies showed that frequently scheduled short
rest breaks reduce time for unofficial breaks (Bhatia/Murell 1969; Graf et al. 1970; Lehmann
1958; McGehee/Owen 1940). Accordingly, such rest breaks improve the task-related use of
total working time, increasing task performance.
Inhibiting effects of supplementary short rest breaks
Prior research also found that at least three inhibiting mechanisms can decrease task perfor-
mance under fixed schedules with supplementary short rest breaks: A potential increase in
work intensity, demands from interruptions of the work flow, and a loss of practice and moti-
Increasing work intensity. Supplementary rest breaks reduce the total potentially produc-
tive working time. Therefore, task goals have to be accomplished in shorter time and, in turn,
work intensity and time pressure might increase. Such indicators of workload have been
linked to higher mental and physical strain (Bowling et al. 2015). This might suggest a de-
crease in task performance. However, in the meta-analysis of Bowling et al. (2015) perceived
workload did not significantly correlate with a broad measure of task performance (k=16,
rc=-.03). In contrast, Szalma et al. (2008) used a larger sample of intervention studies (k=125)
and examined type of task and type of performance measure more closely. They found that
time pressure increased work speed (.12<rc<.28) and reduced task accuracy (-.16<rc<-.32) in
tasks with perceptual and cognitive demands. For motor tasks time pressure reduced work
speed (rc=-.29). Thus, assuming that supplementary rest breaks might actually increase work
intensity, beneficial but also detrimental effects on task performance might develop.
Interruptions. As outlined above a rest break is, by definition, an interruption of the work-
flow. Some studies reported that employees perceive frequently scheduled short rest breaks as
disruptive and impairing events (Dababneh et al. 2001; Henning et al. 1997) because (sub-)
goals cannot be reached. Work interruptions increase mental and emotional strain and impair
mental recovery from work (Baethge et al. 2015). However, effects of interruptions on task
performance are more complex (Baethge et al. 2015). Few interruptions increase activation
and effort investment resulting in higher task performance. However, after a certain threshold
of cumulating interruptions, task execution is severely impaired and, thus, performance will
decrease. This is also in line with findings from Graf (1922) suggesting an inverse u-shaped
pattern between total working time and total rest break duration for task performance. Nota-
bly, results of Graf’s lab studies revealed that rest breaks with a length of 5 to 10% of total
working time are ‘worthwhile’.
Loss of practice and motivation. After a rest break performance might temporarily de-
crease. Such a warm-up decrement has been explained by a loss of practice and motivation
(Adams 1961; Graf 1927; Rivers/Kraepelin 1896). However, at least for motor tasks this effect
decreases after a few days due to improvements in motor skills (Rutenfranz/Iskander 1966).
The Present Study
Based on these insights, the aim of this study was to investigate the impact of within-shift,
supplementary short rest breaks that are externally scheduled in advance on measures of task
performance. More specifically, our major question is whether such rest breaks compensate
the associated loss of potentially productive working time. We use meta-analysis to quantita-
tively review the strength of evidence from current research. Moreover, we will also explore
effects of potential moderating variables such as study and sample characteristics, task charac-
teristics, and characteristics of the rest break schedule.
We conducted a meta-analysis to answer our research questions. Below, we describe the litera-
ture search, the coding procedure, and the meta-analytic computations.
Literature Search and Study Inclusion
Our literature search follows the four-step approach according to the PRISMA statement
(Moher et al. 2009) which are identification and screening of studies, checking their eligibility,
and, finally, study inclusion.
At first, we conducted a search of German and English studies in scholarly literature databases
(EBSCO, PubMed, PSYNDEX, SCOPUS) for the years January 1990 to December 2014. We
selected this time period to find studies that are representative concerning the present work
situation and sample characteristics. An initial search with relatively broad search terms (e.g.,
rest, break, pause) yielded over one million results. Thus, we developed a search string with
about 200 German and English word combinations (i.e., “work break*” OR “rest break*”; the
complete search string can be requested from the first author of this paper) that was combined
with a work context string (work* OR occupation* OR job* or employ* OR drive*) and an
exclusion string (i.e., “pregnant” OR “school”). This initial search yielded 9,259 studies. In
addition, a further free hand search yielded 530 studies. After removing duplicates 8,350 stud-
ies remained.
Next, we checked the thematic fit of the titles and abstracts for the remaining studies, i.e., the
study investigated rest breaks. Interrater reliability of coding for a subsample of 100 randomly
selected studies was good (two raters; κ=.70; 95%CI [.52; .89]). The first two authors discussed
reasons for differences in study coding (nine of 100 studies) and harmonized decision criteria
for the full screening sample. Finally, we excluded 7,301 studies after screening (main reasons:
earlier editions of book chapters, Non-German or Non-English publication, editorials, not
about within-shift breaks).
We checked k = 1,049 full-text articles for inclusion. We used the following criteria for study
inclusion: (a) (quasi-)experimental comparison (between-subject or within-subject design)
between a condition with within-shift, supplementary short rest breaks and a control condi-
tion without, (2) at least two supplementary short rest breaks were implemented, (3) the
length of short rest breaks was stable over the shift or total working time and between 3 and
10 minutes (Richter/ Hacker 2014), (4) the start and end of these rest breaks were externally
scheduled (i.e., not by the participants), (5) measures of task performance were assessed, (6)
the author(s) reported sufficient data to calculate an effect size measure and its standard error.
For instance, we excluded the study of Dababneh et al. (2001) that was cited in two prior re-
views (Tucker 2003; van Holland et al. 2015) as information on productivity was only report-
ed for the final shift period. Moreover, we found a difference of more than three standard de-
viations between the estimated effect size and the mean in our final sample, indicating it as
outlier. We also excluded studies that relied on rest breaks in a sport or educational context.
Within this stage, we identified ten systematic narrative reviews about effects of rest breaks.
Accordingly, we checked the cited references. However, this yielded no further studies. Alto-
gether, we excluded k = 1,039 studies, which left ten publications for inclusion.
In our meta-analysis, we finally combined data from k = 11 independent study samples (N =
705; see Table 1 for an overview)
Coding Procedure
Effect size estimation
We used Hedges’ g (Hedges/Olkin 1985) for effect size estimation as many studies had small
total sample sizes (Median = 42; Borenstein et al. 2009). For repeated-measurement (within-
subject or crossover) designs, we considered the published lagged correlation of measures or,
if not reported, estimated this with a conservative value of r = .50 (see meta-analysis of Stur-
man et al. 2005). To ensure independence of effect sizes, we used average effect size estimates
for studies that reported multiple performance measures. In such cases, we considered their
dependence and corrected variances as outlined by Borenstein et al. (2009). For studies inves-
tigating multiple rest break interventions (e.g., manipulation of rest break length) conditional
effect sizes were combined for the overall analyses and used separately for moderator analyses.
The first author conducted all effect size calculations and double-checked them. Incon-
sistencies were discussed and resolved with the second author.
Coding of performance measures
We recorded effect sizes for quality and quantity measures of task performance. Quality varia-
bles were measures of accuracy (errors, error rate). One study examined rest breaks of doctors
during surgery. For this study, we coded the number of critical intraoperative events and the
urine production of patients as quality measures (see Engelmann et al. 2011, 2012). Quantity
variables were measures representing the amount of work performed in a specific time period
(e.g., productivity rate during farm work, number of calls in call-center work, number of key
strokes for data-entry work). All performance measures were objectively measured.
Table 1: Characteristics of studies included in the meta-analysis
Sample size,
sample, country
Mean age, %
Setting, design,
intervention length
Task demands,
working time
Rest break
Effect size est
mates (g, SE),
number of out-
come variables
Engelmann et al.
(2011, 2012)
N = 7, employees,
N.A., 17%
Field, WS
R, 4
d, 180
0, 25, 5, 25, 5, 14
QA: 0.08, 0.28, 1
QL: 0.45, 0.24, 2
Faucett et al. (2007,
Study 1)
N = 66, emplo
ees, US
25, 21%
Field, BS
R, 2
physical, 480
50, 20, 5, 60, 4, 4
0.04, 0.24, 1
Faucett et al. (2007,
Study 2)
N = 32, emplo
ees, US
41, 72%
ield, BS
R, 3
physical, 480
50, 20, 5, 60, 4, 4
0.17, 0.25, 1
Galinsky et al.
N = 42, emplo
ees, US
30, 74%
Field, WS
R, 20
mental, 450
60, 20, 5, 60, 4, 4
0.04, 0.13, 2
Galinsky et al.
N = 51, emplo
ees, US
36, 92%
Field, WS
R, 2
mental, 450
60, 20, 5, 60, 4, 4
QA: 0.20, 0.13, 2
Henning et al.
(1997, Study 1)
N = 34, emplo
ees, US
26, 89%
Field, BS
R, 20
mental, 450
60, 12, 3, 60, 4, 3
QA: 0.00, 0.35, 1
Henning et al.
(1997, Study 2)
N = 10, emplo
ees, US
25, 79%
Field, WS
mental, 450
60, 12, 3, 60, 4, 3
QA: 0.41, 0.27, 1
(2002), Hüttges et
al. (2005)
N = 19, emplo
ees, Germany
N.A., 90%
Field, WS
NR, 5
mental, 450
A: 30, 60, 5,
25, 12, 13; Trial B:
30, 60, 10, 50, 6,
0.05, 0.19, 1
et al.
Study 2)
N = 107, students,
27, N.A.
Lab, BS
R, 1
mental, 30
0, 6, 3, 10, 2, 20
QA: 0.41, 0.17, 2
Paulus et al. (2006,
Study 3)
N = 104, students,
27, N.A.
Lab, BS
R, 1
mental, 30
0, 6, 3, 10, 2, 20
QA: 0.12, 0.17, 2
Van den Heuvel e
al. (2003)
N = 233, emplo
ees, Netherlands
39, 48%
Field, BS
R, 36
mental, 390
30, 40, 5, 35, 8, 10
QA: 0.21, 0.14, 1
QL: 0.18, 0.12, 3
Note: N = sample size, g = Hedges g, SE = standard error of g, N.A. = no information available, WS = within-subject design, BS = between-
subject design, R = randomized trial, NR = non-randomized trial, QA = quantitative task performance, QL = qualitative task performance.
a Total time for longer breaks in minutes, total time for supplementary short rest breaks in minutes, length of short rest breaks in minutes,
rest break interval in minutes, number of supplementary short rest breaks (rest break frequency), time loss due to supplementary short rest
breaks in %.
Coding of moderator variables
We assessed several moderating variables related to characteristics of the samples and studies,
the tasks, and the rest break schedules.
Sample and study characteristics. We recorded data about the samples’ mean age in years,
the percentage of females in the samples, the type of sample (employees vs. students), the
study location (US vs. Europe vs. others), the study setting (field study vs. lab study), the study
design (between-subject vs. within-subject), and the randomization of participants (with vs.
without randomization). Furthermore, we coded the length of interventions in days.
Task characteristics. The total working time in minutes and the predominant type of task
demands (mental vs. physical vs. mixed) according to task descriptions in the study were cod-
Rest break characteristics. The total time for additional longer breaks, the total time for
short rest breaks, the time for single short rest breaks, the frequency of short rest breaks, the
rest break interval, two indicators for the relative work-to-rest-ratio (i.e., total working time
divided by (a) the total time for all rest breaks and (b) the total time for short rest breaks) was
recorded. Moreover, we calculated the time reduction due to supplementary rest breaks in
percent (i.e., total time for short rest breaks relative to the total working time).
Meta-analytic computations
We used a stepwise approach for the meta-analytic computations. First, we checked our data
for potential outliers. We found no extreme values differing more than two standard devia-
tions from the mean. Thus, we included all data for further calculations. Second, we ran a
random-effects model to calculate pooled effect sizes (Borenstein et al. 2009; Hedges/Vevea
1998). In our meta-analysis, we only corrected for sampling error. We report k as the num-
ber of studies for effect size estimation, N as the sum of individuals for k studies, pooled
Hedges’s g as effect size estimate, and the 95% confidence interval (CI) for g. If the 95%CI
excludes zero, the effect size estimate is significant from zero with p < .05 (two-tailed).
In addition, we report indices for heterogeneity indicating between study variations of true
effect sizes. To quantify heterogeneity, we report the QWithin- and I²-statistics. If QWithin is signif-
icant or I² is higher 25% we regard effect sizes as heterogeneous. In contrast to the Q-statistics,
I² is not sensitive to the number of aggregated studies. However, it cannot be used for testing
significance of heterogeneity. Thus, both measures should be used to analyze heterogeneity.
A substantial amount of heterogeneity indicates the impact of moderating variables (Boren-
stein et al. 2009). For categorical moderators, we applied random effects subgroup analyses
and report results of QBetween-tests of heterogeneity (Borenstein et al. 2009). Furthermore, we
used random effects meta-regression (unrestricted maximum likelihood method) for metric
moderators. We conducted these analyses according to the procedures and recommendations
proposed by Borenstein et al. (2009).
We conducted effect size estimation and all further analyses with Comprehensive Meta-
Analysis (CMA) software 2.2 (2010, Biostat, Inc, Englewood, NJ). We considered effect sizes
around 0.2 as small effect, around 0.5 as medium effect, and around 0.8 or higher as large ef-
fect (Cohen 1992). Moreover, we used the U3 index to calculate relative improvement rates in
task performance in percent (Lipsey/Wilson 2001). Finally, we examined the chance that a
publication bias might have affected our results with funnel plot analyses, Eggers’ regression
test, and the trim and fill method (see Borenstein et al. 2009).
Sample and Study characteristics
The mean age of employees was 30.6 years and the mean percentage of females in the studies
was 64%. Most of the samples were from the US (k = 8), the others from Europe. Only two
studies were lab studies. Both studies were the only ones with student samples. The median
for the length of one single short rest break was five minutes (range: three to ten minutes), the
median for rest break frequency was four times per shift, and the median for the rest break
interval was 55 minutes. The median of total working time was 450 minutes. Mean working
time reduction due to supplementary short rest breaks was about 9.3% of the total working
time. In most studies (k = 8) participants had time for further longer breaks (M = 36 minutes).
In most studies (k = 8) participants performed rather mentally demanding tasks such as
office work, brainstorming tasks, or data entry. Two studies examined rest breaks in more
physically demanding farm work. One study examined doctors during surgery which is
characterized by a mixture of high mental and physical demands.
Effects of Supplementary Short Rest Breaks on Task Performance
The effect of supplementary short rest breaks on quantity measures of task performance was
positive and significant with g = 0.12 (95%CI [0.02, 0.23], k = 11, N = 705, 18 combined effect
sizes). This effect can be considered as negligible small or an improvement of 5%. Effect sizes
were homogeneous (QWithin(10) = 9.20, p = .513, I² = 0%).
Furthermore, we found a significantly positive effect of supplementary short rest breaks on
quality measures of task performance with g = 0.23 (95%CI [0.02, 0.45], k = 2, N = 240, eight
combined effect sizes). This effect can be considered as small or as an improvement of 9%.
Effect sizes were homogeneous (QWithin(1) = 1.04, p = .309, I² = 3.4%).
Effect sizes estimates of both performance measures did not differ significantly
(QBetween(1) = 0.79, p = .374).
Moderator Analyses
Although our results suggest that effects sizes were homogenous, results of these hetero-
geneity analyses should be interpreted with caution. First, under conditions that the number
of studies is low, the Q-statistic suffers from problems of low statistical power (Higgins et al.
2003). Second, Borenstein et al. (2009) note that if studies’ effect size estimates have low preci-
sion (see Forest plots in Figure 1), heterogeneity can be masked and therefore resulting in es-
timates of near zero. For instance, 95% uncertainty intervals for I2 (see Borenstein et al.
2009 for the formulas) were 0 to 57% for quantity measures of task performance. Therefore,
we examined the potential impact of further moderating variables. Due to sample size re-
strictions this was only possible for quantity measures of task performance as outcome. Table
2 shows the results of these moderator analyses.
Sample and Study characteristics
Effect sizes were independent of samples’ mean age, samples’ percentage of females, type of
sample, and sample size.
Moreover, we found no significant moderating impact of study location, study setting,
study design, and randomization procedure. The pooled effect size in studies using a random-
ized control-group design was g=0.16, 95%CI [-0.01, 0.32]. Length of intervention had no
linear moderating effect. Since one early study reported u-shaped effects (Vernon 1925), we
ran a further subgroup analysis (intervention length: one day vs. two to five days vs. more
than five days). There were still no significant subgroup differences (QBetween(2)=3.64, p=.162).
However, on a descriptive level such an u-shaped pattern of intervention length was indicated
(one day: k=2, g=0.26, 95%CI [0.03, 0.50]; two to five days: k=4, g=-0.05, 95%CI [-0.28, 0.18];
more than five days: k=5, g=0.14, 95%CI [-0.00, 0.28]).
Figure 1: Forest plots for quality and quantity measure of task performance
Note: Trapezoid symbols represent mean effect size estimates. Thickness of boxes represents the study weight.
Task characteristics
We found no significant moderating effects of total working time and task demands.
Rest break schedule
Finally, we examined several characteristics of the rest break schedules. First, we found no
moderating impact of total time for further longer breaks and total time for short rest breaks.
Furthermore, time for single short rest breaks, frequency of short rest breaks, and rest break
interval were no significant moderating variables.
To account for between-study differences in total working time, we conducted some fur-
ther analyses. However, also relative work-to-rest-ratios were no significant moderating vari-
Moreover, we found no moderating effect of time reduction due to supplementary rest
-1,00 -0,50 0,00 0,50 1,00
Hedges‘ g and 95%CI
Van den Heuvel et al. (2003)
Engelmann et al. (2011, 2012)
Faucett et al. (2007), Study 2
Hüttges et al. (2005)
Galinsky et al. (2000)
Faucett et al. (2007), Study 1
Henning et al. (1997), Study 1
Engelmann et al. (2011, 2012)
Paulus et al. (2006), Study 3
Galinsky et al. (2007)
Van den Heuvel et al. (2003)
Paulus et al. (2006), Study 2
Henning et al. (1997), Study 2
Table 2: Results of moderator analyses for performance quantity as outcome
Moderator k N
Difference test
Study and sample characteristics
Mean age 9 679
b -0.03
Females [%] 9 494
b 0
Study sample
Q(1) = 1.73, p =.189
Students 2 211
g 0.26
Employees 9 494
g 0.09
Sample size 11 705
b 0.001
Study location
Q(1) = 0.02, p =.902
US 8 446
g 0.13
Europe 3 294
g 0.11
Study setting
Q(1) = 1.73, p =.189
Field 9 494
g 0.09
Lab 2 211
g 0.26
Study design
Q(1) = 0.41, p =.524
Between-subject 6 576
g 0.16
Within-subject 5 129
g 0.09
Q(1) = 0.02, p =.902
Randomized 9 676
g 0.13
Nonrandomized 2 29
g 0.11
Intervention length 11 705
b 0.002
Task characteristics
Total working time 11 705
b -0.001
Task demands
Q(2) = 1.89, p =.389
Mental 8 600
g 0.15
Physical 2 98
g -0.1
Mixed 1 7
g 0.08
Characteristics of rest break schedule
Time longer breaks (total) 11 705
b -0.002
Time short breaks (total) 11 705
b -0.004
Time short break 11 705
b -0.06
Frequency short breaks 11 705
b -0.01
Rest break interval 11 705
b -0.004
Relative work-rest-ratio
Total rest break time 11 705
b -0.1
Total short break time 11 705
b -0.003
Time reduction [%] 11 705
b 0.009
Note: k = number of studies, N = total sample size, PE = point estimate of effect sizes measure, CI = confidence interval of effect size measure,
LL = lower limit, UL = upper limit, g = Hedges g, b = unstandardized regression weight.
Supplementary Analyses
Publication bias
A meta-analysis might yield inaccurate effect size estimates if results rely on an incomplete
sample of relevant studies. Below, we report results of some supplementary analyses investi-
gating such a potential publication bias. To perform such analyses with CMA at least three
studies are needed. Thus, these analyses were only possible for quantity measures of task per-
First, we inspected the funnel plot where standard errors and effect size estimates of all
studies are plotted against each other. An asymmetrical distribution of effect sizes indicates
such a bias. However, after inspecting the graph, we rejected this assumption.
Second, results of Egger’s regression test (Egger et al. 1997) revealed no statistical asym-
metric effect size distribution (b = -0.52, 95% CI [-2.77, 1.73]).
Third, we applied a random effects trim and fill model (Duval/Tweedie 2000). In such a
model, pooled effect sizes are recomputed until the funnel plot is symmetric by adding effect
sizes from hypothetical missing studies. Results of this analysis yielded one potential leaving
study with a positive effect size estimate. The pooled effect size estimate for quantity measures
of task performance did only marginally change (g = 0.14, 95%CI [0.03, 0.25]) and remained
positive and significant.
Thus, according to these results the likelihood that a publication bias might have affected
our results is relatively low.
Consequences of mental and physical strain
Supplementary short rest breaks reduce total working time. We found that such rest break
schedules improve mean quantitative task performance. Hence, it can be argued that supple-
mentary short rest breaks increase work demands as the work has to be done in less time. Ac-
cordingly, persons might have to invest compensatory effort for goal-attainment at costs of
higher negative short-term strain consequences (Hockey 1997; Meijman/Mulder 1998). We
examined this assumption in our sample of studies and added further effect size information
from the study of Dababneh et al. (2001). We coded intervention effects of supplementary
short rest breaks on measures of short-term consequences of self-reported mental strain (e.g.,
fatigue, positive and negative affect, monotony, stress experience, mental effort) and self-
reported physical strain (e.g., physical discomfort in different body areas). Negative effect sizes
represent lower negative strain consequences.
We found that supplementary short rest breaks significantly decreased negative strain con-
sequences. Pooled effect sizes were g=-0.20 (95%CI [-0.38, -0.03], k = 9, N=296) for mental
strain outcomes and g=-0.36 (95%CI [-0.58, -0.14], k=10, N=529) for physical strain out-
comes. Both effect sizes are small. For both outcomes heterogeneous effect sizes were indicat-
ed (mental strain: QWithin(8)=11.43, p=.179, I²=30.0%; physical discomfort: QWithin(9)=36.54,
p<.001, I²=75.4%). Funnel plot inspection and results of further statistical tests did not indi-
cate a publication bias. Overall, these results do not support the assumption that supplemen-
tary short rest breaks increase quantitative task performance at costs of higher negative short-
term strain consequences.
Results of several prior reviews suggest that within-shift rest breaks are important to protect
employees’ well-being, health, and work safety (Ariens et al. 2001; Barredo/Mahon 2007;
Brewer et al. 2006; da Costa/Vieira 2008; Folkard/Lombardi 2006; Goodman et al. 2012; Grif-
fiths et al. 2007; van Holland et al. 2015; Kennedy et al. 2010; Nachreiner et al. 2010; Tucker
2003). However, less is known whether a managerial investment in more paid time for rest
breaks also pays off by improving employees’ task performance. Thus, the aim of our meta-
analysis was to uncover the impact of supplementary short rest breaks on quantity and quality
measure of task performance. To answer this question, we aggregated data from 11 independ-
ent study samples.
Theoretical Implications
The overall effect sizes of supplementary short rest breaks on task performance measures were
significantly positive. More specifically, a mean reduction of about 9.3% daily working time
for further rest breaks increased employees’ quantitative task performance for about 5% and
their work quality for about 9%.
Our results support findings of prior studies revealing that schedules with fixed supple-
mentary short rest breaks do not decrease (Galinsky et al. 2000; Henning et al. 1997) but ra-
ther increase (Dababneh et al. 2001) task performance. Our meta-analytic findings revealed
that beneficial effects of supplementary frequent short rest breaks on task performance are
rather small. However, at least with regard to effects on quantitative task performance, we
should not forget that individuals outperformed a reduction of potentially productive working
Several lines of reasoning have been discussed to explain increases in task performance by
fixed schedules of supplementary short rest breaks. Referring to the seminal work of Graf
(1922, 1927) improvements in task performance develop if effects of several beneficial mecha-
nisms connected to rest breaks (e.g., recovery from and prevention of negative short-term
strain consequences, optimal effort investment, less time for unofficial breaks and unneces-
sary tasks) exceed effects of accompanying impairing mechanisms (e.g., interruption of work
flow, loss of motivation). Thus, according to our results beneficial effects of scheduled short
rest breaks seem to prevail. This is also supported by one further finding of our study: Rest
breaks significantly improved individuals’ self-reported short-term mental and physical well-
being. This suggests no adverse compensatory costs of work intensification due to reduced
working time (Hockey 1997; Meijman/Mulder 1998) which has been also shown earlier for
machine-paced in contrast to self-paced work (Graf 1930). However, we could not disentangle
the interplay of the assumed rest break mechanisms more precisely. For instance, the latter
effect on strain outcomes might be due to successful recovery from impairing strain effects
during rest breaks and/or lower total load because of reduced working time. Thus, future
studies should examine the proposed mediating variables in combination to shed light on the
hitherto “black box”.
Along these lines a further question concerns the importance of rest breaks for job design.
From a theoretical point of view both the Effort-Recovery Model (Meijman/Mulder 1998) and
the Motivational Control Theory of Cognitive Fatigue (Hockey 1997, 2011) suggest that rest
breaks and task changes reduce impairing short-term effects of strain. Accordingly, task per-
formance should increase when interruption periods represent a change from task demands.
Thus, some previous experimental lab studies found rest breaks and task changes were both
effective for improving task performance (Bennett et al. 1974; Sio/Ormerod 2009). According
to ergonomic principles in the design of work systems (e.g., DIN EN ISO 6385 2004) task
changes should be favored as primary preventive strategy over rest breaks (i.e., secondary pre-
vention) as they increase task variety, learning opportunities, and individuals’ skill use. Thus,
certainly, there is a strong need for field studies investigating effects of both interventions, also
for other strain-related outcome variables, and also in combination. The latter point is im-
portant because in our meta-analysis we found several studies on duties with high task variety
(e.g., office work, surgeons, and brainstorming) where supplementary rest breaks additionally
improved task performance and well-being.
We further investigated the impact of several moderating variables such as sample charac-
teristics (age, gender, profession), study characteristics (sample size, study location, study de-
sign, randomization procedure), task characteristics (working time, task demands), and rest
break characteristics (total time for rest breaks of different length, rest break frequency, rest
break interval, work-to-rest ratios, time reduction due to supplementary rest breaks, and
length of intervention). In this sample of studies, none of these moderator tests were signifi-
cant for quantitative task performance as outcome. As our moderator analyses relied on
modest samples of substudies, low statistical power might be a limitation. However, our anal-
yses revealed homogeneous effect sizes for both performance measures. This rather suggests
no further impact of moderating variables here, but might also base on variance restrictions in
the primary studies. However, at least some experimental studies found that schedule charac-
teristics affect task performance (e.g., Balci/Aghazadeh 2003; Bhatia/Murrell 1969). Therefore,
future research should examine moderating variables more systematically and also in combi-
nation (e.g., different rest break schedules for task with different demands).
Limitations and Future Research Directions
What are potential limitations of our study? First, the scope of our literature search was
limited concerning the time period of publications (1990 to 2014) and language (studies pub-
lished in German or English). However, after a series of analyses, we found no support for a
potential publication bias. Importantly, pooled effect size estimates should be interpreted with
caution as in our meta-analysis the number of combined study results and also the sample
size, especially for qualitative task performance measures, was limited. We further note that
generalization of results to different working conditions might be limited. For instance, most
studies were conducted in the US and with jobs or tasks that are mental demanding. Moreo-
ver, in most studies short rest breaks were added to further longer breaks. However, this is
also a strength of our study, since nowadays such working conditions cover a wide and in-
creasing range of jobs in industrialized countries.
A second limitation concerns the estimation of lagged correlations for performance
measures in within-subject designs and, moreover, intercorrelations of multiple performance
measures within single studies. However, this procedure does not affect pooled effect size es-
timates but rather their precision (Borenstein et al. 2009). As our approach here was rather
conservative, significant effects of rest breaks on task performance are even more impressive.
Third, all intervention periods were relatively short. An early study of Vernon (1925)
found that performance effects of rest breaks followed an u-shaped pattern with time and an
increase of effects after 11 weeks. We found no significant moderating effect of intervention
length but descriptive data indicated such an u-shaped pattern. In our review the longest rest
break intervention was eight weeks (van den Heuvel et al. 2003). Thus, reported direct effect
sizes for supplementary short rest breaks might be rather conservative and more research is
necessary to evaluate rest break interventions over longer periods, also to uncover more long-
term effects on performance and strain outcomes.
Fourth, at least some studies reported that a certain percentage of participants skipped
their supplementary rest breaks, also as they feared a loss of productivity and conflicts with
productivity goals (Henning et al. 1997; Rutenfranz/Stoll 1966, van den Heuvel et al. 2003).
Between-study variance in compliance rate might have biased our results. Unfortunately,
compliance was poorly documented in the studies. Thus, we would like to encourage the
authors of future studies to mandatory report such measures.
Fifth, in our meta-analysis we only considered studies investigating fixed rest break sched-
ules. Prior studies found individuals’ recovery from strain might be less successful under self-
scheduled rest breaks (Tucker 2003). However, two field studies (Claus/Willamowski 2002;
McLean et al. 2001) and four lab studies (Hahn 1989; Karwowski et al. 1994; Praetsch 2013;
Schmatz/Klingebiel 2012) found no significant effect of timing control on task performance.
One lab study found a significantly higher task performance under self-scheduled than fixed
rest breaks (Ho 2008) whereas O’Donnell and colleagues (2015) report a significant inverse
pattern. However, short intervention periods and small study sample sizes limit interpretation
of these results. Moreover, the additional moderating impact of work-rest-sequences is un-
known so far. Therefore, future studies should also examine timing control as independent
variable, stratified for different work-rest-patterns, when disentangling effects of supplemen-
tary short rest breaks on task performance.
Finally, several other rest break characteristics were less well documented and examined in
the studies. This concerns individuals’ rest break activities (e.g., relaxation or physical activi-
ties), physical conditions at the rest break location, and, most importantly, the quality, intensi-
ty, and temporal dynamics of work demands. Future studies need to examine these variables
as moderators as they might additionally influence recovery processes (Trougakos/Hideg
2009; Tucker 2003).
Practical Implications
As argued in the introduction, sufficient time for within-shift rest breaks is important for em-
ployees’ well-being, health, and safety. Thus, in most countries over the world national legisla-
tion assures employees’ to get at least a minimum of mandatory rest break time during the
working day (McCann 2005). However, in European countries, this only affects employees
being on duty for longer than six hours and primarily concerns one or two longer and unpaid
rest breaks (e.g., for meals). In agreement with previous work, we found that employees’ per-
formance and well-being benefit from more frequently scheduled, supplementary short rest
Our findings suggest that employers should extend employees’ daily within-shift recovery
time by supplementary and frequently scheduled short rest breaks as they increase task per-
formance and decrease impairing effects of strain over the shift. Moreover, to motivate em-
ployees for such additional rest breaks, they should be scheduled as paid working time. It
might be counterintuitive for managers, but according to our results this investment in less
working time and more recovery time pays off twice, thus, in higher performance output and
lower negative strain levels of their employees.
Although not under the scope of this review, there are further recommendations from the
literature how to implement such break schedules. For instance, managers should actively
improve employees’ compliance with such rest breaks. Thus, rest break schedules should be
developed in participation with employees, employees should be given the official permission
for such breaks, and they should be encouraged and reinforced to take these breaks (Hüttges
et al. 2005; Zacher et al. 2014). As short breaks are by definition only of short duration, in
most cases employees will take them at their workplace. Thus, stressors that might impede
recovery, e.g., noise or heat, should be switched off at this time. If this is not possible, for in-
stance at an industrial assembly line, employees should have opportunities to reach designated
rest break areas quickly.
From our results we cannot deduce general rules-of-thumb for timing of optimal rest
break schedules (see also Konz 1998; Tucker 2003). According to Bokranz (1985) and
Rohmert (1973a,b) rest breaks must be longer with increasing rest break intervals to reach
sufficient recovery from impairing consequences of short-term strain. Several authors
(Bokranz 1985; Nachreiner et al. 2010; Tucker 2003) argue that the nature of work has to be
considered when planning optimal rest break schedules. Thus, the more mental and/or physi-
cal demanding the work, the earlier the worker must rest. However, it is also important that
employees can finish their task before a break, also to prevent feelings of being interrupted
during the work flow. Thus, hourly five minute breaks might be appropriate for work that is
repetitive or highly physically or mentally demanding while longer rest breaks after longer
periods of work might be better in other jobs (Richter/Hacker 2014; Tucker 2003; Wegge et al.
2014). From a more practical perspective, managers might probe and evaluate different
schemes with their employees and select the most optimal one.
Finally, nowadays, in many jobs, especially in the service and healthcare sector, the lack of
mandatory longer rest breaks or their premature interruption are very common (Lohmann-
Haislah 2012; Sarna et al. 2009; Wendsche et al. 2014). Actually, employers have to prevent
such situations by improving work organization. However, the opportunity for authorized
supplementary rest breaks might help employees to get at least a minimum of within-shift
recovery time, even on days were work demands impede a longer mandatory rest breaks dur-
ing the work shift.
In our meta-analysis, we reviewed the literature published within the last 25 years on effects of
fixed schedules of supplementary short rest breaks on task performance.
Our results indicate that even though such paid rest breaks incorporate a loss of potential-
ly productive working time, they increase task performance and improve mental and physical
well-being. Notably, these effects on task performance even existed when short breaks were
added to mandatory longer breaks. Therefore, our results reveal that earlier experimental
findings from this research (Graf 1922, 1927) are also valid in more complex, modern work
However, our review also uncovered serious gaps in the literature. This concerns the
boundary conditions and actual mechanisms for these reported effects, indicating a strong
need for further research, especially under improved conceptual conditions.
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... Individuals have several possibilities to recover and build new resources, and during more extended periods of free time, such as evenings [12], weekends [13], or vacations and sabbaticals [14]. Importantly, recovery happens also at shorter intervals during formal working hours, such as lunch breaks [15], scheduled breaks [16], or micro-breaks [17]. The concept of micro-breaks originates in the ergonomics literature, defined as scheduled rests that individuals take to prevent the onset or progression of physical symptoms, such as musculoskeletal pain or discomfort [18]. ...
... In the organizational literature, this concept was introduced as a brief resource-replenishing strategy, taken informally between work tasks [19,20]. Besides micro-breaks [21], several other terms are widely used to refer to short internal recovery, such as work breaks [22], rest breaks [16], energy management strategies [5], recovery behaviors [23], restorative activities [24], and mini-breaks [25]. Micro-breaks are beneficial for the worker's well-being and job performance [19,26], even if the total work time is reduced because of the breaks [16]. ...
... Besides micro-breaks [21], several other terms are widely used to refer to short internal recovery, such as work breaks [22], rest breaks [16], energy management strategies [5], recovery behaviors [23], restorative activities [24], and mini-breaks [25]. Micro-breaks are beneficial for the worker's well-being and job performance [19,26], even if the total work time is reduced because of the breaks [16]. For the purpose of this paper, we adopted a general definition of micro-breaks as short discontinuities in one's tasks of no longer than 10 minutes [17,27]. ...
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Recovery activities during short breaks taken between work tasks are solutions for preventing the impairing effects of accumulated strain. No wonder then that a growing body of scientific literature from various perspectives emerged on this topic. The present meta-analysis is aimed at estimating the efficacy of micro-breaks in enhancing well-being (vigor and fatigue) and performance, as well as in which conditions and for whom are the micro-breaks most effective. We searched the existent literature on this topic and aggregated the existing data from experimental and quasi-experimental studies. The systematic search revealed 19 records , which resulted in 22 independent study samples (N = 2335). Random-effects meta-analyses shown statistically significant but small effects of micro-breaks in boosting vigor (d = .36, p < .001; k = 9, n = 913), reducing fatigue (d = .35, p < .001; k = 9, n = 803), and a non-significant effect on increasing overall performance (d = .16, p = .116; k = 15, n = 1132). Subgroups analyses on performance types revealed significant effects only for tasks with less cognitive demands. A meta-regression showed that the longer the break, the greater the boost was on performance. Overall, the data support the role of micro-breaks for well-being, while for performance, recovering from highly depleting tasks may need more than 10-minute breaks. Therefore, future studies should focus on this issue.
... Allotting time for disconnection Managers seeking to improve cognitive performance and stimulate creativity within their teams can begin by encouraging employees to take scheduled breaks, preferably in natural settings. This recommendation is supported by research demonstrating the positive effects of such breaks on cognitive performance and creative thinking (Atchley et al., 2012;Berto, 2005;Wendsche et al., 2016). Managers can facilitate this by organizing outdoor team-building activities, establishing office spaces with natural views or access to green spaces, or simply promoting a culture that values disconnection during break times. ...
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Purpose The Industry 4.0 environment is characterized by fast data, vertically and horizontally interconnected systems, and human–machine interfaces. In the middle stands the manager, whose sustained performance is critical to the organization's success. Business disturbances—such as supply chain disruptions during the pandemic—can quickly test the manager's resiliency. While creativity and flexibility are critical for success in these situations, these skills are often not promoted directly. This paper will discuss strategies for enhancing managers' creativity and resiliency and give suggestions for improving professional development training and post-secondary business education. Design/methodology/approach A synthesis of the literature in business and psychology provides a foundation for creating a conceptual model incorporating strategies to promote managerial creativity and resiliency. While the model focuses on managerial performance under adverse conditions, the tenets of the model also apply during times of relative stability. Findings Findings based on a synthesis of the literature on creativity in business and psychology provide the foundation for a conceptual model to identify potential elements in training and curriculum design to further managers' creativity and resiliency. This model recommends clear, actionable training and program-level curriculum design suggestions for improved managerial performance. Originality/value This paper identifies a conceptual model to enhance managerial creativity leading to increased resiliency through professional development programs and suggestions for educators in post-secondary business education. This model provides tools for managers to deal with adverse and rapidly changing conditions flexibly, promoting employee productivity and satisfaction.
... Occupational health research in Germany has found that nurses who experience frequent work interruptions and multitasking not only show signs of mental exhaustion, but they also rate their overall work ability lower [57]. It is known, especially among older employees, that the negative stress consequences decrease with additional short rest breaks [58,59]. Short breaks not only have the potential to reduce stress but also to promote social interaction [60]. ...
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Background The care sector is characterized by high absenteeism of nursing care employees due to illness. Organizational determinants that can affect the mental health of nurses are known. Although nurses are confronted with different framework conditions in different care settings, there is a lack of comparative data in Germany. Methods The purpose of this study was to examined the relationship between work demands and employee health in different care settings. This cross-sectional survey was conducted between June and October 2021 in four acute care hospitals, seven inpatient care facilities, and five outpatient care services in Germany. 528 nursing care employees (acute care hospitals n = 234; inpatient care facilities n = 152; outpatient care services n = 142) participated in the survey (participation rate: 22.6%-27.9%). For each care setting, data was collected via questionnaire on individual determinants ( gender, age, profession, working time ), organizational work demands ( quantitative workload, qualitative workload, organization of work, social work climate, after work situation, verbal violence, threats, physical violence ) and employee health ( subjective health status and work ability ). Descriptive statistics and binary logistic regressions were performed. Results Increasing age (OR = 0.650, 95% CI = 0.424—0.996) as an individual determinant and organization of work (OR = 0.595, CI = 0.362—0.978) as an organizational determinant were negatively associated with subjective health. Furthermore, age (OR = 0.555, 95% CI = 0.353—0.875), a demanding organization of work (OR = 0.520, CI = 0.315—0.858), increasing quantitative workloads (OR = 0.565, CI = 0.343—0.930) and a poorer perceived social work climate (OR = 0.610, CI = 0.376—0.991) were associated with lower work ability. Conclusions Based on the study results, health programs should target both individual and organizational factors. The findings seem to support the importance to include nursing care employees in the planning process, as it can have an impact on their health. Trial registration The project was registered in the German Clinical Trial Register (DRKS00024961, 09/04/2021).
... Past research shows that breaks can help employees maintain high levels of energy and performance throughout the day (e.g., Henning et al., 1997;Wendsche et al., 2016;Zacher et al., 2014). As such, although breaks involve a temporary stoppage of specific work tasks, break-taking is an important work activity that allows employees to replenish the energy needed for work. ...
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Unlabelled: Although breaks can help employees stay energized and maintain high levels of performance throughout the day, employees sometimes refrain from taking a break despite wanting to do so. Unfortunately, few studies have investigated individuals' reasons for taking and for not taking a break at work. To address this gap, we developed a model for predicting employees' break-taking behaviors. We developed hypotheses by integrating theories of work stress, self-regulation, and the results of a qualitative survey conducted as part of the current research (Study 1). Specifically, we predicted that high workloads would be positively related to the desire to detach from work, but that at the same time, high workloads would also deter employees from actually taking breaks. Furthermore, we predicted that employees would be less likely to act upon their desire to take a break within an environment where breaks are frowned upon by supervisors and coworkers, relative to an environment where breaks are allowed and encouraged. The results of a daily diary study of full-time employees (Study 2) provided general support for these predictions. Altogether, this research provides insights into the manner in which employees' psychological experiences and characteristics of the work environment combine to predict break-taking. Supplementary information: The online version contains supplementary material available at 10.1007/s10869-022-09866-4.
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Schulpausen gehören zum Alltag von allen Grundschüler*innen. Doch für Lehrkräfte sind Pausen meist eher wenig erholsam, da diese meist mit organisationalen Aufgaben beschäftigt sind, den Unterrichtsraum wechseln oder ihrer Aufsichtspflicht nachkommen müssen. Schlichtweg: Die Schulpause ist für die Lehrkraft eher normale Arbeitszeit. In diesem Beitrag beantworten wir aus Sicht der Forschung, warum Arbeitspausen für Lehrkräfte so wichtig sind, wie sie gestaltet sein können und unter welchen Rahmenbedingungen eine gesundheitsförderliche Pausenorganisation gelingen kann.
This study aimed to investigate the prevalence of three characteristics of work break organization, namely skipping work breaks, interruptions of work breaks, and meal break duration, and their relationships with physical and mental health. We used data from the BAuA-Working Time Survey 2017, a representative workforce survey in Germany, and restricted the sample to 5979 full-time employees. Logistic regression analyses were conducted with in total five health complaints as dependent variables: back pain and low back pain, pain in the neck and shoulder region, general tiredness, faintness, or fatigue, physical exhaustion, and emotional exhaustion. Many employees often skipped their work breaks (29%) and experienced break interruptions (16%). Frequent skipping of work breaks was significantly positively, that is detrimentally, related to all five health complaints and frequent interruptions of work breaks also, except for neck and shoulder pain. Meal break duration was significantly negatively, that is beneficially, related to physical exhaustion.
Based on the Conservation of Resources theory, we develop dual mechanisms by which lunchtime recovery activities predict creativity. Specifically, by conceptualizing the quality of lunchtime naps and meals as examples of recovery activities, we expect these recovery activities help individuals replenish their psychological resources in the form of more work engagement (affective process) and less cognitive depletion (cognitive process). Further, individuals are expected to utilize these available psychological resources to generate creative ideas. To test our model, we used a group‐mean centering approach to focus on within‐person effects by recruiting 230 employees working at construction sites in South Korea. Overall, after removing 242 invalid observations (omitting at least two items and not reporting the duration of a nap), we finalized a total of two‐wave 1598 daily questionnaires. A high quality of lunchtime naps and meals helps individuals recover their emotional resources (more work engagement) and cognitive resources (less cognitive depletion), which predict individuals' creativity. Finally, although indirect effects of the two recovery activities on creativity via affective and cognitive processes were generally supported, the indirect effect of lunch nap quality on creativity via work engagement was not significant, suggesting most of the effect is due to meal quality rather than nap quality.
There are 2 families of statistical procedures in meta-analysis: fixed- and random-effects procedures. They were developed for somewhat different inference goals: making inferences about the effect parameters in the studies that have been observed versus making inferences about the distribution of effect parameters in a population of studies from a random sample of studies. The authors evaluate the performance of confidence intervals and hypothesis tests when each type of statistical procedure is used for each type of inference and confirm that each procedure is best for making the kind of inference for which it was designed. Conditionally random-effects procedures (a hybrid type) are shown to have properties in between those of fixed- and random-effects procedures.