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Micro-breaks matter: A diary study on the effects of energy management strategies on occupational well-being

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Organizational researchers and practitioners are increasingly interested in self-regulatory strategies employees can use at work to sustain or improve their occupational well-being. A recent cross-sectional study on energy management strategies suggested that many work-related strategies (e.g., setting a new goal) are positively related to occupational well-being, whereas many micro-breaks (e.g., listening to music) are negatively related to occupational well-being. We used a diary study design to take a closer look at the effects of these energy management strategies on fatigue and vitality. Based on conservation of resources theory, we hypothesized that both types of energy management strategies negatively predict fatigue and positively predict vitality. Employees (N = 124) responded to a baseline survey and to hourly surveys across one work day (6.7 times on average). Consistent with previous research, between-person differences in the use of work-related strategies were positively associated with between-person differences in vitality. However, results of multilevel analyses of the hourly diary data showed that only micro-breaks negatively predicted fatigue and positively predicted vitality. These findings suggest that taking micro-breaks during the work day may have short-term effects on occupational well-being, whereas using work-related strategies may have long-term effects.
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ENERGY MANAGEMENT STRATEGIES 1
Micro-Breaks Matter: A Diary Study on the Effects of Energy Management Strategies on
Occupational Well-Being
Hannes Zacher1, Holly A. Brailsford2, and Stacey L. Parker2
1University of Groningen
2University of Queensland
Author Note
Hannes Zacher, Department of Psychology, University of Groningen, The Netherlands.
Holly A. Brailsford and Stacey L. Parker, School of Psychology, University of Queensland,
Australia. We thank Brenda Hughes and Michelle Oberg for helping with data collection.
Correspondence concerning this article should be addressed to Hannes Zacher,
Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712TS Groningen,
The Netherlands, e-mail: h.zacher@rug.nl
ENERGY MANAGEMENT STRATEGIES 2
Abstract
Organizational researchers and practitioners are increasingly interested in self-regulatory
strategies employees can use at work to sustain or improve their occupational well-being. A
recent cross-sectional study on energy management strategies suggested that many work-related
strategies (e.g., setting a new goal) are positively related to occupational well-being, whereas
many micro-breaks (e.g., listening to music) are negatively related to occupational well-being.
We used a diary study design to take a closer look at the effects of these energy management
strategies on fatigue and vitality. Based on conservation of resources theory, we hypothesized
that both types of energy management strategies negatively predict fatigue and positively predict
vitality. Employees (N = 124) responded to a baseline survey and to hourly surveys across one
work day (6.7 times on average). Consistent with previous research, between-person differences
in the use of work-related strategies were positively associated with between-person differences
in vitality. However, results of multilevel analyses of the hourly diary data showed that only
micro-breaks negatively predicted fatigue and positively predicted vitality. These findings
suggest that taking micro-breaks during the work day may have short-term effects on
occupational well-being, whereas using work-related strategies may have long-term effects.
Keywords: energy management; micro-breaks; fatigue; vitality; diary study
ENERGY MANAGEMENT STRATEGIES 3
Organizational psychologists are increasingly interested in self-regulatory strategies
employees can use throughout the work day to sustain or improve their occupational well-being
(Breevaart, Bakker, & Demerouti, 2014; Daniels & Harris, 2005; Petrou, Demerouti, Peeters,
Schaufeli, & Hetland, 2012; Schmitt, Zacher, & Frese, 2012). A recent cross-sectional study
explored associations of two types of energy management strategies with fatigue and vitality
(Fritz, Lam, & Spreitzer, 2011). On a bivariate and item-specific level, Fritz et al. (2011) found
that eight out of 20 work-related strategies (e.g., learning something new, reflecting on the
meaning of work) were positively associated with vitality. In contrast, and rather surprisingly,
they found that 11 out of 22 micro-breaks (e.g., drinking a caffeinated beverage, listening to
music) were positively related to fatigue and eight out of 22 micro-breaks were negatively
related to vitality. However, the cross-sectional design of their exploratory study did not allow
for definitive conclusions about the utility of energy management strategies.
The goal of the current study, therefore, was to use a diary study design to take a closer
look at micro-breaks and work-related strategies and their effects on subsequent occupational
well-being. Diary studies not only mitigate biases associated with retrospective reporting and
common method variance, they also allow investigation of a) within-person trends in
occupational well-being over time, b) short-term lagged effects of energy management strategies
on subsequent well-being, c) reverse lagged effects of well-being on subsequent use of energy
management strategies, and d) a comparison of within- and between-person relationships (Fisher
& To, 2012). While diary studies are typically used to explain variation in outcome variables
across multiple work days, they can also be used to study variation across shorter time spans
(e.g., hours) within days or variation across multiple work weeks (Bakker & Bal, 2010; Cropley
& Millward Purvis, 2003; Grech, Neal, Yeo, Humphreys, & Smith, 2009).
ENERGY MANAGEMENT STRATEGIES 4
We aim to contribute to the literature by further probing the criterion-related validity of
two promising sets of self-regulatory strategies: micro-breaks and work-related strategies (Fritz
et al., 2011). Moreover, we intend to contribute to organizational practice by developing
suggestions for training employees in the use of energy management strategies and providing
them with opportunities to use these strategies. An overview of our conceptual model and
hypotheses is shown in Figure 1. In a nutshell, we expect that taking micro-breaks and using
work-related strategies constitute personal resources that have negative short-term effects on
momentary fatigue and positive short-term effects on momentary vitality across the work day.
Micro-Breaks and Work-Related Strategies as Two Types of Energy Management Strategies
Human energy is a dynamic affective experience that entails having psycho-physiological
resources to act (Cole, Bruch, & Vogel, 2012; Ryan & Frederick, 1997; Thayer, 1989). Research
has shown that employees may recover their energy levels at home after work by engaging in
activities associated with experiences of psychological detachment, mastery, and relaxation
(Sonnentag & Fritz, 2007; Sonnentag, Kuttler, & Fritz, 2010). However, these off-the-job
activities may not be sufficient for sustaining energy throughout the work day. Fritz et al. (2011)
therefore introduced the concept of energy management strategies, which refers to activities that
employees engage in to replenish and increase their energy while at work. This implies that these
strategies are thought to have short-term effects on occupational well-being during the work day.
Based on research on specific and short respite activities (Trougakos & Hideg, 2009),
Fritz et al. (2011) distinguished between two distinct categories of energy management
strategies. First, work-related strategies are techniques that employees use while doing their
work to manage their energy (e.g., switching tasks, making a to-do list; Fritz et al., 2011). In
their cross-sectional study of knowledge workers, Fritz et al. (2011) found that several (i.e., 8 out
ENERGY MANAGEMENT STRATEGIES 5
of 22) work-related strategies were positively associated with general vitality on a bivariate and
item-specific level, whereas the expected negative relationships with fatigue were not found. In
addition, Fritz et al. (2011) stated that “the most commonly used strategies (e.g., switching to
another task or browsing the Internet) are not associated with higher levels of human energy at
work. Rather, strategies related to learning, to the meaning of one’s work, and to positive
workplace relationships were most strongly related to employees’ energy” (p. 28).
Second, micro-breaks are energy management strategies that are not directly related to
doing work (e.g., having a snack, doing some form of physical activity; Fritz et al., 2011;
Trougakos & Hideg, 2009). Surprisingly, Fritz et al. (2011) found that many commonly used
micro-breaks were positively associated with fatigue and not significantly associated with
vitality (in total, 11 out of 22). Moreover, several less common micro-breaks (8 out of 22) were
negatively associated with vitality. Fritz et al. (2011) suggested that “It may be that employees
seek out these strategies when they are already fatigued” (p. 34). Thus, it is important to
investigate energy management strategies and occupational well-being throughout the work day
using a diary study design to find out which of these constructs precedes the other.
Fritz et al. (2011) explored relationships between specific energy management strategies
(i.e., single items) and occupational well-being, and even though they conceptually grouped
strategies into two categories, they did not aggregate the items into broader constructs. In our
study, we conceive micro-breaks and work-related strategies as two distinct constructs with
formative measures. According to MacKenzie, Podsakoff, and Jarvis (2005), a formative
measurement model entails that a) the combination of items defines the meaning of the construct,
b) changes in items explain changes in the construct, c) items do not necessarily have a common
theme, and d) items are not necessarily expected to have the same antecedents and consequences
ENERGY MANAGEMENT STRATEGIES 6
(see also Diamantopoulos & Siguaw, 2006; Edwards & Bagozzi, 2000; MacCallum & Browne,
1993). In contrast, a reflective measurement model assumes that all items reflect the underlying
latent construct and thus should be highly correlated (MacKenzie et al., 2005).
Consistent with the four criteria for formative measurement, we argue that the
combinations of specific micro-breaks and work-related strategies give meaning to the broader
constructs. Moreover, taking specific micro-breaks and using specific work-related strategies
should determine changes in these constructs and not vice versa. We also propose that different
micro-breaks (e.g., listening to music, having a snack) and different work-related strategies (e.g.,
making a to-do list, learning something new) do not necessarily have to be correlated, and that
these specific energy management strategies may have different predictors and outcomes (even
though in this paper we assume that all strategies positively predict occupational well-being).
Finally, we agree with Wilcox, Howell, and Breivik (2008), who suggested that the
conceptualization of a construct as having formative or reflective measures also depends on the
framing of the survey questions. Specifically, these researchers argued that if the items pertain to
specific actions in the past, a formative measurement model may be more appropriate. Based on
these theoretical considerations, we assume that the two types of energy management strategies
can be operationalized using formative measures on the daily level (Figure 1). However, if items
ask about a person’s general action tendencies, the responses could reflect a propensity to show
certain behaviors and thus a reflective measurement model may be more appropriate (Wilcox et
al., 2008). We therefore report additional analyses on the between-person level that explore
shared variation among the specific energy management strategies. Moreover, we report results
of additional analyses on relationships between specific strategies and occupational well-being.
Effects of Energy Management Strategies on Occupational Well-Being
ENERGY MANAGEMENT STRATEGIES 7
Consistent with Fritz et al.’s (2011) study, we examined fatigue and vitality as two forms
of occupational well-being. Fatigue involves the experience of energy depletion and a sense of
mental strain and exhaustion (Schmitt et al., 2012), whereas vitality refers to the experience of
feeling full of energy, vigor, and activity (Ryan & Frederick, 1997). Based on conservation of
resources theory (Hobfoll, 1989; Hobfoll & Shirom, 2001), we expect that both micro-breaks and
work-related strategies positively predict vitality and negatively predict fatigue. The theory
argues that resources are valued objects, conditions, personal characteristics, and energies that
people want to obtain, retain, and protect to avoid stress and gain additional resources (Hobfoll,
1989). Moreover, the theory suggests that transient resources such as energy are depleted as
employees face demands during the work day, but can be replenished and increased by activating
additional resources such as self-regulatory strategies (Schmitt et al., 2012).
The effects of resources in one domain (e.g., energy management strategies) on resources
in another domain (e.g., occupational well-being) have been called “resource caravans” and can
be explained by three underlying processes (Hobfoll, 2002; ten Brummelhuis & Bakker, 2012).
First, employees who use energy management strategies should be better at avoiding stressful
situations in the first place (e.g., by making a to-do list), which helps them sustain or improve
their occupational well-being instead of investing resources in order to prevent declines in well-
being. Second, employees who use energy management strategies should be better able to cope
with unavoidable stressors during the work day (e.g., by taking a walk outside or by talking to a
co-worker), which in turn helps sustain their occupational well-being. Finally, even when work-
related stressors are absent, employees who use energy management strategies should be
motivated to improve their occupational well-being in order to have additional resources as
buffers for more resource demanding times (Hobfoll, 2002; ten Brummelhuis & Bakker, 2012).
ENERGY MANAGEMENT STRATEGIES 8
Conservation of resources theory further distinguishes between more or less volatile and
structural resources along a transience dimension (Hobfoll, 2002; ten Brummelhuis & Bakker,
2012). On the one hand, volatile resource can be fleeting or temporal, and have short-term effects
on other resources. On the other hand, structural resources are permanently available and have
long-term effects on other resources (ten Brummelhuis & Bakker, 2012). At the within-person
level, we argue that energy management strategies are volatile resources and should have
positive short-term effects on momentary occupational well-being. On the between-person level,
we assume that the general propensity to use energy management strategies is a structural
resource and thus should be positively related to general (or chronic) occupational well-being.
Hypothesis 1: Micro-breaks (a) negatively predict fatigue and (b) positively predict vitality.
Hypothesis 2: Work-related strategies (a) negatively predict fatigue and (b) positively predict
vitality.
Method
Participants and Procedure
Participants in this online diary study were 124 full-time employees, who worked in
managerial, professional, and administrative roles for a large public university in Australia. Job
descriptions included administrative officer, event manager, facilities officer, finance officer,
human resources officer, marketing officer, personal assistant, publications and external relations
officer, and senior library officer. Ninety-seven participants were female and 27 were male. Ages
ranged from 19 to 64 years (M = 35.64, SD = 11.57). In terms of education, 10.9% had
completed high school, 12.8% held a vocational degree, 49.1% held an undergraduate degree,
and 27.3% held a postgraduate degree. On average, participants had worked within their
organization for 5.36 years (SD = 7.74) and in their current job for 3.26 years (SD = 5.02).
ENERGY MANAGEMENT STRATEGIES 9
We recruited employees through an online newsletter, door-knocking at offices, and
word-of-mouth. Employees who volunteered for the study selected one work day of their choice
to participate. Afterwards, they received an AUD10 coffee voucher as thanks for their
participation. In the morning of the chosen work day, employees received an email with a link to
a baseline survey. Subsequently, employees received an email with a link to an hourly survey at
the start of every hour throughout their work day (which lasted typically from 8am or 9am to
4pm or 5pm). The online survey software recorded the times when participants started and
submitted their hourly surveys, which allowed us to check compliance with instructions.
Initially, 136 employees signed up for the study and provided at least one hourly survey. We
excluded participants who did not comply with instructions and who provided less than four
hourly responses. This resulted in a final sample of 124 participants who together provided 829
hourly data points (on average, 6.7 per person; 111 participants completed the baseline survey).
Measures
Energy management strategies. We assessed energy management strategies with Fritz
et al.’s (2011) lists of 22 micro-breaks and 20 work-related strategies (Table 1). In the baseline
survey, participants indicated the extent to which they generally use these strategies at work (5-
point scale from 1 = never to 5 = always). Thus, these measures tapped a general propensity to
use energy management strategies. Example items for micro-breaks are: having a snack, drinking
a caffeinated beverage, and listening to music. Example items for work-related strategies are:
switching to another task, making a to-do list, and talking to a co-worker. In the hourly surveys,
items were written in past tense and participants indicated which strategies they had used within
the past hour (0 = did not use strategy, 1 = used strategy). Thus, these measures tapped the
number of energy management strategies employees had used in the previous hour at work. Fritz
ENERGY MANAGEMENT STRATEGIES 10
et al. (2011) aimed to include in their two lists a broad variety of strategies that employees use to
manage their energy at work. They reported that they had validated the content of their lists by
conducting pilot studies in which they consulted with organizational scholars and practitioners.
As Fritz et al.’s (2011) study was exploratory, we conducted our own content validation
study with a sample of 12 experts to further examine the nature and operationalization of the two
energy management strategies. All experts had a PhD in organizational psychology or closely
related fields and worked at different universities. They were not familiar with Fritz et al.’s
(2011) study and did not know our research question and hypotheses. Using an online survey
tool, we presented the 42 items developed by Fritz et al. (2011) in randomized order, and asked
the experts to sort each item into one of three categories: a) micro-breaks (defined as “energy
management activities that employees might use at work that are not directly related to doing
work”; Fritz et al., 2011), b) work-related strategies (defined as techniques that employees
might use while doing their work to manage their energy at work”; Fritz et al., 2011), and c)
activities that would not be used by employees to manage their energy at work.
The results of the content validation study are shown in Table 1. Of the 22 original
micro-breaks items, all but one (show compassion to someone who needs help) were categorized
as micro-breaks by at least 10 experts (83%). Thus, we did not include the show compassion item
in our micro-break measure (note however that we ran additional analyses which indicated that
our results were very similar when this item was included). Of the 20 original work-related
strategies items, 14 were categorized as work-related strategies by at least nine experts (75%, see
Table 1). For five items, agreement was lower than 75% and therefore we did not include these
items in our work-related strategies measure (our results were very similar when these item were
included). Moreover, one item (clean the office) was categorized as a micro-break by 10 experts
ENERGY MANAGEMENT STRATEGIES 11
(83%) and was therefore included in the micro-breaks measure (results were very similar when
this item was not included and when it was included in the work-related strategies measure).
Thus, our final measures of micro-breaks and work-related strategies included 22 and 14 items,
respectively. We computed average scores for the baseline survey and for each hourly survey.
In contrast to Fritz et al.’s (2011) exploratory study, we aggregated the items into two
overall scores to avoid committing Type I error due to a large number of statistical tests. Even
though we did not use structural equation modeling to analyze our data, we note that the
measures are assumed to be formative rather than reflective, as each construct is defined by the
combination of its measures (Figure 1; Diamantopoulos & Siguaw, 2006). This means that a
change in any of the specific measures changes the overall construct and not vice versa.
For constructs with formative measures, computing internal consistency estimates to
assess their reliability is not appropriate (Howell, Breivik, & Wilcox, 2007). We note, however,
that Cronbach’s alphas for the measures in the baseline survey were .84 (micro-breaks) and .83
(work-related strategies). MacKenzie et al. (2005) suggested that test-retest reliability may be a
more appropriate way to assess the reliability of formative constructs, but only if the items are
stable over time. It is unlikely that employees will use the same energy management strategies
(e.g., making plans for the evening or weekend) again after one or more hours during their work
day. Thus, estimating test-retest reliability based on the hourly assessments in this study is not
appropriate. However, our analyses showed that employees’ general use of energy management
strategies was positively related to their aggregated hourly use of strategies (Table 2).
Specifically, the correlation between general micro-breaks and aggregated hourly micro-breaks
across the work day was r = .53 (p < .001) and the correlation between general work-related
strategies and aggregated hourly work-strategies was r = .37 (p < .001). These findings provide
ENERGY MANAGEMENT STRATEGIES 12
initial evidence for the reliability of our measures. Moreover, the weak and non-signifi-cant
correlations between general micro-breaks and aggregated hourly work-related strategies (r =
.12, p = .203) as well as between general work-related strategies and aggregated hourly micro-
breaks (r = .17, p = .072) provide further evidence for the discriminant validity of our measures.
Fatigue. We measured fatigue with a 5-item scale from the Profile of Mood States
(POMS) questionnaire (McNair, Lorr, & Droppleman, 1971). The baseline survey measured the
extent to which participants “generally” feel worn-out, fatigued, exhausted, weary, and bushed
(alpha = .93), whereas the hourly surveys measured the extent to which participants felt these
emotions “right now” (alpha = .95). Both surveys used a 5-point scale ranging from 1 (very
slightly or not at all) to 5 (extremely).
Vitality. We measured vitality with a 6-item scale from the POMS (McNair et al., 1971).
The baseline survey measured the extent to which participants “generally feel lively, active,
energetic, cheerful, full of pep, and vigorous (alpha = .91), whereas the hourly surveys measured
whether participants felt these emotions “right now” (alpha = .95). Again, 5-point scales ranging
from 1 (very slightly or not at all) to 5 (extremely) were used.
Analyses
Our data had a multilevel structure with data at the hourly level nested within persons.
Therefore, we used hierarchical linear modeling (HLM) with person-mean centered predictor
variables to test our hypotheses (Hoffman, Griffin, & Gravin, 2000). In the first set of within-
person analyses, we regressed hourly fatigue and vitality on the time of day, fatigue or vitality
experienced when completing the previous hourly survey, and energy management strategies
used during the previous hour. It is important to note here that the fatigue and vitality measures
asked for current occupational well-being, whereas the energy management measures asked for
ENERGY MANAGEMENT STRATEGIES 13
the use of strategies in the past hour. Thus, for the first set of analyses, we created a lagged
format for the dependent variables, and used energy management strategies assessed at the same
time as the lagged dependent variables as predictors.
We conducted a second set of analyses in order to address concerns about reversed
causality. Specifically, we wanted to rule out the possibility that employees’ occupational well-
being predicts their use of energy management strategies (e.g., it could be argued that high
fatigue and low vitality motivate employees to take more micro-breaks, or that high fatigue and
low vitality prevent employees from using work-related strategies). We examined the reversed
effects by regressing the two energy management strategies on the time of day, energy manage-
ment strategy use according to the previous hourly survey, and fatigue and vitality experienced
when completing the previous hourly survey. Thus, for this set of analyses, we also created a
lagged format for the dependent variables, and used fatigue and vitality as predictors of the use
of energy management strategies in the following hour at work. We controlled for time of day
because research suggests there are changes in occupational well-being throughout the work day
(Grech et al., 2009). Finally, to compare our findings with Fritz et al.’s (2011), we report results
of analyses at the between-person level using regression analyses with the baseline survey data.
Results
Table 2 shows the descriptive statistics and correlations of the within- and between
person variables. Results of a series of null models computed in HLM showed that 38% of the
variance in fatigue and 30% of the variance in vitality resided at the within-person level. Table 3
shows the results of the HLM analyses. The first set of analyses showed that time of day
positively predicted fatigue (γ = .07, p < .001) and negatively predicted vitality (γ = -.07, p <
.001). Consistent with Hypotheses 1a and 1b, micro-breaks negatively predicted fatigue (γ = -.63,
ENERGY MANAGEMENT STRATEGIES 14
p = .009) and positively predicted vitality (γ = .76, p = .002). In contrast, work-related strategies
did not significantly predict fatigue (γ = -.21, p = .388) and vitality (γ = -.16, p = .504). Thus,
Hypotheses 2a and 2b were not supported. We additionally examined longer-term lagged effects
of energy management strategies on occupational well-being two, three, and four hours later
during the work day; none of these effects were significant. Overall, these findings at the within-
person level suggest that taking micro-breaks has positive short-term effects on employees’
momentary occupational well-being, while the use of work-related strategies has no such effects.
The second set of analyses showed that time of day did not significantly predict micro-
breaks and work-related strategies (Table 3). Use of micro-breaks in the previous hour reduced
the likelihood that employees used micro-breaks again in the following hour (γ = -.19, p < .001),
whereas this was not the case for the use of work-related strategies. Fatigue and vitality did not
significantly predict the subsequent use of either of the energy management strategies. Moreover,
additional analyses of longer-term lagged effects of occupational well-being on the use of energy
management strategies two, three, and four hours later did not reveal significant effects. These
findings suggest that reversed causality was not a concern in this study.
Finally, regression analyses at the between-person level with data from the baseline
survey showed that general micro-breaks and work-related strategies did not significantly predict
general fatigue (β = -.001, p = .990, and β = -.11, p = .265, respectively). General micro-breaks
also did not predict general vitality (β = .17, p = .072), whereas general work-related strategies
significantly predicted general vitality (β = .37, p < .001).
Additional Analyses
Constructs with reflective measures and occupational well-being. It could be argued
that some of the specific energy management strategies may constitute reflective indicators of
ENERGY MANAGEMENT STRATEGIES 15
latent constructs. To investigate this possibility, we conducted an exploratory principle axis
factor analysis with Varimax rotation using employees’ ratings of the 42 energy management
strategies from the baseline survey. The factor analysis identified 14 factors with Eigenvalues
greater than 1 (72% of total variance explained). Further inspection of the rotated factor solution
showed that 10 items had factor loadings of .70 or greater on four common factors. We labeled
the first factor “private activities” as it included the four items check and send personal emails
and text messages (factor loading of .78), surf the web (.81), check in with a friend or family
member (.70), and make plans for the evening or weekend (.76; Cronbach’s alpha = .76). We
called the second factor “prosocial activitiesbecause it included the two items make time to
show gratitude to someone I work with (.80) and do something that will make a colleague happy
(.76; alpha = .80). We labeled the third factor “Reflection” because it included the two items
reflect on the meaning of my work (.83) and reflect on how I make a difference at work (.70;
alpha = .74). Finally, we labeled the fourth factor “Organizing” because it included the two items
Check and update schedule (.73) and make a to-do list (.71; alpha = .68).
Regression analyses with the baseline survey data showed that general private activities,
prosocial activities, reflection, and organizing did not significantly predict general fatigue.
General vitality was significantly predicted by prosocial activities (β = .21, p = .042) and
reflection (β = .26, p = .012), but not by general private activities and organizing. Multilevel
analyses with the hourly survey data (controlling for time of day and fatigue or vitality in the
previous hour) revealed that private activities, prosocial activities, reflection, and organizing did
not significantly predict fatigue, and only prosocial activities significantly predicted vitality (γ =
.31, p = .005). Moreover, tests of the reversed effects indicated that neither fatigue nor vitality
significantly predicted private activities, prosocial activities, reflection, and organizing in the
ENERGY MANAGEMENT STRATEGIES 16
following hour at work. Overall, these analyses suggest that employees’ general propensity to
engage in prosocial activities and to reflect on the meaning of their work are positively related to
vitality. Moreover, prosocial activities also appear to have beneficial effects on vitality during
the work day. We note that this prosocial activities measure was operationalized using two of the
items that we excluded from our formative measure of work-related strategies (Table 1).
Specific energy management strategies and occupational well-being. Consistent with
Fritz et al. (2011), the three most frequent hourly micro-breaks in our study were drinking water
(indicated 521 out of 829 possible times), going to the bathroom (310 times), and having a snack
(292 times). The three most frequently used work-related strategies were checking email (544
times), talking to a co-worker or supervisor (443 times), and switching to another task (318
times). Fritz et al. (2011) found checking email and switching to another task were the most
common strategies, whereas talking to a co-worker or supervisor ranked fifth on their list.
Analyses at the between-person level showed that none of the specific energy
management strategies were significantly correlated with general fatigue, whereas eight specific
micro-breaks were positively correlated with general vitality (rs between .20 and .41 for micro-
breaks items 1, 2, 5, 6, 11, 15, 19, and 22 in Table 1) and 11 of the specific initial work-related
strategies were positively correlated with general vitality (rs between .21 and .33 for work-
related strategies items 1, 4, 6, 7, 8, 9, 10, 12, 15, 16, and 18). Contrary to Fritz et al. (2011),
none of the energy management strategies were negatively related to general vitality.
Analyses at the within-person level showed that five of the specific micro-breaks were
negatively related to fatigue (items 2, 3, 5, 6, and 7 in Table 1) and five specific micro-breaks
were positively related to vitality (items 1, 2, 6, 11, and 14). Moreover, four specific work-
related strategies were negatively related to fatigue (items 1, 2, 3, and 5) and seven specific
ENERGY MANAGEMENT STRATEGIES 17
work-related strategies were positively related to vitality (items 3, 4, 5, 7, 9, 10, and 11). Only
one specific work-related strategy from the original list was negatively related to vitality (vented
about a problem); we excluded this item from our final measure (Table 1). Overall, these
findings suggest that several specific energy management strategies are positively related to
vitality at the general and hourly levels, and several specific strategies are negatively related to
fatigue at the hourly (but not general) level. With one exception, all of the relationships between
specific strategies and occupational well-being were in the expected direction.
Discussion
In a cross-sectional study on energy management strategies and occupational well-being,
Fritz et al. (2011) found that many work-related strategies were positively associated with
vitality, whereas several micro-breaks were negatively related to vitality and positively related to
fatigue. Especially the latter findings were “contrary to popular belief” and led Fritz et al. (2011)
to the conclusion that “individuals looking to effectively manage their energy at work would do
well to focus on work-related strategies rather than micro-breaks” (p. 34). The goal of the current
diary study was to take a closer look at the effects of energy management strategies on
occupational well-being. At the between-person level, our results were consistent with Fritz et al.
(2011) in that employees who generally used more work-related strategies reported higher levels
of general vitality. However, we did not replicate their findings at the between-person level that
general micro-breaks increased fatigue and reduced vitality. Indeed, at the within-person level,
we found that taking micro-breaks reduced fatigue and increased vitality throughout the work
day, whereas work-related strategies had no significant effects on occupational well-being.
While the within-person effects of micro-breaks on occupational well-being are
consistent with conservation of resources theory (Hobfoll, 1989; Hobfoll & Shirom, 2001; Ito &
ENERGY MANAGEMENT STRATEGIES 18
Brotheridge, 2003), it remains a question for future research why work-related strategies may not
predict occupational well-being on an hourly basis. On the one hand, it may be possible that
using work-related strategies involves an expenditure of resources which, in turn, leads to lower
well-being. On the other hand, the notion of “resource caravans” in conservation of resources
theory (Hobfoll, 1989) together with Fritz et al.’s (2011) and our findings of a positive
association between work-related strategies and occupational well-being at the between-person
level may suggest that work-related strategies unfold their beneficial effects in the long-term.
Researchers have suggested that within-person designs can lead to different, and even
contrary, conclusions compared to between-person designs. For instance, there is some evidence
that self-efficacy is positively related to performance at the between-person level, and negatively
related to performance at the within-person level (Vancouver, Thompson, & Williams, 2001).
Our findings suggest that the effects of different energy management strategies on occupational
well-being may also depend on the level of analysis. A possible explanation for such divergent
effects across levels may be that both types of energy management strategies have positive
effects on occupational well-being, but that micro-breaks have beneficial effects in the short term
(i.e., on an hourly basis), whereas work-related strategies are beneficial in the long term (i.e.,
across days and weeks). While we were not able to address this possibility in our study, future
research could examine the effects of energy management strategies across different time spans.
Our additional analyses, based on an exploratory factor analysis with the baseline survey
data, showed that employees’ propensity to engage in prosocial activities and reflection about
their work were positively related to their general vitality, and that prosocial activities on the
within-person level positively predicted momentary vitality. These findings are consistent with
research demonstrating the importance of prosocial behavior and experienced meaningfulness for
ENERGY MANAGEMENT STRATEGIES 19
occupational well-being (Grant & Sonnentag, 2009). It is interesting to note, however, that the
prosocial activities items were not included in our formative measure of work-related strategies,
which was based on the results of our content validation study. The experts in our validation
study did not conceive helping others at work as an energy management strategy, possibly
because they perceived prosocial activities as expenditure and not as conservation of resources.
Limitations and Future Research
A strength of our diary study design is that we were able to test lagged effects of energy
management strategy use on occupational well-being, and to eliminate the possibility of reverse
effects of well-being on strategy use. However, limitations of this study include the sole use of
self-report data; employees may have over- or underestimated their use of energy management
strategies. However, the “yes/no” format of the hourly checklists used to assess strategies should
have minimized bias. Moreover, reporting self-regulatory behaviors in the past hour requires
minimal self-reflection, is not overly cognitively demanding, and has been shown to be
positively correlated with actual behaviors (Smolders, de Kort, Tenner, & Kaiser, 2012).
A second potential limitation is that our study covered only one work day; employees
may have chosen to participate on a day that is not representative of their typical work day.
Nevertheless, our theoretical assumptions on short-term momentary effects of energy
management strategies requires a research design with hourly assessments, and the within-person
trends in occupational well-being found across the day were consistent with previous studies
(Grech et al., 2009). It is also unlikely that the magnitude of the effects of energy management
strategies on occupational well-being one hour later depends on the specific day of the week (cf.
Cropley & Millward Purvis, 2003; Grech et al., 2009). Thus, we argue that our design did not
limit the generalizability of our findings at the within-person level. Future research could,
ENERGY MANAGEMENT STRATEGIES 20
however, examine the effects of energy management strategies on occupational well-being
throughout the work day across multiple days. This would allow examination of trends in
occupational well-being across a work week, and the cumulative effects of the use of energy
management strategies on these trends (cf. Grech et al., 2009).
Third, we focused on two energy management strategies in this study and neglected other
self-regulatory strategies (e.g., selection, optimisation, and compensation; Schmitt et al., 2012;
Weigl, Müller, Hornung, Zacher, & Angerer, 2013) as well as factors such as work events and
stable individual differences as predictors of occupational well-being. Future research could
examine interactive effects of energy management strategies with positive and negative work
events and individual differences such as proactive personality or neuroticism.
Fourth, future research could develop shorter scales to assess energy management
strategies. It may be a limitation of our study design that participants were asked to read through
42 energy management items every hour and select those that they had used in the previous hour.
The completion of the hourly surveys themselves may have represented work breaks or stressors
which could have impacted on our results. Specifically, the time to complete the surveys may
have had recovery effects, such as a negative influence on fatigue or a positive influence on
vitality. Alternatively, survey completion time may have had negative effects on occupational
well-being, such that employees who needed longer to fill in the surveys reacted with increased
fatigue and decreased vitality. It may also be possible that employees with high fatigue or low
vitality subsequently took longer to complete the surveys as they needed a break from work. We
addressed this potential confound0 by examining relationships between the time employees took
to complete the hourly surveys (i.e., number of minutes calculated based on the online survey
tool’s recordings of the start and completion times) and their previous (i.e., one hour before),
ENERGY MANAGEMENT STRATEGIES 21
concurrent, and subsequent (i.e., one hour later) levels of fatigue and vitality. We included time
of day as a control variable in these analyses (note, however, that the results were similar with
and without inclusion of this variable).
Results showed that employees spent on average approximately three minutes per hourly
survey (M = 2.54 minutes, SD = 1.74; note that we excluded 11 out of the 829 hourly surveys
which had completion times of longer than 17 minutes [i.e., more than 2 standard deviations],
possibly because participants forgot to submit their answers immediately online; however the
results did not change when these 11 entries were included in the analyses). Results indicated
that employees’ fatigue and vitality in the previous hour did not significantly predict their survey
completion time. Survey completion time was not significantly correlated with concurrent
fatigue and vitality, and did not significantly predict fatigue and vitality one hour later (with and
without controlling for previous levels of fatigue and vitality). The analyses suggest that the time
spent completing the surveys did not bias our findings. Nevertheless, researchers should be
aware of this issue and account for possible effects of survey completion time in future studies.
Fifth, there is an ongoing debate regarding the usefulness of formative measures in the
literature. While some researchers encourage the use of formative constructs based on careful
theoretical considerations (Diamantopoulos, Riefler, & Roth, 2008; Diamantopoulos & Siguaw,
2006), other researchers have argued that formative measurement is generally not a viable
alternative to reflective measurement because it has inherent weaknesses that relate to the
measures’ dimensionality, internal consistency, identification, measurement error, construct
validity, and causality (Edwards, 2011; Howell et al., 2007; Wilcox et al., 2008). Howell et al.
(2007) recommended that formative measures should also be analyzed separately, which we
have done in this study by examining relationships between specific energy management
ENERGY MANAGEMENT STRATEGIES 22
strategies and occupational well-being. Edwards (2011; Edwards & Bagozzi, 2000) proposed
alternatives to formative measurement (e.g., adding reflective items to each item of a formative
measure). While our assumptions regarding the formative nature of the energy management
measures are based on established criteria and theoretical considerations (MacKenzie et al.,
2005), researchers could make use of our exploratory factor analysis results as a first step toward
developing multidimensional reflective scales to assess energy management strategies.
Last here, our sample was limited to managerial, professional, and administrative
employees with access to a computer, and future research is needed before we can generalize our
findings to other occupations (e.g., blue-collar workers) and industries such as manufacturing.
Implications for Theory and Practice
Our findings have implications for conservation of resources theory, particularly for its
discussion of temporal processes (Hobfoll, 2002; ten Brummelhuis & Bakker, 2012).
Specifically, our findings suggest that energy management strategies can be used on a more
general, trait-like basis or during specific work days to influence employees’ chronic and
momentary well-being, respectively. As a resource on a temporally stable or structural level,
general use of work-related strategies appears to be associated with chronic vitality. In contrast,
on a given work day micro-breaks can be considered personal resources in one domain (i.e., self-
regulatory behavior) that influence volatile energy resources in another domain (i.e., well-being).
Our differential findings on the within- and between-person levels raise the question
which processes mediate these temporal effects of resources in one domain on resources in
another domain (“resource caravans”; Hobfoll, 1989). For instance, energy management
strategies could help employees avoid stressful situations, cope with unavoidable stressors, or
enhance their resources even in the absence of stressors (Hobfoll, 2002; ten Brummelhuis &
ENERGY MANAGEMENT STRATEGIES 23
Bakker, 2012). Future conceptual work on energy management strategies as self-regulatory
resources should attempt to clarify the specific processes that operate on daily and chronic levels.
More theoretical work is also needed on potential curvilinear effects of energy
management strategies on occupational well-being. Based on the “too-much-of-a-good-thing
effect” (Pierce & Aguinis, 2013), it could be argued that intermediate levels of strategy use may
be most beneficial, whereas low and high strategy use may be ineffective or resource-draining,
respectively. We examined curvilinear effects in our data and did not find significant effects.
However, research based on further theoretical considerations could re-examine this possibility.
In conclusion, our findings suggest two implications for practice. First, our findings at the
between-person level imply that organizations could train employees in the general (or long-term
oriented) use of work-related strategies, as these strategies, over time, may enable employees to
achieve higher levels of chronic vitality. Researchers have suggested that a concrete training
program for self-regulatory strategies could involve informative and motivational lectures on the
theoretical background and actual use of strategies, including specific work-related examples and
opportunities to practice (Searle, 2008; Zacher & Frese, 2011). Second, organizations could
encourage employees to take short-term respites in the form of micro-breaks during the work day
in order to improve their occupational well-being on an hourly basis. This would involve that the
organization provides employees’ with structural opportunities for micro-breaks (e.g., provide
designated break areas). Moreover, supervisors should give employees permission as well as
provide encouragement and positive reinforcement to take micro-breaks (e.g., going outside to
get some fresh air). More generally, companies could aim to establish an organizational culture
and climate that prioritizes employees’ daily and chronic occupational well-being and therefore
facilitates and rewards taking hourly micro-breaks and the general use of work-related strategies.
ENERGY MANAGEMENT STRATEGIES 24
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ENERGY MANAGEMENT STRATEGIES 28
Table 1
Categorization of Energy Management Strategies by Experts
Items
Micro-
break
Work-
related
strategy
Not an energy
management
strategy
Micro-breaks according to Fritz et al. (2011)
1. Drink water
92
8
0
2. Have a snack
100
0
0
3. Go to the bathroom
83
8
8
4. Drink a caffeinated beverage
100
0
0
5. Do some form of physical activity, including walks or stretching
100
0
0
6. Talk to someone about common interests (like sports or hobbies)
100
0
0
7. Check in with a friend or family member
92
0
8
8. Listen to music
92
8
0
9. Surf the web
100
0
0
10. Show compassion to someone who needs helpa
42
50
8
11. Go outside for some fresh air
100
0
0
12. Check and send personal e-mails and text messages
100
0
0
13. Make plans for the evening or weekend
92
0
8
14. Look out the window
100
0
0
15. Do an errand
92
8
0
16. Read something for fun
100
0
0
17. Daydream
92
8
0
18. Shop
83
0
17
19. Meditate
100
0
0
20. Nap
92
0
8
21. Write in my journal
83
8
8
22. Smoke
92
0
8
Work-related strategies according to Fritz et al. (2011)
1. Check email
17
83
0
2. Switch to another task
0
100
0
3. Make a to-do list
8
92
0
4. Offer help to someone at worka
8
67
25
5. Talk to a co-worker/supervisor
17
83
0
6. Learn something new
25
75
0
7. Focus on what gives me joy at work
17
83
0
8. Set a new goal
0
100
0
9. Do something that will make a colleague happya
17
58
25
10. Make time to show gratitude to someone I work witha
25
58
17
11. Check and update schedule
8
92
0
12. Seek feedback
0
92
8
13. Vent about a problema
33
58
8
14. Shut out interruptions
8
92
0
15. Reflect on how I make a difference at work
8
92
0
16. Get out of the office for a meeting
17
83
0
17. Find ways to delegate
8
92
0
18. Reflect on the meaning of my work
25
75
0
19. Make a phone calla
42
58
0
20. Clean the officeb
83
17
0
Note. N = 12. Numbers represent percentage of experts who classified an item into a category. Highest
values are underlined. aItem was excluded from measure. bItem was added to micro-breaks measure.
ENERGY MANAGEMENT STRATEGIES 29
Table 2
Means (M), Standard Deviations (SD), and Correlations of Variables
Variable
M
SD
1-ICC
1
2
3
4
5
6
7
8
9
Between-person (level 2) variables
1. General micro-breaks
2.51
0.42
2. General work-related strategies
3.07
0.43
.30**
3. General fatigue
2.54
0.96
-.04
-.11
(.93)
4. General vitality
2.81
0.82
.27**
.41**
-.47**
(.91)
Within-person (level 1) variables
5. Time of day
13.05
2.29
-.03
-.08*
.21**
-.18**
6. Micro-breaks
0.15
0.10
57.12
.53**
.17
-.12
.23*
.17**
-.18**
.15**
7. Work-related strategies
0.15
0.11
55.35
.12
.37**
-.19*
.36**
.43**
-.16**
.12**
8. Fatigue
1.53
0.71
37.77
-.08
-.06
.57**
-.32**
-.21*
-.22*
(.95)
-.26**
9. Vitality
2.41
0.90
30.04
.17
.39**
-.24*
.66**
.20*
.27**
-.20*
(.95)
Note. Correlations below the diagonal are based on between-person data (N = 111 for the between-person variables and N = 124 for
the aggregated within-person variables) and correlations above the diagonal are based on within-person data (n = 829). The intraclass
correlation coefficient (ICC) is calculated by dividing the between-person variance (τ00) by the sum of τ00 and the within-person
variance (σ2). 1-ICC refers to the percentage of within-person variance observed for the variable. Where available, reliability estimates
(α) are shown in parentheses along the diagonal.
* p < .05; ** p < .01.
ENERGY MANAGEMENT STRATEGIES 30
Table 3
Results of Hierarchical Linear Modeling Analyses
Fatiguet2
Vitalityt2
Micro-breakst2
Work-related
strategiest2
Predictor
γ
SEγ
p
γ
SEγ
p
γ
SEγ
p
γ
SEγ
p
Intercept
1.54
.05
<.001
2.34
.07
<.001
.15
.01
<.001
.15
.01
<.001
Time of day
.07
.01
<.001
-.07
.01
<.001
.00
.00
.891
-.00
.00
.076
Micro-breakst1
-.63
.24
.009
.76
.25
.002
-.19
.04
<.001
Work-related strategiest1
-.21
.24
.388
-.16
.25
.504
-.05
.04
.236
Fatiguet1
.02
.05
.729
-.01
.01
.553
-.00
.01
.616
Vitalityt1
.08
.04
.064
-.01
.01
.376
.01
.01
.411
R2within-person level
.08
.24
.08
.07
Note. γ = unstandardized coefficient; SEγ = standard error of γ. t1 = Time 1, t2 = Time 2 (one hour later). Degrees of freedom at the
between-person level: 122; degrees of freedom at the within person-level: 518. R2within-person level = (within-person variance [σ2] of null
model σ2 of predictor model) / σ2 of null model.
ENERGY MANAGEMENT STRATEGIES 31
Figure 1
Conceptual Model and Hypotheses
Note. t1 = Time 1, t2 = Time 2 (one hour later). The measures for micro-breaks and work-related
strategies are assumed to be formative, whereas the measures for fatigue and vitality are assumed
to be reflective. The relationships between measures and constructs depicted are only conceptual
as we did not use structural equation modeling to analyze our data.
Micro-
Breakst1
Work-Related
Strategiest1
Fatiguet2
Vitalityt2
Fatiguet1
Vitalityt1
Item1
Item2
Item22
Item1
Item2
Item5
Item1
Item2
Item5
Item1
Item2
Item14
Item1
Item2
Item6
Item1
Item2
Item6
- (Hypothesis 1a)
+ (Hypothesis 1b)
- (Hypothesis 2a)
+ (Hypothesis 2b)
... In particular, we focus on employees' conscious, volitional decisions to take breaks from their work tasks. That is, in addition to formal break periods (e.g., lunch breaks), most individuals also have some degree of autonomy over the decision to take shorter breaks during the day, which are sometimes referred to as "micro-breaks" (Kim et al., 2021;Niu, 2016;Zacher et al., 2014). Yet, the current literature provides only limited insights into the antecedents of these decisions. ...
... More so, energy is a finite resource; employees deplete their energy by completing work tasks (Quinn et al., 2012) and recover energy via rest (Meijman & Mulder, 1998). Critically, because breaks allow individuals to recover the energy needed for work (Hunter & Wu, 2016;Zacher et al., 2014), workload provides a natural starting point for understanding break-taking behavior. Specifically, workload is defined as the number of goals that need to be pursued, the difficulty of those goals, and the amount of progress remaining to be made to meet the goals (Bowling et al., 2015;Spector and Jex 1998). ...
... In response, employees engage in behaviors to help them restore their energy. One such behavior is taking a break (e.g., Hunter & Wu, 2016;Zacher, et al., 2014). As such, these theories suggest employees may take breaks to recover from fatigue that is caused by high workloads, thereby implying a positive relationship between workload and break-taking. ...
Article
Full-text available
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.
... Previous studies have investigated micro-breaks and work-related strategies as ways that employees manage (replenish and increase) their energy during work hours [7,8]. Work-related strategies are supposed to be techniques that "employees use while doing their work to manage their energy" (e.g., switching tasks, making a to-do list; p. 31) [7]. ...
... The literature identified two crucial energy management strategies: micro-breaks and work-related strategies [7,8]. Micro-breaks are linked to lower energy levels than other strategies, which increase energy at work [20]. ...
... This suggests that micro-breaks may be more beneficial as sources of recovery during leisure time (e.g., chatting with a friend on social occasions or going for a walk in the afternoon). In contrast, workrelated strategies help employees maintain optimal energy levels during work [3,7,8,22]. In other words, the observed tendency is that work-related strategies are more efficient for managing energy proactively during work compared to the micro-breaks used when energy depletion is installed, and employees need to rest. ...
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Drawing on the Job Demands-Resources theory, we investigate the effectiveness of an online intervention based on training in energy management strategies using an experimental design. The intervention focused on creating awareness about the importance of energy in completing tasks, shaping the present state, and proactively identifying valuable strategies to manage vitality during work. Additionally, we expected an increase in work-related strategies (i.e., setting a new goal) and a decrease in micro-breaks (i.e., mental and physical). Participants were enrolled voluntarily in the intervention and randomly assigned to the experimental group (N = 42) and the control group (N = 44). Results of the ANCOVA showed that, in the experimental group, the intervention positively impacted changes in proactive vitality management. Furthermore, the results indicated that the participants from the experimental group used fewer physical micro-breaks after the intervention. Additionally, after the training and weekly level, the results showed a decrease in work-related strategies and physical micro-breaks in the experimental group. Thus, organizations could facilitate employees to learn to engage in different energy management strategies according to their preferences.
... Making plans in the form of to-do lists was unrelated also to well-being outcomes. Zacher and colleagues (Zacher et al., 2014) included setting new goals and making to-do lists into a broader category of work-related strategies, for which they found positive relations with vitality when compared between persons. However, within persons over time, there was no effect on fatigue or vitality. ...
... Without opportunities for recovery, a buildup effect of work demands on fatigue, and a cycle of fatigue building up into longer-term negative effects, such as chronic fatigue and burnout, is to be expected. Several factors have been pointed out to help recovery (for an overview of effective interventions, see Verbeek et al., 2019), such as breaks during a workday (e.g., Zacher et al., 2014), relaxation , and positive feelings related to mastery and relatedness, such as volunteer work (Mojza et al., 2010). The beneficial effects of these factors are at least partly due to the fact they facilitate psychological detachment (de Vries et al., 2017). ...
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Several types of interventions to help people detach from work have been tested, but so far, no tests of different types of planning have been conducted. This field experiment tested the effects of goal setting combined with making implementation intentions on psychological detachment in the evening, and its effect on fatigue the next day, compared to an only goal setting condition and a control group without an intervention. The effects of the interventions were measured by means of a daily diary for a period of two weeks. We hypothesized a stronger effect on detachment in the evening and fatigue the next day of the implementation intention intervention for those not habitually planning. Contrary to our expectation, neither intervention had a positive effect in comparison to the control group. The daily effects on psychological detachment of the combined goal-setting implementation intention condition were negative for individuals who had a high general tendency to plan, as shown by the significant cross-level interactions of the moderated mediation model. We discuss these results in light of future interventions.
... While this method of study continues to be common amongst students, prolonged focus can often result in feelings of burnout, lack of productivity, decreased engagement, stress, and anxiety. While adults often replenish their energy levels by participating in relaxing and engaging activities after work and class, micro-breaks are needed to keep adults refreshed and engaged throughout the day (Zacher et al., 2014). Academic libraries are working to create a conducive study atmosphere which encourages individuals to take regular breaks, socialize with friends and classmates, and focus on an unrelated task by providing access to colouring materials, Legos, video games, and more. ...
... Academic libraries are working to create a conducive study atmosphere which encourages individuals to take regular breaks, socialize with friends and classmates, and focus on an unrelated task by providing access to colouring materials, Legos, video games, and more. Providing these resources allows patrons to socialize, step away from the computer, meditate, and refocus of their studies which has shown to have a positive impact on fatigue, vitality, and engagement (Zacher et al., 2014). Similarly, focus based activities such as colouring, building/sculpting, and playing video games can help patrons by allowing them to be present in the moment rather than worrying about the future. ...
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Over the past decade there has been an increasing awareness of the need for wholistic means of support for college and university students. With the rate of mental health concerns continuing to rise, institutions of higher education are working more collaboratively to create increased access to mental health support on campuses. Although academic libraries have customarily been responsible for bolstering their parent institution’s commitment to academic success as well as student and faculty research initiatives – it has become increasingly clear that prioritizing their community’s mental health plays a critical role in achieving these goals. As a result, many academic libraries have begun utilizing methods of play to support the mental health and wellbeing of their patronage. Most commonly art supplies, videogame consoles/games, and kinetic resources such as Legos and Play-Doh are offered year-round for check out at many institutions, while events including coloring and crafting opportunities are offered monthly with an increased presence near midterms and finals. This paper will utilize a combination of annual reports, scholarly articles, and library websites to identify and convey trends, emerging practices, and initiatives.
... Findings of the reviewed literature suggest that, due to its easy and quick accessibility, digital leisure activities can function as a micro-break. Micro-breaks are short, informal breaks (i.e., not a 'regular' break) that last only a few minutes [41] and earlier research showed that micro-breaks can have an effect on-at least short-termwell-being at work [42]. Similar to recent findings on cyberloafing [43], these digital microbreaks are, thus, potentially a 'recovery tool' for employees. ...
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The rise of the Internet and information and communication technologies (ICTs) has led to employees spending increasingly more time on non-work-related digital activities at work. A vast literature base exists that is devoted to the potential adverse effect of such activities in the form of cyberloafing. However, not much is known about the positive outcomes of such activities conceptualized as digital leisure. The present review systematically examines current literature on digital leisure activities and how these contribute to positive outcomes in the workplace. Additionally, possible moderating and mediating variables are investigated. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, eight peer-reviewed studies were identified that met inclusion criteria. The results indicate that resource recovery processes and employee well-being, as well as employee productivity are positively associated with digital leisure in the workplace. Age was found to moderate the relationship between digital leisure and self-reported employee productivity, while employee satisfaction was found to mediate the relationship between digital leisure and employee productivity. Future research directions are outlined and implications for the work context are discussed.
... Such strategies could involve facilitating periods of lower workload after relationship conflicts arise or providing activities that can generate short-term recovery. Such activities can range from fairly simple actions (e.g., taking micro-breaks; Zacher, Brailsford, & Parker, 2014) to more extensive initiatives (e.g., virtual reality escapes; Ahmaniemi, Lindholm, Muller, & Taipalus, 2017). Nevertheless, any of these practices have the potential to facilitate psychological recovery that reduces the extent that workload reinforces a given conflict. ...
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Humanity will mount interplanetary exploration missions within the next two decades, supported by a growing workforce operating in isolated, confined, and extreme (ICE) conditions of space. How will future space workers fare in a closed social world while subjected to persistent stressors? Using a sample of 32 participants operating in ICE conditions over the course of 30-45 days, we developed and tested a dynamic model of conflict and strain. Drawing on conservation of resources (COR) theory, we investigated reciprocal relationships between different forms (i.e., task and relationship) of conflict, and between conflict and strain. Results demonstrated evidence for a resource threat feedback loop as current-day task conflict predicted next-day relationship conflict and current-day relationship conflict predicted next-day task conflict. Additionally, results indicated support for a resource loss feedback loop as current-day relationship conflict predicted next-day strain, and current-day strain predicted next-day relationship conflict. Moreover, we found that job conditions affected these associations as current-day relationship conflict was more associated with next-day task conflict when next-day workload was high, but not when next-day workload was low. Similarly, current-day relationship conflict was more associated with next-day strain when next-day workload was high; however, this association decreased when next-day workload was low. Therefore, the results suggest that workload plays a critical role in weakening the effect of these spirals over time, and suggests that targeted interventions (e.g., recovery days) can help buffer against the negative impact of relationship conflict on strain and decrease the extent that relationship conflict spills over into task disputes.
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Nowadays, many organisations steadily have to face new challenges due to an increasing competition, new technologies and manpower shortage. While dealing with this growth of challenges and confronting employees with higher demands, organizations have to pay attention to employees’ personal resources that are connected to their well-being and health. Human energy is a psychological construct connected to different states of experiencing e.g. vigor and vitality, or otherwise fatigue. The subjective experience of human energy varies during the working day. In order to promote employees’ health and well-being, an objective measure to determine human energy levels is needed. In this paper, we report on first insights from a pilot study with 12 healthy participants investigating if human’s glucose concentration can serve as an objective measure of human energy. We analyse the possible interplay between subjective human energy perception during work and sensor-based blood sugar levels, assessed by a continuous glucose monitoring (CGM) system in healthy adults.
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Based on the conservation of resources theory, this two‐wave study investigated the mediating role of work–nonwork conflict in the relationship between job crafting and employee recovery experience and examined the moderating role of work demands in this relationship. Participants were 486 employees (39.3% male and 60.7% female) from a medical company in the central region of China who responded to a paper‐and‐pencil survey twice with a 1‐month interval. Regression‐based results indicated that job crafting positively predicted recovery experience after work through lower work–nonwork conflict. Furthermore, the association between job crafting and work–nonwork conflict was moderated by work demands, such that the effect was stronger for employees with higher work demands. The present study explains how job crafting may improve employees' after‐work recovery experience and addresses whether this process could be more significant for employees with higher work demands. The conclusion has practical implications for improving employee recovery experience.
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Recently, Pfeffer (2010) called for a better understanding of the human dimension of sustainability. Responding to this call, we explore how individuals sustain an important human resource-their own energy-at work. Specifically, we focus on strategies that employees use at work to sustain their energy. Our findings show that the most commonly used strategies (e.g., switching to another task or browsing the Internet) are not associated with higher levels of human energy at work. Rather, strategies related to learning, to the meaning of one's work, and to positive workplace relationships were most strongly related to employees' energy.
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Work ability describes employees' capability to carry out their work with respect to physical and psychological job demands. This study investigated direct and interactive effects of age, job control, and the use of successful aging strategies called selection, optimization, and compensation (SOC) in predicting work ability. We assessed SOC strategies and job control by using employee self-reports, and we measured employees' work ability using supervisor ratings. Data collected from 173 health-care employees showed that job control was positively associated with work ability. Additionally, we found a three-way interaction effect of age, job control, and use of SOC strategies on work ability. Specifically, the negative relationship between age and work ability was weakest for employees with high job control and high use of SOC strategies. These results suggest that the use of successful aging strategies and enhanced control at work are conducive to maintaining the work ability of aging employees. We discuss theoretical and practical implications regarding the beneficial role of the use of SOC strategies utilized by older employees and enhanced contextual resources at work for aging employees. Copyright © 2012 John Wiley & Sons, Ltd.
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Drawing from research on personal resources (e.g., Baumeister, Bratslavsky, Muraven, & Tice, 1998; Fredrickson, 1998) and the episodic nature of work (Beal, Weiss, Barros, & MacDermid, 2005), we examine research and theory relevant to the study of momentary recovery in the workplace. Specifically, we propose that the nature of within workday breaks influences the levels of psychological resources, which in turn influence various workplace outcomes. First, we discuss the momentary approach to studying workplace breaks and consequent resource levels. In doing so, we distinguish between two types of breaks, respites and chores; and we detail two types of psychological resources, regulatory and affective resources. Consequences of psychological resource levels on emotional exhaustion and performance are considered. We also explore possible moderators of the proposed relationships; we discuss job and individual characteristics, and motivation to perform. Finally, we conclude the chapter with a brief discussion on future research and possible applications of the momentary approach to work recovery in organizations.
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