Content uploaded by Marjaana Sianoja
Author content
All content in this area was uploaded by Marjaana Sianoja on Aug 30, 2016
Content may be subject to copyright.
Sianoja, M., et al. (2016). Recovery during Lunch Breaks: Testing Long-Term Relations
with Energy Levels at Work.
Scandinavian Journal of Work and Organizational Psychology
,
1(1): 7, 1–12, DOI: http://dx.doi.org/10.16993/sjwop.13
* University of Tampere, FI
† Radboud University Nijmegen, NL
Corresponding author: Marjaana Sianoja (marjaana.sianoja@uta.)
ORIGINAL ARTICLE
Recovery during Lunch Breaks: Testing Long-Term
Relations with Energy Levels at Work
Marjaana Sianoja*
, Ulla Kinnunen*
, Jessica de Bloom*
, Kalevi Korpela* and Sabine Geurts†
This study had two aims. First, we examined whether lunch break settings, activities, and recovery
experiences were associated with lunchtime recovery cross-sectionally. Second, we investigated whether
lunchtime recovery was related to energy levels (i.e., exhaustion and vigor) across a 12-month period.
We collected longitudinal questionnaire data among 841 Finnish workers (59% female, mean age
47 years) from 11 dierent organizations in various elds at two time points (spring 2013 and 2014).
We used hierarchical regression analysis to test our hypotheses.
We found that recovery experiences, that is, psychological detachment from work and control during
the lunch break, were related to successful lunchtime recovery. After controlling for background factors,
main job characteristics (workload and autonomy), and the outcomes at baseline, successful lunchtime
recovery was related to a decrease in exhaustion and to an increase in vigor one year later.
To conclude, lunch breaks oer an important setting for internal recovery during working days and seem
to relate to energy levels at work over time.
Keywords: lunch breaks; recovery; detachment; control; exhaustion; vigor
Introduction
Recovery from work stress, that is, psycho-physiological
unwinding after effort expenditure at work that restores
employees’ energy and mental resources, is a mechanism
explaining how employees can protect their well-being
and health in demanding working conditions (Craig &
Cooper, 1992; Geurts & Sonnentag, 2006; Meijman &
Mulder, 1998). Recovery plays an intervening role in the
relationship between stressful job characteristics and the
development of chronic load reactions, such as prolonged
fatigue, sleep disorders, and cardiovascular diseases
(Geurts & Sonnentag, 2006). Therefore, a more profound
understanding of recovery processes is essential in pro-
moting sustainable working life.
Recovery occurs during breaks from work when job
demands are no longer present (Meijman & Mulder,
1998). Different forms of breaks range from sabbaticals
and vacations to short micro-breaks within the working
day. Recovery within working days, referred to as internal
recovery, has received far less attention in the recovery
research literature than off-job recovery, referred to as
external recovery (Sonnentag & Fritz, 2015). Although
recovery during breaks within the working day may not
be as self-evident as recovery during leisure time, internal
recovery has potential in preventing stress from
accumulating early on, helping to maintain perfor-
mance throughout the day and preventing high need for
recovery at the end of the working day (Coffeng, van Sluijs,
Hendriksen, van Mechelen, & Boot, 2015; Geurts, Beckers, &
Tucker, 2014).
As workers typically spend a third to a half of their day
at the workplace it is important to recognize the recovery
potential of within working day breaks, and especially of
the lunch break, which is typically the longest and most
common of breaks in the course of the working day.
Furthermore, organizations have a greater opportunity
to influence employees’ internal recovery than external
recovery and, therefore, lunch breaks as a recovery set-
ting may be of special interest to employers. For example,
organizations may encourage regular lunch breaks and
provide restorative environments (e.g., quiet rooms for
relaxation). The question of how to recover successfully
during lunch breaks has recently gained some research
attention (Brown, Barton, Pretty, & Gladwell, 2014;
Krajewski, Wieland, & Sauerland, 2010; Trougakos, Hideg,
Cheng, & Beal, 2014). Nevertheless, research on internal
recovery is still scarce (Sonnentag & Fritz, 2015).
In this study we examine which lunchtime settings,
activities, and recovery experiences are related to lunch-
time recovery (i.e., how often employees recuperate
successfully from work during lunch breaks) in a cross-
sectional sample (Study 1). Furthermore, we test whether
lunchtime recovery is related to energy levels at work, that
is, exhaustion and vigor, over a 12-month period (Study 2).
Our study contributes to the literature on work stress
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 2 of 12
recovery by extending the understanding of how to pro-
mote internal recovery and exploring its relation with
maintaining energy at work. Figure 1 presents the model
of the study with hypothesized relationships.
Recovery during lunch break: Theoretical and
empirical perspectives
In the effort-recovery (E-R) model (Meijman & Mulder,
1998) recovery has been defined as a process of the
psycho-biological system returning to its pre-stressor
level. Recovery occurs when the demands causing strain
are no longer present (Meijman & Mulder, 1998). When
recovery is insufficient, an individual has to invest addi-
tional effort at work, which may cause strain and lead to
accumulating strain reactions in the long term. Recovery
therefore plays a significant role in counteracting
strain caused by job demands and helps in maintaining
well-being and energy at work.
Besides seeing recovery as a passive process (i.e., caused
by mere absence of demands), active perspectives on
recovery have also been introduced. According to conser-
vation of resources (COR) theory (Hobfoll, 2002; Hobfoll,
1989), people are motivated to gain new resources and
protect their existing resources. Resources are defined
broadly as “objects, personal characteristics, conditions,
or energies that are valued by the individual” (Hobfoll,
1989, p. 516). When resources are lost, threatened with
loss, or new resources are not gained after effort invest-
ment, strain occurs. During breaks from work, people
have the opportunity to engage in pleasant activities
and to regain resources (e.g., energy and positive mood).
Thus, to recover during a break, a break must ensure
absence of job demands and provide an opportunity for
employees to regain valued resources (Hobfoll, 2002;
Meijman & Mulder, 1998). This also implies that breaks
should be regular and long enough to allow enough time
for recovery.
Additionally, break location (the place where the break
is spent), break activities, and experiences during the
break may influence its recovery potential as they are
closely related to the absence of job demands and oppor-
tunities for resource gain. These aspects have been argued
(Sonnentag & Natter, 2004) and shown (Sonnentag &
Fritz, 2015; Sonnentag & Zijlstra, 2006; van Hooff & Baas,
2013) to be of importance in terms of recovery during lei-
sure time. Some of these (e.g., activities) have also been
identified in earlier research as important aspects of inter-
nal recovery (see Sianoja, Kinnunen, De Bloom, & Korpela,
2015).
When looking at recovery research on where breaks
are spent, a recent 5-day diary study found no difference
between spending breaks inside or outside the office in
terms of resource recovery (Hunter & Wu, 2016). However,
in this study carried out among 95 university staff mem-
bers, the outside condition also included different spaces
inside the office building (e.g., a break room), which may
not offer as beneficial conditions for detachment from
work as spaces outside the office building (e.g., a café or
restaurant). Other studies have been specifically interested
in natural environments. According to an intervention
study by Brown et al. (2014), spending one’s lunchtime
walking in a natural environment was beneficial in terms
of improved mental health when compared to walking in
built environments. Accordingly, this study suggests that
Figure 1: Model of the study.
Sianoja et al: Lunchtime Recovery and Long-Term Outcomes Art. 7, page 3 of 12
break location may be significant in terms of recovery.
To conclude, spending the lunch break outside the office
building should, in theory, aid recovery, as it ensures
better mental detachment from work offering a “change
of scenery” where job demands are not present (e.g.,
Korpela, De Bloom, & Kinnunen, 2015).
Concerning break activities, earlier studies on internal
recovery have associated relaxing, physical, and social
activities with positive recovery outcomes (Coffeng
et al., 2015; Krajewski et al., 2010; Trougakos et al., 2014).
Of these, we focused on social activities. Wendsche
et al. (2014) showed that collective rest breaks (i.e., breaks
including social activities) were associated with less
turnover than breaks spent alone. In addition, a study by
Trougakos et al. (2014) focusing on different lunch break
activities revealed that social activities that were based on
individuals’ own choice, were conducive to recovery.
In sum, in addition to absence of job demands, as sug-
gested by the E-R model, earlier research shows that
recovery may be also enhanced by engaging in activities
that enable resource replenishment, as suggested by the
COR theory.
Hypothesis 1: a) Having lunch breaks regularly, b) hav-
ing longer lunch breaks, c) spending lunch breaks outside
the office building and d) spending lunch breaks with oth-
ers are positively associated with recovery during lunch
breaks.
Furthermore, it has been argued that a recovering
break should promote recovery experiences (Coffeng
et al., 2015; Trougakos et al., 2014). According to
Sonnentag and Fritz (2007), there are four such mecha-
nisms: psychological detachment, relaxation, mastery,
and control. Of these, we examined detachment, that is,
not thinking about work, and control, that is, getting to
choose how to spend one’s free time (e.g., lunch breaks).
These two experiences were chosen as they have gained
most support in earlier studies. In studies focusing on
recovery during leisure time, detachment has been
identified as a core recovery experience (Sonnentag &
Fritz, 2015). Psychological detachment from work, in
addition to physical detachment, is crucial, as continuing
to think about job demands during breaks may result
in strain (Sonnentag & Fritz, 2007). In fact, in a cross-
sectional study detachment during work breaks was
connected to less need for recovery at the end of the
day (Coffeng et al., 2015). Furthermore, autonomy (i.e.,
control) during lunch breaks has previously been linked
to beneficial outcomes (Trougakos et al., 2014). More
specifically, autonomy during lunch breaks was recog-
nized as a moderator between lunch break activities and
recovery outcomes: autonomy strengthened the positive
effects of the activities. In addition, preferred work break
activities have been associated with increased resources
after the break (Hunter & Wu, 2016). Therefore break
characteristics that enhance psychological detachment
from work and allow control, may provide beneficial
setting for recovery.
Hypothesis 2: Recovery experiences (detachment and
control) during lunch breaks are positively associated with
recovery during lunch breaks.
Long-term associations between lunchtime recovery
and energy levels at work
As long-term outcomes of recovery we focused on energy,
specifically on exhaustion and vigor at work. According to
the E-R model (Meijman & Mulder, 1998), when recovery
is insufficient, high and continuous demands lead to nega-
tive load effects and depletion of energy, which in the long
term can lead to emotional exhaustion. Emotional exhaus-
tion is one of the core burnout dimensions and refers to
“feelings of being overextended and depleted of one’s
emotional and physical resources” (Maslach, Schaufeli, &
Leiter, 2001, p. 399). Research has shown that emotional
exhaustion predicts mental and physical illness, such as
depression and cardiovascular diseases (Ahola, 2007),
as well as increased sickness absence (Toppinen-Tanner,
Ojajärvi, Väänänen, Kalimo, & Jäppinen, 2005).
Hunter and Wu (2016) found that resource recovery
during workday breaks across one working week was asso-
ciated with lower levels of exhaustion at the end of the
week. As far as we know, the long-term effects between
poor recovery during lunch breaks and exhaustion have
not yet been examined. However, over time employees go
through numerous cycles of daily lunchtime recovery pro-
cesses, which may ultimately result in either gain or loss
of energy depending on whether recovery is successful
or incomplete. Therefore, insufficient recovery may, over
time, result in cumulative resource loss in terms of higher
exhaustion.
Hypothesis 3: Insufficient recovery during lunch
breaks is related to high level of emotional exhaustion
over time.
In contrast, successful recovery ensures that energy
levels are sufficient for people to experience vigor at
work (Meijman & Mulder, 1998). Vigor is one of the core
dimensions of work engagement and is characterized
by high activation, energy, and mental resilience while
working (Schaufeli, Salanova, González-Romá, & Bakker,
2002). Work engagement, and particularly vigor, has been
shown to be important in terms of motivation and perfor-
mance at work (Bakker, Demerouti, & Sanz-Vergel, 2014).
It has also been shown that exhaustion and vigor are
not endpoints of the same energy construct (Demerouti,
Mostert, & Bakker, 2010; Mäkikangas, Feldt, Kinnunen, &
Tolvanen, 2012). Thus we cannot conclude that absence
of exhaustion automatically implies high levels of vigor. It
is therefore important to measure both when examining
the energy levels of individuals.
To the best of our knowledge, studies on internal
recovery and its relation to vigor are so far lacking.
However, on a daily level taking micro-breaks at work has
been associated with vitality, a concept related to vigor
(Zacher, Brailsford, & Parker, 2014). Furthermore, earlier
research has established a positive link between external
recovery and work engagement (Kühnel, Sonnentag, &
Westman, 2009; Sonnentag, 2003). If recovery is repeatedly
insufficient during lunch breaks, it may lead to loss of
energy and over time reduce vigor. In addition, recovery is
associated with resource gain (e.g., energy), and resources
tend to accumulate and generate other resources in
the long term (Hobfoll, 2002). Accordingly, successful
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 4 of 12
recovery during lunch breaks may result in energy gain,
resulting in higher levels of vigor over time.
Hypothesis 4: Successful recovery during lunch breaks is
related to high level of vigor over time.
Methods
Participants and Procedure
The data were collected as a part of larger project on
recovery from work (see Kinnunen et al., 2016). The par-
ticipants of this study were Finnish employees working in
11 different organizations in various fields, mostly work-
ing in cognitively or emotionally demanding jobs. The
most common fields were education, information tech-
nology, and media. The questionnaire data were collected
in two phases. First, in spring 2013 (Time 1), an electronic
questionnaire was sent either directly to the employees’
work e-mail addresses (in seven organizations) or the
link to the questionnaire was delivered to the employees
by our contact persons (in four organizations). Of the
employees contacted (N = 3,593), 1,347 returned the
completed questionnaire after two reminders, yielding a
response rate of 37.5%. Second, in spring 2014 (Time 2)
the electronic questionnaire was sent to those employees’
e-mail addresses who responded in 2013 and who were
still employed in the same organizations (N = 1,192). Of
these, a total of 841 employees returned the completed
questionnaire, yielding a response rate of 70.6%. In
both study phases the employees were informed about
the goals of the study, assured that responses would be
treated confidentially and reminded that participation
was voluntary.
In Study 1, we used the cross-sectional sample col-
lected at T2, because not all variables (i.e., spending lunch
breaks outside, spending breaks with others, lunchtime
detachment, and lunchtime control) were measured at
T1. A cross-sectional design was considered appropriate
because we were interested in the immediate relations of
break settings, activities, and experiences with lunchtime
recovery. Study 2 was based on the longitudinal sample
covering both measurements with a 12-month time lag
between the measurements. It is difficult to theoretically
determine the most appropriate time lag as we lack the-
ories of change, and therefore even descriptive research
on the time courses of important relationships has been
recommended (Kelloway & Francis, 2013). We consider
one year to be an appropriate time lag, as it is so far the
most typical time period used in earlier recovery stud-
ies showing long-term effects (Kinnunen & Feldt, 2013;
Siltaloppi, Kinnunen, Feldt, & Tolvanen, 2011; Sonnentag,
Binnewies, & Mojza, 2010). Additionally, the reality of
data collection in organizations imposed certain limita-
tions. We were not able to schedule measurements more
frequently because we had to consider the organizations’
wishes and time constraints.
Of the sample used in both studies (N = 841), 58.6%
were women. The participants’ average age was 47.1 years
(range 21–67, SD = 10.0). Most of the participants (76.4%)
were living with a partner (either married or cohabit-
ing), and 45.6% had children (average of two) living at
home. Of the sample, 38.2% held a university degree
(master’s level or higher), 26.6% had a polytechnic degree,
and the rest (35.2%) had a vocational school qualification
or less. Of the participants, 8.3% were blue-collar workers
(e.g., cleaners), 30.0% lower white-collar workers
(e.g., office workers), 57.8% senior white-collar workers
(e.g., teachers) and 3.8% senior-level managers (e.g., chief
executive officers). The majority had a permanent job
(89.0%), worked full-time (96.8%) and had a regular day
shift (89.7%). Average weekly working hours were 39.1
(SD = 5.9). Of the participants, 53.6% worked in the public
sector as teachers or administrative staff in vocational or
upper secondary schools, or in a polytechnic (university of
applied sciences). The rest (46.4%) worked in the private
sector in various jobs.
In analyzing sample attrition we compared the respond-
ents (n = 841) of the longitudinal sample with the non-
respondents. The results indicated that the respondents
did not differ from the non-respondents in terms of gen-
der, age, having a partner, number of children or level of
education. They also did not differ in terms of the study
variables measured at both time points (regularity of
taking lunch breaks, lunch break length, lunchtime recov-
ery, exhaustion, or vigor). However, the respondents were
more often employed as senior white-collar workers (58%
vs. 50%) than the non-respondents (p < .05) and more
often on a permanent job contract (89% vs. 79%) than the
non-respondents (p < .001). Also, the respondents worked
more hours per week (39.1 vs. 37.9 hours, p < .01) and
more often on regular day shifts (90% vs. 83%, p < .01)
than the non-respondents. As we used the data collected
at T2 in our cross-sectional study, this sample attrition
concerns both Study 1 and Study 2.
Measures
Recovery during lunch breaks
To measure the degree of recovery during lunch breaks
at T1 and T2, we used one item “I recuperate from work
during my lunch break” from the Recovery after Breaks
Scale (Demerouti, Bakker, Sonnentag, & Fullagar, 2012)
aiming to capture specifically how well and regularly
employees recover during their lunch breaks. The item
was rated on a scale from 1 (very seldom or never) to 5
(very often or always). Earlier studies have provided sup-
port for the validity of single item measures (e.g., Drolet &
Morrison, 2001; Elo, Leppänen, & Jahkola, 2003). Con-
cerning recovery, it has been shown that recovery
from work measured with one item correlated highly
with longer recovery scales, such as need for recovery
(Kinnunen, Feldt, Siltaloppi, & Sonnentag, 2011).
Break settings, activities and experiences
Of break settings and activities, we measured regularity of
lunch breaks [dichotomized to 0 = occasionally (1–3 times
a week), 1 = regularly (4–5 times a week)] and length of
the lunch break (in minutes). Those participants (n = 36 at
T1 and n = 32 at T2) who reported not taking lunch breaks,
were not asked to answer any further lunch break related
questions (recovery during lunch break, break activities,
or experiences) and as lunch break recovery was the main
focus in our study, they were excluded from the analyses.
Sianoja et al: Lunchtime Recovery and Long-Term Outcomes Art. 7, page 5 of 12
In addition at T2, we asked whether the employees
habitually spent their lunch breaks outside the office
building [“I spend my lunch break outside my company
building (e.g., in a restaurant or in a café)”] or with others
[“I spend my lunch break with others (e.g., with colleagues,
acquaintances, friends or family members)”]. The answers
were dichotomized [0 = no (hardly ever or once a week),
1 = yes (2–5 times a week)].
Of recovery experiences, we measured detachment and
control during lunchtime at T2. Both detachment and
control were measured with one item (respectively: “I dis-
tance myself mentally from my work during lunch breaks”
and “I decide myself how to spend my lunch breaks”)
from the Finnish version of the Recovery Experience
Questionnaire (Kinnunen et al., 2011; Sonnentag & Fritz,
2007). The items were adapted to concern lunch breaks
and measured on a scale from 1 (very seldom or never) to
5 (very often or always).
Potential long-term outcomes
Emotional exhaustion was measured at T1 and T2 with
the five-item scale (e.g., “I feel emotionally drained from
my work”) from the Maslach Burnout Inventory (Kalimo,
Hakanen, & Toppinen-Tanner, 2006; Maslach, Jackson, &
Leiter, 1996) with response options on a seven-point
response scale from 0 (never) to 6 (every day). The Cron-
bach’s alphas were .93 at T1 and .93 at T2.
Vigor was measured at T1 and T2 with the three-item
shortened scale (e.g., “At my work, I feel bursting with
energy”) from the Utrecht Work Engagement Scale
(Schaufeli, Bakker, & Salanova, 2006) using a seven-point
response scale ranging from 0 (never) to 6 (every day). The
Cronbach’s alphas were .89 at T1 and .90 at T2.
Control variables
Of the background factors, we controlled for age (in years),
gender (1 = woman, 2 = man) and working hours per week,
as these may play a role in recovery (e.g., Mohren, Jansen, &
Kant, 2010; Siltaloppi et al., 2011). Working hours were
measured with a single question: “How many hours do
you actually work per week? (Include paid and unpaid
overtime, but not your commuting time)”.
We also controlled for main job characteristics, namely
workload and autonomy, measured at T1 and T2 as they
may act as confounding variables in our study. First,
appropriate job design may above all promote internal
recovery (Geurts et al., 2014) as it enables the employees
to adjust their work according to their current need for
recovery. Furthermore, job demands and resources play a
pivotal role in maintaining energy as job demands may
start a health deteriorating process leading to exhaustion,
and job resources, in turn, to a health promoting pro-
cess leading to an increase in vigor (Bakker et al., 2014).
Workload was measured with three items (e.g., “How
often does your job require you to work under time pres-
sure?”, Cronbach’s alphas .88 at T1 and .87 at T2) from the
Quantitative Workload Inventory (Spector & Jex, 1998).
Autonomy was measured with five items (e.g., “I can influ-
ence decisions that are important for my work”, Cronbach’s
alphas .77 at T1 and .78 at T2) from the QPS Nordic-ADW
(Dallner et al., 2000). All job characteristics were meas-
ured with a five-point scale from 1 (very seldom or never)
to 5 (very often or always).
Analyses
In both studies (Studies 1 and 2), we used hierarchical
regression analyses to test our hypotheses. In the cross-
sectional Study 1 lunchtime recovery served as a depend-
ent variable. At the first step, we added the control vari-
ables (age, gender, weekly working hours, workload, and
autonomy). At the second step we added variables describ-
ing lunch break settings and activities (regularity of the
lunch breaks, length of the lunch break, break outside,
and with others). Finally, at the third step we added recov-
ery experiences (detachment and control) during lunch
breaks.
In the longitudinal Study 2, we followed similar steps
with both outcomes (exhaustion and vigor). At the first
step, we controlled for the outcome at Time 1. At the sec-
ond step, we added control variables (age, gender, weekly
working hours, workload, and autonomy). Lunchtime
recovery at Time 1 was added at the final step, as we were
interested in its explanatory power after controlling for
the outcome at Time 1, background factors and work
characteristics.
Results
Descriptive results
Means, standard deviations, and zero-order correlations of
the study variables are presented in Table 1 (Study 1) and
Table 2 (Study 2). We first looked at the frequencies of
lunch break characteristics examined in Study 1, in which
all variables were measured at T2. To have a regular lunch
break was common in our sample, as 86% of the partici-
pants reported taking a lunch break 4–5 times a week. Of
those participants who took lunch breaks at least once a
week, 37% reported habitually spending the break out-
side the office building and 71% with other people. In
Study 1, of the lunch break settings and activities, regular
lunch breaks (r = .21), longer lunch breaks (r = .16), breaks
outside the office building (r = .17), and breaks with oth-
ers (r = .08) showed positive associations with lunchtime
recovery. However, both recovery experiences – detach-
ment and control – during lunch breaks showed the
strongest correlations: high level of detachment (r = .59)
and control (r = .30) during lunch breaks were associated
with successful lunchtime recovery. In addition, of the
control variables, workload was negatively (r = −.12) and
autonomy positively (r = .33) associated with recovery dur-
ing lunch breaks.
In Study 2 there were significant longitudinal correla-
tions between lunchtime recovery and both potential
long-term outcomes (Table 2). Lunchtime recovery at T1
was negatively related to exhaustion at T2 (r = −.35) and
positively related to vigor at T2 (r = .36). Of the control var-
iables gender (female), long weekly working hours, high
workload and low autonomy were related to exhaustion at
T2, and gender (female) and high level of autonomy were
related to vigor at T2. In addition, lunchtime recovery
(r = .48) and both outcomes (r = .69 for exhaustion and
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 6 of 12
M / % SD 1 2 3 4 5 6 7 8 9 10 11
1. Lunchtime recovery 3.42 0.97 –
2. Age 48.13 10.02 .04 –
3. Gender158.6% – .03 .04 –
4. Weekly working hours 38.65 5.90 −.03 −.06 .07* −
5. Workload 3.82 0.79 −.12*** −.07 −.15*** .27*** –
6. Autonomy 3.20 0.80 .33*** −.06 .16*** .05 −.24*** –
7. Regularity of lunch breaks286.2% – .21*** −.13*** −.04 .04 −.05 .10** –
8. Length of lunch break 29.02 8.55 .16*** −.05 .11** −.02 −.05 .06 .08* −
9. Lunch break outside336.9% – .17*** −.04 .11 ** .12** .08* .14*** .15*** .15*** –
10. Lunch break with others471.4% – .08* −.09** −.13*** .03 .02 .05 .21*** .07* .12** –
11. Detachment at lunch break 3.33 1.06 .59*** .01 .01 −.09* −.16*** .18*** .21*** .18*** .19*** .03 –
12. Control at lunch break 4.27 0.96 .30*** .03 .11** .03 −.05 .38*** .05 .19*** .22*** .05 .24***
Table 1: Means, standard deviations, and zero-order correlations of the study variables in Study 1.
Note. 1Gender: 1 = female, 2 = male; 2Regularity of lunch breaks: 0 = occasionally (1–3 times a week), 1 = regularly (4–5 times a week); 3Break outside: 0 = no (hardly ever or once
a week), 1 = yes (2–5 times a week); 4Break with others: 0 = no (hardly ever or once a week), 1 = yes (2–5 times a week).
The second column shows percentages for categorical variables: 1 % of female participants; 2 % of participants taking lunch breaks regularly; 3 % of participants typically spending
lunch breaks outside; 4 % of participants typically spending lunch breaks with others.
* p < .05; ** p < .01; *** p < .001; 807 < N < 841.
Sianoja et al: Lunchtime Recovery and Long-Term Outcomes Art. 7, page 7 of 12
M / % SD 1 2 3 4 5 6 7 8 9
1. Lunchtime recovery T1 3.39 1.00
2. Age T1 47.13 10.02 .08*
3. Gender158.6% – .01 .04
4. Weekly working hours T1 39.09 5.94 −.09* −.01 .06
5. Workload T1 3.89 0.82 −.16*** .03 −.16*** .28***
6. Autonomy T1 3.18 0.82 .30*** −.08* .16*** .05 −.30***
7. Exhaustion T1 1.92 1.45 −.41*** .04 −.11** .12** .36*** −.35***
8. Vigor T1 4.53 1. 21 .43*** .01 −.06 .05 .01 .26*** −.45***
9. Exhaustion T2 1.92 1.41 −.35*** −.02 −.15*** .08* .31*** −.30*** .69*** −.35***
10. Vigor T2 4.37 1.32 .36*** .00 −.09** .07 .04 .19*** −.37*** .68*** −.45***
Table 2: Means, standard deviations, and zero−order correlations of the study variables in Study 2.
Note. 1Gender: 1 = female, 2 = male.
The second column shows percentages for categorical variables: 1 % of female participants.
* p < .05; ** p < .01; *** p < .001; 785 < N < 841.
r = .68 for vigor) were relatively stable between T1 and T2.
No mean level changes occurred in lunchtime recovery or
exhaustion between T1 and T2. However, vigor was signifi-
cantly lower at T2 than at T1 (p < .001).
Testing the hypotheses
Study 1
The results of the hierarchical regression analysis con-
cerning the associations between lunch break settings,
activities, recovery experiences, and lunchtime recovery
are shown in Table 3. At step 1, control variables (back-
ground variables and job characteristics) explained 12%
of the variance in lunchtime recovery and autonomy at
work significantly contributed to lunchtime recovery.
Regular lunch breaks, longer lunch breaks and habitu-
ally spending lunch breaks outside the office building
contributed to successful lunchtime recovery, increas-
ing the explanation rate of the model to 17%. Spend-
ing lunch breaks with others did not contribute to
lunchtime recovery. After adding recovery experience
variables to the model at step 3, only regularity of the
lunch breaks (of the lunchtime characteristics entered at
step 2) continued to be associated with lunchtime recov-
ery. Both detachment and control were positively related
to recovery, and they raised the explanation rate of the
model to 41%. Detachment (β = .51, p < .001) predicted
lunchtime recovery more strongly than control (β = .09,
p < .01).
In sum, Hypothesis 1 was partially supported, as most
of the positive effects of lunchtime settings and activities
disappeared when lunchtime recovery experiences were
entered into the model. More specifically, Hypothesis 1a
was fully supported, as taking lunch breaks regularly con-
tributed to successful lunchtime recovery. Hypotheses
1b and 1c were partially supported, as longer lunch
breaks and spending breaks outside were only significant
before recovery experiences were entered into the model.
Hypothesis 1d did not receive support, as spending
lunch breaks with others did not contribute to recovery.
Furthermore, Hypothesis 2 was fully supported, as both
high levels of detachment and control during lunch break
contributed to successful lunch break recovery.
Study 2
The results of hierarchical regression analyses exploring
the longitudinal relationships of lunchtime recovery with
exhaustion and vigor are shown in Table 4. Concerning
exhaustion, at step 1, exhaustion at T1 strongly predicted
exhaustion at T2 explaining 47% of the variance. At the
second step, adding the control variables, the explanation
rate of the model increased by 1 %, as gender (female)
was significantly related to exhaustion. At the final
step, lunchtime recovery at T1 contributed significantly
(β = −.07) to exhaustion at T2. The increase in the explana-
tion rate was significant, although it increased only 0.3%.
The explanation rate of the final model was 48%. Thus,
in line with Hypothesis 3, successful recovery at lunch
breaks seems to explain – to a minor degree – a decrease
in exhaustion across one year.
In the model predicting vigor, at step 1, vigor at T1
strongly predicted vigor at T2 explaining 47% of the
variance. At the second step, adding the control variables
neither background factors nor job characteristics were
significant predictors of vigor. At the final step, lunchtime
recovery at T1 contributed significantly (β = .10) to vigor
at T2 and added 1% to the explanation rate. The explana-
tion rate of the final model was 48%. Thus, in line with
Hypothesis 4 successful recovery at lunch breaks seems to
explain – to a minor degree – an increase in vigor across
one year.
Discussion
This study had two main aims. First, we investigated
whether certain lunch break settings, activities, and expe-
riences were related to recovery during lunch breaks.
Second, we examined whether lunchtime recovery was
associated with energy levels at work one year later. We
based our study on the E-R model and the COR theory.
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 8 of 12
Lunchtime recovery
Step 1 Step 2 Step 3
Predictors β β β
Age .06 .09* .06*
Gender1−.03 −.04 −.03
Weekly working hours −.04 −.05 .00
Workload −.04 −.04 .01
Autonomy .33*** .30*** .22***
Regularity of lunch breaks2.15*** .07*
Length of lunch break .11** .03
Lunch break outside3.11** .01
Lunch break with others4−.00 .02
Detachment at lunch break .51***
Control at lunch break .09**
ΔR2.12*** .06*** .24***
R2.12*** .17*** .41***
Table 3: Results of hierarchical regression analysis for lunchtime recovery (Study 1), N = 774.
Note. 1Gender: 1 = female, 2 = male; 2Regularity of lunch breaks: 0 = occasionally (1–3 times a week), 1 = regularly
(4–5 times a week); 3Break outside: 0 = no (hardly ever or once a week), 1 = yes (2–5 times a week); 4Break with others:
0 = no (hardly ever or once a week), 1 = yes (2–5 times a week).
* p < .05; ** p < .01; *** p < .001.
Model 1 Model 2
Exhaustion Vigor
Step 1 Step 2 Step 3 Step 1 Step 2 Step 3
Predictors at T1 β β β β β β
Dependent variable at T11.68*** .65*** .63*** .69*** .68*** .64***
Age T1 −.04 −.03 −.03 −.04
Gender2−.07* −.07* −.05 −.05
Weekly working hours T1 −.02 −.02 .04 .05
Workload T1 .05 .06 .02 .03
Autonomy T1 −.03 −.02 .03 .01
Lunchtime recovery T1 −.07* .10**
ΔR2.47*** .01** .003* .47*** .01 .01**
R2.47*** .48*** .48*** .47*** .48*** .49***
Table 4: Results of hierarchical regression analysis for exhaustion (Model 1) and vigor (Model 2) at T2 (Study 2), N = 745.
Note. 1Dependent variable at T1: For the first model Exhaustion at T1, for the second model Vigor at T1. 2Gender: 1 = female,
2 = male.
* p < .05; ** p < .01; *** p < .001.
Among our sample of Finnish workers, having lunch
breaks was common, as 86% of the participants took them
4–5 times a week. On average, the participants felt occa-
sionally recovered after their lunch breaks and no changes
in this regard were observed across one year. In line with
our expectations, of the break settings or activities, regu-
larity of the lunch breaks, length of the lunch break and
spending lunch breaks outside the office contributed to
successful lunchtime recovery. Thus, our study supports
the importance of taking regular lunch breaks. However,
associations between break length and breaks outside
were no longer significant after taking recovery experi-
ences into account. As expected, we found that higher
levels of detachment and control during lunch breaks
were related to more successful lunchtime recovery. This
finding concurred with earlier research on internal recov-
ery (Coffeng et al., 2015; Trougakos et al., 2014). In light
of our results, it seems that detachment is more mean-
ingful in terms of lunchtime recovery than control. This
is logical, as detachment ensures total absence of job
Sianoja et al: Lunchtime Recovery and Long-Term Outcomes Art. 7, page 9 of 12
demands, whereas employees with high level of control
may still choose to engage, for example, in discussing
work issues. Our result therefore extends the earlier find-
ing that detachment from work is a powerful recovery
experience during non-work time (Sonnentag & Fritz,
2015). However, our one-item measure for control did not
necessarily capture all dimensions of control as a recovery
experience, for example control over when to take lunch
breaks (cf. Sonnentag & Fritz, 2007). The measure used
may therefore have underestimated the importance of
control during breaks. We recommend future studies to
assess recovery experiences at lunchtime with multiple
items to capture their full meaning.
Both taking longer lunch breaks and habitually spend-
ing breaks outside the workplace premises were corre-
lated with higher levels of detachment. Thus our results
suggest that lunch break length and spending lunch
breaks outside the office building may matter for lunch-
time detachment, which in turn relates to lunchtime
recovery. We recommend that future studies, with longi-
tudinal designs enabling appropriate mediation analysis,
test whether lunchtime recovery experiences mediate the
effects of lunchtime settings and activities on recovery.
One earlier study found that spending the break inside
versus outside one’s office (outside = in the same build-
ing or outside the building) did not have an effect on
recovery after breaks during the working day (Hunter &
Wu, 2016). As our results suggest that where lunch
breaks are spent could matter, it is important to note that
our measure (outside = outside the office building) was
different from the one used by Hunter and Wu (2016).
Therefore we suggest that future studies use more com-
prehensive measures in differentiating where breaks are
spent to disentangle these differing results. For example,
spending breaks in the break room of the department
could have different recovery outcomes from spend-
ing breaks outside the office building in a restaurant.
Furthermore, our outside condition was quite general,
and did not take specific recovery enhancing environ-
mental factors (e.g., natural settings) into account. Given
that natural settings are more likely to afford restorative
experiences than are built environments, comparing
them would be a good option for future studies (Brown
et al., 2014).
Furthermore, in our study, spending the lunch breaks
with others was not associated with recovery. This is
surprising, as earlier research suggests that breaks
including social activities are more beneficial for recovery
than breaks spent alone (Wendsche et al., 2014).
However, earlier research has also suggested that social
activities are more beneficial when based on one’s
own choice (Trougakos et al., 2014). Our study took
no account of this issue, which may explain our non-
significant finding. Additionally, we did not distinguish
between spending the break with colleagues and spend-
ing the break with other people, like friends and family.
This may be important, as in theory spending the lunch
break with friends or family may relate to more success-
ful detachment from work than spending the break with
colleagues. Therefore we recommend that future stud-
ies take into account whether social activities are based
on employees’ own choice and with whom employees
spend their breaks.
When looking at lunchtime recovery and its long-term
relationship with energy levels, we found that successful
lunchtime recovery was associated with less exhaustion
one year later, as expected. Although the effects we found
were small, it is worth noting that this relationship was
still valid after controlling for baseline level of exhaustion
and several controls. Thus successful lunchtime recovery
explained a minor decrease in exhaustion in the long
term. Our findings lend tentative support to our expec-
tations derived from the E-R model: insufficient recovery
during lunch breaks is related to loss of energy. When
this loss of energy accumulates over time due to repeated
episodes of insufficient recovery, it may partly explain
increased levels of exhaustion. Furthermore, our result
is in line with the conclusions of earlier studies linking
internal recovery with less exhaustion in the short term
(Hunter & Wu, 2016).
Similarly, the connection between lunchtime recovery
and vigor was supported. Successful recovery was related
to a minor increase in vigor one year later after controlling
for baseline level of vigor and several other controls. These
findings tentatively support our expectations derived
from the E-R and COR theories that successful recovery
prevents energy loss and increases internal resources
(e.g., energy). When lunchtime recovery is repeatedly
successful, it accumulates and generates new resources
across time, relating to a small increase in vigor. As the
levels of exhaustion and vigor at work were reasonably
stable over one year (i.e., the T1 level explained about half
of their variance at T2), our findings estimating the long-
term change in energy levels due to lunchtime recovery
can be considered promising. Taken together, lunchtime
recovery seems to be of importance in terms of energy at
work over time.
Limitations, strengths, and suggestions for future
studies
This study has certain limitations that should be consid-
ered. First, choosing the best time lag for studying lon-
gitudinal relations between internal recovery and energy
is not self-evident and the one-year time lag used in our
study is debatable. Our results explained variation in
energy levels only to a minor degree. The effects would
likely be stronger if more frequent measures over shorter
time lags (e.g., every couple of months) were applied.
Future research may benefit from testing similar long-
term effects with more frequent measurements over dif-
ferent time spans. Nevertheless, our longitudinal analysis
supported long-term relationships between lunchtime
recovery, exhaustion and vigor, supporting the view that
employees’ degree of recovery during their lunch breaks
may have significance, not only on a daily level, but also
in the long-term.
Second, although previous studies have demonstrated
one item measures to be valid substitutes for longer
scales (Drolet & Morrison, 2001; Elo et al., 2003; Fisher,
Matthews, & Gibbons, 2016; Kinnunen et al., 2011) future
research may benefit from using multiple item measures
for lunchtime recovery and recovery experiences. Third,
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 10 of 12
a further limitation concerning the measures is that our
study relies solely on self-report measures and may there-
fore suffer from common method bias. This limitation
mainly concerns the cross-sectional part of this study, as
temporal separation can be an effective way to reduce
common method bias (Spector, 2006). Still, future studies
may benefit from using measures that are more objective,
such as physiological measures, in examining internal
recovery. Also, the cross-sectional study permits no causal
interpretations. In the future the question of what factors
promote recovery during lunch breaks may best be tested
with intervention studies.
Fourth, the response rate was relatively low (37.5% at
T1 and 23.4% at T2 relative to baseline respondents) and
self-selection occurred between T1 and T2 in terms of a
permanent job contract, occupational status (more often
senior white-collar workers), working more often on
regular day shifts, and longer working hours per week.
This self-selection also concerns the cross-sectional part
of our study, where we used the sample collected at T2.
This was due to the fact that our T1 questionnaire did
not include all items related to lunch breaks (spend-
ing lunch breaks outside, spending breaks with others,
detachment, or control). Therefore, the generalizability
of our results may be limited. However, the response
rate is similar to those of other studies conducted in
organizational settings (see Baruch & Holtom, 2008,
for a review), and our large and diverse sample makes
the results more generalizable to wider populations.
Nevertheless, it would be useful to replicate our results
in other samples in future.
Fifth, our study included a limited variety of lunch-
time activities and only examined their frequency. For
example, we asked how often employees engaged in
social activities or spent their breaks outside the office
building, but did not differentiate with whom and where
exactly the breaks were spent. Therefore we recommend
that future studies take these issues into account using
more specific and comprehensive measures. We also
recommend measuring other experiences in addition
to detachment and control during workday breaks. For
example, relaxation may be important in terms of inter-
nal recovery, as it reduces psycho-physiological activa-
tion and elicits positive affect (Sonnentag & Fritz, 2007).
It may be possible to increase the experience of relaxa-
tion during breaks by engaging in relaxation exercises
(Krajewski et al., 2010) or less deliberately by engaging
in other relaxing activities, such as listening to music or
going for a walk.
Despite these limitations, our study has several
strengths. Earlier research on recovery has focused almost
exclusively on external recovery. This study provides new
insights on recovery during within working day breaks.
Specifically, it demonstrated that although lunch breaks
are limited in time, taking regular lunch breaks, which
enhance mental detachment and control over how to
spend the break, relate positively to successful recovery.
Our study also demonstrated that lunchtime recovery has
importance in terms of long-term exhaustion and vigor.
Our results on lunchtime recovery may be of particular
interest to organizations, as compared to external recov-
ery, organizations may influence the settings they provide
for recovery during within working day breaks.
Conclusions
This study demonstrated that lunchtime recovery may
best be promoted by ensuring control and especially
detachment during lunch breaks. In practice, organiza-
tions could promote lunchtime recovery by giving options
to spend lunch breaks in different ways that enable
detachment, such as spending the break in a non-work
environment or offering a space for relaxing activities.
This recommendation is suitable for fields where workers
are at risk of insufficient recovery, for example, employees
in cognitively or emotionally demanding jobs, and where
the work tasks enable flexibility in terms of lunch break
settings and activities. Furthermore, our study suggests
that recovery during lunch breaks and energy levels at
work are related across time. Thus if lunchtime recovery
is repeatedly successful, it may contribute to a decrease
in exhaustion and an increase in vigor. In summary, lunch
breaks offer an important recovery setting to promote
occupational health and well-being alongside recovery
during leisure time.
Funding Statement
This study was supported by the Academy of Finland
(grant 257682).
Competing Interests
The authors declare that they have no competing interests.
References
Ahola, K. (2007). Occupational Burnout and Health.
People and Work Research Reports, 81. Helsinki:
Finnish Institute of Occupational Health.
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I.
(2014). Burnout and work engagement: The JD–R
approach. Annu. Rev. Organ. Psychol. Organ. Behav.,
1, 389–411. DOI: http://dx.doi.org/10.1146/
annurev-orgpsych-031413-091235
Baruch, Y., & Holtom, B. C. (2008). Survey response rate
levels and trends in organizational research. Human
Relations, 61, 1139–1160. DOI: http://dx.doi.org/
10.1177/0018726708094863
Brown, D. K., Barton, J. L., Pretty, J. N., & Gladwell, V.
(2014). Walks4Work: Assessing the role of the
natural environment in a workplace physical activity
intervention. Scandinavian Journal of Work, Environ-
ment & Health, 40, 390–399. DOI: http://dx.doi.
org/10.5271/sjweh.3421
Coffeng, J. K., van Sluijs, E. M., Hendriksen, I.,
van Mechelen, W., & Boot, C. (2015). Physical
Activity and Relaxation During and After Work are
Independently Associated With Need for Recovery.
J Phys Act Health, 12, 109–115. DOI: http://dx.doi.
org/10.1123/jpah.2012-0452
Craig, A., & Cooper, R. E. (1992). Symptoms of acute
and chronic fatigue. In Smith, A., & Jones, D.
(Eds.), Handbook of Human Performance. London:
Sianoja et al: Lunchtime Recovery and Long-Term Outcomes Art. 7, page 11 of 12
Academic Press, Vol. 3, pp. 289–339. DOI: http://
dx.doi.org/10.1016/b978-0-12-650353-1.50017-4
Dallner, M., Elo, A.-L., Gamberale, F., Hottinen, V.,
Knardahl, S., Lindström K., et al. (2000). Valida-
tion of the General Nordic Questionnaire (QPSNordic)
for Psychological and Social Factors at Work. Nordic
Council of Ministers, Copenhagen. Nord.
Demerouti, E., Bakker, A. B., Sonnentag, S., &
Fullagar, C. J. (2012). Work-related flow and energy
at work and at home: A study on the role of daily
recovery. Journal of Organizational Behavior, 33,
276–295. DOI: http://dx.doi.org/10.1002/job.760
Demerouti, E., Mostert, K., & Bakker, A. B. (2010).
Burnout and work engagement: A thorough inves-
tigation of the independency of both constructs.
Journal of Occupational Health Psychology, 15,
209–222. DOI: http://dx.doi.org/10.1037/a0019408
Drolet, A. L., & Morrison, D. G. (2001). Do we really need
multiple-item measures in service research? Journal
of Service Research, 3, 196–204. DOI: http://dx.doi.
org/10.1177/109467050133001
Elo, A.-L., Leppänen, A., & Jahkola, A. (2003). Validity
of a single-item measure of stress symptoms. Scan-
dinavian Journal of Work, Environment & Health,
29, 444–451. DOI: http://dx.doi.org/10.5271/
sjweh.752
Fisher, G. G., Matthews, R. A., & Gibbons, A. M. (2016).
Developing and investigating the use of single-item
measures in organizational research. Journal of
Occupational Health Psychology, 21(1), 3–23. DOI:
http://dx.doi.org/10.1037/a0039139
Geurts, S., Beckers, D., & Tucker, P. (2014). Recovery
from demanding work hours. In Peeters, M., De
Jonge, J., & Taris, T. (Eds.), An introduction to Contem-
porary Work Psychology. Chichester: Wiley.
Geurts, S., & Sonnentag, S. (2006). Recovery as an explan-
atory mechanism in the relation between acute
stress reactions and chronic health impairment.
Scandinavian Journal of Work, Environment & Health,
32, 482–492. DOI: http://dx.doi.org/10.5271/
sjweh.1053
Hobfoll, S. E. (1989). Conservation of resources: A
new attempt at conceptualizing stress. American
Psychologist, 44, 513–524. DOI: http://dx.doi.
org/10.1037/0003-066X.44.3.513
Hobfoll, S. E. (2002). Social and psychological resources and
adaptation. Review of General Psychology, 6, 307–324.
DOI: http://dx.doi.org/10.1037/1089-2680.6.4.307
Hunter, E. M., & Wu, C. (2016). Give Me a Better Break:
Choosing Workday Break Activities to Maximize
Resource Recovery. Journal of Applied Psychology,
101, 302–311. DOI: http://dx.doi.org/10.1037/
apl0000045
Kalimo, R., Hakanen, J., & Toppinen-Tanner, S. (2006).
Maslachin yleinen työuupumuksen arviointime-
netelmä MBI-GS (The Finnish version of the Maslach
burnout inventory –general survey). Helsinki: Finn-
ish Institute of Occupational Health.
Kelloway, E. K., & Francis, L. (2013). Longitudinal
research and data analysis. In Tetric, L., Wang, M., &
Sinclair, R. (Eds.), Research Methods in Occupational
Health Psychology: Measurement, Design, and Data
Analysis. New York: Routledge, pp. 303–374.
Kinnunen, U., & Feldt, T. (2013). Job characteristics,
recovery experiences and occupational Well-being:
Testing cross-lagged relationships across 1 year.
Stress and Health, 29, 369–382. DOI: http://dx.doi.
org/10.1002/smi.2483
Kinnunen, U., Feldt, T., de Bloom, J., Sianoja, M.,
Korpela, K., & Geurts, S. (2016). Linking Boundary
Crossing from Work to Non-work to Work-Related
Rumination: A Variable- and Person-Oriented
Approach. Journal of Occupational Health Psychology.
DOI: http://dx.doi.org/10.1037/ocp0000037
Kinnunen, U., Feldt, T., Siltaloppi, U., & Sonnentag, S.
(2011). Job demands-resources model in the context
of recovery: Testing recovery experiences as media-
tors. European Journal of Work and Organizational
Psychology, 20, 805–832. DOI: http://dx.doi.org/
10.1080/1359432X.2010.524411
Korpela, K., De Bloom, J., & Kinnunen, U. (2015). From
restorative environments to restoration in work.
Intelligent Buildings International, 7, 215–223. DOI:
http://dx.doi.org/10.1080/17508975.2014.959461
Krajewski, J., Wieland, R., & Sauerland, M. (2010).
Regulating strain states by using the recovery
potential of lunch breaks. Journal of Occupational
Health Psychology, 15, 131–139. DOI: http://dx.doi.
org/10.1037/a0018830
Kühnel, J., Sonnentag, S., & Westman, M. (2009). Does
work engagement increase after a short respite?
The role of job involvement as a double-edged
sword. Journal of Occupational and Organiza-
tional Psychology, 82, 575–594. DOI: http://dx.doi.
org/10.1348/096317908X349362
Mäkikangas, A., Feldt, T., Kinnunen, U., & Tolvanen, A.
(2012). Do low burnout and high work engagement
always go hand in hand? Investigation of the energy
and identification dimensions in longitudinal data.
Anxiety, Stress & Coping: An International Journal,
25, 93–116. DOI: http://dx.doi.org/10.1080/10615
806.2011.565411
Maslach, C., Jackson, S. E., & Leiter, M. P. (1996).
Maslach Burnout Inventory manual (3rd ed.).
Mountain View, CA: CPP, Inc.
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001).
Job burnout. Annual Review of Psychology, 52,
397–422. DOI: http://dx.doi.org/10.1146/annurev.
psych.52.1.397
Meijman, T. F., & Mulder, G. (1998). Psychological aspects
of workload. In Drenth, P., & Thierry, H. (Eds.),
Handbook of Work and Organizational Psychology
(Vol. 2 Work psychology,). Hove: Psychology Press,
pp. 5–33.
Mohren, D. C. L., Jansen, N. W. H., & Kant, I. J. (2010).
Need for recovery from work in relation to age: a
prospective cohort study. International Archives
of Occupational and Environmental Health, 83,
553–561. DOI: http://dx.doi.org/10.1007/s00420-
009-0491-9
Sianoja et al: Lunchtime Recovery and Long-Term OutcomesArt. 7, page 12 of 12
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006).
The measurement of work engagement with a short
questionnaire a cross-national study. Educational
and Psychological Measurement, 66, 701–716. DOI:
http://dx.doi.org/10.1177/0013164405282471
Schaufeli, W. B., Salanova, M., González-Romá, V., &
Bakker, A. B. (2002). The measurement of engagement
and burnout: A two sample confirmatory factor ana-
lytic approach. Journal of Happiness Studies, 3, 71–92.
DOI: http://dx.doi.org/10.1023/A:1015630930326
Sianoja, M., Kinnunen, U., De Bloom, J., & Korpela, K.
(2015). Insufficient recovery from job stress as a risk
factor for poor health and well-being. In Hopkins, C. R.
(Ed.), Job Stress: Risk Factors, Health Effects and Cop-
ing Strategies. New York: Nova Publishing Company,
pp. 27–48.
Siltaloppi, M., Kinnunen, U., Feldt, T., & Tolvanen, A.
(2011). Identifying patterns of recovery experiences
and their links to psychological outcomes across
one year. International Archives of Occupational and
Environmental Health, 84, 877–888. DOI: http://
dx.doi.org/10.1007/s00420-011-0672-1
Sonnentag, S. (2003). Recovery, work engagement, and
proactive behavior: a new look at the interface
between nonwork and work. Journal of Applied
Psychology, 88, 518–528. DOI: http://dx.doi.
org/10.1037/0021-9010.88.3.518
Sonnentag, S., Binnewies, C., & Mojza, E. J. (2010).
Staying well and engaged when demands are high:
the role of psychological detachment. Journal of
Applied Psychology, 95, 965–976. DOI: http://
dx.doi.org/10.1037/a0020032
Sonnentag, S., & Fritz, C. (2007). The Recovery Expe-
rience Questionnaire: Development and valida-
tion of a measure for assessing recuperation and
unwinding from work. Journal of Occupational
Health Psychology, 12, 204–221. DOI: http://dx.doi.
org/10.1037/1076-8998.12.3.204
Sonnentag, S., & Fritz, C. (2015). Recovery from job
stress: The stressor-detachment model as an integra-
tive framework. Journal of Organizational Behavior,
36, 72–103. DOI: http://dx.doi.org/10.1002/job.1924
Sonnentag, S., & Natter, E. (2004). Flight attendants’ daily
recovery from work: Is there no place like home?
International Journal of Stress Management, 11(4),
366–391. DOI: http://dx.doi.org/10.1037/1072-5245.
11.4.366
Sonnentag, S., & Zijlstra, F. R. H. (2006). Job character-
istics and off-job activities as predictors of need for
recovery, well-being, and fatigue. Journal of Applied
Psychology, 91(2), 330–350. DOI: http://dx.doi.
org/10.1037/0021-9010.91.2.330
Spector, P. E. (2006). Method variance in organizational
research: truth or urban legend. Organizational
Research Methods, 9, 221–232. DOI: http://dx.doi.
org/10.1177/1094428105284955
Spector, P. E., & Jex, S. M. (1998). Development of four
self-report measures of job stressors and strain:
Interpersonal Conflict at Work Scale, Organizational
Constraints Scale, Quantitative Workload Inventory,
and Physical Symptoms Inventory. Journal of Occu-
pational Health Psychology, 3, 356–367. DOI: http://
dx.doi.org/10.1037/1076-8998.3.4.356
Toppinen-Tanner, S., Ojajärvi, A., Väänänen, A.,
Kalimo, R., & Jäppinen, P. (2005). Burnout as a
predictor of medically certified sick-leave absences
and their diagnosed causes. Behavioral Medi-
cine, 31, 18–32. DOI: http://dx.doi.org/10.3200/
BMED.31.1.18-32
Trougakos, J. P., Hideg, I., Cheng, B. H., & Beal, D. J.
(2014). Lunch breaks unpacked: The role of
autonomy as a moderator of recovery during lunch.
Academy of Management Journal, 57, 405–421. DOI:
http://dx.doi.org/10.5465/amj.2011.1072
van Hooff, M. L. M., & Baas, M. (2013). Recovering by
means of meditation: The role of recovery experi-
ences and intrinsic motivation. Applied Psychology:
An International Review, 62, 185–210. DOI: http://
dx.doi.org/10.1111/j.1464-0597.2011.00481.x
Wendsche, J., Hacker, W., Wegge, J., Schrod, N.,
Roitzsch, K., Tomaschek, A., & Kliegel, M. (2014).
Rest break organization in geriatric care and turnover:
A multimethod cross-sectional study. International
Journal of Nursing Studies, 51, 1246–1257. DOI:
http://dx.doi.org/10.1016/j.ijnurstu.2014.01.006
Zacher, H., Brailsford, H. A., & Parker, S. L. (2014). Micro-
breaks matter: A diary study on the effects of energy
management strategies on occupational well-being.
Journal of Vocational Behavior, 85, 287–297. DOI:
http://dx.doi.org/10.1016/j.jvb.2014.08.005
How to cite this article: Sianoja, M., Kinnunen, U., de Bloom, J., Korpela, K. and Geurts, S. (2016). Recovery during Lunch Breaks:
Testing Long-Term Relations with Energy Levels at Work.
Scandinavian Journal of Work and Organizational Psychology
, 1(1): 7,
1–12, DOI: http://dx.doi.org/10.16993/sjwop.13
Submitted: 04 April 2016 Accepted: 08 August 2016 Published: 30 August 2016
Copyright: © 2016 The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
OPEN ACCESS
Scandinavian Journal of Work and Organizational Psychology
is a peer-reviewed open
access journal published by Stockholm University Press.
