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Objective: To examine the extent to which vacationing behavior is associated with metabolic outcomes. Specifically, we consider how total vacation episodes and total vacation days from the past 12 months relate to metabolic syndrome and metabolic symptoms. Design: Sixty-three workers eligible for paid vacation attended a lab visit during which their blood was drawn and they completed an interview assessing vacationing behavior in the past 12 months. Main outcome measures: Metabolic syndrome and metabolic symptoms are the main outcome measures. Results: Over the past 12 months, participants took approximately five vacations (M = 5.44, SD = 3.16) and used about 2 weeks of their paid vacation days (M = 13.80, SD = 7.25). Participants rated vacations positively, expressing low levels of travel-, childcare- and financial burden-related stress. As vacation episodes increased, metabolic syndrome incidence (OR = 0.76, p = 0.051) and number of metabolic symptoms met (IRR = 0.92, p = 0.035) decreased. Notably, risk for metabolic syndrome decreased by nearly a quarter with each additional vacation taken by participants. Conclusions: Overall, vacations are experienced as positive events. This positive subjective experience may translate into physical health benefits given that vacation frequency may protect against metabolic syndrome and symptoms.
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This is an original preprint of an article published by Taylor & Francis in Psychology & Health
on June 19, 2019, available online:
Vacation frequency is associated with metabolic syndrome and symptoms
Bryce Hruska, Ph.D.a, Sarah D. Pressman, Ph.D.b,
Kestutis Bendinskas, Ph.D.c, Brooks B. Gump, Ph.D. MPHa
a Syracuse University, David B. Falk College of Sport & Human Dynamics, Department of
Public Health, Food Studies, and Nutrition, Syracuse NY, USA
b University of California, Irvine, School of Social Ecology, Irvine CA, USA
c State University of New York at Oswego, Department of Chemistry, Oswego NY, USA
Funding Details: This work was supported by Project: Time Off under funding agreement
Corresponding author: Bryce Hruska, PhD, 444 White Hall
Department of Public Health, Food Studies and Nutrition, Falk College, Syracuse University,
Syracuse, NY 13244, Ph: 315-443-5430, Fax: 315-342-2046, Email:
Words: 5,014
Tables: 2
Figures: 0
Objective: To examine the extent to which vacationing behavior is associated with metabolic
outcomes. Specifically, we consider how total vacation episodes and total vacation days from the
past 12 months relate to metabolic syndrome and metabolic symptoms.
Design: Sixty-three workers eligible for paid vacation attended a lab visit during which their
blood was drawn and they completed an interview assessing vacationing behavior in the past 12
Main Outcome Measures: Metabolic syndrome and metabolic symptoms
Results: Over the past 12 months, participants took approximately 5 vacations (M = 5.44, SD =
3.16) and used about 2 weeks of their paid vacation days (M = 13.80, SD = 7.25). Participants
rated vacations positively, expressing low levels of travel-, childcare-, and financial burden-
related stress. As vacation episodes increased, metabolic syndrome incidence (OR = 0.76, p =
0.051), and number of metabolic symptoms met (IRR = 0.92, p = 0.035) decreased. Notably, risk
for metabolic syndrome decreased by nearly a quarter with each additional vacation taken by
Conclusions: Overall, vacations are experienced as positive events. This positive subjective
experience may translate into physical health benefits given that vacation frequency may protect
against metabolic syndrome and symptoms.
Keywords: vacation; paid time off; physical health; recovery experience; metabolic syndrome;
metabolic symptoms
Vacation Frequency is Associated with Metabolic Syndrome and Symptoms
The United States is the only industrial nation that does not guarantee paid vacation time
to its workers (Ray, Sanes, & Schmitt, 2013). Even among the 77% of American workers who
have paid vacation time available, less than half fully utilize it (U.S. Travel Association, 2018;
Van Giezen, 2013). Importantly, a small but compelling literature suggests that this lost vacation
time may incur a detrimental effect on the physical health of American workers.
With regard to subjective health outcomes, research has consistently shown that vacations
have a positive effect. In a meta-analysis representing one of the most comprehensive
examinations to date, de Bloom and Colleagues (2009) found that vacationers perceive
improvements in life satisfaction and mood as well as decreases in exhaustion and negative
mood as a result of vacationing. Far fewer studies have examined the relationship between
vacationing and objective physical health outcomes. The most compelling research comes from
secondary analyses of two large-scale cohort studies demonstrating a dose-response relationship
between vacationing amount and risk for myocardial infarction, cardiovascular-specific
mortality, and all-cause mortality (Eaker, Pinsky, & Castelli, 1992; Gump & Matthews, 2000).
More recently, Strandberg and colleagues (2017) replicated these findings: over the course of a
26-year follow up, shorter vacations at baseline predicted high rates of mortality. Aside from
these studies directly linking vacationing to objective physical health, research more broadly
examining restorative behaviors also supports a link between vacationing and physical health
outcomes. In a study of restorative leisure behavior that included vacationing, more time spent
on leisure was tied to lower blood pressure, lower cortisol levels, and enhanced feelings of self-
reported health (Pressman et al., 2009). This is echoed by the larger literature finding consistent
benefits on physiology and health for those spending more time in restorative leisure behaviors
(e.g., Tominaga et al., 1998; Ulrich et al., 1991). Collectively, this literature has been taken as
evidence that vacations may provide a recovery opportunity during which elevated physiological
load (e.g., sympathetic nervous system activity from stress) returns to resting levels (reviewed in
Geurts & Sonnentag, 2006). In turn, this reduction in physiological load may reduce risk factors
for cardiovascular disease such as metabolic syndrome, which has been consistently associated
with chronic stress (Almadi, Cathers, & Chow, 2013; Chandola, Brunner, & Marmot, 2006;
Edwards, Stuver, Heeren, & Fredman, 2012).
Despite these compelling findings, research examining the association between
vacationing and objective physical health outcomes remains limited and, in some cases,
conflicting (Cooper & Tokar, 2016; Epel et al., 2016; Tarumi, Hagihara, & Morimoto, 1998).
One significant impediment contributing to these mixed findings is the absence of a standardized
instrument that provides a systematic means to assess vacations. Existing vacation research
typically relies on single-item measures that require participants to retrospectively report on
vacation experiences occurring at various points in the past (e.g., de Bloom et al., 2011).
Consequently, they are prone to multiple sources of error stemming from memory limitations;
mood and recency effects; biases in interpretation; and construct under coverage. Furthermore,
limited research has considered the many dimensions that may influence whether a vacation is
related to health. To date, existing research has focused on only a few factors such as vacation
frequency (Eaker et al., 1992; Gump & Matthews, 2000), yet many other potentially important
vacation characteristics likely exist. For example, a growing literaure suggests that some
environments are more conducive to psychological restoration than others (e.g., Berman,
Jonides, & Kaplan, 2008; Bowler, Buyung-Ali, Knight, & Pullin, 2010). This research, combined
with more recent vacation habits such as the ‘staycation’ (e.g., Shankman, 2014), suggests that
the location of a vacation may be an important factor to consider as it relates to vacationing and
As part of a recent pilot study examining the health effects of vacations, we developed a
vacation assessment tool intended to overcome the measurement limitations of existing research
by employing a systematic, semi-structured interview yielding information about key vacation
characteristics that may be tied to health outcomes. The goal in creating this instrument was to
provide a tool that may aid in clarifying the role that vacations play in alleviating and/or
buffering work stress and promoting workers health. Furthermore, we hope that this instrument
will provide researchers with a much needed device that will enhance the scientific rigor in this
field of inquiry and allow for easier comparison of future vacation research studies.
As a proof of concept we provide descriptive information on the vacation characteristics
assessed with special consideration given to the variability observed on these dimensions. By
demonstrating this variability, we aim to provide evidence for the further examination of these
characteristics in larger studies considering the vacation-health association. In addition, as a
preliminary test of the instrument’s validity, we considered the relationship between the amount
of vacationing taken by participants in the past 12 months and its relationship to physical health.
Specifically, we operationalized vacationing amount in two ways the total number of
vacation episodes and the total number of vacation days used in the past 12 months. Given the
paucity of vacation research considering objective physical health outcomes, we chose to
examine the association between vacationing amount and risk for both metabolic syndrome and a
total count of metabolic symptoms. Metabolic syndrome is a constellation of cardiometabolic
risk factors that are associated with a reduced health-related quality of life, a greater utilization of
illness-related absent days from work, and a nearly doubled risk for cardiovascular disease the
leading cause of death for both men and women in the U.S. (Burton, Chen, Schultz, & Edington,
2008; CDC, 2017; Motillo et al., 2010). Importantly, metabolic syndrome and the symptoms that
define it are modifiable and thus demonstrating its association with vacationing will provide
evidence for the importance of this health behavior in reducing this cardiovascular disease risk.
As a secondary aim, we also consider how the aforementioned relationships varied as a function
of the vacation’s location.
Participants were recruited from the community using flyers, the internet, and media
advertisements. Inclusionary criteria included (a) being at least 18 years of age, (b) being
employed full-time, (c) being eligible for paid time off intended for vacationing, (d) being able to
read and understand English, (e) having access to the internet, (f) having a working email
address, and (g) having a vacation planned using at least three paid days that was at least one
month away. Individuals with vacations planned over Thanksgiving or Christmas were excluded
as these particular vacations represent unique experiences with mixed benefits and costs
(Greenberg & Berktold, 2006). University faculty and school teachers were excluded from
participation given that their vacation behavior varies cyclically with the academic schedule of
their students and thus is not representative of the larger population of working adults. In
addition, participants had to be free of adrenal gland (e.g., Cushing’s d isease, Addison’s disease),
pituitary gland (e.g., tumors or cancers of the pituitary gland, acromegaly), and
inflammatory/auto-inflammatory disorders (e.g., rheumatoid arthritis, lupus). Finally,
participants were excluded if they used any medications having long-term effects on the
hypothalamic pituitary adrenal axis (e.g., systemic steroids, anticonvulsants, opioid agonists). In
total, 67 individuals met inclusionary criteria and 63 (94.0%) were enrolled. The four individuals
not included in the final sample declined participation due to a lack of time to commit to the
The sample was predominantly female (69.8%) and Caucasian (93.6%) with a reported
family income between $75,000-$99,000 (63.5%), and an average age of 43.3 years (SD = 10.30,
range = 24-61). Participants were employed in a variety of industries; however, healthcare (27%)
and education (24%) were the most frequently represented industries consistent with the
employment characteristics of the Syracuse, NY area (Data USA, 2018).
All study procedures were approved by the Human Subjects Review Board of Syracuse
University. The procedures described here were part of a larger study considering the effects of
past vacationing behavior on current health outcomes, as well as the effects of an upcoming
vacation on acute health changes. The analyses reported in this manuscript are based upon the
data examining past vacationing behavior.
Participants completed surveys and visited the study’s lab to have their blood drawn and
the vacation interview administered. Participants were instructed to fast overnight and to refrain
from eating anything in the morning prior to the lab visit. Blood samples for lipid and glucose
testing were drawn by one of two professional phlebotomists. Waist circumference and blood
pressure was measured by trained research assistants. A trained interviewer conducted the
vacation interview.
Metabolic Syndrome/Symptoms. Metabolic syndrome was defined using the revised
criteria of the National Cholesterol Education Program Adult Treatment Program III (Grundy,
Brewer, Cleeman, Smith, & Lenfant, 2004). We chose this definition given its widespread use in
clinical settings, as well as the ease with which symptoms could be measured using unobtrusive
blood and physical measurements (Kaur, 2014). Specifically, the presence of 3 or more of the
following symptoms was indicative of metabolic syndrome: (1) a waist circumference 35
inches in females or 40 inches in males, (2) blood pressure 130/85 mmHg, (3) triglycerides
150 mg/dL, (4) HDL cholesterol < 50 mg/dL in females or < 40 mg/dL in males, and (5) fasting
blood glucose 100 mg/dL.
Waist circumference was measured in inches using a non-stretchable measuring tape
placed on the bare abdomen at the midpoint between the lower most rib and the top of the hip
bone (World Health Organization, 2008). Measurements were taken by two independent raters.
Inter-rater reliability was excellent (ICC = 0.98).
Blood pressure was measured using the SunTech Tango + (Model 2120; SunTech
Medical, Inc.) Following a demonstration of the blood pressure measurement, intended to
acclimate participants to the blood pressure cuff inflation, automated readings were taken in 2-
minute intervals for 10 minutes. During this time, participants watched a non-evocative historical
video. In total, five blood pressure measurements were taken and averaged to yield a single
summary score.
Triglycerides, HDL cholesterol, and fasting blood glucose were measured using the
CLIA-waived Alere Cholestech LDX. Blood samples drawn from the antecubital vein were
collected in 2 mL heparin tubes. Following centrifugation, plasma samples were stored in 1.6
mL LowBind Eppendorf tubes on ice until analysis.
Vacationing. Interviews were conducted in a private office with a trained interviewer to
ensure confidentiality. By request, participants brought employer records reflecting any paid
time off that they had taken in the prior 12 months. The interviewer reviewed with the participant
each paid time off episode that consisted of at least one full day. For each episode, participants
reported (a) whether they considered the paid time off a vacation, (b) whether they held a second
job during the paid time off, and (c) what they did during the paid time off (e.g., went to Florida,
stayed at home). Official New York state holidays were not counted as time off unless
participants’ had to draw from their bank of paid time in order to take the day off from work (i.e.,
the holiday was not recognized by their employer). In addition, non-New York state holidays
(e.g., Good Friday) were not counted as paid time off if they were given to participants as
holidays by their employers.
Interviewers recorded all paid time off reported by the participant on a 12-month calendar
and highlighted those vacation episodes meeting the criteria described above. The completed
calendars were used as a visual record of the participants’ vacation frequency over the past 12
months and were given to participants to use as a reference as they answered questions (see
Appendix 1) about each episode that they classified as a vacation and that they did not use to
work a second job. These questions were administered in an electronic survey format delivered
through Qualtrics (Qualtrics, Provo, UT). All participants in the study completed the interview as
well as the questions about each vacation episode that they took resulting in a 100% completion
rate. The total time to complete the interview and associated questions about each vacation
episode ranged between 30-120 minutes.
This assessment resulted in an overall measure of past 12-month vacation frequency as
well as a measure of 11 different facets of each vacation episode. These dimensions include each
vacation’s length, location, financial burden, and social context as well as the participant’s
positive appraisal of the vacation, the degree to which the participant disengaged from work and
their personal life, the activities they engaged in, their alcohol use and sleep habits, and any
negative events that occurred during the vacation (see Appendix 1 for the full interview). These
characteristics were selected based upon known associations with stress, vacationing, and
physical health as observed in the literature (de Bloom, 2015). Items for each characteristic were
created using existing measures, face valid questions, and expert opinion. The final interview
was reviewed and approved by the authors prior to its implementation in the current project.
Unless otherwise noted, all items that required participants to make a rating were placed on a
scale ranging from 0 (‘Not at all’) to 4 (‘A lot’).
Vacation frequency was calculated by totaling the number of vacation episodes meeting
inclusionary criteria that took place over the prior 12 months. The length of each vacation
episode was calculated by totaling the number of paid days used for the vacation in the previous
12 months. The location of each vacation episode was coded as either home or away from home.
This variable was used to determine the total number, as well as percentage, of vacation episodes
and vacation days occurring in each location. Hours spent traveling was recorded with an open-
ended question about the number of hours spent traveling and the stressfulness of traveling was
assessed using a single-item rating.
The financial burden associated with each vacation episode was assessed by asking
participants how much money that spent on the vacation, as well as with a single-item rating of
any financial stress occurring as a result.
The social context of each vacation episode was measured by asking participants about
who was present during the vacation, the number of hours spent on childcare, and a rating of
childcare stressfulness.
The positive appraisal of each vacation episode was assessed using five items that were
averaged to yield a single score. In the current study, Cronbach’s alpha was 0.67.
Disengagement from work during each vacation episode was measured using six items
that inquired about the frequency with which vacationers detached themselves from work-related
activities. Disengagement from life during each vacation was measured using three items that
asked about the frequency with which respondents disengaged from worries and stressors
associated with their personal lives while vacationing. Items on both dimensions were rated on a
scale ranging from 0 (‘Never’) to 4 (‘Very often’). Cronbach’s alpha was 0.82 for the work
disengagement subscale and 0.57 for the life disengagement subscale.
Activities taking place during each vacation episode were assessed using an adapted
version of the Pittsburgh Enjoyable Activities Test (PEAT: Pressman et al., 2009). We modified
the PEAT to include activities pertinent to vacationing. For each activity described, respondents
indicated whether they engaged in the activity (yes/no), and the number of hours they spent
doing any endorsed activities.
Alcohol use was measured using a modified version of the Alcohol Use Disorders
Identification Test-Consumption (AUDIT-C: Bush, Kivlahan, McDonell, Fihn, & Bradley,
1998). Specifically, the questions were presented as open-ended and revised so that they inquired
about drinking behavior occurring during the vacation rather than in the past year.
Sleep habits during each vacation were assessed using two items. The first item inquired
about the frequency with which sufficient sleep was obtained during the vacation rated on a scale
from 0 (‘Never’) to 4 (‘Very often’); the second item was adapted from the Pittsburgh Sleep
Quality Index (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) and inquired about the
quality of sleep obtained during the vacation rated on a scale ranging from 0 (‘Very good) to 3
(‘Very bad’).
Negative events that occurred during each vacation episode were measured using three
items informed by prior research (de Bloom et al., 2011) and that asked respondents to indicate
whether they had experienced any negative events, to report the total number experienced, and to
provide a brief description of any events experienced .
Covariates. Demographic characteristics including age, gender, and family income were
assessed by survey. Current smoking status was assessed by survey using a single item (0 = no, 1
= yes). In addition to using a modified version of the AUDIT-C to assess alcohol use during each
vacation episode, we also included the unmodified AUDIT-C (Bush et al., 1998) to assess
harmful drinking patterns that cut across various life domains and that reflect habitual alcohol
use behaviors. In females a score 3 and in a males a score 4 is indicative of harmful drinking.
Due to the low base rate of smoking in the sample (11.1%), smoking and harmful drinking status
were combined to yield a single measure of health behaviors (0 = no smoking or harmful
drinking, 1 = smoking or harmful drinking). Overall, 33.3% of the sample reported engaging in
harmful drinking or smoking. We selected these covariates based upon their known associations
with metabolic syndrome (Hildrum, Mykletun, Hole, Midthjell, & Dahl, 2007; Loucks et al.,
2015; Slagter et al., 2014).
Data analysis
Data analyses were conducted using Stata IC 13 (StataCorp, 2013). For models
examining the presence of metabolic syndrome as the outcome, logistic regression was used,
while for models considering the number of metabolic symptoms present, Poisson regression
was used. For the logistic regression analyses, odds ratios (ORs) and 95% confidence intervals
were computed and for the Poisson regression analyses, incidence rate ratios (IRRs) and 95%
confidence intervals were calculated. The IRR is a statistic that provides a multiplying factor for
each unit of change in the predictor (Hilbe, 2014).
Model predictors consisted of two broad aspects of a person’s vacation behavior: the total
number of vacation episodes and the total number of paid vacation days taken in the past 12-
months. Within each of those categories, we also considered the location of the vacation as
represented by the total number of vacation episodes taken home versus away as well as the total
number of paid vacation days taken home versus away. In all models tested, demographic and
health behavior covariates were included. Finally, to facilitate model interpretation, in any
models in the vacation predictor was statistically significant, we calculated the predicted
probability of meeting criteria for metabolic syndrome and the predicted number of metabolic
symptoms met at various levels of the vacation predictor, while holding all covariates constant.
Descriptive Statistics
Over the previous 12 months, participants took approximately 5 vacations (at home: M =
2.43, SD = 2.60; away from home: M = 2.94, SD = 2.02) and used roughly 14 paid vacation days
(at home: M = 5.13, SD = 5.93; away from home: M = 8.67, SD = 6.25). On average, 38.9% (SD
= 31.8) of the vacation episodes taken by each participant took place at home while 56.3% (SD =
33.1) took place away from home. Similarly, on average 33.5% (SD = 31.5) of the paid vacation
days taken by each participant took place at home and 61.7% (SD = 33.6) took place away from
[Table 1 near here]
Table 1 contains the descriptive statistics of the other vacation characteristics assessed by
the vacation interview. Overall, participants reported relatively low levels of stress related to
traveling and childcare, as well as low levels of financial burden associated with vacationing.
Vacations were generally appraised positively, with relatively high levels of work and life
disengagement occurring during vacationing. The most common activities participants engaged
in while on vacation included social activities, passive activities, and light aerobic activities.
Drinking was relatively infrequent and sleep quality was high despite sleep quantity typically
falling below what was desired. Finally, negative events occurring while vacationing were rare.
Notably, variability was observed on all of the dimensions, with participants utilizing the full
range of the scale for each characteristic.
Overall, 20.6% of the participants met criteria for metabolic syndrome. This rate is lower
than the 35% prevalence rate observed in the general U.S. population (Aguilar, Bhuket, Torres,
Liu, & Wong, 2015). The average number of metabolic symptoms met by participants was 1.48
(SD = 1.19).
Primary Analysis: Total Vacation Episodes and Vacation Days Predicting Metabolic
Syndrome and Symptoms
Metabolic Syndrome. The relationship between the total number of vacation episodes
taken in the past 12 months and metabolic syndrome was statistically significant such that the
odds of having metabolic syndrome decreased by 24% with each additional vacation taken (OR =
0.76, p = 0.051; see Table 2). Furthermore, the predicted probability of meeting metabolic
syndrome criteria was 16.0% for participants taking the average number of vacations, while it
was 46.7% for participants who did not take any vacations and 1.3% for participants who took
the maximum number of vacations observed in the sample. In contrast, the relationship between
total vacation days and metabolic syndrome was not statistically significant (OR = 0.99, p =
Metabolic Symptoms. An inverse relationship was observed between the number of
vacation episodes taken in the past 12 months and the number of metabolic symptoms met (IRR
= 0.92, p = 0.035; see Table 2). Specifically, each additional vacation episode in the past 12
months was associated with an 8% decrease in the number of metabolic symptoms. Additionally,
the predicted number of metabolic symptoms met was 1.40 for participants taking the average
number of vacations, while it was 2.16 for participants who did not take any vacations and 0.65
for participants who took the maximum number of vacations observed in the present sample. The
total number of vacation days was not related to metabolic symptoms (IRR = 0.98, p = 0.11).
[Table 2 near here]
Secondary Analysis: Vacation Episodes and Vacation Days by Vacation Characteristics
Predicting Metabolic Syndrome and Symptoms
Metabolic Syndrome. The total number of home vacations (OR = 0.52, 95% CI = 0.30,
0.89, p = 0.017) but not the total number of away vacations (OR = 1.05, 95% CI = 1.01, 1.08, p
= 0.75) was associated with a reduced odds of metabolic syndrome. The predicted probability
of meeting metabolic syndrome was 38.0%, 11.0%, and 1.0% for participants taking no home
vacations, the average number of home vacations, and the maximum number of home vacations
observed in the sample. Neither the total number of home vacation days (OR = 0.80, 95% CI =
0.64, 1.01, p = 0.057), nor the total number of away vacation days (OR = 1.09, 95% CI = 0.99,
1.20, p = 0.098) was related to metabolic syndrome.
Metabolic Symptoms. Neither the number of home vacations (IRR = 0.92, 95% CI = 0.84,
1.01, p = 0.080) nor the number of away vacations (IRR = 0.95, 95% CI = 0.95, 1.06, p = 0.35)
was related to the number of metabolic symptoms. Further, the number of home vacation days
(IRR = 0.97, 95% CI = 0.93, 1.01, p = 0.10) as well as the number of away vacation days (IRR =
1.00, 95% CI = 0.96, 1.03, p = 0.82) were unrelated to the number of metabolic symptoms met.
Despite the established relationships between work stress and health, research examining
the health benefits of vacation experiences is limited. While existing research suggests that
vacations may confer a beneficial health effect (e.g., de Bloom et al., 2009; Gump & Matthews,
2000), studies examining the relationship between vacationing and health have been constrained
by the absence of a standard instrument that can be used across studies to assess vacations and
their key characteristics. The creation and utilization of such an instrument across vacation
research will facilitate the comparison of studies considering different populations, industries,
and research designs. The current study introduces a vacation interview that addresses this gap in
the literature and demonstrates that considerable variability exists on a number of dimensions
that may influence the beneficial effects that vacations have on health. Furthermore, as a
preliminary examination of the instrument’s validity and utility, it considered how vacationing
amount relates to metabolic syndrome and symptoms, with a secondary aim considering how the
location of vacations relate to these health outcomes.
Descriptive statistics of the vacation characteristics assessed indicate that overall
vacationers experienced their vacations as positive. Notably, relatively little stress was associated
with the vacations taken as reflected on multiple dimensions rated by participants: stress incurred
from traveling, spending money, and providing childcare while vacationing was rated low.
Furthermore, vacations were appraised as positive experiences overall with a high degree of
work and life disengagement achieved while vacationing. Importantly, one of the most
commonly reported activities occurring during vacations were social activities such as spending
time with family or sharing a meal with friends. This is significant given the influential role that
social relationships have on physical and psychological health (e.g., Cacioppo et al., 2002;
Umberson, Crosnoe, & Reczek, 2010). Future research should consider whether these
characteristics assessed by our vacation instrument are observed in studies utilizing larger
Findings from the present research indicated that the number of vacation episodes was
related to a reduced odds of meeting metabolic syndrome such that each additional vacation
decreased the odds of meeting criteria for this syndrome by nearly a quarter. Additionally, the
total number of vacation episodes in the previous 12 months were related to a reduction in the
number of metabolic symptoms met. In contrast, the total number of vacation days from the prior
12 months was not related to either metabolic syndrome or metabolic symptoms. This finding
replicates prior research demonstrating a relationship between vacation frequency and
cardiovascular events (Eaker, Pinsky, & Castelli, 1992; Gump & Matthews, 2000). Furthermore,
it extends this finding to include an additional health outcome in the form of metabolic
syndrome. This extension is important given that metabolic syndrome is modifiable and thus the
risk that it confers for cardiovascular disease and diabetes can be reduced by addressing its
etiological factors. Work stress and its associated sympathetic nervous system cardiovascular
activity represents one such factor that may be effectively addressed by more frequent
vacationing (e.g., Fan, Blumenthal, Hinderliter, & Sherwood, 2013; Joseph et al., 2016).
Aside from this main finding, the present study also found that the total number of home
vacations were related to a reduced odds of meeting metabolic syndrome. While this finding
seems to suggest that home vacations and not away vacations are associated with positive
health effects, the cross-sectional nature of the study design precludes conclusions about the
directionality of the observed relationships. Thus, while it is possible that vacationing is
associated with improved physical health as we hypothesize, it is also possible that people
experiencing poorer health were more likely to take certain types of vacations. That is, it is
possible that people with better health were more inclined to take home vacations, while people
experiencing health difficulties may have been more inclined to take away vacations. For
example, some research suggests that people experiencing chronic health problems are more
likely to use distraction based, emotion focused coping strategies to manage their illness (e.g.,
Endler, Kocovski, & Macrodimitris, 2001; Spendelow, Joubert, Lee, & Fairhurst, 2018).
Traveling away from home may be an example of this type of coping behavior. Aside from this
possibility, people with poorer health may have been more likely to vacation at home to remain
close to their primary care physicians. To our knowledge, this study represents the first empirical
comparison of home and away vacations and their relationship to physical health outcomes.
Thus, additional research using larger samples and prospective designs is necessary before any
firm conclusions can be drawn from this particular finding.
Several limitations constrain the findings from the current study. First, the retrospective
design makes it difficult to firmly establish the direction of the relationships observed. Future
research should employ a prospective design in which vacationing behavior assessed via the
vacation interview is examined as a predictor of future risk for metabolic syndrome and
symptoms. However, a clinically detectable impact of vacationing behaviors may require many
years to develop; thus, a longitudinal design is not without its own limitations. Second,
Cronbach’s alphas for the positive appraisal and life disengagement subscales were relatively
low, which may reflect the need to revise these subscales. Future research utilizing larger
samples and testing the factor structure of the vacation interview subscales should be performed
to explore the potential reasons for the Cronbach’s alpha values observed in the current study.
Third, the sample was primarily female and Caucasian potentially limiting its generalizability.
However, it should be noted that the seminal studies demonstrating a relationship between
vacationing and objective physical health outcomes consisted of samples that were either entirely
male (Gump & Matthews, 2000) or entirely female (Eaker et al., 1992). Thus, the composition of
the present study sample, in terms of the representation of both sexes, is actually better than prior
research. That being said, future research should consider more racially/ethnically diverse
samples with a more equal proportion of males and females. Finally, given the inclusionary
criteria of the study, it is possible that people who take vacations may have been more willing to
participate. However, the number of vacations taken by participants ranged from 0-15 (see Table
1). Thus, we did have participants who reported taking 0 vacations in the past 12 months.
Furthermore, even if the current sample consisted of people who were more likely to take
vacations and who may have been less stressed to begin with, this would have worked against
our ability to detect the relationships observed. However, the findings were consistent with prior
research demonstrating that less vacationing is associated with poorer health.
Despite these limitations, the present study contributes to the research literature by
demonstrating that vacation frequency is associated objective physical health status as
represented by metabolic syndrome and symptoms. Furthermore, it introduces a new instrument
allowing for the systematic assessment of vacations and their characteristics across vacation
research, and in so doing, aiding the case for the importance of vacationing in improving health
and reducing work-related disease.
Acknowledgements: The authors thank Alexandrah Gichingiri, Ivan Castro, Rachel Zajdel,
Aylonna Whitney, Samantha Henderson, Ian Thompson, Tatiana Gregory, Tessila Abbott,
Barbara Samson, and Jessica Fleming for their assistance in ensuring the successful completion
of the project.
Disclosure Statement: As part of the funding agreement, Project: Time Off retained no legal
authority regarding data analysis, manuscript preparation, or publication decisions.
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Table Captions
Table 1. Descriptive Statistics of the Vacation Characteristics Assessed by the Vacation
Interview (N = 63)
Table 2. Regression of Metabolic Syndrome and Metabolic Symptoms on Total Past 12-Month
Vacation Episodes and Vacation Days (N=63)
Table 1.
M (SD)a
Total vacation episodes
5.44 (3.16)
Total vacation days
13.80 (7.25)
% home
Hours spent traveling
Stressfulness of traveling
9.68 (6.56)
0.74 (0.83)
Financial burden
Money spent
Financial stress
744.94 (1824.18)
0.46 (0.76)
Social context
Hours spent childcare
Childcare stressfulness
6.16 (8.06)
0.33 (0.64)
Positive appraisal
2.95 (0.66)
Work disengagement
2.91 (0.82)
Life disengagement
3.37 (0.66)
86.09 / 22.32 (73.50)
60.95 / 9.55 (60.24)
Table 1 Continued.
M (SD)a
11.54 / 1.05 (4.19)
23.67 / 2.04 (6.56)
57.69 / 3.99 (6.11)
Light aerobic
Non-fitness sporting
78.11 / 6.61 (10.61)
47.04 / 3.63 (7.32)
33.43 / 2.09 (4.07)
65.09 / 5.12 (7.59)
7.40 / 0.32 (1.47)
24.56 / 0.53 (1.51)
55.62 / 8.21 (18.40)
Alcohol use
Drinking days
Number drinks/day
Number 6+ drink days
1.75 (2.28)
1.54 (1.88)
0.12 (0.53)
Sleep habits
2.63 (0.95)
0.97 (0.57)
Negative eventsc
11.83 / 0.12 (0.32)
a All statistics reported represent the mean and standard deviation unless otherwise noted.
b The statistics reported reflect the percentage of vacations on which each activity was endorsed followed
by the mean (standard deviation) number of hours spent on each activity. The range for these items refers
to the hours spent on each activity.
c The statistics reported reflect the percentage of vacations for which a negative event was endorsed
followed by the mean (standard deviation) number of events reported across all vacation. The range for
these items refers to the number of events across all vacations.
Table 2.
Metabolic Syndrome
Metabolic Symptoms
OR [95% CI]
IRR [95% CI]
0.76 [0.57, 1.00]
1.08 [1.00, 1.16]
0.69 [0.15, 3.17]
0.61 [0.31, 1.20]
0.69 [0.15, 3.10]
0.92 [0.86, 0.99]
1.03 [1.01, 1.05]
0.73 [0.46, 1.15]
0.83 [0.68, 1.02]
0.78 [0.49, 1.25]
0.99 [0.90, 1.09]
1.06 [0.99, 1.09]
0.64 [0.14, 2.88]
0.64 [0.33, 1.22]
0.78 [0.18, 3.40]
0.98 [0.95, 1.01]
1.02 [1.01, 1.05]
0.71 [0.45, 1.13]
0.84 [0.69, 1.04]
0.76 [0.47, 1.23]
Note. Metabolic syndrome: 0 = ‘metabolic syndrome criteria not met’ and 1 = ‘metabolic
syndrome criteria met’. Gender: 0 = ‘male’ and 1 = ‘female’. Health behaviors: 0 = ‘current
smoking or hazardous alcohol consumption present’ and 1 = ‘current smoking or hazardous
alcohol consumption absent’.
OR = odds ratio; IRR = incidence rate ratio
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Meditation is becoming increasingly practiced, especially for stress-related medical conditions. Meditation may improve cellular health; however, studies have not separated out effects of meditation from vacation-like effects in a residential randomized controlled trial. We recruited healthy women non-meditators to live at a resort for 6 days and randomized to either meditation retreat or relaxing on-site, with both groups compared with ‘regular meditators' already enrolled in the retreat. Blood drawn at baseline and post intervention was assessed for transcriptome-wide expression patterns and aging-related biomarkers. Highly significant gene expression changes were detected across all groups (the ‘vacation effect') that could accurately predict (96% accuracy) between baseline and post-intervention states and were characterized by improved regulation of stress response, immune function and amyloid beta (Aβ) metabolism. Although a smaller set of genes was affected, regular meditators showed post-intervention differences in a gene network characterized by lower regulation of protein synthesis and viral genome activity. Changes in well-being were assessed post intervention relative to baseline, as well as 1 and 10 months later. All groups showed equivalently large immediate post-intervention improvements in well-being, but novice meditators showed greater maintenance of lower distress over time compared with those in the vacation arm. Regular meditators showed a trend toward increased telomerase activity compared with randomized women, who showed increased plasma Aβ42/Aβ40 ratios and tumor necrosis factor alpha (TNF-α) levels. This highly controlled residential study showed large salutary changes in gene expression networks due to the vacation effect, common to all groups. For those already trained in the practice of meditation, a retreat appears to provide additional benefits to cellular health beyond the vacation effect.
Introduction There are few longitudinal studies of relationships between vacation and later health outcomes. We studied these during a 26-year follow-up of the Helsinki Businessmen Study. Methods In 1974, at mean age of 47 years, 2741 members of a cohort of executives and businessmen born 1919–1934 were clinically examined and reported their annual vacation time (dichotomized >21 [n = 2001] vs. ≤21 days [n = 740]), self-rated health (SRH) and perceived physical fitness using a five-step scale. In old age in 2000 (mean age 73 years), the survivors filled in the RAND-36/SF-36 health-related quality of life (HRQoL) questionnaire. Mortality between 1974 and 2000 was retrieved from national registers. Results At baseline, shorter vacation was associated with longer work time, higher BMI, more coffee consumption and worse SRH. During the 26-year follow-up, 778 men out of 2741 (28.4%) had died. Shorter annual vacation was associated with higher mortality with curves starting to diverge after 18 years of follow-up, (fully adjusted hazard ratio 1.29, 95% confidence interval 1.08–1.55, P = 0.005). In old age, shorter vacation in midlife was tentatively associated with worse general health. Conclusions Shorter vacation time in midlife was associated with characteristics related to lifestyle and with worse perceived health status, and predicted mortality up to old age in men.
Objectives: The objectives of this study were to determine whether job strain is more strongly associated with higher ambulatory blood pressure (ABP) among blue-collar workers compared with white-collar workers, to examine whether this pattern generalizes across working and nonworking days and across sex, and to examine whether this pattern is accounted for by psychosocial factors or health behaviors during daily life. Methods: A total of 480 healthy workers (mean age = 43 years, 53% female) in the Adult Health and Behavior Project-Phase 2 completed ABP monitoring during 3 working days and 1 nonworking day. Job strain was operationalized as high psychological demand (> sample median) combined with low decision latitude (<sample median; Karasek model; Job Content Questionnaire). Results: Covariate-adjusted multilevel random coefficient regressions demonstrated that associations between job strain and systolic and diastolic ABP were stronger among blue-collar workers compared with white-collar workers (b = 6.53 [F(1,464) = 3.89, p = .049] and b = 5.25 [F(1,464) = 6.09, p = .014], respectively). This pattern did not vary by sex, but diastolic ABP findings were stronger when participants were at work. The stronger association between job strain and ABP among blue-collar workers was not accounted for by education, momentary physical activity, or substance use, but was partially accounted for by covariation between higher hostility and blue-collar status. Conclusions: Job strain is associated with ABP among blue-collar workers. These results extend previous findings to a mixed-sex sample and nonworking days and provide, for the first time, comprehensive exploration of several behavioral and psychosocial explanations for this finding.
Purpose: To determine if a 1- to 3-week vacation in adults leads to weight gain and whether that gain persists 6 weeks later. Methods: 122 adults going on a 1- to 3-week vacation completed 3 visits. The visits were 1 week prior to, 1 week post, and 6 week post vacation. Height, weight, blood pressure, and waist-to-hip ratio, physical activity (International Physical Activity Questionnaire - IPAQ) and stress (Perceived Stress Scale - PSS) were measured. Results: Body weight increased on vacation (0.32 ± 0.08 kg, p < 0.05) and this increase persisted so that total weight gain was 0.41 ± 0.11 kg (p < 0.05). No difference in weight gain based on BMI was found (0.28 ± 0.13 kg, 0.39 ± 0.14 kg, and 0.48 ± 0.27 kg for normal weight, overweight, and obese, respectively). PSS decreased for the study (17.1 ± 0.5 to 14.9 ± 0.6 for pre-vacation to 6-weeks post-vacation, respectively; p < 0.001), and total physical activity tended to increase on vacation (3940 ± 235 vs. 4313 ± 344METs, for pre- vs. post-vacation, respectively; p = 0.10) and decreased in the post-vacation period (4313 ± 344 vs. 3715 ± 306METs, p < 0.05). Conclusions: Vacations resulted in significant weight gain (0.32 kg), and this weight gain persisted at the 6-week follow-up period. The weight gain appeared to be driven by increased energy intake above energy requirements. This gain could be a significant contributor to yearly weight gain in adults and therefore affect obesity prevalence.
‘How long should a man’s vacation be?’ was the title of an article in the New York Times in 1910, in which businessmen, academics and politicians discussed whether vacations for the working classes were necessary and why. Today, European workers enjoy a minimum of 20 days paid annual leave (plus statutory days) and the tourism industry employs more than 260 million people worldwide. Does that mean we have the answers to the intriguing questions raised in a newspaper more than 100 years ago? Not quite. Our journey will pass major theories and milestones of research on recovery from work, and get an overview of findings on vacation effects. As a souvenir of your trip, you’ll be given some practical tips to make the most of your next holiday.