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Experiential avoidance and well-being: A
daily diary analysis
Kyla A. Machell
a
, Fallon R. Goodman
a
& Todd B. Kashdan
a
a
Department of Psychology, George Mason University, Fairfax, VA, USA
Published online: 06 May 2014.
To cite this article: Kyla A. Machell, Fallon R. Goodman & Todd B. Kashdan (2014): Experiential avoidance
and well-being: A daily diary analysis, Cognition & Emotion, DOI: 10.1080/02699931.2014.911143
To link to this article: http://dx.doi.org/10.1080/02699931.2014.911143
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BRIEF REPORT
Experiential avoidance and well-being: A daily diary
analysis
Kyla A. Machell, Fallon R. Goodman, and Todd B. Kashdan
Department of Psychology, George Mason University, Fairfax, VA, USA
Experiential avoidance (EA) is a regulatory strategy characterised by efforts to control or avoid
unpleasant thoughts, feelings and bodily sensations. Most studies of EA have used trait measures
without considering the effects of EA on psychological functioning in naturalistic settings. To address
this gap, we used daily diary methodology to examine the influence of EA of anxiety on everyday well-
being. For two weeks, 89 participants provided daily reports of EA, positive and negative affect,
enjoyment of daily events and meaning in life (MIL). Daily EA predicted higher negative affect,
lower positive affect, less enjoyment of daily events (exercising, eating food and listening to music) and
less MIL. The effect of EA on positive affect was not accounted for by the amount of negative affect
experienced. Our daily measure of EA was a stronger predictor of daily well-being than a traditional
trait measure (The Acceptance and Action Questionnaire). Taken together, results offer insights into
the adverse effects of EA on daily well-being and suggest that EA is a context-specific regulatory
strategy that might be best captured using a state-dependent measure.
Keywords: Experiential avoidance; Well-being; Daily diary methodology.
Experiential avoidance (EA) is a regulatory strategy
characterised by efforts to control or avoid unpleas-
ant thoughts, feelings and bodily sensations (Hayes,
Luoma, Bond, Masuda, & Lillis, 2006). A growing
body of evidence suggests that EA tends to be a
maladaptive emotion regulation strategy linked
with a lower quality of life (Hayes et al., 2004)
and worse emotional and psychological well-being
(Kashdan, Barrios, Forsyth, & Steger, 2006).
Unfortunately, nearly every published study of EA
has relied on a global trait questionnaire to assess
the presence of EA—participants are asked to
endorse general statements about unwanted
thoughts, feelings and sensations across time and
context such as “Anxiety is bad” and “I am able to
take action on a problem even if I am uncertain
Correspondence should be addressed to: Todd B. Kashdan, Department of Psychology, George Mason University, MS 3F5,
Fairfax, VA 22030, USA. E-mail: tkashdan@gmu.edu
Todd B. Kashdan was financially supported as the Senior Scientist of the Center for the Advancement of Well-Being, George
Mason University; Fallon Goodman was financially supported by the same centre with a fellowship.
© 2014 Taylor & Francis
1
COGNITION AND EMOTION, 2014
http://dx.doi.org/10.1080/02699931.2014 .911143
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what is the right thing to do” (rated from 1 = never
true to 7 = always true) (Hayes et al., 2004).
Emotion regulation theorists have argued that EA
is not a static feature of humanity and, instead, is a
state-dependent process that is sensitive to social
contexts (Gross & John, 2003; Kashdan, Farmer
et al., 2013). The current study adopted this
dynamic within-person approach to EA to study
the association with three core components of well-
being: affect, enjoyment of pleasurable events and a
sense of meaning in life (MIL).
EA AND WELL-BEING
There is a substantial body of research suggesting
that rigid adherence to inhibitory emotion regula-
tion strategies is detrimental to psychological well-
being. Inhibition of outward emotional expression
has been linked to increases in unwanted emoti onal
experiences (Gross & Levenson, 1997). Similarly,
avoidance of internal emotional experience is
thought to be an unhelpful emotion regulation
strategy linked with poor ps ychological functioning
(Kashdan et al., 2006). When people are asked to
suppress negative emotions, they report a tempor-
ary success in down-regulating initial discomfort,
but ultimately experience a rebound effect whereby
undesirable emotions increase (Hayes, Strosahl, &
Wilson, 1999). EA is one such strategy that is
employed to reduce distress, yet has the paradoxical
effect of increasing unwanted emotional experi-
ences (Hayes, Wilson, Gifford, Follette, & Strosahl,
1996). Research has demonstrated the temporal role
of EA of anxiety in increasing the very symptoms
of anxiety that were the target of the avoidance
(Kashdan et al., in press). Although use of inhibitory
regulation strategies increases negative emotions, the
same is not true for positive emotions. Experimental
research on the use of suppression strategies suggests
that attempts to inhibit emotional expression not
only increase negative emotions but also decrease
positive emotions (Gross & John, 2003). Experience
sampling research demonstrates a similar finding for
EA: people who score high on a trait measure of
EA report more daily negative affect and less daily
positive affect than people with lower EA scores
(Kashdan et al., 2006), suggesting that EA interferes
with well-being.
In addition to emotional disturbances, EA may
interfere with the enjoyment of everyday events by
draining resources necessary for attention and
engagement. In any given moment, human beings
possess finite cognitive resources and physical
stamina. Expending valuable energy on EA pulls a
person away from what is unfolding in the present
moment, reducing their potential to devote effort
and make progress towards valued goals (Hayes
et al., 2006). People who are hyper-focused on
regulating, avoiding and concealing emotions direct
their attention inward and, in turn, are less able to
attend and respond to pleasant life events. This
could apply to events beyond those that are very
positive or highly arousing. Devoting excessive time
to avoid unwanted emotions (internally and extern-
ally) is likely to reduce the available mental resources
needed to enjoy the most basic pleasurable events
(e.g., exercising, eating food, listening to music,
having sex). Attempts to rid certain emotions to
prevent adverse experiences may effectively reduce
other emotions that are in fact highly desirable.
EA inhibits the approach-oriented behaviours
necessary to seek out and enjoy valued experiences
(Hayes et al., 1999) that may contribute to a sense
of MIL, which is widely considered a component
of one’s broader subjective well-being (e.g., Steger,
Kashdan, & Oishi, 2008). Indeed, EA (measured
as a stable, global trait) has been associated with
decreased global (Kashda n & Breen, 2007) and
daily (Kashdan et al., 2006) MIL. Because indi-
viduals who use EA are less able to be in contact
with and enjoy daily events, and tend to experience
more daily negative affect and less daily positive
affect, they may perceive their daily lives as less
meaningful.
EA AND WELL-BEING AS DYNAMIC
DAILY PROCESSES
Despite the common conceptualisations of EA as
an inflexible regulatory strategy (e.g., Hayes et al.,
1996; Kashdan et al., 2006), the effects of using EA
are not uniform across all situations and may differ
MACHELL, GOODMAN, KASHDAN
2 COGNITION AND EMOTION, 2014
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between and within individuals. A person-by-
situation approach to emotion regulation suggests
that, rather than categorising regulatory strategies
as either helpful or harmful, the adaptiveness of
emotion regulation strategies depends on the con-
text in which they are used (Aldao, 2013;Kashdan
& Rottenberg, 2010). For example, the effects of EA
have been shown to vary in response to changing
situational demands (Kashdan et al., in press) and
individual goals (Kashda n & Breen, 2007). This
research ties EA back to theoretical frameworks
that sugges t this is a dynamic process (Hayes et al.,
1996, 2004) that cannot be studied with static
measurement approaches. Accordingly, the status
quo of assessing EA as a dispositional trait does not
adequately capture how the effects of EA might
change depending on the situation, nor does it
provide information on how EA affects psycholo-
gical functioning in the context of daily life.
Recent daily diary studies have begun to assess
EA as a state-dependent construct, with findings
that highlight the need to study EA across time
and situation. For example, Shahar and Herr
(2011) found that healthy individuals tend to use
EA on days when they experience more negative
affect, but depressed individu als demonstrate a
more inflexible pattern of use and rely on EA even
at low levels of daily negative affect. This nuanced
relationship would not have emerged had these
researchers used a traditional global measure of
EA, such as both versions of the Acceptance and
Action Questionnaire (AAQ; Bond et al., 2011;
Hayes et al., 2004) and the Multidimensional
Experiential Avoidance Questionnaire (Gamez,
Chmielewski, Kotov, Ruggero, & Watson, 2011).
Well-being also fluctuates on a daily basis and
may vary meaningfully within individuals. A robust
literature on intra-individual variability in mood
demonstrates that levels of positive and negative
affect change from day to day (e.g., Eid & Diener,
1999). Daily diary studies have illustrated daily
fluctuations in MIL, with one study demonstrating
that the majority of variability in MIL judgments
was within people (82%) rather than between
people (King, Hicks, Krull, & Del Gaiso, 2006).
To fully understand how EA impacts well-being,
we must examine these relationships in the context
of people’s daily lives. The current study is the first
to use a dynamic approach to EA and well-being.
THE PRESENT STUDY
The inverse association between EA and well-being
is largely based on measures of EA as a fixed trait
rather than a dynamic state. As a result, researchers
possess limited knowledge of how EA impacts
well-being in the context of daily life. To address
this gap, we conducted a daily diary study examin-
ing the influence of daily EA of anxiety on daily
well-being. Rather than creating a single, global
construct of well-being as an outcome, we relied on
core components of subjective well-being from
dominant theories and empirical research (e.g.,
Diener, Suh, Lucas, & Smith, 1999; McKnight &
Kashdan, 2009)— positive and negative affect,
enjoyment of daily events and daily MIL. We
hypothesised that daily EA of anxiety would predict
less positive affect, more negative affect, less enjoy-
ment of daily events and less daily MIL. To address
the importance of using a dynamic approach, we
examined whether daily EA predicted indicators of
well-being over and above an exi sting trait measure
of EA.
METHOD
We report how we determined our sample size, all
data exclusions, all manipulations and all measures
in the study. The current study is one of a series of
papers derived from a larger data-set collected to
understand emotion and emotion regulation in
daily life. Measures not examined in the current
study are reported elsewhere (DeWall, Lambert,
Pond, Kashdan, & Fincham, 2013, Stu dy 2; Farmer
& Kashdan, 2012; Kashdan, Dewall et al., 2013,
Study 3; Kashdan & Nezlek, 2012; Kashdan,
Yarbro, McKnight, & Nezlek, 2014 ). Data were
collected in two waves from a total of 173 participants.
Measures of EA were only collected during the
second wave, which included the 95 participants
we discuss below.
EXPERIENTIAL AVOIDANCE IN DAILY LIFE
COGNITION AND EMOTION, 2014
3
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Participants
Participants were 95 college students (82% women;
54% Caucasian; mean age = 21.10, SD = 2.12) who
participated for course credit. Of these participants,
89 (81% women; 54% Caucasian; mean age =
21.18, SD = 2.22) completed questions about EA
and well-being at the end of the day for two weeks.
These 89 participants provided 1261 valid daily
entries (M = 14.17, SD = 2.25). A valid entry had to
be completed between 6:00 pm of the day in
question and 10:00 am of the following day.
Procedure
Participants were recruited via flyers and online
advertisements. Small groups of participants
attended meetings (1.5 hours) during which they
completed demographic and trait measures, and
where instructions were given about web-based
daily data collection. Participants were asked to
complete their daily reports at the end of each day
(before going to sleep) for 14 consecutive days.
Date and time stamps were inspected to ensure
compliance with these guidelines. Throughout the
study, participants received weekly email remin-
ders, and all study instructions were available
online.
Daily measures
At the end of each day of the study, participants
logged onto the secure website to provide daily
measures of EA, positive and negative affect,
enjoyment of exercising, eating food, listening to
music, having sex, and MIL. Daily measures used
modifications of items from corresponding trait
measures to include a specific focus on the day as
the unit of analysis. Reliability estimates for the
daily measures are presented in the Results section.
Experiential avoidance
Daily EA was measured using a 4-item state
measure of EA (Kashdan, Farmer, et al., 2013).
Participants answered, “How upset and distressed
over anxiety were you?”, “How much effort did
you put into making anxiety-related feelings or
thoughts go away?”, “How much did you struggle
to control your anxiety-related feelings or
thoughts?” and “To what extent did you give up
saying or doing what you like (or mattered to you)
in order to control and manage your anxiety?”
Participants answered using a 7-point scale with
endpoints 1 = “not at all” and 7 = “very much”.In
the current sample, this measure had a correlation
of .82 with a measure of daily suppression of
negative emotions, demonstrating acceptable con-
vergent validity, and has also shown acceptable
reliability and validity in past research (Kashdan,
Farmer et al., 2013).
Positive and negative affect
Daily positive and negative affect was measured by
responses to six positively valenced adjectives
(excited, enthusiastic, happy, relaxed, calm and
satisfied) and six negatively valenced adjectives
(nervous, embarrassed, upset, sad, bored and disap-
pointed). Participants answered using a 7-point
scale with endpoints 1 = “Did not feel this way at
all” and 7 = “Felt this way very strongly”.
Event enjoyment
Daily enjoyment of exercising, eating food, listen-
ing to music and having sex was measured by
responses to the item “How much pleasure did you
experience today from (respective activity)?” Parti-
cipants answered using a 7-point scale with end-
points 1 = “not at all” and 7 = “very much”.
Meaning in life
Daily MIL was measured with the Daily Meaning
Scale (Steger et al., 2008), a 2-item scale asking
“How meaningful did you feel your life was
today?” and “How much did you feel your life
had purpose today?” Participants answered using a
7-point scale with endpoints 1 = “not at all” and
7=“very much”.
Trait measures
Experiential avoidance
The AAQ (Hayes et al., 2004) is a 9-item measure
of EA that assesses tendencies to make negative
MACHELL, GOODMAN, KASHDAN
4 COGNITION AND EMOTION, 2014
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evaluations of private events (e.g., “anxiety is bad ”),
unwillingness to remain in contact with these
events, the need/desire to control or alter these
events and the inability to take action in the face of
negatively evaluated private events. The psycho-
metric properties of this scale have been established
in both clinical and non-clinical samples (e.g.,
Feldner, Zvolensky, Eifert, & Spira, 2003 ; Hayes
et al., 2004). Participants responded to items using
a 7-point Likert scale (α = .69). Higher scores
indicate greater EA. The mean score on the AAQ
was 33.40 (SD = 8.59).
Anxiety
Because our measure of EA emphasised avoidance
of anxiety, the Social Interaction Anxiety Scale
(SIAS; Mattick & Clarke, 199 8 ) was included to
measure baseline levels of anxiety in our sample.
The SIAS is a 20-item measure of participants’ fear
and avoidance of social interactions. Participants
responded to items using a 5-point Likert scale
(α = .89). The mean score on the SIAS was 24.94
(SD = 14.18), with 20 participants (23%) reporting
clinical levels of anxiety (scores > 34). Participants’
social anxiety scores were comparable to other
undergraduate samples. In the original validation
paper for the SIAS, Mattick and Clarke (1998 )
reported a mean score of 19.0 (SD = 10.1) for
undergraduate participants. Another study exam-
ining social anxiety in college students reported a
mean SIAS score of 22.38 (SD = 15.40; Purdon,
Antony, Monteiro, & Swinson, 2001).
RESULTS
Analytic strategy
Our prim ary interest was in the slope between EA
on a given day and (1) positive and negative affect,
(2) event enjoyment and (3) MIL. The data were
conceptualised as hierarchically nested with days
nested within persons. Analyses were conducted
with a series of multilevel models using the
programme HLM (Raudenbush, Bryk, Cheong,
& Congdon, 2000).
Daily measures: Descriptive statistics
The reliability of the daily measures was examined
by conducting three-level models with items
nested within days, and days nested within people.
These reliability estimates (presented in Table 1)
provide evidence for the acceptable reliability of
each daily measure. Because the event enjoyment
measures consisted of only one item, the reliability
estimate was not calculated. Upon examining the
partitioning of variance, we found that there was
greater within-person variability than between-
person variability for each measure. These results
support our approach of conducting within-person
(day-level) analyses.
Table 1. Descriptive statistics for daily measures
M (SD)
Between-person
variability
Within-person
variability
Item-level
reliability
Daily experiential avoidance 2.48 (1.37) .31 .61 .80
Positive affect 4.20 (1.25) .27 .73 .79
Negative affect 2.20 (1.05) .33 .68 .64
Enjoyment of exercising 2.32 (1.86) .26 .74 –
Enjoyment of eating food 4.05 (1.67) .30 .70 –
Enjoyment of listening to
music
4.42 (2.03) .29 .71 –
Enjoyment of having sex 2.18 (2.10) .42 .58 –
Meaning in life 9.02 (3.21) .47 .53 .86
Note: The four enjoyment items are single items and thus, reliability cannot be calculated. We kept them separate because they describe
discrete, concrete activities.
EXPERIENTIAL AVOIDANCE IN DAILY LIFE
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Daily EA and daily well-being
Our initial analyses examined within-person rela-
tionships between daily EA and daily positive and
negative affect, event enjoyment and MIL. Daily
EA served as the predictor and daily well-being
variables served as outcomes. Daily EA was entered
group-mean centred, which meant that coefficients
described relationships between deviations from a
person’s mean score on daily EA and the outcome
measures. The null hypothesis in these analyses was
that the mean within-person relationship between
EA and a measure of well-being was 0. This was
tested by the γ
10
coefficient in the last of the three
equations below.
Day level: y
ij
¼ b
0j
þ b
1j
Daily EAðÞþr
ij
Person-level intercept: b
0j
¼ c
00
þ u
0j
Person-level slope: b
1j
¼ c
10
þ u
1j
As expected, EA predicted less positive affect,
B = −.39, t(88) = −11.60, p < .001, and more
negative affect, B = .42, t(88) = 16.00, p < .001.
Daily EA predicted less enjoyment of exercising,
B = −.14, t(88) = −3.00, p < .01, eating food,
B = −.18, t(88) = −4.06, p < .001, and listening to
music, B = −.12, t(88) = −2.20, p < .05. Daily
EA was unrelated to enjoyment of having sex,
B = −.01, t(88) = −.20, p = .84. Daily EA
predicted less daily MIL, B = − .46, t(88) =
−5.63, p < .001.
Trait EA and daily well-being
Additional analyses examined the relationship
between tra it EA measured by the AAQ and daily
indicators of well-being. In these models, trait and
daily EA served as predictors and the daily well-
being variables served as outcomes. Entering both
trait and daily EA as simultaneous predictors of
daily well-being revealed that only daily EA
significantly predicted less positive affect, B =
−.39, t(84) = −11.53, p < .001, more negative
affect, B = .42, t(84) = 16.16, p < .001, less
enjoyment of exercising, B = −.15, t(84) = −3.28,
p < .01, eating food, B = −.18, t(84) = −4.36, p <
.001, listening to music, B = −.12, t(84) = −2.24,
p < .05, and less MIL, B = −.46, t(84) = −5.47,
p < .001. Trait EA did not significantly predict
variance in any of the daily indicators of well-
being beyond that being predicted by the state
measure of EA (ps ranged from .10 to .80).
Construct specificity with negative affect
As expected, daily EA and daily negative affect
were positively related, B = .43, t(88) = 15.63, p <
.01. To test whether the effects of trying to avoid
unpleasant experiences is best explained by the
intensity/frequency of negative experiences, we
used negative affect as a covariate in subsequent
analyses. We conducted multilevel analyses with
daily EA and daily negative affect as simultaneous
predictors of daily well-being variables. Upon
controlling for daily negative affect (a conservative
test), EA still predi cted less daily positive affect,
B = −.16, t (88) = −5.01, p < .001, but no longer
predicted decreased daily MIL, B = −.06,
t(88) = − .74, p >.40. When we included negative
affect as a predictor, EA approached significance
for predicting enjoyment of eating food, B = −.09,
t(88) = −1.94, p = .055; when negative affect
was added as a covariate, EA was no longer a
significant predictor of enjoyment of exercising or
listening to music (ps >.05).
DISCUSSION
The present study assessed EA as a state-depend-
ent variable to determine the effects of EA of
anxiety on everyday experiences. Results suggest
that EA is detrimental to daily well-being and
may influence the extent to which people’s well-
being fluctuates from day to day. Using EA on a
given day predicted more daily negative affect, and
less daily positive affect, enjoyment of daily events
and daily MIL. Daily EA was a better predictor of
these indicators of daily well-being than disposi-
tional (trait) levels of EA. One explanation is that
MACHELL, GOODMAN, KASHDAN
6 COGNITION AND EMOTION, 2014
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some effects of EA might be a function of the
amount of negative affect experienced on a given
day. When negative affect was accounted for, daily
EA was still related to less daily positive affect and
approached significance for less enjoyment of
eating food. EA no longer predicted enjoyment
of exercising or listening to music, or daily MIL.
Our data support prior work that EA prevents
reward responsiveness to daily life activities. While
there is a rich theoretical framework that suggests
EA negatively impacts well-being by interfering
with the ability to pursue valued life goals (Hayes
et al., 1999), few studies have examined how this
process unfolds over the course of daily life. Our
results provide evidence that the pernicious effects
of EA can be observed on a daily basis and that
using EA disrupts indicators of daily well-being.
Further, we provide contextual evidence of how
EA can be problematic in everyday life by hinder-
ing the ability to extract rewards from everyday
experiences.
Our results emphasise the importance of study-
ing EA as a dynamic process rather than as a fixed
trait. While trait measures like the AAQ predict
other trait phenomena (such as global indicators of
well-being), these traditional global approaches to
measurement may be problematic if one is interes-
ted in capturing dynamic, contextual phenomena.
Measuring EA as a stable, between-person con-
struct fails to capture the natural fluctuations in the
use of EA to regulate emotions and as a result
appears to offer less utility in understanding peo-
ple’s quality of life.
Although our methods extended the study of
EA beyond traditional trait measures and single-
occasion measurement, our results should be inter-
preted in the light of several limitations. First,
several researchers have highlighted concerns about
our trait measure of EA (the AAQ-9), including
issues with item wording and scale brevity, and
suggest the use of the updated, more psychome-
trically sound version of the AAQ (AAQ-II, Bond
et al., 2011). In addition, the AAQ represents a
broader, global measure of EA, whereas our 4-item
measure of EA is a narrower, facet measure of EA.
As a result, conclusions drawn about the predictive
validity of our state measure over the more
traditional trait measure might be limited. Because
our measure of EA was restricted to the avoidance
of anxiety, and not global/general negative affect,
the implications of our results cannot extend to all
instances of EA. Future research should examine
how state measures of EA directe d to thoughts,
feelings, impulses and bodily sensations beyond the
scope of anxiety impact daily well-being. Future
research should examine how state measures of
other types of EA impact daily well-being. Our
state measures are self-report measures that parti-
cipants completed at the end of the day, albeit in the
context of an intense, repeated measurement
design. Future research might consider additional
measurement approaches that minimise recall bias,
such as event-contingent reporting where partici-
pants report events as they occur.
Our convenience sample of college students,
mostly women, limits the generalisability of these
results to other populations. Replications are neces-
sary to explore whether the same relationship
between daily EA and decreased daily well-bein g
is consistent within other age groups. Additionally,
while positive and negative affect, event enjoyment
and MIL are important elements of well-being,
there are a large number of well-being indicators
that were not measured in this study. We look
forward to future research that expands upon our
results by examining the effect s of EA on additional
components of well-being, such as life satisfaction,
personal growth, wisdom and the capacity to love
and be loved.
Despite these limitations, our findings provide
new insights into the adverse consequences of EA
on well-being and the importance of moving
beyond trait measures when attempting to under-
stand emotion regulation. Future research should
examine the use of EA in various situatio ns (i.e.,
before and after specific social interactions) to
further explore how and when EA is detrimental
to well-being. Researchers also might benefit from
using multiple daily assessments to assess temporal
sequences that may exist between daily EA and
well-being. For example, a recent study demon-
strated the temporal role of EA in eliciting
feelings of social anxiety (Kashdan et al., in press).
Increasing our knowledge of how EA disrupts
EXPERIENTIAL AVOIDANCE IN DAILY LIFE
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7
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well-being is important, but equally and perhaps
even more important is to begin investigating ways
to counteract the negative impact of using EA.
Insight into other daily processes that might
dampen or strengthen the relationship between
EA and daily well-being will provide guidance to
both researchers and clinicians interested in
decreasing distress and promoting well-being.
Manuscript received 14 October 2013
Revised manuscript received 24 March 2014
Manuscript accepted 28 March 2014
First published online 2 May 2014
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