Depressive symptoms predict inflexibly high levels of experiential avoidance in response to daily negative affect: A daily diary study

Behaviour Research and Therapy 49 (2011) 676e681

Contents lists available at ScienceDirect

Behaviour Research and Therapy journal homepage: www.elsevier.com/locate/brat

Depressive symptoms predict inflexibly high levels of experiential avoidance in response to daily negative affect: A daily diary study Ben Shahar a, *, Nathaniel R. Herr b a b

School of Psychology, Interdisciplinary Center, Herzliya Israel Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 March 2010 Received in revised form 20 June 2011 Accepted 13 July 2011

Experiential avoidance (EA) is an emotion-regulation strategy used to control or avoid unpleasant internal experiences. Despite the important role of avoidance in depressive disorders, there is relatively little research directly examining the role of EA in the development and maintenance of depression, and most of this research relies on measurement of EA as a global and stable personality trait. In this study we sought to extend the research on EA and depression by using a daily diary design and multilevel analysis to examine how the daily relationship between EA and negative affect (NA) varies as a function of baseline depressive symptoms. In order to achieve this goal we created a new state measure of EA assessing several avoidant behaviors. The findings revealed that participants with more depressive symptoms used more daily EA overall. Additionally, the difference in daily EA between those with higher versus lower depressive symptoms was greater on days when participants experienced less NA. This moderation effect was found only concurrently whereas one-day lagged analysis failed to reveal this effect. These findings provide preliminary support for the hypothesis that depression is associated with an inflexibly high level of avoidant emotion regulation. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Depression Experiential avoidance Emotion regulation Negative affect

Experiential avoidance (EA) is an emotion-regulation process involving attempts to suppress, avoid, control, or otherwise downregulate unpleasant emotions, thoughts, memories, or bodily sensations (Hayes, Wilson, Gifford, Follette, & Strosahl, 1996; Hayes et al., 2004). Hayes et al. (1996) suggested that EA might be viewed as a trans-diagnostic process playing a central role in the development of emotional disorders., The unified treatment for anxiety and unipolar depressive disorders designed by Barlow, Allen, and Choate (2004) also emphasizes emotional avoidance as a toxic process and teaches patients to accept their (mainly negative) emotional experiences. Furthermore, emotional acceptance is an important target in other therapeutic modalities such as Acceptance and Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 1999), Dialectical Behavior Therapy (Linehan, 1993), MindfulnessBased Cognitive Therapy (MBCT; Segal, Williams, & Teasdale, 2002), and emotion-focused therapy (Greenberg & Watson, 2006). Most research on EA has focused on the role of avoidance in the context of anxiety (for example see Levitt, Brown, Orsillo, & Barlow, 2004) whereas much less research has focused on understanding the link between EA and depressive experiences (Kashdan, Breen,

* Corresponding author. E-mail address: [email protected] (B. Shahar). 0005-7967/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.brat.2011.07.006

Afram, & Terhar, 2010). However, the role of avoidance in depression has long been noted. Ferster (1973) argued that avoidance is a central process in depression and that depressed patients engage in various avoidant behaviors in an attempt to reduce aversive internal experiences. In addition, behavioral activation treatment (Jacobson, Martell, & Dimidjian, 2001; Martell, Addis, & Jacobson, 2001) is based on the principle that avoidant behaviors (mainly rumination) prevent depressed patients from engaging in adaptive problem-solving activities, limiting access to positive antidepressant environmental stimuli, and thereby perpetuating depressive symptoms. The development of behavioral activation treatment has led to renewed interest in the role of avoidance in depression (Ottenbreit & Dobson, 2004). Despite these therapeutic advances, empirical investigation of the link between EA behaviors and depression is still lacking. Existing studies are mostly correlational and cross-sectional, and often seek to link global trait measures of EA with depressive symptoms. For example, research has shown that the Acceptance and Action Questionnaire (AAQ; Hayes et al., 2004), the most common trait measure of EA, is correlated with depressive symptoms (Hayes, Luoma, Bond, Masuda, & Lillis, 2006; Hayes et al., 2004; Tull, Gratz, Salters, & Roemer, 2004). Depressive symptoms were also found to be related to cognitive, behavioral, and experiential avoidance using the Cognitive-Behavioral Avoidance Scale

B. Shahar, N.R. Herr / Behaviour Research and Therapy 49 (2011) 676e681

(CBAS; Ottenbreit & Dobson, 2004; see Cribb, Moulds, & Carter, 2006; Moulds, Kandris, Starr, & Wong, 2007). In a recent study (Shallcross, Troy, Boland, & Mauss, 2010), trait emotional acceptance (operationalized as low scores on the AAQ) was found to predict less depressive symptoms in the face of high stress in a group of community females at risk for developing depression. In one treatment study (Berking, Neacsiu, Comtois, & Linehan, 2009), reductions in EA (measured with the AAQ) were predictive of improvements in depressive symptoms among patients with borderline personality disorder. More importantly, higher levels of EA predicted less subsequent reduction in depression during treatment, suggesting that EA may not be a mere consequence of depressive experiences. Although the research examples described above are important, they provide little information regarding the actual daily processes by which EA strategies (as opposed to mere trait attitudes) are associated with depressive symptoms or negative affect (NA). For example, do depressed individuals react to daily negative affect, an aversive state, with more or less EA behaviors? Does this response depend upon the intensity of the NA experienced? Answering such questions require measuring EA behaviors and NA repeatedly as opposed to using global trait measures of EA or manipulating EA in the laboratory. A central goal of the present study, therefore, was to provide more in-depth information regarding the way individuals with depressive symptoms regulate negative affect on a daily basis. A secondary goal of this study was to further clarify the EA construct and to provide initial validation data for a state measure of EA strategies that is sensitive to daily fluctuations. Because a state measure of EA does not exist in the literature, we sought to create a state measure of EA that assesses several different behaviors, all hypothesized to serve an avoidant function. Hayes et al. (1996) argued that EA represents anything that a person does to avoid unpleasant internal experiences. Therefore, although emotion suppression or thought suppression may be the most obvious experiential avoidant behaviors, other behaviors may also serve the same function. For example, in ACT (Hayes et al., 1999), reason-giving is considered an experientially avoidant behavior that prevents people from being in the present moment (Hayes, 2002; Hayes et al., 2004, 2006). People may spend a considerable amount of time “in their heads” trying to understand the reasons for their depression, which may prevent direct contact with internal painful experiences. Reason-giving is similar to rumination and they are highly correlated (r ¼ .65, Addis & Carpenter, 1999). This is not surprising given that rumination is defined as thinking about the reasons and consequences of one’s symptoms (Nolen-Hoeksema, 1991). There is very little research on reason-giving in the context of psychopathology and psychological treatment. Addis and Jacobson (1996) found that clients who gave more reasons for their depression (regardless of the content of the reasons) responded more poorly to a behavioral treatment. Addis and Carpenter (1999) replicated these results, showing that individuals endorsing more reasons for depression were more resistant to an action-oriented treatment rational. Similarly, distraction might also be viewed as an avoidant behavior (Campbell-Sills & Barlow, 2007; Linehan, 1993). Distraction was initially conceptualized as an adaptive response style to negative emotional states as compared to rumination in NolenHoeksema response style theory of depression (Just & Alloy, 1997; Nolen-Hoeksema, 1987, 1991). Experimental studies showed that inducing rumination produced impaired performance in a number of cognitive correlates of depression compared to inducing distraction (see examples in Lavender & Watkins, 2004; Lyubomirsky & Nolen-Hoeksema, 1995); however, recent research has conceptualized distraction as a form of emotional avoidance

677

(e.g., Campbell-Sills & Barlow, 2007) and a growing literature suggests that distraction has poorer outcomes than acceptance for tolerating physical pain (Gutiérrez, Luciano, Rodríguez, & Fink, 2004; McMullen et al., 2008). Given the evidence that a number of different regulatory behaviors may serve an avoidant function, we created an internally consistent state EA measure by using representative items from measures of reason-giving, distraction, thought suppression, emotion suppression, and overall lack of experiential acceptance (see Method section for more details). In summary, the primary goal of this study was to explore whether individuals’ use of EA strategies in response to daily NA differs as a function of levels of depression symptoms. It was predicted 1) that participants reporting more depressive symptoms at baseline would report using more EA on a daily basis (main effect for baseline BDI) and 2) that less depressed participants would report a direct relationship between EA and NA (thus their EA would be related to how much NA they experience each day), while more depressed participants would report greater use of EA regardless of the extent to which they experience NA on a given day (i.e., a cross-level interaction). To facilitate a test of this hypothesis, our secondary goal was to create an internally consistent and valid state measure of EA that is sensitive to daily fluctuations and includes several regulatory behaviors that (theoretically) serve a similar avoidant function. Method Participants The sample consisted of 178 introductory psychology students (mean age 18.99, 67.4% females) at the University of Arizona who participated in exchange for course credit. Most of the participants were Caucasian (136, 76.4%), while 22 (12.4%) were Hispanic, 5 (2.8%) were African-American, 8 (4.5%) were Asian American, and 7 (3.9%) indicated other race/ethnicity. Measures Baseline depressive symptoms The Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996), a widely used and well-validated self-report measure of depressive symptoms, was administered at baseline before the 21-day daily diary period. Trait experiential avoidance The 9-item Acceptance and Action Questionnaire (Hayes et al., 2004), administered at baseline, was used to measure trait experiential avoidance. The questionnaire asks participants to indicate how true each of the 9 items is for them on a seven-point scale and assesses concepts such as need for cognitive control and avoidance of negative private events. Hayes and colleagues reported adequate reliability for the AAQ (a ¼ .70) and demonstrated convergent and divergent validity across ten studies totaling over 2000 participants. In the current sample, internal consistency coefficient for the AAQ was .65. Daily negative affect NA was measured on a daily basis with the Negative Affect Scale of the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). The PANAS is comprised of two 10-item independent subscales that measure negative and positive affect. Each item on the PANAS is a single word that describes a feeling state. The negative affect subscale contains words such as distressed, guilty, and nervous. Respondents are asked to indicate the extent to which each item describes how they are feeling on a 1 (slightly or

678

B. Shahar, N.R. Herr / Behaviour Research and Therapy 49 (2011) 676e681

not at all) to 5 (extremely) scale. The PANAS can be used to measure affective states in various time frames, such as today, during the past few days, or during the past week. The “today” version was used in the daily diary. The psychometric properties of the PANAS are well established (Crawford & Henry, 2004; Watson et al., 1988). Daily experiential avoidance A nine-item instrument that assesses engagement in various EA strategies on a daily basis was designed for this study. Participants were given the following introduction: “We would like to ask you a few questions about how you coped with negative thoughts and feelings today. When you had negative thoughts or feelings today (even if you didn’t have many) to what extent did you do the following things?” Participants then indicated on a 1 (not at all) to 5 (a great deal) scale the extent to which they engaged in several EA behaviors. Representative items from several measures of EA traits were selected based on their item-total correlations or factor loadings within their original scale where such data was available or according to subjective assessment of fit made by the authors, and modified for use on a daily basis. Two items from the White Bear Suppression Inventory (WBSI; Wegner & Zanakos, 1994) were used to measure daily thought suppression, one item from the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003) was used to measure daily emotion suppression, and two items from the Accept Without Judgment of the Kentucky Inventory of Mindfulness Skills (KIMS; Baer, Smith, & Allen, 2004) were used to measure daily lack of experiential acceptance. In addition, two items were generated to measure reason-giving (“tried to understand why I was feeling this way” and “tried to come up with reasons for these feelings”). Finally, one item was used to measure daily distraction (“Try to distract myself from them”) and one generic item (“Tried to control them”) designed to capture the overall construct of EA were used. Several analyses were conducted to provide initial support for the psychometric acceptability of the EA state instrument. First, the participants’ aggregated (across all 21 days) responses to the 9-item daily questionnaire were submitted to principal component analysis with varimax rotation and clearly yielded one factor accounting for 75.11% of the variance. Second, the EA state measure demonstrated excellent internal consistency when the items were aggregated across all 21 days of the diary study (Cronbach’s a ¼ .91). When internal consistency was examined on each individual day, coefficient alphas ranged from .83 to .94 (mode ¼ .91; only one day was less than .88), indicating that the EA state measure demonstrated excellent reliability on every day of the study. Third, to support the construct validity of the daily measure, a multilevel model predicting state EA from the AAQ was conducted (following Nezlek, 2007). This model showed that the AAQ (representing trait EA) significantly predicted state EA, B ¼ .33, SE ¼ .06, t(176) ¼ 5.21, p < .001. Conducting similar models with the individual state EA items showed a similar pattern. For each of the nine individual items, trait EA predicted higher daily levels of the corresponding EA behavior. Finally, because the main goal of the daily diary study was to assess the daily relationship between EA and NA, it was important to ensure that these two constructs do not excessively overlap with each other. Therefore, another principal component analysis with varimax rotation was conducted with the aggregated data. The analysis showed that 3 factors were extracted, accounting for 50.91%, 17.71%, and 5.48% of the total variance. The scree plot, however, clearly showed that a two factor solution explains the data better and the analysis was repeated extracting 2 factors. In this analysis, the pattern matrix showed that all the EA daily items loaded on the first factor and all the NA daily items loaded on the

second factor. Taken together, these factor analyses confirmed that daily EA and NA are sufficiently differentiated from each other. The EA state measure, the negative affect adjectives, and their factor loadings after rotation are presented in Table 1. Procedure The study was approved by the institutional review board of the University of Arizona. Participants were enrolled and started the daily diary study during several time points throughout the spring and fall semesters between February 2006 and October 2006. After written informed consent was obtained, they completed a battery of questionnaires (as part of a larger project), including the BDI. They were then told that for the next 21 days they would receive a daily email at 5 PM with a link directing them to a website where they would complete a questionnaire assessing their NA and their EA behaviors that day. Participants were instructed to complete the daily questionnaire as close as possible to bed time in order to capture their experiences throughout the day. Because the exact timestamp for each day for all participants was available, a research assistant verified that the daily questionnaires were entered on a daily basis. Overall, participants completed between 6 and 21 daily diaries with a mean of 18.22  3 days. 90% of the participants completed at least 15 diaries. The number of diaries completed was not related to mean NA and Mean EA (aggregated across days), but was negatively correlated with depressive symptoms (r ¼ .16, p < .05) indicating that participants who were more depressed completed fewer diary entries. Approach to data analysis Given the nested structure of the dataset (daily diaries nested within individuals), we used multilevel modeling using SAS PROC MIXED (SAS Institute, 2004) to examine how the daily NAeEA relationship varies as a function of baseline depressive symptoms. We examined both the concurrent (same-day) and one-day lagged

Table 1 Experiential avoidance (EA) and negative affect (NA) items and their factor loadings after rotation. Item

1. Tried to control them 2. Try to distract myself from them 3. Tried to push unwanted thoughts out of my mind 4. Tried to understand why I was feeling this way. 5. Told myself that I shouldn’t be feeling the way I’m feeling. 6. Tried to come up with the reasons for these feelings. 7. Tried to control my feelings by not expressing them. 8. Criticized myself for having irrational or inappropriate emotions. 9. Tried to get rid of unwanted thoughts Scared Afraid Nervous Ashamed Upset Guilty Irritable Hostile Distress Jittery

Rotated factor loadings EA

NA

.84 .88 .91

.13 .24 .26

.81

.14

.82

.26

.86

.18

.79

.28

.72

.34

.90 .16 .14 .30 .21 .3 .21 .19 .06 .22 .23

.19 .85 .83 .80 .78 .78 .75 .72 .72 .70 .68

B. Shahar, N.R. Herr / Behaviour Research and Therapy 49 (2011) 676e681

Results Means and standard deviations of baseline BDI, NA, and EA are presented in Table 2. Within- and between-person variance was calculated using HLM 7 software (Raudenbush, Bryk, & Congdon, 2011) to test unconditional models with each variable entered as the dependent variable. The sample included 99 individuals (55.6%) scoring 0e9 on the BDI, 62 individuals (34.8%) scoring between 10 and 19 indicating mild to moderate depression, and 17 individuals (9.5%) scoring 20 or above indicating moderate-severe depression. The first model predicted daily EA from the same-day NA, baseline depressive symptom, linear effect of time, and the crosslevel 2-way interaction between baseline BDI and NA (See Table 3 for all fixed effects). This model revealed a significant main effect for baseline BDI indicating the participants with more depressive symptoms at baseline used overall more EA on a daily basis. The main effect for daily NA was also significant indicating that on days when participants felt more NA they also used more EA strategies. Finally, the cross-level interaction between baseline BDI and daily NA was also significant indicating that the NAeEA daily association was moderated by baseline depressive symptoms, such that participants with more baseline depressive symptoms showed

Table 2 Means and standard deviations.

BDI Daily negative affect Daily experiential avoidance

na

M

SD level 2

SD level 1

178 3244 3244

10.11 1.92 2.19

7.94 .55 .58

n/a .48 .71

Note: M, SD level 2, and SD level 1 are the intercept, standard deviation of e, and standard deviation of r0 in unconditional HLM models with the listed variable entered as the dependent variable. a For diary measures, n is total number of daily observations.

Table 3 Daily negative affect, baseline depressive symptoms, and their interaction predicting daily experiential avoidance.

Time Negative affect BDI NA  DS

B

SE B

df

t

.007 .360 .036 .007

.003 .030 .006 .003

176 3063 176 3063

2.44* 13.11*** 6.29*** 2.00*

Note. BDI ¼ Beck Depression Inventory. *p < .05; **p < .01; ***p < .001.

a weaker daily NAeEA association. Fig. 1 shows the simple slopes (Aiken & West, 1991; Preacher, Curran, & Bauer, 2006) for the daily NAeEA associations for participants with 1 SD above the mean and 1 SD below the mean on baseline BDI scores. Fig. 1 shows that participants 1 SD below the mean on the BDI (participants with a baseline BDI value of 2.17) had a positive NAeEA simple slope (B ¼ .41, SE ¼ .04, p < .05) whereas participants with 1 SD above the mean on the BDI (participants with a baseline BDI value of 18.05) also had a positive but weaker NAeEA simple slope (B ¼ .30, SE ¼ .03, p < .05). The region of significance for the simple slopes (Preacher et al., 2006) indicated that at a BDI value of 33 the simple slope becomes not significantly different than zero. Thus, Fig. 1 suggests that participants with more depressive symptoms use more EA even on days when they feel relatively less NA. Finally, to determine the prospective relationship between daily NA and EA at different levels of baseline depressive symptoms, two lagged multilevel models were examined. The first predicted EA from NA and EA on the previous day as well as baseline BDI and the interaction between BDI and previous day NA. The second one was identical but NA was the dependent variable, predicted from EA and NA on the previous day and the interaction between BDI and previous day EA. These models showed neither a significant main effect for EA predicting NA in the following day nor a significant main effect for NA predicting EA in the following day. In both models the interaction between BDI and the level-1 predictor was nonsignificant. Discussion The present study employed a daily diary design to examine how the relationship between daily NA and daily EA vary as a function of depressive symptoms reported at baseline. This study extends previous research on EA by examining EA at a day-to-day state level rather than relying on a global trait measure of EA and by using a within-person analysis in addition to between-person 5 Daily Experiential Avoidance

associations between NA and EA. Daily NA was centered on each person’s mean (i.e., each participant’s mean NA, aggregated across all 21 days, was subtracted from each day’s score). The use of person-mean centering provides a pure assessment of the withinperson relationship between level-1 predictors and the dependent variable that is not contaminated with between-person variability and reduces unnecessary collinearity (Enders & Tofighi, 2007; Nezlek, 2001; Paccagnella, 2006; Schwartz & Stone, 1998). Baseline BDI was centered on the grand mean. Because an unconditional growth model predicting daily EA as a function of time (assessment day) showed a significant linear decrease of EA (B ¼ .007, SE ¼ .00, t(3065) ¼ 2.82, p ¼ .005), the linear effect of time was accounted for in all multilevel models. Random slopes were entered into the model only if their variance estimate was significantly different than zero. In the lagged models, NA on day j  1 was used to predict EA on day j, after statistically accounting for the autoregressive effect of EA on j  1. In other words, NA on day j  1 was used to predict changes in EA from day j  1 to day j. Similar models were used to predict NA from previous day EA. In all multilevel models (except the time-lagged models), the residual variance at level 1 was also explained by an autoregressive error structure (Affleck, Zautra, Tennen, & Armeli, 1999; Schwartz & Stone, 1998). This error structure accounts for the fact that, for each participant, two measurement points that are closer in time (i.e., days 4 and 5) are likely to be more strongly correlated than two measurement points further away in time (i.e., days 5 and 15). The lagged models do not include an autoregressive error structure because this is already accounted for by including the level-1 predictor on day j  1.

679

4.5 4 3.5 + 1 SD

3

- 1SD

2.5

BDI = 33

2 1.5

1

-2

-1

0

1

2

Daily Negative Affect Fig. 1. Same-day associations between NA and EA for participants 1 SD below the baseline BDI mean, participants 1 SD above the baseline BDI mean, and participants with baseline BDI score of 33. The range of NA indicates the difference from the person-centered mean on the 5 point NA scale.

680

B. Shahar, N.R. Herr / Behaviour Research and Therapy 49 (2011) 676e681

analysis. The results indicated that baseline depressive symptoms moderated the same-day but not the one-day lagged relationship between daily NA and daily EA. Specifically, it was found that participants reporting more depressive symptoms at baseline also reported more use of daily EA strategies but this difference was particularly pronounced at low levels of daily NA. Thus, even on days when participants with higher BDI scores felt less NA they still used relatively high levels of EA. This pattern suggests a less flexible style of EA, a style that has been emphasized by some researchers (e.g., Wilson & Murrell, 2004) as particularly toxic. These findings suggest that depressive symptoms are not necessarily associated with EA only when individuals experience high levels of distress; rather, depressive symptoms appear to be associated with high use of EA regardless of distress levels. A possible concern for interpreting these findings is the notion that more depressed individuals may experience more NA in general and therefore use more EA strategies regardless of NA levels (i.e., a ceiling effect or a restricted range of NA levels). We conducted several analyses to examine this possibility. First, we examined the frequencies of NA among more depressed participants (BDI > 15). Frequency counts showed that among these participants, NA scores were less than 2.5 on 71.3% of all days (NA ranged from 1 to 5) and less than 2 on 52.9% of all days. Second, for each participant we computed the standard deviation of NA scores. These standard deviations quantify the deviation of each daily NA score from the mean NA (across all days) for each participant. We then compared the standard deviations of more depressed participants (BDI score of 16 or above) with those of less depressed participant (BDI scores lower than 16). Independent sample t-test indicated that standard deviations were similar in these two groups, suggesting that the distribution of NA did not vary as a function of depressive symptoms. These findings reduce concerns that the results of the study are influenced by differential NA patterns for depressed versus non-depressed individuals. Interestingly, a significant positive NAeEA simple slope among participants with lower levels of depressive symptoms indicate that they use more EA on days where they felt more NA. These findings suggest that a flexible manner of using EA may be adaptive, thus challenging the idea that EA is a toxic process in general. As such, a more careful assessment of the context in which people use EA is warranted and will likely include parameters such as when, how, or what avoided experiences may not produce negative outcomes. For example, some intense emotions may need to be down-regulated at some point to facilitate adaptive functioning, whereas other emotions need to be accepted and even accessed to facilitate adaptive functioning. Contrary to predictions, the one-day lagged association between NA and EA did not vary as a function of baseline depression. Furthermore, regardless of levels of baseline depression, NA on one day did not predict more EA on the next day and EA on one day did not predict more NA on the next day. Thus, conclusions regarding the prospective nature of the NAeEA relationship cannot be made. One possible explanation for these null findings is that one day is simply not the correct time frame in which NA and EA affect each other. In fact, it is more likely that people use EA immediately when they are distressed. It is also possible that an ironic increase in NA following avoidance is also relatively immediate, as demonstrated in previous experiments (Levitt et al., 2004). Thus, it is likely that a one-day time frame, which was selected for convenience in the current study, cannot accurately capture the manner in which EA and NA reciprocally affect each other. Future studies need to assess the ongoing relationship between NA and EA by using more rigorous designs, specifically by assessing these constructs repeatedly over the course of a day or immediately after an aversive experience (failure). Overall, it seems that when studying

psychopathological processes, the question of when effects occur is equally important to the question of how strong are the effects. A secondary goal of this study was to create a useful state measure of EA that includes several strategies that people often use to down-regulate negative internal experiences. Defining and measuring EA is not an easy task as many behaviors are used to control negative experiences at different times and different contexts. Distinguishing between attitudes and active strategies (cognitive and behavioral) makes operationalizing this construct even more complicated. Our decision to include thought suppression, emotion suppression, distraction, reason-giving, and selfcriticizing reflected an attempt to include commonly studied psychological constructs that represents active behaviors designed to control negative affect. Our argument, which needs further study, is that EA is a broad latent construct that is made up of a variety of avoidant behaviors that are often situation specific. There is room to consider other avoidant behaviors (e.g., worry and rumination) or to question the constructs chosen here; however, the high internal consistency of the state EA measure in the current study suggests that it may be an adequate starting point for integrating the various EA constructs. If our proposition is correct, it would indicate the need to theoretically converge the study of processes such as thought suppression, emotion suppression, reason-giving, and distraction in order to provide a more parsimonious conceptualization of emotional avoidance in depression and other psychopathologies. The present study was limited by the use of a student population. Replication in other populations is necessary for the results to be generalizable to a wider range of ages and socioeconomic backgrounds. Furthermore, while many participants in the present study had moderate levels of depressive symptoms, the sample used was nonclinical and may not be representative of individuals who have met full diagnostic criteria for depression. The findings must be extended to a clinical population to determine if the same pattern of results is found among those individuals. In addition, as discussed before, measuring EA and NA once a day at the end of the day and the lack of a subsequent day relationship between the variables precludes conclusions regarding the temporal relationship between these variables. Future studies need to incorporate more assessments throughout the day. Conclusions By using a daily diary method and state measures of EA, the present study demonstrated that depressive symptoms moderate the same-day (but not lagged) relationship between NA and EA. Previous research has used cross-sectional data and trait measures to show that depressed individuals are more likely to engage in EA than non-depressed individuals. The results of the present study support this finding but suggest that the model of the relationship between depression and EA is more complex when fluctuations in daily NA are taken into account. Specifically, while all individuals endorsed more EA behaviors at times when they had higher NA, individuals with more depressive symptoms engage in high levels of EA regardless of NA levels. When applied to treatment, this pattern suggests that depressed individuals may benefit not only from reducing EA when they feel bad, but also when they are continuing to engage in EA when they are feeling less negative. In other words, it may be important to help depressed individuals to notice that some days are just not as bad as others. Acknowledgments This research was supported by a Dissertation Research Award from the Social and Behavioral Sciences Research Institute of the University of Arizona.

B. Shahar, N.R. Herr / Behaviour Research and Therapy 49 (2011) 676e681

References Addis, M. E., & Carpenter, K. M. (1999). Why, why, why?: reason-giving and rumination as predictors of response to activation- and insight-oriented treatment rationales. Journal of Clinical Psychology, 55, 881e894. Addis, M. E., & Jacobson, N. S. (1996). Reasons for depression and the process and outcome of cognitive-behavioral psychotherapies. Journal of Consulting & Clinical Psychology, 64, 1417e1424. Affleck, G., Zautra, A., Tennen, H., & Armeli, S. (1999). Multilevel daily process designs for consulting and clinical [sychology: a preface for the perplexed. Journal of Consulting & Clinical Psychology, 67, 746e754. Aiken, L. S., & West, S. G. (1991). Multiple regression: testing and interpreting interactions. Thousand Oaks, CA, US: Sage Publications, Inc. Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of mindfulness by selfreport: the Kentucky inventory of mindfulness skills. Assessment, 11, 191e206. Barlow, D. H., Allen, L. B., & Choate, M. (2004). Toward a unified treatment for emotional disorders. Behavior Therapy, 35, 205e230. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for Beck depression inventory-II. San Antonio, TX: Psychological Corporation. Berking, M., Neacsiu, A., Comtois, K., & Linehan, M. (2009). The impact of experiential avoidance on the reduction of depression in treatment for borderline personality disorder. Behaviour Research and Therapy, 47, 663e670. Campbell-Sills, L., & Barlow, D. (2007). Incorporating emotion regulation into conceptualizations and treatments of anxiety and mood disorders Handbook of emotion regulation. New York, NY, US: Guilford Press, (pp. 542e559). Crawford, J. R., & Henry, J. D. (2004). The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43, 245e265. Cribb, G., Moulds, M., & Carter, S. (2006). Rumination and experiential avoidance in depression. Behaviour Change, 23(3), 165e176. Enders, C., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods, 12, 121e138. Ferster, C. (1973). A functional analysis of depression. American Psychologist, 28, 857e870. Greenberg, L. S., & Watson, J. C. (2006). Emotion-focused therapy for depression. Washington, DC: American Psychological Association. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality & Social Psychology, 85, 348e362. Gutiérrez, O., Luciano, C., Rodríguez, M., & Fink, B. (2004). Comparison between an acceptance-based and a cognitive-control-based protocol for coping with pain. Behavior Therapy, 35, 767e783. Hayes, S. C. (2002). Buddhism and acceptance and commitment therapy. Cognitive and Behavioral Practice, 9(1), 58e66. Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J. (2006). Acceptance and commitment therapy: model, processes and outcomes. Behaviour Research and Therapy, 44, 1e25. Hayes, S. C., Strosahl, K., & Wilson, K. G. (1999). Acceptance and commitment therapy: An experiential approach to behavior change. New York: Guilford Press. Hayes, S. C., Strosahl, K., Wilson, K. G., Bissett, R. T., Batten, S. V., Bergan, J., et al. (2004). Measuring experiential avoidance: a preliminary test of a working model. The Psychological Record, 54, 553e578. Hayes, S. C., Wilson, K. G., Gifford, E. V., Follette, V. M., & Strosahl, K. D. (1996). Experiential avoidance and behavioral disorders: a functional dimensional approach to diagnosis and treatment. Journal of Consulting and Clinical Psychology, 64, 1152e1168. Jacobson, N., Martell, C., & Dimidjian, S. (2001). Behavioral activation treatment for depression: returning to contextual roots. Clinical Psychology: Science and Practice, 8, 255e270. Just, N., & Alloy, L. (1997). The response styles theory of depression: tests and an extension of the theory. Journal of Abnormal Psychology, 106(2), 221e229. Kashdan, T., Breen, W., Afram, A., & Terhar, D. (2010). Experiential avoidance in idiographic, autobiographical memories: construct validity and links to social anxiety, depressive, and anger symptoms. Journal of Anxiety Disorders, 24, 528e534.

681

Lavender, A., & Watkins, E. (2004). Rumination and future thinking in depression. British Journal of Clinical Psychology, 43(2), 129e142. Levitt, J. T., Brown, T. A., Orsillo, S. M., & Barlow, D. H. (2004). The effects of acceptance versus suppression of emotion on subjective and psychophysiological response to carbon dioxide challenge in patients with panic disorder. Behavior Therapy, 35, 747e766. Linehan, M. (1993). Cognitive-behavioral treatment of borderline personality disorder. New York, NY, US: Guilford Press. Lyubomirsky, S., & Nolen-Hoeksema, S. (1995). Effects of self-focused rumination on negative thinking and interpersonal problem solving. Journal of Personality and Social Psychology, 69(1), 176e190. Martell, C., Addis, M., & Jacobson, N. (2001). Depression in context: Strategies for guided action. New York, NY, US: W W Norton & Co. McMullen, J., Barnes-Holmes, D., Barnes-Holmes, Y., Stewart, I., Luciano, C., & Cochrane, A. (2008). Acceptance versus distraction: brief instructions, metaphors and exercises in increasing tolerance for self-delivered electric shocks. Behaviour Research and Therapy, 46, 122e129. Moulds, M., Kandris, E., Starr, S., & Wong, A. (2007). The relationship between rumination, avoidance and depression in a non-clinical sample. Behaviour Research and Therapy, 45(2), 251e261. Nezlek, J. (2001). Multilevel random coefficient analyses of event- and intervalcontingent data in social and personality psychology research. Personality and Social Psychology Bulletin, 27, 771e785. Nezlek, J. B. (2007). A multilevel framework for understanding relationships among traits, states, situations and behaviours. European Journal of Personality, 21(6), 789e810. Nolen-Hoeksema, S. (1987). Sex differences in unipolar depression: evidence and theory. Psychological Bulletin, 101, 259e282. Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of the depressive episode. Journal of Abnormal Psychology, 100, 569e582. Ottenbreit, N., & Dobson, K. (2004). Avoidance and depression: the construction of the cognitive-behavioral avoidance scale. Behaviour Research and Therapy, 42(3), 293e313. Paccagnella, O. (2006). Centering or not centering in multilevel models?: the role of the group mean and the assessment of group effects. Evaluation Review, 30, 66e85. Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437e448. Raudenbush, S. W., Bryk, A. S., & Congdon, R. (2011). HLM 7 for Windows [computer software]. Lincolnwood, IL: Scientific Software International, Inc. SAS Institute. (2004). SAS/STAT 9.1 user’s guide. Cary, NC: Author. Schwartz, J., & Stone, A. (1998). Strategies for analyzing ecological momentary assessment data. Health Psychology, 17, 6e16. Segal, Z. V., Williams, J. M. G., & Teasdale, J. D. (2002). Mindfulness-based cognitive therapy for depression: A new approach to preventing relapse. New York: Guilford Press. Shallcross, A., Troy, A., Boland, M., & Mauss, I. (2010). Let it be: accepting negative emotional experiences predicts decreased negative affect and depressive symptoms. Behaviour Research and Therapy, 48(9), 921e929. Tull, M., Gratz, K., Salters, K., & Roemer, L. (2004). The role of experiential avoidance in posttraumatic stress symptoms and symptoms of depression, anxiety, and somatization. Journal of Nervous and Mental Disease, 192(11), 754e761. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 47, 1063e1070. Wegner, D. M., & Zanakos, S. (1994). Chronic thought suppression. Journal of Personality, 62, 615e640. Wilson, K., & Murrell, A. (2004). Values work in acceptance and commitment therapy: setting a course for behavioral treatment Mindfulness and acceptance: Expanding the cognitive-behavioral tradition. New York, NY, US: Guilford Press, (pp. 120e151).