# استفاده از درمان مبتنی بر پذیرش و تعهد برای افزایش خوددلسوزی: مطالعه کنترل شده تصادفی

کد مقاله | سال انتشار | مقاله انگلیسی | ترجمه فارسی | تعداد کلمات |
---|---|---|---|---|

38918 | 2014 | 10 صفحه PDF | سفارش دهید | محاسبه نشده |

**Publisher :** Elsevier - Science Direct (الزویر - ساینس دایرکت)

**Journal :** Journal of Contextual Behavioral Science, Volume 3, Issue 4, October 2014, Pages 248–257

#### چکیده انگلیسی

Abstract Self-compassion has been shown to be related to several types of psychopathology, including traumatic stress, and has been shown to improve in response to various kinds of interventions. Current conceptualizations of self-compassion fit well with the psychological flexibility model, which underlies acceptance and commitment therapy (ACT). However, there has been no research on ACT interventions specifically aimed at self-compassion. This randomized trial therefore compared a 6-hour ACT-based workshop targeting self-compassion to a wait-list control. From pretreatment to 2-month follow-up, ACT was significantly superior to the control condition in self-compassion, general psychological distress, and anxiety. Process analyses revealed psychological flexibility to be a significant mediator of changes in self-compassion, general psychological distress, depression, anxiety, and stress. Exploratory moderation analyses revealed the intervention to be of more benefit in terms of depression, anxiety, and stress to those with greater trauma history.

#### مقدمه انگلیسی

. Introduction The concept of self-compassion has been put forth as a healthy alternative to both self-criticism and high self-esteem and has been conceptualized as consisting of self-kindness, mindfulness, and common humanity (Neff, 2003b). Self-kindness involves extending understanding, patience, and benevolence to the self, especially in difficult times; Common humanity refers to a sense in which one is connected to others in and even through one׳s suffering, as suffering is in fact common to all human beings; And mindfulness involves holding painful experiences in awareness (that is, not denying or distracting from them) but at a distance so that one does not become overly identified with them. The relevance of self-compassion has been supported by recent research showing that self-compassion correlates negatively with depression, anxiety, worry, rumination, and PTSD avoidance symptoms (Neff, 2003a, Neff et al., 2007, Raes, 2010 and Thompson and Waltz, 2008). In addition, self-criticism and low self-compassion play a role in the development of psychological disorders in response to stressful life events, such as exposure to trauma (Cox et al., 2004, Sharhabani-Arzy et al., 2005 and Thompson and Waltz, 2008). Interventions of various lengths and formats, from mindfulness-based stress reduction programs to very brief rationales, have been shown to increase self-compassion, as measured by Neff׳s Self-Compassion Scale (SCS; Neff, 2003a; for a review of research using the SCS, see Neff, 2012). One study showed that an 8-week mindful self-compassion course based on Neff׳s conceptualization improved self-compassion, mindfulness, compassion towards others, life satisfaction, avoidance, depression, anxiety, and stress significantly more than a wait-list control, with all improvements maintained at 6-month follow-up (Neff & Germer, 2013). Some authors have suggested that acceptance and commitment therapy (ACT; Hayes, Strosahl, & Wilson, 2012) overlaps with Neff׳s conceptualization of self-compassion considerably and that Relational Frame Theory (RFT; Hayes, Barnes-Holmes, & Roche, 2001), the basic science of language and cognition behind ACT, may be relevant to self-compassion as well (Neff & Tirch, 2013). While research on ACT has not extensively examined self-compassion, ACT׳s process of change, psychological flexibility, which is measured by the Acceptance and Action Questionnaire-II (AAQ-II; Bond et al., 2011), correlates with the SCS at r=.65 (Neff, unpublished data cited in Neff & Tirch, 2013). Psychological flexibility from an ACT perspective has 6 different dimensions. It consists of (1) deliteralizing language and cognition (defusion), (2) openly and willingly experiencing emotions and bodily sensations (acceptance), (3) flexibly and voluntarily attending to what is present (present moment awareness), (4) having a sense of self as the perspective from which life is experienced, as distinguished from one׳s identity or self-image (self-as-context), (5) flexible yet persistent self-directed behavior (committed action), and (6) freely chosen qualities of action that make behavior intrinsically reinforcing (values). There are parallels and similarities between the concepts of psychological flexibility and self-compassion. First, from an ACT perspective, Neff׳s central concept of self-kindness may be closely linked to self-acceptance. The opposite of experiential acceptance, experiential avoidance, is viewed within ACT to include excessive evaluation of one׳s experiences as bad or wrong and is therefore highly self-invalidating. Acceptance of one׳s painful experiences, and of oneself when one is hurting, can thus be a stance of profound self-kindness. Further, contacting pain openly is necessary for extending understanding to oneself, a coping skill that is included in Neff׳s definition of self-kindness. Second, from an RFT point of view, extending such self-understanding involves deictic relational frames (or perspective taking), which are defined as frames “that specify a relation in terms of the perspective of the speaker” (Hayes et al., 2001, p. 38). These very same deictic frames are involved in a sense of common humanity (an aspect central to self-compassion), since they allow one to see that both the self and others have moment to moment perspectives that can bear witness to difficult experiences. As perspective taking is strengthened, RFT argues that a larger common consciousness emerges that is extended across time, place, and person. Third, Neff׳s self-compassion conceptualization and ACT both emphasize mindfulness, which from an ACT perspective consists of defusion, acceptance, contact with the present moment, and self-as-context (Fletcher & Hayes, 2005). Defusion is important for self-compassion because it allows self-criticisms to pass through the mind without having to be believed, proven wrong, or otherwise engaged—a stance that is likely more workable than an agenda of cognitive change. Defusion from self-criticism is particularly well-suited to self-critics because instructions to be less self-critical will likely be taken as criticisms, and will strengthen the self-critical repertoire. Self-as-context, or the observing self, is a sense of self that emerges from defusion from self-conceptualizations. Unlike self-esteem, which depends on positive self-evaluations, self-as-context cannot be threatened by failures and is therefore consummately stable.

#### نتیجه گیری انگلیسی

4. Results 4.1. Participant characteristics A total of 532 participants were screened, of which 225 qualified; approximately 85 attended the informed consent session (38%). Of these, 78 (92%) agreed to participate, and 73 (86%) actually did so (as defined by supplying at least 1 assessment point). The ACT group was smaller (n=34) than the waitlist group (n=44) because of the unblocked randomization procedure, which could not ensure equivalence of group size. There was no difference on any screening measure or demographic variable (gender, racial/ethnic background, sexual orientation, or grade point average) between the two conditions, except that that ACT participants were significantly younger than waitlist control participants (p=.04), although the means differed by less than 1 year. See Table 1 for details on participant characteristics. 4.2. Distributional assumptions Before conducting formal statistical analyses, underlying distributional assumptions were examined, particularly skewness, kurtosis, outliers, and homogeneity of variance. Data from each condition at each time point were required to exhibit skewness between −2.00 and 2.00 and kurtosis between −4.00 and 4.00. Only the Anxiety subscale of the DASS failed to meet criteria, but did so after 2 outliers were removed from follow-up. A summary of the means and standard deviations of all outcome and process variables are shown in Table 3. Table 3. Means, standard deviations, and between-condition comparisons of outcome and process variables at each time point. Pre Post Follow-up Mean SD N Mean SD N Mean SD N Self-compassion (SCS) ACT 14.64 3.72 30 17.95 2.95 28 19.28 2.92 28 Control 13.87 3.00 43 14.98 3.31 39 15.26 3.96 40 P for between-group t .33 .00 .00 General psychological distress (GHQ) ACT 14.63 6.56 30 9.96 4.37 28 7.68 4.59 28 Control 15.35 5.12 43 15.05 5.92 39 11.88 5.07 40 P for between-group t .60 .00 .00 Depression (DASS-D) ACT 11.53 9.69 30 7.50 7.42 28 6.00 7.16 28 Control 12.84 8.00 43 14.09 12.30 39 11.28 9.18 40 P for between-group t .53 .01 .01 Anxiety (DASS-A) ACT 10.47 7.84 30 7.36 8.02 28 6.64 7.46 28 Control 8.09 6.73 43 9.54 9.85 39 8.25 7.57 40 P for between-group t .17 .34 .39 Stress (DASS-S) ACT 15.33 10.13 30 12.90 8.26 28 10.43 9.05 28 Control 15.98 8.27 43 17.18 10.45 39 14.98 8.31 40 P for between-group t .76 .08 .04 Psychological flexibility (AAQ-II) ACT 25.40 9.41 30 20.86 7.61 28 17.61 7.93 28 Control 26.23 8.29 43 24.65 10.29 39 23.87 9.44 40 P for between-group t .69 .10 .01 Note: All comparisons were independent samples 2-tailed t-tests. SCS=Self-Compassion Scale; GHQ=General Health Questionnaire; DASS=Depression Anxiety and Stress Scales; AAQ-II=Acceptance and Action Questionnaire-II. Table options 4.3. Outcomes 4.3.1. Analytic strategy Although hierarchical linear modeling (HLM) was explored as a method of data analysis, in virtually all cases modeling time categorically rather than as a linear covariate provided a better fit as determined by a comparison of nested models using restricted log-likelihoods, and thus a mixed model repeated measures (MMRM) analysis was used. MMRM is a mixed regression model that retains most of the advantages of HLM for an intent-to-treat analysis (Raudenbush & Bryk, 2002) in using all available data from all subjects and taking into account the obtained outcome and missingness, thus reducing the problem of missing data. Although treatment occurred in groups, the analysis was not fully nested since the comparison condition contained no nesting variable at that level. Several simpler and restrictive covariance assumptions were tested (compound symmetry, compound symmetry heterogeneous, Toeplitz) and the simplest model was used that was not significantly different than the unspecified covariance structure as determined by comparison of nested models through the restricted log-likelihood. Denominator degrees of freedom for the fixed effects test statistics was based on the Sattherthwaite approximation. Effect sizes (converted to Cohen׳s d), were be derived from the F- test statistic for the regression coefficients using the formula View the MathML sourced=2F/df (with df constrained to be no larger than the number of participants), which is suggested for repeated measures and multilevel designs ( Rosenthal and Rosnow, 1991 and Verbeke and Molenberghs, 2000). Effect sizes for within group contrasts were calculated by the formula [Mdiff/√{V(1)+V(2)−2 Cov(1,2)}] where V=variance, Cov=covariance, and numbers refer to the measurement occasions compared ( Wackerly, Mendenhall, & Scheaffer, 2008, p. 271). Effect sizes are discussed using the cutoffs suggested by Cohen (1988). 4.3.2. Self-compassion An MMRM analysis with a heterogeneous compound symmetry covariance structure best fit the data from the SCS and revealed a significant effect for treatment condition (p<.001) and time (p<.001), and a significant and medium time-by-condition interaction [F(2, 102.61)=10.18, p<.001, effect size=.74]. The interaction reflected the differences in magnitude of improvement between conditions. More specifically, the waitlist participants showed a small significant improvement from pre to post (p=.03, effect size=.35) and from pre to follow-up [Estimate=1.52, SE=.49, t (109.56)=3.08, p<.01, 95% CI:.54, 2.49, effect size=.48], while ACT participants exhibited a significant large improvement from pre to post (p<.001, effect size=1.15) and from pre to follow-up [Estimate=4.82, SE=.59, t (109.37)=8.21, p<.001, 95% CI: 3.66, 5.99, effect size=1.54]. The pre to follow-up changes were significantly different between the two conditions [Estimate=−3.31, SE=.77, t (109.45)=−4.32, p<.001, 95% CI: −4.83, −1.79, effect size=1.06, a large effect]. Fig. 2 displays changes in the SCS across time points for each condition. Changes in SCS by condition. Fig. 2. Changes in SCS by condition. Figure options 4.3.3. General psychological distress An MMRM analysis with a heterogeneous compound symmetry covariance structure best fit the data and revealed a significant effect for treatment condition (p<.001), time (p<.001), and a significant and small time-by-condition interaction [F(2, 106.80)=3.69, p=.03, effect size=.45]. The interaction reflected the between-condition differences in the degree to which participants improved. More specifically, while the waitlist participants showed no improvement from pre to post (p=.77) and showed a significant medium improvement from pre to follow-up [Estimate=−3.54, SE=1.06, t (108.29)=−3.34, p<.01, 95% CI: −5.64, −1.44, effect size=.52], those in the ACT condition showed a significant medium improvement from pre to post (p<.001, effect size=.67) and large and significant improvement from pre to follow-up [Estimate=−7.06, SE=1.27, t (108.08)=−5.56, p<.001, 95% CI: −9.57, −4.54, effect size=1.03]. The pre to follow-up changes were significantly different between the two conditions [Estimate=3.52, SE=1.65, t (108.17)=2.13, p=.04, 95% CI:.24, 6.80, effect size=.52, a medium effect]. 4.3.4. Depression An MMRM analysis with a Toeplitz covariance structure best fit the data for the DASS-D and revealed a significant effect for treatment condition (p=.01) and time (p=.03), and a significant and small time-by-condition interaction [F(2, 87.45)=3.11, p=.0498, effect size=.41]. The interaction reflected the fact that participants in the waitlist condition showed no improvement from pre to post (p=.41) or from pre to follow-up (p=.34), while ACT participants showed a significant small improvement from pre to post (p=.01, effect size=.48) and a significant medium improvement from pre to follow-up [Estimate=−5.72, SE=2.09, t (63.18)=−2.73, p=.01, 95% CI: −9.91, −1.54, effect size=.51]. Although pre to post changes were significantly different between the two treatment conditions [Estimate=5.50, SE=2.21, t (133.02)=2.49, p=.01, 95% CI: 1.13, 9.86, effect size=.61, a medium effect], the pre to follow-up changes were not significantly different between conditions (p=.15). 4.3.5. Anxiety For DASS-A scores, an MMRM analysis with an unstructured covariance structure best fit the data and revealed no effect for treatment condition (p=.48), a significant effect for time (p=.04), and a significant and medium time-by-condition interaction [F(2, 67.21)=7.48, p<.01, effect size=.67]. The interaction reflected the fact that the waitlist participants showed a significant and small deterioration from pre to post (p=.04, effect size=.34) and no change from pre to follow-up (p=.92), while ACT participants showed a significant medium improvement from pre to post (p=.01, effect size=.53) and from pre to follow-up [Estimate=−5.17, SE=1.48, t (71.37)=−3.48, p<.01, 95% CI: −8.12, −2.21, effect size=.66]. The pre to follow-up changes were significantly different between the two conditions [Estimate=5.29, SE=1.92, t (70.47)=2.76, p=.01, 95% CI: 1.46, 9.11, effect size=.68, a medium effect]. 4.3.6. Stress For the DASS-S, an MMRM analysis with a heterogeneous compound symmetry covariance structure fit the data best and revealed a marginally significant effect for treatment condition (p=.06), a significant effect for time (p=.02), and no significant time-by-condition interaction (p=.13). The waitlist participants showed no change from pre to post (p=.42) or from pre to follow-up (p=.50). Those in the ACT condition also showed no change from pre to post (p=.16) but showed a significant medium improvement from pre to follow-up [Estimate=−5.25, SE=1.74, t (111.53)=−3.02, p<.01, 95% CI: −8.70, −1.80, effect size=.56]. Evaluation of the difference in the pre to follow-up changes between conditions revealed a non-significant trend towards a small effect in favor of the ACT participants [Estimate=4.25, SE=2.27, t (111.64)=1.88, p=.06, 95% CI: −.24, 8.75, effect size=.46]. 4.4. Mediation analyses 4.4.1. Analytic strategy The functional role of psychological flexibility (AAQ-II) in producing effects on the outcome measures was examined by mediation analysis. Testing the significance of the “a” and “b” cross product is recognized as perhaps the best all-around available method to test mediation ( MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). A nonparametric method using bootstrapped samples developed by Preacher and Hayes, 2004 and Preacher and Hayes, 2008 was used in the current study to test the statistical significance of the cross product of the coefficients. In the present set of analyses, parameter estimates were based on 3000 bootstrap samples. The bias corrected and accelerated 95% confidence intervals were then examined. These confidence intervals are similar to the 2.5 and 97.5 percentile scores of the obtained distribution of the cross products over the k samples, but with z-score based corrections for bias due to the underlying distribution ( Preacher and Hayes, 2004 and Preacher and Hayes, 2008). If the confidence intervals do not contain zero, the point estimate was considered significant at the level indicated. In each mediational analysis, all time points were included, and therefore participants who missed 1 or more assessments were excluded (N=8 of 73). Pre to follow-up changes in the outcome variable were entered as outcomes, and pre to post changes in the process variable (AAQ-II) were entered as mediators. 4.4.2. Self-compassion Pre to post changes in psychological flexibility (AAQ-II) significantly mediated (p<.05) pre to follow-up changes in self-compassion as measured by the SCS (bootstrapped point estimate=2.51, SE=.86, 95% CI: .07, 2.01). The significantly better impact of the intervention on SCS pre to follow-up changes, t(63)=3.72, p<.01, was reduced but continued to be significant after including the mediator, t(63)=3.03, p<.01 (proportion of effect mediated=28.1%). 4.4.3. General psychological distress Pre to post changes in psychological flexibility significantly mediated (p<.05) pre to follow-up changes in general psychological distress as measured by the GHQ (bootstrapped point estimate=−1.78, SE=1.10, 95% CI: −4.74, −.21). The significantly better impact of the workshop on GHQ pre to follow-up changes, t(63)=−2.01, p=.049, was no longer significant after including the mediator, t(63)=−1.14, p=.26 (proportion of effect mediated=46.9%). 4.4.4. Depression Pre to post changes in psychological flexibility significantly mediated (p<.05) pre to follow-up DASS-D changes (bootstrapped point estimate=−2.89, SE=1.79; 95% CI: −7.70, −.34). Although the decrease in DASS-D scores from pre to follow-up was non-significant, t(63)=−1.79, p=.08, it was reduced after adjusting for the mediator, t(63)=−.80, p=.43 (proportion of difference mediated=59.9%). 4.4.5. Anxiety Pre to post AAQ-II changes significantly mediated (p<.05) pre to follow-up DASS-A change scores (bootstrapped point estimate=−1.78, SE=1.27, 95% CI: −6.02, −.26). The significantly better pre to follow-up DASS-A change scores observed in the ACT condition, t(63)=−2.40, p=.02, became non-significant after including the mediator, t(63)=−1.59, p=.12 (proportion of effect mediated=37.7%). 4.4.6. Stress Pre to post changes in AAQ-II significantly mediated (p<.05) pre to follow-up changes in stress as measured by the DASS-S (bootstrapped point estimate=−2.09, SE=1.52, 95% CI: −7.04, −.09). The non-significant trend towards superiority of the ACT condition in terms of pre to follow-up DASS-S change scores, t(63)=−1.86, p=.07, disappeared after including the mediator in the analysis, t(63)=−1.09, p=.28 (proportion of effect mediated=44%). 4.5. Moderation analyses 4.5.1. Analytic strategy To evaluate whether the workshop was differentially effective for individuals according to trauma history, the SLESQ-R (taken at pretreatment) was evaluated as a moderator of the effect of treatment condition on outcomes. Following the recommendations of Hayes (2013), linear regression was used to construct a model with the following predictors of outcome variables: SLESQ-R, treatment condition, and the interaction (i.e., product) of SLESQ-R and treatment condition. If the coefficient for the interaction term in a given analysis is significant, SLESQ-R may be regarded as a moderator of that outcome. Significant interactions were then probed by examining the conditional effects of condition on outcome at the 25th, 50th, and 75th percentiles of the SLESQ-R. So that coefficients may be interpretable within the range of the data, both SLESQ-R and condition were mean centered prior to analysis (Hayes, Glynn, & Huge, 2012). Data for both pre and follow-up could only be collected from 93% of participants (68 of 73), meaning that 7% of pre to follow-up change data were missing. Because listwise deletion and single imputation may bias results in datasets with more than 5% missing data (Graham, 2009 and Schafer, 1999), multiple imputation (Rubin, 1987) was used in these analyses. Multiple imputation is a Monte Carlo technique for handling missing data, in which multiple complete datasets are constructed by imputing missing data points with values generated based on individuals׳ scores on other variables. Each of the imputed datasets is then analyzed using standard techniques, and pooled estimates and confidence intervals for the coefficients of interest are constructed (Croy and Novins, 2005, Graham, 2009 and Schafer, 1999). The statistical package mi from R statistics (R-3..1; R Core Team, 2013) was used to carry out this procedure ( Su, Gelman, Hill, & Yajima, 2011). Using bootstrapping, 30 imputed datasets were generated, and missing data were imputed based on values for all other variables in the dataset. Confidence intervals constructed for each coefficient were used to evaluate statistical significance, and these statistics were complemented with visual inspection of plots of the interaction using the car package in R ( Fox & Weisberg, 2011). Because statistical procedures have not yet been developed to incorporate multiple imputation into additional dismantling analyses, such as the probing of interactions, these analyses were performed on a non-imputed data set in which missingness was handled through listwise deletion. 4.5.2. Self-compassion The SLESQ-R did not significantly moderate the effect of treatment condition on pre to follow-up SCS change scores (coefficient of interaction term=.73, SE=.43, 95% CI: −.14, 1.59). However, confidence intervals and visual inspection (see Fig. 3) suggest a stronger association between the ACT intervention and self-compassion for those with higher scores in the SLEQ-R, suggesting a moderation trend. Visual inspection of moderation analyses.Note: Each figure provides a scatter ... Fig. 3. Visual inspection of moderation analyses. Note: Each figure provides a scatter plot between the outcome and the ACT intervention, with a linear regression line indicated in dashes. Within each figure, and from left to right, each panel indicates the association between the outcome and the ACT intervention at each level of trauma history (1st, 2nd , 3rd and 4th quartiles). As shown in the each figure, as trauma history increases, the association between the outcome and the ACT intervention increased in the expected direction for each outcome. Trauma History=SLESQ-R; Self-Compassion=SCS; Depression=Depression subscale of the DASS; Anxiety=Anxiety subscale of the DASS; and Stress=Stress subscale of the DASS. Figure options 4.5.3. General psychological distress The SLESQ-R did not significantly moderate the effect of treatment condition on pre to follow-up GHQ change scores (coefficient of interaction term=−.41, SE=1.12, 95% CI: −2.64, 1.83). As noted by the confidence interval, this moderation effect clearly included 0, suggesting a null effect. 4.5.4. Depression The SLESQ-R was a significant moderator of the impact of treatment condition on the change in DASS-D scores from pre to follow-up (coefficient of interaction term=−3.11, SE=1.55, 95% CI: −6.20, −.02). Visual inspection (Fig. 3) confirmed this pattern. Follow-up analyses based on a non-imputed dataset revealed that 8.2% of the total variance in DASS-D change scores was uniquely attributable to the interaction [F(1,64)=6.18, p=.02]. Probing the interaction revealed that among those scoring low in depression (25th percentile) on the SLESQ-R, the effect of condition on DASS-D pre to follow-up change scores was non-significant (p=.46). However, among those scoring moderate (50th percentile) or high (75th percentile), DASS-D pre to follow-up change scores for the ACT treatment group were significantly better than those of the waitlist control (conditional effects of −5.11 and −9.04 respectively, ps both <.05). Thus, ACT was helpful with depression for the more traumatized participants. 4.5.5. Anxiety The SLESQ-R significantly moderated the relationship between treatment condition and DASS-A pre to follow-up change scores (coefficient of interaction term=−2.63, SE=1.16, 95% CI: −4.94, −.32; see Fig. 3). Analyses based on a non-imputed dataset showed that 9.5% of the total variance in DASS-A change scores was uniquely attributable to the interaction [F(1,64)=7.55, p<.01]. Probing the interaction showed that among participants scoring low on the SLESQ-R, there was no significant effect of condition on pre to follow-up DASS-A change scores (p=.59). By contrast, among those scoring moderate or high, DASS-A change scores were significantly better in the ACT group (conditional effects of −5.09 and −8.40, respectively, ps both <.01). Thus, ACT was helpful with anxiety for the more traumatized participants. 4.5.6. Stress The SLESQ-R did not significantly moderate the impact of treatment condition on DASS-S pre to follow-up changes (coefficient of interaction term=−2.85; SE=1.51; 95% CI: −5.86, .016; 90% CI: −5.38, −.33). As shown in Fig. 3 and the interaction׳s confidence interval, a finer grain analysis and visual inspection of this association suggest a moderation trend. Additional analysis also showed that 7.2% of the total variance in DASS-S change scores in a non-imputed dataset could be uniquely attributed to the interaction [F(1, 64)=5.41, p=.02]. Probing the interaction showed that among participants scoring low on the SLESQ-R, the effect of condition on DASS-S pre to follow-up changes was not significant (p=.58). However, among those scoring moderate or high, DASS-S changes were significantly better for ACT participants (conditional effects of −5.16 and −8.75, respectively, ps both <.05). Thus, ACT was helpful with stress for the more traumatized participants.