Pre-deployment trait anxiety, anxiety sensitivity and experiential avoidance predict war-zone stress-evoked psychopathology

Author’s Accepted Manuscript Pre-deployment Trait Anxiety, Anxiety Sensitivity and Experiential Avoidance Predict War-zone Stress-Evoked Psychopathology Adam R. Cobb, Cynthia L. Lancaster, Eric C. Meyer, Han-Joo Lee, Michael J. Telch www.elsevier.com/locate/jcbs

PII: DOI: Reference:

S2212-1447(17)30052-2 http://dx.doi.org/10.1016/j.jcbs.2017.05.002 JCBS186

To appear in: Journal of Contextual Behavioral Science Received date: 9 January 2017 Revised date: 1 May 2017 Accepted date: 15 May 2017 Cite this article as: Adam R. Cobb, Cynthia L. Lancaster, Eric C. Meyer, HanJoo Lee and Michael J. Telch, Pre-deployment Trait Anxiety, Anxiety Sensitivity and Experiential Avoidance Predict War-zone Stress-Evoked Psychopathology, Journal of Contextual Behavioral Science, http://dx.doi.org/10.1016/j.jcbs.2017.05.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Running head: MODERATORS OF STRESS REACTIONS IN THE WAR-ZONE

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Pre-deployment Trait Anxiety, Anxiety Sensitivity and Experiential Avoidance Predict War-zone Stress-Evoked Psychopathology

Adam R. Cobba, Cynthia L. Lancastera, , Eric C. MeyerbHan-Joo Leec, , Michael J. Telchd*

a

b

University of Texas at Austin

U.S. Department of Veterans Affairs VISN 17 Center of Excellence for Research on Returning

War Veterans, Waco, Texas and Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, College of Medicine c

University of Wisconsin – Milwaukee 

Author Note

Adam R. Cobb and Cindy L. Lancaster, Department of Psychology, University of Texas at Austin; Eric C. Meyer, U. S. Department of Veterans Affairs VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, Texas and Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, College of Medicine; HanJoo Lee, Department of Psychology, University of Wisconsin-Milwaukee; Michael J. Telch, Department of Psychology, University of Texas at Austin. This research was funded by the U.S. Army RDECOM Acquisition Center, Natick Contracting Division, and U. S. Defense Advanced Agency under Contract No. W911QY-07-C0002 awarded to Michael J. Telch. Views expressed this article are those of the authors may not necessarily be endorsed by the U. S. Army or the Department of Veterans Affairs. Study findings were previously presented at the 34th Annual Convention for the Anxiety and Depression Association for America in March 2014.

MODERATORS OF WAR-ZONE STRESS d

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University of Texas at Austin

*

Corresponding author. Michael J. Telch, Department of Psychology, University of Texas

at Austin, 108 E. Dean Keaton, Austin, TX 78712.

Abstract Identifying modifiable risk factors is requisite for preventing stress-related psychopathology, but, few prospective studies have examined their impact on the emergence of psychological, dysfunction. Trait anxiety (TA), anxiety sensitivity (AS), and experiential avoidance (EA) were, assessed in 161 soldiers awaiting deployment. Soldiers also completed repeated in-theater, assessments of stressors, post-traumatic stress (PTSS), anxiety, and depression symptoms. Multilevel models tested predictions that each trait would independently and jointly amplify, stressors’ impact on symptoms. TA increased risk for anxiety (r = .19, p = .020), but not, stressors’ anxiogenic effects (r = .14, p = .080), whereas TA reduced stressors’ impact on, depression (r = .18, p = .038) and PTSS (r = .28, p = .001). AS increased risk across symptoms, (r’s = .26 to .31, p’s ≤ .002), but did not moderate stressors’ anxiogenic effects (r = .15, p = .074). EA’s stress-moderating effects depended on levels of TA and AS (r’s = .22-.27, p’s ≤ .010). Findings suggest TA and EA may interact in ways to enhance resilience, whereas AS may reliably potentiate the pathogenic effects of stress. Support is given for examining the contextualized influence of individual differences, and their dynamic interactions in predicting soldiers’ reactions to war-zone stressors.

Keywords: War-zone stress, PTSD, trait anxiety, anxiety sensitivity, experiential avoidance.

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Most soldiers are resilient (McNally, 2012). However, war-zone stress is associated with a myriad of problems (Tanielian & Jaycox, 2009), including post-traumatic stress (PTSS), anxiety and depression (Hoge et al., 2004; Seal, Bertenthal, Minger, Sen, & Marmar, 2007), substance use problems (Jacobson et al., 2008), reduced quality of life (Schnurr, Lunney, Bovin, & Marx, 2009) and suicide (e.g., Jakupcak et al., 2010). Deployment-related trajectories of mental health are also heterogeneous, and predictors only modestly account for this variability (e.g., Berntsen et al., 2012; Bonanno et al., 2012). Combat is the most established predictor of negative mental health outcomes among deployed soldiers (e.g., Smith et al., 2008; Vasterling et al., 2010), but individual differences, and other deployment stressors contribute to a range of psychopathology, as it emerges in-theater1 (e.g., Telch, Rosenfield, Lee & Pai, 2012; Telch et al, 2015), and upon return to civilian life (e.g., Heron, Bryan, Dougherty & Chapman, 2013). Mitigating these problems requires more complete functional models of stress-related psychopathology, targeting individual difference and contextual factors that are modifiable, and determining how they operate together. Diathesis-stress frameworks are well-suited for this purpose, by positing that while stress is a critical impetus, the onset, severity, and course of adverse outcomes are governed by many pre-disposing factors (McKeever & Huff, 2003; Elwood, Hahn, Olatunji, & Williams, 2009) that generate and modulate experience (Hammen, 2006; Riskind, Black, & Shahar, 2010), interact 1

Meaning “in the war-zone”.

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within persons (Kendler, Zachar, & Craver, 2011), and exert fluctuating influence over time. These frameworks have addressed reactions to several major life stressors (e.g., Shell, Gazelle, & Faldowski, 2014; Elwood, Mott, Williams, Lohr, & Schroeder, 2009; Edmondson et al., 2014; Reinelt et al., 2013), but have rarely been used to identify factors that confer risk and resilience for in-theater psychological dysfunction (e.g., Telch et al., 2012; Beevers, Lee, Wells, Ellis, & Telch, 2011), or to examine how such factors work together to cause psychopathology (see Kraemer, Stice, Kazdin, Offord & Kuper, 2001). Consistent with process-oriented, integrative approaches to examining how individual differences interact with self-regulation in stress-related psychopathology (e.g., Elwood et al., 2009; Zvolensky et al., 2015; see Borsboom & Cramer, 2013), the present study examined how pre-deployment trait anxiety (TA; Spielberger, 1972), anxiety sensitivity (AS; Reiss, Peterson, Gursky, & McNally, 1986), and experiential avoidance (EA; Hayes, Wilson, Gifford, Follette, & Strosahl, 1996) interact with war-zone stressors to predict the emergence of psychological symptoms in-theater. TA, AS, and EA have each been linked to psychopathology, they share moderate to large inter-correlations (r’s = .30 to .65; e.g., Kelly & Forsyth, 2009), and can be viewed as related, but distinct trait-like tendencies implicated in the experience and regulation of emotion, especially in the context of stress. Specifically, TA reflects tendencies to respond anxiously to real or imagined threat, and towards general negative emotionality (Spielberger, 1972); AS reflects fear of anxiety and arousal-based reactions to somatic, social or cognitive threats (Reiss & McNally, 1985; Taylor et al., 2007), and EA reflects avoidance of aversive private internal experiences (Hayes et al., 2004). Theory and empirical work suggests interactions between these traits are likely, and probably reciprocal. In theory, averseness to emotions promotes avoidance, which in turn,

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reduces opportunities for extinction and self-regulation, thereby maintaining heightened threat reactivity and diminishing emotional processing of stressful experiences (e.g., Ehlers & Clark, 2000). In support, previous studies have revealed EA strengthens relations of AS with PTSD (Naifeh, Tull, & Gratz, 2012; Bardeen, Tull, Stevens & Gratz, 2015; Bardeen, 2015), anxiety (Bardeen, 2015; Bardeen, Fergus & Orcutt, 2014), and depression (Zvolensky et al., 2015). AS has also been theorized to be a potentiator of anxiety and other aversive internal states (Reiss, 1991; Reiss & McNally, 1985), and TA and AS have been shown to operate together in amplifying stress reactions (e.g., Orsillo, Lilienfeld & Heimberg, 1994; see Olatunji & WolitzkyTaylor, 2009). Thus, the guiding assumption of this investigation is that these traits operate together, with war-zone stressors, to confer risk for in-theater symptoms (see Kraemer et al, 2001). Study Overview In the present study, U.S. soldiers with no prior deployments completed a battery of assessments at pre-deployment, and monthly web-based assessments of war-zone stressors and symptoms while deployed to Iraq. Within a diathesis-stress framework, and applying standard criteria for moderation (Kraemer et al, 2001; Kraemer, Kiernan, Essex, & Kupfer, 2008), the effects of pre-deployment TA, AS, and EA were evaluated as moderators of the effects in-theater stressor exposure on PTSS, anxiety, and depression symptoms. Two primary questions were addressed: (1) Does TA, AS, or EA independently moderate the subsequent effects of war-zone stressors on in-theater symptoms? and (2) Do TA, AS, and EA interact in moderating the effects of stressors on symptoms? It was hypothesized that TA, AS, and EA would each independently potentiate stressor

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effects on PTSS, anxiety, and depression (Hypotheses 1a, 1b, and 1c) 2 even after controlling for other known risk factors, including gender (e.g., Vogt, Pless, King, & King, 2005), and lifetime Axis I psychopathology (e.g., LeardMann et al., 2013). With respect to interactions between the traits3, it was predicted EA would amplify risk conferred by AS in amplifying stressors’ effects on in-theater symptoms (Hypothesis 2a). It was also explored whether the expected AS x EA amplification patterns would replicate in relations between TA and EA (Hypothesis 2b), which would suggest non-specific effects of the AS x EA relation. Finally, TA and AS were likewise expected to interact, with each amplifying the stress-moderating effects of the other (Hypothesis 2c). Method Participants Participants were U.S. soldiers (N = 223) from 9 Army units (4 combat arms, 1 combat support, and 4 combat service support units) preparing for deployment from Ft. Hood, TX to Iraq (between August 2007 to 2009); 82% (N = 184) provided informed consent. To mitigate perceived coercion, commanders were not present during briefing and consent. Eligible participants were ≥ 18 years of age, had no prior deployments, and were scheduled to deploy within 3 months. Among consenters, 6 did not deploy, 1 withdrew, and 16 did not complete intheater assessments. The final sample (N = 161) was mostly enlisted (92.5%), male (80.12%), between 19 and 25 years of age (68.1%), and was racially diverse (18% Hispanic / Latino, 72.7% Caucasian, 11.8% Native American, 9.9% African American, and 5.6% Asian). Education was typical for military samples: 43.5% graduated high school, 37.9% attended college, and 9.9% obtained a baccalaureate or higher degree. At pre-deployment, 51% had a history of one or more

2 3

Evaluated in models referred to as “incremental diathesis-stress models”. Evaluated in models referred to as “interacting diatheses-stress models.”

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Axis I disorders (42% substance use, 18% mood disorders and 9% anxiety disorders; see Table S3 in supplement). Procedures All soldiers completed an extensive battery of assessments at pre-deployment that took ~8 hours to complete. Pre-deployment assessments relevant to this report are described below (see section Pre-deployment Assessments), whereas other risk assessment domains have been described elsewhere (e.g., see Beevers et al, 2011; Lancaster et al, 2014; Telch et al., 2012; Telch et al., 2015). Demographics and the diathesis traits (i.e., TA, AS, and EA) were assessed using self-report measures administered at the beginning of the pre-deployment assessment. This was followed by clinician-based interviews assessing the presence of current and past DSM-IV diagnoses. Once deployed, soldiers completed the Combat Experience Log (CEL) each month for the duration of their deployment (see Lee et al., 2011). The CEL is a web-based battery of self-report assessments consisting of two major sections designed to capture stressor exposure, and psychological symptoms experienced in the past 30 days. Reminders with links to the CEL were emailed 6 days before, and 7 days following (if needed) the end of each 30-day reporting period, and was accessible between periods. Pre-deployment Assessments Diagnostic status Past and current psychopathology were assessed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I-IV; First, Spitzer, Gibbon, & Williams, 1996). All modules were administered by doctoral students with a ≥ 1 year of experience using this instrument, supervised by the PI. Diagnoses were confirmed by the PI in follow-up interviews with participants, and there was perfect agreement between evaluators.

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Trait anxiety (TA) TA was assessed with the Trait Scale from the State-Trait Anxiety Inventory (STAI-T; Spielberger, 1983), which includes 20-items assessing the tendency to experience anxiety across a variety of situations (0 = “Almost never”; 3 = “Almost always”). The STAI-T exhibits good convergent (Spielberger, 1983) and discriminative validity (Kabacoff et al., 1997; Bieling et al., 1998), test-retest reliability (.73-.86; Spielberger, 1983), and internal consistency (Bieling et al., 1998; Kabacoff, et al., 1997). Internal consistency was also excellent in the present study (= .91). Anxiety sensitivity (AS) AS was assessed using the Anxiety Sensitivity Index-3 (Taylor et al., 2007), a validated scale with 18-items assessing fearful reactivity to the sensations and symptoms of anxious and fearful states (Osman et al., 2010; Taylor et al., 2007), rated by the extent of agreement with one’s experiences (0 = “Very little”; 4 = “Very much”). This measure has good convergent validity with an earlier, validated version of the ASI (Taylor et al., 2007; Reiss et al., 1986), and has good internal consistency (= .89; Osman et al., 2010), comparable to that found in the present sample (=.91). Experiential Avoidance (EA) EA was assessed using the 9-item version of the Acceptance and Action Questionnaire-I (AAQ-I; Hayes et al., 2004). Higher scores indicate greater tendencies to avoid internal private experiences (0 = “Never True”; 6 = “Always True”). The AAQ-I exhibits good test-retest reliability (.64), and correlates well with related constructs (r = .33-.50; Wegner & Zanakos, 1994; Wells & Davies, 1994; Bernstein & Putnam, 1986). Further, AAQ-I scores are highly correlated with the newer AAQ-II (r = .72 to .97; Bond et al.,

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2011; Gámez et al., 2011; Meyer et al., 2013) and the Multidimensional Experiential Avoidance Questionnaire (MEAQ; Gámez et al., 2011; r = .66 to .74).

The AAQ-I is also incrementally

valid over related measures in predicting psychopathology-related outcomes (Hayes et al., 2004; Marx & Sloan, 2005). Similar to reports showing marginal internal consistency (= .74 or lower; Gámez, Chmielewsky, Kotov, Ruggero & Watson., 2011; Hayes et al., 2004; Marx & Sloan, 2005; Boelen & Reijntjes, 2008), internal consistency was just satisfactory in the present sample (= .62)4. In-Theater Assessments War-zone stressors Exposure to combat (e.g., hostile incoming fire) and non-combat stressors (e.g., bad news from home) was assessed using the CEL – War-zone Stressor Checklist (Lee et al., 2011; see Supplemental Table S1 for items, and their respective frequencies), which was adapted from the Deployment Risk and Resilience Inventory (DRRI; King, King & Vogt, 2003). Stressor occurrence since the last CEL entry was assessed with 18 items describing common deployment stressors, and 2 free-response items for reporting stressors not included in the checklist. Post-traumatic stress symptoms (PTSS) PTSS were assessed with the PTSD Checklist, Short Version (PCL-Short; Weathers, Litz, Herman, Huska & Keane, 1993), consisting of 4-items assessing each DSM-IV PTSD symptom cluster (re-experiencing: 2 items; avoidance: 1 item; and arousal: 1 item), rated according to distress (1 = “Not at all”; 4 = “Extremely”). The PCL-Short has diagnostic accuracy equivalent to that of the full PCL (Bleise et al, 2008). Internal consistency in the present sample was acceptable (= .76). Removal of items resulted in either no change, or decrements to internal consistency of the AAQ-I. The AAQII was not available at the time of study initiation. 4

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Depression symptoms Depression symptoms were assessed with the 10-item Center for Epidemiological Studies Depression Scale (CES-D-10; Andresen, Malmgren, Carter, & Patrick, 1994). Items reflecting the core symptoms of depression were rated on a 4-point scale (ranging from 0 = “Rarely / None of the time” to 3 = “Most / All of the time”). The CES-D-10 has good test-retest reliability (r = .83, Irwin et al., 1999), and has similar predictive accuracy (Kappa = .82-.97; Andresen et al., 1994; Zhang et al., 2012) and correlates highly with the 20-item version (r = .97, p < .001; Zhang et al., 2012). Internal consistency was acceptable in the present sample (= .79), but slightly lower than prior reports (= .88-.92; Irwin et al., 1999; Zhang et al., 2012). Anxiety symptoms Anxiety symptoms were assessed with the 19-item Combat Experience Log Anxiety Subscale (CEL-ANX), an author-constructed instrument (Lee et al, 2011) developed for the parent project. Soldiers rated the presence and severity of general anxiety symptoms (e.g., panic, fear, excessive worry) using a 5-point scale (1 = “not at all”; 5 = “extremely”). Internal consistency was excellent in the present sample (= .92). Statistical Analyses Data were analyzed using multilevel growth models predicting outcomes of monthly intheater PTSS, anxiety, and depression symptoms, with observations nested within individuals (Raudenbush & Byrk, 2002; see Supplemental Table S2 for detailed model specifications). Time for each observation was entered as days since deployment, centered at 8 months (Curran, Bauer, & Willoughby, 2006) 5. All other continuous predictors were z-transformed. Predictors

5

Centering time at 8 months allowed for more meaningful interpretation of intercept variance as the between-soldier dispersion of symptoms at mid-deployment, and secondarily provided the best stabilization across the length of the growth curves.

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included: (a) the fixed and random effects of time; (b) control variables including gender and presence of a lifetime DSM-IV Axis I disorder (dichotomously coded); (c) the hypothesized stress-moderators, TA, AS, and EA 6, and (d) two stressor variables including: (1) each soldier’s (time-invariant) average monthly stressor count (i.e., indicating between-soldier differences in average stressor exposure across deployment months; STRESSBP), and (2) monthly (timevarying) deviation from this average (i.e., indicating within-soldier monthly changes in stressor exposure; STRESSWP). Parsing stressor effects into these between- and within-solider components avoids misleading results, because variance explained by time-varying stressors confounds individual differences and within-person change (Hoffman & Stawski, 2009). All statistical tests were two-tailed, with alpha set at .05. Because multilevel models more efficiently address error inflation by using precision weighted estimation, no p-value adjustments for multiple comparisons were made (see Gelman, Hill & Yajima, 2012). Our analytic approach consisted of six separate steps. In step 1, main effects were estimated by omitting interactions and controlling for all covariates (see Hayes, Glynn, & Huge, 2012), with the exception of the three putative pre-deployment diatheses. This allowed for a baseline estimation of stressor effects for later comparison after controlling for TA, AS, and EA. In step 2, TA, AS, and EA were entered separately, and in step 3, all three diatheses were included to estimate their unique contribution to symptoms. Following the approach outlined in steps 2 and 3, in steps 4 and 5, we included two-way diathesis-stress interactions, with each diathesis-stress interaction modeled separately (referred to as “singular diathesis-stress models”), followed by including all three diathesis-stress interactions to estimate incremental effects on symptoms (referred to as “incremental diathesis-stress models”; Hypotheses 1a, 1b, and 1c). In a

6

We examined intercorrelations, regressed each on the other two, and assessed for multicollinearity; there was no indication of estimation problems.

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final step, we evaluated our primary predictions by including three-way interaction terms to test whether the diatheses interact, or operate together in moderating stressors (referred to as “interacting diatheses-stress models”; Hypotheses 2a, 2b, and 2c). To probe interaction terms, diatheses were centered 1 SD above and below the mean to estimate the conditional effects of stressors, given low or high levels of each pre-deployment trait (Aiken & West, 1991). Results Baseline Growth Curve Models of In-Theater Symptoms The number of soldiers and in-theater assessments varied across outcomes. For anxiety symptoms, 161 soldiers contributed an average of 6.46 (range = 1 to 18; SD = 5.47) and total of 1040 assessments. For PTSS, 153 soldiers completed an average of 4.19 (range = 1 to 14; SD = 3.35) and total of 641 assessments. Across soldiers, 17% of the PTSS observations were in the clinically significant range (i.e., ≥ 7; Bliese et al., 1994). For depression symptoms, 150 soldiers completed an average of 4.45 (range = 1 to 13; SD = 3.52) and total of 667 assessments. Across soldiers, 28% of the depression observations were in the clinically significant range (≥ 10, Andresen et al., 1994). Intra-class correlation coefficients were 69%, 61%, and 43%, respectively, for anxiety, post-traumatic stress, and depression symptoms. Growth curves were consistent with previous reports (see Lee et al., 2011). Post-traumatic stress and depression symptoms were best modeled by fixed quadratic, random linear growth curves, whereas anxiety was best modeled by fixed cubic, random quadratic terms (see Table S2 in supplement). Diathesis-stress Models of In-Theater Symptoms Results presented below are limited to our primary analyses aiming to characterize how the pre-deployment traits operate together in moderating the impact of war-zone stressors on symptoms. Descriptive statistics are presented in Table 1. Main effect findings are presented in

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Table 2, and described in the supplement (see Section 2 and Table S4 of the supplement). Findings for the singular diathesis-stress models, in which stress-moderation effects of each predeployment trait were examined separately, are presented in the supplement (see Section 3 and Table S5 of the supplement). Incremental diathesis-stress models What follows are more stringent tests of stress-moderation of each pre-deployment trait (TA, AS, and EA), controlling for the stress-moderation effects of the other two. These are referred to as incremental diathesis-stress models. Omnibus findings are presented in Table 3, and conditional effects are presented in Table S6 in the supplement. Global effect size calculations indicated these models explained 24%, 24%, and 21% of the variance in PTSS, anxiety, and depression symptoms across deployment months. Trait anxiety x war-zone stressors (Hypothesis 1a) Significant stress-moderation by TA was observed for PTSS (TA x STRESSWP: b = -.25, t = 3.48, p = .001) and depression (TA x STRESSBP: b = -.80, t = 2.09, p = .038), although effects were not significant for anxiety (TA x STRESSWP: b = .32, t = 1.75, p = .080; see Figure 1). Contrary to expectations, TA was negatively related to stressor’s effects on PTSS, evidenced by increases in symptoms in response to monthly increases in stressors for those with low (STRESSWP: b = .46, t = 5.82, p < .001), but not high TA (STRESSWP: b = -.04, t = -.33, p = .738; see Figure 1). A similar pattern was observed for depression, indicating TA was associated with reduced depressogenic effects of average stressors (STRESSBP: low TA: b = .96, t = 1.97, p = .050; high TA: b = -.63, t = -1.21, p = .229; see Figure 1). Anxiety sensitivity x war-zone stressors (Hypothesis 1b) AS’s stress-moderation effects were mostly robust to controlling for the other diathesis-stress

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interactions (see Figure 2; cf. Supplement, Section 3 and Table S5). AS strengthened the effect of stressors on PTSS (AS x STRESSBP: b = .45, t = 2.34, p = .021; AS x STRESSWP: b = .31, t = 3.29, p = .001) and depression (AS x STRESSWP: b = .70, t = 3.10, p = .002), although effects on anxiety were no longer significant (AS x STRESSWP: b = .42, t = 1.79, p = .074). Probing revealed significant positive associations between stressors and PTSS for those with high AS (STRESSBP: b = 1.13, t = 4.53, p < .001; STRESSWP: b = .53, t = 4.33, p < .001), but not low AS (STRESSBP: b = .23, t = .99, p = .326; STRESSWP: b = -.10, t = -.93, p = .353; see Figure 2). Similarly, there was a significant positive relation between monthly increases in stressors and depression among soldiers with high AS (STRESSWP: b = 1.01, t = 3.46, p = .001), but not low AS (STRESSWP: b = - .39, t = -1.48, p = .140; see Figure 2). Experiential avoidance x war-zone stressors (Hypothesis 1c) Controlling for the main and interactive stress-moderation effects of TA and AS, all EA x stressor interactions were non-significant. Interacting diatheses-stress models In a final step, we tested each of the three diathesis pairs (AS x EA, TA x EA, and TA x AS) in what we refer to as interacting diatheses-stress models. Omnibus findings are presented in Table 4, and conditional effects are presented in Table S6 of the supplement. Estimates of global effect size indicated these models explained 29%, 26%, and 25% of the variance in PTSS, anxiety and depression symptoms, respectively. AS x EA x war-zone stressors (Hypothesis 2a) The interaction of AS, EA, and monthly changes in stressors significantly predicted anxiety (STRESSWP: b = .47, t = 2.68, p = .007). Increases in stressors were positively associated with anxiety for soldiers with low AS and EA (STRESSWP: b = 1.81, t = 4.71, p <

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.001), high AS and EA (STRESSWP: b = 1.39, t = 5.75, p < .001), and high AS, but low EA (STRESSWP: b = 1.27, t = 2.52, p = .012). However, increases in stressors were not associated with in-theater anxiety symptoms for soldiers with low AS and high EA (STRESSWP: b = .03, t = .06, p = .951; see Figure 3). TA x EA x war-zone stressors (Hypothesis 2b) The interaction of TA, EA, and average stressors significantly predicted PTSS (TA x EA x STRESSBP: b = .53, t = 3.21, p = .002) and anxiety (TA x EA x STRESSBP: b = 1.92, t = 2.62, p = .010; TA x EA x STRESSWP: b = -.53, t = -2.92, p = .004; see Figure 4). Specifically, higher average stressor exposure was associated with higher PTSS in soldiers with low levels of both TA and EA (STRESSBP: b = .98, t = 3.06, p = .003) or high levels of both TA and EA (STRESSBP: b = .80, t = 2.82, p = .005; see Figure 3). However, soldiers with low TA and high EA exhibited a non-significant relationship between average stressors and PTSS (STRESSBP: b = .51, t = 1.57, p = .119), and in soldiers with high TA and low EA, there was a marginally significant negative relation between average stressors and PTSS (STRESSBP: b = -.83, t = 1.94, p = .054). Average stressors were also significantly positively associated with anxiety among those with low TA and EA (STRESSBP: b = 4.10, t = 2.67, p = .008), but not for those with high levels of one or both traits (STRESSBP | High TA, High EA: b = 1.62, t = 1.37, p = .173; High TA, Low EA: b = -.37, t = -.19, p = .848; Low TA, High EA: b = -1.57, t = -1.06, p = .292; see Figure 4). In contrast, when examining monthly increases in stressors, strong positive associations between stressors and anxiety were found for those with high TA and low EA (STRESSWP: b = 2.86, t = 5.64, p < .001), followed by those with high TA and high EA (STRESSWP: b = .97, t = 3.13, p = .002). However, there was no significant relation between monthly changes in stressors and

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anxiety for those with low TA and low EA (STRESSWP: b = .22, t = .65, p = .519), or low TA and high EA (STRESSWP: b = .45, t = 1.07, p = .285; see Figure 4). TA x AS x war-zone stressors (Hypothesis 2c) Across outcomes, the interaction of pre-deployment TA and AS did not significantly moderate stressors’ effects on symptoms. Discussion This study provides preliminary evidence for how three related, but functionally distinct pre-deployment traits implicated in stress-reactivity (TA, AS, and EA) influence risk and resilience for the emergence of war-zone stress-evoked psychological symptoms. By assuming these factors operate together (Borsboom et al., 2013; Kendler et al., 2011; Kraemer et al., 2001), the present approach extended conventional diathesis-stress frameworks in estimating the incremental effects of the targeted traits, and the effects of their specific configurations within soldiers. This helped reveal the functional distinctions between the traits (e.g., Bardeen, 2015), by considering how specific profiles relate to the impact of stressors in-theater. Overview of Main Findings The incremental diathesis-stress models addressed the question of whether TA, AS, and EA exhibit independent effects in moderating the impact of war-zone stressors. Consistent with predictions, TA was associated with an amplified impact of stressors on anxiety symptoms (Hypothesis 1a), and AS showed pathogenic stress-moderation effects across symptom domains when controlling for the other diathesis-stress interactions (Hypothesis 1b). However, contrary to predictions, TA was associated with a reduced impact of stressors on PTSS and depression (Hypothesis 1a), and EA did not moderate stressor effects when controlling for the other diathesis-stress terms (Hypothesis 1c).

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Beyond examining independent stress-moderation effects, the interacting diatheses-stress models addressed whether the pre-deployment traits interact in moderating the impact of warzone stressors. Theory (Hayes et al, 1996) and evidence from cross-sectional (e.g., Bardeen, 2015) and prospective (Bardeen et al., 2014) studies supported the prediction that EA would amplify AS in moderating stressors’ impact on symptoms (Hypothesis 2a). Results were consistent with theory that sensitivity to threatening emotions should pose problems for individuals who tend to avoid or suppress them (e.g., Hayes et al, 1996), but inconsistent with reports that AS is pathogenic only among those with high EA (e.g., Bardeen, 2015; Naifeh et al, 2012). Instead, AS potentiated monthly increases in stressors’ effects on anxiety, irrespective of EA; and EA reduced the effects of increases in stressors on anxiety, but only in those with low AS. However, when the effect of stressors was held constant, AS was more strongly linked with anxiety in those with high EA. (see supplement, Table S6). Thus, these results partially replicated findings supporting EA as an amplifier of AS’s direct effects, but the stressmoderation findings suggest amplification may not characterize AS and EA in moderating the link between stressors and anxiety. Similarly, only partial support was found for the prediction that EA would amplify the stress-potentiating effects of TA (Hypothesis 2b). Given high TA, EA modestly enhanced the effects average stressors on anxiety and PTSS; but also reduced the effects of monthly increases in stressors on anxiety. Given high EA, TA enhanced the effects of average stressors on anxiety, but not PTSS; and only marginally enhanced the effects of monthly increases in stressors on anxiety. Only two of these observations support amplification and both appear better accounted for by protective effects. Specifically, TA was negatively associated with the effects of average stressors on PTSS only in those with low EA. EA was also negatively associated with the effects

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of average stressors on anxiety only in those with low TA. However, similar to the AS x EA findings, when the effect of stressors was held constant, TA was positively associated with anxiety, but only among those with high EA (see supplement, Table S6). The prediction that TA and AS would act as overlapping risk factors in amplifying the effects of war-zone stressors on symptoms (Hypothesis 2c) was not supported, regardless of the operationalization of stressors or symptom type. Consistent with the literature (e.g., Lilienfeld et al., 1993), findings support both functional overlap and distinctiveness of TA and AS both with respect to risk for in-theater symptoms, and vulnerability to war-zone stressors. For instance, although TA and AS operated as overlapping risk factors in amplifying the impact of monthly increases in stressors on anxiety, they exhibited clearly different stress-moderation effects on PTSS and depression, as well as different functional relations with EA in moderating the effects of stressors across symptom types.

The Adaptiveness of Seemingly Pathogenic Traits While preliminary, the present findings indicate potential adaptiveness of seemingly maladaptive traits. In contrast to AS, neither TA nor EA were invariably pathogenic, and under some conditions, protective effects were observed. Findings suggesting resilience among those conventionally construed as at risk may at first seem counterintuitive. However, they align with theories that broaden diathesis-stress models to encompass ideas of differential susceptibility (Belsky & Pluess, 2009) and sensitivity to context (Ellis, Boyce, Belsky, BakermansKranenburg, & van IJzendoorn, 2011; Telch, Harrington, Smits, & Powers, 2011; Telch et al., 2010). The premise shared by these theories is that individual differences exert differential effects as a function of context, and even seemingly maladaptive traits can be beneficial under

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certain conditions. Consistent with most prior findings, TA directly predicted anxiety and depression. However, TA’s stress-moderating effects varied across symptom types. While TA promoted anxiety in response to stressors, consistent with TA’s association with apprehension and hypervigilance to threats (e.g., Butler & Mathews, 1987), it was also associated with a reduced impact of stressors on depression and PTSS (see Figure 1). Importantly, these effects were limited to the context of mounting stressors, consistent with the lack of a direct association between TA and PTSS. This suggests TA may exert a specific stress-buffering effect. Unfortunately, there is a lack of research informing how TA relates to adaptation to stress and the emergent course of symptoms over time. Some evidence suggests TA can be beneficial (e.g., Mellanby & Zimdars, 2011; Lee, Wadsworth, & Hotopf, 2006), but most evidence has linked TA with negative mental health outcomes. This includes prospective findings that TA confers risk for post-deployment PTSD after adjusting for self-regulation and resilience factors (McNally et al., 2011). Thus, we must assume the observed protective effects of TA are contextually bound, and would not generalize to post-deployment outcomes. Given the observed context dependence of TA’s effects, this warrants discussion of how TA’s effects may vary as a function of context. The cardinal features of TA of apprehension and worry have been conceived as mental efforts to prevent or prepare for dreaded outcomes (e.g., Borkovec et al., 2004), and as a means of rehearsing reactions to stressors (e.g., Newman & Llera, 2011). Furthermore, intolerance of uncertainty is a key feature of generalized anxiety (e.g., Dugas, Buhr & Ladouceur, 2004), suggesting clear and present danger could serve a palliative function. For instance, allocation of attention to current threat may reduce the generalized preoccupation with imagined, potential threats, thereby reducing a prototypical

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source of distress associated with TA (Grillon, 2002). Moreover, while worry is itself activating, it is also negatively reinforcing by blunting emotional and physiological reactions to stressors (e.g., Borkovec et al., 2004; Newman & Llera, 2011). This has been theorized to be core driver of generalized anxiety, by promoting avoidance of downward emotional shifts through negative reinforcement of maintaining the worried state (see Newman & Llera, 2011 for review). Theory also suggests that persistently expecting the worst increases the likelihood of a positive contrast. Taken together, these findings suggest TA may simultaneously confer risk, but also be linked to more adaptive affective and behavioral mobilization in response to stressors. The findings for EA suggest that prior challenges to its incremental validity over related constructs (e.g., Berman, Wheaton, McGrath, & Abramowitz, 2010) may be an artifact of contingencies on its effects. This view is consistent with calls for more integrative approaches to the EA construct, and with the view of EA as an adaptive or maladaptive form of emotion regulation that critically depends on context (Hayes et al., 1996). Avoidance of negative experiences is thought to provide short-term relief, but ultimately maintain distress by reducing opportunities for threat disconfirmation and emotional processing following stress (Ehlers & Clark, 2000; Foa & Kozak, 1986). Thus, although evidence supports EA as conferring risk for psychopathology in the long run, it is also reasonable to expect EA to benefit soldiers during deployment, especially when heightened emotion could hinder performance. Importantly, EA’s interactions with the other trait dimensions as war-zone stress moderators produced the largest effect sizes (i.e., small to medium, r = -.24 to .27) across models. EA’s effects appear to critically depend on TA and AS, and on whether acute or more chronic stressor exposure is considered (see Figures 3 and 4). The findings indicate EA may be an effective short-term strategy, serving to buffer the impact of increases in deployment stress,

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but only in those with high TA (or low AS). However, EA may be ineffective for managing anxiety and PTSS in response to more prolonged elevations in stressors among those with high TA. These results are consistent with literature implicating avoidance as a time-dependent protective or pathogenic process – the adaptiveness of which depends on context, and flexible, judicious use (e.g., Hayes et al., 1996). In addition to contributing to efforts examining interactions between these traits (e.g., Zvolensky et al., 2015; Bardeen, 2015), these findings specifically promote person-as-context approaches (see Hoffman & Stawski, 2009), which will especially benefit efforts to determine the affect regulatory functions of EA, and to reconcile its pathogenic and adaptive effects. Clinical Implications Identifying malleable risk factors has the potential for enhancing secondary prevention. There is an extensive literature indicates AS operates as a vulnerability factor in anxiety-related psychopathology (e.g., Schmidt et al., 2006). The present findings are further support that AS is a potent risk factor for anxiety symptoms, but also indicate AS increases risk for in-theater depression. Fortunately, AS appears modifiable (Keough & Schmidt, 2012; Smits, Berry, Tart, & Powers, 2008), and in fact, reductions in AS have been shown to mediate the effects of cognitive-behavioral treatment for anxiety-related psychopathology (Smits, Powers, Cho & Telch, 2004). Future work evaluating pre-deployment screening and interventions that specifically target AS appears warranted. With respect to TA and EA, the clinical implications are less clear, given that the effects of both traits were partially dependent on the extent of stressor exposure, symptom domain and pre-deployment levels of AS. The findings suggest that when evaluating the adaptiveness of both traits, they should be examined in operation with other factors – an approach that

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compliments recommendations to examine the influence of both intra- and extra-personal contexts (e.g., S.C. Hayes et al., 2012). While evidence suggests TA and EA are modifiable (Jorm, 1989; Hayes, Orsillo, & Roemer, 2010; Walser, Karlin, Trockel, Mazina, & Taylor, 2013; Wolitzky-Taylor, Arch, Rosenfield, & Craske, 2012), it is unknown whether pre-deployment interventions targeting these traits would enhance psychological adjustment during or following deployment. Study Limitations Some limitations deserve mention. First, although the present study was more than sufficiently powered to detect small to medium effects (see Figure S1 in supplement), the presented models are complex, requiring estimation of many parameters. Hence, small effects should be interpreted cautiously pending replication with larger samples. Second, our analyses assume constant effects across levels and explanatory factors (Raudenbush & Bryk, 2002). Diagnostics indicated effects were stable, and model assumptions were met, but we cannot rule out the possibility that diathesis or stressor effects varied over time, or as a function of temporal proximity to their measurement. Third, most soldiers provided in-theater data (91%), but missing CEL entries were not uncommon. However, there were no apparent patterns in missing data across months, and multilevel models are well suited for data with missing observations. Fourth, the internal reliability for the AAQ-I was in the just satisfactory range, which may have reduced the ability to detect effects. Unfortunately, more recent and psychometrically superior measures of EA were not available at the time of study initiation. Finally, this study was limited to examining pre-deployment vulnerability for in-theater symptoms in a relatively homogenous sample. Inferences regarding long-term effects on re-adjustment to civilian life, and whether the findings would generalize to other populations and settings await further investigation.

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Conclusions This study presents preliminary prospective evidence for the singular and joint influence of three widely studied affect-related traits in moderating the impact of war-zone stressors on psychological symptoms. Considered together, TA and AS exerted independent stressmoderation effects, EA augmented these effects, and varied configurations of these traits either potentiated or tempered the impact of war-zone stressors. Importantly, these contextualized effects would have been obscured had we solely modeled single diathesis-stress interactions, or omitted interactions between the traits. This underscores the importance of considering how putative risk factors operate together, and in context, in governing the emergence of war-zone stress-related symptoms. Because constellations of individual differences and contextual factors, and the dynamic interactions among and between them allow many combinations to confer advantage, and disadvantage (cf. Belsky et al., 2007; Belsky & Pluess, 2009), they must be considered together. Importantly, the presented models captured only up to 29% of the variance in war-zone symptoms, with effect sizes ranging from small to medium in magnitude, which indicates a large proportion of the variance in war-zone symptoms was unexplained. Future work should aim to develop more complete and nuanced models of psychopathology by implementing similar multifactor approaches, with priority given to modifiable individual difference factors that moderate the impact of environmental stress.

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Table 1 Descriptive Statistics for Modeled Variables Variable Male Female Lifetime Axis I Disorder a Average Stressors b Deployment Months c Trait Anxiety (STAI-T) Anxiety Sensitivity (ASI-3) Experiential Avoidance (AAQ-I) PTSD Symptoms (PCL-4) Anxiety Symptoms (CEL-ANX) Depression Symptoms (CES-D-10) Bivariate Correlation Matrix Variable 1. Deployment Months 2. Lifetime Axis I Disorder 3. Gender d

a

b

N 129 32 94 161 161 161 161 161 153 161 150

% 80.12 19.87 70.19 -

M 3.42 14.30 37.95 10.17 31.51 5.25 10.60 7.65

SD 2.82 3.39 9.49 10.05 7.66 2.10 10.53 5.11

1 -

2 .354

3 .503

4 .710

5 .000***

6 .639

7 .747

8 .679

.03

-

.000***

.000***

.999

.000***

.420

.718

.999

***

-.02

.15

-

.000

***

4. Average War-zone Stressors 5. Changes in War-zone Stressors e

.01

.14

-.17

-

-.13

.00

.00

6. Trait Anxiety (STAI-T)

.01

.25

.13

7. Anxiety Sensitivity (ASI-3) 8. Experiential Avoidance (AAQ-I)

-.01 -.01

.03 .01

-.17 .09

.07 -.05

.000

.000

***

.005**

.030*

.999

.184

.110

.00

-

.999

.999

.999

-.04

.00

-

.000***

.000***

.00 .00

.37 .51

.48

.000*** -

Note. Table 1 presents descriptive statistics for all modeled variables. In bottom section, Bivariate correlations for all modeled predictors are depicted below the diagonal, whereas corresponding p-values are above the diagonal. STAI-T = State-Trait Anxiety Inventory (Form Y). AAQ-I = Acceptance and Action Questionnaire. ASI-3 = Anxiety Sensitivity Index - III. PCL-4 = Post-traumatic Stress Checklist – 4-Item Version. CEL-ANX = General Anxiety Scale. CES-D-10 = Center for Epidemiological Studies Depression Scale - 10 Items. a

Dichotomously coded (yes or no) presence of lifetime, including current, DSM-IV-TR Axis I

Disorders.

b

Average monthly stressors across soldiers and deployment months.

c

Average total

duration of deployment in months, across soldiers. d Coded: male = 0, female = 1. e Within-

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soldier monthly deviations from average monthly stressor exposure across deployment months. *p < .05. **p < .01, ***p < .001

Table 2 Main Effects of Pre-deployment Variables and Deployment Stressors on In-Theater PTSD, Anxiety, and Depression Symptoms Effect PTSD Symptoms (PCL-4) b t p 95% CI Size Gender .95 2.29 .024* .19 (.02, .34) Lifetime or Current Axis I Disorder .42 1.44 .153 .12 (-.05, .28) Trait Anxiety (STAI-T) -.09 -.51 .610 -.04 (-.21, .12) Anxiety Sensitivity (ASI-3) .59 3.51 .001** .28 (.13, .42) Experiential Avoidance (AAQ-I) .08 .44 .662 .04 (-.13, .20) Average War-zone Stressors .66 4.43 < .001** .35 (.20, .47) Changes in War-zone Stressors .27 4.80 < .001** .37 (.23, .50) 2 Global Effect Size Pseudo R = .19 Effect Anxiety Symptoms (CEL-ANX) b t p 95% CI Size Gender 2.76 1.77 .079 .14 (-.02, .30) Lifetime or Current Axis I Disorder 2.35 2.14 .034* .17 (.01, .32) Trait Anxiety (STAI-T) 1.62 2.36 .020* .19 (.03, .34) Anxiety Sensitivity (ASI-3) 2.50 3.93 .001** .31 (.16, .44) Experiential Avoidance (AAQ-I) -.73 1.06 .293 -.09 (-.24, .08) Average War-zone Stressors 1.69 2.69 .008* .22 (.06, .36) Changes in War-zone Stressors 1.15 7.70 .001** .53 (.42, .62) 2 Global Effect Size Pseudo R = .24 Effect Depression Symptoms (CES-D-10) b t p 95% CI Size Gender 2.02 2.26 .025* .19 (.02, .34) Lifetime or Current Axis I Disorder 1.36 2.23 .028* .19 (.02, .34) Trait Anxiety (STAI-T) .93 2.45 .015* .20 (.04, .35) Anxiety Sensitivity (ASI-3) .58 1.60 .112 .13 (-.03, .29) Experiential Avoidance (AAQ-I) .31 .78 .437 .07 (-.10, .23) Average War-zone Stressors .15 .47 .640 .04 (-.13, .21) Changes in War-zone Stressors .37 2.55 .011* .21 (.05, .36) 2 Global Effect Size Pseudo R = .18

Note. Main effects for each parameter above were derived from models including all covariates,

MODERATORS OF WAR-ZONE STRESS

40

but omitting interaction terms in order to estimate the impact of each variable across all months of deployment. STAI-T = State-Trait Anxiety Inventory (Form Y). AAQ-I = Acceptance and Action Questionnaire. ASI-3 = Anxiety Sensitivity Index - III. PCL-4 = Post-traumatic Stress Checklist – 4-Item Version. CEL-ANX = General Anxiety Scale. CES-D-10 = Center for Epidemiological Studies Depression Scale - 10 Items. *p < .05. **p < .01.

Table 3 Regression Coefficients for the Incremental Diathesis-Stress Models Effect PTSD Symptoms (PCL-4) b t p Size AS x Average War-zone Stressors .45 2.34 .021* .19 TA x Changes in War-zone Stressors -.25 -3.48 .001** -.28 AS x Changes in War-zone Stressors .31 3.29 .001** .27

TA x Changes in War-zone Stressors AS x Changes in War-zone Stressors

b

t

p

.32 .42

1.75 1.79

.080 .074

Effect Size .14 .15

95% CI (-.02, .30) (-.02, .30)

Pseudo R2 = .24 / .01 / .23

Global Effect Size / vs. Main / vs. Growth Depression Symptoms (CES-D-10)

b

t

p

TA x Average War-zone Stress AS x Changes in War-zone Stressors

-.80 .70

-2.09 3.10

.038* .002*

Global Effect Size / vs. Main / vs. Growth

(.03, .35) (-.42, -.12) (.11, .41)

Pseudo R2 = .24 / .05 / .22

Global Effect Size / vs. Main / vs. Growth General Anxiety Symptoms (CEL-ANX)

95% CI

Effect Size -.18 .26

95% CI (-.33, -.01) (.09, .40)

Pseudo R2 = .21 / .03 / .18

Note. Coefficients presented above were estimated by fully conditional models, as detailed in Table S2 of the supplement. TA = Trait anxiety, as measured by the State-Trait Anxiety Inventory (Form Y). EA = Experiential avoidance, as measured by the Acceptance and Action Questionnaire. AS = Anxiety sensitivity, as measured by the Anxiety Sensitivity Index - III. PCL-4 = Post-traumatic Stress Checklist – 4-Item Version. CEL-ANX = General Anxiety Scale. CES-D-10 = Center for Epidemiological Studies Depression Scale - 10 Items.

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41

*p < .05. **p < .01

Table 4. Regression Coefficients for the Interacting Diatheses-Stress Models Effect PTSD Symptoms (PCL-4) b t p Size TA x EA x Average Stressors .53 3.21 .002** .27 Global Effect Size for 3-way / vs. 2-Way / vs. Growth

General Anxiety Symptoms (CEL-ANX) TA x EA x Average Stressors TA x EA x Changes in Stressors AS x EA x Changes in Stressors

AS x EA x Changes in Stressors Global Effect Size for 3-way / vs. 2-Way / vs. Growth

(.11, .42)

Pseudo R2 = .29 / .05 / .27 b

t

p

1.92 -.53 .47

2.62 -2.92 2.68

.009* .004** .007**

Global Effect Size for 3-way / vs. 2-Way / vs. Growth

Depression Symptoms (CES-D-10)

95% CI

Effect Size .22 -.24 .22

95% CI (.05, .37) (-.39, -.08) (.06, .37)

Pseudo R2 = .26 / .02 / .24 b

t

p

.25

1.49

.137

Effect Size .13

95% CI (-.05, .30)

2

Pseudo R = .25 / .04 / .22

Note. See Table S2 in the supplement for detailed model specifications. TA = Trait anxiety, as measured by the State-Trait Anxiety Inventory (Form Y). EA = Experiential avoidance, as measured by the Acceptance and Action Questionnaire. AS = Anxiety sensitivity, as measured by the Anxiety Sensitivity Index - III. PCL-4 = Post-traumatic Stress Checklist – 4-Item Version. CEL-ANX = General Anxiety Scale. CES-D-10 = Center for Epidemiological Studies Depression Scale - 10 Items. *p < .05. **p < .01

MODERATORS OF WAR-ZONE STRESS

42

Fig 1. Pre-deployment trait anxiety (TA) x war-zone stressors predicting in-theater depression, PTSD, and anxiety symptoms. The top figure depicts the effects of average monthly war-zone stressor count (STRESSBP), given low versus high levels of TA at pre-deployment, on in-theater depression symptoms (CES-D-10). The bottom two figures represent the effects of withinsoldier changes in war-zone stressor exposure in any single deployment month (STRESSWP) on PTSD (PCL-4; middle) and anxiety symptoms (CEL-ANX; bottom) for soldiers with low versus high TA at pre-deployment. Low TA = gray dashed lines. High TA = gray solid lines. Fig 2. Pre-deployment anxiety sensitivity (AS) x war-zone stressors predicting in-theater PTSD, depression, and anxiety symptoms. The top left figure depicts the effects of average monthly war-zone stressors (STRESSBP) on PTSD symptoms (PCL-4) for soldiers with low versus high levels of AS at pre-deployment. The other figures represent the effects of within-soldier changes in monthly war-zone stressor exposure (STRESSWP) on PTSD symptoms (PCL-4; top right), depression symptoms (CES-D-10; bottom left) and anxiety symptoms (CEL-ANX; bottom right) for soldiers with low versus high AS. Low AS = gray dashed lines. High AS = gray solid lines. Fig 3. Pre-deployment anxiety sensitivity (AS) x experiential avoidance (EA) x war-zone stressors predicting in-theater anxiety symptoms. This figure represents the effects of withinsoldier changes in war-zone stressor exposure in any single deployment month (STRESSWP) on anxiety symptoms (CEL-ANX) for soldiers with low or high levels of AS and EA at predeployment. Low AS, low EA = gray dashed lines. Low AS, high EA = gray solid lines. High AS, low EA = gray dashed lines with triangles. High AS, high EA = gray solid lines with triangles. Fig 4. Pre-deployment trait anxiety (TA) x experiential avoidance (EA) x war-zone stressors

MODERATORS OF WAR-ZONE STRESS

43

predicting in-theater PTSD and anxiety symptoms. The top two figures depict the effects of average monthly war-zone stressors (STRESSBP) on anxiety (CEL-ANX; top) and PTSD symptoms (PCL-4; middle) for soldiers with low versus high TA and EA at pre-deployment. The bottom figure represents the effects of within-soldier changes in war-zone stressor exposure in any single deployment month on anxiety symptoms (CEL-ANX) for soldiers with low or high levels of TA and EA at pre-deployment. Low TA, low EA = gray dashed lines. High TA, low EA = gray solid lines. High TA, low EA = gray dashed lines with triangles. High TA, high EA = gray solid lines with triangles.

Highlights 

Anxiety sensitivity (AS) conferred risk directly and amplified stressors’ impact.



Trait anxiety (TA) and experiential avoidance (EA) did not invariably confer risk.



TA and EA were protective in the context of elevated stressor exposure.



Pre-dispositions and context interact in the emergence of stress-evoked symptoms.

MODERATORS OF WAR-ZONE STRESS

Fig. 1

44

MODERATORS OF WAR-ZONE STRESS Fig. 2

45

MODERATORS OF WAR-ZONE STRESS Fig. 3

46

MODERATORS OF WAR-ZONE STRESS Fig. 4

47

MODERATORS OF WAR-ZONE STRESS

48