Predictors of response to an attention modification program in generalized social phobia.

A number of randomized, double blind, placebo controlled studies support the efficacy of computerized Attention Modification Programs (AMP) in the treatment of anxiety (Amir, Beard, Taylor et al., 2009; Amir, Beard, Burns, & Bomyea, 2009; Schmidt, Richey, Buckner, & Timpano, 2009). AMP is based on cognitive theories of anxiety that propose a causal role for selective attention to threat-relevant information in the maintenance of anxiety (e.g., Mathews & MacLeod, 2005; Williams, Watts, MacLeod, & Mathews, 1997). Consistent with these theories, 25 years of research provides evidence demonstrating that patients meeting diagnostic criteria for an anxiety disorder preferentially attend to threat-relevant stimuli over neutral stimuli when the two compete for processing resources (for a review see Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007). More relevant to the causality hypothesis, recent studies have demonstrated that experimentally manipulating attentional allocation to threatening information confers differential susceptibility to anxiety under stress (e.g., Amir, Weber, Beard, Bomyea, & Taylor, 2008; Clarke, MacLeod, & Shirazee, 2008; MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002). To modify attention, previous studies have used a variant of the probe detection task (MacLeod, Mathews, & Tata, 1986) designed to induce selective processing of neutral cues when these cues compete for processing resources with threat-relevant cues. In the original probe detection task participants were presented with neutral and threat-relevant information and their response latency to detect a visual probe replacing these words was measured. In the AMP training version of the task, participants respond to a visual probe that consistently replaces stimuli of neutral valence, thereby directing attention away from threat. To our knowledge, three published studies have examined the efficacy of AMP in reducing symptoms in treatment-seeking individuals meeting diagnostic criteria for an anxiety disorder; two in generalized social phobia (GSP; Amir, Beard, Taylor et al., 2009; Schmidt et al., 2009) and one in generalized anxiety disorder (GAD; Amir, Beard et al., 2009). All three studies were randomized placebo-controlled double-blind trials. The placebo group (Attention Control Condition, ACC) was identical to AMP except that the location of the probe relative to the threatening or neutral stimuli was random (i.e., the probe replaced neutral stimuli on 50% of trials, and threatening stimuli on the other 50% of trials). Participants completed AMP or ACC twice weekly for four weeks. Each training session was approximately 20 minutes in duration. Across all three RCTs, participants in the AMP group showed significantly larger reductions in clinician- and self-rated symptoms of anxiety and functional impairment relative to the control group. A recent meta-analysis found a medium-to-large effect of AMP on anxiety symptoms (averaged across all anxiety measures) relative to the ACC group (d = .78 in patient samples; Hakamata et al., 2010). Moreover, a significantly larger proportion of participants in the AMP group compared to the ACC group no longer met diagnostic criteria for the principal anxiety disorder diagnosis at post-assessment (loss of diagnosis: AMP, range 50% to 72%; ACC, range 11% to 14%). Finally, two of the trials conducted a formal mediation analysis (Mackinnon, Lockwood, Hoffman, West, & Sheets, 2002), demonstrating that change in attentional bias for threat mediated the reduction in interviewer-rated anxiety from pre- to post-assessment (Amir, Beard, Taylor et al., 2009; Amir, Beard et al., 2009). Considered together, these studies suggest that AMP may be an efficacious treatment for at least GSP and GAD. Despite these initial promising findings, not all individuals who complete AMP show a reduction of symptoms. Therefore, it is important to examine patient characteristics that may account for variability in response to AMP. Doing so may ultimately inform treatment selection as well as methods for augmenting existing treatments. As Steketee and Chambless (1992, p. 387) pointed out, “If we can identify characteristics of clients that are associated with poor response to treatment, we may: (a) gain clues that lead to needed modifications to treatment procedures and (b) be able to match patients to treatments that work best for those with their particular characteristics”. As a first step in this direction, we examined predictors of response to AMP in a large randomized clinical trial of patients seeking treatment for GSP. There are at least two types of pre-treatment variables that can have predictive utility: Prognostic and Prescriptive (Kraemer, Wilson, Fairburn, & Agras, 2002, Fournier et al., 2009). A prognostic variable predicts outcome irrespective of treatment condition. Prognostic variables are either identified in single-treatment designs (i.e., when there is no comparison condition) or when a variable is associated with outcome across multiple treatments (i.e., a main effect of the pre-treatment variable on outcome). A prescriptive variable or moderator (Baron & Kenny, 1986) predicts differential treatment response between two or more treatment conditions (i.e. an interaction effect). Our selection of predictor variables was guided by previous recommendations in prediction research (e.g., Kazdin, 2008; Kraemer et al., 2002; Steketee & Chambless, 1992). We began by identifying baseline demographic and clinical characteristics in our sample that have been examined in previous research examining predictors of treatment response in SP (e.g., Hofmann, 2000; Rodebaugh, Holaway, & Heimberg, 2004) as well as the extant literature more broadly (e.g., Fournier et al., 2009). These variables were categorized into the following domains: (1) demographic characteristics (e.g., age, gender), and (2) clinical characteristics (e.g., severity of co-occurring depression, Axis I comorbidity). Next, we followed recommendations that the selection of predictor variables should be informed by theory about the mechanisms hypothesized to underlie to treatment effects (e.g., Kazdin, 2008; Kraemer et al., 2002). Accordingly, we selected potential predictor variables based on information processing theories of anxiety (e.g., Mathews & MacLeod, 2005) and prior research on the mechanisms of action in AMP (e.g., Amir, Beard, Taylor et al., 2009). These variables were categorized as cognitive disturbance factors. We identified two cognitive disturbance factors hypothesized to be central to the maintenance of SP, namely (1) attentional bias for threat (Mathews & MacLeod, 2005; Williams et al., 1997; see also Bogels & Mansell, 2004) and (2) social interpretation bias (e.g., Clark & Wells, 1995; Hofmann, 2007; Rapee & Heimberg, 1997). Previous research in analogue (Amir et al., 2008) and treatment-seeking samples (Amir, Beard, Taylor et al., 2009; Amir, Beard et al., 2009) suggest that anxiety reduction associated with AMP occurs through a change in attention bias for threatening cues. Thus, to the extent that the modification of attentional bias for threat is a key target of AMP, we expected that patients with GSP who displayed the greatest attentional bias at baseline would be the ones who would benefit most from this intervention; that is, attentional bias is hypothesized to have a prescriptive effect on response to AMP. We also chose to examine social interpretation bias as a predictor of response to AMP because this cognitive bias is a core feature of cognitive models of SP (e.g., Clark & Wells, 1995; Hofmann, 2007; Rapee & Heimberg, 1997) and has been shown to be associated with social anxiety reduction in CBT regimens (e.g., Foa, Franklin, Perry, & Herbert, 1996; Hofmann, 2004; Smits, Rosenfield, McDonald, & Telch, 2006; Taylor & Alden, 2008; Wilson & Rapee, 2005). In the current study, we explored the prognostic and prescriptive predictive value of pre-treatment variables assessed in the context of two RCTs testing the efficacy of AMP for GSP. We followed the data analytic approach adopted by Fournier et al. (2009), first employing a stepwise procedure within the three broad predictor domains (demographics, clinical characteristics, and cognitive disturbance factors) and then testing a final model wherein we examined whether each of the previously identified predictors remained a significant predictor of outcome when all of the predictors were tested simultaneously in the same statistical model.