Running Head : CLUSTER ANALYSIS OF ED VARIABLES 1 Cluster Analysis of Eating Disorder Behaviors using EMA Technology : A Secondary Analysis of Randomized Clinical Trial Data by

Eating disorders are a dangerous form of mental illness and very little is understood regarding individual behavioral patterns. The goal of current study is to determine if meaningful eating disorder behavioral patterns can be identified within and among eating disorder (ED) diagnostic categories by using a cluster analytic approach. This study is a secondary analysis of data collected as part of a randomized clinical trial of an identity focused cognitive-behavioral psychotherapy designed to promote recovery and health women with AN and BN. Sixty-five women diagnosed with either threshold or subthreshold anorexia nervosa (AN) or bulimia nervosa (BN) completed 21 days eventand signal-triggered EMA methodology to prospectively measure eight ED behaviors. Bivariate correlations and cluster analyses were used to explore the natural groupings of 11 eating disorder variables. The average number of behavioral episodes (M = 36.40) and percentage of days with no recorded behavior (M = 16.62). The most frequent behaviors included restricting (M = 35.76%), vomiting (M = 28.71%), and bingeing (M = 14.63%). Significant correlations were found between a few important variables, including restricting and vomiting (r = -.583, p < .01) and other structural behaviors representative of daily behavioral patterning. This study supported the notion that by examining a wide spectrum of eating disorder behaviors, natural groupings of eating disorder individuals can be found through cluster analysis. The goal of this study was to add depth to our knowledge regarding daily behavioral engagements of women diagnosed with an ED and in doing so holds potentially improve approaches to assessment and treatment. CLUSTER ANALYSIS OF ED VARIABLES 3 Cluster Analysis of Eating Disorder Behaviors using EMA Technology: A Secondary Analysis of Randomized Clinical Trial Data Each year, over five-million Americans are diagnosed with an eating disorder (Becker, Grinspoon, Klibanski, & Herzog, 1999). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IVTR; 2000), eating disorders (ED) include three diagnostic categories: anorexia nervosa (AN), bulimia nervosa (BN), and eating disorder not otherwise specified (EDNOS) and these three diagnostic categories are defined by specific symptom profiles that are used to determine the presence or absence of each eating disorder. However, current research on eating disorders is beginning to question the homogeneity of individuals within these diagnostic categories (Wonderlich, Joiner, Keel, Williamson, & Crosby, 2007). Although clear objective boundaries are inherent in the DSM system, there is emerging evidence that important variability within a diagnosis can be masked by the diagnosis itself. For example, research has shown that individuals with the same diagnosis engage in multiple different purging and non-purging behaviors (Clinton et al., 2004), thus making it difficult to identify and measure individual behavioral change. The goal of current study is to determine if meaningful eating disorder behavioral patterns can be identified within and among the ED diagnoses categories by using a cluster analytic approach. Recent studies provide preliminary evidence to suggest behavioral patterns within the eating disorder diagnoses. As suggested by Wonderlich, et al. (2007), an exploration and expansion of behaviors constituting the eating disorder is necessary. Studies have shown that eating disordered behaviors may be entrained to days of the week and times of the day. For example, Smyth and colleagues (2004) found day of week effects on eating disorder behaviors, in that there was a significant day of week effect on vomiting and a marginally significant effect CLUSTER ANALYSIS OF ED VARIABLES 4 on bingeing. Both events occurred with greatest frequency on Sunday, even as compared with Friday and Saturday. Similarly, other studies have examined correlations between the behaviors themselves. Reba and colleagues (2005) found that laxative use negatively predicted other forms of purging behaviors including diuretic use and vomiting; Reba-Harrelson and colleagues (2008) found that diet pill use positively predicted vomiting, and similarly, that diet pill use positively predicted other types of purging; and Shroff and colleagues (2006) found a positive correlation between excessive exercise and the diagnosis of anorexia nervosa-purging type. Important behavioral correlations can be overlooked by current diagnostic categories. The diagnosis of BN, which is characterized by alternating episodes of binge eating and compensatory behavior to avoid weight gain, is not specific to the type of compensatory behaviors used. Similarly, the diagnosis of AN is based on physical outcomes including amenorrhea and low BMI, but is mute on the specific behaviors that lead to the physical outcomes. Thus, from a diagnostic perspective, a single diagnosis may encompass several distinct behavioral patterns and, conversely, behavioral patterns may exist across diagnoses. By looking at the prevalence and temporal occurrence of eating disorder variables, this study will broaden the spectrum of knowledge regarding daily behavioral engagements of women diagnosed with an ED and in doing so holds potential for improving approaches to assessment