The Role of Alexithymia and Affective Lability in Disordered Eating Behaviors

University of Kentucky UKnowledge

Theses and Dissertations--Psychology Psychology

2021

THE ROLE OF ALEXITHYMIA AND AFFECTIVE LABILITY IN DISORDERED EATING BEHAVIORS

Anna Marie Lilia Ortiz University of Kentucky, [email protected] Author ORCID Identifier: https://orcid.org/0000-0002-4761-2595 Digital Object Identifier: https://doi.org/10.13023/etd.2021.280

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Recommended Citation Ortiz, Anna Marie Lilia, "THE ROLE OF ALEXITHYMIA AND AFFECTIVE LABILITY IN DISORDERED EATING BEHAVIORS" (2021). Theses and Dissertations--Psychology. 196. https://uknowledge.uky.edu/psychology_etds/196

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Anna Marie Lilia Ortiz, Student

Dr. Gregory T. Smith, Major Professor

Dr. Mark T. Fillmore, Director of Graduate Studies

THE ROLE OF ALEXITHYMIA AND AFFECTIVE LABILITY IN DISORDERED EATING BEHAVIORS

______

DISSERTATION ______A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Arts and Sciences at the University of Kentucky

By Anna Marie Lilia Ortiz Lexington, Kentucky Director: Dr. Gregory T. Smith, Professor of Psychology Lexington, Kentucky 2021

ABSTRACT OF DISSERTATION

THE ROLE OF ALEXITHYMIA AND AFFECTIVE LABILITY IN DISORDERED EATING BEHAVIORS

Affective distress may influence eating disorder behaviors in multiple ways. Affective lability refers to the tendency to experience frequent and striking fluctuations in mood. There is considerable evidence that it predicts eating disorder symptoms. Further, alexithymia is characterized by difficulties identifying and describing feelings and an externally oriented thinking style. Alexithymia has also been implicated in the development and maintenance of eating disorders. To date, these risk factors have been studied separately. Given the evidence that both alexithymia and affective lability are affective components associated with eating disordered behaviors, it may be important to examine possible relationships among alexithymia, affective lability, binge eating, and purging. Therefore, I investigated possible moderation and mediation effects longitudinally in 587 college students. Specifically, I tested the presence of joint effects of the two risk factors on binge eating and purging, such that individuals that experience frequent and rapid changes in mood may be more likely to binge eat or purge if they also have a difficult time identifying their emotions. Additionally, I examined the viability of the following mediational processes: that affective lability predicts increases alexithymia, which in turn predicts increases in binge eating and in purging; and that alexithymia predicts increases in affective lability, which in turn predicts increases in binge eating and in purging. Results supported my hypothesis the alexithymia predicts increases in affective lability, which in turn predicts increases in purging. Results were not consistent with the other mediational processes. Moreover, none of the moderation hypotheses were supported. The implications of these results are discussed.

KEYWORDS: affective lability, alexithymia, binge eating, purging, eating disorders

Anna Marie Lilia Ortiz

June 22, 2021 Date

THE ROLE OF ALEXITHYMIA AND AFFECTIVE LABILITY IN DISORDERED EATING BEHAVIORS

By Anna Marie Lilia Ortiz

Gregory T. Smith, Ph.D. Director of Dissertation

Mark T. Fillmore, Ph.D. Director of Graduate Studies

June 22, 2021 Date

ACKNOWLEDGMENTS

The following dissertation, while an individual work, benefited from the insights and direction of several people. First, my Dissertation Chair, Dr. Gregory Smith, for the opportunity to learn and grow throughout graduate school. Next, I wish to thank the complete Dissertation Committee, and outside reader, respectively: Dr. Shannon Sauer-

Zavala, Dr. Thomas Widiger, Dr. Carrie Oser, and Dr. Joseph Hammer. Each individual provided insights that guided and challenged my thinking, substantially improving the finished product. Finally, I would like to thank Dr. Cheri Levinson for her guidance and mentorship.

In addition to the technical and instrumental assistance above, I received equally important assistance from family and friends. My parents, Mary Sue and Fernando Ortiz, sisters and brothers-in-law, Emily and Michael Bianco and Melissa and Kevin

McCartney, and nieces and nephew, Thérèse, Gianna, and Francis Bianco, all provided invaluable and on-going support throughout the dissertation process and graduate school in general. Thank you for believing in, loving, and encouraging me. I would not be where or who I am today without you all. I would also like to express my deepest gratitude to my friends, both within and outside of graduate school, for the time, laughter, and adventures shared together. I am incredibly grateful for each of you. I also wish to thank the respondents of my study. Finally, I would like to thank God for His faithfulness, grace, and guidance.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

CHAPTER 1. INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Affect-Based Risk for Eating Disorders ...... 2 1.2.1 Alexithymia...... 2 1.2.2 Affective Lability ...... 4 1.3 The Interplay Between Risk Factors ...... 5 1.4 The Current Study ...... 7

CHAPTER 2. METHODS ...... 9 2.1 Participants ...... 9 2.2 Measures ...... 9 2.3 Data Analytic Procedure ...... 11

CHAPTER 3. RESULTS ...... 15 3.1 Participants Retention and Treatment of Missing Data ...... 15 3.2 Descriptive Statistics ...... 15 3.3 Model Tests Results...... 16 3.3.1 Autoregressive Predictive Effects ...... 17 3.3.2 Mediation Model Tests ...... 17 3.4 Interaction Effects ...... 17

CHAPTER 4. DISCUSSION ...... 24 4.1 Mediation Processes ...... 24 4.2 Moderation Effects ...... 27 4.3 Strengths ...... 27 4.4 Limitations and Future Directions...... 28 4.5 Conclusions ...... 30

REFERENCES ...... 32

VITA ...... 40

LIST OF TABLES

Table 1 Descriptive Statistics of Affective Lability, Alexithymia, Negative Affect, and Body Mass Index ...... 19 Table 2 Presence of Binge Eating and Purging at Waves 1-3 ...... 20 Table 3 Bivariate Correlations Between Study Variables ...... 21

LIST OF FIGURES

Figure 1 Structural equation model with pathways pertaining to main model...... 22 Figure 2 Full structural equation model with all significant pathways...... 23

vii

CHAPTER 1. INTRODUCTION

1.1 Background

Eating disorders are a significant public health concern and are associated with many negative consequences, including medical complications, cognitive dysfunction, and the development of other comorbid psychiatric conditions (American Psychiatric

Association, 2013; Keski-Rahkonen & Mustelin, 2016; Treasure & Schmidt, 2013). Of note, eating disorders have the highest rate of mortality of any mental illness (Mitchell &

Crow, 2006). Eating disordered behaviors, such as binge eating and purging, typically begin to develop during adolescence and emerging adulthood (Allen et al., 2013; Hoek & van Hoeken, 2003) and, importantly, engagement in these behaviors is predictive of the development of clinically significant eating disorders later on in life. It is clear from the above that the identification of risk or causal factors for these disorders is critical.

A crucial area of research and public health concern is identifying factors that contribute to the development and maintenance of eating disorders. The determination of such mechanisms provides insight into how certain individuals develop eating disorders and others do not, which may help to identify individuals who are at greatest risk and decrease the stigma associated with this mental illness. Over the years, a substantial amount of effort has been dedicated to the identification of risk factors and the development of validated risk theories. For example, research has examined the role of individual differences in personality (Fischer et al., 2008), pubertal onset (Klump, 2013), and learned expectancies regarding reinforcement from eating and from thinness

(Hohlstein et al., 1998).

An additional area of documented risk concerns affect. Much of the research on affect and eating disorders has primarily focused on the role of negative affect

(Heatherton & Baumeister, 1991). Although negative affect is surely an important component in the development and maintenance of eating disorders, it is also vital to explore the role of other affective factors as well. Further, limited work has been done investigating how factors transact together to potentiate risk. Doing so may clarify risk processes, aid in identifying high-risk groups, help to create targeted prevention programs and intervention efforts, and inform public policy. Therefore, in the current study, I examined the interplay between two under-appreciated risk factors, namely alexithymia and affective lability, in the development of eating disordered behaviors. I next review relevant research for each of these attributes as well as existing work that supports the hypotheses that these risk factors transact.

1.2 Affect-Based Risk for Eating Disorders

1.2.1 Alexithymia

Alexithymia is a personality trait that is characterized by three primary features:

(1) difficulties identifying feelings, (2) difficulties describing or communicating feelings, and (3) a cognitive style that is focused on the external environment as opposed to internal states. Individuals with low awareness of their emotions tend to be less cognizant of their psychological needs (Dizén et al., 2005). Further, a lack of clarity into one’s feelings may impede one’s ability to regulate emotions effectively. Since emotions aid in informing whether one’s goals and needs are being met (Lazarus, 1991), individuals who struggle to identify or differentiate between emotions may also have difficulties determining the best course of action when needs and goals are going unmet. It is thus

important to consider the possibility that alexithymia could result in ineffective regulation of emotions.

Research has demonstrated that individuals with eating disorders tend to be high in alexithymia (Bourke et al., 1992; Corcos et al., 2000). It has been posited that alexithymia may underlie the emotional difficulties that individuals with eating disorders often experience (Brewer et al., 2015). Consistent with this possibility, alexithymia has been implicated in the development and maintenance of eating disorders (Giombini et al.,

2019) and is related to poorer treatment outcomes (Gramaglia et al., 2020; Pinna et al.,

2014). In Nowakowski, McFarlane, and Cassin’s (2013) review of the literature, both individuals with eating disorders, as well as nonclinical samples with disturbed eating, appear to have deficits in identifying and communicating affective states. A cross- sectional study of a non-clinical sample of undergraduate females found that individuals in the alexithymia range were found to engage in more consistent body checking behaviors and had higher body dissatisfaction. They also reported a higher potential risk for developing an eating disorder (De Berardis et al., 2007). Finally, patients with bulimia nervosa often engage in binge eating and purging as a response to stress but find it difficult to connect their behavior to an emotional trigger (Schmidt et al., 1993).

Although few longitudinal studies have been conducted, there is some evidence to support a prospective relationship between alexithymia and eating disorders. A 3-year longitudinal study of patients with an eating disorder found that difficulties identifying feelings, which is a subscale of the alexithymia measure, significantly predicted treatment outcome, controlling for depression and the severity of the eating disorder (Speranza et al., 2007). Further, in a sample of women with bulimia nervosa, the examination of

pretreatment and posttreatment levels of alexithymia demonstrated that abstinence from binge/purge episodes was associated with a significant reduction in alexithymia (de Groot et al., 1995).

1.2.2 Affective Lability

An additional form of affective risk may be affective lability, which refers to the degree to which an individual experiences striking and frequent shifts in mood states. The variable and sudden intensity of emotional shifts is different from consistent or steady- state negative affect. Compared to a stable predisposition to negative emotionality, the unpredictability of fluctuating negative and positive emotion states may cause a feeling of being out of control, which may lead to more distress overall. This compounded stress from the dysregulation of affect may lead individuals to behave impulsively (e.g., binge eat, purge) as a means of coping with their changing affective states and associated distress.

Studies have demonstrated that affective lability is a distinct construct from neuroticism, of which negative emotionality is a key component, and is associated with different correlates, prospectively predictive of different outcomes, and incrementally predictive beyond negative affect (Kamen et al., 2010; Miller & Pilkonis, 2006). For example, affective lability has been found to predict social and academic functioning above and beyond neuroticism (Bagge et al., 2004). One explanation may be that measures of broad negative affect do not capture dynamic processes that occur over time.

It has been suggested that the typical items that measure negative affect/neuroticism do not use a clearly defined time frame; use vague qualifiers of intensity, duration, and frequency; and do not provide information about dynamic processes because they reflect

an individual’s mean level of negative emotionality over a significant period of time

(Ormel et al., 2004). Therefore, studying affective lability may provide important information that is not captured by negative affect alone.

Affective lability is thought to be a risk factor for eating disorders in general

(Lavender et al., 2013) and binge eating and purging behaviors in particular (Anestis et al., 2010; Benjamin & Wulfert, 2005; Berner et al., 2017; Santangelo et al., 2014; Yu &

Selby, 2013). Evidence suggests that binge eating and purging are used as a means of regulating affect (Haedt-Matt & Keel, 2011). When individuals are experiencing rapid and recurring changes in mood, they may be more inclined to engage in maladaptive behaviors to provide immediate relief from the distress associated with the changing mood states (Anestis et al., 2009; Berner et al., 2017). Consistent with this possibility, ecological momentary assessment (EMA) research has demonstrated that bulimic behaviors, such as binge eating and purging, tend to occur on days in which emotional variability is higher (Selby et al., 2012). Specifically, elevations in affective lability predict the number of daily binge eating episodes and account for variance in global eating disorder symptoms (Anestis et al., 2010).

1.3 The Interplay Between Risk Factors

What is particularly under-appreciated is the likelihood of transactions occurring among these risk factors and eating disordered behaviors. Two such forms of transaction are mediation and moderation. There are plausible theoretical grounds to consider the possibility that affective lability and alexithymia transact to increase risk. First, both affective variability and affective intensity are inversely related to emotion clarity

(Thompson et al., 2009) and emotion differentiation (Boden et al., 2013; Demiralp et al.,

2012; Pond et al., 2012). Additionally, researchers have posited that the ability to identify and label emotions would assist individuals in understanding and managing their emotions (Kashdan et al., 2015; Vine & Aldao, 2014). It follows that alexithymia has been found to lead to disturbances in affect regulation. Recent work by Edwards and colleagues (2021) also supports such a mediational relationship, i.e., that disrupted affective regulation may partially mediate the predictive influence of alexithymia on symptoms of borderline personality disorder.

To elaborate on this proposed mechanism: A person with alexithymia may have limited capacities to use cognitive mechanisms to understand and regulate emotions, and, in turn, misinterpret, focus on, and amplify the bodily sensations associated with the emotional arousal (Taylor et al., 1997). It follows that over time decreased emotional clarity, as experienced in alexithymia, may result in an increase in affective instability due to an inability to effectively tend to the experienced emotion. As a result, one may engage in actions that offer a quick resolution to unidentifiable distress. Therefore, a mediational process may occur in which alexithymia predicts instability in affect, which in turn predicts behaviors that provide immediate reductions in a given affective state, such as binge eating and purging.

Similarly, the reverse may be true, such that affective lability may lead to an increase in alexithymia, which in turn predicts binge eating and purging. Experiencing emotions as rapidly changing, intense, and unpredictable may lead to increased difficulty identifying and labeling emotional states. Moreover, binge eating disorder is associated with higher levels of alexithymia and more engagement in emotional eating compared to healthy controls (Pinaquy et al., 2003). What is more is that individuals with bulimia

nervosa, which is characterized by binge eating and purging episodes, tend to have difficulties in identifying feelings and triggers associated with these cycles (de Groot et al., 1995). Consequently, eating disorder behaviors, such as binge eating and purging, may be used to avoid, stifle, and/or organize indeterminate affect states (Goodsitt, 1983).

Thus, it is possible that alexithymia mediates the relationship between affective lability and binge eating and purging. The identification of the possible relationships among these risk factors represents a novel, and as yet unstudied, extension of the existing literature.

A third possibility is that the two factors interact to increase risk. Perhaps the influence of affective lability on binge eating and purging is stronger at higher levels of alexithymia. It may be that rapid shifts in emotional states leave one less prepared to cope when suddenly distressed. Among such individuals, it may be that those high in alexithymia, and hence unable to identify their emotional experiences, are even less likely to respond in ways that effectively manage their affect. Thus, they may be more likely to respond to abrupt and unidentifiable distress with behaviors that alter their current mood state. One example of such behaviors may be eating disordered behaviors.

1.4 The Current Study

To address moderation and mediational hypotheses involving alexithymia, affective lability, binge eating, and purging, I conducted a three-wave longitudinal study

(N = 587) of university students. First, I tested a set of mediational hypotheses involving alexithymia and affective lability. As described above, I investigated whether longitudinal data would be consistent with the possibility that affective lability mediates the predictive influence of alexithymia on binge eating and purging. I then tested the

reverse mediation: that the data would be consistent with the possibility that alexithymia mediates the predictive influence of affective lability on subsequent binge eating and purging. In each case, I tested the hypothesized mediation using models that also included prediction from negative affect, body mass index (BMI), and gender. My goal was to test predictive effects involving alexithymia and affective lability beyond prediction from those other variables. In particular, I sought to determine whether prediction from affective lability and alexithymia operated differently when negative affect was included or excluded from the model. Thus, I examined the same model without the inclusion of negative affect.

Additionally, I hypothesized that the interaction of wave 1 affective lability and wave 1 alexithymia would predict wave 2 binge eating and wave 2 purging. I anticipated that this effect would be present from wave 2 to wave 3 as well. Specifically, I anticipated that the relationship between affective lability and each eating disorder behavior would be stronger at higher levels of alexithymia. To my knowledge, the current study is the first test of a model identifying both alexithymia and affective lability as contributing mechanisms to eating disordered behaviors.

CHAPTER 2. METHODS

2.1 Participants

Participants (N = 587) were recruited via SONA, an online research pool of students in an undergraduate psychology class at a midwestern university. To participate in this study, subjects had to be between the ages of 18 and 20 and in their first year of college at wave 1. Current or historic eating disorder diagnosis did not preclude study participation. The sample consisted of 469 (79.9%) women and 118 (20.1%) men; no participants identified as non-binary or gender fluid. The mean age of the participants at the start of the study was 18.21. The majority of the sample identified as European

American (85.4%), followed by Black (9.3%); the remainder of the participants identified themselves as Latinx (6.1%), Asian (4.2%), Native American (0.8%), Pacific Islander

(0.7%), and other (0.3%). Participants were allowed to select multiple ethnicities; therefore, the total percentage exceeds 100.

2.2 Measures

Demographic/Background Questionnaire. Demographic information such as participant age, gender, race, marital/relationship status, education, occupational status, personal income, and family of origin income was obtained from participants.

Eating Disorder Examination-Questionnaire (EDE-Q; Fairburn & Beglin, 1994). This measure is a self-report version of the Eating Disorder Examination semi-structured interview used to assess eating disorder symptoms during the preceding four weeks

(Cooper & Fairburn, 1993; Fairburn & Beglin, 1994). It has been shown to have good reliability and validity (Cooper & Fairburn, 1993; Luce & Crowther, 1999; Mond et al.,

2004). I defined binge eating as objective binge eating with loss of control (see Tanofsky- 9

Kraff et al., 2007). To be scored positively for binge eating, participants needed to positively endorse two items. The first question assessed objectively large eating episodes and the second assessed loss of control during these episodes. Binge eating status was measured dichotomously (yes or no). Therefore, if participants’ response on both items was greater than 0, then they received a score of “1.” Purging was measured with the question “Over the 28 days, how many times have you made yourself sick (vomit) as a means of controlling your shape or weight?” Purging status was also measured dichotomously (yes or no). Therefore, participants received a score of “1” if they indicated a number greater than 0. This measure also asks participants to report their height and weight. This information was used to calculate body mass index (BMI). To calculate BMI using the standard formula (kg/m2), participants’ weight in pounds was converted to kilograms, and their height in inches converted to meters.

Five-Factor Borderline Personality Inventory: Affective Dysregulation (FFBI; Mullins-

Sweatt et al., 2012). The 10-item Affective Dysregulation scale was taken from this 120- item measure. This measure has been found to have good convergent validity with other measures of affective lability in a sample of undergraduate students. It also has demonstrated good internal consistency (Mullins-Sweat et al., 2012). Items are rated on a

5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). A sample question from this scale is “My mood shifts rapidly from one feeling to another.” Internal consistencies in the current study, by wave, were α = .91, .93, and .93.

Toronto Alexithymia Scale (TAS-20; Bagby, Parker, et al., 1994) is a 20-item questionnaire is comprised of three subscales that measure the key components of alexithymia, including difficulties identifying emotions, difficulties describing emotions,

and an externally oriented thinking style. The TAS-20 has demonstrated adequate retest reliability (Bagby, Parker, et al., 1994), convergent validity, as well as divergent validity

(Bagby, Taylor, et al., 1994). In the current study, internal consistencies ranged from α =

.83 to .86.

The Positive and Negative Affect Schedule, Negative Affect Scale (PANAS; Watson et al.,

1988). The 10-item negative affect subscale of the PANAS was used in this study. Using a 5-point Likert scale (from 1 = very slightly or not at all, to 5 = extremely), participants indicated how often they felt a given negative mood during the previous two weeks. The negative affect subscale score is the sum of the ten items, ranging from 10 to 50, with higher scores indicating more frequent experiences of negative emotions. The validity, internal consistency, and test-retest reliability of the PANAS are good (Watson et al.,

1988). The internal consistency for the current study ranged from α = .88 to .90.

2.3 Data Analytic Procedure

Data Collection. Participants were consented and completed questionnaires online at three different time points during their first and second year of college: the fall of their first year, the spring of their first year, and the fall of their second year. Participants received credit for research participation at wave 1 and received payment ($10) for completing waves 2 and 3. This procedure was approved by the University’s Institutional

Review Board. Retention was challenging because the pandemic began partway through the longitudinal period. Education became remote and the dormitories were closed. As a result, students faced a variety of challenges that complicated retention. To secure the best possible retention, the following strategies were employed: daily automatic study

reminder emails and weekly personalized reminder emails and text messages from study coordinators.

Data Analysis. I report on two sets of analyses. First, I tested a predictive model using structural equation modeling (SEM); the software I used was Mplus (Muthen & Muthen,

2004-2010). I used the robust weighted least squares (WLSMV) method. This method does not assume that the data is normally distributed and provides the best option for modeling categorical data (Brown, 2006). Binge eating and purging were modeled dichotomously. I tested mediation by using a bias-corrected bootstrapping method. This method does not impose the assumption of normality of data and increases statistical power. This procedure, which is available in MPlus, generated 1,000 bootstrapped samples to empirically approximate the true sampling distribution because the assumption of a normal sampling distribution is not likely to be accurate (Muthen &

Muthen, 2004-2010). I assessed the model fit using two relative fit indices, the comparative fit index (CFI) and the Tucker-Lewis index (TFI), and two absolute fit indices, the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR). Guidelines for these indices vary. Using the most stringent guidelines, CFI and TFI values of .95 or higher are described as representing good fit. RMSEA values less than .05 indicate a close fit and SRMR values of .09 or lower tend to indicate good fit (Hu & Bentler, 1999) Additionally, I reported the model chi-square.

The full model is represented in Figure 1. This model included the following: (1)

Cross-sectional associations among all variables, which included affective lability, alexithymia, binge eating, purging, BMI, gender, and negative affect, at each wave (not

depicted in the model). (2) Autoregressive predictive effects from each variable at each wave to itself at the following wave. (3) Longitudinal predictive effects from each variable to each other variable, with the exception that gender measured at wave 1 predicted only wave 2 binge eating and wave 2 purging. (4) Four mediational pathways representing the central hypotheses of my model. These included: wave 1 affective lability through wave 2 alexithymia to wave 3 binge eating; wave 1 affective lability through wave 2 alexithymia to wave 3 purging; wave 1 alexithymia through wave 2 affective lability to wave 3 binge eating; and wave 1 alexithymia through wave 2 affective lability to wave 3 purging.

Additionally, the same model was tested without the inclusion of negative affect to determine the value of predicting above and beyond this affective risk factor. I then compared the model fit between these two models.

The second set of analyses regarding my moderation hypotheses were conducted using linear regression analysis in SPSS 27.0. To construct the interaction terms of affective lability and alexithymia at waves 1 and 2, I centered both variables and then calculated their product term. Descriptive statistics and bivariate correlations were calculated in advance of hypothesis tests. Using the procedure described next, I then tested the prediction of binge eating at wave 2 and purging at wave 2 from the interaction of affective lability and alexithymia at wave 1. Similarly, I tested the prediction of binge eating at wave 3 and purging at wave 3 from the interaction of affective lability and alexithymia at wave 2.

For prediction of wave 2 binge eating, I entered gender, wave 1 BMI, and wave 1 binge eating. At step two, I entered wave 1 affective lability and wave 1 alexithymia. At

step three, I entered the interaction term of wave 1 affective lability by wave 1 alexithymia. For prediction of wave 3 binge eating, I entered gender, wave 2 BMI, and wave 2 binge eating in step 1. At step two, I entered wave 2 affective lability and wave 2 alexithymia. At step three, I entered the interaction term of wave 2 affective lability by wave 2 alexithymia.

To predict wave 2 purging, I entered gender, wave 1 BMI, and wave 1 purging in the first step. I then entered wave 1 affective lability and wave 1 alexithymia in the second step. At step three, I entered the interaction term of wave 1 affective lability by wave 1 alexithymia. Finally, to predict wave 3 purging, I entered gender, wave 2 BMI, and wave 2 purging in step 1. At step two, I entered wave 2 affective lability and wave 2 alexithymia. At step three, I entered the interaction term of wave 2 affective lability by wave 2 alexithymia.

CHAPTER 3. RESULTS

3.1 Participants Retention and Treatment of Missing Data

A total of 587 subjects participated at wave 1 and 409 at wave 2 (retention of

70.0%). Wave 3 included 341participants, which was 83.4% of those studied in wave 2.

Regarding missing data, at each wave, participating and non-participating participants did not differ on any study variables. Therefore, I inferred that data were missing at random and used the expectation-maximization procedure to impute missing data. It has been demonstrated that this procedure more accurately approximates estimates of population parameters compared to traditional methods, such as case deletion or mean substitution

(Enders, 2006). Thus, I was able to make use of the full sample.

3.2 Descriptive Statistics

Table 1 presents the mean and standard deviations for affective lability, alexithymia, negative affect, and BMI for the full sample. Table 2 presents the presence or absence of binge eating and purging. At wave 1, 13.3% of the sample endorsed at least some binge eating and 2.7% endorsed purging over the preceding 28 days. At wave 2,

19.8% of participants reported binge eating and 3.6% reported purging over the preceding

28 days. Finally, at wave 3, 19.3% of individuals reported binge eating and 3.4% indicated purging within the preceding 28 days. The bivariate correlations among the study variables at all three waves are presented in Table 3. As expected, affective lability and alexithymia were positively correlated at each wave, and each positively correlated with negative affect, binge eating, purging, and the interaction between affective lability and alexithymia within each wave and across waves. Further, BMI positively correlated with binge eating at all three waves. Binge eating also correlated with alexithymia within

and across each wave. Binge eating correlated with negative affect within each respective wave. When looking at correlations across waves, there were limited correlations. For example, binge eating at wave 1 did not correlate with negative affect at wave 2. See

Table 3 for all correlations.

3.3 Model Tests Results

Next, I summarize the results of the model test. All fit indices indicated that the model fit well. Specifically, the RMSEA value indicated good model fit (RMSEA = 0.03,

90% confidence interval 0.00 – 0.06), as did the SRMR (SRMR = .01). Both absolute fit indices indicated good model fit as well: CFI = 1.0 and TLI = .99. The χ2 (11) = 16.08, p

= 0.14, indicating a non-significant difference between the model and the observed correlations. Figure 1 depicts the model with the significant pathways pertaining to the hypothesized processes. Arrows are included for all statistically significant pathways.

Beta weights are also provided for prospective effects. Of note, gender did not predict any variable prospectively. The full model with all significant pathways is depicted in

Figure 2.

Further, the same model tests were conducted without the inclusion of negative affect and produced the same results. All fit indices indicated that the model without negative affect fit well. Specifically, the RMSEA value indicated good model fit

(RMSEA = 0.04, 90% confidence interval 0.00 – 0.06), as did the SRMR (SRMR = .01).

Both absolute fit indices indicated good model fit as well: CFI = 1.0 and TLI = .99. The

χ2 (9) = 15.33, p = 0.08, indicating a non-significant difference between the model and the observed correlations.

3.3.1 Autoregressive Predictive Effects

All autoregressive predictions were significant for affective lability, alexithymia,

negative affect, binge eating, and purging. Each variable predicted itself longitudinally.

3.3.2 Mediation Model Tests

As indicated in Figure 1, affective lability at wave 1 predicted wave 2 alexithymia, b = .18, z = 5.225, p < .001. However, alexithymia at wave 2 did not predict wave 3 binge eating. Therefore, my first hypothesis that alexithymia mediated the relationship between affective lability and binge eating was not supported.

Next, affective lability in the fall of the first year of college predicted alexithymia in the spring of the first year of college, which in turn predicted purging in the fall of the second year of college. Statistical tests of mediation indicated that the pattern of effects was consistent with the hypothesized mediational process, b = .02, z = 2.05, p < .05. The mediation effect appeared to be full, rather than partial; there was no remaining direct effect of wave 1 affective lability on wave 3 purging.

I found no evidence for the mediational processes of wave 1 alexithymia through wave 2 affective lability to wave 3 binge eating or to wave 3 purging. Specifically, wave

2 affective lability did not predict wave 3 binge eating or wave 3 purging. However, wave 1 alexithymia did predict wave 2 affective lability, b = .29, z = 8.57, p < .001.

3.4 Interaction Effects

No significant interaction effects were detected at either time point in the prediction of binge eating behavior, indicating that there were no differential effects of alexithymia on the relationship between affective lability and binge eating across any

time lag. Further, there were no significant interaction effects in the prediction of purging behavior across any time lag. Thus, no evidence of interactive effects were observed.

Table 1 Descriptive Statistics of Affective Lability, Alexithymia, Negative Affect, and Body Mass Index Total Sample Wave 1 Wave 2 Wave 3 Mean (SD) Affective Lability 21.52 (8.36) 22.30 (8.04) 22.33 (7.80) Alexithymia 48.51 (11.55) 47.79 (11.31) 47.59 (11.02) Negative Affect 21.18 (6.90) 23.47 (4.50) 21.39 (6.23) BMI 23.58 (4.59) 23.87 (4.55) 24.18 (4.79) Note. N = 587. SD = standard deviation. BMI = Body Mass Index.

Table 2 Presence of Binge Eating and Purging at Waves 1-3 Wave 1 Wave 2 Wave 3 N (% of N (% of N (% of

sample) sample) sample) Binge 78 (13.3) 116 (19.8) 113 (19.3) eating Purging 16 (2.7) 21 (3.6) 20 (3.4) Note. N = 587. In each cell, N = the number of participants reporting engagement in the behavior; % refers to the percentage of the sample represented by the number. All behaviors are dichotomized yielding a range of 0–1.

Table 3 Bivariate Correlations Between Study Variables

Note. AL-1, AL-2, and AL-3 = affective lability at waves 1, 2, and 3; TAS-1, TAS-2, and TAS-3 = alexithymia at waves 1, 2, and 3; NA-1, NA-2, NA-3 = negative affect at waves 1, 2, and 3; P-1, P-2, and P-3 = purging at waves 1, 2, and 3; B-1, B-2, and B-3 = binge eating at waves 1, 2, and 3; LxA-1, LxA-2, and LxA-3 = the interaction of affective lability and alexithymia at waves 1, 2, and 3; BMI-1, BMI-2, and BMI-3 = body mass index at waves 1, 2, and 3. N = 587. * p < .05. ** p < .01.

Figure 1 Structural equation model with pathways pertaining to main model.

Note: N = 587. *p < .05, **p < .01, ***p < .001. Covariation among all variables cross-sectionally was modeled but not included in figure for clarity. Solid lines represent the significant pathways. Dashed lines represent hypothesized paths that were not significant.

Figure 2 Full structural equation model with all significant pathways.

Note: N = 587. *p < .05, **p < .01, ***p < .001. Covariation among all variables cross-sectionally was modeled but not included in figure for clarity.

CHAPTER 4. DISCUSSION

The purpose of this study was to assess possible processes occurring between alexithymia and affective lability and their affect on binge eating and purging behaviors.

The results supported the hypothesized mediational process that alexithymia in the fall of the first year of college predicts increases in affective lability in the spring of the first year, which in turn predicts increases in purging in the fall of the second year of college.

Results were not consistent with the possibility that affective lability mediates the relationship between alexithymia and binge eating. Additionally, contrary to our hypotheses, the data did not support the mediational processes of alexithymia mediating the predictive influence of affective lability on binge eating and purging. Finally, none of the moderation hypotheses regarding the interaction of affective lability and alexithymia predicting binge eating and purging across each time lag were supported.

4.1 Mediation Processes

My first finding supported the presence of a mediational path from affective lability through alexithymia to purging. This observation suggests the possibility that consistently experiencing frequent changes in affective states may lead to being less sure of, comfortable with, and able to identify one’s mood at any given time, possibly because mood states are so transient that one does not have prolonged experience with them.

Perhaps that sequence of events increases risk for purging. This significant finding expands on prior cross-sectional research which has demonstrated that emotional variability is inversely associated with clarity of emotion (Thompson et al., 2009).

Similarly, the current results potentially provide support and directionality to the cross-

sectional finding that alexithymia mediates the relationship between affective lability and borderline personality disorder (Edwards et al., 2021).

It was surprising that this same mediational path of affective lability to alexithymia was not significant in predicting binge eating. While it is consistent with my clinical experience and theoretical perspective that affective lability could lead to difficulty identifying emotions, I do not have a sound theoretical reason as to why alexithymia would be an antecedent to purging and not binge eating. At the same time, the results of these two mediational tests are similar to those of de Groot and colleagues who found a significant correlation between alexithymia and purging frequency, but not between alexithymia and binge eating frequency posttreatment in a sample of women with bulimia nervosa (1995). This is contrary to the findings of de Zwaan and associates

(1995) who demonstrated a higher prevalence of alexithymia for women who binge eat compared to those with anorexia nervosa or bulimia nervosa. Because of the limited longitudinal investigations of the predictive value of alexithymia on eating disorders, my results cannot be easily compared with the existing literature. The longitudinal work to date has primarily focused on alexithymia’s impact on clinical outcomes for eating disorders (Speranza et al., 2007; Vrieze, 2018). Therefore, replication of the current study’s findings and further theoretical exposition of why alexithymia predicts purging behaviors and not binge eating is needed before placing strong confidence in these findings.

My findings did not support the reverse hypotheses that alexithymia predicts increases in affective lability, which in turn predicts binge eating and purging. Although alexithymia significantly predicted increases in affective lability, it is striking that

affective lability did not then predict subsequent binge eating or purging. Although affective lability at wave 1 predicted wave 2 binge eating, the same effect was not observed from wave 2 to wave 3. Therefore, these findings provide limited support for the retrospective and EMA research indicating that affective lability predicts bulimic behaviors (Anestis et al., 2010; Berner et al., 2017). However, two important differences exist between prior literature and the current study. First, a large portion of the literature investigating affective lability has utilized the EMA design. This type of research provides information regarding variation within the individual. On the other hand, the prospective design of the present study is assessing between-person variance; therefore, investigating a different question. The second important difference is how affective lability has been measured. Prior work investigating affective lability in the context of eating disorders has measured affective lability in diverse ways, using various measures as well as different means of calculating the represented score. For example, studies have used measures such as the Dimensional Assessment of Personality Pathology Basic

Questionnaire – Affective Lability subscale and the PANAS, and have created an index for affective lability by using methods such as the mean square successive difference statistic to average the change from one score to the next as well as the probability of the acute index to capture the occurrence of extreme increases in positive and negative affect

(Anestis et al., 2009; Lavender et al., 2013; Yu & Selby, 2013). Because scales that claim to measure the same construct are not always interchangeable and the means of deriving a representative score has varied, I am not able to fully compare the results of this study to previous literature using different scales.

Contrary to past findings, negative affect did not predict binge eating or purging across any time lag (Lavender et al., 2016). Further, the predictive models produced the same results with and without the inclusion of negative affect; the one significant mediation effect among those hypothesized was observed in both models.

4.2 Moderation Effects

Finally, none of the predicted interactions between affective lability and alexithymia predicted subsequent binge eating or purging. Few studies have examined the moderating effect of these variables on binge eating and purging (Ortiz et al., 2021;

Pinaquy et al., 2003).

Although the results of this study do demonstrate that both alexithymia and affective lability predict each other over time, no evidence of a joint effect was found.

Specifically, there was no evidence to suggest that alexithymia moderates the relationship between affective lability and binge eating or purging. This negative finding suggests that individuals who experience rapid and frequent changes in mood states are equally likely to engage in binge eating and purging if they have high or low levels of alexithymia.

Although it is plausible that there is an interactive effect present that was not captured by this study, given the current negative findings it is unclear how much, if any, future effort should be dedicated to further investigation of this possibility.

4.3 Strengths

The current study had several strengths. First, the longitudinal nature of this study allowed for investigations of temporal effects. Of course, as important as documenting temporal relationships is, doing so is not the same as demonstrating causality. Second, I

employed a large sample which allowed for a more precise estimate of the effect. Finally, the measures I used had strong psychometric properties.

4.4 Limitations and Future Directions

Despite these strengths, the study had several limitations. First, the majority of the sample was female and Caucasian. Additionally, the generalizability of these findings is constrained to college students. The results of this study may not be representative of individuals within the same age range who do not attend college. Thus, larger samples that are more diverse with respect to race, ethnicity, and gender identity are needed to investigate these risk processes for individuals of diverse racial backgrounds, gender identity and expression, and young adults outside of the college setting.

Next, the low rates of binge eating and purging in my sample limits my ability to generalize these findings to the development of symptoms in a clinical population. It would be useful to conduct this study with a much larger representation of dysfunction.

The current study had reasonable, predictable amounts of binge eating and purging for college students, but to better tease apart these processes it would be beneficial to have a sample with a bigger range of dysfunction and more systematic dysfunction represented.

Therefore, future studies should consider examining these processes in samples structured to include more individuals with clinical levels of symptomatology.

Third, the model driving the current research is casual in nature. The significant finding of this prospective correlational study is consistent with, but not proof of, the underlying casual model.

Additionally, one unique limitation of this longitudinal study is that it took place during a global pandemic. Only one full wave of the study was conducted prior to the

COVID-19 pandemic. The reality is that different participants had very different experiences of the pandemic. The residential college experience was disrupted for virtually all students, but what that meant varied from person to person (e.g., some individuals lived in apartments with friends, some moved back home). As a result, there are sources of variability that could not have been anticipated and that I therefore did not control for in my design. Additionally, there was likely variability in the impact of this departure from the norm on students (e.g., some had unexpected or exacerbated financial concerns, became food insecure, had sick relatives). While there are always factors that differ person to person in any study that contribute to error variance, it is likely that the varied experiences of the pandemic increased the magnitude of unmodeled variability across participants.

There is no way to fully understand the impact of the pandemic on the results of the study. The study began before the pandemic and proceeded throughout different stages of the pandemic; thus, there was variability in context for each person over time and variability in the impact of the pandemic. Therefore, future studies should seek to examine these effects in a more traditional, non-pandemic context to tease apart these processes from both a theoretical and empirical standpoint.

Although I found no differences among participants who participated in all three waves of the study and those who did not, I cannot know for certain that differences would not have emerged had every person completed every wave. Further, it is certainly true that the rate of longitudinal attrition was higher than desired. Loss of study

participants from wave to wave was no doubt due to a number of factors; one unique possible source of attrition in the current study was the global pandemic. Anecdotally, many participants appeared less engaged in this research, and their academic life more broadly, than has been the case in prior, similarly designed studies in our laboratory. This observation further underscores the importance of replicating this study’s findings in a traditional context. Regardless of the cause, the attrition was a limitation in the present study.

4.5 Conclusions

There is difficulty in deciding what conclusions to draw from the predominately negative findings. For some or many reasons, the study largely did not work as anticipated. It is possible that the large change in context of the study, involving assessment both before and during the global pandemic, altered the results, thus producing a false negative impression of how these risk factors operate in typical times.

However, it is unclear whether these findings would be different in a traditional context.

It is certainly possible that the theoretical basis of this study is not as sound as was originally thought. Therefore, critical consideration should be made regarding the value of continued exploration of these risk factors together. Over the course of the years of the design and execution of this study, I became less confident in the theoretical basis supporting the relationship between these risk factors. One conclusion that could be drawn from the largely non-significant findings is that investigating the relationships among affective lability, alexithymia, binge eating, and purging may not be a fruitful direction for eating disorder research. It is important as scientists to critically evaluate why our work failed to produce the hypothesized results, examine the trajectory of our

work, recognize when a certain course of study may not be as important or fruitful as originally suspected, and modify accordingly. Perhaps a useful inference for risk researchers is to focus their efforts elsewhere. I hope that this work does provide value in guiding future research and generating additional aims worth studying.

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VITA

ANNA MARIE LILIA ORTIZ, M.S.

EDUCATION

2018 Master of Science in Clinical Psychology University of Kentucky; Lexington, KY

2016 Bachelors of Arts, Clinical Psychology, Minor in Exercise Science Franciscan University of Steubenville, Steubenville, OH

HONORS AND AWARDS

PEO Scholar Nomination, 2020-2021

University of Kentucky • Arts & Sciences Dean's Competitive Graduate Fellowship, 2020 • Excellent Clinical Performance Award Recognition, 2020 • Research Challenge Trust Fund Fellowship, 2018 – 2020 • Lyman T. Johnson Fellow, 2016 – 2019 Franciscan University of Steubenville • Baconian Society Award, 2016 • Outstanding Scholar in Experimental Psychology “Gemelli” Award, Honorary Student of the Year, 2016 • Psi Chi Honor Society Award, 2016 • Dean’s List, 2012-2016 • Chancellor’s Scholarship, 2012-2016

PROFESSIONAL POSITIONS

Graduate Research Assistant August 2016 – Present Adolescent Risky Behavior Research Laboratory

Graduate Student Collaborator October 2019 - Present Eating Anxiety Treatment Research Laboratory, University of Louisville, Louisville, KY

Project SHORT Volunteer September 2020 - Present

Teaching Assistant August 2016 – May 2021 40

University of Kentucky, Lexington, KY

Practicum Therapist June 2019 – July 2020 Louisville Center for Eating Disorders, Louisville, KY

Practicum Therapist August 2017 – July 2020 Jesse G. Harris Psychological Services Center, Lexington, KY

Interventionist Jan 2019 – Jan 2020 Dialectical Behavior Therapy, University of Kentucky, Lexington, KY

Clinic Assistant Coordinator July 2018 – July 2019 Jesse G. Harris, Jr. Psychological Services Center, Lexington, KY

Youth Assessment Therapist April 2019 – May 2019 The Lexington School, Lexington KY

Practicum-Level Individual Therapist Aug 2017 – May 2018 University of Kentucky Counseling Center, Lexington, KY

Interpersonal Process Therapy Group Therapist Jan 2018 – May 2018 University of Kentucky Counseling Center, Lexington, KY

Happy Healthy Kids Group Leader 2018 Jesse G. Harris, Jr. Psychological Services Center, Lexington, KY

Personality Assessment Practicum Interventionist 2017 University of Kentucky, Lexington, KY

Intelligence Assessment Practicum Interventionist 2016 University of Kentucky, Lexington, KY

Franciscan Institute for Science and Health Psychology Laboratory Jan 2014 – May 2016 Franciscan University of Steubenville, Steubenville, OH

Klump Lab May 2015 – Aug 2015 Michigan State University, East Lansing, MI

PROFESSIONAL PUBLICATIONS

9. Ralph-Nearman, C., Williams, B. M., Ortiz, A. M. L., Smith, A. R., & Levinson, C. A. (2021). Pinpointing Core and Pathway Symptoms Among Sleep Disturbance, Anxiety, Worry, and Eating Disorder Symptoms In Anorexia Nervosa and Atypical Anorexia Nervosa. Journal of Affective Disorders. 8. Ortiz, A. M. L., Davis, H. A., Riley, E. R., & Smith, G. T. (2021). The interaction between affective lability and eating expectancies predicts binge eating. Eating Disorders: The Journal of Treatment and Prevention. 7. Christian, C., Bridges-Curry, Z., Hunt, R.A., Ortiz, A.M.L., Drake, J.E., Levinson, C.A. (2021). Latent Profile Analysis of Impulsivity and Perfectionism Dimensions and Associations with Psychiatric Symptoms. Journal of Affective Disorders, 283, 293-301. 6. Atkinson, E. A., Ortiz, A. M. L., & Smith, G. T. (2020). Affective Risk for Problem Drinking: Reciprocal Influences Among Negative Urgency, Affective Lability, and Rumination. Current Drug Research Review, 12(1), 42-51. 5. Ortiz, A. M. L., Peterson, S. J., D’Agostino, A. R., & Smith, G. T. (2020). Expectancy Theory And Addictive Behaviors. In Advances In Psychology Research (pp. 1-33). New York: Nova Science Publishers, New York, NY. 4. Davis, H. A., Ortiz, A. M. L., & Smith, G. T. (2019). Transactions between early binge eating and personality predict transdiagnostic risk. European Eating Disorders Review. 27(6), 614-627. 3. Ortiz, A. M. L., Davis, H. A., & Smith, G. T. (2019). Transactions among thinness expectancies, depression, and binge eating in the prediction of adolescent weight control behaviors. International Journal of Eating Disorders, 52(2), 142-152. 2. Davis, H. A., Ortiz, A. M. L., & Smith, G. T. (2017). The occurrence and covariation of binge eating and compensatory behaviors across early to mid- adolescence. Journal of Pediatric Psychology, 43(4), 402-412. 1. Davis, H. A., Ortiz, A. M., D’Agostino, A. R., & Smith, G. T. (2016). A two-stage risk model for bulimic behavior. In N. Morton (Ed.), Eating Disorders: Prevalence, Risk Factors and Treatment Options. (pp. 97-120). New York: Nova Science Publishers, Inc.