Projects and Research Interests

1. Ecological momentary assessment and ambulatory assessment: Ambulatory assessment (AA) encompasses a wide range of methods used to study people in their natural environment, including momentary self-report (e.g., ecological momentary assessment [EMA]), observational (e.g., audio- or video-recording; activity monitoring), and physiological (e.g., cardiac and respiratory activity assessed using physiological sensors worn by participants) methods. Data from AA methods can characterize and test dynamic psychological processes, including emotions, cognitive styles and expectations, behavior patterns, and physiological correlates of daily life. In particular, AA methods can provide real-time (or near real-time) assessments which minimize retrospective and heuristic biases that are known to systematically distort past experiences and events. In addition, the collection of many assessments over time provides a temporal dimension to the monitoring of psychological constructs, especially crucial in the case of processes like emotion that are known to be dynamic, to fluctuate over time, and to change as a result of both external and internal influences. Finally, obtaining assessments in individuals’ natural environment improves external validity, allowing one to observe and evaluate potential influences on psychological processes which are often experimentally eliminated or controlled in the laboratory.

Since 2004, I have worked on several projects that use AA to examine mood, impulsivity, personality, and substance use in those with BPD or other emotional dysregulation disorders. In the first of these studies, we used methods, techniques, and findings from the fields of affect and emotion, behavioral assessment, and psychometrics to shed light on affective instability. Specifically, we conducted an intensive study of affective instability using ecological momentary assessment (EMA) --- a real time assessment of behaviors, emotions, and cognitive variables via hand held computers.  Study participants included BPD outpatients and psychiatric controls with current depressive disorder who rated their mood states, behaviors, and life events six times per day for a 28-day period. Results from this study have shed light on the nature and measurement of affective instability in BPD, the relation between EMA and retrospective reports of affective instability, and the relations between affective instability and substance use or interpersonal problems. We are also analyzing several other recently collected ambulatory assessment data sets (self-report, physiology, activity and location) that include those with BPD, depressive disorder, anxiety disorder, and community controls. Finally, I have co-authored several articles on the use of EMA/ESM approaches in clinical and psychological science. I have received funding from NIAAA and NIMH to continue this line of research on emotion dysregulation and affective instability, and the use of EMA/ambulatory assessment in the study of psychopathology will be a major focus for me in the future. For the research described above, I played critical roles in all aspects of the research (i.e., study planning, obtaining funding, framing the research question, data analytic approach, and ms. preparation).

1.   Trull, T. J., Solhan, M. B., Tragesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., & Watson, D. (2008). Affective instability: Measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of Abnormal Psychology, 117, 647-661. PMID: 18729616

2.   Trull, T. J., & Ebner-Priemer, U. W. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151-176. PMC4249763

3.   Trull, T. J., & Ebner-Priemer, U. W. (2014). The role of ambulatory assessment in psychological science. Current Directions in Psychological Science, 23, 466-470. PMC4269226 [Available on 2015-12-01]

4.   Scheiderer, E. M., Wang, T., Tomko, R. L., Wood, P. K., & Trull, T. J. (in press). Negative Affect Instability among Individuals with Comorbid Borderline Personality Disorder and Posttraumatic Stress Disorder. Clinical Psychological Science.

2. Intensive longitudinal data analysis: The amount of data that can be collected using AA methods is exponentially larger than what many psychological scientists are used to managing. AA data are often collected over much of the 24 hour day and over many days. In some of our studies, participants contribute over 10,000 data points a day. Therefore, expert data management and expertise in quantitative methods appropriate for “big data” like these are needed. We have published several papers proposing unique approaches to analyzing data for these types of studies. For example, our papers have focused on conceptualizing and quantifying dynamic processes like affective instability, impulsivity, and undifferentiated negative affect. For the research described above, I played critical roles in all aspects of the research (i.e., study planning, obtaining funding, framing the research question, data analytic approach, and ms preparation).

1.   Jahng, S., Wood, P.K., & Trull, T.J. (2008). Analysis of affective instability in EMA: Indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13, 354-375. PMID: 19071999

2.   Ebner-Priemer, U. W., Eid, M., Stabenow, S., Kleindienst N., & Trull, T. (2009). Analytic strategies for understanding affective (in) stability and other dynamic processes in psychopathology. Journal of Abnormal Psychology, 118, 195-202. PMID: 19222325

3.   Tomko, R. L., Lane, S. P., Pronove, L. M., Treloar, H. R., Brown, W. C., Solhan, M. B., Wood, P. K., & Trull, T. J. (in press). Undifferentiated Negative Affect and Impulsivity in Borderline Personality Disorder: A Momentary Perspective.  Journal of Abnormal Psychology.

4.   Trull, T. J., Lane, S. P., Koval, P., & Ebner-Priemer, U. W. (in press). Affective dynamics and psychopathology. Emotion Review.

3. Borderline Personality Disorder: Diagnosis, description, and etiology (including genetics): Over the last 30 years, I have studied borderline personality disorder (BPD), and I have been funded by the NIMH, NIAAA, and research foundations for these projects. Early on, I was impressed by the fact that while there were many published articles on this disorder, there were relatively few methodologically-sound, empirically-based studies. I became interested in (1) how best to define the disorder, given the great degree of heterogeneity within this diagnostic category; (2) how to understand the symptoms of this disorder by considering the major personality traits that underlie the symptoms; and (3) developing and testing etiological models of the disorder. My first studies focused on the development of BPD features in young adults. Published results from these projects have demonstrated the reliability and validity of methods to identify features of BPD in young adults, evaluated several etiological models of BPD and outcomes of BPD features, examined the comorbidity (co-occurrence) of BPD and substance use disorders (e.g., alcohol or drug dependence) from both an empirical and theoretical perspective, and reviewed evidence for alternative dimensional models of BPD and other personality disorders. Other studies focused on attachment and parental bonding, relations between BPD features and negative outcome two years later and the assessment of childhood abuse. Finally, we have investigated treatment utilization in those with BPD features; personality and psychopathology; BPD features and substance use; and the relations among BPD features over time.

Another set of projects focusing on the genetics of BPD and related phenotypes is of great relevance to this proposal. A large behavior genetic study on BPD features was funded by the Borderline Personality Disorder Research Foundation to conduct a behavior genetic study of BPD features. Specifically, with colleagues Nick Martin (Australia) and Dorret Boomsma (Netherlands), we initiated a twin study of the genetics of Borderline Personality Disorder, as well as of the personality dimensions that underlie the symptoms of this disorder. To date, we have phenotyped approximately 8,000 Australian twins, and approximately 28,000 Dutch twins and family members. Outcomes of this study to date include better estimates of the distribution of BPD features in the population, of genetic influences on BPD features, and of the extent to which the covariation between BPD features and personality traits is determined by underlying genetic factors. To date our publications have addressed the heritability of BPD features, the invariance of BPD feature scores across age and gender, genetic linkage of BPD features, and a genetic analysis of the comorbidity between BPD and five factor model traits. Data collection on another cohort of Australian twins is almost complete, and we will collect BPD feature data in the next wave of data collection for the Netherlands Twin Registry. Our ultimate aim is to obtain GWAS data from a large portion of these individuals to conduct molecular genetic analyses on BPD and related phenotypes. For the research described above, I played critical roles in all aspects of the research (i.e., study planning, obtaining funding, framing the research question, data analytic approach, and ms. preparation).

  1. Trull, T. J. (2001). Structural relations between borderline personality disorder features and putative etiological correlates. Journal of Abnormal Psychology, 110, 471-481. PMID: 11502090
  2. Bagge, C., Nickell, A., Stepp, S., Durrett, C., & Trull, T. J. (2004). Borderline personality disorder features predict negative outcome two years later. Journal of Abnormal Psychology, 113, 279-288. PMID: 15122948
  3. Distel, M. A., Trull, T. J., Willemsen, G., Derom, C., Thiery, E., Grimmer, M., Martin, N. G., & Boomsma, D. I. (2008). Heritability of Borderline Personality Features is similar across Three Countries. Psychological Medicine, 38, 1219-1229. PMID: 17988414
  4. Distel, M. A., Trull, T. J., Vink, J. M., Willemsen, G., Derom, C. A., Lynskey, M., Martin, N. G., & Boomsma, D. I. (2009). The five factor model of personality and borderline personality disorder: A genetic analysis of comorbidity. Biological Psychiatry, 66, 1131-1138. PMID: 19748081

4. Dimensional models of personality disorder: There are a number of problems with the categorical system of personality disorder diagnosis that is codified in DSM-5. For example, the personality disorder categories are quite heterogeneous with regard to symptoms and traits, and a great deal of comorbidity among personality disorder diagnoses is frequently observed. An attractive alternative to representing personality pathology and disorder in a categorical manner (i.e., present versus absent) is a dimensional model of classification. My work in this area has been both conceptual and empirical. I have published several papers on the problems inherent in categorical systems of personality disorder diagnoses, as well as on the available dimensional models of personality and personality pathology that might be adopted. Further, several of my published studies have presented data directly relevant to this topic, focusing primarily on the five-factor model of personality. For the research described above, I played critical roles in all aspects of the research (i.e., study planning, obtaining funding, framing the research question, data analytic approach, and ms. preparation).

  1. Trull, T. J., & Durrett, C. (2005). Categorical and dimensional models of personality disorders. Annual Review of Clinical Psychology, 1, 355-380. PMID: 17716092
  2. Widiger, T. A., & Trull, T. J. (2007). Plate tectonics in the classification of personality disorder: Shifting to a dimensional model. American Psychologist, 62, 71-83. PMID: 17324033
  3. Trull, T. J., Vergés, A., Wood, P. K., Jahng, S., & Sher, K. J. (2012). The Structure of DSM-IV-TR Personality Disorder Symptoms in a Large National Sample. Personality Disorders: Theory, Research, and Treatment, 3, 355-369. PMC3779622
  4. Trull, T. J., & Widiger, T. A. (2013). Dimensional models of personality: The five-factor model and the DSM-5. Dialogues in Clinical Neuroscience, 15(2), 135-146. PMC3811085

5. Personality, Personality Disorders, and Substance Use Disorders: Because BPD is frequently associated with substance use disorders (SUDs), I began to study the reasons why these conditions frequently co-occur within the same individuals. To date, I have published many papers on the topic of personality disorders and SUDs. In addition, I have served as Co-Investigator on two federally-funded grants (both from the NIAAA) that support projects that will provide data to address these and related questions. I am also a senior faculty member of the NIAAA-funded Alcoholism Research Center (ARC) that is based at Washington University in St. Louis (Andrew Heath, PI). For example, I was the Principal Investigator of Project 6, “Ecological momentary assessment of emotional regulation.” This project uses ecological momentary assessment (EMA) to examine the use of alcohol to regulate emotions, how these efforts compare to other forms of emotional regulation observed in BPD (e.g., smoking, self-harm), and the effects of these regulation strategies on subsequent mood. BPD exhibits exceptionally high comorbidity with alcohol use disorders (AUDs) in both clinical and population-based samples. Cardinal symptoms of BPD, impulsivity and affective instability, are central constructs in theories of AUD etiology, so BPD represents a type of “model system” for studying the role of emotion regulation and disordered self-control in the genesis of AUD. We recently finished this data collection which included EMA data from both random and event-based (e.g., drinking, smoking) assessments. Our papers that are submitted or in preparation will address: (1) The role of both positive and negative moods in alcohol use in BPD and in non-affected controls (CON). (2) Are BPD patients drinking episodes will be associated with heavier consumption (sex and body weight adjusted) and estimated blood alcohol concentrations than CON? Do both affective instability and by impulsivity moderate this effect? (3) Is alcohol consumption’s effect on mood characterized by positive and/or negative reinforcement? (4) Are negative post-drinking effects of alcohol on mood larger in BPD patients than controls?

In addition, I was a Co-Investigator on an NIAAA-funded project that uses NESARC data from over 40,000 US residents to examine the course and development of alcohol problems and related comorbidities across the life span. Most of my work focuses on personality disorder-substance dependence comorbidities. Papers from this project presented an alternative and (we believe) more accurate scoring of the personality disorder items from the NESARC interview, as well as an examine PD-SUD comorbidities, and methods for clustering symptoms and evaluating both general/shared and specific factors associated with comorbid conditions. For the research described above, I played critical roles in all aspects of the research (i.e., study planning, obtaining funding, framing the research question, data analytic approach, and ms. preparation).

  1. Trull, T. J., Sher, K. J., Minks-Brown, C., Durbin, J., & Burr, R. (2000). Borderline personality disorder and substance use disorders: A review and integration. Clinical Psychology Review, 20, 235-253. PMID: 10721499
  2. Trull, T. J., Waudby, C. J., & Sher, K. J. (2004). Alcohol and substance use disorders and personality disorder symptoms. Experimental and Clinical Psychopharmacology, 12, 65-75. PMID: 14769101
  3. Jahng, S., Solhan, M. B., Tomko, R., Wood, P. K., Piasecki, T. A., & Trull, T. J. (2011). Affect and alcohol use: An EMA study of Outpatients with borderline personality disorder. Journal of Abnormal Psychology, 120, 572-584. PMC4262451
  4. Jahng, S., Trull, T. J., Wood, P. K., Tragesser, S. L., Tomko, R., Grant, J. D., Bucholz, K. K., & Sher, K. J. (2011). Distinguishing General and Specific Personality Disorder Features and Implications for Substance Dependence Comorbidity. Journal of Abnormal Psychology, 120, 656-669. PMC4241053


Near Complete List of Published Work in PubMed: