Personality and psychopathology are composed of dynamic and interactive processes among diverse psychological systems, manifesting over time and in response to an individual's natural environment. Ambulatory assessment techniques promise to revolutionize assessment practices by allowing access to the dynamic data necessary to study these processes directly. Assessing manifestations of personality and psychopathology naturalistically in an individual's own ecology allows for dynamic modeling of key behavioral processes. However, advances in dynamic data collection have highlighted the challenges of both fully understanding an individual (via idiographic models) and how s/he compares with others (as seen in nomothetic models). Methods are needed that can simultaneously model idiographic (i.e., person-specific) processes and nomothetic (i.e., general) structure from intensive longitudinal personality assessments. Here we present a method, group iterative multiple model estimation (GIMME) for simultaneously studying general, shared (i.e., in subgroups), and person-specific processes in intensive longitudinal behavioral data. We first provide an introduction to the GIMME method, followed by a demonstration of its use in a sample of individuals diagnosed with personality disorder who completed daily diaries over 100 consecutive days.
Public Significance StatementAmbulatory assessment techniques (e.g., ecological momentary assessment) generate information that can be used to develop personalized models of an individual's behavior. Techniques are presented that allow for the simultaneous development of many personalized models and searches for shared features across models.
Objective: Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. Method: We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N ϭ 78) and a subsample with a single diagnosis (n ϭ 24). Results: We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. Conclusions: The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets.
What is the public health significance of this article?This study highlights the importance of understanding psychopathology using individualized models. We demonstrate research techniques that can be used to develop personalized models of psychopathology as well as search for commonalities across individuals.
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