Using dynamic factor analysis to model intraindividual variation in borderline personality disorder symptoms.

摘要

Borderline personality disorder (BPD) is a highly prevalent, debilitating, and costly form of mental illness that involves instability in self-concept, emotions, and behavior, including chronic suicidality and self-injury. The essential psychological dynamics underlying the disorder are poorly understood. In particular, the role of identity disturbance in the disorder is largely unexplained, although several prominent theories have been advanced. Psychodynamic theories posit that identity disturbance underlies emotional and behavioral dysregulation, whereas biosocial theory suggests that identity disturbance is the result and largely not the cause of these other symptoms. Research to date has been largely cross-sectional and based on retrospective report, making it difficult to untangle the temporal dynamics of BPD symptoms. In addition, new research based on ecological momentary assessment (EMA), which could potentially shed light on this question, has focused on groupwise hypotheses and analyzed interindividual data. This approach does not account for potential heterogeneity in these processes, whereas person-specific methods based on intraindividual variation can account for this heterogeneity. The current study uses dynamic factor analysis, a person-specific modeling approach, to investigate the longitudinal covariation of anger, impulsivity, and identity disturbance. 11 psychiatric outpatients who were diagnosed either with BPD (n = 4) or with a mood or anxiety disorder, but not BPD (n = 7) completed a 21-day EMA protocol by rating these symptoms six times per day at quasi-random times at roughly 2-hour intervals. Cubic spline interpolation was used to produce time series with equal spacing between measurements, and models were created to describe the relationship between these symptoms, both in synchronous ratings and at successive time points. Models were created using examination of modification indices from a baseline autoregressive model, and multiple fit indices were used to determine good fit. Results revealed extensive variability between individuals in the dynamics of anger, impulsivity, and identity disturbance, although a simple autoregressive model fit data well for six participants. The results support neither psychodynamic nor behavioral theories of BPD symptom dynamics but imply that each may account for symptom variation in different individuals with the disorder. Results also show that a person-specific approach to modeling EMA data is feasible and may support the development of theories of psychological processes in BPD. This class of methods may also be useful for the study of psychotherapy process and outcome and may aid in treatment planning, outcome monitoring, and diagnostic assessment in clinical settings.