边缘型人格障碍
心理学
临床心理学
特质
五大性格特征
神经性贪食症
人格
心理信息
情绪失调
人际交往
情感(语言学)
发展心理学
饮食失调
梅德林
程序设计语言
法学
政治学
社会心理学
沟通
计算机科学
作者
Carolyn M. Pearson,Jason M. Lavender,Li Cao,Stephen A. Wonderlich,Ross D. Crosby,Scott G. Engel,James E. Mitchell,Carol B. Peterson,Scott J. Crow
摘要
Borderline personality disorder (BPD) traits are common among those with bulimia nervosa (BN). However, how these traits impact the state experience of precipitants of BN behavior, such as stressful events and emotional reactivity, has not been determined. Thus, the purpose of this naturalistic study was to examine this trait-state association in BN. Women with DSM-IV BN (N = 133) completed a baseline measure of personality pathology traits, and subsequently recorded their affective state and the frequency and perception of 3 types of stressful events (interpersonal, work/environment, and daily hassles) several times per day for 2 weeks using ecological momentary assessment (EMA). Pearson correlations assessed the associations between BPD traits (affective lability, identity problems, insecure attachment, and cognitive dysregulation) and (a) frequency of stressful events and (b) perception of stressful events. Generalized linear models (GLM) were used to evaluate the relationship between BPD traits and changes in negative affect following stressful events. Results revealed that while all traits were significantly associated with perceived stressfulness, certain BPD traits were significantly associated with the frequency of stressful events. Individuals with higher trait insecure attachment experienced larger increases in negative affect following interpersonal stressful events. These findings suggest that interventions focused on addressing stressful events and enhancing adaptive emotional responses to interpersonal events may be particularly useful for a subset of individuals with BN with BPD-related personality characteristics, including insecure attachment, affective lability, and identity problems. (PsycINFO Database Record
科研通智能强力驱动
Strongly Powered by AbleSci AI