JOURNAL ARTICLE
Predictors of Physical and Psychosocial Quality of Life Among Young People With Borderline Personality Disorder Features.
Published In: Journal of Personality Disorders, 2026, v. 40, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Delahunty, Georgia; Betts, Jennifer K.; Nicol, Katie; Cotton, Sue; Andrewes, Holly; Chanen, Andrew M. 3 of 3
Abstract
Borderline personality disorder (BPD) is associated with poor quality of life (QoL), but little is known about which QoL dimensions are affected or what might predict QoL outcomes. Baseline data were collected from 208 participants (aged 15–25 years, with three or more DSM-5 BPD features) in one of three randomized controlled trials conducted at youth mental health services in Melbourne, Australia. Hierarchical regression analyses revealed that BPD severity, avoidant personality disorder diagnosis (AVPD), and psychotic disorder each independently predicted poorer overall QoL. Both BPD severity and AVPD independently predicted all psychosocial subscale dimensions of QoL. For the physical dimensions, AVPD independently predicted pain and senses, whereas BPD severity and psychotic disorder independently predicted independent living. Co-occurring mood or antisocial personality disorder predicted neither overall QoL nor any dimension of QoL. These findings provide additional weight to the argument that young people with BPD should be a high-priority group for early intervention. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2026/02, Vol. 40, Issue 1, p1
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2026
- ISSN:0885-579X
- DOI:10.1521/pedi.2026.40.1.1
- Accession Number:191632275
- Copyright Statement:Copyright of Journal of Personality Disorders is the property of Guilford Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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