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Adjunctive avatar therapy for mentalization based treatment of borderline personality disorder: a mixed methods feasibility study
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Abstract
- Background: Borderline personality disorder (BPD) is characterized by severe instability in emotions, identity, relationships, and impulsive behaviour. One contributing factor to BPD is deficient mentalizing - our ability to understand the mental states of others and ourselves. Psychotherapies can be effective at reducing symptoms of BPD but effects are small. Innovative ways of enhancing existing therapies are therefore essential. Objectives: In a mixed-methods, feasibility and acceptability study we adjuncted conventional mentalization based treatment (MBT) for BPD with avatar software (avatar-MBT). We wanted to test whether the enhanced visual narrative afforded by the software would facilitate therapy. Methods: We used proprietary avatar software in four group MBT sessions. We collected data on up-take (n=15), drop-out (n=4), and self-report measures (n=11) of mentalization and mood, and conducted qualitative interviews to assess attitudes and beliefs (n=9). Findings: Thematic analysis revealed five themes on the usefulness of avatar-MBT, including facilitating perspective taking, expression, emotional distancing, the big picture, and group participation. The sixth theme suggested avatar-MBT is best placed within a group setting. There was no deterioration in symptoms as monitored by self-report measures. Conclusions: Qualitative data suggests that avatar-MBT is acceptable to patients with BPD who described it as enhancing conventional MBT and expressed a wish to continue using it. However, controlled trials are required to assess efficacy. Clinical Implications: Results suggest that avatar-MBT may be a viable option to enhance existing BPD treatment. Furthermore, we provide initial evidence that it is feasible to implement a digital adjunct within a group therapy setting.
Details
- Database :
- OAIster
- Notes :
- doi:10.1136/eb-2017-102761
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1312912402
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1136.eb-2017-102761