Abstract
Abstract
Background
Predicting response to cognitive behavior therapy (CBT) assists efforts to enhance treatment outcome when predictive factors are modifiable prior to, or during, treatment. The extent to which clients hold beliefs and attitudes consistent with CBT (termed CBT-mindedness) is a relatively new concept with research suggesting it predicts response to CBT amongst small samples of adults with anxiety. This study aimed to investigate CBT-mindedness amongst a larger clinical population receiving internet-delivered CBT (iCBT).
Method
1132 adults with anxiety, depression or mixed anxiety and depression who accessed iCBT with or without therapist support via the THIS WAY UP clinic completed a brief self-report measure of CBT-mindedness along with measures of distress, anxiety, and depression. Measures were completed pre- and post-treatment.
Results
The 3-factor structure of the CBT Suitability Scale (CBT-SUITS) was confirmed and scores were unrelated or very weakly related to symptoms/distress. CBT-mindedness increased amongst treatment completers. CBT-mindedness predicted post-treatment distress (but not symptoms), and change in CBT-mindedness predicted lower post-treatment symptoms and distress.
Conclusions
The CBT-SUITS represents a psychometrically sound measure of CBT-mindedness. Results amongst this large sample of adults accessing iCBT in a community service indicate that CBT-mindedness (or CBT-mindedness change) is an important predictor of therapy response.
Publisher
Springer Science and Business Media LLC
Subject
Clinical Psychology,Experimental and Cognitive Psychology
Reference42 articles.
1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723. https://doi.org/10.1109/TAC.1974.1100705
2. Andersson, G., Carlbring, P., & Rozental, A. (2019). Response and remission rates in internet-based cognitive behavior therapy: An individual patient data meta-analysis. Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2019.00749
3. Andrews, G., Basu, A., Cuijpers, P., Craske, M. G., English, C. L., & Newby, J. M. (2018). Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: An updated meta-analysis. Journal of Anxiety Disorder, 55, 70–8. https://doi.org/10.1016/j.janxdis.2018.01.001
4. Andrews, G., & Slade, T. (2001). Interpreting scores on the Kessler psychological distress scale (K10). Australian and New Zealand Journal of Public Health, 25(6), 494–497. https://doi.org/10.1111/j.1467-842X.2001.tb00310.x
5. Australian Bureau of Statistics. (2013). Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure. Retrieved from https://www.abs.gov.au/ausstats/abs@.nsf/mf/1270.0.55.005