BackgroundInternet-based cognitive–behavioural treatment (ICBT) for anxiety disorders has shown some promise, but no study has yet examined unguided ICBT in primary care. This randomized controlled trial (RCT) investigated whether a transdiagnostic, unguided ICBT programme for anxiety disorders is effective in primary care settings, after a face-to-face consultation with a physician (MD). We hypothesized that care as usual (CAU) plus unguided ICBT would be superior to CAU in reducing anxiety and related symptoms among patients with social anxiety disorder (SAD), panic disorder with or without agoraphobia (PDA) and/or generalized anxiety disorder (GAD).MethodAdults (n = 139) with at least one of these anxiety disorders, as reported by their MD and confirmed by a structured diagnostic interview, were randomized. Unguided ICBT was provided by a novel transdiagnostic ICBT programme (‘velibra’). Primary outcomes were generic measures, such as anxiety and depression symptom severity, and diagnostic status at post-treatment (9 weeks). Secondary outcomes included anxiety disorder-specific measures, quality of life, treatment adherence, satisfaction, and general psychiatric symptomatology at follow-up (6 months after randomization).ResultsCAU plus unguided ICBT was more effective than CAU at post-treatment, with small to medium between-group effect sizes on primary (Cohen's d = 0.41–0.47) and secondary (Cohen's d = 0.16–0.61) outcomes. Treatment gains were maintained at follow-up. In the treatment group, 28.2% of those with a SAD diagnosis, 38.3% with a PDA diagnosis, and 44.8% with a GAD diagnosis at pretreatment no longer fulfilled diagnostic criteria at post-treatment.ConclusionsThe unguided ICBT intervention examined is effective for anxiety disorders when delivered in primary care.
Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients we are able to show that we can predict therapy outcome with an Area Under the Curve (AUC) of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants it is hard to generalize the results, but they do show great potential in this type of information.
Transdiagnostic treatments span a heterogeneous group of interventions that target a wider range of disorders and can be applied to treat several disorders simultaneously. Several meta-analyses have highlighted the evidence base of these novel therapies. However, these meta-analyses adopt different definitions of transdiagnostic treatments, and the growing field of transdiagnostic therapies has become increasingly difficult to grasp. The current narrative review proposes a distinction of “one size fits all” unified and “my size fits me” individualized approaches within transdiagnostic therapies. Unified treatments are applied as “broadband” interventions to a range of disorders without tailoring to the individual, while individualized treatments are tailored to the specific problem presentation of the individual, e.g., by selecting modules within modular treatments. The underlying theoretical foundation and relevant empirical evidence for these different transdiagnostic approaches are examined. Advantages and limitations of the transdiagnostic treatments as well as future developments are discussed.
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