| 1. |
- Andersson, E, et al.
(författare)
-
Internet-based cognitive behaviour therapy for obsessive-compulsive disorder: a randomized controlled trial
- 2012
-
Ingår i: Psychological Medicine. - : Cambridge University Press (CUP). - 0033-2917 .- 1469-8978. ; 42:10, s. 2193-2203
-
Tidskriftsartikel (refereegranskat)abstract
- Background. Cognitive behaviour therapy (CBT) is an effective treatment for obsessive-compulsive disorder (OCD) but access to CBT is limited. Internet-based CBT (ICBT) with therapist support is potentially a more accessible treatment. There are no randomized controlled trials testing ICBT for OCD. The aim of this study was to investigate the efficacy of ICBT for OCD in a randomized controlled trial. less thanbrgreater than less thanbrgreater thanMethod. Participants (n=101) diagnosed with OCD were randomized to either 10 weeks of ICBT or to an attention control condition, consisting of online supportive therapy. The primary outcome measure was the Yale-Brown Obsessive Compulsive Scale (YBOCS) administered by blinded assessors. less thanbrgreater than less thanbrgreater thanResults. Both treatments lead to significant improvements in OCD symptoms, but ICBT resulted in larger improvements than the control condition on the YBOCS, with a significant between-group effect size (Cohens d) of 1.12 (95% CI 0.69-1.53) at post-treatment. The proportion of participants showing clinically significant improvement was 60% (95% CI 46-72) in the ICBT group compared to 6% (95% CI 1-17) in the control condition. The results were sustained at follow-up. less thanbrgreater than less thanbrgreater thanConclusions. ICBT is an efficacious treatment for OCD that could substantially increase access to CBT for OCD patients. Replication studies are warranted.
|
|
| 2. |
- Andersson, Gerhard, et al.
(författare)
-
Förekomst av tinnitus i Sverige
- 2002
-
Ingår i: Läkaresällskapets Rikstämma 27-29 november 2002. ; , s. 130-
-
Konferensbidrag (refereegranskat)
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Rozental, Alexander, et al.
(författare)
-
Consensus statement on defining and measuring negative effects of Internet interventions
- 2014
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: Internet interventions have a great potential for alleviating emotional distress and promoting mental health. A number of clinical trials have demonstrated their efficacy for several psychiatric conditions, and Internet interventions will likely become a common alternative to face-to-face treatments. Meanwhile, research has paid little attention to the potential negative effects associated with treatment, warranting further investigation of the possibility that some patients might deteriorate or experience adverse events. Evidence from face-to-face treatments suggests that negative effects afflict 5-10% of all patients undergoing treatment in terms of deterioration alone. However, there is currently a lack of consensus on how to define and measure negative effects in psychotherapy research in general, leaving researchers without practical guidelines for monitoring and reporting negative effects in clinical trials.Method: The current paper seeks to provide recommendations that could promote the study of negative effects in Internet interventions with the aim of increasing the knowledge of its occurrence and characteristics. Ten leading experts in the field of Internet interventions were invited to participate and share their perspective on how to explore negative effects, using the Delphi technique to facilitate a dialogue and reach an agreement.Results: The importance of conducting further research on negative effects is emphasized, and suggestions on how to classify and measure negative effects in Internet interventions are provided, involving methods from both quantitative and qualitative research. Potential mechanisms underlying negative effects are also presented, differentiating common factors shared with face-to-face treatments from those unique to treatments delivered via the Internet.Conclusion: Negative effects are to be expected and need to be acknowledged to a greater extent, advising researchers to systematically probe for negative effects whenever conducting clinical trials involving Internet interventions, as well as to share their findings in scientific journals.
|
|
| 6. |
- Kaldo, V., et al.
(författare)
-
AI-driven adaptive treatment strategies in internet-delivered CBT
- 2021
-
Ingår i: European psychiatry. - : Cambridge University Press. - 0924-9338 .- 1778-3585. ; 64, s. S20-S20
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: Adaptive Treatment Strategies warns therapists of patients at risk of treatment failure to prompt an adaption of the intervention. Internet-delivered Cognitive Behavioural Therapy (ICBT) collects a wide range of data before and during treatment and can quickly be adapted by adjusting the level of therapist support. Objectives: To evaluate how accurate machine learning algorithms can predict a single patient’s final outcome and evaluate the opportunities for using them within an Adaptive Treatment Strategy. Methods: Over 6000 patients at the Internet Psychiatry Clinic in Stockholm receiving ICBT for major depression, panic disorder or social anxiety disorder composed a training data set for eight different machine learning methods (e.g. k-Nearest Neighbour, random forest, and multilayer perceptrons). Symptom measures, messages between therapist and patient, homework reports, and other data from baseline to treatment week four was used to predict treatment success (either 50% reduction or under clinical cut-off) for each primary symptom outcome. Results: The Balanced Accuracy for predicting failure/success always were significantly better than chance, varied between 56% and 77% and outperformed the predictive precision in a previous Adaptive Treatment Strategy trial. Predictive power increased when data from treatment weeks was cumulatively added to baseline data. Conclusions: The machine learning algorithms outperformed a predictive algorithm previously used in a successful Adaptive Treatment Strategy, even though the latter also received input from a therapist. The next steps are to visualize what factors contributes most to a specific patient’s prediction and to enhance predictive power even further by so called Ensemble Learning.No significant relationships.
|
|
| 7. |
|
|
