New study: Adapted CBT for adults with OCD and ASD

We have just released a pre-print1 where we describe an adapted cognitive behavior therapy for adults with obsessive-compulsive disorder (OCD) and autism spectrum disorder (ASD). You can find the full paper here.

OCD and ASD often co-occur but effective treatment options for this patient group are sorely lacking. We extended an adapted CBT protocol developed in the UK at our specialist clinic for OCD and related disorders (OCD-programmet).

Our results show that OCD-symptoms (both when rated by a clinician and by the participants themselves) decrease over the course of treatment, but that few participants were completely symptom free.

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Results on the main outcome: Yale-Brown Obsessive-compulsive Scale (YBOCS) rated by clinicians.

We discuss three ways to develop the treatment further: adding more support in between sessions to help participants do exposure exercises on their own, including interventions to help participants find meaningful daily activities, and intensifying the treatment over a shorter time span.

If you are interested in learning more, you can find the pre-print at the pre-print server PsyArXiv.


  1. A pre-print is a manuscript that has been read and approved by all authors but has not gone through peer-review yet. It’s a popular way to quickly disseminate results in fields like genetics, physics and mathematics. It is gaining popularity in other fields as well. Wikipedia article ↩︎

Internet-KBT för tvångssyndrom och dysmorfofobi i reguljär vård

Nu finns internet-KBT för tvångssyndrom (OCD) och dysmorfofobi (BDD) tillgängligt för vuxna personer i hela Sverige!

Vi har utvecklat och utvärderat dessa behandlingar i flera vetenskapliga studier sedan 2011 (OCD) respektive 2014 (BDD) och funnit att de fungerar bra vid båda tillstånden. Det slutgiltiga målet har alltid varit att fler patienter ska få tillgång till effektiv behandling och vi är stolta över att ha nått fram till den här punkten.

Lina Lundström är den person som varit projektledare för implementeringen i reguljär vård och det är tack vare hennes ihärdiga arbete som behandlingarna nu finns tillgängliga.

Instruktioner för egenanmälan till behandlingen

Boende i Stockholms län kan logga in via Vårdguidens e-tjänster för att anmäla sitt intresse för behandling.

Data Science for Mental Health Transfer Learning Roundtable

This week RISE/KTH arranged a roundtable on AI in mental heath research with participants from KI, KTH, Stockholm University and KCL, UCL and Anna Freud Institute in London. We are starting in this exiting field now.

 

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New study: PRIMED

In collaboration with researchers at Linnéuniversitet, Royal Institute of Technology and University of North Carolina at Chapel Hill we are planning a new study called PRIMED: Predicting Response to CBT in Mental Disorders using multimodal data and machine learning.

Common mental disorders such as depression, social anxiety, panic disorder and OCD have a point prevalence of 15%. CBT is the treatment of choice, yet 30-60% of patients undergoing CBT will not respond to treatment. Identifying non-responders before treatment would allow alternative treatment choices.

Predictor studies have so far not been able to reach an acceptable predictive power to guide clinical decisions so adding fine-grained and multiple types data, including the genetic footprint, may be a way forward.

Hence, the aim of PRIMED is to better predict treatment response to Cognitive behavioral therapy (CBT) in depression, social anxiety disorder, panic disorder, and OCD by using clinical, register-based, and genetic data from 6000 individuals in Sweden.

In PRIMED, potential predictors include clinical information, register based data and genetic variation. Outcomes are short-term clinical data, long-term register based medical data e.g. prescriptions, diagnoses, suicide attempts and social data such as unemployment, sickness absence, and disability pension.

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In addition to study predictors on these different levels, we also want to add artificial intelligence, or Machine Learning to identify individuals at risk of not responding to treatment. So-called ‘Learning Machines’ can learn from historical cases and then apply what it has learnt to predict outcomes in a single, new case. Rather than considering the effect of one variable on an outcome of interest at a group level, Learning Machines identify patterns of information that can be used to predict the outcome for an individual.

The Learning Machine will learn from a training dataset consisting of 4 000 individuals and then validate its ability to correctly identify patients at risk on 2 000 new patients.

This project pursues a novel research area that can only be developed by a multidisciplinary team involving experts in disparate fields: psychiatry, psychology, epidemiology, AI, and genetics. We have assembled a strong interdisciplinary team from four different universities in two countries and are very excited about this collaboration!

Doctor Evelyn Andersson Hagen!

Congrats Evelyn! She is the 4th PhD student to graduate from our lab and she defended her thesis very well the past Friday. Big thank you to everyone involved!

 

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Evelyn in defense mode.
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From left: Co-supervisor Erik Hedman-Lagerlöf, examination board member Lisa Thorell, main supervisor Christian Rück, Evelyn, co-supervisor Martin Schalling and Cattis Lavebratt, opponent Jönsson, examination board member Lars Westberg and session chair Viktor Kaldo.
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Party!
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Winning shoes!
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Opponent Erik Jönsson, Oslo University.
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ICBT FTW!

Evelyn nailed it!

Evelyn Andersson Hagen today nailed her thesis at the KI Library. This is symbol of her thesis now being out in the public. She will defend her thesis June 1st. Everyone is welcome! Details here.

Big thanks to all the participants of the studies, the involved clinicians of Internetpsykiatrienheten and to the co-supervisors Nils Lindefors, Martin Schalling, Catharina Lavebratt and Erik Hedman-Lagerlöf.

The thesis is here (pdf).

 

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