Treatment for alcohol use disorders on the internet, long-term outcomes, cost-effectiveness, video therapy, family support and prediction of outcomes thru language processing and machine learning

With the development of new technology and the distancing due to the current pandemic, the way councelling and psychological treatments are delivered are changing quickly.

Without research on the effects and delivery of such intervetion, there is a risk of usingor spreading interventions that have no or negative effects. Alcohol use disorders (AUD) are highly prevalent worldwide, and causes a number of health problems.

Internet interventions are effective in reducing alcohol consumption and could help those that do not receive treatment.

But there is a lack of studies on long term effects, economic evaluations, video counselling and support for family members.

There is also a lack of knowledge on how to predict outcome in treatment of AUD, which can be adressed by using data from internet interventions. In this projekt data from internet interventions will be analyzed in five separate studies.

The research questions are:is internet treatment for AUD more effective than information control and as effectivce as face-to-face treatment in alcohol consumption and alcohol use disorder 12 and 24 months after randomisation? (I)is the costs of delivery lover and the cost-effectiveness higher in internet treatment for AUD compared with face-to-face treatment? (II)can analysis of free text using artificial intelligence and machine learning predict outcome in internet treatment of AUD? (III)what is the effect on treatment utilization, alcohol consumption and symptoms of AUD, of transitioning from face-to-face meetings to video meetings in the treatment of AUD at a specialized care clinic during the COVID pandemic? (IV)what are the effects of an Internet based self-help program for family members fo people with AUD on level of stress, anxiety and depression, alcohol use and treatment seeking? (V)The results will be important to end users and can help guide clinicians and policy makers in planing and funding internet interventions in health care and public health settings.