Depression is the third largest contributor to global disease burden (outranking heart disease and HIV/AIDS) and the number one contributor to disease burden in developed countries, costing approximately US $150 billion per year in Europe, where it is the most costly mental disorder. Subthreshold depression, a condition characterized by impairments and reduced quality of life, almost doubles these costs, bringing them to a staggering quarter of a trillion dollars.
Given the exceptionally high financial burden associated with clinical and subclinical depression, improving the preventative measures, as well as the efficacy and delivery of the interventions is imperative.
Cognitive-behavioral therapy (CBT) is the psychological standard of care in the treatment of depression, recommended by leading US and European health agencies. CBT can be delivered in classical format (face-to-face), or via the Internet (e.g., two-way online video communication). Internet-delivered CBT comes with cost benefits, as well as with greater reach.
In the recent years, automated CBT (interventions delivered on the computer, or online, which use no or minimal therapist support) has emerged as a solution that can, on some dimensions, be as effective as the classical CBT.
However, the existing computerized interventions for depression also come with less desirable outcomes, such as high dropout rates (50%-60%), limited endurance of long-term benefits, or limited improvement in functioning.
We believe that these limitations characterizing the existing computerized solutions are caused by (1) reduced or non-existent personalization of the intervention (e.g., same standard intervention delivered to various people, making some unable to identify with the treatment); (2) reduced immersion (and attractiveness) of the treatment experience (e.g., compared to other online activities, some intervention platforms may be perceived as uninteresting or repetitive), and (3) lack of a customized, personalized manner of providing feedback (most solutions present total scores for quizzes and scales).
Recognizing these shortcomings, we are using insights from Graphic Design (e.g., user interfaces), gamification theories (e.g., “serious games”) and Artificial Intelligence to develop an automated application aimed at both prevention and intervention for depression, which will substantially increase the quality of the user experience, thus leading to better outcomes (e.g., reduced attrition rates, more stable improvements, increased functioning).
To achieve this goal, we have assembled an inter- and multi-disciplinary team of researchers, psychologists, programmers and designers in order to create a customizable and reactive cross-platform application. The application comes with specific, native apps for various platforms (smartphones, desktops), presenting materials and interactive exercises that react and adapt to users’ preferences. Elements of gamification are included in the delivery of cognitive-behavioral interventions and in the feedback module. Mobile notifications and achievements, presented in a visually appealing way, will further incentivize the use of the application.
The efficacy of the platform is examined via two randomized clinical pilot studies: One testing its utility as a prevention tool, the other testing its utility as an intervention tool for depression.
As a result of this project, a new treatment option will be available to the public, based on the latest research and development in psychology and computer sciences, raising the bar in what is possible in the field of automated interventions for depression.