The Argument

Depression is the largest contributor to global disease burden (World Health Organization), costing approximately $150 billion per year in Europe. 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 annually.

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:

  • 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)
  • 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)
  • Lack of a customized, personalized manner of providing feedback (most solutions present total scores for quizzes and scales)

Our Solution

Recognizing these shortcomings, we used insights from Graphic Design (e.g., user interfaces), gamification theories (e.g., “serious games”) and Artificial Intelligence to develop an automated app aimed at both prevention and intervention for depression, which can 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 doctoral-level team of researchers, psychologists, programmers and designers in order to create a customizable and reactive cross-platform mobile app. The application presents 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 mobile app links to a therapist interface. The therapist uses this interface to customize the intervention, get real-time information about the client's progress, and communicate with the client.

The efficacy of the platform was examined via two randomized clinical pilot studies: One testing its utility as a preventative tool, the other testing its utility as an intervention tool for depression.

As a result of this project, a new treatment option is 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.

[ Home ]