Personalized online intervention based on a risk algorithm for the universal prevention of anxiety disorders: Design and development of the prevANS intervention.

Objective: To describe the design and development of prevANS, a personalized online intervention for the universal prevention of anxiety disorders based on a predictive risk algorithm. A user-centered approach was followed, considering the feedback of potential users and mental health professionals.

Methods: The study had three phases: (a) designing the intervention based on existing scientific literature; (b) piloting and evaluating the beta version involving potential users and health professionals; and (c) refining the intervention based on participants' suggestions. This iterative process aimed to refine the prevANS intervention before testing in a randomized controlled trial.

Results: The prevANS intervention provides personalized anxiety risk reports and components tailored to individuals' needs. Participants at low risk receive psychoeducation had access to a set of tools enhance protective factors. Moderate/high-risk individuals also receive cognitive-behavioral training. Both groups have access to a reward system and forum. Results from the design evaluation indicate that the prevANS interface is attractive and user-friendly and the psychoeducational materials helpful and engaging. The cognitive-behavioral training module received positive feedback. Participants suggested changes related to usability, content clarity, attractiveness, and engagement, which were implemented afterwards.

Conclusions: This article describes the development of a personalized intervention for preventing anxiety disorders using a validated risk prediction algorithm. The prevANS intervention was designed based on current scientific literature by a team of experts employing a user-centered approach. Research on the effectiveness of information and communication technologies in mental health prevention interventions considering user needs and preferences is warranted.