Presentation Information
[SY-43-01]Personalized & Optimized Therapies (POTs) using the Resilience Training App® for subthreshold depression in the community: a report from the RESiLIENT trial
*Toshi A. Furukawa (Kyoto University(Japan))
Keywords:
Depression,Prevention,Cognitive-Behavioural Therapy,Smartphone,Precision medicine
In 1984, a report by the US National Institute of Mental Health concluded that preventing depression was impossible. However, research over the past 30 years has shown that depression prevention is, in fact, achievable. Despite this progress, health losses due to common mental disorders have continued to rise, and depression remains the leading cause of disability related to mental health worldwide. Simply put, we have failed in our professional mission to provide people with the mental health support they deserve.
A shift in this trajectory now seems finally within reach, driven by the widespread adoption of internet technologies and rapid advancements in artificial intelligence (AI).
Over the past decade, we have been developing a smartphone CBT (cognitive behavioral therapy) app called the Resilience Training App ®. We recently completed the largest individually randomized trial to date, aimed at treating subthreshold depression and promoting mental well-being in the general population (total n=5361). The app delivers five CBT-based skills: behavioral activation, cognitive restructuring, problem-solving, assertiveness training, and behavior therapy for insomnia. These skills, both individually and in combination, demonstrated varying levels of efficacy in reducing depression symptoms, with effect sizes ranging from -0.67 (95% CI: -0.81 to -0.53) to -0.16 (95% CI: -0.30 to -0.02). The interventions also showed differential effects on anxiety, insomnia, and mental well-being. These benefits were sustained for at least 26 weeks.
Building on these findings, we developed the AI-based Personalized & Optimized Therapy (POT) algorithm to match interventions with individuals' characteristics. When individuals received their POT, the overall effect size for the population increased by 35% compared to the conventional approach of providing everyone with the group average best intervention.
Scaling up the implementation of this app and its POT algorithm is both timely and crucial in the global fight against depression.
A shift in this trajectory now seems finally within reach, driven by the widespread adoption of internet technologies and rapid advancements in artificial intelligence (AI).
Over the past decade, we have been developing a smartphone CBT (cognitive behavioral therapy) app called the Resilience Training App ®. We recently completed the largest individually randomized trial to date, aimed at treating subthreshold depression and promoting mental well-being in the general population (total n=5361). The app delivers five CBT-based skills: behavioral activation, cognitive restructuring, problem-solving, assertiveness training, and behavior therapy for insomnia. These skills, both individually and in combination, demonstrated varying levels of efficacy in reducing depression symptoms, with effect sizes ranging from -0.67 (95% CI: -0.81 to -0.53) to -0.16 (95% CI: -0.30 to -0.02). The interventions also showed differential effects on anxiety, insomnia, and mental well-being. These benefits were sustained for at least 26 weeks.
Building on these findings, we developed the AI-based Personalized & Optimized Therapy (POT) algorithm to match interventions with individuals' characteristics. When individuals received their POT, the overall effect size for the population increased by 35% compared to the conventional approach of providing everyone with the group average best intervention.
Scaling up the implementation of this app and its POT algorithm is both timely and crucial in the global fight against depression.