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AI-Driven Efficacy for Eating Disorder Recovery

CE Hours 1

About this course

Eating disorder recovery is nonlinear, deeply personal, and often unsupported between sessions or after formal treatment ends. Many individuals in recovery seek ongoing, accessible tools that can help them stay engaged, manage setbacks, and continue building healthier patterns. This workshop introduces a novel solution: a clinically grounded, AI-powered digital companion that transforms evidence-based therapy into personalized, gamified support designed to meet users wherever they are in their recovery journey. The tool integrates cognitive behavioral therapy (CBT), dialectical behavior therapy (DBT), acceptance and commitment therapy (ACT), and unified transdiagnostic principles into micro-interventions-delivered as bite-sized, emotionally resonant 'quests.' These daily tasks are dynamically adapted to a user's unique coping style, emotional patterns, and core challenges. With over a thousand modular activities, the platform supports skill progression through layered, personalized paths that evolve as users demonstrate growth or encounter difficulty. Each user is guided by an AI accountability companion that offers positive reinforcement, reframing, emotion labeling, and values-aligned nudges in real time. The platform has been piloted with 100s of individuals recovering from a range of eating disorders including anorexia nervosa, bulimia nervosa, binge eating disorder, and ARFID. Participants engaged with the tool for a median of 7 minutes per day across four weeks. Preliminary data show an average 23% decrease in EDE-Q scores, with some highly engaged users seeing up to 65% symptom reduction. Patients showed significant improvements in subjective distress (SUDS), emotion labeling, meal structure, and interruption of disordered behavior patterns. Many users reported reaching personal recovery milestones for the first time, including reframing body image beliefs and navigating high-risk scenarios using the app's real-time interventions. This session will include: -A walkthrough of the clinical framework, core therapeutic mechanisms, and personalization engine behind the app -A demonstration of key features, including skill progression and real-time adaptations based on user behavior -A discussion of pilot data and user-reported outcomes -An interactive experience where attendees explore several evidence-based tasks from the platform Attendees will learn how this tool can serve as an adjunct to therapy, a scaffold for those transitioning out of higher levels of care, or a long-term recovery companion for individuals seeking sustained support. Special attention will be given to ethical considerations, diversity of clinical presentation, and how AI can extend the therapeutic alliance outside the traditional treatment room. This workshop is ideal for clinicians, researchers, and treatment center leaders interested in integrating technology into outpatient and post-treatment recovery support. It is also relevant to those exploring digital behavioral health, relapse prevention tools, or innovation in emotion- and behavior-focused care. By blending clinical rigor with user-centered design, this approach offers a scalable, low-burden way to enhance continuity, engagement, and recovery outcomes. By the end of this session, participants will understand how dynamic, personalized micro-interventions can complement existing treatment protocols, and walk away with practical insights into the future of digital recovery care.

Learning Objectives

  • Utilize an AI-powered tool to support clients in between sessions through personalized, gamified micro-interventions that align with clinical goals.
  • Identify core evidence-based strategies embedded in digital interventions for eating disorder recovery, including CBT, DBT, and ACT-informed techniques.
  • Explain how adaptive technologies can improve emotional regulation, engagement, and treatment adherence among individuals recovering from eating disorders.

Learning Levels

  • Intermediate
  • Advanced

Course Instructor(s)

  • Mehek Mohan, MBA

    Mehek Mohan is the CEO and co-founder of Kahani, a mental health startup focused on eating disorder care coordination through personalized, gamified recovery tools. She earned her MBA from Stanford Graduate School of Business. Previously, she was the youngest Product Manager at Genentech, where she led key AI initiatives across Early Clinical Development. She studied Molecular and Cell Biology at UC Berkeley, researching novel gene editing techniques under Nobel Laureate Dr. Jennifer Doudna. Passionate about increasing access to care, Mehek is also a lifelong adventurer who has traveled to 113 countries and counting.

  • Daisy Cephas

    Daisy Cephas is a fourth-year doctoral candidate in the PAU-Stanford PsyD Consortium, specializing in child and adolescent psychology with a focus on eating disorders. She is trained in Adolescent-Focused Therapy (AFT) and Family-Based Treatment (FBT) and has provided outpatient care for youth and families at the Stanford School of Medicine. Her research has focused on ARFID, binge eating disorder, and anorexia nervosa, particularly diagnostic assessment, through the Stanford Eating Disorders Research Program. Since its inception, she has served as a consultant with Kahani, a gamified eating disorder recovery app that integrates therapy tasks, psychoeducation, and social support to build regulation and coping skills. Daisy is committed to advancing evidence-based treatments and combining research and advocacy to better support youth and families affected by eating disorders.

References

  • Wisting, L., Stice, E., Ghaderi, A., & Dahlgren, C. L. (2023). Effectiveness of virtually delivered Body Project groups to prevent eating disorders in young women at risk: a protocol for a randomized controlled trial. Journal of Eating Disorders, 11(1), 209. https://doi.org/10.1186/s40337-023-00932-7
  • Thompson-Brenner, H., Singh, S., Gardner, T., Brooks, G. E., Smith, M. T., Lowe, M. R., & Boswell, J. F. (2021). The Renfrew unified treatment for eating disorders and comorbidity: Long-term effects of an evidence-based practice implementation in residential treatment. Frontiers in Psychiatry, 12, 641601. https://doi.org/10.3389/fpsyt.2021.641601
  • Wiltsey Stirman, S., Marques, L., Creed, T. A., Gutner, C. A., DeRubeis, R., Barnett, P. G., ... & La Bash, H. (2018). Leveraging routine clinical materials and mobile technology to assess CBT fidelity: the Innovative Methods to Assess Psychotherapy Practices (imAPP) study. Implementation Science, 13(1), 69. https://doi.org/10.1186/s13012-018-0756-3

CE Process Info

Content

  • Recording
    1 parts
    • AI-Driven Efficacy for Eating Disorder Recovery
AI-Driven Efficacy for Eating Disorder Recovery
You Have Completed This course
$25
You are enrolled
  • CE Hours
    1
  • Type
    Self-Paced
  • Publication Date
    Feb 15th, 2026

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