All Teachings

Digital Health in Practice, Fall 2025, University of Zurich

Generative AI Chatbot for Mental Health Treatment (NEJM AI, 2025) Mobile health intervention CanRelax reduces distress in people with cancer, (npj Digital Medicine 2025) Therabot for the treatment of mental disorders (Nature Mental Health 2025), A Hybrid Rule- and LLM-based Embodied Voice Assistant for Cognitive Stimulation in Older Adults (JMIR Preprints 2025), Success factors of growth-stage digital health companies (BMC Health Services Research 2025) Next-generation Wearable Sensors for Biopsychosocial Care in Mental Health (BMJ Digital Health and AI 2025) Digital phenotyping of diet, physical activity, and glycemia in adults (npj Digital Medicine, 2024), Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management (Scientific Reports 2025)

Digital health applications use information, sensor, and communication technology to understand, prevent, manage, or treat diseases. The design of these applications requires interdisciplinary expertise at the intersection of medicine, psychology, computer science, technology, management, economics, and law. Only a close collaboration between experts from these disciplines and a specific target population can lead to a shared understanding of the problem at hand and, as a result, highly effective digital health applications. For this reason, national and international students studying computer science, business informatics, psychology, management, economics, or law are invited to work collaboratively with medical students.

Digital health applications and companies have the goal of advancing health care services to fight the ongoing increase of non-communicable diseases (NCDs) and common mental disorders (CMDs) in developed countries. To this end, the question arises of how to develop evidence-based digital health interventions (DHI) that allow medical doctors and other caregivers to scale and tailor long-term treatments to individuals in need at sustainable costs. Through input lectures and practical applications, this module has, therefore, the objective to help students to understand better the need, design, implementation, and evaluation of DHIs.

After the course, students will be able …

  1. to understand the importance of digital health interventions for the prevention, management, and treatment of non-communicable diseases and common mental disorders
  2. to discuss the opportunities and challenges of digital health interventions (e.g., data collection with wearables, smartphone- and chatbot-delivered health interventions)
  3. to gain hands-on experience in the conceptual design, implementation, and evaluation of a wearable- and smartphone-based digital health intervention

To reach these learning objectives, the following topics are covered:

  1. DHIs for the prevention, management, and treatment of NCDs and CMDs
  2. Strategies for long-term compliance with DHI
  3. Conceptual design of a wearable- and smartphone-based DHI
  4. Technical implementation of a wearable- and smartphone-based DHI
  5. Evaluation of a wearable- and smartphone-based DHI

Course Structure

The module consists of live input sessions with interactive group exercises and discussions. Complementary learning material is provided through tutorial video clips, multiple-choice questions, and exercises. Additional coaching sessions are offered to support the groups with the development of their DHI and with the preparation of their presentations. In addition to the MobileCoach platform (www.mobile-coach.eu), an open-source software platform for developing digital biomarkers and health interventions, students will gain hands-on experience with wearable fitness trackers (Fitbits). Programming skills are not required.

The module is structured as follows:

  1. Students are equipped with relevant foundational knowledge (2 sessions)
  2. Students build groups and develop a wearable- and smartphone-based DHI (3 sessions)
  3. Students use the wearable- and smartphone-based DHIs of their peers (ca. 10 days)
  4. Students in groups evaluate their wearable- and smartphone-based DHI (1 session)
  5. Students in groups present and critically discuss their findings (1 session)

Course Literature

All relevant learning material will be made available via the online learning platform. Moreover, the content of this module is drawn from the experience of the lecturers and the following work:

  1. Balbim GM, IG Marques, DX Marquez, et al. (2021) Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions, Journal of Medical Internet Research (JMIR) Mhealth Uhealth 9(3):e25289, 10.2196/25289
  2. Bakoyiannis, I. Therabot for the treatment of mental disorders. Nat. Mental Health 3, 485 (2025). https://doi.org/10.1038/s44220-025-00439-x
  3. Barth, J., Schläpfer, S., Schneider, F., Santhanam, P., Kowatsch, T., Heinz, P., Held, U., Eicher, M., Witt, C., Mobile health intervention CanRelax reduces distress in people with cancer in a randomized controlled trial, npj Digital Medicine 8, 269 (2025), 10.1038/s41746-025-01688-x
  4. Bitomsky, L., Pfitzer, E., Nißen, M.K., Kowatsch, T. (2025) Advancing health equity and the role of digital health technologies: a scoping review, BMJ Open 2025;15:e099306, 10.1136/bmjopen-2025-099306
  5. Brill, E., Vinay, R., Nißen, M., Joshi, P., Klöppel, S., Kowatsch, T. (2025) Towards a smartphone-based and conversational agent delivered just-in-time adaptive holistic lifestyle intervention for seniors affected by cognitive decline: Two-week proof-of-concept study, JMIR Formative Research, 12/05/2025:66885 10.2196/66885
  6. Castro, O., Mair, J. L., … Kowatsch, T. (2023). Development of “LvL UP 1.0”: a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Frontiers in Digital Health, 5. https://doi.org/10.3389/fdgth.2023.1039171
  7. Collins, L. M. (2018). Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST). Springer. https://doi.org/10.1007/978-3-319-72206-1
  8. Heinz M., … Jacobson Nicholas, C. (2025). Randomized Trial of a Generative AI Chatbot for Mental Health Treatment. NEJM AI, 2(4), AIoa2400802. https://doi.org/10.1056/AIoa2400802
  9. Jacobson, N., Kowatsch, T., & Marsch, L. (Eds.). (2023). Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future (1st ed.). Elsevier, Academic Press. https://doi.org/10.1016/C2020-0-02801-X.
  10. Pfitzer, E., Giger, O.F., Kausch, C., Kowatsch, T. (2025) Success Factors and Measures for Scaling Patient-Facing Digital Health Technologies from Leaders Insights, BMC Health Services Research 25(632) 10.1186/s12913-025-12748-z
  11. Pfitzer, E., Bitomsky, L., Nißen, M., Kausch, C., Kowatsch, T. (2024) Success factors of growth-stage digital health companies: a systematic literature review, J Med Internet Res 2024;26:e60473 10.2196/60473
  12. Kowatsch, T., Otto, L., Harperink, S. et al. (2019). A design and evaluation framework for digital health interventions. it – Information Technology, 61(5-6), 253-263. https://doi.org/10.1515/itit-2019-0019
  13. Kowatsch, T., & Fleisch, E. (2021). Digital Health Interventions. In O. Gassmann & F. Ferrandina (Eds.), Connected Business: Create Value in a Networked Economy (pp. 71-95). Springer International Publishing. https://doi.org/10.1007/978-3-030-76897-3_4
  14. Kowatsch, T., Schachner, T., Harperink, S. et al. (2021). Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Healthcare Professionals, Patients, and Family Members: Intervention Design and Results from a Multi-site, Single-arm Feasibility Study. J Med Internet Res, 23(2). https://doi.org/10.2196/25060
  15. Kowatsch, T., Lohse, K.-M., Erb, V. et al. (2021). Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies. Journal of Medical Internet Research (JMIR), 23(2). https://doi.org/10.2196/23612
  16. Mishra, V., Künzler, F., Kramer, J.-N., Fleisch, E., Kowatsch, T., & Kotz, D. (2021). Detecting Receptivity for mHealth Interventions in the Natural Environment. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 5(2), Article 74. https://doi.org/10.1145/3463492
  17. Nahum-Shani, I., Smith, S. N., Spring, B. J. et al. (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med, 52(6), 446-462. https://doi.org/10.1007/s12160-016-9830-8
  18. Sim, I. (2019). Mobile Devices and Health. N Engl J Med, 381(10), 956-968. https://doi.org/10.1056/NEJMra1806949
  19. Vinay, R., Uetova, E., Tommila, N., Biller-Andorno, N., Kowatsch, T. (2025) GRACE, A Hybrid Rule- and LLM-based Embodied Voice Assistant for Cognitive Stimulation in Older Adults: A Pilot Study Assessing Technical Feasibility, Technology Acceptance, and Working Alliance, JMIR Preprints. 09/05/2025:76489, 10.2196/preprints.76489
  20. Wang, C., Lee, C., & Shin, H. (2023). Digital therapeutics from bench to bedside. npj Digital Medicine, 6(1), 38. https://doi.org/10.1038/s41746-023-00777-z

Share this post

Summary

Digital Health in Practice, University of Zurich, Fall 2025, 4 ECTS, Tuesday, 8:15 a.m. – 12:00 a.m., Links:

  1. UZH Course Catalogue Entry (MeF)
  2. UZH Course Catalogue Entry (WWF)
  3. MobileCoach Video Tutorials
  4. MobileCoach Exercises
Lecturers
Prof. Dr. Tobias Kowatsch
Prof. Dr. Tobias Kowatsch
Professor for Digital Health Interventions, Institute for Implementation Science in Health Care, University of Zurich (UZH); Director, School of Medicine, University of St.Gallen (HSG); Scientific Director, Centre for Digital Health Interventions, UZH, HSG & ETH Zurich, Switzerland
Prof. Dr. Viktor von Wyl
Prof. Dr. Viktor von Wyl
Professor for Mobile and Digital Health, Institute for Implementation Science in Health Care, University of Zurich, Switzerland
Contact Person
Felix Moser
Felix Moser
Ph.D. candidate, School of Medicine, University of St. Gallen; BSc and MSc in Informatics, Technical University of Munich
Teaching Support
Andreas Baumer
Andreas Baumer
Doctoral Candidate, Mobile and Digital Health, Institute for Implementation Science in Health Care, University of Zurich
Sintieh Nchinda Ngek Ekongefeyin, Dr. med., MSc
Sintieh Nchinda Ngek Ekongefeyin, Dr. med., MSc
Ph.D. Candidate, Mobile and Digital Health, Institute for Implementation Science in Health Care, University of Zurich
Felix Moser
Felix Moser
Ph.D. candidate, School of Medicine, University of St. Gallen; BSc and MSc in Informatics, Technical University of Munich