Optimizing Digital Therapeutics, Spring 2024, University of Zurich

Digital Therapeutics from Bench to Bedside (npj Digital Medicine, 2023), Digital Therapeutics for Mental Health and Addiction (Elsevier 2023), Large Language Models in Medicine (Nature Medicine, 2023), How to e‐mental health (Nature Mental Health, 2023), Artificial Intelligence and Machine Learning in Medicine (New England Journal of Medicine, 2023), LLM‐based AI Chatbots in Medicine (Nature Medicine, 2023)

Digital therapeutics (DTx) 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.

DTx and DTx 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 design and optimize evidence-based DTx 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 better understand the need, design, optimization, implementation, and evaluation of DTx.

After the course, students …

  1. understand the relevance of digital therapeutics (DTx)
  2. understand design and evaluation frameworks for DTx
  3. can explain optimization strategies for DTx
  4. can draft study designs for DTx optimization trials
  5. can implement an optimization trial for a smartphone-based and chatbot-delivered DTx
  6. can critically assess the results of a DTx optimization trial
  7. can discuss the opportunities and challenges of DTx optimization trials

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

  1. Introduction to digital therapeutics (motivation, definition, application areas)
  2. Introduction to design and evaluation frameworks for digital therapeutics
  3. Introduction to optimization strategies for digital therapeutics and corresponding study designs
  4. Workshop on optimization trial designs (in groups)
  5. Design of an optimization trial (in groups)
  6. Technical implementation of an optimization trial for a smartphone-based and chatbot-delivered DTx (in groups)
  7. Data collection and analysis with a DTx (individually)
  8. Presentation and discussion of DTx optimization trial results (in groups)

Course Structure

The module consists of live input sessions with interactive group exercises and discussions. Complementary online learning material is provided. Additional coaching sessions are offered to support the groups with the design of their optimization trials, the technical implementation of the study designs, and the preparation of the final group presentations. Students will use MobileCoach (www.mobile-coach.eu), an open-source software platform for developing and evaluating DTx, to implement the optimization trial. Programming skills are not required.

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. Collins LM (2018) Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST), New York: Springer, 10.1007/978-3-319-72206-1
  2. Jacobson N, T Kowatsch & LA Marsch (2023) Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future (1st ed.), Cambridge: Elsevier, Academic Press, 10.1016/C2020-0-02801-X
  3. Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., Hekler, E. B., & Murphy, S. A. (2019) Efficacy of Contextually Tailored Suggestions for Physical Activity: A Optimization Trial of HeartSteps Ann Behav Med, 53(6), 573-582, 10.1093/abm/kay067
  4. Kowatsch T & Fleisch E (2021) Digital Health Interventions, in: Gassmann, O.; Ferrandina, F. (eds): Connected Business, Springer: Cham, 10.1007/978-3-030-76897-3_4
  5. Kowatsch T, L Otto, S Harperink, A Cotti & H Schlieter (2019) A Design and Evaluation Framework for Digital Health Interventions, it- Information Technology 61(5-6), 253-263, 10.1515/itit-2019-0019
  6. Kramer, J. N., Künzler, F., Mishra, V., Presset, B., Kotz, D., Smith, S., Scholz, U., & Kowatsch, T. (2019) Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial JMIR Res Protoc, 8(1), e11540. 10.2196/11540
  7. Kramer, J., Künzler, F., Mishra, V., Smith, S. N., Kotz, D. F., Scholz, U., Fleisch, E., & Kowatsch, T. (2020) Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results from an Optimization Trial Ann Behav Med, 54(7), 518–528, 10.1093/abm/kaaa002 
  8. Qian, T., Walton, A. E., Collins, L. M., Klasnja, P., Lanza, S. T., Nahum-Shani, I., Rabbi, M., Russell, M. A., Walton, M. A., Yoo, H., & Murphy, S. A. (2022) The Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations, Psychological methods, 27(5), 874–894 10.1037/met0000283
  9. Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2016) Sample size calculations for trials in mHealth Stat Med, 35(12), 1944-1971, 10.1002/sim.6847
  10. Walton, A., Nahum-Shani, I., Crosby, L., Klasnja, P., & Murphy, S. (2018) Optimizing Digital Integrated Care via Trials Clin Pharmacol Ther, 104(1), 53-58. 10.1002/cpt.1079

Share this post


Optimizing Digital Therapeutics (04SM22MAS014), University of Zurich, Spring 2024, 4 ECTS

  1. UZH Course Information
  2. MobileCoach Video Tutorials
  3. MobileCoach User Forum
  4. MobileCoach Exercises
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
Teaching Support
Davinny Sou
Davinny Sou
MSc in Information Systems, PhD Student, University of St.Gallen & ETH Zurich
Prabhakaran Santhanam
Prabhakaran SanthanamM.Sc. in Computer Science
MobileCoach Software Engineer and Community Manager at the Center for Digital Health Interventions