Digital Biomarker Development, Fall 2023, University of St.Gallen

Hypoglycemia Detection in People With Diabetes Using Smartwatch Data (Diabetes Care 2023), Engineering digital biomarkers of glucose from non-invasive smartwatches (npj Digital Medicine, 2021); Wearable-based accelerometer activity as digital biomarker of inflammation, biological age, and mortality (Scientific Reports (2023) A digital biomarker of diabetes from smartphone-based vascular signals (Nature Medicine 2020), Wearable sensors enable personalized predictions of clinical laboratory measurements (Nature Medicine 2021).

What are recent developments in digital biomarkers? A digital biomarker is quantifiable and objective behavioral or physiological data collected through digital devices. Digital biomarkers explain and predict health-related outcomes and can be classified depending on their purpose (e.g., risk, diagnostic, monitoring,  prognostic, predictive, or response biomarkers). They can be gathered continuously and non-invasively, providing valuable insights into an individual’s health status. Digital biomarkers can revolutionize healthcare by enabling remote monitoring, personalized, just-in-time adaptive interventions (e.g., digital therapeutics), and early detection of diseases.

To this end, the question arises of how to develop evidence-based digital biomarkers that allow medical doctors and other caregivers to scale and tailor long-term treatments to individuals in need at sustainable costs. This lecture aims to help students and upcoming healthcare executives better understand digital biomarkers’ relevance, development, and assessment at the intersection of health economics, information systems research, computer science, and medicine.

After the course, students will be able to…

  1. understand the relevance of digital biomarkers for the prevention, management, and treatment of disease
  2. understand several classes of digital biomarkers
  3. know how to design and evaluate digital biomarkers
  4. know digital health technologies used for digital biomarker development
  5. assess data of a digital biomarker study in the area of metabolic health
  6. propose digital biomarkers for healthy longevity (e.g., biological age)
  7. discuss the advantages and disadvantages of digital biomarkers

Course Content

  1. On the relevance of digital biomarkers for the prevention, management, and treatment of disease
  2. Classification of digital biomarkers
  3. Study designs for digital biomarkers
  4. Assessment of digital biomarkers
  5. Advantages and disadvantages of digital biomarkers

Course structure

The lecture is structured in three parts, with on-site sessions and complementary online exercises. In the first part, an overview of digital biomarkers is provided. In the second part, a controlled laboratory experiment is conducted with the course participants to develop a digital biomarker in metabolic health. For that purpose, various physiological data will be collected (e.g., with wearables). In the third part, students in groups will assess digital biomarker data and design a digital biomarker for healthy longevity, for example, determining biological age. The groups will finally present and discuss their results with fellow students. Moreover, coaching sessions are offered to support the students in preparing their presentations. Complementary learning material and multiple-choice questions are provided online.

The lecture takes place at the School of Medicine, Room 62-112.

Course literature

  1. Adler, D. A., Wang, F., Mohr, D. C., Estrin, D., Livesey, C., & Choudhury, T. (2022). A call for open data to develop mental health digital biomarkers. BJPsych Open, 8(2), e58, Article e58. 10.1192/bjo.2022.28
  2. Alfalahi, H., Khandoker, A. H., Chowdhury, N., Iakovakis, D., Dias, S. B., Chaudhuri, K. R., & Hadjileontiadis, L. J. (2022). Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports, 12(1), 7690. 10.1038/s41598-022-11865-7
  3. Bartolome, A., & Prioleau, T. (2022). A computational framework for discovering digital biomarkers of glycemic control. npj Digital Medicine, 5(1), 111. 10.1038/s41746-022-00656-z
  4. Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., Cho, P., Zingler, K., Ogbeide, F. I., Zhao, A., Runge, R., Sim, I., & Dunn, J. (2021). The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. Journal of Clinical and Translational Science, 5(1), e19, Article e19. 10.1017/cts.2020.511
  5. Coravos, A., Khozin, S., & Mandl, K. D. (2019). Developing and adopting safe and effective digital biomarkers to improve patient outcomes. npj Digital Medicine, 2(1). 10.1038/s41746-019-0090-4
  6. Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schüssler-Fiorenza Rose, S. M., Li, X., Bahmani, A., Delp, S. L., Hastie, T., & Snyder, M. P. (2021). Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature Medicine, 27(6), 1105-1112. 10.1038/s41591-021-01339-0
  7. Föll, S., Maritsch, M., Spinola, F., Mishra, V., Barata, F., Kowatsch, T., Fleisch, E., & Wortmann, F. (2021). FLIRT: A Feature Generation Toolkit for Wearable Data. Computer Methods and Programs in Biomedicine, 106461. 10.1016/j.cmpb.2021.106461
    Lehmann, V., Föll, S., Maritsch, M., van Weenen, E., Kraus, M., Lagger, S., Odermatt, K., Albrecht, C., Fleisch, E., Zueger, T., Wortmann, F., & Stettler, C. (2023). Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data. Diabetes Care, 46(5), 993-997. 10.2337/dc22-2290
  8. Manta, C., Patrick-Lake, B., & Goldsack, J. C. (2020). Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health. Digital Biomarkers, 4(3), 69-77. 10.1159/000509725
  9. Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., La Marca, R., von Wangenheim, F., Hoelscher, C., & Ferrario, A. (2023). An interpretable machine learning approach to multimodal stress detection in a simulated office environment. J Biomed Inform, 139, 104299. 10.1016/j.jbi.2023.104299
  10. Rassouli, F., Tinschert, P., Barata, F., Steurer-Stey, C., Fleisch, E., Puhan, M. A., Baty, F., Kowatsch, T., & Brutsche, M. H. (2020). Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker. J Asthma Allergy, 13, 649-657. 10.2147/JAA.S278119
  11. Shim, J., Fleisch, E., & Barata, F. (2023). Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011–2014. Scientific Reports, 13(1), 9326. 10.1038/s41598-023-36062-y
  12. Sieberts, S. K., Schaff, J., Duda, M., et al. (2021). Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. npj Digital Medicine, 4(1), 53. 10.1038/s41746-021-00414-7
  13. Shandhi, M. M. H., Goldsack, J. C., Ryan, K., Bennion, A., Kotla, A. V., Feng, A., Jiang, Y., Wang, W. K., Hurst, T., Patena, J., Carini, S., Chung, J., & Dunn, J. (2021). Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review. J Med Internet Res, 23(9), e29875. 10.2196/29875
  14. Sim, I. (2019). Mobile Devices and Health. N Engl J Med, 381(10), 956-968. 10.1056/NEJMra1806949
  15. Tams, S., Hill, K., de Guinea, A. O., Thatcher, J., & Grover, V. (2014). NeuroIS – Alternative or Complement to Existing Methods? Illustrating the Holistic Effects of Neuroscience and Self-Reported Data in the Context of Technostress Research. Journal of the Association for Information Systems, 15(10), Article 1. 10.17705/1jais.00374
  16. Teepe, G. W., Lukic, Y. X., Kleim, B., Jacobson, N. C., Schneider, F., Santhanam, P., Fleisch, E., & Kowatsch, T. (2023). Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study. BMC Psychology, 11(1), 186. 10.1186/s40359-023-01215-1
  17. Vasudevan, S., Saha, A., Tarver, M. E., & Patel, B. (2022). Digital biomarkers: Convergence of digital health technologies and biomarkers. npj Digital Medicine, 5(1), 36. 10.1038/s41746-022-00583-z

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Digital Biomarker Development (11,208,1.00), University of St.Gallen, Fall Semester 2023, Room 62-112, 3 ECTS, Course & Examination Fact Sheet

Prof. Dr. Tobias Kowatsch
Prof. Dr. Tobias Kowatsch
Associate 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), and Scientific Director, Centre for Digital Health Interventions, UZH, HSG & ETH Zurich, Switzerland
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Teaching Assistants
Marc-Robin Gruener
Marc-Robin Gruener
Teaching Assistant, Digital Biomarker Development & Student research assistant at the Centre for Digital Health Interventions, School of Medicine, University of St.Gallen
Akshaye Shenoi
Akshaye Shenoi
Ph.D. candidate and Teaching Assistant for Digital Biomarker Development at the Centre for Digital Health Interventions, Singapore-ETH Centre, Singapore
M.Sc. in Computer Science, National University of Singapore

Focus: Health Sensing using Machine Learning and Signal Processing

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