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375-0003-00L 4 Credits WBZ , NDS D-HEST , D-MTEC

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Designing a Digital Biomarker (Group Project 2)

Lecturers & Examiners: Dr. Tobias Kowatsch, Dr. Mia Jovanova

VVZ CR n/a

Last Updated: 2026-06-01 11:30:45

Abstract

The course introduces the concept of digital biomarkers. More specifically, the course will cover fundamental topics such as designing observational studies, collecting and exploring data generated by consumer-centric devices, and applying analytical methods to predict health-related outcomes.

Objective

The widespread use of mobile technologies (e.g., wearable sensors, mobile applications, social media, and location-tracking technologies) can meet the health monitoring needs of the world's aging population and the ever-growing number of chronic patients. However, this premise is based on applying information and communication technologies that allow us to monitor patient data in many different ways. In this course, we will analyze systematic ways to collect data, review the most appropriate methods and applications in healthcare, discuss the main challenges, and apply the newly gained knowledge in a project. The course has four learning objectives, i.e., to 1. understand the concept of digital biomarkers in general 2. understand the various application areas of digital biomarkers 3. to critically reflect and assess existing digital biomarkers 4. to understand how to design a digital biomarker

Content

The course will cover the following topics: 1. Introduction to digital biomarkers 2. Design of digital biomarker studies 3. Exploration and assessment of digital biomarker candidates 4. Digital biomarker project and critical reflection

Resources

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. [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. [3] 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), 14.Dagum, P. (2018). Digital biomarkers of cognitive function. npj Digital Medicine, 1(1), 10. [4] Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. [5] Haug, C. J., & Drazen, J. M. (2023). Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. New England Journal of Medicine, 388(13), 1201-1208. [6] Jacobson, N. C., & Bhattacharya, S. (2022). Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy, 149, 104013. [7] Kourtis, L. C., Regele, O. B., Wright, J. M., & Jones, G. B. (2019). Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. npj Digital Medicine, 2(1), 9. [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. [9] Meskó, B., & Görög, M. (2020). A short guide for medical professionals in the era of artificial intelligence. NPJ digital medicine, 3(1), 126. [10] McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020). Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet Digital Health, 2(5), e221-e223.. [11] Sim, I. (2019). Mobile devices and health. New England Journal of Medicine, 381(10), 956-968. [12] 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. [13] 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.

General Information

Language: English
Levels: WBZ , NDS
Frequency: Yearly recurring

Examination

Type: ungraded semester performance

This module is assessed based on the participant's combined pass/fail status of the group project (including a presentation) and the online exercises in Moodle. The group project and online exercises are ungraded.

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type	Title	Time & Place	Hours
lecture with exercise	Designing a Digital Biomarker (Group Project 2) Irregular lecture. Takes place online via zoom The lecturer will communicate the exact lesson times of ONLINE courses. 1. Digital Biomarkers and Data Analysis Theory Lectures: [29.08, 05.09, 12.09] [13:00-17:00], Zoom 2. Coaching Sessions for Groups: [19.09, 26.09, 10.10, 17.10] [15:00-17:00], Zoom 3. Group Presentations: [07.11] [13:00-17:00], Zoom	No time listed	24 h semesterly