Data Science for Digital Health: Relaxation Digital Biomarker
The WHO assumes that depression will be the leading cause of years lived with a disease by 2030. While effective treatment (antidepressants and therapy) exist, patients face barriers such as long waiting lists, stigma, and a shortage of healthcare professionals. Researchers, startups, and companies alike have started to develop digital interventions such as apps or websites to address the rising prevalence and insufficient resources. One approach is the tailoring of treatment to passively collected symptoms. For this purpose, we have collected voice data within a gamified slow-paced breathing exercise and self-reported relaxation scores. We have a unique dataset of breathing and voice recordings derived from a gamified slow-paced breathing exercise. We want to analyze this dataset to answer questions related to psychophysiological changes in relaxation.
Business Model Development for the AI-based Mindfulness Tool “Breeze”
The thesis has the objective to develop a business model for a novel digital health tool that aims at the prevention of noncommunicable diseases.
Master thesis/internship at ETH Zurich: Dropout Prediction in Digital Health Interventions
We aim to identify factors influencing non-adherence and explore machine learning methods that can effectively predict dropouts based on longitudinal data from real-world mHealth apps.

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