The Health Intervention Analytics Lab is a junior research group headed by Dr. Stefan Konigorski, Senior Researcher in the chair of Digital Health & Machine Learning and Adjunct Assistant Professor at the Icahn School of Medicine at Mount Sinai in New York. Currently, he is visiting faculty at Harvard University in the Statistical Reinforcement Learning lab, Department of Statistics.

The group was founded in May 2021 and is pioneering work on open source tools to perform N-of-1 trials in order to investigate the effect of digital health interventions on an individual level. We have developed the StudyU platform and StudyMe app that allow researchers, physicians and citizens to easily design and conduct N-of-1 trials and find out if health interventions have an effect. The aim is to allow everyone to setup and conduct N-of-1 trials digitally and support open, reproducible science through the platform. First clinical studies in collaboration with clinicians are running using StudyU. In addition to N-of-1 trials, the group is working on adaptive trials and micro-randomized trials in order to implement and analyze effects of adaptive health interventions.

The software tools and clinical studies provide empirical clinical data for our methodological work on statistical and machine learning methods to analyze the resulting complex time series data. We employ methods from causal inference by viewing the data embedded in its causal structure. Further analytic methods of interest encompass clinical risk prediction models. 

• StudyU: Platform for designing designing, implementing, and conducting digital health interventions through N-of-1 trials
• StudyMe: App that allows everyone in the general population to design and perform an N-of-1 trial to investigate the effectiveness of one or more health interventions on a health outcome that they are interested in.
• Statistical and machine learning methods for N-of-1 trials and their design
• Causal inference in prediction models

• Susan Murphy, Statistical Reinforcement Learning lab, Harvard University
• Jane Nikles, University of Queensland
• Sungho Won, Health Statistics lab, Seoul National University

• Collaboration with the Clinical Psychology group at Helmut Schmidt University/University Medical Center Hamburg-Eppendorf in an ongoing series of N-of-1 trials using StudyU on open-label placebo treatment for antidepressant discontinuation symptoms.
• In an ongoing study with Glucofit GmbH on diabetes, the goal is to better understand how food, insulin, and exercise affect blood glucose levels in people with type 1 diabetes. This is essential to create better, individualized therapy. To do this, we are developing AI models based on causal inference and prediction models, and testing them with data from patients. We are collaborating with GlucoFit, a Berlin-based startup that aims to create a digital assistant for diabetics. We are still looking for test persons who would like to participate in the two-week, remunerated study. The study will be conducted fully digitally, visits to Potsdam or Berlin are not necessary. Details are available here: https://glucofit.de/lass-uns-diabetes-besser-verstehen/.

We are looking for interested Master students and HiWis to support our work on apps (StudyU, StudyMe) and our work on statistical & machine learning models for their analysis. See here for more details.

• Dr. Stefan Konigorski (Head of group)
• Juliana Schneider (PhD student)
• Francisco Rios (Research Associate)
• Lasse von der Heydt (Research Associate)
• Thomas Gärtner (PhD student, funded by abacus GmbH)
• Dr. Luca Meoli (Guest scientist, scientist at Charité Berlin)
• Sandro Steinwand (Master student)
• ... and close collaborations with the Digital Health Design Lab and chair of Digital Health & Personalized Medicine and Digital Health & Connected Healthcare

Alumni:

• Alexander Zenner (Master student 2020-21, now at Google)
• Nils Strelow (Master student 2020-21, now at Google)
• Lukas Ehrig (Master student 2021)
• Mohammad Wasil Saleem (Master student 2021)