Prof. Dr. Christoph Lippert

N-of-1 Trials: Digital Health Interventions


Traditionally, in our health system, treatment guidelines and health recommendations have been developed based on results of studies of large groups of individuals. However, the analysis of such large population-based studies only gives insights into factors affecting diseases and treatments on average. Hence, these results do not allow meaningful predictions whether an intervention will help a given single individual. For example, major drugs in therapeutic areas including asthma, depression, diabetes or rheumatoid arthritis have only shown an efficacy in 50%-60% of patients. Similarly, the effect of health interventions and lifestyle changes on chronic disease and health outcomes on an individual level if often unclear.

N-of-1 trials provide a framework to evaluate personalized treatments and derive invididual treatment effects. More specifically, N-of-1 trials are multi-crossover randomized controlled trials in a single participant, i.e. where the participant follows the different interventions of the study in a pre-specified or randomized order, and the outcome (e.g. improvement of lower back pain or rheumatoid arthritis pain) is compared between interventions.

Here, we are building a platform called StudyU, developing and evaluating necessary tools and methods for the platform, and applying the platform to derive and evaluate individualized treatment effects. Also, we are working on user-centric N-of-1 trials and have developed the StudyMe tool.

StudyU Platform for N-of-1 Trials

  • Develop a platform for designing, implementing, and conducting digital health interventions
  • See here for further details

StudyMe App for User-Centric N-of-1 Trials

  • Develop an 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.
  • See here for further details


  • Investigate statistical and machine learning models to estimate and test individual treatment effects in N-of-1 trials

Clinical use cases

  • Assess the effect of digital physical exercise interventions on chronic unspecific low back pain

  • Assess the short-term and medium-term effect of drinking coffee on heart rate and blood pressure

  • Assess the effectiveness of open label placebo for reducing side effects when stopping to take antidepressive medication


  • Stefan Konigorski
  • Tamara Slosarek
  • Sarah Wernicke (now at MH Brandenburg)
  • Alexander Zenner (now at Google)
  • Nils Strelow (now at Google)
  • Fabian Pottbäcker
  • Manisha Manaswini
  • Darius Ruether (now at UKE Hamburg)
  • Florian Henschel
  • Thomas Gärtner
  • Ariane Morassi Sasso
  • Saniya Adeel
  • Raza Ali