  Personalized adaptive interventions offer the opportunity to increase patient
benefits, however, there are challenges in their planning and implementation.
Once implemented, it is an important question whether personalized adaptive
interventions are indeed clinically more effective compared to a fixed gold
standard intervention. In this paper, we present an innovative N-of-1 trial
study design testing whether implementing a personalized intervention by an
online reinforcement learning agent is feasible and effective. Throughout, we
use a new study on physical exercise recommendations to reduce pain in
endometriosis for illustration. We describe the design of a contextual bandit
recommendation agent and evaluate the agent in simulation studies. The results
show that, first, implementing a personalized intervention by an online
reinforcement learning agent is feasible. Second, such adaptive interventions
have the potential to improve patients' benefits even if only few observations
are available. As one challenge, they add complexity to the design and
implementation process. In order to quantify the expected benefit, data from
previous interventional studies is required. We expect our approach to be
transferable to other interventions and clinical interventions.
