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 adaptive interventions add complexity to the design and
implementation process, but have the potential to improve patients' benefits
even if only few observations are available. 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