In healthcare, AI systems support clinicians and patients in diagnosis,
treatment, and monitoring, but many systems' poor explainability remains
challenging for practical application. Overcoming this barrier is the goal of
explainable AI (XAI). However, an explanation can be perceived differently and,
thus, not solve the black-box problem for everyone. The domain of
Human-Centered AI deals with this problem by adapting AI to users. We present a
user-centered persona concept to evaluate XAI and use it to investigate
end-users preferences for various explanation styles and contents in a mobile
health stress monitoring application. The results of our online survey show
that users' demographics and personality, as well as the type of explanation,
impact explanation preferences, indicating that these are essential features
for XAI design. We subsumed the results in three prototypical user personas:
power-, casual-, and privacy-oriented users. Our insights bring an interactive,
human-centered XAI closer to practical application
