The increasing adoption of wearable-based mental health monitoring platforms offers significant potential to enhance patient care and support general practitioners. Because mental health platforms rely on continuous data collection and clinician adoption, effective onboarding is crucial for ensuring long-term adherence and usability. However, onboarding remains a challenge due to the diverse technological proficiencies of users, the complexity of integrating patient-generated health data into clinical workflows, and the necessity of sustaining user engagement. This study develops an onboarding concept for wearable-based mental health monitoring platforms and iteratively derives principles for designing such a concept, focusing on modularity, flexibility, and seamless integration into clinical practice. The resulting design principles highlight (1) modular onboarding to address varying user needs, (2) a hybrid approach to optimize flexibility and resource efficiency, and (3) supervised experimentation to build user confidence through structured hands-on learning. These principles provide actionable insights for designing scalable, user-centered onboarding frameworks in mental health monitoring.