Examining a social-based system with personalized recommendations to promote mental health for college students

College students experience a high amount of stress due to different factors such as life transitions, heavy coursework, and adjustment to an independent lifestyle. Dealing with stress may lead students to not prioritize their wellness. As a result, most students’ mental health declines which may affect their overall well-being. In this paper, we propose the FreeMind system which is a social application for students to interact with peers as well as receive support for mental health promotion. FreeMind system aims to focus on self-care and uses machine learning (ML) with expert knowledge to recommend different behavioral strategies to manage stress based on users’ activity preferences and reported well-being. To assess our system, we conducted a study with 21 college students to evaluate the systems’ usability and feasibility. Based on our findings, we discuss strategies to increase social support and access to local resources to benefit students’ mental health, and the ways in which the target population envisions the recommendations styles to effectively move them to action in self-care.