Work in this space has shown the potential of sensing technology to identify, track, and improve stress and mental wellness: stress has been detected through typing analysis and computer mouse pressure, while wearable sensors such as AutoSense and SPIRE show potential to detect stress using physiological signals. Just as it is important to manage health behaviors such as sleep and exercise, the HCI community has long recognized the importance of tracking psychological elements of health. This paper makes the following contributions: This definition guided our work but, importantly, we also aimed to better understand how student stress is perceived by students and clinicians specifically, how they approach subjective evaluations of students’ ability to cope with the demands posed by student life, and affective responses to that evaluation. We further define stress in the context of this work as negative stress which, while sometimes adaptive, can lead to negative psychological and physical effects over time. We focus our scope of mental wellness on the issues-both clinical and non-clinical-of stress, anxiety, and depression, as they are the three mental health concerns most prominent for students. Together, these viewpoints contribute to a foundational understanding of the role that self-tracking can play for the student population, and its potential to help-or hinder-stress management and mental wellness. We aim in this work to better understand both the general and mental wellness-focused practices of student self-trackers and the expert perspectives of student health professionals. Understanding user preferences is crucial to the adoption of any health monitoring system, yet no prior studies, of which we are aware, bring to light the experiences and challenges of those who actually self-track to achieve mental wellness goals. By combining this passive sensing with ecological momentary assessment (EMA) responses, researchers have begun to draw correlations between tracked behaviors and self-reported indicators of mental well-being. Meanwhile, the StudentLife study at Dartmouth College has demonstrated that it is possible to use automatic smartphone sensing to gather passive data corresponding to certain behaviors (e.g., attending a class or a party). Companies like Ginger.io use mobile sensing to provide app-based mental health coaching at opportune moments, while Computerized Cognitive Behavioral Therapy (CCBT) allows patients to undergo remote treatment for anxiety, and insomnia. Ī variety of efforts aim to make the identification and treatment of mental health concerns more readily accessible. A widely-recognized college mental health crisis has led to overwhelming demand on counseling services at campuses across the country. In a recent national survey of college students, 85% of students reported that they felt overwhelmed by demands over the last year and 47.8% reported feeling that things were hopeless. Student emotional health is at an all-time low as students face increasing stress and academic pressures. Although people of all ages are affected by stress and poor mental health, these concerns are particularly rampant in undergraduate and graduate student populations. Mental health and wellness concerns are a major public health issue, with an economic cost in the US of over $2.5 trillion per year. We conclude with a discussion of challenges and opportunities in leveraging self-tracking for mental wellness, highlighting several design considerations. We draw on findings from these studies to characterize students’ motivations, challenges, and preferences in collecting and viewing self-tracked data related to mental wellness, and we compare findings between students with diagnosed mental illnesses and those without. Second, an online survey of 297 students examines personal experiences with self-tracking and attitudes toward sharing self-tracked data with others. First, focus groups and card sorting activities with 14 student health professionals reveal expert perspectives on the usefulness of tracking for three scenarios. We present two studies conducted to better understand self-tracking for stress management and mental wellness in student populations. However, these studies do not account for students’ experiences and challenges adopting self-tracking technologies to support mental wellness goals. Recent studies with college student populations have examined the feasibility of collecting everyday mood, activity, and social data.
Previous research suggests an important role for self-tracking in promoting mental wellness.
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