Advances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.

Figure 1: Data manipulation is an approach that involves altering the value or structure of self-tracking data.With five data manipulation typology (Insert, Delete, Transform, Increase, and Reduce), we applied data manipulation through exploratory workshops and a one-week field trial.The above cards, which served as key materials for this study, provide an overview of the different types of data manipulation, along with definitions and examples for each type.

Stress is caused by a variety of events in our daily lives. By anticipating stressful situations, we can prepare and better cope with stressors when they actually occur. However, many past-centric personal informatics (PI) tools focus on capturing events that already happened and analyzing the data. In this work, we examine how anticipation — a future-centric self-tracking practice — could be used to manage daily stress levels. To address this, we built MindForecaster, a calendar- mediated stress anticipation application that allows users to expect stressful events in advance, generates activities to mitigate stress, and evaluates actual stress levels compared to previously estimated stress levels. In a 30-day deployment with 47 users, the users who explicitly planned and executed coping interventions reported reduced stress more than those who only expected stressful events. We suggest design implications for stress management by incorporating the properties of anticipation into current PI models.

Mental wellness is a desirable health outcome for students. However, current personal informatics systems do not adequately support students in creating concrete mental health-related goals and turning them into actionable plans. In this paper, we introduce MindNavigator - a workshop in which groups of college students were invited to generate behavioral change goals to manage daily life stress and practice personalized interventions for two weeks. We describe the manner in which participants identified both stressors and pleasures to create actionable, engaging, and open-ended behavioral plans that aided in stress relief. We found that the social nature of the workshop helped participants understand themselves and execute self-intervention in new ways. Through this practice, we build on prior studies to propose an analytical framework of personal informatics for mental wellness.

Self-tracking technologies have been developed to understand the self. Emotions are critical to understanding one's daily life; however, tracking the emotion is challenging due to the implicit form of data. In this paper, we introduce MindTracker, an approach for tracking emotion through a tangible interaction with plasticine clay. We explored the benefits and challenges of MindTracker via a two-week data collection study with 16 college students as well as via interviews with three clinical mental health experts. MindTracker is designed for users to craft a form that represents emotion using clay and to describe the experience that evokes the emotion using a diary. We found that the tangible modality of MindTracker motivated the participants to express various aspects of emotions. In addition, MindTracker's data collection and reflection process could have therapeutic properties, such as expressive therapy, self-soothing, and emotional self-regulation. We conclude this paper by discussing the design features of emotion-tracking tools and opportunities to use MindTracker to promote mental health.