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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.

We investigate the application of aesthetic stimuli's influence on emotional response during product interaction; implications for novel yet acceptable product experiences. Adopting a research-through-design approach, three tape-dispenser products were designed. Together with a control, the dispensers offered visual and auditory stimulation during product use. Results indicate the importance of consistency between function and stimulus for elicitation of positive response towards novel aesthetic interventions. User profiles also appeared to implicate response to aesthetic stimuli.

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.

Cell population data (CPD) are new morphologic parameters including volume, conductivity, and five light scattering characteristics used for leukocyte classification by an automated hematology analyzer, the UniCel DxH 800. We developed a discriminating CPD model to predict the leukemia lineage during routine complete blood cell count (CBC).We analyzed the CPD of 405 blood samples containing more than 10% blasts that were randomly divided into test and validation sets. With the test set, we produced a model for categorizing acute lymphoblastic leukemia (ALL) or acute promyelocytic leukemia (APL), using ranges of the CPD and regarding the remainder as non-APL acute myeloid leukemia. We verified these models against the validation set.In the test set, we formulated a 21-parameter model which identified 43 of 47 ALL cases (91.5% sensitivity) and ruled out 151 of 156 other leukemia cases (96.8% specificity), and a 13-parameter model which distinguished all 10 APL cases (100% sensitivity) and excluded 193 other leukemia cases (100% specificity). In the validation set, the ALL model showed 85.1% sensitivity and 94.2% specificity, and the APL model 100% sensitivity and 100% specificity.This study demonstrated a new solution for predicting blast lineage using the CPD on a CBC and leukocyte differential.