Phil Adams is a PhD student in Information Science, who has received several awards during his time here, including the Information Science Outstanding Service Award for 2015. Phil worked for Professor Geri Gay's Interaction Design Lab and wrote his dissertation on, "Moments of Measurement for Affect, Stress, and Chronic Pain with Mobile Devices." See the abstract below. Phil is going to work for Google in Mountain View, CA at the end of the summer.

Abstract: With the goal of supporting daily wellbeing, this dissertation explores, unites, and extends several research domains: measurement and psychometrics, mobile HCI and user experiences, and the in situ assessment of affect, stress, and chronic pain. Inspired by the promises of mobile health (Richardson & M. C. Reid, 2013), this work contributes both artifacts (measures and software) and research findings that further the momentary measurement of these often unobservable human states.

Each of the three case studies involves investigating momentary measurement with mobile devices (Shiffman, Stone, & Hufford, 2008; Hektner, Schmidt, & Mihaly Csikszentmihalyi, 2007):

In assessing affect, I report on the Photographic Affect Meter (Pollak, Adams, & Gay, 2011), a novel image-based single-item measure of emotion; underlying the PAM grid is the Russell’s circumplex model of affect: a two-dimensional valence/arousal space within which each emotion can be placed (Russell, 1980). In assessing stress, I report on SESAME, a field trial comparing minimally invasive techniques for assessing stress in the wild (Adams et al., 2014); I am interested in supporting long-term engagement with one’s own perceived stress levels through measurement by determining an effective balance between reliability and intrusiveness. In assessing pain intensity, I report on two projects intended to support the management of chronic pain by providing novel and effective self-report assessments of pain intensity. Meter is a multi-stage user-centered research through design (Gay, 2004; Zimmerman, Forlizzi, & Evenson, 2007) investigation seeking optimal visual interfaces for the self-report of pain intensity on smartphone screens. Keppi is a novel pressure-based user input device for the self-report of scalar values; users are able to consistently report pain intensity with four degrees of freedom, as well as continuously map pressure to visual cues.

I then reflect on the case studies speaking to (1) the continued essential value of self-report in a sensor-centric world, (2) the idea of a minimally viable moment of self-assessment, (3) the advantages and disadvantages of personalizing the measurement interfaces themselves, and (4) that there is not a one-size-fits all approach for developing novel self-reports for the EMA domain. This work then contributes several design patterns useful to others continuing this work in new health and measurement spaces.

Thesis committee: Geri Gay, Tanzeem Choudhury, and Elaine Wethington