Personal Informatics in the Wild: Hacking Habits for Health & Happiness — CHI 2013 Workshop

Personal Informatics in the Wild: Hacking Habits for Health & Happiness — CHI 2013 Workshop

Accepted Papers | Call for Participation

Personalized Tracking of Goals and Gains after Psychotherapy Using Behavioral Data
Andrew J. White, Tillmann Neben, Aliona von der Trenck, Armin Heinzl, Georg W. Alpers

A multitude of stimuli can trigger anxiety or fear. If anxiety becomes pronounced and begins to impinge on a person's social functioning, psychologists speak of anxiety disorders. Most fears and anxieties are strongly tied to location: for example, fear of bridges, crowded places, or elevators. Even when fear is elicited by specific, animate stimuli such as dogs or spiders, there are often strong ties with certain locations (e.g. the dog park). As a consequence of this marked worry and anxiety, people with phobias often cease to approach these locations – they avoid them. Besides the tremendous deleterious impact on general wellbeing and personal life, the economic consequences of anxiety disorders also impacts society. Exposure therapy is the treatment of choice for anxiety disorders and involves deliberate, systematic confrontation of feared stimuli. Although highly effective, return of fear post-treatment remains a significant problem for many individuals. There is evidence to suggest that fears return due to a lack of regular self-exposure to feared situations. We outline a software tool that allows feared situations to be identified within psychotherapy sessions that can be later used to create dynamic “fear maps”. These maps update as patients systematically confront these locations. In addition, we outline how principles from gamification can be used to depict quantified gains, and performance generally. Our application collects GPS and self-report data collected by mobile phones. Tracking the location of patients allows i) identification of movement patterns and ii) tagging of user's emotional ratings at specific locations. This information helps the users to better quantify and understand the extent of their avoidance behavior, their progress and achievements, and importantly, provides an individualized measure of relapse potential.




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Organized by

Ian Li
Jon Froehlich
Jakob Eg Larsen
Catherine Grevet
Ernesto Ramirez


  • Papers Due  January 11, 2013
    January 18, 2013
  • Notification  February 8, 2013
  • Workshop  April 27–28, 2013

CHI 2013

CHI 2013
April 27–May 2, 2013
Paris, France

Created by Ian Li. HCII, Carnegie Mellon University.