Notes taken by Yevgeniy Medynskiy during the workshop.
Introductions 9:15 AM
Name/affiliation/what made you interested in self-tracking (1 min each)
- Clinical health & wellness: clinical outcomes; wellness; mental health; social wellbeing
- Domains: email; attention/information overload; sleep; location; experience sampling
- Branching Out: Persuasive (not manipulating!) technology; Social networking; Barriers to PI
- Theories: Value-sensitive design; Psychology
- Visualizing archives of personal data
- Privacy and security
Madness #1 9:35 AM
- Halimat Alabi
- App for anxiety tracking; mental health; Cognitive Behavioral Therapy (CBT).
- Sudheendra Hangal
- Sentiment analysis via email; passive lifelogging; sensemaking of text archives with Muse (Memories Using Email).
- Catherine Grevet
- Integrated model for multi-faceted PI systems; Transtheoretical Model; Theory of Planned Behavior; Stage-based Model of Personal Informatics.
- Elizabeth Bales
- Share PI data with their social network/community; "Interpersonal" informatics.
- Laura Dabbish
- SeeMail for email visualization; Patterns of communication over time, including responsiveness.
- Matthew Kay
- Sleep tracking and self-improvement; Environmental factors that disturb sleep; Lullaby sensor system + sleep tracking device like FitBit or Zeo.
- Eric Hekler
- Android apps for self-tracking and social sharing; Goal setting; Ability to influence behavior via confederates; Operant conditioning via fun experiences.
- Jed Brubaker
- Challenges and barriers when working with PI ecologies; Estrellita case study; Challenge of Context, Challenge of "Personal" and Challenge of Represenation.
- Blaine Price
- Lifelogging (specifically, location) and privacy; Persuasive technology -- engagement, non-invasiveness. Summary of available devices
Breakout #1 11:30 AM
Group 1: Design of Tools
- Synchronize data formats. Are you measuring that you think you're measuring?
- Presentation and sharing. What levels of privacy controls should you give to users?
- Keeping users' engagement, based on their expertise and motivation.
Group 2: Making Sense of Data
- Collection of Data --> Visualization (Two different domains)
- Venn Diagram between Collection, Visualization and Persuasion
- Iteration of understanding... Why do we collect data?
Group 3: Theory
- Not just use theory, but test and improve theory
- Where does theory come in: related work; prototyping (conceptual review/feedback)
- Models of motivation and action; theories of behavior change and behavior change strategies; measurement theory
Group 4: Social Implication
- Personal vs. Interpersonal/social
- The best use is "non-use" e.g. quitting smoking, if you quit
Madness #2 3:00 PM
- Mark Matthews
- Relapse prevention and mental health; bipolar disorder -- tracking common triggers/signs/risk factors; PI architecture.
- Eric Hofer
- Course for PI design; Arduino as technology for teaching PI; Adding art to the design and research in most HCI work.
- Amy Gonzales
- Vera photographing and rating health behaviors, with and without social interaction and reflection.
- Neema Moraveji
- Calming technologies -- technologies that induce calm; "calm alertness"; stressors vs calmers
- Alina Pommeranz
- Personal values of PI -- value-sensitive technology; value elicitation via experience sampling
- Alexander Meschtscherjakov
- Experience Sampling (ESM) Tools; MyExperience, PocketBee; Created a new tools: Maestro which uses a client-server architecture
- Noreen Kamal
- Personal Health Informatics: What is the role of the [online] social network?
Can the later motivate the use of the former? The social impact on health.
- Jack Weeden
- Monitor waste and recycling behavior. Social competition for scores, with MTurk integration for waste labeling. Personal reflection.
- Natalia Romero
- Collecting and sharing experiences with family.
Breakout #2 4:40 PM
Group A: Barriers to Expanding the Reach of PI Tools
- Barriers: Collection, motivation, finding the users for your tools, getting critical mass.
- Strategies for overcoming: Gamification, establishing social norms,
integrating PI with everyday activity, bootstapping (value w/ little setup time),
improving usability, making sure users can utilize the data they are collecting, making applications mobile.
Group B: Novel interactions and data
- Relationships between different types of data -- aggregation.
- Self-experimentation (life-hackers, tinkerers & play).
- Separation of quantitative and qualitative tasks between computer/human.
Group C: Narrative and Storytelling
- Journaling, "fridge magnet" app, story editing.
- Hero's journey as narrative structure.
- Re-telling someone's life to them based on their personal information.
Group D: Ethics/Values of Personal Informatics
- Promote self-awareness. Stimulate cognitive activity.
- "Value transparency" -- tools articulate what they're trying to do and users can choose.