Tag Archives: survey
For about a year QS Labs has been working on a small scale project to learn how we can get more meaning out our personal data. The program involves small group discussions in which participants share data in advance, collaborate on visualization and analysis, and discuss the results. With a research grant from Intel, we’ve been able to schedule another round of these for the coming months. They are called ”QS Co-Labs.” The events take place online (over Google Hangout). If you’d like to participate, please let us know by filling out the survey below.
We’re focusing this round of QS Co-Labs on a few specific themes. If you’re using self-tracking tools, apps, web services, or devices to track any of the following we especially want to hear from you!
- Data Aggregation (e.g. Tictrac, Runkeeper, Fluxtream, etc.)
- Physical Activity
- Email (meta data)
We can’t do this without the generous help of our community. We have some great data visualization experts and analysts who are helping us, but we can always use more. If you have visualization or analysis experience and want to participate, share your skills, and help others get meaning out of their data we would love to have you. Fill out our survey or let us know directly.
If this sounds interesting and you’d like to take part then please fill out our short survey so we can get to know you better.
This is a guest post from Ken Snyder of Quantified Self London. Thanks Ken!
I have personally found Magnesium to be a great tool in getting better sleep, although I believe a more common use is to help get more sleep. In any event, I thought it would be interesting to hear from the community IF and HOW they use Magnesium. The survey will only take 5 minutes if you can spare the time (and less than a minute if you’ve not tried Magnesium):
This is a guest post by Chloe Fan:
Hi! I’m a graduate student at Carnegie Mellon in Human-Computer Interaction, and I’m interested in learning about the barriers that you may encounter while collecting or reflecting on your personal information (e.g., too tedious to collect, information not useful, forgetting to collect).
I’m also interested in learning how long-term users have overcome these barriers. The area of personal informatics/self-tracking is not yet well-studied in academic research, so this research on how people respond and cope with barriers to tracking and reflecting can help us design better tools for tracking personal information.
This study consists of a short 5-10 minute online survey about the personal information you’re collecting, the tool you are using, and any barriers that come up during the collecting and reflecting process. At the end of the survey, you will have the option to enter your contact information if we can follow up with a phone interview. This interview should last about an hour.
If you complete the online survey, you will be entered into a $25 online gift card raffle. If you are chosen for the interview and choose to participate, you will be compensated $10 an hour.
Thank you, and I look forward to hearing about your experiences!
Survey Link: https://www.surveymonkey.com/s/F77P7Q7
Many people participated describing their experiences using existing tools to track and reflect on personal information. The survey helped us develop a model to describe personal informatics systems (Figure 1).
The model is a series of five stages: Preparation, Collection, Integration, Reflection, and Action, with four properties: problems in earlier stages cascade to later stages; stages are iterative; they are user-driven and/or system-driven; and they are uni-faceted and/or multi-faceted.
From these properties, we suggest that personal informatics systems should:
1) be designed in a holistic manner across the stages;
2) support iteration between stages;
3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and
4) provide support for associating multiple facets of people’s lives to enrich the value of systems.
In the rest of this post, I will talk about our findings in further detail and discuss how the model can guide the evaluation and design of personal informatics systems.
Figure 1. The stage-based model of personal informatics systems and its four properties.
(Full report after the jump.)
Ian Li, a PhD student studying personal informatics at Carnegie Mellon University, wrote in to ask some questions for his research project. Here is his story, with a link to the survey he created:
Like most people who read The Quantified Self, I have
tracked various aspects of my life. I have logged my gas
mileage, kept my bank statements, wrote down how much I worked and slept, and many more, planning some day to sit down and graph the data to better understand my own behavior.
When I entered the Human-Computer Interaction Institute at Carnegie Mellon University, I started studying how computers can help people understand their own selves. For the first few years, I did some projects related to personal informatics (http://www.ianli.com/research), but focused primarily on physical activity, creating a system that monitors and graphs when, where, and with whom people take the most steps.
Currently, I am looking beyond physical activity to understand general challenges with personal informatics: What information are people interested in recording about their lives? What problems are they experiencing in monitoring their behavior? How can exploring one’s data be improved?