Fitness data visualization helpful for tracking physical activity

Many people are using fitness apps on their smartphones to meet fitness goals or track their sleep patterns, but new research suggests that the ability to visualize the data over space or time adds greater benefit to users.

“Personal activity tracking is getting more robust and there are more applications to choose from, but people often don’t get any value from their data because you can’t see it displayed over time or in a larger context,” James Fogarty, PhD, associate professor of computer science and engineering at University of Washington, said in a press release.

While many people are using fitness-tracking smartphone apps such as FitBit, Sleep Cycle and MyFitnessPal, as well as location tracking services like FourSquare, the data are not easy to compare or view over time.

Researchers at the University of Washington assessed prior research on the use of fitness apps and associated behaviors, and sought to extend the studies. They surveyed 113 respondents who self-tracked their physical activity and used their data to identify 13 subsets of activity, or “cuts,” representing areas of daily life, These included arrival and departure times from work, time spent using different modes of transit, the number of trips by distance, travel time to work according to weather, categories of food outlets visited by day or time of day and minutes of physical activity, among other factors.

“This is about learning how people want to engage with their data,” Sean Munson, PhD, an assistant professor of human centered design and engineering, said in the press release. “We really wanted to target a much more casual audience with this study, because these tools are becoming much more common.”

Next, 14 participants, including both regular and casual trackers, were recruited to participate in a self-tracking data collection experiment. All participants indicated a desire to increase or maintain their physical activity level. The group had Moves, a smartphone app that passively tracks locations and user activity throughout the day while in use, installed on their phones, and were interviewed about their initial expectations. They were instructed to launch the app at least once daily, and were interviewed about their use, experiences and reaction to the app at 2 weeks and 1 month of use.

Researchers used a MySQL database to build visualizations of the data based on the 13 established cuts, and presented the visualizations to the participants at 1 month. Participants provided feedback on the cuts, indicating up to five that they considered most valuable, as well as one that they perceived as having no value.

While the majority of participants shared the same goals for their activity, the cuts that users considered most important varied substantially, with all but one of the cuts (an indicator of trips to and from a location according to type of transit) being selected as most valuable by at least one user.

The researchers reported that, upon viewing the data in a visual and temporal format, users were able to discern surprising trends in their own activity: For example, one participant realized that he was most active on Tuesdays, which led him to speculate about what promotes this behavior; Another noticed that she did not spend much time bicycling on Tuesdays, and thought about how she could change this behavior. Some of those who contemplated behavioral changes reported discussing these changes with family members.

The researchers suggested that the ability for participants to point out areas of their schedules that were counterproductive to their goals could also help users to make beneficial changes to their health and fitness.

“Discovery about your patterns and habits happens when you see something you weren’t expecting to see,” Daniel Epstein, a University of Washington doctoral student in computer science and engineering, said in the release. “Some participants already had an intuition about patterns in their lives, but it hit home for them when we started showing the supporting data to them in a visual way.”

For more information:

Epstein DA. Taming data complexity in lifelogs: Exploring visual cuts of personal informatics data. Presented at: Proceedings of the ACM Conference on Designing Interactive Systems; June 21-25, 2014; Vancouver, BC, Canada.