Once the domain of buttons and knobs, car dashboards are increasingly home to large touch screens. While that makes following a mapping app easier, it also means drivers can’t feel their way to a control; they have to look. But how does that visual component affect driving?

New research from the 91̽�� and Toyota Research Institute, or TRI, explores how drivers balance driving and using touch screens while distracted. In the study, participants drove in a vehicle simulator, interacted with a touch screen and completed memory tests that mimic the mental effort demanded by traffic conditions and other distractions. The team found that when people multitasked, their driving and touch screen use both suffered. The car drifted more in the lane while people used touch screens, and their speed and accuracy with the screen declined when driving. The effects increased further when they added the memory task. 

These results could help auto manufacturers design safer, more responsive touch screens and in-car interfaces.

The team Sept. 30 at the ACM Symposium on User Interface Software and Technology in Busan, Korea. 

“We all know ,” said co-senior author , a 91̽��professor in the Paul G. Allen School of Computer Science & Engineering. “But what about the car’s touch screen? We wanted to understand that interaction so we can design interfaces specifically for drivers.”

As the study’s 16 participants drove the simulator, sensors tracked their gaze, finger movements, pupil diameter and electrodermal activity. The last two are common ways to measure mental effort, or “cognitive load.” For instance, pupils tend to grow when people are concentrating. 

While driving, participants had to touch specific targets on a 12-inch touch screen, similar to how they would interact with apps and widgets. They did this while completing three levels of an “N-back task,” a memory test in which the participants hear a series of numbers, 2.5 seconds apart, and have to repeat specific digits. 

The participants’ performance changed significantly under different conditions:

• When interacting with the touch screen, participants drifted side to side in their lane 42% more often. Increasing cognitive load had no effect on the results.
• Touch screen accuracy and speed decreased 58% when driving, then another 17% under high cognitive load.
• Each glance at the touchscreen was 26.3% shorter under high cognitive load.
• A “hand-before-eye” phenomenon, in which drivers’ reached for a control before looking at it, increased from 63% to 71% as memory tasks were introduced.

The team also found that increasing the size of the target areas participants were trying to touch did not improve their performance. 

“If people struggle with accuracy on a screen, usually you want to make bigger buttons,” said , a 91̽��doctoral student in the Allen School. “But in this case, since people move their hand to the screen before touching, the thing that takes time is the visual search.”

Based on these findings, the researchers suggest future in-car touch screen systems might use simple sensors in the car — eye tracking, or touch sensors on the steering wheel — to monitor drivers’ attention and cognitive load. Based on these readings, the car’s system might adjust the touch screen’s interface to make important controls more prominent and safer to access.

“Touch screens are widespread today in automobile dashboards, so it is vital to understand how interacting with touch screens affects drivers and driving,” said co-senior author , a 91̽��professor in the Information School. “Our research is some of the first that scientifically examines this issue, suggesting ways for making these interfaces safer and more effective.”

, a 91̽��doctoral student in the Information School, is co-lead author. Other co-authors include , , and of TRI. This research was funded in part by TRI.

For more information, contact Wobbrock at wobbrock@uw.edu and Fogarty at jfogarty@cs.washington.edu.

Personal tracking tools — technologies that meticulously count our daily steps, map our runs, account for each purchase – fall in and out of favor in users’ lives.

People abandon self-tracking for different reasons, 91̽�� researchers have found. Some don’t like what their Fitbit or financial tracking tools reveal, others find collecting data a hassle, don’t quite know how to use the information or simply learn what they need to know about their habits and move on.

“We got curious about what it’s like for people after they stop using self-tracking tools,” said , a 91̽��assistant professor of human centered design and engineering. “Do they feel great, do they feel guilty, do they feel like they’ve gotten everything they need?”

In research that surveyed 193 people who had abandoned personal informatics tracking, the team found many people experienced no real difference in their lives. Other emotions, however, ranged from guilt over not being able to keep it up to relief from the tyranny of self-tracking.

Now, in a to be presented Sept. 15 at the Association for Computing Machinery’s (UbiComp 2016), the researchers explore how different design approaches may better support people who have lapsed in their Fitbit use.

“People feel more guilt when it comes to abandoning health tracking, as compared to something like location tracking, which is more of a fun thing that people do for a while and move on from,” said lead author , a 91̽��doctoral student in computer science and engineering.

“We definitely don’t think that everyone should be tracking forever, but we wanted to see if there are design opportunities to better support people who have had different experiences using Fitbit.”

The research team surveyed 141 people who had lapsed in using Fitbit. They showed the subjects seven different visual representations and ways of framing previously collected data, to see if the data could offer additional support and encouragement to be healthy if portrayed in new and interesting ways.

Half of these Fitbit users described feeling guilty about their lapsed Fitbit use, and nearly all of those said they would like to return to activity tracking. Twenty-one said they got no value out of tracking, found it annoying, or struggled to connect the data to behavior change. Five participants felt they had learned enough about their habits, and 45 reported mixed feelings about abandoning their Fitbit.

The researchers found that lapsed users responded differently to seeing their old Fitbit data presented in new ways, depending on their personal tracking history.

Participants who had tracked their fitness levels for less than four months preferred visualizations that showed them which days of the week or time of day they were active, while those with a longer track record preferred visualizations that highlighted the length of their activity record.

Most people preferred social comparisons that made them look better than their peers, such as “you walked more than 70 percent of people,” over those that were framed negatively, such as “30 percent of people walked more than you” – even if the comparisons represented the same information.

The team also found that people who felt guilty about abandoning their Fitbit use were very receptive to recommendations that they return to tracking, while people who felt they had gotten what they had wanted out of self-tracking felt those same suggestions were judgmental and unhelpful.

The responses show, researchers say, that a one-size-fits-all design approach misses opportunities to support different types of users.

“Right now self-tracking apps tend to assume everyone will track forever, and that’s clearly not the case,” said co-author , a 91̽��associate professor of computer science and engineering.

“Given that some people feel relief when they give it up, there may be better ways to help them get better value out of the data after they’re done, or reconnect them to the app for weeklong check-ins or periodic tune-ups that don’t presume they’ll be doing this every day for the rest of their lives.”

Co-authors include Jennifer Kang, a recent bachelors graduate from the UW’s department of computer science and engineering and the information school, and , a postdoctoral researcher in computer science and engineering and human centered design and engineering.

The research was funded by the Intel Science and Technology Center for Pervasive Computing, Nokia Research, the Agency for Healthcare Research and Quality and the National Science Foundation.

For more information, contact the authors at lapsedtracking@uw.edu.

Researchers studied how mobile-based food journals integrate into everyday life and specific challenges when using food journaling technology. Their research suggests how future designs might make it easier and more effective.

Their paper, “,” will be presented at the ACM Conference on Human Factors in Computing Systems (CHI 2015) in Seoul, South Korea, April 18-23.

The study uncovered three problem areas: barriers to reliable food entry, negative nudges in current food journal apps and challenges in social features. The findings resulted from data collected in a survey of 141 current and former food loggers as well as analysis of 5,526 public posts on the community forums of mobile-based MyFitnessPal, FatSecret and CalorieCount.

“Community contributions to the databases allow journalers to publish nutritional entries themselves and create a diverse food base from which to pick, but it also raises concerns about reliability,” said Edison Thomaz, a researcher on the study and a doctoral student in human-centered computing at Georgia Tech.

Some users said logging meals took too much effort and was time-consuming. They sometimes loosely followed recipes or only ate partial portion sizes, making it difficult to log meals. Another issue was that food databases contained inaccuracies and some common foods were missing, or had multiple listings for a single food because of user-generated listings.

Researchers found that not all foods are created equal when it comes to logging them. On a seven-point scale, packaged foods and fast food were a breeze to log (6.5 and 6.3 mean scores), while counting up finger foods at a friend’s house or party took dedication (3.2 and 2.9 mean scores).

This made the mobile journals themselves less effective, with some participants straying from their goals or eating the same thing every day to ease the logging ritual. As one respondent put it, it was easier to “scan a code on some processed stuff and be done with it.”

Participants also wanted to develop social connections around food goals. Encouragement of goal attainment and mutual support helped strengthen journaling habits. Conversely, when people received no comments, had online friends stop journaling or had comparatively less progress than others, it negatively impacted their food-tracking goals.

The findings led to several recommendations, including one for designing goal-specific systems.

“Food journals are an important method for tracking food consumption and can support a variety of goals, including weight loss, healthier food choices, detecting deficiencies, identifying allergies and determining foods that trigger other symptoms,” said co-author , a 91̽��associate professor of computer science and engineering.

“Instead of attempting to capture the elusive ‘everything,’ the results suggest creating a diversity of journal designs to support specific goals,” Fogarty said.

Reputation systems were suggested to allow users to filter for specific needs (e.g. tracking sodium intake) or vote on accuracy of entries. Also a priority: streamlining databases with similar foods and providing context for food entry, such as indicating restaurant items or vegan meals.

The results have also led to separate research by team members to implement new journaling solutions. Georgia Tech researchers are testing the feasibility of using a mobile device’s built-in microphone to capture ambient sounds related to eating that, when recognized by the mobile device, nudge users to log their food. Washington researchers are using photo-based journaling to augment or replace methods focused on detailed nutritional input in an attempt to remove or reduce barriers to journaling.

Other co-authors are Felicia Cordeiro, Daniel Epstein and Elizabeth Bales at the 91̽��and Arvind Jagannathan and Gregory Abowd at Georgia Tech.

The work is funded in part by the Intel Science and Technology Center for Pervasive Computing, the National Science Foundation and the National Institutes of Health.

###

This story was adapted from a Georgia Tech .

For more information, contact Fogarty at jfogarty@cs.washington.edu and 206-685-8081 or Joshua Preston with Georgia Tech communications at jpreston@cc.gatech.edu or 678-231-0787.

Grant numbers:

National Science Foundation: OAI-1028195 and SCH-1344613

National Institutes of Health: 1U54EB020404-01

More people are opting to use their phones as “life-logging” devices, but is the data they collect actually useful? Massive amounts of information showing your life patterns over a week, month or year are going untapped because these applications don’t have a way to interpret the data over the long term.

91̽�� researchers have developed visual tools to help self-trackers understand their daily activity patterns over a longer period and in more detail than current life-logging programs can offer. Their found that people generally had an easier time meeting personal fitness and activity goals when they could see their data presented in a broader, more visual way.

“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,” said , a 91̽��associate professor of computer science and engineering.

“We think visualizations like these are the future of how people will look back at their own data to find meaningful or actionable information.”

The research team presented its findings in June at the Association for Computing Machinery’s conference on in Vancouver, British Columbia.

Smartphone life-logging applications such as “Moves” and “Saga” that passively record location and physical activity are becoming more popular, as are other tracking tools like “FitBit,” “FourSquare,” “MyFitnessPal” and “SleepCycle.” But while these programs are useful for tracking day-to-day workouts or activities, there isn’t a way to help people pinpoint why they behave the way they do or what, specifically, they might do differently to meet their goals.

The 91̽��team wants to anticipate what people want and need from these tools, and develop ways to provide them with insights into their behavior and factors that affect it.

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

For the study, 14 West Coast participants ages 23 to 66 used the “Moves” application – recently acquired by Facebook – on their smartphones for one month last summer, passively recording types of activities and locations visited. During the month, the researchers interviewed participants several times about their preferences and ease of use.

Afterward, the researchers sliced into the data generated by each person to pull out “cuts,” or subsets, to help participants explore their data and discover trends. Examples are the type of transportation chosen based on trip distance, or the average work commute time based on the weather that day.

They then displayed these relationships through a series of visualizations, including graphs, tables and maps.

All of the participants found the information to be more helpful in achieving fitness and activity goals than if they simply used the smartphone app.

“Discovery about your patterns and habits happens when you see something you weren’t expecting to see,” said , a 91̽��doctoral student in computer science and engineering. “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 example, one participant realized that if a destination was more than 3 miles away, she usually opted to drive instead of walk. Another realized that Tuesdays were by far his most active day of the week, prompting him to think about what promoted that behavior on Tuesdays.

The researchers hope these findings will influence the data analysis capabilities of life-logging applications. They have plans to develop tools that target specific aspects of a person’s life, including reaching step goals and making healthy food choices.

Other team members are , a 91̽��doctoral student in computer science and engineering, and , a former 91̽��post-doctoral researcher who is now at Google.

The research was funded by the and the at the UW.

###

For more information, contact Fogarty at jfogarty@cs.washington.edu and Munson at smunson@uw.edu.