Getting to ‘Know Thyself’

What if you could know – really know – how getting only five hours of sleep a night affected your productivity at work? Or, if you could get an affirmation that yes, “this is why you’re fat”? Today, there are numerous gadgets, apps, and services available to the average consumer for collecting everything from vital signs to how many calories were in that Starbucks latte. Personal tracking has grown tremendously popular in recent years and companies such as FitBit, Jawbone, and Nike have made great advances in increasing awareness and making sense of everyday activities. The most effective systems would have to hit the sweet spot – the overlap of data gathered and experience offered in order to give users the best picture of their overall health.

At projekt202, several of us track our sleep and steps with FitBits or apps such as RunKeeper, our meals with LoseIt or MyFitnessPal, and log our workouts through a myriad of apps. Some of us also rely on Facebook groups to share workout progress, recipes, and encouragement. Recently, some of us at p202 labs asked ourselves, “What if there was a system that ‘knew’ us and could help us be better?” We could see two initial problems. First, despite the fact that there are so many devices and apps, no one system has successfully pulled all the data together in a way that tells a compelling and motivating story. For example, as I log my sleep hours with a FitBit, I rely on my Polar heart rate monitor for an accurate snapshot of a morning workout. I can’t easily overlay these two data streams and see trends or patterns that might help me adjust my lifestyle in order to reach my goals. Second, in order for any system to tell a good ‘story’ that would have an impact, accurate detailed input is required. Data entry is so painful and tedious in current devices and apps that keeping up consistent records are demotivating. While most modern meal tracking applications allow users to select from a large (and growing) database of foods, entering a meal can be frustrating if the exact food isn’t in the database. The ability to scan barcodes of packaged foods is a brilliant feature, but (hopefully) we don’t only eat packaged foods. In my own experience, I often found myself guessing and making up food data when the correct choices were not present. LoseIt’s analysis was only as good as the data I seeded it with and I knew it wasn’t accurate. After a while, I finally gave up in disgust because the app was telling me one thing but my reflection was showing me something else.

Early sketches

Sense-making sketches for the home screen

Two quick concepts

Two concept sketches

As we often see when another floodgate of information is opened (in this case, health information that was previously only accessible in the domain of a hospital or doctor’s office), it’s not long before the user is drowning in data. After pooling our collective experiences and looking for evidence of a possible savior, we realized we weren’t the only ones intrigued by this problem. Far from it. The Quantified Self movement (formalized in 2011) seeks to gather data from daily life (food, activity, mood, etc.) via technology. Nicholas Felton has been charting his stats in Personal Annual Reports and has made Daytum available so that others can visualize their own data. As p202 labs, we decided to tackle a bit of this quandary ourselves. Perfectly accurate data entry is one of the holy grails, but we see an opportunity dig deep creatively and invent a system to deliver simple, elegant, fun, and useful data visualization and storytelling. To that end, we’re going to devote some of our cycles to bringing out what our brains already know but aren’t telling us and displaying this in an easy to understand way. The great part is that as a p202 labs project, we are starting from scratch and asking ourselves, “What do we want this to be?” The sky is the limit and we’re set to imagine and create something that is both useful and amazing.

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