An Example of the Quantified Self: Steps

Wearable trackers are all the latest rage!  From a FitBit to a Microsoft Band to old school pedometers, the latest health craze is about steps.   Whether you joined the office fitness challenge or just want to get moving a bit more, the concept of tracking steps is the most popular metric of the quantified self in 2015.   What a perfect metric to start a series of posts showing examples of the quantified self.  The objective is to show the wide variety of metrics an individual can use to make their life healthier, more productive, and happier.

Tracking Activity: Steps

The graphic below is my steps history as recorded using an app called Argus on my iPhone 5S.  The app is simple, turn it on, and it does the rest.  It records steps as you move through the motion sensor.  Of course, the drawback is, it only records steps if the phone is with you and on your body.  For example, if I was on an elliptical, the phone would have to be in my pocket to record the steps, placing it on the machine doesn’t work.

Creating a Custom Quantified Data Visualization

To create the dashboard below, I entered my step data into a Microsoft Excel spreadsheet, added a few formulas for day of week and location and picked a data visualization platform. While I could have created a few charts from a pivot table in Excel, I decided to give Qlik Sense a test drive.  Qlik Sense is a lighter, consumer oriented version of the powerful Qlikview data discovery and reporting platform.  Qlik Sense is easy to use, just select your data source (Excel in my case), select your dimensions and measures and you are off to the races.

quantified self data visualization

Using Qlik Sense to visualize steps data from Argus

Raw Data: Steps by Day

The top graph shows raw steps by calendar date.  While there isn’t too much to see here at first glance, you can see clear dips in the pattern which maybe the weekend.  You can also see the data at the right tend to be a slightly higher than the data to the left side.

Steps by Day of Week

By adding a dimension called “DayName”, we can average steps by day of week.  In the orange chart to the right, the most active day is Monday, followed by Thursday and Sunday.  The lowest is Wednesday.  Fitness improvements come with consistency.  Being the difference between highest and lowest is fairly great, we can focus on being more active on Wednesday’s.  Tracking the change over time will help us be more consistent with our activity.

Steps by Geography

I also added a dimension called “location” by tagging each day with where I was.  I often split time between Washington state and Santa Barbara with days in between flying. The green chart to the left shows a pretty interesting pattern, I am far more active when in Washington state compared to Santa Barbara, This is probably due to the recreational abundance in Washington, such as the hiking trails around Mt. Baker and the walking we do while downtown.  Santa Barbara is also a much more isolated location by comparison.  Perhaps, if I want to be more active, I should stay in Washington state.

The Fundamental, Visual Flaw

You might be asking, what flaw is there is there in the above dashboard?  It is hard to see because it is not there. One of the best CEO’s I ever had the honor of working for said, “if it is important enough to put on a chart, you better damn well have a goal indicator with it.”  I agree.

Throughout this post, I mentioned goals such as covering 10,000 steps in a day, and increasing Wednesday activity.  The charts above should include an indication of these numbers.  The charts are essentially naked without the indicators and the user viewing them loses the context of the rest of the data.

Always include a goal indicator when creating data visuals, the context is essential.

Conclusion

This is one example of using data visualization to improve personal life.  Activity is one of the primary factors in achieving and maintaining good health.  Using a fitness tracker and visualization tool like Qlik Sense can be effective.  Just understand the privacy policy and how the wearable tech company may use your private data.

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