Lunchtime Ignite talk for Quantified Self Amsterdam on May 11th 2013

1. Insights from Tracking
Walking Patterns
Per Sandholm
per@quantisproject.com
www.quantisproject.com
Using Steps Mania

3. The Dataset
• 4 million activities collected during three
months (February-April)
• An activity is defined by steps taken, class
(running or walking), duration and location
• The Quantis Cloud service also maintains
information about friendships, awards and
weight measurements

4. Location distribution of users included in dataset

5. 51% female users vs. 22% male users (not all users entered their gender)

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Age 0-9 Age 10-19 Age 20-29 Age 30-39 Age 40-49 Age 50-59 Age 60-69 Age 70-79 Age 80-89
Percentage
Age distribution of users included in dataset

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Average Daily Step Count
Average number of steps taken vs. age of user

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BMI 10-14 BMI 15-19 BMI 20-24 BMI 25-29 BMI 30-34 BMI 35-39 BMI 40-44
Percentage
46,7% of the Danish population have BMI>25 (54,2% male and 39,4% female)

9. Clustering of all users vs. loyal users by average daily steps taken
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<2000 <4000 <6000 <8000 <10000 <12000 <14000 >14000
All Users Loyal Users

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Average steps taken by all Danish users
Pink bars are weekends or bank holidays

11. -4
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Copenhagen temperature in Celsius graph vs. average steps taken
Pink bars are weekends or bank holidays

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Copenhagen sunshine days vs. average steps taken
Green bars are sunny days

13. Awarded
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14. Mon
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00:00 12:00 23:59
Punch card visualization showing a single user (average over one month)

15. 00:00 12:00 23:59
Mon
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Punch card visualization showing all users (average over full three months)

16. Only 5% of the activities were classified as running

17. 60
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Age 10-19 Age 20-29 Age 30-39 Age 40-49 Age 50-59 Age 60-69 Age 70-79
Average walking vs. running pace of all users

18. Observations
• Using a mobile for self-tracking has some
inherent problems
• Award system progression could be
improved
• Weather and especially sunshine affects
users activity level

19. Future Ideas
• Impact of high score rankings and friends
on steps taken
• Long term variations such as seasonal
changes
• Detecting when users are about to loose
motivation

20. Thank You!
per@quantisproject.com
@quantisproject
quantisproject.com