Embed Size (px)
DESCRIPTION
Lunchtime Ignite talk for Quantified Self Amsterdam on May 11th 2013
Citation preview
Insights from Tracking Walking Patterns
Per Sandholm
www.quantisproject.com
Using Steps Mania
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
Location distribution of users included in dataset
51% female users vs. 22% male users (not all users entered their gender)
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 80-890
5
10
15
20
25
30
35
Percentage
Age distribution of users included in dataset
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 80-890
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Average Daily Step Count
Average number of steps taken vs. age of user
BMI 10-14 BMI 15-19 BMI 20-24 BMI 25-29 BMI 30-34 BMI 35-39 BMI 40-440
5
10
15
20
25
30
35
40
45
Percentage
46,7% of the Danish population have BMI>25 (54,2% male and 39,4% female)
Clustering of all users vs. loyal users by average daily steps taken
<2000 <4000 <6000 <8000 <10000 <12000 <14000 >140000
5
10
15
20
25
30
35
40
45
All Users Loyal Users
Fr Su Tu Th Sa Mo We Fr Su Tu Th Sa Mo W
e Fr Su Tu Th Sa Mo We Fr Su Tu Th Sa Mo W
e Fr Su Tu Th Sa Mo0
1000
2000
3000
4000
5000
6000
Average steps taken by all Danish usersPink bars are weekends or bank holidays
Fr Su Tu Th Sa Mo We Fr Su Tu Th Sa Mo W
e Fr Su Tu Th Sa Mo We Fr Su Tu Th Sa Mo W
e Fr Su Tu Th Sa Mo0
1000
2000
3000
4000
5000
6000
-4
-2
0
2
4
6
8
Copenhagen temperature in Celsius graph vs. average steps takenPink bars are weekends or bank holidays
1 3 5 7 9 11 13 15 170
1000
2000
3000
4000
5000
6000
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Copenhagen sunshine days vs. average steps takenGreen bars are sunny days
couch
potato
doctorso
rders
strict
lybusin
ess
first1000
paperb
oy
weeke
ndgetaw
ay
primeti
me
firstmara
thon
early
bird
moonwalker
spee
dygonzal
es
oldfaithful
sleep
walker
marathonman
zombiew
alk
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Mon
Tue
Wed
Thu
Fri
Sat
Sun 00:00 12:00 23:59
Punch card visualization showing a single user (average over one month)
00:00 12:00 23:59
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Punch card visualization showing all users (average over full three months)
Only 5% of the activities were classified as running
Age 10-19 Age 20-29 Age 30-39 Age 40-49 Age 50-59 Age 60-69 Age 70-7960
70
80
90
100
110
120
130
140
Average walking vs. running pace of all users
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
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
Thank You!
@quantisproject
quantisproject.com
