Quantified Self: Self Tracking for Health

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1 Quantified Self: Self Tracking for Health Andreas Schreiber 4 th International TEMOS Conference, Bonn,

2 Introduction Scientist, Head of department Co-Founder, CEO Co-Founder Slide 2

3 My Motivation Quantified Self track myself With sensors With smartphone apps Slide 3

4 Quantified Self Slide 4

5 What is Quantified Self? Self-knowledge through numbers Analyze trends and set goals to improve yourself Recording of daily activities Fitness, sleep, location, Monitoring and display of information from various devices, services, and applications Slide 5

6 Other Terms Self Tracking Life Hacking Life Logging Slide 6

7 Google Trends: Quantified Self & Health Apps Slide 7

8 Quantified Self Meetups Slide 8

9 Objects of Tracking: Health-oriented Well-being-oriented Body Mood Addictions Physical activities Nutrition Other Directly health-oriented Chronic diseases No-chronic diseases General medication Symptoms Blood test results Insulin intake Blood sugar General daily records about health state Slide 9

10 Objects of Tracking: Not-health-oriented Environmental Temperature Ozone conzentration Atmospheric pressure Location Rain Clouds Relationships Frequency Quality Sex Other Finance ToDos Delays (Train, etc.) Slide 10

11 Motivation Slide 11

12 What People are Tracking? 70% 60% 50% 40% 30% 20% 10% 0% Health Environment Finance Nutrition Fitness Germany Slide 12 Studie QlikTech, August 2013

13 Motivations Five motivations Self-Design Self-Entertainment Self-Association Self-Discipline Self-Healing Slide 13

14 Motivation Self-Design Self-Design motivated by the possibilities of self-optimization Slide 14

15 Motivation Self-Entertainment Self-Entertainment motivated due to the pleasure-bringing aspects of selftracking Slide 15

16 Motivation Self-Association Self-Association motivated by the prospect of community citizenship and selfindividualizing aspects within a community Slide 16

17 Motivation Self-Discipline Self-Discipline motivated due to the self-gratification possibilities of selftracking Slide 17

18 Motivation Self-Healing Self-Healing motivated by the self-healing possibilities of self-tracking Slide 18

19 Mean Motivation (Range 1-5) Self-Healing Self-Discipline Self-Association Self-Entertainment Self-Design 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 Well-being and health Well-being All Source: Marcia Nißen, Quantified Self An Exploratory Study on the Profiles and Motivations of Self-Tracking, Bachelor Thesis (2013) Slide 19

20 Self-Assesment of Self-Tracking Categories Source: Marcia Nißen, Quantified Self An Exploratory Study on the Profiles and Motivations of Self-Tracking, Bachelor Thesis (2013) Slide 20

21 Self Experiments Design experiments Measure results Improve yourself Slide 21

22 Wearable Sensors, Devices, and Apps Slide 22

23 Technologies for Self-Tracking 70% Deployed technologies for self-tracking 60% 50% 40% 30% 20% 10% 0% Mobile hardware (smartphones) and software (apps) Web- and desktop applications Self-tracking hardware Self-made desktop tools (spreadsheets etc.) Pen and paper Other Source: Marcia Nißen, Quantified Self An Exploratory Study on the Profiles and Motivations of Self-Tracking, Bachelor Thesis (2013) Slide 23

24 Steps Slide 24

25 Activity Slide 25

26 Blood Pressure Slide 26

27 Weight Slide 27

28 Stress Slide 28

29 Sleep Slide 29

30 Coffee, Medication, Expenses, Slide 30

31 Medando: BloodpressureCompanion Slide 31

32 Medando: WeightCompanion Slide 32

33 Wearable Technology Source: Slide 33

34 Many Devices, Sensors and Apps Data Exchange: The Internet of Things Slide 34

35 Notification on Android Slide 35

36 Sharing Slide 36

37 Data Analytics Slide 37

38 Steps (Fitbit) Slide 38

39 Steps (Fitbit) Slide 39

40 Weight (Withings) Slide 40

41 Body Fat (Withings) Slide 41

42 Activity (Nike Fuelband) Slide 42

43 Activity & Location (Moves) Slide 43

44 Blood Pressure vs. Weight Slide 44

45 hgraph Slide 45

46 To be continued Big Data Slide 46

47 Conclusions Quantified Self community is growing Many more devices and apps Mobile! Wearable! Data analytics at the beginning Slide 47

48 Discussion Slide 48

Quantified Self: Analyzing the Big Data of our Daily Life. Andreas Schreiber <[email protected]> PyData Berlin 2014

DLR.de Chart 1 Quantified Self: Analyzing the Big Data of our Daily Life Andreas Schreiber PyData Berlin 2014 DLR.de Chart 2 Introduction Scientist, Head of department Co-Founder,