This document discusses the capabilities of wearable technology to track and analyze human behavior and physiology. It summarizes research showing that wearables can detect stress levels, predict personality traits, and identify physical activities like sitting, walking or cycling with high accuracy by collecting data from sensors. However, it notes that sustained long-term use of wearables is challenging, with many users stopping use within 6-12 months. It also discusses challenges regarding data access, ownership and privacy.

1. Behavioural science
for wearable technology
and the quantified
workplace
Dr Lukasz Piwek

3. GPS traces
can be used
to tell your identity
and predict
where you are likely
to go next
?
de Montjoye et al. (2013a)
Song et al. (2010)
Do & Garcia-Perez (2014)

4. stress level can be detected
with 80% accuracy
by capturing only a few words
with microphone
Lu et al. (2012)

5. call logs, SMS logs,
Bluetooth scans,
and application usage
can be used to predict
personality traits
with up to 70% accuracy
de Montjoye et al. (2013b)
Chittaranjan et al. (2013)

6. accelerometers
can be used to predict
whether you’re sitting,
walking, jogging,
cycling, driving or sleeping
with up to 95% accuracy
Khalil & Glal, 2009
He & Li, 2013
Behar et al. (2013)

7. Image source: Apple, Nike, Fitbit, Motorola, Jawbone, Microsoft, Withings, Neurosky, Duoferility, Nuubo

8. =

9. Headbands
Camera clips
Sensors embedded in clothing
Smartwatches
Sociometric badges
OXI
ALT ECG EMG
EEG
OXI
ALT
ECG
EMG
EEG
Accelerometer
Electrocardiogram
Microphone
Oximeter
Electromyorgaph
Electroencephalogram
Thermometer
Electrodermograph
Location GPS
Digital camera
Bluetooth proximity
Pressure
Altimeter
Consumer health wearables
Piwek, Ellis, Andrews, Joinson (2016)
PLOS Medicine: 13 (2): e1001953

10. self-monitoring + feedback
continuous
intelligent
detailed
instant
personalised
Festinger, 1954
Mcfall and Hammen,1971
Kirsenbaum et al., 1981
Bandura, 1997
Wilbur et al., 2003
Womble et al., 2004
Williams et al., 2007
Du et al., 2011
Swan, 2012
Bird et al., 2013

11. high blood pressure
high cholesterol level
physical inactivity
obesity
45%*
*NHS The Atlas of Risk (2014)

12. Those three devices accounted for
97%
of all smartphone-enabled
activity trackers sold in 2013*
*cmo.com
Fitbit Jawbone UP Nike Fuelband

13. Battery life
Data access
User interface
Reliability SecurityData ownership
Build qualityFeedback
Fitbit Jawbone UP Nike Fuelband

14. distance
per day
(km)
days
squash
gym
squash
5
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
fuelband
jawbone
average distance
over 30 days (km)
0 2 4 6 8

16. “Each new device should
reduce the complexity of the
system and increase the value
of everything else in the
ecosystem.”
Bill Buxton

17. *Ledger et al. (2014)
0%
25%
50%
75%
100%
0 3 6 9 12 15 18 21 24
time (months)
rate of
sustained
use (%)
32% drop rate
within 6 months
50% drop rate
after 12 months

18. Self-monitoring in cycling
Piwek, Joinson, Morvan (2015)
Transportation Research Part A: 77, 126–136

19. 33 features
performance (e.g. tracking speed, distance traveled)
social (e.g. joining challenges against other cyclists)
mapping (e.g. viewing map with directions)
other (e.g. answering a call)

20. speed distance duration
simplicity,
utility
& relevance
is critical

21. vs
free1 year

22. 13
Non-trackers
Initial survey
Debriefing
interview
5 weeks
12
Trackers
+
5 weeks

23. yourtime?16th
monday
traveled
to campus?
why not
cycling?
how?
L ?
=
=

24. >

26. norms
beliefs
attitudes
motivations
personality traits
cultural influences
socio-economic factors

27. “Strava louts” & Big Behavioural Cycling Data
Project in progress

28. “Quantified runner”
Project in progress

29. Project in progress
Quantified workplace

30. on off
Andrews, Ellis, Shaw, Piwek (2015)
PLOS One 10 (10): e0139004
Smartphone research methods
PSYCHOLOGY
SENSOR LAB
TM
NetPropagate
Systems
TM

31. Project in preparation
PhD student co-supervision
Digital footprints for behavioural profiling
PSYCHOLOGY
SENSOR LAB
TM
NetPropagate
Systems
TM

32. Project in preparation
Awaiting grant application outcomes
Sociometric wearables for research
PSYCHOLOGY
SENSOR LAB
TM
NetPropagate
Systems
TM

33. “(…) without the proper behavioural design,
without considering how new products and
services fit into people's day-to-day lives, any
new technology can be terrifying. That's where
the challenge comes in. The task of making
(new technology, new world version) can't
be left up to engineers and technologists
alone - otherwise we will find ourselves
overrun with amazing capabilities that
people refuse to take advantage of.”
Cliff Kuang Wired 10/2013

34. Prof Adam Joinson | Bath
Dr David Ellis | Lancaster
Dr Phoebe Moore | Middlesex
Dr Sally Andrews | Nottingham Trent
Prof Alan Tapp | UWE
Dr Fiona Spotswood | UWE

35. lpiwek@gmail.com
@lukaszpiwek
motioninsocial.com
Thank you!