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Behavioural science
for wearable technology
and the quantified
workplace
Dr Lukasz Piwek 
GPS traces
can be used
to tell your identity
and predict
where you are likely
to go next
?
de Montjoye et al. (2013a)
Song...
stress level can be detected
with 80% accuracy
by capturing only a few words
with microphone
Lu et al. (2012)
call logs, SMS logs,
Bluetooth scans,
and application usage
can be used to predict
personality traits
with up to 70% accur...
accelerometers
can be used to predict
whether you’re sitting,
walking, jogging,
cycling, driving or sleeping
with up to 95...
Image source: Apple, Nike, Fitbit, Motorola, Jawbone, Microsoft, Withings, Neurosky, Duoferility, Nuubo
=
Headbands
Camera clips
Sensors embedded in clothing
Smartwatches
Sociometric badges
OXI
ALT ECG EMG
EEG
OXI
ALT
ECG
EMG
EE...
self-monitoring + feedback
continuous
intelligent
detailed
instant
personalised
Festinger, 1954
Mcfall and Hammen,1971
Kir...
high blood pressure
high cholesterol level
physical inactivity
obesity
45%*
*NHS The Atlas of Risk (2014)
Those three devices accounted for
97%
of all smartphone-enabled
activity trackers sold in 2013*
*cmo.com
Fitbit Jawbone UP...
Battery life
Data access
User interface
Reliability SecurityData ownership
Build qualityFeedback
Fitbit Jawbone UP Nike Fu...
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 ...
“Each new device should
reduce the complexity of the
system and increase the value
of everything else in the
ecosystem.”
B...
*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
wit...
Self-monitoring in cycling
Piwek, Joinson, Morvan (2015)
Transportation Research Part A: 77, 126–136
33 features
performance (e.g. tracking speed, distance traveled)
social (e.g. joining challenges against other cyclists)
m...
speed distance duration
simplicity,
utility
& relevance
is critical
vs
free1 year
13
Non-trackers
Initial survey
Debriefing
interview
5 weeks
12
Trackers
+
5 weeks
yourtime?16th
monday
traveled
to campus?
why not
cycling?
how?
L ?
=
=
>
norms
beliefs
attitudes
motivations
personality traits
cultural influences
socio-economic factors
“Strava louts” & Big Behavioural Cycling Data
Project in progress
“Quantified runner”
Project in progress
Project in progress
Quantified workplace
on off
Andrews, Ellis, Shaw, Piwek (2015)
PLOS One 10 (10): e0139004
Smartphone research methods
PSYCHOLOGY
SENSOR LAB
TM
...
Project in preparation
PhD student co-supervision
Digital footprints for behavioural profiling
PSYCHOLOGY
SENSOR LAB
TM
Net...
Project in preparation
Awaiting grant application outcomes
Sociometric wearables for research
PSYCHOLOGY
SENSOR LAB
TM
Net...
“(…) without the proper behavioural design,
without considering how new products and
services fit into people's day-to-day...
Prof Adam Joinson | Bath
Dr David Ellis | Lancaster
Dr Phoebe Moore | Middlesex
Dr Sally Andrews | Nottingham Trent
Prof A...
lpiwek@gmail.com
@lukaszpiwek
motioninsocial.com
Thank you!
Behavioural science for wearable technology and the quantified workplace
Behavioural science for wearable technology and the quantified workplace
Behavioural science for wearable technology and the quantified workplace
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Behavioural science for wearable technology and the quantified workplace

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An invited talk for "McLaren Applied Technologies" in Woking on 13th of April 2016.

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Behavioural science for wearable technology and the quantified workplace

  1. 1. Behavioural science for wearable technology and the quantified workplace Dr Lukasz Piwek 
  2. 2. 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)
  3. 3. stress level can be detected with 80% accuracy by capturing only a few words with microphone Lu et al. (2012)
  4. 4. 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)
  5. 5. 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)
  6. 6. Image source: Apple, Nike, Fitbit, Motorola, Jawbone, Microsoft, Withings, Neurosky, Duoferility, Nuubo
  7. 7. =
  8. 8. 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
  9. 9. 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
  10. 10. high blood pressure high cholesterol level physical inactivity obesity 45%* *NHS The Atlas of Risk (2014)
  11. 11. Those three devices accounted for 97% of all smartphone-enabled activity trackers sold in 2013* *cmo.com Fitbit Jawbone UP Nike Fuelband
  12. 12. Battery life Data access User interface Reliability SecurityData ownership Build qualityFeedback Fitbit Jawbone UP Nike Fuelband
  13. 13. 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
  14. 14. “Each new device should reduce the complexity of the system and increase the value of everything else in the ecosystem.” Bill Buxton
  15. 15. *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
  16. 16. Self-monitoring in cycling Piwek, Joinson, Morvan (2015) Transportation Research Part A: 77, 126–136
  17. 17. 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)
  18. 18. speed distance duration simplicity, utility & relevance is critical
  19. 19. vs free1 year
  20. 20. 13 Non-trackers Initial survey Debriefing interview 5 weeks 12 Trackers + 5 weeks
  21. 21. yourtime?16th monday traveled to campus? why not cycling? how? L ? = =
  22. 22. >
  23. 23. norms beliefs attitudes motivations personality traits cultural influences socio-economic factors
  24. 24. “Strava louts” & Big Behavioural Cycling Data Project in progress
  25. 25. “Quantified runner” Project in progress
  26. 26. Project in progress Quantified workplace
  27. 27. on off Andrews, Ellis, Shaw, Piwek (2015) PLOS One 10 (10): e0139004 Smartphone research methods PSYCHOLOGY SENSOR LAB TM NetPropagate Systems TM
  28. 28. Project in preparation PhD student co-supervision Digital footprints for behavioural profiling PSYCHOLOGY SENSOR LAB TM NetPropagate Systems TM
  29. 29. Project in preparation Awaiting grant application outcomes Sociometric wearables for research PSYCHOLOGY SENSOR LAB TM NetPropagate Systems TM
  30. 30. “(…) 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
  31. 31. 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
  32. 32. lpiwek@gmail.com @lukaszpiwek motioninsocial.com Thank you!

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