Paper presented at #ubicomp13. Full paper pdf here: http://www.cl.cam.ac.uk/~nkl25/publications/papers/lathia_ubicomp13.pdf

1. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods
@neal_lathia, k. rachuri, c. mascolo (@cecim), j. rentfrow
computer laboratory, university of cambridge
#ubicomp13

2. Contextual Dissonance?

3. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods

4. You are tasked with researching X (e.g., X = emotions) in
daily life using ubiquitous tech; so you decide to build a
system that will:
● Ask participants for assessments of
the X they experience
● Collect sensor data to 'objectively'
measure participants' contexts and
quantify their behaviour
Research Scenario

5. why would you do this?
● … to explore whether machine learning
approaches could infer people's
subjective responses/complex
behaviours
● … to understand the extent that the
broad set of sensor data reflects self-
reported behaviour

6. “...automated tracing is widely used to
provide insight into what and when;
however, it does not provide the why...”
Froehlich et al.

7. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods

9. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods

10. “...researchers are faced with concrete
decisions regarding design [...] studies
have often been classifed into the three
categories of interval-, signal-, and
event-contingent protocols...”
Bolger et. al
ESM design: how should I ask questions?

11. “...sampling to capture data from the
sensors of the phone cannot be
performed continuously, as this will
drain the battery rapidly. However,
conservative sampling leads to the loss
of valuable behavioural data...”
K. Rachuri
sensor design: how should I sample from sensors?

12. Both of these design protocols will
affect the quantity and quality of data
that you receive from participants.

13. ● Shouldn't sense everything all the
time: triggers a survey based on a
particular sensor
● Ask for subjective responses and,
while doing so, sample data from
other sensors to gather behavioural
signals
Research Scenario

14. We built a system like
this. It includes: sensor
data collection, ESM
interfaces, etc., and
remote reconfguration.

15. Open Source Smartphone Libraries for
Computational Social Science
N. Lathia, K. Rachuri, C. Mascolo, G. Roussos. 2nd
ACM
Workshop on Mobile Systems for Computational Social
Science.
as an aside...

16. 22 users; 1-month;
questions about mood
& current context
(location, sociability);
background sensing
from many sensors;
triggers remotely
reconfgured weekly.

17. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods
… by asking the “what if” question:

18. Your ESM protocol is driven by the
accelerometer's state: questionnaires
will be triggered based on when the
participant is moving.
Example Research Scenario

19. ...skews sampling towards
the later hours of the day

20. But all sensors have their own distribution...
… so how have I skewed my other results?

21. P( state(sensor1) = b | state(accelerometer) = a) ~ P( state(sensor1)) = b

22. Data that I would
have received by
continuous
sampling

23. Data received by
triggering on one
sensor's state.

24. Bias?

25. Accelerometer ~ Non-Stationary
10.61% of the data is non-stationary.
When it is, participants are:
95.23% non-silent; 39.24% at home;
14.43% communicating with others.

26. Full Sample vs. Accelerometer Trigger
Non-silent?
37.78% | 95.23%
Communicating with others?
4.60% | 14.43%

27. More Examples?
Microphone ~ Silent/Non-Silent
Accelerometer ~ Moving/Not-Moving
Location ~ Home/Away
Screen ~ Using the device
SMS/Calls ~ Communicating with others
Proximity ~ Near the phone

28. Microphone ~ Non-Silent
37.78% of the data is non-silent.
When it is, participants are:
26.75% non-stationary; 47.12% at
home; 9.48% communicating with
others.

29. Full Sample vs. Microphone Trigger
Moving?
10.61% | 26.75%
Communicating with others?
4.60% | 9.48%

30. Dissonance; a tension or clash
resulting from the combination of two
disharmonious elements

31. Dissonance; between using sensor
states to trigger ESM surveys while
using sensor data to quantify context
and behaviour.

32. Ok; so replace the accelerometer
trigger with sampling uniformly across
time.
Example Research Scenario

33. temporal
sampling is more
likely to fnd your
participant in
“dominant”
contexts, e.g., at
home.

34. But the response data I get back from
participants will not be affected by the
choices that I make... right?
Research Scenario

35. 1-month; 4 groups with
random weekly trigger
orders: (a) screen, (b)
communication events,
(c) immediately during
non-silence, (d) some
time after non-silence

37. “4 of the 6 tests found that the negative
affect ratings (and 2 out of 6 for the
positive ratings) were signifcantly
different from one another with at least
90% confdence.”

38. who are you with?
alone 33.33% of the time (screen
trigger) to 60.77% of the time
(microphone trigger)

39. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods

40. Where do we go from here?
Opportunities for more research...

43. Generalise sensor-
enhanced experience
sampling tool. Currently
in alpha testing.

44. Working with Android sensors?
Try out library!
One of the goals is to enable easy and quick access to
sensor data in 2 lines of code.
https://github.com/nlathia/SensorManager

45. Contextual Dissonance:
Design Bias in Sensor-Enhanced
Experience Sampling Methods
@neal_lathia, k. rachuri, @cecim, j. rentfrow
ACM Ubicomp 2013

46. References
● Smyth and Stone. “Ecological Momentary Assessment Research in
Behavioral Medicine.” Journal of Happiness Studies 2003.
● Froehlich et al. “MyExperience: A System for In Situ Tracing and
Capturing User Feedback on Mobile Phones.” ACM MobiSys 2007.
● Froehlich et al. “UbiGreen: Investigating a Mobile Tool for Tracking and
Supporting Green Transportation Habits” ACM CHI 2009.
● Rachuri. “Smartphones Based Social Sensing: Adaptive Sampling,
Sensing and Computation Offloading.” PhD Thesis 2013.
● Bolger et. al. “Diary Methods: Capturing Life as it is Lived” Ann. Rev.
Psychology 2003.