Case study on mobile-based experience sampling using the Q-Sense and EmotionSense platform. Presented by Dr. Neal Lathia of Cambridge University at LSHTM's 'Enhancing data capture in health research' RDM event on November 20th, 2015.

1. Mobile-Based Experience Sampling for
Behaviour Research
@neal_lathia
Computer Laboratory
University of Cambridge

2. Background
Smartphones as Research Tools
Case 1: Subjective Wellbeing & Behaviour
Case 2: Smoking Cessation
Challenges, Opportunities, Questions

3. Smartphones as Research Tools

4. 71% 195 87%Technological cultural HEALTH

5. “by 2025, when most of today’s psychology
undergraduates will be in their mid-30s, more
than 5 billion people on our planet will be using
ultra-broadband, sensor-rich smartphones far
beyond the abilities of today’s iPhones, Androids,
and Blackberries.”
G. Miller. The Smartphone Psychology Manifesto. Perspectives on Psychological
Science. 7:3 May 2012.

8. Accelerometer
GPS / Wi-Fi
Gyroscope
Bluetooth
Microphone
Humidity
Temperature
Phone / Text Logs
Device Logs
Social Media APIs
App Usage

9. Accelerometer | Physical Activity
GPS / Wi-Fi | Mobility
Gyroscope | Orientation
Bluetooth | Co-Location
Microphone | Ambient Audio
Humidity | Environment
Temperature | Environment
Phone / Text Logs | Socialising
Device Logs | Network
Social Media APIs | Socialising
App Usage | Information Needs

10. Case 1: Subjective Wellbeing & Behaviour
N. Lathia, G. Sandstrom, P. Rentfrow, C. Mascolo (in prep). Happy
People Live Active Lives. 2015.

18. “A sample of 222 undergraduates was screened
for high happiness using multiple confirming
assessment filters. We compared the upper 10%
of consistently very happy people with average
and very unhappy people. The very happy people
were highly social, and had stronger romantic and
other social relationships than less happy
groups...”
Diener, Seligman. Very Happy People. In Psychological Science 13 (1). Jan 2002.

19. “A sample of 222 undergraduates was screened
for high happiness using multiple confirming
assessment filters. We compared the upper 10%
of consistently very happy people with average
and very unhappy people. The very happy people
were highly social, and had stronger romantic and
other social relationships than less happy
groups...”
Diener, Seligman. Very Happy People. In Psychological Science 13 (1). Jan 2002.

21. Hemminki, Nurmi, Tarkoma. Accelerometer-Based Transportation Mode Detection
on Smartphones. In ACM Sensys 2013.
Statistical: mean, standard deviation, median, etc.
Time: auto-correlation, mean-crossing rate, etc.
Frequency: FFT, spectral energy, etc.
Peak: volume, intensity, skewness, etc.
Segment: e.g., velocity change rate

22. r(24,201) = .37, p < .001 d = .80

23. r(10,376) = .03, p < .001 d = .07 r(2,969) = .10, p < .001 d = .19

26. F(2, 10,288) = 39.08, p < .
001
F(2, 9,627) = 32.52, p < .001
M = .57
M = .22
M = -.14
Happiness: M = .23
M = .27
M = -.26
26Hour of the Day Hour of the Day
Sensedphysicalactivity

27. Case 2: Smoking Cessation
Naughton et al. (in prep). The feasibility of a context aware smoking
cessation app (Q Sense): A mixed methods study. 2015.

28. Ferguson, Shiffman. The relevance and treatment of cue-induced cravings in
tobacco dependence. In J Subst Abuse Treat. April 2009.
“cue-induced cravings: intense,
episodic cravings typically
provoked by situational cues
associated with drug use [...]
smokers exposed to smoking-
related cues demonstrate
increased craving [...]”

32. Stress, Depression, Urges,
Situation, Social (Other Smokers).

33. Challenges, Opportunities, Questions

35. 11,311
5,212
3,902
2,978
2,401
1,924
1,514

38. N. Lathia et al. Smartphones for Large-scale Behaviour Change Interventions. In IEEE
Pervasive Computing, Special Issue on Understanding and Changing Behavior, 2013.
Monitor
Learn
Change
Behaviour

39. Mobile-Based Experience Sampling for
Behaviour Research
@neal_lathia
neal.lathia@cl.cam.ac.uk