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Using Smartphones to Measure (and Intervene in) Daily Life

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Invited Talk @ Workshop on Innovations in Online Experiments, Nuffield Centre for Experimental Social Sciences, 13/03/2015

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Using Smartphones to Measure (and Intervene in) Daily Life

  1. 1. Using Smartphones to Measure (and Intervene in) Daily Life @neal_lathia Computer Laboratory University of Cambridge
  2. 2. Background Smartphones as Research Tools Case 1: Subjective Wellbeing & Behaviour Case 2: Smoking Cessation Challenges, Opportunities, Questions
  3. 3. Background
  4. 4. Smartphones as Research Tools
  5. 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.
  6. 6. Accelerometer GPS / Wi-Fi Gyroscope Bluetooth Microphone Humidity Temperature Phone / Text Logs Device Logs Social Media APIs App Usage
  7. 7. 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
  8. 8. Case 1: Subjective Wellbeing & Behaviour N. Lathia, G. Sandstrom, P. Rentfrow, C. Mascolo (in prep). Happy People Live Active Lives. 2015.
  9. 9. “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.
  10. 10. “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.
  11. 11. 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
  12. 12. r(24,201) = .37, p < .001 d = .80
  13. 13. r(10,376) = .03, p < .001 d = .07 r(2,969) = .10, p < .001 d = .19
  14. 14. 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 27Hour of the Day Hour of the Day Sensedphysicalactivity
  15. 15. 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.
  16. 16. 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 [...]”
  17. 17. Stress, Depression, Urges, Situation, Social (Other Smokers).
  18. 18. Stress, Depression, Urges, Situation, Social (Other Smokers).
  19. 19. Challenges, Opportunities, Questions
  20. 20. https://github.com/xsenselabs http://easym.emotionsense.com/
  21. 21. 11,311 5,212 3,902 2,978 2,401 1,924 1,514
  22. 22. Using Smartphones to Measure (and Intervene in) Daily Life @neal_lathia neal.lathia@cl.cam.ac.uk

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