skip to main content
research-article

MyTraces: Investigating Correlation and Causation between Users’ Emotional States and Mobile Phone Interaction

Published:11 September 2017Publication History
Skip Abstract Section

Abstract

Most of the existing work concerning the analysis of emotional states and mobile phone interaction has been based on correlation analysis. In this paper, for the first time, we carry out a causality study to investigate the causal links between users’ emotional states and their interaction with mobile phones, which could provide valuable information to practitioners and researchers. The analysis is based on a dataset collected in-the-wild. We recorded 5,118 mood reports from 28 users over a period of 20 days.

Our results show that users’ emotions have a causal impact on different aspects of mobile phone interaction. On the other hand, we can observe a causal impact of the use of specific applications, reflecting the external users’ context, such as socializing and traveling, on happiness and stress level. This study has profound implications for the design of interactive mobile systems since it identifies the dimensions that have causal effects on users’ interaction with mobile phones and vice versa. These findings might lead to the design of more effective computing systems and services that rely on the analysis of the emotional state of users, for example for marketing and digital health applications.

References

  1. 2017. Android’s Notification Listener Service. (2017). http://developer.android.com/reference/android/service/notification/NotificationListenerService.html.Google ScholarGoogle Scholar
  2. 2017. Google Now. (2017). http://www.google.com/landing/now/.Google ScholarGoogle Scholar
  3. 2017. Google Play Store. (2017). https://play.google.com/store/apps.Google ScholarGoogle Scholar
  4. 2017. Google’s Activity Recognition Application. http://developer.android.com/training/location/activity-recognition.htmll. (2017).Google ScholarGoogle Scholar
  5. 2017. HeadSpace. (2017). https://www.headspace.com.Google ScholarGoogle Scholar
  6. 2017. HelloMind. (2017). http://www.hellomind.com.Google ScholarGoogle Scholar
  7. Jorge Alvarez-Lozano, Venet Osmani, Oscar Mayora, Mads Frost, Jakob Bardram, Maria Faurholt-Jepsen, and Lars Vedel Kessing. 2014. Tell me your apps and I will tell you your mood: correlation of apps usage with bipolar disorder state. In PETRA’14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C Daniel Batson, Laura L Shaw, and Kathryn C Oleson. 1992. Differentiating affect, mood, and emotion: toward functionally based conceptual distinctions. Sage Publications.Google ScholarGoogle Scholar
  9. Christopher Beedie, Peter Terry, and Andrew Lane. 2005. Distinctions between emotion and mood. Cognition 8 Emotion 19, 6 (2005), 847--878.Google ScholarGoogle Scholar
  10. Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In UbiComp’15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Chun-Chu Chen and James F Petrick. 2013. Health and wellness benefits of travel experiences a literature review. Journal of Travel Research 52, 6 (2013), 709--719.Google ScholarGoogle ScholarCross RefCross Ref
  12. Sunny Consolvo, David W McDonald, Tammy Toscos, Mike Y Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, and others. 2008. Activity Sensing in the Wild: a Field Trial of UbiFit Garden. In CHI’08. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Mihaly Csikszentmihalyi and Reed Larson. 1983. The experience sampling method. New Directions for Methodology of Social and Behavioral Science 15 (1983), 41--56.Google ScholarGoogle Scholar
  14. Mihaly Csikszentmihalyi and Reed Larson. 2014. Validity and reliability of the experience-sampling method. In Flow and the Foundations of Positive Psychology. 35--54.Google ScholarGoogle Scholar
  15. Martin Fishbein. 1995. Developing effective behavior change interventions: some lessons learned from behavioral research. NIDA Research Monograph 155 (1995), 246--261.Google ScholarGoogle Scholar
  16. Ronald Aylmer Fisher and Frank Yates. 1938. Statistical tables for biological, agricultural and medical research. Longman.Google ScholarGoogle Scholar
  17. Joseph P Forgas, Gordon H Bower, and Susan E Krantz. 1984. The influence of mood on perceptions of social interactions. Journal of Experimental Social Psychology 20, 6 (1984), 497--513.Google ScholarGoogle ScholarCross RefCross Ref
  18. Ke Huang, Chunhui Zhang, Xiaoxiao Ma, and Guanling Chen. 2012. Predicting mobile application usage using contextual information. In UbiComp’12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Neal Lathia, Veljko Pejovic, Kiran K. Rachuri, Cecilia Mascolo, Mirco Musolesi, and Peter J. Rentfrow. 2013. Smartphones for Large-Scale Behaviour Change Intervention. Pervasive Computing 12, 12 (July 2013), 66--73. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Peter D Leathwood and Patricia Pollet. 1983. Diet-induced mood changes in normal populations. Journal of Psychiatric Research 17, 2 (1983), 147--154.Google ScholarGoogle ScholarCross RefCross Ref
  21. Hosub Lee, Young Sang Choi, Sunjae Lee, and I.P. Park. 2012. Towards unobtrusive emotion recognition for affective social communication. In CCNC’12.Google ScholarGoogle Scholar
  22. Robert LiKamWa, Yunxin Liu, Nicholas D Lane, and Lin Zhong. 2013. Moodscope: Building a mood sensor from smartphone usage patterns. In MobiSys’13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Bo Lu, Elaine Zanutto, Robert Hornik, and Paul R Rosenbaum. 2001. Matching with doses in an observational study of a media campaign against drug abuse. J. Amer. Statist. Assoc. 96, 456 (2001), 1245--1253.Google ScholarGoogle ScholarCross RefCross Ref
  24. Hong Lu, Gokul T. Chittaranjan Mashfiqui Rabbi, Denise Frauendorfer, Marianne Schmid Mast, Andrew T. Campbell, Daniel Gatica-Perez, and Tanzeem Choudhury. 2012. StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones. In UbiComp’12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hong Lu, Wei Pan, Nicholas D. Lane, Tanzeem Choudhury, and Andrew T. Campbell. 2009. SoundSense: Scalable Sound Sensing for People-centric Applications on Mobile Phones. In MobiSys’09. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Prasanta Chandra Mahalanobis. 1936. On the generalized distance in statistics. Proceedings of the National Institute of Sciences (Calcutta) 2 (1936), 49--55.Google ScholarGoogle Scholar
  27. Abhinav Mehrotra, Robert Hendley, and Mirco Musolesi. 2016. PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management. In Proceedings of UbiComp’16. Heidelberg, Germany. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing Content-driven Intelligent Notification Mechanisms for Mobile Applications. In UbiComp’15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My Phone and Me: Understanding User’s Receptivity to Mobile Notifications. In CHI’16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Abhinav Mehrotra, Jo Vermeulen, Veljko Pejovic, and Mirco Musolesi. 2015. Ask, But Don’t Interrupt: The Case for Interruptibility-Aware Mobile Experience Sampling. In UbiComp’15 Adjunct. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Kewei Ming and Paul R Rosenbaum. 2001. A note on optimal matching with variable controls using the assignment algorithm. Journal of Computational and Graphical Statistics 10, 3 (2001), 455--463.Google ScholarGoogle ScholarCross RefCross Ref
  32. Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: designing intelligent prompting mechanisms for pervasive applications. In UbiComp’14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Richard E Petty, Joseph R Priester, and Duane T Wegener. 1994. Cognitive processes in attitude change. Handbook of Social Cognition 2 (1994), 69--142.Google ScholarGoogle Scholar
  34. Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver. 2015. When attention is not scarce-detecting boredom from mobile phone usage. In UbiComp’15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Karl H Pribram and Diane McGuinness. 1975. Arousal, activation, and effort in the control of attention. Psychological Review 82, 2 (1975), 116--149.Google ScholarGoogle ScholarCross RefCross Ref
  36. Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and In-situ Assessment of Mental and Physical Well-being using Mobile Sensors. In UbiComp’11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Kiran K. Rachuri, Mirco Musolesi, Cecilia Mascolo, Jason Rentfrow, Chris Longworth, and Andrius Aucinas. 2010. EmotionSense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research. In UbiComp’10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. JA Ressel. 1980. A circumplex model of affect. Journal of Personality and Social Psychology 39 (1980), 1161--78.Google ScholarGoogle ScholarCross RefCross Ref
  39. Donald B Rubin. 1973. Matching to remove bias in observational studies. Biometrics 29, 1 (1973), 159--183.Google ScholarGoogle ScholarCross RefCross Ref
  40. Donald B Rubin. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 5 (1974), 688--701.Google ScholarGoogle ScholarCross RefCross Ref
  41. Adam Sadilek and John Krumm. 2012. Far Out: Predicting Long-Term Human Mobility. In AAAI’12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Antti Salovaara, Antti Lindqvist, Tero Hasu, and Jonna Häkkilä. 2011. The phone rings but the user doesn’t answer: unavailability in mobile communication. In MobileHCI’11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Ulrich Schimmack and Reisenzein Rainer. 2002. Experiencing activation: energetic arousal and tense arousal are not mixtures of valence and activation. Emotion 2, 4 (2002), 412--417.Google ScholarGoogle ScholarCross RefCross Ref
  44. Martin EP Seligman. 2004. Can happiness be taught? Daedalus 133, 2 (2004), 80--87.Google ScholarGoogle ScholarCross RefCross Ref
  45. Hans Selye. 1956. The stress of life. McGraw-Hill.Google ScholarGoogle Scholar
  46. Sandra Servia-Rodríguez, Kiran K Rachuri, Cecilia Mascolo, Peter J Rentfrow, Neal Lathia, and Gillian M Sandstrom. 2017. Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study. In WWW’17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. William R Shadish, Thomas D Cook, and Donald Thomas Campbell. 2002. Experimental and quasi-experimental designs for generalized causal inference. Houghton, Mifflin and Company.Google ScholarGoogle Scholar
  48. Choonsung Shin, Jin-Hyuk Hong, and Anind K Dey. 2012. Understanding and prediction of mobile application usage for smart phones. In UbiComp’12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Vijay Srinivasan, Saeed Moghaddam, Abhishek Mukherji, Kiran K Rachuri, Chenren Xu, and Emmanuel Munguia Tapia. 2014. MobileMiner: Mining your frequent patterns on your phone. In UbiComp’14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Elizabeth A Stuart. 2010. Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics 25, 1 (2010), 1--21.Google ScholarGoogle Scholar
  51. Robert E Thayer. 1989. The biopsychology of mood and arousal. Oxford University Press.Google ScholarGoogle Scholar
  52. Arvind Thiagarajan, Lenin Ravindranath, Katrina LaCurts, Samuel Madden, Hari Balakrishnan, Sivan Toledo, and Jakob Eriksson. 2009. VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones. In SenSys’09. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Dorthe Kirkegaard Thomsen, Mimi Yung Mehlsen, Søren Christensen, and Robert Zachariae. 2003. Rumination--relationship with negative mood and sleep quality. Personality and Individual Differences 34, 7 (2003), 1293--1301.Google ScholarGoogle ScholarCross RefCross Ref
  54. Fani Tsapeli and Mirco Musolesi. 2015. Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach. EPJ Data Science 4, 1 (2015), 1--15.Google ScholarGoogle ScholarCross RefCross Ref
  55. Fani Tsapeli, Mirco Musolesi, and Peter Tino. 2017. Model-free Causality Detection: An Application to Social Media and Financial Data. Physica A 183, 1 (2017), 139--155.Google ScholarGoogle ScholarCross RefCross Ref
  56. Tianyu Wang, Giuseppe Cardone, Antonio Corradi, Lorenzo Torresani, and Andrew T Campbell. 2012. WalkSafe: A Pedestrian Safety App for Mobile Phone Users Who Walk and Talk While Crossing Roads. In HotMobile’12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. Fast app launching for mobile devices using predictive user context. In MobiSys’12. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. MyTraces: Investigating Correlation and Causation between Users’ Emotional States and Mobile Phone Interaction

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
          Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 3
          September 2017
          2023 pages
          EISSN:2474-9567
          DOI:10.1145/3139486
          Issue’s Table of Contents

          Copyright © 2017 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 11 September 2017
          • Accepted: 1 July 2017
          • Revised: 1 May 2017
          • Received: 1 February 2017
          Published in imwut Volume 1, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader