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The Experience Sampling Method on Mobile Devices

Published:06 December 2017Publication History
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Abstract

The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intra-psychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.

References

  1. F. B. Abdesslem, I. Parris, and T. Henderson. 2010. Mobile experience sampling: Reaching the parts of facebook other methods cannot reach. In Proceedings of the Privacy and Usability Methods Pow-wow.Google ScholarGoogle Scholar
  2. G. D. Abowd, G. R. Hayes, G. Iachello, J. A. Kientz, S. N. Patel, M. M. Stevens, and K. N. Truong. 2005. Prototypes and paratypes: Designing mobile and ubiquitous computing applications. IEEE Pervas. Computing, 4, 4, 67--73. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. S. Adams. 1963. Toward an understanding of inequity. J. Abnorm. Social Psychol. 67, 5, 422--436.Google ScholarGoogle ScholarCross RefCross Ref
  4. P. Adams, M. Rabbi, T. Rahman, M. Matthews, A. Voida, G. Gay, T. Choudhury, and S. Voida. 2014. Towards personal stress informatics: Comparing minimally invasive techniques for measuring daily stress in the wild. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare, ICST, 72--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Alcañiz, A. Rodríguez, B. Rey, and E. Parra. 2014. Using serious games to train adaptive emotional regulation strategies. In Proceedings of the International Conference on Social Computing and Social Media, Springer International Publishing, 541--549. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Anthony, T. Henderson, and D. Kotz. 2007. Privacy in location-aware computing environments. IEEE Pervas. Computing, 6, 4, 64--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. K. Ara, N. Sato, S. Tsuji, Y. Wakisaka, N. Ohkubo, Y. Horry, N. Moriwaki, K. Yano, and M. Hayakawa. 2009. Predicting flow state in daily work through continuous sensing of motion rhythm. In Proceedings of the International Conference on Networked Sensing Systems, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. O. Asan and E. Montague. 2014. Using video-based observation research methods in primary care health encounters to evaluate complex interactions. Informat. Primary Care, 21, 4, 161--170.Google ScholarGoogle Scholar
  9. M. Ashour, K. Bekiroglu, C. H. Yang, C. Lagoa, D. Conroy, J. Smyth, and S. Lanza. 2016. On the mathematical modeling of the effect of treatment on human physical activity. In Proceedings of the IEEE Conference on Control Applications (CCA), 1084--1091.Google ScholarGoogle Scholar
  10. Y. Ayzenberg and R. W. Picard. 2014. FEEL: A system for frequent event and electrodermal activity labeling. IEEE J. Biomed. Health Informat. 18, 1, 266--277.Google ScholarGoogle ScholarCross RefCross Ref
  11. S. Barclay, C. Todd, I. Finlay, G. Grande, and P. Wyatt. 2002. Not another questionnaire! Maximizing the response rate, predicting non-response and assessing non-response bias in postal questionnaire studies of GPs. Family Pract. 19 1, 105--111.Google ScholarGoogle Scholar
  12. L. F. Barrett and D. J. Barrett. 2001. An introduction to computerized experience sampling in psychology. Soc. Sci. Comput. Rev. 19, 2, 175--185. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W. Barta, H. Tennen, and M. Litt. 2012. Measurement reactivity in diary research. In Handbook of Research Methods for Studying Daily Life, M. R. Mehl, and T. S. Conner (Eds.). Guilford Press, New York.Google ScholarGoogle Scholar
  14. P. Baudisch and G. Chu. 2009. Back-of-device interaction allows creating very small touch devices. In Proceedings of the Conference on Human Factors in Computing Systems, ACM, 1923--1932. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Ben-Zeev, E. A. Scherer, R. Wang, H. Xie, and A. T. Campbell. 2015. Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. Psychiatric Rehab. J., 38, 3, 218--226.Google ScholarGoogle ScholarCross RefCross Ref
  16. G. E. Bevans. 1913. How Workingmen Spend Their time, Columbia University Press (1913).Google ScholarGoogle Scholar
  17. N. Bolger and J.-P. P. Laurenceau. 2013. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research, Guilford Press (2013).Google ScholarGoogle Scholar
  18. J. E. Broderick, J. E. Schwartz, S. Shiffman, M. R. Hufford, and A. A. Stone. 2003. Signaling does not adequately improve diary compliance. Ann. Behav. Med. 26, 2, 139--148.Google ScholarGoogle ScholarCross RefCross Ref
  19. C. J. Burgin, P. J. Silvia, K. M. Eddington, and T. R. Kwapil. 2013. Palm or cell? Comparing personal digital assistants and cell phones for experience sampling research. Soc. Sci. Comput. Rev., 31, 2, 244--251. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. D. Buschek, F. Hartmann, E. V. Zezschwitz, A. D. Luca, and F. Alt. 2016. SnapApp: Reducing authentication overhead with a time-constrained fast unlock option. In Proceedings of the CHI Conference on Human Factors in Computing Systems, ACM, 3736--3747. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. K. Caine. 2016. Local standards for sample size at CHI. In Proceedings of the CHI Conference on Human Factors in Computing Systems, ACM, 981--992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Carter and J. Mankoff. 2005. When participants do the capturing: The role of media in diary studies. In Proceedings of the Conference on Human Factors in Computing Systems, ACM, 899--908. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Y.-J. J. Chang, G. Paruthi, and M. W. Newman. 2015. A field study comparing approaches to collecting annotated activity data in real-world settings. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 671--682. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. K. Church, M. Cherubini, and N. Oliver. 2014. A large-scale study of daily information needs captured in situ. ACM Trans. Comput.-Hum. Interact. 21, 2, 1--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. Church and R. de Oliveira. 2013. What's up with whatsapp?: Comparing mobile instant messaging behaviors with traditional SMS. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, 352--361. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. K. Church, D. Ferreira, N. Banovic, and K. Lyons. 2015. Understanding the challenges of mobile phone usage data. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services, 505--514. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Ciman and K. Wac. 2016. Individuals' stress assessment using human-smartphone interaction analysis. IEEE Trans. Affect. Comput.Google ScholarGoogle Scholar
  28. L. M. Collins and J. W. Graham. 2002. The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug Alcoh. Depend. 68, 85--96.Google ScholarGoogle ScholarCross RefCross Ref
  29. T. Conner Christensen, L. Feldman Barrett, E. Bliss-Moreau, K. Lebo, and C. Kaschub. 2003. A practical guide to experience-sampling procedures. J. Happiness Stud. 4, 1, 53--78.Google ScholarGoogle ScholarCross RefCross Ref
  30. T. Conner. 2015. Experience sampling and ecological momentary assessment with mobile phones. Retrieved 20 February 2017 from http://www.otago.ac.nz/psychology/otago047475.pdf.Google ScholarGoogle Scholar
  31. S. Consolvo, I. E. Smith, T. Matthews, A. LaMarca, J. Tabert, and P. Powledge. 2005. Location disclosure to social relations: Why, when, 8 what people want to share. In Proceedings of the Conference on Human Factors in Computing Systems, ACM, 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. S. Consolvo and M. Walker. 2003. Using the experience sampling method to evaluate Ubicomp applications. IEEE Pervas. Comput. 2, 2, 24--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. P. M. Costa, J. Pitt, T. Galvão, and J. F. e. Cunha. 2013. Assessing contextual mood in public transport: A pilot study. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices and Services, ACM, 498--503. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. L. Cowan, W. G. Griswold, L. Barkhuus, and J. D. Hollan. 2010. Engaging the periphery for visual communication on mobile phones. In Proceedings of the Hawaii International Conference on System Sciences, 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. M. Csikszentmihalyi, R. Larson, and S. Prescott. 1977. The ecology of adolescent activity and experience. J. Youth Adoles. 6, 3, 281--294.Google ScholarGoogle ScholarCross RefCross Ref
  36. E. Cutrell, M. Czerwinski, and E. Horvitz. 2001. Notification, disruption, and memory: Effects of messaging interruptions on memory and performance. In Proceedings of the Human-Computer Interaction -- INTERACT, 263--269.Google ScholarGoogle Scholar
  37. A. K. Dey. 2001. Understanding and using context. Pers. Ubiq. Comput. 5, 1, 4--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. T. Dingler and M. Pielot. 2015. I'll be there for you: Quantifying attentiveness towards mobile messaging. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 1--5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. U. W. Ebner-Priemer and T. J. Trull. 2009. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol. Assess. 21, 4, 463--475.Google ScholarGoogle ScholarCross RefCross Ref
  40. R. M. Ellingson and B. Oken. 2011. Ambulatory physiologic monitoring system supporting EMA with self-administered visual evoked potential recording at randomized intervals. In Proceedings of the International Instrumentation and Measurement Technology Conference. IEEE, 1--4.Google ScholarGoogle Scholar
  41. A. Faiola and P. Srinivas. 2014. Extreme mediation: Observing mental and physical health in everyday life. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, 47--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. D. Ferreira, J. Goncalves, V. Kostakos, L. Barkhuus, and A. K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 91--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. D. Ferreira, V. Kostakos, A. R. Beresford, J. Lindqvist, and A. K. Dey. 2015a. Securacy: An empirical investigation of android applications' network usage, privacy and security. In Proceedings of the Conference on Security and Privacy in Wireless and Mobile Networks. ACM, 1--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. D. Ferreira, V. Kostakos, and A. K. Dey. 2015b. AWARE: Mobile context instrumentation framework. Frontiers in ICT, 2, 6, 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  45. J. E. Fischer and S. Benford. 2009. Inferring player engagement in a pervasive experience. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 1903--1906. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. J. E. Fischer, C. Greenhalgh, and S. Benford. 2011. Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 181--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. J. E. Fischer, N. Yee, V. Bellotti, N. Good, S. Benford, and C. Greenhalgh. 2010. Effects of content and time of delivery on receptivity to mobile interruptions. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 103--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. R. Fisher and R. Simmons. 2011. Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning. In Proceedings of the International Conference on Machine Learning and Applications and Workshops. 436--441. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. R. R. Fletcher, S. Tam, O. Omojola, R. Redemske, and J. Kwan. 2011. Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD. In Proceedings of the Engineering in Medicine and Biology Society. IEEE, 1802--1805.Google ScholarGoogle Scholar
  50. J. Froehlich, M. Y. Chen, S. Consolvo, B. Harrison, and J. A. Landay. 2007. MyExperience: A system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of the International Conference on Mobile Systems, Applications and Services. ACM, 57--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. J. Froehlich, M. Y. Chen, I. E. Smith, and F. Potter. 2006. Voting with your feet: An investigative study of the relationship between place visit behavior and preference, In Proceedings of UbiComp 2006: Ubiquitous Computing, P. Dourish, and A. Friday (Eds.). Springer, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. A. Gaggioli, G. Pioggia, G. Tartarisco, G. Baldus, D. Corda, P. Cipresso, and G. Riva. 2013. A mobile data collection platform for mental health research. Pers. Ubiq. Computing, 17, 2, 241--251. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. S. Ghosh, V. Chauhan, N. Ganguly, B. Mitra, and P. De. 2015. Impact of experience sampling methods on tap pattern based emotion recognition. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the International Symposium on Wearable Computers (Adjunct). ACM, 713--722. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. P. Gomes, M. Kaiseler, C. Queirós, M. Oliveira, B. Lopes, and M. Coimbra. 2012. Vital analysis: Annotating sensed physiological signals with the stress levels of first responders in action. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6695--6698.Google ScholarGoogle Scholar
  55. A. L. Gonzales. 2014. Text-based communication influences self-esteem more than face-to-face or cellphone communication. Comput. Human Behavior, 39, 197--203. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. R. Gouveia and E. Karapanos. 2013. Footprint tracker: Supporting diary studies with lifelogging. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 2921--2930. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. S. Grandhi and Q. Jones. 2010. Technology-mediated interruption management. Int. J. Hum.-Comput. Stud. 68, 5. 288--306. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. S. A. Grandhi and Q. Jones. 2015. Knock, knock! Who's there? Putting the user in control of managing interruptions. Int. J. Hum.-Comput. Stud. 79, 35--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. GSMA. 2016. Mobile Economy 2016. Retrieved 20 February 2017 from http://gsmamobileeconomy.com/global/.Google ScholarGoogle Scholar
  60. S. Guha and S. B. Wicker. 2015. Spatial subterfuge: An experience sampling study to predict deceptive location disclosures. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1131--1135. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. M. Gustarini, K. Wac, and A. K. Dey. 2016. Anonymous smartphone data collection: Factors influencing the users' acceptance in mobile crowd sensing. Pers. Ubiq. Comput. 20, 1, 65--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. J. P. Haas and E. L. Larson. 2007. Measurement of compliance with hand hygiene. J. Hosp. Inf. 66, 1, 6--14.Google ScholarGoogle ScholarCross RefCross Ref
  63. A. A. Haedt-Matt and P. K. Keel. 2011. Revisiting the affect regulation model of binge eating: A meta-analysis of studies using ecological momentary assessment. Psychol. Bull. 137, 4, 660--681.Google ScholarGoogle ScholarCross RefCross Ref
  64. M. Harbach, E. von Zezschwitz, A. Fichtner, A. De Luca, and M. Smith. 2014. It's a hard lock life: A field study of smartphone (un)locking behavior and risk perception. In Proceedings of the Symposium on Usable Privacy and Security. 213--230.Google ScholarGoogle Scholar
  65. D. H. Hareva, K. Tomoki, O. Hisao, N. Takao, O. Hiroki, and K. Hiromi. 2007. Development of real-time biological data collection system using a cellular phone. In Proceedings of the SICE Annual Conference 2007. 316--321.Google ScholarGoogle Scholar
  66. S. S. Hasan, R. Brummet, O. Chipara, Y. H. Wu, and T. Yang. 2015. In-situ measurement and prediction of hearing aid outcomes using mobile phones. In Proceedings of the International Conference on Healthcare Informatics. 525--534. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. S. S. Hasan, O. Chipara, Y.-H. Wu, and N. Aksan. 2014. Evaluating auditory contexts and their impacts on hearing aid outcomes with mobile phones. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare. ICST, 126--133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. S. S. Hasan, F. Lai, O. Chipara, and Y.-H. Wu. 2013. Audiosense: Enabling real-time evaluation of hearing aid technology in-situ. In Proceedings of the IEEE International Symposium on Computer-Based Medical Systems. 167--172.Google ScholarGoogle ScholarCross RefCross Ref
  69. J. M. Hektner, J. A. Schmidt, and M. Csikszentmihalyi. 2007. Experience Sampling Method: Measuring the Quality of Everyday Life. Sage (2007).Google ScholarGoogle Scholar
  70. J. Hernandez, D. McDuff, C. Infante, P. Maes, K. Quigley, and R. Picard. 2016. Wearable ESM: Differences in the experience sampling method across wearable devices. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 195--205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. K. E. Heron and J. M. Smyth. 2010. Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behavior treatments. Brit. J. Health Psychol. 15, 1, 1--39.Google ScholarGoogle ScholarCross RefCross Ref
  72. S. E. Hormuth. 1986. The sampling of experiences in situ. J. Pers.Google ScholarGoogle Scholar
  73. G. Hsieh, I. Li, A. Dey, J. Forlizzi, and S. E. Hudson. 2008. Using visualizations to increase compliance in experience sampling. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 164--167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. S. Ickin, K. Wac, and M. Fiedler. 2013. QoE-based energy reduction by controlling the 3G cellular data traffic on the smartphone. In Proceedings of the ITC Specialist Seminar on Energy Efficient and Green Networking. 13--18.Google ScholarGoogle Scholar
  75. S. Ickin, K. Wac, M. Fiedler, L. Janowski, J.-H. H. Hong, and A. K. Dey. 2012. Factors influencing quality of experience of commonly used mobile applications. IEEE Communications Magazine, 50, 4, 48--56.Google ScholarGoogle ScholarCross RefCross Ref
  76. M. Iida, P. E. Shrout, J.-P. P. Laurenceau, and N. Bolger. 2012. Using diary methods in psychological research. APA PsycNET.Google ScholarGoogle Scholar
  77. S. Intille, C. Haynes, D. Maniar, A. Ponnada, and J. Manjourides. 2016. μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1124--1128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. S. S. Intille, E. M. Tapia, J. Rondoni, J. Beaudin, C. Kukla, S. Agarwal, L. Bao, and K. Larson. 2003. Tools for studying behavior and technology in natural settings, in ubicomp 2003: ubiquitous computing. UbiComp 2003. Lecture Notes in Computer Science, vol. 2864, A. K. Dey, A. Schmidt, and J. F. McCarthy (Eds.). Springer, Berlin, Heidelberg.Google ScholarGoogle Scholar
  79. E. Isaacs, A. Konrad, A. Walendowski, T. Lennig, V. Hollis, and S. Whittaker. 2013. Echoes from the past: How technology mediated reflection improves well-being. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 1071--1080. Google ScholarGoogle ScholarDigital LibraryDigital Library
  80. L. A. Jelenchick, J. C. Eickhoff, and M. A. Moreno. 2013. “Facebook depression?” Social networking site use and depression in older adolescents. J. Adoles. Health. 52, 1, 128--130.Google ScholarGoogle ScholarCross RefCross Ref
  81. S. K. Johansen and A. M. Kanstrup. 2016. Expanding the locus of control: Design of a mobile quantified self-tracking application for whiplash patients. In Proceedings of the Nordic Conference on Human-Computer Interaction, ACM, 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. D. Kahneman, A. B. Krueger, D. A. Schkade, N. Schwarz, and A. A. Stone. 2004. A survey method for characterizing daily life: The day reconstruction method. Science (New York, N.Y.), 306 (5702), 1776--1780.Google ScholarGoogle Scholar
  83. C. Karr. 2015. Purple robot. Retrieved 20 February 2017 from http://tech.cbits.northwestern.edu/purple-robot/.Google ScholarGoogle Scholar
  84. M. Kay, G. L. Nelson, and E. B. Hekler. 2016. Researcher-centered design of statistics: Why bayesian statistics better fit the culture and incentives of HCI. In Proceedings of the Conference on Human Factors in Computing Systems, ACM, 4521--4532. Google ScholarGoogle ScholarDigital LibraryDigital Library
  85. I. Ketykó, K. D. Moor, W. Joseph, L. Martens, and L. D. Marez. 2010. Performing QoE-measurements in an actual 3G network. In Proceedings of the International Symposium on Broadband Multimedia Systems and Broadcasting. IEEE, 1--6.Google ScholarGoogle Scholar
  86. A. Khalil and K. Connelly. 2006. Context-aware telephony: Privacy preferences and sharing patterns. In Proceedings of the Conference on Computer Supported Cooperative Work. ACM, 469--478. Google ScholarGoogle ScholarDigital LibraryDigital Library
  87. V.-J. J. Khan, P. Markopoulos, B. Eggen, W. IJsselsteijn, and B. de Ruyter. 2008. Reconexp: A way to reduce the data loss of the experiencing sampling method. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 471--476. Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. V. J. Khan, P. Markopoulos, and B. Eggen. 2009. An experience sampling study into awareness needs of busy families. In Proceedings of the Conference on Human System Interactions. 338--343. Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. J. Kim, T. Nakamura, H. Kikuchi, and Y. Yamamoto. 2015a. Psychobehavioral validity of self-reported symptoms based on spontaneous physical activity. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4021--4024.Google ScholarGoogle Scholar
  90. J. Kim, T. Nakamura, H. Kikuchi, K. Yoshiuchi, T. Sasaki, and Y. Yamamoto. 2015b. Covariation of depressive mood and spontaneous physical activity in major depressive disorder: Toward continuous monitoring of depressive mood. IEEE J. Biomed. Health Informat. 19, 4, 1347--1355.Google ScholarGoogle ScholarCross RefCross Ref
  91. J. Kim, J. J. Tran, T. W. Johnson, R. Ladner, E. Riskin, and J. O. Wobbrock. 2011. Effect of mobileasl on communication among deaf users. In Proceedings of the Conference on Human Factors in Computing Systems (Extended Abstracts), ACM, 2185--2190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  92. P. Klasnja, B. L. Harrison, L. LeGrand, A. LaMarca, J. Froehlich, and S. E. Hudson. 2008. Using wearable sensors and real time inference to understand human recall of routine activities. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 154--163. Google ScholarGoogle ScholarDigital LibraryDigital Library
  93. C. Korunka, R. Prem, and B. Kubicek. 2012. Diary studies as a macro-ergonomic evaluation tool: Development of a shift diary and its application in ergonomic evaluations. In Proceedings of the Southeast Asian Network of Ergonomics Societies Conference. 1--6.Google ScholarGoogle Scholar
  94. S. Kvale. Doing Interviews, SAGE (2007).Google ScholarGoogle Scholar
  95. R. Larson and M. Csikszentmihalyi. 1983. The experience sampling method, In Flow and the Foundations of Positive Psychology, M. Csikszentmihalyi (Eds.). Wiley Jossey-Bass.Google ScholarGoogle Scholar
  96. N. Lathia, K. K. Rachuri, C. Mascolo, and P. J. Rentfrow. 2013. Contextual dissonance: Design bias in sensor-based experience sampling methods. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 183--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. J. A. Lee, C. Efstratiou, and L. Bai. 2016. OSN mood tracking: Exploring the use of online social network activity as an indicator of mood changes. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (Adjunct). ACM, 1171--1179. Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. S. Lee, J. Seo, and G. Lee. 2010. An adaptive speed-call list algorithm and its evaluation with ESM. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 2019--2022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  99. B. Lepri, J. Staiano, G. Rigato, K. Kalimeri, A. Finnerty, F. Pianesi, N. Sebe, and A. Pentland. 2012. The sociometric badges corpus: A multilevel behavioral dataset for social behavior in complex organizations. In Proceedings of the International Conference on Privacy, Security, Risk and Trust and International Conference on Social Computing. 623--628. Google ScholarGoogle ScholarDigital LibraryDigital Library
  100. G. Liang, J. Cao, and W. Zhu. 2013. CircleSense: A pervasive computing system for recognizing social activities. In Proceedings of the International Conference on Pervasive Computing and Communications. IEEE, 201--206.Google ScholarGoogle Scholar
  101. M. Linnap and A. Rice. 2014. The effectiveness of centralised management for reducing wasted effort in participatory sensing. In Proceedings of the International Conference on Pervasive Computing and Communication (Adjunct). IEEE, 68--73.Google ScholarGoogle Scholar
  102. Y. Liu, J. Goncalves, D. Ferreira, B. Xiao, S. Hosio, and V. Kostakos. 2014a. CHI 1994-2013: Mapping two decades of intellectual progress through co-word analysis. In Proceedings of the Conference on Human Factors in Computing Systems. 3553--3562. Google ScholarGoogle ScholarDigital LibraryDigital Library
  103. Z. Liu, J. Shan, R. Bonazzi, and Y. Pigneur. 2014b. Privacy as a tradeoff: Introducing the notion of privacy calculus for context-aware mobile applications. In Proceedings of the Hawaii International Conference on System Sciences. 1063--1072. Google ScholarGoogle ScholarDigital LibraryDigital Library
  104. V. López, L. Ahumada, S. Galdames, and R. Madrid. 2012. School principals at their lonely work: Recording workday practices through ESM logs. Computers 8 Education, 58, 1. 413--422. Google ScholarGoogle ScholarDigital LibraryDigital Library
  105. T. Lovett and E. O'Neill. 2012. Mobile Context Awareness, Springer (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  106. P. Lynn. 2001. The impact of incentives on response rates to personal interview surveys: Role and perceptions of interviewers. Int. J. Pub. Opin. Res. 13, 3, 326--336.Google ScholarGoogle ScholarCross RefCross Ref
  107. T. Maekawa, N. Yamashita, and Y. Sakurai. 2016. How well can a user's location privacy preferences be determined without using GPS location data? IEEE Trans. Emerg. Topics Comput.Google ScholarGoogle Scholar
  108. C. Mancini, K. Thomas, Y. Rogers, B. A. Price, L. Jedrzejczyk, A. K. Bandara, A. N. Joinson, and B. Nuseibeh. 2009. From spaces to places: Emerging contexts in mobile privacy. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  109. P. Markopoulos, N. Batalas, and A. Timmermans. 2015. On the use of personalization to enhance compliance in experience sampling. In Proceedings of the European Conference on Cognitive Ergonomics. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  110. A. Maxhuni, A. Matic, V. Osmani, and O. M. Ibarra. 2011. Correlation between self-reported mood states and objectively measured social interactions at work: A pilot study. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare (Adjunct). 308-311.Google ScholarGoogle Scholar
  111. J. M. Mayer, S. R. Hiltz, L. Barkhuus, K. Väänänen, and Q. Jones. 2016. Supporting opportunities for context-aware xocial matching: An experience sampling study. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 2430--2441. Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. K. O. McCabe, L. Mack, and W. Fleeson. 2011. A guide for data cleaning in experience sampling studies, In Handbook of Research Methods for Studying Daily Life, M. R. Mehl, and T. S. Conner (Eds.). Guilford Press, New York.Google ScholarGoogle Scholar
  113. A. Mehrotra, M. Musolesi, R. Hendley, and V. Pejovic. 2015. Designing content-driven intelligent notification mechanisms for mobile applications. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 813--824. Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. A. Mehrotra, V. Pejovic, J. Vermeulen, R. Hendley, and M. Musolesi. 2016. My phone and me: Understanding people's receptivity to mobile notifications. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 1021--1032. Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. A. Meschtscherjakov, A. Weiss, and T. Scherndl. 2009. Utilizing emoticons on mobile devices within ESM studies to measure emotions in the field. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services (Adjunct).Google ScholarGoogle Scholar
  116. K. Mihalic and M. Tscheligi. 2007. ‘Divert: Mother-in-law': Representing and evaluating social context on mobile devices. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 257--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  117. T. R. Mitchell, L. Thompson, E. Peterson, and R. Cronk. 1997. Temporal adjustments in the evaluation of events: The “rosy view”. J. Experim. Soc. Psychol. 33, 4, 421--448.Google ScholarGoogle ScholarCross RefCross Ref
  118. T. Miu, P. Missier, and T. Plötz. 2015. Bootstrapping personalised human activity recognition models using online active learning. In Proceedings of the Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, 1138--1147.Google ScholarGoogle Scholar
  119. M. A. Moreno, L. Jelenchick, R. Koff, J. Eikoff, C. Diermyer, and D. A. Christakis. 2012. Internet use and multitasking among older adolescents: An experience sampling approach. Comput. Hum. Behav. 28, 4, 1097--1102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  120. S. Motahari, S. Ziavras, and Q. Jones. 2009a. Preventing unwanted social inferences with classification tree analysis. In Proceedings of the International Conference on Tools with Artificial Intelligence, IEEE, 500--507. Google ScholarGoogle ScholarDigital LibraryDigital Library
  121. S. Motahari, S. Ziavras, M. Naaman, M. Ismail, and Q. Jones. 2009b. Social inference risk modeling in mobile and social applications. In Proceedings of the International Conference on Computational Science and Engineering, 125--132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  122. S. T. Moturu, I. Khayal, N. Aharony, W. Pan, and A. Pentland. 2011a. Sleep, mood and sociability in a healthy population. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 5267--5270.Google ScholarGoogle Scholar
  123. S. T. Moturu, I. Khayal, N. Aharony, W. Pan, and A. Pentland. 2011b. Using social sensing to understand the links between sleep, mood, and sociability. In Proceedings of the International Conference on Privacy, Security, Risk and Trust and International Conference on Social Computing. IEEE, 208--214.Google ScholarGoogle Scholar
  124. H. Muukkonen, K. Hakkarainen, M. Inkinen, K. Lonka, and K. Salmela-Aro. 2008. CASS-methods and tools for investigating higher education knowledge practices. In Proceedings of the International Conference on International Conference for the Learning Sciences. International Society of the Learning Sciences, 107--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  125. I. Myin-Germeys, M. Oorschot, D. Collip, J. Lataster, P. Delespaul, and J. van Os. 2009. Experience sampling research in psychopathology: Opening the black box of daily life. Psychol. Med. 39, 9, 1533--1547.Google ScholarGoogle ScholarCross RefCross Ref
  126. T. Nguyen, S. Gupta, S. Venkatesh, and D. Phung. 2014. A Bayesian nonparametric framework for activity recognition using accelerometer data. In Proceedings of the International Conference on Pattern Recognition. 2017--2022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  127. T. Nguyen, S. Gupta, S. Venkatesh, and D. Phung. 2016. Nonparametric discovery of movement patterns from accelerometer signals. Patt. Recognition Lett. 70, 52--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  128. E. Niforatos and E. Karapanos. 2014. EmoSnaps: A mobile application for emotion recall from facial expressions. Pers. Ubiq. Comput. 19, 2, 425--444. Google ScholarGoogle ScholarDigital LibraryDigital Library
  129. PACO. 2016. PACO - The personal analytics companion. Retrieved 20 February 2017 from https://www.pacoapp.com/.Google ScholarGoogle Scholar
  130. W. K. Park. 2005. Mobile Phone Addiction, in Mobile Communications. R. Ling, and P. E. Pedersen (Eds.). Springer, London.Google ScholarGoogle Scholar
  131. J. Pärkkä, J. Merilahti, E. M. Mattila, E. Malm, K. Antila, M. T. Tuomisto, A. V. Saarinen, M. V. Gils, and I. Korhonen. 2009. Relationship of psychological and physiological variables in long-term self-monitored data during work ability rehabilitation program. IEEE Trans. Inf. Tech. Biomed., 13, 2, 141--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  132. S. Patil, R. Hoyle, R. Schlegel, A. Kapadia, and A. J. Lee. 2015. Interrupt now or inform later?: Comparing immediate and delayed privacy feedback. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 1415--1418. Google ScholarGoogle ScholarDigital LibraryDigital Library
  133. S. Patil, R. Schlegel, A. Kapadia, and A. J. Lee. 2014. Reflection or action?: How feedback and control affect location sharing decisions. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 101--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  134. V. Pejovic, N. Lathia, C. Mascolo, and M. Musolesi. 2016. Mobile-based experience sampling for behaviour research, In Emotions and Personality in Personalized Services: Models, Evaluation and Applications, M. Tkalčič, B. De Carolis, M. de Gemmis, A. Odić, and A. Košir (Eds.). Springer International Publishing, Cham.Google ScholarGoogle Scholar
  135. V. Pejovic and M. Musolesi. 2014. InterruptMe: Designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 897--908. Google ScholarGoogle ScholarDigital LibraryDigital Library
  136. E. Pergler, R. Hable, E. Rico-Schmidt, C. Kittl, and R. Schamberger. 2014. A context-sensitive tool to support mobile technology acceptance research. In Proceedings of the Hawaii International Conference on System Sciences. 1015--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  137. T. D. Pessemier, K. D. Moor, A. Juan, W. Joseph, L. D. Marez, and L. Martens. 2011. Quantifying QoE of mobile video consumption in a real-life setting drawing on objective and subjective parameters. In Proceedings of the International Symposium on Broadband Multimedia Systems and Broadcasting. IEEE, 1--6.Google ScholarGoogle Scholar
  138. M. Pielot, K. Church, and R. de Oliveira. 2014. An in-situ study of mobile phone notifications. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices 8 Services. ACM, 233--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  139. M. Pielot, T. Dingler, J. S. Pedro, and N. Oliver. 2015. When attention is not scarce - detecting boredom from mobile phone usage. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 825--836. Google ScholarGoogle ScholarDigital LibraryDigital Library
  140. PsyMate. 2016. PsyMate {EN}. Retrieved 20 February 2017 from http://www.psymate.eu/psymate--en-.html.Google ScholarGoogle Scholar
  141. M. Raento, A. Oulasvirta, and N. Eagle. 2009. Smartphones: An emerging tool for social scientists. Sociol. Meth. Res. 37, 3, 426--454.Google ScholarGoogle ScholarCross RefCross Ref
  142. W. M. Randall and N. S. Rickard. 2013. Development and trial of a mobile experience sampling method (m-ESM) for personal music listening. Music Perception: An Interdisciplinary Journal. 31, 2, 157--170.Google ScholarGoogle ScholarCross RefCross Ref
  143. H. T. Reis and S. L. Gable. 2000. Event sampling and other methods for studying everyday experience, In Handbook of Research methods in Social and Personality Psychology, H. T. Reis, and C. M. Judd (Eds.). Cambridge University Press, New York, NY, US.Google ScholarGoogle Scholar
  144. S. Reyal, S. Zhai, and P. O. Kristensson. 2015. Performance and user experience of touchscreen and gesture keyboards in a lab setting and in the wild. In Proceedings of the Conference on Human Factors in Computing Systems, ACM, 679--688. Google ScholarGoogle ScholarDigital LibraryDigital Library
  145. A. Rieger, S. Neubert, S. Behrendt, M. Weippert, S. Kreuzfeld, and R. Stoll. 2012. 24-Hour ambulatory monitoring of complex physiological parameters with a wireless health system: Feasibility, user compliance and application. In Proceedings of the International Multi-Conference on Systems, Sygnals 8 Devices, 1--3.Google ScholarGoogle Scholar
  146. S. Rosenthal, A. K. Dey, and M. Veloso. 2011. Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In Proceedings of the International Conference on Pervasive Computing. Springer-Verlag, 170--187. Google ScholarGoogle ScholarDigital LibraryDigital Library
  147. D. Rough and A. Quigley. 2015. Jeeves -- a visual programming environment for mobile experience sampling. In Proceedings of the Visual Languages and Human-Centric Computing (Symposium). IEEE, 121--129.Google ScholarGoogle Scholar
  148. M. Sabatelli, V. Osmani, O. Mayora, A. Gruenerbl, and P. Lukowicz. 2014. Correlation of significant places with self-reported state of bipolar disorder patients. In Proceedings of the International Conference on Wireless Mobile Communication and Healthcare. 116--119.Google ScholarGoogle Scholar
  149. J. B. Sabra, H. J. Andersen, and K. Rodil. 2015. Hybrid cemetery culture: Making death matter in cultural heritage using smart mobile technologies. In Proceedings of the International Conference on Culture and Computing. 167--174. Google ScholarGoogle ScholarDigital LibraryDigital Library
  150. S. Saeb, Z. Mi, M. Kwasny, C. J. Karr, K. Kording, and D. C. Mohr. 2015. The relationship between clinical, momentary, and sensor-based assessment of depression. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare. 229--232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  151. A. Sahami Shirazi, N. Henze, T. Dingler, M. Pielot, D. Weber, and A. Schmidt. 2014. Large-scale assessment of mobile notifications. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 3055--3064. Google ScholarGoogle ScholarDigital LibraryDigital Library
  152. M. C. Sala, K. Partridge, L. Jacobson, and J. B. Begole. 2007. An exploration into activity-informed physical advertising using PEST, In Pervasive Computing. A. LaMarca, M. Langheinrich, and K. N. Truong (Eds.). Springer, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  153. C. E. Schwartz, M. A. G. Sprangers, A. Carey, and G. Reed. 2004. Exploring response shift in longitudinal data. Psychology 8 Health, 19, 1, 51--69.Google ScholarGoogle Scholar
  154. C. N. Scollon, C. Kim-Prieto, and E. Diener. 2003. Experience sampling: promises and pitfalls, strengths and weaknesses. J. Happiness Stud. 4, 1, 5--34.Google ScholarGoogle ScholarCross RefCross Ref
  155. E. Seto, J. Hua, L. Wu, A. Bestick, V. Shia, S. Eom, J. Han, M. Wang, and Y. Li. 2014. The Kunming Calfit study: Modeling dietary behavioral patterns using smartphone data. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 6884--6887.Google ScholarGoogle Scholar
  156. S. Shiffman. 2009. Ecological momentary assessment (EMA) in studies of substance use. Psychol. Assess. 21, 4, 486--497.Google ScholarGoogle ScholarCross RefCross Ref
  157. S. Shiffman, A. A. Stone, and M. R. Hufford. 2008. Ecological momentary assessment. Ann. Rev. Clin. Psychol., 4, 1--32.Google ScholarGoogle ScholarCross RefCross Ref
  158. F. Shih, I. Liccardi, and D. Weitzner. 2015. Privacy tipping points in smartphones privacy preferences. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 807--816. Google ScholarGoogle ScholarDigital LibraryDigital Library
  159. J. Smith, A. Lavygina, J. Ma, A. Russo, and N. Dulay. 2014. Learning to recognise disruptive smartphone notifications. In Proceedings of the International Conference on Human-computer Interaction with Mobile Devices 8 Services. ACM, 121--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  160. J. M. Smyth and K. E. Heron. 2016. Is providing mobile interventions “just-in-time” helpful? An experimental proof of concept study of just-in-time intervention for stress management. In Proceedings of the Wireless Health. IEEE, 1--7.Google ScholarGoogle Scholar
  161. G. Spanakis, G. Weiss, B. Boh, and A. Roefs. 2016. Network analysis of ecological momentary assessment data for monitoring and understanding eating behavior, In Proceedings of the Internationsl Conference on Smart Health (ICSH 2015). (Phoenix, AZ, Nov. 17--18, 2015). Revised Selected Papers, X. Zheng, D. D. Zeng, H. Chen, and S. J. Leischow (Eds.). Springer, Cham. Google ScholarGoogle ScholarDigital LibraryDigital Library
  162. A. Stone, R. Kessler, and J. Haythomthwatte. 1991. Measuring daily events and experiences: Decisions for the researcher. J. Pers. 59, 3, 575--607.Google ScholarGoogle ScholarCross RefCross Ref
  163. A. A. Stone and S. Shiffman. 2002. Capturing momentary, self-report data: A proposal for reporting guidelines. Ann.Behav. Med. 24, 3, 236--243.Google ScholarGoogle ScholarCross RefCross Ref
  164. C. B. B. Taylor, L. Fried, and J. Kenardy. 1990. The use of a real-time computer diary for data acquisition and processing. Behav. Res. Therapy 28, 1, 93--97.Google ScholarGoogle ScholarCross RefCross Ref
  165. N. Tejani, T. R. Dresselhaus, and M. B. Weinger. 2010. Development of a hand-held computer platform for real-time behavioral assessment of physicians and nurses. J. Biomed. Informat. 43, 1, 75--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  166. G. H. ter Hofte. 2007. Xensible interruptions from your mobile phone. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 178--181. Google ScholarGoogle ScholarDigital LibraryDigital Library
  167. S. Teso, J. Staiano, B. Lepri, A. Passerini, and F. Pianesi. 2013. Ego-centric graphlets for personality and affective states recognition. In Proceedings of the International Conference on Social Computing. 874--877. Google ScholarGoogle ScholarDigital LibraryDigital Library
  168. K. Tollmar and C. Huang. 2015. Boosting mobile experience sampling with social media. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 525--530. Google ScholarGoogle ScholarDigital LibraryDigital Library
  169. P. Totterdell and S. Folkard. 1992. In situ repeated measures of affect and cognitive performance facilitated by use of a hand-held computer. Behav. Res. Meth., Instrum., Comput. 24, 4, 545--553.Google ScholarGoogle ScholarCross RefCross Ref
  170. C. C. Tsai, G. Lee, F. Raab, G. J. Norman, T. Sohn, W. G. Griswold, and K. Patrick. 2006. Usability and feasibility of PmEB: A mobile phone application for monitoring real time caloric balance. In Proceedings of the Pervasive Health Conference and Workshops. 1--10.Google ScholarGoogle Scholar
  171. H. Väätäjä and V. Roto. 2010. Mobile questionnaires for user experience evaluation. In Proceedings of the Conference on Human Factors in Computing Systems (Extended Abstracts). ACM, 3361--3366. Google ScholarGoogle ScholarDigital LibraryDigital Library
  172. N. van Berkel, J. Goncalves, S. Hosio, and V. Kostakos. 2017. Gamification of mobile experience sampling improves data quality and quantity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1, 3, 107:1--107:21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  173. N. van Berkel, S. Hosio, T. Durkee, V. Carli, D. Wasserman, and V. Kostakos. 2016a. Providing patient context to mental health professionals using mobile applications. In Proceedings of the CHI workshop on Computing and Mental Health. 1--4.Google ScholarGoogle Scholar
  174. N. van Berkel, C. Luo, T. Anagnostopoulos, D. Ferreira, J. Goncalves, S. Hosio, and V. Kostakos. 2016b. A systematic assessment of smartphone usage gaps. In Proceedings of the Conference on Human Factors in Computing Systems. 4711--4721. Google ScholarGoogle ScholarDigital LibraryDigital Library
  175. N. van Berkel, C. Luo, D. Ferreira, J. Goncalves, and V. Kostakos. 2015. The curse of quantified-self: An endless quest for answers. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (Adjunct). 973--978. Google ScholarGoogle ScholarDigital LibraryDigital Library
  176. K. Van den Broucke, D. Ferreira, J. Goncalves, V. Kostakos, and K. De Moor. 2014. Mobile cloud storage: A contextual experience. In Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services. 101--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  177. S. Vhaduri and C. Poellabauer. 2016. Human factors in the design of longitudinal smartphone-based wellness surveys. In Proceedings of the International Conference on Healthcare Informatics. IEEE, 156--167.Google ScholarGoogle Scholar
  178. C. R. Walker. 1956. The Foreman on the Assembly Line. Harvard University Press (1956).Google ScholarGoogle ScholarCross RefCross Ref
  179. E. I. Walsh and J. K. Brinker. 2016. Should participants be given a mobile phone, or use their own? effects of novelty vs utility. Telemat. Inform. 33, 1, 25--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  180. Z. Wang, J. M. Tchernev, and T. Solloway. 2012. A dynamic longitudinal examination of social media use, needs, and gratifications among college students. Comput. Human Behav. 28, 5, 1829--1839. Google ScholarGoogle ScholarDigital LibraryDigital Library
  181. D. Weber, A. Voit, P. Kratzer, and N. Henze. 2016. In-situ investigation of notifications in multi-device environments. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1259--1264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  182. J. Weppner, P. Lukowicz, S. Serino, P. Cipresso, A. Gaggioli, and G. Riva. 2013. Smartphone based experience sampling of stress-related events. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare and Workshops. 464--467. Google ScholarGoogle ScholarDigital LibraryDigital Library
  183. J. Westerink, M. Ouwerkerk, G. J. d. Vries, S. d. Waele, J. v. d. Eerenbeemd, and M. V. Boven. 2009. Emotion measurement platform for daily life situations. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction and Workshops, 1--6.Google ScholarGoogle Scholar
  184. L. Wheeler and H. T. Reis. 1991. Self-recording of everyday life events: Origins, types, and uses. J. Pers. 59, 3, 339--354.Google ScholarGoogle ScholarCross RefCross Ref
  185. M. L. Wilson, D. Craggs, S. Robinson, M. Jones, and K. Brimble. 2012. Pico-ing into the future of mobile projection and contexts. Pers. Ubiq. Comput. 16, 1, 39--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  186. R. Xiao, G. Laput, and C. Harrison. 2014. Expanding the input expressivity of smartwatches with mechanical pan, twist, tilt and click. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 193--196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  187. D. Xu, L. Qian, Y. Wang, M. Wang, C. Shen, T. Zhang, and J. Zhang. 2015. Understanding the dynamic relationships among interpersonal personality characteristics, loneliness, and smart-phone use: evidence from experience sampling. In Proceedings of the International Conference on Computer Science and Mechanical Automation. 19--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  188. Y. Yang, G. D. Clark, J. Lindqvist, and A. Oulasvirta. 2016. Free-form gesture authentication in the wild. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 3722--3735. Google ScholarGoogle ScholarDigital LibraryDigital Library
  189. Z. Yue, E. Litt, C. J. Cai, J. Stern, K. Baxter, Z. Guan, N. Sharma, and G. Zhang. 2014. Photographing information needs: The role of photos in experience sampling method-style research. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 1545--1554. Google ScholarGoogle ScholarDigital LibraryDigital Library
  190. X. Zhang, L. R. Pina, and J. Fogarty. 2016. Examining unlock journaling with diaries and reminders for in situ self-report in health and wellness. In Proceedings of the Conference on Human Factors in Computing Systems. ACM, 5658--5664. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. The Experience Sampling Method on Mobile Devices

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                  cover image ACM Computing Surveys
                  ACM Computing Surveys  Volume 50, Issue 6
                  November 2018
                  752 pages
                  ISSN:0360-0300
                  EISSN:1557-7341
                  DOI:10.1145/3161158
                  • Editor:
                  • Sartaj Sahni
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                  Publication History

                  • Published: 6 December 2017
                  • Accepted: 1 July 2017
                  • Revised: 1 June 2017
                  • Received: 1 September 2016
                  Published in csur Volume 50, Issue 6

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