Can smartphones be used to bring computer-based tasks from the lab to the field? A mobile experience-sampling method study about the pace of life

  • Stefan Stieger
  • David Lewetz
  • Ulf-Dietrich Reips


Researchers are increasingly using smartphones to collect scientific data. To date, most smartphone studies have collected questionnaire data or data from the built-in sensors. So far, few studies have analyzed whether smartphones can also be used to conduct computer-based tasks (CBTs). Using a mobile experience-sampling method study and a computer-based tapping task as examples (N = 246; twice a day for three weeks, 6,000+ measurements), we analyzed how well smartphones can be used to conduct a CBT. We assessed methodological aspects such as potential technologically induced problems, dropout, task noncompliance, and the accuracy of millisecond measurements. Overall, we found few problems: Dropout rate was low, and the time measurements were very accurate. Nevertheless, particularly at the beginning of the study, some participants did not comply with the task instructions, probably because they did not read the instructions before beginning the task. To summarize, the results suggest that smartphones can be used to transfer CBTs from the lab to the field, and that real-world variations across device manufacturers, OS types, and CPU load conditions did not substantially distort the results.


Pace of life Experience sampling Smartphone Well-being Psychological pressure 


  1. Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: Guilford.Google Scholar
  2. Conner, T. S., Tennen, H., Fleeson, W., & Barrett, L. F. (2009). Experience sampling methods: A modern idiographic approach to personality. Social and Personality Psychology Compass, 3, 292–313. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619. CrossRefPubMedPubMedCentralGoogle Scholar
  4. Dufau, S., Duñabeitia, J. A., Moret-Tatay, C., McGonigal, A., Peeters, D., Alario, F.-X., . . . Grainger, J. (2011). Smart phone, smart science: How the use of smartphones can revolutionize research in cognitive science. PLoS ONE, 6, e24974.
  5. Freeman, J. B., & Ambady, N. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42, 226–241. CrossRefPubMedGoogle Scholar
  6. Garhammer, M. (2002). Pace of life and enjoyment of life. Journal of Happiness Studies, 3, 217–256. CrossRefGoogle Scholar
  7. Götz, F. M., Stieger, S., & Reips, U.-D. (2017). Users of the main smartphone operating systems (iOS, Android) differ only little in personality. PLoS ONE, 12, e0176921. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480.CrossRefPubMedGoogle Scholar
  9. Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using Smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11, 838–854. CrossRefPubMedPubMedCentralGoogle Scholar
  10. Kassavetis, P., Saifee, T. A., Roussos, G., Drougkas, L., Kojovic, M., Rothwell, J. C., . . . Bhatia, K. P. (2016), Developing a tool for remote digital assessment of Parkinson’s disease. Movement Disorders Clinical Practice, 3, 59–64.
  11. Keller, F., & Gunasekharan, S., Mayo, N., & Corley, M. (2009). Timing accuracy of Web experiments: A case study using the WebExp software package. Behavior Research Methods, 41, 1–12.
  12. Kulas, J. T., & Stachowski, A. A. (2009). Middle category endorsement in Likert-type response scales: Associated item characteristics, response latency, and intended meaning. Journal of Research in Personality, 43, 489–493. CrossRefGoogle Scholar
  13. Lee, C. Y., Kang, S. J., Hong, S.-K., Ma, H.-I., Lee, U., Kim, Y. J. (2016). A validation study of a smartphone-based finger tapping: Application for quantitative assessment of bradykinesia in Parkinson’s disease. PLoS ONE, 11, e0158852. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Levine, R., & Bartlett, K. (1984). Pace of life, punctuality and coronary heart disease in six countries. Journal of Cross-Cultural Psychology, 15, 233–255.CrossRefGoogle Scholar
  15. Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30, 178–205. CrossRefGoogle Scholar
  16. Mehl, M. R., Pennebaker, J. W., Crow, D. M., Dabbs, J., & Price, J. H. (2001). The Electronically Activated Recorder (EAR): A device for sampling naturalistic daily activities and conversations. Behavior Research Methods, Instruments, & Computers, 33, 517–523. CrossRefGoogle Scholar
  17. Miller, G. (2012). The smartphone psychology manifesto. Perspectives on Psychological Science, 7, 221–237. CrossRefPubMedGoogle Scholar
  18. Raento, M., Oulasvirta, A., & Eagle, N. (2009). Smartphones: An emerging tool for social scientists. Sociological Methods and Research, 37, 426–454. CrossRefGoogle Scholar
  19. Reips, U.-D., & Funke, F. (2008). Interval-level measurement with visual analogue scales in Internet-based research: VAS generator. Behavior Research Methods, 40, 699–704. CrossRefPubMedGoogle Scholar
  20. Rosa, H. (2003). Social acceleration: Ethical and political consequences of a desynchronized high-speed society. Constellations, 10, 3–33.CrossRefGoogle Scholar
  21. Schwarz, S., & Reips, U.-D. (2001). CGI versus JavaScript: A Web experiment on the reversed hindsight bias. In U.-D. Reips & M. Bosnjak (Eds.), Dimensions of Internet science (pp. 75–90). Lengerich, Germany: Pabst.Google Scholar
  22. Stieger, S., Göritz, A. S., & Voracek, M. (2011). Handle with care: The impact of using Java applets in web-based studies on dropout and sample composition. Cyberpsychology, Behavior, and Social Networking, 14, 327–330.CrossRefGoogle Scholar
  23. Stieger, S., & Reips, U.-D. (2010). What are participants doing while filling in an online questionnaire: A paradata collection tool and an empirical study. Computers in Human Behavior, 26, 1488–1495.CrossRefGoogle Scholar
  24. Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., . . . Jensen, M. M. (2015). Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. In J. Song, T. Abdelzahar, & C. Mascolo (Eds.), Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys) (pp. 127–140). New York, NY: Association for Computing Machinery.
  25. Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2002). Patient noncompliance with paper diaries. British Medical Journal, 324, 1193–1194. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. CrossRefGoogle Scholar
  27. Witt, S. T., Laird, A. R., & Meyerand, M. E. (2008). Functional neuroimaging correlates of finger-tapping task variations: An ALE meta-analysis. NeuroImage, 42, 343–356. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Wrzus, C., & Mehl, M. R. (2015). Lab and/or field? measuring personality processes and their social consequences. European Journal of Personality, 29, 250–271. CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Stefan Stieger
    • 1
  • David Lewetz
    • 2
  • Ulf-Dietrich Reips
    • 1
  1. 1.Department of PsychologyUniversity of KonstanzKonstanzGermany
  2. 2.Department of PsychologyUniversity of ViennaViennaAustria

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