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Understanding the Challenges of Mobile Phone Usage Data

Published: 24 August 2015 Publication History

Abstract

Driven by curiosity and our own three diverse smartphone application usage datasets, we sought to unpack the nuances of mobile device use by revisiting two recent Mobile HCI studies [1, 17]. Our goal was to add to our broader understanding of smartphone usage by investigating if differences in mobile device usage occurred not only across our three datasets, but also in relation to prior work. We found differences in the top-10 apps in each dataset, in the durations and types of interactions as well as in micro-usage patterns. However, it proved very challenging to attribute such differences to a specific factor or set of factors: was it the time frame in which the studies were executed? The recruitment procedure? The experimental method? Using our somewhat troubled analysis, we discuss the challenges and issues of conducting mobile research of this nature and reflect on caveats related to the replicability and generalizability of such work.

References

[1]
Banovic, N., Brant, C., Mankoff, J., and Dey, A. Proactivetasks: The short of mobile device use sessions. In Proceedings of MobileHCI '14, ACM (2014), 243--252.
[2]
Bentley, F. R., and Metcalf, C. J. Location and activity sharing in everyday mobile communication. In CHI '08 Extended Abstracts, ACM (2008), 2453--2462.
[3]
Böhmer, M., Hecht, B., Schöning, J., Krüger, A., and Bauer, G. Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage. In Proceedings of Mobile HCI'11, ACM (2011), 47--56.
[4]
Brandt, J., Weiss, N., and Klemmer, S. R. Txt 4 l8r: Lowering the burden for diary studies under mobile conditions. In CHI '07 Extended Abstracts, ACM (2007), 2303--2308.
[5]
Brown, B., McGregor, M., and McMillan, D. 100 days of iphone use: Understanding the details of mobile device use. In Proceedings of the MobileHCI '14, ACM (2014), 223--232.
[6]
Carrascal, J. P., and Church, K. An in-situ study of mobile app & mobile search interactions. In Proceedings of CHI '15, ACM (2015), 2739--2748.
[7]
Carter, S., Mankoff, J., and Heer, J. Momento: support for situated ubicomp experimentation. In Proceedings of CHI '07, ACM (2007), 125--134.
[8]
Cherubini, M., and Oliver, N. A refined experience sampling method to capture mobile user experience. In International Workshop on Mobile User Experience Research held at CHI '09 (2009).
[9]
Church, K., Cherubini, M., and Oliver, N. A large-scale study of daily information needs captured in situ. In Transactions on Human-Computer Interaction (TOCHI) 21, 2 (2014), 10.
[10]
Consolvo, S., and Walker, M. Using the experience sampling method to evaluate ubicomp applications. IEEE Pervasive Computing 2, 2 (4 2003), 24--31.
[11]
Dearman, D., and Truong, K. Evaluating the implicit acquisition of second language vocabulary using a live wallpaper. In Proceedings of CHI '12, ACM (2012), 1391--1400.
[12]
Do, T. M. T., Blom, J., and Gatica-Perez, D. Smartphone usage in the wild: A large-scale analysis of applications and context. In Proceedings of the ICMI'11, ACM (2011), 353--360.
[13]
Drummond, C. Replicability is not reproducibility: nor is it good science. In Proceedings of the Evaluation Methods for Machine Learning Workshop at ICML (2009).
[14]
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., and Estrin, D. Diversity in smartphone usage. In Proceedings of MobiSys '10, ACM (2010), 179--194.
[15]
Ferreira, D., Dey, A. K., and Kostakos, V. Understanding human-smartphone concerns: a study of battery life. In Pervasive Computing, Springer-Verlag (Berlin, Heidelberg, 2011), 19--33.
[16]
Ferreira, D., Ferreira, E., Goncalves, J., Kostakos, V., and Dey, A. K. Revisiting human-battery interaction with an interactive battery interface. In Proceedings of Ubicomp '13, ACM (2013), 563--572.
[17]
Ferreira, D., Goncalves, J., Kostakos, V., Barkhuus, L., and Dey, A. K. Contextual experience sampling of mobile application micro-usage. In Proceedings of MobileHCI '14, ACM (2014), 91--100.
[18]
Ferreira, D., Kostakos, V., Beresford, A. R., Janne, L., and Dey, A. K. Securacy: An empirical investigation of android applications? network usage, privacy and security. In Proceedings of the 8th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) (2015).
[19]
Ferreira, D., Kostakos, V., and Dey, A. K. Lessons learned from large-scale user studies: Using android market as a source of data. International Journal of Mobile Human Computer Interaction 4, 3 (1 2012), 28--43.
[20]
Ferreira, D., Kostakos, V., and Dey, A. K. Aware: mobile context instrumentation framework. Frontiers in ICT 2, 6 (2015).
[21]
Fischer, J. E. Experience-sampling tools: a critical review. Mobile Living Labs 9 (2009), 1--3.
[22]
Froehlich, J., Chen, M. Y., Consolvo, S., Harrison, B., and Landay, J. A. MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of the 5th international conference on Mobile systems, applications and services, ACM (2007), 57--70.
[23]
Henze, N., Pielot, M., Poppinga, B., Schinke, T., and Boll, S. My app is an experiment: Experience from user studies. Developments in Technologies for Human-Centric Mobile Computing and Applications (2012), 294.
[24]
Intille, S. S., Rondoni, J., Kukla, C., Ancona, I., and Bao, L. A context-aware experience sampling tool. In CHI '03 Extended Abstracts, ACM (2003), 972--973.
[25]
Kamisaka, D., Muramatsu, S., Yokoyama, H., and Iwamoto, T. Operation prediction for context-aware user interfaces of mobile phones. SAINT'09. Ninth Annual International Symposium on Applications and the Internet (2009), 16--22.
[26]
Lee, U., Lee, J., Ko, M., Lee, C., Kim, Y., Yang, S., Yatani, K., Gweon, G., Chung, K.-M. . M., and Song, J. Hooked on smartphones: An exploratory study on smartphone overuse among college students. In Proceedings of CHI '14, ACM (2014), 2327--2336.
[27]
McMillan, D., Morrison, A., Brown, O., Hall, M., and Chalmers, M. Further into the Wild: Running Worldwide Trials of Mobile Systems. Springer Berlin Heidelberg, 2010, 210--227.
[28]
Oulasvirta, A., Rattenbury, T., Ma, L., and Raita, E. Habits make smartphone use more pervasive. Personal and Ubiquitous Computing 16, 1 (1 2012), 105--114.
[29]
Palen, L., and Salzman, M. Voice-mail diary studies for naturalistic data capture under mobile conditions. In Proceedings of CSCW '02, ACM (2002), 87--95.
[30]
Pejovic, V., and Musolesi, M. Interruptme: Designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the UbiComp '14, ACM (2014), 897--908.
[31]
Pielot, M. Large-scale evaluation of call-availability prediction. In Proceedings of UbiComp '14, ACM (2014), 933--937.
[32]
Pielot, M., Church, K., and de Oliveira, R. An in-situ study of mobile phone notifications. In Proceedings of MobileHCI '14, ACM (2014), 233--242.
[33]
Rachuri, K. K., Musolesi, M., Mascolo, C., Rentfrow, P. J., Longworth, C., and Aucinas, A. Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In Proceedings of UbiComp '10, ACM (2010), 281--290.
[34]
Raento, M., Oulasvirta, A., Petit, R., and Toivonen, H. Contextphone: A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing 4, 2 (4 2005), 51--59.
[35]
Rahmati, A., and Zhong, L. Studying smartphone usage: Lessons from a four-month field study.
[36]
Ramanathan, N., Alquaddoomi, F., Falaki, H., George, D., Hsieh, C., Jenkins, J., Ketcham, C., Longstaff, B., Ooms, J., Selsky, J., Tangmunarunkit, H., and Estrin, D. ohmage: An open mobile system for activity and experience sampling. In PervasiveHealth, IEEE (2012), 203--204.
[37]
Shepard, C., Rahmati, A., Tossell, C., Zhong, L., and Kortum, P. Livelab: Measuring wireless networks and smartphone users in the field. ACM SIGMETRICS Performance Evaluation Review 38, 3 (2011).
[38]
Shin, C., Hong, J.-H. . H., and Dey, A. K. Understanding and prediction of mobile application usage for smart phones. In Proceedings of UbiComp '12, ACM (2012), 173--182.
[39]
Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K. K., Xu, C., and Tapia, E. M. Mobileminer: Mining your frequent patterns on your phone. In Proceedings of UbiComp '14, ACM (2014), 389--400.
[40]
Truong, K. N., Shihipar, T., and Wigdor, D. J. Slide to x: unlocking the potential of smartphone unlocking. In Proceedings of CHI '14, ACM (2014), 3635--3644.
[41]
Vaish, R., Wyngarden, K., Chen, J., Cheung, B., and Bernstein, M. S. Twitch crowdsourcing: crowd contributions in short bursts of time. In Proceedings of CHI '14, ACM (2014), 3645--3654.
[42]
Vastenburg, M. H., and Herrera, N. R. Adaptive experience sampling: addressing the dynamic nature of in-situ user studies. In Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010), Springer (2010), 197--200.
[43]
Vetek, A., Flanagan, J. A., Colley, A., and Keränen, T. Smartactions: Context-aware mobile phone shortcuts. In Proceedings of INTERACT '09, Springer-Verlag (2009), 796--799.
[44]
Wagner, D. T., Rice, A., and Beresford, A. R. Device analyzer: Large-scale mobile data collection. SIGMETRICS Performance Evaluation Review 41, 4 (4 2014), 53--56.
[45]
Yan, T., Chu, D., Ganesan, D., Kansal, A., and Liu, J. Fast app launching for mobile devices using predictive user context. In Proceedings of MobiSys '12 (6 2012), 113--126.
[46]
Zhang, C., Ding, X., Chen, G., Huang, K., Ma, X., and Yan, B. Nihao: A Predictive Smartphone Application Launcher. Springer Berlin Heidelberg, 2013, 294--313.

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    cover image ACM Conferences
    MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
    August 2015
    611 pages
    ISBN:9781450336529
    DOI:10.1145/2785830
    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]

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    Published: 24 August 2015

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    Author Tags

    1. Device usage
    2. Evaluation
    3. Generalizability
    4. Methodology
    5. Micro-usage
    6. Mobile HCI
    7. Mobile usage
    8. Replication
    9. Smartphone usage
    10. User Studies

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    • (2023)Scanning or Simply Unengaged in Reading? Opportune Moments for Pushed News Notifications and Their Relationship with Smartphone Users' Choice of News-reading ModesProceedings of the ACM on Human-Computer Interaction10.1145/36042687:MHCI(1-26)Online publication date: 13-Sep-2023
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    • (2022)To What Extent We Repeat Ourselves? Discovering Daily Activity Patterns Across Mobile App UsageIEEE Transactions on Mobile Computing10.1109/TMC.2020.302198721:4(1492-1507)Online publication date: 1-Apr-2022
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