ABSTRACT
Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.
- Khaled Abdel-Kader, Manisha Jhamb, Lee Anne Mandich, Jonathan Yabes, Robert M. Keene, Scott Beach, Daniel J. Buysse, and Mark L. Unruh. 2014. Ecological momentary assessment of fatigue, sleepiness, and exhaustion in ESKD. BMC nephrology 15, 1 (2014), 29.Google Scholar
- Saeed Abdullah, Mark Matthews, Elizabeth L. Murnane, Geri Gay, and Tanzeem Choudhury. 2014. Towards circadian computing: early to bed and early to rise makes some of us unhealthy and sleep deprived. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 673--684. Google ScholarDigital Library
- Saeed Abdullah, Elizabeth L. Murnane, Jean M. R. Costa, and Tanzeem Choudhury. 2015. Collective Smile: Measuring Societal Happiness from Geolocated Images. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 361--374. Google ScholarDigital Library
- John A. E. Anderson, Karen L. Campbell, Tarek Amer, Cheryl L. Grady, and Lynn Hasher. 2014. Timing is everything: Age differences in the cognitive control network are modulated by time of day. Psychology and aging 29, 3 (2014), 648.Google Scholar
- Amelia M. Arria and Robert L. DuPont. 2010. Nonmedical prescription stimulant use among college students: why we need to do something and what we need to do. Journal of addictive diseases 29, 4 (2010), 417--426.Google ScholarCross Ref
- Yin Bai, Bin Xu, Yuanchao Ma, Guodong Sun, and Yu Zhao. 2012. Will you have a good sleep tonight?: sleep quality prediction with mobile phone. In Proceedings of the 7th International Conference on Body Area Networks. 124--130. Google ScholarDigital Library
- Brian P. Bailey and Shamsi T. Iqbal. 2008. Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Transactions on Computer-Human Interaction (TOCHI) 14, 4 (2008), 21. Google ScholarDigital Library
- Mathias Basner, Daniel Mollicone, and David F. Dinges. 2011. Validity and sensitivity of a brief psychomotor vigilance test (PVT-B) to total and partial sleep deprivation. Acta astronautica 69, 11 (2011), 949--959.Google Scholar
- Katharina Blatter and Christian Cajochen. 2007. Circadian rhythms in cognitive performance: methodological constraints, protocols, theoretical underpinnings. Physiology & behavior 90, 2 (2007), 196--208.Google Scholar
- Matthias Böhmer, Brent Hecht, Johannes Schöning, Antonio Krüger, and Gernot Bauer. 2011. Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage. In Proceedings of the 13th international conference on Human computer interaction with mobile devices and services. 47--56. Google ScholarDigital Library
- Braun Research Center. 2015. Trends in Consumer Mobility Report. (2015).Google Scholar
- Barry Brown, Moira McGregor, and Donald McMillan. 2014. 100 days of iPhone use: understanding the details of mobile device use. In Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services. ACM, 223--232. Google ScholarDigital Library
- Geir Scott Brunborg, Rune Aune Mentzoni, Helge Molde, Helga Myrseth, Knut Joachim Mår Skouverøe, Bjørvatn, and Ståle Pallesen. 2011. The relationship between media use in the bedroom, sleep habits and symptoms of insomnia. Journal of sleep research 20, 4 (2011), 569--575.Google ScholarCross Ref
- Julie Carrier and Timothy H. Monk. 2000. Circadian rhythms of performance: new trends. Chronobiology international 17, 6 (2000), 719--732.Google ScholarCross Ref
- Mei-Yen Chen, Edward K. Wang, and Yi-Jong Jeng. 2006. Adequate sleep among adolescents is positively associated with health status and health-related behaviors. BMC Public Health 6, 1 (2006), 59.Google ScholarCross Ref
- Angel Correa, Tania Lara, and Juan Antonio Madrid. 2013. Influence of Circadian Typology and Time of Day on Temporal Preparation. Timing & Time Perception 1, 2 (2013), 217--238.Google ScholarCross Ref
- Giuseppe Curcio, Michele Ferrara, and Luigi De Gennaro. 2006. Sleep loss, learning capacity and academic performance. Sleep medicine reviews 10, 5 (2006), 323--337.Google Scholar
- Serge Daan and Martha Merrow. 2002. External time--internal time. Journal of biological rhythms 17, 2 (2002), 107--109.Google ScholarCross Ref
- William C. Dement and Christopher Vaughan. 1999. The promise of sleep: A pioneer in sleep medicine explores the vital connection between health, happiness, and a good night's sleep. Dell Publishing Co.Google Scholar
- Nancy L. Digdon and Andrew J. Howell. 2008. College students who have an eveningness preference report lower self-control and greater procrastination. Chronobiology international 25, 6 (2008), 1029--1046.Google ScholarCross Ref
- Trinh Minh Tri Do, Jan Blom, and Daniel Gatica-Perez. 2011. Smartphone usage in the wild: a large-scale analysis of applications and context. In Proceedings of ICMI. 353--360. Google ScholarDigital Library
- S. M. Doran, HPA Van Dongen, and David F. Dinges. 2001. Sustained attention performance during sleep deprivation: evidence of state instability. Archives italiennes de biologie 139, 3 (2001), 253--267.Google Scholar
- Jillian Dorrian, Nicole Lamond, Alexandra L. Holmes, Helen J. Burgess, Gregory D. Roach, Adam Fletcher, Drew Dawson, and others. 2003. The ability to self-monitor performance during a week of simulated night shifts. SLEEP 26, 7 (2003), 871--877.Google ScholarCross Ref
- B. Drust, J. Waterhouse, G. Atkinson, B. Edwards, and T. Reilly. 2005. Circadian rhythms in sports performance---an update. Chronobiology international 22, 1 (2005), 21--44.Google ScholarCross Ref
- John D. Eastwood, Alexandra Frischen, Mark J. Fenske, and Daniel Smilek. 2012. The unengaged mind defining boredom in terms of attention. Perspectives on Psychological Science 7, 5 (2012), 482--495.Google ScholarCross Ref
- Benedict Evans. 2014. Mobile is Eating the World.Google Scholar
- Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in smartphone usage. In Proc. of the 8th international conference on Mobile systems, applications, and services. ACM, 179--194. Google ScholarDigital Library
- Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th international conference on Human computer interaction with mobile devices & services. ACM, 91--100. Google ScholarDigital Library
- Denzil Ferreira, Vassilis Kostakos, and Anind K. Dey. 2015. AWARE: mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.Google ScholarCross Ref
- R. Foster and L. Kreitzman. 2011. The Rhythms Of Life: The Biological Clocks That Control the Daily Lives of Every Living Thing. (2011).Google Scholar
- Susannah Fox and Maeve Duggan. 2013. Tracking for health. Pew Research Center.Google Scholar
- Namni Goel, Mathias Basner, Hengyi Rao, and David F. Dinges. 2013. Circadian rhythms, sleep deprivation, and human performance. Progress in molecular biology and translational science 119 (2013), 155.Google Scholar
- Scott A. Golder and Michael W. Macy. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 6051 (2011), 1878--1881.Google ScholarCross Ref
- Alina Hang, Alexander De Luca, Jonas Hartmann, and Heinrich Hussmann. 2013. Oh app, where art thou?: on app launching habits of smartphone users. In Proc. of the 15th international conference on Human-computer interaction with mobile devices and services. 392--395. Google ScholarDigital Library
- Lynn Hasher, David Goldstein, and Cynthia P. May. 2005. It's About Time: Circadian Rhythms, Memory, & Aging.Google Scholar
- Helene Hembrooke and Geri Gay. 2003. The laptop and the lecture: The effects of multitasking in learning environments. Journal of computing in higher education 15, 1 (2003), 46--64.Google ScholarCross Ref
- Jim A. Horne and Olov Ostberg. 1975. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. International journal of chronobiology 4, 2 (1975), 97--110.Google Scholar
- James A. Horne and Olov östberg. 1977. Individual differences in human circadian rhythms. Biological Psychology 5, 3 (1977), 179--190.Google ScholarCross Ref
- Ke Huang, Xiang Ding, Jing Xu, Guanling Chen, and Wei Ding. 2015. Monitoring Sleep and Detecting Irregular Nights through Unconstrained Smartphone Sensing. (2015).Google Scholar
- Simon L. Jones, Denzil Ferreira, Simo Hosio, Jorge Goncalves, and Vassilis Kostakos. 2015. Revisitation analysis of smartphone app use. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1197--1208. Google ScholarDigital Library
- Myriam Juda, Céline Vetter, and Till Roenneberg. 2013. The Munich chronotype questionnaire for shift-workers (MCTQShift). Journal of biological rhythms 28, 2 (2013), 130--140.Google ScholarCross Ref
- Richard Kadison and Theresa Foy DiGeronimo. 2004. College of the overwhelmed: The campus mental health crisis and what to do about it. Jossey-Bass.Google Scholar
- Biren B. Kamdar, Katherine A. Kaplan, Eric J. Kezirian, and William C. Dement. 2004. The impact of extended sleep on daytime alertness, vigilance, and mood. Sleep medicine 5, 5 (2004), 441--448.Google Scholar
- Ilia N. Karatsoreos, Sarah Bhagat, Erik B. Bloss, John H. Morrison, and Bruce S. McEwen. 2011. Disruption of circadian clocks has ramifications for metabolism, brain, and behavior. Proceedings of the national Academy of Sciences 108, 4 (2011), 1657--1662.Google Scholar
- Matthew Kay, KyIe Rector, Sunny Consolvo, Ben Greenstein, Jacob O. Wobbrock, Nathaniel F. Watson, Julie Kientz, and others. 2013. PVT-touch: Adapting a reaction time test for touchscreen devices. In Pervasive Computing Technologies for Healthcare. IEEE, 248--251. Google ScholarDigital Library
- Nicole Lamond and Drew Dawson. 1999. Quantifying the performance impairment associated with fatigue. Journal of sleep research 8, 4 (1999), 255--262.Google ScholarCross Ref
- Nicole Lamond, Sarah M. Jay, Jillian Dorrian, Sally A Ferguson, Gregory D. Roach, and Drew Dawson. 2008. The sensitivity of a palm-based psychomotor vigilance task to severe sleep loss. Behavior research methods 40, 1 (2008), 347--352.Google Scholar
- Hosub Lee, Young Sang Choi, and Yeo-Jin Kim. 2011. An adaptive user interface based on spatiotemporal structure learning. Communications Magazine, IEEE 49, 6 (2011), 118--124.Google ScholarCross Ref
- Gilly Leshed and Phoebe Sengers. 2011. I lie to myself that i have freedom in my own schedule: productivity tools and experiences of busyness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 905--914. Google ScholarDigital Library
- Julian Lim and David F. Dinges. 2008. Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences 1129, 1 (2008), 305--322.Google ScholarCross Ref
- Julian Lim and David F. Dinges. 2010. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological bulletin 136, 3 (2010), 375.Google Scholar
- Gloria Mark, Shamsi T. Iqbal, Mary Czerwinski, and Paul Johns. 2014a. Bored mondays and focused afternoons: The rhythm of attention and online activity in the workplace. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 3025--3034. Google ScholarDigital Library
- Gloria Mark, Yiran Wang, and Melissa Niiya. 2014b. Stress and multitasking in everyday college life: an empirical study of online activity. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 41--50. Google ScholarDigital Library
- Aleksandar Matic, Martin Pielot, and Nuria Oliver. 2015. Boredom-computer interaction: Boredom proneness and the use of smartphone. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 837--841. Google ScholarDigital Library
- Jun-Ki Min, Afsaneh Doryab, Jason Wiese, Shahriyar Amini, John Zimmerman, and Jason I. Hong. 2014. Toss'n'turn: smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 477--486. Google ScholarDigital Library
- Timothy H. Monk. 2005. The post-lunch dip in performance. Clinics in sports medicine 24, 2 (2005), e15--e23.Google Scholar
- Timothy H. Monk, Daniel J. Buysse, Charles F. Reynolds, and David J. Kupfer. 1996. Circadian determinants of the postlunch dip in performance. Chronobiology international 13, 2 (1996), 123--133.Google ScholarCross Ref
- Sai T. Moturu, Inas Khayal, Nadav Aharony, Wei Pan, and Alex Pentland. 2011. Using social sensing to understand the links between sleep, mood, and sociability. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom). IEEE, 208--214.Google Scholar
- Elizabeth L. Murnane, Saeed Abdullah, Mark Matthews, Tanzeem Choudhury, and Geri Gay. 2015. Social (media) jet lag: how usage of social technology can modulate and reflect circadian rhythms. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 843--854. Google ScholarDigital Library
- Danny O'Brien. 2004. Life Hacks: Tech Secrets of Overprolific Alpha Geeks. In Emerging Tech Conference.Google Scholar
- Halszka Oginska and Janusz Pokorski. 2006. Fatigue and mood correlates of sleep length in three age-social groups: School children, students, and employees. Chronobiology international 23, 6 (2006), 1317--1328.Google ScholarCross Ref
- Antti Oulasvirta, Tye Rattenbury, Lingyi Ma, and Eeva Raita. 2012. Habits make smartphone use more pervasive. In Personal & Ubiquitous Computing, Vol. 16. 105--114. Google ScholarDigital Library
- Sanjay R. Patel, Najib T. Ayas, Mark R. Malhotra, David P. White, Eva S. Schernhammer, Frank E. Speizer, Meir J. Stampfer, and Frank B. Hu. 2004. A prospective study of sleep duration and mortality risk in women. SLEEP 27, 3 (2004), 440--444.Google ScholarCross Ref
- Martin Pielot, Rodrigo de Oliveira, Haewoon Kwak, and Nuria Oliver. 2014. Didn't you see my message?: predicting attentiveness to mobile instant messages. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 3319--3328. Google ScholarDigital Library
- Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver. 2015. When attention is not scarce-detecting boredom from mobile phone usage. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 825--836. Google ScholarDigital Library
- Jacob Poushter. 2016. Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies. Pew Research Center.Google Scholar
- Ahmad Rahmati, Chad Tossell, Clayton Shepard, Philip Kortum, and Lin Zhong. 2012. Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability. In Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services. 11--20. Google ScholarDigital Library
- Gregory D. Roach, Drew Dawson, and Nicole Lamond. 2006. Can a Shorter Psychomotor Vigilance Task Be Used as a Reasonable Substitute for the Ten-Minute Psychomotor Vigilance Task? Chronobiology international 23, 6 (2006), 1379--1387.Google ScholarCross Ref
- Till Roenneberg. 2012. Internal time: Chronotypes, social jet lag, and why you're so tired. Harvard Press.Google Scholar
- Till Roenneberg. 2013. Chronobiology: the human sleep project. Nature 498, 7455 (2013), 427--428.Google Scholar
- Till Roenneberg, Karla V. Allebrandt, Martha Merrow, and Céline Vetter. 2012. Social jetlag and obesity. Current Biology 22, 10 (2012), 939--943.Google ScholarCross Ref
- Till Roenneberg, Lena K. Keller, Dorothee Fischer, Joana L. Matera, Céline Vetter, and Eva C. Winnebeck. 2015. Human Activity and Rest In Situ. Methods in enzymology 552 (2015), 257--283.Google Scholar
- Till Roenneberg, Tim Kuehnle, Myriam Juda, Thomas Kantermann, Karla Allebrandt, Marijke Gordijn, and Martha Merrow. 2007. Epidemiology of the human circadian clock. Sleep medicine reviews 11, 6 (2007), 429--438.Google Scholar
- Till Roenneberg, Tim Kuehnle, Peter P. Pramstaller, Jan Ricken, Miriam Havel, Angelika Guth, and Martha Merrow. 2004. A marker for the end of adolescence. Current Biology 14, 24 (2004), R1038--R1039.Google ScholarCross Ref
- Till Roenneberg, Anna Wirz-Justice, and Martha Merrow. 2003. Life between clocks: daily temporal patterns of human chronotypes. Journal of biological rhythms 18, 1 (2003), 80--90.Google ScholarCross Ref
- Christina Schmidt, Fabienne Collette, Christian Cajochen, and Philippe Peigneux. 2007. A time to think: circadian rhythms in human cognition. Cognitive Neuropsychology 24, 7 (2007), 755--789.Google ScholarCross Ref
- Choonsung Shin, Jin-Hyuk Hong, and Anind K. Dey. 2012. Understanding and prediction of mobile application usage for smart phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 173--182. Google ScholarDigital Library
- Aaron Smith and Dana Page. 2015. The Smartphone Difference. Pew Research Center.Google Scholar
- Mark R. Smith and Charmane I. Eastman. 2012. Shift work: health, performance and safety problems, traditional countermeasures, and innovative management strategies to reduce circadian misalignment. Nature and science of sleep 4 (2012), 111.Google Scholar
- Daniel J. Taylor and Adam D. Bramoweth. 2010. Patterns and consequences of inadequate sleep in college students: substance use and motor vehicle accidents. Journal of Adolescent Health 46, 6 (2010), 610--612.Google ScholarCross Ref
- Sara Thomée, Annika Härenstam, and Mats Hagberg. 2011. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC public health 11, 1 (2011).Google Scholar
- Bill Thornton, Alyson Faires, Maija Robbins, and Eric Rollins. 2015. The Mere Presence of a Cell Phone May be Distracting. Social Psychology (2015).Google Scholar
- HPA Van Dongen and David F. Dinges. 2005. Circadian rhythms in sleepiness, alertness, and performance. In Principles and practice of sleep medicine. 435--443.Google Scholar
- Céline Vetter, Myriam Juda, and Till Roenneberg. 2012. The influence of internal time, time awake, and sleep duration on cognitive performance in shiftworkers. Chronobiology international 29, 8 (2012), 1127--1138.Google ScholarCross Ref
- J. Victoria, MA Rideout, G. Ulla, PD Foehr, F. Donald, and PD Roberts. 2010. Generation M2. Media in the lives of 8- to 18-year-olds. Kaiser Foundation Research (2010).Google Scholar
- Ullrich Wagner, Steffen Gais, Hilde Haider, Rolf Verleger, and Jan Born. 2004. Sleep inspires insight. Nature 427, 6972 (2004), 352--355.Google Scholar
- Ye Xu, Mu Lin, Hong Lu, Giuseppe Cardone, Nicholas Lane, Zhenyu Chen, Andrew Campbell, and Tanzeem Choudhury. 2013. Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns. In Proceedings of the 2013 International Symposium on Wearable Computers. ACM, 69--76. Google ScholarDigital Library
- Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th international conference on Mobile systems, applications, and services. ACM, 113--126. Google ScholarDigital Library
Index Terms
- Mobile manifestations of alertness: connecting biological rhythms with patterns of smartphone app use
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