Nediyana Daskalova
ABSTRACT
The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.
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- Amid Ayobi, Paul Marshall, and Anna L Cox. 2020. Trackly: A Customisable and Pictorial Self-Tracking App to Support Agency in Multiple Sclerosis Self-Care. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, 1–15.Google Scholar
Digital Library
- Amid Ayobi, Paul Marshall, Anna L Cox, and Yunan Chen. 2017. Quantifying the body and caring for the mind: self-tracking in multiple sclerosis. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 6889–6901.Google ScholarDigital Library
- Amid Ayobi, Tobias Sonne, Paul Marshall, and Anna L Cox. 2018. Flexible and Mindful Self-Tracking: Design Implications from Paper Bullet Journals. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 28.Google ScholarDigital Library
- Colin Barr, Maria Marois, Ida Sim, Christopher H Schmid, Barth Wilsey, Deborah Ward, Naihua Duan, Ron D Hays, Joshua Selsky, Joseph Servadio, 2015. The PREEMPT study-evaluating smartphone-assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial. Trials 16, 1 (2015), 67.Google ScholarCross Ref
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101.Google Scholar
- Colleen E Carney, Daniel J Buysse, Sonia Ancoli-Israel, Jack D Edinger, Andrew D Krystal, Kenneth L Lichstein, and Charles M Morin. 2012. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep 35, 2 (2012), 287–302.Google ScholarCross Ref
- Ting-Ray Chang, Eija Kaasinen, and Kirsikka Kaipainen. 2012. What influences users’ decisions to take apps into use?: A framework for evaluating persuasive and engaging design in mobile Apps for well-being. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia. ACM.Google ScholarDigital Library
- Connie Chen, David Haddad, Joshua Selsky, Julia E Hoffman, Richard L Kravitz, Deborah E Estrin, and Ida Sim. 2012. Making sense of mobile health data: an open architecture to improve individual-and population-level health. Journal of medical Internet research 14, 4 (2012), e112.Google ScholarCross Ref
- Eun Kyoung Choe, Bongshin Lee, Haining Zhu, Nathalie Henry Riche, and Dominikus Baur. 2017. Understanding self-reflection: how people reflect on personal data through visual data exploration. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 173–182.Google ScholarDigital Library
- Eun Kyoung Choe, Nicole B Lee, Bongshin Lee, Wanda Pratt, and Julie A Kientz. 2014. Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1143–1152.Google ScholarDigital Library
- Chia-Fang Chung, Elena Agapie, Jessica Schroeder, Sonali Mishra, James Fogarty, and Sean A Munson. 2017. When personal tracking becomes social: Examining the use of Instagram for healthy eating. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 1674–1687.Google ScholarDigital Library
- John Concato, Nirav Shah, and Ralph I Horwitz. 2000. Randomized, controlled trials, observational studies, and the hierarchy of research designs. New England journal of medicine 342, 25 (2000), 1887–1892.Google Scholar
- Felicia Cordeiro, Elizabeth Bales, Erin Cherry, and James Fogarty. 2015. Rethinking the mobile food journal: Exploring opportunities for lightweight photo-based capture. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 3207–3216.Google ScholarDigital Library
- Felicia Cordeiro, Daniel A Epstein, Edison Thomaz, Elizabeth Bales, Arvind K Jagannathan, Gregory D Abowd, and James Fogarty. 2015. Barriers and negative nudges: Exploring challenges in food journaling. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 1159–1162.Google ScholarDigital Library
- Mary Czerwinski, Eric Horvitz, and Susan Wilhite. 2004. A diary study of task switching and interruptions. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 175–182.Google ScholarDigital Library
- Nediyana Daskalova, Karthik Desingh, Alexandra Papoutsaki, Diane Schulze, Han Sha, and Jeff Huang. 2017. Lessons learned from two cohorts of personal informatics self-experiments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 46.Google ScholarDigital Library
- Nediyana Daskalova, Danaë Metaxa-Kakavouli, Adrienne Tran, Nicole Nugent, Julie Boergers, John McGeary, and Jeff Huang. 2016. SleepCoacher: A personalized automated self-experimentation system for sleep recommendations. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology. ACM, 347–358.Google ScholarDigital Library
- Nediyana Daskalova, Jina Yoon, Yibing Wang, Cintia Araujo, Guillermo Beltran Jr, Nicole Nugent, John McGeary, Joseph Jay Williams, and Jeff Huang. 2020. SleepBandits: Guided Flexible Self-Experiments for Sleep. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, 1–13.Google ScholarDigital Library
- Orianna DeMasi, Sidney Feygin, Aluma Dembo, Adrian Aguilera, and Benjamin Recht. 2017. Well-being tracking via smartphone-measured activity and sleep: cohort study. JMIR mHealth and uHealth 5, 10 (2017), e137.Google Scholar
- Markéta Dolejšová and Denisa Kera. 2017. Soylent Diet Self-Experimentation: Design Challenges in Extreme Citizen Science Projects. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2112–2123.Google Scholar
- Daniel A Epstein, Nicole B Lee, Jennifer H Kang, Elena Agapie, Jessica Schroeder, Laura R Pina, James Fogarty, Julie A Kientz, and Sean Munson. 2017. Examining menstrual tracking to inform the design of personal informatics tools. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 6876–6888.Google ScholarDigital Library
- Daniel A Epstein, An Ping, James Fogarty, and Sean A Munson. 2015. A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 731–742.Google ScholarDigital Library
- Bob Evans. 2019. PACO: The Personal Analytics Companion. https://pacoapp.com/Google Scholar
- Miguel Farias and Catherine Wikholm. 2016. Has the science of mindfulness lost its mind?BJPsych bulletin 40, 6 (2016), 329–332.Google Scholar
- Brian J Fogg. 2002. Persuasive technology: using computers to change what we think and do. Ubiquity 2002, December (2002), 5.Google ScholarDigital Library
- Brian J Fogg. 2019. Tiny Habits: The Small Changes That Change Everything. Houghton Mifflin Harcourt.Google Scholar
- Susannah Fox and Maeve Duggan. 2013. Tracking for Health. https://www.pewinternet.org/2013/01/28/tracking-for-health/Google Scholar
- Daniel Harrison, Paul Marshall, Nadia Bianchi-Berthouze, and Jon Bird. 2015. Activity tracking: barriers, workarounds and customisation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 617–621.Google ScholarDigital Library
- Eiji Hayashi and Jason Hong. 2011. A diary study of password usage in daily life. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2627–2630.Google ScholarDigital Library
- Steven C Hayes. 1981. Single case experimental design and empirical clinical practice.Journal of consulting and clinical psychology 49, 2(1981), 193.Google Scholar
- Reetta Heinonen, Riitta Luoto, Pirjo Lindfors, and Clas-Håkan Nygård. 2012. Usability and feasibility of mobile phone diaries in an experimental physical exercise study. Telemedicine and e-Health 18, 2 (2012), 115–119.Google ScholarCross Ref
- Miguel A Hernán and Sonia Hernández-Díaz. 2012. Beyond the intention-to-treat in comparative effectiveness research. Clinical Trials 9, 1 (2012), 48–55.Google ScholarCross Ref
- Mieke Heyvaert and Patrick Onghena. 2014. Randomization tests for single-case experiments: State of the art, state of the science, and state of the application. Journal of Contextual Behavioral Science 3, 1 (2014), 51–64.Google ScholarCross Ref
- Alexis Hiniker, Sungsoo Ray Hong, Tadayoshi Kohno, and Julie A Kientz. 2016. Mytime: Designing and evaluating an intervention for smartphone non-use. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 4746–4757.Google ScholarDigital Library
- Tero Jokela, Jarno Ojala, and Thomas Olsson. 2015. A diary study on combining multiple information devices in everyday activities and tasks. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 3903–3912.Google ScholarDigital Library
- Ravi Karkar, Jessica Schroeder, Daniel A Epstein, Laura R Pina, Jeffrey Scofield, James Fogarty, Julie A Kientz, Sean A Munson, Roger Vilardaga, and Jasmine Zia. 2017. Tummytrials: a feasibility study of using self-experimentation to detect individualized food triggers. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 6850–6863.Google ScholarDigital Library
- Ravi Karkar, Jasmine Zia, Roger Vilardaga, Sonali R Mishra, James Fogarty, Sean A Munson, and Julie A Kientz. 2015. A framework for self-experimentation in personalized health. Journal of the American Medical Informatics Association 23, 3(2015), 440–448.Google ScholarCross Ref
- Joseph Jofish Kaye, Mary McCuistion, Rebecca Gulotta, and David A Shamma. 2014. Money talks: tracking personal finances. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 521–530.Google ScholarDigital Library
- Christina Kelley, Bongshin Lee, and Lauren Wilcox. 2017. Self-tracking for mental wellness: understanding expert perspectives and student experiences. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 629–641.Google ScholarDigital Library
- John M Kelley and Ted J Kaptchuk. 2010. Group analysis versus individual response: the inferential limits of randomized controlled trials. Contemporary clinical trials 31, 5 (2010), 423–428.Google Scholar
- Ian Kerridge. 2003. Altruism or reckless curiosity? A brief history of self experimentation in medicine. Internal medicine journal 33, 4 (2003), 203–207.Google Scholar
- Elisabeth T Kersten-van Dijk, Joyce HDM Westerink, Femke Beute, and Wijnand A IJsselsteijn. 2017. Personal informatics, self-insight, and behavior change: A critical review of current literature. Human–Computer Interaction 32, 5-6 (2017), 268–296.Google ScholarDigital Library
- Julie A Kientz. 2019. In Praise of Small Data: When You Might Consider N-of-1 Studies. GetMobile: Mobile Computing and Communications 22, 4(2019), 5–8.Google ScholarDigital Library
- Young-Ho Kim, Eun Kyoung Choe, Bongshin Lee, and Jinwook Seo. 2019. Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 615.Google ScholarDigital Library
- Young-Ho Kim, Jae Ho Jeon, Eun Kyoung Choe, Bongshin Lee, KwonHyun Kim, and Jinwook Seo. 2016. TimeAware: Leveraging framing effects to enhance personal productivity. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 272–283.Google ScholarDigital Library
- Young-Ho Kim, Jae Ho Jeon, Bongshin Lee, Eun Kyoung Choe, and Jinwook Seo. 2017. OmniTrack: A Flexible Self-Tracking Approach Leveraging Semi-Automated Tracking. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 67.Google ScholarDigital Library
- Judy Kopp. 1988. Self-monitoring: A literature review of research and practice. In Social Work Research and Abstracts, Vol. 24. Oxford University Press, 8–20.Google Scholar
- Thomas R Kratochwill, John H Hitchcock, Robert H Horner, Joel R Levin, Samuel L Odom, David M Rindskopf, and William R Shadish. 2013. Single-case intervention research design standards. Remedial and Special Education 34, 1 (2013), 26–38.Google ScholarCross Ref
- Reed Larson and Mihaly Csikszentmihalyi. 2014. The experience sampling method. In Flow and the foundations of positive psychology. Springer, 21–34.Google Scholar
- Gary P Latham. 2003. Goal setting: A five-step approach to behavior change.Organizational Dynamics(2003), 309–318.Google Scholar
- Jisoo Lee, Erin Walker, Winslow Burleson, Matthew Kay, Matthew Buman, and Eric B Hekler. 2017. Self-experimentation for behavior change: Design and formative evaluation of two approaches. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 6837–6849.Google ScholarDigital Library
- Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 557–566.Google ScholarDigital Library
- Elizabeth O Lillie, Bradley Patay, Joel Diamant, Brian Issell, Eric J Topol, and Nicholas J Schork. 2011. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?Personalized medicine 8, 2 (2011), 161–173.Google Scholar
- David Mant. 1999. Can randomised trials inform clinical decisions about individual patients?The Lancet 353, 9154 (1999), 743–746.Google Scholar
- Sean A Munson, Jessica Schroeder, Ravi Karkar, Julie A Kientz, Chia-Fang Chung, and James Fogarty. 2020. The Importance of Starting With Goals in N-of-1 Studies. Frontiers in Digital Health 2 (2020), 3.Google ScholarCross Ref
- Gina Neff and Dawn Nafus. 2016. Self-tracking. MIT Press.Google Scholar
- Rosemery O Nelson and Steven C Hayes. 1981. Theoretical explanations for reactivity in self-monitoring. Behavior Modification 5, 1 (1981), 3–14.Google ScholarCross Ref
- Doug Oman, Shauna L Shapiro, Carl E Thoresen, Thomas G Plante, and Tim Flinders. 2008. Meditation lowers stress and supports forgiveness among college students: A randomized controlled trial. Journal of American College Health 56, 5 (2008), 569–578.Google ScholarCross Ref
- Leysia Palen and Marilyn Salzman. 2002. Voice-mail diary studies for naturalistic data capture under mobile conditions. In Proceedings of the 2002 ACM conference on Computer supported cooperative work. ACM, 87–95.Google ScholarDigital Library
- Vineet Pandey. 2018. Creating Scientific Theories with Online Communities using Gut Instinct. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. 109–112.Google ScholarDigital Library
- Vineet Pandey, Tushar Koul, Chen Yang, Daniel McDonald, Mad Price Ball, Bastian Greshake Tzovaras, and Scott Klemmer. 2021. Galileo: Citizen-led Experimentation using a Social Computing System. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM.Google ScholarDigital Library
- Sun Young Park and Yunan Chen. 2015. Individual and social recognition: challenges and opportunities in migraine management. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 1540–1551.Google ScholarDigital Library
- James O Prochaska and Wayne F Velicer. 1997. The transtheoretical model of health behavior change. American journal of health promotion 12, 1 (1997), 38–48.Google Scholar
- Seth Roberts and Allen Neuringer. 1998. Self-experimentation. In Handbook of research methods in human operant behavior. Springer, 619–655.Google Scholar
- John Rooksby, Parvin Asadzadeh, Mattias Rost, Alistair Morrison, and Matthew Chalmers. 2016. Personal tracking of screen time on digital devices. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 284–296.Google ScholarDigital Library
- John Rooksby, Mattias Rost, Alistair Morrison, and Matthew Chalmers. 2014. Personal tracking as lived informatics. In Proceedings of the 2014 CHI Conference on Human Factors in Computing Systems. ACM, 1163–1172.Google ScholarDigital Library
- Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, 2018. A tutorial on Thompson sampling. Foundations and Trends® in Machine Learning 11, 1(2018), 1–96.Google Scholar
- Johnny Saldaña. 2015. The coding manual for qualitative researchers. Sage.Google Scholar
- Akane Sano, Sara Taylor, Craig Ferguson, Akshay Mohan, and Rosalind W Picard. 2017. QuantifyMe: An Automated Single-Case Experimental Design Platform. In International Conference on Wireless Mobile Communication and Healthcare. Springer, 199–206.Google Scholar
- Jessica Schroeder, Chia-Fang Chung, Daniel A Epstein, Ravi Karkar, Adele Parsons, Natalia Murinova, James Fogarty, and Sean A Munson. 2018. Examining self-tracking by people with migraine: goals, needs, and opportunities in a chronic health condition. In Proceedings of the 2018 on Designing Interactive Systems Conference 2018. ACM, 135–148.Google ScholarDigital Library
- Jessica Schroeder, Ravi Karkar, James Fogarty, Julie A Kientz, Sean A Munson, and Matthew Kay. 2019. A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health. Journal of healthcare informatics research 3, 1 (2019), 124–155.Google ScholarCross Ref
- Timothy Sohn, Kevin A Li, William G Griswold, and James D Hollan. 2008. A diary study of mobile information needs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 433–442.Google ScholarDigital Library
- Katarzyna Stawarz, Anna L Cox, and Ann Blandford. 2015. Beyond self-tracking and reminders: designing smartphone apps that support habit formation. In Proceedings of the 33rd annual ACM conference on Human Factors in Computing Systems. ACM, 2653–2662.Google ScholarDigital Library
- Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. Sage publications Thousand Oaks, CA.Google Scholar
- Melanie Swan. 2013. The quantified self: Fundamental disruption in big data science and biological discovery. Big data 1, 2 (2013), 85–99.Google Scholar
- Jakob Tholander and Maria Normark. 2020. Crafting Personal Information-Resistance, Imperfection, and Self-Creation in Bullet Journaling. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
- Eric Topol. 2019. The A.I. Diet. The New York Times (Mar 2019). https://www.nytimes.com/2019/03/02/opinion/sunday/diet-artificial-intelligence-diabetes.htmlGoogle Scholar
- Niels van Berkel, Jorge Goncalves, Peter Koval, Simo Hosio, Tilman Dingler, Denzil Ferreira, and Vassilis Kostakos. 2019. Context-Informed Scheduling and Analysis: Improving Accuracy of Mobile Self-Reports. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 1–12.Google ScholarDigital Library
- Heather P Whitley and Wesley Lindsey. 2009. Sex-based differences in drug activity. American family physician 80, 11 (2009), 1254–1258.Google Scholar
- Steve Whittaker, Vaiva Kalnikaite, Victoria Hollis, and Andrew Guydish. 2016. ‘Don’t Waste My Time’: Use of Time Information Improves Focus. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1729–1738.Google ScholarDigital Library
- Hanlu Ye, Meethu Malu, Uran Oh, and Leah Findlater. 2014. Current and future mobile and wearable device use by people with visual impairments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3123–3132.Google ScholarDigital Library
Index Terms
- Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation
Index terms have been assigned to the content through auto-classification.
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