References
- Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 1994 International Conference on Very Large Databases. San Francisco, CA: Morgan Kaufmann.
- Barton, A. J. (2012). The regulation of mobile health applications. BMC Medicine, 10, 46. doi:10.1186/1741-7015-10-46
- Bawden, D., & Robinson, L. (2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191. doi:10.1177/0165551508095781
- Belkin, N. J., & Croft, W. B. (1992). Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12), 29–38. doi:10.1145/138859.138861
- Bentley, F., Tollmar, K., Stephenson, P., Levy, L., Jones, B., Robertson, S., & Wilson, J. (2013). Health Mashups: Presenting statistical patterns between wellbeing data and context in natural language to promote behavior change. ACM Transactions on Computer-Human Interaction (TOCHI), 20(5), 30. doi:10.1145/2503823
- Bernstein, M., Hong, L., Kairam, S., Chi, E., & Suh, B. (2010). A torrent of tweets: Managing information overload in online social streams. Proceedings of the CHI 2010 Workshop on Microblogging: What and How Can We Learn From It? New York, NY: ACM.
- Chewning, E. G., & Harrell, A. M. (1990). The effect of information load on decision makers’ cue utilization levels and decision quality in a financial distress decision task. Accounting, Organizations and Society, 15(6), 527–542. doi:10.1016/0361-3682(90)90033-Q
- Choe, E. K., Lee, B., & Schraefel, M. C. (2015). Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Computer Graphics and Applications, 35(4), 28–37. doi:10.1109/MCG.2015.51
- Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014). Understanding quantified-selfers’ practices in collecting and exploring personal data. Proceedings of the CHI 2014 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9. doi:10.1111/ecor.2012.88.issue-s1
- Chung, C. F., Cook, J., Bales, E., Zia, J., & Munson, S. A. (2015). More than telemonitoring: Health provider use and nonuse of life-log data in irritable bowel syndrome and weight management. Journal of Medical Internet Research, 17(8), e203. doi:10.2196/jmir.4364
- Corbin, J., & Strauss, A. (2008). Basics of qualitative research. London, UK: Sage.
- Cosley, D., Akey, K., Alson, B., Baxter, J., Broomfield, M., Lee, S., & Sarabu, C. (2009). Using technologies to support reminiscence. Proceedings of the British HCI Group 2009 Annual Conference on People and Computers. Swinton, UK: British Computer Society.
- Cuttone, A., Petersen, M. K., & Larsen, J. E. (2014). Four data visualization heuristics to facilitate reflection in personal informatics. In C. Stephanidis & A. Antona (Eds.), Universal Access in Human-Computer Interaction. Design for All and Accessibility Practice. UAHCI 2014. Lecture Notes in Computer Science (Vol. 8516, pp. 541–552). Cham, Switzerland: Springer International.
- Dabbish, L. A., Kraut, R. E., Fussell, S., & Kiesler, S. (2005). Understanding email use: Predicting action on a message. Proceedings of the CHI 2005 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Dingler, T., Sahami, T., & Henze, N. (2014). There is more to well-being than health data: Holistic lifelogging through memory capture. Proceedings of the CHI 2014 Workshop on Beyond Quantified Self: Data for Wellbeing. New York, NY: ACM.
- Elsden, C., Kirk, D. S., & Durrant, A. C. (2015). A quantified past: Toward design for remembering with personal informatics. Human–Computer Interaction, 31(6), 518–557. doi:10.1080/07370024.2015.1093422
- Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20(5), 325–344. doi:10.1080/01972240490507974
- Exist. (2016). Homepage. Retrieved from https://exist.io
- Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2005). Weka. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 1305–1314). Boston, MA: Springer.
- Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. doi:10.1287/inte.4.3.28
- Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR), 38(3), 9. doi:10.1145/1132960.1132963
- Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014). Lifelogging: Personal big data. Foundations and Trends in Information Retrieval, 8(1), 1–125. doi:10.1561/1500000033
- Haddadi, H., & Brown, I. (2014). Quantified self and the privacy challenge. SCL Technology Law Futures Forum. Retrieved from http://www.eecs.qmul.ac.uk/%7Ehamed/papers/qselfprivacy2014.pdf
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. doi:10.1145/1656274
- Hallowell, E. M. (2005). Overloaded circuits: Why smart people underperform. Harvard Business Review, 83(1), 54–62.
- Hanani, U., Shapira, B., & Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11(3), 203–259. doi:10.1023/A:1011196000674
- Hello Code. (2015). 2015 in review. Retrieved March 31, 2016, from http://blog.hellocode.co/post/2015-review/
- Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. Proceedings of the SIGIR 1999 International Conference on Research and Development in Information Retrieval. New York, NY: ACM.
- Hiltz, S. R., & Turoff, M. (1985). Structuring computer-mediated communication systems to avoid information overload. Communications of the ACM, 28(7), 680–689. doi:10.1145/3894.3895
- Huckvale, K., Car, M., Morrison, C., & Car, J. (2012). Apps for asthma self-management: A systematic assessment of content and tools. BMC Medicine, 10(1), 144. doi:10.1186/1741-7015-10-144
- Jones, S. L. (2015). Exploring correlational information in aggregated quantified self data dashboards. Proceedings of the UbiComp/ISWC 2015 Workshop on New Frontiers of Quantified Self. New York, NY: ACM.
- Jones, S. L., Ferreira, D., Hosio, S., Goncalves, J., & Kostakos, V. (2015). Revisitation analysis of smartphone app use. Proceedings of the UbiComp 2015 International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY: ACM.
- Jones, S. L., & Kelly, R. (2016). Sensemaking challenges in personal informatics and self-monitoring systems. Proceedings of the CHI 2016 Workshop on Interactive Systems in Healthcare. New York, NY: ACM.
- Karkar, R., Fogarty, J., Kientz, J. A., Munson, S. A., Vilardaga, R., & Zia, J. (2015). Opportunities and challenges for self-experimentation in self-tracking. Adjunct Proceedings of the UbiComp 2015 International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ISWC 2015 International Symposium on Wearable Computers. New York, NY: ACM.
- Kay, M., Choe, E. K., Shepherd, J., Greenstein, B., Watson, N., Consolvo, S., & Kientz, J. A. (2012). Lullaby: A capture & access system for understanding the sleep environment. Proceedings of the UbiComp 2012 Conference on Ubiquitous Computing. New York, NY: ACM.
- Kay, M., Patel, S. N., & Kientz, J. A. (2015). How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy. Proceedings of the CHI 2015 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Keller, K. L., & Staelin, R. (1987). Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research, 14, 200–213. doi:10.1086/jcr.1987.14.issue-2
- Kelly, R., & Payne, S. J. (2014). Collaborative web search in context: A study of tool use in everyday tasks. Proceedings of the CSCW 2014 Conference on Computer Supported Cooperative Work & Social Computing. New York, NY: ACM.
- Koroleva, K., Krasnova, H., & Günther, O. (2010).‘STOP SPAMMING ME!’ - exploring information overload on facebook. Proceedings of the AMCIS 2010 Americas Conference on Information Systems. Atlanta, GA: Association for Information Systems.
- Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. doi:10.1073/pnas.1218772110
- Lara, O. D., & Labrador, M. A. (2013). A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials, 15(3), 1192–1209. doi:10.1109/SURV.2012.110112.00192
- Lee, S., Kim, S.-H., Hung, Y.-H., Lam, H., Y.-A, K., & Yi, J. S. (2016). How do people make sense of unfamiliar visualizations?: A grounded model of novice’s information visualization sensemaking. IEEE Transactions on Visualization and Computer Graphics, 22(1), 499–508. doi:10.1109/TVCG.2015.2467195
- Li, I. (2011). Personal informatics and context: Using context to reveal factors that affect behavior ( Unpublished doctoral dissertation). Pittsburgh, PA: Carnegie Mellon University.
- Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. Proceedings of the CHI 2010 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Li, I., Dey, A. K., & Forlizzi, J. (2011). Understanding my data, myself: Supporting self-reflection with ubicomp technologies. Proceedings of the UbiComp 2011 International Conference on Ubiquitous Computing. New York, NY: ACM.
- Li, I., Forlizzi, J., & Dey, A. (2010). Know thyself: Monitoring and reflecting on facets of one’s life. Proceedings of the CHI 2010 Conference on Human Factors in Computing Systems. New York, NY: ACM.
- Lupton, D. (2013). Understanding the human machine [Commentary]. IEEE Technology and Society Magazine, 32(4), 25–30. doi:10.1109/MTS.2013.2286431
- Mamykina, L. A., Smaldone, M., & Bakken, S. R. (2015). Adopting the sensemaking perspective for chronic disease self-management. Journal of Biomedical Informatics, 56, 406–417. doi:10.1016/j.jbi.2015.06.006
- Mano, R. S., & Mesch, G. S. (2010). E-mail characteristics, work performance and distress. Computers in Human Behavior, 26, 61–69. doi:10.1016/j.chb.2009.08.005
- McGrath, M. J., & Scanaill, C. N. (2013). Wellness, fitness, and lifestyle sensing applications. In M. J. McGrath & C. N. Scanaill (Eds.), Sensor Technologies (pp. 217–248). New York, NY: Springer.
- Oulasvirta, A., Hukkinen, J. P., & Schwartz, B. (2009). When more is less: The paradox of choice in search engine use. Proceedings of the SIGIR 2009 International Conference on Research and Development in Information Retrieval. New York, NY: ACM.
- Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. London, UK: Penguin.
- Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation, 9, 1. doi:10.1186/1743-0003-9-21
- Paul, S. A., & Morris, M. R. (2009). CoSense: Enhancing sensemaking for collaborative web search. Proceedings of the CHI 2009 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Pegoraro, R. (2011, February 14). Facebook news feed filtering can make friends vanish. The Washington Post. Retrieved from http://voices.washingtonpost.com/fasterforward/2011/02/facebook_news_feed_filters_can.html
- Petersen, D., Steele, J., & Wilkerson, J. (2009). WattBot: A residential electricity monitoring and feedback system. Proceedings of the CHI 2009 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Phillips, J. K., & Battaglia, D. A. (2003). Instructional methods for training sensemaking skills. Proceedings of the Interservice/Industry Training, Simulation, and Education Conference. Orlando, FL: National Training Systems Association.
- Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684. doi:10.1038/srep01684
- Rader, E., & Gray, R. (2015). Understanding user beliefs about algorithmic curation in the Facebook news feed. Proceedings of the CHI 2015 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Rapp, A., & Cena, F. (2014). Self-monitoring and technology: Challenges and open issues in personal informatics. Proceedings of the International Conference on Universal Access in Human-Computer Interaction. Cham, Switzerland: Springer International.
- Rapp, A., & Cena, F. (2016). Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data. International Journal of Human-Computer Studies, 94, 1–17. doi:10.1016/j.ijhcs.2016.05.006
- Ricci, F., Rokach, L., & Shapira, B. (2011). In F. Ricci, R. Lior, B. Shapira & P. B. Kantor (Eds.), Introduction to recommender systems handbook (pp. 1–35). Boston, MA: Springer.
- Rooksby, J., Rost, M., Morrison, A., & Chalmers, M. C. (2014). Personal tracking as lived informatics. Proceedings of the CHI 2014 Conference on Human Factors in Computer Systems. New York, NY: ACM.
- Rose, E. (2010). Continuous partial attention: Reconsidering the role of online learning in the age of interruption. Educational Technology Magazine: the Magazine for Managers of Change in Education, 50(4), 41–46.
- Russell, D. M., Stefik, M. J., Pirolli, P., & Card, S. K. (1993). The cost structure of sensemaking. In Proceedings of the CHI 1993 conference on human factors in computer systems. New York, NY: ACM.
- Savolainen, R. (2007). Filtering and withdrawing: Strategies for coping with information overload in everyday contexts. Journal of Information Science, 33(5), 611–621. doi:10.1177/0165551506077418
- Schick, A. G., Gordon, L. A., & Haka, S. (1990). Information overload: A temporal approach. Accounting, Organizations and Society, 15(3), 199–220. doi:10.1016/0361-3682(90)90005-F
- Schneider, S. C. (1987). Information overload: Causes and consequences. Human Systems Management, 7(2), 143–153.
- Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178. doi:10.1037/0022-3514.83.5.1178
- Shah, N. H. (2015). Using big data. In P. R. O. Payne & P. J. Embi (Eds.), Translational informatics (pp. 119–128). London, UK: Springer.
- Shapira, B., Hanani, U., Raveh, A., & Shoval, P. (1997). Information filtering: A new two-phase model using stereotypic user profiling. Journal of Intelligent Information Systems, 8(2), 155–165. doi:10.1023/A:1008676625559
- Soleymani, M., Pantic, M., & Pun, T. (2012). Multimodal emotion recognition in response to videos. IEEE Transactions on Affective Computing, 3(2), 211–223. doi:10.1109/T-AFFC.2011.37
- Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85–99. doi:10.1089/big.2012.0002
- Sweeny, K., Melnyk, D., Miller, W., & Shepperd, J. A. (2010). Information avoidance: Who, what, when, and why. Review of General Psychology, 14(4), 340. doi:10.1037/a0021288
- Teevan, J., Dumais, S. T., & Horvitz, E. (2005). Personalizing search via automated analysis of interests and activities. Proceedings of the SIGIR 2005 International Conference on Research and Development in Information Retrieval. New York, NY: ACM.
- Tollmar, K., Bentley, F., & Viedma, C. (2012).Mobile health mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. Proceedings of the PervasiveHealth 2012 International Conference on Pervasive Computing Technologies for Healthcare. New York, NY: IEEE Press.
- van Rijsbergen, C. J. (1975). Evaluation. In C. J. Van Rijsbergen (Ed.), Information retrieval (pp. 95–132). London, UK: Butterworth & Co.
- Weick, K. E. (1995). Sensemaking in organizations (Vol. 3). Thousand Oaks, CA: Sage.
- Whittaker, S. (2008). Making sense of sensemaking. In T. Erickson & D. W. McDonald (Eds), HCI remixed: Reflections on works that have influenced the HCI community. Boston, MA: MIT Press.
- Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Cambridge, MA: Morgan Kaufmann.
- Wolf, G. (2010, April 28). The data-driven life. The New York Times. Retrieved from http://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html
- Zhong, N., Yao, Y. Y., & Ohishima, M. (2003). Peculiarity oriented multidatabase mining. IEEE Transactions on Knowledge and Data Engineering, 15(4), 952–960. doi:10.1109/TKDE.2003.1209011