Author(s):
- White, Gary
- Liang, Zilu
- Clarke, Siobhan
Abstract:
A variety of self-Tracking applications and devices have been developed in recent years to support users in tracking their weight, calories eaten, physical activities, sleep and productivity. The availability of all this data from multiple streams provides a rich environment for experimentation that allows users to improve certain aspects of their lives such as losing weight, getting better sleep or being more productive. In this paper we propose a framework that guides users to define, track, analyse, improve and control goals for better personal productivity. We present the outcome of a single-subject case study that was implemented over one year based on the proposed framework for academic productivity. This pilot study demonstrates how longitudinal multistream self-Tracking data can be leveraged to gain actionable insights into personal productivity.
Document:
https://doi.org/10.1109/CBMI.2019.8877475
References:
1. C. Gurrin, A. F. Smeaton, A. R. Doherty et al., “Lifelogging: Personal big data”, Foundations and Trends in information retrieval, vol. 8, no. 1, pp. 1-125, 2014. CrossRef Google Scholar 2. L. Hood and M. Flores, “A personal view on systems medicine and the emergence of proactive p4 medicine: predictive preventive personalized and participatory”, New biotechnology, 2012. CrossRef Google Scholar 3. P. Moore and A. Robinson, “The quantified self: What counts in the neoliberal workplace”, New Media & Society, vol. 18, no. 11, pp. 2774-2792, 2016. CrossRef Google Scholar 4. Z. Liang and M. A. Chapa-Martell, “Framing self-quantification for individual-level preventive health care”, Proceedings of the 28th Australian Conference on Computer-Human Interaction, pp. 336-343, 2015. CrossRef Google Scholar 5. S. Vamos and K. Klein, “Our self-tracking movement and health literacy: are we really making every moment count?”, Global health promotion, vol. 25, no. 2, pp. 85-89, 2018. CrossRef Google Scholar 6. G. White, C. Cabrera, A. Palade and S. Clarke, “Augmented reality in iot”, Service-Oriented Computing: 16th International Conference ICSOC 2018 Workshop (CIoTs), November 12–15, 2018. Google Scholar 7. G. White, A. Palade, C. Cabrera and S. Clarke, “Autoencoders for QoS prediction at the edge”, 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom) (PerCom 2019), Mar. 2019. View Article Full Text: PDF (3022KB) Google Scholar 8. G. White and S. Clarke, “Smart cities with deep edges”, ECML PKDD 2018 Workshops, pp. 53-64, 2019. CrossRef Google Scholar 9. R. Ravichandran, S.-W. Sien, S. N. Patel, J. A. Kientz and L. R. Pina, “Making sense of sleep sensors: How sleep sensing technologies support and undermine sleep health”, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 6864-6875, 2017. Access at ACM Google Scholar 10. Z. Liang and B. Ploderer, “Sleep tracking in the real world: a qualitative study into barriers for improving sleep”, Proceedings of the 28th Australian Conference on Computer-Human Interaction, pp. 537-541, 2016. Access at ACM Google Scholar 11. M. Kay, D. Morris, J. A. Kientz et al., “There’s no such thing as gaining a pound: Reconsidering the bathroom scale user interface”, Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 401-410, 2013. Access at ACM Google Scholar 12. J. Weatherall, Y. Paprocki, T. M. Meyer, I. Kudel and E. A. Witt, “Sleep tracking and exercise in patients with type 2 diabetes mellitus (step-d): Pilot study to determine correlations between fitbit data and patient-reported outcomes”, JMIR mHealth and uHealth, vol. 6, no. 6, 2018. CrossRef Google Scholar 13. S. Taylor, A. Sano, C. Ferguson, A. Mohan and R. Picard, “Quantifyme: An open-source automated single-case experimental design platform”, Sensors, vol. 18, no. 4, pp. 1097, 2018. CrossRef Google Scholar 14. R. Karkar, J. Schroeder, D. A. Epstein, L. R. Pina, J. Scofield, J. Fogarty, et al., “Tummytrials: a feasibility study of using self-experimentation to detect individualized food triggers”, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 6850-6863, 2017. Access at ACM Google Scholar 15. E. O. Lillie, B. Patay, J. Diamant, B. Issell, E. J. Topol and N. J. Schork, “The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?”, Personalized medicine, vol. 8, no. 2, pp. 161-173, 2011. CrossRef Google Scholar 16. R. D. Mirza and G. H. Guyatt, “A randomized clinical trial of n-of-1 trialstribulations of a trial”, JAMA internal medicine, vol. 178, no. 10, pp. 1378-1379, 2018. CrossRef Google Scholar 17.Method and system for tracking employee productivity via electronic mail, Oct. 2000. Google Scholar 18.Method and system for tracking employee productivity in a client/server environment, May 2004. Google Scholar 19. Y. W. Ramírez and D. A. Nembhard, “Measuring knowledge worker productivity : A taxonomy”, Journal of intellectual capital, vol. 5, no. 4, pp. 602-628, 2004. CrossRef Google Scholar 20. J. Rooksby, P. Asadzadeh, M. Rost, A. Morrison and M. Chalmers, “Personal tracking of screen time on digital devices”, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 284-296, 2016. Access at ACM Google Scholar 21. Y.-H. Kim, J. H. Jeon, E. K. Choe, B. Lee, K. Kim and J. Seo, “Timeaware: Leveraging framing effects to enhance personal productivity”, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 272-283, 2016. Access at ACM Google Scholar 22. S. Whittaker, V. Kalnikaite, V. Hollis and A. Guydish, “‘don’t waste my time’: Use of time information improves focus”, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 1729-1738, 2016. Access at ACM Google Scholar 23. Y.-H. Kim, E. K. Choe, B. Lee and J. Seo, “Understanding personal productivity”, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019. Access at ACM Google Scholar 24. B. P. Haynes, “An evaluation of office productivity measurement”, Journal of Corporate Real Estate, vol. 9, no. 3, pp. 144-155, 2007. CrossRef Google Scholar 25. A. P. Brief and R. J. Aldag, “Employee reactions to job characteristics: A constructive replication”, Journal of Applied Psychology, vol. 60, no. 2, pp. 182, 1975. CrossRef Google Scholar 26. N. G. Dodd and D. C. Ganster, “The interactive effects of variety autonomy and feedback on attitudes and performance”, Journal of organizational behavior, vol. 17, no. 4, pp. 329-347, 1996. CrossRef Google Scholar 27. D. Clements-Croome and Y. Kaluarachchi, “Assessment and measurement of productivity”, Creating the productive workplace, vol. 15, no. 11, pp. 129-166, 2000. Google Scholar 28. J. R. Hackman and G. R. Oldham, “Motivation through the design of work: Test of a theory”, Organizational behavior and human performance, vol. 16, no. 2, pp. 250-279, 1976. CrossRef Google Scholar 29. A. B. Bakker and M. P. Bal, “Weekly work engagement and performance: A study among starting teachers”, Journal of occupational and organizational psychology, vol. 83, no. 1, pp. 189-206, 2010. CrossRef Google Scholar 30. T. A. Wright, R. Cropanzano and D. G. Bonett, “The moderating role of employee positive well being on the relation between job satisfaction and job performance”, Journal of occupational health psychology, vol. 12, no. 2, pp. 93, 2007. CrossRef Google Scholar 31. A. Mathur, M. Van den Broeck, G. Vanderhulst, A. Mashhadi and F. Kawsar, “Tiny habits in the giant enterprise: Understanding the dynamics of a quantified workplace”, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015. Access at ACM Google Scholar 32. R. W. Weisberg, Modes of expertise in creative thinking: Evidence from case studies, 2006. Google Scholar 33. Z. Liang, B. Ploderer, W. Liu, Y. Nagata, J. Bailey, L. Kulik, et al., “Sleepexplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors”, Personal and Ubiquitous Computing, vol. 20, no. 6, pp. 985-1000, 2016. CrossRef Google Scholar 34. G. White, A. Palade and S. Clarke, “Forecasting qos attributes using lstm networks”, 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2018. View Article Full Text: PDF (3935KB) Google Scholar 35. R. Agrawal, T. Imieliński and A. Swami, “Mining association rules between sets of items in large databases” in Acm sigmod record, ACM, vol. 22, no. 2, pp. 207-216, 1993. Access at ACM Google Scholar 36. Z. Liang, M. A. Chapa Martell and T. Nishimura, “Framing self-quantification for individual-level preventive health care”, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 547-552, 2016. CrossRef Google Scholar 37. M. E. Rollo, T. Bucher, S. P. Smith and C. E. Collins, “Servar: An augmented reality tool to guide the serving of food”, International Journal of Behavioral Nutrition and Physical Activity, vol. 14, no. 1, pp. 65, 2017. CrossRef Google Scholar 38. Z. Liang and M. A. Chapa Martell, “Validity of consumer activity wristbands and wearable eeg for measuring overall sleep parameters and sleep structure in free-living conditions”, Journal of Healthcare Informatics Research, vol. 2, no. 1–2, pp. 152-178, 2018. CrossRef Google Scholar 39. A. Tajbakhsh, M. Rahmati and A. Mirzaei, “Intrusion detection using fuzzy association rules”, Applied Soft Computing, vol. 9, no. 2, pp. 462-469, 2009. CrossRef Google Scholar 40. C. Creighton and S. Hanash, “Mining gene expression databases for association rules”, Bioinformatics, vol. 19, no. 1, pp. 79-86, 2003. CrossRef Google Scholar