Author(s):
- Choe, Eun Kyoung
- Abdullah, Saeed
- Rabbi, Mashfiqui
- Thomaz, Edison
- Epstein, Daniel A.
- Cordeiro, Felicia
- Kay, Matthew
- Abowd, Gregory D.
- Choudhury, Tanzeem
- Fogarty, James
- Lee, Bongshin
- Matthews, Mark
- Kientz, Julie A.
Abstract:
The authors present an approach for designing self-monitoring technology called “semi-automated tracking,” which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.
Document:
https://doi.org.10.1109/MPRV.2017.18
References:
1. S. Consolvo et al., “Activity Sensing in the Wild: A Field Trial of UbiFit Garden”, Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 1797-1806, 2008. Show Context Access at ACM Google Scholar
2. W.J. Korotitsch and R.O. Nelson-Gray, “An Overview of Self-Monitoring Research in Assessment and Treatment”, Psychological Assessment, vol. 11, no. 4, pp. 415-425, 1999. Show Context CrossRef Google Scholar
3. J. Froehlich et al., “The Design and Evaluation of Prototype Eco-Feed-back Displays for Fixture-Level Water Usage Data”, Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 2367-2376, 2012. Show Context Google Scholar
4. D.A. Epstein et al., “A Lived Informatics Model of Personal Informatics”, Proc. 2015 ACM Int’l J. Conf. Pervasive and Ubiquitous Computing, pp. 731-742, 2015. Show Context Access at ACM Google Scholar
5. J. Rodgers and L. Bartram, “Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback”, IEEE Trans. Visualization and Computer Graphics, vol. 17, no. 12, pp. 2489-2497, 2011. Show Context View Article Full Text: PDF (580KB) Google Scholar
6. F. Cordeiro et al., “Barriers and Negative Nudges: Exploring Challenges in Food Journaling”, Proc. 33rd Ann. ACM Conf. Human Factors in Computing Systems, pp. 1159-1162, 2015. Show Context Access at ACM Google Scholar
7. A.A. Stone and S. Shiffman, “Ecological Momentary Assessment (EMA) in Behavioral Medicine”, Annals of Behavioral Medicine, vol. 16, no. 3, pp. 199-202, 1994. Show Context Google Scholar
8. M.S. Patel, D.A. Asch and K.G. Volpp, “Wearable Devices as Facilitators Not Drivers of Health Behavior Change”, J. Am. Medical Assoc., vol. 313, no. 5, pp. 459-460, 2015. Show Context CrossRef Google Scholar
9. Subjective Well-being: Measuring Happiness Suffering and other Dimensions of Experience, National Academies Press, 2014. Show Context Google Scholar
10. I. Li, A.K. Dey and J. Forlizzi, “Using Context to Reveal Factors that Affect Physical Activity”, ACM Trans. Computer-Human Interaction, vol. 19, no. 1, 2012. Show Context Access at ACM Google Scholar
11. E.K. Choe et al., “Understanding Quantified-Seifers’ Practices in Collecting and Exploring Personal Data”, Proc. 32nd Ann. ACM Conf. Human Factors in Computing Systems, pp. 1143-1152, 2014. Show Context Google Scholar
12. E.K. Choe et al., “SleepTight: Low-Burden Self-Monitoring Technology for Capturing and Reflecting on Sleep Behaviors”, Proc. ACM Int’l J. Conf. Pervasive and Ubiquitous Computing, pp. 121-132, 2015. Show Context Access at ACM Google Scholar
13. M. Kay et al., “Lullaby: A Capture & Access System for Understanding the Sleep Environment”, Proc. ACM Int’l J. Conf. Ubiquitous Computing, pp. 226-234. Show Context Access at ACM Google Scholar
14. P. Adams et al., “Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild”, Proc. 8th Int’l Conf. Pervasive Computing Technologies for Healthcare, pp. 72-79, 2014. Show Context CrossRef Google Scholar
15. E. Thomaz, I. Essa and G.D. Abowd, “A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing”, Proc. ACM Int’l J. Conf. Pervasive and Ubiquitous Computing, pp. 1029-1040, 2015. Show Context Access at ACM Google Scholar
16. M. Rabbi et al., “MyBehavior: Automatic Personalized Health Feedback from User Behaviors and Preferences Using Smart-phones”, Proc. ACM Int’l J. Conf. Ubiquitous Computing, pp. 707-718, 2015. Show Context Access at ACM Google Scholar
17. F. Cordeiro et al., “Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture”, Proc. 33rd Ann. ACM Conf. Human Factors in Computing Systems, pp. 3207-3216, 2015. Show Context Access at ACM Google Scholar
18. J. Choi and R. Gutierrez-Osuna, “Using Heart Rate Monitors to Detect Mental Stress”, Wearable and Implantable Body Sensor Networks, pp. 219-223, 2009. Show Context View Article Full Text: PDF (696KB) Google Scholar
19. N.B. Anderson et al., Stress in America: Paying with our Health, Amer. Psychological Assoc., Feb. 2015, [online] Available: www.apa.org/news/press/releases/stress/2014/stress-report.pdf. Show Context Google Scholar
20. G. Valenza et al., “Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis”, IEEE J. Biomedical and Health Informatics, vol. 18, no. 5, pp. 1625-1635, 2014. Show Context View Article Full Text: PDF (1134KB) Google Scholar
21. J. Blum and E. Magill, “M-Psychiatry: Sensor Networks for Psychiatric Health Monitoring”, Proc. 9th Ann. Postgraduate Symp. Convergence of Telecommunications Networking and Broadcasting, pp. 33-37, 2008. Show Context Google Scholar
22. J.E. Bardram et al., “Designing Mobile Health Technology for Bipolar Disorder: A Field Trial of the Monarca System”, Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 2627-2636, 2013. Show Context Access at ACM Google Scholar
23. E. Frank, H.A. Swartz and D.J. Kupfer, “Interpersonal and Social Rhythm Therapy: Managing the Chaos of Bipolar Disorder”, Biological Psychiatry, vol. 48, no. 6, pp. 593-604, 2000. Show Context CrossRef Google Scholar
24. K. Yatani and K.N. Truong, “BodyScope: A Wearable Acoustic Sensor for Activity Recognition”, Proc. ACM Int’l J. Conf. Ubiquitous Computing, pp. 341-350, 2012. Show Context Access at ACM Google Scholar
25. K.M. Stawarz, A.L. Cox and A. Bland-ford, “Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation”, Proc. 33rd Ann. ACM Conf. Human Factors in Computing Systems, pp. 2653-2662, 2015. Show Context Access at ACM Google Scholar