Abstract
Diabetes self-management continues to present a significant challenge to millions of individuals around the world, as it often requires significant modifications to one’s lifestyle. The highly individual nature of the disease presents a need for each affected person to discover which daily activities have the most positive impact on one’s health and which are detrimental to it. Data collected with self-monitoring can help to reveal these relationships, however interpreting such data may be non-trivial. In this research we investigate how individuals with type 2 diabetes and their healthcare providers reason about data collected with self-monitoring and what computational methods can facilitate this process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alex ADA 1701 NBS, ria, 1-800-Diabetes V 22311. Statistics about diabetes - American Diabetes Association® [Internet]. American Diabetes Association [cited 2014 May 5]. http://www.diabetes.org/diabetes-basics/statistics/. American Diabetes Association, Statistics about Diabetes, http://www.diabetes.org/diabetesbasics/statistics/ Last updated on 04/05/2017. Retrieved on 04/05/2017
Amos AF, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabet Med. 1997;14(Suppl 5):S1–85.
Cordeiro F, Bales E, Cherry E, Fogarty J. 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 [Internet]. New York, NY: ACM; 2015 [cited 2015 Jun 1]. p. 3207–16. [CHI ’15]. http://doi.acm.org/10.1145/2702123.2702154
Dalla Man C, Rizza RA, Cobelli C. Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng. 2007 Oct;54(10):1740–9.
Drummond N, Abdalla M, Beattie JAG. Effectiveness of routine self monitoring of peak flow in patients with asthma. Grampian Asthma Study of Integrated Care [GRASSIC]. BMJ. 1994;308(6928):564–7.
Fenech M, El-Sohemy A, Cahill L, Ferguson LR, French T-AC, Tai ES, et al. Nutrigenetics and nutrigenomics: viewpoints on the current status and applications in nutrition research and practice. J Nutrigenet Nutrigenomics. 2011;4(2):69–89.
Glasziou P, Irwig L, Mant D. Monitoring in chronic disease: a rational approach. BMJ. 2005;330(7492):644–8.
Gove JH, Hollinger DY. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface-atmosphere exchange. J Geophys Res. 2006;111(D8):D08S07.
Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010;363(4):301–4.
Karter AJ, Ackerson LM, Darbinian JA, D’Agostino Jr RB, Ferrara A, Liu J, et al. Self-monitoring of blood glucose levels and glycemic control: the Northern California Kaiser Permanente Diabetes registry∗. Am J Med. 2001;111(1):–9.
Law K, Stuart A, Zygalakis K. Data assimilation: a mathematical introduction. Springer International Publishing Switzerland: Springer; 2015. 256 p.
Ledger D. Inside Wearables - Part 2 [Internet]. Endeavour Partners LLC; 2014. http://endeavourpartners.net/assets/Endeavour-Partners-Inside-Wearables-Part-2-July-2014.pdf
Li I, Dey A, Forlizzi J. A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems [Internet]. New York, NY: ACM; 2010 [cited 2015 May 27]. p. 557–66. [CHI ’10]. http://doi.acm.org/10.1145/1753326.1753409
MacLeod H, Tang A, Carpendale S. Personal informatics in chronic illness management. In: Proceedings of Graphics Interface [Internet]. Toronto, Ont., Canada: Canadian Information Processing Society; 2013. p. 149–56 [cited 2015 May 28]. [GI ’13]. http://dl.acm.org/citation.cfm?id=2532129.2532155
Mamykina L, Mynatt ED, Kaufman DR. Investigating health management practices of individuals with diabetes. Montréal, Québec, Canada: ACM; 2006 [cited 2010 Oct 12]. p. 927–36. http://portal.acm.org/citation.cfm?id=1124772.1124910&coll=ACM&dl=ACM&CFID=108403113&CFTOKEN=94372641
Mamykina L, Heitkemper B, Smaldone A, Kukafka R, Cole-Lewis H, Davidson PG, et al. Structured scaffolding for reflection and problem-solving in diabetes self-management: qualitative study of mobile diabetes detective. J Am Med Inform Assoc. 2016a 23 129–136.
Mamykina L, Levine ME, Davidson PR, Smaldone A, Elhadad N, Albers D. Data-driven health management: reasoning about personally generated data in diabetes with information technologies. J Am Med Inform Assoc. 2016b 23, 526–531
Martin S, Schneider B, Heinemann L, Lodwig V, Kurth H-J, Kolb H, et al. Self-monitoring of blood glucose in type 2 diabetes and long-term outcome: an epidemiological cohort study. Diabetologia. 2006;49(2):271–8.
National Diabetes Statistics Report [Internet]. 2014. http://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf
Noronha J, Hysen E, Zhang H, Gajos KZ. Platemate: crowdsourcing nutritional analysis from food photographs. In: Proceedings of the 24th annual ACM symposium on User interface software and technology [Internet]. New York, NY: ACM; 2011 [cited 2012 Oct 16]. p. 1–12. [UIST ’11]. http://doi.acm.org/10.1145/2047196.2047198
O’Kane MJ, Bunting B, Copeland M, Coates VE. Efficacy of self monitoring of blood glucose in patients with newly diagnosed type 2 diabetes [ESMON study]: randomised controlled trial. BMJ. 2008;336(7654):1174–7.
Paterson B, Thorne S. Expert decision making in relation to unanticipated blood glucose levels. Res Nurs Health. 2000;23(2):147–57.
Polonsky WH, Skinner TC. Perceived treatment efficacy: an overlooked opportunity in diabetes care. Clin Diabetes. 2010;28(2):89–92.
Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn. 1999;37(3):297–336.
Sedigh-Sarvestani M, Albers DJ, Gluckman BJ. Data assimilation of glucose dynamics for use in the intensive care unit. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012. p. 5437–40.
Sevick MA, Stone RA, Novak M, Piraino B. A PDA-based dietary self-monitoring intervention to reduce sodium intake in an in-center hemodialysis patient. PPA. 2008;2:177–84.
Sturis J, Polonsky KS, Mosekilde E, Van Cauter E. Computer model for mechanisms underlying ultradian oscillations of insulin and glucose. Am J Phys. 1991;260(5 Pt 1):E801–9.
The Cost of Diabetes - American Diabetes Association® [Internet]. 2013 [cited 2013 Aug 9]. http://www.diabetes.org/advocate/resources/cost-of-diabetes.html
Welschen LMC, Bloemendal E, Nijpels G, Dekker JM, Heine RJ, Stalman WAB, et al. Self-monitoring of blood glucose in patients with type 2 diabetes who are not using insulin a systematic review. Diabetes Care. 2005;28(6):1510–7.
Acknowledgements
This work was funded by the National Library of Medicine grant “Training in Biomedical Informatics at Columbia University”, T15 LM007079, National Institute of Diabetes and Digestive and Kidney Disease [NIDDK] grant, 1R01DK090372-01A1 and National Library of Medicine grant LM006910.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Mamykina, L., Levine, M.E., Davidson, P.G., Smaldone, A.M., Elhadad, N., Albers, D.J. (2017). From Personal Informatics to Personal Analytics: Investigating How Clinicians and Patients Reason About Personal Data Generated with Self-Monitoring in Diabetes. In: Patel, V., Arocha, J., Ancker, J. (eds) Cognitive Informatics in Health and Biomedicine. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-51732-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-51732-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51731-5
Online ISBN: 978-3-319-51732-2
eBook Packages: MedicineMedicine (R0)