I have been a Type 1 Diabetic since I was 13 years old. Over the past 13 years I have had to get used to constantly self-monitoring and recording various aspects of my lifestyle and health on a daily basis. For me this has always been a manual and imprecise practice, and from the perspective of a diabetic the Internet of Things (IoT) presents a lot of opportunities to make diabetes management and control and much simpler and more precise process.
The Internet of Things describes a complex network ecology made up of physical objects that produce their own constant data stream using sensors that detect environmental changes (Mitew 2014). Devices that participate within the IoT ecology are characterized by their ability to track where they are and where they’ve been, store and record the primary data they have gathered, and act with some sort of agency within the online environment whether it be communication with other devices or human beings (Bleecker 2006). It is from the logic of this IoT ecology that the movement (and philosophy) of “quantified self” has grown; which describes a process of self-monitoring and self-tracking using IoT objects that produces data on a range of personal everyday practices such as food consumption, emotional state, and physical/mental performance (Lupton 2013). http://quantifiedself.com/2013/11/doug-kanter-year-diabetes-data/ A look at the “diabetes” archive of the Quantified Self website shows a thriving culture of sharing, aggregation and prosumerism in regards to the personal data they have compiled in regards to their diabetes management and lifestyle (Lupton 2013). For example Type-1 diabetic Doug Kanter (see above video) used data and data visualizations in understand relationships between his diet, blood sugar levels, and insulin (read: medication) doses (Ramirez 2013). But IoT has more potential in this area than simply a thorough self-studying tool. Technology has reached a point where it is now entirely possible to embed a small sensor under the skin that constantly monitors blood sugars and communicates this data directly to an insulin pump that adjusts doses according in response; establishing a dynamic, actionable conversation between two different online devices (Swan 2012). In this sense a network of IoT devices shows great potential in artificially emulating the regular body functions of a healthy, non-diabetic. But of course even with this leap in technology there are still issues of cost and internet availability that will present barriers to accessing this kind of treatment in many developing parts of the world (Mitew 2014).