Abstract for debate 10th of December 2015
Can Self-Tracking be a bigger promise in healthcare?
As Self-tracking devices get more and more in the mainstream, we see different usage, motivation and engagement. The literature that arises form different domains, looks at different perspectives towards the domain of self-tracking. In this debate we would like to talk about the design perspective of wearable devices or ‘things’ and look at it from a sociological perspective.
In recent literature we learn that these self-tracking tools and devices have a limited usage. People tend to use these devices for a very short time. In (Fogg, 2010) terms we could map these users as span behavior types, with different purposes or aims. Users are excited and curious for the data, set certain goals if possible within the environment that they use, but after two weeks, a month some literature talks about a 6 month (Shih, 2015) maximum before users drop out.
Question arise if Personal Informatics can have more meaning in a healthcare environment. And how can we then create more engagement within this environment. From a design perspective this is rather challenging. In order to come to an engagement design approach, we also need to look at the wellbeing and happiness that could bring these devices. What does it mean for a patient to see all the objective facts of their life, (Ancker JS1, 2015) came to four major themes in interviewing patients and care givers: ‘ (1) tracking this data feels like work for many patients, (2) personal medical data for individuals with chronic conditions are not simply objective facts, but instead provoke strong positive and negative emotions, value judgments, and diverse interpretations, (3) patients track for different purposes, ranging from sense-making to self-management to reporting to the doctor, and (4) patients often notice that physicians trust technologically measured data such as lab reports over patients’ self-tracked data’.
In a broader perspective we need to ask how society will go about in this matter from an ethic and privacy perspective. If we want to create more engagement with these devices, the user needs to trust this environment. Trust not only in accurateness of the data, but also in who owns the data and what happens with the data.
(Lupton D. , 2014) speaks of 5 modes of self-tracking private, communal, pushed, imposed and exploited. These modes can intersect or overlap with each other. In our research we limit ourselves to private self-tracking, pushed self-tracking and to a certain extend communal self-tracking.
(Lupton D. , 2014), defines these modes as follows:
Private self-tracking, as espoused in the Quantified Self’s goal of ‘self knowledge through numbers’, is undertaken for purely personal reasons and the data are kept private or shared only with limited and selected others.
Pushed self-tracking departs from the private self-tracking mode in that the initial incentive for engaging in self-tracking comes from another actor or agency. Self-monitoring may be taken up voluntarily, but in response to external encouragement or advocating rather than as a wholly self-generated and private initiative.
Communal Self-tracking, while self-tracking, in its very name and focus on the ‘self’ may appear to be an individualistic practice, many self-trackers view themselves as part of a community of trackers (Boesel, 2013a; Lupton, 2013a; Nafus & Sherman, 2014; Rooksby, et al., 2014). They use social media, platforms designed for comparing and sharing personal data and sites such as the Quantified Self website to engage with and learn from other self-trackers.
Considering these 3 modes we would like to look deeper into the possibilities of creating more engagement through the more ‘leisure’ wearable devices that are used in conjunction with the ‘medical’ self-tracking devices chronic patients already use. This will give a more holistic view on the behaviour and lifestyle of the patient, create more context to the data and therefor could be analysed to enable a happiness and wellbeing state for the patient.
By learning about attitudes and behaviour of patients we can empathize more and come to a more engaged relationship between patient and care provider. Help the patient in his or her lifestyle improvement or life comfort.
Ancker JS1, W. H. (2015, Aug 19). You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions. J Med Internet Res.
Boesel, W. E. (2013). Retrieved from Cyberology: http://thesocietypages.org/cyborgology/2013/05/22/what-is-the-quantified-self-now/#more-15719
Fogg, B. &. (2010). Behavior Wizard: A Method for Matching Target Behaviors with Solutions. Stanford University.
Lupton, D. (2014). Self-tracking Modes: Reflexive Self-Monitoring and Data Practices. Imminent Citizenships: Personhood and Identity Politics in the Informatic Age’ workshop.
Lupton, D. (2013a). Understanding the human machine. IEEE Technology & Society Magazine, 32(4), 25-30 .
Nafus, D. &. (2014). This one does not go up to 11: the Quantified Self movement as an alternative big data practice. International Journal of Communication, 8, 1785-1794.
Rooksby, J. R. (2014). Personal tracking as lived informatics. Proceedings of the 32nd annual ACM conference on Human factors in computing systems, Toronto.
Shih, P. H. (2015). Use and Adoption Challenges of Wearable Activity Trackers. iConference 2015 Proceedings.