The State of Now in Quantified Self – Survey

With this survey we want to get a more in depth view on Quantified Self, behaviour and trends. Click here to fill in the survey!

We want to see if you as a respondent tracked yourself in the past 12 months with devices such as Fitbit, Jawbone Up, Withings Pulse or Aura, Body Media and so forth. Maybe you use mobile apps such as Runkeeper, Moves, Human, Fitness Pal. Maybe you started in the past 12 months and you quit or maybe you are tracking yourself for years already. Looking forward to see your answers! This survey is part of PHD on designing for more engagement, wellbeing and happiness in Personal Informatics!  We will donate €1 to the ‘International Alzheimer LIga’ and ‘Doctors withouth borders’ for each respondent. Thanks for your collaboration!

Click here to fill in the survey!

Self-Tracking Cultures and the Emergence of Hybrid Beings?

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photo credit: Arman Zhenikeyev/Shutterstock

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.

 

Works Cited

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.

Thesis Designing for Personal Health

View the thesis here!

We observe that more “smart devices” are becoming part of our daily life, and people that quantify aspects of their lifestyle are becoming more mainstream. In doing so, they leave a huge digital footprint behind in an active and passive way.

We notice that the Quantified Self is mainly focused on creating awareness towards a healthier lifestyle. We learn that there are opportunities for realizing healthcare that is more oriented and organized around prevention. Not only on an individual level, but also on a population level. Patterns might be discovered in user data helping to support predictions in a more granular and personalized way.  At the same time, a lot of questions arise when using Quantified Self. How do these device integrate in people’s daily life? Are they as effective as we think? Do they create enough awareness and persuasion to create a sustainable and healthier lifestyle? Do they facilitate a structural behavior change with the user?  Do they continue the lifestyle they adopted during the tracking period? Or are we seeing more a temporary phenomenon in the usage and behavior changes?

RunKeeper and Goalsetting

A few months ago RunKeeper implemented goalsetting in their application. What I like about is of course the different options to set goals. Especially the tiny incremental goalssetting possibilities and the immediate feedback you get accordingly.

My routine for more then a year is to do a workout 3 times a week, if possible more, but 3 times a week for sure. I usually run 10 minutes – 10 minutes Tai Chi – another 10 minute run. So it is not a lot, it takes 30 minutes of my time, and it is easy to manage.

Since the RunKeeper update I tried different goals, but the one I really like ‘run total distance by x date. Every week I try to set another goal, just a tiny little bit higher then the previous one. So my end goal is to run 10 km a week. What the trigger is, is really simple, seeing the progress bar grow during the week in achieving my goal, this is great feedback! And after my run the awesome feeling when I get when I get immediatly the overview of what I achieved! Below the different screens that show me my evolution in reaching my goal!

If you want to start working out, try this method to aim at a long term of behavior change. It will guide you smoothly towards your next goal.

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Progress screen immediately available after your workout is finished, gives the ‘Awesome’ feeling.

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Screen two more global overview of the achievement, result of the previous week, workouts per week

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Screen 3 where the different goals that are passed, aligned with dates.

Awesome! 97% Sleep efficiency

Sat_14_09_2013

I think this never happened before! When I woke up I did’t had the feeling that my sleepscore would be so high, although I felt I fell in a coma from the moment my head hit the pillow last night. I was really relaxed yesterday, especially in the evening. First I went to the hairdresser, and the guy who washes your hair, is really good in head massages, it is a real pleasure to feel his hands on your head, really smooth, no words for it really. After that went to the market to buy some good food, and then had a complete body massage. Getting rid off all the stress that I had in June and July and went home. Cooked watch some tele and went to bed around 11.00. I guess it was a nice day and spoiled my brain and body. Resulting in a 97 % sleep effeciency, just awesome!

Sleepdata during a cold and breathing problems

Last night was my first refreshing night since days. Being sick with a serious cold and breathing problems I had a hard time sleeping well. But this morning I all of sudden felt energized again, and better, not so tired and kind a ready to start my day.

I like to share my sleeping data during these days, because even you know you are not sleeping well or feel like you slept very well, the data is always interesting to look at. Last night was very peculiar with almost no wake ups, and a high score of sleep effeciency 91%, which is very special, it doens’t happen that much, usually my score is in the 80″s.

Thursday18_04

Friday19_04

Sunday21_04

Monday22_4

H(app)athon Project

Yesterday I was at a H(app)athon meet up to brainstorm on new ideas to measure happiness. Based on a range of measurements already done like hapiness at the workplace, life satisfaction, health and so forth, we came up with an e-mirror app. Based on e-harmony dating websites. Working with mentors who are 5-10 % feel more happy then you are, and creating kind of a baseline, either matched to your mentor, or to a group of people with whom you have a match, and work your way to a more happy life. Like the idea?