VS - Quantified Self (wearables, data and its impact on users) - ANM50601

• Anglický název: VS - Quantified Self (wearables, data and its impact on users)
• Zajišťuje: Ústav informačních studií - studia nových médií (21-UISKNM)
• Fakulta: Filozofická fakulta
• Platnost: od 2019
• Semestr: zimní
• Body: 0
• E-Kredity: 6
• Způsob provedení zkoušky: zimní s.:
• Rozsah, examinace: zimní s.:0/2, Zk [HT]
• Počet míst: neurčen / neurčen (15)
• Minimální obsazenost: neomezen
• 4EU+: ne
• Virtuální mobilita / počet míst pro virtuální mobilitu: ne
• Stav předmětu: nevyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Způsob výuky: prezenční
• Další informace: http://novamedia.ff.cuni.cz
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu
• Garant: Mgr. Jakub Fiala, Ph.D.
• Je neslučitelnost pro: ANM50621

Anotace

čeština

Poslední úprava: Mgr. Jakub Fiala, Ph.D. (03.02.2019)

Výzvou tohoto kurzu je zamyslet se nad tím, jak porozumět problematice sebeměření, protože znalost (a to se týká i poznání nás samotných) je sociálním konstruktem. Odhodlání mnoho lidí jako jsou různí datoví aktivisté a nadšenci do sebeměření a sebozorování pro nás mohou být inspirací v tom, co všechno je možné, když se lidé zapojí a aktivně používají technologie, které jsou pro ně aktuální a důležité. Během kurzu se ponoříte do světa nejrůznějšího měření a vaším úkolem bude realizovat vlastní malý etnografický výzkum (viz podmínky zakočení předmětu). Právě kritické myšlení a dobře zvolená argumentace jsou tím, co bude v kurzu hodnoceno, skrze reflexi vlastních dat je možné porozumět problematice Quantified self hlubokým a velmi intenzivním způsobem, které sebeměření vyžaduje.

angličtina

Poslední úprava: Mgr. Jakub Fiala, Ph.D. (03.02.2019)

The course encouraged us to think about how to communicate the issues of self-tracking, because knowledge - even about the self (or ourselves) - is a social product. The dedication of patient advocates, data activists, and quantified-self enthusiasts can inspire you with a view of what is possible when people get involved in the technologies that matter to them. You are guided into the wild jungle - veritable explosion of self-directed tracking, and challenged to conduct your own small etnography research (see section below). It is your critical thinking and well-balanced argumentation what is graded in the course, thanks to your own reflection you learn about Quantified self topic in deep and very intimate way.

Literatura

Poslední úprava: Mgr. Jakub Fiala, Ph.D. (03.02.2019)

Core

LUPTON, Deborah. The quantified self: a sociology of self-tracking. Cambridge: Polity, 2016. ISBN 978-1-5095-0059-8.

NEFF, Gina a Dawn NAFUS. Self-tracking. Cambridge: MIT Press, 2016. The MIT Press essential knowledge series. ISBN 978-0-262-52912-9.

 

Monography

NAFUS, Dawn. Quantified: biosensing technologies in everyday life. Cambridge: The MIT Press, [2016]. ISBN 978-0-262-03417-3.

BOUK, Daniel B. How our days became numbered: risk and the rise of the statistical individual. Chicago: University of Chicago Press, 2015. ISBN 978-0-226-25917-8.

LUPTON, Deborah. Digital sociology [online]. London: Routledge, [2015] [cit. 2019-02-03]. ISBN 978-1-138-02276-8. Dostupné z: http://site.ebrary.com/lib/cuni/Doc?id=10962197

RYAN, Susan Elizabeth. Garments of paradise: wearable discourse in the digital age. Cambridge: MIT Press, 2014. ISBN 978-0-262-02744-1.

SCHÄFER, Mirko Tobias a Karin VAN ES. The datafied society: studying culture through data. Amsterdam: Amsterdam University Press, 2017. ISBN 978-94-6298-136-2.

YOM-TOV, Elad. Crowdsourced health: how what you do on the Internet will improve medicine. Cambridge: MIT Press, [2016]. ISBN 978-0-262-03450-0.

PORTER, Theodore M. Trust in numbers: the pursuit of objectivity in science and public life. Second printing, and first paperback printing. Princeton: Princeton Universtiy Press, 1996. History and philosophy of science. ISBN 0-691-02908-3. Dostupné také z: http://www.gbv.de/dms/bowker/toc/9780691037769.pdf

CHRISTIAN, Brian a Tom GRIFFITHS. Algorithms to live by: the computer science of human decisions. New York: Henry Holt and Company, 2016. ISBN 978-1-62779-036-9.

DOURISH, Paul a Genevieve BELL. Divining a digital future: mess and mythology in ubiquitous computing. Cambridge: MIT Press, [2011]. ISBN 978-0-262-52589-3.

EYAL, Nir a Ryan HOOVER. Hooked: how to build habit-forming products. London: Portfolio Penguin, 2014. ISBN 978-0-241-18483-7.

CARR, Nicholas G. The glass cage: how our computers are changing us. Norton paperback. New York: W. W. Norton & Company, [2014]. ISBN 978-0-393-35163-7.

UĞUR, Seçil. Wearing embodied emotions: a practice based design research on wearable technology. Milan: Springer, [2013]. SpringerBriefs in applied sciences and technology. PoliMI SpringerBriefs. ISBN 978-88-470-5246-8.

 

Articles

LUPTON, Deborah, 2014. Self-tracking cultures: Towards a sociology of personal informatics. Proceedings of the 26th Australian Computer-Human Interaction Conference on Designing Futures the Future of Design. Sydney, Australia: ACM Press, 2014, 77-86. DOI: 10.1145/2686612.2686623. ISBN 9781450306539. Dostupné také z: http://dl.acm.org/citation.cfm?doid=2686612.2686623

LUPTON, Deborah, 2013. Understanding the Human Machine. IEEE Technology and Society Magazine. 32(4), 25-30. DOI: 10.1109/MTS.2013.2286431. ISSN 0278-0097. Dostupné také z: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6679313

LUPTON, Deborah, 2013. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Critical Public Health. 23(4), 393-403. DOI: 10.1080/09581596.2013.794931. ISSN 0958-1596. Dostupné také z: http://www.tandfonline.com/doi/abs/10.1080/09581596.2013.794931

LUPTON, Deborah, 2012. M-health and health promotion: The digital cyborg and surveillance society. 10(3), 229-244. DOI: 10.1057/sth.2012.6. ISSN 1477-8211. Dostupné také z: http://link.springer.com/10.1057/sth.2012.6

BANDURA, Albert, 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review [online]. 84(2), 191-215. DOI: 10.1037/0033-295X.84.2.191. ISSN 0033-295X.

RODDENBERRY, Angela a Kimberly RENK, 2010. Locus of control and self-efficacy: potential mediators of stress, illness, and utilization of health services in college students. Child Psychiatry And Human Development [online]. 41(4), 353-70. DOI: 10.1007/s10578-010-0173-6. ISSN 15733327.

SOLOMON, Paul, 2002. Discovering information in context. Annual Review of Information Science and Technology. 36(1), 229-264.

MORSE, Jack, 2018. Using a Fitbit to regulate your cocaine use is not a good idea. Mashable [online]. [cit. 2018-07-16]. Dostupné z: https://mashable.com/2018/07/10/fitbit-cocaine-use

CUTTONE, Andrea, Michael Kai PETERSEN a Jakob Eg LARSEN, 2014. Four Data Visualization Heuristics to Facilitate Reflection in Personal Informatics. Universal Access in Human-Computer Interaction. Design for All and Accessibility Practice. Cham: Springer International Publishing, 2014, 541-552. Lecture Notes in Computer Science. DOI: 10.1007/978-3-319-07509-9_51. ISBN 978-3-319-07508-2. Dostupné také z: http://link.springer.com/10.1007/978-3-319-07509-9_51

NAKAJIMA, Tatsuo, Vili LEHDONVIRTA, Eiji TOKUNAGA a Hiroaki KIMURA, 2008. Reflecting human behavior to motivate desirable lifestyle. Proceedings of the 7th ACM conference on Designing interactive systems - DIS '08. New York, New York, USA: ACM Press, 2008, 405-414. DOI: 10.1145/1394445.1394489. ISBN 9781605580029. Dostupné také z: http://portal.acm.org/citation.cfm?doid=1394445.1394489

ANCKER, Jessica S, Holly O WITTEMAN, Baria HAFEEZ, Thierry PROVENCHER, Mary VAN DE GRAAF a Esther WEI, 2015. "You Get Reminded You’re a Sick Person": Personal Data Tracking and Patients With Multiple Chronic Conditions. Journal of Medical Internet Research. 17(8), e202-. DOI: 10.2196/jmir.4209. ISSN 1438-8871. Dostupné také z: http://www.jmir.org/2015/8/e202/

RUCKENSTEIN, Minna a Mika PANTZAR, 2017. Beyond the Quantified Self: Thematic exploration of a dataistic paradigm. New media & society. 19(3), 401–418. DOI: 10.1177/1461444815609081. ISBN 10.1177/1461444815609081. Dostupné také z: http://journals.sagepub.com/doi/10.1177/1461444815609081

GILMORE, James N, 2016. Everywear: The quantified self and wearable fitness technologies. 18(11), 2524-2539. DOI: 10.1177/1461444815588768. ISSN 1461-4448. Dostupné také z: http://journals.sagepub.com/doi/10.1177/1461444815588768

VETROVSKY, Tomas, Jozef CUPKA, Martin DUDEK, Blanka KUTHANOVA, Klaudia VETROVSKA, Vaclav CAPEK a Vaclav BUNC, 2017. Mental health and quality of life benefits of a pedometer-based walking intervention delivered in a primary care setting. Acta Gymnica. 47(3), 138-143. DOI: 10.5507/ag.2017.017. ISSN 23364912. Dostupné také z: http://gymnica.upol.cz/doi/10.5507/ag.2017.017.html

LUBANS, David Revalds a Philip James MORGAN, 2007. Social, psychological and behavioural correlates of pedometer step counts in a sample of Australian adolescents. Journal of Science and Medicine in Sport. 2009(12), 141—147. DOI: 10.1016/j.jsams.2007.06.010. ISBN 10.1016/j.jsams.2007.06.010. Dostupné také z: http://linkinghub.elsevier.com/retrieve/pii/S1440244007001892

KNEIDINGER-MÜLLER, Bernadette, 2018. Self-Tracking Data as Digital Traces of Identity: A Theoretical Analysis of Contextual Factors of Self-Observation Practices. International Journal of Communication. 12(2018), 629-646. ISSN 1932-8036.

CHOE, Eun Kyoung, Nicole B. LEE, Bongshin LEE, Wanda PRATT a Julie A. KIENTZ, 2014. Understanding quantified-selfers' practices in collecting and exploring personal data. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14. New York, New York, USA: ACM Press, 2014, s. 1143-1152. DOI: 10.1145/2556288.2557372. ISBN 9781450324731. Dostupné také z: http://dl.acm.org/citation.cfm?doid=2556288.2557372

Požadavky ke zkoušce

Poslední úprava: Mgr. Jakub Fiala, Ph.D. (03.02.2019)

Student (or group of two) is encouraged to conduct own small etnography research (in case of group both must track same area/topic), prepare paper (between 9K and 18K characters with spaces) and in-class presentation. At the course, we usually think about our first hand experiences using self-tracking methods and tools. This approach is embodied in Three Prime Questions:

1. What did you do?
2. How did you do it?
3. What did you learn?

Anybody share a self-tracking project, within the constraints of time and common sense. Every talk about actual practice has value because it lets us learn and think about one person’s approach. Since the goal is collaborative learning, rather than killing time through entertainment, a speaker who is struggling due to nervousness, confusion, or lack of preparation can be helped along by questions from the group.

Scientific theories, demos of tools and apps, and/or philosophical speculation are greetly welcomed. But in the context of a Quantified self they distract unless they are grounded in actual attempts at self-tracking and self-experiment. When theory or demonstrations are embedded in an account of personal experience, however, they work great. Tell us what you’ve done, how you did it, and what it means to you.

Topics include, but are not limited to:

• Fitness and health tracking,
• Chemical body load counts,
• Personal genome sequencing,
• Lifelogging,
• Metabolic monitoring,
• Self experimentation,
• Behavior monitoring,
• Location tracking,
• Sleep tracking,
• Mood and emotion tracking,
• Medical self-diagnostics.

Sylabus

Poslední úprava: Mgr. Jakub Fiala, Ph.D. (03.02.2019)

1. An Intoduction to Quantified self

1.1 We Have Always Been Quantified

1.2 The Practices

Communal tracking or what increasingly is referred to as "citizen science" involves donating privately tracked data to public health research for the greater good.
Pushed tracking can be where people are given economic incentives - such as when employers "incentivize" employees through various sticks and carrots to self-track - or receive social pressure that makes the cost of not tracking high.
Imposed tracking is when there is no meaningful alternative, such as when activity tracking becomes a prerequicite for employment or insurance coverage.

1.3 The Tools

Ubiquitous computing, the idea that computers would one day be a part of bodies and environments, not just offices, arose in the 1980s and has been na important idea within technology communities ever since.
Persuasive computing is the idea that computers can "nudge" people to act in particular ways, and this idea, too, helped create common design strategies in wearables.
This subfield of computer science also championed gamification, or using game techniques to encourage users to perform a certain action.

1.4 The Communities

 

2. When Personal (Data) Gets Political

2.1 Am I Normal?

This conflation of mathematically normal distribution with "normal" as kind of ideal gives tremendous power to those who decide what to measure. Many of us lives in the sort of society that valorizes "self-improvement" and "taking-action". Choosing not to do something about potential problem makes one double outlier, most definitely "not normal" in the sense of failling short of the cultural ideal of the striving self-improver.
Accepting this view of a "failed step-taker" leaves no room for questions about the social situations that create the near impossibility of "active lifestyles" for many people. Believing or rejecting the failed step-taker model is a choice that people can make as technology users, but as long as the technologies are designed in this way, it is not a model that users can simply escape.

2.2 Who Asks the Questions?

2.3 Public Health Outcomes

Many established programs for self-improvement, whether a diet, a financial plan, or a productivity program, do start with data that brings to life patterns of behavior through some form of tracking. But,  simply knowing (and agreeing) that the behavior is healthy or unhealthy may not be enough to change it.

2.4 Who Has Access to Data?

2.5 Who profits?

 

3. Making Sense of Data

3.1 Tracking to Monitor and Evaulate

Many self-trackers learn how to make judgments about which data is most appropriate for which goal, and whatever the gap between reality and goal is one that even needs closing. Many self-trackers think of this process as a kind of feedback loop, a term from computer science for a system that generates information and then adjusts in response to that information.

3.2 Self-tracking to Elicit Sensations

This form of tracking, perhaps more stronger than the others, is often what people have in mind when they say their tracking is connected to mindfulness. Through the numbers they become aware of their bodily states.

3.3 Aesthetic Curiosity

3.4 Debugging a Problem

3.5 Cultivating a Habit

Many self-trackers use data to support "habit hacking," or creating new habits and changing old ones.
Who cultivated a flossing habit by starting to floss one tooth only. Easier to do than the whole mouth, and therefore easier to in initiate, Fogg's single gesture came to feel overtime part of the "natural" flow of things. He calls the process "tiny habits" were triggering small behaviors can lead to change over time. Self-trackers sometimes talk about "chaining" habits together by timing a new habit like doing sit-ups just after pre-existing habit, like drinking coffee, so that they effectively become one long gesture - a morning routine, say.

 

4. Self-Tracking and the Technology Industry

4.1 What We Mean by "Industry"?

4.2 How Industrial Actors See Their Markets

4.2 The Economic Role of Data

4.3 Making Markets in Self-Tracking

Self-tracking tools are emerging at the intersections of key social arenas - between health and wellness, between work and life, and between accessibility nad luxury.

 

5. Self-Tracking and Medicine

5.1 Empowering Patients

Smartphones and other devices change where healthcare happens.
The process of biomedicalization also means that doctors must compete with app stores and shopping malls for people's attention as they look to lose weight, sleep better, and manage symptoms of chronic diseases, even though widespread belief in the importance of medicine is what created this situation in the first place. Biomedicalization blurs the lines between patient and consumer, and between self-care and doctor's orders.
Putting data in the hands of people eventually creates new ways for them to solve their own problems without clinical intervention, and this is a good reason for medical organization to pursue it. However, data alone cannot create new ways for people to engage in their own health.
Assumption that people should have access to the same type of information that doctors have - such as heart rate, blood pressure, blood oxygen saturation - is built into the design of medical self-tracking devices.

5.2 Bridging Home and Clinic

5.3 Data Driven Health Innovation and Discovery

 

6. Future Directions for Quantified self

6.1 The Fight for Data Access

6.2 The Fight for Data Privacy and Security

6.3 The Legal and Regulatory Questions about Data (USA versus EU)

6.4 Future Directions for Technology Innovation

6.5 Debates about Health and Equity

6.6 The Fights over Meaning

Datafication means that societies privilege data, and data-driven outcomes, over other kinds of knowing. When data mediates so many things, control over the meanings of data is a type of power.
Every time we glance at our smartphone to see how many steps we've taken is an opportunity to ask questions about how we want to make sense of our worlds, our experiences, and our bodies, and what we want to say to the company that make it their business to help us do those things. The line between ourselves and our data is where we choose to draw it.