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.