Self-Tracking – Gina Neff and Dawn Nafus

Thoughts: This was an OK book. In addition to laying out many of the forms self-tracking can take, Neff and Nafus outline a range of social and political factors relating to various self-tracking technologies and practices, both now and in the future. Chapter 3 had the most useful information, but I experienced no great epiphanies while reading it.

(The notes below are not a summary of the book, but rather raw notes - whatever I thought, at the time, might be worth remembering.)

Neff, Gina and Dawn Nafus. 2016. Self-Tracking. MIT.

1. An Introduction to Self-Tracking

• 7: tension in self-quantification: self-tracking can be used for surveillance, privacy violation, for tailored ads etc., but can also be very practical for individuals when mindfully implemented.
• 9: new term: exposome (the pollutants that bodies are exposed to) cf. genome, microbiome…
• 11: many current self-tracking applications just measure things, but don’t help people figure out what questions they should be asking, how they can run experiments, etc.
• 12: goals of the book: outline how self-tracking practitioners go about tracking – some best practices. Identify areas where policies can be implemented so that self-tracking is better aligned with public interest
• 12: authors write from more of a cultural/social/observation standpoint, as opposed to a behavioural or technological one
• 15: Ben Franklin famously kept a daily log, but such diaries and journals were actually common in the 1700s
• 15-16: three general approaches:
• “self-tracking” (e.g. Franklin - plays an active role in changing one’s life) and “life-logging” (more passive, supports personal reflection), and self-experimentation.
• 24: various researchers argue that when people self track, they are:
  • Outsourcing bodily management - relieves mental burden
  • Demonstrating to themselves that they can properly care for themselves
• 25: authors think of self-trackers as transducers: capture some qualities of a signal but not all of them, and the ones they capture may be more or less correlated with the things we really want to measure
• 25: self-tracking lies near one end of a spectrum, with “other-tracking” lying at the opposite end
  • 25-26 E.g. pushed tracking (people are incentivized to self-track), imposed tracking
• 28: persuasive computing: the idea that computers can nudge people towards certain actions. Related concept: gamification

2. What Is at Stake? The Personal Gets Political

• Many (but not all) biological traits lie along a normal distribution- people can get confused when comparing any one trait to the rest of a population. A value that is not “normal” may be perceived as a problem, whether or not it actually is. Cf. Medical model of disability
• 65: who owns data? We often think of ownership being binary, but anthropologist Bill Maurer suggests it may make most sense to think of data as having degrees of ownership (like kinship, he argues - you can be more or less closely related to someone). If data is from your accelerometer is turned into data on the number of steps you take using proprietary software, the data is a bit like a child - neither you nor the company could have made it alone

3. Making Sense of Data

• 69: “Data is a strange product in the sense that it often reveals more than its designers intended, yet less than is required to be useful.” -for data to be useful, it often needs to be manipulated, tinkered with, combined, added to, etc.
• 70: authors list 5 common styles/purposes for self-tracking: monitoring/evaluating, eliciting sensations, aesthetic curiosity, debugging a problem, cultivating a habit

Tracking to monitor and evaluate

• 71: usually done to see whether one is meeting a goal/target

Practical Considerations when tracking to evaluate

• 74: try to keep the measure as simple as possible, while being relevant to the goal at hand. Often, qualitative/subjective measures do the job.

Self-tracking to elicit sensations

• 75: a “prosthetic of feeling” -can make yourself more attuned to your own state, lead to increased awareness.
• 77: can help foster increased mindfulness
• 77: can generate ideas about cause and effect. Seth Roberts: “you can’t falsify a hypothesis you don’t have”

Practices for tracking to elicit sensations

• 78: often requires a lot of trial and error

Aesthetic curiosity

• tracking in order to create art
• 82: to look up: Trace, app which turns doodles into walking directions

Practical considerations for aesthetic projects

Debugging a problem

• 85: “diagnostic categories and tests are designed for the people who fall in the center of the bell curve, not outliers.” Diagnoses that are uncommon or recently recognized might not actually be arrived at by doctors, so represent a situation where personal debugging may be useful

Practical considerations for debugging

• 86: in general, three factors are worth recording: symptoms, possible triggers, possible things that bring relief
• 88: one approach is ABAB (alternating) testing, where a treatment is started and then paused

Cultivating a habit

• Aka “habit hacking”
• Can be one aspect of setting up one’s environment to make good habits easy

Practical considerations for new habits

What makes good self-tracking practice?

• 94: one basic measure: did you learn something?

Further practical considerations

• 96-99: authors offer some suggestions:

  1. Start with brevity - begin with a short experiment
  2. Focus on one or two things
  3. Name those things with care
  4. Time and location are good data curators
  5. Be realistic about the work
  6. Numbers, words and pictures all count
  7. Numbers have qualities - consider how you set up the scale for whatever you’re measuring
  8. Words and pictures have quantities
     1. Check out tools like 750 Words
  9. Self tracking tools do not have to be fancy
  10. Do a few trial runs

• 99-103: They offer suggestions about how to interpret data

  1. Time and location are strong clues
     1. Watch for spikes, be aware of time lag effects
     2. Some useful tools: Data Sense, Fluxtream
  2. Rolling averages can clarify an underlying trend
  3. Annotate. “numbers don’t tell stories, people do.”
     1. Thought: this could be a good reason to take more pictures, to give me a sense of what I was doing around a particular time
  4. Collages support visual sensemaking - “visual patterns can yield more viscerally powerful feedback than numbers”
  5. Missing data isn’t really absent - patches of missing data have meaning
  6. Telling the story is an opportunity to craft it - people think more carefully about data when they need to explain/present it to someone else
  7. Comparisons with other people can provide context

• 103: authors state that “nutritionists, naturopaths[!] mental health professionals, and others who take holistic approaches” are more often interested in self tracking data than doctors

4. Self-Tracking and the Technology Industry

• 111: “Probabilities ’tame chance’, as historian Ian Hacking would say, and when chance is tamed, investment can flow” - personal differences are important, but tech companies are likely to design for people in the hump of the statistical curve.
• 112: “technological solutionism“: the idea that complex social problems can be solved by technology alone

5. Self-Tracking and Medicine

• 148: self-tracking devices, esp. ubiquitous ones like smartphones, can be used for large epidemiological studies, e.g. Apple’s ResearchKit
• 151-152: medical research has traditionally been conducted on the population-level. Eric Topol has argued that self tracking can lead to both a better understanding of population-level effects, but also of individuals (e.g. some individuals may react differently to a specific treatment than the average person in a population)

6. Future Directions for Self-Tracking

• Issues that are likely to become/remain important:
  • Who owns data generated by trackers?
  • How will privacy related to tracking data be preserved?
  • Will self-tracking tools be available to those who need them most, or just to those who can afford them?