Quantified Self: From Sousveillance to Personal Science and Phenotyping

The quantified-self movement which involves using technology to track various aspects of one's daily life and behaviors, could be traced back to the sousveillance-like monitoring described in 1970. One of the first platforms for these activities - Nike+ website publicly launched in 2006 - was helping runners to track and share their workouts. 

The term "quantified self" was coined in 2007 by Wired magazine editors Gary Wolf and Kevin Kelly who co-founded the Quantified Self Institute and "Quantified Self Meetups." The movement experienced a period of rapid growth in popularity in the 2010s. Forbes has even called 2013 "The Year of the Quantified Self". 

As the technology for self-tracking has become more advanced and widespread, it has attracted the attention of commercial hardware developers. Fitbit founded in 2007 as Healthy Metrics Research, released their first tracker in 2009. In the 2010s, a number of major tech companies, including Apple, Google, and Samsung, began to develop and market wearable devices and self-tracking apps.

Quantified Self movement has not become a mainstream trend due to a combination of cost, technical barriers, and privacy concerns. Many people resented self-tracking being pushed by their employers, health and life insurers in order to monitor them. And despite many attempts to develop analysis tools, most people are still lacking the skills to process their data in order to make better decisions in everyday life.

"Personal science" (the use of scientific methods and principles to analyze personal lifelogging) and N-of-1 studies (when individual is studied in isolation, rather than as part of a larger group in a clinical study) are related to the quantified-self movement in that they both involve the use of technology and data to track and understand one's own health and behavior. These approaches, however, are not yet widely used or understood by the general public. 

The use of self-tracking data has the potential to inform the study of various medical conditions through the process of phenotyping, as several papers have demonstrated (eg, for vaccine-triggered anorexia and endometriosis). However, the understanding of how to effectively use this type of data for this purpose is still in the early stages, and it has not yet been widely adopted by traditional medical science. In contrast to what was expected 20 years ago, phenotyping has taken a back seat in human genetics research. It was thought that having a precise or well-measured phenotype was far less relevant than having a huge sample. However, now that the field of genetics has a working strategy for gene discovery, and AI is getting more sophisticated, the importance of phenotype is re-emerging, and this will likely lead to a renewed interest in the quantified self.

REFERENCES

McClusky M. The Nike experiment: how the shoe giant unleashed the power of personal metrics. Wired. 2009 Jun 22;17(07).

Gabashvili IS. Why Red Beans and Rice Are Good ... But Not with Coffee, Forbes 2012, April 30. DOI: 10.6084/m9.figshare.13600517

Osozawa S. Case report: anorexia as a new type of adverse reaction caused by the COVID-19 vaccination: a case report applying detailed personal care records. F1000Res 2022 Jan 4;11:4

Urteaga I, McKillop M, Elhadad N. Learning endometriosis phenotypes from patient-generated data. NPJ digital medicine. 2020 Jun 24;3(1):1-4.

Dick DM. The Promise and Peril of Genetics. Curr Dir Psychol Sci. 2022 Dec;31(6):480-485. doi: 10.1177/09637214221112041. Epub 2022 Sep 16. PMID: 36591341; PMCID: PMC9802013.

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Special thanks to OpenAI's Assistant for their help with illustrating and writing this article.

QS#14: There are more questions than answers

Quantifying self: there are more questions than answers

Notes from QS Show&Tell #14 held in the San Francisco Bay Area Tech Museum of San Jose on June 22nd.

Quantifying Productivity

Bill Jarrold used a simple script to record his Unix activity with timestamps on the commands he typed. By quantifying the number of operations per hour he determined that he is usually on the roll at about 3pm, but is slowing down around 10am and midnight. The talk spurred discussions about other ways to analyze productivity - number of builds per time unit? keystrokes? content analysis of the commands? There also were suggestions on using GUI-based tools. For example, CoScripter Reusable History that records everything one does on the web. Or DeliciousDiscovery that analyzes Delicious bookmarks and tags. There are even commercial software tools such as SpyAgent that capture everything a computer user does: keystrokes typed, websites visited, chat conversations, applications ran, emails sent and received, files opened, and more.

One needs not only sophisticated screening and recording of performance related measures but also more sophisticated data analysis methods. There is a good discussion on HN about More-Hours-Worked not equal to More-Work-Getting-Done (it started from this post: Something Deeply Wrong With Chemistry). Dependence of productivity on hours worked is bell shaped and very individual. Some may be most productive when working 35 hours a week, others could increase workload to 60 hours. Number of keystrokes may not necessarily correlate with meaningful output either. Remember Jack Nicholson in The Shining? Besides, sometimes we need to think before turning ideas into action. And how could we measure what is going on in the brain?

Quantifying Thoughts

Mark Carranza is probably the most notable collector of thoughts among the Bay area quantifiers. His database has more than one and a quarter of a million thoughts and keeps growing, with more entries than the diary of Samuel Pepis and the collection of Lion Kimbro, the man who wrote down every though he had.
Jim, the second presenter of QS#14 is collecting thoughts too - he has 65,000 of them connected by associations and represented by colorful visualizations. He uses spreadsheets and Personal Brain software to create and display the results. TheBrain's display is organized around a central Thought, surrounded by all its Children, Siblings and Jumps - like an ontology - helping to follow a train of hought, flowing from one to the next or just wandering around. Navigation through the data is interesting although rather chaotic. Questions from QS participants addressed the usefulness of the tool. Does it really help to to leverage the power of visual thinking and understand the context of information before taking action?

Quantifying Stress

Bharat Vasan of PulseTrace Technologies gave a great impromptu talk on how he is using his watch reading real time heart rate from his wrist. Data may be uploaded via USB or wireless connection. One good application is managing stress. Bharat wants to be a good public speaker - and he certainly is, but his pulse rate always skyrockets during a presentation. Of course, some of us don't need to have a watch like this to know if pulse rate is elevated - the tendency to blush lets everybody around to take the readings. Shortness of breath, nausea and sweating may add to the picture. As Jerry Seinfeld said about delivering the eulogy, most people would rather be in the casket than speaking in public. One the other hand, some increase in pulse when talking is rather good and he trick is to use it as energy to fuel the presentation.
Pulse rate is a good measure of stress levels. Other measures would complement it and contribute to meaningful analytics. For example, recovery heart rate, a measure of how quickly ones heart could return to resting state. Standing vs sitting - normally there is 5 to 10 beats difference. Temperature in the room. Coffee or spices in food...

I have not tried the PT100 sensor-based watch, but my complaints about similar monitoring watches include their inability to measure heart rate during running at full speed, sensitivity to humidity, rain, food vapors, limited time intervals between repeated measurements. I also question reliability of measurements in 1-5% of cases.

Quantifying Motion

Indeed, quantifiers are improving their lives not only with spreadsheets and software - gadget usage continues to rise. ("Zeo's broken" was among two-word introductions of meetup participants). There were many Fitbit fans proudly showing their devices and less proudly measurements for the day - Wow, we are becoming ashamed for not stepping enough! Popular motion gadgets do not work for everyone and are generation 1.0 or rather 0.5 - some like swimming instead of running and elliptical trainers instead of treadmills, others are surprised to find that rocking baby in their arms was counted as calorie-burning steps. BodyMedia Fit (GoWear Fit), for example, measures not only acceleration, but also skin temperature and galvanic skin response reflective of physical stress, but is not water proof and can't measure emotional stress. Fitbit is less pricey but also not quite ready to do what most people want. The next talk was devoted to fitbit's corporate competitor - DirectLife.

Alex Bangs, co-founder of Entelos now working on a spinoff - DigitalSelf, likes this little white plastic box and DirectLife program. The device has accelerometers to quantify how much you move. The program starts with a one week assessment to get a good idea of individual's current activity and create a baseline. The plan has daily activity goals that increase slowly week by week (hopefully not as in The Red Shoes by Andersen!). Alex showed how his device was flashing congratulating him with good activity levels for the day. It could be even better, he said, the little box can almost sing a tribute to you if you walked more than your goal. The device fits in the pocket, but can be forgotten at home - which makes every wearer sad as the accomplishments of the day will not be logged and recognized. We love to be tapped on the back and told well done, are not we all?