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.
==
Special thanks to OpenAI's Assistant for their help with illustrating and writing this article.
No comments :
Post a Comment