What Is Quantified Self?

The term “quantified self” refers to the practice of using wearable devices and other modern technologies to collect personalized data about one’s own life and health. It can be seen as a type of lifestyle pursued by technology enthusiasts and is associated with early adopters of wearable devices such as those made by Apple Inc. (AAPL) and Fitbit Inc. (FIT).

The term "quantified self" was popularized in 2007 by the writer Gary Wolf, who wrote about the concept in Wired magazine and gave a TED talk on the subject in 2010.

Key Takeaways

  • Quantified self is a subculture of people who aim to use technology to learn about and improve their habits and their lives.
  • It is often associated with fitness and health tracking, but it also applies to other areas such as personal finances and subjective well-being.
  • The technologies used in quantified self have improved significantly in recent years, and this trend may accelerate due to advances in artificial intelligence (AI).

How Quantified Self Works

The basic concept behind the quantified self is to gain insight into one’s life through the use of technology. In this sense, it can be seen as a type of self-improvement philosophy that aims to make use of the latest advances in wearable technology and other fields. To that end, enthusiasts use devices to track metrics such as their heart rate, blood pressure, and sleep hours. Beyond its health applications, quantified self also applies to other areas of life such as using smartphone and desktop applications to track budgets, investments, and other areas of personal finance.

Some enthusiasts also use supplemental applications, such as mood trackers or electronic journals, to gather data about their subjective well-being. By combining this information with the objective data gained from wearable devices, the user can potentially uncover valuable correlations and insights regarding their lifestyle and habits. For instance, a user might discover that certain types of food are correlated with noticeably better sleep outcomes.

In some cases, the type of health-related information gathered by many devices can be of substantial medical value. Some doctors’ offices and hospitals are now reviewing the information provided by these devices to better understand a patient’s health condition or responsiveness to prescribed treatments and medications. In the future, developments in the field of artificial intelligence (AI) could help to make these devices even more useful in diagnosing and predicting health problems.

Real-World Example of Quantified Self

Luke is a technology enthusiast who enjoys using modern applications and devices to learn about his health and habits. He regularly monitors personal metrics such as his income, spending, and mood, as well as health metrics such as his weight, heart rate, and hydration. Over the years, Luke has noticed that he can improve his overall well-being by experimenting with new lifestyle changes and then monitoring the resulting data to see whether they contribute to or detract from his overall health and well-being.

In the past, the technology Luke used for this pursuit was relatively basic, only managing to capture basic metrics and without the ability to easily synchronize or analyze data between devices. In recent years, however, there has been a steady improvement in this area, with this kind of data beginning to be used in the formal health system to help diagnose problems and prescribe solutions. Luke hopes that this trend will continue, particularly as software and wearable devices begin to incorporate increasingly advanced AI.