Quantification is powerful. It gives us a sense of perceived objectivity, a clear, comparable data point by which to gauge a concept. As Espeland and Stevens explain in “A Sociology of Quantification,” numerical benchmarks are inherently reactive (they drive action) and lend authority to ideas.

Quantification is commonly used as an evaluation mechanism. It provides guidance to managers on the performance of employees, as is the case in the trucking industry, in which Karen Levy first explained how advanced systems allow dispatchers to manage truck driver performance from afar. While quantification allows for forms of external evaluation in the corporate world, the growth of datafication and algorithmic living have exposed an equally powerful use for quantification: socially driven self-regulation.

This use of quantification benchmarks is especially relevant to the growing popularity of fitness quantification. New apps such as MapMyRun and Strava have enabled the vast collection of personal data related to individual’s workout habits. These data can then be compared with the data of others who use the applications, breeding a form of comparative self-regulation, wherein individuals use the fitness data of themselves and others to drive improvements in their own performance.

In these applications, users are given the ability to “track” their exercise and view a running feed of their peers. For runners and bikers, clear benchmarks of heart rate, distance, and speed can lend a quantitative angle to a run, noting areas for improvement and implying whether or not training has been effective. These quantitative values provide the basis for self-regulation and the tracking of individual goals. Unlike most industries, where quantification is used as a basis for external performance evaluation, the individualized fitness app has developed a more intimate application for these data. Quantified metrics are generated in real time, as demonstrated below.

real-time data

However, the inherently social nature of fitness applications does provide a form of socially driven regulation as well. By broadcasting fitness data to one’s network, fitness becomes inherently competitive. For example, when I go on a run in DC, I can afterward view exactly how I match up to others in my area on predefined routes. With the quantified data in the application, I can directly compare my stats with others, giving me further motivation to push myself. Strava's leaderboard function is pictured below:

In many cases, this social pressure is an illusion. As Hwang and Levy first explained in “The Presentation of the Machine in Everyday Life,” systems can be designed to drive certain actions or elicit specific feelings, even if such designs are illusory. In the case of Strava and MapMyRun, the social pressures and competition exist primarily in the minds of the users, as their specific times are likely rarely actually seen. The apps use this design feature to trigger an athlete’s desire to beat others in their area and improve their abilities.

In the case of both applications, the perceived external competition and clear quantitative benchmarks give users the data to regulate their exercise habits. Unlike industry, where performance benchmarks are more closely linked to institutional incentives determined by management, the datafication of exercise uses quantification in a more intimate way. The power of this approach should not be overlooked. Being able to quantify my improvements and evaluate myself with regularity has transformed the way I train. It also helps me push harder to compete with those around me and “level up” in my fitness. At the end of the day, I am the one that needs to get up and go for a run, but the power of quantification certainly helps.