
From metrics to muscle: AI evolution in fitness tech
Beyond steps and scores
The term “quantified self,” coined in 2007 by Wired editors Gary Wolf and Kevin Kelly, refers to using technology to track and analyze aspects of daily life, like steps, sleep, and heart rate. Early products like Fitbit paved the way, with advances in sensor tech and integration into smartphones and wearables accelerating the trend. Algorithms soon turned raw data into composite scores, such as Whoop’s Strain and Recovery Score, which track trends rather than specific units, helping users gauge effort and recovery over time.
Despite progress, we’re still grappling with moving from data to action. While quantified self tools offer self-knowledge and curiosity-driven insights, true behavior change hinges on knowing what to change and how to change it.
Smart Design’s work with Gatorade and Epicore on the Gx Sweat Patch shows how wearables are most powerful when they move beyond raw data. While the patch can track fluid loss and sodium levels, those numbers aren’t the point; most users wouldn’t remember them. What matters is what to do next. The real value comes from the patch’s personalized guidance: how much to drink and how to replenish electrolytes, both tailored to your workout and the weather. In this case, the data serves the recommendation, not the other way around.

The passive power of AI in fitness

Competitive metrics vs personalized progress
