Health Metrics and the Goodhart Trap: Beyond the Target

The Corrosion of Proxy Measures

Monday established the theoretical floor: when a measure becomes a target, it ceases to be a good measure. This principle, articulated by economist Charles Goodhart and further refined by Donald Campbell, describes a systemic failure in how humans interact with data. In the context of a high-performance life, this failure is most lethal within the health domain. We live in an era of unprecedented quantification—sleep scores, heart rate variability, blood glucose levels, and body fat percentages are delivered to our wrists in real-time. Yet, as the volume of data increases, the quality of our physiological reality often declines. This occurs because the human mind is a sophisticated optimizer; when given a number, it seeks the shortest path to increase that number, even if the path destroys the very thing the number was meant to represent. This is metrics gaming at its most intimate level.

In the health domain, we must differentiate between the latent variable—your actual biological resilience and functional longevity—and the proxy—the number on the scale or the ring. Ray Dalio, in his principles of radical truth, emphasizes the necessity of looking at the machine rather than just the output. If you treat your health as a series of KPIs to be manipulated rather than a system to be managed, you fall into the Campbell’s Law trap: the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the processes it is intended to monitor. When you optimize for a sleep score rather than sleep itself, you have corrupted the process.