Personal Measurement

Machine-learning-driven health insights are presented as an aspiration rather than a finished capability. Recent device adoption has created large health datasets that can support deeper analysis. The quantified-self movement began with fragmented individual tracking around a decade earlier. The protocol quantifies organ systems and biological fluids as part of its measurement foundation. Observing hidden serious findings motivated personal self-measurement. Ongoing measurement and monitoring provide new data for the framework to evolve. Continued measurement provides new data after a protocol has been implemented. Blood markers, saliva, stool samples, advanced imaging, and fitness testing are used to build a complete health profile. Self-measurement was tied to concern about the severity of future hidden health issues. Comprehensive self-measurement is the foundation of the approach. Data collection is treated as the starting point for all subsequent decisions. The next phase is correlating health data across domains rather than merely collecting more data. Pendulum offered free HbA1c testing at baseline and after three months so users could objectively compare their results. The…