Training my smartwatch to track intelligence

Summary

Some mornings I’d wake ready for the world—I’d feel alert, clear-headed, present. Other days, I’d retrace my steps 20 times to find my keys, dreading the long day ahead. My sleep was erratic and I didn’t know why. I had invested in a Garmin watch to clarify trends, and it was helping, but it was also missing something. Though great at tracking the body, it was mediocre at tracking the mind. There’s another signal I was using to track the mind, which is chess. I play almost daily, and I found winning or losing to be a good proxy for mental clarity. Chess was the signal absent from my Garmin that I hoped could bridge body to mind. Time to verify if I was right. Building a Model I downloaded my chess data (date, start ELO, ΔELO) from Lichess, and my Garmin data (about 1.5 years of cross-referenced signal) and put on my data science hat. I was tracking ~20 signals related to exercise and sleep and from them built various statistical models to predict my daily ELO fluctuations. What worked best was also the simplest: tried and true logistic regression. I could predict winning/losing with about 60% accuracy (and confirmed that number through cross-validation). 60% seemed pretty good: absent any signal, the chance of winning should be about 50% because chess apps pair you with like-ELO players. That I could do better than chance wasn’t too surprising. A lot of cognition depends on how well you sleep and exercise. But what exactly does it mean to sleep and exercise well? Which of the signals were actually important? Using a sparse logistic regression solver is a straightforward method to show which features have little (or redundant) predictive power, and I was pretty surprised by the results. Here’s a sample of independent signals from most positively correlated to most negatively correlated Feature Correlation REM sleep duration Positive Stress* duration Positive Stress* average Positive Deep sleep duration None Sedentary duration None Active calories Negative Light sleep...