Prediction of Overall Mortality from Fitbit heart rate data

From what I can tell, the Fitbit API returns heart rate data at an effective temporal resolution of 9.98 seconds (min: 5 s, median: 10 s, max: 15 s). Curiously, you are more likely to get either a 5 or 15 s interval than a 10 s interval. Using Mathematica, as before, we can plot the distribution of times between samples returned by the Fitbit API,


fitbitHR
That is still (although just barely) usable for measuring heart rate recovery, the change in your heart rate some time t after you stop your exercise. For most things you can measure on a wearable, any one datapoint is next to useless; the key is to look at first and second derivatives, such as gradual trends in how your heart rate drops following a few minutes on the treadmill. The key medical study is probably the October 1999 article in NEJM, Heart-rate recovery immediately after exercise as a predictor of mortality. The conclusion of that paper is that “A delayed decrease in the heart rate during the first minute after graded exercise, which may be a reflection of decreased vagal activity, is a powerful predictor of overall mortality”. Their standard for a ‘delayed’ decrease was a drop of ≤ 12 beats per minute from the heart rate at peak exercise, measured 1 minute after cessation of exercise. Since Fitbit is probably not in the “mortality prediction” market, ~10 s temporal resolution is fine; for medical researchers, however, it would be nice to have slightly higher temporal resolution data.

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