Counting Every Heart Beat: Observation of a Quantified Selfie

Ideally, if enough were known about all the variables affecting heart rate, the time of the next beat could be known. Of course this means knowing: the environment e.g. temperature, air density, wind, air quality; activity level e.g. sleeping, sitting, standing, walking, running, biking, rowing; diet and digestive loads including stimulants; physical health including allergies, sicknesses, and chronic ailments e.g. asthma, bronchitis; and all the kinds and levels of stress. I’ve observed all of these conditions as qualitatively affecting my own heart’s behavior, angina pain, and lung function e.g. shortness of breath, but none have been isolated and quantified sufficiently to be useful in predicting the quantitative changes. I understand a few obvious situations well enough to change behavior e.g. the importance of sleep, starting off rapidly in cold weather insures high heart rate resulting in shortness of breath, drinking more wine requires a longer absorption time, and contentious or stressful situations elevate HR degree and duration. A lung infection or a heavy cold requires an additional 2-5 bpm.

Based on the use and understanding of current wrist HR monitors and their evolution, an uncanny amount is known or can be deduced about the exact state of person, 24×7 including their overall state of health, stress and the specific activity they are engaged in. However to get to this state will require a good model of the heart-lung system that is validated my thousands of users who wear the devices and record their own status along with exogenous conditions to a data collection and analytics platform.