For the past several months, we’ve been working with Fit3D to create a cardiovascular risk score based on anthropomorphic measurements (waist-hip ratio, thigh circumference, torso-to-leg volume ratios, etc.) obtained from Fit3D scans. Our overall goal was to produce a single score that (a) was easy to interpret, (b) was backed by the scientific literature, and (c) was composed of simpler disease-specific scores so users could see exactly where their risks were coming from and feel motivated to improve. The project has been interesting and challenging from several angles, so I’m including some technical details about our process and calculations in this post.
In the past year, Fit3D has been collecting more and more body measurement data from Fit3D scanners all around the world to continually refine and improve its algorithms. The body fat prediction model we developed earlier has been steadily improving with the addition of more training data based on a combination of Fit3D scans and DEXA scans.
Nearly one in three people in the United States track their fitness in some way, be it with fitness bands, smart watches, smart phone apps, or home blood pressure and other monitors. Most of us use these devices to improve or maintain our health and fitness, collecting information on our activity level, sleep, heart rate and other factors. But how effectively are we using that data? Your Fitbit might tell you that you slept poorly after having a few drinks, or that your heart rate or blood pressure spiked after a stressful work presentation. Though those observations may be interesting, how meaningful are they? Do they actually lead to better health, or insights into our own wellbeing?
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