Longitudinal Data From Sensors Can Help Assess Health Risks
Trillions of data points collected by wearable sensors now can be translated into empirically verified measures of health risk and benefit for patients, which can be used to quantify and enhance their length and quality of life.
That’s the contention of researchers who say they are the “first scientists to successfully translate a common piece of information from wearable sensors—step count—into a verifiable measure of health risk,” while estimating its impact on health and longevity.
The study, funded by Lapetus Solutions—a Wilmington, N.C.-based company that leverages analytics and cloud computing to more accurately predict mortality, morbidity and healthy lifespan—was published this month in the journal Computer.
“There are hundreds, if not thousands, of companies that are generating data from wearable sensors. However, most of that data isn’t used for anything,” says Jay Olshansky, chief scientist at Lapetus Solutions and professor at the University of Illinois at Chicago School of Public Health.
“We took it upon ourselves to develop the algorithms that are required to translate data from wearable sensors into something useful—in this case, we translated data from step count into actual risk of death,” adds Olshansky, who contends that the metrics have significant value for healthcare providers and patients.
To calculate the risk of death, expected gain in life expectancy and healthy-life expectancy, researchers used a plethora of data, including step count, age, sex, height, weight, walking speed, stride length, steps per mile as well as calories burned.
