Geneticist Michael Snyder was wearing seven biosensors collecting data about his health when he noticed changes in his heart rate and oxygen level during a flight. When he later developed a fever, he suspected he had been infected with Lyme disease. Subsequent tests confirmed his suspicion. Credit: Steve Fisch
In the future your smart watch may be able to monitor your heart rate, count your steps and suggest whether or not you are catching a cold.
Researchers at the Stanford University School of Medicine have shown that fitness monitors and other wearable biosensors can indicate the onset of infection, inflammation and even insulin resistance by establishing a range of normal or baseline values for each person in a study, and compare it to when they are ill.
Michael Snyder, Ph.D., professor and chair of genetics and senior author of the study, said the aim of the research was to look at people on an individual level. The study followed 60 people through their everyday lives, collecting 2 billion measurements, including continuous data from each participant’s wearable biosensor devices and periodic data from laboratory tests of their blood chemistry, gene expression and other measures.
The participants wore between one and seven commercially available activity monitors and other monitors that collected more than 250,000 measurements a day, including weight, heart rate, oxygen in the blood, skin temperature, activity, calories expended, acceleration and exposure to gamma rays and X-rays.
One of the benefits of the advancements of biosensor technology is generally people only have their blood pressure and body temperature measured every year or two and often ignore the results unless they are outside of the normal range. However, these measurements may be monitored continuously if technological advancements continue.
“We have more sensors on our cars than we have on human beings,” Snyder said in a statement.
The study proved that if given a baseline range of values, it is possible to monitor deviations from normal and associate the deviations with environmental conditions, illness and other factors that impact health.
Algorithms can then be designed to pick up patterns of change that could potentially contribute to clinical diagnostics and research.
The study might be particularly important for the participants with insulin resistance, a precursor for Type 2 diabetes.
Another discovery during the study was that declines in blood-oxygen levels during flights were correlated with fatigue. However, the study also showed that people tend to adapt on long flights and their oxygen levels eventually increase, and they generally feel less fatigued as the hours go by.
The study was published in PLOS Biology.
Postdoctoral scholars Xiao Li, Ph.D. and Jessilyn Dunn, Ph.D. and software engineer Denis Salins share lead authorship.
Other Stanford-affiliated co-authors of the study are researcher Gao Zhou; postdoctoral scholars Wenyu Zhou, Ph.D. and Dr. Sophia Miryam Schüssler-Fiorenza Rose, Ph.D.; research dietician Dalia Perelman; undergraduate summer intern Ryan Runge; genetic counselor Shannon Rego; high school student Ria Sonecha; Somalee Datta, Ph.D., director of the Genetics Bioinformatics Service Center; and Dr. Tracey McLaughlin, associate professor of medicine.
