Fitness activity trackers and smartwatches may soon be able to do more than what they are traditionally equipped to do. The data from wearables could potentially perceive and forecast diseases according to new research.
According to a new study conducted by researchers at the Stanford University School of Medicine, wearable sensors, which are able to detect the skin temperature of the body, monitor the heart rate and other parameters, can aid in predicting what diseases a person may potentially be prone to. The wearable's data can reveal information such as inflammation, any infections, or even resistance to insulin.
For the purpose of the study, the researchers observed 43 participants using a Basis Peak or Basis B1 smartwatch and recorded over 250,000 daily measurements. The researchers observed the participants for a year and mapped the data, which included their skin temperature, heart rate, and sleep patterns.
"We want to study people at an individual level," shared Michael Snyder, PhD, professor and chair of genetics.
The Findings Of The Study
In addition to the 43, the team also monitored one person dubbed Participant #1 for two years. This participant would often wear nearly seven wearables. The team observed his data and selected four dates where his body measurements were not normal and his skin and heart rates were elevated.
During one of these four periods, the participant had developed Lyme disease. In the other period he suffered from common cold, all of which tie in to inflammation. This led the researchers to deduce that the wearables could detect early inflammation signals.
Upon analyzing this information further, the researchers deduced that abnormal measurements, particularly for one's heart rate, were signs that suggested ailments such as Lyme disease and even common cold.
The devices were useful for detecting inflammatory conditions and could also find the difference between people who are sensitive to insulin and those who are resistant to insulin. With this capability, it would potentially be easier for doctors to ascertain Type-2 diabetes.
The researchers also found that a drastic change in environment, such as being on flights, also altered the parameters of the individual. .
The study shows that if one has a baseline range for individuals, it could be possible to observe and predict the health patterns thanks to any deviations that occur. Any distinctive deviation pattern which seems abnormal and is detected by wearable, could signal a health problem.
"Algorithms designed to pick up on these patterns of change could potentially contribute to clinical diagnostics and research," shared the researchers.
The study has been published in the Jan. 12 issue of the journal PLOS Biology.