Smartwatches and other wearable devices may be used to sense illness, dehydration, and even changes to the red blood cell count, according to biomedical engineers and genomics researchers. With the help of machine learning, wearable device data on heart rate, body temperature and daily activities may be used to predict health measurements that are typically observed during a clinical blood test.
The experiment showed that there were multiple connections between the smartwatch data and clinical blood tests. For example, if a participant’s watch indicated they had a lower sweat gland activation, as measured by an electrodermal sensor, that indicated that the patient was consistently dehydrated.
The team also found that measurements that are taken during a complete blood lab, like hematocrit, hemoglobin, and red and white blood cell count, had a close relationship to the wearables data. A higher sustained body temperature coupled with limited movement tended to indicate illness, which matched up with a higher white blood cell count in the clinical test. A record of decreased activity with a higher heart rate could also indicate anemia, which occurs when there isn’t enough iron in a patient’s blood.
Although the wearables data isn’t specific enough to accurately predict the precise number of red or white blood cells, Dunn and the team are highly optimistic that it could be a noninvasive and fast way to indicate when something in a patient’s medical data is abnormal.