A new approach tested by researchers at the University of Iowa shows that de-identified data from a “smart thermometer” connected to a mobile phone app can track flu activity in real time at both population and individual levels and the data can be used to significantly improve flu forecasting. The findings are published online in the journal Clinical Infectious Diseases.
Lead study author Aaron Miller, PhD, a UI postdoctoral scholar in computer science and senior study author Philip Polgreen, MD, UI associate professor of internal medicine and epidemiology, analyzed de-identified data from the commercially available Kinsa Smart Ear Thermometers and accompanying app, which recorded users’ temperature measurement over a study period from Aug. 30, 2015 to Dec. 23, 2017. There were over 8 million temperature readings generated by almost 450,000 unique devices. The smart thermometers encrypt device identities to protect user privacy and also give users the option of providing anonymized information on age or sex. Readings were reported from all 50 states and were aggregated to provide region and age-group specific flu activity estimates.
Inder Singh and Erin Koehler at Kinsa, Inc., the company that makes and sells the smart thermometers, were also part of the study team. They conceived and designed the Kinsa products for purposes of tracking the spread of illness, and provided full access to de-identified data for Miller and Polgreen to independently analyze. Neither Miller nor Polgreen have a financial relationship with Kinsa, Inc.
The UI team compared the data from the smart thermometers to influenza-like illness (ILI) activity data gathered by the Centers for Disease Control and Prevention (CDC) from healthcare providers across the country. They found that the de-identified smart thermometer data was highly correlated with ILI activity at national and regional levels and for different age groups.
Current forecasts rely on this CDC data, but even at its fastest, the information is almost two weeks behind real-time flu activity. The UI study showed that adding thermometer data, which captures clinically relevant symptoms (temperature) likely even before a person goes to the doctor, to simple forecasting models, improved predictions of flu activity. This approach accurately predicted influenza activity at least three weeks in advance.
Knowing that flu activity is about to increase in a community may prompt individuals to get a flu shot, stay home from work when they get sick, and seek medical help if their illness worsens.
Miller notes that the smart thermometers also provide a way to estimate which age groups are being most affected during a flu season, using de-identified data.
Monitoring the duration of fever from the smart thermometer readings revealed that fevers occurring during flu season were more likely to last three to six days and much less likely to last only one day. Fevers lasting even or more days were not at all seasonal. The data also identified instances where users had fever that went away for a few days and then returned. The researchers believe this so-called “biphasic” fever pattern may reflect more serious illnesses. The second temperature spike can indicate a secondary bacterial infection like pneumonia that sets in after the flu and can lead to more severe health problems, especially in older individuals.