Principal Findings and Comparison With Prior Work
INFLUENZA
Our results provide evidence of the potential for wearable sensors to discriminate between individuals with and without influenza. We characterized the wearable sensor “phenotype” of participants with laboratory-confirmed influenza. Across all wearable sensor measures, the participants who tested positive for influenza exhibited greater deviations from baseline values and longer TTRB than those who reported ILI symptoms only. Changes in wearable sensor data generally emerged from the day before symptom onset and peaked on the day of or day after symptom onset. Similar observations were made using commercial wearable sensors for the detection of COVID-19, suggesting that wearable sensors may provide an objective early warning signal for the onset of ILI before individuals become aware of their symptoms [A retrospective study of 200,000 Fitbit users suggested that wearable sensor data can predict ILI at individual and population levels [Visual inspection of the group-level time series (We have previously reported that individuals (from the same data set, FluStudy2020) who sought care for their ILI experienced more severe symptoms than those who did not [We also found a consistent, statistically significant though weak correlation between patient-reported severity and duration of key ILI symptoms and data from commercial wearable sensors, including the magnitude of change from baseline, cumulative burden, and TTRB of multiple wearable sensor measures. This study was conducted in a fully remote setting, using data from commercially available wearable sensors and participants’ self-reported symptoms; these data are inherently more variable than clinical measures, such as implanted medical devices, physician- or expert-reported assessments, and medical records. Although the observed correlations are not “strong” by conventional standards, they are statistically significant, coherent, and directionally meaningful.