Limitations
symptoms-only
COMPLICATIONS, COVID-19 INFECTION, INFLUENZA
This study has some limitations. First, the sample is not representative of the general population. The participants were predominantly White, female, and aged <50 years. The study design may also have had some biases owing to the inclusion criteria. For example, the participants were required to own a Fitbit device; those who own a Fitbit device may be more health conscious and physically active than the general population. Indeed, the population examined in this study had a mean daily step count (approximately 8000) that was somewhat higher than the estimates for the US adult population (approximately 6500) [It is noteworthy that a portion of the study period coincided with the COVID-19 pandemic, which may have also influenced care-seeking behaviors. The participants may have been more likely to delay or avoid seeking treatment owing to the overwhelmed health care services and to minimize their risk of COVID-19 infection. Moreover, COVID-19 mitigation measures may have reduced circulating influenza during the portion of the study period coinciding with the pandemic. In addition, logistical challenges were posed by the remote collection of influenza diagnostic tests, which may have resulted in miscataloging influenza-positive cases as “symptoms-only” cases. Logistical complications resulted in some influenza diagnostic kits not being delivered to the participants in a timely manner, and some testing materials became unavailable during the COVID-19 pandemic. Participants may have erroneously tested negative because of delays or errors in collecting and returning their samples to the laboratory. The timing discrepancy of influenza test kit delivery between the 2 studies also suggests that the FluStudy2020 participants may have collected their influenza test samples slightly later than the HTRI study participants, whose diagnostic kits were kept on hand rather than shipped on demand. It is not possible to estimate the impact of these factors, but their net effect is likely to be an underestimation of the magnitude of the observed differences in wearable sensor data features between participants who tested positive for influenza and those who reported symptoms only, as the population that reported symptoms only may have included individuals who were influenza positive but could not be diagnosed. Finally, nonlinear models or other machine learning techniques may be better suited for these investigations.