Assessing adherence to multi-modal Oura Ring wearables from COVID-19 detection among healthcare workers

Shiba, S. K., Temple, C. A., Krasnoff, J., Dilchert, S., Smarr, B. L., Robishaw, J., Mason, A. E., Shiba, S. K., Temple, C. A., Krasnoff, J., Dilchert, S., Smarr, B. L., Robishaw, J., & Mason, A. E.
Cureus, 15(9)
(2023)

Identifying early signs of a SARS-CoV-2 infection in healthcare workers could be a critical tool in reducing disease transmission. To provide this information, both daily symptom surveys and wearable device monitoring could have utility, assuming there is a sufficiently high evel of participant adherence. The aim of this study is to evaluate adherence to a daily symptom survey and a wearable device (Oura Ring) among healthcare professionals (attending physicians and other clinical staff) and trainees (residents and medical students) in a hospital etting during the early stages of the COVID-19 pandemic. Wearable device adherence was significantly higher than the daily symptom survey adherence for most participants. Overall, participants were highly adherent to the wearable device, wearing the device an average of 87.8 1.6% of study nights compared to survey submission, showing an average of 63.8 ± 27.4% of study days. In subgroup analysis, we found that healthcare professionals (HCPs) and medical students had the highest adherence to wearing the wearable device, while medical residents had ower adherence in both wearable adherence and daily symptom survey adherence.These results indicated high participant adherence to wearable devices to monitor for impending infection in the course of a research study conducted as part of clinical practice. Subgroup analysis ndicated HCPs and medical students maintained high adherence, but residents’ adherence was lower, which is likely multifactorial, with differences in work demands and stress contributing to the findings. These results can guide the development of adherence strategies for a wearable device to increase the quality of data collection and assist in disease detection in this and future pandemics.