Monitoring Beliefs And Physiological Measures Using Wearable Sensors And Smartphone Technology Among Students At Risk Of Covid-19: Protocol For A Mhealth Study

Background: The COVID-19 pandemic has significantly impacted lives and greatly affected the mental health and public safety of an already vulnerable population-college students. Social distancing and isolation measures have presented challenges to students' mental health. mHealth apps and wearable sensors may help monitor students at risk of COVID-19 and support their mental well-being.Objective: This study aimed to monitor students at risk of COVID-19 by using a wearable sensor and a smartphone-based survey.Methods: We conducted a prospective study on undergraduate and graduate students at a public university in the Midwest United States. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal smartphone devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints: at baseline, 1 month later, 2 months later, 3 months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population.Results: This study enrolled 2158 college students between September 2020 and January 2021. Subjects are currently being followed-up for 1 academic year. Data collection and analysis are currently underway.Conclusions: This study examined student health and well-being during the COVID-19 pandemic and assessed the feasibility of using a wearable sensor and a survey in a college student population, which may inform the role of our mHealth tools in assessing student health and well-being. Finally, using data derived from a wearable sensor, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data toward the development of a model for the early prediction and detection of COVID-19.