Monitoring Beliefs and Physiological Measures Using Wearable Sensors and Smartphone Technology Among Students at Risk of COVID-19: Protocol for a mHealth Study

Abstract

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. Trial Registration ClinicalTrials.gov NCT04766788; https://clinicaltrials.gov/ct2/show/NCT04766788 International Registered Report Identifier (IRRID) DERR1-10.2196/29561

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

“…We analyzed participants from three independent studies 12 , 27 , 28 that collected wearable data concurrently with COVID-19 symptom and diagnosis information. A total of 29 participants were from a dataset from Mishra et al.…”

“…To test this, we collected Fitbit data before and after symptom onset of COVID-19 from 43 medical interns and 72 undergraduate and graduate students, from 2 cohorts described elsewhere. 26 , 27 , 28 We also compared these data with a previously published dataset used for pre-detection. 12 When analyzed with our previously validated algorithm, the separate signals provided a multidimensional picture of the course of the illness, which varied between individuals who showed different symptoms.…”

Risk Factors for COVID-19 in College Students Identified by Physical, Mental, and Social Health Reported During the Fall 2020 Semester: Observational Study Using the Roadmap App and Fitbit Wearable Sensors

“…The study protocol has been previously published with more details [ 25 ]. Briefly, eligibility for study participation included the following: age ≥18 years, being a confirmed undergraduate or graduate U-M student (eg, on campus or at home).…”

“…Participants were instructed to complete surveys at baseline (preintervention: T0, monthly: T1, T2, T3, and upon study exit [T4] using the Roadmap platform). A list of the survey questionnaires is provided in the Research Protocol [ 25 ]. Psychometric properties of these measures are provided in Multimedia Appendix 3 .…”

“…Herein, this Roadmap platform was leveraged to (1) characterize self-reported outcomes of physical, mental, and social health by COVID-19 status during the fall 2020 semester; (2) assess physical activity through consumer-grade wearable sensors (Fitbit) by COVID-19 status [ 25 ]; and (3) evaluate potential risk factors associated with COVID-19 positivity, including student demographics (eg, gender, race, and ethnicity), substance use, and physical, mental, and social health constructs [ 25 ]. This work is important because it may inform future mHealth design interventions for this population.…”