Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study.
Journal
JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504
Informations de publication
Date de publication:
04 Jun 2021
04 Jun 2021
Historique:
entrez:
11
6
2021
pubmed:
12
6
2021
medline:
12
6
2021
Statut:
aheadofprint
Résumé
The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being. Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19. We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile 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. This study enrolled 2,158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing. This study examined student health and well-being during the COVID-19 pandemic. It also assessed the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19. ClinicalTrials.gov NCT04766788.
Sections du résumé
BACKGROUND
BACKGROUND
The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being.
OBJECTIVE
OBJECTIVE
Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19.
METHODS
METHODS
We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile 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
RESULTS
This study enrolled 2,158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing.
CONCLUSIONS
CONCLUSIONS
This study examined student health and well-being during the COVID-19 pandemic. It also assessed the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19.
CLINICALTRIAL
BACKGROUND
ClinicalTrials.gov NCT04766788.
Identifiants
pubmed: 34115607
doi: 10.2196/29561
pmc: PMC8386373
doi:
Banques de données
ClinicalTrials.gov
['NCT04766788']
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NHLBI NIH HHS
ID : K24 HL156896
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL146354
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007622
Pays : United States
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