학술논문
Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19
Document Type
article
Author
Sun, Shaoxiong; Folarin, Amos A; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; Cummins, Nicholas; Matcham, Faith; Dalla Costa, Gloria; Simblett, Sara; Leocani, Letizia; Lamers, Femke; Sørensen, Per Soelberg; Buron, Mathias; Zabalza, Ana; Guerrero Pérez, Ana Isabel; Penninx, Brenda WJH; Siddi, Sara; Haro, Josep Maria; Myin-Germeys, Inez; Rintala, Aki; Wykes, Til; Narayan, Vaibhav A; Comi, Giancarlo; Hotopf, Matthew; Dobson, Richard JB
Source
Journal of Medical Internet Research, Vol 22, Iss 9, p e19992 (2020)
Subject
Language
English
ISSN
1438-8871
Abstract
BackgroundIn the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. ObjectiveWe aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. MethodsWe analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. ResultsWe were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P