Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.
Adolescent
Adult
Aged
Aged, 80 and over
Body Mass Index
COVID-19
Coronavirus Infections
/ epidemiology
Data Collection
Denmark
/ epidemiology
Female
Humans
Italy
/ epidemiology
Male
Middle Aged
Mobile Applications
Monitoring, Physiologic
Netherlands
/ epidemiology
Pandemics
/ prevention & control
Pneumonia, Viral
/ epidemiology
Smartphone
Social Isolation
Social Media
Spain
/ epidemiology
Telemedicine
United Kingdom
/ epidemiology
Wearable Electronic Devices
Young Adult
COVID-19
behavioral monitoring
mobile health
mobility
phone use
smartphones
wearable devices
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
25 09 2020
25 09 2020
Historique:
received:
08
05
2020
accepted:
26
07
2020
revised:
20
07
2020
pubmed:
3
9
2020
medline:
6
10
2020
entrez:
3
9
2020
Statut:
epublish
Résumé
In 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. We 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. We 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. We 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<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
Sections du résumé
BACKGROUND
In 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.
OBJECTIVE
We 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.
METHODS
We 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.
RESULTS
We 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<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.
CONCLUSIONS
RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
Identifiants
pubmed: 32877352
pii: v22i9e19992
doi: 10.2196/19992
pmc: PMC7527031
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e19992Subventions
Organisme : Medical Research Council
ID : MC_PC_17214
Pays : United Kingdom
Organisme : Department of Health
ID : RP-PG-0407-10314
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M501633/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K006584/1
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : 171
Pays : United Kingdom
Organisme : Department of Health
ID : 05/40/04
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_13041
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0902393
Pays : United Kingdom
Informations de copyright
©Shaoxiong Sun, Amos A Folarin, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Nicholas Cummins, Faith Matcham, Gloria Dalla Costa, Sara Simblett, Letizia Leocani, Femke Lamers, Per Soelberg Sørensen, Mathias Buron, Ana Zabalza, Ana Isabel Guerrero Pérez, Brenda WJH Penninx, Sara Siddi, Josep Maria Haro, Inez Myin-Germeys, Aki Rintala, Til Wykes, Vaibhav A Narayan, Giancarlo Comi, Matthew Hotopf, Richard JB Dobson, RADAR-CNS Consortium. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2020.
Références
JAMA. 2020 Apr 7;323(13):1239-1242
pubmed: 32091533
Lancet Respir Med. 2020 Apr;8(4):420-422
pubmed: 32085846
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1699-702
pubmed: 19163006
N Engl J Med. 2020 Apr 30;382(18):1708-1720
pubmed: 32109013
Med Eng Phys. 2008 May;30(4):466-77
pubmed: 17869159
BMC Psychiatry. 2019 Feb 18;19(1):72
pubmed: 30777041
JMIR Mhealth Uhealth. 2019 Aug 01;7(8):e11734
pubmed: 31373275
J Travel Med. 2020 Mar 13;27(2):
pubmed: 32052846
Lancet. 2020 Feb 15;395(10223):497-506
pubmed: 31986264
Lancet Glob Health. 2020 May;8(5):e641-e642
pubmed: 32199072
J Travel Med. 2020 May 18;27(3):
pubmed: 32181488
Sensors (Basel). 2014 Sep 16;14(9):17235-55
pubmed: 25230307
J Infect Dis. 2020 May 11;221(11):1757-1761
pubmed: 32067043
JAMA. 2020 Apr 14;323(14):1406-1407
pubmed: 32083643