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Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

Shaoxiong Sun, Amos A Folarin, Yatharth Ranjan and 24 more

Journal of Medical Internet Research · 2020

doi:10.2196/19992

Abstract

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.

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References 31

  1. 1.

    Bai Y, Yao L, Wei T, Tian F, Jin D, Chen L, Wang M. Presumed asymptomatic carrier transmission of COVID-19. JAMA. 2020;1406-1407.

    Not in our library10.1001/jama.2020.2565

  2. 2.

    Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, Liu S, Zhao P, Liu H, Zhu L, Tai Y, Bai C, Gao T, Song J, Xia P, Dong J, Zhao J, Wang F. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420-422.

    Not in our library10.1016/s2213-2600(20)30076-x

  3. 3.

    Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med. 2020;27(2):taaa021.

    Not in our library10.1093/jtm/taaa021

  4. 4.

    WHO coronavirus disease (COVID-19) dashboard. World Health Organization. 2020.

    Not in our library

  5. 5.

    Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506.

    Not in our library10.1016/s0140-6736(20)30183-5

  6. 6.

    Yu P, Zhu J, Zhang Z, Han Y. A familial cluster of infection associated with the 2019 novel coronavirus indicating possible person-to-person transmission during the incubation period. J Infect Dis. 2020;221(11):1757-1761.

    Not in our library10.1093/infdis/jiaa077

  7. 7.

    Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, Liu L, Shan H, Lei C, Hui DS, Du B, Li L, Zeng G, Yuen K, Chen R, Tang C, Wang T, Chen P, Xiang J, Li S, Wang J, Liang Z, Peng Y, Wei L, Liu Y, Hu Y, Peng P, Wang J, Liu J, Chen Z, Li G, Zheng Z, Qiu S, Luo J, Ye C, Zhu S, Zhong N, China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.

    Not in our library10.1056/nejmoa2002032

  8. 8.

    Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;A.

    Not in our library10.1001/jama.2020.2648

  9. 9.

    Lau H, Khosrawipour V, Kocbach P, Mikolajczyk A, Schubert J, Bania J, Khosrawipour T. The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China. J Travel Med. 2020;27(3):taaa037.

    Not in our library10.1093/jtm/taaa037

  10. 10.
  11. 11.

    World economic outlook update. International Monetary Fund. 2020.

    Not in our library

  12. 12.

    Pandian P, Mohanavelu K, Safeer K, Kotresh T, Shakunthala D, Gopal P, Padaki V. Smart Vest: wearable multi-parameter remote physiological monitoring system. Med Eng Phys. 2008;30(4):466-77.

    Not in our library10.1016/j.medengphy.2007.05.014

  13. 13.

    Cancela J, Pastorino M, Tzallas A, Tsipouras M, Rigas G, Arredondo M, Fotiadis D. Wearability assessment of a wearable system for Parkinson's disease remote monitoring based on a body area network of sensors. Sensors (Basel). 2014;14(9):17235-55.

    Not in our library10.3390/s140917235

  14. 14.

    Paradiso R, Alonso A, Cianflone D, Milsis A, Vavouras T, Malliopoulos C. Remote health monitoring with wearable non-invasive mobile system: The Healthwear project. IEEE. 2008.

    Not in our library10.1109/iembs.2008.4649503

  15. 15.

    RADAR-base.

    Not in our library

  16. 16.
  17. 17.

    Stewart C, Rashid Z, Ranjan Y, Sun S, Dobson R, Folarin A. RADAR-base: major depressive disorder and epilepsy case studies. UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 2018;1735-1743.

    Not in our library10.1145/3267305.3267540

  18. 18.

    RADAR-CNS.

    Not in our library

  19. 19.

    Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, Folarin AA, Haro JM, Kerz M, Lamers F, Li Q, Manyakov NV, Mohr DC, Myin-Germeys I, Narayan V, Bwjh P, Ranjan Y, Rashid Z, Rintala A, Siddi S, Simblett SK, Wykes T, Hotopf M, RADAR-CNS consortium. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry. 2019;19(1):72.

    Not in our library10.1186/s12888-019-2049-z

  20. 20.

    hersenonderzoek.nl.

    Not in our library

  21. 21.

    Ester M, Kriegel H, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD-96 Proceedings. 1996;226-231.

    Not in our library

  22. 22.

    Sleep logs. Fitbit SDK.

    Not in our library

  23. 23.

    Communication. Google Play.

    Not in our library

  24. 24.

    Hale T, Petherick A, Phillips T, Webster S, Kira K, Angrist N. Coronavirus government response tracker. Blavatnik School of Government. 2020.

    Not in our library

  25. 25.

    Ostertagová E, Ostertag O, Kováč J. Methodology and application of the Kruskal-Wallis test. AMM. 2014;611:115-120.

    Not in our library10.4028/www.scientific.net/amm.611.115

  26. 26.

    Dinno A. Nonparametric pairwise multiple comparisons in independent groups using Dunn's test. Stata J. 2018;15(1):292-300.

    Not in our library10.1177/1536867x1501500117

  27. 27.

    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Statistics. 2001;29(4):1165-1188.

    Not in our library10.1214/aos/1013699998

  28. 28.

    Number of mobile phone users worldwide from 2015 to 2020. Statista.

    Not in our library

  29. 29.

    Number of connected wearable devices worldwide by region from 2015 to 2022. Statista.

    Not in our library

  30. 30.

    COVID-19 community mobile reports. Google.

    Not in our library

  31. 31.

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