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. 2020 Sep 25;22(9):e19992.
doi: 10.2196/19992.

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

Shaoxiong Sun  1 , Amos A Folarin  1   2 , Yatharth Ranjan  1 , Zulqarnain Rashid  1 , Pauline Conde  1 , Callum Stewart  1 , Nicholas Cummins  3 , Faith Matcham  4 , Gloria Dalla Costa  5 , Sara Simblett  6 , Letizia Leocani  5 , Femke Lamers  7 , Per Soelberg Sørensen  8 , Mathias Buron  8 , Ana Zabalza  9 , Ana Isabel Guerrero Pérez  9 , Brenda Wjh Penninx  7 , Sara Siddi  10   11   12 , Josep Maria Haro  10   11   12 , Inez Myin-Germeys  13 , Aki Rintala  13 , Til Wykes  6   14 , Vaibhav A Narayan  15 , Giancarlo Comi  16 , Matthew Hotopf  4   14 , Richard Jb Dobson  1   2 ; RADAR-CNS Consortium  17
Affiliations

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

Shaoxiong Sun et al. J Med Internet Res. .

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.

Keywords: COVID-19; behavioral monitoring; mobile health; mobility; phone use; smartphones; wearable devices.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: VAN is an employee of Janssen Research & Development LLC and may own equity in the company.

Figures

Figure 1
Figure 1
Behavioral changes for Milan, Italy (208 participants). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
Figure 2
Figure 2
Behavioral changes for Spain (329 participants). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
Figure 3
Figure 3
Behavioral changes for Copenhagen, Denmark (106 participants). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
Figure 4
Figure 4
Behavioral changes for the United Kingdom (316 participants). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
Figure 5
Figure 5
Behavioral changes for the Netherlands (103 participants). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
Figure 6
Figure 6
Box plots for comparisons among baseline, prelockdown, and during lockdown phases for different features. (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, and (i) social app duration. *means P<.05, **means P<.01, ***means P<.001.
Figure 7
Figure 7
Zoomed in time series plots for Copenhagen, Denmark (left) and the United Kingdom (right). (a) homestay duration, (b) maximum distance from home, (c) Fitbit step count, (d) maximum number of nearby devices, (e) total sleep duration, (f) bedtime, (g) heart rate, (h) unlock duration, (i) social app duration, (j) COVID-19 confirmed and death cases. Solid line: median; shade: 25th percentile to 75th percentile. dur: duration; WHO: World Health Organization.
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