Sun, S; Folarin, AA; Ranjan, Y; Rashid, Z; Conde, P; Stewart, C; Cummins, N; ... Dobson, RJ; + view all Sun, S; Folarin, AA; Ranjan, Y; Rashid, Z; Conde, P; Stewart, C; Cummins, N; Matcham, F; Dalla Costa, G; Simblett, S; Leocani, L; Sørensen, PS; Buron, M; Guerrero Pérez, AI; Zabalza, A; Penninx, BW; Lamers, F; Siddi, S; Haro, JM; Myin-Germeys, I; Rintala, A; Wykes, T; Narayan, VA; Comi, G; Hotopf, M; Dobson, RJ; - view fewer (2020) Using smartphones and wearable devices to monitor behavioural changes during COVID-19. Journal of Medical Internet Research , 22 (9) , Article e19992. 10.2196/19992.

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Abstract

Background: In the absence of a vaccine or highly 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 urgently required. Objective: We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiting the spread of COVID-19. Methods: We analysed data extracted from smartphone and wearable devices and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the UK, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, 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’s tests to assess differences in these features among baseline, pre-, and during-lockdown periods. We also studied behavioural differences by age, gender, body mass index (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 pre- 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 usage. People were more active on their phones (P < .001 for Italy, Spain, and the UK), spending more time using social media apps (P < .001 for Italy, Spain, the UK, and the Netherlands), particularly around major news events. Furthermore, participants had lower heart rate (P < .001 for Italy, Spain; P = .02 for Denmark), went to bed later (P < .001 for Italy, Spain, the UK, and the Netherlands), and slept more (P < .001 for Italy, Spain, and the UK). We also found that young people had longer homestay than older people during 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 behavioural 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 epi/pandemics and could be particularly vital in helping ease out of lockdown

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