Background In the absence of a vaccine or effective treatment for COVID-19, countries have adopted Non-Pharmaceutical Interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of monitoring the impact and response of these interventions at a local level is urgently required. Here we explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to  test the effect and response to NPIs aimed at limiting the spread of COVID-19. Methods We included 1062 participants recruited in Italy, Spain, Denmark, the UK, and the Netherlands. We derived features from the phone and wearable device for length of time spent at home,  distance travelled from home,  heart rate, sleep , and patterns of phone use. We visualised data using time series plots and performed statistical tests to assess differences in behaviour during baseline, pre-, and post-lockdown periods. Findings We found significant changes in behaviours between baseline/pre-lockdown and post-lockdown for all features except total sleep duration. In general, participants spent more time at home, travelled much less, and were more active on their phones, interacting with others by using social apps. Nevertheless, the response across nations differed with Denmark showing attenuated changes in behaviour. Interpretation Differences in the extracted features by country may reflect variations in communication and implementation of different NPIs as well as cultural differences. We have demonstrated that generalised open-source mobile health monitoring platforms such as RADAR-base which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented in response to  infectious outbreaks such as COVID-19.
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. 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 Dunns 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. 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. We saw reduced sociality as measured through mobility features, and increased virtual sociality through phone usage. People were more active on their phones, spending more time using social media apps, particularly around major news events. Furthermore, participants had lower heart rate, went to bed later, and slept more. 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. RADAR-base 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.
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Background In the absence of a vaccine or effective treatment for COVID-19, countries have adopted Non-Pharmaceutical Interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of monitoring the impact and response of these interventions at a local level is urgently required. Here we
We aimed to
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rapidly
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Methods We included
We analysed data extracted from smartphone and wearable devices and managed by the RADAR-base from
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features from the phone and wearable device for length of
nine features on a daily basis including
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maximum
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maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average
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, and patterns of phone use. We visualised data using time series plots and performed statistical tests
duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post-hoc Dunns tests
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behaviour during
these features among
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post-lockdown periods. Findings We found significant changes in behaviours between baseline/pre-lockdown and post-lockdown for all features except total sleep duration. In general, participants spent more time
during-lockdown periods. We also studied behavioural differences by age, gender, body mass index (BMI), and educational background. We were able to quantify expected changes in time spent
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travelled much less, and
distance travelled, and the number of nearby Bluetooth-enabled devices between pre- and during-lockdown periods. We saw reduced sociality as measured through mobility features, and increased virtual sociality through phone usage. People
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interacting with others by using social apps. Nevertheless, the response across nations differed with Denmark showing attenuated changes in behaviour. Interpretation Differences in the extracted features by country may reflect variations in communication and implementation of different NPIs as well as cultural differences. We have demonstrated that generalised open-source mobile health monitoring platforms such as
spending more time using social media apps, particularly around major news events. Furthermore, participants had lower heart rate, went to bed later, and slept more. 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.
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which leverages data from wearables and mobile technologies are valuable tools for helping understand the behavioural impact of public health interventions implemented
can be used to rapidly quantify and provide a holistic view of behavioural changes
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public health interventions as a result of