 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 of, and compliance with, 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. We included 1062 participants recruited in Italy, Spain, Denmark, the UK  and the Netherlands. We analysed phone GPS, phone usage data and Fitbit activity, heart rate, sleep, which were collected and managed by the RADAR-base platform. Daily features were derived reflecting mobility, functional measures, and phone usage . We visualised data using time series plots and performed statistical tests to assess differences in behaviour during baseline, pre-  and post-lockdown periods.  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 and travelled much less  and were more active on their phones, interacting with others by using social apps. Nevertheless, the level of compliance across nations differed with Denmark showing attenuated changes in behaviour.  Differences in the extracted features by country may reflect cultural differences as well as variations in communication and implementation of different NPIs  . 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.
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.
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Background
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of, and compliance with,
and response of
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COVID- 19.
COVID-19. Methods
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,
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analysed phone GPS, phone usage data and Fitbit activity,
derived features from the phone and wearable device for length of time spent at home, distance travelled from home,
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which were collected and managed by the RADAR-base platform. Daily features were derived reflecting mobility, functional measures, and phone usage
and patterns of phone use
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,
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Findings
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post- lockdown
post-lockdown
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and
,
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,
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level of compliance
response
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Interpretation
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cultural differences as well as
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as well as cultural differences