Statistics from Altmetric.com
The COVID-19 pandemic has brought unparalleled destruction to global health, social and economic systems. To control the spread of COVID-19, most countries have enforced a societal-level lockdown. This mass disruption of civil life provides opportunities for observational ‘natural experiments’, mandating lifestyle changes overnight. Big data, such as Google Trends (GT), have been used to identify outbreaks1 and monitor risk communication strategies,2 public awareness3 and misinformation4 during COVID-19. The real-time nature of data, together with ubiquitous internet access and Google’s dominance of online search traffic, has uniquely positioned GT as a useful tool for ‘nowcasting’ social trends and lifestyle changes.5
An area of life significantly impacted by COVID-19 lockdown is physical activity. Closure of gyms and restrictions on ‘non-essential’ travel may lead to declines in overall physical activity. Conversely, changing circumstances may interrupt ‘automatic’ behavioural patterns through ‘habit discontinuity’6 leading to formation of new health habits, as demonstrated by previous research on residential relocation.7 Here, we used GT data to explore community interest in physical activity before and during COVID-19 outbreaks in Australia, the UK and the USA.
We extracted GT data of nation-level online queries for the topic ‘exercise’, which included all related search terms sharing the same concept, such as ‘workouts’ and ‘fitness training’ (online supplementary file). GT normalises search data within a defined time frame and geography on a scale of …
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