COVID-19 Highlights the Potential for a More Dynamic Approach to Physical Activity Surveillance

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Alex V. Rowlands Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, United Kingdom
National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC), University Hospitals of Leicester NHS Trust and the University of Leicester, Leicester, United Kingdom

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Pedro F. Saint-Maurice Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA

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Philippa M. Dall School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, United Kingdom

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The emergence of severe acute respiratory syndrome coronavirus 2, has urged the scientific community and industry to obtain population snapshots of lifestyle behaviors to characterize changes in behaviors during relatively short windows of time (e.g., weekly) so that the impact of lockdowns and other related public health measures could be assessed. Consumer device companies have taken the lead with wearables, tracking ambulatory movement worldwide by harnessing the data from their millions of users and exemplified how it can be used to monitor trends. In March 2020, Fitbit reported steps per day dropped by −7% (Germany) to −38% (Spain) in the week restrictions were brought in (Fitbit, 2020b). In October 2020, steps and number of “active minutes” remained down; however, the intensity of “active minutes” and duration of sleep had increased (Fitbit, 2020a). Garmin also mined the data of their users, reporting that while steps per day decreased steeply when restrictions started, they did start to pull back over the following months (Garmin, 2021). Smartphones with built-in accelerometers, similarly, have potential for widespread monitoring of physical activity. Notably, in 2017, Althoff et al. (2017) used smartphone data to compare activity across 111 countries in >700,000 people.

Consumer devices tracking physical activity have been around for a while now, but the pandemic has made it perhaps clearer than ever that these devices might have an important role capturing multiple snapshots of ambulatory movement and physical activity in large groups of individuals. Thus, a snapshot of physical activity, as typically obtained in national/international surveys, can be complemented by long-term, more dynamic assessments as generated from these tools. Such data would not only inform the planning of future pandemic control measures but, further be valuable in monitoring the impact of other artificial interventions, for example, changes to the built environment and/or green space, and examinations of activity/sleep patterns across multiple countries.

However, the application of consumer devices in the context of physical activity surveillance comes with multiple challenges. For example, wearable users are not representative of the wider population and studies have demonstrated that wearable users are notably younger, fitter, and from higher socioeconomic backgrounds (Pontin et al., 2021; Strain et al., 2019). Two insightful commentaries in this issue ask whether consumer devices could be appropriate for population-level surveillance of physical activity (Mair et al., 2022; Strain et al., 2022). In the first commentary, Mair et al. (2022) discuss the potential benefits and limitations of using consumer devices to understand population physical activity patterns across a number of different study designs, and suggest that leveraging access to retrospective data presents a unique opportunity, especially in response to natural events. In an invited response to Mair et al. (2022), Strain et al. (2022) focus on the limitations specifically for population-level surveillance and examine four key issues to tackle before inferring population level of activity from consumer devices. If consumer devices are appropriate for use for in population-level surveillance, then this could facilitate surveillance of other physical behaviors (e.g., purposeful activity/exercise sessions and sleep). However, while the use of consumer devices for surveillance appears very attractive, there are important challenges with this approach.

Acknowledgments

A.V. Rowlands is supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or Department of Health. P.F. Saint-Maurice was supported by the National Institutes of Health’s Intramural Research Program: National Cancer Institute.

References

  • Althoff, T., Sosič, R., Hicks, J.L., King, A.C., Delp, S.L., & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, 547(7663), 336339. https://doi.org/10.1038/nature23018

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  • Fitbit. (2020a, October 20). Finding your pandemic flow: New Fitbit data reveals your new favorite activities. Fitbit News. https://blog.fitbit.com/finding-your-pandemic-flow/

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  • Fitbit. (2020b, March 23). The Impact of coronavirus on global activity. Fitbit News. https://blog.fitbit.com/covid-19-global-activity/

  • Garmin. (2021, January 29). How Garmin users prioritize movement in a global pandemic? Garmin Blog. https://www.garmin.com/en-US/blog/health/how-garmin-users-prioritized-movement-in-a-global-pandemic/

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  • Mair, J.L., Hayes, L.D., Campbell, A.K., & Sculthorpe, N. (2022). Should we use activity tracker data from smartphones and wearables to understand population physical activity patterns? Journal for the Measurement of Physical Behaviour. Advance online publication. https://doi.org/10.1123/jmpb.2021-0012

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  • Pontin, F., Lomax, N., Clarke, G., & Morris, A. (2021). Socio-demographic-determinants of physical activity and app usage from smartphone data. Social Science & Medicine, 284, Article 114235. https://doi.org/10.1016/j.socscimed.2021.114235

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  • Strain, T., Wijndaele, K., Pearce, M., & Brage, S. (2022). Considerations for the use of consumer-grade wearables and smartphones in population surveillance of physical activity. Journal for the Measurement of Physical Behaviour. Advance online publication.

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  • Strain, T., Wijndaele, K., & Brage, S. (2019). Physical activity surveillance through smartphone apps and wearable trackers: Examining the UK potential for Nationally representative sampling. JMIR mHealth and uHealth, 7(1), Article e11898. https://doi.org/10.2196/11898

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  • Althoff, T., Sosič, R., Hicks, J.L., King, A.C., Delp, S.L., & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, 547(7663), 336339. https://doi.org/10.1038/nature23018

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    • Search Google Scholar
    • Export Citation
  • Fitbit. (2020a, October 20). Finding your pandemic flow: New Fitbit data reveals your new favorite activities. Fitbit News. https://blog.fitbit.com/finding-your-pandemic-flow/

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    • Export Citation
  • Fitbit. (2020b, March 23). The Impact of coronavirus on global activity. Fitbit News. https://blog.fitbit.com/covid-19-global-activity/

  • Garmin. (2021, January 29). How Garmin users prioritize movement in a global pandemic? Garmin Blog. https://www.garmin.com/en-US/blog/health/how-garmin-users-prioritized-movement-in-a-global-pandemic/

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    • Export Citation
  • Mair, J.L., Hayes, L.D., Campbell, A.K., & Sculthorpe, N. (2022). Should we use activity tracker data from smartphones and wearables to understand population physical activity patterns? Journal for the Measurement of Physical Behaviour. Advance online publication. https://doi.org/10.1123/jmpb.2021-0012

    • Search Google Scholar
    • Export Citation
  • Pontin, F., Lomax, N., Clarke, G., & Morris, A. (2021). Socio-demographic-determinants of physical activity and app usage from smartphone data. Social Science & Medicine, 284, Article 114235. https://doi.org/10.1016/j.socscimed.2021.114235

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strain, T., Wijndaele, K., Pearce, M., & Brage, S. (2022). Considerations for the use of consumer-grade wearables and smartphones in population surveillance of physical activity. Journal for the Measurement of Physical Behaviour. Advance online publication.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strain, T., Wijndaele, K., & Brage, S. (2019). Physical activity surveillance through smartphone apps and wearable trackers: Examining the UK potential for Nationally representative sampling. JMIR mHealth and uHealth, 7(1), Article e11898. https://doi.org/10.2196/11898

    • Crossref
    • Search Google Scholar
    • Export Citation
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