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Should we use activity tracker data from smartphones and wearables to understand population physical activity patterns?

  • Jacqueline L. Mair*
  • , Lawrence D. Hayes
  • , Amy K. Campbell
  • , Nicholas Sculthorpe
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    42 Downloads (Pure)

    Abstract

    Researchers, practitioners, and public health organisations from around the world are becoming increasingly interested in using data from wearable activity trackers, from companies such as Fitbit Inc, Garmin Ltd, Xiaomi, and Apple Inc, to measure physical activity. Indeed, large-scale, easily accessible, and autonomous data collection concerning physical activity as well as other health behaviours is becoming ever more attractive. There are several benefits of using wearable activity trackers to collect physical activity data, including the ability to obtain big data, retrospectively as well as prospectively, to understand individual level physical activity patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level physical activity. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from wearable activity trackers to understand large-scale physical activity patterns and stimulate discussion amongst the scientific community on what the future holds with respect to physical activity measurement and surveillance.
    Original languageEnglish
    Pages (from-to)3-7
    Number of pages5
    JournalJournal for the Measurement of Physical Behaviour
    Volume5
    Issue number1
    DOIs
    Publication statusPublished - 25 Oct 2021

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • m-health
    • quantified-self
    • big data
    • surveillance
    • wearables

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