TY - JOUR
T1 - Should we use activity tracker data from smartphones and wearables to understand population physical activity patterns?
AU - Mair, Jacqueline L.
AU - Hayes, Lawrence D.
AU - Campbell, Amy K.
AU - Sculthorpe, Nicholas
PY - 2021/10/25
Y1 - 2021/10/25
N2 - 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.
AB - 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.
KW - m-health
KW - quantified-self
KW - big data
KW - surveillance
KW - wearables
U2 - 10.1123/jmpb.2021-0012
DO - 10.1123/jmpb.2021-0012
M3 - Article
SN - 2575-6605
VL - 5
SP - 3
EP - 7
JO - Journal for the Measurement of Physical Behaviour
JF - Journal for the Measurement of Physical Behaviour
IS - 1
ER -