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Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults

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  • Tom White
  • Kate Westgate
  • Nicholas J Wareham
  • Soren Brage

Abstract

Background: Wrist-worn accelerometers are emerging as the most common instrument for measuring physical activity in large-scale epidemiological studies, though little is known about the relationship between wrist acceleration and physical activity energy expenditure (PAEE). Methods: 1695 UK adults wore two devices simultaneously for six days; a combined sensor and a wrist accelerometer. The combined sensor measured heart rate and trunk acceleration, which was combined with a treadmill test to yield a signal of individually-calibrated PAEE. Multi-level regression models were used to characterise the relationship between the two time-series, and their estimations were evaluated in an independent holdout sample. Finally, the relationship between PAEE and BMI was described separately for each source of PAEE estimate (wrist acceleration models and combined-sensing). Results: Wrist acceleration explained 44–47% between-individual variance in PAEE, with RMSE between 34–39 J•min-1•kg-1. Estimations agreed well with PAEE in cross-validation (mean bias [95% limits of agreement]: 0.07 [-70.6:70.7]) but overestimated in women by 3% and underestimated in men by 4%. Estimation error was inversely related to age (-2.3 J•min-1•kg-1 per 10y) and BMI (-0.3 J•min-1•kg-1 per kg/m2). Associations with BMI were similar for all PAEE estimates (approximately -0.08 kg/m2 per J•min-1•kg-1). Conclusions: A strong relationship exists between wrist acceleration and PAEE in free-living adults, such that irrespective of the objective method of PAEE assessment, a strong inverse association between PAEE and BMI was observed.

Suggested Citation

  • Tom White & Kate Westgate & Nicholas J Wareham & Soren Brage, 2016. "Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0167472
    DOI: 10.1371/journal.pone.0167472
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    References listed on IDEAS

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    1. Tuomas O Kilpeläinen & Lu Qi & Soren Brage & Stephen J Sharp & Emily Sonestedt & Ellen Demerath & Tariq Ahmad & Samia Mora & Marika Kaakinen & Camilla Helene Sandholt & Christina Holzapfel & Christine, 2011. "Physical Activity Attenuates the Influence of FTO Variants on Obesity Risk: A Meta-Analysis of 218,166 Adults and 19,268 Children," PLOS Medicine, Public Library of Science, vol. 8(11), pages 1-14, November.
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    Cited by:

    1. Xinyue Li & Hongyu Zhao, 2020. "Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-22, October.
    2. Kirsten Corder & André O. Werneck & Stephanie T. Jong & Erin Hoare & Helen Elizabeth Brown & Campbell Foubister & Paul O. Wilkinson & Esther MF van Sluijs, 2020. "Pathways to Increasing Adolescent Physical Activity and Wellbeing: A Mediation Analysis of Intervention Components Designed Using a Participatory Approach," IJERPH, MDPI, vol. 17(2), pages 1-22, January.
    3. Lina Cai & Tomas Gonzales & Eleanor Wheeler & Nicola D. Kerrison & Felix R. Day & Claudia Langenberg & John R. B. Perry & Soren Brage & Nicholas J. Wareham, 2023. "Causal associations between cardiorespiratory fitness and type 2 diabetes," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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