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Prediction of activity-related energy expenditure using accelerometer-derived physical activity under free-living conditions: a systematic review

International Journal of Obesity volume 40, pages 1187–1197 (2016)Cite this article

Subjects

Abstract

Background/Objectives:

Activity-related energy expenditure (AEE) might be an important factor in the etiology of chronic diseases. However, measurement of free-living AEE is usually not feasible in large-scale epidemiological studies but instead has traditionally been estimated based on self-reported physical activity. Recently, accelerometry has been proposed for objective assessment of physical activity, but it is unclear to what extent this methods explains the variance in AEE.

Subjects/Methods:

We conducted a systematic review searching MEDLINE database (until 2014) on studies that estimated AEE based on accelerometry-assessed physical activity in adults under free-living conditions (using doubly labeled water method). Extracted study characteristics were sample size, accelerometer (type (uniaxial, triaxial), metrics (for example, activity counts, steps, acceleration), recording period, body position, wear time), explained variance of AEE (R2) and number of additional predictors. The relation of univariate and multivariate R2 with study characteristics was analyzed using nonparametric tests.

Results:

Nineteen articles were identified. Examination of various accelerometers or subpopulations in one article was treated separately, resulting in 28 studies. Sample sizes ranged from 10 to 149. In most studies the accelerometer was triaxial, worn at the trunk, during waking hours and reported activity counts as output metric. Recording periods ranged from 5 to 15 days. The variance of AEE explained by accelerometer-assessed physical activity ranged from 4 to 80% (median crude R2=26%). Sample size was inversely related to the explained variance. Inclusion of 1 to 3 other predictors in addition to accelerometer output significantly increased the explained variance to a range of 12.5–86% (median total R2=41%). The increase did not depend on the number of added predictors.

Conclusions:

We conclude that there is large heterogeneity across studies in the explained variance of AEE when estimated based on accelerometry. Thus, data on predicted AEE based on accelerometry-assessed physical activity need to be interpreted cautiously.

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Acknowledgements

Author contributions

All authors designed the study. SJ conducted the search and screened the abstracts; SJ and AS conducted full-text screening and data extraction. SJ analyzed the data and all authors interpreted the data. SJ drafted the manuscript. All authors revised and approved the final version.

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Authors and Affiliations

  1. Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany

    S Jeran, A Steinbrecher & T Pischon

  2. Charité–Universitätsmedizin Berlin, Berlin, Germany

    T Pischon

  3. DZHK (German Center for Cardiovascular Research), partner site Berlin, Berlin, Germany

    T Pischon

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  1. S Jeran
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Correspondence to T Pischon.

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Jeran, S., Steinbrecher, A. & Pischon, T. Prediction of activity-related energy expenditure using accelerometer-derived physical activity under free-living conditions: a systematic review. Int J Obes 40, 1187–1197 (2016). https://doi.org/10.1038/ijo.2016.14

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