Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations
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Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus.
The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified.
Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015.
The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific.
This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data.
PROSPERO registration number
KeywordsSedentary Time Vigorous Physical Activity Indirect Calorimetry Multivariate Adaptive Regression Spline Physical Activity Intensity
We are deeply thankful to Patty Freedson, Professor (University of Massachusetts/Amherst, USA) and Catrine Tudor-Locke, PhD (University of Massachusetts/Amherst, USA), for their comments on an earlier draft. This is part of a PhD Thesis conducted in the Biomedicine Doctoral Studies at the University of Granada, Spain.
Compliance with ethical standards
This review was conducted under the umbrella of the ActiveBrains project (DEP2013-47540). Jairo H. Migueles is supported by a Grant from the Spanish Ministry of Education, Culture and Sport (FPU15/02645). Cristina Cadenas-Sanchez is supported by a Grant from the Spanish Ministry of Economy and Competitiveness (BES-2014-068829). Jose Mora-Gonzalez is supported by a Grant from the Spanish Ministry of Education, Culture and Sport (FPU14/06837). Francisco B. Ortega and Jonatan R. Ruiz are supported by Grants from the Spanish Ministry of Science and Innovation (RYC-2011-09011 and RYC-2010-05957, respectively). Ulf Ekelund is supported by Grants from the Research Council of Norway (249932/F20) and the UK Medical Research Council (MC_UU_12015/3). Additional funding was obtained from the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES). In addition, funding was provided by the SAMID III network, RETICS, funded by the PN I + D+I 2017-2021 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/0022) and the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005-00046/ACTI).
Conflict of interest
Jairo H. Migueles, Cristina Cadenas-Sanchez, Ulf Ekelund, Christine Delisle Nyström, Jose Mora-Gonzalez, Marie Löf, Idoia Labayen, Jonatan R. Ruiz, and Francisco B. Ortega declare that they have no conflicts of interest relevant to the content of this review.
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