Research Study Abstract

Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

  • Published on March 16, 2017

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusion: 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.


  • Jairo H. Migueles 1
  • Cristina Cadenas-Sanchez 1
  • Ulf Ekelund 2,3
  • Christine Delisle Nystrom 4
  • Jose Mora-Gonzalez 1
  • Marie Lo¨f 4,5
  • Idoia Labayen 6
  • Jonatan R. Ruiz 1,4
  • Francisco B. Ortega 1,4


  • 1

    PROFITH ‘‘PROmoting FITness and Health through physical activity’’ Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Ctra. Alfacar s/n, 18011 Granada, Spain

  • 2

    Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway

  • 3

    MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital Hills Road, Cambridge, UK

  • 4

    Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden

  • 5

    Department of Clinical and Experimental Medicine, Faculty of the Health Sciences, Linko¨ping University, Linko¨ping, Sweden

  • 6

    Department of Nutrition and Food Science, University of the Basque Country, UPV-EHU, Vitoria-Gasteiz, Spain


Sports Medicine


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