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Related Experiment Video

Updated: May 31, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

International children's accelerometry database (ICAD): design and methods.

Lauren B Sherar1, Pippa Griew, Dale W Esliger

  • 1College of Kinesiology, University of Saskatchewan, Saskatoon, SK, Canada. lauren.sherar@usask.ca

BMC Public Health
|June 23, 2011
PubMed
Summary

Accelerometers provide objective physical activity data in children. Pooling data from multiple studies enhances analysis of physical activity and health relationships.

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Area of Science:

  • Pediatric physical activity research
  • Objective measurement of physical activity
  • Public health and epidemiology

Background:

  • Accelerometers are increasingly used for objective physical activity measurement in children.
  • Objective measures are more effective than subjective tools in detecting physical activity-health associations.
  • The International Children's Accelerometry Database (ICAD) project standardized raw accelerometer data from global studies.

Purpose of the Study:

  • To describe contributing studies to the ICAD project.
  • To outline standardized methods for processing accelerometer data.
  • To establish a novel data repository for future research on physical activity and health in children.

Main Methods:

  • Collected 46,131 raw Actigraph data files from 20 international studies (2008-2010).

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Last Updated: May 31, 2026

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Published on: April 21, 2017

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05:59

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Published on: March 7, 2019

  • Included anthropometric, demographic, and health data for children aged 3-18 years.
  • Reduced data using standardized analytical methods.

    • Main Results:

    • With wear time criteria of ≥ 8, ≥ 10, and ≥ 12 hours/day, 96%, 93.5%, and 86.2% of males, respectively, had valid data.
    • Correspondingly, 96.3%, 93.7%, and 86% of females had at least one valid day of data.

    Conclusions:

    • Pooling accelerometer and phenotypic data increases statistical power and sample representativeness.
    • Standardized methods optimize outcome variable generation and allow study of inter-study variability.
    • Challenges include variability in accelerometry measurements and non-accelerometer data collection methods.