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Related Experiment Video

Updated: Jun 16, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

Applying generalizability theory to estimate habitual activity levels.

Eric E Wickel1, Gregory J Welk

  • 1University of Tulsa, OK 74104, USA. eric-wickel@utulsa.edu

Medicine and Science in Sports and Exercise
|February 9, 2010
PubMed
Summary

Generalizability theory can improve physical activity research by identifying measurement errors. A mixed design using 7-8 days from one season reliably assesses habitual activity levels in youth.

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Last Updated: Jun 16, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

  • Physical activity research
  • Measurement error quantification
  • Generalizability theory

Background:

  • Habitual physical activity assessment often relies on single measurement periods, potentially misrepresenting long-term behavior.
  • Generalizability theory offers a framework to analyze multiple sources of measurement error in physical activity studies.

Purpose of the Study:

  • To apply generalizability theory to quantify measurement error in youth physical activity.
  • To determine the optimal number of days and seasons for reliable assessment of habitual physical activity.

Main Methods:

  • Youth wore pedometers for 7 consecutive days across three months (September, January, May) over two years.
  • Variance was partitioned to identify contributions from participants, days, seasons, and their interactions.
  • Generalizability (g) and phi coefficients were calculated using random and mixed designs to assess reliability.

Main Results:

  • Residual variance was the largest error source (55.64%), followed by participant (18.74%), season (6.59%), and day (2.67%).
  • A random design with a single season did not achieve reliable physical activity estimates.
  • A mixed design using 7-8 days from a single, fixed season achieved acceptable reliability for both relative and absolute decisions.

Conclusions:

  • Generalizability theory is a valuable tool for improving the accuracy of physical activity research.
  • A mixed design with 7-8 days from one season is sufficient for reliable assessment of youth physical activity levels.