Research Study Abstract

Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies

  • Published on Feb 2018

Introduction/Purpose: To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph).

Methods: Participants were 932 adults (50–74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL.

Results: Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from −4% to −13% lower than DLW, but its accuracy was poorer for physical activity duration (−48%) and sedentary time (−18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (−2% to −7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (−1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (ρTX = 0.16–0.34) overall, but four ACT24 recalls had higher correlations (ρTX = 0.48–0.60), as did 4 d of ActiGraph assessment (ρTX = 0.54–0.87).

Conclusions: New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.

Author(s)

  • MATTHEWS, CHARLES E. 1
  • KOZEY KEADLE, SARAH 1,2
  • MOORE, STEVEN C. 1
  • SCHOELLER, DALE S. 3
  • CARROLL, RAYMOND J. 4,5
  • TROIANO, RICHARD P. 6
  • SAMPSON, JOSHUA N. 7

Institution(s)

  • 1

    Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
  • 2

    Kinesiology Department, California Polytechnic State University, San Luis Obispo, CA
  • 3

    University of Wisconsin, Biotech Center and Nutritional Sciences, Madison, WI
  • 4

    Department of Statistics, Texas A&M University, College Station, TX
  • 5

    School of Mathematical and Physical Sciences, University of Technology Sydney, AUSTRALIA
  • 6

    Risk Factor Assessment Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
  • 7

    Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD

Journal

Med Sci Sports Exerc

Categories

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