American Journal of Preventive Medicine
Volume 35, Issue 6 , Pages 578-588 , December 2008

Cost Effectiveness of Community-Based Physical Activity Interventions

  • Larissa Roux, MD, MPH, PhD

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
    • Corresponding Author InformationAddress correspondence and reprint requests to: Larissa Roux, MD, MPH, PhD, 5612 Elm Street, Vancouver BC, Canada V6N-1A4
  • ,
  • Michael Pratt, MD, MPH, MS

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
  • ,
  • Tammy O. Tengs, ScD

      Affiliations

    • Milliman, Denver, Colorado
  • ,
  • Michelle M. Yore, MSPH

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
  • ,
  • Teri L. Yanagawa, MKin, MBA

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
    • Stryker Instruments Manufacturing, Freiburg, Germany
  • ,
  • Jill Van Den Bos, MA

      Affiliations

    • Milliman, Denver, Colorado
  • ,
  • Candace Rutt, PhD

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
  • ,
  • Ross C. Brownson, PhD

      Affiliations

    • Prevention Research Center, School of Public Health, St. Louis University, St. Louis, Missouri
  • ,
  • Kenneth E. Powell, MD, MPH

      Affiliations

    • Chronic Disease, Injury, and Environmental Epidemiology Section, Epidemiology Branch, Division of Public Health, Georgia Department of Human Resources, Atlanta, Georgia
  • ,
  • Gregory Heath, DHSc

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
    • University of Tennessee College, Chattanooga, Tennessee
  • ,
  • Harold W. Kohl III, PhD

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
  • ,
  • Steven Teutsch, MD, MPH

      Affiliations

    • Outcomes Research and Management, Merck & Co., Inc., West Point, Virginia
  • ,
  • John Cawley, PhD

      Affiliations

    • Department of Policy Analysis and Management, Cornell University, Ithaca, New York
  • ,
  • I.-Min Lee, ScD, MD

      Affiliations

    • Harvard Medical School and Harvard School of Public Health, Brigham and Women's Hospital, Boston, Massachusetts
  • ,
  • Linda West, MSPH

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia
  • ,
  • David M. Buchner, MD, MPH

      Affiliations

    • Physical Activity and Health Branch, Division of Nutrition, Physical Activity and Obesity, CDC, Atlanta, Georgia

  • Image Result

    Conceptual overview of the CDC MOVE model. Illustration of the 10-state Markov process represented as a state-transition diagram. In this process, circles represent possible health states, and arrows

    Conceptual overview of the CDC MOVE model. Illustration of the 10-state Markov process represented as a state-transition diagram. In this process, circles represent possible health states, and arrows represent allowed transitions among these discrete health states. In each cycle of the Markov model, transition probabilities denote the likelihood with which people within a particular health state will stay in that state (represented by the tight curvilinear arrows to and from a single circle); transition to a new health state; or die. Death is an absorbing state from which no future transitions are possible. The output from the Markov process is depicted by the box; a running tally of the total costs and quality-of-life benefits generated during each cycle as a result of being in a series of health states over time. MOVE, measurement of the value of exercise

  • Image Result
    Results from probabilistic sensitivity analyses: acceptability curves representing variation of intervention costs and effect sizes

    Results from probabilistic sensitivity analyses: acceptability curves representing variation of intervention costs and effect sizes

 The full text of this article is available via AJPM Online at www.ajpm-online.net.

PII: S0749-3797(08)00770-8

doi: 10.1016/j.amepre.2008.06.040

American Journal of Preventive Medicine
Volume 35, Issue 6 , Pages 578-588 , December 2008