Cardiometabolic risk in younger and older adults across an index of ambulatory activity.
BACKGROUND
Pedometers are increasingly being used to assess population levels of physical activity and as motivational tools for individuals to increase their physical activity. To maximize their utility, a framework for classifying pedometer-determined activity into meaningful health-related categories is needed.


OBJECTIVE
This study investigated whether a pedometer step index proposed by Tudor-Locke and Bassett can effectively group younger and older adults according to cardiometabolic health status.


METHODS
Analyses (conducted in 2008) used cross-sectional data from the Childhood Determinants of Adult Health study (1793 adults aged 26-36 years; collected 2004-2006) and from the Tasmanian Older Adult Cohort study (1014 adults aged 50-80 years; collected 2002-2006). Participants wore a pedometer for 7 days and the prevalence of cardiometabolic health indicators, including the metabolic syndrome, elevated Pathobiological Determinants of Atherosclerosis in Youth risk scores, and elevated Framingham risk scores, was examined across the following step categories: sedentary (< 5000); low-active (5000-7499); somewhat active (7500-9999); active (10,000-12,499); and high-active (> or = 12,500).


RESULTS
With the exception of younger men, individuals achieving > or = 5000 steps had a substantially lower prevalence of adverse cardiometabolic health indicators than those obtaining fewer steps. Differences in the prevalence of adverse indicators were generally modest across higher steps-per-day categories. However, younger men and women in the high-active category had a substantially lower prevalence of some adverse health indicators.


CONCLUSIONS
In general, the proposed index for classifying pedometer activity effectively distinguishes cardiometabolic health risk. Pedometers may be a useful tool for objectively identifying inactive individuals at greatest risk for poor cardiometabolic health.
Schmidt
Michael D
MD
Menzies Research Institute, University of Tasmania, Hobart, Tasmania, Australia. schmidtm@uga.edu
Cleland
Verity J
VJ
Shaw
Kelly
K
Dwyer
Terence
T
Venn
Alison J
AJ
D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure / Q000502:physiology; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D007328:Insulin / Q000097:blood; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods / Q000592:standards; D009042:Motivation; D009043:Motor Activity / Q000502:physiology; D012016:Reference Values; D018570:Risk Assessment; D017678:Sex Distribution; D016138:Walking / Q000502:physiology* / Q000523:psychology / Q000706:statistics & numerical data
D001786:Blood Glucose; D007328:Insulin; D008055:Lipids