Step-Based Physical Activity Metrics and Cardiometabolic Risk: NHANES 2005-2006.
This study aimed to catalog the relationships between step-based accelerometer metrics indicative of physical activity volume (steps per day, adjusted to a pedometer scale), intensity (mean steps per minute from the highest, not necessarily consecutive, minutes in a day; peak 30-min cadence), and sedentary behavior (percent time at zero cadence relative to wear time; %TZC) and cardiometabolic risk factors.



We analyzed data from 3388 participants, 20+ yr old, in the 2005-2006 National Health and Nutrition Examination Survey with ≥1 valid day of accelerometer data and at least some data on weight, body mass index, waist circumference, systolic and diastolic blood pressure, glucose, insulin, HDL cholesterol, triglycerides, and/or glycohemoglobin. Linear trends were evaluated for cardiometabolic variables, adjusted for age and race, across quintiles of steps per day, peak 30-min cadence, and %TZC.



Median steps per day ranged from 2247 to 12,334 steps per day for men and from 1755 to 9824 steps per day for women, and median peak 30-min cadence ranged from 48.1 to 96.0 steps per minute for men and from 40.8 to 96.2 steps per minute for women for the first and fifth quintiles, respectively. Linear trends were statistically significant (all P < 0.001), with increasing quintiles of steps per day and peak 30-min cadence inversely associated with waist circumference, weight, body mass index, and insulin for both men and women. Median %TZC ranged from 17.6% to 51.0% for men and from 19.9% to 47.6% for women for the first and fifth quintiles, respectively. Linear trends were statistically significant (all P < 0.05), with increasing quintiles of %TZC associated with increased waist circumference, weight and insulin for men, and insulin for women.



This analysis identified strong linear relationships between step-based movement/nonmovement dimensions and cardiometabolic risk factors. These data offer a set of quantified access points for studying the potential dose-response effects of each of these dimensions separately or collectively in longitudinal observational or intervention study designs.
1Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA; 2College of Public Health and Human Sciences, Oregon State University, Corvallis, OR; and 3Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA.
Catrine
C
Tudor-Locke
John M
JM
Schuna
H O
HO
Han
Elroy J
EJ
Aguiar
Michael A
MA
Green
Michael A
MA
Busa
Sandra
S
Larrivee
William D
WD
Johnson
D061725:Accelerometry; D000328:Adult; D000886:Anthropometry; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000097:blood / Q000503:physiopathology / Q000517:prevention & control*; D008076:Cholesterol, HDL / Q000097:blood; D015444:Exercise / Q000502:physiology*; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007328:Insulin / Q000097:blood; D008297:Male; D008875:Middle Aged; D009749:Nutrition Surveys; D012307:Risk Factors; D057185:Sedentary Behavior; D014280:Triglycerides / Q000097:blood
D001786:Blood Glucose; D008076:Cholesterol, HDL; D006442:Glycated Hemoglobin; D007328:Insulin; D014280:Triglycerides
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