Article

Accelerometer-Determined Steps/Day and Metabolic Syndrome

Pennington Biomedical Research Center, Baton Rouge, Louisiana 70808, USA.
American journal of preventive medicine (Impact Factor: 4.28). 06/2010; 38(6):575-82. DOI: 10.1016/j.amepre.2010.02.015
Source: PubMed

ABSTRACT There is a lack of knowledge about the relationship between objectively measured physical activity and the odds of having metabolic syndrome (MetS) and cardiovascular (CVD) risk factors.
This study aims to investigate associations between accelerometer-determined steps/day and the odds of having MetS and its individual CVD risk factors in the U.S. population.
Adults in 2005-2006 NHANES with accelerometer-determined steps/day and measurements necessary to determine MetS by AHA/NHLBI were included (n=1446, 48.2% men, 33.5% with MetS, mean age=47.5 years, mean BMI=28.7 kg/m(2)). Logistic regression was used to estimate the odds of having MetS or abnormal CVD risk factors from incrementally higher levels of steps/day.
MetS prevalence decreased as steps/day increased (p<0.0001), with 55.7% of participants in the lowest categoric level of steps/day and 13.3% in the highest level having MetS. The odds of having MetS were 10% lower for each additional 1000 steps/day (OR=0.90, 95% CI=0.86, 0.93). The likelihood of having MetS was OR=0.28 (95% CI=0.18, 0.44) for active to highly active and 0.60 (0.43, 0.82) for low to somewhat-active compared to sedentary adults (p<0.0001). Adults who took more steps/day tended to have lower waist circumference, higher high-density lipoprotein (HDL) cholesterol level, and lower levels of triglycerides.
Adults who maintain an active lifestyle by accumulating more steps are likely to have a lower prevalence of MetS and its individual CVD risk factors. Although other concomitant lifestyle behaviors may influence this lower prevalence, the evidence presented here on steps/day and metabolic syndrome, and elsewhere on physical activity and other health and disease states, suggest that it is a fundamental component of daily living.

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    • "Most current knowledge on PA patterns, however, relies predominantly upon self-reporting with its known limitations affecting accuracy, such as recall bias and social desirability bias [11]. Motion sensors, such as accelerometers and pedometers, overcome many of the limitations related to self-reporting because they provide accurate and reliable estimates of the daily PA level by measuring incidental activities and considering all domains of PA (occupation, transportation , household and yard/garden, leisure time) [12]. furthermore, pedometers have the advantage of being inexpensive, unobtrusive and easy to use. "
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    • "Using these programs, researchers around the world could access the time-series accelerometer data from NHANES and run SAS programs to derive meaningful PA variables to use in statistical analyses. Over the past seven years, the NCI's SAS programs have facilitated great progress in understanding the distribution of PA behaviors in America (Troiano et al., 2008; Tudor-Locke et al., 2010), and have helped to identify numerous cross-sectional associations between PA and health outcomes (Healy et al., 2011; Sisson et al., 2010; Carson and Janssen, 2011). "
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    ABSTRACT: Accelerometers are a valuable tool for measuring physical activity (PA) in epidemiological studies. However, considerable processing is needed to convert time-series accelerometer data into meaningful variables for statistical analysis. This article describes two recently developed R packages for processing accelerometer data. The package accelerometry contains functions for performing various data processing procedures, such as identifying periods of non-wear time and bouts of activity. The functions are flexible, computationally efficient, and compatible with uniaxial or triaxial data. The package nhanesaccel is specifically for processing data from the National Health and NutritionExamination Survey (NHANES), years 2003–2006. Its primary function generates measures of PA volume, intensity, frequency, and patterns according to user-specified data processing methods. This function can process the NHANES 2003–2006 dataset in under one minute, which is a drastic improvement over existing software. This article highlights important features of packages accelerometry and nhanesaccel and demonstrates typical usage for PA researchers.
    The R Journal 12/2014; 6(2):52-62. · 0.90 Impact Factor
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    • "Proportions classified by this step-defined sedentary lifestyle index ranged from 2% in a small sample of male university students in the United States (Mestek et al. 2008) and <5% in a male South African sample (Cook et al. 2010b) and also in a Czech Republic sample (Sigmundova et al. 2011) to 56% in an American sample of multiethnic, low-income housing residents 18 to ≥70 years of age (Bennett et al. 2006), 71% in a small sample of African-American Medicaid recipients aged 31–63 years (Panton et al. 2007), and 76% in overweight–obese individuals recruited to a physical activity intervention to promote weight maintenance following a behavioural and weight-loss program (Villanova et al. 2006). Because at least 8 analyses of the 2005–2006 NHANES accelerometer step data (adjusted to become more in line with a pedometer scaling) have also focused on <5000 steps/day as at least one studied stepbased cut-point (Sisson et al. 2010, 2012; Tudor-Locke et al. 2009a, 2010b, 2011a, 2011b, 2011d; Yang et al. 2011), the table includes only the study with the most inclusive (i.e., largest) sample from the original data source that also specifically reported the weighted proportion classified as taking <5000 steps/day (Sisson et al. 2012). "
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    ABSTRACT: Step counting (using pedometers or accelerometers) is widely accepted by researchers, practitioners, and the general public. Given the mounting evidence of the link between low steps/day and time spent in sedentary behaviours, how few steps/day some populations actually perform, and the growing interest in the potentially deleterious effects of excessive sedentary behaviours on health, an emerging question is "How many steps/day are too few?" This review examines the utility, appropriateness, and limitations of using a reoccurring candidate for a step-defined sedentary lifestyle index: <5000 steps/day. Adults taking <5000 steps/day are more likely to have a lower household income and be female, older, of African-American vs. European-American heritage, a current vs. never smoker, and (or) living with chronic disease and (or) disability. Little is known about how contextual factors (e.g., built environment) foster such low levels of step-defined physical activity. Unfavorable indicators of body composition and cardiometabolic risk have been consistently associated with taking <5000 steps/day. The acute transition (3-14 days) of healthy active young people from higher (>10 000) to lower (<5000 or as low as 1500) daily step counts induces reduced insulin sensitivity and glycemic control, increased adiposity, and other negative changes in health parameters. Although few alternative values have been considered, the continued use of <5000 steps/day as a step-defined sedentary lifestyle index for adults is appropriate for researchers and practitioners and for communicating with the general public. There is little evidence to advocate any specific value indicative of a step-defined sedentary lifestyle index in children and adolescents.
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