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      Influence of socio-economic status on habitual physical activity and sedentary behavior in 8- to 11-year old children

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          Abstract

          Background

          While socio-economic status has been shown to be an important determinant of health and physical activity in adults, results for children and adolescents are less consistent. The purpose of this study, therefore, is to examine whether physical activity and sedentary behavior differs in children by socio-economic status (SES) independent of body mass index.

          Methods

          Data were from two cohorts including 271 children (117 males; 154 females) in study 1 and 131 children in study 2 (63 males; 68 females). The average age was 9.6 and 8.8 years respectively. Height and body mass were assessed according to standard procedures and body mass index (BMI, kg/m 2) was calculated. Parent-reported household income was used to determine SES. Habitual, free-living physical activity (PA) was assessed by a pedometer (steps/day) in study 1 and accelerometer (time spent in moderate-to-vigorous PA) in study 2. Self-reported time spent watching TV and on the computer was used as measure of sedentary behavior. Differences in PA and sedentary behavior by SES were initially tested using ANOVA. Further analyses used ANCOVA controlling for BMI, as well as leg length in the pedometer cohort.

          Results

          In study 1, mean daily steps differed significantly among SES groups with lower SES groups approximating 10,500 steps/day compared to about 12,000 steps/day in the higher SES groups. These differences remained significant (p < 0.05) when controlling for leg length. Lower SES children, however, had higher body mass and BMI compared to higher SES groups (p < 0.05) and PA no longer remained significant when further controlling for BMI. In study 2 results depended on the methodology used to determine time spent in moderate-to-vigorous physical activity (MVPA). Only one equation resulted in significant group differences (p = 0.015), and these differences remained after controlling for BMI. Significant differences between SES groups were shown for sedentary behavior in both cohorts (P < 0.05) with higher SES groups spending less time watching TV than low SES groups.

          Conclusions

          Children from a low SES show a trend of lower PA levels and spend more time in sedentary behavior than high SES children; however, differences in PA were influenced by BMI. The higher BMI in these children might be another factor contributing to increased health risks among low SES children compared to children from with a higher SES.

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          Most cited references48

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          The obesity epidemic in the United States--gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis.

          This review of the obesity epidemic provides a comprehensive description of the current situation, time trends, and disparities across gender, age, socioeconomic status, racial/ethnic groups, and geographic regions in the United States based on national data. The authors searched studies published between 1990 and 2006. Adult overweight and obesity were defined by using body mass index (weight (kg)/height (m)(2)) cutpoints of 25 and 30, respectively; childhood "at risk for overweight" and overweight were defined as the 85th and 95th percentiles of body mass index. Average annual increase in and future projections for prevalence were estimated by using linear regression models. Among adults, obesity prevalence increased from 13% to 32% between the 1960s and 2004. Currently, 66% of adults are overweight or obese; 16% of children and adolescents are overweight and 34% are at risk of overweight. Minority and low-socioeconomic-status groups are disproportionately affected at all ages. Annual increases in prevalence ranged from 0.3 to 0.9 percentage points across groups. By 2015, 75% of adults will be overweight or obese, and 41% will be obese. In conclusion, obesity has increased at an alarming rate in the United States over the past three decades. The associations of obesity with gender, age, ethnicity, and socioeconomic status are complex and dynamic. Related population-based programs and policies are needed.
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            Built environments and obesity in disadvantaged populations.

            In the United States, health disparities in obesity and obesity-related illnesses have been the subject of growing concern. To better understand how obesity-related health disparities might relate to obesogenic built environments, the authors conducted a systematic review of the published scientific literature, screening for studies with relevance to disadvantaged individuals or areas, identified by low socioeconomic status, black race, or Hispanic ethnicity. A search for related terms in publication databases and topically related resources yielded 45 studies published between January 1995 and January 2009 with at least 100 participants or area residents that provided information on 1) the built environment correlates of obesity or related health behaviors within one or more disadvantaged groups or 2) the relative exposure these groups had to potentially obesogenic built environment characteristics. Upon consideration of the obesity and behavioral correlates of built environment characteristics, research provided the strongest support for food stores (supermarkets instead of smaller grocery/convenience stores), places to exercise, and safety as potentially influential for disadvantaged groups. There is also evidence that disadvantaged groups were living in worse environments with respect to food stores, places to exercise, aesthetic problems, and traffic or crime-related safety. One strategy to reduce obesity would involve changing the built environment to be more supportive of physical activity and a healthy diet. Based on the authors' review, increasing supermarket access, places to exercise, and neighborhood safety may also be promising strategies to reduce obesity-related health disparities.
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              Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables.

              Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies.
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                Author and article information

                Journal
                BMC Public Health
                BMC Public Health
                BioMed Central
                1471-2458
                2010
                27 April 2010
                : 10
                : 214
                Affiliations
                [1 ]Department of Kinesiology, Michigan State University, East Lansing, MI, USA
                [2 ]Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI, USA
                [3 ]Department of Kinesiology, Iowa State University, Ames, IA, USA
                [4 ]Department of Health and Physical Education/Human Performance Lab, University of Nebraska-Kearney, Kearney, NE, USA
                [5 ]Department of Psychology, Iowa State University, Ames, IA, USA
                [6 ]National Institute on Media and the Family, Minneapolis, MN, USA
                Article
                1471-2458-10-214
                10.1186/1471-2458-10-214
                2873582
                20423487
                19eddf71-8d53-4092-9449-1c167e127062
                Copyright ©2010 Drenowatz et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 November 2009
                : 27 April 2010
                Categories
                Research article

                Public health
                Public health

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