Examining the Relationship between Green Spaces, Physical Activity, and Obesity - Report Example

Topic: Examining the relationship between green spaces, physical activity, and obesity. Green spaces have come out to be important contributors in obesity reduction and promoting physical activity. It has been noticed that availability of supermarkets in the neighbourhood adds to the consumption of fruit and vegetables and reduces the chances of obesity. In different studies, connectivity, land use mix and population density are related to some sort of physical activity while unsystematic urbanisation has an inverse relation to walking. According to CABE (2006), green spaces, i.e. built environment provides opportunities to reduce obesity and inactivity. Built environment consists of streets and neighbourhoods, parks and workplaces. Obesity and inactivity puts a great burden of £10 billion on the health services of England. According to American Heart Association (2009), children at high risk to obesity residing near parks were found to be physically active. In a Canadian study, a sound association existed between the presence of parks within half a mile from home of girls preferring walking to school and boys spending free time in playing activities. Through environmental and policy-wise interventions, changes in physical activity on a large scale can be brought about by strategizing control on eating habits and reduce obesity. Multidisciplinary research has established a relation between green spaces, physical activity and obesity. Bell et al (2008) have examined the relationship between built environment and density of population taking a period of 2 years by measuring the body mass index (BMI) of children and youths and the impact of race/ethnicity, gender, age or SES on the relationship between obesity, physical activity and green spaces. Method of collecting sample data Data was gathered from electronic medical records of a primary care clinic in Indianapolis IN. children between the 3-16 years in age were made part of the sample who got treated through the clinic network during 1996 to 2002. Addresses of all participants were GEO-coded by using Arc-GIS 9.0 automated address matching routines besides manually entering the details. The sample data was limited to those participants who lived there for 2 years and had same-day clinical body measurements for 2 years separately (n 3901). The sub-group selected had biennial visits to the clinic, was a bit senior (9.16 years vs. 8.76 years, p0.01); it had a higher percentage of male children, youth and those categorised as obese (Bell et al., 2008: 548). Dependent Variable The child’s gender and particular age BMI z-score was dependable variable in relation to the U.S. Growth reference charts for the year 2008 measured at time 2. The child’s BMI z-score at the start of the biennium was taken as a covariate in all models. The BMI of youth was based on their height and weight taken by scales and stadiometers. Algorithms were applied to find far-fetched values. The reason to using the BMI z-scores in stead of total BMI was the potential increase in height and weight of research participants. For measuring the changes in 2 years a dichotomous dependable variable was created to segregate BMI z-scores as increasing between two times vs. equal or down (Bell et al., 2008: 548). Measurement of Green Spaces The normalized difference vegetation index (NDVI) taken by changing pixel values in the satellite images of 30x30-meter areas was taken to measure greenness. As per the urban environment and analysis purpose, NDVI was scaled to a factor of 10 to relate a 1-unit increase in land use changes found not objectionable in urban setting. Further, summer measurement was selected to match to the high green biomass in housing locations. It was assumed that there was no change in mean NDVI in the selected season as correlating coefficients for the selected period were in the range of 0.97 to 0.99. Mean NDVI was measured for 1-kilometer radial network covering the house of each child. The stretch of 1 kilometer was selected on correlates of adult walking capacity. As network buffers differ in size as based on the standard of street connectivity and are more perfect than radial buffers but because it is not a tested proposition both parameters were used in all models. Housing density was measured by taking number of houses per acre put to housing land use within the child’s block-group of houses (Bell et al., 2008: 548). Covariates Covariates tested on all models included racial/ethnic groups, gender and interaction between the two besides age at baseline and status on health insurance to serve as proxy for SES. Linear, quadratic and cubic terms in baseline BMI z-scores were taken to test non-linear connections and Time-1clinical weight division sections. Results were found same for linear BMI z-scores at Time1 (Bell et al., 2008: 549). Statistical Analysis Bell et al. analysed the data by using GeoDa software to examine autocorrelation in the dependent variable. Rest of the data was analysed using Stata version 10.0. They made use of ANOVA to examine the differences in mean NDVI and area density by variable. Multiple linear regression was employed to factor BMI z-score at Time-1, built environment variables and covariates. SES was set within census block-groups by employing Huber-White measure of variance (Bell et al., 2008: 549). Density of population and NDVI together as well as individually were part of the multiple regression models. Finally, the models were used with the NDVI in radial and network buffers. The impact of the combination of NDVI and BMI z-scores by age, race, gender and SES was mapped individually with improved F-tests for the collective importance of the terms. At the end, logistic regression was employed to represent the dichotomous standards of 2-year BMI z-score increase due to green spaces density of housing and all covariates (Bell et al., 2008: 549). Results As per the sample, most children and youth were non-Hispanic black (58%); between the age of 6-10 (35%) and on the roll of Medicaid (83%). The average block-group median family income was lower to that of the country overall ($36,917/year vs. $49,387/year).Time-1 recorded 23% of the children and youth obese, 17% over-weight with an average BMI z-score of 0.68 SDs over the national standard. The average BMI z-score got increased by 0.08 SDs during the study period (Bell et al., 2008: 549). In the index year 2000, the mean NDVI was up in non-Hispanic black children and youth and other children with normal weight, insurance and high income block-groups. NDVI was down in children and youth in the neighbourhoods with housing density over the 50th percentile. Mean housing density was lesser among children and youth with higher SES. As no proof of spatial auto-correlation was noticed in the Time-2 BMI z-scores, analysis was made without spatial regression models. As quadratic and cubic NDVI estimates were not important enough as well as F-tests also showed no changes by age, race and gender, results were gathered for linear NDVI measures and collected models only. The NDVI and housing density were oppositely correlated but the intra-correlation coefficient was not enough to model the variables in combination. An NDVI increase by 0.01-unit was related to lesser Time-2 BMI z-scores. Housing density without the NDVI was not crucially related to Time-2 BMI z-scores. Contrastingly, a higher NDVI was related to lesser Time-2 BMI z-scores and housing density was just on the border with lesser Time-2 BMI z-scores greenness and density were measured together (Bell et al., 2008: 550). Conclusion There is an opposite relation between green spaces and BMI z-scores of children and youth at the given 2 years, as ascertained with baseline BMI z-scores and significant covariates. The impact seems to be irrelevant to housing density, strong by buffer type, of the same direction, capacity and importance by gender, age and race. The study substantiates the previous research that green spaces improve the health of children playing in parks and surrounding green areas in comparison to children residing in less-green areas. Physical activity of children and youth leads to impact the BMI of children and youth. They feel more active in open spaces in stead of streets and sidewalks. The findings back the research on the advancement and maintenance of green spaces within the neighbourhoods as an instrument of reducing childhood obesity. References: American Heart Association, 2009. Children living near green spaces are more active. Health & Medicine. Viewed 7 Feb. 2010, from http://esciencenews.com/articles/2009/03/12/children.living.near.green.spaces.are.more.active Bell, Janice F, Wilson, Jeffrey S, Liu, Gilbert C, 2008, ‘Neighborhood greenness and 2-year changes in body mass index of children and youth. American Journal of Preventive Medicine’, vol. 35 (6) Elsevier Inc. CABE, 2006. Physical activity and the built environment. Viewed 7 Feb. 2010, from http://www.cabe.org.uk/publications/physical-activity-and-the-built-environment Read More