1.0 Introduction
1.1 Overview of the condition
Obesity is defined as having a sex- and age- specific body mass index (BMI) at or above the ninety fifth percentile of national growth standards. Obesity is directly linked to excessive/abnormal fat accumulation. The BMI is a ratio of the body weight and height and is the most common and accurate measure of obesity because it correlates to the amount of body fat. In terms of BMI, a person is considered to be obese if s/he has a BMI of over 30 (which is often ≥20% the normal body weight). A body mass index of between 25 and 29.9 qualifies an individual to be regarded as overweight. A person with a BMI of 40 and above is said to have morbid obesity (Bell & Zimmerman, 2010).
In the last three decade childhood obesity has been on a steady rise with. In the said period, the prevalence of obesity in children has doubled while it has tripled in adolescents. The population of children aged between 6 and11 who are obese in USA rose from 7% in 1980 to almost 18% in 2010. That of adolescents aged between 12 and 19 years rose from 5% to 18% in the same period. The statistics seem to be grimmer among the children from poor families. However, is a good sign with 19 states and USA territories reporting decline in obesity prevalence among the preschoolers from low-income families and only 3 states reporting an increase in prevalence. New York was one of the states that gave a good report on this matter. This decline has been attributed to deliberate concerted efforts by government departments and communities through programs aimed at reducing obesity among children.
1.2 Etiology and risk factors associated with obesity
The etiology and pathophysiology of obesity have been associated with a caloric imbalance between the calories consumed and those expended. In other words, the main cause of obesity is consuming more calories than the body is burning through physical activity . This caloric imbalance is influenced by the quality, quantity and frequency of food consumption are a major cause of obesity coupled with physical inactivity. Other risk factors associated with obesity include: genetic predisposition, gender (women are more vulnerable), age (the body’s metabolic capacity reduces with age), physical activities (sedentary life is a major cause of obesity in the modern society especially the youth and children), psychological factors that influence eating habits, illness like hypothyroidism and some medication like antidepressant and steroids. Most children and adolescents in the USA driven to and from school hence they barely get a chance to engage in physical activity in form of a walk or running. In addition, most children spend most of their leisure time watching television and playing video games rather than engaging in physical exercises. As they watch television and play games, the children consume high calorie foods.
Environmental and societal influences shape an individual’s lifestyle choices making these factors one of the causes of obesity. If an individual lives in an environment where the groceries are a rarity, he or she is likely to opt for whatever is available which may not always be healthy. Other environmental factors associated with obesity are availability of high energy, palatable food, lack of access to safe places to exercise/play, decreasing costs of unhealthy foods and increasing costs of healthy foods, also contribute to higher incidences of obesity. Societal pressures such as the need to fit in could easily sway a child or an adolescent’s food choices. Generally environmental factors tend to prevent or inhibit healthy eating, encourage unhealthy eating and active life. Such factors include: the availability of high energy palatable foods, lack of access to safe places to exercise/play, watching too much TV, decreasing costs of unhealthy foods and increasing costs of healthy foods, also contribute to higher incidences of obesity (Ard, 2007; Khan, et al., 2009)
Recently some scholars have investigated some unconventional obesity risk factors. Among these unconventional risk factors is reduced sleep. The exact mechanisms of how sleep affects body weight remain unclear. However, there are several speculations on how sleep reduction may cause weight gain by increasing food intake or decreasing energy burnt. The most common theory on how sleep reduction can lead to weight gain is the fact that reduced sleep affects the production of hormones that control the appetite. Reduced sleep increases the production of appetite-stimulating hormone (ghrelin) and reduces the secretion of satiety inducing hormone (leptin). This means that sleep deprived persons have hunger pangs and an appetite, particularly for fat and carbohydrate foods (Bell & Zimmerman, 2010). Reduced sleep also increases affinity to high calorie foods and drinks, may be in a bid to compensate for the apparent energy burnt during the prolonged working hours. Reduced sleep also means that people have people more hours to eat, and they often choose less healthy diets. Last but not least, reduced sleep has the effect of reducing physical activity due to tiredness during the day . Another unconventional risk factor is the effect of food advertisment on the food choices . Researchers from Yale university found that exposure to food advertising during TV watching may trigger automatic snacking and thus contribute to obesity. The study established that children consume 45% more snack foods when exposed to food advertisemet.
1.3 Disease burden
Obesity exerts direct and indirect pressure on the health and finances of the individual, family and community. The prevalence of obesity has been on a steady increase in the last 3 decades to the extent that it has become a major public health concern and a global . Compared the 1980s, the world obesity prevalence among children and adolescent has doubled (Bell & Zimmerman, 2010). Up to 17% of children and adolescent were obese in 2003/2004 while 34% were overweight (Bell & Zimmerman, 2010). In 2011, more than 40 million children under the age of 5 years old were overweight.
In the U.S, 17% of the children and adolescent population are currently obese ( CDC, 2013). Statistics indicate that as early as preschool, children are already contending with obesity with one out of eight preschoolers being obese (12%). 19.6% of the children between the ages of 6 and 11 in the USA are obese. The prevalence of obesity also varies with race and socioeconomic status, with 19% black children between the ages of 2 and 5 years being obese compared to 16% of their Hispanic counterparts. Childhood obesity increases the chances of being overweight or obese at adolescence and adult age.
The increasing prevalence of obesity among children and adolescent is particularly worrying because the associated chronic diseases were once considered diseases of the old but are now common among the young, with obesity being attributed to the incidences. Obesity is associated with chronic illnesses such as diabetes, cardiovascular disorders, hypertension, gallstones and cancer. It has also been associated with dyslipidemia, stroke, sleep apnea, Osteoarthritis, respiratory problems, gynecological problems such as infertility and abnormal menses, social and psychological problems. These secondary illness associated with Obesity have led to scholars concluding that “obesity is the fastest growing cause of disease and death in USA. A recent study established that childhood obesity and some of the associated diseases are linked to premature deaths.
In response to these grim statistics, communities, parents and other stakeholders have developed concerted, coordinated and comprehensive policy and environmental intervention strategies to reverse the obesity situation. This strategies target the modifiable risk factors mentioned earlier. Community based programs that incorporate several stakeholders such as teachers, health officials, nutritionists, facilitators, local authorities, fitness and gym instructions have been reported to be successful in helping reduce the obesity prevalence (Khan, et al., 2009; Simmons, et al., 2009; de Groot, Robertson, Swinburn, & de Silva-Sanigorski, 2010; Porter, 2013). These community based interventions have been credited for the decline in obesity rates reported in 19 states, New York state being one of them .
2. 0 Background
2.1 The New York City case
Despite posting good report on childhood obesity, New York State still a childhood obesity crisis with 1 in every 4 children and teenagers under the age of 18 (about 1.1 million) being obese. This population is estimated to increase the annual cost of medical cost by $242. This high prevalence of obesity remains mainly because of noncompliance to nutritional guidelines and failure to participate in physical activities. The National School Lunch and Breakfast program has been adopted in some schools in line with the nutritional guidelines recommended by the Department of Health and Human Services. This has however been met by non compliance particularly from the students with a 2007 survey revealing that students do not consume the recommended five or more portions of fruits on a daily basis. Physical education is mandatory in most schools but most students do not participate. A survey carried out in 2007 revealed that 62% of the children in New York do not engage in the recommended physical activities while 87% do not participate in physical education in school.
Buffalo, New York has not only adopted the aforementioned approaches but also adapted them in order to adequately address the problem. The Healthy Kids, Healthy Communities initiative is touted as being instrumental in encouraging more children to engage in physical activities. The initiative has resulted in the increase in the number of biking lanes, safe routes to school and play zones within the city. Further adaptation of the initiative can be done in order to ensure that lunches and breakfasts are provided by school administrations. Healthy eating ought to be taught to school children in order to boost the efforts to reduce child hood obesity. This implies that there is still room for improvement and the state comptroller has stated by instituting statewide audits of school lunch meal services and physical education programs. In addition, more researchers are conducting studies to monitor and evaluate the effectiveness of the community intervention programs in place.
2.2 Previous intervention studies
One such study by Porter (2013) sought to acsertain that community actions and efforts are useful in preventing obesity. The study involved exploratory analysis of 3 community programs aimed at preventing childhood obesity in Northeastern USA: Eat Well Play Hard Chemung in semirural NY, Whole Community Project in semi-urban NY and Shape Up Somerville in urban Massachusetts. Data was colled through analysis of programs documents from inception, participant observation and stakeholder interviews. The researcher attended about 7 meetings and events for each program to observe how the interventions are delivered, interviewed 23 stakeholders and randomly reviewed approximately 100 documents of each program since its inception. This three methods were used to determine whether there was any improvement in BMI within the life of the programs and whether the participants were consistend. The study established that the programs were successful in modifying the food and activity environments and thus prevented obesity. However, the programs were not as useful in generating policies or improving the economic status of participants. The programs had greater potential if long-term funding and technical assistance was available. The three programs involve children from the school going age to 18yrs old which is the same target population for the proposed program. Eat Well Play Hard Chemung in NY (semirural) can easily be adapted in Buffalo, New York; first by introducing the program to a few district schools on a pilot basis and monitoring its effectiveness for a period not exceeding one year. With the monitoring data collected, the program can be rolled out in as many schools as possible and modified to fit the cultural practices and orientation of each school.
3.0 Methods
3.1 Recruitment of participants
A community centered approach known as Stay and Play initiative will be developed in partnership with parents and teachers. The initiative will be pilot tested for a period of a year among a select group of students drawn from 12 schools in Buffalo city. The participants will be aged between 12 and 15 years. In line with the statistics which indicate the prevalence of childhood obesity is more pronounced among Hispanic and African American children, the study population will be made up of: 38% Hispanics, 29% African Americans, 15% Caucasians, 14% biracial children and 4% multiracial children. All children within the ages of 12 and 15 in the participating schools were eligible for participation. The participants were selected through systematic sampling.
3.2 Treatment of participants
The participants will have their weight and height measurements taken prior to the commencement of the study. They will then undergo a rigorous training in order to acquaint them with the details of the program. The participants will remain in school every day for two hours and play. The ones in the control group will be allowed to go home but will be required to document their activities after school. The students will also have short breaks in between the play sessions where they will receive information on healthy eating and the need for adequate sleep as measures of curbing weight gain.
3.3 Data collection
Like in the study described above data will be colled through analysis of programs documents from inception, participant observation and stakeholder interviews. The researcher will attend 5 events of the program in each school as well as 3 in 4 other programs for the purpose of observation and comparisio. The researcher will interview, 12 students, 7 teachers and all the twelve program facilitaros. In addition the researcher will randomly review documents of 10 students from each of the participating school to establish whether there was a significant change in BMI. The documents from the control groups and the 4 other mentioned will be reviewed for comparison purpose.
3.4 Data analysis
The analysis of the data will be carried out using SPSS version 13.0. Mc Nemar’s test will be used in the comparison of differences in the participants who are obese while they were on other intervention programs and while they were participating in the Stay and Play program. Paired t-tests shall be used in comparing the pre and post intervention differences. The following measures will be considered: the child BMI, physical activity and attitudes..
References
Anderson, S. (2009). Prevalence of obesity among US preschool children in different racial and ethnic groups. Arch Pediatr Adolesc Med , 163(4) , 344-348.
Ard, J. D. (2007). Unique Perspectives on the Obesogenic Environment. Journal of General Internal Medicine , 22 (7), 1058–1060.
Bayer, O., Rosario, A., Wabitsch, M., & von Kries, R. (2009). Sleep duration and obesity in children: is the association dependent on age and choice of the outcome parameter? Sleep , 32 (9), 1183-9.
Bell, J. F., & Zimmerman, F. J. (2010). Shortened Nighttime Sleep Duration in Early Life and Subsequent Childhood Obesity. JAMA Pediatrics , 164 (9), 840-845.
Bickham, D. S., Blood, E. A., Walls, C. E., Shrier, L. A., & Rich, M. (2013). Characteristics of Screen Media Use Associated With Higher BMI in Young Adolescents. Pediatrics , 131 (5), 935-941.
CDC . (2007). The Obesity Epidemic and New York Students. Retrieved January 10, 2014, from Centers for Disease Control and Prevention:: <http://www.cdc.gov/HealthyYouth/yrbs/pdf/obesity/yrbs07_new_york_obesity.pdf>
CDC. (2013, August 16). Adult Obesity Facts. Retrieved January 18, 2014, from http://www.cdc.gov/obesity/data/adult.html
CDC. (2013, July 10). Childhood Obesity Facts. Retrieved January 28, 2014, from http://www.cdc.gov/healthyyouth/obesity/facts.htm
CDC. (2013, August 6). Progress on Childhood Obesity. Retrieved January28 2014, from http://www.cdc.gov/VitalSigns/ChildhoodObesity/
Daniels, S., Arnett, D., & Eckel, R. (2005;). Overweight in children and adolescents: pathophysiology, consequences, prevention, and treatment. Circulation 111 , 1999–2002.
de Groot, F., Robertson, N., Swinburn, B., & de Silva-Sanigorski, A. (2010). Increasing community capacity to prevent childhood obesity: challenges, lessons learned and results from the Romp & Chomp intervention. BMC Public Health , 10 (522).
Dietz, W. (2004). Overweight in childhood and adolescence. . New England Journal of Medicine , 855-857.
DiNapoli, T. P. (2008). Preventing and Reducing Childhood Obesity. New York: Office of the State Comptroller.
Flynn, M., McNeil, D., Maloff, B., Mutasingwa, D., Wu, M., Ford, C., et al. (2006). Reducing obesity and related chronic disease risk in children and youth: a synthesis of evidence with 'best practice' recommendations. Obesity Reviews , 7 (1), 7-66.
Franks, P., Hanson, R., Knowler, W., Sievers, M., Bennett, P., & Looker, H. (2010). Childhood Obesity, Other Cardiovascular Risk Factors, and Premature Death. New England Journal of Medicine , 362 (6), 485-493.
Greer, S. M., Goldstein, A. N., & Walker, M. P. (2013). The impact of sleep deprivation on food desire in the human brain. Nature Communications , 4 (2259).
Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming Effects of Television Food Advertising on Eating Behavior. Health Psychology , 28 (4), 404–413.
Khan, L., Sobush, K., Keener, D., Goodman, K., Lowry, A., Kakietek, J., et al. (2009). Recommended community strategies and measurements to prevent obesity in the United States. Morbidity and Mortality Weekly Report , 58 (RR-7), 1-26.
Nazario, B. (2013, March 21). Obesity. Retrieved January 18, 2014, from http://www.webmd.com/diet/what-is-obesity
Ogden, C., Carroll, M., Kit, B., & Flegal, K. (2012). Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010. Journal of the American Medical Association 307(5): , 483-490.
Ogden, C., Flegal, K., Carroll, M., & Johnson, C. ( 2002). Prevalence and trends in overweight among US children and adolescents, 1999–2000. JAMA , 1728–1732.
Porter, C. (2013 ). Community action to prevent childhood obesity: lessons from three US case studies. Childhood Obesity , 9 (2), 164-74.
Reilly, J., Armstrong, J., Dorosty, A., Emmett, PM, Ness, A., et al. (2005). Early life risk factors for obesity in childhood: cohort study. BMJ:Clinical Research Education , 330 (7504), 1357.
Robert Wood Johnson Foundation . (2013, July 8). New York City: Signs of Progress Toward Reversing the Childhood Obesity Epidemic. Retrieved January 29, 2014, from http://www.rwjf.org/en/about-rwjf/newsroom/newsroom-content/2013/07/new-york-city--signs-of-progress.html
Simmons, A., Mavoa, H., Bell, A., De-Courten, M., Schaaf, D., Schultz, J., et al. (2009). Creating community action plans for obesity prevention using the ANGELO (Analysis Grid for Elements Linked to Obesity) Framework. Health Promotion International , 24 (4), 311-24.
The Editorial Board. (2013, August 6). Hope for Childhood Obesity. New York Times , p. 12.
Vandewater, E., Bickham, D., & Lee, J. (2006). Time well spent? Relating television use to children's free-time activities. Pediatrics , 117 (12), 181-91.
Vasanti, M. S., Matthias, S., & Frank, B. (2006). Intake of Sugar Sweetened beverages and weight gain: A systematic review. American Journal of clinical nutrition , 84 (2), 274-88.
WHO. (2013, March). Obesity and overweight. Retrieved January 18, 2014, from http://www.who.int/mediacentre/factsheets/fs311/en/index.html
