Evidence based research paper on prevention of Obesity

Introduction

Obesity is projected to the third greatest booster to the overall burden of all ailments globally and especially in the United States and Australia respectively. This is mostly evaluated and analyzed using the body mass index approximating 30 grams per meter square or higher. The disadvantages of obesity have been outlined in this paper. The scope of this paper discusses beyond the use of body mass index to evaluate obesity to the real measurements of body fat amounts. Although, techniques like underwater weighing, dual energy x-ray absorption, and biometrical impedance evaluation only measure the total mass of fat in the body are very expensive. These methods are also very time consuming to apply in a large set of population

The anthropometric technique which recognizes high-risk adiposity correctly is still under debate. Several other alternatives have been used to rectify the assessment of high-risk adiposity hence provide effective measurements. Some of these techniques include the, higher waist circumference, waist-hip ratio, and the waist height ratio. High waist circumference has been used over the years as the best predictor of metabolic diseases compared to body mass index (lam, et al 2015). As such, this because waist circumference easily identifies health risk through evaluation of total body fat and the central adiposity

Recent researches and analysis of data have revealed out that, 30 to 60 percent of the population categorized as possessing high-risk waist circumference to have a body mass index which is below the obese level. Studies have also shown out that a substantial portion of the population who have large waist circumference do not possess an obese body mass index. This research is aimed at finding out whether those without an obese mass index but with large waist circumference boosts the analysis of adiposity-associated metabolic outputs (Rodríguez et al 2018).

Methods

Measuring waist circumference and body mass index

Height dimensions were captured to the nearest half centimeter, shoeless using a stadiometer. Body weights were taken without shoes and excessive cloth ware to nearest 0.1 kilograms using a beam balance. The body mass index was computed as weight (kg) divided by height (m) 2. This Data was classified as (a) none obese less than 30 kg/m2 and ( b) obese greater than or equal to 30kg/m2.  Waist circumferences were taken at the midway points between the iliac crests and the costal margin, then the mean of the two was computed. Waist circumference was classified as follows :(a)non-obese less than 100 cm for men, less than 88cm for females and obese if greater than 100cm for males and greater than 88cm for females. Adiposity divisions were formed using the integration of body mass index and waist circumference as follows; (a) BMIN/WCN; (b) BMIN/WCO; (c) BMIO/WCN; and (d) BMIO/WCO, (N = non-obese and O = obese.) (lessi, et al,2017).

Estimating metabolic outcomes

In all the regions blood pressure was estimated using a Dinamap oscillometric blood pressure sensor.in Virginia, blood pressure was estimated using a standard mercury sphygmomanometer and fine-tuned accurately. Blood samples were taken by venipuncture after the participants fasting overnight (Javed et al 2015). All the blood samples were super filtered to separate the plasma from the serum, then transferred to the nearest laboratory as soon as possible. Obesity was defined by fasting plasma glucose if less or 7.0 mmol/l or two-hour plasma glucose ≥11.1 mmol/l, or current medication using insulin or oral hypo-glycaemic components. Dyslipidemia was identified as triglycerides >2.0 mmol/l or maximum density lipoprotein (HDL) cholesterol <1.0 mmol/l. Cardiovascular ailment status was self-reported and was identified as former angina, stroke, or heart artery ailments (ford,et al 2014)

Estimation of covariates

Covariate information and data was collected using interview conducted questionnaires. Educational level was classified as lower, which include high school level, middle (certificate, or diploma) and high including (bachelor’s degree or a post graduate diploma).  Physical exercise was reported in person and was classified as follows inactive if 0 hours, insufficient if less or equal to 150 hours, and sufficient if greater than 150 hours. Smoking status was classified as, (a)regular smoker,(b) non-smoker, and (c)  x-smokers (Nazale et al 2015)

Ten thousand, three hundred and seventy-six participants were involved in this exercise .the activity took 8 weeks. The data captured from this sub-portion of the population included both height, weight and the waist circumference. Adiposity categories were classified as BMIN/WCN, BMIN/WCO, BMIO/WCN, and BMIO/WCO (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated (Diet et al 2016).

Results

All the Members were approximately 50 years of Age and half of them were male. The proportions of BMIN/WCN, BMIN/WCO, BMIO/WCN, and BMIO/WCO were 67, 11, 1 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using body mass index as compared to body mass index and waist circumference integrated (32 percent and 47 percent). Risks for diabetes were also lower when obesity was identified using BMI alone (0.62 vs. 0.66) outputs (kjaer et al 2015). Same observations were recorded for all outputs. The chances for hypertension, dyslipidemia, and diabetes were higher for those with BMIN/WCO

Evidence Table Worksheet

1. PICOT Question:

Plus

Will you have a comparison group or will subjects be their own controls?

1. Is a ‘time’ appropriate with your question—why or why not?

ERADICATING OBESITY

Name

Institutional Affiliation

methods to Use of waist circumference and body mass index to reduce obesity

Introduction

Obesity is an epidemic that has cried to help for generations. It’s a great and alarming health crisis both in United States and the whole globe at large.as the different countries gets industrialized obesity levels increases due to the production of junk food full of cholesterols and carbonated drinks. Due to industrialization, the use of physical energy to execute manual jobs among the citizens has reduced. Every physical activity has been reduced by machines and automation of processes, this leaves people idle hence, increased accumulation of fats caused by lack of physical exercise to burn down the calories. Some studies by the national center for health statistics has revealed out that the United States citizens above the age of 20 years have probability of becoming obese with a percentage 20% in 1997 to a maximum of 31.4 percent in the year 2017(Javed, et al,2017)

These numbers are alarming .As nation we have an epidemic that is taking over.one that we have grown to ignore. These health crisis known as obesity is so widely accepted within our society is not viewed as an epidemic or even an issue.it is simply referred to as “overweight” or “fat “this epidemic has grown to numb many people that suffer from its condition. Many who suffer from obesity are far unaware or they ignore the health consequences that come along with the disease.(kim&jeoung,2017).

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Evidence Table Worksheet

Evidence Synthesis

1. Evaluation Table

• PICOT Question:

“In adults under 50 (P), would monitoring  BMI and waist size (I) reduce the incidence of

obesity (O) compared with not monitoring BMI and waist size (C) within an 8-week time

frame (T)?”

plus

2. Will you have a comparison group or will subjects be their own controls?
3. Is a ‘time’ appropriate with your question—why or why not?

Definition

Obesity is defined as the intensification in amount of fat cells in the body in relation to lean body mass.inorder to diagnose obesity we evaluate individuals waist circumference and the body mass index. Obesity is a dangerous medical problem that can lead to different ailments such metabolism complications, cardiovascular diseases, diabetes, high cholesterols and sickle cell anemia (ford, et al ,2014).

Treatment of obesity depends on conditions of the patients. Some treatment obesity may involve the change of the individual’s lifestyle such as change of diet and having daily physical exercise to burn down the calories and keep the body fit. There are also approved medical substances for reducing weights. Patients with critical obesity conditions, surgery operations maybe there only help. Obesity is an increasing common condition in United States

Epidemiology

More than 80 million adults ranging from 20 years and above, from the both genders in United States are suffering from obesity. This is as per report generated by the national center for health statistics. Moreover, this institution revealed that, the united states citizen aged 20 years ,the probability for obesity has increased gradually and constantly from 1997 to 2013.for every 1 to 6 deaths in united states emanate from obesity. This is according to a research featured in American journal for public health (Di, et al, 2017).

Clinical presentation

In most cases the clinical presentation of obesity is often the co-moralities that often happen from obesity complications. Obesity is a chronic ailment that can cause different complication in our body organ systems. Obese individuals stand a great chance of falling sick under different chronic conditions such as cardio vascular diseases, diabetes and bone related ailments

Complications

Approximately 200 deaths occur annually in United States and are attributed by obesity. Obesity is ranked the second preventable disease killer in United States. Obesity doubles the probability of having multiple health complication .individual having a body mass index ranging above 24.9 stands high chances of facing diseases associated with obesity (masters, et al, 2013)

Body mass index is commonly used to diagnose obesity. Body mass index evaluates the body composition and is applied in diagnosis of obese individuals. Obesity is calculated as body mass index of a minimum of 30kg per meter square of about a quarter of the total fat within the body (men).obesity in women is calculated as 30kg/m2 contained in 30% of the total fat in the body (lee, et al,2017)

Conclusion Picot question

Obesity is a full blown epidemic that brings about even more considerable health risk. Prevention of obesity must be a high priority for health care realm. An objective of health people 2020 is to maximize the issuance of information to health care providers who frequently evaluate the body mass index in adult individuals (lam, et al, 2015).by concentrating on use of both waist circumference and body mass index to diagnose obesity can reduces the cases of obesity in united states

References

Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., … & Lewington, S. (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. The Lancet, 388(10046), 776-786.

Ford, E. S., Maynard, L. M., &  Li, C. (2014).  Trends in mean waist circumference and abdominal obesity among US adults,  1999-2012. Jama, 312(11), 1151-1153.

Javed, A., Jumean, M., Murad, M. H., Okorodudu, D., Kumar, S., Somers, V. K., … & Lopez‐Jimenez, F. (2015). Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta‐analysis. Pediatric obesity, 10(3), 234-244.

Kim, D. W., Kim, J. Y., & Jeong, H. (2017). Comparison of Body Mass Index, Waist Circumference, and Waist-to-Height Ratio as a Predictors of Abdominal Fat Distribution in Male Examinees from the Health Promotion Center. Korean Journal of Family Practice, 7(4), 596-599.

Kjær, I. G. H., Kolle, E., Hansen,  B. H., Anderssen, S. A., & Torstveit, M. K. (2015). Obesity prevalence in N orwegian adults assessed by body mass index, waist circumference and fat mass percentage. Clinical obesity, 5(4), 211-218.

Lam, B. C. C., Koh, G. C. H., Chen, C., Wong, M. T. K., & Fallows, S. J. (2015). Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLoS One, 10(4), e0122985.

Lee, S., Kuk, J. L., Boesch, C., & Arslanian, S. (2017). Waist circumference is associated with liver fat in black and white adolescents. Applied physiology,  nutrition, and metabolism, 42(8), 829-833.

Nazare, J. A., Smith, J., Borel, A. L., Aschner, P., Barter, P., Van Gaal, L., … & Ross, R. (2015). Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). The American j ournal of cardiology, 115(3), 307-315.

Rodríguez, N. V., Fernandez-Britto, J. E., Martinez, T. P., Martinez, R. G., Castañeda, C. G., Garriga, M. R., … & Blanco, F. A. (2018). Waist-height ratio in  children of 7 to 11 years with high weight at birth and its relationship with gender, age and diet. Clinica e investigacion en arteriosclerosis: publicacion oficial de la Sociedad Espanola de Arteriosclerosis.