Obesity affects every continent on the planet. From the tip of the North Pole to the equator to the edge of the South Pole, it is prevalent. Humans are naturally wired to store fat. Our bodies have an unlimited capacity to store fuel into our adipose tissue. More and more adipocytes are able to be created. However, haven’t we gone far enough? In the United States alone we have an obesity epidemic. The health risks that come with obesity are beyond that of other diseases. Often enough, obesity is associated with increased risk of the other top three killers in the United States. Chopping off the head of the snake is the most efficient path towards increasing health in the world and it turns out that physical activity is one of the best ways to accomplish that. This paper will address the obesity epidemic, the risks and causes of obesity, and the measures necessary to overcome it.
Obesity and overweight are characterized by excess body fat resulting from a positive energy balance. According to the body mass index, which is calculated as weight in kilograms (kg) divided by height in meters squared, rounded to one decimal place, obesity I is defined as a BMI of 30.0-34.9 kg/meters squared for males and females aged 20 and over. Obesity II is defined as 35.0-39.9 kg/meters squared and obesity III is defined as a BMI of 40.0 kg/meters squared or higher. Between 1988-1994 and 2009-2012, the prevalence of men and women with grade 1, 2, and 3 obesity increased while the prevalence of men and women aged 20 and over who were overweight but not obese remained stable (Finucane et al., 2011). According to the Center for Disease Control (2015), worldwide the rate of obesity has nearly doubled since 1980, with just over 200 million adult men and just under 300 million adult women considered obese. Currently more than one-third (34.9% or 78.6 million) of U.S. adults are obese and approximately 69% of adults are overweight or obese. No state in the United States has a prevalence rate of obesity less than 20%. Only 6 states have a prevalence of obesity between 20% and 25%. 24 states have a prevalence rate of obesity between 25% and greater than 30%. 19 states have a prevalence rate of obesity between 30% and greater than 35%. 3 states have a prevalence rate of obesity of 35% or higher. The Midwest has the highest prevalence of obesity, followed by the South, the Northeast, and the West (CDC, 2015).
A child’s weight status is determined using an age- and sex-specific percentile for BMI rather than the BMI categories used for adults because children’s body composition varies by age and sex (CDC, 2015). The weight status of children is defined on the basis of the sex-specific smoothed percentile curves for BMI-for-age. Extreme obesity is defined as a BMI at or above the 120% of the 95th percentile for children of the same age and sex. For children and adolescents aged 2-19 years, the prevalence of obesity has remained fairly stable at about 17% and has affected about 12.7 million children and adolescents for the past decade (Ogden, Carroll, Kit, & Flegal, 2014). In 2011-2012, 8.4% of 2-to-5 year-olds had obesity compared with 17.7% of 6-to-11 year-olds and 20.5% of 12-to-19 year-olds. Childhood obesity is more common among certain racial and ethnic groups as well. In 2011-2012, the prevalence of obesity among children and adolescents was higher among Hispanics (22.4%) and non-Hispanic blacks (20.2%) than among non-Hispanic whites (14.1%). The prevalence of obesity was lowest in non-Hispanic Asian youth (8.6%) than in the youth of non-Hispanic white, non-Hispanic blacks, or Hispanic (Ogden et al. 2014). Childhood obesity associated with adult head of household’s education level is inconclusive, with some populations showing greater prevalence trends than others.
Non-Hispanic blacks have the highest age-adjusted rates of obesity (47.8%) followed by Hispanics (42.5%), non-Hispanic whites (32.6%) and non-Hispanic Asians (10.8%) (CDC, 2015). These non-modifiable risk factors leave certain racial and ethnic groups susceptible to obesity and its related health consequences. Obesity is also higher among middle aged adults, 40-59 years old (39.5%) then among younger adults age 20-39 (30.3%) or adults over 60 or above (35.4%). Obesity has shown a link between socioeconomic status and prevalence. Higher income women are less likely to have obesity than low-income women. Among non-Hispanic black and Mexican-American men, those with higher incomes are more likely to have obesity than those with low income.
Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer (uterus, prostate, breast, colon), some of the leading causes of preventable death. Other health risks of obesity include hypertension, gallbladder disease, hypercholesterolemia, and osteoarthritis (Larrson, Svardsudd, Welin, Wilhelmsen, Bjorntop, & Tibblin, 1984). The patterning of body fat affects disease risk. Excess fat in the abdomen out of proportion to total body fat is linked to greater risk for chronic diseases associated with obesity (Canoy, 2008).
Waist circumference is a common measurement tool for identifying those at risk for chronic disease. Girth at the waist greater than 102 cm (40 in.) for men and 88cm (35 in.) for women is considered a risk factor. For children aged 6-19, a girth at the 90th percentile is commonly used to define high risk (Canoy, 2008). Waist circumference as a measurement tool has its limits, as waist circumference does not add to the accuracy of predicting disease risk in people who have a BMI of 35 kg/meters squared or more (Larrson et al., 1984). Another appropriate measurement tool is waist-to-hip ratio. This measurement tool is a predictor of the patterning of visceral fat. Excessive fat above the waist increases risk for coronary heart disease (Finkelstein, Trogdon, Cohen, & Dietz, 2008). The desirable waist-to-hip ratio is less than 1.00 for men and less than 0.80 for women. The waist-to-hip ratio has defined itself as a reliable predictor of coronary heart disease in both women and men.
The medical costs for people who are obese are $1,429 higher than those of normal weight (Cawley & Meyerhoefer, 2012). By one estimate, the U.S. spent $190 billion on obesity-related health care expenses in 2005, which were double the previous estimates (Thompson, Edelsberg, Colditz, Bird, & Oster, 1999). Over the course of a lifetime, per-person costs for obesity are similar to those for smoking (Clifton, Bastiaans & Keogh, 2009).
Many methods are accepted as clinically effective approaches to weight loss. Physical activity, low-calorie diets, behavior therapy, pharmacotherapy, surgery, and combinations of these techniques are all effective. Meal composition has a notable effect on weight loss and body composition. High-protein diets have a significant predisposition to increase weight loss (Wiley, 2014). High fat and sugar diets are associated with increases in abdominal fat and fatty liver (Science Daily, 2016). There are strong evidential links between fatty liver and heart failure in obese individuals (Zeevi, Korem, Zmora, Israeli, Rothschild, Weinberger, & Segal, 2015). It’s been long-assumed that following the glycemic index is useful for lowering blood sugar and warding off obesity. However, now that is contested (Teixeira, Carraca, Marques, Rutter, Oppert, Bourdeaudhuij, & Brug, 2015).
Behavioral change is another powerful tool for weight loss. Many problematic issues concerning weight loss revolve around the ability to maintain consistent behavioral patterns that are associated with continuous weight loss. The same is true of maintaining body weight after weight loss. Mediators for medium/long-term weight control were higher levels of autonomous motivation, self-efficacy, self-regulation skills, flexible eating restraint, and positive body image. Higher autonomous motivation, self-efficacy, and self-regulation skills emerged as the best predictors of beneficial weight loss (Koritzky, Rice, Dieterle & Bechara, 2015). Two opposing skills come together to help maintain weight loss. The concept of recency, described as thinking moment to moment, is associated with poorer weight loss than those who maintain a long-term viewpoint of weight loss (Steinberg, Bennett, Askew, & Tate, 2015). Weighing regularly however, preferably every day, is associated with maintaining weight loss and greater amounts of weight loss than weighing irregularly (Qi & Dennis, 2000). Eating behaviors that were conducive to weight loss included carefully watching a weight graph and weighing daily, and recording the type and quantity of food consumed (Pakpour, Gellert, Dombrowski, & Fridlund, 2015). Changing and solidifying the behaviors of the obese population is necessary in many categories and avenues. However, the ability to create new habits and maintain them is the first step. Motivational interviewing has shown promise in behavioral change over the years and has been specifically used to target unwanted behaviors and quickly move a subject towards readiness to change. It has shown effectiveness with both adolescents and adults, and is fairly easy to teach to health care professionals of all allied health fields (Zhou, Ren, Yin, Wang, & Wang, 2014).
Physical activity is a component that cannot be ignored when considering the obesity epidemic. Well-structured, organized interventions regarding physical activity should be implemented both at the adolescent and adult levels. For the majority of interventions, the original curriculum put into place at the k-12 level are ineffective (Mei, Xiong, Xie, Guo, Li, Guo, & Zhang, 2016). This gives a better understanding of why the obesity epidemic has included adolescents as well, despite being offered a given amount of physical activity per week. Not all interventions are successful, but some are. Structured physical activity intervention alone have shown positive links to warding off obesity in the adolescent population (Jirvaee, Siadat, Zamani & Taleban, 2015). In fact, there are many types of interventions that can occur. For instance, positive goal setting that has been established appropriately has shown tremendous improvements in BMI, waist-to-hip circumference, and the state of well-being of adolescents (Hall, Zeveloff, Steckler, Schneider, Thompson, Pham & McMurray, 2012 ). What has been established by several interventions is that the level of engagement and interest in physical activity is a deciding factor in the effectiveness of the intervention and the degree of weight loss (Corder, Atkin, Ekelund, & van Sluijis, 2013).
It’s no secret that the more active an individual is the greater health benefits they will receive (Seo & Li, 2010). The more sedentary, the more likely to gain weight over time and reduce health. The American College of Sports Medicine recommends that adults participate in at least 150 min/week of moderate-intensity physical activity to protect against excessive weight gain and reduce chronic disease risk factors (Dishman, Health & Lee, 2013). Overweight and obese individuals will likely benefit from 250 or more minutes each week to experience greater weight reduction and prevent weight regain. The ACSM also recommends strength training to increase or maintain fat-free mass and further reduce health risks.
The strength of evidence of the impact physical activities has on reducing obesity is rather conflicted compared to restricting caloric intake and increasing physical activity (Dishman et al., 2013). However, both population-based epidemiological studies and clinical trials support the claim that regular physical activity and exercise training are useful for reducing the primary and secondary risks of excess weight gain. Physical activity, as well as for helping obese or overweight people keep off the weight they’ve lost. 2.5 to 4.5 hours of moderate-intensity exercise (55-69% of maximal heart rate or about 3 to 6 METS depending on the age) that results in an energy expenditure of at least 1200 to 2000 kcal/week is the recommended dose response for reducing obesity, a long with a diet that has a reduced calorie intake of 1000 to 1500 kcal/week (Dishman et al., 2013).
One of the most important factors to control for obese individuals is a steady rate of weight loss and preventing weight regain. Weight loss can result in decreased physical activity energy expenditure and maintaining physical activity after the weight has been lost is critical in preventing weight regain (Wang, Lyles, You, Berry, Rejeski, & Nicklas, 2008). Changes in eating behavior and physical activity can improve long-term weight loss compared with either behavior alone. Interventions targeted to readopt healthy habits in both of these areas are recommended for long-term weight loss and prevention of weight regain (Jakicic, Wing, & Winters-Hart, 2002). The amount of self-motivation, commonly called intrinsic motivation, is a crucial predictor of weight maintenance following weight loss. The initial focus on weight loss results in short-term weight loss while the change in exercise-related motivational factors, which an emphasis on intrinsic motivational factors, plays a more important role in longer term weight loss (Teixeira, Going, Houtkooper, Cussler, Metcalfe, Blew, & Lohman, 2006).
Reducing obesity comes with many benefits beyond looking and feeling better. The risk of developing other diseases are also combatted by reducing body mass and improving body composition. Diabetes, heart disease, and certain forms of cancer are all risks associated with obesity. Those conditions and their risks can be reduced by improving body composition and body weight (Dishman et al., 2013). In fact, obesity has been shown to be associated with several auto-immune diseases such as celiac disease and rheumatoid arthritis. Obese women are shown to be at greater risk of sarcoidosis, type 1 diabetes mellitus, celiac disease, and Raynaud’s phenomenon. Their risks are reduced per reduction in every BMI unit (Harpse, Basit, Andersson, Neilsen, Frisch, Wolhfarht, & Jess, 2014). Obesity can even have adverse effects on unborn children. According to Harpsoe, Basit, Bager, Wohlfarht, Benn, Nohr, & Jess (2013), maternal obesity was associated with doctor-diagnosed asthma, and wheezing in offspring. Since the risk of Hayfever and atopic eczema were not elevated, the pathway to the asthma and wheezing is unlikely allergenic. Losing weight and reducing the chance of weight-regain is the best possible route of action for the obese population. Chronic low-grade inflammation is associated with advanced age and adiposity, and predisposes individuals to several chronic diseases, including cardiovascular disease, diabetes mellitus, and physical disability (Beavers, Ambrosius, Nicklas, & Rejeski, 2013). Weight loss is the lifestyle factor most responsible for improvements in the inflammatory profile in the obese population, not physical activity (Beavers et al., 2013). Reduction in adiposity will reduce the levels of leptin because leptin is produced by adipocytes in the adipose tissue. Increases in low-grade chronic inflammation is seen in advanced age and adiposity because leptin is released by adipose tissue into the blood stream and leptin resistance develops during aging despite adipose tissue levels (Gabriely, Ma, Yang, Rosetti, & Barzilai, 2002). Leptin plays an especially important role in weight regulation by controlling energy balance (Daile, Della-Fera, & Martin, 2000). Suppression of food intake is an important role of leptin, while ghrelin increases appetite. Ghrelin is found in leaner individuals and leptin is found in higher levels in overweight individuals. The level of leptin in adolescents could help predict childhood obesity because leptin interacts directly with the hypothalamus to control energy intake (Venner, Lyon, & Doyle-Baker, 2006). Leptin resistance is associated with diet induced obesity as well (Zhang & Scarpace, 2006). Since Leptin is associated with fuel partitioning and controls body weight by regulating food intake, and because leptin is higher in obese individuals, and their risk of leptin resistance is higher due to chronic expression of leptin, it’s important to reduce adiposity levels. In fact, an individual doesn’t even have to be obese to become leptin resistant and therefore increased risk to diet-induced obesity (Scarpace & Zhang, 2007). This symbolizes that leptin may be a key component in reducing obesity or preventing it.
Obesity is a major concern of health professionals. Counteracting the epidemic that is present today in our youth and adult populations is of the utmost importance. Obesity is expensive, costing the entire world billions of dollars each year to only cover up the effects of the disease. A long-term focus on interventions that promote weight-loss and aim to maintain a healthy body weight are needed. Obesity increases the likelihood of mortality on multiple fronts and some of the risks associated with obesity aren’t fully understood. However, physical activity is an important modifier of obesity. Nutrition, behavioral change, and continued physical activity and exercise have shown to be effective in preventing, reducing, and ending obesity in most populations. However, it is a habit that must be undertaken by all of us. If we are to overcome our natural inclination to store fat, we must learn to override our own instincts. It will take self-motivation, engagement, changes in attitude, new eating behaviors, and increased physical activity. Despite the lengthy measures we must take to battle obesity, in the end it will be worth it. We can look back and know that future generations to come will have role models who won the war against obesity.
- Finucane MM, Stevens GA, Cowan MJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants.
- (Adult Obesity Facts | Data | Adult | Obesity | DNPAO | CDC. (n.d.). Retrieved March 8, 2016, from http://www.cdc.gov/obesity/data/adult.html.
- Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA : The Journal of the American Medical Association, 311(8), 806–14.
- Dishman, R. K., Heath, G. W., & Lee, I.-M. (2013). Physical activity epidemiology (2nd ed.). Physical activity epidemiology (2nd ed.).
- Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. British Medical Journal (Clinical Research Ed.), 288(6428), 1401–4. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1441047&tool=pmcentrez&rendertype=abstract
- Canoy, D. (2008). Distribution of body fat and risk of coronary heart disease in men and women. Current Opinion in Cardiology, 23(6), 591–8. http://doi.org/10.1097/HCO.0b013e328313133a
- Finkelstein, E. A., Trogdon, J. G., Cohen, J. W., & Dietz, W. (2008). Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs (Project Hope), 28(5), w822–31. http://doi.org/10.1377/hlthaff.28.5.w822
- Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31(1), 219–230. http://doi.org/10.1016/j.jhealeco.2011.10.003
- Thompson, D., Edelsberg, J., Colditz, G. A., Bird, A. P., & Oster, G. (1999). Lifetime Health and Economic Consequences of Obesity. Archive of Internal Medicine, 159, 2177–2183. http://doi.org/10.1001/archinte.159.18.2177
- Clifton, P. M., Bastiaans, K., & Keogh, J. B. (2009). High protein diets decrease total and abdominal fat and improve CVD risk profile in overweight and obese men and women with elevated triacylglycerol. Nutrition, Metabolism, and Cardiovascular Diseases : NMCD, 19(8), 548–54. http://doi.org/10.1016/j.numecd.2008.10.006
- (2014, May 6). Snacking contributes to fatty liver, abdominal obesity. ScienceDaily. Retrieved March 8, 2016 from www.sciencedaily.com/releases/2014/05/140506120036.htm
- Radiological Society of North America. (2016, January 26). Study links fatty liver, heart failure in obese people. ScienceDaily. Retrieved March 8, 2016 from sciencedaily.com/releases/2016/01/160126085737.htm
- Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Segal, E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. http://doi.org/10.1016/j.cell.2015.11.001
- Teixeira, P. J., Carraça, E. V, Marques, M. M., Rutter, H., Oppert, J.-M., De Bourdeaudhuij, I., … Brug, J. (2015). Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Medicine, 13, 84. http://doi.org/10.1186/s12916-015-0323-6
- Koritzky, G., Rice, C., Dieterle, C., & Bechara, A. (2015). The Biggest Loser Thinks Long-Term: Recency as a Predictor of Success in Weight Management. Frontiers in Psychology, 6, 1864. http://doi.org/10.3389/fpsyg.2015.01864
- Steinberg, D. M., Bennett, G. G., Askew, S., & Tate, D. F. (2015). Weighing every day matters: daily weighing improves weight loss and adoption of weight control behaviors. Journal of the Academy of Nutrition and Dietetics, 115(4), 511–8. http://doi.org/10.1016/j.jand.2014.12.011
- Qi, B. B., & Dennis, K. E. (2000). The adoption of eating behaviors conducive to weight loss. Eating Behaviors, 1(1), 23–31. Retrieved from http://www.sciencedirect.com/science/article/pii/S1471015300000039
- Pakpour, A. H., Gellert, P., Dombrowski, S. U., & Fridlund, B. (2015). Motivational interviewing with parents for obesity: an RCT. Pediatrics, 135(3), e644–52. http://doi.org/10.1542/peds.2014-1987
- Zhou, Z., Ren, H., Yin, Z., Wang, L., & Wang, K. (2014). A policy-driven multifaceted approach for early childhood physical fitness promotion: impacts on body composition and physical fitness in young Chinese children. BMC Pediatrics, 14, 118. http://doi.org/10.1186/1471-2431-14-118
- Mei, H., Xiong, Y., Xie, S., Guo, S., Li, Y., Guo, B., & Zhang, J. (2016). The impact of long-term school-based physical activity interventions on body mass index of primary school children – a meta-analysis of randomized controlled trials. BMC Public Health, 16(1), 205. http://doi.org/10.1186/s12889-016-2829-z
- Jiryaee, N., Siadat, Z. D., Zamani, A., & Taleban, R. (2015). Comparing of goal setting strategy with group education method to increase physical activity level: A randomized trial. Journal of Research in Medical Sciences : The Official Journal of Isfahan University of Medical Sciences, 20(10), 987–93. http://doi.org/10.4103/1735-1995.172792
- Hall, W. J., Zeveloff, A., Steckler, A., Schneider, M., Thompson, D., Pham, T., McMurray, R. G. (2012). Process evaluation results from the HEALTHY physical education intervention. Health Education Research, 27(2), 307–18. http://doi.org/10.1093/her/cyr107
- Corder, K., Atkin, A. J., Ekelund, U., & van Sluijs, E. M. F. (2013). What do adolescents want in order to become more active? BMC Public Health, 13, 718. http://doi.org/10.1186/1471-2458-13-718
- Seo, D.-C., & Li, K. (2010). Leisure-time physical activity dose-response effects on obesity among US adults: results from the 1999-2006 National Health and Nutrition Examination Survey. Journal of Epidemiology and Community Health, 64(5), 426–431. http://doi.org/10.1136/jech.2009.089680
- Wang, X., Lyles, M. F., You, T., Berry, M. J., Rejeski, W. J., & Nicklas, B. J. (2008). Weight regain is related to decreases in physical activity during weight loss. Medicine and Science in Sports and Exercise, 40(10), 1781–1788. http://doi.org/10.1249/MSS.0b013e31817d8176
- Jakicic, J. M., Wing, R. R., & Winters-Hart, C. (2002). Relationship of Physical Activity to Eating Behaviors and Weight Loss in Women. Medicine & Science in Sports & Exercise, 34(10), 1653–1659.
- Teixeira, P. J., Going, S. B., Houtkooper, L. B., Cussler, E. C., Metcalfe, L. L., Blew, R. M., Lohman, T. G. (2006). Exercise motivation, eating, and body image variables as perdictors of weight control. Medicine and Science in Sports and Exercise, 38(1), 179–188. http://doi.org/10.1249/01.mss.0000180906.10445.8d
- Harpse, M. C., Basit, S., Andersson, M., Nielsen, N. M., Frisch, M., Wohlfahrt, J., Jess, T. (2014). Body mass index and risk of autoimmune diseases: A study within the Danish National Birth Cohort. International Journal of Epidemiology, 43(3), 843–855. http://doi.org/10.1093/ije/dyu045
- Harpsoe, M. C., Basit, S., Bager, P., Wohlfahrt, J., Benn, C. S., Nohr, E. A., Jess, T. (2013). Maternal obesity, gestational weight gain, and risk of asthma and atopic disease in offspring: a study within the Danish National Birth Cohort. The Journal of Allergy and Clinical Immunology, 131(4), 1033–40. http://doi.org/10.1016/j.jaci.2012.09.008
- Beavers, K. M., Ambrosius, W. T., Nicklas, B. J., & Rejeski, W. J. (2013). Independent and combined effects of physical activity and weight loss on inflammatory biomarkers in overweight and obese older adults. Journal of the American Geriatrics Society, 61(7), 1089–1094. http://doi.org/10.1111/jgs.12321
- Gabriely, I., Ma, X. H., Yang, X. M., Rossetti, L., & Barzilai, N. (2002). Leptin Resistance During Aging Is Independent of Fat Mass. Diabetes, 51(4), 1016–1021. http://doi.org/10.2337/diabetes.51.4.1016
- Baile, C. A., Della-Fera, M. A., & Martin, R. J. (2000). Regulation of metabolism and body fat mass by leptin. Annual Review of Nutrition, 20, 105–27. http://doi.org/10.1146/annurev.nutr.20.1.105
- Venner, A. A., Lyon, M. E., & Doyle-Baker, P. K. (2006). Leptin: a potential biomarker for childhood obesity? Clinical Biochemistry, 39(11), 1047–56. http://doi.org/10.1016/j.clinbiochem.2006.07.010
- Zhang, Y., & Scarpace, P. J. (2006). The role of leptin in leptin resistance and obesity. Physiology & Behavior, 88(3), 249–56. http://doi.org/10.1016/j.physbeh.2006.05.038
- Scarpace, P. J., & Zhang, Y. (2007). Elevated leptin: consequence or cause of obesity? Frontiers in Bioscience : A Journal and Virtual Library, 12, 3531–3544. http://doi.org/10.2741/2332
My Signature Method, Your Signature Move!