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Obesity PreventionShellie RayAuburn University/ Auburn MontgomeryAbstract Obesity is one of the most challenging health crises the country has ever faced. Two-thirds of the American population is either overweight or obese. Many people who are overweight or obese are unaware or ignore the health consequences and too few healthcare providers are addressing the problem with their patients. The purpose of this project is to evaluate the effectiveness of including Body Mass Index (BMI) and waist circumference (WC) as screening measurements in an outpatient clinic to identify those at risk for overweight and obesity, and provide weight management education as needed. Patients within a wound healing clinic were approached to participate. BMI and WC were measured and a Patient Readiness Assessment was given to those who consented. Lifestyle changes promoting desirable weight loss were taught. Follow-up BMI and WC were assessed at six weeks. Out of the 15 patients approached, 12 consented to participate. There were 33.3% males and 66.7% females. The mean pre-BMI is 37.667with a std. deviation of 5.9595 and the mean post-BMI is 36.667 with a std. deviation of 6.0050. The mean pre-WC is 46.667 with a std. deviation of 6.4149 and the mean post-WC is 44.583 with a std. deviation of 6.1564. The T-Test showed statistically significant differences of –p=<0.05 for both outcome measures. Assessing BMI and WC measurements during routine exams has improved the ability to identify persons who are overweight or obese and initiate weight management education. Weight loss happens over time so further research is needed to continue with follow-up measurements to determine if the continued measurements are successful in weight management.Obesity Prevention Introduction Obesity is one of the most challenging health crises the country has ever faced. Obesity is defined as an excessively high amount of body fat or adipose tissue in relation to lean body mass CITATION Stu93 \l 1033 (Stunkard & Wadden, 1993). Second only to cigarette smoking, obesity is a leading cause of preventable death in the United States (Mokdad, Marks & Gerbending, 2004). Obesity is a chronic disease that is associated with hypertension, diabetes, hyperlipidemia, coronary artery disease, obstructive sleep apnea, and cancers of the breast, uterus, prostate, and colon. It is also associated with psychological disorders such as depression, anorexia nervosa and bulimia CITATION Dou99 \l 1033 (Douketis, Feightner, Attia, & Feldman, 1999). More than 110,000 deaths per year are associated with obesity CITATION Fle10 \l 1033 (Flegal, Carroll, Ogden, & Curtain, 2010). According to a recently released report F as in fat: How obesity threatens American’s future 2011, put out by the Trust for America’s Health (TFAH, 2011) the following statistics have been reported. An overwhelming number of American adults, 190 million, are either overweight or obese. That is two-thirds, 68% of the American population. Nearly one-third of teenagers and children fall into this category. Alarmingly adult obesity rates rose in sixteen states over the past year. No state reported a decrease in obesity rates. Twelve states now have obesity rates above thirty percent. Nine out of the top ten most obese states in the country were from southern states. Alabama is second only to Mississippi as the fattest state in the nation. The country’s health care costs are much too high and continue to rise. Obesity is enormously expensive. Americans spend more than $150 billion on health care related to obesity (TFAH, 2011). Reducing obesity has the potential to significantly ease this problem. States are searching for ways to reduce the cost of obesity-related health problems. Some states are trying reward systems while others are experimenting with penalties. Alabama has already adopted measures that raise health insurance rates for state workers who are overweight (TFAH, 2011). There are considerable health risks associated with obesity therefore the prevention of obesity should be a high priority for healthcare providers. Although the diagnosis of obesity is at times obvious, clinicians often do not address the issue with their patients. An objective of Healthy People 2020 is to increase the proportion of primary care practitioners who regularly assess body mass index (BMI) in their adult patients. In 2008 only 48% of practitioners regularly assessed BMI and the target for 2020 is 53.8% of practitioners assessing BMI. In a large national study of adults with BMI of 30 or higher, only 42% reported that their health care professional advised them to lose weight CITATION Gal99 \l 1033 (Galuska, Will, Serdula, & Fords, 1999). This indicates an evident gap in practice in the healthcare system. Screening with BMI and waist circumference measurements (WC) could detect a large percent of adults who are obese or overweight. For the past thirty years obesity has been primarily diagnosed using BMI CITATION Rom08 \l 1033 (Romero-Corral, et al., 2008). This simple index of body weight has been used consistently in studies, and has been recommended for individual use in clinical practice to guide recommendations for weight loss and weight control CITATION Sei01 \l 1033 (Seidell, Kahn, Williamson, Lissner, & Valdez, 2001). BMI is calculated by dividing weight in kilograms divided by height squared. Categories of weight are classified as followed: BMI less than 18.5 underweight, 18.5-24.9 normal weight, 25-25.9 overweight, 30-34.9 obese -class I, 35-39.9 obese- class II, and 40 or more extreme obesity-class III. BMI is not a direct measure of adiposity. Abdominal adiposity carries a greater risk of morbidity and mortality. High WC has been shown to increase risk of death by 35% compared to normal WC (Dagenais, Yi, Bosch, Pogue, & Yusuf, 2005). WC provides an additional dimension for assessing abdominal adiposity and clinical risk. WC greater than or equal to 40 inches in males and greater than or equal to 35 inches in females is an additional risk factor for complications related to obesity.PICO Question In primary care patients 19 years and older, will Body Mass Index (BMI) and waist circumference measurements at every visit reduce the incidence of obesity? Purpose and Goals The purpose of the project is to use BMI and WC measurements as a screening program to assess the weight status of individuals to identify those at risk for overweight and obesity. The goal of the project is early identification of overweight and obese individuals and to provide education/counseling to the individual. The education/counseling would provide the individual with risks related to obesity and also health benefits of weight reduction with proper nutrition and exercise. Education will also be provided for healthier eating options and exercising. Target Population The target population is adult patients ages 19 and older in the primary care setting. The number of patients involved in the project is approximately 15-20 for a small test of change. The Wound Healing Center at the Northeast Alabama Regional Medical Center is the area in which I plan to implement my project. This would be an ideal setting since the patients return to the clinic for follow-up weekly or bi-weekly. This is also an area where there is tremendous teaching needs in areas such as diabetes control, weight management, nutrition and lifestyle changes which would make BMI and WC measurements important factors in regards to morbidity and mortality related to obesity.Outcomes The outcomes for my project will include measurement and classification of underweight, overweight, and obesity in the target population according to BMI and also measurement of WC. These measurements can let the individual know if they are overweight or obese thus increasing their risks of disease related comorbidities. Education can be given regarding proper nutrition, physical exercise, and lifestyle changes in order to lose weight to decrease their BMI and reduce their WC. Follow-up with these patients on a weekly or bi-weekly basis will allow for measurement of BMI and WC to track their progress and to provide encouragement and support in their efforts to lose weight.Framework There is increasing recognition that efforts to change practice should be guided by conceptual models or framework (Graham, Tetroe, & the KT Theories Research Group, 2007). There are numerous models designed to assist clinicians to implement an evidence-based change in practice. The model chosen for this project is the Model for Evidence-Based Practice Change. This is a revised version of the model proposed by Rosswurm and Larrabee (1999). The model guides nurses through a systematic process for the change to evidence-based practice. The model consists of six steps CITATION Mel11 \l 1033 (Melnyk & Fineout-Overholt, 2011). Step One Assess the need for change in practice. Identify the problem or opportunity for improvement. Develop a PICO question. In primary care patients 18 years old and older, will Body Mass Index (BMI) and waist circumference measurements at every visit reduce the incidence of obesity? Step Two Locate the best evidence. Plan a search for the best evidence by identifying the types and sources of evidence. Types of evidence should include clinical practice guidelines, systematic reviews, single studies and expert committee reports on obesity, BMI, and WC measurements. The types of literature sought were quantitative and qualitative research studies. Search strategies included utilizing the following databases: CINAHL, PubMed, Medline, Cochrane and Trip Database. The following terms were used in the search engines: obesity, obesity prevention, body mass index, waist circumference, primary prevention and primary care. Step Three Critically analyze the evidence. Judge whether the body of evidence is of sufficient quantity and strength to support a practice change. The evidence located in the search process was analyzed for validity, reliability, and applicability and placed on an evidence grid. Step Four Design a practice change. This includes defining the proposed practice change, identifying needed resources, designing the evaluation of the pilot and designing the implementation plan. This could be done in the form of a protocol.Step Five Implement and evaluate change in practice. This would include implementation of the pilot study, the evaluation process, development of conclusions and recommendations. The team members implement the plan and provide feedback. The feedback is used to make adjustments to the plan. Step Six Integrate and maintain a change in practice. Incorporate the new practice change into the standards of care of your organization and have an ongoing monitoring of the process.Review of the Literature The prevalence of obesity has continued to rise over the past 20 years. The presence of obesity thus further associates individuals with numerous health complications such as type II diabetes, hypertension, cardiovascular disease and metabolic syndrome CITATION Jar10 \l 1033 (Jarrett, Bloch, Bennett, Bleazard, & Hedges, 2010). The objective of this study is to determine the relationship between BMI, age, gender and current health status. This is a descriptive study (cross-sectional) of 9071 women and 8880 men. Data was collected by cross-sectional surveys. Data used was BMI (excluded BMI <19.5), ages <25 and > 70 were excluded, also excluded were psychiatric illnesses because of the known association between many psychotropic drugs and weight gain. The outcome measures were the percent of participants taking medications and number of medications taken. Major findings of the study were that obesity does not substantially affect current health in young people, it is likely that the increased medication load in obese compared to normal-weight older people originate partly from an increase in BMI starting at a younger age. Age, gender and onset of high BMI all require consideration when using BMI to assess current health status. The significance of this study to my project is that high BMI affects your health status. Starting to measure BMI at a younger age can make you aware of your health risks so that you can make lifestyle changes in order to decrease your weight and thus decrease your chance of health risks. For the past thirty years obesity has been primarily diagnosed using the BMI CITATION Rom08 \l 1033 (Romero-Corral, et al., 2008). This study was performed to assess the diagnostic performance of BMI and its correlation with body composition measurements. This is a descriptive study (cross-sectional) of 13,601 participants including 6,580 men and 7,021 women ages 20-79.9 years old. Participants were interviewed and submitted to an examination by a physician at a mobile examination center that included extensive anthropometric, physiological and laboratory testing. All personnel performing anthropometric measurements were trained and strict protocol was followed. Body weight was measured to the nearest 0.01 kg and weight measured to the nearest 0.1 cm. All participants wore identical clothing. The outcomes were that BMI defined obesity (>30) resulted in 19.1 % men and 24.7 % women. Body fat percentage defined obesity resulted in 43.9% men and 52.3% women. The findings were that BMI fails to discriminate between body fat percent and lean mass so the diagnostic accuracy of BMI in detecting obesity is limited. The significance for my project is that BMI is not the only way to measure for obesity but still may be the best way to evaluate for changes in body fatness over time because increases in BMI most likely represent fat gain. Also additional measurements are needed to be used for this project such as waist circumference measurements to account for adiposity. The dramatic increases in the prevalence of obesity have heightened the national awareness of the serious threat to public health. The physicians in the U.S. healthcare system are not adequately addressing weight issues in their patients thus becoming an untapped resource for obesity prevention and treatment interventions CITATION Art10 \l 1033 (Arterburn, et al., 2010). This study was performed to examine the frequency of BMI measurement before the implementation of two new Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for obesity that require U.S. health plans to annually report the frequency of BMI and BMI percentile measurement among all adults and children who had at least one outpatient visit during the past two years. This is a descriptive study (cross-sectional) of subjects who were enrolled in commercial (private payer), Medicaid or Medicare products in the ten health systems. Eligibility criteria are 18 years and older (adult) & 2-17 (child) as of Dec 21, 2006 and had to be continuously enrolled in the health plan for at least one year during the 2005-2006 periods. All subjects who had at least one outpatient visit during the time period was considered eligible for BMI measurement. All electronic medical record (EMR) databases from the ten systems were reviewed for documentation of BMI. The outcomes of the study were that total enrollment in the 10 plans ranged from 175,000 to 3.2 million members. The availability of BMI measurements for adults across the 10 health plans ranged from 28%-88% and in children from 21%-81%. The prevalence of adult overweight subjects (BMI > 25) range from 63%-72% (mean 69%) and obesity (BMI > 30) 29%-41% (mean36%). The significance to my project is that obesity is one of the most common and costly chronic disease in the U.S. and health care plans need to have a protocol to routinely measure and address BMI with their patients. Health care delivery systems are uniquely positioned to implement and evaluate quality improvement initiatives in obesity care. Multiple health organizations recommend using BMI to identify overweight and obese individuals. However there is uncertainty regarding the best approach to measure adiposity CITATION Gel08 \l 1033 (Gelber, Gaziano, Orav, Manson, Buring, & Kurth, 2008). This study examined associations between anthropometric measures of BMI, WC, waist-to-hip ratio, and waist-to-height ratio and the risk of incident cardiovascular disease. Cardiovascular disease includes non-fatal myocardial infarction, nonfatal ischemic stroke and cardiovascular death. This is a cohort (prospective, descriptive) study including 16,332 men in the Physician’s Health Study and 32,700 women in the Women’s Health Study. Both groups completed a randomized trial of aspirin for primary prevention of cardiovascular disease and cancer. After the aspirin trial, remaining participant’s waist and hip circumferences were requested, BMI was calculated from self-reported weight and height. Physical activity, history of cancer, diabetes, elevated cholesterol, hypertension, smoking history, alcohol consumption and parental history of heart attack were self-reported by the men. The same measurements were assessed for the women with the addition of hormone use, level of education, and dietary information. The outcomes were a total of 1,505 incidences of cardiovascular disease. The major findings were that the waist-to-hip ratio demonstrated statistically the best model fit and strongest association with cardiovascular disease because BMI may miss identifying persons at normal BMI levels with increased cardiovascular disease risk related central fat distribution. The higher levels of adiposity increase the risk of cardiovascular disease. The significance for my project is that given the ease of measurement and current standard use in the classification of overweight and obesity, BMI still remains the most clinically practical measure of adiposity. BMI does not directly assess body fat distribution and is not as good as circumference measures for the measurement of the most metabolically active intra-abdominal fat. Waist circumference is easy to measure and easy to access in the clinical settings CITATION Fli10 \l 1033 (Flint, et al., 2010). This purpose of this study was performed to assess the risk of coronary heart disease associated with excess weight measured by BMI and WC. Participants in two prospective cohort studies, the Health Professionals Health Study (27,859 men ages 39-75) and the Nurses’ Health Study (41,534 women ages 39-65) underwent a 16 year follow-up through 2004. Criteria for exclusion for men were: known acute myocardial infarction or angina in 1986 or before, cancer diagnosis or missing data on BMI or WC. Criteria for exclusion for women: known coronary heart disease in 1988 or before, cancer diagnosis, missing data on BMI or WC. Mailed surveys to participants were performed from 1986-2004. Male participants completed and mailed a baseline survey with detailed information about medical history, dietary intake, lifestyle and demographic information. Every two years follow-up questionnaires were mailed with one including a tape measure and instructions for measuring their WC to the nearest one-fourth inch. Female participants completed baseline and follow-up questionnaires reporting medical history and health related behaviors, a separate dietary questionnaire was performed and measurement of WC. During the follow-up form 1988-2004, a total of 1823 cases of coronary heart disease in men and 1173 cases in women were recorded. The major findings of the study were that BMI and WC both strongly predict future risk of coronary heart disease. WC appeared to predict coronary heart disease risk better than BMI among men and women age 60 years and older. The significance for my project is WC and BMI measurements at primary care visits are needed for the diagnosis and intervention of obesity for the prevention of comorbid conditions. Accurate classification of obesity is important to the patient and the clinician in risk assessment and weight management. BMI and WC are the most common anthropometric measurements used for the classification of general and central obesity CITATION Che06 \l 1033 (Chen, Ho, & Chan, 2006). The purpose of this study is to evaluate the validity of currently recommended obesity cutoffs of BMI and WC in Asians by the WHO/IASO/IOTF and Chinese by the Working Group on Obesity in China (WGOC) using the percentage body fat obesity criteria. This is a descriptive study (cross-sectional) of 1122 community-based Hong Kong Chinese women aged 41-63 years who had participated in the baseline assessments in three longitudinal studies of body mass were included in this study. They were recruited from the local community-based population. Subjects went to Prince of Wales Hospital in Hong Kong to have percentage body fat and percentage truncal fat measured. Trained interviewers performed face to face interviews based on structures questionnaire on sociodemographic data. The major findings of the study were a moderate to high association between BMI, WC, percent body fat and percent truncal fat in the prediction of obesity. The significance for my project is that BMI and WC are the most commonly used anthropometric indices used for the classification of obesity and both have a good accuracy among Asians which is similar to other populations. The most commonly used anthropometric method to diagnose obesity is BMI. The simplicity of measuring BMI has allowed it to be used extensively in epidemiological studies CITATION Oko10 \l 1033 (Okorodudu, et al., 2010). It is also used in clinical practice due to the ease of measurement. This article is a systematic review and meta-analysis of studies that assessed the performance of BMI to detect body adiposity. The indicated databases were searched and the predefined inclusion criteria were (1) the study must have assessed the performance of BMI to identify excess body fat, (2) provided standard values of diagnostic performance, and (3) used a body composition technique as the gold standard. The search yielded 3,341 potentially relevant articles however only 25 met all the inclusion criteria. The meta-analysis assessed the diagnostic performance of BMI in 31, 968 individuals from 32 research studies from 12 different countries. The study concludes that BMI to identify excess body adiposity of patients has good specificity but poor sensitivity, with approximately half of the patients who have excess body fat being labeled as non-obese. BMI should not be the only measure of obesity in the patient care settings, particularly in those with a BMI < 30. Prevention and management of obesity is addressed in a clinical practice guideline developed by the Institute for Clinical Systems Improvement (ICIS, 2011). The target population of this guideline is mature adolescents and adult patients. The guideline objectives are: (1) to increase awareness of BMI, (2) to improve the percentage of patients with elevated BMI who have received education and counseling regarding weight loss, (3) to improve the outcome of the treatment for obesity, and (4) to improve community involvement (employers, schools) in the prevention and treatment of obesity. For the prevention and assessment of obesity the guideline recommends calculating BMI annually for screening and as needed for management. Classify according to the BMI category such as underweight, normal weight, overweight and obese. Educate patients about their BMI and associated risks. Educate not only the overweight and obese patient but also the normal weight patients regarding weight maintenance. A high BMI does not address the distribution of visceral adiposity. Waist circumference provides and additional dimension for assessing visceral adiposity and clinical risk. Nutrition and physical activity recommendations are made in this guideline. Encourage at least five servings of fruits and vegetables per day. Aim for 20-35 grams of fiber daily with whole grain food choices. Calories from fat should be less than or equal to 30% of total daily calories. Encourage calorie reduction by paying close attention to portion sizes, measuring food and keeping a food journal. Minimally, all patients should be encouraged to do at least ten minutes of physical activity above what they are already doing each day and gradually increase the amount of time, followed by an increase in intensity. Ideally, all patients should meet the current recommendations of sixty minutes of moderate-intensity activity on most days per week. This may be done in ten minute increments. Encourage small bouts of activity that one might not generally consider exercise such as: taking the stairs, parking farther away, exercising while watching television, standing rather than sitting and taking activity breaks from the computer screen, television, or other media.Critical Appraisal of Evidence The above discussed articles will be evaluated on their strengths and weaknesses. The first article discussed in the literature review was regarding the relationship between BMI, age, gender and health status CITATION Jar10 \l 1033 (Jarrett, Bloch, Bennett, Bleazard, & Hedges, 2010). The strengths of the study are the large sample size and that the actual measurements of height, weight were verified. The weaknesses of the study are the self- reporting questionnaire and that several diseases such as hypertension and hyperlipidemia may not have been diagnosed because they can be asymptomatic resulting in the underestimation of the illness load. The level of evidence is V. The second article was regarding the accuracy of BMI in diagnosing obesity in adults CITATION Rom08 \l 1033 (Romero-Corral, et al., 2008). The strengths of this study are the large sample population, the use of four different ethnic groups and the bias was reduced by training staff with strict protocol in anthropometric measuring. The level of evidence is V. The third article examined the frequency of BMI measurements before the implementation of two new Healthcare Effectiveness Data and Information Set performance measures for obesity that require U.S. health plans to annually report the frequency of BMI among all adults and children in outpatient visits during the past two years CITATION Art10 \l 1033 (Arterburn, et al., 2010). Weaknesses of the study are that some healthcare plans have not implemented EMRs (so may not reflect the overall population). Also the individual chart reviews were not done so there may be an underestimation of BMI capture in plans with less extensive use of EMRs. Another weakness was that the study did not address the accuracy of height and weight. The strengths of the study were the inclusion of ten health plans, the wide geographic and demographic diversity. The level of evidence is V. The fourth article examined associations between anthropometric measures and the risk of incident cardiovascular disease CITATION Gel08 \l 1033 (Gelber, Gaziano, Orav, Manson, Buring, & Kurth, 2008). The strengths of this study were the large sample size, the study design, the long duration of follow-up, the measurement of multiple cofounders and the confirmation of CVD events with medical reviews. The weaknesses were the self-reported information on exposures, comorbid conditions and potential mediators. Another weakness was the study was limited female health professionals and mostly Caucasian male physicians and also the socioeconomic factors of the study population. The level of evidence is IV. The fifth article assessed the risk of coronary heart disease associated with excess weight measured by BMI and WC CITATION Fli10 \l 1033 (Flint, et al., 2010). The strengths of this study were the large study population across a broad age range, the large number of incidents of CHD in men and women without known coronary heart disease at baseline, the length of long-term follow-up and the use of validated measures of exposures and covariates. The weaknesses of the study were that the study population was limited to male health professionals and female nurses and the self-measures of WC. The level of evidence is IV. The sixth article evaluated the validity of currently recommended obesity cutoffs of BMI and WC in Asians and Chinese using the percentage body fat CITATION Che06 \l 1033 (Chen, Ho, & Chan, 2006). The strengths of the study were the large sample size and the multiple socioeconomic levels that were studied. The study’s weakness is that only one ethnic group was included in this study and the participants were recruited and not randomized. The level of evidence is V. The seventh article was a systematic review and a meta-analysis of studies that assessed the performance of BMI to detect body adiposity CITATION Oko10 \l 1033 (Okorodudu, et al., 2010). The strengths of the study are the study design. The weaknesses of the study is the risk for publication bias in which positive results or results with expected findings are more likely to be published. Another weakness is the use of different gold standards for the definition of excess adiposity. The level of evidence is I. The clinical practice guideline could have bias in regards to the potential conflict of interest of two of the guideline developers. The level of evidence is I. In summary there are 4 descriptive studies, 2 cohort studies, a systematic review and a clinical practice guideline supporting the use of BMI and WC measurements as assessment tools in the diagnosis of overweight and obesity in adult patients.Recommendations for Evidence-Based Practice Project Based on the literature review and critical appraisal the following recommendations are made for my project: Assessment of BMI and WC of adult patients at all visits. Recommendation Grade ACounseling regarding proper nutrition. Encourage at least five servings of fruits and vegetables per day, whole grains with a fiber intake of 35 grams or more daily, less than or equal to 30% of calories from fat. For weight loss, encourage calorie reduction. Recommendation Grade A.Counseling regarding physical activity. Encourage all patients to do at least 10 minutes of physical activity above what they are already doing each day and gradually increase the amount of time and intensity. Ideally, all patients should exercise 30 to 60 minutes of moderate intensity activity on most days of the week. Recommendation Grade A.Needs Assessment Many patients who are overweight or obese either do not realize it or are in denial and too few doctors are addressing the problem with their patients. Patients at the Wound Healing Center are treated for chronic, non-healing wounds. Physicians typically see 12-15 patients during a half day clinic. A large number of these patients are overweight or obese. The only time weight is addressed is on the admission paperwork that the patient fills out during their initial visit. BMI is not calculated and WC is not measured. Weight is not assessed or addressed on follow-up visits. Many chronic, non-healing wounds are a result of diabetes and peripheral vascular disease. Weight plays a major role in both of these disease processes. Measuring BMI and WC can raise awareness of weight status to patient’s thus giving them motivation to achieve weight loss and improve wound healing. Stakeholders involved in this small test of change include: patients, medical staff, National Wound Healing Center, and insurance companies. The patients have the potential to become healthier, more active, have faster wound healing and possibly decrease some medications. The medical staff includes physicians and nurses. The National Wound Healing Center may potentially increase their wound healing times by achieving normal BMI Insurance companies are impacted due to the $150 billion dollars spent yearly in healthcare expenses related to obesity.Implementation PlansProposed Plan for Small Test of Change It is evident that this clinic does not address the issue of overweight and obesity due to the fact that BMI is never addressed with the patients. In my planned small test of change I will follow one physician during a weekly half day clinic at the Wound Healing Center. I will speak with each patient who attends the clinic and inform them of my project. A consent letter will be given to each patient and asked to participate. If a patient decides to participate, a consent letter will be signed. The patient consents to having BMI and WC measured initially. If a patient is categorized as overweight or obese, a Patient Readiness Assessment is given. The Patient Readiness Assessment is a three question likert style questionnaire that assesses a patient’s readiness to make lifestyle changes in order to lose weight. The score is based on a 1-10 scale. A score of 1-3 indicates that they have very little intention or interest at this time. A score of 4-7 indicates ambivalence about incorporating lifestyle changes. A score of 8-10 indicates that they are very willing to make lifestyle changes in order to lose weight. If patients are ready to make lifestyle changes then education is provided on lifestyle changes to incorporate in order to lose weight. The education is based on recommendations from clinical practice guideline developed by the Institute for Clinical Systems Improvement (ICIS, 2011). Nutrition and physical activity recommendations are made in this guideline. Encourage at least five servings of fruits and vegetables per day. Aim for 20-35 grams of fiber daily with whole grain food choices. Calories from fat should be less than or equal to 30% of total daily calories. Encourage calorie reduction by paying close attention to portion sizes, measuring food and keeping a food journal. Minimally, all patients should be encouraged to do at least ten minutes of physical activity above what they are already doing each day and gradually increase the amount of time, followed by an increase in intensity. Ideally, all patients should meet the current recommendations of sixty minutes of moderate-intensity activity on most days per week. This may be done in ten minute increments. Encourage small bouts of activity that one might not generally consider exercise such as: taking the stairs, parking farther away, exercising while watching television, standing rather than sitting and taking activity breaks from the computer screen, television, or other media. Patients will be followed during return clinic visits and BMI and WC will be measured. Each patient will have at least three measurements, dependent upon how often they return to clinic for follow-up. The success of the project will be dependent of the patient’s willingness to participate. Support from the clinic staff is also crucial. Anticipated barriers are the fact that weight is a sensitive issue which may cause emotional discomfort to the patients. The clinic does not have access to bed scales so bedridden patients will be excluded from the project. Actual Implementation Plan for Small Test of Change An IRB application was submitted and approved by Auburn University. Once IRB was approved I contacted the Wound Healing Clinic to schedule my first day to implement my project. Fifteen patients were scheduled to see the physician for wound care. After the clinic staff performed the vital signs I spoke with each patient regarding my project. Twelve of the fifteen patients consented to participate in the project. Three were ineligible to participate, two were unable to stand on the scale for measurement and one was discharged from the clinic. Each patient’s height and weight were measured. A BMI calculator, provided by the clinic physician, was used to calculate BMI. Waist circumference was also measured. The patient’s were given a graph with their BMI charted according to normal, overweight and obese. WC was also charted. A Patient Readiness Assessment was performed with each patient. Lifestyle changes to encourage weight loss such as physical activity and healthy food choices were taught with each participant. The purposed plan was to take follow-up measurements in three weeks. These measurements were not taken due to the inconsistency in patients returning to clinic at this interval. In six weeks, follow-up measurements were taken at the clinic. Patients were called to remind them of their appointments. Each participant returned to clinic for their follow-up appointment. BMI and WC were measured and results given to patient.Evaluation Plans The purpose of the project is to use BMI and WC measurements as a screening program to assess the weight status of individuals to identify those at risk for overweight and obesity. The goal of the project is identification of overweight and obese individual and to provide education of lifestyle changes to incorporate in order to decrease their weight. The purpose and goals will be accomplished by measuring BMI and WC on individuals and educating them about lifestyle changes to incorporate in order to decrease their weight. The specific outcomes to be measured during this project are BMI and WC. Project Timeline The ideal timeline for my project would be to monitor BMI and WC over one year. The timeline for implementation of my small test of change will be eight weeks. During week one and two, I will begin with speaking with the patients and obtaining consent for the project. Willing participants will have initial BMI and WC measurements and also given the Patient Readiness Assessment. During weeks three thru seven, patients will be seen in the clinic for their follow-up visits with wound healing. Measurement of BMI and WC will be performed during these visits. I would like to have at least three measurements for each patient. By week eight, all measurements should be obtained. BMI and WC will be analyzed to see if there was a decrease in BMI and WC. The final paper will be prepared. Results will be presented to stakeholders with a poster presentation. Budget Anticipated budget needs are small. Tools needed are scales, a tape measure and a BMI calculator. The BMI calculator was provided to me by the clinic physician. Tape measure and scale was purchased for less than $50. The WHC has a copier that will available to me for copies of the Patient Readiness Assessment and the BMI/WC chart where the measurements will be recorded. Findings Data collected and entered in the Excel spreadsheet included: patient ID, age, gender, race, pre-BMI, pre-WC, post-BMI, post-WC, lifestyle changes (PRA 1), family support (PRA 2) and professional support (PRA 3). The Excel spreadsheet was entered into the statistical software program Statistical Package for the Social Sciences (SPSS) and descriptive statistical analysis were conducted. Figure 1. There are 12 participants. The mean age of the participants is 57.750 with a std.deviation of 16.0631. There are 33.3% male and 66.7% female participants. The mean pre-BMI is 37.667 (obese) with a std. deviation of 5.9595 and the mean post-BMI is 36.667 (obese) with a std. deviation of 6.0050. The mean pre-WC is 46.667 with a std. deviation of 6.4149 and the mean post-WC is 44.583 with a std. deviation of 6.1564 (see Figure 1). The T-Test showed statistically significant differences with –p=<0.05 for both outcome measures of BMI and WC. Figure 2. According to the Patient Readiness Assessment( see Figure 2), the mean findings for lifestyle changes is 8.333 with a std. deviation of 2.7080, family support mean is 8.5000 with a std. deviation of 3.11886, and professional support mean is 9.0 with a std. deviation of 2.33550. The findings for the Patient Readiness Assessment indicate that participants were willing to make lifestyle changes in order to lose weight and also perceive to receive support in their weight loss efforts from family and their healthcare professional. The findings suggest that assessing BMI and WC measurements has improved the ability to identify persons who are overweight or obese. The objective measures help raise awareness of weight status as a health risk and initiate weight management education.Future Research Weight loss happens over time so further research is needed to continue with follow-up BMI and WC measurements to determine if the continued measurements are successful in weight management. Recommendations were made to the Wound Healing Clinic to implement the clinical practice guideline suggestions to assess BMI and WC at least annually and more often for weight management. Their patients return to clinic for follow-up appointments weekly to bi-weekly making follow-up measurements possible. A large number of their patients have been coming to the clinic for treatment of chronic non-healing wounds for many years. These would be the ideal patients to follow over a longer period of time.Conclusions The EBP project concluded that assessing BMI and WC measurements has improved the ability to identify persons who are overweight or obese and raises awareness of weight status as a health risk. Key learning experiences for the researcher have been that weight is a sensitive subject for many individuals. Some participants were shocked that they were classified as obese with one stating that he was not obese, the calculations must be wrong when in fact he was obese. Sticking with factual findings and evidence and not giving personal opinions and judgments has been found to be the best way to deal with the sensitive subject of weight for this researcher. This has been a learning process over the last three semesters. Learning how to build an Evidence Based Practice from the foundation to the finished product has been a challenging but rewarding experience. Taking a simple PICO question and turning it into research and data collection has taught me how to gather the best evidence based practice and implement it into my clinical decision making. ReferencesArterburn, D., Alexander, G., Calvi, J., Coleman, L., Gillman, M., Novotny, R., … Sherwood, N. (2010). Body mass index measurement and obesity prevalence in ten U.S. health plans. Clinical Medicine & Research, 8(3/4), 126-130.Chen, Y., Ho, S., & Chan, S. (2006). Validity of body mass index and waist circumference in the classification of obesity as compared to percent body fat in Chinese middle-aged women. International Journal of Obesity, 30, 918-952. Dagenais, G., Yi, Q., J.F., M., Bosch, J., Pogue, J., & Yusuf, S. (2005). Prognostic impact of body weight and adominal obesity in women and men with cardiovascular disease. American Heart Journal, 149, 54-60.Douketis, J., Feightner, J., Attia, J., & Feldman, W. (1999). Detection, prevention and treatment of obesity. CMAJ, 160, 513-525.Flegal, K., Carroll, M., Ogden, C., & Curtain, L. (2010). Prevalence and trends in obesity among U.S. adults 1999-2008. JAMA, 288, 1723-1727.Flint, A., Rexrode, K. M., Hu, F., Glynn, R. J., Caspard, H., Manson, J., … Rimm, E.B. (2010). Body mass index, waist circumference, and risk of coronary heart disease: A prospective study among men and women. Obesity Research & Clinical Practice, 4, 171-181.Galuska, D., Will, J., Serdula, M., & Fords, E. (1999). Are health care professionals advising obese patients to lose weight? JAMA, 16, 1576-1578.Gelber, R., Gaziano, J., Orav, E., Manson, J., Buring, J., & Kurth, T. (2008). Measures of obesity and cardiovascular risk among men and women. Journal of the American College of Cardiology, 52(8), 605-615.Graham, I.D., Tetroe, J., & the KT Theories Research Group. (2007). Some theoretical underpinnings of kowledge translation. Academic Emergency Medicine, 14(11), 936-941.Jarrett, B., Bloch, G., Bennett, D., Bleazard, B., & Hedges, D. (2010). The influence of body mass index, age and gender on current illness: a cross-sectional study. International Journal of Obesity, 34, 429-436.Melnyk, B., & Fineout-Overholt, E. (2011). Evidence-Based Practice in Nursing & Healthcare (2nd ed. pp.254-257). Philadelphia: Wolters Kluwer and Lippincott Williams & Wilkins.Mokdad, A., Marks, J., D.F., S., & Gerbending, J. (2004). Actual causes of death in the United States, 2000. JAMA, 291, 1238-1245.Okorodudu, D., Jumean, M., Montori, V., Romero-Corral, A., Somers, V., Erwin, P., Lopez-Jimenez, F. (2010). Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. International Journal of Obestiy, 34, 791-799.Romero-Corral, A., Somers, V., Sierra-Johnson, J., Thomas, R., Collazo-Clavell, M., Korinek, J., … Lopez-Jimenez, F. (2008). Accuracy of body mass index in diagnosing obesity in the adult general population. International Journal of Obesity, 32, 959-966.Rosswurm, M., & Larrabee, J. (1999). A model for change to evidence-based practice. Image: Journal of Nursing Scholarship, 31(4), 317-322.Seidell, J., Kahn, H., Williamson, D., Lissner, L., & Valdez, R. (2001). Report from a Centers for Desease Control and Prevention Workshop on use of adult anthroprometry for public health and primary health care. American Journal of Clinical Nutrition, 73, 123-126.Stunkard, A., & Wadden, T. (1993). Obesity: Theory and Therapy. New York, NY: Raven Press.Trust for American's Health. (2011). F as in fat: How obesity threatens America's future. Robert Wood Foundation. . Accessed July 14, 2011. 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