The Science of VFP

VFP™ is an educational tool to assist health and fitness professionals in demonstrating physiological changes that occur with proper nutrition and exercise. The product is based on scientific studies and widely accepted research. The physiological principles that hold true for all humans are illustrated throughout the Visual Fitness Planner. However, there are individual physiological principles that are unique to each human and cannot specifically be represented within the Visual Fitness Planner.

The health risk portion of VFP™ is based on the seven risk factors identified by the American Medical Association, the American Dietetic Association, and the American Heart Association. The seven risk factors are as follows:

  • Age
  • Family History
  • Activity Level
  • Smoking
  • High Blood Pressure
  • High Cholesterol
  • Body Mass Index

These seven risk factors were studied as factors contributing to diabetes, heart disease, and stroke. The Journal of American Medical Association published the Framingham Study, which followed 10,000 Americans and how their chances for developing diabetes, heart disease, and stroke significantly increased based upon their personal risk factors. The Framingham Study also followed these 10,000 Americans for more than a decade, and showed that by decreasing Body Mass Index (BMI) by 10 percent, an individual’s risks for diabetes, heart disease, and stroke collectively fell 40 percent. A study performed by the Cooper Institute for Aerobics Research also showed that an increased activity rate of prescribed 20 minutes of exercise four times a week resulted in a decrease in risk factors by 10 percent.

  • The American Heart Association has published studies stating that decreasing smoking in the first year of prescribed program decreases chances for heart disease and stroke by 10 percent.
  • The American Heart Association studies also show that a decrease in blood pressure and cholesterol to within normal rates decrease an individual’s chance for developing heart disease and stroke by 10 percent.
  • The American Dietetic Association and the American Medical Association both have published statements addressing BMI as the greatest risk factor of the seven.

The Results Prediction Simulator as part of VFP™ is based on the following:

  • Proper Food Intake is based on an individual’s Basal Metabolic Rate (BMR), calculated using the Harris Benedict Equation.  Once an individual’s BMR is calculated, their number of days of compliance under the nutritional program is entered. The nutritional plan is based on the American Diabetic Association’s recommendation of 5 to 6 small meals a day. The plan also recommends low-glycemic foods, lean proteins, and small quantities of fat. The combination of food items produces a 500-calorie per day deficit from the individual’s BMR.
  • Resistance Training is based on the number of days compliant and simply assigned a moderate value of exercise for 20 minutes. The caloric value assigned to resistance training is 200 calories a day.
  • Cardiovascular Training is based on the number of days compliant and assigned a moderate intensity of exercise for a 20 minute session.
  • Pounds of fat burned per week is a calculation utilizing the number of days compliant multiplied by the calories burned in each individual category of nutrition, resistance, cardiovascular, and supplements. Total calories saved or burned per week are then calculated.
  • Pounds of fat burned are calculated by taking the pounds of fat per week multiplied by the number of weeks compliant to the program.
  • The standard weight range utilized by the program to illustrate body transformation from weight is based on the mortality scales created by the insurance industry. Supporting data was also used from the study conducted by the United States Air Force. Both studies provide height and weight recommendations based on mortality.
  • Body fat is the estimation of height and weight charts of an average weight person for that age. If a more accurate means of body fat calculation is available, that data can be entered for a more accurate depiction of the individual’s fat to muscle ratio.

The representation of the before and after picture is calculated using the circumference and girth measurements taken from a study performed by the United States Air Force measuring decreased circumference and girth in pilots who had gone through training.

The transformation plan is a combination of well-known fitness and wellness recommendations from such organizations as American Dietetic Association, American College of Sports Medicine, Yale University, and American Heart Association. These recommendations include:

  • Reward yourself
  • Start today
  • Create a new metabolism (smaller more frequent meals)
  • Resize your meals
  • Cardiovascular train first in the morning
  • Resistance train
  • Keep a journal on goal setting
  • Plan ahead
  • Increase water intake
  • Intake complex carbohydrates
  • Intake lean proteins

Overview of Calculations

Health Age

Solid principles and research were used to create the Health Age component of the VFP™. Health Age is an individual’s actual age and his or her lifestyle risk factors calculated together to produce one’s health age. Health is an age indicator of a person’s likelihood of contracting heart disease, diabetes, stroke and cancer prematurely and is calculated by an individual’s seven health risks: age, BMI, family history, exercise level, high blood pressure, high cholesterol and smoking.

Each one of the seven health risks are given an appropriate weight and value as determined by the medical research. Each value of the health risks was weighted to have a positive or negative effect on an individual’s health age. This tool was constructed with the intention of motivating people to make healthy lifestyle choices while giving them a realistic view of how their lifestyle choices affect their likelihood of contracting of four major diseases.

VFP Disease Risk Calculations

The subject’s BMI is displayed with a vertical progress bar. The range of the BMI bar is 18 to 40. Disease risk factors are also displayed with vertical progress bars. These bars have a range of 0 to 100. The level of risk is expressed by a value between 0 and 100. The calculated risk values are based on assumptions about the relative importance of several well known risk factors. The estimates of the magnitudes of each risk factor attempt to capture the prevailing wisdom about Type II Diabetes, Cardiovascular Heart Disease, and Stroke.

Obesity Factor Calculations

The Obesity factor is a combination of a person’s BMI and a measure of their excess body fat. Excess body fat depends on a person’s age and gender. The obesity factor is calculated using Body Mass Index, acceptable level of body fat based on age and gender and percentage of excess body fat as variables. The calculated resulted is then scaled effectively adjusting the calculated risks for athletes and others with a low percentage of body fat. If the BMI effect is a proxy for the amount of body fat a person carries, then if the person’s body fat is known to be lower, the effect of this factor should be smaller. For example, using this scaling function, the Obesity factor for a person with 7% body fat would be scaled to zero. However, the Obesity factor of a person with 32% body fat would be scaled by 100%.

Diabetes Risk Calculation

A person’s risk of Type II Diabetes is determined by calculated values based on several factors: family history, current exercise activity, age, gender, weight, height, and body composition. The series of formulas are scaled to adjust for levels of obesity. The result is categorized into 6 levels or risk: minimal, low, moderate, high, very high and extremely high risk.

Heart Disease Risk Calculation

Coronary Heart Disease Risk is the sum of nine factors: age, family history, gender, smoking, high blood pressure, exercise history, high cholesterol, BMI, and body fat percentage. The result of the calculation is scaled based on obesity factors and categorized into 6 levels of risk.

Stroke Risk Calculation

Stroke Risk is calculated using the following information: age, family history or stroke, family history of diabetes, smoking habits, gender, blood pressure, exercise history, cholesterol level, height, weight and body composition. The resulting risk value is then scaled and corresponds six categories of risk.

Quantifying a person’s risk with a number does not imply their actual risk – only the risk could be higher or lower if certain factors were different. The main objective of these calculations is to dramatically demonstrate how behaviors increase disease risk while others reduce a person’s risk.

VFP Science Research Calculations

VFP Disease Risk Calculations

The subject’s BMI is displayed with a vertical progress bar. The range of the BMI bar is 18 to 40. Disease risk factors are also displayed with vertical progress bars. These bars have a range of 0 to 100. The level of risk is expressed by a value between 0 and 100. The calculated risk values are based on assumptions about the relative importance of several well known risk factors. The estimates of the magnitudes of each risk factor attempt to capture the prevailing wisdom about Type II Diabetes, Cardiovascular Heart Disease, and Stroke. Please note that quantifying a person’s risk with a number does not imply their actual risk – only that their risk could be higher or lower if certain factors were different. The main objective of these calculations is to dramatically demonstrate how some behaviors increase disease risk while others reduce a person’s risk.


Obesity Factor Calculations

The disease risk calculations take into account the effects of body mass and body fat percentage on a person’s health. The Obesity factor is a combination of a person’s BMI and a measure of their excess body fat. Excess body fat depends on a person’s age and gender. To understand how excess body fat is determined, consider this table which shows an acceptable amount of body fat for persons of various ages:

To determine the excess body fat, subtract the acceptable body fat percentage from the person’s actual body fat percentage.

The Obesity Factor is constructed from these basic functions:Obesity Factor Calculations

Constructing the Obesity factor in this way allows the Visual Fitness Planner to use the power of BMI to predict disease while also taking into account the effect of a high body fat percentage. The Obesity Factor is scaled by the function:

Y = 0.0#x – 0.##

This scaling function effectively lowers the calculated risks for athletes and others with a low percentage of body fat. If the BMI effect is a proxy for the amount of body fat a person carries, then if the person’s body fat is known to be lower, the effect of this factor should be smaller. For example, using this scaling function, the Obesity factor for a person with 7% body fat would be scaled to 0. However, the Obesity factor of a person with 32% body fat would be scaled by 100%.


Diabetes Risk Calculation

A person’s risk of Type II Diabetes is the sum of six factors: family history, exercise, age, gender, BMI, and Percent Body Fat.

Diabetes Risk Calculation

*The Obesity Factor is magnified by 25% for Type II Diabetes

Example:

Consider a female subject 5 ft 6 in, 175 lbs, 36 years old who has a family history of diabetes. She does not exercise. Her BMI is 28.3. Her Body Fat is 35%.

Her risk of Diabetes is:

A value of ## places the subject in the ‘High’ Risk category. Risk factor categories for all diseases are as follows:
These risk categories correspond to the BMI disease risk levels.


Heart Disease Risk Calculation

Coronary Heart Disease Risk is the sum of nine factors: age, family history, gender, smoking, high blood pressure, exercise, high cholesterol, BMI, and body fat percentage.

Heart Disease Risk Calculations

Example:

Consider a male subject 6 ft, 250 lbs, 62 years old who smokes and has a family history of heart disease. He does not have High Blood Pressure or High Cholesterol. His BMI is 34.0. His Body Fat is 36%.

His risk of heart disease is:

A value of ## places the subject in the ‘Very High’ Risk category. Risk factor categories for all diseases are as follows:

Heart Disease Risk Scale Chart

These risk categories correspond to the BMI disease risk levels.


Stroke Risk Calculation

Stroke Risk is the sum of eight factors: age, family history, gender, smoking, high blood pressure, exercise, BMI, high cholesterol.

Stroke Risk Calculation

Example:

Consider a female subject 5 ft 3 in, 165 lbs, 60 years old who smokes, has High Blood Pressure, is physically inactive, and has a family history of stroke. Her BMI is 29.3. Her Body Fat is 38%.

Her risk of stroke is:

Stroke Risk Example 1

A value of ## places the subject in the ‘Very High’ Risk category. Risk factor categories for all diseases are as follows:

Stroke Risk Scale Chart

These values correspond to the BMI disease risk levels.


Disease Risk Factors

Disease Risk Factors


Cancer Addendum

Relative Risk of Cancer on a scale of 100 is calculated as follows. First, consider the relationship between a person’s BMI and their risk of Cancer.

Cancer Risk Addendum

# Please contact your VFP Representative for exact numerical value and further explanation.

 


References and Notes
 

J. T. Fine, G. A. Colditz, E. H. Coakley, G. Moseley, J. E. Manson, W. C. Willett, and I. Kawachi. A Prospective Study of Weight Change and Health-Related Quality of Life in Women. JAMA. 1999; 282: 2136-2142.
Body fat: is the estimation of height and weight charts of an average weight person for that age. If a more accurate means of body fat calculation is available, that data can be entered for a more accurate depiction of the individual’s fat to muscle ratio.
The representation of the before and after picture is calculated using the circumference and girth measurements taken from a study performed measuring decreased circumference and girth in older women who had gone through training.
A. R. Folsom, S. A. Kaye, T. A. Sellers, C. P. Hong, J. R. Cerhan, J. D. Potter, and R. J. Prineas. Body fat distribution and 5-year risk of death in older women. JAMA. 1993; 269: 483-487.
A. Wirth and J. Krause. Long-term Weight Loss With Sibutramine: A Randomized Controlled Trial. JAMA. 2001; 286: 1331-1339.
R. B. D’Agostino, Sr, S. Grundy, L. M. Sullivan, P. Wilson, and for the CHD Risk Prediction Group. Validation of the Framingham Coronary Heart Disease Prediction Scores: Results of a Multiple Ethnic Groups Investigation. JAMA. 2001; 286: 180-187.
D. B. Allison, PhD; K. R. Fontaine, PhD; J. E. Manson, MD, DrPH; J. Stevens, PhD; T. B. VanItallie, MD. Annual Deaths Attributable to Obesity in the United States. JAMA. 1999; 282:1530-1538.
M. Wei, J. B. Kampert, C. E. Barlow, M. Z. Nichaman, L. W. Gibbons, R. S. Paffenbarger, Jr, and S. N. Blair. Relationship Between Low Cardiorespiratory Fitness and Mortality in Normal-Weight, Overweight, and Obese Men. JAMA. 1999; 282: 1547-1553.
A. L. Dunn, B. H. Marcus, J. B. Kampert, M. E. Garcia, H. W. Kohl III, and S. N. Blair. Comparison of Lifestyle and Structured Interventions to Increase Physical Activity and Cardiorespiratory Fitness: A Randomized Trial. JAMA. 1999; 281: 327-334.
A. Hinderliter, A. Sherwood, E. C. D. Gullette, M. Babyak, R. Waugh, A. Georgiades, and J. A. Blumenthal. Reduction of Left Ventricular Hypertrophy After Exercise and Weight Loss in Overweight Patients With Mild Hypertension. Archives of Internal Medicine. 2002; 162: 1333-1339.
K. J. Stewart. Exercise Training and the Cardiovascular Consequences of Type 2 Diabetes and Hypertension: Plausible Mechanisms for Improving Cardiovascular Health. JAMA. 2002; 288: 1622-1631.
M. Tanasescu, M. F. Leitzmann, E. B. Rimm, W. C. Willett, M. J. Stampfer, and F. B. Hu. Exercise Type and Intensity in Relation to Coronary Heart Disease in Men. JAMA. 2002; 288: 1994-2000.
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W. K. Al-Delaimy, J. E. Manson, C. G. Solomon, I. Kawachi, M. J. Stampfer, W. C. Willett, and F. B. Hu. Smoking and Risk of Coronary Heart Disease Among Women With Type 2 Diabetes Mellitus. Archives of Internal Medicine. 2002; 162: 273-279.
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K. R. Fontaine, D. T. Redden, C. Wang, A. O. Westfall, and D. B. Allison. Years of Life Lost Due to Obesity. JAMA. 2003; 289: 187-193.
T. Kurth, J. M. Gaziano, K. Berger, C. S. Kase, K. M. Rexrode, N. R. Cook, J. E. Buring, and J. E. Manson. Body Mass Index and the Risk of Stroke in Men. Archives of Internal Medicine. 2002; 162: 2557-2562.
National Task Force on the Prevention and Treatment of Obesity. Overweight, Obesity, and Health Risk. Archives of Internal Medicine. 2000; 160: 898-904.
R. S. Vasan, A. Beiser, S. Seshadri, M. G. Larson, W. B. Kannel, R. B. D’Agostino, and D. Levy. Residual Lifetime Risk for Developing Hypertension in Middle-aged Women and Men: The Framingham Heart Study. JAMA. 2002; 287: 1003-1010.
B. M. Psaty, C. D. Furberg, L. H. Kuller, M. Cushman, P. J. Savage, D. Levine, D. H. O’Leary, R. N. Bryan, M. Anderson, and T. Lumley. Association Between Blood Pressure Level and the Risk of Myocardial Infarction, Stroke, and Total Mortality: The Cardiovascular Health Study. Archives of Internal Medicine. 2001; 161: 1183-1192.
J. A. Metz, J. S. Stern, P. Kris-Etherton, M. E. Reusser, C. D. Morris, D. C. Hatton, S. Oparil, R. B. Haynes, L. M. Resnick, F. Xavier Pi-Sunyer, S. Clark, L. Chester, M. McMahon, G. W. Snyder, and D. A. McCarron. A Randomized Trial of Improved Weight Loss With a Prepared Meal Plan in Overweight and Obese Patients: Impact on Cardiovascular Risk Reduction. Archives of Internal Medicine. 2000; 160: 2150-2158.

Diabetes Care 25:S50-S60, 2002
©2002 by the American Diabetes Association, Inc.Evidence-Based Nutrition Principles and Recommendations for the Treatment and Prevention of Diabetes and Related Complications
P. G. Shekelle, M. L. Hardy, S. C. Morton, M. Maglione, W. A. Mojica, M. J. Suttorp, S. L. Rhodes, L. Jungvig, and J. Gagné. Efficacy and Safety of Ephedra and Ephedrine for Weight Loss and Athletic Performance: A Meta-analysis. JAMA. 2003; 289: 1537-1545.
R. Bender, Karl-Heinz Jöckel, C. Trautner, M. Spraul, and M. Berger. Effect of Age on Excess Mortality in Obesity. JAMA. 1999; 281: 1498-1504.
Framingham Heart Study
The federal government’s Framingham Heart Study has gone on since 1948. It follows a representative sample of 5,209 adult residents and their offspring aged 28-62 years in Framingham, Massachusetts. These people have been tracked using standardized biennial cardiovascular examination, daily surveillance of hospital admissions, death information and information from physicians and other sources outside the clinic. The study’s goal is to learn the circumstances under which cardiovascular diseases arise, evolve and end fatally in the general population. This information will help researchers find out, over a long time, how those who develop cardiovascular diseases differ from those who don’t. In 1971, the study enrolled a second-generation group to participate in similar examinations. It consisted of 5,124 of the original participants’ adult children and their spouses. This second study is called the Framingham Offspring Study.
BMI – Excess Weight Can Take Years Off Your Life Despite Exercise
Lead author Dr. Frank Hu of the Harvard School of Public Health – published in the New England Journal of Medicine
Fitness Level Predicts Stroke
Investigators from the Research Institute of Public Health and the Kuopio Research Institute of Exercise Medicine in Finland of Internal Medicine, 2003;163:1682-1688
Stroke among “high-fit” men was 72% lower than it was among “low-fit” males.
Medicine & Science in Sports & Exercise, a study conducted at the Cooper Institute in Dallas, TX involving
Is Exercise the Elixir of Youth?
NASA – Circulation in September 2001
Researchers at Southwestern Medical Center in Dallas
Lifestyle Changes Effective in Preventing Type 2 Diabetes in Seniors
U.S. Department of Health and Human Services (HHS)
National Diabetes Education Program (NDEP)
Dr. James R. Gavin III, Chair of the NDEP
Exercise – A Gym Visit a Day Keeps the Doctor Away
Researchers from the University of Missouri-Columbia
Journal of the Physiological Society, 2005; 562:829-838
Exercise – Capacity Affects Heart Disease Risk
Scientists from Norwegian University of Science and Technology, the Medical College of Ohio, Williams College, and the University of Michigan Medical School Science, published online 1/20/05
BMI – Weight Gain Linked To Breast Cancer Death: US Study
Boston’s Brigham and Women’s Hospital and Harvard Medical School – Journal of Clinical Oncology
BMI – Losing Weight Can Cut Cancer Risk
Study from the American Cancer Society (ACS)
BMI – Diet, Exercise Top Drugs in Preventing Diabetes
March 1 issue of the Annals of Internal Medicine
Author Dr. William Herman, a professor of internal medicine at the University of Michigan School of Medicine.
Regular Exercise: Even a Late Start Cuts Heart/Diabetes Risk
Dr. Robert John Petrella and colleagues at the University of Western Ontario, London
BMI – Obesity Epidemic Threatens Life Expectancy
Lead study author S. Jay Olshansky, Ph.D., from the University of Illinois at Chicago
BMI – Obesity Raises Diabetes Risk Up to 80 Times 2004 Reuters Health
Diabetes UK chief executive Douglas Smallwood
ACS Report: At Least Half of US Cancer Deaths Could Be Prevented
American Cancer Society
The information is contained in Cancer Prevention and Early Detection Facts & Figures 2005
For Diabetics, Regular Exercise Can Lower Cardiovascular Risk Factors More Than Medication
Studies in the April issue of Diabetes Care
Study by researchers at the National Public Health Institute in Finland
Cardiovascular Risk – Health Family Tree Screening Questionnaire Approximately 5 percent of families account for about 50 percent of the coronary deaths before age 55.
Texas, Baylor College of Medicine
American Journal of Public Health, October 1988, vol. 78, p. 1283.)
Family History Alone Can Imply Cancer Mutation Risk
November 1, 2005 issue of CANCER
BMI – Medical News Today Lists Health Risks Related To Being Overweight or Obese
Medical News Today
Study is first smoking-related illness data released by Centers for Disease Control and Prevention (CDC).
Jeffrey Fellows, PhD, a senior research associate at Kaiser Permanente’s Center for Health Research in north Portland.

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