As per the records of WHO the prevalence of obesity in the world has risen to epidemic proportions. This isn’t just limited to one part of the world. Nations everywhere have obese citizens. While some think that the richest and the most developed countries in the world are the most obese, this isn’t always the case. In fact, the United States and the United Kingdom are two of the most economically rich and developed countries in the world. However, they are only ranked 12th and 36th, respectively, when compared to other nations. There have been multiple nations with small economies that have been classified as the most obese, and as per the World Health Organization. The number of obese people in the world total is approximately 2.1 billion, which makes up about 30% of the total population. This number continues to rise. Over 3 million people each year die from obesity.
In addition to dramatically decreasing a person’s lifespan, the quality of life of an obese person is affected. This is of major concern as excess fat distribution is strongly associated with different health risk conditions including higher risks of diabetes, heart disease and even certain types of cancer. Other health risks from obesity include osteoarthritis, kidney disease, strokes, and high blood pressure. Hence, a simple and effective measure of obesity is needed for risk assessment in order to guide appropriate management and develop preventive strategies.
Many people want to know the answer to this question: What should I eat? and How much should I weigh? we will be answering the second question through this blog, because the question of what you should eat is highly individualized. So let’s get into answering how much you should weight? However, there is not one ideal healthy weight for each person, because a number of different factors play a role. These include age, muscle-fat ratio, height, gender, and body fat distribution, or body shape.
So the motivation behind developing this application is to track the overall fitness as well as a healthy weight of a person by considering different obesity parameters such as Body Mass Index (BMI), Waist-Hip Ratio (WHR) and Waist-Height Ratio (WHtR). Combining all these three parameters is one of the best ways to get an accurate idea of whether or not you should consider changing your diet or exercise routine. This blog will demonstrate how BMI, WHR, and WHtR are calculated using different body parameters in MATLAB and how their score will impact your health and early prediction of obesity. This Fitness Tracker application is very helpful for anyone who is looking to make strides in their fitness.
Now let's have an understanding of each parameter and how they are going to predict the health status of a person.
Body Mass Index (BMI)
So the most popular indicator of showing what a healthy weight is, Body mass index or BMI. You've all probably heard this. A BMI is a ratio of your weight to your height so it's a math that shows weight in kilograms over height in meters squared.
Check out this BMI score table which will help you to find out your BMI is normal or not. For adults including men and women, a healthy BMI is considered from 18.5 to 25, under 18.5 is considered underweight, 25 to 30 is considered overweight and 30 and above is considered obese.
A lot of people complain that BMI is inaccurate because they know someone who clearly isn't unhealthy and overweight but is classified as overweight or even obese by BMI. Consider the case of athlete or bodybuilder who tends to be very fit and has little body fat. They can have a high BMI because they have more muscle mass, but this does not mean they are overweight. This is because the BMI only uses height and weight and not considering body fat distribution.
On a population-wide basis when they're looking at thousands and thousands even millions of people BMI is actually a decent indicator of body fat percentage which is why they use it in a lot of these studies. While it can be helpful it shouldn’t be the only way to understand an individual human body! So what’s the better measure? The solution is Waist to Hip Ratio and Waist to Height Ratio.
Waist to Hip Ratio (WHR)
Waist to Hip Ratio or WHR, is your waist circumference divided by your hip circumference. This is considered to be the best screening tool to identify the fat distribution in your body compare to BMI. The most dangerous combination is actually being skinny and having an enlarged waist. This is called central obesity or in simple terms thin people with belly fat. This particular fat changes the way our system works and causes diabetes increased blood pressure and basically increases the chances to develop heart diseases. Since the WHR is able to predict central obesity it can be useful for the early prediction of such diseases!
In order to get accurate results, It’s important to make sure that you take your measurements in the right place. There are basically 3 categories for body shape such as; apple, avocado, and pear shapes. Women with a ratio of 0.8 or below and men with 0.95 or below are pear-shaped. People with this shape are in a lower risk category for health problems. Women with a ratio of 0.85 or above and men with 1.0 or above are considered to have apple-shaped bodies. Fat tends to gather around the middle, and apple-shaped bodies face a higher risk of health problems. People whose ratios fall between apple and pear thresholds are at moderate risk of health problems and sometimes called avocados.
Waist to Height Ratio (WHtR)
Next parameter is Waist to height ratio which is a measure of central fat distribution. Waist to height ratio is calculated by dividing the abdominal waist circumference by height, both should be in the same units. Higher values indicate a higher risk of obesity-related cardiovascular diseases. WHtR is equally fair for short or tall people, as well as children and adults. Abdominal fat affects organs like the heart, liver, and kidneys more adversely than fat around the hips and bottom, so this is a crucial parameter to check the risk for obesity, heart diseases, diabetes, stroke, and hypertension.
As a general rule, both men and women should keep their waist circumference to less than half of their height. BMI does not differentiate between body fat and muscle mass, which means it can over/underestimate obesity in some people. A combination of high BMI with a high waist to height ratio is a much stronger indicator of increased health risk. People with a BMI classification of “Overweight” or “Obese” are in a lower risk category if they also have a low to moderate WHtR, compared to those that are in the higher WHtR category.
So this is the score table for WHtR. Let's say a female with a WHtR value of 0.45, so she’s coming to the category of healthy. Which means that with respect to her height her abdominal fat distribution is normal.
Why BMI WHR and WHtR are selected over other parameters?
Body mass index (BMI) is widely used in the diagnosis of overweight and obesity, whereas waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) are employed as surrogate indicators of central obesity to predict fat distribution.
- These anthropometric indices are used in epidemiological studies for population surveillance of risk factors for chronic disease because they can be easily measured and at a low cost.
- Efficient screening requires criteria that are economical, easily accessible, and applicable for all populations.
- WHtR is a reliable, easy-to-use, and less age-dependent index for identifying children and adolescents with increased cardio metabolic risk related to central obesity.
- Waist-to-hip-ratio is a quick and easy way to check how much weight you carry around your middle. It’s just one of several measures along with BMI that you can measure without the help of any clinical experts to evaluate your weight and health.
This makes the measurement and calculation of these parameters easy and thereby you can use it as a guide to control your disease risk factors.
Graphical User Interface
Six panels are used to create the complete look of this application. Once the app starts running, the user first sees the input / user panel. After clicking the "Show My Results" button, the app will take you to the next panel, the result panel. From the result panel, the user can view the score analysis of the BMI, WHR and WHtR, and the caution panel will provide an overview of various preventive measures. The entire graphical user interface was created using MATLAB App Designer, and the webinar video below explains how to create it.
Once the application panel is ready, the next step is to write the code to determine the behavior for the components, the following image shows the overall workflow of the application design.
so in the first stage, data acquisition, we will be collecting the different body parameters including gender, height , weight, waist circumference and hip circumference from the user side as an input to our application. These parameters are then used to calculate the BMI, WHR and WHtR. In the output stage there will be health risk level prediction with respect to your score and gender. There will be a individual score analysis as well as a general conclusion out of it, moreover this application will also give an idea about the precautions to be followed in order to maintain healthy lifestyle.
Once the input parameters are obtained, the model will predict your health by following three steps: unit conversion, BMI, WHR, WHtR calculation, and finally risk level classification based on BMI, WHR and WHtR scores. In the coming sections, we will see the algorithms that were followed to perform these steps.
In this stage, the user body parameters are converted to standard units before calculating the different measures of obesity such as BMI, WHR, and WHtR. Initially, the algorithm will check whether the user has entered all the required entries, and if any fields are empty, it will show a user error message saying "Fill up the data". Once the data is filled in, the algorithm will check whether each of the body parameter units is in predetermined units, otherwise it is converted back to predetermined units, and finally, these parameters will be used in the calculation step.
BMI, WHR and WHtR calculation
The prime objective in this step is to calculate the BMI, WHR, and WHtR from the user parameters such as gender, height, weight, waist measurement, and hip measurement. The equations that are followed to calculate these obesity indices are given bellow:
Risk level classification based on BMI, WHR and WHtR scores
In this stage, the scores are classified into different health conditions based on gender. The results can give you will give you a complete picture of your health and give you an accurate idea of whether or not you should consider changing your diet or exercise routine.
Let's start with BMI, it incorporates height and weight, but not physique, fitness, age or gender. For adults 20 years and older, BMI incorporates weight and height but does not take age or gender into account. A woman who has the same BMI have more body fat than a man. Therefore the following algorithm is used for classifying the BMI score obtained for both men and women.
The WHR and WHtR are less age-dependent indices, but there are significant differences in body composition in men and women, and women having more body fat. The distribution of fat varies by gender, and men have a relatively higher central distribution of fat. These differences start early in life and become more pronounced in adulthood due to changes in sex hormone levels. In both men and women, the waist, hip and hip ratio increases with age. The difference in scores was found to be statistically significant for both genders, so the algorithm considered specific score cut-points based on gender.
Once the health risk levels are categorized for each of the estimated scores, a final decision will be made on the basis of the following algorithm. So, remember the result panel created in the app designer, there were lamps near each parameter to indicate the risk level of each score obtained. The color of the lamp is either green or red to indicate low and health risk levels.
In the case of BMI, the scores within the range 18.5 to 25 will be indicated by green color and the scores above or below this range will be in red color. In case of WHR, the score less than or equal to 1.0 is in green and greater than 1.0 is in red in case of males and the score less than or equal to 0.85 is in green and greater than 0.85 is in red for females. In case of WHtR, the scores within the range 0.43 to 0.53 in male and 0.42 to 0.49 in the female will be indicated by green color and the scores above or below this range will be in red color. Once the color of each lamp is assigned the next step is to make a general conclusion out of that.
The picture above shows the different color conditions of the lamp according to the scores. Here, in the first case, where all the lamps are green, it indicates that the person is healthy, and, in the last case where all the lamps are red, this indicates that the person has a high health risk.
Consider the second case where the BMI is high, and the WHR and WHtR are normal, which occurs when the person is having more muscle mass than fat accumulates. Consider the case of athletes or bodybuilders, they tends to have more muscle, which can lead to obesity in terms of BMI, but they are not obses. So analyzing WHR and WHtR here will help you distinguish between fat and muscle.So in those cases, the person is at a lower risk.
Consider the third case where BMI is normal and WHR and WHTR are not normal, which happens when the person develops more fat in the middle. BMI may fail to identify this central fat distribution because it is only related to height and weight and does not consider fat accumulation. Fat accumulation is a major concern because it is associated with different health risks, so this condition can be considered a moderate health risk case.
Other color conditions of the lamp should not be taken into account, because to some extent WHR and WHtR are directly proportional, there is the possibility of increasing one or the other and decreasing one. An if-else loop based on the above conditions is used in this application to predict the overall health of the individual.
Following are the results obtained by entering different body parameters of different people in the application.
Case 1: Person with Central Obesity or Belly Fat
Here the individual is having fat accumulation in the waist and it's the most dangerous condition where it will changes the way our system works and causes diabetes increased blood pressure and basically increases the chances to develop heart diseases. BMI assessment in this example produces a false positive results masquerading the health risks associated with the patient because. This is because the BMI is associated with height and weight not fat accumulation.
But because this application also considers WHR and WHtR parameters, it is possible to predict central obesity, thereby enabling early detection of diseases caused by the accumulation of fat around the waist. The application was able to accurately predict central obesity as the final decision was made from three individual scores.
Case 2: Highly Overweight Person with Central Obesity
Here the individual is an obese women. Excessive fat distribution is strongly associated with health risks including diabetes, heart disease and certain types of cancer.
In this application, all individual scores obtained indicate that the individual is in high health risk. Thereby the final prediction supports that conclusion.
Case 3: Healthy Person with More Muscle Mass
Here the individual is a bodybuilder or physically fit male. For a bodybuilder BMI is not a very precise representation of body fat percentage or levels. BMI takes into consideration an individual's weight and height. Here weight includes both fat and muscle weight. As a result, bodybuilders will have high values of BMI even with minimal body fat.
Case 4: Underweight Person
The person here is an underweight woman. This raises the risk for serious health problems including anorexia, type 1 diabetes, hyperthyroidism, cancer, or tuberculosis. In this application, all individual scores obtained indicate that the individual is in high health risk. Thereby the final prediction supports that conclusion.
The application accurately predicted overall health status in all four different cases
This fitness tracker intended to be the simple and straightforward application so that everyone can get the most out of it, whether they’re a beginner or a fitness pro. This application gives you a complete picture of your health than providing a general overview which might not be entirely accurate. It also overcomes the drawbacks of using BMI alone to predict health risk. It is also worth noting that the app has been able to accurately predict the health status of different people with different body parameters.
Learning about your BMI, WHR and WHtR rate is a positive step in the process of reaching or maintaining a healthy weight. The more you know, the easier it is to make changes in your life that produce real results. Track your BMI, WHR and WHtR scores, keep a weight loss or weight gain journal, gather support from friends and family, and connect with your healthcare team to find a plan that works over the long-term for you.
There are a few limitations to this Fitness Tracker Application! Here they are:
- There are various factors that affect the fitness scores of BMI, WHR and WHtR, they are age, gender, and fat percentage. Though we are considering gender and fat distribution in our application we are not considering the age factor. So as a future extension of this application we can include age factor while calculating the scores.
- It is not appropriate to use the BMI categories for adults to interpret the BMI of children and teens. The BMI percentile allows us to categorize your child's BMI based where he or she falls in comparison to other boys or girl of the same age group. If your child is, say, in the 75th percentile, that would mean that he or she is higher than 75 percent of children in the group. In terms of weight, higher percentile values correspond to higher weight categories, while lower percentile values correspond to lower weight categories.
Watch the video below for the full code and try to replicate all the results yourself !
Get Standalone Application of Webinar
This Standalone has been created using MATLAB R2018b. Get the application running and start evaluating your fitness.
Standalone Application Installation Steps
Download the above standalone installation file in .exe format to your computer, which is the standard extension for the installation files on Windows computers. You can follow the steps below to install a stand-alone application from an .exe file.
- Locate and double-click the .exe file saved to your computer. (It will usually be in your Downloads folder)
- Wait until the following dialog box disappears.
- Click on Next
- If you want to add a shortcut to the desktop, tick the checkbox, and then click Next.
- Click Next
- Choose Yes and Click Next
- Click Install
- Wait for installation to finish. Once this is done, you can now open the app from the Start menu (Windows 7) or the Start screen (Windows 8) by searching for the app name. Alternatively, you can click on the saved shortcut on the desktop.
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