Obesity risk early warning method, system and equipment

Abstract

The invention relates to the technical field of health management, in particular to an obesity risk early warning method, system and equipment, and the method comprises the following steps: 1, obtaining the body basic data of a user; step 2, calculating a BMI according to the obtained body basic data, and performing preliminary obesity screening on the user according to the calculated BMI; wherein the BMI result is displayed in a normal range, and a user may have recessive obesity and needs to be further examined; if the BMI shows overweight or obesity, executing the step 3; and step 3, calculating the body fat rate according to the obtained body basic data, and judging whether the user is obese or not according to the body fat rate, so that the problem that multi-dimensional and accurate obesity prediction cannot be realized due to the fact that single data is mostly adopted or only one piece of key data is concerned during reference of existing obesity risk early warning is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a method, system and device for early warning of obesity risk. Background Art

[0002] Obesity has become a global public health issue, not only impacting individual quality of life but also increasing the risk of developing various chronic diseases. With improvements in living standards and changes in lifestyles in my country, the obesity problem is becoming increasingly severe. According to statistics, obesity has become the sixth leading risk factor for death and disability in my country. Therefore, scientifically and accurately assessing obesity risk is crucial for individual health management and the development of public health strategies.

[0003] Existing obesity risk warnings often use single data or focus only on one key data for reference, and are unable to achieve multi-dimensional and accurate obesity predictions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and device for obesity risk warning to solve the problem that existing obesity risk warnings often use single data or only focus on a certain key data when referring to them, and cannot achieve multi-dimensional and accurate obesity prediction.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A method for early warning of obesity risk, comprising the following steps:

[0007] Step 1: Obtain the user's basic physical data;

[0008] Step 2: Calculate BMI based on the acquired basic body data, and conduct preliminary obesity screening on the user based on the calculated BMI;

[0009] If the BMI result is within the normal range, the user may have hidden obesity and needs further examination;

[0010] If your BMI shows you are overweight or obese, you need to proceed to step 3;

[0011] Step 3: Calculate the body fat percentage based on the acquired basic body data, and determine whether the user is obese based on the body fat percentage.

[0012] A further technical solution is that the basic body data includes obtaining the user's weight and height, and calculating the BMI based on the user's weight and height, and the calculation formula is: BMI=weight (kg) / height² (m²).

[0013] A further technical solution is that the basic body data also includes the user's waist circumference and waist-to-hip ratio, among which; men with waist circumference ≥90cm and women with waist circumference ≥85cm are diagnosed as central obesity; when the waist-to-hip ratio ≥0.90 (male) or ≥0.85 (female), it is diagnosed as central obesity.

[0014] A further technical solution is that in step 3, the body fat percentage is calculated by gender, where the body fat percentage calculation formula for men is: body fat percentage (male) = 1.2*BMI+0.23*age-5.4-10.8; the body fat percentage calculation formula for women is: body fat percentage (female) = 1.2*BMI+0.23*age-5.4.

[0015] A further technical solution is that in step 3, it also includes the calculation of body fat percentage based on the waist circumference of the adult user, wherein; the calculation formula for body fat percentage of adult males is: a=waist circumference (cm)*0.74; b=weight (kg)*0.082+44.74; body fat weight (kg)=ab; body fat percentage of adult males=(total body fat weight / weight)*100%; the calculation formula for body fat percentage of adult females is: a=waist circumference (cm)*0.74; b=weight (kg)*0.082+34.89; body fat weight (kg)=ab; body fat percentage of adult females=(total body fat weight / weight)*100%.

[0016] A further technical solution is that the body fat percentage calculation method in step 3 also includes measurement by sebum clamp, commercial electrical impedance measurement and dual-energy X-ray absorptiometry.

[0017] A system for early warning of obesity risk comprises a data acquisition module and a model prediction module; wherein the data acquisition module is used to collect basic physical data of a user and calculate the corresponding BMI parameters for each year based on the basic physical data for each year; and the model prediction module is used to predict and obtain first obesity trend information of the target user and predict and obtain second obesity trend information of the target user based on the measured BMI parameters and body fat percentage parameters.

[0018] A device for early warning of obesity risk includes a memory and one or more processors, wherein the memory stores executable code.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] By testing the user's BMI value, which directly reflects the individual's degree of obesity and is a widely used obesity risk assessment indicator in clinical and public health fields, the system also combines the user's body fat percentage, a key indicator for assessing obesity risk. It more accurately reflects body fat content than BMI and can more comprehensively assess an individual's health. Combining BMI and body fat percentage allows BMI to provide a preliminary screening of overall obesity, while body fat percentage more accurately reflects body fat content. Using both together can avoid misclassifying individuals with well-developed muscles as obese, improving the accuracy of obesity warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of a method for obesity risk warning according to the present invention.

[0022] Figure 2 A diagram of a body shape differentiation grid tool for a method of early warning of obesity risk according to the present invention DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] Figures 1 to 2 An embodiment of the present invention is shown.

[0025] Example 1:

[0026] refer to Figure 1 As shown, a method for early warning of obesity risk is disclosed, comprising the following steps:

[0027] Step 1: Obtain the user's basic physical data;

[0028] Step 2: Calculate BMI based on the acquired basic body data, and conduct preliminary obesity screening on the user based on the calculated BMI;

[0029] If the BMI result is within the normal range, the user may have hidden obesity and needs further examination;

[0030] If your BMI shows you are overweight or obese, you need to proceed to step 3;

[0031] Step 3: Calculate the body fat percentage based on the acquired basic body data, and determine whether the user is obese based on the body fat percentage.

[0032] In this invention, the user's BMI is tested. The BMI directly reflects an individual's obesity level and is a widely used obesity risk assessment indicator in clinical and public health fields. It is also combined with the user's body fat percentage, a key indicator for assessing obesity risk. It more accurately reflects body fat content than BMI and enables a more comprehensive assessment of an individual's health. Combining BMI and body fat percentage allows BMI to provide a preliminary screening of overall obesity, while body fat percentage more accurately reflects body fat content. This combined use of the two can prevent individuals with well-developed muscles from being misclassified as obese, improving the accuracy of obesity warnings.

[0033] In step 2, where the BMI result is displayed within the normal range, the user may have hidden obesity and requires further examination. It is worth noting here that even if the user's BMI result is displayed within the normal range, the user may be hidden obese. Hidden obesity refers to an individual's weight or BMI (body mass index) being within the normal range, but the body fat percentage is high, especially the excessive accumulation of visceral fat (fat surrounding the organs in the abdominal cavity), which is a sub-healthy state. Therefore, this application uses a combination of BMI and body fat percentage to warn of obesity, thereby improving the accuracy of obesity warnings for users.

[0034] The basic body data includes obtaining the user's weight and height, and calculating the BMI based on the user's weight and height, and the calculation formula is: BMI=weight (kg) / height² (m²).

[0035] Basic body data also includes the user's waist circumference and waist-to-hip ratio. Among them, men with waist circumference ≥90cm and women with waist circumference ≥85cm are diagnosed as central obesity; when the waist-to-hip ratio ≥0.90 (male) or ≥0.85 (female), it is diagnosed as central obesity.

[0036] In step 3, the body fat percentage is calculated based on gender, where;

[0037] The formula for calculating body fat percentage for men is:

[0038] Body fat percentage (male) = 1.2*BMI+0.23*age-5.4-10.8;

[0039] The formula for calculating body fat percentage for women is:

[0040] Body fat percentage (female) = 1.2*BMI+0.23*age-5.4.

[0041] As can be seen from the formula, under the same BMI and age conditions, the calculated body fat percentage of men is 10.8 percentage points lower than that of women, which reflects the physiological characteristics of women who need more necessary fat.

[0042] In step 3, the body fat percentage is calculated based on the waist circumference of the adult user, wherein;

[0043] The formula for calculating body fat percentage for adult men is:

[0044] a=waist circumference (cm)*0.74;

[0045] b=weight (kg)*0.082+44.74;

[0046] Body fat weight (kg) = ab;

[0047] Body fat percentage of adult men = (total body fat weight / weight) * 100%;

[0048] The formula for calculating body fat percentage for adult women is:

[0049] a=waist circumference (cm)*0.74;

[0050] b=weight (kg)*0.082+34.89;

[0051] Body fat weight (kg) = ab;

[0052] Body fat percentage of adult women = (total body fat weight / body weight) * 100%.

[0053] This calculation method pays special attention to waist circumference, an important indicator reflecting central obesity, and can better assess the accumulation of visceral fat.

[0054] It can also be calculated using the human body roundness calculation formula:

[0055]

[0056] Obesity is judged according to the World Health Organization (WHO) BMI classification standard:

[0057] The WHO standard divides adult BMI into the following levels:

[0058] Thin: BMI < 18.5 kg / m²;

[0059] Normal: BMI 18.5-24.9 kg / m²;

[0060] Overweight: BMI 25.0-29.9 kg / m²;

[0061] Obesity: BMI ≥ 30 kg / m²;

[0062] Obesity level 1: BMI 30-34.9 kg / m²;

[0063] Obesity level 2: BMI 35-39.9 kg / m²;

[0064] Obesity stage 3: BMI ≥ 40 kg / m².

[0065] Taking into account the physical characteristics of the Asian population, a stricter BMI standard that is more suitable for Chinese people has been developed:

[0066] Thin: BMI < 18.5 kg / m²;

[0067] Normal: BMI 18.5-23.9 kg / m²;

[0068] Overweight: BMI 24.0-27.9 kg / m²;

[0069] Obesity: BMI ≥ 28.0 kg / m²;

[0070] Obesity is further subdivided according to Chinese characteristics:

[0071] Mild obesity: BMI 28.0-32.4 kg / m²;

[0072] Moderate obesity: BMI 32.5-37.4 kg / m²;

[0073] Severe obesity: BMI 37.5-49.9 kg / m²;

[0074] Extreme obesity: BMI ≥ 50 kg / m².

[0075] At the same time, the BMI assessment standards for children and adolescents are also taken into consideration. Unlike adults, children and adolescents are in the growth and development stage, and their BMI standards need to refer to the percentile curves of the same age and gender:

[0076] Overweight: BMI ≥ 85th percentile for age and sex;

[0077] Obesity: BMI ≥ 95th percentile for age and sex.

[0078] BMI assessment for children and adolescents needs to be comprehensively evaluated in combination with growth curves, body fat percentage and other indicators to avoid making judgments based solely on weight.

[0079] The role and advantages of BMI in obesity risk assessment:

[0080] Simple and easy: BMI is easy to calculate, only requires measuring height and weight, no complicated equipment is required, and it is suitable for large-scale population screening.

[0081] Strong comparability: BMI provides a unified standard, which facilitates the comparison of obesity conditions in different regions and at different times.

[0082] Risk prediction: BMI is positively correlated with the risk of multiple chronic diseases, including cardiovascular disease, type 2 diabetes, hypertension, etc., and is an effective tool for assessing health risks.

[0083] Widely used in clinical practice: BMI is the most commonly used obesity assessment indicator in clinical practice and an important basis for formulating intervention strategies.

[0084] Combining BMI and body fat percentage: Body fat percentage, which can be determined through methods such as skinfold thickness measurement and bioelectrical impedance analysis, more accurately reflects body fat content. Excessive body fat is defined as a body fat percentage greater than 25% for adult men and greater than 30% for women.

[0085] In the process of combining body fat percentage assessment, the body fat percentage assessment criteria for children are also different from those for adults. Generally speaking, the assessment of body fat percentage for children needs to take age and gender factors into consideration:

[0086] Boys: A body fat percentage >25% can be considered obese.

[0087] Girls: A body fat percentage >30% can be considered obese.

[0088] Body fat percentage, measured through skinfold thickness and bioelectrical impedance analysis, more directly reflects body fat content than BMI. Waist circumference and waist-to-height ratio (waist circumference divided by height) are also important indicators for assessing central obesity in children. Studies have shown that a waist-to-height ratio ≥ 0.5 is associated with increased metabolic risk in children, regardless of age, gender, or race.

[0089] In the process of collecting the user's body fat percentage, the body roundness coefficient can also be used as a basis for judging whether the user is obese. For a long time, the body mass index (BMI) has been widely used by the medical community as a health screening tool. BMI is a ratio based on height and weight, and is often used to assess the degree of obesity of an individual. However, BMI cannot distinguish between muscle and fat, especially visceral fat accumulation, and does not take into account factors such as age, gender and race. Therefore, its accuracy has been questioned, and many doctors and health experts believe that BMI is no longer suitable for objectively reflecting a person's health risks and status. The body roundness index (BRI) combines height and waist circumference to focus on assessing the risk of abdominal fat (central obesity) and chronic diseases such as cancer, heart disease and diabetes. The correlation is closer and can more accurately predict personal health risks, especially those with central obesity.

[0090] The body fat percentage calculation method in step 3 also includes measurement by sebum clamp, electrical impedance measurement and dual-energy X-ray absorptiometry.

[0091] Example 2:

[0092] A system for early warning of obesity risk comprises a data acquisition module and a model prediction module; wherein the data acquisition module is used to collect basic physical data of a user and calculate the corresponding BMI parameters for each year based on the basic physical data for each year; and the model prediction module is used to predict and obtain first obesity trend information of the target user and predict and obtain second obesity trend information of the target user based on the measured BMI parameters and body fat percentage parameters.

[0093] Example 3:

[0094] A device for obesity risk warning includes a memory and one or more processors, wherein the memory stores executable code, and is characterized in that when the processor executes the executable code, it runs the above-mentioned obesity risk warning method.

[0095] Example 4:

[0096] This embodiment uses the measurement data of human height, weight and waist circumference to calculate the body mass index (BMI) and body roundness index (BRI) of the human body, and constructs a two-dimensional body shape differentiation map through BMI and BRI to evaluate the body shape of an individual who is at risk of chronic diseases such as cardiovascular disease, diabetes and hypertension, so as to promote individual introspection, reduce unhealthy lifestyles and behaviors that lead to central obesity, and reduce the risk of chronic diseases such as cardiovascular disease, diabetes and hypertension.

[0097] Step 1: Get the calculation formula:

[0098] 1. BMI calculation formula:

[0099]

[0100] 2. Thomas BRI calculation formula:

[0101]

[0102] Step 2: Calculate data

[0103] The BMI and BRI data were calculated according to the above calculation formula.

[0104] Step 3: Draw a 2D graph

[0105] Based on the BMI and BRI data, a quadrant chart was drawn with BMI on the horizontal axis and BRI on the vertical axis. A 16-square grid chart was also drawn with the normal cutoff values ​​for BMI and BRI, respectively. The normal cutoff values ​​for BMI are 18.5 and 24.0, and the normal cutoff values ​​for BRI are 2.0 and 3.0.

[0106] Step 4: Naming the grid

[0107] Each grid is named according to its BMI and BRI combination characteristics.

[0108] In particular, the central obesity and latent obesity distinguished by this patent have important theoretical significance and practical value for weight management.

[0109] Step 5: Create a body shape grid tool

[0110] Paper and plastic board are used as materials to draw a grid tool diagram for distinguishing body shapes.

[0111] Although the present invention has been described herein with reference to a number of illustrative embodiments thereof, it will be understood that numerous other modifications and implementations may be devised by those skilled in the art that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, the drawings, and the claims, numerous variations and modifications may be made to the components and / or layout of the subject combination arrangement. In addition to variations and modifications to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A method for early warning of obesity risk, characterized in that: The following steps are involved: Step 1: Obtain the user's basic physical data; Step 2: Calculate BMI based on the acquired basic body data, and conduct preliminary obesity screening on the user based on the calculated BMI; If the BMI result is within the normal range, the user may have hidden obesity and needs further examination; If your BMI shows you are overweight or obese, you need to proceed to step 3; Step 3: Calculate the body fat percentage based on the acquired basic body data, and determine whether the user is obese based on the body fat percentage.

2. The obesity risk warning method according to claim 1, characterized in that: The basic body data includes obtaining the user's weight and height, and calculating the BMI based on the user's weight and height. The calculation formula is: BMI=weight (kg) / height² (m²).

3. The obesity risk warning method according to claim 2, characterized in that: Basic body data also includes the user's waist circumference and waist-to-hip ratio. Among them, men with waist circumference ≥90cm and women with waist circumference ≥85cm are diagnosed as central obesity; when the waist-to-hip ratio ≥0.90 (male) or ≥0.85 (female), it is diagnosed as central obesity.

4. The obesity risk warning method according to claim 1, characterized in that: In step 3, the body fat percentage is calculated based on gender. The body fat percentage calculation formula for men is: body fat percentage (male) = 1.2*BMI + 0.23*age - 5.4 - 10.8; the body fat percentage calculation formula for women is: body fat percentage (female) = 1.2*BMI + 0.23*age - 5.

4.

5. The obesity risk warning method according to claim 4, characterized in that: In step 3, the body fat percentage is also calculated based on the waist circumference of the adult user, where the body fat percentage calculation formula for adult males is: a=waist circumference (cm)*0.74; b=weight (kg)*0.082+44.74; body fat weight (kg)=ab; body fat percentage for adult males=(total body fat weight / weight)*100%; the body fat percentage calculation formula for adult females is: a=waist circumference (cm)*0.74; b=weight (kg)*0.082+34.89; body fat weight (kg)=ab; body fat percentage for adult females=(total body fat weight / weight)*100%.

6. The obesity risk warning method according to claim 1, characterized in that: The body fat percentage calculation method in step 3 also includes measurement by sebum clamp, electrical impedance measurement and dual-energy X-ray absorptiometry.

7. A system for early warning of obesity risk, characterized by: The data acquisition module and the model prediction module; The data collection module is used to collect the user's basic body data and calculate the corresponding BMI parameters for each year based on the basic body data each year; The model prediction module is used to predict and obtain the first obesity trend information of the target user, and predict and obtain the second obesity trend information of the target user based on the measured BMI parameter and body fat percentage parameter.

8. A device for early warning of obesity risk, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, it runs the obesity risk warning method according to any one of claims 1 to 6.

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