Evaluation methods for body composition data

Abstract

The assessment method of this invention first provides a bioelectrical impedance analyzer with a database storing population data for different ethnic groups. A subject then inputs parameters such as ethnicity, gender, and age into the analyzer. The analyzer then obtains measurement data based on the subject's height, weight, and the input parameters. Finally, the analyzer analyzes and compares the subject's measurement data with the population data in the database. This allows for the assessment of the subject's body composition within their ethnic group, thereby meeting the body composition assessment needs of different ethnic groups and improving the accuracy of the assessment results.

Description

Technical Field

[0001] This invention relates to methods for evaluating body composition data, and in particular to a method for evaluating body composition data of different ethnic groups using bioelectrical impedance analysis. Prior Technology

[0002] Body composition data (such as bone mineral density, fat percentage, muscle mass, and bone mineral density) can be assessed using several different methods, such as advanced medical equipment like dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI), or methods like underwater weighing, air displacement plethysmography (ADP), skinfold measurement, circumference measurement, isotope dilution, ultrasound, potassium-40 counting, and bioelectrical impedance analysis (BIA).

[0003] Because body composition data varies significantly across different races—for example, Black people have higher muscle mass than other races, while Asians have higher body fat percentages—currently, among the aforementioned methods, there is a lack of evaluation methods that assess body composition data across different races. This means that the accuracy of the assessment results may be affected by racial differences. Therefore, traditional methods for assessing body composition data still need improvement. Summary of the Invention

[0004] The main objective of this invention is to provide a method for evaluating body composition data, which uses bioelectrical impedance analysis to evaluate body composition data of different ethnic groups, so as to meet the body composition evaluation needs of different ethnic groups and improve the accuracy of evaluation results.

[0005] To achieve the aforementioned main objectives, the method provided by the present invention comprises the following steps: (a) providing a bioelectrical impedance analyzer having a database storing maternal data of different ethnicities; (b) a subject inputting multiple parameters, including ethnicity, gender, and age, into an input unit of the bioelectrical impedance analyzer; (c) a data construction unit of the bioelectrical impedance analyzer obtaining measurement data of the subject based on the subject's height, weight, and the parameters input by the subject in step (b); and (d) a data analysis unit of the bioelectrical impedance analyzer analyzing and comparing the subject's measurement data with the maternal data in the database.

[0006] As can be seen from the above, the assessment method of the present invention can assess the body composition of the test subject within their respective ethnic group, thereby meeting the body composition assessment needs of different ethnic groups and improving the accuracy of the assessment results.

[0007] Preferably, the population data includes body composition data of Mexican, Black, White, Latino, Asian, and other races at different sexes and ages.

[0008] Preferably, the measurement data is at least one of bone mineral percentage, muscle percentage, fat percentage, and bone mineral density.

[0009] Preferably, after the analysis and comparison are completed, the database is updated periodically by one of the dynamic update units of the bioelectrical impedance analyzer based on the comparison results, so as to improve the accuracy and reliability of the parent data.

[0010] Preferably, after the analysis and comparison are completed, one of the output units of the bioelectrical impedance analyzer generates a personalized body composition assessment report based on the comparison results. In addition to allowing the test subject to have a clearer understanding of their own body composition, the personalized body composition assessment report also enables the test subject to manage their personal body composition.

[0011] The detailed construction, features, assembly, and usage of the method for evaluating body composition data provided by this invention will be described in the following detailed description of embodiments. However, those skilled in the art will understand that such detailed descriptions and the specific embodiments listed for implementing this invention are merely illustrative and not intended to limit the scope of this patent application. Simple Explanation of the Diagram

[0012] Figure 1 is a flowchart of the evaluation method of the present invention. Figure 2 is a schematic diagram of the evaluation method of the present invention, showing the subject operating the bioelectrical impedance analyzer in a standing position. Figure 3 is a block diagram of the evaluation method of the present invention. Figure 4 is a graph of the evaluation method of the present invention, showing the median total body fat percentage of Asian women. Figure 5 is a graph of the evaluation method of the present invention, showing the median bone mineralization rate of the trunk of white males. Figure 6 is a graph of the evaluation method of the present invention, showing the standard deviation of the fat percentage of the left upper limb of Mexican-American men. Figure 7 is a graph of the evaluation method of the present invention, showing the standard deviation of bone mineralization rate of the right upper limb of Latina women. Figure 8 is a graph of the evaluation method of the present invention, showing the median muscle rate of the right lower limb of a Black male. Figure 9 is a graph of the evaluation method of the present invention, showing the standard deviation of muscle rate of the left lower limb of women of other races. Figure 10 is a graph of the evaluation method of the present invention, showing the median bone mineral density of a Black male. Implementation

[0013] The applicant hereby clarifies that throughout this specification, including the embodiments described below and the claims in the patent application, all directional terms are based on the directions shown in the drawings. Secondly, in the embodiments and drawings described below, the same element reference numerals represent the same or similar elements or their structural features.

[0014] Please refer to Figure 1. The method for evaluating body composition data of the present invention includes the following steps:

[0015] Step (a): As shown in S1 of Figure 1, a bioelectrical impedance analyzer 10 is provided. As shown in Figures 1 and 3, the bioelectrical impedance analyzer 10 has a database 20, which stores maternal data 22 of different races (curves shown in Figures 4 to 10).

[0016] The bioelectrical impedance analyzer 10 provided in this step utilizes the characteristic that different frequencies of current generate different resistance and reactance values ​​by inputting a small amount of current into the human body, based on the differences in water content and cell membrane properties of different tissues in the human body, thereby obtaining body composition data. Since the detailed structure and operating principle of the bioelectrical impedance analyzer 10 are not the focus of this case, they will not be elaborated here.

[0017] In addition, the parent data 22 in this embodiment is body composition data of Mexican, Black, White, Latino, Asian and other races at different sexes and ages. The aforementioned body composition data includes at least four types: bone mineral content percentage (BMC %), lean body mass percentage (LBM %), body fat percentage (BF %) and bone mineral density (BMD). Moreover, the aforementioned four types of data can be further subdivided into median, power transformation and standard deviation, as shown in the curves in Figures 4 to 10.

[0018] Step (b): As shown in S2 of Figure 1, a subject 80 inputs multiple parameters into one of the input units 30 of the bioelectrical impedance analyzer 10. These parameters include race, gender and age.

[0019] In this step, the subject 80 first inputs parameters such as race, gender and age using the input unit 30, and then operates the bioelectrical impedance analyzer 10 to measure the subject 80, thereby simultaneously obtaining parameters such as resistance, reactance, phase angle, impedance parameters and impedance value.

[0020] It should be noted that the input unit 30 may be, but is not limited to, a keyboard or a touch panel. Secondly, the test site can be the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb; the test subject can choose the test site according to their own needs, and there are no restrictions on this. Furthermore, the test subject 80 can operate the bioelectrical impedance analyzer 10 in different postures, such as standing, lying down, or sitting. Figure 2 shows an example of a standing posture.

[0021] Step (c): The data construction unit 40 of one of the bioelectrical impedance analyzers 10 obtains measurement data 82 of the subject 80 based on the subject 80's height, weight, and the parameters input by the subject 80 in step (b).

[0022] In this step, the method of obtaining height and weight is not limited. In this embodiment, height and weight are directly provided to the bioelectrical impedance analyzer 10 by the subject 80 using the input unit 30 (e.g., keyboard or touch panel). After obtaining height and weight, the data construction unit 40 of the bioelectrical impedance analyzer 10 will perform calculations in conjunction with all the parameters in step (b) to obtain one of the subject 80's measurement data 82. The measurement data 82 includes at least one of bone mineral mass percentage, muscle mass percentage, body fat percentage, and bone mineral density. In this embodiment, all four types of data are included.

[0023] The data construction unit 40 of the bioelectrical impedance analyzer 10 calculates the standard score of the measurement data 82 using any of the following standardized formulas: or and Where Z is the standard score of measurement data 82, X is measurement data 82, M is the median of population data 22, L is the power transformation of population data 22, and S is the standard deviation of population data 22.

[0024] When the measured data 82 represents bone mineral mass percentage, the relevant value of bone mineral mass percentage is calculated using the following formula:

[0025] XMi=a0+a1x+a2x2+a3x3+a4x4......(1)

[0026] XLi=b0+b1x+b2x2+b3x3+b4x4......(2)

[0027] XSi=c0+c1x+c2x2+c3x3+c4x4......(3)

[0028] Where XM represents the median of measurement data 82 (bone mineral mass percentage), XL represents the power transform of measurement data 82 (bone mineral mass percentage), XS represents the standard deviation of measurement data 82 (bone mineral mass percentage), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, and a0~a4, b0~b4, and c0~c4 are all regression coefficients. The aforementioned regression coefficients may vary depending on the item, and the range of the aforementioned regression coefficients is between -100 and 100.

[0029] After obtaining the median (XMi) of the bone mineral density of the whole body or each limb segment using the above formula (1), the median (XMi) is substituted into X of any of the above standardization formulas to obtain the standard score of the median (XMi); after obtaining the power transformation (XLi) of the bone mineral density of the whole body or each limb segment using the above formula (2), the power transformation (XLi) is substituted into X of any of the above standardization formulas to obtain the standard score of the power transformation (XLi); after obtaining the standard deviation (XSi) of the bone mineral density of the whole body or each limb segment using the above formula (3), the standard deviation (XSi) is substituted into X of any of the above standardization formulas to obtain the standard score of the standard deviation (XSi).

[0030] When the measured data is 82, representing muscle rate, the relevant value of muscle rate is calculated using the following formula:

[0031] XMi= d0+d1x+d2x2+d3x3+d4x4......(4)

[0032] XLi= e0+e1x+e2x2+e3x3+e4x4......(5)

[0033] XSi= f0+f1x+f2x2+f3x3+f4x4......(6)

[0034] Where XM represents the median of measurement data 82 (muscle rate), XL represents the power transform of measurement data 82 (muscle rate), XS represents the standard deviation of measurement data 82 (muscle rate), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, d0~d4, e0~e4, and f0~f4 are all regression coefficients. The aforementioned regression coefficients may vary depending on the item, and the range of the aforementioned regression coefficients is between -100 and 100.

[0035] After obtaining the median (XMi) of muscle rate of the whole body or each limb segment using the above formula (4), the median (XMi) is substituted into X of any of the above standardization formulas to obtain the standard score of the median (XMi); after obtaining the power transformation (XLi) of muscle rate of the whole body or each limb segment using the above formula (5), the power transformation (XLi) is substituted into X of any of the above standardization formulas to obtain the standard score of the power transformation (XLi); after obtaining the standard deviation (XSi) of muscle rate of the whole body or each limb segment using the above formula (6), the standard deviation (XSi) is substituted into X of any of the above standardization formulas to obtain the standard score of the standard deviation (XSi).

[0036] When the measured data is 82, representing body fat percentage, the relevant value of body fat percentage is calculated using the following formula:

[0037] XMi= g0+g1x+g2x2+g3x3+g4x4......(7)

[0038] XLi= h0+h1x+h2x2+h3x3+h4x4......(8)

[0039] XSi= j0+j1x+j2x2+j3x3+j4x4......(9)

[0040] Where XM represents the median of measurement data 82 (fat percentage), XL represents the power transformation of measurement data 82 (fat percentage), XS represents the standard deviation of measurement data 82 (fat percentage), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, g0~g4, h0~h4, and j0~j4 are all regression coefficients. The aforementioned regression coefficients may vary depending on the item, and the range of the aforementioned regression coefficients is between -100 and 100.

[0041] After obtaining the median (XMi) of the body fat percentage or each limb segment using the above formula (7), the median (XMi) is substituted into X of any of the above standardization formulas to obtain the standard score of the median (XMi); after obtaining the power transformation (XLi) of the body fat percentage or each limb segment using the above formula (8), the power transformation (XLi) is substituted into X of any of the above standardization formulas to obtain the standard score of the power transformation (XLi); after obtaining the standard deviation (XSi) of the body fat percentage or each limb segment using the above formula (9), the standard deviation (XSi) is substituted into X of any of the above standardization formulas to obtain the standard score of the standard deviation (XSi).

[0042] When the measured data is bone mineral density (BMD), the relevant BMD value is calculated using the following formula:

[0043] XMi= k0+k1x+k2x2+k3x3+k4x4......(10)

[0044] XLi= l0+l1x+l2x2+l3x3+l4x4......(11)

[0045] XSi= m0+m1x+m2x2+m3x3+m4x4......(12)

[0046] Where XM represents the median of measurement data 82 (bone mineral density), XL represents the power transform of measurement data 82 (bone mineral density), XS represents the standard deviation of measurement data 82 (bone mineral density), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, k0~k4, l0~l4, and m0~m4 are all regression coefficients. The aforementioned regression coefficients may vary depending on the item, and the range of the aforementioned regression coefficients is between -100 and 100.

[0047] After obtaining the median (XMi) of bone mineral density of the whole body or each limb segment using the above formula (10), the median (XMi) is substituted into X of any of the above standardization formulas to obtain the standard score of the median (XMi); after obtaining the power transform (XLi) of bone mineral density of the whole body or each limb segment using the above formula (11), the power transform (XLi) is substituted into X of any of the above standardization formulas to obtain the standard score of the power transform (XLi); after obtaining the standard deviation (XSi) of bone mineral density of the whole body or each limb segment using the above formula (12), the standard deviation (XSi) is substituted into X of any of the above standardization formulas to obtain the standard score of the standard deviation (XSi).

[0048] Step (d): The data analysis unit 50 of the bioelectrical impedance analyzer 10 analyzes and compares the measurement data 82 of the subject 80 with the parent data 22 of the database 20 to assess the body composition of the subject 80 within its ethnic group.

[0049] In this step, to facilitate the explanation of how to analyze and compare, the following examples, illustrated with diagrams, illustrate several implementation methods for different ethnic groups and different test sites:

[0050] Please refer to Figure 4. The curve shown in Figure 4 represents the median body fat percentage of female maternal data 22. The ethnicity of the aforementioned maternal data 22 is Asian, and the measurement site of the aforementioned maternal data 22 is the whole body. When the first subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 4 into the input unit 30, the median body fat percentage can be calculated by the above formula (7). Then, by analyzing and comparing the aforementioned measurement data 82 and the maternal data 22, the difference between them can be known, thereby allowing the first subject 80 to assess the body fat percentage of their ethnicity.

[0051] Please refer to Figure 5. The curve shown in Figure 5 represents the median bone mineral density percentage of male maternal data 22. The ethnicity of the aforementioned maternal data 22 is Caucasian, and the test site of the aforementioned maternal data 22 is the torso. When the second test subject 80 inputs the same ethnicity, gender, and age parameters as the maternal data 22 in Figure 5 into the input unit 30, the median bone mineral density percentage can be calculated by the above formula (1). Then, by analyzing and comparing the aforementioned measurement data 82 and the maternal data 22, the difference between them can be known, thereby allowing the second test subject 80 to assess the status of their own torso's bone mineral density percentage within their ethnicity.

[0052] Please refer to Figure 6. The curve shown in Figure 6 represents the male maternal data 22 with the standard deviation of body fat percentage. The ethnicity of the aforementioned maternal data 22 is Mexican, and the test site for the aforementioned maternal data 22 is the left upper limb. When the third test subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 6 into the input unit 30, the standard deviation of body fat percentage can be calculated using the above formula (9). Then, by analyzing and comparing the aforementioned measurement data 82 with the maternal data 22, the difference between the two can be determined, thereby allowing the third test subject 80 to assess the body fat percentage of the left upper limb within their ethnic group.

[0053] Please refer to Figure 7. The curve shown in Figure 7 represents the standard deviation of the bone mineral density percentage for female maternal data 22. The ethnicity of the aforementioned maternal data 22 is Latino, and the test site for the aforementioned maternal data 22 is the right upper limb. When the fourth subject 80 inputs the same ethnicity, gender, and age parameters as the maternal data 22 in Figure 7 into the input unit 30, the standard deviation of the bone mineral density percentage can be calculated using the above formula (3). Then, by analyzing and comparing the aforementioned measurement data 82 with the maternal data 22, the difference between the two can be determined, thereby allowing the fourth subject 80 to assess the bone mineral density percentage of the right upper limb within their ethnic group.

[0054] Please refer to Figure 8. The curve shown in Figure 8 represents the median muscle rate of male subjects 22. The ethnicity of the subjects 22 is Black, and the test site for the subjects 22 is the right lower limb. When the fifth subject 80 inputs the same ethnicity, gender, and age as the subjects 22 in Figure 8 into the input unit 30, the median muscle rate can be calculated using the above formula (4). Then, by analyzing and comparing the measured data 82 with the subjects 22, the difference between the measured data 82 and the subjects 22 can be determined, thereby allowing the fifth subject 80 to assess the muscle rate of the right lower limb within the range of their ethnicity.

[0055] Please refer to Figure 9. The curve shown in Figure 9 represents the female population data 22, which represents the standard deviation of muscle mass. The ethnicity of the population data 22 includes all ethnicities except Mexican, Black, White, Latino, and Asian. The test site for the population data 22 is the left lower limb. When the sixth subject 80 inputs the same ethnicity, gender, and age as the population data 22 in Figure 9 into the input unit 30, the standard deviation of muscle mass can be calculated using the above formula (6). Then, by analyzing and comparing the aforementioned measurement data 82 with the population data 22, the difference between the two can be determined, allowing the subject 80 to assess the distribution of muscle mass in the left lower limb within their ethnic group.

[0056] Please refer to Figure 10. The curve shown in Figure 10 represents the median bone mineral density (BMD) of male maternal data 22. The ethnicity of the aforementioned maternal data 22 is Black, and the measured area of ​​the aforementioned maternal data 22 is the whole body. When the seventh subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 10 into the input unit 30, the median BMD can be calculated by the above formula (10). Then, by analyzing and comparing the aforementioned measurement data 82 and the maternal data 22, the difference between them can be known, thereby allowing the subject 80 to assess the distribution of BMD throughout the body within their ethnicity.

[0057] The above is for illustrative purposes only and is not intended to limit the race or test site of the test subject 80. The test subject 80 can choose the test site according to their own needs. They only need to enter their race, gender and age to obtain the comparison results of the body composition data of the test site and the parent body data 22.

[0058] In addition, during this step, the dynamic update unit 60 of the bioelectrical impedance analyzer 10 will periodically update the database 20 according to the comparison results, so that the measurement data 82 becomes part of the parent data 22, thereby improving the accuracy and reliability of the parent data 22.

[0059] On the other hand, as shown in Figure 3, after the analysis and comparison are completed, the output unit 70 of the bioelectrical impedance analyzer 10 will generate a personalized body composition assessment report 72 based on the comparison results. In addition to allowing the test subject 80 to better understand their own body composition, the personalized body composition assessment report 72 also allows the test subject 80 to manage their personal body composition.

[0060] In summary, this invention utilizes bioelectrical impedance analysis technology to assess the body composition data of subject 80, enabling subject 80 to understand their body composition status within their own ethnic group, thereby meeting the body composition assessment needs of different ethnic groups and improving the accuracy of assessment results.

[0061] 10: Bioelectrical Impedance Analyzer 20: Database 22: Maternal Data 30: Input Unit 40: Data Construction Unit 50: Data Analysis Unit 60: Dynamic Update Unit 70: Output Unit 72: Personalized Body Composition Assessment Report 80: Test subjects 82: Measurement data S1: Steps S2: Steps S3: Steps S4: Steps

Claims

1. A method for assessing body composition data, comprising the following steps: (a) providing a bioelectrical impedance analyzer having a database storing parental data for different ethnic groups; (b) a subject inputting multiple parameters, including ethnicity, sex, and age, into an input unit of the bioelectrical impedance analyzer; (c) a data construction unit of the bioelectrical impedance analyzer obtaining measurement data of the subject based on the subject's height, weight, and the parameters input in step (b); and (d) a data analysis unit of the bioelectrical impedance analyzer analyzing and comparing the subject's measurement data with the parental data in the database to assess the subject's body composition within their ethnic group; wherein, In step (c), the standard score of the measurement data is calculated by any of the following formulas: or, where Z is the standard score of the measurement data, X is the measurement data, M is the median of the population data, L is the power transform of the population data, and S is the standard deviation of the population data.

2. The method for evaluating body composition data as described in claim 1, wherein in step (a), the parent data is body composition data of Mexican, Black, White, Latino, Asian and other races at different sexes and ages.

3. The method for evaluating body composition data as described in claim 1, wherein, The measurement data includes at least one of bone mineral percentage, muscle percentage, fat percentage, and bone mineral density.

4. The method for evaluating body composition data as described in claim 3, wherein when the measurement data is bone mineral mass percentage, the median of the measurement data is calculated by the following formula: XMi = a0 + a1x + a2x² + a3x³ + a4x⁴, the power transformation of the measurement data is calculated by the following formula: XLi = b0 + b1x + b2x² + b3x³ + b4x⁴, and the standard deviation of the measurement data is calculated by the following formula: XSi = c0 + c1x + c2x² + c3x³ + c4x⁴, where, XM represents the median of the measurement data, XL represents the power transform of the measurement data, XS represents the standard deviation of the measurement data, i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, and a0~a4, b0~b4, and c0~c4 are all regression coefficients.

5. The method for evaluating body composition data as described in claim 3, wherein when the measurement data is muscle rate, the median of the measurement data is calculated by the following formula: XMi = d0 + d1x + d2x2 + d3x3 + d4x4, the power transform of the measurement data is calculated by the following formula: XLi = e0 + e1x + e2x2 + e3x3 + e4x4, and the standard deviation of the measurement data is calculated by the following formula: XSi = f0 + f1x + f2x2 + f3x3 + f4x4, where, XM represents the median of the measurement data, XL represents the power transform of the measurement data, XS represents the standard deviation of the measurement data, i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, and d0~d4, e0~e4, and f0~f4 are all regression coefficients.

6. The method for evaluating body composition data as described in claim 3, wherein when the measurement data is body fat percentage, the median of the measurement data is calculated using the following formula: XMi = g0 + g1x + g2x² + g3x³ + g4x⁴, the power transformation of the measurement data is calculated using the following formula: XLi = h0 + h1x + h2x² + h3x³ + h4x⁴, and the standard deviation of the measurement data is calculated using the following formula: XSi = j0 + j1x + j2x² + j3x³ + j4x⁴, where, XM represents the median of the measurement data, XL represents the power transform of the measurement data, XS represents the standard deviation of the measurement data, i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, and g0~g4, h0~h4, and j0~j4 are all regression coefficients.

7. The method for evaluating body composition data as described in claim 3, wherein when the measurement data is bone mineral density, the median of the measurement data is calculated by the following formula: XMi = k0 + k1x + k2x² + k3x³ + k4x⁴, the power transform of the measurement data is calculated by the following formula: XLi = l0 + l1x + l2x² + l3x³ + l4x⁴, and the standard deviation of the measurement data is calculated by the following formula: XSi = m0 + m1x + m2x² + m3x³ + m4x⁴, where, XM represents the median of the measurement data, XL represents the power transform of the measurement data, XS represents the standard deviation of the measurement data, i represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject, x represents the age of the subject, and k0~k4, l0~l4, and m0~m4 are all regression coefficients.

8. In the body composition data evaluation method as described in claim 1, in step (d), a dynamic update unit of the bioelectrical impedance analyzer periodically updates the database based on the comparison results.

9. The method for evaluating body composition data as described in claim 1, in step (d), an output unit of the bioelectrical impedance analyzer generates a human body composition evaluation report based on the comparison results of step (d).

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