JP5044346B2 - Pet obesity prevention support system - Google Patents

Description

  The present invention relates to a pet obesity prevention support system and an obesity prevention support method for calculating the lean body mass and proper calorie calories of a pet using a computer.

  Obesity has become a problem due to the rich food situation in recent years. Obesity has been recognized as a major factor in lifestyle-related diseases such as heart disease, arteriosclerosis, hypertension, and diabetes, so managing the amount of fat accumulated in the body maintains and promotes daily health. It is considered important to do.

  As for pets, such as dogs and cats, the food situation is becoming richer and obesity is a problem. Owners who are fond of pets are trying to maintain and improve the health of dogs and cats by controlling the amount of food they supply so that dogs and cats do not become obese. However, there are many cases where such a request is made.

  Against this background, the type and amount of pet food necessary for pets is automatically calculated according to the pet's weight and body condition score (BCS), and a prescription is automatically created based on this. A system has been proposed (see Patent Document 1). However, since BCS is determined by a veterinarian by visual inspection and palpation, it may lack objectivity. Further, in the system described in this document, the calculation formula that is the basis of the calculation is unknown, and it cannot be determined whether or not the calculation result has scientific validity.

JP 2006-25605 A

  An object of the present invention is to provide a pet obesity prevention support system that calculates a lean body weight of a pet using a computer.

  The present invention is a pet obesity prevention support system that calculates a lean body weight of a pet using a computer, and the pet's current weight, body condition score (hereinafter referred to as BCS) on the input screen of the computer, Means for displaying on the input screen that a current data set including gender, castration presence and age data should be input, data input means for inputting the current data set, and storing the input current data set Current data set storage means, lean body weight calculating means for calculating the lean body weight of a pet using the stored current data set, and lean body mass for storing the lean body weight calculated by the lean body weight calculating means Pet obesity prevention support system comprising weight storage means and means for outputting the stored lean body mass to an output device Those that achieve the object by providing a beam.

  Further, the present invention is a pet obesity prevention support method for calculating a lean body weight of a pet via network communication between a user terminal device and a management server computer, and the information processing in the server includes: Display on the input screen that the current data set including the pet's current weight, body condition score (hereinafter referred to as BCS), gender, castration status, and age data should be entered on the input screen of the terminal device A step of receiving the current state data set input in the terminal device from the terminal device via network communication, a current state data set storing step of storing the received current state data set, and the stored current state Using the data set, the lean body mass calculating step for calculating the lean body weight of the pet, To provide a pet obesity prevention support method comprising a lean body mass storage step for storing the lean body mass calculated in the fat body weight calculating step, and a step of distributing the stored lean body mass to a terminal device. The above-mentioned purpose is achieved.

  In the obesity prevention support system of the present invention, since the value of body fat percentage is corrected based on the sex / age / type of pet, castration / infertility data, it is possible to accurately estimate the body fat percentage, The lean body mass can be calculated more accurately. For this reason, it is possible to obtain a more accurate amount of pet food supply, and to more appropriately support prevention of obesity.

  First, a first embodiment of the present invention will be described with reference to FIG. The pet obesity prevention support system (hereinafter referred to as “the present system”) of this embodiment is operated using a single computer and an input device, a storage device, and an output device (including a printer) connected thereto. It is a system realized by executing a program installed in the computer.

  As shown in FIG. 1, the system includes a computer, a printer, a storage device that stores data to be displayed as an output screen, and an input / output device that includes input means such as a monitor, a keyboard, and a mouse. The system of the present embodiment displays an input screen on a computer monitor, receives each data input on the input screen by operating a keyboard or the like, performs an operation based on the data, and monitors the result as an output screen. It is displayed on the top.

In this system, a program installed on a computer, that is, application software, includes the following functional steps s1 to s8.
・ Input screen providing step s1
This is a step to realize the input screen providing means in the present system, which is a current state consisting of pet's current weight, body condition score (hereinafter referred to as BCS), gender, castration status and age data on the computer input screen. The fact that a data set should be input is displayed on the input screen. You may provide the column which inputs the data of a dog breed as needed.
・ Data input step s2
This is a step of realizing data input means in this system, and data input is performed.
-Current data set storage step s3
This is a step for realizing the current data set storage means in the present system, and stores the input data as a current data set.
・ Lean body weight calculation step s4
This is a step for realizing the lean body mass calculating means in this system, and the lean body weight of the pet is calculated using the stored current data set.
・ Lean body mass storage step s5
This is a step for realizing the lean body mass storage means in the present system, and stores the calculated lean body weight.
・ Salary calorie calculation step s6
This is a step for realizing an appropriate salary calorie calculating means in this system, which calculates an appropriate salary calorie using lean body mass.
・ Appropriate pet food salary calculation step s7
This is a step for realizing an appropriate pet food salary calculating means in the present system, and calculates an appropriate pet food salary based on the calculated appropriate calorie value.
-Output screen providing step s8
This is a step of realizing the output screen providing means in the present system, and the obtained lean body weight of the pet, the appropriate calorie required per day, and the appropriate daily salary of the pet food product are displayed on the monitor.
In realizing each of the above steps, a dedicated arithmetic IC corresponding to each step may be used.

The lean body mass calculating step s4 is further configured by the following steps s4-1 to s4-6 and s4-9 to s4-10, but preferably includes s4-7 to s4-8.
・ Provisional body fat percentage calculation step s4-1
The provisional body fat percentage is obtained by referring to the BCS table from the BCS value stored in the current data set storage means for storing the current data set.
Temporary body fat percentage storage step s4-2
The provisional body fat percentage obtained is stored.
-Body fat percentage correction value calculation step s4-3 based on gender, etc.
Body fat percentage correction value based on gender and castration by referring to gender and castration correction table from gender and castration presence / absence stored in current data set storage means for storing current data set (hereinafter referred to as “sex gender correction value”) Get).
-Body fat percentage correction value storage step s4-4 based on gender, etc.
The obtained gender correction value is stored.
-Body fat percentage correction value calculation step according to age s4-5
A body fat percentage correction value by age (hereinafter referred to as “age correction value”) is obtained by referring to the age correction value storage table from the age stored in the current data set storage means for storing the current data set.
-Body fat percentage correction value storage step according to age s4-6
The obtained age correction value is stored.
-Body fat percentage correction value calculation step s4-7 by dog breed
A body fat percentage correction value (hereinafter referred to as “dog breed correction value”) is obtained by referring to the dog breed correction value table from the dog breed stored in the current data set storage means for storing the current data set.
-Body fat percentage correction value storage step s4-8 by dog breed
The obtained breed correction value is stored.
Body fat percentage correction value calculation step s4-9
The provisional body fat percentage and sex, etc., age, and each body fat percentage correction value by dog breed are calculated.
-Lean body weight calculation step s4-10
The lean body mass is calculated from the obtained corrected body fat percentage and the input current body weight.

Hereinafter, this system will be described in detail based on FIG. 2 and FIG.
FIG. 2 is a flowchart showing the operation of the system, and FIG. 3 is a schematic diagram of a screen displayed on the monitor by the system. As shown in FIG. 2, when the present system is activated, the input screen providing step s1 is first executed. In this step, the input screen shown in FIG. 3 is displayed on the monitor. The input screen includes fields for inputting the current weight, BCS, sex, and age of the pet. Furthermore, as shown in FIG. 3, you may provide the column which inputs a dog breed as needed. Although not shown in the drawing, the presence / absence of castration (in the case of males) and the presence / absence of infertility (in the case of females) are also entered in the sex column.

  In the subsequent data input step s2, when the execution key is selected by inputting BCS, gender, age dog breed, castration / infertility on the input screen, and the current data set storage step s3, the input data is Stored as a data set.

  Subsequently, a lean body mass calculation step s4 is executed, and the lean body weight of the pet is calculated according to the input data. In this step, a BCS table that stores the relationship between BCS and body fat percentage, a gender and other correction table that stores the relationship between gender and castration, and body fat percentage correction value, and the relationship between age and body fat percentage correction value A body fat percentage calculation unit comprising an age correction table to be stored and, optionally, a dog type correction table for storing a relationship between a dog type and a body fat rate correction value, and a body fat obtained by the body fat rate calculation unit It is executed by a lean body mass calculator that obtains lean body mass from the rate. Further, this step is further composed of 10 steps S4-1 to S4-10 as described above, and these steps are sequentially executed. The body fat percentage calculator corresponds to s4-1 to s4-9, and the lean body mass calculator corresponds to s4-10.

First, in provisional body fat percentage calculation step s4-1, provisional body fat percentage is obtained by referring to the BCS table from the BCS values stored in the current data set storage step s3, and in subsequent provisional body fat percentage storage step s4-2. The provisional body fat percentage obtained is stored.
Specifically, the BCS table is a memory area in the computer in which data on the correspondence between the BCS and the provisional body fat percentage is stored, and the data stored in the memory area is a key for the BCS value. The provisional body fat percentage value can be extracted by referring to the data reference instruction from the computer arithmetic unit.

Subsequently, in the body fat percentage correction value calculation step s4-3 by gender, the gender correction value is obtained from the gender correction table according to the gender and castration stored in the current data set storage step s3, and the subsequent gender correction is performed. The gender correction value obtained in the value storage step s4-4 is stored. This step includes not only case classification by male and female but also case classification by presence or absence of castration and infertility.
The gender correction table is a memory area in the computer in which data on the correspondence between gender and castration presence / absence and the corresponding body fat percentage correction value is stored, and is stored in the memory area. The data is referred to in accordance with a data reference command from the computing unit of the computer using gender and castration as keys, and the corresponding body fat percentage correction value can be extracted.

Further, in the fat percentage correction value calculation step s4-5 by age, the age correction value is obtained from the age correction value storage table according to the age stored in the current data set storage step s3, and in the subsequent age correction value storage step s4-6. The obtained age correction value is stored.
Specifically, the age correction value storage table is a memory area in the computer in which data on the correspondence between the age and the corresponding body fat percentage correction value is stored, and the data stored in the memory area is Referring to age as a key, it is referred to in accordance with a data reference command by a computing unit of a computer, and a corresponding body fat percentage correction value can be extracted.

Next, in step s4-7 for calculating body fat percentage correction value by dog breed, the body fat percentage correction value is obtained from the dog breed correction value table in accordance with the dog breed stored in the current data set storage step s3, and the following dog breed correction is performed. The dog type correction value obtained in the value storage step s4-8 is stored.
Specifically, the dog type correction value table is a memory area in the computer in which data on the correspondence between the dog type and the body fat percentage correction value is stored, and the data stored in the memory area Is referred to in accordance with a data reference command by a computing unit of a computer using the breed of dog as a key, and a corresponding body fat percentage correction value can be extracted.

  In this embodiment, the correction steps s4-3 to s4-8 based on gender, age, and breed are performed in the order of s4-3 → s4-4 → s4-5 → s4-6 → s4-7 → s4-8. The three steps (s4-3 → s4-4), (s4-5 → s4-6) and (s4-7 → s4-8) can be executed in any order. is there. Therefore, the execution order of each step described here is merely an example.

  Next, in the body fat percentage correction value calculation step s4-9, the obtained provisional body fat percentage and each body fat percentage correction value according to age, breed, etc. are calculated. In the present embodiment, calculation is performed to add the provisional body fat percentage, the gender correction value, the age correction value, and the dog breed correction value.

  Next, in lean body mass calculating step s4-10, the lean body mass is calculated from the obtained corrected body fat percentage and the input current body weight. The body fat percentage value used for the calculation of lean body mass is not the body fat percentage value itself obtained from BCS, but is corrected based on gender, age, etc. as described above. The body fat percentage value is used.

  Following the execution of step s4, the lean body mass calculated in s4 is stored in the lean body mass storage step s5, and further, the lean body mass stored in s5 is used in the appropriate calorie calculating step s6. The appropriate salary calorie is calculated, and the appropriate pet food salary calculation step s7 calculates the appropriate pet food salary based on the calculated appropriate salary calorie value. Here, pet food products that are subject to food salary can be pre-defined in the system.

  The calculation result obtained by the execution up to step s7 is displayed on the monitor as an output screen as shown in FIG. 8 in the execution stage of the subsequent output screen providing step s8. When the “print” icon is selected, the information on the output screen is printed out by the printer, and a hard copy of the output screen is obtained.

  In the present embodiment, it is preferable to target mammals represented by dogs and cats, and it is particularly preferable to target dogs from the viewpoint of accurate estimation of body fat percentage. Therefore, in the following explanation, dogs are targeted as specific pet species.

  Prior to inputting each data on the screen displayed in step s1, first, the BCS of the target dog is determined. BCS is used to grade the degree of obesity based on the external features derived from animal body shape and palpation. In general, the BCS has five stages of determination criteria of 1, 2, 3, 4, and 5. BCS3 is the standard, and the larger the value, the higher the obesity level.

  The determination site | part of BCS is a rib, a waist | lumbar part, and an abdomen. The veterinarian comprehensively determines the external features of these parts, and the BCS of the dog is determined. The specific determination method in each part is as follows.

The above BCS determination method is merely a guideline, and even if the conditions of all three regions are not satisfied at the same time, the veterinarian comprehensively determines the characteristics of each region and determines the BCS. In the present system, subdivided steps are provided by further providing 0.5 increments between the five stages of the BCS. That is, the degree of obesity is determined using a total of 9 criteria, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0. Use classified BCS. Judgments regarding BCS 1.5, 2.5, 3.5, and 4.5 are made by a veterinarian based on the above BCS criteria.
The BCS also uses a 9-step criterion from 1 to 9. In this case, 1 (9 steps) → 1 (5 steps), 2 (9 steps) → 1.5 (5 steps), 3 (9 steps) → 2 (5 steps),. . . 9 (9 levels) → 5 (5 levels).

  Originally, from the viewpoint of appropriately determining the degree of obesity of a dog, it is more desirable to make an objective evaluation based on the body fat percentage rather than a qualitative index such as BCS. As an apparatus that can easily measure the body fat percentage of a dog, the present applicant has previously proposed a body fat scale for dogs in Japanese Patent Application Laid-Open No. 2007-195931. According to this body fat scale, it is possible to easily and accurately measure the body fat of small animals for pets including dogs without requiring much labor. However, body fat scales are only used in pet clinics and are not yet widely used. Therefore, qualitative judgment such as body weight and BCS is generally used for the evaluation of the degree of obesity. Therefore, a method for accurately estimating the body fat percentage by a method that does not use a body fat scale has been desired.

  As described above, the BCS is determined based on the characteristics of the body when the veterinarian touches the body of the dog, but when the present inventors have further investigated, even in the same BCS, body fat depends on gender and age. It was found that there was a significant difference in rate. Furthermore, castrated females and infertile females tend to be thicker than normal dogs, and there is a tendency for a gap between the touch that the veterinarian touches for BCS determination and the measured body fat percentage. This has also been found as a result of the study by the present inventors. Based on the above knowledge, in the system of this embodiment, the body fat percentage is estimated in consideration of the sex and age of the dog. Therefore, the problem of deviation between the BCS determination and the body fat percentage as described above is solved.

The inventors of the present invention collected actual body fat percentage data for approximately 20,000 dogs for the purpose of improving accuracy in the correspondence between BCS and body fat percentage. As a result, the present inventors have found that there is a relationship shown in FIG. 4 between BCS and body fat percentage. The breakdown of BCS with a total of 20 and 624 heads is as follows.
BCS1: 27, BCS1.5: 49, BCS2: 430, BCS2.5: 1, 563, BCS3: 7,015, BCS3.5: 4, 648, BCS4: 4,031, BCS4 .5: 1,433, BCS5: 870.

Based on the result shown in FIG. 4, the body fat percentage corresponding to each numerical value of BCS can be obtained. Based on the relationship thus obtained, in the present embodiment, a specific correspondence between BCS and body fat percentage is defined as follows. Based on this correspondence, the body fat percentage is obtained from the BCS in step s4-1. That is, the following correspondence data between BCS and body fat percentage is stored in the computer as a BCS table, so that the corresponding body fat percentage correction is performed in accordance with the data reference command using BCS as a key by the computing unit of the computer. The value is extracted.
BCS1 is 16.7%, BCS1.5 is 17.9%, BCS2 is 19.3%, BCS2.5 is 21.8%, BCS3 is 26%, BCS3.5 is 31.6%, and BCS4 is 35. 4%, BCS4.5 38.1%, BCS5 40.0%. Note that the numerical values shown in the correspondence relationship between the BCS and the body fat percentage are merely examples in the present embodiment, and are not necessarily limited to the numerical values in the practice of the present invention.

  The correction of the body fat percentage in step s4-3 is based on the inventors' knowledge that the body fat percentage is different even in the same BCS depending on gender and the presence or absence of castration / infertility. Specifically, the relationship between the measured body fat percentage data and the BCS for four groups of males, females, castrated males, and infertile females is as shown in FIG. As shown in FIG. 5, castrate males and non-castrated males (intact males) tend to have higher body fat percentage in castrated males even in the same BCS. The same applies to females, and even for the same female, infertile females and non-infertile females (intact females) tend to have higher body fat percentage. Furthermore, females tend to have higher body fat percentages between males and females.

  Therefore, in the present embodiment, based on the knowledge shown in FIG. 5, the body fat percentage value is corrected according to gender and the presence or absence of castration / infertility. This correction is performed in step s4-3. The specific details of the correction are as follows. That is, by storing the following data on the correspondence between gender, castration / infertility and the corresponding body fat percentage correction value in the computer as a gender correction table, the gender and castration / infertility by the computing unit of the computer is stored. The corresponding body fat percentage correction value is extracted in response to the data reference command using the presence or absence of the key as a key.

[Correction of body fat percentage by gender]
Male is 2.5% plus, castrated male is 0.9% minus, female is ± 0%, infertile female is 1.3% minus.
In addition, the numerical value shown above is only the illustration in this embodiment to the last, and in implementation of this invention, it is not necessarily restrained by said numerical value.

  The correction of the body fat percentage in step s4-5 is based on the knowledge of the present inventors that the body fat percentage varies with the same BCS depending on the age. Specifically, the relationship between the measured body fat percentage data and the BCS for five groups of 1 year old, 2-3 years old, 4-5 years old, 6-9 years old, 10 years old and over is as shown in FIG. As shown in FIG. 6, there is a significant difference with respect to age as well as sex, and the body fat percentage tends to increase with age in both males and females.

  Therefore, in the present embodiment, the value of the body fat percentage is corrected according to the age based on the knowledge shown in FIG. This correction is performed in step s4-5. Specifically, the data of the correspondence relationship between the age shown in Table 2 below and the body fat percentage correction value defined based on the actual measurement data of the body fat percentage is used as an age correction value storage table. Remember in. As a result, the corresponding body fat percentage correction value can be extracted by the data reference command using the age as a key by the computing unit of the computer.

  In addition, the numerical value shown above is only the illustration in this embodiment to the last, and in implementation of this invention, it is not necessarily restrained by said numerical value.

  The correction of the body fat percentage in step s4-7 is based on the knowledge of the present inventors that the body fat percentage varies even with the same BCS depending on the dog breed. Specifically, for body dogs with BCS 3.0, the body fat percentage for each type of dog is measured using a body fat meter, so that there is a significant difference in the correspondence between BCS and body fat percentage due to differences in dog breeds. I found a difference. The actual measurement results are shown in Table 2. As shown in Table 3, a significant difference was also observed for dog breeds, and even with the same BCS, for example, there was a difference of 10% or more in body fat percentage between Shetland sheepdog and French bulldog. It is done.

Therefore, in the present embodiment, based on the knowledge shown in Table 3, the body fat percentage value is corrected according to the dog type. This correction is performed in step s4-7. Specific contents of the correction are shown in Table 4 below. That is, by storing the data of the correspondence relationship between the breed shown in Table 4 below and the body fat percentage correction value thereby as a dog breed correction value table in the computer, the data using the dog breed as a key by the computing unit of the computer A corresponding body fat percentage correction value is extracted according to the reference command.
In addition, all the correction | amendment numerical values in Table 4 were calculated based on the result of the experimental data (refer Table 3) by measurement.

  In addition, the numerical value shown above is only the illustration in this embodiment to the last, and in implementation of this invention, it is not necessarily restrained by said numerical value.

In step s4-10, the lean body mass is calculated by the following calculation formula (1) based on the body fat percentage and the current weight obtained in step s4-9.
Lean body mass (kg) = (current body weight (kg)) × (100−body fat percentage (%)) / 100 (1)
Here, the lean body mass is the body weight of the dog, excluding the weight of fat, out of the total body weight. Since the fat portion does not consume energy, the energy required by the dog should be determined based on lean body mass. Since this system calculates lean body weight, it is possible to calculate more accurate fitness calories and fitness pet food consumption. The formula (1) is determined based on “Clinical Nutrition 4th Edition of Small Animals” (Gakushinsha).
The present system may include an appropriate weight calculating means and means for outputting the appropriate weight to the output device.
The suitable weight calculating means includes, for example, a means for obtaining an appropriate weight based on lean body mass using the formula (3).
Suitable body weight (kg) = lean body weight (kg) / ((100-F (%)) / 100) (3)
Here, F (%) is an appropriate body fat percentage and can be appropriately set according to the animal species, sex, age, etc. In the case of a dog, for example, 18 to 27% is preferably used.
By being able to provide such an appropriate weight, it is possible to know the weight that should be targeted, and to provide convenience for the owner.

In step s6, an appropriate feeding calorie is calculated from the lean body weight obtained in step s4-10 by the following calculation formula (2).
Proper calorie calories (kcal) = A x (lean body weight (kg)) ^0.75 (2)
In the formula, A is a value selected from the range of 80 to 130 as a calorie coefficient.

  The above formula (2) is determined based on “Clinical Nutrition 4th Edition of Small Animals” (Gakushinsha). The reason why the calorie coefficient is given a range is that the absorption efficiency varies depending on the constituents of the pet food, and the calorie coefficient varies depending on the breeding situation. In the case of feeding a normal meal in a Japanese indoor dog, the calorie coefficient is preferably a value selected from the range of 80 to 110, more preferably 100. When specially treated food is fed to a domestic dog in Japan, the calorie coefficient is preferably a value selected from the range of 90 to 120, more preferably 110.

  In the subsequent step s7, the appropriate salary calorie obtained in s6 is converted into the amount of pet food, and the appropriate amount of pet food per day is calculated. Further, in step s8, the calculation result obtained in the execution up to step s7 is output to the output screen. An example of the output screen displayed on the monitor is shown in FIG.

  Next, a second embodiment of the present invention will be described with reference to FIG. Regarding the second embodiment, the description in detail regarding the first embodiment is applied as appropriate to points that are not particularly described.

  Whereas the first embodiment described above is a computer system composed of one computer and various devices connected thereto, this embodiment is a user terminal device and a server computer for management. The present invention relates to a method for preventing obesity of pets that are operated through network communication. The method according to the present embodiment is realized by executing a server computer system in which a program is installed. This program displays the input screen on the monitor of the terminal device, receives each data input on the input screen by operating the keyboard, etc., performs calculations based on the received data, and displays the result as an output screen on the monitor Is displayed. As shown in FIG. 9, a system for realizing the method according to the present embodiment includes a server computer, a printer, a storage device storing attribute data such as calories of pet food products to be displayed as an output screen, and a network. And an input / output screen display terminal device having input means such as a monitor, a keyboard and a mouse. Also in this embodiment, the body fat percentage of a dog can be estimated with high accuracy as in the above-described embodiment.

  Hereinafter, the present invention will be described in more detail with reference to examples.

Example 1
One veterinarian was asked to determine the BCS of 100 dogs. The determination result and the data of the dog's sex, age, breed, presence / absence of castration / infertility were input to the system shown in FIGS. 1 to 3 to determine the body fat percentage of each dog. In the system shown in FIGS. 1 to 3, all corrections for gender, age, dog type, castration / infertility were performed. Separately, body fat percentage was measured using a body fat scale. FIG. 9A shows the relationship between the body fat percentage obtained by the system shown in FIGS. 1 to 3 and the body fat percentage measured using a body fat scale. The body fat scale used for this measurement is “Health Lab Dog Body Fat Scale” manufactured by Kao Corporation.

  A specific measuring method using the body fat scale is as follows. The measurement site is 20 mm laterally from the center of the dog's spine. First, the measurement part is wiped with cotton soaked with ethanol solution for disinfection 6 to 7 times to remove sebum on the skin. Then use a comb to scrape the hair and expose the skin. Place the body fat meter electrode on the exposed area and press the measurement switch. Read the body fat percentage displayed on the display.

Comparative Example 1
In Example 1, the relationship between the BCS determined by the veterinarian and the body fat percentage measured by a body fat scale was determined. The result is shown in FIG.

  As is clear from the results shown in FIG. 9A, the body fat percentage obtained using the system shown in FIGS. 1 to 3 is extremely high with the determination result of the body fat percentage measured using the body fat scale. It can be seen that there is a correlation. On the other hand, as is clear from the results shown in FIG. 9B, a certain degree of correlation is observed between the BCS and the body fat percentage measured by the body fat scale, but the accuracy is shown in FIG. -It turns out that it is lower than the body fat rate calculated | required using the system shown in FIG.

Example 2
The appropriate salary calories were calculated using the system shown in FIGS. 1 to 3 for the following dogs. Based on the results, the amount of food to be fed was controlled for 8 weeks, and the weight change before and after the test was measured.
Dog breed: Miniature dachshund Gender: Female (intact)
Age: 2 years BCS: 4
Weight: 6.70kg

The system shown in FIGS. 1 to 3 was used to calculate the appropriate calorie calorie using BCS, age, gender, castration / infertility, and body fat percentage corrected from dog breed. The salary food was a health lab special therapy meal, and the salary coefficient was 110.
Provisional body fat percentage: 35.4%
Gender correction: 0%
Age correction: -0.3%
Dog breed correction: -1.1%
Body fat percentage: 34.0%
Lean body mass: 4.4kg
Appropriate salary calories: 335 kcal

Based on this calorie supply calorie, the contents of the salary food during the test period were as follows, the amount of salary food was controlled for 12 weeks, and the change in body weight before and after the test was measured. The results are shown in Table 5 and FIG.
Dry food (3.35 kcal / g): 275 kcal (82 g)
Snack (3.1 kcal / g): 60 kcal (19 g)
Total: 335 kcal

  The change in body weight during the test period was -9.0%. BCS and body fat percentage could be reduced without greatly reducing lean body mass, and the weight was successfully reduced. That is, it can be seen that this system is useful for weight management for preventing obesity in dogs.

Example 3
The appropriate salary calories were calculated using the system shown in FIGS. 1 to 3 for the following dogs. Based on the results, the amount of food to be fed was controlled for 8 weeks, and the weight change before and after the test was measured.
Dog breed: Jack Russell Terrier Gender: Male (castration)
Age: 3 years BCS: 4
Weight: 8.60kg

The system shown in FIGS. 1 to 3 was used to calculate the appropriate calorie calorie using BCS, age, gender, castration / infertility, and body fat percentage corrected from dog breed. The salary food was a health lab special therapy meal, and the salary coefficient was 110.
Provisional body fat percentage: 35.4%
Gender correction: -0.9%
Age correction: -0.3%
Dog breed correction: -4.9%
Body fat percentage: 29.3%
Lean body mass: 6.1kg
Appropriate salary calories: 426 kcal

Based on this calorie supply calorie, the contents of the salary food during the test period were as follows, the amount of salary food was controlled for 12 weeks, and the change in body weight before and after the test was measured. The results are shown in Table 6 and FIG.
Dry food (3.35 kcal / g): 346 kcal (103 g)
Snack (3.1 kcal / g): 80 kcal (26 g)
Total: 426 kcal

  The change in body weight during the test period was -5.8%. BCS and body fat percentage could be reduced without greatly reducing lean body mass, and the weight was successfully reduced. That is, it can be seen that this system is useful for weight management for preventing obesity in dogs.

  As mentioned above, although this invention was demonstrated about the preferable embodiment, this invention is not restrict | limited to embodiment mentioned above. For example, the embodiment described above is intended for dogs, but the present invention can be similarly applied to pets other than dogs, for example, small mammals such as cats.

It is the schematic which shows the function of the obesity prevention assistance system of the pet in the 1st Embodiment of this invention. It is a flowchart which shows the process performed in the obesity prevention support system of the pet shown in FIG. It is a schematic diagram which shows the input screen displayed on a monitor in the obesity prevention support system of the pet shown in FIG. It is a graph which shows a response | compatibility with the measured value of the body fat rate of the dog by a body fat meter, and each BCS determination value. It is a graph which shows a response | compatibility with the measured value of the body fat rate of each dog of a male, a female, a castrated male, and an infertile female, and each BCS determination value. It is a graph which shows the correspondence of the measured value of the body fat rate of the dog by the difference in age, and each BCS determination value. It is a schematic diagram which shows the output screen displayed on a monitor in the obesity prevention support system of the pet shown in FIG. It is the schematic which shows the function of the obesity prevention assistance system of the pet in the 2nd Embodiment of this invention. (A) is the graph which showed the correspondence of the body fat rate converted by the BCS correction method used in the pet obesity prevention support system shown in FIG. 1, and the body fat rate by a body fat meter, (b) These are the graphs which showed the correspondence of BCS (no correction | amendment) and the body fat rate by a body fat meter. 5 is a graph showing the results of dog weight change in Example 2. FIG. 6 is a graph showing the results of dog weight change in Example 3. FIG.

Claims (10)

A pet obesity prevention support system that calculates a lean body weight of a pet using a computer,
Display on the input screen that the current data set including pet's current weight, body condition score (hereinafter referred to as BCS), gender, castration status, and age data should be input on the computer input screen. Means,
Data input means for inputting the current data set;
Current data set storage means for storing the input current data set;
Using the stored current data set, a lean body mass calculating means for calculating a lean body weight of the pet;
Lean body mass storage means for storing the lean body mass calculated by the lean body mass calculating means;
A pet obesity prevention support system comprising: means for outputting the stored lean body mass to an output device. The lean body mass calculating means includes a BCS table that stores the relationship between BCS and body fat percentage, a gender correction table that stores the relationship between gender and castration, and body fat percentage correction value, and a correction value corresponding to age A body fat percentage calculating unit that stores an age correction value storage table for storing, and a lean body mass calculating unit that obtains lean body mass from the body fat percentage obtained by the body fat percentage calculating unit,
The body fat percentage calculation unit includes a means for obtaining a provisional body fat percentage by referring to the BCS table from the BCS values stored in the current data set storage means, and a provisional body fat percentage storage for storing the provisional body fat percentage. Means, gender correction value storage means for storing a gender correction value obtained from the gender correction table according to the gender and castration stored in the current data set storage means, and stored in the current data set storage means An age correction value storage means for storing an age correction value obtained from the age correction value storage table according to a given age, and a provisional body fat rate and a sex correction value stored in the provisional body fat ratio storage means Calculating means for calculating the sex correction value stored in the means for calculating and the age correction value stored in the age correction value storage means, and further storing the body fat percentage obtained by the calculating means. And a body fat ratio storage means for,
2. The lean body mass calculation unit calculates a lean body mass from the current body weight stored in the current data set storage means and the body fat percentage stored in the body fat percentage storage means according to equation (1). The pet obesity prevention support system described.
Lean body mass (kg) = (current body weight (kg)) × (100−body fat percentage (%)) / 100 (1) In the means for displaying on the input screen that the current data set of the pet should be input, it is further displayed that the dog breed data should be input,
The data input means for inputting the current data set includes data input means for inputting the dog breed,
The body fat percentage calculation unit comprises a dog breed correction value table for storing the relationship between the dog breed and the body fat percentage correction value,
A dog type correction value storage unit for storing a dog type correction value obtained from a dog type correction value table according to the dog type stored in the current data set storage unit;
The calculating means includes a provisional body fat percentage stored in the provisional body fat percentage storage means, a gender correction value stored in the means for storing the gender correction value, and an age stored in the age correction value storage means. The pet obesity prevention support system according to claim 2, wherein the correction value and the dog type correction value stored in the dog type correction value storage means are calculated. Furthermore, it has a suitable salary calorie calculation means,
The pet obesity prevention support system according to any one of claims 1 to 3, wherein the appropriate calorie calorie calculating means calculates the appropriate calorie calorie according to the formula (2) using the lean body mass.
Calories (kcal) = A × (lean body mass (kg)) ^0.75
In the formula, A is a value selected from the range of 80 to 130 as a calorie coefficient. (2)   The pet food salary amount calculating means further comprises an appropriate pet food salary amount calculating means for calculating the appropriate pet food salary amount based on the value of the appropriate salary calorie calculated by the aptitude salary calorie calculating means. The pet obesity prevention support system according to any one of 1 to 4. A pet obesity prevention support method for calculating a lean body weight of a pet via network communication between a user terminal device and a management server computer,
Information processing in the server should input a current data set including pet's current weight, body condition score (hereinafter referred to as BCS), gender, castration status, and age data on the input screen of the terminal device. Displaying the effect on the input screen;
Receiving the current data set input in the terminal device from the terminal device via network communication;
A current data set storage step for storing the received current data set;
Using the stored current data set, the lean body mass calculating step for calculating the lean body weight of the pet;
A lean body mass storage step for storing the lean body mass calculated in the lean body mass calculating step;
Distributing the stored lean body mass to a terminal device. The lean body mass calculating step includes a BCS table storing a relationship between BCS and body fat percentage, a gender correction table storing a relationship between gender and castration presence / absence, and a body fat percentage correction value, and a correction value corresponding to age. A body fat percentage calculating unit that stores an age correction value storage table for storing, and a lean body mass calculating unit that obtains lean body mass from the body fat percentage obtained by the body fat percentage calculating unit,
The body fat percentage calculation unit obtains a provisional body fat percentage by referring to the BCS table from the BCS values stored in the current data set storage means, and a provisional body fat percentage storage for storing the provisional body fat percentage A gender correction value storing step for storing a gender correction value obtained from a gender correction table in accordance with gender and castration stored in the current data set storage means, and storing in the current data set storage means An age correction value storage step for storing an age correction value obtained from the age correction value storage table in accordance with the determined age, and a provisional body fat rate stored in the provisional body fat rate storage means and a correction value for gender, etc. A calculating step for calculating the sex correction value stored in the means to be corrected and the age correction value stored in the age correction value storage means. And a body fat rate storage step of storing the obtained body fat percentage,
The body fat calculation step includes at least a provisional body fat rate calculation step for obtaining a provisional body fat rate from a BCS, a gender correction value calculation step for obtaining a gender correction value from the presence or absence of gender and castration, and an age correction value from age Calculating an age correction value, and calculating a provisional body fat percentage, a gender correction value, and an age correction value to obtain a body fat percentage,
The lean body mass calculating step calculates the lean body mass from the body fat rate obtained by the body fat rate calculating step and the current body weight of the current data set according to the formula (1). Pet obesity prevention support method.
Lean body mass (kg) = (current body weight (kg)) × (100−body fat percentage (%)) / 100 (1) In the step of displaying on the input screen that the current data set of pets should be input, it is further displayed that dog type data should be input,
The received breed data is included in the received current data set, and the body fat percentage calculation step further includes a dog breed correction value calculation step for calculating a relationship between the dog breed and the body fat percentage correction value, and The pet obesity prevention support method according to claim 6 or 7, wherein the provisional body fat percentage, gender correction value, age correction value, and dog breed correction value are calculated to obtain the body fat percentage. In addition, there is an appropriate salary calorie calculation step,
The pet obesity prevention support method according to any one of claims 6 to 8, wherein the proper calorie calorie calculating step calculates the proper calorie calorie according to the formula (2) using the lean body mass.
Calories (kcal) = A × (lean body mass (kg)) ^0.75
In the formula, A is a value selected from the range of 80 to 130 as a calorie coefficient. (2)   The pet food salary amount calculating step further includes an appropriate pet food salary amount calculating step, wherein the appropriate pet food salary amount calculating step calculates the appropriate pet food salary amount based on the value of the appropriate salary calorie calculated in the appropriate salary calorie calculating step. The pet obesity prevention support method according to any one of 6 to 9.

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