CN112349388B - Data processing method and device for blood sugar, storage medium and electronic equipment - Google Patents
Data processing method and device for blood sugar, storage medium and electronic equipment Download PDFInfo
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- CN112349388B CN112349388B CN202011400046.4A CN202011400046A CN112349388B CN 112349388 B CN112349388 B CN 112349388B CN 202011400046 A CN202011400046 A CN 202011400046A CN 112349388 B CN112349388 B CN 112349388B
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The embodiment of the disclosure provides a data processing method, a device, a storage medium and electronic equipment for blood sugar, wherein the method comprises the steps of acquiring at least one blood sugar related parameter within a preset time based on a preset diet proportion; comparing the blood glucose related parameter with a corresponding reference value; based on the comparison, a recommended diet ratio is determined. According to the embodiment of the disclosure, the glucose metabolism characteristics of the subject can be determined or estimated according to the blood glucose data of the subject after the diet with the preset proportion is completed, so that a personalized nutrition strategy can be provided for the subject, the blood glucose related parameters are prevented from being increased, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of medical data processing, in particular to a data processing method, device, storage medium and electronic equipment for blood sugar.
Background
The effects of different types and amounts of macronutrients and foods on human health have been controversial. In addition to other explanations of inconsistencies between studies (e.g., study design, sample size, etc.), an important argument is that there are large individual differences in human responses to dietary factors. Several studies have been conducted in the prior art with a milestone significance to capture or PREDICT human personalized responses to diet, including, for example, israel personalized nutritional cohort studies and the latest pre 1 study, which challenge "one-shot" diet advice.
Most people are mainly in a postprandial state during non-sleep periods, while postprandial hyperglycemia is associated with a high risk of heart metabolic disease. Postprandial blood glucose is thus a good indicator for personalized response studies on the same meal. Although some progress has been made in describing the pattern of an individual's response to the same or different foods, it is currently unclear whether and how to apply it clinically, due to the lack of high level evidence (e.g., evidence from the clinic).
The number of studies in the prior art focused on personalized or precise nutrition is limited and no widely used cost-effective method for individual situations has been established. To date, personalized nutritional strategies either require a large algorithmic basis or lack an effective assessment method at the individual level.
Disclosure of Invention
In order to solve the above-mentioned problems, an object of an embodiment of the present disclosure is to provide a data processing method, apparatus, storage medium and electronic device for blood glucose, so as to solve the above-mentioned problems in the prior art.
In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:
the present disclosure provides a data processing method for blood glucose, comprising the steps of:
Acquiring at least one blood glucose related parameter within a predetermined time based on a predetermined dietary ratio; comparing the blood glucose related parameter with a corresponding reference value; based on the comparison, a recommended diet ratio is determined.
In some embodiments, obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined dietary ratio comprises: a plurality of predetermined dietary fractions are determined, and at least one blood glucose related parameter of the user for each of the predetermined dietary fractions is acquired over a predetermined time.
In some embodiments, the predetermined diet ratios include at least a high fat-low water ratio and a high water-low fat ratio.
In some embodiments, comparing the blood glucose-related parameter to a corresponding reference value comprises: comparing the blood sugar related parameter with a corresponding first blood sugar reference value to obtain a first proportional value based on the first blood sugar reference value; comparing the blood glucose related parameter with a corresponding second blood glucose reference value to obtain a second proportion value based on the second blood glucose reference value; the first and second scale values are compared with a predetermined scale reference value.
In some embodiments, the determining the recommended dietary ratio based on the comparison results comprises: determining a eating trend feature when the first ratio value or the second ratio value based on at least one of the blood glucose-related parameters is greater than a corresponding predetermined ratio reference value; based on the dietary trend characteristics, a recommended dietary ratio is determined.
In some embodiments, the blood glucose-related parameter comprises at least one of a postprandial maximum blood glucose value, an average amplitude of blood glucose value fluctuations, and a total area under a glucose meter measurement curve.
In some embodiments, the dietary ratio comprises at least the following ingredients: fat, carbohydrate, protein.
The present disclosure also provides a data processing apparatus for blood glucose, comprising: an acquisition module for acquiring at least one blood glucose related parameter over a predetermined time based on a predetermined meal ratio; a comparison module for comparing the blood glucose related parameter with a corresponding reference value; and a determination module for determining a recommended diet ratio based on the comparison result.
The present disclosure also provides a storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of any of the methods described above.
The present disclosure also provides an electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program on the memory, implements the steps of any of the methods described above.
The beneficial effects of the embodiment of the disclosure are that: according to the embodiment of the disclosure, the glucose metabolism characteristics of the subject can be determined or estimated according to the blood glucose data of the subject after the diet with the preset proportion is completed, so that a personalized nutrition strategy can be provided for the subject, the blood glucose related parameters are prevented from being increased, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of diet ratios in a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a data processing apparatus for blood glucose according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various aspects and features of the disclosure are described herein with reference to the drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this disclosure will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person skilled in the art will certainly be able to achieve many other equivalent forms of the present disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the disclosure in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
Embodiments of the present disclosure provide a data processing method for blood glucose, for performing a dietary intervention with respect to a subject, so as to acquire and process blood glucose data of the subject subjected to the dietary intervention, so as to recommend a corresponding dietary ratio to the subject based on the blood glucose data, as shown in fig. 1, including the steps of:
S101, acquiring at least one blood glucose related parameter in a preset time based on a preset diet proportion.
In this step, at least one blood glucose-related parameter is obtained for a predetermined time based on a predetermined diet ratio; specifically, during a dietary intervention of a subject, the subject is required to consume only provided food or beverage having a predetermined dietary ratio for a predetermined time, and a biological sample of the subject is collected and personal data is acquired by continuously wearing a blood glucose monitor (CGMS) to acquire at least one blood glucose-related parameter based on the predetermined dietary ratio for the subject based on the biological sample. Note that, in this step, the predetermined time here may be set to, for example, 24 days; furthermore, in order to obtain a true and accurate blood glucose-related parameter, the subject is required not to change his lifestyle or physical exercise program for the whole predetermined time, such that the factors affecting the blood glucose-related parameter of the subject are limited to the diet ratio.
Further, in the step S101, the step of obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined diet ratio, as shown in fig. 2, includes the steps of:
S201, determining a plurality of predetermined diets.
In this step, it is necessary to first determine a plurality of predetermined dietary ratios, where the predetermined dietary ratios represent the distribution of different substances in the diet; this pre-determination of the diet of the subject directly determines the manner in which the subject is to be subjected to the diet intervention, where the diet intervention is to have the subject consume different diets of equal calories at different diets for a predetermined period of time (e.g., 2300 kcal per day for men and 1900 kcal per day for women). In a specific embodiment, the predetermined diet ratios include at least a high fat-low water ratio and a high water-low fat ratio.
In particular, the diet ratio of the isocaloric diet herein may be a high fat-low carbohydrate (HF-LC) ratio and a low fat-high carbohydrate (LF-HC) ratio, wherein the distribution of the major components of the HF-LC ratio diet is fat based, the specific composition may be that the carbohydrates provide 25% of the energy, the proteins provide 15% of the energy and the fats provide 60% of the energy, of course the diet ratio herein may be adjusted during the diet intervention, for example 15% of the energy for the carbohydrates, 15% of the energy and 70% of the energy for the proteins; accordingly, the distribution of the main components in the diet of the LF-HC ratio is based on carbohydrates, and may specifically be that the carbohydrates provide 65% of the energy, the proteins provide 15% of the energy and the fat provides 20% of the energy, although the ratio of the diet may be adjusted here, for example, to 75% of the energy, the proteins provide 15% of the energy and the fat provides 10% of the energy.
During the subject's dietary intervention, the subject completes a crossover intervention of at least two diets of different proportions, sometimes requiring a setup purge period (e.g., 1-7 days) between diets of different proportions to reduce the residual effects of the food of the previous stage.
In a specific embodiment, 28 healthy young people were recruited as subjects to compare the effect of the diet of the LF-HC and HF-LC ratios on postprandial blood glucose levels of the subjects, with 3 sets of dietary intervention activities being completed consecutively during the dietary intervention, and each subject being provided with an isocaloric diet throughout the course of the dietary intervention. The arrangement of the 3 groups of dietary intervention activities here is shown in fig. 3, each group comprising 4 times of 6 days, i.e. a total of 24 days. During each set of dietary interventions, each subject completed the dietary interventions for two crossover experimental conditions (i.e., LF-HC ratio and HF-LC ratio). A clearance period (6 days) may be provided during the diet intervention, this clearance being provided between the consumption of different proportions of diet by the subject, to eliminate the food-residue effect of the previous stage. It should be noted that, the intervention sequence of HF-LC and LF-HC ratio diets is formed according to a computer-generated randomization schedule in 3 intervention actions, the intervention sequence may be exemplified by the following: LF-HC, HF-LC, HF-LC, LF-HC, HF-LC and LF-HC.
Specifically, the HF-LC ratio diet included two groups of diets each for 3 days, wherein the first 3 days diet had a total energy intake of fat (E) of 70%, a total energy intake of protein of 15% and a total energy intake of carbohydrates of 15%, and the other 3 days diet included a total energy intake of fat of 60%, a total energy intake of protein of 15%, and a total energy intake of carbohydrates of 25%; the LF-HC dosing diet included two groups of 3-day diets, with the first 3-day diet having 20% energy intake from fat, 15% energy intake from protein and 65% energy intake from carbohydrate, and the other 3-day diet having 10% energy intake from fat, 15% energy intake from protein and 75% energy intake from carbohydrate. In addition, the 6 day purge diet includes 30% energy intake from fat, 15% energy intake from protein and 55% energy intake from carbohydrate.
S202, acquiring at least one blood glucose related parameter of a user for each preset diet ratio in a preset time.
In determining a plurality of predetermined diet ratios by the above step S201, it is necessary to analyze continuous blood glucose data after the subject has consumed an intervention diet having a different ratio when the subject receives a diet dry. The blood glucose data here corresponds to its suitability for the predetermined diet ratio, and is represented by blood glucose-related parameters, which are used herein to describe the condition of the subject's Postprandial Blood Glucose (PBG), and include at least postprandial maximum blood glucose level (MPG), mean amplitude of fluctuation of blood glucose level (MAGE), total area under blood glucose measurement curve (AUC 24).
Specifically, in order to evaluate the adaptability of the subject to different diets, the interstitial glucose level of the subject needs to be measured every 15 minutes by a blood glucose monitor or other device, a blood glucose measurement curve is obtained, blood glucose related parameters are obtained based on the blood glucose measurement curve and the like, especially blood glucose related parameters after the subject consumes all diets with different proportions, wherein the blood glucose related parameters can be a maximum postprandial blood glucose value (MPG), a mean amplitude of fluctuation of blood glucose value (MAGE) and a total area under the blood glucose measurement curve (AUC 24), wherein the maximum blood glucose value (MPG) is a peak value measured by the blood glucose monitor within 3 hours after each meal, or a maximum value measured by the blood glucose monitor between two meals with an interval of less than 3 hours; the mean amplitude of blood glucose level fluctuation (MAGE) is obtained by measuring the arithmetic mean of the differences between successive peaks and valleys; the total area under the blood glucose measurement curve (AUC 24) refers to the total area under the blood glucose monitor measurement curve for the day 0:00 to 24:00.
S102, comparing the blood glucose related parameter with a corresponding reference value.
After obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined diet ratio in step S101, in this step, the obtained blood glucose related parameter needs to be compared with a corresponding reference value, so as to obtain adaptability of a subject to different diet ratios and determine tendency of the subject to the diet ratio under the condition of ensuring that the blood glucose condition is maintained within a relatively normal range, as shown in fig. 4, specifically including the following steps:
s301, comparing the blood glucose related parameter with a corresponding first blood glucose reference value to obtain a first proportional value based on the first blood glucose reference value;
s302, comparing the blood sugar related parameter with a corresponding second blood sugar reference value to obtain a second proportion value based on the second blood sugar reference value;
s303, comparing the first proportion value and the second proportion value with a preset proportion reference value.
In this step, the effect of diets such as HF-LC and LF-HC ratios on Postprandial Blood Glucose (PBG) levels in each subject is further investigated by obtaining a comparison between the blood glucose-related parameters of the subject and corresponding reference values. This step uses mainly a bayesian analysis model to calculate the posterior probability of the mean differences and clinically significant differences in the postprandial maximum blood glucose level (MPG), mean amplitude of blood glucose fluctuations (MAGE) and total area under the blood glucose measurement curve (AUC 24) caused by the different dietary patterns of the subject individual.
The reference value herein is determined in such a manner that the difference between the average levels of the maximum postprandial blood glucose values of healthy persons aged 25-45 and 45 years old or older, i.e. + -. 3mg/dL (. + -. 0.167 mmol/L) is the reference value for the maximum blood glucose value; the difference between the average levels of mean amplitude of fluctuation in blood glucose levels of healthy persons 25-45 and over the age of 45, i.e. + -. 1.3mg/dL (. + -. 0.072 mmol/L) was taken as the mean value for the blood glucoseA reference value of the average amplitude of the value fluctuation; the difference between the average area under the blood glucose measurement curve of healthy people and pre-diabetic people is + -1.5X10 as far as the total area under the blood glucose measurement curve is concerned 4 mg/dL.min (+ -13.889 mmol/L.h) is a reference value for the total area under the blood glucose measurement curve.
After determining the corresponding blood glucose reference value for different blood glucose related parameters, further, comparing each blood glucose related parameter with the corresponding blood glucose reference value, specifically comparing the blood glucose related parameter with the corresponding first blood glucose reference value to obtain a first ratio value based on the first blood glucose reference value, and simultaneously comparing the blood glucose related parameter with the corresponding second blood glucose reference value to obtain a second ratio value based on the second blood glucose reference value; the two ratio values are posterior probabilities characterizing that the subject exceeds two blood glucose reference values, and further, the first ratio value and the second ratio value are compared with a predetermined ratio reference value so as to judge the tendency of the subject to different ratio diets based on the comparison result. For example, as shown in table 1, taking any subject as an example, comparing its maximum blood glucose value difference with 0.167mmol/L as the first blood glucose reference value and 0.167mmol/L as the second blood glucose reference value respectively to obtain a first ratio value of 4.5% greater than the first blood glucose reference value and a second ratio value of 35.43% less than the second blood glucose reference value, and/or comparing its average blood glucose value fluctuation amplitude with 0.072mmol/L as the first blood glucose reference value and 0.072mmol/L as the second blood glucose reference value respectively to obtain a first ratio value of 4.37% greater than the first blood glucose reference value and a second ratio value of 83.17% less than the second blood glucose reference value, the total area under its blood glucose measurement curve may of course be compared with 13.889mmol/l·h as the first blood glucose reference value and 13.889mmol·h as the second blood glucose reference value respectively to obtain the first ratio value and the second ratio value.
Finally, the first and second ratio values for each of the blood glucose-related parameters are compared with a predetermined ratio reference value. The predetermined ratio reference value here may be 80%.
TABLE 1
The comparing the blood glucose related parameter with the corresponding reference value in the step S102 includes the following steps:
s103, determining a recommended diet proportion based on the comparison result.
After comparing the blood glucose related parameter with the corresponding reference value through the above-described step S102, in the present step, a recommended diet ratio is determined to the subject based on the comparison result such that the postprandial blood glucose level of the subject is within a relatively normal range. For example, if the subject's comparison results tend to be higher in blood glucose levels after they have consumed a high fat-low carbohydrate (HF-LC) proportioned diet, the subject is recommended a low fat-high carbohydrate (LF-HC) proportioned diet; conversely, if the subject's comparison results tend to have a higher blood glucose level after they consume the low fat-high carbohydrate (LF-HC) diet, the subject is recommended a low fat-low carbohydrate (HF-LC) diet.
In the present embodiment, it is necessary to obtain a difference in the effect of the intervention of the subject between the diets of the LF-HC ratio and the HF-LC ratio to obtain the posterior probability, and if the blood glucose condition of the subject after the intervention satisfies a predetermined condition, it will be defined as a high-fat responder or a high-carbohydrate responder, and a subject who does not satisfy the predetermined condition will be considered as a non-responder. The subject's dietary tendency profile tag (e.g., high carbohydrate responders, high fat responders or non-responders) is a determinant of his nutritional recommendation. For high carbohydrate responders, the HF-LC ratio diet is a better choice for controlling blood glucose and vice versa. For non-responders, there was no difference in blood glucose-related parameters between HF-LC and LF-HC diets.
In the determining the recommended diet ratio based on the comparison result in the above step S103, as shown in fig. 5, the method includes the steps of:
s401, determining a eating trend feature when the first ratio value or the second ratio value based on at least one of the blood glucose related parameters is greater than a corresponding predetermined ratio reference value.
In this step, in case that said first or second ratio value based on at least one of said blood glucose related parameters is larger than the corresponding predetermined ratio reference value, a dietary tendency characteristic of the subject is determined based on the first or second ratio value being larger than the corresponding predetermined ratio reference value, where the dietary tendency characteristic is expressed in the form of a responder to a substance. For example, taking 28 subjects in table 1 as an example, for example, of all the ratio values of subject No. 1 for the different blood glucose-related parameters, the second ratio value 83.17% for the average amplitude of fluctuation of blood glucose values is less than 80% as a predetermined ratio reference value, then subject No. 1 is determined to be a high-fat responder, and of all the ratio values of subject No. 5 for the different blood glucose-related parameters, the first ratio value 93.97% for the average amplitude of fluctuation of blood glucose values is greater than plus 0.072mmol/L is greater than 80% as a predetermined ratio reference value, then subject No. 5 is determined to be a high-carbohydrate responder.
All subjects were traversed, and for the blood glucose-related parameters of postprandial maximum blood glucose level, there were 9 subjects defined as respondents, no. 7, no. 8, no. 11, no. 13, no. 17, no. 19, no. 22, no. 23 and No. 27, respectively, and these subjects were considered high-fat respondents, and were high-carbohydrate respondents, based on the comparison between the first and second ratio values for maximum blood glucose level and the predetermined ratio reference value, with the second ratio value of No. 8, no. 11, no. 13 being less than-0.167 mmol/L being greater than 80%, with the first ratio value of No. 7, no. 17, no. 19, no. 22, no. 23 and No. 27 being greater than +.0.167 mmol/L being greater than 80%. Similarly, for the blood glucose-related parameters of the average amplitude of the fluctuation of the blood glucose level, no. 1, no. 4, no. 8 were determined as high-fat responders, and No. 5, no. 19, no. 22, no. 25, and No. 27 were determined as high-carbohydrate responders. It is clear from this that for subject No. 8, both blood glucose-related parameters were considered to be high-fat responders, and for test takers No. 19, no. 22 and No. 27, both blood glucose-related parameters were considered to be high-carbohydrate responders.
In general, when the difference in the ratio of carbohydrate to fat in the diet increases in a high-carbohydrate responder, the ratio value of the high-carbohydrate responder to the blood glucose-related parameter tends to increase, whereas the ratio value of the high-carbohydrate responder to the blood glucose-related parameter tends to decrease.
S402, determining a recommended diet proportion based on the diet tendency characteristics.
In the above step, after determining the eating trend feature, the recommended eating proportion may be determined based on the eating trend feature, in a case where the first proportion value or the second proportion value based on at least one of the blood glucose-related parameters is greater than a corresponding predetermined proportion reference value. Specifically, for example, the LF-HC diet is recommended to a high-fat responder and the HF-LC diet is recommended to a high-carbohydrate responder to avoid an increase in blood glucose-related parameters, which is a precise nutritional suggestion for preventing metabolic diseases such as diabetes.
A second embodiment of the present disclosure provides a data processing apparatus for blood glucose, for performing a dietary intervention with respect to a subject, so as to acquire and process blood glucose data of the subject subjected to the dietary intervention, so as to recommend a corresponding diet ratio to the subject based on the blood glucose data, as shown in fig. 6, which includes an acquisition module 10, a comparison module 20, and a determination module 30, and specifically, the modules are coupled to each other:
An acquisition module 10 for acquiring at least one blood glucose related parameter over a predetermined time based on a predetermined meal size. Acquiring, by the acquisition module 10, at least one blood glucose related parameter over a predetermined time based on a predetermined meal ratio; specifically, during a dietary intervention of a subject, the subject is required to consume only provided food or beverage having a predetermined dietary ratio for a predetermined time, and a biological sample of the subject is collected and personal data is acquired by continuously wearing a blood glucose monitor (CGMS) to acquire at least one blood glucose-related parameter based on the predetermined dietary ratio for the subject based on the biological sample. Note that the predetermined time here may be set to, for example, 24 days; furthermore, in order to obtain a true and accurate blood glucose-related parameter, the subject is required not to change his lifestyle or physical exercise program for the whole predetermined time, such that the factors affecting the blood glucose-related parameter of the subject are limited to the diet ratio.
Further, in the acquiring of at least one blood glucose related parameter for a predetermined time based on a predetermined diet ratio by the acquiring module 10 described above, the acquiring module 10 includes:
A determining unit for determining a plurality of predetermined diets.
By means of the determination unit it is necessary to first determine a plurality of predetermined meal proportions, where the predetermined meal proportions represent the distribution of different substances in the diet; this pre-determination of the diet of the subject directly determines the manner in which the subject is to be subjected to the diet intervention, where the diet intervention is to have the subject consume different diets of equal calories at different diets for a predetermined period of time (e.g., 2300 kcal per day for men and 1900 kcal per day for women). In a specific embodiment, the predetermined diet ratios include at least a high fat-low water ratio and a high water-low fat ratio.
An acquisition unit for acquiring at least one blood glucose related parameter of the user for each of the predetermined dietary fractions within a predetermined time. After a plurality of predetermined diet ratios are determined by the above-described determination unit, when the subject receives a diet dry, it is necessary to analyze continuous blood glucose data after the subject consumes an intervention diet having a different ratio. The blood glucose data here corresponds to its suitability for the predetermined diet ratio, and is represented by blood glucose-related parameters, which are used herein to describe the condition of the subject's Postprandial Blood Glucose (PBG), and include at least postprandial maximum blood glucose level (MPG), mean amplitude of fluctuation of blood glucose level (MAGE), total area under blood glucose measurement curve (AUC 24).
A comparison module 20 for comparing the blood glucose related parameter with a corresponding reference value.
After at least one blood glucose related parameter is obtained by the above-mentioned obtaining module 10 based on a predetermined diet ratio for a predetermined period of time, the obtained blood glucose related parameter needs to be compared with a corresponding reference value by the comparing module 20, so as to obtain adaptability of a subject to different diet ratios and determine tendency of the subject to the diet ratio under the condition of ensuring that the blood glucose condition is maintained within a relatively normal range, which specifically comprises the following parts:
a first ratio unit for comparing the blood glucose related parameter with a corresponding first blood glucose reference value to obtain a first ratio value based on the first blood glucose reference value;
a second proportion unit for comparing the blood glucose related parameter with a corresponding second blood glucose reference value to obtain a second proportion value based on the second blood glucose reference value;
and a comparison unit comparing the first and second ratio values with a predetermined ratio reference value.
The effect of diets such as HF-LC and LF-HC ratios on Postprandial Blood Glucose (PBG) levels of each subject is further investigated by a comparison unit by obtaining a comparison between the blood glucose related parameter of the subject and a corresponding reference value. The comparison unit mainly adopts a Bayes analysis model to calculate the posterior probability of mean value difference and clinically significant difference of postprandial maximum blood glucose level (MPG) caused by different diet modes of subjects, mean amplitude of blood glucose fluctuation (MAGE) and total area under blood glucose measurement curve (AUC 24).
A determination module 30 for determining a recommended dietary ratio based on the comparison.
After comparing the blood glucose related parameter with the corresponding reference value by the comparison module 20 described above, a recommended diet ratio is determined to the subject by the determination module 30 based on the comparison result such that the postprandial blood glucose level of the subject is within a relatively normal range. For example, if the subject's comparison results tend to be higher in blood glucose levels after they have consumed a high fat-low carbohydrate (HF-LC) proportioned diet, the subject is recommended a low fat-high carbohydrate (LF-HC) proportioned diet; conversely, if the subject's comparison results tend to have a higher blood glucose level after they consume the low fat-high carbohydrate (LF-HC) diet, the subject is recommended a high fat-low carbohydrate (HF-LC) diet.
The determination module 30 includes the following: a feature determination unit for determining a eating trend feature in case the first or second ratio value based on at least one of the blood glucose-related parameters is larger than a corresponding predetermined ratio reference value. And a ratio determination unit for determining a recommended diet ratio based on the diet tendency characteristics.
According to the embodiment of the disclosure, the glucose metabolism characteristics of the subject can be determined or estimated according to the blood glucose data of the subject after the diet with the preset proportion is completed, so that a personalized nutrition strategy can be provided for the subject, the blood glucose related parameters are prevented from being increased, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
A third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program that, when executed by a processor, implements the method provided by the first embodiment of the present disclosure, including steps S11 to S13 as follows:
s11, acquiring at least one blood glucose related parameter in a preset time based on a preset diet proportion;
s12, comparing the blood glucose related parameters with corresponding reference values;
and S13, determining a recommended diet proportion based on the comparison result.
Further, the computer program, when executed by a processor, implements the other methods provided by the first embodiment of the present disclosure.
According to the embodiment of the disclosure, the glucose metabolism characteristics of the subject can be determined or estimated according to the blood glucose data of the subject after the diet with the preset proportion is completed, so that a personalized nutrition strategy can be provided for the subject, the blood glucose related parameters are prevented from being increased, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
The fourth embodiment of the present disclosure provides an electronic device, which may include at least a memory 901 and a processor 902, as shown in fig. 7, where the memory 901 stores a computer program, and the processor 902 implements the method provided by any embodiment of the present disclosure when executing the computer program on the memory 901. Exemplary, the electronic device computer program steps are as follows S21 to S23:
s21, acquiring at least one blood glucose related parameter in a preset time based on a preset diet proportion;
s22, comparing the blood glucose related parameters with corresponding reference values;
s23, determining a recommended diet proportion based on the comparison result.
Further, the processor 902 also executes a computer program to implement the other methods in the first embodiment described above.
According to the embodiment of the disclosure, the glucose metabolism characteristics of the subject can be determined or estimated according to the blood glucose data of the subject after the diet with the preset proportion is completed, so that a personalized nutrition strategy can be provided for the subject, the blood glucose related parameters are prevented from being increased, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
The storage medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the passenger computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., connected through the internet using an internet service provider).
It should be noted that the storage medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While various embodiments of the present disclosure have been described in detail, the present disclosure is not limited to these specific embodiments, and various modifications and embodiments can be made by those skilled in the art on the basis of the concepts of the present disclosure, which modifications and modifications should fall within the scope of the claims of the present disclosure.
Claims (7)
1. A data processing method for blood glucose, comprising the steps of:
obtaining at least one blood glucose-related parameter of a subject within a predetermined time after a meal is dry and the meal is dry based on a predetermined meal ratio, wherein the blood glucose-related parameter is used for describing the blood glucose condition of the subject after meal, the predetermined time is 24 days, and the blood glucose-related parameter comprises at least one of a maximum postprandial blood glucose value, an average amplitude of fluctuation of the blood glucose value and a total area under a blood glucose meter measurement curve;
Comparing the blood glucose related parameter with a corresponding reference value;
determining a recommended diet ratio based on the comparison;
wherein, the comparing the blood sugar related parameter with the corresponding reference value comprises the following steps:
comparing the blood sugar related parameter with a corresponding first blood sugar reference value to obtain a first proportional value based on the first blood sugar reference value;
comparing the blood glucose related parameter with a corresponding second blood glucose reference value to obtain a second proportion value based on the second blood glucose reference value;
comparing the first and second ratio values with a predetermined ratio reference value;
the method for determining the recommended diet ratio based on the comparison result comprises the following steps:
determining a eating trend characteristic in the form of a responder to a substance, comprising at least a high fat responder, a high carbohydrate responder and a non-responder, when said first ratio value or said second ratio value based on at least one of said blood glucose related parameters is greater than a corresponding predetermined ratio reference value.
2. The data processing method according to claim 1, wherein the obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined diet ratio comprises the steps of:
Determining a plurality of predetermined diets;
at least one blood glucose related parameter of the user for each of said predetermined meal proportions is acquired within a predetermined time.
3. The data processing method of claim 2, wherein the predetermined diet ratio comprises at least a high fat-low water ratio and a high water-low fat ratio.
4. The data processing method according to claim 1, wherein the diet ratio comprises at least the following components: fat, carbohydrate, protein.
5. A data processing device for blood glucose, comprising the following:
an acquisition module for acquiring, based on a predetermined meal ratio, at least one blood glucose-related parameter of a subject within a predetermined time after a meal dry matter of the meal ratio has passed, the blood glucose-related parameter being for describing a blood glucose condition of the subject after a meal, the predetermined time being 24 days, the blood glucose-related parameter comprising at least one of a postprandial maximum blood glucose value, an average amplitude of blood glucose value fluctuations, a total area under a blood glucose meter measurement curve;
a comparison module for comparing the blood glucose related parameter with a corresponding reference value; the method specifically comprises the following steps: a first ratio unit for comparing the blood glucose related parameter with a corresponding first blood glucose reference value to obtain a first ratio value based on the first blood glucose reference value; a second proportion unit for comparing the blood glucose related parameter with a corresponding second blood glucose reference value to obtain a second proportion value based on the second blood glucose reference value; a comparison unit comparing the first and second ratio values with a predetermined ratio reference value;
A determination module for determining a recommended diet ratio based on the comparison result; the method specifically comprises the following steps: a characteristic determining unit for determining a eating propensity characteristic in the form of a responder to a certain substance, including at least a high fat responder, a high carbohydrate responder and a non-responder, in case that the first ratio value or the second ratio value based on at least one of the blood glucose related parameters is larger than a corresponding predetermined ratio reference value; and a ratio determination unit for determining a recommended diet ratio based on the diet tendency characteristics.
6. A storage medium storing a computer program, which when executed by a processor performs the steps of the method of any one of claims 1 to 4.
7. An electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program on the memory, realizes the steps of the method according to any of claims 1 to 4.
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| KR101777747B1 (en) * | 2015-12-15 | 2017-09-13 | 주식회사 아이센스 | Method for individualized recommanding food for glycosuria patient |
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| CN109243580A (en) * | 2018-11-01 | 2019-01-18 | 广州仁生健康科技有限公司 | A kind of hypoglycemic intelligent nutrition catering system and dietary management method |
| US20200163626A1 (en) * | 2018-11-27 | 2020-05-28 | International Business Machines Corporation | Glucose level prediction and management |
| CN109767825A (en) * | 2019-01-31 | 2019-05-17 | 北京卡路里信息技术有限公司 | Diet control method, apparatus, equipment and storage medium |
| CN109727658A (en) * | 2019-02-22 | 2019-05-07 | 上海鹰瞳医疗科技有限公司 | Dietary recommendations continued method, apparatus and system |
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| CN103984847A (en) * | 2014-04-14 | 2014-08-13 | 小米科技有限责任公司 | Food and drink recommendation method and related device |
| KR20180002937A (en) * | 2016-06-29 | 2018-01-09 | 주식회사 아이센스 | Strip discharger for blood glucose meter |
| CN111508582A (en) * | 2020-04-03 | 2020-08-07 | 三七二二(北京)健康咨询有限公司 | Method and device for controlling postprandial blood sugar and electronic equipment |
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