JP2012063979A - Method and system for predicting blood sugar level - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a goal setting person (a user) with a method for predicting blood sugar level which offers appropriate information and support for goal achievement.SOLUTION: The method for predicting blood sugar level by means of a management server for managing blood sugar level information from a plurality of terminals comprises: an authentication step for determining whether communication with the terminals is allowed or not; a blood sugar level information obtaining step for obtaining the blood sugar level information from the authenticated terminals; prediction curve calculating steps (S10 and S13) for calculating respective blood sugar level prediction curves on the basis of the blood sugar level information from the terminals; and an information sharing step for enabling mutual exchange of the information on the blood sugar level and the blood sugar level prediction curves among the terminals.

Description

  The present invention relates to a blood sugar level prediction method and a blood sugar level prediction system using this method.

  2. Description of the Related Art Conventionally, a goal management method is known in which a goal setter is motivated to achieve a goal by receiving support from a supporter in order to achieve the goal set by himself / herself. According to this method, first, the target setter performs supporter registration, input of a target value, and input of a current value at any time to the server. The server refers to these values and sends an e-mail requesting support to the goal setter to the supporter. The supporter who has received the support request e-mail returns a message supporting the target setter by e-mail. By such a method, the target setter can be encouraged to achieve the target without being frustrated supported by the supporter (for example, Patent Document 1).

JP 2010-9526 A

  However, in the conventional technology, the support message comes from an unspecified number of supporters even though registered by the goal setter, and does not necessarily share the goal. There was a problem that there was a case that it was not appropriate support.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following application examples or forms.

  Application Example 1 A blood glucose level prediction method according to this application example is a method using a second device that manages blood glucose level information from a plurality of first devices, and determines whether communication with the first device is possible. An authentication step, a blood glucose level information acquisition step for acquiring the blood glucose level information from the authenticated first device, and a prediction curve calculation for calculating each blood glucose level prediction curve based on the blood glucose level information of the first device And an information sharing step for exchanging the blood glucose level information and the blood glucose level prediction curve information between the first devices.

  According to this blood glucose level prediction method, in the blood glucose level information acquisition step, the second device acquires blood glucose level information from the first device permitted to access the second device in the authentication step. Then, in the prediction curve calculation step, the second device calculates a blood sugar level prediction curve indicating future changes in blood sugar level based on the blood sugar level information. For calculating the blood glucose level prediction curve, a method using an algorithm relating to a change in blood glucose level is known. For example, an actual measurement curve based on blood glucose level information that has already been measured corresponding to calorie intake and calorie consumption is analyzed. Calculated according to an algorithm. In the information sharing step, the second device enables the first device related to blood glucose level information to exchange information on each other's blood glucose level information and blood glucose level prediction curve, thereby sharing the information. . Thereby, the user who has the 1st device can refer to the blood glucose level information etc. of other users who had the same subject and target of blood glucose level, and appropriate support for the target of blood glucose level management, etc. It is possible to obtain

  Application Example 2 In the blood sugar level prediction method according to the application example described above, the blood glucose level prediction method further includes a stratification step of stratifying the blood glucose level prediction curve into groups for each similar curve, and in the information sharing step, the same group It is preferable that the blood glucose level information and the blood glucose level prediction curve information can be exchanged between the first devices belonging to each other.

  According to this method, in the stratification step, the second device stratifies blood glucose level prediction curves corresponding to each of the first devices. In this stratification, blood glucose level prediction curves having similar curve patterns are grouped into one group, and the user of the first device belonging to the group has almost the same state regarding blood glucose level, for example, blood glucose level for calorie intake and calorie consumption. It can be said that the tendency of the increase or decrease of the value is similar. In the information sharing step, the second device enables only the first devices belonging to the same group to exchange information with each other via the second device. Thereby, the user who has the 1st device can refer only to the information of the user with which blood glucose level information, the blood glucose level prediction curve, etc. were approximated among other users who had the same subject and target called blood glucose level. It is possible to obtain more appropriate information and support for the target of blood glucose level management.

  Application Example 3 In the blood sugar level prediction method according to the application example, in the prediction curve calculation step, it is preferable that the blood sugar level prediction curve is a curve based on a glycohemoglobin concentration (HbA1c) of the blood sugar level information. .

  According to this method, HbA1c is used as blood glucose level information for calculating the blood glucose level prediction curve in the prediction curve calculation step. HbA1c is a ratio (%) of hemoglobin combined with blood glucose to total hemoglobin, and is a value reflecting an average blood glucose level for a corresponding period. By calculating a blood sugar level prediction curve according to an algorithm based on this HbA1c, it is possible to grasp a long-term blood sugar level tendency.

  Application Example 4 In the blood sugar level prediction method according to the application example described above, it is preferable that the blood glucose level prediction curve is stratified by template matching in the stratification step.

  According to this method, so-called template matching in which the curve pattern of the blood sugar level prediction curve is stratified by similarity to the template is used for stratification in the stratification step. Thereby, the blood glucose level prediction curve can be easily stratified.

  Application Example 5 A blood glucose level prediction system according to this application example includes a first device that transmits blood glucose level information, and a second device that acquires and manages the blood glucose level information. An operation unit for inputting the blood sugar level information, and a first communication unit for communicating with the second device, wherein the second device determines whether or not communication with the first device is possible. Based on the blood glucose level information of the authentication unit, the storage unit storing the blood glucose level information acquired from the authenticated first device, and the blood glucose level information of the first device, A control unit configured to exchange the blood glucose level information and the blood glucose level prediction curve information between the first devices; and a second communication unit configured to communicate with the first device. It is characterized by that.

  According to the blood glucose level prediction system, the first device and the second device are provided, and the second device receives blood glucose level information from the first device permitted to access the second device by the authentication unit. The blood glucose level prediction curve indicating the change in the blood glucose level in the future is calculated by the control unit based on the blood glucose level information. For this blood sugar level prediction curve, a method using an algorithm relating to changes in blood sugar level is known. For example, a blood sugar level actual measurement curve based on blood sugar level information that has already been actually measured corresponding to calorie intake and calorie consumption is analyzed. Calculated according to an algorithm. And a control part enables the 1st apparatus in connection with blood glucose level information to mutually exchange blood glucose level information and the information of a blood glucose level prediction curve, and to share information. Thereby, the user who has the 1st device can refer to the blood glucose level information etc. of other users who had the same subject and target of blood glucose level, and appropriate support for the target of blood glucose level management, etc. It is possible to obtain

  Application Example 6 In the blood sugar level prediction system according to the application example described above, it is preferable that the first device further includes a measurement unit that measures energy consumption of the blood sugar level information.

  According to this configuration, the first device has a measurement unit, and the measurement unit consumes, for example, by grasping the activity state such as exercise or grasping the strength of the activity by the pulse. Measure calories directly. Thereby, the 1st apparatus can measure the calorie consumption which changes every moment in real time, and can contribute to the accuracy improvement of blood glucose level information. Further, it is possible to save the user from inputting these data from the operation unit.

  Application Example 7 In the blood sugar level prediction system according to the application example described above, the control unit stratifies the blood sugar level prediction curve into groups for each similar curve, and the first devices belonging to the same group mutually. It is preferable that the blood glucose level information and the blood glucose level prediction curve information can be exchanged.

  According to this configuration, the control unit of the second device stratifies blood glucose level prediction curves corresponding to each of the first devices. In this stratification, blood glucose level prediction curves having similar curve patterns are grouped into one group, and the user of the first device belonging to the group has almost the same state regarding blood glucose level, for example, blood glucose level for calorie intake and calorie consumption. It can be said that the tendency of the increase or decrease of the value is similar. Then, the control unit allows only the first devices belonging to the same group to exchange information with each other via the second device. Thereby, the user who has the 1st device can refer only to the information of the user with which blood glucose level information, the blood glucose level prediction curve, etc. were approximated among other users who had the same subject and target called blood glucose level. It is possible to obtain more appropriate information and support for the target of blood glucose level management.

The schematic diagram which shows the blood glucose level prediction system which concerns on this invention. The block diagram which shows the structure of a terminal. The block diagram which shows the structure of a management server. The graph which shows a blood glucose level prediction curve. (A) List figure which shows the kind of meal menu information, (b) List figure which shows activity classification information. (A) The graph which shows the prediction of the blood glucose level by ingested calories, (b) The graph which shows the prediction of the blood glucose level by consumption calories. The graph which shows the fluctuation | variation of HbA1c. The flowchart which shows calculation of a blood glucose level prediction. The flowchart which shows grouping based on a blood glucose level prediction curve or a HbA1c fluctuation | variation prediction curve. (A) The graph which shows the blood glucose level at the time of waking up, (b) The graph which shows the correlation with the blood glucose level at the time of waking up and HbA1c.

Hereinafter, an example of a preferred embodiment in a blood sugar level prediction method and a blood sugar level prediction system of the present invention will be described with reference to the accompanying drawings.
(Embodiment)

  FIG. 1 is a schematic diagram showing a blood sugar level prediction system according to the present invention. The blood glucose level prediction system 1 includes a plurality of portable terminals (first devices) 2 (2a, 2b... 2n), and a management server (second device) 3 that acquires and manages blood glucose level information from the terminals 2. And a communication line 4 for wirelessly connecting the terminal 2 and the management server 3. In the blood glucose level prediction system 1 having such a configuration, blood glucose level information, which is information related to blood glucose levels, is transmitted from each user having the terminal 2 to the management server 3 via the communication line 4. Management such as analysis of blood glucose level information is performed.

  FIG. 2 is a block diagram showing the configuration of the terminal, and FIG. 3 is a block diagram showing the configuration of the management server. As shown in FIG. 2, the terminal 2 includes an activity amount measurement unit 21, a pulse measurement unit 22, an operation unit 23, a display unit 24, a terminal communication unit (first communication unit) 25, and a ROM (Read Only). A memory (RAM) 26; a RAM (Random Access Memory) 27; a CPU (Central Processing Unit) 28; and a bus 29 for connecting these components.

  The CPU 28 uses the RAM 27 as a working area, and controls each unit of the terminal 2 by executing a control program (not shown) stored in advance in the ROM 26. Further, the CPU 28 also measures the current date and time by counting the clock.

  The activity amount measuring unit 21 includes an acceleration sensor, a speed sensor, and the like for detecting the movement state of the user having the terminal 2. The terminal 2 is attached to the user's waist, arm, etc. Operates according to the operation. These sensors output an output signal in accordance with the exercise intensity for a certain period by the user. Then, the CPU 28 converts the output signal into calorie consumption using a predefined arithmetic expression. The converted calorie consumption is transmitted via the terminal communication unit 25 as activity amount information. In order to obtain calorie consumption more accurately, biological data such as pulse wave RR interval (R wave peak interval), body temperature, blood pressure, sleep, etc. may be detected by optical detection, electrical signal detection, pressure detection, etc. It is valid.

  The pulse measurement unit 22 includes a specific wavelength light irradiation unit (not shown) that emits light of a specific wavelength, and the specific wavelength light irradiation unit irradiates light of a specific wavelength to a user's blood vessel and is reflected by the blood vessel. The intensity of light having a wavelength or the intensity of light having a specific wavelength transmitted through a blood vessel is detected by a light receiving element (not shown) such as a photodiode. The specific wavelength light irradiation unit is attached to, for example, a user's arm or finger. Then, the CPU 28 converts the output signal from the light receiving element into a pulse per minute using a predefined arithmetic expression. The converted pulse is transmitted as pulse information via the terminal communication unit 25. Based on this pulse, the exercise state measured by the activity amount measuring unit 21 can be further examined, and the calorie consumption can be grasped more accurately. The pulse measurement unit 22 functions as a measurement unit together with the activity amount measurement unit 21.

  The operation unit 23 includes an operation button group having input keys such as numbers and letters, and outputs an operation signal corresponding to the input key operated by the user. The operation unit 23 receives input of various types of information such as meal contents, exercise contents, and their implementation times, measured blood sugar levels, and the like by user operations. In this case, as for the implementation time, the date and time when the contents of meal and the contents of exercise are input are automatically input.

  In this case, the display unit 24 is a liquid crystal display, and displays the calorie consumption, the pulse, and the input content from the operation unit 23 under the control of the CPU 28.

  The terminal communication unit 25 is connected to the communication line 4 and transmits / receives various data such as blood glucose level information to / from the management server 3 that is previously authenticated to be communicable under the control of the CPU 28.

  The ROM 26 is a nonvolatile memory or the like, and stores meal menu information 26a and activity type information 26b as blood glucose level information. The meal menu information 26a and the activity type information 26b will be described later with reference to FIG.

  Next, as shown in FIG. 3, the management server 3 is a storage unit using a CPU 35 as a control unit, a RAM 34 functioning as a working area of the CPU 35, a nonvolatile memory, and the like, and a control program executed by the CPU 35 And a ROM 33 that stores blood glucose level information from the user, a prediction algorithm for predicting the blood glucose level, and the like. In addition, the management server 3 is connected to the authentication unit 31 that authenticates whether or not the accessed terminal 2 is communicable, and the communication line 4, and is a terminal that is authenticated in advance to be communicable under the control of the CPU 35. 2, a management communication unit (second communication unit) 32 that transmits and receives various data such as blood glucose level information, and a bus 36 that connects these components.

  The ROM 33 as a storage unit is configured by a storage medium such as a hard disk, and stores blood glucose level information and the like from the terminal 2. The ROM 33 stores, as blood glucose level information, meal information 33a that is information on the type of meal eaten by the user and calorie intake, pulse information 33b that is information on calorie consumption, and activity amount information 33c. Further, the ROM 33 groups the history information 33d including information on the user's past blood glucose level and future predicted blood glucose level, and group information described later with reference to FIG. 9, that is, users having similar blood glucose level information. Group information 33e, which is converted into information, and software 33f such as the described control program are stored.

  The CPU 35 includes a calculation unit 35a and an analysis unit 35b. The analysis unit 35b extracts meal information 33a acquired from the terminal 2 as intake energy and information corresponding to the meal information from the history information 33d, and uses the extracted history information 33d to determine the blood glucose level of the user for the meal information. Analyze change trends. At the same time, the analysis unit 35b extracts pulse information 33b and activity amount information 33c, which are exercise information acquired from the terminal 2 as energy consumption, and information corresponding to these exercise information from the history information 33d, and extracts the extracted history information 33d. It is used to analyze the change tendency of the user's blood glucose level with respect to the exercise information.

  Then, the calculation unit 35a combines the blood glucose level change tendency due to the intake energy and the blood glucose level change tendency due to the consumed energy, which are analyzed by the analysis unit 35b, and calculates a blood sugar level prediction curve. The blood sugar level prediction curve corresponds to information on the predicted blood sugar level in the future in the history information 33d.

  Here, the blood sugar level prediction curve of the history information 33d will be described. FIG. 4 is a graph showing a blood sugar level prediction curve. FIG. 5A is a list showing the types of meal menu information. The meal menu information 26a includes, for example, information that Japanese meal A and its intake calories are 500 Kcal (kilocalories) as a meal menu. These pieces of information are stored in the ROM 26 of the terminal 2. FIG. 5B is a list showing activity type information. The activity type information 26 b includes, for example, information on walks and walking, and these information are stored in the ROM 26 of the terminal 2. Has been. In addition, calorie consumption such as a walk or walking is measured by the activity amount measuring unit 21.

  As shown in FIG. 4, the blood glucose level prediction curve 10 is a daily blood glucose level and an action history (meal information, for example, measured during an educational hospitalization period when a user has previously had an educational hospitalization related to diabetes. And a prediction curve 12 that predicts a blood sugar level based on the meal information 33a, the pulse information 33b, and the activity amount information 33c acquired from the terminal 2 It has become.

  FIG. 4 shows, as an example, a user's prediction curve 12 on date A and an actual measurement curve 11 of a similar past date P that serves as a reference for the action history of the prediction curve 12. These curves represent the user's blood glucose level over time, the vertical axis shows the blood glucose level, the horizontal axis shows the passage of time, "breakfast" "walk" " “Lunch”... Indicates an action history such as meals taken by the user and timing of exercise.

  The action history is information acquired from the terminal 2, and the acquired meal menu information 26a is information selected from the meal menu shown in FIG. 5A by operating the operation unit 23 of the terminal 2. is there. Similarly, the acquired activity type information 26b is information selected from the activity types shown in FIG. 5B by the user operating the operation unit 23 of the terminal 2.

  For example, the breakfast meal on date A in FIG. 4 is “Western food C”, and “500 Kcal” corresponding to Western food C is obtained from the terminal 2 and used for calculation of the blood sugar level. In addition, “underwater walking” performed on the morning of the same day is information selected by the user from the list of FIG. 5B, and the calorie consumption thereof is measured by the activity amount measuring unit 21. In this case, “200 Kcal” "(Not shown) is acquired from the terminal 2 and used for calculation of the blood glucose level. As described above, in the blood glucose level prediction system 1, the history information 33d of the management server 3 stores information on the actual blood glucose level change in the past behavior of the user, and refers to the information on the actual blood glucose level change. Thus, it is possible to predict a change in blood glucose level from blood glucose level information of calorie intake and calorie consumption for the user's own behavior. That is, the blood sugar level prediction curve 10 can be calculated.

  Next, calculation of the prediction curve 12 for predicting a blood glucose level change in the blood glucose level prediction curve 10 will be described. FIG. 6A is a graph showing prediction of blood glucose level based on calorie intake, and FIG. 6B is a graph showing prediction of blood glucose level based on calorie consumption. The prediction curve 12 is calculated based on these two graphs. First, with reference to Fig.6 (a), calculation of the 1st prediction curve which shows the prediction of the blood glucose level by ingested calories is demonstrated. The first prediction curve is obtained based on the calorie intake and the first prediction algorithm included in the prediction algorithm. The first prediction curve has a delay period d1, a rising period d2, an equilibrium period d3, and a falling period d4.

  Hereinafter, an example of the 1st prediction algorithm which calculates | requires the curve which shows the blood glucose level in each period is demonstrated. The delay period d1 indicates a period from the start of a meal until the blood glucose level (reference value) C0 at the start of the meal is exceeded. In the delay period d1, a predetermined time (for example, 15 minutes) from the start of the meal is set, and the blood glucose level C0 at the start of the meal is maintained. Note that the blood glucose level C0 at the start of the meal uses the blood glucose level measured by the user at the time, but if the measurement cannot be made, for example, a preset standard value of the user's blood glucose level is used. May be.

  The rising period d2 indicates a period starting from the end of the delay period d1 until the blood sugar level starts to rise and reaches a peak value (peak value). The peak value is a value obtained by adding the blood glucose level increase value h1 to the blood glucose level C0 at the start of the meal, with the blood glucose level increasing at the slope s1. The increase value h1 of the blood glucose level is obtained by, for example, h1 = (calorie intake) × (insulin secretion amount) × (coefficient α). In the blood sugar level prediction system 1, the insulin secretion amount and the coefficient α (> 0) are fixed values set in advance according to the user. The insulin secretion amount and the coefficient may be not only fixed values set in advance, but also values or variable values determined according to user attributes (age, sex, height, weight).

  The equilibrium period d3 is a period during which the blood sugar level peak value is maintained from the end of the rising period d2. In the blood sugar level prediction system 1, a predefined fixed value is set. In addition, for example, the value obtained by multiplying the calorie intake by a coefficient specific to the user is divided by a coefficient corresponding to the difference from the previous calorie intake, and the equilibrium period using the calorie intake and a predetermined arithmetic expression d3 may be obtained.

  The falling period d4 indicates a period from the end of the equilibrium period d3 until the blood sugar level starts to decrease at the slope s2 and reaches the reference value. That is, the descent period d4 is a period until the blood glucose level returns from the peak value to the reference value (blood glucose level C0 at the start of the meal). The slope s2 is obtained by, for example, s2 = (calorie intake) × (coefficient β). In the blood sugar level prediction system 1, the coefficient β is a fixed value (<0) determined in advance according to the user, but the value β determined in accordance with the user's attributes (age, gender, height, weight) It may be a variable value.

  Subsequently, with reference to FIG. 6B, calculation of a second prediction curve indicating prediction of blood glucose level by calorie consumption will be described. The second prediction curve is obtained based on the calorie consumption and the second prediction algorithm included in the prediction algorithm. The second prediction curve includes a delay period e1 and a falling period e2. An example of the 2nd prediction algorithm which calculates | requires the blood glucose level curve in each period is demonstrated.

  The delay period e1 indicates a period from the start of exercise until the blood sugar level starts to decrease, and is a period during which the blood sugar level at the start of exercise is maintained. In the blood sugar level prediction system 1, a predetermined period (for example, 2 minutes) is set as the delay period e1. The falling period e2 is a period during which the blood sugar level falls with a slope s3 (a blood sugar level reduction amount h2 per unit time) from the end of the delay period e1. The decrease amount h2 is obtained by, for example, h2 = (calorie consumption) × (insulin secretion amount) × (coefficient γ). The calorie consumption is the user's calorie consumption measured by the activity amount measurement unit 21. In the blood sugar level prediction system 1, the activity amount measurement unit 21 consumes the user even during normal operation when the user is not conscious of exercise. Calories are calculated and input sequentially. The amount of insulin secretion is a fixed value preset according to the user, and the coefficient γ (<0) may be a variable value according to the blood glucose level, or a fixed value determined according to the user's attribute It may be.

  The calculation of the prediction curve 12 will be described based on the calculation of the first prediction curve and the second prediction curve. Returning to FIG. 4, when the input meal information is Western food C on date A, the next action (meal or exercise) from the time when Western food C is ingested as a waveform representing a change in blood glucose level when Western food C is ingested. Waveform data for a period until the) is performed is extracted. In the example shown in FIG. 4, waveform data is extracted for a period from when the breakfast on date A is consumed until the next action, that is, underwater walking. If underwater walking is not performed, waveform data for a period from when breakfast is consumed until lunch is extracted is extracted. In this way, a waveform representing a change in blood glucose level when the same meal content is ingested is extracted as the first model waveform. In addition, when a plurality of waveforms are extracted, the analysis unit 35b performs processing such as averaging the plurality of extracted waveforms, and generates a first model waveform indicating a change tendency of the blood sugar level with respect to the meal information. To do.

  In addition, the analysis unit 35b extracts the history information 33d corresponding to the exercise information input from the terminal 2 using a predetermined extraction condition, and uses the extracted history information 33d, the user's blood glucose level for the exercise information Analyzing the change trend of The blood glucose level prediction system 1 is configured so that the calorie consumption of the user is sequentially calculated by the activity amount measuring unit 21. However, it is possible to determine what operation the calculated calorie consumption has performed. In order to distinguish, for the exercise other than the normal operation, the activity type (FIG. 5B) is input before the user performs the exercise.

  Specifically, when the input activity information is underwater walking, the next action from the start of underwater walking on the date A in FIG. That is, the waveform data until the lunch is taken is extracted. In this way, a waveform representing a change in blood glucose level when the same exercise is performed is extracted as the second model waveform. When a plurality of waveforms are extracted, the analysis unit 35b performs a process such as averaging the plurality of extracted waveforms, and generates a second model waveform indicating a change tendency of the blood sugar level with respect to the activity information. Generate.

  The calculation unit 35a deforms the generated first prediction curve and second prediction curve based on the analysis result of the analysis unit 35b, and integrates the deformed first prediction curve and second prediction curve to generate the prediction curve 12. Generate. That is, the analysis of the analysis unit 35b compares the increase value h1 up to the peak value of the blood sugar level in the increase period d2 of the first prediction curve with the increase amount up to the peak value of the first model waveform, When the difference is equal to or greater than a predetermined threshold, the coefficient α is adjusted so that the increase value h1 of the first prediction curve becomes the increase amount of the first model waveform.

  In addition, the analysis unit 35b compares the equilibrium period d3 of the first prediction curve with the continuation period in which the peak value of the blood sugar level continues in the first model waveform, and the difference between the equilibrium period d3 and the continuation period is determined in advance. If it is equal to or greater than the threshold, the equilibrium period d3 is set so that the equilibrium period d3 of the first prediction curve matches the duration. In the first model waveform, it is assumed that the peak value continues during the period when the peak value of the blood glucose level falls within the predetermined threshold range, and the time point when the peak value falls below the threshold range is Judged to be the end.

  Then, the analysis unit 35b compares the amount of decrease (h1) in which the blood sugar level has decreased from the peak value in the falling period d4 of the first prediction curve with the amount of decrease in which the blood sugar level has decreased from the peak value of the first model waveform. When the difference in the amount of decrease is equal to or greater than a predetermined threshold, the coefficient β of the slope s2 so that the amount of decrease (h1) in the decrease period d4 of the first prediction curve becomes the amount of decrease in the first model waveform. Adjust.

  Furthermore, the analysis unit 35b performs the second prediction curve in the same manner as the first prediction curve, and reduces the amount h2 of blood glucose level per unit time in the falling period e2 of the second prediction curve and the blood glucose in the second model waveform. The value is compared with the amount of decrease per unit time, and if the difference between the amounts of decrease is equal to or greater than a predetermined threshold, a coefficient is set so that the amount of decrease h2 of the second prediction curve becomes the amount of decrease in the second model waveform. Adjust γ.

  Based on the analysis result of the analysis unit 35b, the calculation unit 35a generates the prediction curve 12 by deforming the first prediction curve and the second prediction curve and integrating the deformed first prediction curve and second prediction curve. To do. The generated prediction curve 12 is stored in the history information 33d (FIG. 3) of the ROM 33.

  As described above, according to the blood sugar level prediction curve 10, the user can not only confirm the daily change tendency of the blood glucose level (mg / dl) measured in the past, but also the meal information 33a and the pulse information 33b. Further, it is possible to confirm the future change tendency of the blood glucose level based on the blood glucose level information of the activity amount information 33c without actually measuring the blood glucose level.

  Further, as a method of grasping the change tendency of the blood sugar level, HbA1c, glycoalbumin, or 1.5-AG may be used in addition to the method of confirming the blood sugar level itself. FIG. 7 is a graph showing fluctuations in HbA1c. HbA1c indicates how much hemoglobin (HbA) in red blood cells is bound to glucose, which is blood glucose, and represents a percentage of the total hemoglobin amount in percentage. This percentage value reflects an average blood glucose level in a certain period (a time when the binding is stable) in which red blood cells having a life span of about 120 days circulate in the body. Therefore, it can be said that it is suitable for grasping a long-term change in blood glucose level without being influenced by a short-term change in blood glucose level due to overeating or rapid exercise. FIG. 7 shows an HbA1c fluctuation prediction curve (blood glucose level prediction curve) 14 indicating HbA1c for each month.

  Similar to the blood sugar level prediction curve 10, the HbA1c fluctuation prediction curve 14 is composed of a fluctuation actual measurement curve 15 that is a past actual measurement value of HbA1c and a fluctuation prediction curve 16 that predicts future fluctuations. From the HbA1c fluctuation prediction curve 14, it is possible to grasp the long-term change tendency of the user's blood glucose level, and the tendency can be grasped by the degree of the slope of the curve, which is a long-term trend, and the fluctuation cycle. It is also possible to grasp from the descending (rising) pattern.

  If this HbA1c fluctuation prediction curve 14 is used, not only the long-term change tendency of the user's own blood glucose level but also other users having similar HbA1c fluctuation prediction curves can be identified and Various information can be shared, and blood sugar levels can be managed and appropriate support and the like can be provided to achieve the target. Of course, even if the blood sugar level prediction curve 10 is used, similar sharing, support, and the like are possible.

  In the following, calculation of blood sugar level prediction, which is data information for generating the blood sugar level prediction curve 10 and the HbA1c fluctuation prediction curve 14, and information sharing between users using the blood sugar level prediction curve 10 or the HbA1c fluctuation prediction curve 14 and Explain how to support each other.

  FIG. 8 is a flowchart showing calculation of blood glucose level prediction, and FIG. 9 is a flowchart showing grouping based on a blood glucose level prediction curve or an HbA1c fluctuation prediction curve. First, referring to FIG. 8, calculation of blood glucose level prediction by the management server 3, that is, data information for generating the prediction curve 12 of the blood glucose level prediction curve 10 and the fluctuation prediction curve 16 of the HbA1c fluctuation prediction curve 14 is calculated. How to do will be described.

  First, in step S1, it is determined whether or not the terminal 2 is permitted by the authentication unit 31. This is based on the identification (ID) of the terminal 2 that has accessed the management server 3 and the user ID of the blood glucose level information (meal information 33a, pulse information 33b, activity amount information 33c) stored in the ROM 33. 31 collates and determines. This step corresponds to an authentication step. If the ID of the terminal 2 and the user ID of the ROM 33 match, the process proceeds to step S2, while if not, the flow ends.

  If the IDs match, it is determined in step S2 whether the meal information 33a has been received. The meal information 33a is information selected by the user from the meal menu information 26a on the terminal 2. This determination is made by the CPU 35. Step S2 corresponds to a blood glucose level information acquisition step. If the meal information 33a is received, the process proceeds to step S3. If not received, the process proceeds to step S5.

  When the meal information 33a is received, in step S3, the received information (meal information 33a) and the blood sugar level change tendency due to the meal in the past are analyzed. This analysis is performed by the analysis unit 35b, and a blood glucose level change is predicted from the received meal information 33a based on an analysis result referring to information on the actual blood glucose level change in the past meal information 33a of the user in the history information 33d. After the analysis, the process proceeds to step S4.

  In step S4, a dietary blood glucose prediction is calculated based on the analysis result. This calculation is performed by the calculation unit 35a. The dietary blood glucose prediction here is data information for creating the first prediction curve indicating the prediction of the blood glucose level based on the calorie intake as described above. After the calculation, the process proceeds to step S5.

  In step S5, it is determined whether or not the activity amount information 33c and the pulse information 33b are received. These pieces of information are information from the terminal 2 by the user. This determination is made by the CPU 35. Step S5 corresponds to a blood glucose level information acquisition step. If the activity amount information 33c and the pulse information 33b are received, the process proceeds to step S6, and if not received, the process proceeds to step S8.

  In step S6, the received information (activity amount information 33c and pulse information 33b) and the past activity amount and blood glucose level change tendency due to the pulse are analyzed. This analysis is performed by the analysis unit 35b, and a blood glucose level change is predicted from the received information based on an analysis result referring to information on the actual blood glucose level change in the past behavior of the user in the history information 33d. After the analysis, the process proceeds to step S7.

  In step S7, an active blood glucose prediction is calculated based on the analysis result. This calculation is performed by the calculation unit 35a. The active blood glucose prediction here is data information for creating the second prediction curve that indicates the prediction of the blood glucose level based on calorie consumption as described above. After the calculation, the process proceeds to step S8.

  In step S8, a blood sugar level prediction is calculated from the meal blood sugar prediction and the active blood sugar prediction. This calculation is performed by the calculation unit 35a. The blood glucose level prediction here is data information for creating the prediction curve 12 and the fluctuation prediction curve 16 from the diet blood glucose prediction and the active blood glucose prediction. After the calculation, the flow ends. This flow is executed for each terminal 2.

  Next, a method for information sharing and mutual support between users using the blood glucose level prediction curve 10 or the HbA1c fluctuation prediction curve 14 will be described with reference to FIG.

  First, in step S10, a blood sugar level prediction curve 10 showing daily blood sugar level changes is generated from blood sugar level prediction data. That is, as shown in FIG. 4, the prediction curve 12 is generated from blood glucose level prediction (meal blood glucose prediction and active blood glucose prediction) calculated based on the meal information 33a, the pulse information 33b, and the activity amount information 33c acquired from the terminal 2. To do. As the actual measurement curve 11, for example, it is conceivable to select a past actual measurement curve similar to the prediction curve 12 or to select the previous day with respect to the corresponding day of the prediction curve 12. Thereby, the blood glucose level prediction curve 10 is generated. This step S10 corresponds to a prediction curve calculation step. After the blood sugar level prediction curve 10 is generated, the process proceeds to step S11.

  In step S11, it is determined whether or not the processing related to the target terminal 2 that has received the information has been completed. This determination is made by the CPU 35 and is made for each terminal 2 that can access the management server 3. If the process for all of the terminals 2 that have acquired the blood glucose level information is completed, the process proceeds to step S12. If the process is not completed, the process returns to step S10.

  In step S12, the blood sugar level prediction curves 10 are grouped by template matching. That is, the blood glucose level prediction curves 10 of the terminals 2 in FIG. 4 that are similar are classified as one group. Template matching is a technique in which blood glucose level prediction curves 10 having a shape close to a certain template curve are collected to form a group of similar ones. Thereby, users belonging to the same group can determine that the short-term change tendency of the blood glucose level is similar. The information held by these users can confirm changes in blood glucose levels in response to changes in blood glucose levels due to sudden factors, and is considered to be useful for each other. After grouping, the process proceeds to step S13.

  In step S13, the HbA1c fluctuation prediction curve 14 indicating the monthly change in HbA1c is generated from data such as history information 33d. That is, as shown in FIG. 7, the fluctuation measurement curve 15 of the HbA1c fluctuation prediction curve 14 based on the history information 33d, and the fluctuation prediction curve 16 based on blood sugar level prediction for generating the prediction curve 12 of the blood sugar level prediction curve 10 , Is generated. This step S13 corresponds to a prediction curve calculation step. After the generation of the HbA1c fluctuation prediction curve 14, the process proceeds to step S14.

  In step S14, it is determined whether or not the processing related to the target terminal 2 having the history information 33d is finished. This determination is performed by the CPU 35 for each terminal 2 stored in the history information 33d, and the HbA1c fluctuation prediction curve 14 is generated. If the process of generating the HbA1c fluctuation prediction curve 14 is completed for all the target terminals 2, the process proceeds to step S15. If the process is not completed, the process returns to step S13.

  In step S15, the HbA1c fluctuation prediction curve 14 is grouped by template matching. That is, those having similar HbA1c fluctuation prediction curves 14 of the terminals 2 in FIG. 7 are classified as one group. Thereby, users belonging to the same group can determine that the long-term change tendency of the blood glucose level is similar. If the HbA1c fluctuation prediction curves 14 are similar, it can be said that the long-term changes in blood glucose levels are certainly similar to each other without being confused by changes in blood glucose levels due to sudden factors. The information held by these users is considered to be more useful for achieving the long-term goal (blood glucose level improvement) for each other. This step S15 corresponds to a stratification step. After grouping, the process proceeds to step S16.

  In step S16, for each group of the blood sugar level prediction curve 10 or the HbA1c fluctuation prediction curve 14, the terminals 2 can exchange data with each other within the group. That is, in this case, the authentication unit 31 authenticates the information related to the user ID stored in the ROM 33 so that even the terminal 2 having the ID that is recognized as the same group as the user ID can access. This step S18 corresponds to an information sharing step, and the flow ends here.

  According to the blood sugar level predicting method and the blood sugar level predicting system described above, by sharing mutual information among users having similar blood sugar level change trends, each user can keep the same blood sugar level change tendency as his / her own. Some other users can be known, and lifestyles such as eating and exercising can be exchanged as useful information for immediate reference. In addition, by exchanging various types of information regarding blood glucose levels between users, it is possible to provide appropriate support and the like to achieve the goal of managing blood glucose levels.

  Further, if the blood glucose level prediction curve 10 is used to grasp the progress and future trend in the change in blood glucose level, it is possible to grasp daily short-term changes and to know the effects of individual meals and exercise. Other users in the group have similar trends, so it is possible to take actions to improve blood glucose levels by exchanging experiences with each other regarding precautions such as timing and frequency of meals and exercise. . On the other hand, if the HbA1c fluctuation prediction curve 14 is used, it is possible to know a change in blood glucose level from a long-term viewpoint that is not affected by the influence of a single meal and exercise. In this case as well, other users in the group share similar trends in the fluctuations in HbA1c, so exchange their experiences with each other about the relationship between diet and exercise trends and changes in blood glucose levels over a long span. It is possible to take better actions to improve blood glucose levels without being constrained.

  Further, the blood sugar level predicting method and the blood sugar level predicting system are not limited to the above-described embodiment, and the same effects as those of the embodiment can be obtained even in the following modifications.

  (Modification 1) Step S16 (information sharing step) in the blood glucose level prediction method is a setting that enables data exchange between users in the group, but is not limited to this setting. For example, even if the users are in a group, a step that allows mutual data exchange only when mutual agreement is obtained may be further set. Thereby, the user can select a user who wants to exchange information and support, and can prevent personal information from being disclosed unexpectedly.

  (Modification 2) In steps S12 and S15, the blood sugar level prediction curve 10 or the HbA1c fluctuation prediction curve 14 is grouped by template matching, but may be grouped from other methods and viewpoints. That is, the blood glucose level prediction curve 10 or the HbA1c fluctuation prediction curve 14 having similar long-term trends (slopes) are grouped, those having similar fluctuation periods are grouped, and the descending (rising) patterns are similar. Grouping, and grouping those that have similar values at the present time. Thereby, the user can select the user who exchanges information in view of each of the viewpoints such as the trend and pattern that he is interested in.

  (Modification 3) In order to show the change tendency of a blood glucose level, the blood glucose level prediction curve 10 shown in FIG. 4 or the HbA1c fluctuation prediction curve 14 shown in FIG. 7 is used, but the wake-up blood glucose level may be used. FIG. 10A is a graph showing the blood glucose level when waking up. FIG. 10A shows an actual measurement value indicating a daily blood glucose level and a moving average indicating an average blood glucose level for one week until the day. In actual measurement values, daily changes may be severe and it may be difficult to grasp the trend of changes in blood glucose levels. With moving averages, patterns of rising and falling blood glucose levels can be read, and groups such as pattern matching are easy to group. . For this reason, it is also effective to use the blood glucose level at wake-up to achieve grouping.

  (Modification 4) Moreover, the knowledge that the blood glucose level at the time of waking and HbA1c has a correlation is acquired. FIG. 10B is a graph showing the correlation between the rising blood glucose level and HbA1c. In this case, HbA1c is a value for each month, and the wake-up blood glucose level is a value obtained by averaging the values for 20 days before the day when HbA1c was measured. As can be seen from FIG. 10B, the averaged wake-up blood glucose level and HbA1c change similar to each other. Therefore, it is possible to use the wake-up blood glucose level instead of HbA1c, and it can be used as a curve indicating the change tendency of the blood glucose level, similarly to the HbA1c fluctuation prediction curve 14 shown in FIG. Furthermore, the knowledge that there is a correlation between the rising blood glucose level and the fasting blood glucose level is also obtained, and if the fasting blood glucose level is used, the change in the blood glucose level is almost the same as the rising blood glucose level and HbA1c. The tendency can be grasped.

  (Modification 5) In the blood glucose level prediction curve 10 shown in FIG. 4, only the actual measurement curve 11 or the prediction curve 12 may be displayed. Similarly, in the HbA1c fluctuation prediction curve 14 shown in FIG. 7, only the fluctuation actual measurement curve 15 or the fluctuation prediction curve 16 may be displayed.

  (Modification 6) Although the terminal 2 is an integrated device as shown in FIGS. 1 and 2, the terminal shape is suitable for carrying such as a wristwatch type, a mobile phone type, a stopwatch type, and a pencil type. If it is good. Moreover, the active mass measurement part 21 and the pulse measurement part 22 which are built in the terminal 2 may each be a separate configuration. According to this configuration, the activity amount measuring unit 21 and the pulse measuring unit 22 can be arranged on the measurement appropriate parts of the user's body, respectively, and can be measured more accurately. Further, the terminal 2 and the management server 3 may be connected by wired communication other than wireless communication, infrared communication, or the like.

  (Modification 7) The ROM 26 of the terminal 2 may store the history information 33d of the management server 3 and the like. According to this, the user can confirm the change tendency of the blood glucose level even with the terminal 2 alone. In addition, the management server 3 may have a configuration including an operation unit and a display unit. In this way, since the blood glucose level information can be confirmed on the management server 3 side, for example, the management server 3 side is connected to a medical institution ( Doctors) can be expected to further support blood glucose level management. Even if the medical institution (doctor) has the terminal 2, the same support can be expected.

  DESCRIPTION OF SYMBOLS 1 ... Blood glucose level prediction system, 2 ... Terminal as 1st apparatus, 3 ... Management server as 2nd apparatus, 10 ... Blood glucose level prediction curve, 11 ... Actual measurement curve, 12 ... Prediction curve, 14 ... Blood glucose level prediction curve HbA1c fluctuation prediction curve, 15 ... fluctuation measurement curve, 16 ... fluctuation prediction curve, 21 ... activity amount measurement section as measurement section, 22 ... pulse measurement section as measurement section, 25 ... terminal communication section as first communication section , 31... Authentication unit, 32... Management communication unit as a second communication unit, 34... ROM as a storage unit, 35.

Claims (7)

A blood glucose level prediction method by a second device that manages blood glucose level information from a plurality of first devices,
An authentication step of determining whether communication with the first device is possible;
Blood glucose level information acquisition step for acquiring the blood glucose level information from the authenticated first device;
A prediction curve calculating step of calculating each blood glucose level prediction curve based on the blood glucose level information of the first device;
A blood glucose level prediction method comprising: an information sharing step that enables the blood glucose level information and the blood glucose level prediction curve information to be exchanged between the first devices. In the blood glucose level prediction method according to claim 1,
Further comprising a stratification step of stratifying the blood glucose level prediction curve into groups for each similar curve;
In the information sharing step, the blood glucose level prediction method allows the blood glucose level information and the blood glucose level prediction curve information to be exchanged between the first devices belonging to the same group. In the blood sugar level prediction method according to claim 1 or 2,
In the prediction curve calculation step, the blood sugar level prediction curve is a curve based on a glycated hemoglobin concentration (HbA1c) of the blood sugar level information. In the blood glucose level prediction method according to claim 2 or 3,
In the stratification step, the blood sugar level prediction curve is stratified by template matching. A first device that transmits blood glucose level information; and a second device that acquires and manages the blood glucose level information;
The first device includes:
An operation unit for inputting the blood sugar level information;
A first communication unit for communicating with the second device,
The second device includes:
An authentication unit for determining whether communication with the first device is possible;
A storage unit for storing the blood glucose level information acquired from the authenticated first device;
A control unit that calculates each blood sugar level prediction curve based on the blood sugar level information of the first device, and exchanges the blood sugar level information and the information of the blood sugar level prediction curve between the first devices. When,
A blood glucose level prediction system comprising: a second communication unit for communicating with the first device. In the blood sugar level prediction system according to claim 5,
The blood glucose level prediction system, wherein the first device further includes a measurement unit that measures energy consumption of the blood glucose level information. In the blood sugar level prediction system according to claim 5 or 6,
The control unit stratifies the blood glucose level prediction curve into groups for each similar curve, and the blood glucose level information and the information on the blood glucose level prediction curve between the first devices belonging to the same group. A blood glucose level prediction system characterized by enabling exchange.

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