Exercise management method and system for managing blood sugar variability

Abstract

The present invention relates to an exercise management method and system for managing blood sugar variability. The method according to the present invention comprises the steps of: obtaining data on the timing of postprandial blood sugar spikes on the basis of a user's blood sugar data acquired during a first period; calculating an optimal start time for postprandial aerobic exercise on the basis of the data on the timing of postprandial blood sugar spikes; and providing an alert for the start time of postprandial aerobic exercise on the basis of the optimal start time for postprandial aerobic exercise during a second period. According to the present invention, it is possible to recommend an effective postprandial aerobic exercise time tailored to a user's blood sugar pattern. In addition, it is possible to prompt a user to start aerobic exercise at the optimal time point for postprandial aerobic exercise according to the meal. Furthermore, by analyzing and providing the improvement in blood sugar levels of postprandial aerobic exercise, the user's will to perform postprandial aerobic exercise can be encouraged and adherence to exercise can be enhanced.

Description

Exercise management method and system for managing glycemic variability

[0001] The present invention relates to an exercise management method and system for managing blood sugar variability.

[0002] Frequent or wide glycemic variability (GV) due to postprandial spikes or hypoglycemic events is known to aggravate cardiovascular disease in diabetic patients, and therefore minimizing glycemic variability is very important.

[0003] To reduce glycemic variability, it is necessary to minimize postprandial blood sugar spikes and increase the time in range (TIR) ​​within the target blood sugar level to stably control blood sugar levels without declining to hypoglycemia.

[0004] Aerobic exercise, including walking, is known to be effective in minimizing postprandial blood sugar spikes before they peak. Therefore, postprandial aerobic exercise helps bring blood sugar levels within the target glycemic range (TIR) ​​relatively quickly.

[0005] However, there is still no method to systematically manage postprandial aerobic exercise in users with diabetes symptoms.

[0006] The technical problem to be solved by the present invention is to provide a method for managing postprandial aerobic exercise in a user with diabetes symptoms.

[0007] An exercise management method for managing blood sugar variability according to the present invention is implemented in a computing device including a processor; and a memory storing instructions or programs executable by the processor.

[0008] The above exercise management method includes a step of obtaining postprandial blood sugar spike occurrence time data based on the user's blood sugar data acquired during a first period, a step of calculating an optimal postprandial aerobic exercise start time based on the postprandial blood sugar spike occurrence time data, and a step of providing an alarm for the start time of postprandial aerobic exercise based on the optimal postprandial aerobic exercise start time during a second period.

[0009] The above postprandial blood sugar spike occurrence time data is divided into predetermined meal time groups and analyzed, and the optimal postprandial aerobic exercise start time can be calculated for each predetermined meal time group.

[0010] The above predetermined meal time groups include a breakfast group, a lunch group, and a dinner group, and the optimal post-meal aerobic exercise start time can be calculated by dividing the optimal post-meal aerobic exercise start time by breakfast, lunch, and dinner.

[0011] The step of calculating the optimal post-prandial aerobic exercise start time may include: calculating the average value, standard deviation, and coefficient of variation of the post-prandial blood sugar spike occurrence time from the post-prandial blood sugar spike time data for each of the predetermined meal time groups; and if the coefficient of variation is greater than or equal to a predetermined standard, excluding the maximum value among the post-prandial blood sugar spike occurrence times and then calculating the average value, standard deviation, and coefficient of variation of the post-prandial blood sugar spike occurrence times again; repeating this until the coefficient of variation becomes less than or equal to a predetermined standard; calculating a first value obtained by multiplying the maximum value of the post-prandial blood sugar spike occurrence time by a predetermined first coefficient for each of the predetermined meal time groups, and a second value obtained by multiplying the minimum value of the post-prandial blood sugar spike occurrence time by a predetermined second coefficient for each of the predetermined meal time groups, and calculating a third value as the average value of the first value and the second value; calculating the third value calculated for each of the predetermined meal time groups as the optimal post-prandial aerobic exercise start time; and calculating the fourth value as the optimal post-prandial aerobic exercise start time if the third value is less than a predetermined fourth value.

[0012] The step of providing an alarm for the start time of post-meal aerobic exercise based on the above-mentioned optimal post-meal aerobic exercise start time may include a step of receiving input from a user a meal start time or an expected meal start time, and a step of providing an alarm for inducing post-meal aerobic exercise when the optimal post-meal aerobic exercise start time has elapsed from the meal start time or expected meal start time input by the user.

[0013] The step of receiving input of a meal start time or an expected meal start time from the user may include a step of outputting a meal time input screen at a predetermined time before a preset meal time, and a step of receiving input of a meal start time or an expected meal time from the user on the meal time input screen.

[0014] The method may further include a step of providing a result of analyzing the effect of postprandial aerobic exercise based on the user's blood sugar data acquired during the first period and the user's blood sugar data acquired during the second period.

[0015] The blood sugar data of the user can be obtained from a continuous blood sugar monitoring device attached to the user.

[0016] The present invention can provide customized recommendations for effective post-prandial aerobic exercise timing tailored to each user's blood sugar patterns. Furthermore, it can guide users to initiate aerobic exercise at the optimal post-prandial aerobic exercise timing for each meal. Furthermore, by analyzing and providing the effects of post-prandial aerobic exercise on improving blood sugar levels, it can encourage users to engage in post-prandial aerobic exercise and increase exercise compliance.

[0017] Figure 1 is a configuration diagram of an exercise management system according to one embodiment of the present invention.

[0018] FIG. 2 is a flowchart provided to explain an exercise management method according to one embodiment of the present invention.

[0019] Figure 3 is a graph exemplifying a user's postprandial blood sugar pattern for each meal.

[0020] Fig. 4 illustrates an example of a meal time input screen according to one embodiment of the present invention.

[0021] FIG. 5 is a flowchart provided to explain a method for calculating an optimal postprandial aerobic exercise start time according to one embodiment of the present invention.

[0022] Figure 6 is an exemplary illustration of an alarm screen for inducing postprandial aerobic exercise according to one embodiment of the present invention.

[0023] Figure 7 is an exemplary screen showing the effect analysis of aerobic exercise after a meal according to one embodiment of the present invention.

[0024] Then, with reference to the attached drawings, an embodiment of the present invention will be described in detail so that a person having ordinary skill in the art to which the present invention pertains can easily carry out the present invention.

[0025] The terminology used herein is for the purpose of describing embodiments only and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise. As used herein, the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like components throughout the specification, and "and / or" includes each and any combination of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms. These terms are only used to distinguish one component from another. Therefore, it should be understood that a first component mentioned below may also be a second component within the technical spirit of the present invention.

[0026] Figure 1 is a configuration diagram of an exercise management system according to one embodiment of the present invention.

[0027] Referring to FIG. 1, the exercise management system according to the present invention may include a user terminal (100) and a blood glucose meter (200). Depending on the embodiment, the exercise management system according to the present invention may further include a server (300).

[0028] The user terminal (100) may be a computing device such as a smart phone, a tablet PC, a cellular phone, a PCS phone (Personal Communication Service phone), a synchronous / asynchronous IMT-2000 (International Mobile Telecommunication-2000) mobile terminal, a Palm PC (Palm Personal Computer), or a personal digital assistant (PDA).

[0029] The user terminal (100) can have a computer program installed and executed to execute an exercise management method according to one embodiment of the present invention.

[0030] The user terminal (100) can obtain postprandial blood sugar spike occurrence time data from the user's blood sugar data acquired from the blood sugar meter (200) and analyze it to calculate the user's optimal postprandial aerobic exercise start time. Furthermore, the user terminal (100) can provide an alarm for the start time of postprandial aerobic exercise based on the calculated optimal postprandial aerobic exercise start time for the user. The alarm can be provided in various ways, such as voice, sound, vibration, or message output.

[0031] A blood glucose meter (200) is attached to a user to measure the user's blood glucose at regular intervals and transmit the measured blood glucose data of the user to a user terminal (100) in real time through a wireless communication method such as Bluetooth, Wi-Fi, or near field communication (NFC).

[0032] The blood glucose meter (200) can be implemented as a device that can measure the user's blood glucose level and transmit it wirelessly to the user terminal (100), such as a continuous glucose monitoring (CGM).

[0033] The server (300) may be a computing device implemented to collect and store the user's blood sugar data measured by the blood sugar meter (200), the postprandial blood sugar spike occurrence time data calculated by the user terminal (100), the optimal postprandial aerobic exercise start time, etc.

[0034] The user terminal (100) and the server (300) can exchange various information and data through a communication network (10).

[0035] The communication network (10) may include a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, 2G, 3G, 4G, 5G, LTE mobile communication networks, Bluetooth, Wi-Fi, Wibro, satellite communication networks, LoRa, Sigfox, and other LPWA (Low Power Wide Area) networks, and the communication method may be either wired or wireless, and any communication method may be used.

[0036] FIG. 2 is a flowchart provided to explain an exercise management method according to one embodiment of the present invention.

[0037] Referring to FIGS. 1 and 2, first, the user terminal (100) receives and stores the user's blood sugar data acquired from a blood sugar meter (200) attached to the user for a first predetermined period of time, and can obtain postprandial blood sugar spike occurrence time data based on the user's blood sugar data acquired for the first period of time (S210).

[0038] The first period may be set to, for example, one week, two weeks, etc. from the time the user wears the blood glucose meter (200) and begins measuring blood glucose data. During the first period, the user may be guided to eat and engage in activities as usual, without performing post-meal aerobic exercise, etc.

[0039] Figure 3 is a graph exemplifying a user's postprandial blood sugar pattern for each meal.

[0040] Referring to Figure 3, the postprandial blood sugar pattern shows a pattern where blood sugar levels increase over time after a meal, peak, and then decline. Furthermore, postprandial blood sugar patterns can vary depending on the meal: breakfast, lunch, and dinner.

[0041] The time of occurrence of a postprandial blood sugar spike is defined as the time (Tp) elapsed from the start of a meal to the time of occurrence of a blood sugar peak, as illustrated in Figure 3.

[0042] The user terminal (100) may receive a meal start time or a meal scheduled time from the user during a first period to calculate the time of occurrence of a postprandial blood sugar spike. For example, the user terminal (100) may receive a meal time preset by the user, such as breakfast, lunch, and dinner. Furthermore, the user terminal (100) may output a meal time input screen, such as the one illustrated in FIG. 4, at a predetermined time prior to the preset meal time, thereby receiving a meal start time or a meal scheduled time input from the user.

[0043] The user terminal (100) may consider the time at which the user inputs the start of a meal or the scheduled meal time as the meal start time. Of course, the user may also directly input the meal start time when he or she starts the meal or after finishing the meal.

[0044] For reference, the Ts indicated for breakfast, lunch, and dinner in Figure 3 represent the optimal postprandial aerobic exercise start time for each meal. The optimal postprandial aerobic exercise start time (Ts) is calculated based on the time of postprandial blood sugar spike.

[0045] Fig. 4 illustrates an example of a meal time input screen according to one embodiment of the present invention.

[0046] Referring to FIG. 4, for example, the user terminal (100) may display a screen for entering a meal time at a predetermined time (15 minutes in the example of FIG. 4) from a preset lunch time. The meal time input screen may include a button for starting a meal (41), a button for starting later (42), and a UI for directly entering a scheduled time (43). When the user clicks the button (41), the user terminal (100) may process the time at which the button (41) is clicked as the meal start time. In addition, when the user clicks the button (42), the user terminal (100) may display the meal time input screen again at a predetermined time interval to enter a meal start time from the user. Meanwhile, when the user selects the UI (43), the user terminal (100) may output a user interface screen through which the user may input a scheduled meal start time, directly enter a scheduled meal start time from the user, and process the time as the meal start time.

[0047] Of course, Fig. 4 is only an example, and the user's meal time can be input in other ways than those exemplified here.

[0048] As for the postprandial blood sugar spike occurrence time data, if the first period is set to one week, and the user eats breakfast, lunch, and dinner during the first period, and also inputs the start times of all meals, the postprandial blood sugar spike occurrence time data as exemplified in Table 1 can be obtained.

[0049] (minutes)Day1Day2Day3Day4Day5Day6Day7Breakfast504060807060150Lunch40304535485055Dinner90809510012011090

[0050] Referring back to FIG. 2, the user terminal (100) can calculate the optimal post-prandial aerobic exercise start time based on the post-prandial blood sugar spike occurrence time data (S220). The user terminal (100) can analyze the post-prandial blood sugar spike occurrence time data by meal group, such as breakfast, lunch, and dinner, and calculate the optimal post-prandial aerobic exercise start time by meal group. The following description assumes that the meal groups are divided into breakfast, lunch, and dinner.

[0051] FIG. 5 is a flowchart provided to explain a method for calculating an optimal postprandial aerobic exercise start time according to one embodiment of the present invention.

[0052] Referring to FIG. 5, the user terminal (100) can calculate the mean, standard deviation, and coefficient of variation (CV) of the postprandial blood sugar spike time from the postprandial blood sugar spike time data for breakfast, lunch, and dinner (S221). For example, the coefficient of variation can be calculated by dividing the standard deviation by the mean.

[0053] Table 2 shows examples of the mean, standard deviation, and coefficient of variation of the postprandial blood sugar spike time data for breakfast, lunch, and dinner, as exemplified in Table 1.

[0054] (Minutes) Mean Standard Deviation CV Breakfast 72.936.450% Lunch 43.38.820% Dinner 97.913.514%

[0055] Next, the user terminal (100) checks whether there is a meal among breakfast, lunch, and dinner with a coefficient of variation (CV) exceeding a predetermined standard (S223). If there is a meal with a coefficient of variation exceeding the predetermined standard (S223-Yes), the maximum value of the postprandial blood sugar spike occurrence time is excluded for the meal, and the average value, standard deviation, and coefficient of variation of the postprandial blood sugar spike occurrence time are recalculated (S225).

[0056] The user terminal (100) repeats steps (S223) and (S225) until the coefficient of variation for each of breakfast, lunch, and dinner becomes less than a predetermined standard.

[0057] For example, assuming that the standard for the coefficient of variation in step (S223) is 30%, since the coefficient of variation of breakfast exceeds 30%, the mean, standard deviation, and coefficient of variation of the postprandial blood sugar spike occurrence time for breakfast can be recalculated by excluding the maximum value among the postprandial blood sugar spike occurrence times for breakfast (corresponding to '150', the postprandial blood sugar spike occurrence time for breakfast on Day 7 in Table 1).

[0058] Table 3 shows an example of recalculating the mean, standard deviation, and coefficient of variation of the postprandial blood sugar spike occurrence time from Day 1 to Day 6, excluding the maximum postprandial blood sugar spike occurrence time of '150' for breakfast.

[0059] (Minute) Average Standard Deviation CV Morning 6014.124%

[0060] As shown in Table 3, since the coefficient of variation calculated again for breakfast is 24%, which is less than 30%, the user terminal (100) finishes calculating the mean, standard deviation, and coefficient of variation for breakfast. Meanwhile, since the coefficients of variation for lunch and dinner are 20% and 14%, which are less than 30%, step (S225) is not performed for lunch and dinner.

[0061] Referring again to FIG. 5, if the condition that the coefficient of variation is less than a predetermined standard for each of breakfast, lunch, and dinner is satisfied (S223-No), the user terminal (100) can calculate the optimal post-meal aerobic exercise start time for each of breakfast, lunch, and dinner (S227).

[0062] The optimal postprandial aerobic exercise start time (Ts) for breakfast, lunch, and dinner can be calculated using the same method as illustrated in Equation 1.

[0063] [Mathematical Formula 1]

[0064] Optimal postprandial aerobic exercise start time (Ts) =

[0065] if Average(A×Tp max , B×Tp min ) ≤ Tmin, then Tmin

[0066] else Average(A×Tp max , B×Tp min )

[0067] 1. The maximum time of occurrence of postprandial blood sugar spike for the meal (Tp) max ) is multiplied by a predetermined first coefficient (A) to obtain the first value (A×Tp max ) and the minimum time of occurrence of postprandial blood glucose spike (Tp min ) is multiplied by a predetermined second coefficient (B) to obtain a second value (B×Tp min ) is found.

[0068] 2. First value (A×Tp max ) and the second value (B×Tp min ) as the average value of the third value (Tp avrg ) = Average(A×Tp max , B×Tp min ) is found.

[0069] 3. Third value (Tp avrg) and the optimal postprandial aerobic exercise start time (Tmin). The third value (Tp avrg ) is less than the minimum postprandial optimal aerobic exercise start time (Tmin), the postprandial optimal aerobic exercise start time is set to Tmin. The third value (Tp avrg ) is greater than or equal to the minimum postprandial optimal aerobic exercise start time (Tmin), then the postprandial optimal aerobic exercise start time is Tp avrg It is decided as follows.

[0070] If we assume A = 0.25, B = 0.75, and Tmin = 30, the maximum value of breakfast (Tp) from the data in Table 1 max ) is 80 (150 of Day7 was excluded in the process of satisfying the CV condition), and the minimum value (Tp min ) is 40, so the first value (A×Tp max ) and the second value (B×Tp min ) are 20 (= 0.25 × 80) and 30 (= 0.75 × 40), respectively, as shown in Table 4. Tp avrg ≤ Tmin, the optimal postprandial aerobic exercise start time for breakfast is 30 minutes, which is set as Tmin. Table 4 below shows this process in a tabular form.

[0071] (minute) Time coefficient calculation value morning - maximum value Tp max = 80A = 0.25A×Tp max = 20 morning - minimum value Tp min = 40B = 0.75B × Tp min = 30 average Tp avrg = 25

[0072] Referring back to FIG. 2, after the user's optimal post-prandial aerobic exercise start time is calculated, the user terminal (100) can provide an alarm for the start time of the post-prandial aerobic exercise based on the optimal post-prandial aerobic exercise start time (S230). In step (S230), the user terminal (100) can output a meal time input screen as exemplified in FIG. 4 above to receive input of the meal start time or the expected meal start time from the user. In addition, the user terminal (100) can provide an alarm for inducing post-prandial aerobic exercise as exemplified in FIG. 6 when the optimal post-prandial aerobic exercise start time has elapsed from the meal start time or the expected meal start time input by the user.

[0073] Figure 6 is an exemplary illustration of an alarm screen for inducing postprandial aerobic exercise according to one embodiment of the present invention.

[0074] Referring to Fig. 6, the post-meal aerobic exercise induction alarm screen may include phrases that induce post-meal aerobic exercise, such as "Shall we start walking?" In addition, a button (61) may be included to receive input from the actual user regarding whether to start post-meal aerobic exercise, or a button (62) may be included to select a re-notification for post-meal aerobic exercise induction. Of course, in addition to the screens exemplified in Fig. 6, various user interface screens that induce post-meal aerobic exercise may be utilized.

[0075] Meanwhile, the user terminal (100) can provide the results of analyzing the effect of postprandial aerobic exercise based on the user's blood sugar data acquired during the first period and the user's blood sugar data acquired during the second period (S240).

[0076] In step (S230), the second period may simply refer to the period following the first period, and depending on the embodiment, it may be set as a predetermined period for exercise effect analysis. For example, if the first period is set to one week, the subsequent periods, such as the first week, the second week, etc., may be set as the second period, and the post-prandial aerobic exercise effect may be analyzed and provided for each week, as exemplified in FIG. 7. Of course, the post-prandial aerobic exercise effect analysis may be provided in various ways other than those exemplified here.

[0077] Figure 7 is an exemplary screen showing the effect analysis of aerobic exercise after a meal according to one embodiment of the present invention.

[0078] So far, an embodiment has been described in which the user terminal (100) directly calculates the optimal post-meal aerobic exercise start time for the user and provides an alarm for the post-meal aerobic exercise start time accordingly.

[0079] According to an embodiment, the user terminal (100) may be implemented to collect the user's blood sugar data from the blood sugar meter (200) and transmit it to the server (300), and the server (300) may analyze the blood sugar data to calculate postprandial blood sugar spike occurrence time data and postprandial optimal aerobic exercise start time, and perform analysis of the postprandial aerobic exercise effect, and provide the results to the user terminal (100). To this end, the user terminal (100) may also transmit data such as meal times input by the user to the server (300). In this case, the server (300) may be implemented to calculate the postprandial optimal aerobic exercise start time using the method described above.

[0080] Meanwhile, the user terminal (100) and / or the server (300) can continuously collect the user's blood sugar data and mealtime information to build learning data consisting of the user's postprandial blood sugar data patterns. Furthermore, the user terminal (100) and / or the server (300) can use the learning data to train an artificial intelligence implemented with a pre-defined learning model. The trained artificial intelligence can then receive the user's blood sugar data and output whether or not the user has eaten and the mealtime.

[0081] Therefore, after the artificial intelligence is learned, it is possible to obtain the meal start time based on the user's blood sugar data obtained from the blood sugar meter (200) without receiving input from the user about the meal start time or meal schedule time, and to implement the system so that the user terminal (100) provides an alarm about the start time of aerobic exercise after a meal based on this.

[0082] AI learning models can be implemented using deep neural networks (DNNs) such as CNNs (Long Short-Term Memory), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), and GRUs (Long Short-Term Memory), as well as liquid time constant networks, spiking neural networks, and multi-layer profiling (MLPs). Of course, other learning models can be applied in addition to the ones exemplified here. Furthermore, training for each learning model can be achieved through any of several learning techniques, including supervised, semi-supervised, or unsupervised learning, and is not limited to these.

[0083] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0084] Software may include computer programs, codes, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device. The software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium, or device for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0085] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0086] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the above. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

Claims

1. A method for managing exercise implemented in a computing device including a processor; and a memory storing instructions or programs executable by the processor; Step of obtaining postprandial blood sugar spike occurrence time data based on the user's blood sugar data acquired during the first period; A step of calculating the optimal post-prandial aerobic exercise start time based on the post-prandial blood sugar spike occurrence time data, and An exercise management method comprising the step of providing an alarm for the start time of a postprandial aerobic exercise based on the optimal postprandial aerobic exercise start time during a second period.

2. In paragraph 1, The above postprandial blood sugar spike occurrence time data is divided into pre-determined meal time groups and analyzed. An exercise management method in which the optimal aerobic exercise start time after the above meal is calculated for each predetermined meal time group.

3. In paragraph 2, The above predetermined meal time groups include a breakfast group, a lunch group, and a dinner group, The above optimal aerobic exercise start time after a meal is an exercise management method calculated by dividing it into optimal aerobic exercise start times after breakfast, lunch, and dinner.

4. In paragraph 2, The steps for calculating the optimal start time for aerobic exercise after the above meal are: A step of calculating the mean value, standard deviation and coefficient of variation of the postprandial blood sugar spike time from the postprandial blood sugar spike time data for each of the above-determined meal time groups, and if the coefficient of variation is greater than or equal to a predetermined standard, excluding the maximum value among the postprandial blood sugar spike times, and then calculating the mean value, standard deviation and coefficient of variation of the postprandial blood sugar spike times again, repeating this until the coefficient of variation is less than or equal to a predetermined standard. A step of obtaining a first value obtained by multiplying the maximum value of the postprandial blood sugar spike occurrence time by a predetermined first coefficient for each of the above-determined meal time groups, and a second value obtained by multiplying the minimum value of the postprandial blood sugar spike occurrence time by a predetermined second coefficient, and obtaining a third value as the average value of the first value and the second value. An exercise management method comprising the step of calculating a third value obtained for each of the predetermined meal time groups as the optimal post-meal aerobic exercise start time, and calculating the fourth value as the optimal post-meal aerobic exercise start time if the third value is smaller than the predetermined fourth value.

5. In paragraph 1, The step of providing an alarm for the start time of post-meal aerobic exercise based on the above optimal post-meal aerobic exercise start time is as follows. A step for receiving a meal start time or an expected meal start time from the user, and An exercise management method comprising the step of providing an alarm for inducing post-meal aerobic exercise when the optimal post-meal aerobic exercise start time has elapsed from the meal start time or expected meal start time input by the user.

6. In paragraph 5, The step of receiving the meal start time or expected meal start time from the above user is as follows: A step for outputting a meal time input screen at a predetermined time before a predetermined meal time, and An exercise management method including a step of receiving a user's input of a meal start time or a meal scheduled time on the meal time input screen.

7. In paragraph 5, An exercise management method further comprising a step of providing a result of analyzing the effect of aerobic exercise after a meal based on the user's blood sugar data acquired during the first period and the user's blood sugar data acquired during the second period.

8. In paragraph 1, An exercise management method in which the user's blood sugar data is obtained from a continuous blood sugar monitoring device attached to the user.

9. As a computing device, processor; and A memory storing instructions or programs executable by the processor; When the above instruction or program is executed by the processor, a step of obtaining postprandial blood sugar spike occurrence time data based on the user's blood sugar data acquired during the first period; A step of calculating the optimal post-prandial aerobic exercise start time based on the post-prandial blood sugar spike occurrence time data, and A computing device executing a step of providing an alarm for starting postprandial aerobic exercise based on the postprandial optimal aerobic exercise start time during a second period.

10. In paragraph 9, The above postprandial blood sugar spike occurrence time data is divided into pre-determined meal time groups and analyzed. The optimal start time for aerobic exercise after the above meal is calculated for each of the predetermined meal time groups. The above predetermined meal time groups include a breakfast group, a lunch group, and a dinner group, A computing device that calculates the optimal aerobic exercise start time after meals by dividing it into the optimal aerobic exercise start time after meals for breakfast, lunch, and dinner.

11. In Article 10, The steps for calculating the optimal start time for aerobic exercise after the above meal are: A step of calculating the mean value, standard deviation and coefficient of variation of the postprandial blood sugar spike time from the postprandial blood sugar spike time data for each of the above-determined meal time groups, and if the coefficient of variation is greater than or equal to a predetermined standard, excluding the maximum value among the postprandial blood sugar spike times, and then calculating the mean value, standard deviation and coefficient of variation of the postprandial blood sugar spike times again, repeating this until the coefficient of variation is less than or equal to a predetermined standard. A step of obtaining a first value obtained by multiplying the maximum value of the postprandial blood sugar spike occurrence time by a predetermined first coefficient for each of the above-determined meal time groups, and a second value obtained by multiplying the minimum value of the postprandial blood sugar spike occurrence time by a predetermined second coefficient, and obtaining a third value as the average value of the first value and the second value. A computing device comprising a step of calculating a third value obtained for each of the predetermined meal time groups as the optimal post-meal aerobic exercise start time, and calculating the fourth value as the optimal post-meal aerobic exercise start time if the third value is smaller than the predetermined fourth value.

12. In paragraph 9, The step of providing an alarm for the start time of post-meal aerobic exercise based on the above optimal post-meal aerobic exercise start time is as follows. A step for receiving a meal start time or an expected meal start time from the user, and A computing device comprising a step of providing an alarm for inducing post-meal aerobic exercise at a time when the optimal post-meal aerobic exercise start time has elapsed from a meal start time or expected meal start time input by a user.

13. In paragraph 12, The step of receiving the meal start time or expected meal start time from the above user is as follows: A step for outputting a meal time input screen at a predetermined time before a predetermined meal time, and A computing device including a step of receiving a meal start input or a meal scheduled time input from a user on the meal time input screen.

14. In paragraph 12, A computing device further executing a step of providing a result of analyzing the effect of aerobic exercise after a meal based on the user's blood sugar data acquired during the first period and the user's blood sugar data acquired during the second period.

15. A server that obtains postprandial blood sugar spike occurrence time data based on the user's blood sugar data acquired during the first period, and calculates the optimal postprandial aerobic exercise start time based on the postprandial blood sugar spike occurrence time data, and An exercise management system including a user terminal that provides an alarm for the start time of aerobic exercise after a meal based on the optimal start time of aerobic exercise after a meal during a second period.

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