Method for producing views related to data on glucose and ketone bodies and related system

Abstract

The present disclosure provides a method for producing views related to data on glucose and ketone bodies and a related system. The method comprises receiving analyte data from an analyte sensor; determining, on the basis of the received analyte data, analyte concentration data comprising glucose concentration data and ketone body concentration data; and displaying, on the basis of the analyte concentration data, on a display unit a plurality of views comprising a first view, wherein the first view comprises a graphical display generated on the basis of the analyte concentration data over a time period, the graphical display comprises a region representing the distribution of a plurality of data points, and the position of each data point is determined by the glucose concentration and the ketone body concentration at the same time. The present disclosure enables an observer to more conveniently analyze energy metabolism and is friendlier to the observer.

Description

Methods and related systems for generating views associated with glucose and ketone body data Technical Field

[0001] This disclosure relates to health-related information systems, and more particularly to a method and related system for generating views related to data on glucose and ketone bodies. Background Technology

[0002] With the fast pace of modern life, people are paying increasing attention to health management. As a primary energy source for the human body, managing carbohydrate intake is crucial for maintaining good health. For example, properly controlling carbohydrate intake plays an important role in preventing and managing chronic diseases such as diabetes and obesity.

[0003] Currently, existing solutions typically estimate a user's total carbohydrate intake by recording the nutritional composition and quantity of food, and then estimate energy expenditure by calculating physical activity and basal metabolic rate, thereby determining the user's carbohydrate intake and expenditure. Some solutions utilize wearable technology to monitor key physiological indicators such as glucose concentration and calorie consumption, allowing users to gain preliminary information about their energy metabolism (e.g., how many kilocalories they burn daily).

[0004] However, existing solutions rely on users meticulously recording and calculating their food intake, requiring a high degree of self-monitoring ability and recording habits. Furthermore, estimating energy expenditure typically necessitates additional equipment to monitor physical activity levels, undoubtedly increasing the difficulty of analyzing energy metabolism. Other solutions employ isolated data presentation methods, displaying the values ​​and fluctuations of various physiological indicators separately. For example, different wearable device manufacturers' systems might generate separate graphs of glucose concentration changes or calorie consumption trends. This isolated data presentation is not user-friendly for understanding energy metabolism. Summary of the Invention

[0005] This disclosure is made in view of the above-mentioned situation, and its purpose is to provide a method and related system for generating data related to glucose and ketone bodies that can improve the convenience of the observer in analyzing energy metabolism and is more observer-friendly.

[0006] To this end, a first aspect of this disclosure provides a method for generating views related to glucose and ketone body data, comprising receiving analyte data from an analyte sensor; determining analyte concentration data including glucose concentration data and ketone body concentration data based on the received analyte data; and displaying multiple views including a first view on a display unit based on the analyte concentration data, wherein the first view includes a graphical display generated based on the analyte concentration data over a time period, the graphical display including a distribution area representing the distribution of multiple data points, the position of each data point being determined by the glucose concentration and ketone body concentration at the same time. In this case, the first view facilitates the identification of the relationship between changes in glucose concentration and changes in ketone body concentration, and makes the comparison between glucose concentration and ketone body concentration more intuitive, thereby improving the readability of the concentration data, making it more user-friendly for observers, and helping observers analyze energy metabolism. Furthermore, the view displaying glucose concentration data and ketone body concentration data helps observers analyze energy metabolism, which, compared to existing solutions that rely on the habits of the target subject or require additional equipment, improves the convenience of observers in analyzing energy metabolism.

[0007] Additionally, in the method according to the first aspect of this disclosure, optionally, the plurality of views further includes a second view generated based on energy information. This second view graphically represents the changes in the energy information over multiple time periods. The energy information is an estimated relationship between the energy intake and energy expenditure of the target object, where the energy intake is the energy from carbohydrates consumed by the target object, and the energy expenditure is the energy consumed by the target object. In this case, incorporating energy information helps the observer interpret the energy metabolism reflected in the concentration data. Furthermore, observing the changes in energy information over multiple time periods helps the observer understand the trend of energy information changes.

[0008] Additionally, in the method according to the first aspect of this disclosure, optionally, the displayed graphic is assigned to a dietary period type and the dietary period type is displayed. This helps the observer analyze energy metabolism in conjunction with the dietary period type.

[0009] Additionally, in the method according to the first aspect of this disclosure, optionally, the graphical display further includes lines drawing the boundaries of heat regions graphically, where the heat regions are areas where the density of data points is greater than a preset density. In this case, it can help the observer distinguish between areas with concentrated and sparse data point distribution, facilitating the observer's identification of important information and noise information in the concentration data, thereby improving the accuracy of the observer's analysis of energy metabolism.

[0010] Additionally, in the method according to the first aspect of this disclosure, the data points may optionally be generated by using color changes to represent the density of the data points. In this case, the concentration data has high repeatability, and using color changes to represent the data points not only reflects the distribution of the data points but also the concentration of the distribution, thus obtaining more information and helping the observer to focus on important information in high-density areas, thereby improving the accuracy of the observer's analysis of energy metabolism.

[0011] Additionally, in the method according to the first aspect of this disclosure, optionally, in response to receiving a first user event from a single graphical display, a first subview is displayed, wherein the first subview includes the glucose concentration curve and ketone body concentration curve of the single graphical display; and / or in response to receiving a second user event from a single graphical display, a second subview is displayed, wherein the second subview includes the glucose concentration curve and ketone body concentration curve for the nighttime period. In this case, the first subview facilitates the observer's analysis of energy metabolism in conjunction with the concentration curves. Furthermore, the nighttime data displayed in the second subview reflects energy metabolism, which helps the observer analyze energy metabolism.

[0012] Additionally, in the method according to the first aspect of this disclosure, optionally, the time period is one day. In this case, the concentration data includes at least data on energy intake and energy expenditure processes, facilitating the analysis of the energy metabolism of the target object.

[0013] Additionally, in the method according to the first aspect of this disclosure, optionally, the graphical displays are arranged in a matrix in the first view, wherein the graphical displays in the rows or columns of the matrix are arranged in ascending order of time period; the number of graphical displays in the rows or columns of the matrix is ​​determined by a preset size, the graphical displays in the rows or columns of the matrix represent graphical displays within one week, the graphical displays in the rows or columns of the matrix represent graphical displays within the service life of the analyte sensor, and / or the graphical displays in the rows or columns of the matrix represent graphical displays within one month. In this case, the matrix arrangement facilitates the display of more graphical displays in a limited display space, and the continuous display of graphical displays helps the observer observe changes in energy metabolism over time, thereby helping the observer to take improvement measures. In addition, the preset size facilitates the limitation of the distribution of graphical displays within a specific display space. Furthermore, displaying data weekly helps track the progress of short-term goals, urging the target to complete improvement measures on time. In addition, displaying data according to the service life, the concentration data corresponding to the graphical displays in the same row or column comes from the same analyte sensor, which can reduce the negative impact of the deviation between different analyte sensors on the observer's analysis of energy metabolism. In addition, displaying data monthly helps track progress toward long-term goals and helps understand long-term changes in energy metabolism.

[0014] Additionally, in the method according to the first aspect of this disclosure, optionally, the plurality of views further include at least one of the following views: a third view generated based on the analyte concentration data within a time period, the third view including glucose concentration curves and ketone body concentration curves within a plurality of time periods; a fourth view generated based on the analyte concentration data within a time period, the fourth view including lines representing the average glucose concentration and the average ketone body concentration for each time period within the plurality of time periods; and a fifth view generated based on the analyte concentration data during the nighttime period within the time period, the fifth view including a plurality of data points, the position of each data point being determined by the average glucose concentration and the average ketone body concentration during the nighttime period of a single time period.

[0015] A second aspect of this disclosure provides a system for generating views related to glucose and ketone body data, comprising: a receiving module, a determining module, and an output module; the receiving module is used to receive analyte data from an analyte sensor; the determining module is used to determine analyte concentration data, including glucose concentration data and ketone body concentration data, based on the received analyte data; the output module is used to display multiple views, including a first view, on a display unit based on the analyte concentration data, wherein the first view includes a graphical display generated based on the analyte concentration data over a time period, the graphical display including a distribution area representing the distribution of multiple data points, the position of each data point being determined by the glucose concentration and ketone body concentration at the same time. In this case, the first view facilitates the identification of the relationship between changes in glucose concentration and changes in ketone body concentration, and makes the comparison between glucose concentration and ketone body concentration more intuitive, thereby improving the readability of the concentration data, making it more user-friendly for observers, and helping observers analyze energy metabolism. Furthermore, the view displaying glucose concentration data and ketone body concentration data helps observers analyze energy metabolism, improving the convenience of energy metabolism analysis compared to existing solutions that rely on the habits of the target subject or require additional equipment.

[0016] According to this disclosure, a method and related system are provided that can improve the convenience of observers in analyzing energy metabolism and generate views related to glucose and ketone body data in a more observer-friendly manner. Attached Figure Description

[0017] This disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings.

[0018] Figure 1 is a schematic diagram illustrating a monitoring environment for simultaneous monitoring of glucose and ketone body concentrations as described in this disclosure example.

[0019] Figure 2 is a schematic diagram illustrating the control device involved in the example of this disclosure.

[0020] Figure 3 is an exemplary flowchart illustrating the generation method involved in this disclosure.

[0021] Figure 4A is a schematic diagram illustrating the first view involved in the example of this disclosure.

[0022] Figure 4B is a schematic diagram of a first embodiment showing a graphical display of the first view of the arrangement involved in the example of this disclosure.

[0023] Figure 4C is a schematic diagram of a second embodiment showing a graphical display of the first view of the arrangement involved in the example of this disclosure.

[0024] Figure 5 is a schematic diagram showing a single graphical display of glucose concentration curves and ketone body concentration curves as described in the examples of this disclosure.

[0025] Figure 6 is a schematic diagram illustrating the second view involved in the example of this disclosure.

[0026] Figure 7 is a schematic diagram illustrating a third view relevant to the examples of this disclosure.

[0027] Figure 8 is a schematic diagram illustrating the fourth view involved in the example of this disclosure.

[0028] Figure 9 is a schematic diagram illustrating the fifth view involved in the example of this disclosure.

[0029] Figure 10 is an exemplary block diagram illustrating a production system according to an example of this disclosure. Detailed Implementation

[0030] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same reference numerals are used for the same components, and repeated descriptions are omitted. Furthermore, the drawings are merely schematic diagrams, and the proportions of the components or the shapes of the components may differ from actual figures. It should be noted that the terms "comprising" and "having," and any variations thereof, in this disclosure, do not necessarily limit the process, method, system, product, or apparatus to the explicitly listed steps or units, but may include or have other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0031] First, let me introduce the relevant terminology used in this disclosure.

[0032] "Graphical display" can refer to displaying relevant information in a graphical manner.

[0033] "Switching displays" can refer to switching between different content or views in the content display of a display unit, such as switching between different pages or functions in a webpage or application.

[0034] An "observer" can refer to any object that needs to obtain information from the view. For example, an observer can be a guardian, medical staff, the target object, or the target model.

[0035] "Sample object" can refer to a user who provides data related to the training samples (such as analyte data) during the training phase of the model, or a user who provides data for analysis and statistics to determine some thresholds (such as empirical values).

[0036] As mentioned above, existing solutions require users to have a high level of self-monitoring ability and a habit of recording data, which is quite challenging. Other solutions are not user-friendly in helping users understand the relationship between the energy from ingested carbohydrates (i.e., the calories from ingested carbohydrates) and the energy expended. Hereinafter, the energy from ingested carbohydrates will be referred to as energy intake, the energy expended as energy expenditure, and the relationship between energy intake and energy expenditure as energy metabolism.

[0037] The inventors discovered through research that combining glucose concentration data and ketone body concentration data can serve as an indicator of the energy metabolism status (e.g., whether it is balanced) of a target subject. Specifically, carbohydrate intake can store glycogen, glycogen depletion leads to gluconeogenesis, and the intensification of gluconeogenesis leads to an increase in ketone body concentration. Therefore, combining glucose concentration data and ketone body concentration data can identify features related to energy metabolism status to determine energy metabolism status. Taking the range of glucose concentration and ketone body concentration as an example, if the range of glucose concentration is not particularly small and the range of ketone body concentration is not particularly large, it can be estimated that energy intake basically offsets energy expenditure. If the range of glucose concentration is particularly small and the range of ketone body concentration is more than large, it can be estimated that energy intake cannot offset energy expenditure. It should be noted that the specific range of glucose concentration and ketone body concentration is not specifically limited in this disclosure, and appropriate thresholds can be set by statistically analyzing analyte data of sample subjects with known energy metabolism status.

[0038] Therefore, the inventors have provided several solutions, and the corresponding embodiments can at least address some of the problems described above, thereby improving the ease of observation for analyzing energy metabolism and making the process more user-friendly. Examples of this disclosure will be described in detail below.

[0039] The analyte monitoring system disclosed herein can be configured to continuously monitor analyte levels. In some examples, the analyte monitoring system may include a sensor control device (hereinafter referred to as the control device). The control device may be configured to acquire signals related to the analyte level (hereinafter referred to as analyte signals), process the analyte signals, and / or transmit information related to the analyte signals (hereinafter referred to as analyte information).

[0040] In some examples, the control device may include an analyte sensor and electronic components. The analyte sensor may be configured to generate an analyte signal, and the electronic components may be configured to receive the analyte signal, process the analyte signal, and / or transmit analyte information to a computing device. In some examples, the analyte monitoring system may also include a computing device that receives data from the control device.

[0041] In some examples, analyte information can be an analyte signal and / or an analyte level. The analyte signal can be any signal that corresponds to an analyte level, and the analyte signal can be converted into an analyte level. In some examples, when the analyte sensor is based on electrochemical measurement, the analyte signal can be an electrical signal, from which the corresponding analyte level can be obtained.

[0042] In some examples, for the analyte being glucose, the analyte sensor can be a glucose sensor, and the analyte level can be the glucose concentration. In some examples, for the analyte being ketone bodies, the analyte sensor can be a ketone body sensor, and the analyte level can be the ketone body concentration.

[0043] In some examples, the analyte sensor can be a multi-analyte sensor. Specifically, the analyte sensor can be a sensor that simultaneously monitors glucose concentration and ketone body concentration. In this case, the time synchronization of glucose concentration data and ketone body concentration data can be improved, and compared to using separate devices to collect glucose concentration data and ketone body concentration data separately, the usage process can be simplified, the purchase cost can be reduced, and the wearing space can be saved.

[0044] Additionally, analyte data can be data related to analyte levels. Analyte data may include analyte information at at least one time point. Furthermore, analyte concentration data may include analyte levels at at least one time point (hereinafter referred to as concentration data).

[0045] For ease of description, some examples below use the simultaneous monitoring of glucose concentration and ketone body concentration as examples. It should be noted that this does not imply any limitation on this disclosure. Unless there is a contradiction, the relevant description is equally applicable to any other monitoring method that can obtain continuous glucose concentration and ketone body concentration.

[0046] Examples of this disclosure will now be described in detail with reference to the accompanying drawings. Figure 1 is a schematic diagram illustrating a monitoring environment for simultaneously monitoring glucose and ketone body concentrations as described in an example of this disclosure.

[0047] Referring to Figure 1, the monitoring environment may include a control device 7. The control device 7 can be configured to acquire analyte data of the target object 8. Specifically, a portion of the control device 7 can be located on the surface of the target object 8, and another portion can be located subcutaneously, thereby enabling the monitoring of analyte levels. It should be noted that although the application scenario of the control device 7 described herein is exemplarily based on monitoring analyte levels by placing a portion of the control device 7 subcutaneously on the target object 8, this does not constitute a limitation of this disclosure.

[0048] As described above, in some examples, the analyte monitoring system may further include a computing device 1 that receives analyte data from the control device 7. In some examples, the computing device 1 may also be included in the monitoring environment. In some examples, the computing device 1 may be a device with a display unit. The display unit can be used to display information related to the received analyte data. This facilitates the provision of rich information display. In some examples, the computing device 1 may also be used to determine energy information based on concentration data. Additionally, the concentration data may be determined from the analyte data.

[0049] In addition, the display unit can be a device for displaying information. For example, the display unit can be a monitor, a display screen, or electronic paper.

[0050] In some examples, computing device 1 may be independent of control device 7. Specifically, computing device 1 may be an electronic device disposed outside of control device 7 and communicatively connected to control device 7. For example, computing device 1 may be a mobile device. As another example, computing device 1 may also be a smartphone, tablet computer, or wearable device. In some examples, control device 7 may be communicatively connected to computing device 1 directly or indirectly. Control device 7 may be communicatively connected to computing device 1 via one or more communication links. For example, communication links may include at least one of proprietary wireless protocols, wired communication links (e.g., serial communication), and wireless communication links (e.g., Bluetooth).

[0051] In some examples, computing device 1 can be integrated into control device 7. That is, control device 7 can be used to display information related to the received analyte data and / or to determine energy information based on concentration data. In other words, the operations of displaying information related to the received analyte data and / or determining energy information based on concentration data can be performed in control device 7.

[0052] Figure 2 is a schematic diagram illustrating the control device 7 involved in the example of this disclosure.

[0053] Before describing the generated views in detail, let's first introduce the control device 7 involved in this disclosure example. Referring to FIG2, in some examples, the control device 7 may include an analyte sensor 71 and an electronic component 72. The analyte sensor 71 may be configured to generate a signal related to glucose concentration (hereinafter referred to as a first signal) and a signal related to ketone body concentration (hereinafter referred to as a second signal), respectively, and the electronic component 72 may be configured to receive the first signal and the second signal.

[0054] In some examples, the analyte sensor 71 can be configured to be at least partially implanted under the skin of the target object 8. This allows at least a portion of the analyte sensor 71 to come into contact with the subcutaneous fluid of the target object 8 to generate an analyte signal. In other examples, the analyte sensor 71 may not come into contact with the subcutaneous fluid of the target object 8. For example, when the analyte sensor 71 is based on optical signal measurement, the analyte signal can be obtained without contact with the subcutaneous fluid of the target object 8 via optical signal.

[0055] In some examples, the analyte sensor 71 may include a working electrode. In some examples, there may be multiple working electrodes, and each working electrode may be used to detect different analytes. Thus, it is possible to monitor multiple analytes. In some examples, the working electrode may include a first working electrode and a second working electrode, the first working electrode being used to detect glucose and the second working electrode being used to detect ketone bodies. In some examples, glucose and ketone bodies can be monitored separately by placing enzyme layers of two analytes on the two working electrodes.

[0056] Furthermore, ketone bodies can be a collective term for acetoacetic acid, β-hydroxybutyric acid, and acetone, intermediate products of fatty acid oxidation in the liver. In the examples of this disclosure, ketone bodies can be monitored by detecting any one or more of acetoacetic acid, β-hydroxybutyric acid, and acetone. That is, ketone bodies can be at least one of acetoacetic acid, β-hydroxybutyric acid, and acetone. Preferably, parameters related to ketone bodies can be obtained by monitoring β-hydroxybutyric acid. In this case, β-hydroxybutyric acid is more sensitive and representative in reflecting ketone body levels, which can improve the reliability of measuring ketone body concentration.

[0057] Referring again to Figure 2, in some examples, the electronic component 72 can be connected to the analyte sensor 71 to receive analyte signals (i.e., the first signal and the second signal) from the analyte sensor 71. In some examples, the electronic component 72 can be electrically connected to the analyte sensor 71. In some examples, the electronic component 72 can be configured to adhere to the surface of the target object 8 (i.e., the skin surface). That is, in some states (e.g., when using the control device 7), the electronic component 72 can adhere to the surface of the target object 8. This facilitates cooperation with the analyte sensor 71 to receive analyte signals.

[0058] Figure 3 is an exemplary flowchart illustrating the generation method involved in this disclosure.

[0059] As described above, the display unit of computing device 1 can be used to display information related to the received analyte data. To this end, an example of this disclosure provides a method for generating a view related to data on glucose and ketone bodies (hereinafter simply referred to as the generation method). The generation method can be implemented by computing device 1. Furthermore, the generation method can also be called a data display method or a generation method.

[0060] Referring to Figure 3, the generation method may include: receiving analyte data from the analyte sensor 71 (step S101), determining concentration data including glucose concentration data and ketone body concentration data based on the received analyte data (step S102), and displaying multiple views on the display unit based on the concentration data (step S103).

[0061] In some examples, in step S101, analyte data can be obtained by the analyte sensor 71 continuously monitoring at least two analytes of the target object 8. This allows for the acquisition of continuous analyte information. For example, the analyte sensor 71 can acquire analyte signals at preset intervals, and the analyte signals and / or the analyte levels determined based on the analyte signals can be used for subsequent display. Furthermore, the preset interval can be set according to monitoring requirements. In some examples, the preset interval can be 1 minute, 3 minutes, 4 minutes, 5 minutes, 8 minutes, 10 minutes, or 15 minutes.

[0062] In some examples, in step S102, if the analyte information in the analyte data is an analyte level, the analyte data can be directly used as concentration data. For example, if the analyte information is glucose concentration and ketone body concentration, the glucose concentration data can include multiple consecutive glucose concentrations, and the ketone body concentration data can include multiple consecutive ketone body concentrations. In some examples, if the analyte information in the analyte data is an analyte signal, the analyte signal can be converted into an analyte level to obtain concentration data.

[0063] In some examples, in step S103, the multiple views may include various views related to concentration data within a time period. In some examples, data for the appropriate time period can be selected from the determined concentration data using the time period as a search criterion for displaying the view.

[0064] Furthermore, the time period can be a period that involves at least both energy intake and energy expenditure processes. In some examples, the time period can be one day. That is, a view can be generated based on daily concentration data. In this case, the concentration data includes data on at least both energy intake and energy expenditure processes, facilitating the observer's analysis of energy metabolism. Additionally, daily glucose concentration data can also reflect the energy intake throughout the day, making the energy intake information carried in the glucose concentration data more accurate.

[0065] In some examples, the multiple views may include at least one of a first view 11, a second view 12, a third view 13, a fourth view 14, and a fifth view 15. Each view is described in detail below.

[0066] In some examples, the first view 11 may include a graphical display 111. The graphical display 111 may be generated based on concentration data over a time period.

[0067] Figure 4A is a schematic diagram showing the first view 11 according to the present disclosure example. Figure 4B is a schematic diagram showing a first embodiment of the graphic display 111 of the arrangement of the first view 11 according to the present disclosure example. Figure 4C is a schematic diagram showing a second embodiment of the graphic display 111 of the arrangement of the first view 11 according to the present disclosure example. Figure 5 is a schematic diagram showing the glucose concentration curve and ketone body concentration curve of a single graphic display 111 according to the present disclosure example, wherein the green curve represents the glucose concentration curve (i.e., the upper curve) and the red curve represents the ketone body concentration curve (i.e., the lower curve).

[0068] Referring to Figure 4A, in some examples, the graphical display 111 of the first view 11 may include a distribution region D1, which may represent the distribution of multiple data points. The location of each data point can be determined by the glucose concentration and ketone body concentration at the same time. In this case, the first view 11 facilitates the identification of the relationship between changes in glucose concentration and changes in ketone body concentration, and makes the comparison between glucose concentration and ketone body concentration more intuitive, thereby improving the readability of concentration data, making it more user-friendly for observers, and helping observers analyze energy metabolism.

[0069] For example, by graphically displaying the horizontal and vertical spans of the analyte in section 111, changes in ketone body concentration can be identified as glucose concentration changes over a time period. These changes can provide a preliminary assessment of energy metabolism. An exemplified scenario is as follows: assuming the horizontal span represents the glucose concentration span and the vertical span represents the ketone body concentration span, if the graph shows section 111 to be narrower horizontally and longer vertically—that is, if section 111 is roughly a narrow strip with the narrower side representing glucose concentration—it can be preliminarily concluded that the target subject 8's energy intake is very low, insufficient to cover energy expenditure.

[0070] In addition, the display of glucose concentration data and ketone body concentration data provides an easier view for observers to analyze energy metabolism, which improves the convenience of energy metabolism analysis compared to existing methods that rely on the habits of the target subject or require additional equipment.

[0071] In addition, the first view 11 can more effectively display and analyze analyte data, making it more observer-friendly and able to reflect the relationships between analyte data, thus helping observers analyze energy metabolism.

[0072] Additionally, "same time" can be any time that allows for the correlation of glucose and ketone body concentrations collected at similar or identical times. In some examples, "same time" can refer to the same instant or the same time period.

[0073] In some examples, the graph 111 may also include lines depicting the boundaries of heat regions, which can be areas where the density of data points is greater than a preset density. In this case, it helps the observer distinguish between areas with concentrated and sparse data point distribution, facilitating the identification of important and noisy information in the concentration data, thereby improving the accuracy of the observer's analysis of energy metabolism. Furthermore, the preset density can be an empirical value.

[0074] In some examples, data points can be generated by using color variations to represent their density. In this case, the concentration data is highly repetitive, and using color variations to represent data points not only shows their distribution but also their concentration, providing more information and helping observers focus on important information in high-density areas, thereby improving the accuracy of the observer's analysis of energy metabolism. As an example, referring to Figure 4A, the brighter the color, the greater the density.

[0075] In some examples, an observer can analyze energy metabolism by observing at least one of the following information: the outline of the graph, the range of glucose concentration, and the range of ketone body concentration.

[0076] Specifically, the outline of the graph 111 helps observers to roughly judge the relationship between changes in glucose concentration and ketone body concentration. For example, the height or width of the outline can roughly indicate the energy metabolism status. Furthermore, if there is little noise in the concentration data, and energy metabolism is relatively balanced, the outline of the graph 111 may exhibit a hysteretic distribution. That is, there may be no data points in the middle of the graph 111. Specifically, when ketone body concentration is high, if carbohydrate intake begins, glucose concentration starts to rise. At this point, gluconeogenesis decreases, and ketone body concentration begins to slowly decrease. When glucose concentration begins to decrease, gluconeogenesis has not yet occurred due to the presence of glycogen reserves, and ketone body concentration remains essentially unchanged. When glucose concentration decreases to a relatively stable level, ketone body concentration begins to rise. These processes are reflected in the graph 111 as a hysteretic distribution of its outline.

[0077] In some examples, in response to receiving a user event from a single graphical display 111, a subview related to the concentration data of the graphical display 111 can be displayed. This facilitates in-depth analysis of energy metabolism by the observer. In some examples, the display of the subview may include at least one of jumping, hovering, and toggling.

[0078] In some examples, a first subview may be displayed in response to receiving a first user event from a single graphical display 111. The first subview may include the glucose concentration curve and ketone body concentration curve of the single graphical display 111 (i.e., the glucose concentration curve and ketone body concentration curve over the time period of the single graphical display 111). This allows the observer to analyze energy metabolism by combining the concentration curves. As an example, Figure 5 shows a schematic diagram of the glucose concentration curve and ketone body concentration curve of the single graphical display 111 plotted on the same coordinate system.

[0079] In some examples, a second subview can be displayed in response to receiving a second user event from a single graphical display 111. The second subview may include glucose concentration curves and ketone body concentration curves for the nighttime period (i.e., glucose and ketone body concentration curves for the nighttime period within the time period of the single graphical display 111). In this case, nighttime data is generally less susceptible to external interference and exhibits more regular fluctuations compared to daytime concentration data (e.g., daytime ketone body concentration fluctuations are generally not very regular), and some of the target object's daytime behaviors will also be reflected in the nighttime data. For example, the inventors found that under a normal diet, ketone body concentration is more stable at night, while under a low-carb diet, ketone body concentration gradually increases at night, and nighttime glucose concentration remains stable within a relatively narrow range compared to a normal diet. That is, nighttime data can reflect energy metabolism and helps observers analyze energy metabolism.

[0080] In addition, the nighttime hours can be set according to the daily routine of the target object 8, and this disclosure does not specifically limit them. For example, the nighttime hours can be from 10 pm to 6 am.

[0081] In some examples, in response to an isolated data point, a third subview can be displayed upon receiving a third user event indicating that the isolated data point is isolated. This third subview can include detailed information corresponding to the isolated data point. In this case, it facilitates analysis of the reasons for the isolated data point to determine whether it needs to be excluded, thereby enabling a more accurate analysis of energy metabolism using data points. Furthermore, the detailed information can include at least the acquisition time, glucose concentration, and ketone body concentration.

[0082] In some examples, the third subview may also include glucose concentration curves and ketone body concentration curves marking the locations of isolated data points. This further facilitates the analysis of the reasons for the isolated data points.

[0083] Furthermore, user events (e.g., first user event, second user event, and third user event) can be any event from the user interface. For example, taking cursor events as an example, user events can include at least one of click, double click, hover, move in, and move out.

[0084] Furthermore, the first user event, the second user event, and the third user event can be different. This makes it easier to distinguish between different requests from the user interface.

[0085] In some examples, the first view 11 may also include a target area. The target area can be a region corresponding to an energy target. This helps the observer analyze the gap between the target and the desired energy level. Additionally, the energy target can be the desired energy metabolism level. In some examples, the energy target can be set by the observer. In some examples, the target area can be displayed in association with distribution region D1. This facilitates comparison between distribution region D1 and the target area.

[0086] In some examples, the first view 11 may also include energy information over a single time period. Additionally, the energy information may be an estimate of the relationship between energy intake and energy expenditure of the target object 8. This helps the observer interpret the energy metabolism depicted in the graphical display 111. In some examples, the energy information may be displayed in association with the graphical display 111. For example, the energy information corresponding to each graphical display 111 may be displayed near that graphical display 111.

[0087] In some examples, the relationship can be the difference between energy intake and energy expenditure. This allows for a quantitative assessment of energy metabolism. In some examples, the relationship can be whether energy intake and expenditure are balanced. In some examples, the relationship can be whether energy intake is insufficient. Additionally, balance can indicate that energy intake exactly covers energy expenditure.

[0088] In some examples, at least one feature extracted from historical concentration data over a time period can be input into the target model to determine energy information. Alternatively, the historical concentration data can be historical data of concentration data. In some examples, the at least one feature may include at least one of the following: raw historical concentration data, graphical display 111, features extracted from graphical display 111, spans of glucose concentration, spans of ketone body concentration, and average glucose concentration.

[0089] In some examples, the target model can be a regression model. In this case, because the regression model predicts continuous values, it allows energy information to be continuous, which is helpful for quantitative analysis of energy metabolism. In other examples, the target model can also be a non-regression model, such as a classification model.

[0090] In some examples, the labels for training samples can be the relationship between energy intake and energy expenditure for the target model. In some examples, the labels for training samples can be estimated based on physiological indicators related to the physical activity level of the sample objects. In some examples, heart rate can be used to estimate energy expenditure to determine the labels for training samples. For example, using the difference as a relation, the energy expenditure of target object 8 can be estimated using heart rate, and then the difference can be obtained by subtracting the energy intake of the sample object. In some examples, the labels for training samples can be determined through a user interface. That is, the labels for training samples can be input by the user (e.g., target object 8).

[0091] In some examples, if the target model is trained online, the sample object can also be target object 8. That is, the target model can be trained using the data of target object 8 itself.

[0092] In some examples, the first view 11 may also include the average glucose concentration (hereinafter referred to as the glucose average) and the span of ketone body concentration data for the nighttime period of a single time period. In this case, the glucose average for the nighttime period is affected by daytime energy intake, and combining the glucose average can improve the accuracy of energy metabolism analysis. Furthermore, regarding the span of ketone body concentration, as mentioned above, ketone body concentration fluctuates relatively regularly at night and can reflect energy metabolism; combining the span of ketone body concentration can improve the accuracy of energy metabolism analysis.

[0093] In some examples, the style of the graphic display (111) can be set according to the energy information. This makes it easier to distinguish different types of energy information and draw the observer's attention to specific types of energy information. For example, the style can be a background color, a border color, or the addition of icons.

[0094] In some examples, the first view 11 may also include a first coordinate axis and a second coordinate axis perpendicular to the first coordinate axis, wherein the scale of the first coordinate axis may be a scale for glucose concentration, and the scale of the second coordinate axis may be a scale for ketone body concentration. As an example, referring back to Figure 4A, a first view 11 for a single time period is shown, wherein the scale of the horizontal axis is a scale for glucose concentration, and the scale of the vertical axis is a scale for ketone body concentration, with the unit of the scale being mmol / L (millimoles per liter).

[0095] In some examples, the first view 11 may also include target ranges for glucose concentration data and ketone body concentration data, displayed graphically. These target ranges may include at least an upper and a lower limit. This facilitates the observer's identification of whether the analyte level is within the target range. Additionally, the target range may refer to a relatively safe interval for the analyte level.

[0096] Referring to Figures 4B and 4C, in some examples, the graphic displays 111 can be arranged in a matrix in the first view 11, wherein the rows or columns of the matrix are arranged in ascending order of time period. In this case, it is convenient to display more graphic displays 111 in a limited display space. In addition, the continuous display of graphic displays 111 helps the observer to observe the changes in energy metabolism over time, thereby helping the observer to take improvement measures.

[0097] In some examples, the number of graphic displays 111 shown in the rows or columns of the matrix is ​​determined by a preset size. This facilitates defining the distribution of the graphic displays 111 within a specific display space.

[0098] Additionally, the preset size can be a size that defines the size of the first view 11. In some examples, the preset size can be determined by the size of the display unit. In some examples, the preset size can be determined by the display space allocated to the first view 11.

[0099] Referring to Figure 4C, in some examples, the graphical display 111 of the matrix rows or columns can be a graphical display 111 of a week. In this case, displaying data weekly helps track the progress of short-term goals, urging target object 8 to complete improvement measures on time. Additionally, it also helps to further improve the readability of concentration data.

[0100] Referring again to Figure 4C, in some examples, when the graph displays 111 by week, the first view may also include a time indicator, which can be used to display time information of the time period of the first graph display 111 in the row or column of the matrix.

[0101] In some examples, the graphical display 111 shown in the rows or columns of the matrix can be a graphical display 111 within the service life of the analyte sensor 71. In this case, the concentration data corresponding to the graphical display 111 in the same row or column comes from the same analyte sensor 71, which can reduce the negative impact of the deviation between different analyte sensors 71 on the observer's analysis of energy metabolism. In some examples, the service life can be 14 days, 1 month, or 1 year, etc.

[0102] In some examples, the rows or columns of the matrix can be graphically displayed as a single month. In this case, displaying data monthly helps track progress toward long-term goals and aids in understanding long-term changes in energy metabolism.

[0103] Referring to Figures 4B and 4C, in some examples, the displayed graph 111 can be assigned to the dietary period type, and the dietary period type can be shown. This helps the observer analyze energy metabolism in conjunction with the dietary period type. Additionally, the dietary period type can indicate the energy intake of the target subject 8. For example, whether the target subject 8 is in a low-carb diet or a normal diet period. In some examples, the dietary period type can include at least a low-carb diet period and a normal diet period.

[0104] Referring to Figures 4B and 4C, in some examples, the first view 11 may include a graphically displayed diet period diagram 112. In some examples, the diet period diagram 112 may include multiple different graphical elements representing different types of diet periods. In some examples, the diet period diagram 112 may also include text labels 1121, which may be used to help understand the types of diet periods represented by the graphical elements.

[0105] In some examples, the graphical elements of the diet diagram 112 can be distributed along a continuous time period. This helps the observer understand the duration of the corresponding diet.

[0106] In some examples, the first view 11 may also include a filtering component. The filtering component can be used to control the time period for which the displayed graphics 111 are shown. For example, the filtering component could be a progress bar.

[0107] Figure 6 is a schematic diagram illustrating the second view 12 involved in the example of this disclosure.

[0108] As described above, multiple views may include a second view 12. In some examples, the second view 12 may be generated based on energy information. This helps observers interpret the energy metabolism reflected in the concentration data. Furthermore, the energy information facilitates quantitative assessment of energy metabolism, enabling observers to analyze energy metabolism more accurately.

[0109] In some examples, the second view 12 can graphically represent the changes in energy information over multiple time periods. This makes it easier for the observer to understand the trends in energy information changes. As an example, Figure 6 shows a schematic diagram of how the difference between energy intake and energy expenditure (i.e., the relationship is the difference) changes over time periods.

[0110] In some examples, energy information can be assigned to relational intervals, with different intervals represented by different colors, and each interval can include at least a balanced interval (see the shaded area in Figure 6). This makes it easier for the observer to track the distribution of energy information. In some examples, the relational intervals may also include intervals of insufficient energy intake and intervals of excessive energy intake.

[0111] In some examples, the first view 11 and the second view 12 can be displayed side by side or switched. In this case, it is convenient to browse the first view 11 and the second view 12, making it easier for the observer to analyze energy metabolism by combining the first view 11 and the second view 12.

[0112] Figure 7 is a schematic diagram illustrating the third view 13 involved in the example of this disclosure.

[0113] As described above, multiple views may include a third view 13. In some examples, the third view 13 may be generated based on concentration data over a time period. Referring to Figure 7, in some examples, the third view 13 may include glucose concentration curves and ketone body concentration curves over multiple time periods. For example, if the time period is one day, the third view 13 may include glucose concentration curves and ketone body concentration curves for multiple days. In this case, combining the third view 13 makes it easier for the observer to identify the changes in energy intake and energy expenditure of the target subject 8, and helps to highlight the fluctuation characteristics of the concentration data. The fluctuation characteristics of glucose concentration help to identify energy intake, and the fluctuation characteristics of ketone body concentration help to identify energy expenditure, thereby improving the accuracy of the observer's analysis of energy metabolism.

[0114] In some examples, in the third view 13, glucose concentration curves for multiple time periods can be represented by different colors, and ketone body concentration curves for multiple time periods can also be represented by different colors. In some examples, the glucose concentration curve and ketone body concentration curve for the same time period can be the same color. This facilitates comparison of glucose and ketone body concentrations for the same time period.

[0115] In some examples, in the third view 13, clicking on the glucose concentration curve highlights the ketone body concentration curve for the same time period, and clicking on the ketone body concentration curve highlights the glucose concentration curve for the same time period. This facilitates comparison of glucose and ketone body concentrations for the same time period.

[0116] Figure 8 is a schematic diagram illustrating the fourth view 14 involved in the example of this disclosure. In Figure 8, circles represent the average glucose concentration over a single time period, and squares represent the average ketone body concentration over a single time period (hereinafter referred to as the ketone body average).

[0117] As described above, multiple views may include a fourth view 14. In some examples, the fourth view 14 may be generated based on concentration data over a time period. Referring to Figure 8, in some examples, the fourth view 14 may include average lines of glucose and average lines of ketone bodies for each time period across multiple time periods. In this case, incorporating the fourth view 14 facilitates the observer's further confirmation of the general changes in energy intake and expenditure of the target subject 8 over time periods, thereby improving the accuracy of the observer's analysis of energy metabolism.

[0118] Figure 9 is a schematic diagram illustrating the fifth view 15 involved in the example of this disclosure.

[0119] As mentioned above, multiple views can include a fifth view 15. In some examples, the fifth view 15 may be generated based on concentration data during the nighttime period within a time period. In this case, as mentioned above, the nighttime data can reflect energy metabolism and help the observer analyze energy metabolism.

[0120] Referring to Figure 9, in some examples, the fifth view 15 may include multiple data points, the location of which can be determined by the average glucose and ketone body values ​​during the nighttime period of a single time period. This facilitates the analysis of the relationship between the average glucose and ketone body values ​​during the nighttime period.

[0121] Specifically, by fitting average glucose and ketone body data during the nighttime period, the inventors discovered that when the average glucose level increases, the average ketone body level decreases, and vice versa; that is, there is an inverse relationship between average glucose and average ketone body levels. Furthermore, changes in average glucose are related to energy intake (e.g., the average glucose level of a normal diet is higher than that of a low-carbohydrate diet). In this context, incorporating fifth view 15 can improve the accuracy of the observer's analysis of energy metabolism.

[0122] In some examples, at least two views from multiple views can be displayed simultaneously or switched between. In some examples, fifth view 15 and fourth view 14 can be displayed simultaneously. This allows the observer to simultaneously observe multiple display methods for average glucose and average ketone bodies.

[0123] Figure 10 is an exemplary block diagram illustrating the production system 2 as described in this disclosure.

[0124] Additionally, this disclosure provides an example of a system for generating views related to glucose and ketone bodies (hereinafter referred to as generation system 2), which may also be called a data display system or a generation system. Generation system 2 can be used to implement the generation method described above. It should be noted that, unless there is a contradiction, the above description of the generation method also applies to generation system 2.

[0125] Referring to Figure 10, in some examples, the generating system 2 may include a receiving module 21, a determining module 22, and an output module 23. The receiving module 21 may be used to receive analyte data from the analyte sensor 71. The determining module 22 may be used to determine concentration data, including glucose concentration data and ketone body concentration data, based on the received analyte data. The output module 23 may be used to display multiple views on a display unit based on the concentration data.

[0126] In some examples, multiple views may include various views related to concentration data within a time period. In some examples, multiple views may include a first view 11, which may include a graphical display 111. The graphical display 111 may be generated based on concentration data within a time period. In some examples, the graphical display 111 of the first view 11 may include a distribution region D1, which may represent the distribution of multiple data points, the location of each data point being determined by the glucose concentration and ketone body concentration at the same time. See the relevant description of step S103 for details.

[0127] Examples of this disclosure also disclose a computer-readable storage medium that can store at least one instruction, which, when executed by a processor, implements one or more steps of the above-described generation method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0128] While the present disclosure has been specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the foregoing description does not limit the present disclosure in any way. Those skilled in the art can make modifications and variations to the present disclosure as needed without departing from its essential spirit and scope, and all such modifications and variations shall fall within the scope of the present disclosure.

Claims

1. A method for generating a view related to data on glucose and ketone bodies, characterized in that, include: Analytical data is received from an analyte sensor; analyte concentration data, including glucose concentration data and ketone body concentration data, is determined based on the received analyte data; and multiple views, including a first view, are displayed on a display unit based on the analyte concentration data, wherein the first view includes a graphical display generated based on the analyte concentration data over a time period, the graphical display including a distribution area representing the distribution of multiple data points, the position of each data point being determined by the glucose concentration and ketone body concentration at the same time.

2. The method according to claim 1, characterized in that, The plurality of views also includes a second view generated based on energy information, which graphically represents the changes in the energy information over multiple time periods. The energy information is an estimated relationship between the energy intake and energy consumption of the target object, wherein the energy intake is the energy from carbohydrates ingested by the target object, and the energy consumption is the energy consumed by the target object.

3. The method according to claim 1, characterized in that, The displayed graphic will be assigned to the dietary period category and the dietary period category will be displayed.

4. The method according to claim 1, characterized in that, The graphical display also includes lines drawing the boundaries of heat regions in a graphical manner, where the heat regions are areas where the density of the data points is greater than a preset density.

5. The method according to claim 1, characterized in that, The data points are generated by using color changes to represent the density of the data points.

6. The method according to claim 1, characterized in that, In response to receiving a first user event from a single graphical display, a first subview is displayed, wherein the first subview includes the glucose concentration curve and ketone body concentration curve of the single graphical display; and / or in response to receiving a second user event from a single graphical display, a second subview is displayed, wherein the second subview includes the glucose concentration curve and ketone body concentration curve for the nighttime period.

7. The method according to any one of claims 1 to 6, characterized in that, The time period is one day.

8. The method according to claim 7, characterized in that, In the first view, the graphic displays are arranged in a matrix, wherein the graphic displays in the rows or columns of the matrix are arranged in ascending order of time period; the number of graphic displays in the rows or columns of the matrix is ​​determined by a preset size; the graphic displays in the rows or columns of the matrix are graphic displays within one week; the graphic displays in the rows or columns of the matrix are graphic displays within the service life of the analyte sensor; and / or the graphic displays in the rows or columns of the matrix are graphic displays within one month.

9. The method according to any one of claims 1 to 6 and 8, characterized in that, The plurality of views also includes at least one of the following views: A third view is generated based on the analyte concentration data over a time period, the third view including glucose concentration curves and ketone body concentration curves over multiple time periods; A fourth view is generated based on the analyte concentration data within a time period, the fourth view including lines of average glucose concentration and average ketone body concentration for each time period in multiple time periods. A fifth view is generated based on the analyte concentration data during the nighttime period within a time period. The fifth view includes multiple data points, the location of which is determined by the average of the glucose concentration and the average of the ketone body concentration during the nighttime period of a single time period.

10. A system for generating views related to data on glucose and ketone bodies, characterized in that, include: The system includes a receiving module, a determining module, and an output module; the receiving module is used to receive analyte data from the analyte sensor. The determining module is used to determine analyte concentration data, including glucose concentration data and ketone body concentration data, based on the received analyte data; the output module is used to display multiple views, including a first view, on a display unit based on the analyte concentration data, wherein the first view includes a graphical display generated based on the analyte concentration data within a time period, the graphical display including a distribution area representing the distribution of multiple data points, the position of each data point being determined by the glucose concentration and ketone body concentration at the same time.

Images