Systems, devices and methods for analyte monitoring

Abstract

A glucose monitoring system includes a sensor control device including an analyte sensor coupled to the sensor electronics and configured to transmit data indicative of an analyte level of a subject, and a reader device, the reader device including wireless communication circuitry configured to receive data indicative of an analyte level and a glycated hemoglobin level of the subject, a non-transitory memory, and at least one processor communicatively coupled to the non-transitory memory and the analyte sensor, the at least one processor configured to calculate a plurality of personalized glucose metrics for the subject using the at least one physiological parameter and at least one of the received data indicative of the analyte level or the received glycated hemoglobin level, and display a report on a display of the reader device including a plurality of interfaces including at least two or more of the received data indicative of the analyte level, the received glycated hemoglobin level, or the calculated plurality of personalized glucose metrics, the plurality of interfaces comprising the report being based on a user type.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit under 35 U.S.C. § 119 of U.S. Provisional Patent Application No. 63 / 279,509, filed November 15, 2021, which is incorporated herein by reference in its entirety for all purposes.

[0002] The subject matter described herein relates generally to improved analyte monitoring systems, and related methods and devices. [Background technology]

[0003] Detecting and / or monitoring analyte levels such as glucose, ketones, lactate, oxygen, hemoglobin A1C, albumin, alcohol, alkaline phosphatase, alanine transaminase, aspartate aminotransferase, bilirubin, blood urea nitrogen, calcium, carbon dioxide, chloride, creatinine, hematocrit, lactate, magnesium, oxygen, pH, phosphorus, potassium, sodium, total protein, or uric acid may be important to the health of individuals suffering from diabetes. Patients suffering from diabetes may suffer from complications including impaired consciousness, cardiovascular disease, retinopathy, neuropathy, and nephropathy. Diabetic patients generally need to monitor their glucose levels to ensure that they are maintained within a clinically safe range, and this information may also be used to determine whether and / or when insulin is needed to lower glucose levels in the body, or when more glucose is needed to raise glucose levels in the body.

[0004] There is a growing body of clinical data demonstrating a strong correlation between frequency of glucose monitoring and glycemic control. However, despite this correlation, many individuals diagnosed with diabetes do not monitor their glucose levels as frequently as they should due to a combination of factors including the convenience, sensitivity of testing, and the discomfort and expense associated with glucose testing.

[0005] To increase patient compliance with a frequent glucose monitoring regimen, in vivo analyte monitoring systems may be utilized in which a sensor control device may be worn on the body of an individual requiring analyte monitoring. The sensor control device may have a small form factor for increased comfort and convenience of the individual and may be applied by the individual using a sensor applicator. The application process includes inserting, using the applicator or insertion mechanism, at least a portion of a sensor that detects an analyte level in a user's bodily fluid present within a layer of the human body such that the sensor is in contact with the bodily fluid. The sensor control device may also be configured to transmit the analyte data to another device from which the individual, a healthcare provider ("HCP"), or a caregiver may review the data and make treatment decisions. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] U.S. Patent No. 9,913,600 [Patent Document 2] International Publication No. 2018 / 136898 [Patent Document 3] International Publication No. 2019 / 236850 [Patent Document 4] US Patent Application Publication No. 2020 / 01969191 [Patent Document 5] U.S. Patent No. 6,175,752 [Patent Document 6] US Patent Application Publication No. 2011 / 0213225 [Patent Document 7] US Patent Application Publication No. 2018 / 0235524 [Patent Document 8] International Publication No. 2020 / 086934 [Patent Document 9] International Publication No. 2021 / 108419 [Patent Document 10] International Publication No. 2021 / 108431 [Patent Document 11] U.S. Provisional Patent Application No. 62 / 939,970 [Patent Document 12] U.S. Provisional Patent Application No. 63 / 015,044 [Patent Document 13] U.S. Provisional Patent Application No. 63 / 081,599 [Patent Document 14] U.S. Provisional Patent Application No. 62 / 939,956 [Patent Document 15] US Patent Application Publication No. 2018 / 052,805 [Patent Document 16] US Patent Application Publication No. 2017 / 832,537 Summary of the Invention [Problem to be solved by the invention]

[0007] However, some people are reluctant to use sample monitoring systems despite their benefits for a variety of reasons, including the complexity and volume of data presented, the learning curve associated with the software and user interface of the sample monitoring systems, and the overall lack of actionable information presented.

[0008] Thus, there is a need for improved digital and graphical user interfaces for analyte monitoring systems and related methods and apparatus that are robust and user-friendly and provide timely and actionable responses. [Means for solving the problem]

[0009] The objects and advantages of the presently disclosed subject matter will be set forth in and apparent from the following description, as well as will be learned by the practice of the presently disclosed subject matter. Additional advantages of the presently disclosed subject matter will be realized and attained by the methods and systems particularly pointed out in the specification and claims hereof, as well as from the appended drawings.

[0010] To achieve these and other advantages, and in accordance with the objectives of the presently disclosed subject matter embodied and broadly described, the presently disclosed subject matter relates to a system for monitoring glucose. According to an embodiment, the system for monitoring glucose can include a sensor controller and a reader device. The sensor controller can include an analyte sensor coupled to the sensor electronics and can be configured to transmit data indicative of an analyte level of the subject. The reader device can include wireless communication circuitry configured to receive data indicative of an analyte level and a glycated hemoglobin level of the subject, a non-transitory memory, and at least one processor communicatively coupled to the non-transitory memory and the analyte sensor, the at least one processor configured to calculate a plurality of personalized glucose metrics for the subject using the at least one physiological parameter and at least one of the received data indicative of the analyte level or the received glycated hemoglobin level, and display a report on a display of the reader device including a plurality of interfaces including at least two or more of the received data indicative of the analyte level, the received glycated hemoglobin level, or the calculated plurality of personalized glucose metrics, the plurality of interfaces comprising the report being based on a user type.

[0011] As embodied herein, the plurality of personalized glucose metrics may include one or more of adjusted A1c or personalized A1c, calculated A1c, adjusted calculated A1c, personalized glucose, personalized average glucose, or personalized time in range. Additionally, the at least one processor may be configured to calculate a plurality of personalized glucose targets corresponding to the plurality of calculated personalized glucose metrics. The plurality of interfaces may further include a plurality of personalized glucose targets. Additionally, the plurality of personalized glucose targets may include one or more of a target glucose range or a target average glucose. As embodied herein, the personalized target glucose range may include a personalized lower glucose limit. Alternatively, the personalized target glucose range may include a personalized upper glucose limit.

[0012] As embodied herein, the at least one physiological parameter can be selected from the group consisting of an red blood cell glucose uptake rate, an red blood cell life span, an red blood cell glycation rate constant, an erythropoiesis rate constant, an red blood cell removal constant, and an apparent glycation constant. Further, the plurality of interfaces can include the at least one physiological parameter of the subject.

[0013] As embodied herein, the user type can include a medical professional. Further, the plurality of interfaces can include a glucose monitoring data interface, a glycated hemoglobin interface, a personalized a1c interface, a personalized glucose interface, a personalized average glucose interface, and a personalized time in range interface.

[0014] As embodied herein, the user type can include a subject. Further, the plurality of interfaces can include a glucose monitoring data interface, a glycated hemoglobin interface, an average glucose interface, and a time in range interface.

[0015] As embodied herein, multiple interfaces for constructing a report can be predetermined based on the user type.

[0016] In this specification, multiple interfaces for constructing a report can be selected by the user.

[0017] As embodied herein, the at least one processor may be further configured to output a notification when at least one of the plurality of personalized glucose metrics is equal to or greater than a corresponding plurality of personalized glucose goals. As embodied herein, the notification may be a visual notification. Alternatively, the notification may be an audio notification. The notification may be an alarm. As embodied herein, the notification may be a prompt.

[0018] As embodied herein, the reader device can wirelessly receive the subject's glycated hemoglobin level from an electronic medical record system.

[0019] As embodied herein, the reader device can wirelessly receive the subject's glycated hemoglobin level from a cloud-based database.

[0020] As embodied herein, a reader device can wirelessly receive the subject's glycated hemoglobin level from the QR code.

[0021] As embodied herein, the reader device can wirelessly receive the subject's glycated hemoglobin level from a home test kit.

[0022] Details of the subject matter described herein, both as to its structure and operation, can be made apparent by examining the accompanying figures, in which like elements are designated by like reference numerals. The components in the figures are not necessarily to scale, with emphasis instead being placed upon illustrating the principles of the subject matter. Moreover, all figures are intended to convey concepts, and relative sizes, shapes and other detailed attributes may be shown diagrammatically, rather than literally or precisely. [Brief description of the drawings]

[0023] [Figure 1] 1 is a system overview of an analyte monitoring system including a sensor applicator, a sensor control device, a reader device, a network, a trusted computer system, and a local computer system. [Figure 2A] FIG. 2 is a block diagram illustrating an example embodiment of a reader device. [Figure 2B] FIG. 1 is a block diagram showing an example embodiment of a sensor control device. [Figure 2C] FIG. 1 is a block diagram showing an example embodiment of a sensor control device. [Figure 2D] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2E] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2F] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2G] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2H] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2I] 1 is an example embodiment of a GUI including a sensor results interface. [Figure 2J] 1 is an example embodiment of a GUI including a glucose monitoring data interface and a calculated A1c interface. [Figure 2K]1 is an example embodiment of a GUI including a glucose monitoring data interface and a calculated A1c interface. [Figure 2L] 1 is an example embodiment of a GUI including a glucose monitoring data interface and a calculated A1c interface. [Figure 3A] 1 is an example embodiment of a GUI including a within range time interface. [Figure 3B] 1 is an example embodiment of a GUI including a within range time interface. [Figure 3C] 1 is an example embodiment of a GUI including a within range time interface. [Figure 3D] 1 is an example embodiment of a GUI including a within range time interface. [Figure 3E] 1 is an example embodiment of a GUI including a within range time interface. [Figure 3F] 1 is an example embodiment of a GUI including a within range time interface. [Figure 4A] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4B] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4C] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4D] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4E] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4F] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4G] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4H] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4I] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4J] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4K] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4L] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4M] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4N] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 4O] 1 is an example embodiment of a GUI including a specimen level and trend alert interface. [Figure 5A] 1 is an example embodiment of a GUI that includes a sensor usage interface. [Figure 5B] 1 is an example embodiment of a GUI that includes a sensor usage interface. [Figure 5C] 13 is an example embodiment of a report GUI including sensor usage information. [Figure 5D] 13 is an example embodiment of a report GUI including sensor usage information. [Figure 5E] 13 is an example embodiment of a report GUI including sensor usage information. [Figure 5F] 13 is an example embodiment of a report GUI including sensor usage information. [Figure 5G] 1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 5H] 1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 5I] 1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 5J]1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 5K] 1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 5L] 1 is an example embodiment of a GUI associated with an analyte monitoring software application. [Figure 6A] FIG. 1 is a flow diagram illustrating an example embodiment of a method for backfilling data in an analyte monitoring system. [Figure 6B] FIG. 1 is a flow diagram illustrating an example embodiment of a method for backfilling data in an analyte monitoring system. [Figure 6C] FIG. 1 is a flow diagram illustrating an example embodiment of a method for disconnection and reconnection event aggregation in an analyte monitoring system. [Figure 7] FIG. 1 is a flow diagram illustrating an example embodiment of a method for failed or lapsed sensor transmissions in an analyte monitoring system. [Figure 8A] FIG. 1 is a flow diagram illustrating an example embodiment of a method for merging data in an analyte monitoring system. [Figure 8B] FIG. 1 is a flow diagram illustrating an example embodiment of a method for merging data in an analyte monitoring system. [Figure 8C] 13A-13C are graphs illustrating data at various processing stages according to an example embodiment of a method for data merging in an analyte monitoring system. [Figure 8D] 13A-13C are graphs illustrating data at various processing stages according to an example embodiment of a method for data merging in an analyte monitoring system. [Figure 8E] 13A-13C are graphs illustrating data at various processing stages according to an example embodiment of a method for data merging in an analyte monitoring system. [Figure 9A] FIG. 1 is a flow diagram illustrating an example embodiment of a method for sensor migration in an analyte monitoring system. [Figure 9B] 13 illustrates an example embodiment of a GUI displayed according to an example embodiment of a sensor migration method in an analyte monitoring system. [Figure 9C] 13 illustrates an example embodiment of a GUI displayed according to an example embodiment of a sensor migration method in an analyte monitoring system. [Figure 9D] 13 illustrates an example embodiment of a GUI displayed according to an example embodiment of a sensor migration method in an analyte monitoring system. [Figure 10A] FIG. 1 is a flow diagram illustrating an example embodiment of a method for generating a failed sensor insertion system alarm. [Figure 10B] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a failed sensor insertion system alarm. [Figure 10C] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a failed sensor insertion system alarm. [Figure 10D] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a failed sensor insertion system alarm. [Figure 11A] FIG. 11 is a flow diagram illustrating an example embodiment of a method for generating a sensor termination system alarm. [Figure 11B] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a sensor termination system alarm. [Figure 11C] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a sensor termination system alarm. [Figure 11D] 11 is an example embodiment of a GUI that is displayed in accordance with an example embodiment of a method for generating a sensor termination system alarm. [Figure 12] FIG. 1 illustrates an example timeline 100 showing the collection of at least one HbA1c value and multiple glucose levels over a period of time. [Figure 13] FIG. 1 illustrates an example of a physiological parameter analysis system that provides physiological parameter analysis in accordance with some embodiments of the present disclosure. [Figure 14] FIG. 1 illustrates an example of a physiological parameter analysis system that provides physiological parameter analysis in accordance with some embodiments of the present disclosure. [Figure 15]FIG. 1 illustrates an example of a post-calculated HbA1c (eHbA1c) report that can be generated as output by a physiological parameter analysis system according to some embodiments of the present disclosure. [Figure 16A] FIG. 1 illustrates an example of a method for determining a personalized target glucose range according to some embodiments of the present disclosure. [Figure 16B] FIG. 13 illustrates an example of a personalized target glucose range report that can be generated as output by a physiological parameter analysis system according to some embodiments of the present disclosure. [Figure 17] FIG. 1 illustrates an example of a personalized target average glucose report that can be generated as output by a physiological parameter analysis system according to some embodiments of the present disclosure. [Figure 18A] FIG. 18B shows a comparison of laboratory HbA1c levels at day 200 (±5 days) against estimated HbA1c (eHbA1c) values ​​with different models than FIG. 18B. [Figure 18B] FIG. 18B shows a comparison of laboratory HbA1c levels at day 200 (±5 days) against estimated HbA1c (eHbA1c) values ​​with different models than in FIG. 18A. [Figure 18C] 1A and 1B show calculated HbA1c (cHbA1c) values ​​for the kinetic model of the present disclosure. [Figure 19] FIG. 1 shows example data for a study subject including measured glucose levels (solid line), laboratory HbA1c measurements (open circles), modeled cHbA1c values ​​(long dashed line), and modeled 14-day eHbA1c values ​​(dotted line). [Figure 20] FIG. 1 shows (1) the relationship between steady-state glucose and equilibrium HbA1c determined using a standard conversion of HbA1c to estimated mean glucose (dashed line with error bars), and (2) the relationship between steady-state glucose and equilibrium HbA1c measured for 90 participants (solid line). [Figure 21] 1 is a graph showing the relationship between K (dL / mg) and average glucose level target (mg / dl) with varying HbA1c target values ​​using the kinetic model of the present disclosure. [Figure 22]1 is a graphical representation of mean glucose and laboratory A1c. [Figure 23] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 24] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Diagram 25] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 26] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 27] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 28] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 29] FIG. 1 illustrates an example embodiment of a report of the present disclosure. [Figure 30-1] FIG. 1 illustrates an exemplary healthcare provider interface according to an embodiment of the present disclosure. [Figure 30-2] FIG. 1 illustrates an exemplary healthcare provider interface according to an embodiment of the present disclosure. [Figure 30-3] FIG. 1 illustrates an exemplary healthcare provider interface according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] Before describing the present subject matter in detail, it is to be understood that the disclosure is not limited to particular embodiments described, as such may, of course, vary, and the scope of the disclosure will be limited only by the appended claims, and the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0025] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include their plural referents unless the context clearly indicates otherwise.

[0026] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the publication dates provided may be different from the actual publication dates, which may require independent confirmation.

[0027] In general, embodiments of the present disclosure include GUIs and digital interfaces for analyte monitoring systems, as well as related methods and devices. Thus, many embodiments include an in vivo analyte sensor that is structurally configured such that at least a portion of the sensor is placed or can be placed within a user's body to obtain information about at least one analyte in the body. However, the embodiments disclosed herein may also be used with in vivo analyte monitoring systems that incorporate in vitro capabilities, as well as purely in vitro or ex vivo analyte monitoring systems, including completely non-invasive systems.

[0028] Additionally, for any of the method embodiments disclosed herein, systems and devices capable of performing each of these embodiments are also within the scope of the present disclosure. For example, sensor control device, reader device, local computer system, and trusted computer system embodiments are disclosed, which devices and systems may include one or more sensors, analyte monitoring circuitry (e.g., analog circuitry), memory (e.g., for storing instructions), power sources, communication circuitry, transmitters, receivers, processors (e.g., for executing instructions), and / or controllers capable of performing or facilitating the performance of any and all method steps.

[0029] As discussed above, the embodiments described herein provide an improved GUI for an analyte monitoring system that is highly intuitive, user friendly, and provides a user with rapid access to physiological information. According to some embodiments, a Time-in-Ranges GUI for an analyte monitoring system is provided that includes a number of bars or bar segments, each bar or bar segment indicating the time that the user's analyte level is within a predefined analyte range that correlates with the bar or bar segment. According to another embodiment, an analyte level / trend alert GUI for an analyte monitoring system is provided that includes visual notifications (e.g., prompts, alerts, alarms, pop-up windows, banner notifications, etc.) that include an alarm condition, an analyte level measurement associated with the alarm condition, and a trend indicator associated with the alarm condition. In summary, these embodiments provide a robust, user-friendly interface that can increase user engagement with the analyte monitoring system and provide timely, actionable responses by the user, to name a few advantages.

[0030] Additionally, embodiments described herein provide improved digital interfaces for analyte monitoring systems. According to some embodiments, improved methods and associated systems and devices are provided for data backfilling, aggregation of wireless communication link disconnection and reconnection events, expired or failed sensor transmissions, merging of data from multiple devices, transitioning from a previously working sensor to a new reader device, generating a sensor insertion failure system alarm, and generating a sensor termination system alarm. These digital interfaces collectively and individually improve the accuracy and completeness of the analyte data collected by the analyte monitoring system, the flexibility of the analyte monitoring system by allowing users to transition between different reader devices, and the alarm capabilities of the analyte monitoring system by providing more robust inter-device communication during certain adverse conditions, to name just a few examples. Other improvements and advantages are also provided. Various configurations of these devices are described in detail through exemplary embodiments only.

[0031] However, before describing these aspects of the embodiments in detail, it is first desirable to describe example devices that may be present, for example, in an in vivo analyte monitoring system, and examples of their operation, all of which may be used in conjunction with the embodiments described herein.

[0032] There are various types of in vivo analyte monitoring systems. A "Continuous Analyte Monitoring" system (or "Continuous Glucose Monitoring" system) can transmit data from a sensor controller to a reader device continuously without prompting, for example automatically according to a schedule. As another example, a "Flash Analyte Monitoring" system (or "Flash Glucose Monitoring" system, or simply "Flash" system) can transfer data from a sensor controller in response to a scan or request for data by a reader device, such as using Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocols. In vivo analyte monitoring systems can also operate without the need for finger stick calibration.

[0033] In vivo analyte monitoring systems can be distinguished from "in vitro" systems which include a meter device that contacts a biological sample outside the body (or "ex vivo"), typically having a port for receiving an analyte test strip that carries a user's bodily fluid that can be analyzed to determine the user's glucose level.

[0034] An in-vivo monitoring system may include a sensor that contacts a user's bodily fluid while located within a living body to detect an analyte level therein. The sensor may be part of a sensor control device that resides on the user's body, including the electronics and power source that enable and control the analyte detection. Sensor control devices and variations thereof may also be referred to as "sensor control units," "on-body electronics" devices or units, "on-body" devices or units, or "sensor data communication" devices or units, to name a few.

[0035] In-vivo monitoring systems may also include devices that receive sensed analyte data from the sensor control device, process the sensed analyte data, and / or display the sensed analyte data to a user in any number of forms. This device and variations thereof may be referred to as a "handheld reader device," "reader device" (or simply "reader"), "handheld electronics" (or simply "handheld"), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply "receivers"), or "remote" devices or units, to name a few. Other devices, such as personal computers, are also utilized with and incorporated into in-vivo and in-vitro monitoring systems.

[0036] Example Embodiments of an In-Vivo Analyte Monitoring System FIG. 1 is a conceptual diagram illustrating an example embodiment of an analyte monitoring system 100 including a sensor applicator 150, a sensor control device 102, and a reader device 120. Here, the sensor control device 102 is delivered to a monitoring location on a user's skin using the sensor applicator 150, and an adhesive patch 105 may maintain the sensor 104 in place for a period of time. The sensor control device 102 may communicate with the reader device 120 over a communication path 140 using wired or wireless technology, as further described in FIG. 2B and FIG. 2C. Example wireless protocols include Bluetooth, Bluetooth Low Energy (BLE, BTLE, Bluetooth SMART, etc.), and Near Field Communication (NFC). A user may view and use applications installed in memory on the reader device 120 using a screen 122 (which may include a touch screen in many embodiments) and an input 121. The device battery of the reader device 120 may be charged using a power port 123. Although only one reader device 120 is shown, the sensor control device 102 may also communicate with multiple reader devices 120. Each reader device 120 may communicate with each other to share data. Further details regarding the reader devices 120 are described below with respect to FIG. 2A. The reader device 120 may communicate with a local computer system 170 over a communication path 141 using a wired or wireless communication protocol. The local computer system 170 may include one or more of a laptop, desktop, tablet, phablet, smartphone, set-top box, video game console, or other computing device, and the wireless communication may include any of a number of applicable wireless networking protocols including Bluetooth, Bluetooth Low Energy (BTLE), Wi-Fi, or others.The local computer system 170 can communicate with the network 190 via communication path 143, similar to the manner in which the reader device 120 can communicate with the network 190 via communication path 142 by wired or wireless communication protocols as described above. The network 190 can be any of a number of networks, such as private and public networks, local area networks or wide area networks. The trusted computer system 180 can include a cloud-based platform or server, can provide authentication services, protected data storage (e.g., storage of analyte measurement data received from the reader device), report generation, and can communicate with the network 190 via communication path 144 by wired or wireless technology. Also, while FIG. 1 illustrates the trusted computer system 180 and the local computer system 170 communicating with a single sensor control device 102 and a single reader device 120, one skilled in the art will appreciate that the local computer system 170 and / or the trusted computer system 180 can also communicate with multiple reader devices and sensor control devices, respectively, via wired or wireless communication.

[0037] Further details of suitable analyte monitoring devices, systems, methods, components and their operation and related features are described in U.S. Pat. No. 9,913,600 to Taub et al., WO 2018 / 136898 to Rao et al., WO 2019 / 236850 to Thomas et al., and U.S. Patent Application Publication No. 2020 / 01969191 to Rao et al., each of which is incorporated by reference in its entirety.

[0038] Example of a Reader Device 2A is a block diagram illustrating an example embodiment of a reader device 120, which in some embodiments may include a smartphone or a smartwatch. Here, the reader device 120 may include a display 122, an input component 121, and a processing core 206 including a communication processor 222 coupled to a memory 223 and an application processor 224 coupled to a memory 225. It may also include another memory 230, an RF transceiver 228 having an antenna 229, and a power source 226 having a power management module 238. Additionally, the reader device 120 may include a multi-function transceiver 232 that may include wireless communication circuitry and be configured to communicate with one or more antennas 234 via Wi-Fi, NFC, Bluetooth, BTLE, and GPS. As will be appreciated by those skilled in the art, these components are communicatively coupled electrically to form a functional device.

[0039] Example of an embodiment of a sensor control device 2B and 2C are block diagrams illustrating an example embodiment of a sensor controller 102 having an analyte sensor 104 and sensor electronics 160 (including analyte monitoring circuitry) that may have most of the processing power to render final result data suitable for display to a user. FIG. 2B shows a single semiconductor chip 161, which may be a custom application specific integrated circuit (ASIC). Within the ASIC 161 are shown certain high level functional units including an analog front end (AFE) 162, a power management (or control) circuit 164, a processor 166, and a communication circuit 168 (which may be implemented as a transmitter, receiver, transceiver, passive circuitry, or otherwise, depending on the communication protocol). In this embodiment, both the AFE 162 and the processor 166 are used as analyte monitoring circuitry, although in other embodiments, either circuitry may perform the analyte monitoring function. The processor 166 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or may be distributed (and portions thereof) among several different chips.

[0040] Also included within the ASIC 161 is memory 163, which may be shared by the various functional units present within the ASIC 161 or may be distributed among two or more of these functional units. The memory 163 may also be an independent chip. The memory 163 may be a volatile and / or non-volatile memory. In this embodiment, the ASIC 161 is coupled to a power source 170, which may be a button cell or the like. The AFE 162 interfaces with the in-vivo analyte sensor 104 to receive measurement data therefrom and outputs this data in digital form to a processor 166, which further processes the data to derive final result glucose discrete and trend values, etc. This data may then be provided to a communication circuit 168 for transmission via an antenna 171 to a reader device 120 (not shown), with minimal further processing required to display the data, for example by a resident software application. According to some embodiments, for example, current glucose values ​​can be transmitted from the sensor control device 102 to the reader device 120 every minute, and past glucose values ​​can be transmitted from the sensor control device 102 to the reader device 120 every five minutes.

[0041] In some embodiments, to conserve power and processing resources of the sensor controller 102, the digital data received from the AFE 162 may be transmitted to the reader device 120 (not shown) with minimal or no processing. In yet other embodiments, the processor 166 may be configured to generate certain predefined data types (e.g., current glucose value, past glucose values) for storage in the memory 163 or transmission to the reader device 120 (not shown) and to identify certain alarm conditions (e.g., sensor fault conditions), while other processing and alarm functions (e.g., high / low glucose threshold alarms) may be performed on the reader device 120. Those skilled in the art will appreciate that the methods, functions and interfaces described herein may be performed in whole or in part by processing circuitry on the sensor controller 102, the reader device 120, the local computer system 170 or the trusted computer system 180.

[0042] FIG. 2C is similar to FIG. 2B, but instead includes two discrete semiconductor chips 162 and 174, which may be packaged together or separately. Here, AFE 162 resides on ASIC 161. Processor 166 is integrated with power management circuitry 164 and communication circuitry 168 on chip 174. AFE 162 may include memory 163, and chip 174 may include memory 165, which may be separate or distributed within. In one example embodiment, AFE 162 is combined with power management circuitry 164 and processor 166 on one chip, and communication circuitry 168 resides on another chip. In another example embodiment, AFE 162 and communication circuitry 168 both reside on one chip, and processor 166 and power management circuitry 164 reside on another chip. However, other chip combinations are possible, including three or more chips, each performing separate functions as described, or sharing one or more functions for fail-safe redundancy.

[0043] Example Embodiments of a Graphical User Interface for an Analyte Monitoring System Exemplary embodiments of GUIs for the analyte monitoring system are described herein. As an initial matter, those skilled in the art will appreciate that the GUIs described herein include instructions stored in memory of the reader device 120, the local computer system 170, the trusted computer system 180, and / or any other device or system that is part of or in communication with the analyte monitoring system 100. These instructions, when executed by one or more processors of the reader device 120, the local computer system 170, the trusted computer system 180, or any other device or system of the analyte monitoring system 100, cause the one or more processors to perform the method steps described herein and / or output a GUI. Those skilled in the art will further appreciate that the GUIs described herein may be stored as instructions in the memory of a single centralized device or may be otherwise distributed across multiple individual elements at geographically dispersed locations.

[0044] Example Embodiments of a Model for Personalized Glucose-Related Metrics Described herein are example embodiments of models for personalized glucose-related metrics. The present disclosure describes methods, devices, and systems for determining physiological parameters generally related to the kinetics of red blood cell glycation, clearance, production, and reticulocyte maturation in a subject. Such physiological parameters can be used, for example, to calculate, among other things, a more reliable calculated HbA1c (cHbA1c), adjusted or personalized HbA1c (aHbA1c), adjusted calculated HbA1c (acHbA1c), and / or personalized target glucose range for a subject-specific diagnostic, treatment, and / or monitoring protocol.

[0045] The terms "HbA1c level", "HbA1c value", and "HbA1c" are used interchangeably herein. The terms "personalized A1c", "personalized HbA1c", "aHbA1c level", "aHbA1c value", and "aHbA1c" are used interchangeably herein. The terms "cHbA1c level", "cHbA1c value", "cHbA1c", and "GD-A1c" are used interchangeably herein and / or are, inter alia, personalized target glucose ranges. The terms "acHbA1c level", "acHbA1c value", and "acHbA1c" are used interchangeably herein.

[0046] Dynamic Model High glucose exposure in certain organs (especially the eyes, kidneys, and nerves) is an important factor for the progression of diabetic complications. Although laboratory HbA1c (also referred to in the art as measured HbA1c) is routinely used to assess glycemic control, studies have reported discrepancies between this glycemic marker and diabetic complications in some individuals. The exact mechanisms underlying the failure of laboratory HbA1c to predict diabetic complications are often unclear, but in some cases are likely related to inaccurate estimation of intracellular glucose exposure in affected organs.

[0047] Equation 1 below illustrates the kinetics of red blood cell hemoglobin glycation (or simply referred to herein as red blood cell glycation), red blood cell clearance, and red blood cell production, where "G" is free glucose, "R" is non-glycated red blood cells, and "GR" is glycated red blood cell hemoglobin. The rate at which glycated red blood cell hemoglobin (GR) is formed is referred to herein as the red blood cell hemoglobin glycation rate constant (typically dl_ * mg _1* day ! k has units of gly ) is called JPEG2024546442000002.jpg4134 formula 1

[0048] Red blood cells, including glycated red blood cells, are continually removed from a subject's circulatory system over time, and new red blood cells are generated, typically at a rate of about 2 million per second. As used herein, the rates associated with removal and generation are each referred to as the red blood cell removal constant (typically, day -1 k has units of age ) and the erythropoiesis rate constant (typically M 2 k with units of / day gen ) In most cases, the amount of red blood cells in the body is maintained at a stable level, so k age and k gen The ratio of must be an individual constant that is the square of the red blood cell concentration.

[0049] In relation to glycation, Equation 2 below further details the mechanism by which glucose transporter 1 (GLUT1) facilitates glucose (G) transport into red blood cells. In this case, intracellular glucose (GI) interacts with hemoglobin (Hb) to produce glycated hemoglobin (HbG), where the glycation reaction rate constant for hemoglobin is (typically dl_ * mg _1 *day ! (has units of k) g A typical experimentally measured kg is 1.2 x 10 3 db / mg / day. Hemoglobin glycation is a multi-step nonenzymatic chemical reaction, so k g must be a universal constant. The rate constant for glucose transport into red blood cells and glycating Hb to HbG is kgly. And k age represents the removal of red blood cells (along with hemoglobin), also referred to herein as the red blood cell turnover rate. JPEG2024546442000003.jpg61129 formula 2

[0050] While elevated intracellular glucose contributes to diabetic complications, extracellular hyperglycemia selectively damages cells with limited ability to effectively regulate glucose transport across their membranes. HbA1c has been used as a biomarker of diabetes-associated intracellular hyperglycemia for two main reasons. First, glycation occurs within red blood cells (RBCs), and thus HbA1c is modulated by intracellular glucose levels. Second, RBCs do not have the ability to regulate glucose transporter GLUT1 levels and therefore cannot modify the glucose uptake rate across the cell membrane, behaving similarly to cells that are selectively damaged by extracellular hyperglycemia. Thus, under conditions of constant RBC lifespan and constant glucose uptake rate across the membrane, HbA1c reflects intracellular glucose exposure in organs affected by diabetic complications. However, given the individual variability in both glucose uptake rate across the membrane and RBC lifespan, laboratory HbA1c does not always reflect intracellular glucose exposure. Variability in the rate of glucose uptake across the RBC membrane is likely related to the estimated risk of diabetic complications in susceptible organs, whereas red blood cell life span is RBC specific and therefore independent of complication risk in other tissues. This explains the clinical lack of reliance on laboratory HbA1c in patients with hematological disorders characterized by abnormal RBC turnover and provides a possible explanation for the apparent "misalignment" between laboratory HbA1c and progression of complications in some diabetic patients (Figure 1).

[0051] To overcome the limitations of laboratory HbA1c, we developed a personalized HbA1c measure that takes into account individual differences in red blood cell turnover and cellular glucose uptake rates. This study aims to extend this model by adjusting for a standard RBC life span of 100 days (corresponding to a 1% RBC turnover rate per day or a mean RBC age of 50 days) and establish a new clinical marker, called adjusted HbA1c (aHbA1c). We propose that aHbA1c is the optimal glycemic marker for estimating organ exposure to hyperglycemia and the risk of future diabetes-related complications.

[0052] As mentioned above, HbA1c is a commonly used analyte that indicates the percentage of glycated hemoglobin found in red blood cells. Thus, a kinetic model can be used to derive HbA1c after calculation, for example, based on at least the glucose level measured for the subject. However, the kinetic model can also be applied to HbA1. For simplicity, HbA1c is used uniformly herein, but HbA1 can be substituted except in cases where a specific HbA1c value is used (see, for example, Equations 15 and 16). In such cases, a similar equation can be derived using a specific HbA1 value.

[0053] Typically, when modeling a physiological process dynamically, assumptions are made to simplify some of the calculations and to focus on the factors that most affect the physiological process.

[0054] This disclosure uses only the following set of assumptions to kinetically model the physiological process shown in Equation 1. First, glucose concentration is high enough to be unaffected by red blood cell glycation. Second, there are no abnormal red blood cells that affect HbA1c measurements, and therefore the hematocrit is constant over the time period of interest. This assumption is made to exclude extreme conditions or life events that are not usually present and may adversely affect the accuracy of the model. Third, the glycation process has a linear dependence on both red blood cell and glucose concentrations. Fourth, newly generated red blood cells have only a small amount of glycated hemoglobin, based on previous reports that reticulocyte HbA1c is very low and almost undetectable. Fifth, erythropoiesis is inversely correlated with total cellular concentration, while removal is a linear process.

[0055] Using the five assumptions mentioned above for this kinetic model, the rates of change of glycated and non-glycated red blood cells can be modeled by the following differential equations 1 and 2: d[GR\ / dt=kgly[G][R]-k age[GR] Equation 1 (d[R]) / dt=k gen / Ck age [R]-k gly [G][R] Equation 2 C is the total red blood cell population, C = [ff] + [GR] (Equation 2a). C typically has units of M (mol / L), [R] and [GR] typically have units of M, and [G] typically has units of mg / dl.

[0056] Assuming a steady state where the glucose concentration is constant and the glycated and non-glycated red blood cell concentrations are stable (d[GR] / dt=(d[R]) / dt=0), the following two equations are derived. Equation 3 below expresses the apparent glycation constant K (typically in units of dL / mg) as k gly and k age whereas equation 4 establishes the dependence between the erythropoiesis rate and the removal rate. K=k gly / k age =[GR] / [G][R] Equation 3 k gen / k age =C 2 equation 4

[0057] For simplicity, hereinafter, k is used to refer to the methods, apparatus, and systems of the present disclosure. age Unless otherwise specified, k gen k age It can also be used as a substitute for k gen k age To substitute for k gen -k age *Reorganize to C.

[0058] HbA1c is the percentage of glycated hemoglobin, as shown in Equation 5 below.

[0059] HbA1c=[GR] / C=(C-[R]) / C Equation 5

[0060] In a hypothetical situation where a person maintains the same glucose level indefinitely, the HbA1c in Equation 5 can be defined as "equilibrium HbA1c" (EA) (typically reported as a %, e.g., 6.5%, but used in decimal form, e.g., 0.065, in calculations). For a given glucose level, the EA (Equation 6) can be derived from Equations 2a, 3, and 5. JPEG2024546442000004.jpg7103 Equation 6

[0061] EA is an estimate of HbA1c based on constant glucose concentration [G] over time. This relationship effectively approximates the average glucose and HbA1c of individuals with stable daily glucose profiles. EA depends on K, whose value is characteristic for each subject. Equation 6 shows that steady-state glucose is not linearly correlated with EA. Steady-state glucose and EA can be approximated using linear functions within a certain range of glucose levels, but not across the entire typical clinical range of HbA1c. Furthermore, in real life, where glucose levels fluctuate continuously, there is no reliable linear relationship between laboratory HbA1c and an individual's average glucose.

[0062] Other researchers have come to this conclusion and have developed kinetic models that correlate measured HbA1c values ​​with average glucose levels. For example, the American Diabetes Association has an online calculator to convert HbA1c values ​​to estimated average glucose levels. However, this model is based on the assumption that kage and kgly do not vary substantially between subjects, which is shown to be incorrect in Example 1 below. Thus, the model currently adopted by the American Diabetes Association is based on the assumption that kage and kgly do not vary substantially between subjects. age and k gly is considered to be a constant that does not vary across subjects.

[0063] Higgens et al. (Sci. Transl. Med. 8, 359ral30, 2016) developed an updated model that removed the assumption of constant red blood cell lifetime. However, this updated model still shows a large intersubject variation in k gly is assumed to remain substantially unchanged.

[0064] In contrast, in the kinetic model described here, k age and k gly In addition, in some embodiments, the k gly is used to derive personalized parameters for the subject's diabetes status and treatment (eg, medication dosage, supplement dosage, exercise plan, and diet / meal plan, etc.).

[0065] Continuing with the kinetic model of this disclosure, given the starting HbA1c (HbA1co) and assuming a constant glucose level [G] during a period t, we obtain from Equation 1 the final HbA1c value for that period (HbA1c t ) (Equation 7) can be derived. JPEG2024546442000005.jpg8117 Equation 7

[0066] To accommodate for changing glucose levels over time, we approximate each individual's glucose history as a series of time intervals t with corresponding mean glucose levels [G, ]. Applying Equation 7 recursively, we obtain the HbA1C at the end of a time interval tz by the following Equation 8 for numerical calculation: z can be represented as: JPEG2024546442000006.jpg8141 Equation 8 Decay term D t = e- (y [G;]+ / c ag e ) t ￡(Equation 8a).

[0067] Using Equation 6, 7 or 8, k age and k gly When solving, kage and k gly 5.0 as a non-limiting example * 10 6 dl_ * mg^day _1 <k gly <8.0 * 10 6 dl_ * mg^day 1 and 0.006day 1 <k age <0.024day -1 In addition or alternatively, the estimated k gly and k age The initial value of (e.g., k gly =4.4 * 10 -6 dl_ * mg^day 1 and k age =0.0092day x An empirical approach using the Broyden-Fletcher-Goldfarb-Shanno algorithm, along with the 1H glucose level data points, can also be used. The more glucose level data points and measured HbA1c data points, the more accurate the physiological parameters described herein will be.

[0068] The value of the time interval t can be selected (e.g., by a user or developer, or by software instructions executing on one or more processors) based on many factors that can vary between embodiments, and thus the value of the time interval t can vary. One such factor is the duration from one glucose data value (e.g., a measured glucose level at a discrete time point, a value representing a glucose level over a particular period spanning multiple discrete time points, or the like) to another glucose data value in the individual's glucose history. This interval between glucose data values ​​is referred to as the time interval t g The time interval t g can vary over an individual's glucose history, and thus a single glucose history can be calculated over a time interval t g In this specification, the time interval tg Many example embodiments are described that result in different values ​​of t. In some embodiments of the glucose monitoring system, g Glucose data points are determined after every minute (e.g., every 1 minute, every 10 minutes, every 15 minutes, etc.) and the resulting glucose history is calculated such that each point is taken over a period of time t g At the expiration of or at a fixed time interval t g In another embodiment, the glucose concentration is a series of glucose data points representing glucose over a period of time t (e.g., a series of glucose data points at 1 minute intervals). g For example, in some flash analyte monitoring systems (described in further detail herein), a user may request a first relatively short time interval t g (e.g., every minute, every two minutes, etc.), and all other data outside of this recent period (up to 8 hours, 12 hours, 24 hours, etc. in some cases) is requested over a second, relatively long time interval t g (e.g., every 10 minutes, 15 minutes, 20 minutes, etc.). The data stored at the second, relatively long time interval is stored at the originally relatively short time interval t g In such an example, the resulting glucose history may be determined based on the frequency with which the user requests glucose data, such as the frequency with which the user requests glucose data, over a first time interval t g Several glucose data points at and a second time interval t g Of course, the glucose concentration can be in combination with other glucose data points at, for example, three or more time intervals t g More complex variations are possible, including: gThere may also be glucose data collected with ad hoc adjunctive measurements (eg, fingersticks and test strips) that can provide additional variability in the glucose level.

[0069] Generally, the time interval is 1 to 15 minutes. g An example analysis performed on the glucose histories of a sample (about 400 subjects) with glucose data points at 10 ... g Depending on other factors such as the above, other values ​​of the time interval t outside the range of 3 hours to 24 hours may be suitable.

[0070] Another factor in the selection of the time interval t is the longest time interval t in the individual's glucose history. gThe presence of gaps or missing data that are longer than 100 or significantly longer than 1000. The presence of one or more such gaps can bias the results. These gaps can occur, for example, due to failure to collect glucose data for a period of time (e.g., the user did not wear the sensor, the user forgot to scan the sensor for data collection, a failure occurred, etc.). The presence and duration of gaps should be considered when selecting the time interval t. In general, the number and duration of gaps should be minimized (or eliminated) as much as possible. However, since such gaps are often difficult to eliminate, to the extent that such gaps exist, in many embodiments the time interval t should be selected to be at least twice as long as the largest (maximum) gap between glucose data points. For example, if the time interval t is selected as 3 hours, the maximum gap should be no more than 90 minutes, and if the time interval t is selected as 24 hours, the maximum gap should be no more than 12 hours.

[0071] ValueHbA1c z is the estimated HbA1c of this kinetic model, which is referred to herein as cHbA1c (calculated HbA1c) to distinguish it from other eHbA1c described herein.

[0072] As previously explained and shown in Equation 8, EA and D are both dependent on the glucose level, [G,], k gly , and k age D also depends on the length of the time interval t. Equation 8 is a recursive form of Equation 7. Equations 7 and 8 are related by HbA1c, glucose level, and the kinetic constant k of individual red blood cells. gly and k age Describes the relationship between.

[0073] k age can be measured directly through expensive and laborious methods, where k age We extend the kinetic model to incorporate reticulocyte maturation as a way to estimate

[0074] Reticulocytes are immature red blood cells that typically comprise about 1% of all red blood cells. The rate at which reticulocytes mature into mature red blood cells (typically within days -1 (has units of k) mat The maturation half-life of a normal reticulocyte is approximately 4.8 hours, and Equation 9 holds true: JPEG2024546442000007.jpg785Equation 9

[0075] This kinetic model makes two assumptions: (1) at time 0, all red blood cells are reticulocytes, and (2) reticulocytes are not eliminated (i.e., they do not mature into mature red blood cells and die). The probability density of reticulocyte age (PRET) can be expressed by the following Equation 10: JPEG2024546442000008.jpg880Equation 10 t is the cell age.

[0076] The reticulocyte production index (RPI), also known as the corrected reticulocyte count (CRC), is the percentage of reticulocytes among all red blood cells. Thus, the RPI is the ratio of P to cell age as shown in Equation 11 below: RET where RPI is the decimal form of the reported RPI (e.g., an RPI reported at 2% is 0.02 in Equation 11). JPEG2024546442000009.jpg885Equation 11

[0077] Typical k mat 3.47days -1 Assuming that kage can be estimated from the measured RPI, the RPI can be calculated in the usual way. For example, the RPI is expressed as the hematocrit percentage (HM mThe reticulocyte percentage (RP) in the RNA stained blood smear can be determined by determining the maturation correction (MC) from the measured hematocrit percentage, and calculating the RPI based on the following equation 12: m is used as a percentage rather than in decimal form (i.e., an RP reported as 3% is 3 in the formula, not 0.03).

[0078] Typical k mat 3.47days -1 Assuming that kage can be estimated from the measured RPI, the RPI can be calculated in the usual way. For example, the RPI is expressed as the hematocrit percentage (HM m ), measure the percentage of reticulocytes (RP) in the RNA-stained blood smear, determine the maturation correction (MC) from the measured hematocrit percentage, and calculate the RPI based on the following equation 12, where RP and HM m is used as a percentage rather than in decimal form (i.e., an RP reported as 3% is 3 in the formula, not 0.03). RPI=(RP * HM m / HM n ) / MC Equation 12 HM n has a normal hematocrit (typically 45).

[0079] Unless otherwise specified, the typical units described relate to the respective values. Those skilled in the art will recognize other units and correct conversions. For example, [G] is typically measured in mg / dL, but can also be converted to M using the molar mass of glucose. If [G] is used in M, or any other variable is used in different units, the equations herein should be adjusted to account for the difference in units.

[0080] Calculation of physiological parameters from kinetic models Embodiments of the present disclosure provide a kinetic model of red blood cell glycation, removal and production, and reticulocyte maturation in a subject's body.

[0081] Physiological parameter k age can be estimated from one or more RPI measurements. age can be estimated from a single RPI measurement using Equation 11 above, although two or more RPI measurements can increase the accuracy of the RPI value. Additionally, RPI can also change over time, in response to treatment, and in response to improvement or worsening of the disease state. Thus, RPI can be measured at any desired time interval (e.g., weekly to yearly), but is preferably measured once every 3 to 6 months.

[0082] k age Once HbA1c is calculated, the physiological parameter k can be calculated from the equations described herein given at least one measured HbA1c value (also referred to as a measured HbA1c level) and a number of glucose levels (also referred to as measured glucose levels) over a period of time immediately preceding the HbA1c measurement. gly and / or K can be estimated.

[0083] FIG. 12 illustrates an example timeline 100 showing the collection of at least one measured HbA1c value 12102a, 12102b, 12102c, a number of glucose levels 12104a, 12104b, and at least one measured RPI value 110a, 110b, 110c over time periods 106 and 108.

[0084] k gly The number of measured HbA1c values ​​12102a, 12102b, 12102c required to calculate Kage depends on the frequency and duration of multiple glucose levels. The number of measured RPI values ​​110a, 110b, 110c required to calculate Kage depends on the stability of the individual kmat and the typical kage. mat (3.47day -1The RPI is preferably measured once every 3-6 months, but can also be measured monthly or weekly if necessary.

[0085] In a first embodiment, one measured RPI value 110b can be used to calculate kage, and one measured HbA1c 12102b can be used to calculate kage. age and combined with multiple glucose measurements over a period of 10 gly Such an embodiment is applicable to subjects whose daily glucose measurements have been stable over an extended period 106 (e.g., more than about 200 days). K can be calculated at time 101 using Equation 6 by replacing EA with the measured HbA1c value 12102b and [G] with the average daily glucose over the period 106. Then, k from Equation 3 gly Therefore, in this embodiment, an initial HbA1c level measurement 12102a is not necessarily required.

[0086] Since the first HbA1c value is not measured, it may be necessary to increase the time interval 106 between the initial glucose level measurements with frequent measurements to obtain an accurate representation of the average glucose and reduce error. In this manner, error can be reduced by using a steady glucose pattern of greater than 100 days. Increasing the length, such as 200 days or more or 300 days or more, can further reduce error.

[0087] An embodiment in which one measured HbA1c value 12102b can be used includes a period 106 of about 100 to about 300 days (or longer) in which glucose levels are measured at least about 72 times per day (e.g., about every 20 minutes) to about 96 times per day (e.g., about every 15 minutes) or more frequently. Furthermore, in such an embodiment, the time between glucose level measurements can be somewhat consistent, and the interval between two glucose level measurements should not exceed about one hour. When only one measured HbA1c value is used, glucose measurements with some missing data are acceptable. More missing data may result in larger errors.

[0088] Alternatively, in some cases using a single measured HbA1c value 12102b, the time period 106 can be shortened if the subject has an existing glucose level monitoring history that includes a stable and consistent glucose profile. For example, for a subject who has been tested over a long period of time (e.g., six months or more), but perhaps less frequently or tightly controlled, the existing glucose level measurements can be used to determine and analyze the glucose profile. In this case, if the HbA1c 12102b and RPI 110b measurements are taken after more frequent and tightly controlled glucose monitoring over the time period 106 (e.g., about 72 to about 96 times per day or more for about 14 days or more), a combination of the four data sets can be used to determine the glucose profile of one or more physiological parameters (k giy , k age , and / or K) can be calculated.

[0089] Alternatively, in some embodiments, one or more measured RPI values ​​110a, 110b, two measured HbA1c values ​​(a first measured HbA1c value 12102a at the beginning of the time period 106 and a second measured HbA1c value 12102b at the end of the time period 106), and multiple glucose levels 12104a measured during the time period 106 are used to calculate one or more physiological parameters (k gly , kage and / or K). In these embodiments, Equation 11 can be used to calculate k age Using Equation 8, we can calculate k gly and / or K. In such embodiments, multiple glucose levels 12104a can be measured, on average, from about 4 times per day (e.g., about every 6 hours) to about 24 times per day (e.g., about every hour) or more frequently, over a period of about 10 days to about 30 days or longer.

[0090] In the embodiments described above, the RPI value(s) may be measured at times other than those shown, as the measured RPI value is relatively stable over time, and therefore the RPI value(s) may be measured at any time during the period 106 and applied to these embodiments.

[0091] The above-described embodiments are not limited to the illustrated example glucose level measurement periods and frequency ranges. Glucose levels can be measured over a period of about several days to about 300 days or more (e.g., about 1 week or more, about 10 days or more, about 14 days or more, about 30 days or more, about 60 days or more, about 90 days or more, about 120 days or more, etc.). In some embodiments, the period is 7 days or more, preferably 1 to 10 months, and less than 1 year. The frequency of such glucose levels is about 14,400 times per day on average (e.g., at a time interval t of about every 6 seconds). g ) (or more frequently) to about three times daily (e.g., about every 8 hours at time intervals t g ) (e.g., 1,440 times per day (e.g., at a time interval of t g ), approximately 288 times per day (e.g., at time intervals of approximately 5 minutes t g ), approximately 144 times per day (e.g., at time intervals of approximately 10 minutes t g ), approximately 96 times per day (e.g., at time intervals of approximately 15 minutes t g ), approximately 72 times per day (e.g., at time intervals of approximately 20 minutes t g ), approximately 48 times per day (e.g., at intervals of approximately 30 minutes t g ), approximately 24 times per day (e.g., at time intervals of approximately every hour tg ), approximately 12 times per day (e.g., approximately every 2 hours at time intervals t g ), approximately eight times a day (e.g., at time intervals of approximately every 3 hours t g ), approximately six times daily (e.g., approximately every 4 hours at time intervals t g ), approximately four times daily (e.g., approximately every 6 hours at time intervals t g ) etc. In some cases, less frequent monitoring (such as once or twice daily) can be used, with glucose measurements taken at approximately the same time each day (within about 30 minutes) to more directly compare day-to-day glucose levels and reduce error in subsequent analyses.

[0092] The above-described embodiments may further include calculating an error or uncertainty associated with one or more physiological parameters. In some embodiments, the error may be used to determine whether to measure another HbA1c value (not shown) about the time point 101, whether to measure one or more glucose levels 12104b (e.g., about the time point 101), whether to extend the monitoring and analysis (e.g., throughout the period 108 from time point 101 to time point 12103, including measuring the glucose level 12104b during the period 108 and the HbA1c value 12102c at time point 12103), and / or whether to increase the frequency of glucose level measurements 12104b during the extended period 108 relative to the frequency of glucose level measurements 12104a during the period 106 ... gly , k age And / or one or more of the above actions can be taken when the error associated with K is about 15% or more, preferably about 10% or more, preferably about 7% or more, and preferably about 5% or more. If the subject has a pre-existing medical condition (e.g., cardiovascular disease), a lower error may be preferred in order to allow for more stringent monitoring and reduce error in the analyses described herein.

[0093] Alternatively, or if the error is acceptable, in some embodiments, one or more physiological parameters (kgly , k age and / or K) may also be used to determine one or more parameters or characteristics for the subject's personalized diabetes management (e.g., cHbA1c at the end of time period 108, a personalized target glucose range, and / or the subject's treatment or treatment changes in the near future), each of which parameters or characteristics are described in further detail herein. In some cases, in addition to the embodiments described above, the HbA1c value may be measured at time 12103 and one or more physiological parameters may be recalculated and applied to a future time period (not shown).

[0094] Additionally or alternatively, equations 8 and 11 can be used to find k age A comparison of these two values ​​can be used to determine whether to measure another HbA1c value (not shown) about time 101, whether to measure one or more glucose levels 12104b (e.g., about time 101), whether to extend the monitoring and analysis (e.g., through the period 108 from time 101 to time 12103, including measuring the glucose level 12104b at time 12103 and the HbA1c value 12102c), and / or whether to increase the frequency of glucose level measurements 12104b during the extension period 108 relative to the frequency of glucose level measurements 12104a during the period 106. For example, k age If the two values ​​differ by more than 10% (e.g. the lower value is not within 10% of the higher value based on the higher value), then the kmat of that individual is considered to be within the typical kmat (3.47day -1 ) may differ from the typical kmat of Equation 11. If a large difference (e.g., greater than 20%) is observed, then the kmat of the individual may be determined. If the kmat of the individual is stable over a period of time (e.g., 3-6 months), then the determined kmat of the individual should be used in place of the typical kmat of Equation 11 in the methods, systems, and devices described herein. Variations in kmat may also be indicative of other health problems.

[0095] One or more physiological parameters and / or one or more parameters or characteristics for personalized diabetes management of a subject can be measured and / or calculated over two or more times (e.g., time 101 and time 12103) and compared. For example, kgly at time 101 and k at time 12103. gly In another example, the cHbA1c at time 12103 may be compared to the cHbA1c at a future time. Some embodiments described further herein may use such a comparison to (1) monitor the progress and / or effectiveness of the subject's personalized diabetes management and optionally modify the subject's personalized diabetes management, (2) identify abnormal or pathological physiological conditions, and / or (3) identify subjects taking supplements and / or medications that affect erythropoiesis and / or metabolism.

[0096] Each of the exemplary methods, devices, and systems described herein includes measuring one or more physiological parameters (k gly , k age and K) can be used to perform one or more relevant analyses (e.g., personalized target glucose range, personalized target average glucose, and cHbA1c, etc.). gly , k age and K) and related analyses can be updated periodically (e.g., about every 3 months to annually). The frequency of updates can depend, among other things, on the subject's glucose levels and diabetes history (e.g., how well the subject is staying within defined thresholds), other medical conditions, etc.

[0097] Other factors One or more physiological parameters (k gly , k ageIn embodiments that apply K and / or K), one or more other subject-specific parameters may be used in addition to one or more physiological parameters. Examples of subject-specific parameters may include, but are not limited to, vital information (e.g., heart rate, temperature, blood pressure, or any other vital information), body chemistry information (e.g., drug levels, blood levels, troponin levels, cholesterol levels, or any other body chemistry information), dietary data / information (e.g., carbohydrate content, sugar content, or any other dietary information), activity information (e.g., occurrence and / or duration of sleep and / or exercise), pre-existing conditions (e.g., cardiovascular disease, heart valve replacement, cancer, and systemic diseases such as autoimmune diseases, hormonal disorders, blood cell disorders, etc.), family history of pre-existing conditions, current treatments, age, race, sex, geographic location (e.g., where the subject grew up or currently lives), diabetes type, duration of diabetes diagnosis, etc., and any combination thereof.

[0098] system In some embodiments, one or more physiological parameters (kgly, k age and / or K) can be performed using a physiological parameter analysis system.

[0099] 13 is a diagram illustrating an example of a physiological parameter analysis system 211 for performing physiological parameter analysis, according to some embodiments of the present disclosure. The physiological parameter analysis system 211 includes one or more processors 212 and one or more machine-readable storage media 214. The one or more machine-readable storage media 214 include a set of instructions for performing physiological parameter analysis routines that are executed by the one or more processors 212.

[0100] In some embodiments, these instructions are based on input 216 (e.g., one or more RPI values, one or more glucose levels, one or more HbA1c levels, one or more previously determined physiological parameters (k gly , kage and / or K), or one or more other subject-specific parameters, and / or one or more times associated with any of the above; gly , k age and / or K), an error associated with one or more physiological parameters, one or more parameters or characteristics for personalized diabetes management of the subject (e.g., cHbA1c, personalized target glucose range, average target glucose level, supplements or dosages, among other parameters or characteristics, and a matching participant group, etc.), and communicating an output 218. In some embodiments, the communication of the input 216 can be, for example, via a user interface (which can be part of a display), a data network, a server / cloud, another device, a computer, or any combination thereof. In some embodiments, the communication of the output 218 can be, for example, to a display (which can be part of a user interface), a data network, a server / cloud, another device, a computer, or any combination thereof.

[0101] As used herein, the term "machine-readable medium" includes any mechanism that can store information in a form accessible by a machine (which can be, for example, a computer, a network device, a mobile phone, a personal digital assistant (PDA), a manufacturing tool, any device having one or more processors, etc.). For example, machine-accessible media includes recordable / non-recordable media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

[0102] In some cases, the one or more processors 212 and the one or more machine-readable storage media 214 may reside within a single device (e.g., a computer, a network device, a mobile phone, a PDA, an analyte monitor, etc.).

[0103] In some embodiments, the physiological parameter analysis system may also include other components. Figure 14 illustrates another example of a physiological parameter analysis system 311 for performing physiological parameter analysis according to some embodiments of the present disclosure.

[0104] The physiological parameter analysis system 311 includes a health monitoring device 14320 having a subject interface 14320A and an analysis module 14320B. The health monitoring device 14320 is or can be operably coupled to a data network 14322. Also provided within the physiological parameter analysis system 311 is a glucose monitor 324 (e.g., an in vivo and / or in vitro (ex vivo) device or system) and a data processing terminal / personal computer (PC) 326, each of which is operably coupled to the health monitoring device 14320 and / or the data network 14322. FIG. 14 further illustrates a server / cloud 328 operably coupled to the data network 14322 for bidirectional data communication with one or more of the health monitoring device 14320, the data processing terminal / PC 326, and the glucose monitor 324. A physiological parameter analysis system 311 within the scope of the present disclosure may also exclude one or more of the server / cloud 328, the data processing terminal / PC 326 and / or the data network 14322.

[0105] In some embodiments, the analysis module 14320B is programmed or configured to perform physiological parameter analysis, and optionally other analyses (e.g., cHbA1c, personalized target glucose range, and other analyses described herein). As illustrated, the analysis module 14320B is part of the health monitoring device 14320 (e.g., executed by a processor in the analysis module 14320B). However, the analysis module 14320B may also be associated with one or more of the server / cloud 328, the glucose monitor 324, and / or the data processing terminal / PC 326. For example, one or more of the server / cloud 328, the glucose monitor 324, and / or the data processing terminal / PC 326 may include a machine-readable storage medium (or media) having a set of instructions that cause one or more processors to execute a set of instructions corresponding to the analysis module 14320B.

[0106] Although the health monitoring device 14320, the data processing terminal / PC 326 and the glucose monitor 324 are each shown operatively coupled to a data network 14322 for communication with the server / cloud 328, one or more of the health monitoring device 14320, the data processing terminal / PC 326 and the glucose monitor 324 may also be programmed or configured to bypass the data network 14322 and communicate directly with the server / cloud 328. Modes of communication between the health monitoring device 14320, the data processing terminal / PC 326, the glucose monitor 324 and the data network 14322 may include one or more of wireless communication, wired communication, RF communication, BLUETOOTH® communication, WiFi data communication, radio frequency identification (RFID) enabled communication, ZIGBEE® communication, or any other suitable data communication protocol, optionally supporting data encryption / decryption, data compression, data decompression, and the like.

[0107] As described in further detail below, the physiological parameter analysis may be performed by one or more of the health monitoring device 14320, the data processing terminal / PC 326, the glucose monitor 324, and the server / cloud 328, and the resulting analysis output is shared in the physiological parameter analysis system 311.

[0108] Additionally, although the glucose monitor 324, health monitoring device 14320 and data processing terminal / PC 326 are shown operably coupled to each other via communication links, they may also be modules within a single integrated device (e.g., a sensor having a processor and communication interface for transmitting, receiving and processing data).

[0109] Measurement of glucose and HbA1c levels The measurements of multiple glucose levels over various time periods described herein may be performed using in vivo and / or in vitro methods, devices, or systems that measure at least one analyte, such as glucose, in a bodily fluid, such as blood, interstitial fluid (ISF), subcutaneous fluid, dermal fluid, sweat, tears, saliva, or other biological fluid. In some cases, a combination of in vivo and in vitro methods, devices, or systems may be used.

[0110] Exemplary in vivo methods, devices, or systems measure glucose levels, and optionally other analytes, in blood or ISF where at least a portion of the sensor and / or sensor control device is or can be placed inside the subject's body (e.g., under the subject's skin surface). Exemplary devices include, but are not limited to, continuous analyte monitoring devices and flash analyte monitoring devices. Specific devices or systems are further described herein, but are also described in U.S. Pat. No. 6,175,752 and U.S. Patent Application Publication No. 2011 / 0213225, each of which is incorporated by reference in its entirety for all purposes. In vitro methods, devices, or systems (including those that are completely non-invasive) include sensors that contact bodily fluids outside the body to measure glucose levels. For example, an in vitro system can use a meter device having a port for receiving an analyte test strip containing the subject's bodily fluid, which can be analyzed to determine the subject's glucose level in the bodily fluid. Additional devices and systems are further described below.

[0111] As discussed above, the frequency and duration of measuring glucose levels can vary, on average, from about 3 times per day (e.g., about every 8 hours) to about 14,400 times per day (e.g., about every 10 seconds) (or more frequently), and from a few days to more than about 300 days, respectively.

[0112] Once the glucose level is measured, the glucose level can be used to estimate one or more physiological parameters (k gly , k ageand / or K), and in some cases other analyses (e.g., cHbA1c, personalized target glucose ranges, and others described herein). In some cases, such analyses can be performed using a physiological parameter analysis system. For example, referring again to FIG. 14, in some embodiments, the glucose monitor 324 can include a glucose sensor coupled to electronics for (1) processing a signal from the glucose sensor and (2) communicating the processed glucose signal to one or more of the health monitoring device 14320, the server / cloud 328, and the data processing terminal / PC 326.

[0113] The measurement of one or more HbA1c levels at various times described herein can be according to any suitable method. Typically, HbA1c levels are measured in a laboratory using a blood sample from a subject. Examples of laboratory tests include, but are not limited to, chromatography-based assays, antibody-based immunoassays, and enzyme-based immunoassays. HbA1c values ​​can also be measured using electrochemical biosensors.

[0114] The frequency of measuring HbA1c levels can vary from once a month to once a year on average (or less frequently if the subject's average glucose level remains stable).

[0115] HbA1c after calculation (cHbA1c) 14, in some embodiments, the HbA1c level may be measured in a laboratory test, the results of which are entered into the server / cloud 328, subject interface 14320A and / or display by a testing entity, a medical professional, a subject or other user. One or more of the health monitoring device 14320, the server / cloud 328 and the data processing terminal / PC 326 may then receive the HbA1c level for analysis by one or more methods described herein.

[0116] One or more physiological parameters (k gly , k age After calculation of K and / or K), multiple glucose measurements can be measured over a next period of time and HbA1c can be calculated using these glucose measurements during and / or at the end of the next period of time. For example, referring again to FIG. 12, at time point 101, one or more physiological parameters (k and / or K) can be calculated based on one or more measured RPI values ​​110a, 110b, multiple glucose level 12104a measurements over a period of time 106, a measured HbA1c level 12102b at the end of the period of time 106, and optionally a measured HbA1c level 12102a at the beginning of the period of time 106. gly , k age Then, multiple glucose levels 12104b can be measured over a subsequent period of time 108. Equation 8 can then be used to calculate a cHbA1c value (HbA1c in Equation 8) during and / or at the end of the period 108. z ), where HbA1co is the measured HbA1c level 12102b at the end of the period 106 (which is the beginning of the period 108), [G,] is the glucose level or average glucose level during the period 108 (or the c portion of the period 108 during which HbA1c is determined), and one or more provided physiological parameters (k gly , k age and / or K) are used.

[0117] A subject's cHbA1c is a function of one or more physiological parameters (kA1c) determined using the most recently measured HbA1c level, the most recently measured RPI value(s), and the intervening glucose level measurements. gly , k age and / or K) over a number of consecutive time periods. The RPI value can be determined based on k ageThe RPI value or an average of two or more RPI values ​​can be used in the calculation. HbA1c can be measured periodically (e.g., every 6 months to one year) to recalculate one or more physiological parameters. The time between remeasurement of the RPI value and the measured HbA1c can be determined based on (1) the consistency of glucose level measurements, (2) the frequency of glucose level measurements, (3) the subject's and corresponding family's diabetes history, (4) the length of time since the subject was diagnosed with diabetes, (5) changes in the subject's personalized diabetes management (e.g., medication / dosage changes, dietary changes, and exercise changes, etc.), (6) changes in the k mat The presence of a disease or disorder affecting the HbA1c level (e.g., anemia, bone marrow disease, genetic conditions, immune system disorders, and combinations thereof) may be dependent upon the presence of anemia, bone marrow disease, genetic conditions, immune system disorders, and combinations thereof. For example, a subject who has consistent glucose level measurements (e.g., less than 5% variation in [G]) and who measures their glucose levels frequently (e.g., continuous glucose monitoring) may measure HbA1c levels less frequently than a subject who has recently (e.g., within the past 6 months) changed their dosage of glycation medication, even if they are consistently measuring their glucose levels frequently.

[0118] 15 illustrates an example of a cHbA1c report that the physiological parameter analysis system 211 of the present disclosure can generate as output 218. The example report illustrated includes a plot of average glucose levels over a period of time. The report also includes the most recently measured RPI value (open circle), the most recently measured HbA1c level (cross), and the cHbA1c level (asterisk) calculated by the physiological parameter analysis system 211. Note that although the most recently measured RPI value and the most recently measured HbA1c value are shown as being measured on different days, these two measurements could be taken during the same visit to a healthcare provider.

[0119] Although two cHbA1c levels are shown, one or more cHbA1c levels may be displayed on the report, including a line tracking the cHbA1c over time. Alternatively, the output 218 of the physiological parameter analysis system 211 may include a single numerical value for the currently or most recently calculated cHbA1c, a table corresponding to the data in FIG. 15, or any other report that provides at least one cHbA1c level to the subject, health care provider, or the like.

[0120] In some cases, cHbA1c can be compared to a previous cHbA1c and / or a previously measured HbA1c level to monitor the effectiveness of the subject's personalized diabetes management. For example, if a diet and / or exercise plan is being implemented as part of the subject's personalized diabetes management, all other factors (e.g., medications and other diseases) being equal, a change in cHbA1c compared to a previous cHbA1c and / or a previously measured HbA1c level can indicate whether the diet and / or exercise plan is effective, ineffective, or in transition.

[0121] In some cases, the cHbA1c can also be compared to a previous cHbA1c and / or a previously measured HbA1c level to determine whether another HbA1c measurement should be performed. For example, if the cHbA1c has changed by 0.5% units or more (e.g., from 7.0% to 6.5% or from 7.5% to 6.8%) compared to the previous cHbA1c and / or previously measured HbA1c level in the absence of a significant glucose profile change, another measured HbA1c level can be tested.

[0122] In some cases, a comparison of cHbA1c to a previous cHbA1c and / or a previous measured HbA1c level can indicate whether an abnormal or pathological physiological condition exists. For example, if a subject has maintained a cHbA1c and / or measured HbA1c level over time and then a change in cHbA1c is identified without other apparent causes, the subject may have a new abnormal or pathological physiological condition. An indication of what this new abnormal or pathological physiological condition is may be a change in one or more physiological parameters (k gly , k age and / or K). Details of abnormal or pathological physiological conditions associated with one or more physiological parameters are described further herein.

[0123] Personalized Target Glucose Range Typically, glucose levels in diabetic subjects are preferably maintained between 54 mg / dL and 180 mg / dL. However, the kinetic model described herein (see Equation 6) assumes that intracellular glucose levels are dependent on the physiological parameter k gly , k age and K. Thus, the measured glucose level may not correspond to the actual physiological state of the subject. For example, a subject with a higher than normal K may be more likely to glycate glucose. Thus, a measured glucose level of 180 mg / dL may be too high for this subject, which may worsen the effects of the subject's diabetes in the long term. In another example, a subject with a lower than normal kgly may be less likely to glycate glucose. Thus, at a glucose level of 54 mg / dL, this subject's intracellular glucose level is quite low, which may make the subject feel weak and hypoglycemic in the long term.

[0124] Using the accepted lower normal glucose limit (LGL) and the accepted upper normal HbA1c limit (AU), equations for the personalized lower glucose limit (GL) (Equation 13) and personalized upper glucose limit (GU) (Equation 14) can be derived from Equation 6. GL=(LGL * 3 / 4J) / / c3 / 4? Equation 13 where k re t ^ is the normal person's k gly and kjffi is the subject's k gly It is. GU=AU / (K(1-AU)) Equation 14

[0125] Since the lower glucose range is based on equivalent intracellular glucose levels, Equation 13 is gly Equation 14 is based on K because the upper glucose range value is based on an equivalent extracellular glucose level (e.g., the upper normal HbA1c limit accepted).

[0126] The currently accepted values ​​above are LGL = 54 mg / dL, k g e ^=6.2 * 10 -6 dL * mg _1* day _1 , and AU=0.08 (i.e., 8%). Using currently accepted values, the following Equations 15 and 16 can be derived: GL=3.35 * 10 4 day -! / K Equation 15 GU=0.087 / K Equation 16

[0127] FIG. 16A illustrates an example of a method for determining a personalized target glucose range 16530. gly , k ageand / or K) 16538, a desired intracellular glucose range 16532 (e.g., a currently tolerated glucose range) having a lower limit 16534 and an upper limit 16536 can be personalized using Equation 13 and Equation 14, respectively. This results in a personalized lower glucose limit (GL) 16540 (Equation 13 ± 7%) and a personalized upper glucose limit (GU) 16542 (Equation 14 ± 7%) that define a personalized target glucose range 16530. gly , k age and / or K), a personalized target glucose range can be determined, where the lower glucose limit value can be modified according to Equation 13 (or Equation 15) ±7%, and / or the upper glucose limit value can be modified according to Equation 14 (or Equation 16) ±7%. The ±7% for each of the above calculated values ​​allows for the use of different values ​​that are substantially closer to the calculated values, such that the personalized nature of the personalized target glucose range 16530 is maintained. Alternatively, the ±7% can be ±10%, ±5%, or ±3%.

[0128] For example, if K is 4.5 * 10 ~4 dL / mg k gly is 7.0 * 10 6 dL * mg _1* day 1 A subject with a glucose tolerance of 0.01 mg / dl may have a personalized target glucose range of about 48±3.4 mg / dl to about 193±13.5 mg / dl, and thus may have a wider range of acceptable glucose levels than currently implemented glucose ranges.

[0129] 13, FIG. 16B illustrates an example of a personalized target glucose range report that the physiological parameter analysis system 211 of the present disclosure can generate as output 218. The illustrated example report includes a plot of daily glucose levels against the personalized target glucose range (area between the dashed lines) described above. Alternatively, other reports can include, but are not limited to, an ambulatory glucose profile (AGP) plot, a numerical display of the personalized target glucose range using the most recent glucose level measurements, and the like, and any combination thereof.

[0130] In another example, K is 6.5 * 10 -4 dL / mg k gly is 6.0 * 10 6 dL * mg _1 *day 1 A subject with a glucose level above 100 mg / dL may have a personalized target glucose range of about 56±3.5 mg / dL to about 134±10 mg / dL. With the upper glucose level limit significantly lowered, the personalized diabetes management for this subject may include more frequent glucose level measurements and / or dosing to remain substantially within the personalized target glucose range.

[0131] In yet another example, K is 5.0 * 10 -4 dL / mg k gly 5.0 * 10 -6 dL * mg _1 A subject who is *day^ may have a personalized target glucose range of about 67±4.5 mg / dL to about 174±12 mg / dL who is more sensitive to lower glucose levels and may feel weak, hungry, dizzy, etc. more frequently if using currently implemented glucose ranges (54 mg / dL and 180 mg / dL).

[0132] Although the above examples all include a personalized lower glucose limit and a personalized upper glucose limit, a personalized target glucose range may include only a personalized lower glucose limit or a personalized upper glucose limit, and the currently implemented lower or upper glucose limit may also be used as another value within the personalized target glucose range.

[0133] The personalized target glucose range can be determined and / or implemented in the physiological parameter analysis system. For example, an instruction set or program associated with a glucose monitor and / or health monitoring device that determines a therapy (e.g., insulin dosage) can use the personalized target glucose range in such analysis. In some cases, a display or a subject interface that includes a display can display the personalized target glucose range.

[0134] The personalized target glucose range can be updated over time as one or more physiological parameters are recalculated.

[0135] Personalized Target Average Glucose In some cases, the subject's personalized diabetes management may include having a HbA1c value target for a future time point. For example, referring to FIG. 12, a subject may have a measured RPI value 110b and a measured HbA1c value 12102b at time point 101, and multiple previous glucose level measurements over time period 106. The subject's personalized diabetes management may include a target HbA1c value (AT) at time point 12103 that is believed to correlate with the subject's improved health. A personalized target average glucose level (GT) for the next time period 108 may be calculated using the following Equation 17, which may be based on the target HbA1c value (AT) and the subject's K calculated at time point 101: GT=AT / (K(l-AT) Equation 17

[0136] In some embodiments, the physiological parameter analysis system can determine the subject's average glucose level during the time period 108, and in some cases can display the average glucose level and / or the target average glucose level. The subject can use the current average glucose level and the target average glucose level to self-monitor their progress over the time period 108. In some cases, the physiological parameter analysis system can be used to transmit (periodically or periodically) the current average glucose level to a healthcare provider for monitoring and / or analysis.

[0137] 17 shows an example of a personalized target average glucose report that the physiological parameter analysis system 211 of the present disclosure can generate as an output 218. The example report shown includes a plot of a subject's average glucose over a period of time (solid line) and personalized target average glucose (shown at 150 mg / dL, dashed line). Alternatively, other reports may include, but are not limited to, a personalized target average glucose over a given time frame (e.g., the past 12 hours) and a numerical display of the subject's average glucose level, and any combination thereof.

[0138] The personalized target average glucose can be updated over time as one or more physiological parameters are recalculated. EXAMPLES

[0139] Data from 148 type 2 subjects and 139 type 1 subjects enrolled in two clinical trials with 6 months of continuous glucose monitoring were analyzed. Only 90 subjects had sufficient data to meet the assumptions of the kinetic model described above, with continuous glucose data of 12 hours or more without gaps. Study participants had three HbA1c measurements on days 1, 100 (± 5 days), and 200 (± 5 days), as well as frequent subcutaneous glucose monitoring throughout the analysis period, which allowed the analysis of two independent data portions per participant (days 1-100 and days 101-200).

[0140] Using the first data part (days 1 to 100), gly and k age The numerical estimation of allows for the calculation of a predicted final cHbA1c for the second data portion (days 101-200). This final cHbA1c can be compared to the observed final HbA1c to validate the kinetic model described herein. The comparison involves the calculation of an estimated HbA1c for the second data portion based on (1) the 14-day average glucose and (2) the 14-day weighted average glucose transformed by an acceptable regression model from the Ale-Derived Average Glucose (ADAG) study, both of which are based on k gly is assumed to be a constant, which is the currently accepted method of relating HbA1cw to glucose measurements as described above.

[0141] 18A-18C show a comparison of laboratory HbA1c levels on day 200 (±5 days) against estimated HbA1c values, where the eHbA1c values ​​in the plot of FIG. 18A were calculated using a 14-day average model, the eHbA1c values ​​in the plot of FIG. 18B were calculated using a 14-day weighted average model, and the cHbA1c values ​​in the plot of FIG. 18C were calculated using the kinetic model (Equation 8) described herein. The solid lines in all graphs show the linear regression of the comparative HbA1c values ​​of the corresponding model. The dashed lines are one-to-one lines, and the closer the solid linear regression is to this, the better the model. It is clear that the kinetic model described herein models the data better, and this model is k age and k gly This study demonstrates a personalized approach to correlating HbA1c with glucose measurements.

[0142] FIG. 19 shows example data for a test subject including measured glucose levels (solid line), laboratory HbA1c readings (open circles), model cHbA1c values ​​(long dashed line), and 14-day model eHbA1c values ​​(dotted line). The model cHbA1c values ​​in FIG. 19 are based on physiological parameters (k age and k gly ) Physiological parameters were calculated based on the first two laboratory HbA1c measurements and the measured glucose levels between the first two laboratory HbA1c measurements. The 14-day eHbA1c values ​​are the glucose level 14-day running averages during the study.

[0143] The examples in Figure 19 show the dynamic nature of the relationship between glucose and cHbA1c and the relationship between glucose and eHbA1c. Further examples were determined across the range of predicted deviations at the 25th, 50th and 75th percentiles of the cHbA1c method for study participants with type 1 and type 2 diabetes. The discrepancy between cHbA1c and 14-day average glucose in these examples shows the exaggerated amplitude of fluctuations inherent in the simple 14-day method.

[0144] FIG. 20 shows the relationship between steady-state glucose and equilibrium HbA1c, (1) as determined using a standard conversion of HbA1c to estimated mean glucose (dashed line with error bars) and (2) as measured for 90 participants (solid line). These individual curves (solid lines) represent the agreement between mean glucose and laboratory-measured HbA1c in the context of mean glucose levels remaining stable over days to weeks. The model suggests that the relationship between glucose and HbA1c is not constant, and that as levels of markers of HbA1c increase, larger changes in glucose are required to achieve the same change in HbA1c. In contrast to traditional assessments of the glycation index, the kinetic model of the present disclosure suggests that an individual's glycation index is not constant across all HbA1c levels. Unlike eHbA1c, an important advantage of cHbA1c is that it takes into account individual differences in glycation. Individuals with low K values ​​are "low glycators" and have higher average glucose levels at a given HbA1c value, whereas individuals with high K values ​​have the opposite.

[0145] The relationship between K (dL / mg) and average glucose level target (mg / dL) using the kinetic model of the present disclosure is plotted for various HbA1c target values ​​in Figure 21. That is, if a subject is targeting a particular HbA1c value (e.g., for subsequent HbA1c measurements or cHbA1c estimations) and has a known value of K (e.g., based on multiple measured glucose levels and at least one measured HbA1c), then an average glucose target for the subject over the time period that the subject is targeting that HbA1c value can be derived and / or identified.

[0146] Further details of methods, devices and systems for determining physiological parameters related to the dynamics of glycation, excretion and production of red blood cells in a subject's body are described in U.S. Patent Application Publication No. 2018 / 0235524 to Dunn et al., WO 2020 / 086934 to Xu, WO 2021 / 108419 to Xu, WO 2021 / 108431 to Xu, U.S. Provisional Patent Application No. 62 / 939,970, U.S. Provisional Patent Application No. 63 / 015,044, U.S. Provisional Patent Application No. 63 / 081,599, and U.S. Provisional Patent Application No. 62 / 939,956, each of which is incorporated by reference in its entirety. Such physiological parameters can be used to obtain, for example, more reliable calculated HbA1c (cHbA1c) or glucose derived A1c (GD-A1c), adjusted HbA1c (aHbA1c or personalized A1c), adjusted cA1c (or K age A personalized glucose metric or personalized analyte measurement, such as cHbA1c adjusted by , and / or a personalized target glucose range, can be calculated for a subject's unique diagnostic, treatment and / or monitoring protocol, among other things.

[0147] For purposes of illustration and not limitation, the processor in the reader device is configured to execute the model described herein to calculate the physiological parameters and the personalized glucose metric. As embodied herein, the laboratory A1c measurements required for the calculation of the physiological parameters and the personalized glucose metric can be received, for example, but not by way of limitation, by the reader device using a camera (such as, but not by way of limitation, one built into the reader device) to scan a QR code containing relevant laboratory A1c data. As embodied herein, the laboratory A1c measurements can be received or obtained by the reader device from a cloud-based database. As embodied herein, the user can also use a home test kit to measure HbA1c in a blood sample and input it into the reader device instead of the laboratory A1c measurements.

[0148] A1c-glucose discrepancies can confound and adversely affect subject care. For example, as shown in Table 1 below, subjects A, B, and C have the same laboratory measured A1c values ​​but different mean glucose levels (125 mg / dL, 154 mg / dL, and 188 mg / dL, respectively). Similarly, subjects B, D, and E have the same mean glucose level of 154 mg / dL but different laboratory measured A1c values ​​(7.0%, 6.0%, and 8.0%, respectively). This information is depicted graphically in FIG. 22. Table 1 JPEG2024546442000010.jpg62150

[0149] The models described herein improve hyperglycemic risk assessment by allowing quantitative removal of red blood cell artifacts. For example, by way of illustration and not limitation, consider subjects A-E with the following characteristics: Table 2 JPEG2024546442000011.jpg62150

[0150] As can be seen in Table 2, subjects A, B, and C have different RBC life spans (i.e., measured in days (123, 87, and 110, respectively)) but the same laboratory measured A1c of 7.0%. The personalized or adjusted A1c of subjects A, B, and C, measured by the model disclosed above, are 6, 8.4, and 6.7, respectively, based on the different RBC life spans. Because the laboratory measured A1c of these three subjects is the same, it is expected that their respective healthcare providers would consider all three to be diabetic and prescribe the same treatment plan based on these values. However, because these subjects have different RBC life spans, their glycemic control is actually very different, as indicated by the quite different personalized A1c. In fact, based on their respective personalized A1c, person A is pre-diabetic (based on a personalized A1c of 6.0), person B is clearly diabetic (based on a personalized A1c of 8.4), and person C is also diabetic (based on a personalized A1c of 6.7). Thus, subjects A, B, and C may actually require different treatment regimens. Similarly, subject D may be considered pre-diabetic based on a laboratory A1c of 6.0, but diabetic based on a personalized A1c of 7.1. Furthermore, subject E may be considered diabetic based on a laboratory A1c of 8.0, but pre-diabetic based on a personalized A1c of 6.9.

[0151] 23-29 show example case studies illustrating the application of the models described herein. For example, as can be seen in FIG. 23, exemplary subjects J17, J33, and J5 have measured mean glucose of 148 mg / dL, 149 mg / dL, and 153 mg / dL, respectively, and laboratory A1c of 7.7%, 6.8%, and 8.1%. However, the personalized mean glucose and personalized A1c for these subjects determined using the models described herein are significantly different. Specifically, J17, J33, and J5 have personalized mean glucose of 141 mg / dL, 250 mg / dL, and 130 mg / dL, respectively, and laboratory A1c of 6.7%, 9.5%, and 6.8%. Of note is that the laboratory glucose metrics for J33 are quite different from the personalized glucose metrics. A graphical representation of these metrics is shown in FIG. 23. These and other metrics shown in FIG. 23 can also be seen in FIGS.

[0152] Example Sensor Results Interface Embodiments 2D-2I illustrate example embodiments of a sensor result interface or GUI for an analyte monitoring system. In accordance with the disclosed subject matter, the sensor result GUI described herein is configured to display analyte data and other health information through a user interface application (e.g., software) installed on a reader device, such as a smartphone or receiver as described with respect to FIG. 2B. Those skilled in the art will also appreciate that a user interface application having a sensor result interface or GUI can also be implemented on a local computing system or other computing device (e.g., a wearable computing device, a smart watch, a tablet computer, etc.).

[0153] Referring initially to FIG. 2D, the sensor results GUI 235 illustrates an interface including a first portion 236 that may include a numerical representation of a current analyte concentration value (e.g., current glucose value), a directional arrow indicating the analyte trend direction, and a textual description providing contextual information such as whether the user's analyte level is within range (e.g., "in range glucose"). According to an embodiment, the first portion 236 may include a numerical representation of a personalized analyte concentration value (e.g., personalized glucose value) determined using a kinetic model as disclosed below. The first portion 236 may also include a color or shade indicating the analyte concentration or trend. For example, as shown in FIG. 2D, the first portion 236 is a green shade indicating that the user's analyte level (e.g., but not limited to, a current or personalized glucose level) is within a target range. For example, according to some embodiments, a red shade may indicate an analyte level below a low analyte level threshold, an orange shade may indicate an analyte level above a high analyte level threshold, and a yellow shade may indicate an analyte level outside of the target range. According to an embodiment, the target range may be a personalized target glucose range determined using a kinetic model as disclosed below.

[0154] According to some embodiments, the sensor results GUI 235 also includes a second portion 237 that includes a graphical representation of the analyte data. Specifically, the second portion 237 includes an analyte trend graph reflecting the analyte concentration, as indicated by the y-axis, over a predetermined time period, as indicated by the x-axis. According to embodiments, the second portion 237 can include a personalized analyte trend graph reflecting a personalized analyte concentration, as indicated by the y-axis, determined using a kinetic model disclosed below, over a predetermined time period, as indicated by the x-axis. In some embodiments, the predetermined time period can be shown in 5 minute increments, for a total of 12 hours of data. However, one of ordinary skill in the art will appreciate that other time increments and periods of analyte data can be utilized and are fully within the scope of the present disclosure. The second portion 237 can also include a point 239 on the analyte trend graph indicating the current analyte concentration value, a shaded green area 240 indicating a target analyte range, and two dotted lines 238a and 238b indicating high and low analyte thresholds, respectively. According to embodiments, a point 239 on the personalized analyte trend graph may show a current personalized concentration value, a shaded green area 240 indicating a personalized target analyte range, and / or two dotted lines 238a and 238b indicating a personalized high analyte threshold and a personalized low analyte threshold, respectively. According to some embodiments, the GUI 235 may also include a third portion 241 that includes a graphic indicator and text information representing the remaining sensor life.

[0155] 2E, another example embodiment of the sensor results GUI 245 is shown. According to the disclosed subject matter, the first portion 236 is shown in a yellow shade to indicate that the user's current analyte concentration is not within the target range. According to an embodiment, the current analyte concentration can include a current personalized analyte concentration and / or the target range can be a personalized target range determined using a kinetic model as described herein. Additionally, the second portion 237 includes an analyte trend line 241, which can reflect past analyte levels over a period of time, and a current analyte data point 239 indicative of a current analyte concentration value (shown in yellow to indicate that the current value is outside the target range). According to an embodiment, the analyte trend line 241 can include past personalized analyte levels over a period of time, and the current analyte data point 239 can indicative of a personalized analyte concentration value.

[0156] According to another aspect of the embodiment, the data on the sensor result GUI 245 is automatically updated or refreshed according to an update interval (e.g., every second, every minute, every five minutes, etc.). For example, according to many of the embodiments, when the reader device receives the analyte data, the sensor result GUI 245 updates (1) the current analyte concentration value shown in the first portion 236, and (2) the analyte trend line 241 and the current analyte data points 239 shown in the second portion 237. Additionally, in some embodiments, the automatically updated analyte data may be such that older analyte data from the past (e.g., the left portion of the analyte trend line 241) is no longer displayed. According to embodiments, the current analyte concentration value may include a current personalized current value, the analyte trend line 241 may include a personalized analyte trend line 241, and the current analyte data points 239 may include a current personalized analyte data point 239.

[0157] 2F is another example embodiment of a sensor results GUI 250. According to the illustrated embodiment, the sensor results GUI 250 includes a first portion 236 that is shown in an orange shade to indicate that the user's analyte level is above a high glucose threshold (e.g., greater than 250 mg / dL). According to an embodiment, the illustrated user's analyte level may include a current personalized analyte concentration, and the high glucose threshold may include a personalized high glucose threshold. The sensor results GUI 250 also shows health information icons 251, such as an exercise icon or an apple icon, that reflect user log entries indicating times the user exercised or ate.

[0158] FIG. 2G is another example embodiment of the sensor results GUI 255. According to the illustrated embodiment, the sensor results GUI 255 includes a first portion 236 that is also shown in an orange shade to indicate that the user's analyte level is above the high glucose threshold. As described above, according to an embodiment, the illustrated user's analyte level can include a current personalized analyte concentration and the high glucose threshold can include a personalized high glucose threshold. As can be seen in FIG. 2G, instead of reporting a numerical value, the first portion 236 displays the text "HI" to indicate that the current analyte concentration value is outside the upper glucose reporting range limit. Conversely, although not shown in FIG. 2G, one skilled in the art will appreciate that when the analyte concentration is below the lower glucose reporting range limit, instead of displaying a numerical value, the first portion 236 will display the text "LO." According to an embodiment, the first portion 236 can display the text "HI" to indicate that the personalized analyte concentration value is outside the upper personalized glucose reporting range limit and conversely, will display "LO" when the personalized analyte concentration is below the lower glucose reporting range limit.

[0159] 2H is another example embodiment of the sensor results GUI 260. According to the illustrated embodiment, the sensor results GUI 260 includes a first portion 236 that is shown in a green shade to indicate that the user's current analyte level is within a target range. According to an embodiment, the user's current analyte level can include a current personalized analyte level and the target range can include a personalized target range. Also, according to the illustrated embodiment, the first portion 236 of the GUI 260 includes text that reads "GLUCOSE GOING LOW," which can indicate to the user that the user's analyte concentration value is predicted to fall below a predicted low analyte level threshold within a predetermined time (e.g., predicted glucose will fall below 75 mg / dL within 15 minutes). One skilled in the art will appreciate that the sensor results GUI 260 can display a "GLUCOSE GOING HIGH" message if the user's analyte level is predicted to rise above a predicted high analyte level threshold within a predetermined time. According to embodiments, the analyte concentration value may comprise a personalized analyte concentration value, and the predicted low analyte level and the predicted high analyte level may comprise personalized predicted low analyte level and predicted high analyte level, respectively.

[0160] FIG. 2I is another example embodiment of the sensor results GUI 265. According to the illustrated embodiment, the sensor results GUI 265 shows the first portion 236 when a sensor error exists. According to the disclosed subject matter, the first portion 236 includes three dashed lines 266 in place of a current analyte concentration value to indicate that the current analyte concentration is unavailable. According to an embodiment, the current analyte concentration value can include a current personalized analyte concentration value. In some embodiments, the three dashed lines 266 can indicate one or more error conditions, such as, for example, (1) a no signal condition, (2) a loss of signal condition, (3) a sensor too hot / cold condition, or (4) an unavailable glucose level condition. Additionally, as can be seen in FIG. 2I, the first portion 236 includes a shade of gray (instead of green, yellow, orange, or red) to indicate that the current analyte data (or the current personalized analyte data) is unavailable. According to another aspect of these embodiments, the second portion 237 may be configured to display historical analyte data on the analyte trend graph even if an error condition exists that prevents the display of the current analyte concentration value in the first portion 236. According to embodiments, the historical analyte data may include historical personalized analyte data. However, as shown in FIG. 2I, the current analyte concentration value data point is not shown on the analyte trend graph in the second portion 237.

[0161] FIG. 2J is a glucose monitoring data interface including a graphical representation including three laboratory HbA1c values ​​(left y-axis) and estimated HbA1c values ​​(left y-axis) based on a 14-day eHbA1c model overlaid on 200 days of glucose monitoring data (right y-axis), as disclosed in International Publication Nos. WO 2021 / 108419 to Xu and WO 2020 / 086934 to Xu, both of which are incorporated by reference in their entireties. As shown, the estimated HbA1c derived from the 14-day HbA1c model shows a very dramatic change over time. However, it is unlikely that HbA1c would change this quickly.

[0162] Figure 2K is a glucose monitoring data interface that includes calculated HbA1c (left Y-axis) for the first 100 days determined using kgly and kage according to the methods described in International Publication Nos. 2021 / 108419 and 2020 / 086934 to Xu, the entireties of which are incorporated by reference herein, overlaid on the graphical representation of Figure 2J.

[0163] FIG. 2L is a glucose monitoring data interface including a graph representation of FIG. 2K overlaid with the calculated HbA1c over the next 100 days (extending from day 100 to day 200, left y-axis) using kgly and kage determined for FIG. 2K according to the methods described in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated by reference in their entireties. Although the method did not consider the third HbA1c value, the model described predicts the measured third HbA1c value, showing that the model described herein closely matches reality.

[0164] Example Implementation of the Time-in-Ranges Interface 3A-3F illustrate example embodiments of GUIs for analyte monitoring systems. Specifically, FIGS. 3A-3F illustrate time-in-range GUIs, each of which includes a number of bars or bar segments (also referred to as time-in-range and / or time-in-target), each of which indicates the time that a user's analyte level is within a predefined analyte range that correlates with the bar or bar segment. For example, in some embodiments, this time may be expressed as a percentage of the predefined time. As described below, according to embodiments, FIGS. 3A-3F may also illustrate personalized time-in-range GUIs, each of which includes a number of bars or bar segments (also referred to as personalized time-in-target), each of which indicates the time that a user's personalized analyte level is within a predefined personalized analyte range that correlates with the bar or bar segment.

[0165] 3A and 3B illustrate example embodiments of a times within range GUI 305 that includes a “custom” times within range view 305A and a “standard” times within range view 305B and a slideable element 310 that allows a user to select between the two views. In accordance with the disclosed subject matter, the times within range views 305A, 305B can each include a number of bars, each bar indicating the time the user's analyte level is within a predefined analyte range that correlates with that bar. In accordance with embodiments, the user's analyte level can include a personalized analyte level. In some embodiments, the times within range views 305A, 305B further include a date range indicator 308 that indicates the date associated with the displayed number of bars and a data availability indicator 314 that indicates the time period(s) for which analyte data is available for the displayed analyte data (e.g., “data available for 7 out of 7 days”).

[0166] 3A, the "custom" time in range view 305A includes six bars (from top to bottom): a first bar indicating the user's glucose range is above 250 mg / dL for 10% of the time, a second bar indicating the user's glucose range is 141-250 mg / dL for 24% of the time, a third bar 316 indicating the user's glucose range is 100-140 mg / dL for 54% of the time, a fourth bar indicating the user's glucose range is 70-99 mg / dL for 9% of the time, a fifth bar indicating the user's glucose range is 54-69 mg / dL for 2% of the time, and a sixth bar indicating the user's glucose range is below 54 mg / dL for 1% of the time. One skilled in the art will recognize that the glucose ranges and percentages of time associated with each bar can vary depending on the ranges defined by the user and the user's available analyte data, and that the user's glucose range can include the user's personalized glucose range. Additionally, although FIGS. 3A and 3B show a predetermined time period 314 equal to seven days, one of ordinary skill in the art will appreciate that other predetermined times (e.g., 1 day, 3 days, 14 days, 30 days, 90 days, etc.) may be utilized and are fully within the scope of the present disclosure.

[0167] According to another aspect of the embodiment, the "custom" times in range view 305A also includes a user definable custom goal range 312 with an available "edit" link that allows the user to define and / or modify the custom goal range. As shown in the "custom" times in range view 305A, the custom goal range 312 is defined as a glucose range of 100-140 mg / dL and corresponds to the third bar 316 of the plurality of bars. One skilled in the art will appreciate that in other embodiments, the ranges may be adjusted by the user and such embodiments are fully within the scope of the present disclosure. According to an embodiment, the custom goal range 312 may include a custom personalized goal range.

[0168] 3B, the "standard" time in range view 305B includes five bars (from top to bottom): a first bar indicating the user's glucose range is above 250 mg / dL for 10% of the time, a second bar indicating the user's glucose range is 181-250 mg / dL for 24% of the time, a third bar indicating the user's glucose range is 70-180 mg / dL for 54% of the time, a fourth bar indicating the user's glucose range is 54 mg / dL-69 mg / dL for 10% of the time, and a fifth bar indicating the user's glucose range is below 54 mg / dL for 2% of the time. Those skilled in the art will recognize that, as with the "custom" time in range view 305A, the percentage of time associated with each bar can vary depending on the user's available analyte data. Additionally, according to an embodiment, the user's glucose range can include the user's personalized glucose range, and the numerical glucose ranges associated with the five bars can be adjusted to the user's personalized glucose range. For example, but not by way of limitation, personalized glucose ranges for each of the five bars may be calculated using a model as disclosed below. However, unlike the "custom" time in range view 305A, the glucose ranges shown in the "standard" view 305B are not user adjustable.

[0169] 3C and 3D illustrate another example embodiment of a Times Within Range GUI 320 having multiple views 320A and 320B similar to the views shown in FIGS. 3A and 3B, respectively. According to some embodiments, the Times Within Range GUI 320 may further include one or more selectable icons 322 (e.g., radio buttons, checkboxes, sliders, switches, etc.) that allow a user to select a predetermined time for which the user's analyte data will be displayed in the Times Within Range GUI 320. For example, as shown in FIGS. 3C and 3D, the selectable icons 322 may be used to select a predetermined time of 7 days, 14 days, 30 days, or 90 days. One of ordinary skill in the art will appreciate that other predetermined times may be utilized and are fully within the scope of the present disclosure.

[0170] 3E illustrates an example embodiment of a time in target GUI 330 that may be visually output on a display of a reader device (e.g., a dedicated reader device, a meter device, etc.). In accordance with the disclosed subject matter, the time in target GUI 330 includes three bars (from top to bottom) including a first bar indicating that the user's glucose range is above the predetermined target range for 34% of the time, a second bar indicating that the user's glucose range is within the predetermined target range for 54% of the time, and a third bar indicating that the user's glucose range is below the predetermined target range for 12% of the time. One skilled in the art will recognize that the percentage of time associated with each bar can vary depending on the user's available analyte data, and that the user's glucose range can include the user's personalized glucose range. 3E shows a predetermined time 332 equal to the past 7 days and a predetermined target range 334 of 80-140 mg / dL, one of ordinary skill in the art will appreciate that other predetermined times (e.g., 1 day, 3 days, 14 days, 30 days, 90 days, etc.) and / or predetermined target ranges (e.g., 70-180 mg / dL) may be utilized and are fully within the scope of the present disclosure. According to an embodiment, the predetermined target range may be a predetermined personalized target range determined using a kinetic model as disclosed herein.

[0171] FIG. 3F illustrates another example embodiment of a Time In Range GUI 340 that includes a single bar that includes five bar portions (from top to bottom): a first bar portion indicating the user's glucose range is "very high" or above 250 mg / dL for 1% of the time (14 minutes); a second bar portion indicating the user's glucose range is "high" or between 180 and 250 mg / dL for 18% of the time (4 hours and 19 minutes); a third bar portion indicating the user's glucose range is within the "target range" or between 70 and 180 mg / dL for 78% of the time (18 hours and 43 minutes); a fourth bar portion indicating the user's glucose range is "low" or between 54 and 69 mg / dL for 3% of the time (43 minutes); and a fifth bar portion indicating the user's glucose range is "very low" or below 54 mg / dL for 0% of the time (0 minutes). 3F, according to some embodiments, the time in range GUI 340 may display text adjacent to each bar portion indicating the actual time in hours and / or minutes, etc. According to embodiments, the numerical values ​​associated with the five bars may be adjusted to fit the user's personalized glucose target range.

[0172] According to one aspect of the embodiment shown in FIG. 3F, each bar segment of the time in range GUI 340 may include a different color. In some embodiments, the bar segments may be separated by dashed or dotted lines 342 to indicate the range reflected by adjacent bar segments, and / or numeric markers 344 may be interspersed between the bar segments. In some embodiments, the time in range reflected by the bar segments may be further represented as a percentage, as actual time (e.g., 4 hours and 19 minutes), or both, as shown in FIG. 3F. Additionally, one skilled in the art will recognize that the percentage of time associated with each bar segment may vary depending on the user's analyte data. In some embodiments of the time in range GUI 340, the user may configure a target range. In other embodiments, the time in range GUI 340 may not allow the user to modify the target range. Additionally, the time in range GUI 340 may include target goals (e.g., "Goal:>70%" for "Target" time in range) in addition to the numeric markers 344, which may be pre-set or user-defined. The GUI 340 may also include text prompts that provide instructions to the user regarding the benefits or adverse effects of staying within a particular range.

[0173] Example Embodiments of Specimen Level and Trend Alert Interfaces 4A-4O illustrate example embodiments of an analyte level / trend alert GUI for an analyte monitoring system. In accordance with the disclosed subject matter, the analyte level / trend alert GUI includes an audio or visual notification (e.g., a prompt, an alert, an alarm, a pop-up window, a banner notification, etc.) including the alarm condition, the analyte level measurement associated with the alarm condition, and a trend indicator associated with the alarm condition. In accordance with an embodiment, the at least one processor is configured to output a notification if at least one of the plurality of personalized glucose metrics is equal to or greater than a corresponding plurality of personalized glucose target values. The notification may include an audio or visual notification (e.g., a prompt, an alert, an alarm, a pop-up window, a banner notification, etc.).

[0174] 4A-4C illustrate example embodiments of a high glucose alarm 410, a low glucose alarm 420, and a severely low glucose alarm 430, respectively, where each alarm includes a pop-up window 402 that includes alarm status text 404 (e.g., "Low Glucose Alarm"), an analyte level measurement 406 associated with the alarm status (e.g., a current glucose level of 67 mg / dL), and a trend indicator 408 (e.g., a trend or directional arrow) associated with the alarm status. In some embodiments, an alarm icon 412 may be adjacent to the alarm status text 404. According to an embodiment, the analyte level measurement 406 may include a personalized analyte level measurement (e.g., a current personalized glucose level of 67 mg / dL).

[0175] 4D-4G, example embodiments of low glucose alarms 440, 445, severe low glucose alarm 450, and high glucose alarm 455 are shown, respectively. As shown in FIG. 4D, low glucose alarm 440 is similar to the low glucose alarm of FIG. 4B (e.g., includes a pop-up window including alarm status text, analyte level measurements related to the alarm status, and trend indicators related to the alarm status), but further includes an alert icon 442 indicating that the alarm is configured as an alert (e.g., displays, plays a sound, and vibrates even if the device is locked or the device's "Do Not Disturb" setting is enabled). With reference to FIG. 4E, low glucose alarm 445 is also similar to the low glucose alarm of FIG. 4B, but instead of including a trend arrow, low glucose alarm 445 includes a text trend indicator 447. According to one aspect of some embodiments, the text trend indicator 447 can be enabled through the device's accessibility settings, causing the device to "speak" the text trend indicator 447 to the user via its text-to-speech feature (e.g., Voiceover on iOS or Select-to-Speak on Android).

[0176] 4F, a low glucose alarm 450 is similar to the low glucose alarm of FIG. 4D (including an alert icon), but instead of displaying a analyte level measurement associated with the alarm condition and a trend indicator associated with the alarm condition, an out-of-range indicator 452 is displayed to indicate that the current glucose level is above or below a predetermined reportable analyte level range (e.g., "HI" or "LO"). According to an embodiment, the current glucose level can include a current personalized glucose level, and the predetermined reportable analyte level range can include a predetermined reportable personalized analyte level range. With reference to FIG. 4G, a high glucose alarm 455 is similar to the high glucose alarm of FIG. 4A (e.g., including a pop-up window including the alarm condition text, a analyte level measurement associated with the alarm condition, and a trend indicator associated with the alarm condition), but further includes instructions to the user 457. For example, in some embodiments, the instructions can be a prompt for the user to "check blood glucose." One skilled in the art will appreciate that other instructions or prompts (e.g., administer a corrective bolus, eat a meal, etc.) can also be implemented.

[0177] Additionally, while Figures 4A-4G depict example embodiments of the analyte level / trend alert GUI displayed on a smartphone having an iOS operating system, one skilled in the art will appreciate that the analyte level / trend alert GUI may be implemented on other devices including smartphones having other operating systems, smart watches, wearables, reader devices, tablet computing devices, blood glucose meters, laptops, desktops, and workstations, to name a few. For example, Figures 4H-4J depict example embodiments of high glucose alarm, low glucose alarm, and severe low glucose alarm for a smartphone having an Android operating system. Similarly, Figures 4K-4O depict example embodiments of severe low glucose alarm, low glucose alarm, high glucose alarm, severe low glucose alarm (including a glucose level check icon), and high glucose alarm (including an out of range indicator), respectively, for a reader device.

[0178] Example embodiments of a sensor-based interface 5A-5F illustrate example embodiments of a sensor-based interface in conjunction with a GUI for an analyte monitoring system. In accordance with the disclosed subject matter, the sensor-based interface provides technical improvements including the ability to quantify and encourage user engagement with the analyte monitoring system. For example, the sensor-based interface encourages more frequent interaction with the device and improved expected outcomes so that the user can benefit from small behavioral modifications. Additionally, as described in further detail below, the user can also benefit from improvements in multiple metabolic parameters due to increased interaction frequency.

[0179] In some embodiments, the HCP may receive reports of user interaction frequency and the patient's recorded metabolic parameter history (e.g., estimated HbA1c level, time in range 70-180 mg / dL, etc.). If the HCP determines that a particular patient in their care is less engaged than others, they may focus efforts on improving the engagement of the less engaged user / patient. The HCP may benefit from more cumulative statistics (such as average glucose view per day, average pre- / post-prandial glucose view, average glucose view for "controlled" days or hours vs. average glucose view for "uncontrolled" days or hours) obtained from the records of user interaction frequency with the analyte monitoring system that can be used to understand why the patient is not realizing the expected gains from the analyte monitoring system. If the HCP finds that the patient is not benefiting as expected from the analyte monitoring system, they may recommend increasing the interaction level (e.g., increasing the interaction target level). Thus, the HCP may modify the predetermined target interaction level.

[0180] In some embodiments, a caregiver may receive reports of the user's interaction frequency so that the caregiver can nudge the user to improve their interaction with the analyte monitoring system. The caregiver may use this data to better understand and improve the user's level of engagement with the analyte monitoring system or to change treatment decisions.

[0181] For example, according to some embodiments, the sensor usage interface may include a visual display of one or more "view" metrics that may each indicate an indication of a user's engagement or interaction with the analyte monitoring system. For example, a "view" may include an instance in which the sensor results interface is rendered or displayed in the foreground (e.g., in some embodiments, to view any of the GUIs described herein). In some embodiments, as described above, the data on the sensor results GUI 245 is automatically updated or refreshed according to an update interval (e.g., every second, every minute, every five minutes, etc.). Thus, a "view" may include one instance per update interval in which the sensor results interface is rendered or displayed in the foreground. For example, if the update interval is every minute, then only one "view" is included when the sensor results GUI 245 is rendered or displayed in the foreground several times within that minute. Similarly, if the sensor results GUI 245 is rendered or displayed in the foreground for 20 consecutive minutes, the data on the sensor results GUI 245 is updated 20 times (i.e., once per minute). However, this would only constitute 20 "views" (i.e., one "view" per update interval). Similarly, if the update interval is every 5 minutes, rendering or foregrounding the sensor results GUI 245 several times within those 5 minutes would only constitute one "view". If the sensor results interface is rendered or foregrounded for 20 consecutive minutes, this would constitute four "views" (i.e., one "view" for each of the four 5-minute intervals). According to other embodiments, a "view" may be defined as the first instance within a sensor life count where the user sees a sensor results interface with a valid sensor reading. According to disclosed embodiments, the user may receive a notification, as described below, indicating when an instance of rendering or foregrounding the sensor results GUI will not be counted as a "view".For example, the user may receive a visual notification indicating "results not updating," or "views not counted," or "please check your glucose levels again," etc. In some embodiments, the user may receive a check-in for each instance that counts as a "view," as described in more detail below.

[0182] According to disclosed embodiments, the one or more processors can be configured to record only one instance of a user operation of the reader device during a prescribed period of time. For example, but not limited to, the prescribed period of time can include one hour. Those skilled in the art will appreciate that the prescribed period of time can include any suitable period of time, such as one hour, two hours, three hours, 30 minutes, 15 minutes, etc.

[0183] According to some embodiments, a "view" may include, for example, a visual notification (e.g., a prompt, an alert, an alarm, a pop-up window, a banner notification, etc.). In some embodiments, the visual notification may include an alarm condition, an analyte level measurement associated with the alarm condition, and a trend indicator associated with the alarm condition. For example, an analyte level / trend alert GUI such as the embodiments shown in Figures 4A-4O may constitute a "view."

[0184] In some embodiments, the sensor user interface may include a visual display of a "scan" metric that indicates another measure of a user's engagement or interaction with the analyte monitoring system. A "scan" may include an instance where a user scans a sensor control device using a reader device (e.g., a smartphone, a dedicated reader, etc.), such as in a flash analyte monitoring system. As discussed above in connection with a "view," a "scan" may include one instance per update interval where a user scans a sensor control device using a reader device.

[0185] 5A and 5B show example embodiments of sensor-based interfaces 500 and 510, respectively. In accordance with the disclosed subject matter, the sensor-based interfaces 500 and 510 may be rendered and displayed by a mobile app or software residing in a non-transitory memory of the reader device 120, for example as described with respect to FIGS. 1 and 2A. In some embodiments, the software may record the date and time of a user's interaction with the system for each instance of a "view" or "scan." In some embodiments, the software may record the current glucose value for each instance of a "view" or "scan." 5A, the sensor user interface 500 can include a predetermined time period 508 indicating a time period (e.g., a date range) for which the views metric is displayed, a Total Views metric 502 indicating a total number of views over the predetermined time period 508, a Views Per Day metric 504 indicating an average number of views per day over the predetermined time period 508, and a Percentage Time Sensor Active metric 506 indicating a percentage of the predetermined time period 508 during which the reader device 120 is in communication with the sensor control device 102 as described with respect to FIGS. 1, 2B, and 2C. Referring to FIG. 5B, the sensor user interface 510 can include the Views Per Day metric 504 and the Percentage Time Sensor Active metric 508, each measured over the predetermined time period 508.

[0186] According to another aspect of the embodiment, the predetermined time period 508 is shown as one week, although one skilled in the art would recognize that other predetermined time periods (e.g., 3 days, 14 days, 30 days) may be utilized. Additionally, the predetermined time period 508 may be a discrete time period including a start date and an end date as shown in the sensor usage interface 500 of FIGURE 5A, or may be a time period relative to the current day or time (e.g., "last 7 days," "last 14 days," etc.) as shown in the sensor usage interface 510 of FIGURE 5B.

[0187] 5C illustrates an example embodiment of a sensor usage interface 525 as part of an analyte monitoring system report GUI 515. In accordance with the disclosed subject matter, the GUI 515 is a snapshot report covering a predefined time period 516 (e.g., 14 days) and includes multiple report portions on a single report GUI, including a sensor usage interface portion 525, a glucose trends interface 517 that may include a glucose trend graph, a low glucose event graph, and other relevant glucose metrics (e.g., glucose control indicators), a health information interface 518 that may include information about the user's average daily carbohydrate intake and medication amounts (e.g., insulin dosages) logged by the user, and a comments interface 519 that may include additional information about the user's analyte and medication patterns presented in a narrative format. In accordance with an embodiment, the health information interface 518 may include a graphical representation of the average daily glucose levels against the target glucose ranges described above (shown with horizontal lines at 80 and 180 mg / dL). The glucose trends interface 517 may also include a personalized A1C percentage and / or a glucose variability percentage. In some embodiments, the health information interface 518 can be segmented to indicate which range the user is in. For example, in some embodiments, the segmentation can be according to color. Specifically, low glucose ranges can be red, good glucose ranges can be green, high glucose ranges can be yellow, and very high glucose ranges can be orange, although one of ordinary skill in the art would understand that different means for segmentation are possible. According to embodiments, the user or healthcare provider can define the segmentation. According to embodiments, the health information interface 518 can include a personalized target glucose range report as disclosed in International Publication No. WO 2020 / 086934 to Xu, which is incorporated herein by reference in its entirety.According to an embodiment, the personalized target glucose range report may include a graphical representation of the daily glucose levels relative to the personalized target glucose ranges described above. According to another aspect of the embodiment, the sensor usage interface 525 may include a percent time sensor active metric 526, an average scans / views metric 527 (e.g., showing the average sum of the number of scans and the number of views), and a percent time sensor active graph 528. As can be seen in FIG. 5C, the axes of the percent time sensor active graph may be aligned with corresponding axes of one or more other graphs (e.g., average glucose trend graph, low glucose events graph) to allow a user to visually correlate data between multiple graphs from two or more portions of the report GUI by common units (e.g., time) from the aligned axes.

[0188] FIG. 5D illustrates an example embodiment of another analyte monitoring system report GUI 530 including sensor usage information. According to the disclosed subject matter, the GUI 530 is a monthly summary report including a first portion including a legend 531, which includes a plurality of graphic icons, each adjacent to a legend. As shown in FIG. 5D, the legend 531 includes an icon and legend for "average glucose," an icon and legend for "scans / views," and an icon and legend for "low glucose events." The GUI 530 also includes a second portion including a calendar interface 532. For example, as shown in FIG. 5D, the GUI 530 includes a monthly calendar interface, where each day of the month may include one or more of an average glucose metric, a low glucose event icon, and a sensor usage metric 532. In some embodiments as shown in FIG. 5D, the sensor usage metric ("scans / views") indicates the total number of scans and views for each day. According to an embodiment, the average glucose metric may include a personalized average glucose metric.

[0189] FIG. 5E illustrates an example embodiment of another analyte monitoring system report GUI 540 including sensor usage information. According to the disclosed subject matter, the GUI 540 is a weekly summary report including multiple report portions, each representing a different day of the week, with each report portion including a glucose trend graph 541 that may include a user's measured glucose levels over a 24 hour period, and a health information interface 543 that may include information regarding the user's daily average glucose, carbohydrate intake, and / or insulin dose. In some embodiments, the glucose trend graph 541 may include a sensor usage marker 542 that indicates that a scan, view, or both occurred at a particular time during the 24 hour period. According to an embodiment, the glucose trend graph 541 may include a personalized glucose level for the user over the 24 hour period. According to an embodiment, the glucose trend graph 541 may include a personalized target average glucose report that may include a graphical representation of the subject's average glucose and personalized target average glucose over a period of time (e.g., but not limited to, shown by solid lines). According to an embodiment, the health information interface 543 may include information regarding the user's daily personalized average glucose.

[0190] 5F illustrates an example embodiment of another analyte monitoring system report GUI 550 including sensor usage information. According to the disclosed subject matter, the GUI 550 is a daily log report including a glucose trend graph 551 that may include a user's glucose levels over a 24 hour period. According to an embodiment, the glucose trend graph 541 may include a user's personalized glucose levels over a 24 hour period. In some embodiments, the glucose trend graph 551 may include a sensor usage marker 552 that indicates that a scan, view, or both occurred at a particular time during the 24 hour period. The glucose trend graph 551 may also include logged event markers, such as logged carbohydrate intake marker 553 and logged insulin administration marker 554, as well as glucose event markers, such as low glucose event marker 555.

[0191] According to an embodiment, Figures 5A-5F may also include laboratory measured HbA1c ("Lab A1c").

[0192] Figures 5I-5L show various GUIs for improving usability and user privacy for analyte monitoring software. GUI 5540 in Figure 5G shows a research consent interface 5540 that prompts the user to either decline or participate (via button 5542) with regard to allowing the user's analyte data and / or other product related data to be used for research purposes. According to embodiments of the disclosed subject matter, the analyte data can be anonymized (de-identified) and stored in an international database for research purposes.

[0193] Referring now to FIG. 5H, a GUI 5550 shows a “Vitamin C” warning interface 5550 that displays a warning to the user that daily use of more than 500 mg of vitamin C supplements may result in erroneously high sensor readings.

[0194] FIGURE 5I is a GUI 5500 illustrating a first time start interface that may be displayed to a user upon starting the analyte monitoring software for the first time. In accordance with the disclosed subject matter, GUI 5500 may include a "Get Started Now" button 5502 that, when pressed, navigates the user to GUI 5510 of FIGURE 5J. GUI 5510 illustrates a country confirmation interface 5512 that prompts the user to confirm the user's country. In accordance with another aspect of an embodiment, for regulatory compliance purposes, certain interfaces within the analyte monitoring software application may be restricted and / or enabled depending on the selected country.

[0195] 5K, a GUI 5520 illustrates a user account creation interface that allows a user to initiate a cloud-based user account creation process. In accordance with the disclosed subject matter, a cloud-based user account may allow a user to share information with medical professionals, family, and friends, utilize a cloud-based reporting platform to review more advanced analyte reports, and back up the user's past sensor readings to a cloud-based server. In some embodiments, the GUI 5520 may also include a "skip" link 5522 that allows a user to use the analyte monitoring software application in an "accountless mode" (e.g., without creating or linking to a cloud-based account). Selecting the "skip" link 5522 may display an information window 5524 informing the user that certain functionality is not available in the "accountless mode." The information window 5524 may further prompt the user to return to the GUI 5520 or to proceed without account creation.

[0196] 5L is a GUI 5530 illustrating a menu interface that is displayed within the analyte monitoring software application while the user is in the “no account mode.” According to aspects of these embodiments, the GUI 5530 includes a “sign in” link 5532 that allows the user to leave the “no account mode” and create a cloud-based user account from within the analyte monitoring software application, or sign in with an existing cloud-based user account.

[0197] Those skilled in the art will understand that the GUIs, report interfaces, or portions thereof described herein are intended to be illustrative only, and that any individual element or any combination of elements illustrated and / or described with respect to a particular embodiment or figure may be freely combined with any element or any combination of elements illustrated and / or described with respect to any of the other embodiments.

[0198] Example Embodiments of a Digital Interface for an Analyte Monitoring System Exemplary embodiments of a digital interface of an analyte monitoring system are described herein. In accordance with the disclosed subject matter, the digital interface may include a set of instructions, routines, subroutines, and / or algorithms, such as software and / or firmware, stored in non-transitory memory that are executed by one or more processors of one or more devices in the analyte monitoring system, and that are configured to enable specific functionality and inter-device communication. As an initial matter, those skilled in the art will appreciate that the digital interface described herein may include instructions stored in non-transitory memory of the sensor control device 102, the reader device 120, the local computer system 170, the trusted computer system 180, and / or any other device or system that is part of or in communication with the analyte monitoring system 100, as described with respect to FIGS. 1, 2A, and 2B. These instructions, when executed by one or more processors in the sensor control device 102, the reader device 120, the local computer system 170, the trusted computer system 180, or any other device or system in the analyte monitoring system 100, cause the one or more processors to perform the method steps described herein. One skilled in the art will further recognize that the digital interfaces described herein can be stored as instructions in the memory of a single centralized device, or distributed across multiple discrete devices at geographically dispersed locations.

[0199] Example Embodiments of the Data Backfilling Method Next, an example embodiment of a method for data backfilling in an analyte monitoring system will be described. In accordance with the disclosed subject matter, gaps in analyte data and other information may result from interruptions in communication links between various devices in the analyte monitoring system 100. Such interruptions may occur, for example, when a device is powered off (e.g., a user's smartphone runs out of battery) or when a first device is temporarily out of wireless communication range of a second device (e.g., a user wearing the sensor control device 102 accidentally leaves their smartphone at home when they leave for work). As a result of these interruptions, the reader device 120 may not be able to receive analyte data and other information from the sensor control device 102. It would therefore be beneficial to have a robust and flexible method for data backfilling in an analyte monitoring system to ensure that each analyte monitoring device is able to receive a complete data set as intended once the communication link is re-established.

[0200] FIG. 6A is a flow diagram illustrating an example embodiment of a method 600 for data backfilling in an analyte monitoring system. In accordance with the disclosed subject matter, the method 600 may be implemented to provide data backfilling between the sensor control device 102 and the reader device 120. At step 602, analyte data and other information is autonomously communicated between the first device and the second device at predetermined intervals. In some embodiments, the first device may be the sensor control device 102 and the second device may be the reader device 120, as described with respect to FIGS. 1, 2A, and 2B. In accordance with the disclosed subject matter, the analyte data and other information may include one or more of, but are not limited to, data indicative of an analyte level in a bodily fluid, a rate of change of the analyte level, a predicted analyte level, a low or high analyte level alert condition, a sensor fault condition, or a communication link event. According to another aspect of these embodiments, autonomously communicating at predetermined intervals may include streaming the analyte data and other information at one or more predetermined rates (e.g., every minute, every 5 minutes, every 15 minutes, etc.) according to a standard wireless communication network protocol, such as the Bluetooth or Bluetooth Low Energy protocol. In some embodiments, different types of analyte data or other information (e.g., historical glucose data every 5 minutes, current glucose values ​​every minute, etc.) may be autonomously communicated between the first and second devices at different predetermined rates.

[0201] In step 604, a disconnection event or condition occurs that causes an interruption of the communication link between the first device and the second device. As described above, the disconnection event can be due to the second device (e.g., the reader device 120, a smartphone, etc.) running out of battery power or being manually turned off by a user. The disconnection event can also be due to the first device moving out of wireless communication range of the second device, the presence of a physical barrier obstructing the first device and / or the second device, or anything that otherwise prevents wireless communication from occurring between the first device and the second device.

[0202] In step 606, a communication link is re-established between the first device and the second device (e.g., the first device returns within wireless communication range of the second device). Once re-connected, the second device requests past analyte data according to the last life count metric for which data was received. According to the disclosed subject matter, the life count metric can be a numerical value that is incremented and tracked in units of time (e.g., minutes) on the second device and indicates the elapsed time since the sensor control device was activated. For example, in some embodiments, the second device (e.g., reader device 120, smartphone, etc.) can determine the last life count metric for which data was received after re-establishing a Bluetooth wireless communication link with the first device (e.g., sensor control device 120). Thereafter, according to some embodiments, the second device can send a request to the first device for past analyte data and other information having a life count metric greater than the determined last life count metric for which data was received.

[0203] In some embodiments, instead of the second device requesting historical specimen data associated with a life count metric greater than the last life count metric for which determined data was received, the second device may send a request to the first device for historical specimen data or other information associated with a particular life count range.

[0204] Upon receiving the request, the first device retrieves the requested historical analyte data from storage (e.g., non-transitory memory of the sensor control device 102) at step 608 and then transmits the requested historical analyte data to the second device at step 610. Upon receiving the requested historical analyte data, the second device stores the requested historical analyte data in storage (e.g., non-transitory memory of the reader device 120) at step 612. In accordance with the disclosed subject matter, when the second device stores the requested historical analyte data, it may do so along with an associated life count metric. In some embodiments, the second device may also output the requested historical analyte data to a display of the second device, such as in a glucose trend graph of a sensor results GUI as described with respect to FIGS. 2D-2I. For example, in some embodiments, the requested historical analyte data may be used to fill gaps in a glucose trend graph by displaying the requested historical analyte data along with previously received analyte data.

[0205] Furthermore, one skilled in the art will appreciate that the data backfilling method may be implemented between multiple different devices in a wired or wireless communication with each other within an analyte monitoring system.

[0206] 6B is a flow diagram illustrating another example embodiment of a method 620 for data backfilling in an analyte monitoring system. In accordance with the disclosed subject matter, the method 620 can be implemented to provide data backfilling between a reader device 120 (e.g., a smartphone, a dedicated reader) and a trusted computer system 180, such as a cloud-based platform that generates reports. At step 622, analyte data and other information is communicated between the reader device 120 and the trusted computer system 180 based on a number of upload triggers. In accordance with the disclosed subject matter, the analyte data and other information can include one or more of the following, but are not limited to: data indicative of an analyte level in a bodily fluid (e.g., current glucose level, historical glucose data), a rate of change of the analyte level, a predicted analyte level, a low analyte level or high analyte level alert condition, information logged by a user, information regarding the sensor control device 102, alarm information (e.g., alarm settings), wireless connection events, and reader device settings, to name a few examples.

[0207] According to another aspect of the embodiment, the upload triggers may include one or more of (but are not limited to) activation of the sensor control device 102, a user entering or deleting a note or log entry, a wireless communication link being re-established between the reader device 102 and the sensor control device 102, a change in an alarm threshold, an alarm being raised, updated or cleared, an internet connection being re-established, a reader device 120 being rebooted, receiving one or more current glucose readings from the sensor control device 102, a shutdown of the sensor control device 120, a signal loss alarm being raised, updated or cleared, a signal loss alarm being switched on / off, a sensor results screen GUI being viewed, or a user signing in to the cloud-based platform.

[0208] According to another aspect of these embodiments, the reader device 120 may "mark" the analyte data and other information to be sent to the trusted computer system 180 in order to track data transmission between the devices. For example, in some embodiments, once the trusted computer system 180 receives the analyte data and other information, it may send a reply back to the reader device 120 to acknowledge successful receipt of the analyte data and other information. The reader device 120 may then mark the data as successfully transmitted. In some embodiments, the reader device 120 may mark the analyte data and other information before transmitting the analyte data and other information and after receiving the reply back. In other embodiments, the reader device 120 may mark the analyte data and other information only after receiving the reply back from the trusted computer system 180.

[0209] 6B, in step 624, a disconnection event occurs that causes an interruption of the communication link between the reader device 120 and the trusted computer system 180. For example, the disconnection event can be caused by a user putting the reader device 120 into "airplane mode" (e.g., disabling the wireless communication module), by a user powering off the reader device 120, or by the reader device 120 moving out of wireless communication range.

[0210] In step 626, one of multiple upload triggers occurs to re-establish the communications link between the reader device 120 and the trusted computer system 180 (and the Internet). The reader device 120 then determines the last successful data transmission to the trusted computer system 180 based on previously marked transmitted specimen data and other information. The reader device 120 may then transmit specimen data and other information not yet received by the trusted computer system 180 in step 628. In step 630, the reader device 120 receives an acknowledgement of successful receipt of the specimen data and other information from the trusted computer system 180.

[0211] 6B is described above with respect to a reader communicating with a trusted computer system, those skilled in the art will appreciate that the data backfilling method may also be applied between other devices and computer systems in an analyte monitoring system (e.g., between a reader and a local computer system, between a reader and a medical delivery device, between a reader and a wearable computer device, etc.) These embodiments, as well as variations and permutations thereof, are fully within the scope of this disclosure.

[0212] In addition to data backfilling, example embodiments of methods for aggregating wireless communication link disconnection and reconnection events in an analyte monitoring system are also described. In accordance with the disclosed subject matter, there can be many and varied causes for interruptions in wireless communication links between various devices in an analyte monitoring system. These causes can be technical in nature (e.g., a reader device being out of wireless communication range of a sensor control device) or related to user behavior (e.g., a user leaving a reader device at home). Thus, to improve the connectivity and data integrity of an analyte monitoring system, it may be beneficial to collect information regarding disconnection and reconnection events between various devices in the analyte monitoring system.

[0213] 6C is a flow diagram illustrating an example embodiment of a method 640 for aggregating wireless communication link disconnection and reconnection events in an analyte monitoring system. For example, in some embodiments, the method 640 may be used to detect, log, and upload Bluetooth or Bluetooth Low Energy disconnection and reconnection events between the sensor control device 102 and the reader device 120 to the trusted computer system 180. In accordance with the disclosed subject matter, the trusted computer system 180 may aggregate disconnection and reconnection events sent from multiple analyte monitoring systems. The aggregated data may then be analyzed to determine whether any conclusions can be made regarding how to improve connectivity and data integrity in the analyte monitoring system.

[0214] At step 642, analyte data and other information is communicated between the reader device 120 and the trusted computer system 180 based on a number of upload triggers such as those described above with respect to method 620 of FIG. 6B. At step 644, a disconnection event occurs that causes an interruption of the wireless communication link between the sensor control device 102 and the reader device 120. Example disconnection events may include, but are not limited to, a user putting the reader device 120 into "airplane mode," a user powering off the reader device 120, the reader device 120 running out of battery, the sensor control device 102 moving out of wireless communication range of the reader device 120, or a physical barrier obstructing the sensor control device 102 and / or the reader device 120, to name a few examples.

[0215] 6C, in step 646, one of multiple upload triggers is re-establishment of the wireless communication link between the sensor control device 102 and the reader device 120. The reader device 120 then determines a disconnect time, which is the time when the interruption of the wireless communication link began, and a reconnect time, which is the time when the wireless communication link between the sensor control device 102 and the reader device 120 is re-established. According to some embodiments, the disconnect time and reconnect time may also be stored locally in an event log of the reader device 120. In step 648, the reader device 120 transmits the disconnect time and reconnect time to the trusted computer system 180.

[0216] According to some embodiments, the disconnection and reconnection times may be stored in non-transitory memory, such as a database, on the trusted computer system 180 and aggregated with disconnection and reconnection times collected from other analyte monitoring systems. In some embodiments, the disconnection and reconnection times may also be transmitted to and stored on a cloud-based platform or server separate from the trusted computer system 180 that stores the analyte data. In still other embodiments, the disconnection and reconnection times may be anonymized.

[0217] One skilled in the art will also appreciate that method 640 can also be used to collect disconnection and reconnection times between reader device 120 and trusted computer system 180, between reader device 120 and wearable computing devices (e.g., smart watches, smart glasses), between reader device 120 and drug delivery devices (e.g., insulin pumps, insulin pens), between sensor controller 102 and wearable computing devices, between sensor controller 102 and drug delivery devices, and between other devices in the analyte monitoring system, including any other combination of devices in the analyte monitoring system. One skilled in the art will further appreciate that method 640 can also be used to analyze disconnection and reconnection times of different wireless communication protocols, such as, for example, Bluetooth or Bluetooth Low Energy, NFC, 802.11x, UHF, cellular connections, or any other standard or proprietary wireless communication protocol.

[0218] Example Embodiments for Remediating Stale / Failed Sensor Transmissions Next, an example embodiment of a method for ameliorating expired and / or failed sensor transmissions in an analyte monitoring system is described. In accordance with the disclosed subject matter, when the sensor control device 102 detects an expired or failed sensor condition, an alert can be triggered on the reader device 120. However, the reader device 120 may not receive these alerts if it is in "airplane mode", powered off, out of wireless communication range of the sensor control device 102, or otherwise unable to wirelessly communicate with the sensor control device 102. This may result in the user missing information, such as the need to promptly replace the sensor control device 102. If a detected sensor failure is not properly addressed, the user may not be aware of adverse glucose conditions (e.g., hypoglycemia and / or hyperglycemia) due to sensor termination.

[0219] 7 is a flow diagram illustrating an example embodiment of a method 700 for improving a stale or failed sensor transmission in an analyte monitoring system. In accordance with the disclosed subject matter, the method 700 may be implemented to improve a sensor transmission by the sensor controller 102 after a stale or failed sensor condition is detected. At step 702, a stale or failed sensor condition is detected by the sensor controller 102. In some embodiments, the sensor failure condition may include one or both of a sensor insertion failure condition or a sensor termination condition. In some embodiments, the sensor insertion failure condition or the sensor termination condition may include, but is not limited to, one or more of a FIFO overflow condition being detected, a sensor signal being below a predefined insertion failure threshold, moisture ingress being detected, an electrode voltage exceeding a predefined diagnostic voltage threshold, an early signal decay (ESA) condition, or a late signal decay (LSA) condition.

[0220] Referring again to FIG. 7 , at step 704, the sensor controller 102 stops taking analyte level measurements from the analyte sensor in response to detecting the sensor fault condition. At step 706, the sensor controller 102 begins transmitting an indication of the sensor fault condition to the reader device 120 while simultaneously allowing the reader device 120 to connect to the sensor controller 102 for data backfill. According to the disclosed subject matter, transmitting the indication of the sensor fault condition can include transmitting multiple Bluetooth or Bluetooth Low Energy advertisement packets, each of which can include the indication of the sensor fault condition. In some embodiments, the multiple Bluetooth or BLE advertisement packets can be transmitted repeatedly, continuously, or intermittently. Those skilled in the art will recognize that other modes of wirelessly broadcasting or multicasting the indication of the sensor fault condition can also be implemented. According to another aspect of these embodiments, the reader device 120 can visually display an alert or prompt for confirmation by a user in response to receiving the indication of the sensor fault condition.

[0221] In step 708, the sensor control device 102 may be configured to monitor for a reply or acknowledgment of receipt of the indication of the sensor fault condition from the reader device 120. For example, in some embodiments, the reader device 120 may generate a reply or acknowledgment of receipt when a user clears an alert on the reader device 120 regarding the indication of the sensor fault condition or responds to a prompt to acknowledge the indication of the sensor fault condition. If the sensor control device 102 receives a reply or acknowledgment of receipt of the indication of the sensor fault condition, it may enter either a storage state or an end state in step 714. In the storage state, according to some embodiments, the sensor control device 102 is placed in a low power mode and can be reactivated by the reader device 120. In contrast, in the end state, the sensor control device 102 cannot be reactivated and must be removed and replaced.

[0222] If no receipt of a fault condition indication is received by the sensor controller 102, then in step 710 the sensor controller 102 stops sending fault condition indications after a first predetermined period of time. For example, in some embodiments, the first predetermined period of time can be one of 1 hour, 2 hours, 5 hours, etc. Then, in step 712, if no receipt of a fault condition indication is still received by the sensor controller 102, the sensor controller 102 also stops allowing data backfilling after a second predetermined period of time. For example, in some embodiments, the second predetermined period of time can be one of 24 hours, 48 ​​hours, etc. Then, in step 714, the sensor controller 102 enters a save state or an end state.

[0223] Embodiments of the present disclosure reduce the risk of a sensor fault alert not being received by allowing the sensor control device 102 to continue transmitting the sensor fault condition for a predetermined period of time. Additionally, while the embodiments described above refer to the sensor control device 102 communicating with a reader device 120, those skilled in the art will recognize that an indication of a sensor fault condition may also be transmitted between the sensor control device 102 and other types of mobile computing devices, such as, for example, a wearable computing device (e.g., smart watch, smart glasses) or a tablet computing device.

[0224] Example Embodiments of Data Merging in an Analyte Monitoring System Next, an example embodiment of a method for merging data received from one or more analyte monitoring systems will be described. As described above with respect to FIG. 1, a trusted computer system 180, such as a cloud-based platform, can be configured to generate various reports based on analyte data and other information received from multiple reader devices 120 and sensor control devices 102. However, a large and diverse population of reader devices and sensor control devices can create complexities and challenges in generating reports based on the received analyte data and other information. For example, a user may have multiple reader devices and / or sensor control devices, each of which may include different versions, either simultaneously or consecutively over a period of time. This can result in further complexities in that there may be duplicate and / or overlapping data sets for each user. Therefore, it would be beneficial to have a method for merging data in a trusted computer system for report generation purposes.

[0225] FIG. 8A is a flow diagram illustrating an example embodiment of a method 800 for merging user-related data from multiple reader devices and multiple sensor control devices to generate one or more report metrics. In accordance with the disclosed subject matter, the method 800 can be implemented to merge analyte data to generate different types of report metrics for use in various reports. At step 802, data is received from one or more reader devices 120 and combined for merging purposes. At step 804, the merged data is de-duplicated to remove historical data from multiple readers originating from the same sensor control device. In accordance with the disclosed subject matter, the process of de-duplicating data can include (1) identifying or assigning a priority associated with each reader device from which analyte data originates, and (2) storing data associated with a higher priority reader device if "duplicate" data exists. For example, in some embodiments, a higher priority is assigned to newer reader devices (e.g., newer models with newer versions of software installed) than older reader devices (e.g., older models with older versions of software installed). In some embodiments, priority can be assigned by device type (e.g., smart phones have a higher priority than dedicated readers).

[0226] Continuing with reference to FIG. 8A, in step 806, it is determined whether one or more of the reporting metrics to be generated require resolution of duplicate data. If not, in step 808, a first type of reporting metric may be generated based on the de-duplicated data without further processing. For example, in some embodiments, the first type of reporting metric may include an average glucose level for use in reports such as a snapshot or monthly summary report (as described with respect to FIG. 5C and FIG. 5D). If it is determined that one or more of the reporting metrics to be generated require resolution of duplicate data, in step 810, a method for resolving overlapping areas of data is performed. An example embodiment of a method for resolving overlapping areas of data is described below with respect to FIG. 8B. Then, in step 812, a second type of reporting metric is generated based on the de-duplicated and processed data to resolve duplicate data segments. For example, in some embodiments, the second type of reporting metric may include a low glucose event calculation for use in reports such as a daily log report (as described with respect to FIG. 5F).

[0227] FIG. 8B is a flow diagram illustrating an example embodiment of a method 815 for resolving overlapping regions of analyte data, which may be implemented, for example, in step 810 of the method 800 described with respect to FIG. 8A. In step 817, the deduplicated data from each reader (resulting from step 804 of the method 800 described with respect to FIG. 8A) may be sorted from oldest to newest. Next, in step 819, the deduplicated and sorted data is separated according to a predefined time period based on the reporting metric to be generated. For example, in some embodiments, if the reporting metric is a graph reflecting glucose values ​​on a particular day, the deduplicated and sorted data may be separated for that particular day. Next, in step 821, the deduplicated and sorted contiguous data portions for each reader device are separated. In accordance with the disclosed subject matter, non-contiguous data points may be discarded or ignored (e.g., not used) for purposes of generating the reporting metric. In step 823, for each deduplicated and sorted contiguous data portion of a reader device, it is determined whether there is an overlapping region with other deduplicated and sorted contiguous data portions from other reader devices. For each identified overlap region, the deduplicated and sorted data from the higher priority reader device is stored in step 825. If in step 827 it is determined that all contiguous portions have been analyzed according to the previous steps, method 815 ends in step 829. Otherwise, method 815 returns to step 823 to continue identifying and resolving overlap regions between contiguous data portions that have been deduplicated and sorted for different reader devices.

[0228] 8C-8E are graphs (840, 850, 860) illustrating various stages in processing deduplicated and sorted data from multiple reader devices according to method 815 for resolving overlapping regions of data. Referring first to FIG. 8C, graph 840 illustrates deduplicated and sorted data from three different reader devices: a first reader 841 (reflected by circular data points), a second reader 842 (reflected by diamond data points), and a third reader 843 (reflected by square data points). According to one aspect of graph 840, data is illustrated after being deduplicated, sorted, and separated into predetermined time periods in step 821 of method 815. As can be seen in FIG. 8C, contiguous data portions for each of the three reader devices (841, 842, 843) have been identified, and three traces are illustrated. According to another aspect of graph 840, non-contiguous points 844 are not included in the three traces.

[0229] Referring now to FIG. 8D, graph 850 shows data from readers 841, 842, 843 in step 823 of method 815, with three overlapping regions of consecutive data portions identified: a first overlap region 851 where all three consecutive data portions overlap, a second overlap region 852 where two consecutive data portions (from reader device 842 and reader device 843) overlap, and a third overlap region 853 where two consecutive data portions (also from reader device 842 and reader device 843) overlap.

[0230] 8E is a graph 860 showing data at step 825 of method 815, where a single trace 861 shows the merged, deduplicated, and sorted data from each reader device after overlap regions 851, 852, and 853 have been resolved using the priorities of three reader devices 841, 842, 843. According to graph 860, the order of priority from highest to lowest is reader device 843, reader device 842, and reader device 841.

[0231] Although Figures 8C, 8D and 8E show three contiguous data portions with three discrete overlap regions identified, one skilled in the art will appreciate that fewer or more contiguous data portions (and non-contiguous data points) and overlap regions are possible. For example, one skilled in the art will appreciate that if a user has only two reader devices, there may be fewer contiguous data portions and overlap regions, if any. Conversely, if a user has five reader devices, one skilled in the art will appreciate that there may be five contiguous data portions with three or more overlap regions.

[0232] Example Sensor Migration Embodiments Next, an example embodiment of a sensor migration method will be described. According to the disclosed subject matter, as mobile computing and wearable technologies continue to rapidly evolve and become more ubiquitous, users are likely to replace or upgrade their smartphones more frequently. Thus, in the context of an analyte monitoring system, it would be beneficial to have a sensor migration method that allows users to continue using a previously working sensor control device with a new smartphone. It would also be beneficial to ensure that historical analyte data from the sensor control device can be backfilled (and subsequently uploaded to a trusted computer system) into a new smartphone in a user-friendly and secure manner.

[0233] FIG. 9A is a flow diagram illustrating an example embodiment of a method 900 for migrating a sensor control device. According to the disclosed subject matter, the method 900 can be implemented in an analyte monitoring system to allow a user to continue using a previously activated sensor control device with a new reader device (e.g., a smartphone). In step 902, a user interface application (e.g., a mobile software application or app) is installed on the reader device 120 (e.g., a smartphone) such that a new unique device identifier or "device ID" is created and stored on the reader device 120. In step 904, after the app is installed and launched, the user is prompted to enter user credentials for purposes of logging into the trusted computer system 180 (e.g., a cloud-based platform or server). An example embodiment of a GUI 930 for prompting the user to enter user credentials is shown in FIG. 9B. According to one aspect of the embodiment, the GUI 930 can include a username field 932 that can include a unique username or email address, and a masked or unmasked password field 934 that allows the user to enter a password.

[0234] Referring again to Figure 9A, after user credentials are entered into the app at step 906, a prompt is displayed requesting user confirmation to log into the trusted computer system 180. An example embodiment of a GUI 940 for requesting user confirmation to log into the trusted computer system 180 is shown in Figure 9D. According to aspects of these embodiments, the GUI 940 may also include a warning, as shown in Figure 9D, that confirming the login will log the user off of other reader devices (e.g., the user's old smartphone).

[0235] Once the user confirms the login, in step 908, the user's authentication information is transmitted to the trusted computer system 180 for subsequent verification. According to some embodiments, a device ID may also be transmitted from the reader device 120 to the trusted computer system 180 for storage in non-transitory memory of the trusted computer system 180. For example, according to some embodiments, in response to receiving the device ID, the trusted computer system 180 may update a device ID field associated with the user's record in the database.

[0236] After the user credentials are verified by the trusted computer system 180, the app prompts the user to scan for an already-activated sensor control device 102 in step 910. According to the disclosed subject matter, the scan may include bringing the reader device 120 close to the sensor control device 102 and causing the reader device 120 to transmit one or more wireless interrogation signals according to a first wireless communication protocol. For example, in some embodiments, the first wireless communication protocol may be a Near Field Communication (NFC) wireless communication protocol. However, one skilled in the art will recognize that other wireless communication protocols (e.g., infrared, UHF, 802.11x, etc.) may also be implemented. An example embodiment of a GUI 950 prompting the user to scan for an already-activated sensor control device 102 is shown in FIG. 9D.

[0237] 9A , in step 912, if an existing wireless communication link is currently established with the user's previous reader device, the reader device 120 scans the sensor control device 102, causing the sensor control device 102 to terminate the existing wireless communication link. According to aspects of these embodiments, the existing wireless communication link may include a link established according to a second wireless communication protocol that is different from the first wireless communication protocol. For example, in some embodiments, the second wireless communication protocol may be a Bluetooth or Bluetooth Low Energy protocol. The sensor control device 102 then enters a "ready to pair" state in which it can establish a wireless communication link with the reader device 120 according to the second wireless communication protocol.

[0238] At step 914, the reader device 120 initiates a pairing sequence with the sensor control device 102 via a second wireless communication protocol (e.g., Bluetooth or Bluetooth Low Energy). Thereafter, at step 916, the sensor control device 102 completes the pairing sequence with the reader device 120. At step 918, the sensor control device 102 can begin transmitting current glucose data to the reader device 120 according to the second wireless communication protocol. For example, in some embodiments, the current glucose data can be wirelessly transmitted to the reader device 120 at predetermined intervals (e.g., every 1 minute, every 2 minutes, every 5 minutes).

[0239] 9A, in step 920, the reader device 120 receives current glucose data from the sensor control device 102 and stores the received glucose data in the non-transitory memory of the reader device 120. According to some embodiments, the reader device 120 can also request historical glucose data from the sensor control device 102 for backfilling purposes. For example, according to some embodiments, the reader device 120 can request historical glucose data from the sensor control device 102 for the entire wearing period that is stored in the non-transitory memory of the sensor control device 102. In other embodiments, the reader device 120 can request historical glucose data for a certain predetermined time range (e.g., from day 3 to present, from day 5 to present, last 3 days, last 5 days, life count>0, etc.). Those skilled in the art will appreciate that other backfilling schemes (such as those described with respect to FIGS. 6A and 6B) can also be implemented and are fully within the scope of the present disclosure.

[0240] Upon receiving the request at step 922, the sensor control device 102 may retrieve the historical glucose data from the non-transitory memory and transmit it to the reader device 120. As a result, at step 924, the reader device 120 may store the received historical glucose data in the non-transitory memory. According to some embodiments, the reader device 120 may also display the current and / or historical glucose data within the app (e.g., on a sensor results screen). In this regard, the new reader may display all available analyte data over the entire wear period of the sensor control device. In some embodiments, the reader device 120 may also transmit the current and / or historical glucose data to the trusted computer system 180. At step 926, the received glucose data may be stored in the non-transitory memory (e.g., a database) of the trusted computer system 180.

[0241] In some embodiments, the received glucose data may also be deduplicated before being stored in non-transitory memory.

[0242] Example Embodiments of Sensor Check and Sensor Replacement System Alarms Exemplary embodiments of autonomous sensor check and sensor replacement system alarms and related methods are now described. In accordance with the disclosed subject matter, a sensor control device can detect certain adverse conditions affecting the operation of the analyte sensor and sensor electronics. For example, an improperly inserted analyte sensor can be detected if an average glucose level measurement over a predetermined period is determined to be below an insertion failure threshold. However, the sensor control device may not have sufficient alarm capabilities due to its small form factor and limited power capacity. Therefore, it may be advantageous for the sensor control device to transmit indications of adverse conditions to another device, such as a reader device (e.g., a smartphone), to alert a user of these conditions.

[0243] FIG. 10A is a flow diagram illustrating an example embodiment of a method 1000 for generating a sensor insertion failure system alarm (also referred to as a "sensor check" system alarm). At step 1002, a sensor insertion failure condition is detected by the sensor controller 102. For example, in some embodiments, a sensor insertion failure condition can be detected when an average glucose value over a predetermined period of time (e.g., average glucose value over 5 minutes, 8 minutes, 15 minutes, etc.) falls below a failed insertion glucose level threshold. At step 1004, in response to detecting the failed insertion condition, the sensor controller 102 stops taking glucose measurements. At step 1006, the sensor controller 102 generates a sensor check indicator and transmits it via the wireless communication circuitry to the reader device 120. Thereafter, as shown at steps 1012 and 1014, the sensor controller 102 continues to transmit the sensor check indicator until either (1) receipt of the indicator is received from the reader device 120 (step 1012) or (2) a predetermined waiting period has elapsed (step 1014), whichever occurs first.

[0244] According to another aspect of these embodiments, if a wireless communication link is established between the sensor control device 102 and the reader device 120, the reader device 120 receives a sensor check indicator at step 1008. In response to receiving the sensor check indicator, the reader device 120 displays a sensor check system alarm at step 1010. FIGS. 10B-10D are example embodiments of a sensor check system alarm interface displayed on the reader device 120. For example, in some embodiments, the sensor check system alarm can be a notification box, banner, or pop-up window output on a smartphone display, such as interfaces 1020 and 1025 of FIGS. 10B and 10C. In some embodiments, the check sensor alarm can be output on a display on the reader device 120, such as a glucose meter or receiver device, such as interface 1030 of FIG. 10D. According to an embodiment, the reader device 120 can also send a sensor check indicator receipt back to the sensor control device 102. For example, in some embodiments, a sensor check indicator receipt may be generated and transmitted automatically upon a successful display of a sensor check system alarm 1020, 1025, or 1030. In other embodiments, a sensor check indicator receipt is generated and / or transmitted in response to a predetermined user input (e.g., clearing a sensor check system alarm, pressing a confirmation "OK" button 1032, etc.).

[0245] Thereafter, in step 1011, the reader device 120 disconnects the sensor control device 102. For example, in accordance with the disclosed subject matter, step 1011 may include one or more of terminating an existing wireless communication link with the sensor control device 102, unpairing with the sensor control device 102, revoking authorization or digital certificates associated with the sensor control device 102, creating or modifying records stored in the reader device 120 to indicate that the sensor control device 102 is in a saved state, or sending an update to the trusted computer system 180 to indicate that the sensor control device 102 is in a saved state.

[0246] 10A, once a sensor check indicator receipt has been received by the sensor controller 102 (at step 1012) or a predetermined waiting period has elapsed (at step 1014), the sensor controller 102 stops transmitting the sensor check indicator at step 1016. The sensor controller 102 then enters a storage state in which no glucose measurements are taken and the wireless communication circuitry is deactivated or transitions to a dormant mode at step 1018. According to one embodiment, while in the "storage state," the sensor controller 102 can be reactivated by the reader device 120.

[0247] Although the method 1000 of FIG. 10A is described with respect to glucose measurement, one skilled in the art will appreciate that the sensor control device 102 can be configured to measure other analytes (e.g., lactate, ketones, etc.) as well. Additionally, although the method 1000 of FIG. 10A describes certain method steps (e.g., receiving a sensor check indicator, displaying a sensor check system alarm, and transmitting a receipt of a sensor check indicator) that are performed by the reader device 120, one skilled in the art will appreciate that some or all of these method steps can be performed by other devices in the analyte monitoring system, such as, for example, a local computing system, a wearable computing device, or a drug delivery device. Additionally, one skilled in the art will appreciate that the method 1000 of FIG. 10A can be combined with any of the other methods described herein, including, but not limited to, the method 700 of FIG. 7 relating to expired and / or failed sensor transmissions.

[0248] 11A is a flow diagram illustrating an example embodiment of a method 1100 for generating a sensor termination system alarm (also referred to as a "sensor replacement" system alarm). In step 1102, a sensor termination condition is detected by the sensor controller 102. As discussed above, the sensor termination condition may include one or more of a FIFO overflow condition being detected, a sensor signal being below a predetermined insertion failure threshold, moisture ingress being detected, an electrode voltage exceeding a predetermined diagnostic voltage threshold, an early signal decay (ESA) condition, or a late signal decay (LSA) condition, to name a few examples, but not limited to the following.

[0249] In response to detecting a sensor termination condition, the sensor controller 102 stops taking glucose measurements at step 1104. In step 1106, the sensor controller 102 generates a sensor replacement indicator and transmits it to the reader device 120 via the wireless communication circuitry. Thereafter, in step 1112, the sensor controller 102 continues to transmit the sensor replacement indicator while determining whether a sensor replacement indicator receipt has been received from the reader device 102. In accordance with the disclosed subject matter, the sensor controller 102 can continue to transmit the sensor replacement indicator until (1) a predetermined waiting period has elapsed (step 1113) or (2) a sensor replacement indicator receipt has been received (step 1112) and the sensor controller 102 has successfully transmitted backfill data to the reader device 120 (steps 1116, 1120).

[0250] 11A, if a wireless communication link is established between the sensor control device 102 and the reader device 120, the reader device 120 receives a sensor replacement indicator at step 1108. In response to receiving the sensor replacement indicator, the reader device 120 displays a sensor replacement system alarm at step 1110. FIGS. 11B-11D are example embodiments of a sensor replacement system alarm interface displayed on the reader device 120. For example, in some embodiments, the replace sensor system alarm can be a notification box, banner, or pop-up window output on a smartphone display, such as interfaces 1130 and 1135 of FIGS. 11B and 11C. In some embodiments, a check sensor alarm can be output on a display of the reader device 120, such as a glucose meter or receiver device, such as interface 1140 of FIG. 11D. According to an embodiment, the reader device 120 can also send a sensor replacement indicator receipt back to the sensor control device 102 to acknowledge receipt of the indicator. For example, in some embodiments, a sensor replacement indicator receipt may be automatically generated and transmitted upon successful display of a sensor replacement system alarm 1130, 1135, or 1140. In other embodiments, a sensor replacement indicator is generated and / or transmitted in response to a predetermined user input (e.g., clearing a sensor check system alarm, pressing a confirmation "OK" button 1142, etc.).

[0251] In step 1114, the reader device 120 may request historical glucose data from the sensor control device 102 after displaying a sensor replacement system alarm and transmitting a sensor replacement indicator receipt. In step 1116, the sensor control device 102 may collect and transmit the requested historical glucose data to the reader device 120. In accordance with the disclosed subject matter, the steps of requesting, collecting, and communicating historical glucose data may include a data backfill routine, such as the methods described with respect to Figures 6A and 6B.

[0252] 11A , in response to receiving the requested historical glucose data, the reader device 120 may transmit a receipt of the received historical glucose data to the sensor control device 102 at step 1118. The reader device 120 then disconnects the sensor control device 102 at step 1119. For example, in accordance with the disclosed subject matter, step 1119 may include one or more of: terminating an existing wireless communication link with the sensor control device 102, unpairing with the sensor control device 102, revoking authorization or digital certificates associated with the sensor control device 102, creating or modifying a record stored in the reader device 120 to indicate that the sensor control device 102 has been terminated, or sending an update to the trusted computer system 180 to indicate that the sensor control device 102 has been terminated.

[0253] In step 1120, the sensor control device 102 receives a receipt of the received past glucose data. Thereafter, in step 1122, the sensor control device 102 stops transmitting the sensor replacement indicator, and in step 1124, the sensor control device 102 can enter an end state in which the sensor control device 102 is not taking glucose measurements and the wireless communication circuitry is either inactive or in a dormant mode. In accordance with the disclosed subject matter, while in the end state, the sensor control device 102 cannot be reactivated by the reader device 120.

[0254] Although the method 1100 of FIG. 11A is described with respect to glucose measurement, one skilled in the art will appreciate that the sensor control device 102 can be configured to measure other analytes (e.g., lactate, ketones, etc.) as well. Additionally, although the method 1100 of FIG. 11A describes certain method steps (e.g., receiving a sensor replacement indicator, displaying a sensor replacement system alarm, and transmitting a sensor replacement indicator receipt) that are performed by the reader device 120, one skilled in the art will appreciate that some or all of these method steps can be performed by other devices in the analyte monitoring system, such as, for example, a local computing system, a wearable computing device, or a drug delivery device. Additionally, one skilled in the art will appreciate that the method 1100 of FIG. 11A can be combined with any of the other methods described herein, including, but not limited to, the method 700 of FIG. 7 relating to expired and / or failed sensor transmissions.

[0255] Example of a report containing multiple interfaces Next, an example embodiment of a report including multiple interfaces will be described. In accordance with the disclosed subject matter, a user can be presented with a report including multiple interfaces as disclosed herein. In accordance with the disclosed subject matter, the interfaces can include any combination of a measurement interface based on a current or measured analyte value, a physiological parameter interface based on a physiological parameter as disclosed herein, and a personalization interface based on a personalized glucose metric as disclosed herein.

[0256] In light of the above, in accordance with the disclosed subject matter, there is provided a glucose monitoring system including a sensor controller including an analyte sensor coupled to the sensor electronics and configured to transmit data indicative of an analyte level of the subject, and a reader device. The reader device of the disclosed subject matter includes a wireless communication circuit configured to receive data indicative of the analyte level and data indicative of the glycated hemoglobin level of the subject, a non-transitory memory, and at least one processor. The processor is communicatively coupled to the non-transitory memory and the analyte sensor and configured to calculate a plurality of personalized glucose metrics for the subject using at least one physiological parameter and at least one of the received data indicative of the analyte level or the received glycated hemoglobin level, and to display a report on a display of the reader device including a plurality of interfaces including at least two or more of the received data indicative of the analyte level, the received glycated hemoglobin level, or the calculated plurality of personalized glucose metrics, the plurality of interfaces constituting the report being based on a user type. According to embodiments, the at least one physiological parameter is selected from the group including red blood cell glucose uptake rate, red blood cell life span, red blood cell glycation rate constant, erythropoiesis rate constant, red blood cell removal constant, and apparent glycation constant. For example, and without limitation, in further embodiments, a plurality of interfaces includes at least one physiological parameter of the subject.

[0257] According to embodiments, the content of the report can differ based on different user types (e.g., but not limited to, subject, healthcare provider, caregiver, etc.). As embodied herein, the interfaces that make up the report can be predetermined or user selectable based on the user type. According to embodiments, the user type includes a healthcare professional. For example, but not limited to, in further embodiments, the interfaces include a glucose monitoring data interface, a glycated hemoglobin interface, a personalized A1c interface, a personalized glucose interface, a personalized average glucose, and a personalized time within range interface. According to embodiments, the user type includes a subject. For example, but not limited to, in further embodiments, the interfaces include a glucose monitoring data interface, a glycated hemoglobin interface, an average glucose interface, and a time within range interface.

[0258] According to embodiments, a subject using the analyte monitoring system may only see a graphical interface that displays either a measured analyte measurement or a personalized analyte measurement, but not both. For example, it may be beneficial to minimize confusion by displaying graphical interfaces that include slightly different data (such as between measured data and personalized data). As embodied herein, the selection of which interfaces may be included in the report depends on whether personalized glucose metrics have been approved or designated for research or clinical purposes by an appropriate regulatory agency.

[0259] According to embodiments, the personalized glucose metric may include one or more of personalized A1c or adjusted A1c, glucose-determined or calculated A1c, personalized glucose, personalized average glucose, and personalized time in range. According to embodiments, the at least one processor is configured to calculate a plurality of personalized glucose targets corresponding to the calculated plurality of personalized glucose metrics. According to embodiments, the plurality of interfaces further includes a plurality of personalized glucose targets. According to embodiments, the personalized glucose target may include one or more of a personalized glucose target range and a personalized target average glucose. According to embodiments, the personalized glucose target range may include a personalized lower glucose limit and / or a personalized upper glucose limit.

[0260] 24 illustrates an exemplary report 1400 including four different measurement interfaces associated with exemplary subject J17: a glucose monitoring data interface 2401 including a graphical representation of measured glucose measurements from an analyte monitoring device over a predetermined time period; an HbA1c interface 2402 including a graphical representation of HbA1c measurements over a predetermined time period (shown as dots 1402a) and a graphical representation of calculated A1c ("cA1c") or glucose derived A1c; an average glucose interface 1403 including a graphical representation of measured 14-day average glucose (148 mg / dL) over a predetermined time period; and a time in range interface 1404 including a graphical representation of measured time in range metrics (as shown, 75% above 180 mg / dL and 2% below 70 mg / dL) over a predetermined time period. As embodied herein, the HbA1c measurements can include laboratory A1c measurements. For example, in a further embodiment, but not by way of limitation, a reader device receives the subject's glycated hemoglobin level wirelessly from an electronic medical record system, from a cloud-based database, from a QR code by the subject, or optionally from the subject using a home test kit that can be mailed to a laboratory for analysis. As embodied herein, FIG. 24 can include any of the interfaces disclosed herein.

[0261] As embodied herein, as shown in FIG. 24, the glucose monitoring data interface 2401 can include a graphical representation in the foreground of the target glucose ranges 2401b,c (shown in dashed lines). As described herein, the target glucose ranges 2401b,c can include personalized target glucose ranges. As embodied herein, as shown in FIG. 24, the HbA1c interface 2402 can include a graphical representation (shown in solid lines) of the target HbA1c 2402b (e.g., but not limited to, 6.5%). As embodied herein, as shown in FIG. 24, the average glucose interface 1403 can include a graphical representation (shown in solid lines) of the target average glucose 1403a.

[0262] As embodied herein, the predetermined time period can be 45 days, as seen in Figure 24. As embodied herein, the predetermined time period can be a total of 12 hours of data in 5 minute increments. However, one of ordinary skill in the art will appreciate that other time increments (e.g., 30 days) and time periods of analyte data can be utilized and are fully within the scope of the present disclosure. Similarly, Figures 26 and 28 show interfaces for exemplary subjects J33 and J5, respectively.

[0263] 25 illustrates an example report 1500 including eight different interfaces associated with an example subject J17. As shown in FIG 25, the report 1500 can include a measurement interface, a physiological parameter interface, and a personalization interface. As embodied herein, FIG 25 can include any of the interfaces disclosed herein.

[0264] According to embodiments disclosed herein, for example, the measurement interface can include, but is not limited to, a glucose monitoring data interface 2401 and an HbA1c interface 2402 as shown in Figure 25. As embodied herein, the HbA1c interface 2402 can include a calculated HbA1c (cA1c or GD-A1c) curve fitted through the HbA1c measurements, as described herein and in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated by reference in their entireties.

[0265] According to embodiments disclosed herein, for example, the physiological parameter interface may include, but is not limited to, a red blood cell glucose uptake rate interface 2501 and a red blood cell life span interface 2502 as shown in FIG. 25. As embodied herein, the red blood cell glucose uptake rate interface 2501 may include a graphical representation of the subject's red blood cell glucose uptake rate (solid line) 2501a and the reference red blood cell glucose uptake rate (dashed line) 2501b over a predetermined time period. As embodied herein, the red blood cell life span interface 2502 may include a graphical representation of the subject's red blood cell life span (solid line) 2502a and the reference red blood cell life span (dashed line) 2502b over a predetermined time period. As can be seen in FIG. 23 and as shown in FIG. 25, the red blood cell glucose uptake rate of subject J17 is 96% and the red blood cell life span is 121 days. The subject's red blood cell glucose uptake rate and red blood cell life span can be calculated using the models described herein and in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated by reference in their entireties. As embodied herein, the physiological parameter interface can also include any other physiological parameters as described herein and in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated by reference in their entireties.

[0266] According to embodiments disclosed herein, the personalization interface may include, for example and without limitation, a personalized glucose interface 2503, a personalized A1c interface 2504, a personalized 14-day average glucose interface 2505, and a personalized time in range interface 2506, as shown in FIG. 25. As embodied herein, FIG. 25 may include any of the personalization interfaces disclosed herein. The personalized glucose interface 2503 may include a graphical representation of a subject's glucose monitoring data interface personalized using a model as described herein and in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated herein by reference in their entirety. As embodied herein, as shown in FIG. 25, the personalized glucose interface 2503 may include target glucose ranges 2401b,c in the foreground. As described herein, the target glucose ranges 2401b,c may include personalized target glucose ranges.

[0267] According to embodiments disclosed herein, the personalized A1c interface 2504 can include a graphical representation of the subject's adjusted or personalized A1c (shown as point 2504a) and adjusted cHbA1c (shown as curve fit 2504c) calculated using models such as those described in WO 2021 / 108419 and WO 2020 / 086934 to Xu, which are incorporated by reference in their entireties. As embodied herein, the personalized A1c interface 2504 can include a graphical representation (shown as a solid line) of the target HbA1c 2504b (e.g., but not limited to, 6.5%).

[0268] According to embodiments disclosed herein, the personalized 14-day average glucose interface 2505 can include an average glucose interface 1403 that includes a graphical representation of the personalized 14-day average glucose (141 mg / dL as shown) over a predetermined time period. As embodied herein, as shown in FIG. 25, the personalized average glucose interface 2503 can include a graphical representation of the target average glucose 1403a (shown as a solid line).

[0269] According to embodiments disclosed herein, the personalized time in range interface 2506 may include a graphical representation of the personalized time in range metric over a predetermined period of time (as shown, 78% above 180 mg / dL and 3% below 70 mg / dL).

[0270] As embodied herein, the predetermined time period can be 45 days, as seen in Figure 25. As embodied herein, the predetermined time period can be a total of 12 hours of data in 5 minute increments. However, one of ordinary skill in the art will appreciate that other time increments and time periods of analyte data can be utilized and are fully within the scope of the present disclosure. Similarly, Figures 27 and 29 also provide graphical depictions of four different glucose metrics for the J33 and J5, respectively.

[0271] According to embodiments disclosed herein, the report 1400 or 1500 may include various measurement interfaces, physiological parameter interfaces, or personalization interfaces based on user type. For example, healthcare providers (HCPs) and caregivers may benefit from viewing a comparison of the measurement interface and the personalization interface to assess, for example, how different the two are, and evaluate diagnostic and treatment options accordingly. Thus, in an embodiment, the content of the report for the HCP may include a predefined set of measurement interfaces, physiological parameter interfaces, and personalization interfaces, for example, as shown but not limited to, in the report 1500. According to an embodiment, the HCP may best access information including the measured analyte measurements, the personalized analyte measurements, and the physiological parameters determined using the models described herein (for example, but not limited to, RBC glucose uptake rate and RBC life span, as shown in FIG. 25, FIG. 27, FIG. 29, or as a second report as described above). As embodied herein, in an embodiment, the content of the report for the subject may include a predefined set of measurement interfaces, physiological parameter interfaces, and / or personalization interfaces. For example, but not limited to, a report generated for a user may include a measurement interface such as that shown in report 1400. As embodied herein, user types may include, for example, but not limited to, subjects, healthcare providers, caregivers, insurance providers, etc.

[0272] As embodied herein, a user (e.g., subject, HCP, caregiver, insurance provider, etc.) can select which interfaces comprise the report. For example, but not by way of limitation, a user can select any combination of the measurement interfaces, personalization interfaces, and physiological parameter interfaces disclosed herein.

[0273] According to certain embodiments, a user may select whether to view a sensor result interface as disclosed herein displaying measured analyte measurements over a predetermined time period (e.g., as shown, but not limited to, in FIGS. 24, 26, and 28), personalized measurements over the same predetermined time period (e.g., as shown, but not limited to, in FIGS. 25, 27, and 29), or both. As embodied herein, a user may switch or toggle between viewing a sensor result interface including measured analyte measurements over a predetermined time period and viewing the same sensor interface including personalized analyte measurements over the same predetermined time period. For example, but not limited to, a user may switch between an average glucose interface 1403 including a graphical representation of average glucose levels over a 45 day period (e.g., as shown, but not limited to, in FIG. 24) and a personalized average glucose interface 2505 including a graphical representation of personalized average glucose levels over the same predetermined time period (e.g., as shown, but not limited to, in FIG. 25). According to embodiments, a user may also switch between any of the other measurement interfaces shown in Figures 24, 26, 28 and the personalization interfaces shown in Figures 25, 27, 29 (such as, but not limited to, the A1c interface, glucose interface, 14-day average glucose interface, and time in range interface). However, one of ordinary skill in the art will appreciate that other time increments and time periods of analyte data may be utilized and are fully within the scope of the present disclosure. According to embodiments, a user may also select a sensor results interface, analyte level and trend alert interface, time in range interface, and / or sensor usage interface described herein to display measured analyte measurements over a predetermined period of time and / or personalized analyte measurements over a predetermined period of time.

[0274] According to embodiments, the aggregate data can be used with any of the graphical user interfaces described above. According to embodiments of the present disclosure, a user (e.g., a user, a healthcare provider, a caregiver, etc.) can personalize any of the graphical interfaces described above. Additionally, an ambulatory glucose profile report ("AGP report") (e.g., as proposed by the International Diabetes Center ("IDC"), which is incorporated by reference in its entirety and can be found on the website http: / / www.agpreport.org / agp / agpreports) can also be modified to include any of the graphical interfaces or personalized metrics described herein. For example, but not limited to, the IDC AGP report version 5 can be modified by replacing the glucose control indicator (GMI) with a personalized A1c. Additionally, a graphical interface for reporting a personalized A1c can be achieved by combining any of the graphical components described herein. For example, in one embodiment, the graphical user interface 3000 can include at least a time in range GUI 340 as shown in FIG. 3F, a glucose trend interface 517 as described herein, and a health information interface 518 as described herein. The interface 3000 may include the patient's name, date of birth ("DOB"), the time period the report covers, and the percentage of time during that time that the continuous glucose monitor was active. As seen in FIG. 3, this time period may be 14 days. According to embodiments, this time period may also be any other time period (e.g., but not limited to, 1 day, 2 days, 3 days, 7 days, 30 days, 45 days, etc., or any other time period). According to embodiments, this time period may be selected by the patient or by the healthcare provider.

[0275] According to FIG. 30, another GUI can provide an interface for use by a healthcare provider. For example, the healthcare provider interface 3100 can include an input interface 3102 for the healthcare provider to input an A1c record, which can include a laboratory measured A1c value. Similarly, the provider interface 3100 can also include an output interface 3104, which can include the measured A1c and a personalized A1c determined based on the measured A1c. The output interface 3104 can also include other data such as GMI, percentage of time in target, percentage of time below target, and personalized A1c factors (also known as "adjusted glycation rate" or "AGR" and as disclosed in U.S. Patent Application Publication No. 18 / 052,805, the entirety of which is incorporated herein by reference). According to an embodiment of the present disclosure, the provider interface 3100 can also include a medical record interface 3106 for displaying an electronic medical record ("EMR"). According to embodiments of the present disclosure, the EMR can include data such as the time the sensor was worn, the data collected, the time in range, the time above range, the time below range, the measured A1c, the personalized A1c, etc. According to embodiments, the interface 3106 can include a record over a period of time. For example, as can be seen in FIG. 30, the interface 3106 can include a record in tabular form for each month that data was collected and / or analyzed.

[0276] As disclosed in U.S. Patent Application Nos. 17 / 832,537 and 18 / 052,805, which are incorporated by reference in their entirety, HbA1c or HbA1c target measurements can be adjusted by the user's apparent glycation rate ("AGR") (also referred to as "personalized A1c factor" or "personalized HbA1c factor"). For example, Table 3 shows "adjusted" HbA1c target measurements based on AGR. Specifically, as can be seen from Table 3, if an A1c target value of 6.0 is adjusted by an AGR of 60, the adjusted A1c target value becomes 5.5. Similarly, if an A1c target value of 6.0 is adjusted by an AGR of 65, the adjusted A1c target value becomes 6.0. Alternatively, the measured A1c value can be adjusted similarly using the AGR to provide an adjusted A1c value (or personalized A1c value). This information can be presented to the subject and healthcare provider to aid in implementing more accurate and informed diabetes diagnosis and treatment based at least on the subject's individual demographic metrics and / or physiology. Table 3 JPEG2024546442000012.jpg44157

[0277] Thus, a personalized A1c can be determined by measuring A1c, determining a personalized A1c factor, and applying this factor to the measured A1c.

[0278] While the disclosed subject matter has been described herein in terms of specific illustrative examples and embodiments, those skilled in the art will recognize that various modifications and improvements can be made to the disclosed subject matter without departing from the scope thereof. Moreover, although individual features of one embodiment of the disclosed subject matter may be described herein, or shown in the drawings of one embodiment and not shown in other embodiments, it will be understood that individual features of one embodiment can be combined with one or more features of other embodiments, or with features from multiple embodiments.

[0279] In addition to the specific embodiments claimed below, the disclosed subject matter is also directed to other embodiments having the dependent features claimed below and any other possible combinations of the features disclosed above. Thus, the specific features set forth in the dependent claims and disclosed above can be combined with each other in other ways within the scope of the disclosed subject matter, with the understanding that the disclosed subject matter is also specifically directed to other embodiments having any other possible combinations. Thus, the above descriptions of specific embodiments of the disclosed subject matter have been presented for purposes of illustration and description. This description is not intended to be exhaustive or to limit the disclosed subject matter to the disclosed embodiments.

[0280] The description herein merely illustrates the principles of the disclosed subject matter. Various modifications and variations of the described embodiments will be apparent to those skilled in the art in light of the teachings herein. Accordingly, the disclosure herein is intended to be illustrative, but not limiting, of the scope of the disclosed subject matter. [Explanation of symbols]

[0281] 100 Sample Monitoring System 102 Sensor control device 104 Sensors 105 Adhesive Patch 120 Reader Device 121 Input section 122 screens 123 Power Port 140 Communication Paths 141 Communication Paths 142 Communication Paths 143 Communication Paths 144 Communication Paths 150 Sensor Applicator 170 Local Computer System 180 Trusted Computer Systems 190 Network

Claims

1. 1. A glucose monitoring system comprising: a sensor control unit including an analyte sensor coupled to the sensor electronics and configured to transmit data indicative of an analyte level in the subject; A reader device; The reader device comprises: a wireless communication circuit configured to receive data indicative of the analyte level and the glycated hemoglobin level of the subject; A non-transient memory; at least one processor communicatively coupled to the non-transitory memory and the analyte sensor; wherein the at least one processor comprises: calculating a plurality of personalized glucose metrics for the subject using at least one physiological parameter and at least one of the received data indicative of the analyte level or the received glycated hemoglobin level; displaying a report on a display of the reader device, the report including a plurality of interfaces including at least two or more of the received data indicative of the analyte level, the received glycated hemoglobin level, or the calculated plurality of personalized glucose metrics. It is configured as follows: the plurality of interfaces for constructing the report are based on a user type; A system characterized by:

2. the plurality of personalized glucose metrics include one or more of adjusted A1c, calculated A1c, adjusted and calculated A1c, personalized glucose, personalized average glucose, or personalized time in range; The system of claim 1 .

3. the at least one processor is further configured to calculate a plurality of personalized glucose goals corresponding to the calculated plurality of personalized glucose metrics. The system of claim 2.

4. the plurality of interfaces further comprising the plurality of personalized glucose goals. The system of claim 3.

5. the plurality of personalized glucose goals include one or more of a target glucose range or a target average glucose; The system of claim 3.

6. the personalized target glucose range includes a personalized lower glucose limit; The system of claim 5.

7. the personalized target glucose range includes a personalized upper glucose limit; The system of claim 5.

8. The at least one physiological parameter is selected from the group consisting of red blood cell glucose uptake rate, red blood cell life span, red blood cell glycation rate constant, erythropoiesis rate constant, red blood cell removal constant, and apparent glycation constant. The system of claim 1 .

9. the plurality of interfaces further comprising at least one physiological parameter of the subject; The system of claim 8.

10. the user types include medical professionals; The system of claim 1 .

11. the plurality of interfaces include a glucose monitoring data interface, a glycated hemoglobin interface, a personalized a1c interface, a personalized glucose interface, a personalized average glucose interface, and a personalized time in range interface; The system of claim 10.

12. The user types include the subjects. The system of claim 1 .

13. the plurality of interfaces includes a glucose monitoring data interface, a glycated hemoglobin interface, an average glucose interface, and a time in range interface; The system of claim 12.

14. the plurality of interfaces constituting the report are predetermined based on the user type; The system of claim 1 .

15. The plurality of interfaces that configure the report are selectable by the user. The system of claim 1 .

16. The at least one processor is further configured to output a notification if at least one of the plurality of personalized glucose metrics is equal to or greater than the corresponding plurality of personalized glucose goals. The system of claim 4.

17. The notification includes a visual notification.

17. The system of claim 16.

18. The notification includes an audio notification.

17. The system of claim 16.

19. The notification is an alarm.

17. The system of claim 16.

20. The notification is a prompt.

17. The system of claim 16.

21. the reader device wirelessly receives the glycated hemoglobin level of the subject from an electronic medical record system. The system of claim 1 .

22. the reader device wirelessly receives the glycated hemoglobin level of the subject from a cloud-based database; The system of claim 1 .

23. the reader device wirelessly receives the glycated hemoglobin level of the subject from a QR code; The system of claim 1 .

24. the reader device wirelessly receives the glycated hemoglobin level of the subject from a home test kit; The system of claim 1 .

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