Systems, devices, and methods for improving meal and therapy interfaces of analyte monitoring systems

Abstract

To provide systems, devices, and methods for improving meal and therapy interfaces of analyte monitoring systems.SOLUTION: A computer-implemented method of identifying a set of meal start candidates and meal peak response candidates allows for determining a medication dosage to be administered with consumption of a meal, identifying meal start and meal peak response candidates, and recommending user-initiated analyte checks.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The subject matter described herein relates generally to systems, devices, and methods for improving the meal and treatment interface of an analyte monitoring system. In particular, embodiments are provided for determining a drug dose to be administered with intake of a meal, for identifying meal onset and meal peak response candidates, and for providing user-directed analyte testing recommendations. [Background technology]

[0002] The increasing prevalence of type 2 diabetes and metabolic syndrome over the past few decades can be attributed to changes in diet and activity levels. For example, the consumption of more available high glycemic index foods can lead to rapid increases in postprandial blood glucose and insulin levels, which are positively associated with weight gain and obesity. These conditions can further lead to an increased risk of developing the above-mentioned and other diseases.

[0003] Most people understand the importance of diet in general. However, in practice, many individuals struggle to translate this general awareness into specific food choices. These problems arise primarily because people are unable to directly see the impact of their choices. This leads to misconceptions about the amount of food eaten, misconceptions about which foods are relatively healthy, and a general lack of awareness about the duration and intensity of activity needed to maintain good health. These problems are further exacerbated by advice based on advertising, habit, peer pressure, food preferences, and overgeneralizations.

[0004] To address these issues, analyte monitoring systems can track and better understand an individual's physiological response. Because high glucose levels are primarily caused by the ingestion of food, postprandial glucose levels are related to the amount of carbohydrates or other dietary components an individual ingests, and the individual's physiological response to the meal. However, the challenge in analyzing this influx of data is to represent the data in a meaningful form that enables effective action. Data regarding dietary choices and their subsequent effects need to be understood, both on a clinical basis and on a personal basis for individuals, dietary managers, and / or healthcare professionals, to understand and mitigate glucose fluctuations, such as episodes of hyperglycemia.

[0005] Conventional solutions for correlating an individual's analyte data with food intake and pre- and post-prandial responses have many deficiencies. For example, some systems require an individual to perform multiple inconvenient and uncomfortable individual blood glucose measurements (e.g., fingerstick blood glucose tests). These solutions may also provide an insufficient number of data points to adequately determine the glycemic response to a meal. For example, an individual may perform an individual blood glucose measurement at a time before or after the individual's glycemic response peaks, making it difficult to accurately understand the glycemic response and meaningfully compare meals based on glycemic response. The lack of data points also makes it difficult to automatically detect the onset of a meal event in an individual's analyte data.

[0006] Moreover, some existing conventional systems rely heavily on individuals manually recording meals, which can be unreliable. Another approach to determining pre- and post-prandial meal responses involves collecting dense glucose measurements within a predefined time window, where glucose values ​​within the window are assumed to represent, for example, the time before and / or after breakfast. However, with this approach, the reliability of the estimates is highly dependent on the consistency of the patient's meal timing routine, which can also be unreliable.

[0007] Other conventional systems attempt to detect meal events simply based on the presence or absence of elevated glucose levels, such as that described in U.S. Patent No. 6,399,633. However, these systems can be inappropriate because they do not take into account an individual's prior eating history and can overestimate the number of meals an individual has consumed.

[0008] A related challenge is determining a drug dose (e.g., insulin dose) to compensate for the expected blood glucose rise that occurs after an individual with diabetes ingests a meal. This dose is often referred to as a meal bolus. Determining the appropriate amount of insulin to be administered can be difficult and typically requires the use of prior art bolus calculators, which depend on parameters such as the individual's insulin effect, the individual's insulin on-board, and the amount of carbohydrates in the meal. For example, the carbohydrate content of a home-cooked meal can be difficult to determine because it is often based on the amounts of individual ingredients in the recipe and may require the user to estimate based on the weight of the various meal portions. Determination of carbohydrates for each meal portion is also required. For example, for a dinner that includes meat, a casserole, and vegetables, the carbohydrate content must be determined for each ingredient individually and then added together for input into the bolus calculator. The time and effort required to make such calculations can be particularly burdensome for diabetics, who often guess the carbohydrate content. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] US Patent Application Publication No. 2003 / 0208113 Summary of the Invention [Problem to be solved by the invention]

[0010] For these and other reasons, improved diet and therapy interfaces for analyte monitoring systems are needed. [Means for solving the problem]

[0011] Exemplary embodiments of systems, devices, and methods for an improved meal and treatment interface for use in an in vivo analyte monitoring system are described herein that can provide systems, devices, and methods for determining drug doses to be administered with intake of a meal, for identifying meal onset and meal peak response candidates, and for advising user-directed analyte testing.

[0012] According to one embodiment, for example, a computer-implemented method for determining a drug dose to administer in conjunction with consumption of a meal includes receiving a user-entered entry related to the meal, determining one or more nutritional parameters associated with the meal by referencing a first database, identifying a most matching meal in a second database based on the nutritional parameters, and determining a drug dose associated with the most matching meal.

[0013] According to another embodiment, a computer-implemented method for identifying a set of meal start candidates and meal peak response candidates includes determining a time derivative of a data point corresponding to a monitored analyte level; creating a set of meal start candidates and meal peak response candidates by determining an optimal value of acceleration based on the time derivative; obtaining a plurality of user-initiated tests and grouping the tests into time clusters; determining, for each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point; and removing a subset of the meal start candidates from the set, the subset including one or more meal start candidates that are not within a predetermined time range of either the time cluster start point or the time cluster end point.

[0014] According to yet another embodiment, a computer-implemented method for providing user-initiated analyte testing recommendations includes receiving recorded actions by a user; evaluating a history log to determine whether the recorded actions correspond to user historical actions related to glycemic risk; in response to determining that the recorded actions correspond to user historical actions related to glycemic risk, calculating an elapsed time until an actionable time related to glycemic risk is reached; and outputting a notification to the user to perform a user-initiated analyte test after the elapsed time.

[0015] Numerous examples of algorithms and methods for implementing combinations and / or variations of one or both of these detection mechanisms are provided, as well as exemplary embodiments of systems and devices for doing the same.

[0016] Other systems, devices, methods, features, and advantages of the subject matter described herein will become apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. All such additional systems, methods, features, and advantages are intended to be included within this description, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Features of the exemplary embodiments should not be construed in any way as limiting the scope of the appended claims unless those features are expressly recited in the claims. [Brief description of the drawings]

[0017] Details of the subject matter described herein, both as to its structure and operation, will become apparent from examination of the accompanying figures in which like reference numerals refer to like parts. The components in the figures are not necessarily to scale, emphasis instead being placed on illustrating the principles of the subject matter. Moreover, all figures are intended to convey ideas, and relative sizes, shapes, and other detailed attributes may be depicted diagrammatically, rather than literally or precisely. [Figure 1]FIG. 1 illustrates an exemplary embodiment of an in vivo analyte monitoring system. [Diagram 2] FIG. 2 is a block diagram of an example embodiment of a reader device. [Diagram 3] FIG. 2 is a block diagram of an exemplary embodiment of a sensor control device. [Figure 4] FIG. 1 is a block diagram of an exemplary embodiment of a system architecture configured to determine a drug dose to be administered in conjunction with the intake of a meal. [Diagram 5] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for determining a drug dose to be administered with the intake of a meal. [Figure 6A] 1 is a graph showing the distribution of user-initiated analyte tests. [Figure 6B] 1 is a graph showing the distribution of user-initiated analyte tests. [Figure 6C] 1 is a graph showing the distribution of user-initiated analyte tests. [Figure 7A] 1 is a graph showing measurements of various analytes and their characteristics. [Figure 7B] 1 is a graph showing measurements of various analytes and their characteristics. [Figure 8] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for determining a set of meal onset and meal peak response candidates. [Figure 9A] FIG. 11 is a flow diagram illustrating another exemplary embodiment of a method for determining a set of meal onset and meal peak response candidates. [Figure 9B] FIG. 11 is a flow diagram illustrating another exemplary embodiment of a method for determining a set of meal onset and meal peak response candidates. [Figure 9C] FIG. 11 is a flow diagram illustrating another exemplary embodiment of a method for determining a set of meal onset and meal peak response candidates. [Figure 10] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for providing user-directed analyte testing advice. [Figure 11] FIG. 11 is a flow diagram illustrating another exemplary embodiment of a method for providing user-directed analyte testing advice. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Before describing the present subject matter in detail, it is to be understood that the present disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

[0019] 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 need to be independently confirmed.

[0020] In general, embodiments of the present disclosure are used with systems, devices, and methods for detecting at least one analyte, such as glucose, in a bodily fluid (e.g., in interstitial fluid ("ISF") under the skin or in blood, in dermal fluid in the dermis layer, etc.). Thus, many embodiments include an in vivo analyte sensor where at least a portion of the sensor is placed, or is structurally configured to be placed, within the body of a user to obtain information regarding at least one analyte in the body. However, the embodiments disclosed herein may 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 those systems that are completely non-invasive.

[0021] Additionally, for each of the method embodiments disclosed herein, the scope of the disclosure includes systems and devices capable of performing each of these embodiments. For example, embodiments of electronic systems are disclosed that can include non-transitory memory (e.g., for storing instructions), processing circuitry (e.g., for executing instructions), power sources, communication circuitry, transmitters, receivers, and / or controllers that can perform or facilitate the performance of any and all method steps.

[0022] Many embodiments of the present disclosure are designed to improve the computer implementation capabilities of analyte monitoring systems with respect to meal and treatment interfaces. In some embodiments, for example, a drug dose to be administered with the intake of a meal can be determined by identifying the most matching meal in a database based on certain nutritional parameters. These embodiments can improve the accuracy of the dose determination software, for example, by referencing an individual's own glycemic response and drug dose history instead of relying on an individual's guess as to the nutrient content of a meal.

[0023] According to other embodiments, data indicative of monitored analyte levels may be received from the analyte sensor and used by the processing circuitry to identify a set of meal start times and meal peak response candidates. These embodiments may improve the accuracy of the software for determining meal start times and meal peak response times without having to rely on the user's estimation or strict adherence to a daily meal routine. Additionally, these embodiments may present a limited and more accurate set of meal start and meal peak response candidates via a graphical interface, allowing a user to more efficiently view analyte data collected by the analyte monitoring system.

[0024] According to yet other embodiments, if a current recorded behavior by the user is determined to have an associated glycemic risk, a recommendation to perform a user-initiated analyte test (e.g., a sensor scan) may be output to the user after an elapsed time. These embodiments evaluate a historical log of the user's past behavior and associated glycemic risk to determine whether a future user-initiated analyte test is warranted. In this regard, these embodiments improve analyte monitoring systems by increasing and / or maintaining user engagement with the system via an interactive user interface as compared to known systems having passive interfaces.

[0025] Thus, in many respects, the embodiments described herein represent various computer-implemented improvements over conventional analyte monitoring systems and corresponding user interfaces. In particular, these embodiments improve the accuracy of analyte monitoring systems with respect to drug dose determination, meal onset and meal peak response detection, and glycemic risk determination. Furthermore, the embodiments described herein utilize certain types of data (e.g., user-initiated analyte test information) in unconventional ways. Other features and advantages of the disclosed embodiments are further described below.

[0026] However, before describing the embodiments in detail, it is desirable to first describe examples of devices that may be present, for example, in an in vivo analyte monitoring system, and their operation, all of which may be used by the embodiments described herein.

[0027] Exemplary embodiments of an analyte monitoring system There are various types of analyte monitoring systems. A "continuous analyte monitoring" system (or "continuous glucose monitoring" system) is, for example, an in-vivo system that can transmit data repeatedly or continuously from a sensor control device to a reader device 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) is an in-vivo system that can transfer data from a sensor control device in response to a scan or data request 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 fingerstick calibration.

[0028] An in-vivo monitoring system may include a sensor that contacts a user's bodily fluid while positioned in vivo and detects one or more analyte levels therein. The sensor may be part of a sensor control device that resides on the user's body and includes electronics and a power source that enable and control the sensing of the analytes. 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. As used herein, these terms are not limited to devices having analyte sensors, but encompass devices having other types of sensors, whether biometric or non-biometric. The term "on-body" refers to any device that resides directly on or in close proximity to the body, such as a wearable device (e.g., eyeglasses, watches, wristbands or bracelets, neckbands or necklaces, etc.).

[0029] In-vivo monitoring systems may also include one or more reader devices that receive sensed analyte data from the sensor control device. These reader devices may process and / or display the sensed analyte data, i.e., sensor data, to a user in any number of formats. These devices and variations thereof may be referred to as "handheld reader devices," "reader devices" (or simply "readers"), "handheld electronic devices" (or handhelds), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply receivers), "relay" devices or units, or "remote" devices or units, to name a few. Other devices, such as personal computers, have also been utilized by or incorporated into in-vivo and in-vitro monitoring systems.

[0030] In vivo analyte monitoring systems can be distinguished from "in vitro" (rather than "ex vivo") systems, which include a meter device having a port for receiving an analyte test strip that contacts a biological sample outside the body and typically carries a user's bodily fluid that can be analyzed to determine the user's analyte level. As mentioned, the embodiments described herein can be used with in vivo systems, in vitro systems, and combinations thereof.

[0031] The embodiments described herein can be used to monitor and / or process information regarding any number of one or more different analytes. Analytes that may be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, glycated hemoglobin (HbAlc), creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose, glucose derivatives, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. Concentrations of drugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored. In embodiments that monitor more than one analyte, the analytes may be monitored at the same time or at different times.

[0032] FIG. 1 is an illustration showing an example embodiment of an in-vivo analyte monitoring system 100 having a sensor control device 102 and a reader device 120 communicating with each other via a local communication path (or link) 140, which may be wired or wireless and may be unidirectional or bidirectional. According to some embodiments, the in-vivo monitoring system 100 may also include a wearable electronic device 120B, such as a smart watch, that may communicate with the sensor control device 102 via communication path (or link) 144 and / or with the reader device 120 via communication path (or link) 145. The communication paths 144 and 145 may be wired or wireless and may be unidirectional or bidirectional. In embodiments where the paths 140, 144 and 145 are wireless, a near field communication (NFC) protocol, an RFID protocol, a Bluetooth or Bluetooth Low Energy protocol, a Wi-Fi protocol, a proprietary protocol (including those existing as of the date of this application or later developed variations thereof), or the like may be used.

[0033] The reader device 120 may also communicate wired, wireless, or hybrid with a computer system 170 (e.g., a local or remote computer system) via communication path (or link) 141 and with a network 190, such as the Internet or the cloud, via communication path (or link) 142. Communication with the network 190 may include communication with a trusted computer system 180 within the network 190, or communication via the network 190 to the computer system 170 via communication link (or path) 143. The communication paths 141, 142, and 143 may be wireless, wired, or both, may be unidirectional or bidirectional, and may be part of a telecommunications network, such as a Wi-Fi network, a local area network (LAN), a wide area network (WAN), the Internet, or other data network. In some cases, the communication paths 141 and 142 may be the same path. All communications over paths 140, 141 and 142 may be encrypted, and the sensor control device 102, reader device 120, computer system 170 and trusted computer system 180 may each be configured to encrypt and decrypt these communications sent and received.

[0034] Variations of devices 102 and 120, as well as other components of in vivo-based analyte monitoring systems suitable for use with embodiments of the systems, devices, and methods described herein, are described in U.S. Patent Application Publication No. 2011 / 0213225 (the '225 publication), the entirety of which is incorporated by reference herein for all purposes.

[0035] The sensor control device 102 can include a housing 103 that houses an in vivo analyte monitoring circuit and a power source. In this embodiment, the in vivo analyte monitoring circuit is electrically coupled to an analyte sensor 104 that protrudes away from the housing 103 through an adhesive patch 105. The adhesive patch 105 includes an adhesive layer (not shown) for attachment to a skin surface of a user's body. Other forms of physical attachment to the body may be used in addition to or instead of adhesion.

[0036] The sensor 104 is adapted to be at least partially inserted into a user's body, in fluid contact with the user's bodily fluids (e.g., subcutaneous (subdermal) fluids, skin fluids, or blood) and can be used to measure the user's analyte-related data in conjunction with an in vivo analyte monitoring circuit. The sensor 104 and any associated sensor control electronics can be applied to the body in any desired manner. For example, an insertion device (not shown) can be used to place all or a portion of the analyte sensor 104 through an external surface of the user's skin and in contact with the user's bodily fluids. In so doing, the insertion device can also place the sensor control device 102 on the skin by means of an adhesive patch 105. In other embodiments, the insertion device can first place the sensor 104 and then couple the associated sensor control electronics to the sensor 104, either manually or with the aid of a mechanical device. Examples of insertion devices are described in U.S. Patent Application Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, all of which are incorporated by reference in their entirety for all purposes.

[0037] After collecting the raw data from the user's body, the sensor control device 102 can perform analog signal conditioning on the data and convert the data into conditioned raw data in a digital format. In some embodiments, the sensor control device 102 can then algorithmically process the digital raw data into a form that represents the user's measured bioinformation (e.g., analyte levels) and / or one or more analyte metrics based thereon. For example, the sensor control device 102 can include processing circuitry that calculates the analyte metrics and algorithmically performs any of the method steps described herein. The sensor control device 102 can then encode and wirelessly communicate the calculated analyte metrics, processed sensor data, notifications, or any other data to the reader device 120 and / or the wearable electronics 120B, which can format or graphically process the received data for digital display to the user. In other embodiments, in addition to or instead of wirelessly communicating the sensor data to another device (e.g., reader device 120 and / or wearable electronics 120B), the sensor control device 102 can graphically process the data in its final form into a displayable state and display the data on the display of the sensor control device 102. In some embodiments, the biometric data in its final form (prior to graphical processing) is used by the system (e.g., integrated into a diabetes monitoring plan) without processing for display to the user.

[0038] In yet other embodiments, the conditioned raw digital data can be encoded for transmission to another device, such as the reader device 120 and / or the wearable electronics 120B, which then algorithmically processes the raw digital data into a form representative of the user's measured biometric information and / or one or more analyte metrics based thereon (e.g., a form that is readily suitable for display to a user). The reader device 120 and / or the wearable electronics 120B can include processing circuitry that calculates the analyte metrics and algorithmically performs any of the method steps described herein. This algorithmically processed data can then be formatted or graphically processed for digital display to a user.

[0039] In other embodiments, the sensor control device 102 and reader device 120 transmit the raw digital data to another computer system for algorithmic processing and display.

[0040] The reader device 120 may include a display 122 for outputting information to and / or accepting input from a user, and an optional input component(s) 121, such as a button, actuator, touch-sensitive switch, capacitive switch, pressure-sensitive switch, jog wheel, etc., for inputting data, commands, or controlling the operation of the reader device 120. In certain embodiments, the display 122 and the input component 121 may be integrated into a single component, e.g., the display may detect the presence and location of physical contact touches on the display, such as a touchscreen user interface. In certain embodiments, the input component 121 of the reader device 120 may include a microphone, and the reader device 120 may include software configured to analyze voice input received from the microphone such that functions and operations of the reader device 120 may be controlled by voice commands. In certain embodiments, the output component of the reader device 120 includes a speaker (not shown) for outputting information as an audible signal. Similar audio response components, such as a speaker, microphone, and software routines for generating, processing, and storing audio drive signals, may be included in the sensor control device 102. According to some embodiments, the wearable electronics 120B may include components that function in a similar manner to similar components of the reader device 120, including a display 122B (which may have a touch screen user interface) and optional input components 121B.

[0041] The reader device 120 may also include one or more data communication ports 123 for wired data communication with external devices, such as the computer system 170 or the sensor control device 102. Exemplary data communication ports include a USB port, a mini USB port, a USB Type-C port, a USB micro-A and / or micro-B port, an RS-232 port, an Ethernet port, a Firewire port, or other similar data communication ports configured to connect to a compatible data cable. The reader device 120 may also include an integrated or wearable in-vitro glucose meter that includes an in-vitro test strip port (not shown) for accepting in-vitro blood glucose test strips for performing in-vitro blood glucose measurements.

[0042] The reader device 120 and / or the wearable electronics 120B can display the measured biometric data wirelessly received from the sensor control device 102, and can also be configured to output visual, audible, tactile, or any combination thereof, alarms, alert notifications, glucose values, etc. Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011 / 0193704, which is incorporated herein by reference in its entirety and for all purposes.

[0043] The reader device 120 can act as a data conduit to transfer measured data and / or analyte metrics from the sensor control device 102 to the computer system 170 or trusted computer system 180. In certain embodiments, data received from the sensor control device 102 may be stored (permanently or temporarily) in one or more memories of the reader device 120 prior to uploading to the systems 170, 180 or network 190.

[0044] Computer system 170 may be a personal computer, a server terminal, a laptop computer, a tablet, or other suitable data processing device. Computer system 170 may be (or may include) software for data management and analysis, and communication with components in analyte monitoring system 100. Computer system 170 may be used by a user or medical professional to display and / or analyze the biometric data measured by sensor control device 102. In some embodiments, sensor control device 102 may communicate the biometric data directly to computer system 170 without an intermediary such as reader device 120, or indirectly to the computer system using an Internet connection (optionally without also first transmitting to reader device 120). The operation and use of computer system 170 is further described in the '225 publication incorporated herein. Analyte monitoring system 100 may also be configured to operate with a data processing module (not shown), as also described in the incorporated '225 publication.

[0045] The trusted computer system 180 may be within the ownership of the manufacturer or distributor of the sensor control device 102, either physically or virtually via a secure connection, and may be used as a server to perform authentication of the sensor control device 102 and securely store the user's biometric data, and / or to provide data analysis programs (e.g., accessible via a web browser) for performing analysis on the user's measurement data.

[0046] Exemplary embodiments of a reader device The reader device 120 may be a mobile communication device, such as a mobile phone, including but not limited to a dedicated reader device (configured to communicate with the sensor control device 102 and optionally the computer system 170, but not having cellular communication capabilities) or a Wi-Fi or Internet-enabled smartphone, tablet, or personal digital assistant (PDA). Examples of smartphones may include mobile phones based on the Windows operating system, the Android operating system, the iPhone operating system, the Palm WebOS operating system, the Blackberry operating system, or the Symbian operating system, and having an Internet connection and / or data network connectivity for data communication over a local area network (LAN).

[0047] The reader device 120 may also be configured as a portable smart wearable electronics assembly, such as an optical assembly (e.g., one or more smart glasses, such as Google Glass, which are mobile communication devices) worn on or next to the user's eyes. The optical assembly may have a transparent display that displays information to the user regarding the user's analyte levels (as described herein) while allowing the user to see through the display with minimal obstruction to the user's overall field of vision. The optical assembly may be capable of wireless communication similar to a smartphone. Other examples of wearable electronics include devices worn around or near the user's wrist (e.g., a watch, etc.), neck (e.g., a necklace, etc.), head (e.g., a headband, hat, etc.), chest, etc. According to some embodiments, for example, the wearable electronics may include a smart watch 120B that can send and receive data directly to the sensor control device 102 via communication path 144 and / or to the reader device 120 via communication path 145, as shown in FIG. 1. Additionally, in some embodiments, the wearable electronics 120B can include a processing circuit coupled to a memory for storing instructions that, when executed by the processing circuit of the wearable electronics 120B, cause the processing circuit to execute a program to generate an output, such as displaying data indicative of the sensed analyte level in a user interface on the wearable electronics' display 122B or outputting an audio or vibration alert. In some embodiments, the data indicative of the sensed analyte level can be received by the wearable electronics 120B from either or both of the sensor control device 102 or the reader device 120.

[0048] FIG. 2 is a block diagram of an exemplary embodiment of a reader device 120 configured as a smartphone. Here, the reader device 120 includes an input component 121, a display 122, and a processing circuit 206, which may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be individual chips or distributed across (and portions of) many different chips. Here, the processing circuit 206 includes a communication processor 202 having an on-board memory 203 and an application processor 204 having an on-board memory 205. The reader device 120 further includes an RF communication circuit 208 coupled with an RF antenna 209, a memory 210, a multifunction circuit 212 having one or more associated antennas 214, a power supply 216, a power management circuit 218, and a clock 219. FIG. 2 is a schematic diagram of typical hardware and functionality present in a smartphone, and one of ordinary skill in the art will readily recognize that other hardware and functionality (e.g., codecs, drivers, glue logic) may also be included.

[0049] The communications processor 202 interfaces with RF communications circuitry 208 and may perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting voice, video, and data signals into a form suitable for provision to the RF communications circuitry 208 (e.g., in-phase and quadrature), which may then transmit the signals wirelessly. The communications processor 202 may also interface with the RF communications circuitry 208 to perform the inverse functions necessary to receive and convert wireless transmissions into digital data, voice, and video. The RF communications circuitry 208 may include a transmitter and receiver (e.g., integrated as a transceiver) and associated encoder logic.

[0050] The application processor 204 may be adapted to execute an operating system and any software applications resident on the reader device 120, process video and graphics, and perform other functions not related to processing communications transmitted and received via the RF antenna 209. The smartphone operating system works in conjunction with a number of applications on the reader device 120. Any number of applications (also known as "user interface applications") may be executed on the reader device 120 at one time, and these may include one or more applications related to a diabetes monitoring regimen in addition to other commonly used applications not related to such regimens, such as email, calendar, weather, sports, games, and the like. For example, data indicative of sensed analyte levels and in vitro blood analyte measurements received by the reader device may be securely communicated to a user interface application resident in the memory 210 of the reader device 120. Such communication may be performed securely, for example, by use of mobile application containerization or wrapping techniques. Additionally, according to some embodiments, the reader device 120 may also include an application for communicating data indicative of sensed analyte levels with the wearable electronics 120B.

[0051] Memory 210 may be shared by one or more of the various functional units present in reader device 120, or may be distributed across two or more functional units (e.g., as separate memories present in different chips). Memory 210 may also be its own separate chip. Memories 203, 205 and 210 may be non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, Flash memory, F-RAM, etc.).

[0052] The multi-function circuitry 212 may be implemented as one or more chips and / or components (e.g., transmitters, receivers, transceivers, and / or other communication circuitry) that perform other functions, such as, for example, local wireless communication with the sensor control device 102 under an appropriate protocol (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy, Near Field Communication (NFC), Radio Frequency Identification (RFID), proprietary protocols, etc.), determining the geographic location of the reader device 120 (e.g., global positioning system (GPS) hardware). One or more other antennas 214 are associated with the functional circuitry 212 as necessary to operate according to the various protocols and circuits.

[0053] The power source 216 can include one or more batteries, which can be rechargeable or single-use disposable batteries. The power management circuitry 218 can regulate battery charging and power supply monitoring, boost power, DC conversion, etc.

[0054] The reader device 120 may also include or be integrated with a drug (e.g., insulin, etc.) delivery device, for example, to share a common housing. Examples of such drug delivery devices may include a drug pump (e.g., a wearable pump for delivering basal and bolus insulin) with a cannula that remains in the body for infusion over hours or days. When combined with a drug pump, the reader device 120 may include a reservoir for storing the drug, a pump connectable to a transfer tube, and an infusion cannula. The pump may pump the drug from the reservoir through the tube into the diabetic patient's body via a cannula inserted into the diabetic patient's body. Other examples of drug delivery devices that may be included with (or integrated with) the reader device 120 include a portable injection device (e.g., an insulin pen) that pierces the skin only for each delivery and is then removed. When combined with a portable injection device, the reader device 120 may include an injection needle, a cartridge for carrying the drug, an interface for controlling the amount of drug delivered, and an actuator for performing the injection. The device can be used repeatedly until the drug is depleted, at which point the combined device can be discarded or the cartridge can be replaced with a new one, at which point the combined device can be used repeatedly. The needle can be replaced after each injection.

[0055] The combined device can function as part of a closed loop system (e.g., an artificial pancreas system that does not require user intervention to operate) or a semi-closed loop system (e.g., an insulin loop system that requires little user intervention to operate, such as confirming dosage changes). For example, the diabetic patient's analyte levels can be repeatedly and automatically monitored by the sensor control device 102, which can then communicate the monitored analyte levels to the reader device 120, which can automatically determine and subsequently deliver an appropriate drug dose to control the diabetic patient's analyte levels to the diabetic patient's body. Software instructions for controlling the pump and the amount of insulin delivered can be stored in the memory of the reader device 120 and executed by the processing circuitry of the reader device. These instructions can also cause drug delivery amounts and durations (e.g., bolus and / or basal infusion profiles) to be calculated based on analyte level measurements obtained directly or indirectly from the sensor control device 102. In some embodiments, the sensor control device 102 can determine and communicate the drug dose to the reader device 120.

[0056] Exemplary embodiments of a sensor control device FIG. 3 is a block diagram illustrating an exemplary embodiment of a sensor control device 102 having an analyte sensor 104 and sensor electronics 250 (including analyte monitoring circuitry) that may have most of the processing power for rendering final result data suitable for display to a user. In FIG. 3, a single semiconductor chip 251 is shown that may be a custom application specific integrated circuit (ASIC). Within the ASIC 251, certain high level functional units are shown including an analog front end (AFE) 252, a power management (or control) circuit 254, a processing circuit 256, and a communication circuit 258 (which may be implemented as a transmitter, a receiver, a transceiver, a passive circuit, or according to a communication protocol). In this embodiment, both the AFE 252 and the processing circuit 256 are used as analyte monitoring circuitry, although in other embodiments, either circuitry may perform the analyte monitoring function. The processing circuit 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a separate chip or may be distributed across (and portions of) multiple different chips.

[0057] A memory 253 is also included within the ASIC 251 and may be shared by various functional units present within the ASIC 251 or may be distributed across two or more functional units. The memory 253 may be a separate chip. The memory 253 is non-transitory and may be volatile and / or non-volatile memory. In this embodiment, the ASIC 251 is coupled to a power source 260, which may be a coin cell battery or the like. The AFE 252 interfaces with and receives measurement data from the in vivo analyte sensor 104 and outputs the data in digital form to a processing circuit 256, which in some embodiments may process the data in any manner described elsewhere herein. This data, where minimal further processing is required by a resident software application to display the data, may then be provided to a communication circuit 258 for transmission to the reader device 120 (not shown) via an antenna 261. The antenna 261 may be configured according to the needs of the application and communication protocol. Antenna 261 may be, for example, a printed circuit board (PCB) trace antenna, a ceramic antenna, or a separate metal antenna. Antenna 261 may be configured as a monopole antenna, a dipole antenna, an F-type antenna, a loop antenna, etc.

[0058] Information may be communicated from the sensor control device 102 to a second device (e.g., the reader device 120) at the initiative of the sensor control device 102 or the reader device 120. For example, information may be communicated automatically and / or repeatedly (e.g., continuously) by the sensor control device 102 if analyte information is available, or may be communicated according to a schedule (e.g., about every minute, about every five minutes, about every ten minutes, etc.), in which case the information may be stored or recorded in the memory of the sensor control device 102 for later communication. Information may be transmitted from the sensor control device 102 in response to receipt of a request by the second device. The request may be an automated request, e.g., a request sent by the second device according to a schedule, or may be a request generated at the initiative of a user (e.g., an ad-hoc or manual request, i.e., a "user-initiated analyte test"). In some embodiments, a manual request for data is referred to as a "scan" of the sensor control device 102 or an "on-demand" data transfer from the device 102. In some embodiments, the second device can send polling signals or data packets to the sensor control device 102, which can treat each poll (or polls occurring at regular time intervals) as a request for data and can transmit such data to the second device when data is available. While in many embodiments, communication between the sensor control device 102 and the second device is secure (e.g., encrypted and / or between authenticated devices), in some embodiments data can be transmitted from the sensor control device 102 in an insecure manner, for example, as a broadcast to all receiving devices within range.

[0059] Different types and / or formats and / or amounts of information may be transmitted as part of each communication, including, but not limited to, one or more of the current sensor reading (e.g., most recently obtained analyte level information corresponding in time to the time the reading was initiated), the rate of change of the measured metric over a predetermined time, the rate of the rate of change of the metric (acceleration of the rate of change), or metric history information corresponding to metric information obtained prior to a given reading and stored in the memory of the sensor control device 102.

[0060] Some or all of the real-time, historical, rate of change, and rate of change (such as acceleration or deceleration) information may be transmitted to the reader device 120 in a given communication or transmission. In certain embodiments, the type and / or format and / or amount of information transmitted to the reader device 120 may be preprogrammed and / or non-alterable (e.g., preset at time of manufacture) or may not be preprogrammed and / or non-alterable such that it is selectable and / or alterable one or more times in the field (e.g., by actuating a switch in the system). Thus, in certain embodiments, the reader device 120 may output the current (real-time) analyte value obtained from the sensor (e.g., in numerical form), the current rate of change of the analyte (e.g., in the form of an analyte rate indicator such as an arrow pointing in the direction of the current rate), and historical analyte trend data (e.g., in the form of a graphical trace) based on the sensor readings obtained by the sensor control device 102 and stored in its memory. Additionally, on-skin or sensor temperature readings or measurements may be collected by optional temperature sensor 257. These readings or measurements may be communicated (individually or as aggregated measurements over time) from sensor control device 102 to another device (e.g., reader 120). However, the temperature readings or measurements may be used in conjunction with software routines executed by reader device 120 to correct or compensate the analyte measurements output to the user instead of, or in addition to, actually displaying a temperature measurement to the user.

[0061] Exemplary embodiments for determining drug doses administered with a meal Exemplary embodiments of systems, devices, and methods for determining a drug dose to be administered in conjunction with the ingestion of a meal are then described. As previously mentioned, certain individuals, such as diabetics, need to compensate for the expected blood glucose rise that occurs after the ingestion of a meal by administering a drug, such as insulin. The drug dose is often referred to as a meal bolus, since it is a drug infusion intended to compensate for a meal.

[0062] Some conventional systems and methods for determining the drug dosage to be administered with food intake require individuals to manually count or estimate carbohydrates.These systems may lead to inaccurate and inconsistent drug dosages, since individuals may have difficulty accurately estimating the amount of carbohydrates and other nutritional components contained in food.In addition, the blood glucose response to nutrients may vary from individual to individual, since it is not assumed that different individuals all respond to the same nutrients in the same way.

[0063] To address this challenge, other systems and methods have attempted to record repeated instances of food intake in a database, along with a description of the meal and associated drug dose. Corresponding analyte data (e.g., post-prandial glucose data) from an analyte monitoring system, such as an in vivo analyte monitoring system, can also be associated with the record in the database based on the time corresponding to the intake of the meal. By associating the meal with analyte data from previous instances in which a drug dose was administered, an individual or health care provider (HCP) can easily identify beneficial drug dose settings to improve future glycemic responses. These systems and methods are further described in U.S. Patent Application Serial No. 15 / 863279 (U.S. Patent Application Publication No. 2018 / 0197628), the entirety of which is incorporated herein by reference for all purposes.

[0064] The embodiments described herein represent improvements to the systems and methods described above. For example, the embodiments described herein can determine a drug dose to be administered with the consumption of a meal that the individual has not consumed before. At a general level, the exemplary embodiments allow the individual to input meal information into an interface, and an appropriate bolus amount for the meal is determined based on various nutritional parameters associated with the meal. More specifically, the method of the exemplary embodiments described herein includes receiving a user-entered entry associated with a new meal, determining the nutrient content of the new meal by referencing a first database, associating the new meal with the most matching meal in a second database based on the nutrient content, and determining the drug dose associated with the most matching meal.

[0065] The present embodiment may be referred to herein as an "empirical" tool, since it is based in part on the typical experience of an individual. For ease of explanation, the exemplary embodiment is described in the context of determining an insulin bolus dose, and is generally referred to as an "experiential bolus assistant," or "EBA" for short. However, it is emphasized that these exemplary embodiments may be used with all types of insulin (e.g., long-acting insulin, intermediate-acting, short-acting insulin, etc.), as well as with other types of diabetes medications other than insulin. The exemplary embodiments may also be used to determine types of doses other than bolus doses, such as basal doses or basal time-varying dose profiles.

[0066] Before describing the embodiments in detail, it will be understood by those skilled in the art that any one or more of the steps of the exemplary method described herein can be stored as software instructions in a non-transitory memory of a reader device, a remote computing device, a trusted computer system, or a drug delivery device, as described with respect to FIG. 1. The stored instructions, when executed, can cause the processing circuitry of the associated device or computing system to perform any one or more of the steps of the exemplary method described herein. In some embodiments, the stored instructions can be implemented as one or more downloadable software applications ("apps") on a reader device, such as a mobile phone or smartphone, and the software can communicate from the reader device to a remote server (e.g., a cloud-based server), which can provide a more comprehensive and robust analysis that can be accessed by an individual on the same or a second computing device. In other embodiments, the stored instructions can be implemented on the reader device or computing system as a web interface accessible by a standard web browser.

[0067] When used with analyte monitoring system 100, these embodiments can capture, classify, and index glycemic responses to meals and prandial insulin doses (administered to compensate for the meal), thus providing the user with additional data that may improve or "fine-tune" their insulin dose. Additionally, over time, the exemplary embodiments can provide advice regarding titration of bolus amounts for each meal.

[0068] FIG. 4 is a block diagram illustrating an exemplary embodiment of the system 100 configured to operate with a modular EBA, where the EBA 402 is in the form of a downloadable app that is downloaded (e.g., through an “app store” or equivalent) and installed on the smartphone 120. According to some embodiments, a second app 404 may also be downloaded and installed on the smartphone 120, the second app 404 being responsible for interfacing with the sensor control device 102 (not shown), processing analyte data received therefrom, and configuring the data for display to a user. According to an aspect of some embodiments, the app 404 may enable a commercially available smartphone to function as the reader device 120. Although the apps 402 and 404 are shown in FIG. 4 as separate apps, they may also be combined into a single downloadable app (or module) with a single access icon on the reader device 120.

[0069] According to another aspect of the embodiment, the EBA 402 sends a request for recently collected glucose data from the user to the app 404 via a resident application programming interface (API). The app 404 processes the request and returns the queried data to the EBA 402, as shown in the loop in FIG. 4. The EBA 402 can temporally associate the glucose data with a description of the recently ingested meal and can optionally upload the meal and glucose data via the network 190 to the trusted computer system 180, represented here as a central cloud-based database. Medication dose and / or post-prandial glucose data can also be uploaded to the trusted computer system 180. The glucose, meal, and medication dose data can be categorized, indexed, and stored long-term as a historical record in the database of the central cloud system 180, and / or downloaded to the reader device 120 or computing system 170 for long-term storage. Nutritional parameters related to the meal can also be stored in the central cloud-based database 180 for each historical record.

[0070] The user can access this data using, for example, a web browser running on the smartphone 120, as shown in FIG. 4, or via a separate computing device such as a personal computer system 170. According to some embodiments, the central cloud system 180 can also provide data analysis tools via the user's web interface 406, which the user can use to input user-specific information, adjust EBA settings, analyze glycemic responses to ingested meals, and make informed decisions regarding insulin dose adjustments and / or modifications. Additionally, this data can be accessed directly by the user's HCP, either alone or in collaboration with the user during a visit, to investigate the effectiveness of the user's insulin treatment and make adjustments thereto. In some embodiments, the computing device 170 can also be used for the user to input dietary information. Overall, the analysis tools 406 can assist the user in long-term diabetes management and integration with other treatment decisions or user engagement systems.

[0071] 4, the central cloud system 180, according to an aspect of an embodiment, can access a nutrition database system 185 that includes nutrition parameters associated with various meals and meal components. The central cloud system 180 and the nutrition database system 185 can communicate over a network 190, which can be via a local area network, a wide area network, the Internet, or any similar communication network. According to an aspect of an embodiment, the central cloud system 180 and the nutrition database system 185 can be hosted in the same geographic location (i.e., where both systems can be managed by the same entity) or in different geographic locations (i.e., where the nutrition database system 185 is managed by a third party). Those skilled in the art will also appreciate that the central cloud system 180 and the nutrition database system 185 can be implemented as separate physical servers or separate instances of virtual machines on the same physical server. Additionally, while FIG. 4 illustrates nutritional database system 185 within network 190, according to some embodiments, nutritional database system 185 may reside on trusted computer system 170, and optionally apps 402 and 404 may communicate directly with trusted computer system 170 to communicate and send and receive updates, data, and reports.

[0072] According to one aspect of the embodiment, the nutritional database system 185 can include an interface that receives dietary information as input and outputs nutritional parameters associated with the input dietary information. In some embodiments, the nutritional parameters can include carbohydrate parameters, fat parameters, and / or protein parameters, each associated with the nutrient content of the input diet. Those skilled in the art will recognize that other nutritional parameters are available and are fully within the scope of the present disclosure.

[0073] FIG. 5 is a flow chart illustrating an exemplary embodiment of a method for determining a drug dose to be administered with the intake of a meal, which may be implemented, for example, by the system 100 of FIG. 4. Beginning with step 505, a user-entered entry related to a meal is received by the system 100. According to some embodiments, the meal entry may be entered by an app 402 or a web interface (e.g., via a web browser) on a reader device 120, such as a smartphone. In other embodiments, the meal entry may be entered by a computing device 170, such as a personal desktop or laptop computer. According to some embodiments, the user-entered entry may be a text entry, which may be, for example, a description of the meal to be consumed, provided in a "natural language" format. In yet other embodiments, the user-entered entry may be in the form of a photo of the meal to be consumed. Those skilled in the art will appreciate that other similar user input methods (e.g., drop-down menus, selectable fields, checkboxes, radio buttons, voice input, etc.) are available and are within the scope of the present disclosure.

[0074] In step 510, a first database is consulted to determine a plurality of nutritional parameters associated with the meal based on the user-entered meal entry. According to one aspect of the embodiment, the first database can be the nutritional database system 185 (as shown in FIG. 4 ), and the plurality of nutritional parameters can include, for example, carbohydrate parameters, fat parameters, and / or protein parameters for the meal associated with the user-entered entry. Additionally, in some embodiments, the user-entered entry can be transmitted from the reader device 120 or computing device 170 to the central cloud system 180 prior to consulting the first database. The central cloud system 180 can then use the user-entered entry to consult the first database 185 to receive the associated nutritional parameters. In other embodiments, the user-entered entry can be directly transmitted by the reader device 120 or computing device 170 to the first database 185, which can then transmit the associated nutritional parameters to the central cloud system 180. Optionally, at least a portion of the first database, which may include nutritional parameters associated with the user-entered meal entries, may be downloaded to either the central cloud system 180, the reader device 120, and / or the computing device 170 and stored in association with the user-entered meal entries. In yet other embodiments, the first database 185 may reside on the trusted computing device 170, and the user-entered entries may be sent from the reader device 120 to the trusted computing device 170 or entered directly into the same trusted computing device 170 on which the first database 185 resides, without communicating with the central cloud system 180 at this step.

[0075] 5, in step 515, nutritional parameters associated with the meals are used to identify the meals with the best matches in the second database. In many embodiments, the second database may be hosted on the central cloud system 180. In other embodiments, the second database may be located on the reader device 120 and / or the computing device 170.

[0076] According to one aspect of the embodiment, the most matching meal may be the meal history record in the second database that has a set of associated nutritional parameters that are most similar to the nutritional parameters associated with the user-entered meal. This may be determined, for example, by calculating a weighted set of differences between the nutritional parameters of each meal history record and the nutritional parameters of the user-entered meal entry, and selecting the meal history record that has the smallest sum of differences. To illustrate, in one exemplary embodiment, the most matching meal may be determined by calculating the minimum sum of differences resulting from the following formula: 0.5*(absolute % difference in carbohydrates)+0.25*(absolute % difference in fat)+0.25*(absolute % difference in protein), where the "absolute % difference" may be the absolute value of the percentage difference between the nutritional parameters of the meal history record and the nutritional parameters of the user-entered meal entry. Those skilled in the art will recognize that other weighting factors may be used for each nutritional parameter. Similarly, the minimum sum of differences may be calculated without using weighting factors. In some embodiments, after identifying the most closely matching meal in the second database, a new meal history record may be created in the second database for the user-entered meal entry and then linked to the most closely matching meal.

[0077] In step 520, the drug dose associated with the most matching meal in the second database is determined. In some embodiments, the drug dose can be, for example, the most recent insulin dose administered with the intake of the most matching meal (recorded in the second database). In other embodiments, the drug dose can be the average of the conventional insulin doses administered for all or a predetermined number of past instances in which the most matching meal was consumed. In yet other embodiments, the drug dose can be the insulin dose flagged as the optimal drug dose for the most matching meal in the second database. Optionally, the determined drug dose and / or associated nutritional parameters can be stored in the second database along with a history record associated with the user-entered meal entry. Finally, in step 525, the determined drug dose can be visually output, for example, on the display of the reader device 120 and / or the display of the computing device 170.

[0078] Exemplary embodiments for identifying a set of meal onset candidates and meal peak response candidates Exemplary Features of User-Directed Analyte Testing Some of the embodiments disclosed herein utilize analyte data from an analyte monitoring system, such as that described with respect to FIG. 1, in combination with information about user-initiated analyte testing to determine a set of candidate meal starts and meal peak response candidates. As previously discussed, one of the challenges with analyte monitoring systems is being able to accurately correlate an individual's analyte data with the individual's food intake, as well as their pre- and post-prandial responses. This correlation can be useful in many applications, such as, for example, guidance for titration of drug doses. Unlike conventional systems, the embodiments described herein do not rely solely on manual blood glucose measurements or manual food records of an individual, both of which are impractical and difficult for an individual to maintain. However, before describing the embodiments in detail, it is desirable to describe certain aspects of user-initiated analyte testing and meal start times, both of which are relevant to the embodiments described herein.

[0079] FIG. 6A is a graph 600 illustrating the time of day (TOD) distribution of a type of user-initiated analyte test. In particular, the graph 600 illustrates self-monitoring blood glucose (SMBG) measurements from various subpopulations of a sensor study of insulin-using patients, where the SMBG measurements consist of fingerstick blood glucose tests. According to one embodiment of the graph 600, the distribution of SMBG measurements can be characterized by two data clusters 610, 620. The first cluster 610 is composed of two weak modes including pre-breakfast and pre-lunch SMBG measurements. Additionally, the second cluster 620 is composed of two slightly more prominent modes including pre-dinner and pre-bedtime measurements. From this data, a reasonable inference can be drawn that meal start times correspond to the majority of SMBG instances.

[0080] 6B and 6C are graphs 630 and 650, respectively, both showing the distribution of another type of user-initiated analyte testing. In particular, graphs 630 and 650 show the distribution of sensor scan instances from analyte reader devices for a large, anonymized population database of users of analyte reader devices. According to one embodiment of graphs 630 and 650, the distribution of sensor scan instances is plotted as a function of time of day and the average number of scans per day. Similar to the SMBG distribution of FIG. 6A, the distribution of sensor scan instances of FIG. 6B and FIG. 6C shows that in the range of low average number of scans per day, the distribution of sensor scan instances is characterized by a peak similar to the SMBG measurement distribution of FIG. 6A. That is, meal start times also correspond to the majority of sensor scan instances.

[0081] Although Figures 6A-6C show distributions for a particular type of user-initiated analyte test, one skilled in the art can reasonably infer that similar distributions would occur for other types of user-initiated analyte tests, such as instances of using a sensor viewer on a smartphone or instances of activating a receiver display in a continuous glucose monitoring (CGM) system.

[0082] Example for determining time derivative and acceleration characteristics In accordance with embodiments described herein, it is also desirable to account for certain characteristics of the analyte data of an analyte monitoring system, which, depending on the embodiment, can be utilized to identify a set of meal onset candidates and meal peak response candidates.

[0083] FIG. 7A illustrates three graphs 700, 710, and 720, each illustrating a particular characteristic of a sample set of analyte data, e.g., blood glucose concentration data, from an analyte monitoring system. Referring initially to the top graph 700, data points 702 (open circles) correspond to analyte concentrations, e.g., blood glucose concentrations, over a period of several days, as shown on the x-axis. The data points 702 may be raw data received from an analyte sensor, which may include irregularly spaced data points and / or questionable readings. In certain embodiments, after being received from the analyte sensor, the data points 702 may be adjusted to remove questionable readings and smooth the data, resulting in adjusted data points 704 (closed circles). The adjusted data points 704 may be characterized by regularly spaced glucose values. According to some embodiments, the data adjustment may include determining whether the sampled glucose data may be an outlier when compared to other glucose data sampled in close time proximity. Further details regarding performing data conditioning and restoration are described in U.S. patent application Ser. No. 14 / 210,312, entitled "Noise Rejection Methods and Apparatus for Sparsely Sampled Analyte Sensor Data," filed Mar. 13, 2014, the disclosure of which is incorporated herein by reference for all purposes.

[0084] 7A, middle graph 710 and bottom graph 720 each illustrate additional characteristics of the analyte data of graph 700. More specifically, middle graph 710 illustrates multiple line plots of the time derivatives, i.e., slopes, of the analyte data of graph 700. According to one aspect of the embodiment, for each time instance k, a pair of time derivatives associated with a candidate meal peak response can be calculated, as well as a pair of time derivatives associated with a candidate meal onset. In particular, the pair of time derivatives can be calculated by computing the rate of change of the analyte data in a forward time window and the rate of change of the analyte data in a backward time window. For example, as seen in top graph 700, where the circled data point 706 occurs at time instance k, the forward time window is indicated by a double-headed arrow to the right of data point 706, and the backward time window is indicated by a double-headed arrow to the left of data point 706. The forward time window can be a time instance from the current measurement at instance k to a time instance in the near future, e.g., 2-3 hours later. Similarly, a backward time window involves using glucose data sampled in the backward time window, i.e., from the current measurement at instance k to time instances in the near past, e.g., up to 1-2 hours prior. According to some embodiments, the time derivative, i.e., slope, can be determined by fitting a line through the analyte measurements in each corresponding time window using a least squares error fitting method.

[0085] According to one aspect of this embodiment, the forward time window associated with the meal start candidate does not necessarily have the same width as the forward time window associated with the meal peak response candidate, and similarly, the rearward time window associated with the meal start candidate does not necessarily have the same width as the rearward time window associated with the meal peak response candidate.

[0086] Returning to graph 710 of FIG. 7A, a number of line graphs of time derivatives are shown, each of which may be calculated according to the methods described above. In particular, a graph of the time derivative of the forward rate of change associated with a candidate meal peak response is shown as v_peak_fwd(k). A graph of the time derivative of the backward rate of change associated with a candidate meal peak response is shown as v_peak_bck(k). Similarly, a graph of the time derivative of the forward rate of change associated with a candidate meal start is shown as v_start_fwd(k). A graph of the time derivative of the backward rate of change associated with a candidate meal start is shown as v_start_bck(k).

[0087] 7A, the bottom graph 720 shows a number of line graphs for acceleration derived from the time derivatives shown in the middle graph 710. In particular, the bottom graph 720 shows acceleration a_peak(k) associated with a meal peak response candidate, where a_peak(k) is calculated as (v_peak_fwd(k)-v_peak_bck(k)) / T_peak, where T_peak is a predetermined sample period scale factor (e.g., 1-3 hours) for the associated meal peak response candidate. Similarly, the bottom graph 720 shows acceleration a_start(k) associated with a meal start candidate, where a_start(k) is calculated as (v_start_fwd(k)-v_start_bck(k)) / T_start, where T_start is a predetermined sample period scale factor (e.g., 1-3 hours) for the associated meal start candidate.

[0088] Also referring to the bottom graph 720 of FIG. 7A, an initial set of meal start candidates and meal peak response candidates can be identified by determining local optima of acceleration from the acceleration line graph. According to an embodiment, local optima of acceleration can be identified based on signal analysis to identify maximum inflection points. For example, at each time instance k, any a_peak value falling in either the forward or backward time window is identified, except for the value a_peak(k) at time instance k. If the value of a_peak(k) is less than or equal to the minimum a_peak value in the two time windows mentioned above, the current time instance k is determined as a meal peak response candidate. Similarly, at each time instance k, any a_start value falling in either the forward or backward time window is identified, except for the value a_start(k) at time instance k. If the value of a_start(k) is greater than or equal to the maximum a_start value in the two time windows mentioned above, the current time instance k is determined as a meal start candidate.

[0089] According to another aspect of the embodiment, if a time instance k has previously been identified as a meal peak response candidate and is also identified as a meal onset candidate, then the meal onset candidate tag is moved to the next instance k+1. Graph 720 illustrates the local optima of acceleration, i.e., the identification of meal onset and meal peak response candidates, indicated by "upward" triangles 722 and "downward" triangles 724, respectively.

[0090] Further details regarding the above calculations, including determining the local optima of time derivatives and acceleration, are described in U.S. Patent Application Publication No. 2017 / 0185748 ("the '748 Publication"), the disclosure of which is incorporated herein by reference for all purposes.

[0091] Exemplary embodiments utilizing analyte data and user-directed analyte testing to identify candidate meal onsets and candidate meal peak responses Exemplary embodiments of systems, devices, and methods for determining a set of meal start candidates and meal peak response candidates based on analyte data from a user-directed analyte testing and analyte monitoring system are then described.

[0092] Those skilled in the art will appreciate that any one or more of the steps of the exemplary method described herein can be stored as software instructions in a non-transitory memory of a sensor control device, a reader device, a remote computer, or a trusted computer system, such as those described with respect to FIG. 1. The stored instructions, when executed, can cause the processing circuitry of the associated device or computing system to perform any one or more of the steps of the exemplary method described herein. Those skilled in the art will also appreciate that in many embodiments, any one or more of the steps of the method described herein, including the calculation of time derivatives, accelerations, or local optima thereof, can be performed using real-time or near real-time sensor data. In other embodiments, any one or more of the method steps can be performed retrospectively with respect to stored sensor data. In some embodiments, the method steps described herein can be performed periodically, according to a predetermined schedule, and / or in batches of retrospective processing.

[0093] Those skilled in the art will also appreciate that the instructions may be stored in non-transitory memory on a single device (e.g., a sensor control device or a reader device) or, alternatively, may be distributed across multiple separate devices that may be located in geographically distributed locations (e.g., a cloud platform). Similarly, those skilled in the art will recognize that the representation of a computing device in the embodiments disclosed herein, such as that shown in FIG. 1, is intended to cover both physical and virtual devices (or "virtual machines").

[0094] FIG. 8 is a flow diagram illustrating an exemplary embodiment of a method 800 for identifying a set of meal peak response candidates and meal start candidates. The method begins with step 805, receiving a plurality of data points corresponding to a monitored analyte level. According to some embodiments, the monitored analyte level can be a monitored blood glucose concentration. However, one skilled in the art will recognize that other analytes, such as acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, HbAlc, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose derivatives, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxide, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, and drugs, such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored and are fully within the scope of the present disclosure. According to some embodiments, the data points may be adjusted either before or after receipt, as previously described above, to remove questionable readings, smooth the data points, and / or create regularly spaced analyte values.

[0095] Then, in step 810, a time derivative of the plurality of data points corresponding to the monitored analyte level is determined. The time derivative of the plurality of data points may be determined according to the calculations described above with respect to graphs 700 and 710 of FIG. 7A. Thereafter, in step 815, an initial set of meal start candidates and meal peak response candidates is created by determining a local optimum of the acceleration of the plurality of data points based on the time derivatives determined in step 810. The local optimum of the acceleration may be determined according to the calculations described above with respect to graph 720 of FIG. 7A.

[0096] At step 820, a plurality of user-initiated analyte tests are obtained and grouped into a plurality of time clusters. The user-initiated analyte tests may include one or more of fingerstick blood glucose tests, instances of sensor scanning from an analyte reader device, instances of use of a sensor viewer on a smartphone, or instances of activation of a receiver display in a continuous glucose monitoring (CGM) system. According to some embodiments, the plurality of time clusters may include a subset of the user-initiated analyte tests within a given number of minutes.

[0097] For each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point are determined in step 825. In some embodiments, the time cluster central tendency point may be the mean, median, or mode.

[0098] At step 830, a subset of meal onset candidates is removed from the initial set of meal onset candidates and meal peak response candidates. According to one aspect of an embodiment, the subset of meal onset candidates may include one or more meal onset candidates that are not within a predetermined time range of either the time cluster start or the time cluster end.

[0099] At step 835, the revised set of meal onset candidates and meal peak response candidates is output to the individual user. In some embodiments, the output may be in the form of a graphical user interface on a display of a reader device, such as a smart phone. In other embodiments, the output may be an auditory or vibratory signal output to a sensor control device, a reader device, a local computer, or a trusted computer system.

[0100] Although method 800 illustrates user-directed analyte test acquisition, grouping, and time cluster analysis in steps 820 and 825, one of ordinary skill in the art will understand that these steps may be performed prior to or simultaneously with any of the other steps of method 800.

[0101] 9A, 9B, and 9C are flow diagrams illustrating another exemplary embodiment of a method 900 for identifying a set of meal peak response candidates and meal start candidates. Like the previous method 800, the method 900 is also based on analyte data from a user-directed analyte testing and analyte monitoring system, but further includes additional steps of removing a subset of meal start candidates and meal peak response candidates from the set to further refine the set. Further details regarding these additional steps of removing a subset of meal start candidates and meal peak response candidates are described in the '748 publication, the disclosure of which is incorporated herein by reference for all purposes.

[0102] Referring initially to FIG. 9A , steps 905-925 of method 900 are the same as steps 805-825 of method 800 and include receiving a plurality of data points, determining a time derivative, creating an initial set of meal start candidates and meal peak response candidates by determining a local optimum of acceleration, obtaining and grouping a plurality of user-initiated analyte tests into time clusters, and determining a time cluster start point, end point, and central tendency point for each time cluster.

[0103] 9B, in step 930, a subset of meal onset candidates and meal peak response candidates is removed from the initial set that includes one or more meal onset candidates and / or meal peak response candidates that are adjacent to candidates of the same type. Because a meal onset event cannot be adjacent in time to another onset event, and similarly, a meal peak response event cannot be adjacent in time to another peak response event, adjacent candidates of the same type are identified and removed from the set under consideration.

[0104] According to some embodiments, meal peak response candidates are removed from the initial set based on, for example, the following criteria: (1) the next instance in the set is also a meal peak response candidate, (2) the next instance in the set has a larger analyte value than the current instance, and (3) the forward peak calculated velocity of the current instance is greater than a non-negative noise floor v_min_rise (e.g., 0.5 mg / dL / min). Calculated rates with absolute numbers close to zero tend to contain a lot of noise. Additionally, in certain embodiments, meal peak response candidates are also removed if the previous instance in the set is also a meal peak response candidate and the previous instance in the set has a larger analyte value than the current instance.

[0105] Similarly, in certain embodiments, a meal start candidate is removed because the previous instance of the adjacent meal start candidate has a smaller analyte value. That is, a meal start candidate is removed based on the following criteria: (1) the previous instance in the set is also a meal start candidate, (2) the previous instance in the set has a smaller analyte value than the current instance, and (3) the value a_start(m-1) is less than a_start(m), where m is the current instance. Additionally, in certain embodiments, a meal start candidate is also removed if the next instance in the set is also a meal start candidate and has an analyte value less than or equal to the analyte value of the current instance.

[0106] Referring again to FIG. 9B, step 935 of method 900 is the same as step 830 of method 800 and includes removing from the set a subset of meal start candidates that consists of one or more meal start candidates that are not within a predetermined time range of either the time cluster start point or the time cluster end point.

[0107] In step 940, another subset of meal onset candidates and meal peak response candidates is removed from the set, the subset including pairs of meal onset candidates and meal peak response candidates, each pair having an amplitude difference that does not exceed a predetermined level. More specifically, in certain embodiments, the meal peak response candidates in the set are analyzed to determine whether the change in analyte value from a previous instance that could be a meal onset candidate to the current meal peak response candidate is large enough. That is, the current meal peak response candidate is removed from the set if it meets the following criteria: (1) the previous instance m-1 in the set is a meal onset candidate, (2) the current instance m is a meal peak response candidate, and (3) the difference between the amplitude of m and the amplitude of m-1 is less than or equal to a predetermined minimum amplitude. Moreover, in certain embodiments, when removing a meal peak response candidate under these conditions, the corresponding meal onset candidate m-1 is also removed.

[0108] In step 945, another subset of meal onset candidates and meal peak response candidates is removed from the set, the subset including pairs of meal onset candidates and meal peak response candidates, each pair not exceeding the proximity threshold and the analyte level drop threshold. That is, in certain embodiments, meal onset candidates that are too close in time to a previous meal peak response candidate and whose value is not significantly smaller than that of the previous meal peak response candidate are removed from the set. More specifically, in certain embodiments, a meal onset candidate for instance m is removed if it meets the following criteria: (1) the previous instance m-1 is a meal peak response candidate, (2) the current instance m is a meal start candidate, (3) the next instance m+1 is a meal peak response candidate, (4) the average value of v_start_bck(m) and v_peak_fwd(m-1) is greater than the maximum post-meal recovery descent rate v_max_descent (e.g., 1 / 4 mg / dL / min), and (5) the difference between the values ​​of the current instance m and the previous instance m-1 is less than the minimum required drop from the previous peak g_min_drop (e.g., 5 to 10 mg / dL). Moreover, when these criteria are met and the meal start candidate is removed, the meal peak response candidate of the previous instance m-1 is also removed.

[0109] Turning to FIG. 9C, in step 950, another subset of meal onset candidates and meal peak response candidates is removed from the set, the subset including unpaired meal onset candidates or meal peak response candidates, or signal artifacts erroneously identified as either meal onset candidates or meal peak response candidates. According to one aspect of the embodiment, residual spike artifacts may occur, for example, when data adjustment does not completely remove all artifacts. In a particular embodiment, the residual spike artifacts erroneously identified as meal onset candidates and meal peak response candidates are removed from the set. More specifically, in a particular embodiment, the current meal onset candidate at instance m is removed from the set if: (1) the current instance m is a candidate meal start, (2) the next instance m+1 is a candidate meal peak response, and (3) the aggregated rate of change calculated by dividing g(m+1)-g(m) by the time interval between the two instances m+1 and m is greater than the maximum allowable postprandial initial rate of change v_max_initialSpike (e.g., 6 mg / dL / min, which is a rate of change that is assumed not to be sustainable between two candidate points).

[0110] In step 955, the resulting set of candidate meal onset and candidate meal peak responses can be further refined. In some cases, due to the size and asymmetry of the forward and backward time windows used to calculate the time derivatives, and because some post-meal responses may be followed by subsequent post-meal responses without sufficient time for the original post-meal response to return to baseline, the identification of candidate meal onset and candidate meal peak responses may be slightly biased towards either before or after the expected instance. To address these situations, further refinement of the set can be performed after removing the subsets described above.

[0111] According to one aspect of the embodiment, for each analyte data g(k) sampled at instance k, an available sample g_prev(k) is identified that is as close as possible to 30 minutes before k. Also, for each analyte data g(k) sampled at instance k, an available sample g_after(k) is identified that is as close as possible to 30 minutes after k. The forward and backward slopes v_fwd(k) and v_bck(k) are then determined by taking the difference g_after(k)-g(k) and dividing by the time interval (e.g., 30 minutes). Similarly, the backward slope v_bck(k) is determined by taking the difference g(k)-g_prev(k) and dividing by the time interval. The slope difference dv(k) is determined by taking the difference v_fwd(k)-v_bck(k). Those skilled in the art will recognize that analyte data samples of different durations other than 30 minutes (e.g., 15 minutes, 60 minutes, 90 minutes, etc.) are available and are fully within the scope of the present disclosure.

[0112] The set is then analyzed for candidate meal start and meal peak response pairs according to the following steps: For each start and peak pair, define an analyte time series, g_array_start, up to 90 minutes before the candidate start and up to 60 minutes after the candidate start. The defined analyte time series, g_array_start, includes the candidate meal start. Similarly, define a glucose time series, g_array_peak, up to 60 minutes before the candidate peak and up to 180 minutes after the candidate peak. The analyte time series, g_array_peak, includes the candidate peak. Next, "trim" the sampled analyte data, g_array_peak, whose timestamp overlaps with the start time of the next pair. For each value of g_array_start and g_array_peak, determine the corresponding slope value delta dv and define arrays, dv_array_start and dv_array_peak, of these values. Those skilled in the art will recognize that other durations of g_array_start and g_array_peak (e.g., 30 minutes before, 30 minutes after, 45 minutes before, 45 minutes after, etc.) are available and are fully within the scope of the present disclosure.

[0113] Thereafter, in certain embodiments, a subset of time instances is determined such that (1) the analyte value measured at these instances is greater than or equal to the 75th percentile of g_array_peak, and (2) the dv value of these instances is less than or equal to the 25th percentile of dv_array_peak. If such a subset contains data, the maximum analyte value g_max in this subset and its corresponding instance are stored. Similarly, another subset of time instances is determined such that (1) the analyte value measured at these instances is less than or equal to the 25th percentile of g_array_start, and (2) the dv value of these instances is greater than or equal to the 75th percentile of dv_array_start. If such a subset contains data, the minimum glucose value g_min in this subset and its corresponding instance are stored. According to an embodiment, the corresponding peak and start candidates of the pair are replaced with g_max and g_min, respectively, if the following criteria are met: (1) g_min and g_max exist and are finite, (2) g_min occurs before g_max, and (3) g_min is less than g_max. Those skilled in the art will also understand that the 75th and 25th percentiles utilized to determine g_max and g_min, respectively, are not meant to be limiting, and other percentiles (e.g., 80th percentile, 20th percentile) are fully within the scope of the present disclosure.

[0114] Further details regarding refining the set of meal start candidates and meal peak response candidates are provided in the '748 publication, the disclosure of which is incorporated herein by reference for all purposes.

[0115] Referring again to FIG. 9C, after refining the set of meal onset candidates and meal peak response candidates, in step 960, each meal onset candidate in the set may be replaced with the average value of the meal onset candidates and nearest time cluster start points. In step 965, the revised set of meal onset candidates and meal peak response candidates is output to the user. In some embodiments, the output may be in the form of a graphical user interface on a display of a reader device, such as a smartphone. In other embodiments, the output may be an auditory or vibration signal output to a sensor control device, a reader device, a local computer, or a trusted computer system.

[0116] 9A-9C show separate steps performed on the initial set of meal onset candidates and meal peak response candidates, one of skill in the art will appreciate that method 900 can exclude one or more steps. In some embodiments, for example, method 900 can exclude step 960, i.e., after performing a refinement step in step 955, outputting the set of meal onset candidates and meal peak response candidates to the user in step 965. In other embodiments, method 900 can exclude step 935, in which case, after removing the subset of adjacent candidates in step 930, the next step performed is removing the subset of candidates having amplitude differences that do not exceed a predetermined level. One of skill in the art will also appreciate that method 900 can include any of the steps described in any order or combination, and any combination or permutation of such steps is fully within the scope of the present disclosure. Similarly, although method 900 illustrates user-directed analyte test acquisition, grouping, and time cluster analysis in steps 920 and 925, one skilled in the art will understand that these steps may be performed prior to or simultaneously with any of the other steps of method 900.

[0117] Exemplary embodiments for providing user-directed analyte testing advice Some of the embodiments described herein can provide recommendations for future user-initiated analyte testing based on current recorded behaviors if the recorded behaviors correspond to user historical behaviors related to glycemic risk. As previously discussed, analyte monitoring systems can provide a more robust and convenient way of tracking an individual's physiological responses. For example, an analyte monitoring system can include a sensor control device that is worn on the individual's body and continuously collects analyte measurements and transmits the data (such as by using Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocols) in response to scanning by a reader device. However, one challenge with analyte monitoring systems is that an increased influx of data can lead to user disengagement and ultimately less frequent use by individual patients. The embodiments described herein can increase an individual's engagement by suggesting useful instances for performing user-initiated analyte testing (e.g., scanning). In this manner, the embodiments can help to mitigate certain glycemic risks, such as, for example, hypoglycemia or hyperglycemia.

[0118] Before describing the embodiments, those skilled in the art will appreciate that, as with many of the previous embodiments, any one or more of the steps of the exemplary method described herein can be stored as software instructions in a non-transitory memory of a sensor control device, a reader device, a remote computer, or a trusted computer system, such as described with respect to FIG. 1. The stored instructions, when executed, can cause the processing circuitry of the associated device or computing system to perform any one or more of the steps of the exemplary method described herein. Those skilled in the art will also appreciate that in many of the embodiments, any one or more of the method steps described herein can be performed using real-time or near real-time sensor data. In other embodiments, any one or more of the method steps can be performed retrospectively with respect to stored sensor data. In some embodiments, the method steps described herein can be performed periodically, according to a predetermined schedule, and / or in retrospective processing batches.

[0119] Those skilled in the art will also appreciate that the instructions may be stored in non-transitory memory on a single device (e.g., a reader device) or, alternatively, may be distributed across multiple separate devices that may be located in geographically distributed locations (e.g., a cloud platform). Similarly, those skilled in the art will recognize that the representation of a computing device in the embodiments disclosed herein, such as that shown in FIG. 1, is intended to cover both physical and virtual devices (or "virtual machines").

[0120] FIG. 10 is a flow diagram illustrating an exemplary embodiment of a method 1000 for providing user-initiated analyte testing advice. According to one aspect of the embodiment, the user-initiated analyte testing can be one or more of a fingerstick blood glucose test, a sensor scan instance from a reader device, an instance of using a sensor viewer on a smartphone, or an instance of activating a receiver display in a continuous glucose monitoring (CGM) system. The method begins with step 1005, receiving a recorded action by a user. According to some embodiments, the recorded action by the user can be an input of a carbohydrate amount, an application of a medication, or, for example, the use of a bolus calculator to correct glucose to a target glucose value. Step 1010 evaluates the history log to determine whether the current recorded action corresponds to a user historical action associated with a glycemic risk, such as, for example, a hypoglycemic risk or a hyperglycemic risk. According to some embodiments, for example, evaluating the history log can include comparing a time of the recorded action to a time of a user historical action associated with a glycemic risk. In other embodiments, evaluating the history log can include evaluating similar inputs at similar times from past records and evaluating the glycemic impact of past similar inputs.

[0121] Illustratively, the recorded behavior is, for example, the user utilizing a bolus calculator on the reader device to correct blood glucose levels to a target glucose value or range, and the memory of the reader device stores an insulin effect value. If the current insulin bolus target correction value applied by the patient corresponds to an insulin effect value that is significantly larger or smaller than the correction value previously used in the same meal period (e.g., lunch) on the same day, the risk of hypoglycemia or hyperglycemia is determined to be high.

[0122] As another example, in some embodiments, the trend uncertainty estimate can be used to determine whether a trend-based insulin correction recommendation is likely to result in hypoglycemia or hyperglycemia. If the uncertainty of the trend estimate exceeds a predetermined threshold, or if the risk calculation based on the trend uncertainty exceeds a predetermined threshold, a glycemic risk is determined and a reminder to perform a user-directed analyte test at an appropriate time in the future can be generated. The risk calculation may generally depend on one or more glucose readings and may not explicitly depend on the trend estimate.

[0123] As yet another example, the recorded behavior may be the user's entry of an abnormally high carbohydrate amount. In these situations, the patient may be adjusting the carbohydrate amount to account for extra macronutrients (e.g., protein and / or fat) or to account for a larger than normal meal. A higher glycemic risk may be determined because the postprandial glucose fluctuation may be different from normal. As another example, the recorded behavior may be the user's entry of bolus insulin information or meal information into a bolus calculator or meal / medication log application at a time that is significantly different from the previous log. For example, an unforeseen circumstance may cause the patient to have a late lunch or a small lunch early. In these situations, the timing of the meal or insulin may be a possible determination of a higher glycemic risk.

[0124] It should be noted that the above examples of evaluating a history log to determine whether recorded actions correspond to a user's historical behavior related to glycemic risk are merely illustrative and are not intended to limit the scope of the embodiments in any way, as would be apparent to one of ordinary skill in the art.

[0125] More specifically, in some embodiments, evaluating the history log can include retrieving an insulin effect value stored in memory, determining whether an analyte trend uncertainty estimate exceeds a predetermined analyte trend threshold, or determining whether a degree of glycemic risk exceeds a predetermined risk threshold.

[0126] If, at step 1015, it is determined that the recorded behavior does not correspond to a user behavior related to glycemic risk, the method 1000 returns to step 1005. However, if the recorded behavior corresponds to a user behavior related to glycemic risk, at step 1020, an expected elapsed time until the possible time for the action related to glycemic risk is reached is calculated. According to one aspect of the embodiment, the elapsed time can be a single instance in the near future (e.g., 65 minutes from now) or a series of instances (e.g., 65 minutes, 90 minutes, and 100 minutes from now). In some embodiments, after the elapsed time is calculated, the user can be prompted to check the output of a notification after the elapsed time.

[0127] 10, in step 1025, a notification to a user to perform a user-initiated analyte test is output after an elapsed time. According to some embodiments, outputting the notification to a user to perform a user-initiated analyte test can include outputting the notification multiple times at predetermined intervals. In other embodiments, the notification can be output to the user in a single instance. Additionally, in some embodiments, the output can be in the form of a graphical user interface on a display of a reader device, such as a smartphone, that reminds the user to scan the sensor control unit. In other embodiments, the output can be an auditory or vibration signal output to the sensor control device, the reader device, a local computer, or a trusted computer system.

[0128] FIG. 11 is a flow diagram illustrating another exemplary embodiment of a method 1100 for providing a user-initiated analyte test recommendation. In some respects, the method 1100 is similar to the method 1000. For example, the first portion of the method 1100 (e.g., steps 1105, 1110, 1115, and 1120) can be the same as the first portion of the method 1000 described above (e.g., steps 1005, 1010, 1015, and 1125). In addition, with respect to the method 1100 of FIG. 11, after calculating the elapsed time until the actionable time associated with the glycemic risk is reached, in step 1125, the method 1100 monitors for an indication of a user-initiated analyte test before the elapsed time. If no indication is received, the method 1100 proceeds to step 1140 and outputs a notification to the user to perform a user-initiated analyte test after the elapsed time. Similar to step 1025 of method 1000 (FIG. 10), in some embodiments the output may be in the form of a graphical user interface on the display of the reader device reminding the user to scan the sensor control unit, in other embodiments the output may be an auditory or vibratory signal output to the sensor control device, the reader device, a local computer, or a trusted computer system.

[0129] If an indication of a user-initiated analyte test (e.g., a sensor scan) is received before the elapsed time, data associated with the user-initiated analyte test is evaluated to determine if glycemic risk still exists at step 1130. According to one aspect of an embodiment, the data associated with the user-initiated analyte test may be data indicative of an analyte level being monitored, e.g., a blood glucose level.

[0130] If the glycemic risk is no longer present, the method 1100 returns to the beginning, i.e., step 1105. On the other hand, if the glycemic risk is still present as determined in step 1130, the elapsed time until the actionable time is reached is updated as necessary in step 1135. In some embodiments, for example, a second elapsed time until the second actionable time associated with the glycemic risk is reached can be calculated. After the elapsed time (or the second elapsed time) is reached, in step 1140, a notification is output to the user to perform a user-initiated analyte test (or a second user-initiated analyte test). As in the previous embodiment, outputting the notification to the user to perform a user-initiated analyte test can include outputting the notification multiple times at predetermined intervals or in a single instance. In some embodiments, the output can be in the form of a graphical user interface on a display of a reader device, such as a smartphone, reminding the user to scan the sensor control unit. In other embodiments, the output can be an auditory or vibration signal output to the sensor control device, the reader device, a local computer, or a trusted computer system.

[0131] Although the embodiments are described in terms of monitored glucose levels and glycemic risks, one of skill in the art will recognize that these embodiments can be utilized for and may monitor other analytes, such as acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, HbAlc, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose derivatives, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxide, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, and drugs, such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, and are fully within the scope of the present disclosure.

[0132] For each embodiment of the methods disclosed herein, the scope of the disclosure includes systems and devices capable of performing each of these embodiments. For example, sensor control device embodiments are disclosed, which may have one or more analyte sensors, analyte monitoring circuits (e.g., analog circuits), memory (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, timers, temperature sensors, and processors (e.g., for executing instructions) that may perform or facilitate the performance of any and all method steps. These sensor control device embodiments may be used, and may be enabled, to implement the steps performed by the sensor control devices of any and all methods described herein. Similarly, reader device embodiments are disclosed, which may have one or more memory (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, timers, and processors (e.g., for executing instructions) that may perform or facilitate the performance of any and all method steps. These reader device embodiments can be used and enabled to implement the steps performed by the reader device of any and all methods described herein. Computing device and server embodiments are disclosed, which can have one or more memories (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, timers, and processors (e.g., for executing instructions) that can perform or facilitate the performance of any and all method steps. These reader device embodiments can be used and enabled to implement the steps performed by the reader device of any and all methods described herein.

[0133] Computer program instructions for performing operations according to the described subject matter may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, JavaScript, Smalltalk, C++, C#, Transact-SQL, XML, PHP, and traditional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program instructions may execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the latter case, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).

[0134] It should be noted that all features, elements, components, functions, and steps described with respect to any embodiment presented herein are intended to be freely combined or substituted with those of any other embodiment. Even if a feature, element, component, function, or step is described with respect to only one embodiment, it should be understood that the feature, element, component, function, or step can be used with all other embodiments described herein unless expressly stated otherwise. Thus, this paragraph serves as a predicative basis and written support for introducing claims that combine features, elements, components, functions, and steps from different embodiments or substitute features, elements, components, functions, and steps from one embodiment with those from another embodiment, even if the preceding description has not expressly stated in a specific case that such combinations or substitutions are possible. It is expressly recognized that explicitly listing all possible combinations and substitutions would be an undue burden, especially given that the permissibility of each and every such combination and substitution would be readily recognized by one of ordinary skill in the art.

[0135] To the extent that the embodiments disclosed herein include or operate in conjunction with memory, storage, and / or computer-readable medium, the memory, storage, and / or computer-readable medium are non-transitory. Thus, to the extent that the memory, storage, and / or computer-readable medium is included in one or more claims, the memory, storage, and / or computer-readable medium is only non-transitory.

[0136] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0137] The present embodiment is susceptible to various modifications and alternative forms, and examples thereof have been shown in the drawings and described in detail herein. However, it is to be understood that these embodiments are not limited to the particular forms disclosed, but rather, these embodiments include all modifications, equivalents, and alternatives falling within the spirit of the present disclosure. Furthermore, any feature, function, step, or element of the present embodiment may be recited or added to the claims, and negative limitations may be made to define the inventive scope of the claims by features, functions, steps, or elements not within the scope.

[0138] Preferred embodiments of the present invention will be described below in detail.

[0139] EMBODIMENT 1 1. A computer-implemented method for determining a drug dose to be administered with intake of a meal, the method comprising: receiving, by an electronic device, a user-entered entry relating to the meal; determining, by a processing circuit, a plurality of nutritional parameters associated with the meal based on the user-entered entries by referencing a first database; identifying, by the processing circuitry, in a second database, a meal that most closely matches the meal based on the plurality of nutritional parameters associated with the meal; determining, by the processing circuitry, the drug dose associated with the most closely matching meal in the second database; outputting the drug dose to the electronic device; A method comprising:

[0140] EMBODIMENT 2 10. The method of claim 1, further comprising storing the user-entered entries and the determined drug dosage in the second database.

[0141] EMBODIMENT 3 2. The method of claim 1, wherein the plurality of nutritional parameters comprises a carbohydrate parameter, a fat parameter, and a protein parameter.

[0142] EMBODIMENT 4 10. The method of claim 1, wherein the second database comprises a plurality of meal history records, each meal history record comprising a plurality of stored nutritional parameters and a stored medication dosage.

[0143] EMBODIMENT 5 5. The method of claim 4, wherein identifying the most closely matching meal comprises determining a minimum sum of differences between the plurality of nutritional parameters stored for each meal history record and the plurality of nutritional parameters associated with the meal.

[0144] EMBODIMENT 6 6. The method of claim 5, wherein determining the minimum sum of differences comprises calculating, for each meal history record, an absolute percentage difference between each of the stored nutritional parameters and each of the nutritional parameters associated with the meal.

[0145] EMBODIMENT 7 7. The method of claim 6, wherein determining the minimum sum of differences further comprises applying one or more weighting factors to the absolute percentage differences.

[0146] EMBODIMENT 8 8. The method of claim 7, wherein the plurality of nutritional parameters comprises a carbohydrate parameter, a fat parameter, and a protein parameter, and the weighting factor of the carbohydrate parameter is greater than or equal to each of the weighting factor of the fat parameter and the weighting factor of the protein parameter.

[0147] EMBODIMENT 9 The method of claim 1 , further comprising the step of entering the user-entered entries related to the meal via a web interface.

[0148] EMBODIMENT 10 The method of claim 1 , further comprising entering the entered entry related to the meal through a mobile app interface.

[0149] EMBODIMENT 11 The method of claim 10 , wherein the electronic device is a smartphone.

[0150] EMBODIMENT 12 The method of claim 1 , further comprising the step of downloading at least a portion of the first database to the electronic device.

[0151] EMBODIMENT 13 The method of claim 1 , wherein the second database is stored on the electronic device.

[0152] EMBODIMENT 14 10. The method of claim 1, wherein the second database is stored on a location-independent server.

[0153] EMBODIMENT 15 2. The method of claim 1, further comprising creating, by the processing circuitry, a link between the most closely matching meal and the user-entered entry in the second database.

[0154] EMBODIMENT 16 2. The method of claim 1, wherein the pharmaceutical dose comprises an insulin dose.

[0155] EMBODIMENT 17 1. An electronic system configured to determine a drug dose to be administered in conjunction with intake of a meal, the system comprising: A processing circuit; a non-transient memory containing a plurality of instructions; the instructions, when executed, cause the processing circuitry to: receiving a user-entered entry relating to the meal; determining a plurality of nutritional parameters associated with the meal based on the user-entered entries by referencing a first database; identifying in a second database a most closely matching meal based on said plurality of nutritional parameters associated with said meal; determining the most consistent meal-related drug dose in the second database; outputting said drug dose; Electronic system.

[0156] EMBODIMENT 18 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuit to store the user-entered entries and the determined drug dosage in the second database.

[0157] EMBODIMENT 19 20. The electronic system of claim 17, wherein the plurality of nutritional parameters comprises a carbohydrate parameter, a fat parameter, and a protein parameter.

[0158] EMBODIMENT 20 20. The electronic system of claim 17, wherein the second database includes a plurality of meal history records, each meal history record including a plurality of stored nutritional parameters and a stored medication dosage.

[0159] EMBODIMENT 21 21. The electronic system of claim 20, wherein the instructions, when executed, further cause the processing circuitry to determine a minimum sum of differences between the nutritional parameters stored for each meal history record and the nutritional parameters associated with the meal.

[0160] EMBODIMENT 22 22. The electronic system of claim 21, wherein the instructions, when executed, further cause the processing circuitry to calculate, for each historical record, an absolute percentage difference between each of the stored nutritional parameters and each of the nutritional parameters associated with the meal.

[0161] EMBODIMENT 23 23. The electronic system of claim 22, wherein the instructions, when executed, further cause the processing circuitry to apply one or more weighting factors to the absolute percentage difference.

[0162] EMBODIMENT 24 24. The electronic system of claim 23, wherein the plurality of nutritional parameters comprises a carbohydrate parameter, a fat parameter, and a protein parameter, and a weighting factor of the carbohydrate parameter is greater than or equal to each of the weighting factor of the fat parameter and the weighting factor of the protein parameter.

[0163] EMBODIMENT 25 20. The electronic system of claim 17, wherein the electronic system further comprises a web interface configured to receive the user-entered entries related to the meal.

[0164] EMBODIMENT 26 20. The electronic system of claim 17, further comprising a mobile app interface configured to receive the user-entered entries related to the meal.

[0165] EMBODIMENT 27 27. The electronic system of claim 26, further comprising a smartphone.

[0166] EMBODIMENT 28 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuitry to download at least a portion of the first database to a non-transitory memory.

[0167] EMBODIMENT 29 20. The electronic system of claim 17, wherein the second database is stored in the non-transitory memory.

[0168] EMBODIMENT 30 20. The electronic system of claim 17, wherein the second database is stored on a location-independent server.

[0169] EMBODIMENT 31 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuitry to create a link in the second database between the closest matching meal and the user-entered entry.

[0170] EMBODIMENT 32 20. The electronic system of claim 17, wherein the medication dose comprises an insulin dose.

[0171] EMBODIMENT 33 1. A computer-implemented method for identifying a set of meal onset candidates and meal peak response candidates, the method comprising: determining a time derivative of a plurality of data points corresponding to a monitored analyte level; generating a set of candidate meal onset and candidate meal peak responses by determining optimal values ​​of acceleration for said plurality of data points based on said time derivatives; obtaining a plurality of user initiated analyte tests and grouping the user initiated analyte tests into a plurality of time clusters; determining, for each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point; removing a first subset of meal start candidates from said set, said first subset including one or more meal start candidates that are not within a predetermined time range of either said time cluster start or said time cluster end; A method comprising:

[0172] EMBODIMENT 34 34. The method of claim 33, further comprising removing a second subset of meal onset candidates and meal peak response candidates from the set, the second subset including one or more meal onset candidates or meal peak response candidates that are adjacent to candidates of the same type.

[0173] EMBODIMENT 35 35. The method of claim 34, wherein the second subset is removed from the set before the first subset is removed from the set.

[0174] EMBODIMENT 36 35. The method of claim 34, further comprising removing a third subset of meal onset candidates and meal peak response candidates from the set, the third subset comprising a first group of pairs of meal onset candidates and meal peak response candidates, each pair in the first group having an amplitude difference that does not exceed a predetermined level.

[0175] EMBODIMENT 37 37. The method of claim 36, further comprising removing a fourth subset of meal onset candidates and meal peak response candidates from the set, the fourth subset comprising a second group of pairs of meal onset candidates and meal peak response candidates, each pair in the second group not exceeding a proximity threshold and an analyte level drop threshold.

[0176] EMBODIMENT 38 38. The method of claim 37, further comprising removing a fifth subset of meal onset candidates and meal peak response candidates from the set, the fifth subset including one or more unpaired meal onset candidates or one or more signal artifacts that have been erroneously identified as meal onset candidates or meal peak response candidates.

[0177] EMBODIMENT 39 39. The method of claim 38, further comprising refining the set based on new multiple time derivatives of the multiple data points corresponding to the monitored analyte levels after one or more subsets of meal onset candidates and meal peak response candidates are removed from the set.

[0178] EMBODIMENT 40 40. The method of claim 39, wherein said method further comprises replacing each meal start candidate in said set with an average of said meal start candidate and a nearest time cluster start point.

[0179] EMBODIMENT 41 41. The method of claim 40, wherein the step of replacing each meal starting candidate in the set occurs after the step of refining the set.

[0180] EMBODIMENT 42 34. The method of claim 33, wherein the method further comprises adjusting the plurality of data points corresponding to the monitored analyte level.

[0181] EMBODIMENT 43 43. The method of claim 42, wherein adjusting the plurality of data points comprises performing a sample data analysis on the plurality of data points to remove questionable data and smooth the plurality of data points.

[0182] EMBODIMENT 44 34. The method of claim 33, wherein the user-directed analyte test comprises a fingerstick blood glucose test.

[0183] EMBODIMENT 45 34. The method of claim 33, wherein said method further comprises outputting said set of candidate meal onset and candidate meal peak response.

[0184] EMBODIMENT 46 34. The method of claim 33, wherein grouping the user-initiated analyte tests into a plurality of time clusters comprises identifying a subset of user-initiated analyte tests within a predetermined number of minutes.

[0185] EMBODIMENT 47 34. The method of claim 33, wherein the user-initiated analyte test comprises a sensor scan instance from an analyte reader device.

[0186] EMBODIMENT 48 34. The method of claim 33, wherein the user-initiated analyte testing comprises one or more of an instance of using a sensor viewer on a smartphone or an instance of activating a receiver display in a continuous glucose monitoring (CGM) system.

[0187] EMBODIMENT 49 1. An electronic system configured to identify a set of meal onset candidates and meal peak response candidates, the system comprising: A processing circuit; a non-transient memory containing a plurality of instructions; the instructions, when executed, cause the processing circuitry to: determining a time derivative of a plurality of data points corresponding to a monitored analyte level; determining an optimal value for acceleration of said plurality of data points based on said time derivatives to generate a set of candidate meal onset and candidate meal peak responses; acquiring a plurality of user initiated analyte tests and grouping the user initiated analyte tests into a plurality of time clusters; determining, for each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point; removing a first subset of meal start candidates from the set, the first subset including one or more meal start candidates that are not within a predetermined time range of either a time cluster start or a time cluster end; Electronic system.

[0188] EMBODIMENT 50 50. The electronic system of claim 49, wherein the instructions, when executed, further cause the processing circuit to remove a second subset of meal onset candidates and meal peak response candidates from the set, the second subset including one or more meal onset candidates or meal peak response candidates that are adjacent to candidates of the same type.

[0189] EMBODIMENT 51 51. The electronic system of claim 50, wherein the instructions, when executed, further cause the processing circuitry to remove the second subset from the set before removing the first subset from the set.

[0190] EMBODIMENT 52 51. The electronic system of claim 50, wherein the instructions, when executed, further cause the processing circuit to remove a third subset of meal onset candidates and meal peak response candidates from the set, the third subset including a first group of pairs of meal onset candidates and meal peak response candidates, each pair in the first group having an amplitude difference that does not exceed a predetermined level.

[0191] EMBODIMENT 53 53. The electronic system of claim 52, wherein the instructions, when executed, further cause the processing circuit to remove a fourth subset of meal onset candidates and meal peak response candidates from the set, the fourth subset including a second group of pairs of meal onset candidates and meal peak response candidates, each pair in the second group not exceeding a proximity threshold and a falling analyte level threshold.

[0192] EMBODIMENT 54 54. The electronic system of claim 53, wherein the instructions, when executed, further cause the processing circuit to remove a fifth subset of meal onset candidates and meal peak response candidates from the set, the fifth subset including one or more unpaired meal onset candidates or one or more signal artifacts that were erroneously identified as meal onset candidates or meal peak response candidates.

[0193] EMBODIMENT 55 55. The electronic system of claim 54, wherein the instructions, when executed, further cause the processing circuit to refine the set based on new time derivatives of the data points corresponding to the monitored analyte levels after one or more subsets of meal onset candidates and meal peak response candidates are removed from the set.

[0194] EMBODIMENT 56 56. The electronic system of claim 55, wherein the instructions, when executed, further cause the processing circuitry to replace each meal start candidate in the set with an average of the meal start candidate and a nearest time cluster start point.

[0195] EMBODIMENT 57 57. The electronic system of claim 56, wherein the instructions, when executed, further cause the processing circuitry to replace each meal-starting candidate in the set after refining the set.

[0196] EMBODIMENT 58 50. The electronic system of claim 49, wherein the instructions, when executed, further cause the processing circuit to adjust the plurality of data points corresponding to the monitored analyte level.

[0197] EMBODIMENT 59 60. The electronic system of claim 58, wherein the instructions, when executed, further cause the processing circuit to perform sample data analysis on the plurality of data points to remove suspicious data and smooth the plurality of data points.

[0198] EMBODIMENT 60 50. The electronic system of claim 49, wherein the user-initiated analyte test comprises a fingerstick blood glucose test.

[0199] EMBODIMENT 61 50. The electronic system of claim 49, wherein the instructions, when executed, further cause the processing circuitry to output the set of meal onset candidates and meal peak response candidates.

[0200] EMBODIMENT 62 50. The electronic system of claim 49, wherein the instructions, when executed, further cause the processing circuitry to identify a subset of user-initiated analyte tests within a predetermined number of minutes.

[0201] EMBODIMENT 63 50. The electronic system of claim 49, wherein the user-initiated analyte test comprises a sensor scan instance from an analyte reader device.

[0202] EMBODIMENT 64 50. The electronic system of claim 49, wherein the user-initiated analyte testing comprises one or more of an instance of using a sensor viewer on a smartphone or an instance of activating a receiver display of a continuous glucose monitoring (CGM) system. EMBODIMENT 65 1. A computer-implemented method for providing user-directed analyte testing advice, the method comprising: receiving recorded actions by a user; evaluating the history log to determine whether the recorded behavior corresponds to user historical behavior related to glycemic risk; responsive to determining that the recorded behavior corresponds to the user's historical behavior associated with the glycemic risk, calculating an elapsed time until a possible action time associated with the glycemic risk is reached; outputting a notification to the user to perform the user initiated analyte test after the elapsed time; A method comprising:

[0203] EMBODIMENT 66 66. The method of claim 65, further comprising prompting the user to confirm output of the notification after the elapsed time in response to a determination that the recorded behavior corresponds to the user historical behavior associated with the glycemic risk.

[0204] EMBODIMENT 67 66. The method of claim 65, wherein the glycemic risk is a hypoglycemic condition or a hyperglycemic condition.

[0205] EMBODIMENT 68 The method further comprising: receiving an indication that the user performed the user-initiated analyte test before the elapsed time; evaluating data associated with the user-initiated analyte test to determine if the glycemic risk still exists; 66. The method of claim 65, further comprising:

[0206] EMBODIMENT 69 70. The method of claim 68, further comprising updating the elapsed time as necessary in response to a determination that the glycemic risk is still present.

[0207] EMBODIMENT 70 66. The method of claim 65, wherein outputting the notification to the user to perform the user-initiated analyte test further comprises outputting the notification a plurality of times at predetermined intervals.

[0208] EMBODIMENT 71 66. The method of claim 65, wherein the user-initiated analyte testing comprises scanning the sensor control unit with a reader device.

[0209] EMBODIMENT 72 66. The method of claim 65, wherein the user-directed analyte test comprises a fingerstick blood glucose test.

[0210] EMBODIMENT 73 66. The method of claim 65, wherein the recorded actions by the user include calculating a bolus, applying a medication, or entering a carbohydrate intake.

[0211] EMBODIMENT 74 66. The method of claim 65, wherein evaluating the history log to determine whether the recorded behavior corresponds to a user historical behavior associated with glycemic risk further comprises comparing a time of the recorded behavior with a time of the user historical behavior associated with the glycemic risk.

[0212] EMBODIMENT 75 66. The method of claim 65, wherein evaluating the history log to determine if the recorded behavior corresponds to user historical behavior associated with glycemic risk further comprises obtaining an insulin effect value stored in memory.

[0213] EMBODIMENT 76 66. The method of claim 65, wherein evaluating the history log to determine whether the recorded behavior corresponds to user historical behavior associated with glycemic risk further comprises determining whether an analyte trend uncertainty estimate exceeds a predetermined analyte trend threshold.

[0214] EMBODIMENT 77 66. The method of claim 65, wherein evaluating the history log to determine whether the recorded behavior corresponds to user historical behavior associated with glycemic risk further comprises determining whether a degree of glycemic risk exceeds a predetermined risk threshold.

[0215] EMBODIMENT 78 The method further comprising: receiving an indication that the user performed the user-initiated analyte test after the elapsed time; evaluating data associated with the user-initiated analyte test to determine if the glycemic risk still exists; responsive to determining that the glycemic risk still exists, calculating a second elapsed time until a second time window associated with the glycemic risk is reached and outputting a notification to the user to perform a second user-initiated analyte test after the second elapsed time; 66. The method of claim 65, further comprising:

[0216] EMBODIMENT 79 80. The method of claim 78, wherein the data associated with the user-directed analyte test includes data indicative of the monitored analyte level.

[0217] Embodiment 80 80. The method of claim 79, wherein the analyte level monitored is a blood glucose level.

[0218] Embodiment 81 1. An electronic system configured to provide user-directed analyte testing advice, the system comprising: A processing circuit; a non-transient memory containing a plurality of instructions; the instructions, when executed, cause the processing circuitry to: receiving recorded actions by a user; evaluating the history log to determine whether the recorded behavior corresponds to a user historical behavior related to glycemic risk; responsive to a determination that the recorded behavior corresponds to the user's historical behavior related to the blood sugar risk, calculating an elapsed time until a behavioral time related to the blood sugar risk is reached; outputting a notification to the user to perform the user-initiated analyte test after the elapsed time. Electronic system.

[0219] EMBODIMENT 82 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuitry to prompt a user to confirm output of the notification after the elapsed time in response to a determination that the recorded behavior corresponds to the user historical behavior associated with the glycemic risk.

[0220] EMBODIMENT 83 82. The electronic system of claim 81, wherein the glycemic risk is a hypoglycemic condition or a hyperglycemic condition.

[0221] EMBODIMENT 84 The instructions, when executed, further cause the processing circuitry to: receiving an indication that the user performed the user-initiated analyte test before the elapsed time; 82. The electronic system of claim 81, further comprising: evaluating data associated with the user-initiated analyte test to determine if the glycemic risk still exists.

[0222] EMBODIMENT 85 The instructions, when executed, further cause the processing circuitry to: 85. The electronic system of claim 84, wherein, in response to determining that the glycemic risk is still present, the electronic system causes the elapsed time to be updated as necessary.

[0223] EMBODIMENT 86 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuitry to output the notification multiple times at predetermined intervals.

[0224] EMBODIMENT 87 82. The electronic system of claim 81, wherein the user-initiated analyte testing includes scanning the sensor control unit with a reader device.

[0225] EMBODIMENT 88 82. The electronic system of claim 81, wherein the user-directed analyte test comprises a fingerstick blood glucose test.

[0226] EMBODIMENT 89 82. The electronic system of claim 81, wherein the recorded actions by the user include calculating a bolus, applying a medication, or entering a carbohydrate intake.

[0227] EMBODIMENT 90 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuitry to compare a time of the recorded behavior with a time of the user historical behavior associated with the glycemic risk.

[0228] EMBODIMENT 91 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuit to obtain an insulin effect value stored in memory.

[0229] EMBODIMENT 92 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuitry to determine whether an analyte trend uncertainty estimate exceeds a predetermined analyte trend threshold.

[0230] EMBODIMENT 93 82. The electronic system of claim 81, wherein the instructions, when executed, further cause the processing circuitry to determine whether a degree of glycemic risk exceeds a predetermined risk threshold.

[0231] EMBODIMENT 94 The instructions, when executed, further cause the processing circuitry to: receiving an indication that the user performed the user-initiated analyte test after the elapsed time; evaluating data associated with the user-initiated analyte test to determine whether the glycemic risk still exists; responsive to determining that the glycemic risk still exists, calculate a second elapsed time until a second actionable time associated with the glycemic risk is reached, and output a notification to the user to perform a second user-initiated analyte test after the second elapsed time. 82. The electronic system of claim 81.

[0232] EMBODIMENT 95 95. The electronic system of claim 94, wherein the data related to the user-initiated analyte test includes data indicative of a monitored analyte level.

[0233] EMBODIMENT 96 96. The electronic system of claim 95, wherein the monitored analyte level is a blood glucose level.

Claims

1. 1. A computer-implemented method for identifying a set of meal onset candidates and meal peak response candidates, the method comprising: determining a time derivative of a plurality of data points corresponding to a monitored analyte level; generating a set of candidate meal onset and candidate meal peak responses by determining optimal values ​​of acceleration for said plurality of data points based on said time derivatives; obtaining a plurality of user initiated analyte tests and grouping the user initiated analyte tests into a plurality of time clusters; determining, for each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point; removing a first subset of meal start candidates from said set, said first subset including one or more meal start candidates that are not within a predetermined time range of either said time cluster start or said time cluster end; A method comprising:

2. 2. The method of claim 1, further comprising removing a second subset of meal onset candidates and meal peak response candidates from the set, the second subset including one or more meal onset candidates or meal peak response candidates that are adjacent to candidates of the same type.

3. The method of claim 2 , wherein the second subset is removed from the set before the first subset is removed from the set.

4. 3. The method of claim 2, further comprising removing a third subset of meal onset candidates and meal peak response candidates from the set, the third subset comprising a first group of pairs of meal onset candidates and meal peak response candidates, each pair in the first group having an amplitude difference that does not exceed a predetermined level.

5. 5. The method of claim 4, further comprising removing a fourth subset of meal onset candidates and meal peak response candidates from the set, the fourth subset comprising a second group of pairs of meal onset candidates and meal peak response candidates, each pair in the second group not exceeding a proximity threshold and an analyte level drop threshold.

6. 6. The method of claim 5, further comprising removing a fifth subset of meal onset candidates and meal peak response candidates from the set, the fifth subset including one or more unpaired meal onset candidates or one or more signal artifacts that have been erroneously identified as meal onset candidates or meal peak response candidates.

7. 7. The method of claim 6, further comprising refining the set based on new multiple time derivatives of the multiple data points corresponding to the monitored analyte levels after one or more subsets of meal onset candidates and meal peak response candidates are removed from the set.

8. The method of claim 7 , further comprising replacing each meal start candidate in the set with an average of the meal start candidate and a nearest time cluster start point.

9. The method of claim 8 , wherein the step of replacing each meal-starting candidate in the set occurs after the step of refining the set.

10. The method of claim 1 , further comprising adjusting the plurality of data points corresponding to the monitored analyte level.

11. The method of claim 10 , wherein adjusting the plurality of data points comprises performing a sample data analysis on the plurality of data points to remove questionable data and smooth the plurality of data points.

12. The method of claim 1 , wherein the user-directed analyte test comprises a fingerstick blood glucose test.

13. The method of claim 1 , further comprising outputting the set of meal onset candidates and meal peak response candidates.

14. 2. The method of claim 1, wherein grouping the user-initiated analyte tests into a plurality of time clusters comprises identifying a subset of user-initiated analyte tests within a predetermined number of minutes.

15. The method of claim 1 , wherein the user-initiated analyte test comprises a sensor scan instance from an analyte reader device.

16. 10. The method of claim 1, wherein the user-initiated analyte testing comprises one or more of an instance of using a sensor viewer on a smartphone or an instance of activating a receiver display in a continuous glucose monitoring (CGM) system.

17. 1. An electronic system configured to identify a set of meal onset candidates and meal peak response candidates, the system comprising: A processing circuit; a non-transient memory containing a plurality of instructions; the instructions, when executed, cause the processing circuitry to: determining a time derivative of a plurality of data points corresponding to a monitored analyte level; determining an optimal value for acceleration of said plurality of data points based on said time derivatives to generate a set of candidate meal onset and candidate meal peak responses; acquiring a plurality of user initiated analyte tests and grouping the user initiated analyte tests into a plurality of time clusters; determining, for each time cluster, a time cluster start point, a time cluster end point, and a time cluster central tendency point; removing a first subset of meal start candidates from the set, the first subset including one or more meal start candidates that are not within a predetermined time range of either a time cluster start or a time cluster end; Electronic system.

18. 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuitry to remove a second subset of meal onset candidates and meal peak response candidates from the set, the second subset including one or more meal onset candidates or meal peak response candidates that are adjacent to candidates of the same type.

19. 20. The electronic system of claim 18, wherein the instructions, when executed, further cause the processing circuitry to remove the second subset from the set before removing the first subset from the set.

20. 20. The electronic system of claim 18, wherein the instructions, when executed, further cause the processing circuit to remove a third subset of meal onset candidates and meal peak response candidates from the set, the third subset including a first group of pairs of meal onset candidates and meal peak response candidates, each pair in the first group having an amplitude difference not exceeding a predetermined level.

21. 21. The electronic system of claim 20, wherein the instructions, when executed, further cause the processing circuit to remove a fourth subset of meal onset candidates and meal peak response candidates from the set, the fourth subset including a second group of pairs of meal onset candidates and meal peak response candidates, each pair in the second group not exceeding a proximity threshold and a falling analyte level threshold.

22. 22. The electronic system of claim 21, wherein the instructions, when executed, further cause the processing circuit to remove a fifth subset of meal onset candidates and meal peak response candidates from the set, the fifth subset including one or more unpaired meal onset candidates or one or more signal artifacts that were erroneously identified as meal onset candidates or meal peak response candidates.

23. 23. The electronic system of claim 22, wherein the instructions, when executed, further cause the processing circuit to refine the set based on new time derivatives of the data points corresponding to the monitored analyte levels after one or more subsets of meal onset candidates and meal peak response candidates are removed from the set.

24. 24. The electronic system of claim 23, wherein the instructions, when executed, further cause the processing circuitry to replace each meal start candidate in the set with an average of the meal start candidate and a nearest time cluster start point.

25. 25. The electronic system of claim 24, wherein the instructions, when executed, further cause the processing circuitry to replace each meal-starting candidate in the set after refining the set.

26. 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuit to adjust the plurality of data points corresponding to the monitored analyte level.

27. 27. The electronic system of claim 26, wherein the instructions, when executed, further cause the processing circuitry to perform sample data analysis on the plurality of data points to remove suspicious data and smooth the plurality of data points.

28. The electronic system of claim 17 , wherein the user-initiated analyte test comprises a fingerstick blood glucose test.

29. 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuitry to output the set of meal onset candidates and meal peak response candidates.

30. 20. The electronic system of claim 17, wherein the instructions, when executed, further cause the processing circuitry to identify a subset of user-initiated analyte tests within a predetermined number of minutes.

31. The electronic system of claim 17 , wherein the user-initiated analyte test comprises a sensor scan instance from an analyte reader device.

32. 20. The electronic system of claim 17, wherein the user-initiated analyte testing comprises one or more of an instance of using a sensor viewer on a smartphone or an instance of activating a receiver display of a continuous glucose monitoring (CGM) system.

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