Systems, devices and methods for health and nutrition monitoring and management using analyte data - Patents.com

Abstract

Systems, devices, and methods are provided for monitoring and managing personal health and nutrition using analyte data from in-vivo analyte sensors. Generally, a sensor control device for wear on the body is provided. The sensor control device may include: an in-vivo analyte sensor for measuring analyte levels in bodily fluids; an accelerometer for measuring the subject's physical activity level; and communications circuitry for wirelessly transmitting data to a reader device. Additionally, various graphical user interface embodiments are disclosed herein for displaying analyte metrics on the reader device, comparing analyte responses of various foods and / or meals, and revising daily nutrient recommendations based on the analyte metrics and physical activity level measurements, as well as other features described herein. Furthermore, the embodiments disclosed herein can be used to monitor various types of analytes.

Description

[Technical field]

[0001] The subject matter described herein generally relates to systems, devices, and methods for monitoring and managing an individual's health and nutrition based at least in part on analyte data from an in-vivo analyte sensor. [Background technology]

[0002] Monitoring and managing personal health and nutrition can provide significant benefits to people at risk for or currently suffering from chronic health problems, and to those seeking to improve their overall health. These efforts can generate multiple health and economic benefits for individuals and the public at large. For example, according to the CDC, seven in ten people die from chronic disease each year in the United States, and about one in two adults suffer from at least one chronic disease. Similarly, about one in three children in the United States are overweight or obese, making them more susceptible to chronic diseases. Many of these chronic diseases are preventable or can be successfully treated if diagnosed early. In this regard, monitoring and managing personal health and nutrition can significantly reduce the probability of chronic disease, and therefore future medical costs. Additional benefits of health and nutrition monitoring include improved athletic performance during training, recovery, or athletic events.

[0003] To further these goals, wearable technology (e.g., Fitbit) can be utilized. For example, a small electronic device may be worn on the body, such as around the wrist, to monitor an individual's heart rate or physical activity level. As doctor's visits become more infrequent (e.g., once a year), wearable technology can serve the useful function of providing timely physiological information to individuals without the need for a doctor's visit, which may ultimately lead to improved health. However, despite these advantages, many people are reluctant to use wearable technology for a variety of reasons, including the complexity of the data presented, the learning curve associated with using a wearable device, inaccuracies regarding the data, etc. Several recent studies have argued that existing wearable devices do not accurately measure an individual's heart rate or the number of calories burned. Summary of the Invention [Problem to be solved by the invention]

[0004] Thus, a need exists for more accurate, easily-used by individuals, systems, devices, and methods for health and nutrition monitoring and management. [Means for solving the problem]

[0005] Provided herein are exemplary embodiments of systems, devices, and methods for monitoring and managing an individual's health and nutrition based at least in part on analyte data received from an in vivo analyte sensor. In general, a small form factor sensor control device can be provided to an individual for wearing on the body. The sensor control device can include, in a single housing: an in vivo analyte sensor for measuring the subject's analyte level (or analyte levels); and an accelerometer for measuring the subject's activity level. The in vivo analyte sensor can be configured such that at least a portion of the sensor contacts the subject's bodily fluid. The sensor control device can also include communication circuitry for wirelessly transmitting data to a reader device. The sensor control device can be designed as a consumer grade product. In these embodiments, for privacy, security, and regulatory reasons, raw analyte level measurements are not stored in the memory of the sensor control device, and these data are encrypted or otherwise made accessible to the individual.

[0006] The reader device that receives data from the sensor control device can be a smartphone, tablet computer, personal digital assistant, or dedicated or non-dedicated mobile computing platform. One or more applications can be installed on the reader device that analyzes the data transmitted from the sensor control device and displays health and nutrition information to the individual. In some embodiments, for example, a simple carbohydrate graph is displayed without showing the values ​​of the underlying analyte data. Instead, bars of different colors can indicate various analyte levels. In other embodiments, a numerical score is displayed that represents the analyte response to the ingested food or meal. In still other embodiments, daily nutrient recommendations based on physical activity level data can be displayed to the individual. These and other embodiments described herein are improvements in the field of computer-based health and nutrition monitoring over conventional or existing wearable devices. Other improvements and advantages are provided and will be apparent to those skilled in the art. Various configurations of these devices are described by way of several embodiments that are merely examples.

[0007] Other systems, devices, methods, features, and advantages of the subject matter described herein will be or become apparent to one with skill in the art upon examination of the following drawings and Detailed Description. Such additional systems, devices, 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 example embodiments are not to be construed as limiting the appended claims unless those features are expressly recited in the claims.

[0008] Details regarding the structure and operation of the subject matter described herein may become apparent by examination of the accompanying drawings, in which like reference numerals refer to like parts. The components in the drawings are not necessarily to scale, with emphasis instead being placed on illustrating the principles of the subject matter. Moreover, all figures are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be depicted generally, without exaggeration or precision. [Brief description of the drawings]

[0009] [Figure 1] System overview of a sensor control device, a reader device, a network, a local computer system, and a trusted computer system [Diagram 2] FIG. 1 is a block diagram illustrating an example embodiment of a reader device. [Figure 3A] 1 is a block diagram of an exemplary embodiment of a sensor control device; [Figure 3B] 1 is a block diagram of an exemplary embodiment of a sensor control device; [Figure 4] Illustrative graph showing multiple analyte curves over time [Diagram 5] Another exemplary graph showing multiple analyte curves over time [Figure 6] Certain Exemplary Embodiments of a Graphical User Interface for Displaying Analyte Metrics on a Reader Device [Figure 7] Certain Exemplary Embodiments of a Graphical User Interface for Analyzing the Effects of Food [Figure 8] Certain Exemplary Embodiments of a Graphical User Interface for Displaying an Individual's Glucose Tolerance [Figure 9] Another exemplary embodiment of a graphical user interface showing a graph for monitoring a subject's carbohydrate intake DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Before describing the present subject matter in detail, it is to be understood that this disclosure is not limited to particular embodiments described herein, 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, as the scope of the present disclosure will be limited only by the appended claims.

[0011] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0012] 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 publications by virtue of their prior disclosure. Further, the dates of publication provided herein may be different from the actual publication dates, which may need to be independently confirmed.

[0013] In general, embodiments of the present disclosure include systems, devices, and methods for monitoring and managing an individual's health and nutrition based at least in part on analyte data from an in-vivo analyte sensor. Accordingly, many embodiments include an in-vivo analyte sensor, where at least a portion of the sensor is positioned or configured to be positionable on a user's body to obtain information regarding at least one analyte, such as glucose, in a bodily fluid (e.g., in interstitial fluid ("ISF") or blood under the skin, in dermal fluid in a dermal layer, or otherwise) of the body. In some embodiments, for example, the sensor is configured to measure glucose levels. In other embodiments, the sensor can be configured to measure ketone levels instead of or in addition to measuring glucose levels. Additionally, detection of other analytes is within the scope of the present disclosure, including, for example, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, etc. The embodiments disclosed herein can also be used in conjunction with in vivo analyte monitoring systems incorporating in vitro functionality, as well as purely in vitro or ex vivo analyte monitoring systems, including completely non-invasive systems.

[0014] Furthermore, for all of the method embodiments disclosed herein, systems and devices capable of implementing each of these embodiments are encompassed within the scope of the present disclosure. For example, sensor control device embodiments are disclosed, which may have one or more sensors, accelerometers, analyte monitoring circuitry (e.g., analog circuitry), non-transitory memory (e.g., for storing instructions and data), power sources, communication circuitry, transmitters, receivers, processors, and / or controllers (e.g., for executing instructions stored in memory) that may perform or facilitate the performance of any and all method steps.

[0015] Numerous embodiments of the present disclosure are designed to improve the accuracy and ease of use of wearable technology using analyte data received from an in vivo analyte sensor. In some embodiments, for example, a sensor control device is worn on the body, the sensor control device including an in vivo analyte sensor and an accelerometer. Analyte metrics are determined by one or more processors of the sensor control device and transmitted to a reader device along with physical activity level measurements. Analyte level measurements on the sensor control device can be discarded later. In other words, the sensor control device can be specifically configured not to transmit analyte level measurements. In the reader device, various information is presented on a display. In some embodiments, for example, a simple carbohydrate graph can be displayed to the user, where multiple colored bars indicate different ranges of analyte levels. In other embodiments, a numerical score can be presented to the user, where the score reflects the analyte response to ingested food or meals. In still other embodiments, analyte metrics can be analyzed along with physical activity level measurements to determine whether daily nutrient recommendations need to be adjusted. Thus, these embodiments can provide subjects with accurate, customizable, easy to use, and easy to understand health and / or nutrition information based on analyte data received from an in vivo analyte sensor. Embodiments of the present disclosure can improve the accuracy of conventional systems in that actual analyte level measurements are utilized, and in some embodiments are corroborated by physical analyte level measurements from an accelerometer. Additionally, embodiments of the present disclosure can improve user compliance with health and nutrition monitoring regimens by presenting a simple, easy to use interface. Other features and advantages of embodiments of the present disclosure are further discussed below.

[0016] However, before describing these aspects of the embodiments in detail, it is desirable to first describe examples of devices that may reside therein, such as a sensor control device that transmits data from an in vivo analyte sensor, and examples of the operation of such devices, all of which may be used in conjunction with the embodiments described herein.

[0017] There are various types of systems that utilize in vivo analyte sensors. For example, a "Continuous Analyte Monitoring" system (e.g., a "Continuous Glucose Monitoring" system) can transmit data from a sensor control device to a reader device automatically, for example, according to a schedule, with or without prompting, continuously or repeatedly. As another example, a "Flash Analyte Monitoring" system (e.g., a "Flash Glucose Monitoring" system or simply "Flash" system) can transmit data from a sensor control device in response to a user-initiated request for data by a reader device (e.g., a scan), for example, using Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocols. In vivo analyte monitoring systems can also operate without the need for finger stick calibration.

[0018] In vivo analyte monitoring systems can be distinguished from "in vitro" systems, which contact a biological sample outside the body (or "ex vivo") and typically include a measurement device having a port for receiving an analyte test strip carrying a user's bodily fluid, which can be analyzed to determine the user's blood glucose level.

[0019] An in-vivo monitoring system may include a sensor that contacts a user's bodily fluid while positioned in vivo and senses an analyte level contained therein. The sensor may be part of a sensor control device that resides on the user's body and contains electronics and a power source that effectuate and control the sensing of the analyte. Sensor control devices and variations thereof may also be referred to as "sensor control units," "on-body electronics" devices or units, or "sensor data communication" devices or units, to name a few.

[0020] In-vivo monitoring systems can also include a reader device that receives sensed analyte data from the sensor control device and processes and / or displays the sensed analyte data to a user in any of a number of formats. This device and variations thereof may also be referred to as a "handheld device," "reader device" (or simply "reader"), "handheld electronics" (or simply "handheld"), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply "receivers"), or "remote" devices or units, to name a few. Other devices, such as personal computers, smartphones, and similar devices, are also utilized with or incorporated into in-vivo and in-vitro monitoring systems.

[0021] Exemplary embodiments of a personal health and nutrition monitoring system 1 is a conceptual diagram illustrating one exemplary embodiment of a health and nutrition monitoring system 100 including a sensor control device 102 and a reader device 120. The system 100 may also include a sensor applicator 150 that may be used to apply the sensor control device 102 to a monitoring location on a user's skin such that the sensor 104 is maintained in place on the user's body for a period of time by an adhesive patch 105. The sensor control device 102 is further described in connection with FIGS. 3A and 3B and may communicate with the reader device 120 via a communication path 140 using wired or wireless techniques. Exemplary wireless protocols include Bluetooth, Bluetooth Low Energy (BLE, BTLE, Bluetooth SMART, etc.), Near Field Communication (NFC), Wi-Fi, etc.

[0022] An individual can monitor and use one or more health and nutrition applications installed in memory on the reader device 120 using the screen 122 and input 121, and the battery of the reader device can be recharged using the power port 123. Further details regarding the reader device 120 are described below in connection with FIG. 2. The reader device 120 can communicate with a local computer system 170 via a communication path 141 using wired or wireless techniques. The local computer system 170 can be a laptop, desktop, tablet, phablet, smartphone, set-top box, video game console, or other computing device, and the wireless communication can include any of a number of applicable wireless networking protocols, including Bluetooth, Bluetooth Low Energy (BTLE), Wi-Fi, etc. The local computer system 170 can communicate with the network 190 via a communication path 143, similar to how the reader device 120 can communicate with the network 190 via a communication path 142 using wired or wireless techniques, as described above. Network 190 can be any of a number of networks, such as private and public networks, local area networks or wide area networks, etc. Network 190 can be a cloud. Trusted computer system 180 can include a server, can provide authentication services and / or protected data storage, and can communicate with network 190 via communication path 144 using wired or wireless techniques. Trusted computer system 180 can be considered as part of network 190 (or the cloud) from the perspective of devices 102, 120, and 170.

[0023] Exemplary embodiments of a reader device 2 is a block diagram illustrating an example embodiment of a reader device 120 configured as a smartphone. Here, the reader device 120 can include: a display 122; input components 121; and processing circuitry 206 including a communication processor 222 coupled to a non-transitory memory 223 and an application processor 224 coupled to a non-transitory memory 225. It can also include: a non-transitory memory 230; an RF transceiver 228 with an antenna 229; and a power source 226 with a power management module 238. It can further include a multi-function transceiver 232, which can communicate with an antenna 234 via Wi-Fi, NFC, Bluetooth, BTLE, ANT+, and GPS networks. As can be appreciated by those skilled in the art, these components are electrically and communicatively coupled to form one functional device.

[0024] Additionally, although described herein as a smartphone, the reader device 120 may also be a mobile smart wearable electronics assembly, such as an optical assembly worn on or adjacent to a user's eye (e.g., monocular or binocular smart glasses, such as the mobile communication device Google Glass). The optical assembly may have a transparent display that allows the user to view through the display with minimal disruption to the user's overall vision, while simultaneously displaying information about the user's analyte levels (as described above). 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), neck (e.g., a necklace), head (e.g., a headband, a hat), chest, etc. Similarly, the reader device 120 may be a tablet computer, a personal digital assistant, a laptop, or any other mobile or personal computing device. The reader device 120 may also be an extension and / or add-on to a device used to monitor activity or sports performance, such as a device configured to measure and / or display power, cadence, pace, heart rate, speed, etc. It is fully within the scope of the embodiments described herein that such devices may be adapted for display of analyte metrics.

[0025] Exemplary embodiments of a sensor control device 3A and 3B are block diagrams illustrating an exemplary embodiment of a sensor control device 102, which includes an analyte sensor 104, an accelerometer 175, and sensor electronics 160 (including analyte monitoring circuitry), which together may have most of the processing power for rendering end result data, such as analyte metrics suitable for display to a user. In FIG. 3A, a single semiconductor chip 161 is illustrated, which may be a dedicated application specific integrated circuit (ASIC). Shown within the ASIC 161 are certain higher order functional units, including an analog front end (AFE) 162), power management (or control) circuitry 164, a processor 166, and communication circuitry 168 (which may be implemented as a transmitter, receiver, transceiver, passive circuitry, or otherwise, depending on the communication protocol). In this embodiment, both the AFE 162 and the processor 166 are used as in vivo analyte monitoring and accelerometer monitoring circuitry, although in other embodiments, either circuitry may perform the monitoring functions. Processor 166 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or may be distributed across (or part of) many different chips.

[0026] The accelerometer 175 may include one or more piezoelectric, piezoresistive, and / or capacitive materials (e.g., lead zirconate titanate, quartz, or tourmaline) that are used to convert mechanical motion and / or changes in velocity into electrical signals. The accelerometer 175 may also include miniature micro-electromechanical systems (MEMS) and may have a single-axis or multi-axis configuration, including a three-axis configuration.

[0027] A non-transitory memory 163 is also included in the ASIC 161, which may be shared by the various functional units present in the ASIC 161 or distributed across two or more of them. The memory 163 may be a separate chip. The memory 163 may be a volatile and / or non-volatile memory. In this embodiment, the ASIC 161 is coupled to a power source 170, which may be a coin cell battery or the like. The AFE 162 interfaces with and receives measurement data from the in-vivo analyte sensor 104 and the accelerometer 175, and outputs said data in digital form to the processor 166. The processor 166 may then execute one or more instructions stored in the memory 163, which may cause the processor 166 to process the data to determine one or more analyte metrics (e.g., analyte curves, analyte curve profiles (e.g., area, slope and / or length), rate of change of analytes), as well as physical activity level values, trend values, and the like. This data can then be provided to communications circuitry 168 for transmission via antenna 171, for example, to reader device 120 (not shown), where additional processing by a resident software application may be required to display said data. Only the analyte metrics (including aggregated and / or averaged data) are stored in memory 163, while the raw analyte level measurements are subsequently discarded. For example, in some embodiments, the raw analyte data reflecting glucose levels is not stored in persistent memory 163 on the sensor, and therefore cannot be accessed by any means (e.g., by a reader device). In other embodiments, the raw analyte data reflecting glucose levels can be temporarily stored in RAM (random access memory), or another similar type of volatile memory. In these embodiments, the raw analyte level measurements can be used to calculate analyte metrics, for example, a running average glucose level over the past 48 hours, after which the raw analyte measurements can be deleted or discarded.

[0028] FIG. 3B is similar to FIG. 3A, but includes two discrete semiconductor chips 162 and 174, which may be packaged together or separately. Here, AFE 162 resides on ASICS 161. As shown, AFE 162 is coupled to both analyte sensor 104 and accelerometer 175. Referring to chip 174, processor 166 is integrated on chip 174 along with power management circuitry 164 and communication circuitry 168. AFE 162 includes memory 163, and chip 174 includes memory 165, which may be isolated or distributed within. In one exemplary embodiment (not shown), AFE 162 is combined with power management circuitry 164 and processor 166 on one chip, while communication circuitry 168 is on a separate chip. In another exemplary embodiment (also not shown), both AFE 162 and communications circuitry 168 are on one chip, and processor 166 and power management circuitry 164 are on another chip. However, other chip combinations are possible, including more than two chips, each performing the separate functions described above, or sharing one or more functions for fail-safe redundancy.

[0029] In some embodiments, the sensor control device 102 collects raw measurement data from the body and transmits the raw data (with or without signal conditioning, and with or without other data, such as temperature data) to the reader device 120 for further algorithmic processing into a form representative of the wearer's analyte levels, which may then be displayed (or made displayable) by the reader device 120. In other embodiments, the algorithmic processing is performed by the sensor control device 102 prior to transmission to the reader device 120.

[0030] Examples of Analyte Level Measurements and Metrics As described above, the in vivo analyte sensor 104 can be configured to measure the level of one or more analytes (e.g., glucose) in a subject's bodily fluid (e.g., epithelial fluid, interstitial fluid, subcutaneous fluid, or blood). A typical glucose profile in a healthy subject exhibits relatively flat glucose levels with "peaks" or analyte curves associated with meals. The size and shape of the analyte curves can be related to the amount and type of food ingested.

[0031] As an example, FIG. 4 is a graphical representation of analyte level measurements obtained by the analyte sensor 104 illustrating the glucose levels over time of a non-diabetic subject. In particular, FIG. 4 is a graph 200 illustrating the analyte levels of a typical subject in response to three meals (e.g., breakfast, lunch, and dinner) over a 24-hour period. The horizontal dashed line illustrates the subject's baseline analyte level, such as a fasting blood glucose level. Analyte curve AC1 illustrates an increase in blood glucose levels at approximately 7:30 a.m. in response to the subject consuming breakfast. Similarly, analyte curves AC2 and AC3 illustrate an increase in blood glucose levels at noon and 6 p.m. in response to the subject consuming lunch and dinner, respectively. Each analyte curve AC1, AC2, and AC3 can have an analyte curve profile, where each profile includes at least: the area under the analyte curve, illustrated in FIG. 4 by the shaded area bounded between the analyte level measurements and the baseline analyte level (e.g., fasting blood glucose level); the slope of the analyte curve; and the length of the analyte curve.

[0032] FIG. 5 is another graphical representation of analyte level measurements obtained by the analyte sensor 104 over a three day period. In particular, graph 250 of FIG. 5 reflects glucose levels over a 72 hour period from a LIBRE sensor (manufactured by ABBOTT DIABETES CARE INC.) worn by a non-diabetic subject, showing days 8-10 of 14 days of wearing the LIBRE sensor. On day 9 (the middle 24 hour period of the 72 hour period shown on the graph), the non-diabetic subject consumed a low carbohydrate diet with a total carbohydrate intake of approximately 10 g. On the days before and after (days 8 and 10), a relatively high carbohydrate, normal diet was consumed. As evidenced by the small analyte curve, glucose variability on day 9 is small, with the majority of raw glucose values ​​between 90-110 mg / dL.

[0033] Exemplary embodiments of a graphical user interface for displaying analyte metrics Described herein are exemplary embodiments of graphical user interfaces for displaying analyte metrics on a reader device 120. As described above with respect to FIG. 4, an in vivo analyte sensor 104 can measure an analyte level in a subject's bodily fluid and one or more sensor electronics processors can determine one or more analyte metrics. The communication circuitry of the sensor control device 102 can then wirelessly transmit the analyte metrics to the reader device 120. As described above with respect to FIGS. 3A and 3B, the raw analyte level measurements are then discarded from the sensor control device 102. However, in some embodiments, the analyte metrics can be stored in an aggregated format in a memory on the sensor control device 102. For example, in some embodiments, a running average of one or more analyte levels over a predetermined amount of time (e.g., the past 4 hours) may be stored in the memory of the sensor control device 102. In other embodiments, peak analyte levels over a predetermined amount of time (e.g., 48 hours) can be stored in the memory of the sensor control device 102. These examples are intended to be illustrative and not limiting in any way, as one of ordinary skill in the art will readily appreciate that other types and formats of aggregated analyte metrics are within the scope of embodiments of the present disclosure.

[0034] The reader device 120 wirelessly receives the analyte metrics through its communications circuitry. A graphical user interface (GUI) stored as instructions in memory of the reader device 120 can be executed by one or more reader device processors to visually output the analyte metrics on a display of the reader device 120. As described below, in certain examples, the GUI is interactive and the subject can input information into the reader device 120 through the GUI using an input device (e.g., a touch screen, keyboard, or mouse).

[0035] FIG. 6 is an exemplary embodiment of a GUI 300 for displaying analyte metrics on the reader device 120. In general, the GUI 300 can display simple, easy-to-read numerical scores representing analyte curves associated with meals consumed by the subject, such as those described with respect to FIG. 4 (e.g., AC1, AC2, AC3). As shown in FIG. 6, a time and date display 310 is shown at the top of the reader device 120 for ease of reference. Below the time and date display 310, three horizontally oriented numerical scores 320 are shown, which represent three meals consumed by the subject: breakfast, lunch, and dinner. In some embodiments, the occurrence of meals can be determined by routines and / or algorithms stored in the memory of the reader device 120 and configured to detect patterns in the analyte metrics received from the sensor control device 102. Detection of a meal event can include detection of an analyte episode or excursion outside of a desired acceptable (e.g., medically recommended) target range for the user, one or both of which can be notified to the user by the software. Examples of analyte excursions include violating a low glucose threshold, violating a high glucose threshold, violating a rate of change (e.g., increase or decrease) threshold, violating a median glucose threshold, violating a glucose variability threshold, etc. In other embodiments, the occurrence of a meal can be further confirmed by a routine configured to corroborate the detected pattern of analyte metrics at a particular time. Other examples of algorithms, routines, or other sets of instructions for detecting meals and variations thereof are described in U.S. Patent Publication Nos. 2013 / 0085358, 2014 / 0350369, or 2014 / 0088393, or WO 2015 / 153482, all of which are incorporated herein by reference in their entirety for all purposes. Some of the algorithms, routines, or sets of instructions described in these incorporated references are described solely with respect to identifying analyte excursions outside of desired target ranges.These embodiments can be extended to detection of meal events based on the teachings contained in another one of these incorporated references (e.g., WO 2015 / 153482). These embodiments can also be extended to detection of meal events by identifying within-target-range episodes in which glucose values ​​remain between upper and lower limits for a period of time. Detection of these episodes can be performed by extension of threshold-based episode detection algorithms.

[0036] In yet other embodiments, the occurrence of a meal can be determined and / or confirmed manually by a user, for example, by prompting the user to provide input when a pattern is detected in the analyte metrics received from the sensor control device 102. For example, in some embodiments, the user can enter a text-based description of the meal consumed into a text entry box using an input device of the reader device 120, as further described with respect to FIG. 7. Each numerical score is proportional to the area under the corresponding analyte curve, such as those shown in FIG. 4 (e.g., AC1, AC2, AC3). However, for simplicity and ease of reference, the numerical scores are displayed in the GUI 300 rather than the analyte curves.

[0037] Referring again to FIG. 6, the numerical scores can be integers on a scale of 1 to 5, where the integers are proportional to the area under the analyte curve. For example, a numerical score of "3" can reflect the area of ​​the "default" analyte curve, which represents the analyte response to a standard default meal. In contrast, a numerical score of "5" can indicate a food or meal that results in a relatively large analyte response, where the analyte curve has a large area compared to the default meal. Conversely, a numerical score of "1" can indicate an ingested food or meal that results in a relatively small analyte response, where the analyte curve has a small area compared to the default meal. Furthermore, as shown in FIG. 6, in many embodiments, the numerical scores reflect the area under one or more corresponding analyte curves. However, in other embodiments, the numerical scores can be a function of other metrics of the analyte curve profile, such as the slope of the analyte curve and / or the length of the analyte curve. In some embodiments, for example, the numerical scores can be a function of the length of the analyte curve, where the length of the analyte curve is related to the duration of the analyte response. The numerical score may also reflect the analyte curve profile for a particular type of food and / or diet. For example, in some embodiments, a relatively high numerical score may reflect an analyte curve characterized by a steep spike, which may reflect a food or diet containing refined carbohydrates (e.g., white flour pasta). In other embodiments, a relatively low numerical score may reflect an analyte curve characterized by a gentle slope, which may reflect a food or diet high in fiber or complex carbohydrates (e.g., whole wheat pasta). Similarly, in other embodiments, the numerical score may be a function of the rate of change of the analyte level.

[0038] Continuing with reference to FIG. 6, below the numeric score 320 are three summary metrics 330, 340, 350 for breakfast, lunch, and dinner. Today's score 330 may indicate a sum of the numeric scores for the meals consumed on that day. This week's score 340 may indicate a sum of the numeric scores for the week to date. Target weekly score 350 may indicate a target numeric score below which the subject wishes to remain. Additionally, as shown below these summary metrics, daily tips or instructions 352 may be provided to assist the subject in meeting their target weekly score. Thus, this simple GUI 300 presents the user with easy to understand analyte metrics without the subject having to understand or interpret the underlying analyte level measurements taken by the sensor 104.

[0039] As shown in FIG. 6, in many embodiments, the numerical score may be represented as an integer on a scale of 1 to 5. However, in other embodiments, the numerical score may be expressed as a percentage value that is greater or less than the area under the analyte curve in response to a standard default food or meal (e.g., 30% above normal or 30% below normal). Additionally, in some embodiments, a particular analyte metric, such as today's score 330, this week's score 340, or goal weekly score 350, may be expressed as an average rather than a sum as shown in FIG. 6. In some embodiments, other non-numerical representations may be used in addition to or in place of the numerical scores described above. For example, different color indicators (e.g., red, yellow, or green lights), text indicators (e.g., "good," "neutral," "bad"), graphical indicators (thumbs up, thumbs down, happy faces, sad faces, or other emoticons), and letter grades (e.g., A, B, C, D, or F) are all within the scope of the present disclosure.

[0040] Additionally, because the relationship between analyte curves and ingested foods or meals may be unique to each individual, the system may also be calibrated for each individual against an analyte curve for a standard default meal using calibration features (not shown). In some embodiments, calibration may be performed by the calibration features, e.g., starting with a standard default meal, and adjusting the default analyte curve over time against the individual's average analyte curve. In some embodiments, the subject may determine a meal that is representative of a standard diet and manually set the default response to this meal.

[0041] Additionally, the GUI 300 can include features for providing incentives to the subject for reaching his or her target weekly score 350. For example, in certain embodiments, the GUI 300 can include features for keeping the subject's friends informed of the subject's status or for messaging the subject's friends through a social media platform (e.g., Facebook) after the target weekly score is achieved. In other embodiments, a monetary incentive (e.g., a membership discount) can be provided for initial sign-up or for referring a friend to sign-up to use the GUI 300 to encourage use of the GUI 300. In other embodiments, the GUI 300 can provide the subject with one or more monetary rewards, such as a discount code, coupon, or in-app reward, once the target weekly score is achieved.

[0042] Exemplary embodiments of a graphical user interface for analyzing the effects of food FIG. 7 is another exemplary embodiment of a GUI for displaying analyte metrics on the reader device 120. More specifically, the GUI 400 can determine and display the physiological impact of a particular food or diet for a particular individual as compared to a default standard food or diet. It is recognized that diet and nutrition must be customized for each individual. While it is true that universal guidelines exist for all people, there is wide variation in individual responses to foods based on individual physiology. Furthermore, food choices may be limited based on local availability, cultural acceptability, food allergies, etc. The GUI 400 can present information specific to the individual as to which of the available foods are most beneficial nutritionally and have the least adverse impact on factors such as weight gain.

[0043] According to one aspect of this embodiment, the reader device 120 wirelessly receives analyte metrics from the sensor control device 102. As described above, the received analyte metrics may include an analyte curve profile for the ingested food or meal, where the analyte curve profile may include one or more of the area under the analyte curve, the slope of the analyte curve, and the length of the analyte curve. An application in the form of instructions stored in a memory of the reader device 120 may then be executed by one or more reader device processors to cause the processor to associate a particular analyte curve profile for the ingested food or meal with a food entry in a database and to store the analyte curve profile and associated metrics in the database. The application may be further executed to perform a comparison between the analyte curve profile for the ingested food or meal and a default analyte curve profile for a standard default food or meal, and the results of the comparison may be visually output on a display of the reader device 120.

[0044] As shown in FIG. 7, the GUI 400 includes a graph 360 showing the physiological effects of a particular food or meal compared to a standard default food. In particular, an analyte curve 362 for the ingested meal or food, i.e., AC1, is shown overlaid with a default analyte curve 364, i.e., AC0, reflecting the subject's analyte response to the standard default meal or food. A time and date display 310 may again be included at the top of the reader device 120. A numerical score 370 for the standard default meal or food, similar to the numerical score described with reference to FIG. 6, and a numerical score 375 for the currently ingested meal may be displayed on the GUI 400. Additionally, in some embodiments, the GUI 400 may also include a text entry box 380 and / or a pictorial entry box 385. The subject may enter a text-based description of the ingested meal into the text entry box 380 using an input device of the reader device 120. The entered information may then be entered into the database as a food entry. Similarly, some embodiments also include a pictorial input box 385, through which the subject can provide a photo of a meal or food, for example using a camera on the reader device, which can then be associated with a food entry and stored in the database.

[0045] In some embodiments, the food database can reside in memory on the reader device 120. In other embodiments, the database can be stored on a trusted computer system remote from the reader device 120 and accessed via a local network, wide area network, or the Internet. Additionally, the GUI 400 can incorporate third party applications that facilitate the entry of food intake data and third party food databases associated therewith. In some embodiments, the GUI 400 can also store and / or compile analyte metric data in a database, which can include data that is indicative of the impact of a particular food or diet on a population or a portion of a population. Similarly, in some embodiments, the GUI 400 can pull data from the food database regarding the impact of a particular food or diet on a population or a portion of a population. The population data can reflect, for example, means or medians, weighted means, standard deviations, and other statistical parameters for analyte metrics that can reflect the effect of a particular food or diet on a population (or a portion thereof) and can be used to normalize and / or compare an individual's response to that particular food or diet. Additionally, population data collected from multiple individual users can be made accessible to authorized individuals, groups, public health authorities, and / or billers.

[0046] Additionally, in some embodiments, the GUI 400 may interface with one or more social media platforms similar to the social media features described above with respect to FIG. 6. For example, according to one aspect of this embodiment, the GUI 400 may include a feature that sends messages or notifications to the subject's friends to keep them informed of the subject's status with respect to the diet consumed, a numerical score associated with the diet consumed, or one or more indicators reflecting the impact of a particular diet consumed. Additionally, in some embodiments, a monetary incentive (e.g., membership discounts, discount codes, in-app rewards) may be offered for initially signing up or for referring a friend to sign up to use the GUI.

[0047] According to another aspect of this embodiment, the GUI 450 may also assist in the detection of pre-diabetes. As shown in FIG. 8, the chart 390 may initially include three analyte curves: (1) analyte curve 392 representing the glucose response of a diabetic subject; (2) analyte curve 394 representing the glucose response of a pre-diabetic subject; and (3) analyte curve 398 representing the glucose response of a normal (non-diabetic and non-pre-diabetic) subject. A dashed analyte curve 396 may represent a four-week average glucose response, which is overlaid with the analyte curves 392, 394, 398. In this regard, the subject may visually approximate his or her analyte tolerance relative to normal, pre-diabetic, and / or diabetic subjects. Additionally, a text display 395 may be provided below the chart 390 to provide further guidance regarding the information displayed in the chart 390.

[0048] According to other aspects of this embodiment, the GUI described herein can also receive and process data from other types of sensors to provide an assessment of the subject's health. For example, in some embodiments, the other types of sensor data include physical activity level measurements from an accelerometer on the sensor control device 102. In other embodiments, the other sensor data includes data from a heart rate sensor, which may be integrated into the sensor control device 102 or may be deployed remotely from the sensor control device 102. Instructions stored in the memory of the reader device 120, along with the GUI, can cause one or more reader device processors to analyze these multiple types of sensor data to generate and display a comprehensive assessment of the subject's health.

[0049] Exemplary embodiments of a graphical user interface for monitoring carbohydrates FIG. 9 is another example of a GUI for displaying analyte metrics on the reader device 120. In particular, FIG. 9 illustrates a GUI 500 including a carbohydrate graph for monitoring a subject's carbohydrate intake. In general, analyte metrics derived from a raw glucose signal, such as that acquired by an in vivo analyte sensor 104, can represent a subject's carbohydrate intake. For example, analyte metrics reflecting a rate of change or an integral value over a particular threshold glucose value may be more reflective of raw carbohydrate intake than raw glucose values. In this regard, the carbohydrate graph of FIG. 9 reflects a subject's carbohydrate intake by displaying a graphical representation based on one or more analyte metrics received from the sensor control device 102. As shown in FIG. 9, the graphical representation can be a plot line 516. However, in other embodiments, the graphical representation can be a series of discontinuous dots, an extrapolated line or curve, a scatter shot, a shaded area, or other representation of the received analyte metrics. Additionally, as can be further seen in FIG. 9, the horizontal axis 520 reflects hourly increments over a 72 hour time window (e.g., from 00:00 on Jan. 15, 2016 to 00:00 on Jan. 18, 2016). It should be understood that other predefined time windows, such as 8-10 hours or 5-15 hours, may also be utilized. The vertical axis is not labeled and does not display values. The GUI 500 is more intuitive and easier to understand for a subject than, for example, the graph 250 of FIG. 5, since a subject may not have the technical understanding of numerical analyte levels necessary to interpret the raw analyte values.

[0050] Continuing to refer to FIG. 9, plot line 516 may include a series of plotted analyte values ​​associated with one or more of the metrics received from the sensor control device 102. Additionally, each plotted analyte value may be assigned to one or more ranges, such as a high range, a medium range, or a low range, where each range may be represented by an indicator, such as a colored band, extending from the horizontal axis at the bottom of the graph to the plotted value. For example, a red band 514 may indicate analyte values ​​assigned to the high range, a yellow band 510 may indicate analyte values ​​assigned to the medium range, and a green band 512 may indicate analyte values ​​assigned to the low range. Although three ranges (high, medium, and low) and three corresponding colors (red, green, and yellow) are shown in FIG. 9, other ranges, indicators, colors, visual patterns, and gradients may be used and are therefore within the scope of the present disclosure.

[0051] In some embodiments, each plotted analyte value may reflect one analyte level measurement obtained by the in vivo analyte sensor 104. However, in other embodiments, the plotted analyte values ​​may reflect different rates of change in the analyte level. Thus, different color bands may be assigned based on the rate of change integrated over a specified period of time. For example, if the analyte level is changing relatively quickly, the colored band may be red. Similarly, as the rate of change decreases, the colored band may be green.

[0052] According to another aspect of some embodiments, the assignment of a plotted analyte value to a particular range and / or colored band may also include an integral of the past analyte values ​​(or rate of change). For example, in some embodiments, if a predetermined number of preceding plotted analyte values ​​are all assigned to the high range, or similarly, if there is a predetermined time period during which all plotted analyte values ​​are assigned to the high range, then the next plotted analyte value may also be assigned to the high range. In contrast, according to the same exemplary embodiment, if a predetermined number of preceding plotted analyte values ​​are all assigned to the low range, or similarly, if there is a predetermined time period during which all plotted analyte values ​​are assigned to the low range, then the next plotted analyte value may also be assigned to the low range. In this regard, the GUI 500 may include an "inertial resistance" algorithm, where, for example, if there is a long period of low carbohydrate consumption, then a short period of high carbohydrate consumption will not necessarily cause the corresponding colored band to be red, because the GUI 500 "remembers" the relatively long integral period of low carbohydrate consumption. In some embodiments, this "inertance" algorithm can be activated or deactivated by a user through a settings interface (not shown) of the GUI 500. Similarly, in some embodiments, the level of inertance, i.e., the threshold duration or number of preceding consecutive low range (or high range) analyte values, can be set by a user through an input device through a settings interface (not shown) of the GUI 500.

[0053] According to another aspect of this embodiment, the GUI 500 may include multiple user-selected levels to control the "leniency" of the process of assigning the plotted analyte values ​​to certain and / or colored bands. In particular, a subject may select an "easy," "medium," or "hard" level through a setting interface (not shown) accessible through an input device of the reader device 120. Instructions stored in memory on the reader device 120 cause one or more reader device processors to determine a range of values ​​for the low, medium, or high ranges, respectively, based on the user-selected level. In other words, when selecting the easy level, the low range may include a wider range of values ​​than the medium or high ranges. When selecting the medium level, the medium range may include a wider range of values ​​than the low or high ranges. When selecting the hard level, the high range may include a wider range of values ​​than the medium or high ranges. In this regard, a subject may begin using the GUI 500 with an easy setting, in which many of the plotted analyte values ​​are assigned to the low range and thus have a green color. The subject may then decide to proceed to a medium or hard setting, with fewer plotted analyte values ​​assigned to the low range and more prevalent plotted analyte values ​​in the "red." These settings allow the user to determine a level of feedback that provides the user with positive incentives while also providing the ability to distinguish between low and high carbohydrate diets.

[0054] According to another aspect of this embodiment, instructions stored in memory on the sensor control device 102 and / or the reader device 120 may be executed by one or more processors of each device to perform periodic monitoring (e.g., daily, weekly, monthly) of carbohydrate intake to establish a carbohydrate intake profile for a particular user. For example, in some embodiments, the instructions may enable the one or more processors to identify, store, and recognize patterns of carbohydrate intake of the user. Based on the identified patterns of carbohydrate intake of the user associated with specific foods and / or meals, a carbohydrate intake profile may be established. In some embodiments, the instructions may also detect excursions of analytes that are inconsistent with the established carbohydrate intake profile, following which the user is prompted to input additional information regarding the types and amounts of food and meals consumed by the user. The instructions may enable the processor to analyze the excursions and associated food intake to determine the effects of various types and amounts of food and meals. In other embodiments, the instructions may enable the processor to prompt the user to input information regarding the type and / or amount of carbohydrate for each food or meal intake. According to one aspect of these embodiments, the instructions can identify and / or store patterns of a user's carbohydrate intake profile after each intake of a food or meal. If a sufficient number of patterns are detected, or after a predetermined period of time, the instructions can stop requesting user input for each intake of a food or meal. In this regard, according to another aspect of this embodiment, the sensor control device and / or reader device can "learn" a particular user's carbohydrate intake pattern to predict an analyte response to a particular food or meal.

[0055] Exemplary embodiments of interfaces for modifying daily nutrient recommendations In another exemplary embodiment, a GUI is provided for generating and modifying daily nutrient recommendations. The National Food and Nutrition Board of the U.S. Department of Health and Human Services has issued Dietary Reference Intake (DRI) documents to establish principles and guidelines for adequate dietary intake. In particular, the Food and Nutrition Board provides authoritative judgments regarding the relationship between food intake, nutrition, and health. Several interactive tools, such as the Interactive DRI for Health Professionals provided by the USDA, can be used to calculate daily nutrient recommendations based on the DRI. The tool can calculate: (1) estimated caloric needs; (2) macronutrients such as carbohydrates, fiber, protein, fat, and water; and (3) vitamins and minerals, taking into account gender, age, BMI, and activity level.

[0056] The following table is provided as an example of daily nutrient recommendations for a 51-year-old male subject who is 6'1" tall and weighs 170 pounds. In some embodiments, the daily nutrient recommendations set forth below may be visually displayed on the reader device 120. In particular, instructions stored in the memory of the reader device, when executed by the one or more reader device processors, may cause the one or more reader device processors to output the following information to a display of the reader device 120:

[0057] [Table 1]

[0058] [Table 2]

[0059] [Table 3]

[0060] [Table 4]

[0061] According to one aspect of this embodiment, the GUI can be provided to: (1) ensure accurate personalized daily nutrient recommendations by monitoring an individual subject's activity level; and (2) correlate with carbohydrate intake by monitoring an individual's analyte response. As described above, in addition to the in-vivo analyte sensor 104, the sensor control device 102 can also include an accelerometer 175 for measuring the subject's physical activity level. Furthermore, the sensor control device 102 can wirelessly transmit one or more analyte metrics and physical activity level measurements to the reader device 120. Then, in some embodiments, instructions stored in the memory of the reader device 120, when executed by one or more reader device processors, can cause the processor to determine whether to adjust the daily nutrient recommendations based on the one or more analyte metrics and physical activity level measurements.

[0062] According to another aspect of this embodiment, the subject's heart rate and daily heart rate patterns can be measured by a device, such as a heart rate monitor integrated into the sensor control device 102 or deployed separately from the sensor control device 102, and wirelessly transmitted to the reader device 120. Daily nutrient recommendations can then be further revised based on one or more heart rate measurements, as with the analyte metrics and physical activity level measurements described above.

[0063] Similarly, according to yet another aspect of this embodiment, the subject's hydration level can be measured, for example by a sensor device integrated into the sensor control device 102 or deployed separately from the sensor control device 102, and wirelessly transmitted to the reader device 120. As with other physiological data, daily nutrient recommendations can then be further revised based on one or more hydration level measurements.

[0064] Similarly, according to yet another aspect of these embodiments, the subject's glucose response can be correlated with the subject's carbohydrate intake, as described with reference to Figure 9. Thus, the daily nutrient recommendations can be further modified based on the subject's carbohydrate intake, as well as other physiological data.

[0065] Additionally, according to another aspect of these embodiments, other parameters of the daily nutrient recommendations, including estimated recommended daily calorie intake, recommended daily carbohydrate intake, recommended daily fiber intake, recommended daily protein intake, recommended daily fat intake, recommended daily water intake, recommended daily vitamin intake, and recommended daily mineral intake, may be modified by the application based on feedback from any of the sensor devices described above.

[0066] Exemplary Embodiments of a Graphical User Interface for Ketone Monitoring For all embodiments disclosed herein, references to analyte level measurements, analyte metrics, analyte curves, and / or analyte curve profiles may refer to any of a number of analytes found in the subject's bodily fluids that may be sensed by the in vivo analyte sensor 104. In some embodiments, analyte metrics related to ketone monitoring may be displayed to the subject on the reader device 120. In particular, to assist the subject in maintaining a state of ketosis for dietary or medical reasons, a GUI may be provided for detecting when a ketosis threshold has not been met and for displaying one or more recommendations to the subject for achieving a ketosis goal threshold.

[0067] For example, several known diets are associated with reduced carbohydrate intake (e.g., Atkins, Paleo, Ketogenic, fasting). When a subject is on a carbohydrate and / or protein restricted diet, the body must shift to using lipid stores to generate energy. During this process, the body enters nutritional ketosis, a state in which ketone bodies, including three compounds (i.e., acetone, acetoacetate, and most prevalently β-hydroxybutyrate), can be detected in bodily fluids. It is generally understood that complex carbohydrates designed for slow metabolism can be introduced into this diet, which will continue to maintain a state of ketosis. Additionally, monitoring ketones can be important for individuals who need to diet or who suffer from epilepsy. The ketogenic diet is known to be an effective diet for reducing or eliminating the occurrence of seizures in people with epilepsy.

[0068] Conventional or existing methods for determining whether a subject is in a state of nutritional ketosis are similar to those used by diabetics in identifying diabetic ketoacidosis. In both of these cases, urine strips (e.g., Ketostix) or blood-based strips (e.g., Abbott Diabetes Care Ketone Test Strips) can be used to determine the level of ketones in the body. However, urine-based tests provide a historical view of ketone levels that is significantly delayed from the current state. Although blood-based tests provide closer to real-time results, the data set is contingent and dependent on how frequently the subject uses the test strips. This can result in important information being lost to understand how food and exercise affect a person's state of nutritional ketosis.

[0069] All of the above embodiments can be used for ketone monitoring, alone or in combination with glucose monitoring. In some embodiments, a GUI can be provided, for example, for reaching a goal habit score for ketone metrics (FIG. 6) or for determining a ketone response for a particular meal, food, supplement, or medication (FIG. 7). Similarly, daily nutrient recommendations for reaching and maintaining a desired state of ketosis can be determined (or modified) based on: (1) analyte metrics reflecting ketone levels in the subject's bodily fluids; and (2) physical activity level measurements from an accelerometer on the sensor control device 102. According to another aspect of an embodiment, a GUI can be provided for determining and displaying one or more times when a ketosis goal threshold has not been met, and for displaying one or more recommendations for achieving the ketosis goal threshold. For these and other embodiments, instructions can be stored in the memory of the reader device 120 that, when executed by one or more reader device processors, cause the one or more processors to analyze and display specific ketone metrics to assist the subject in reaching and maintaining a state of ketosis.

[0070] It should be noted that all features, elements, components, functions, and steps described with respect to any embodiment provided herein are intended to be freely combinable and replaceable with features, elements, components, functions, and steps from any other embodiment. If a particular 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 otherwise stated. Thus, this paragraph serves as a predicate and express support for introducing claims that combine features, elements, components, functions, and steps from different embodiments at any time, or replace features, elements, components, functions, and steps from one embodiment with those from another embodiment, even if the following description does not expressly state that such combinations or substitutions are possible in a particular example. It is clearly recognized that an explicit enumeration of all possible combinations and substitutions would be an undue burden, especially when a person skilled in the art would readily recognize that all such combinations and substitutions are each permissible.

[0071] To the extent that the embodiments disclosed herein include or operate in conjunction with memory, storage, and / or computer-readable media, the memory, storage, and / or computer-readable media are non-transitory, and thus, to the extent that the memory, storage, and / or computer-readable media are encompassed by one or more claims, the memory, storage, and / or computer-readable media are only non-transitory.

[0072] These embodiments are susceptible to various modifications and alternative forms, specific examples of which are 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 on the contrary, these embodiments can encompass all modifications, equivalents, and alternatives falling within the spirit of the disclosure. Furthermore, any feature, function, step, or element of these embodiments can be recited or added to the claims, and negative limitations that define the scope of the claimed invention by any feature, function, step, or element not within the scope of the claims can also be recited or added to the claims. [Explanation of symbols]

[0073] 100 Health and Nutrition Surveillance System 102 Sensor Control Device 104 Sensor, analyte sensor 105 Adhesive Patch 120 Reader Device 121 Input 122 Screen, Display 123 Power Port 140, 142, 143, 144 Communication paths 150 Sensor Applicator 160 Sensor Electronics 161 Semiconductor chips, ASIC 162 Analog Front End, AFE, Semiconductor Chip 163, 223, 225 Non-transient memory 164 Power management (or control) circuit configuration 166 processors 168 Communication Circuit Configuration 170 Local computer system, power supply 174 Chips 175 Accelerometer, Semiconductor Chip 180 Trusted Computer Systems 190 Network 200, 250, 360 graph 206 Processing circuit configuration 222 Communications Processor 224 Application Processor 226 Power supply 228 RF Transceiver 229 Antenna 230 Non-transient memory 232 Multifunction Transceiver 234 Antenna 238 Power Management Module 300, 400, 450, 500 GUI 310 Date and time display 320 Numerical Score 330 Summary Metrics, Today's Score 340 Summary Metrics, This Week's Score 350 Summary Metrics, Target Weekly Scores 362, 364, AC1, AC2, AC3 analyte curves 370, 375 Numeric score 380 Text Entry Box 385 Image input box 390 Charts 392, 394, 396, 398 analyte curve 395 Text display 510 Yellow Band 512 Green Band 514 Red Band 516 Plot Lines

Claims

1. 1. A method for displaying analyte metrics, the method comprising: collecting one or more measurements reflective of ketone levels with a sensor-control device configured to be worn on the subject's body, the sensor-control device including a sensor configured to be positioned at least in part subcutaneously in contact with interstitial fluid of the subject; calculating, by one or more processors of the sensor control device, a plurality of analyte metrics, the plurality of analyte metrics including at least one ketone metric related to the one or more measurements reflective of the ketone levels; receiving the plurality of analyte metrics by a reader device, the sensor control device being in wireless communication with the reader device using a wireless communication protocol; determining, by the reader device, whether the subject is in a state of ketosis based on a comparison of the ketone metrics to a ketosis threshold; outputting at least a portion of the plurality of analyte metrics, including the at least one ketone metric, and an indication of whether the ketosis threshold has been met, to a graphical user interface on a display of the reader device; and outputting on the graphical user interface on the display of the reader device recommended values ​​for reaching the state of ketosis based on the at least one ketone metric, the recommended values ​​including recommended values ​​for nutrients; A method comprising:

2. 2. The method of claim 1, further comprising collecting, with the sensor control device configured to be worn on a body of a subject, one or more measurements reflective of a glucose level, and wherein calculating the plurality of analyte metrics comprises calculating at least one glucose metric related to the one or more measurements reflective of the glucose level.

3. The method of claim 2 , wherein the at least one glucose metric comprises a glucose curve associated with a glucose excursion.

4. 4. The method of claim 3, wherein the glucose excursion comprises one or more of: a violation of a low glucose level threshold, a violation of a high glucose level threshold, and a violation of a rate of change of glucose level threshold.

5. The method of claim 1 , wherein at least a portion of the outputted analyte metrics include a color indicator and a text indicator.

6. The method of claim 1 , wherein at least a portion of the outputted analyte metrics include a color indicator and a graphic indicator.

7. 2. The method of claim 1, wherein the at least one ketone metric comprises a ketone curve associated with a ketone excursion.

8. 8. The method of claim 7, wherein the ketone excursion comprises breaching a high ketone level threshold.

9. 8. The method of claim 7, wherein the ketone excursion comprises a breach of a rate of change threshold in ketone levels.

10. The method of claim 7 , wherein the ketone curve associated with the ketone excursion is displayed on the graphical user interface of the reader device.

11. The method of claim 1 , wherein the reader device comprises a smartphone.

12. 1. An analyte monitoring system, comprising: a sensor control device configured to be worn on a body of a subject, the sensor control device including a sensor configured to be at least partially subcutaneously positioned in contact with interstitial fluid of the subject, the sensor configured to collect one or more measurements reflective of ketone levels; and A reader device that wirelessly communicates with the sensor control device using a wireless communication protocol; The sensor control device may further include one or more processors and a memory, coupled to the one or more processors, having instructions stored therein, The instructions, when executed by the one or more processors, cause the one or more processors to: calculating a plurality of analyte metrics, the plurality of analyte metrics including at least one ketone metric related to the one or more measurements reflective of the ketone levels in the interstitial fluid; a memory for executing The reader device is receiving the plurality of analyte metrics; determining whether the subject is in a state of ketosis based on a comparison of the ketone metrics to a ketosis threshold; outputting at least a portion of the plurality of analyte metrics, including the at least one ketone metric, and an indication of whether the ketosis threshold has been met, to a graphical user interface on a display of the reader device; and outputting on the graphical user interface on the display of the reader device recommended values ​​for reaching the state of ketosis based on the at least one ketone metric, the recommended values ​​including recommended values ​​for nutrients; Execute system.

13. 13. The system of claim 12, wherein the sensor is further configured to collect one or more measurements reflective of a glucose level, and the processor is configured to calculate at least one glucose metric associated with the one or more measurements reflective of the glucose level.

14. The system of claim 13 , wherein the at least one glucose metric comprises a glucose curve associated with a glucose excursion.

15. 15. The system of claim 14, wherein the glucose excursion comprises one or more of: a violation of a low glucose level threshold, a violation of a high glucose level threshold, and a violation of a rate of change of glucose level threshold.

16. The system of claim 12 , wherein at least a portion of the outputted analyte metrics include a color indicator and a text indicator.

17. The system of claim 12 , wherein at least a portion of the outputted analyte metrics include a color indicator and a graphic indicator.

18. 13. The system of claim 12, wherein the at least one ketone metric comprises a ketone curve associated with a ketone excursion.

19. 20. The system of claim 18, wherein the ketone excursion comprises a breach of a high ketone level threshold.

20. 20. The system of claim 18, wherein the ketone excursion comprises a breach of a rate of change threshold in ketone levels.

21. 20. The system of claim 18, wherein the ketone curve associated with the ketone excursion is displayed on the graphical user interface of the reader device.

22. The system of claim 12 , wherein the reader device comprises a smartphone.

23. 10. The method of claim 1, further comprising indicating at least one time per day when the ketosis threshold has not been met.

24. The method of claim 1 , wherein the recommendations are daily nutrient recommendations.

25. 1. A method for displaying analyte metrics, the method comprising: collecting one or more measurements reflective of ketone levels and one or more measurements reflective of glucose levels with a sensor control device configured to be worn on the body of the subject, the sensor control device including an in-vivo sensor configured to be positioned at least partially subcutaneously and in contact with interstitial fluid of the subject; calculating, by one or more processors of the sensor control device, a plurality of analyte metrics including at least one ketone metric associated with the one or more measurements reflective of the ketone levels in the interstitial fluid and at least one glucose metric associated with the one or more measurements reflective of the glucose levels in the interstitial fluid; receiving the plurality of analyte metrics by a reader device, the sensor control device being in wireless communication with the reader device using a wireless communication protocol; outputting the at least one ketone metric and the at least one glucose metric to a graphical user interface on a display of the reader device; determining, by the reader device, whether the subject is in a state of ketosis based on a comparison of the ketone metrics to a ketosis threshold; and outputting an indication of whether the ketosis threshold has been met to the graphical user interface on the display of the reader device; A method comprising:

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