Systems, devices, and methods for wellness and nutrition monitoring and management using analyte data

Abstract

To provide systems, devices and methods for monitoring and management of an individual's wellness and nutrition using analyte data from an in vivo analyte sensor.SOLUTION: Generally, a sensor control device is provided for wear on the body. The sensor control device can include: an in vivo analyte sensor for measuring an analyte level in a bodily fluid; an accelerometer for measuring the physical activity level of a subject; and communications circuitry for wirelessly transmitting data to a reader device. Furthermore, disclosed herein are embodiments of various graphical user interfaces for displaying analyte metrics on a reader device, comparing the analyte response of various foods and / or meals, and modifying daily nutrient recommendations based on analyte metrics and physical activity level measurements, and other features described herein. Additionally, the embodiments disclosed herein can be used to monitor various types of analytes.SELECTED DRAWING: Figure 1

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 Art

[0002] Monitoring and managing an individual's health and nutrition can provide significant benefits to people at risk of chronic health problems or currently suffering from chronic health problems, and people seeking to improve their overall health. These efforts can produce multiple health and economic benefits for the individual and, more broadly, the public. For example, according to the CDC, seven out of ten people in the United States die each year from chronic diseases, and approximately one in two adults suffers from at least one chronic disease. Similarly, in the United States, approximately one in three children is overweight or obese and is more likely to develop chronic diseases. Many of these chronic diseases are preventable or can be well treated if diagnosed early. In this regard, monitoring and managing an individual's health and nutrition can significantly reduce the probability of chronic diseases, and as a result, reduce future medical costs. Further benefits of health and nutrition monitoring include improved athletic performance during training, recovery, or athletic events.

[0003] Wearable technologies (e.g., Fitbit) can be used to facilitate these goals. 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 visits become less frequent (e.g., once a year), wearable technologies can play a useful role in providing individuals with timely physiological information without requiring a doctor visit, which could ultimately lead to improved health. However, despite these advantages, many people are reluctant to use wearable technologies for a variety of reasons, including the complexity of the data presented, the learning curve associated with using wearable devices, and inaccuracies in the data. Several recent studies argue that existing wearable devices do not accurately measure an individual's heart rate or calories burned. [Overview of the project] [Problems that the invention aims to solve]

[0004] Therefore, there is a need for more accurate, user-friendly systems, devices, and methods for monitoring and managing health and nutrition. [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 analytic data received from an in vivo analytic sensor. Generally, a sensor-control device having a small form factor can be provided to the individual for wear on the body. The sensor-control device may include, within a single housing: an in vivo analytic sensor for measuring the subject's analytic level (or multiple analytic levels); and an accelerometer for measuring the subject's activity level. The in vivo analytic sensor may be configured so that at least a portion of the sensor is in contact with the subject's bodily fluids. The sensor-control device may also include a communication circuit configuration 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 analytic level measurements are not stored in the sensor-control device's memory, and these data are either encrypted or made accessible to the individual.

[0006] The reader device that receives data from the sensor control device may 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, which analyze the data transmitted from the sensor control device and display 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, multiple bars of different colors may indicate various analyte levels. In other embodiments, a numerical score representing the response of the analyte to the food or meal consumed is displayed. In yet another embodiment, daily nutrient recommendations based on physical activity level data can be displayed to the individual. These embodiments and other embodiments described herein represent improvements in the field of computer-based health and nutrition monitoring that surpass conventional or existing wearable devices. Other improvements and advantages are also provided, which will be apparent to those skilled in the art. Various configurations of these devices are illustrated by several embodiments, which are merely examples.

[0007] Other systems, devices, methods, features, and advantages of the subject matter described herein will be apparent to those skilled in the art by examining the following drawings and "Modes for Carrying Out the Invention." Such additional systems, devices, methods, features, and advantages are included herein, within the scope of the subject matter described herein, and are intended to be protected by the appended claims. Features of exemplary embodiments should not be construed as limiting the appended claims unless those features are expressly enumerated in the claims.

[0008] Details relating to the structure and operation of the subject matter described herein may become apparent by examining the accompanying drawings, in which similar reference numbers refer to similar parts. The components in the drawings are not necessarily to scale and are exaggerated to illustrate the principles of the subject matter. Furthermore, all figures are intended to convey concepts, and here relative sizes, shapes, and other detailed attributes may be illustrated schematically, without exaggeration or accuracy. [Brief explanation of the drawing]

[0009] [Figure 1] System overview of sensor control devices, reader devices, networks, local computer systems, and trusted computer systems. [Figure 2] Block diagram showing an exemplary embodiment of a reader device. [Figure 3A] Block diagram of an exemplary embodiment of a sensor control device [Figure 3B] Block diagram of an exemplary embodiment of a sensor control device [Figure 4] An illustrative graph showing multiple analyte curves over time. [Figure 5] Another illustrative graph showing multiple analyte curves over time. [Figure 6] Exemplary Embodiment with a Graphical User Interface for Displaying Analyte Metrics on a Reader Device [Figure 7] Exemplary Embodiment with a Graphical User Interface for Analyzing the Effects of Food [Figure 8] Exemplary Embodiment with a Graphical User Interface for Displaying an Individual's Glucose Tolerance [Figure 9] Another exemplary embodiment of a graphical user interface displaying a graph for monitoring a subject's carbohydrate intake. [Modes for carrying out the invention]

[0010] Before describing the subject matter in detail, it should be understood that this disclosure is not limited to the specific embodiments described herein and is therefore naturally subject to change. It should also be understood that the terms used herein are intended solely to describe specific embodiments and are not intended to limit them, for the scope of this disclosure is limited only by the appended claims.

[0011] As used in this specification and the appended claims, the singular forms "a, an" and "the" refer to multiple subjects unless the context explicitly indicates otherwise.

[0012] The publications discussed herein are provided only with respect to disclosures prior to the filing date of this application. Nothing in this specification should be construed as an acknowledgment that this disclosure has no prior rights over such publications on the grounds that these publications have been previously disclosed. Furthermore, the publication dates provided herein may differ from the actual publication dates and may need to be verified individually.

[0013] Generally, 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, which at least a portion of which is positioned or configured to be positioned on a user's body, thereby obtaining information on at least one analyte of the body in a bodily fluid (e.g., in subcutaneous interstitial fluid ("ISF") or blood, in dermal fluid of the dermis, or other), such as glucose. In some embodiments, for example, the sensor is configured to measure glucose levels. In other embodiments, the sensor may be configured to measure ketone levels instead of, or in addition to, measuring glucose levels. Furthermore, the detection of other analytes is also within the scope of this disclosure, and other analytes include, 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, and the like. The embodiments disclosed herein can also be used in combination with in vitro analyte monitoring systems incorporating in vitro functionality, and purely in vitro or extravital analyte monitoring systems (including completely non-invasive systems).

[0014] Furthermore, with respect to all embodiments of the methods disclosed herein, systems and devices capable of carrying out each of these embodiments are included within the scope of this disclosure. For example, embodiments of sensor control devices are disclosed, which may include one or more sensors, accelerometers, analyzer monitoring circuits (e.g., analog circuits), non-temporary memory (e.g., for storing instructions and data), power supplies, communication circuits, transmitters, receivers, processors, and / or controllers (e.g., for executing instructions stored in memory) that can carry out or facilitate the execution of any of the method steps.

[0015] Numerous embodiments of this disclosure are designed to improve the accuracy and ease of use of wearable technology using analytic data received from an in vivo analytic sensor. In some embodiments, for example, a sensor control device is worn on the body, and the sensor control device includes an in vivo analytic sensor and an accelerometer. Analytic metrics are determined by one or more processors in the sensor control device and transmitted to a reader device along with physical activity level measurements. Analytic 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 analytic level measurements. Various information is presented on the reader device's display. In some embodiments, for example, a simple carbohydrate graph can be displayed to the user, where multiple colored bars represent multiple ranges of analytic levels. In other embodiments, a numerical score can be presented to the user, where the score reflects the analytic response to ingested food or diet. In yet another embodiment, analytic metrics can be analyzed together with physical activity level measurements to determine whether daily nutrient recommendations need to be adjusted. Accordingly, these embodiments can provide subjects with accurate, customizable, easy-to-use, and easy-to-understand information regarding their health and / or nutrition, based on analytic data received from in vivo analytic sensors. Embodiments of the Disclosure can improve the accuracy of conventional systems by utilizing actual analytic level measurements and, in some embodiments, being supported by physical analytic level measurements from accelerometers. Furthermore, embodiments of the Disclosure can improve user adherence to health and nutrition monitoring regimens by presenting a simple and easy-to-use interface. Other features and advantages of embodiments of the Disclosure will be discussed further below.

[0016] However, before discussing these aspects of the embodiments in detail, it is desirable to first describe examples of devices that may be present internally, such as sensor control devices that transmit data from in vivo analyte sensors, and examples of the operation of such devices. All of these examples can 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 automatically transmit data from a sensor control device to a reader device continuously or repeatedly, for example, according to a schedule, regardless of the presence of a prompt. As another example, a "Flash Analyte Monitoring" system (e.g., a "Flash Glucose Monitoring" system or simply a "Flash" system) can transmit data from a sensor control device in response to a request for data initiated by a user, for example, using a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol, by a reader device (e.g., a scan). 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 of the body (or "ex vivo") and typically include a measurement device having a port for receiving an analyte test strip carrying a user's body fluid, and can determine the user's blood glucose level by analyzing the body fluid.

[0019] The in-vivo monitoring system can include a sensor, which, while positioned within the body, contacts the user's body fluid and senses the level of an analyte contained in the body fluid. The sensor can be part of the sensor control device that is present on the user's body and encloses the electronic components and power source that enable and control the sensing of the analyte. The sensor control device and its variations may be referred to, for some examples, as a "sensor control unit", a "body-mounted electronic component" device or unit, or a "sensor data communication" device or unit.

[0020] The in-vivo monitoring system can also include a reader device, which receives the sensed analyte data from the sensor control device, processes the sensed analyte data, and / or displays the sensed analyte data to the user in any of a number of formats. This device and its variations may be referred to, for some examples, as a "handheld device", a "reader device" (or simply "reader"), a "handheld electronic component" (or simply "handheld"), a "portable data processing" device or unit, a "data receiver", a "receiver" device or unit (or simply "receiver"), or a "remote" device or unit. Other devices such as personal computers, smartphones, and similar devices are also being used with or incorporated into in-vivo and in-vitro monitoring systems.

[0021] Exemplary embodiments of a personal health and nutrition monitoring system Figure 1 is a conceptual diagram showing an exemplary embodiment of a health and nutrition monitoring system 100, which includes a sensor control device 102 and a reader device 120. The system 100 may also include a sensor applicator 150, which can be used to apply the sensor control device 102 to a monitoring location on the user's skin so that the sensor 104 is maintained in a predetermined location on the user's body by an adhesive patch 105 for a given period of time. The sensor control device 102 is further described in relation to Figures 3A and 3B, and can 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 the memory on the reader device 120 using the screen 122 and input 121, and can recharge the reader device's battery using the power port 123. Further details regarding the reader device 120 are described below in relation to Figure 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 may be a laptop, desktop, tablet, phablet, smartphone, set-top box, video game console, or other computing device, and wireless communication may be one of many 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 many networks, such as private networks, public networks, local area networks, or wide area networks. Network 190 can be a cloud. Trusted computer system 180 may include servers, provide authentication services and / or secure data storage, and communicate with network 190 via communication paths 144 using wired or wireless techniques. Trusted computer system 180 may be considered part of network 190 (or the cloud) from the perspective of devices 102, 120, and 170.

[0023] Exemplary Embodiments of a Reader Device Figure 2 is a block diagram illustrating an exemplary embodiment of a reader device 120 configured as a smartphone. Here, the reader device 120 may include a processing circuit configuration 206 that includes: a display 122; input components 121; and a communication processor 222 coupled to non-temporary memory 223, and an application processor 224 coupled to non-temporary memory 225. It may also include: non-temporary memory 230; an RF transceiver 228 with an antenna 229; and a power supply 226 with a power management module 238. Furthermore, it may include a multifunction transceiver 232 that can communicate with the antenna 234 via Wi-Fi, NFC, Bluetooth, BTLE, ANT+, and GPS networks. As those skilled in the art will understand, these components are electrically and communicatively coupled to form a single functional device.

[0024] Furthermore, although described herein as a smartphone, the reader device 120 can also be a mobile smart wearable electronic component assembly, such as an optical assembly worn over or adjacent to the user's eyes (e.g., monocular or binocular smart glasses such as Google Glass, which are mobile communication devices). This optical assembly may have a transparent display that allows the user to view through the display while minimizing interference with the user's overall vision, and simultaneously displays information to the user at the user's analysis level (as described above). The optical assembly may be capable of wireless communication similar to that of a smartphone. Other examples of wearable electronic components include devices worn around or near the user's wrist (e.g., a watch), neck (e.g., a necklace), head (e.g., a headband, hat), chest, etc. Similarly, the reader device 120 can also be a tablet computer, a personal digital assistant, a laptop, or any other mobile computing device or personal computing device. The reader device 120 can also be an extension and / or add-on to a device used to monitor the performance of an activity or sport, such as a device configured to measure and / or display power, cadence, pace, heart rate, and speed. Adapting such a device to display analyte metrics is entirely within the scope of the embodiments described herein.

[0025] Exemplary Embodiments of Sensor-Controlled Devices Figures 3A and 3B are block diagrams illustrating exemplary embodiments of the sensor control device 102, which each includes an analyte sensor 104, an accelerometer 175, and a sensor electronic component 160 (including an analyte monitoring circuit configuration), which together can provide most of the processing power for rendering final result data, such as analyte metrics, suitable for display to the user. Figure 3A shows a single semiconductor chip 161, which can be a dedicated application-specific integrated circuit (ASIC). Shown within the ASIC 161 are specific higher-order functional units, including an analog front-end (AFE) 162), a power management (or control) circuit configuration 164, a processor 166, and a communication circuit configuration 168 (which can be implemented as a transmitter, receiver, transceiver, passive circuit, 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 circuit configurations, but in other embodiments, either circuit can perform the monitoring function. The processor 166 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed across a number of different chips (or be part of a number of different chips).

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

[0027] Non-temporary memory 163 is also included in the ASIC 161 and can be shared by various functional units present within the ASIC 161, or distributed among two or more of them. Memory 163 can also be a separate chip. Memory 163 can be volatile and / or non-volatile memory. In this embodiment, the ASIC 161 is connected to a power supply 170, which may be a coin cell battery or the like. The AFE 162 interfaces with the in vivo analyte sensor 104 and the accelerometer 175, receives measurement data from them, and outputs the data to the processor 166 in digital format. The processor 166 can then execute one or more instructions stored in memory 163, which cause the processor 166 to process the data and determine one or more analyte metrics (e.g., analyte curve, analyte curve profile (e.g., area, slope and / or length), rate of change of analyte), as well as physical activity level values, trend values, etc. Next, this data can be supplied to a communication circuit configuration 168 via the antenna 171 to send it to, for example, a reader device 120 (not shown), where additional processing by a resident software application may be required to display the above data. Only analyte metrics (including aggregated and / or averaged data) are stored in memory 163, while raw analyte level measurements are later discarded. For example, in some embodiments, raw analyte data reflecting glucose levels are not stored in the persistent memory 163 on the sensor and are therefore inaccessible by any means (e.g., by a reader device). In other embodiments, 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, raw analyte level measurements can be used to calculate analyte metrics, such as a moving average glucose level over the past 48 hours, and then these raw analyte measurements can be deleted or discarded.

[0028] Figure 3B is similar to Figure 3A, but includes two discrete semiconductor chips 162 and 174, which may be packaged together or separately. Here, the AFE 162 resides on the ASICS 161. As shown here, the AFE 162 is connected to both the analyzer sensor 104 and the accelerometer 175. Referring to chip 174, the processor 166 is integrated on chip 174 together with a power management circuit configuration 164 and a communication circuit configuration 168. The AFE 162 includes memory 163, and chip 174 includes memory 165, which may be internally isolated or distributed. In one exemplary embodiment (not shown), the AFE 162 is combined with the power management circuit configuration 164 and the processor 166 on a single chip, while the communication circuit configuration 168 is on a separate chip. In another exemplary embodiment (also not shown), both the AFE 162 and the communication circuit configuration 168 are located on one chip, while the processor 166 and the power management circuit configuration 164 are located on another chip. Other combinations of chips are also possible, including three or more chips, each performing a distinct function as 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 adjustment, and without other data such as temperature data) to the reader device 120 for further algorithmic processing into a format representing the wearer's analyte levels, which can 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 before 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 body fluids (e.g., epithelial fluid, interstitial fluid, subcutaneous fluid, or blood). A typical glucose profile in a healthy subject shows relatively flat glucose levels with a diet-related “peak” or analyte curve. The size and shape of the analyte curve may be related to the amount and type of food consumed.

[0031] As an example, Figure 4 is a graphical representation of analyte level measurements obtained by the analyte sensor 104, showing the glucose levels over time in a non-diabetic subject. In particular, Figure 4 is a graph 200 representing 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 represents the subject's baseline analyte level, such as the fasting blood glucose level. Analyte curve AC1 shows the rise in blood glucose level at approximately 7:30 a.m., in response to the subject consuming breakfast. Similarly, analyte curves AC2 and AC3 show the rises in blood glucose level at noon and 6 p.m., in response to the subject consuming lunch and dinner, respectively. Each analyte curve AC1, AC2, and AC3 may have an analyte curve profile, where each profile includes at least: the area below the analyte curve, indicated in Figure 4 by the shaded area limited between the analyte level measurement 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] Figure 5 is another graphical representation of the analyte level measurements obtained by the analyte sensor 104 over a three-day period. In particular, graph 250 in Figure 5 reflects the glucose levels over a 72-hour period from the LIBRE sensor (manufactured by ABBOTT DIABETES CARE INC.) worn by a non-diabetic subject, showing days 8 to 10 of the 14-day LIBRE sensor wearing period. On day 9 (the middle 24-hour period of the 72-hour period shown in the graph), the non-diabetic subject consumed a low-carbohydrate diet with a total carbohydrate intake of approximately 10g. On the days before and after this (days 8 and 10), the subject consumed a normal diet with a relatively high carbohydrate content. As is evident from the small analyte curve, glucose variability on day 9 was small, with most raw glucose values ​​being between 90 and 110 mg / dL.

[0033] Exemplary Embodiment of a Graphical User Interface for Displaying Analyte Metrics This specification describes exemplary embodiments of a graphical user interface for displaying analyte metrics on the reader device 120. As described above with respect to Figure 4, the in vivo analyte sensor 104 can measure analyte levels in the subject's bodily fluids and can determine one or more analyte metrics by one or more sensor electronic component processors. The communication circuit configuration of the sensor control device 102 can then wirelessly transmit the analyte metrics to the reader device 120. As described above with respect to Figures 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 memory on the sensor control device 102 in an aggregated format. For example, in some embodiments, a moving average of one or more analyte levels over a predetermined time period (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 time period (e.g., 48 hours) may be stored in the memory of the sensor control device 102. Those skilled in the art will readily understand that other types and formats of aggregated analytic metrics are within the scope of the embodiments of this disclosure; therefore, these examples are illustrative and not intended to limit the scope in any sense.

[0034] The reader device 120 receives analyte metrics wirelessly through its communication circuit configuration. A graphical user interface (GUI) stored as instructions in the reader device 120's memory can be executed by one or more reader device processors to visually output the analyte metrics to the reader device 120's display. In certain examples, as described below, the GUI is interactive, allowing the subject to input information into the reader device 120 through the GUI using an input device (e.g., touchscreen, keyboard, or mouse).

[0035] Figure 6 shows an exemplary embodiment of a GUI 300 for displaying analyte metrics on the reader device 120. Generally, the GUI 300 can display simple, easy-to-read numerical scores representing analyte curves related to meals consumed by the subject, such as those described with respect to Figure 4 (e.g., AC1, AC2, AC3). As shown in Figure 6, a date and time display 310 is shown at the top of the reader device 120 for easy reference. Below the date and time display 310 are three horizontally oriented numerical scores 320, which represent the three meals consumed by the subject, namely breakfast, lunch, and dinner. In some embodiments, meal occurrences can be determined by routines and / or algorithms stored in the memory of the reader device 120 and configured to detect patterns in analyte metrics received from the sensor control device 102. Detection of meal events may include the detection of analyte episodes or excursions outside the desired acceptable (e.g., medically recommended) target range for the user, and the software can notify the user that one or both of these have been detected. Examples of analyte excursions include violations of the low glucose threshold, the high glucose threshold, the rate of change (e.g., increase or decrease) threshold, the median glucose threshold, and the glucose variability threshold. In other embodiments, the occurrence of a meal can be further confirmed by a routine configured to corroborate detected patterns of analyte metrics at specific times. Other examples of sets of algorithms, routines, or other instructions for detecting meals and their variations are described in U.S. Published Patent No. 2013 / 0085358, U.S. Published Patent No. 2014 / 0350369, or U.S. Published Patent No. 2014 / 0088393, or International Publication No. 2015 / 153482, all of which are incorporated into this application in whole for all purposes. Some of the sets of algorithms, routines, or instructions described in these incorporated references are described only in relation to the identification of analyte excursions outside the desired target range.These embodiments can be extended to the detection of meal events based on the teachings contained in another of these references (e.g., International Publication No. 2015 / 153482). These embodiments can also be extended to the detection of meal events by identifying target-range episodes in which glucose values ​​are maintained between upper and lower limits over a period of time. Detection of these episodes can be carried out by extensions of threshold-based episode detection algorithms.

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

[0037] Referring again to Figure 6, the numerical score can be an integer on a scale of 1 to 5, and this integer is proportional to the area under the analyte curve. For example, a numerical score of "3" can reflect the area of ​​the "initial setting" analyte curve, representing the analyte's response to a standard initial setting diet. In contrast, a numerical score of "5" can indicate a food or diet that yields a relatively large analyte response, in which case the analyte curve has a larger area compared to the initial setting diet. Conversely, a numerical score of "1" can indicate an ingested food or diet that yields a relatively small analyte response, in which case the analyte curve has a smaller area compared to the initial setting diet. Furthermore, as shown in Figure 6, in many embodiments, the numerical score reflects the area under one or more corresponding analyte curves. However, in other embodiments, the numerical score can also be a function of other metrics in 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 score 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. Numerical scores can also reflect analyte curve profiles for specific types of foods and / or meals. For example, in some embodiments, relatively high numerical scores may reflect an analyte curve characterized by steep spikes, which may reflect foods or meals containing refined carbohydrates (e.g., refined flour pasta). In other embodiments, relatively low numerical scores may reflect an analyte curve characterized by a gentle slope, which may reflect foods or meals high in fiber or complex carbohydrates (e.g., whole-wheat pasta). Similarly, in other embodiments, the numerical score can be a function of the rate of change of the analyte level.

[0038] Continuing to refer to Figure 6, below the numerical score 320, three summary metrics 330, 340, and 350 related to breakfast, lunch, and dinner are displayed. Today's score 330 may represent the sum of the numerical scores for meals consumed on that day. This week's score 340 may represent the sum of the numerical scores up to the current day for that week. The target weekly score 350 may represent the target numerical score that the subject intends to remain below. Furthermore, as shown below these summary metrics, daily advice or instructions 352 can be provided to help the subject meet their target weekly score. Thus, this simple GUI 300 presents the user with easily understandable analyte metrics without the subject needing to understand or interpret the underlying analyte-level measurements acquired by the sensor 104.

[0039] As shown in Figure 6, in many embodiments, numerical scores can be represented as integers on a scale of 1 to 5. However, in other embodiments, numerical scores can be represented as a percentage greater than or less than the area under the analytic curve in response to a standard initial food or meal (e.g., 30% above or 30% below the standard). Furthermore, in some embodiments, specific analytic metrics, such as a score of 330 today, a score of 340 this week, or a target weekly score of 350, can be represented as an average rather than a sum, as shown in Figure 6. In some embodiments, other non-numerical representations can be used in addition to, or instead of, the numerical scores described above. For example, indicators of different colors (e.g., red light, yellow light, or green light), text indicators (e.g., "good," "neutral," "bad"), graphic indicators (thumbs up, thumbs down, happy face, sad face, or other emoticons), and letter grades (e.g., A, B, C, D, or F) are all within the scope of this disclosure.

[0040] Furthermore, since the relationship between the analyte curve and the food or meal consumed may be unique to each individual, the system can also be calibrated for each individual using a calibration feature component (not shown) against the analyte curve for a standard initial diet. In some embodiments, calibration can be performed by using the calibration feature component to adjust the initial analyte curve over time to the individual's average analyte curve, for example, starting with a standard initial diet. In some embodiments, the subject may determine a meal that represents a standard diet and manually set the initial response to this meal.

[0041] Furthermore, GUI300 may include feature components to incentivize the subject to reach their target weekly score of 350. For example, in certain embodiments, GUI300 may include feature components to keep the subject's friends informed of the subject's status, or to send messages to the subject's friends through a social media platform (e.g., Facebook) after the target weekly score is achieved. In other embodiments, monetary incentives (e.g., membership discounts) may be offered to encourage the use of GUI300 for initial sign-up or for referring friends to sign up to use this GUI. In other embodiments, once the target weekly score is achieved, GUI300 may offer the subject one or more monetary rewards, such as discount codes, coupons, or in-app rewards.

[0042] Exemplary Embodiment of a Graphical User Interface for Analyzing the Effects of Food Figure 7 shows 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 effects of a particular food or diet on a particular individual compared to a standard food or diet with default settings. It is recognized that prescribed diets and nutrition must be customized for each individual. While it is true that there are common guidelines for all people, there is a wide range of individual variability in food responses based on individual physiology. Furthermore, food choices may be limited based on regional availability, cultural acceptance, food allergies, etc. The GUI 400 can present individual-specific information on which foods are most nutritionally beneficial and have the fewest adverse effects on factors such as weight gain among the available foods.

[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 relating to 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. Next, one or more reader device processors can execute an application in the form of instructions stored in the memory of the reader device 120, thereby causing the processors to associate a specific analyte curve profile relating to the ingested food or meal with a food entry in the database, and to store the analyte curve profile and the associated metrics in the database. Further execution of the application can perform a comparison between the analyte curve profile relating to the ingested food or meal and a default analyte curve profile relating to a standard default food or meal, and the results of this comparison can be visually output on the display of the reader device 120.

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

[0045] In some embodiments, the food database may reside in memory on the reader device 120. In other embodiments, the database may be stored on a trusted computer system located remotely from the reader device 120 and accessed via a local network, wide area network, or the Internet. Furthermore, the GUI 400 may incorporate third-party applications that facilitate the input of food intake data, and associated third-party food databases. In some embodiments, the GUI 400 may also store and / or aggregate analytic metric data in the database, which may include data that are indicators 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 may retrieve 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 described above can, for example, reflect the mean or median, weighted mean, standard deviation, and other statistical parameters of analyte metrics that can reflect the impact of a particular food or diet on a population (or a portion thereof), and can be used to normalize and / or compare with an individual's response to that particular food or diet. Furthermore, population data collected from multiple individual users can be made accessible to authenticated individuals, groups, public health authorities, and / or payers.

[0046] Furthermore, in some embodiments, the GUI 400 can also be interfaced with one or more social media platforms, similar to the social media feature portion described above with respect to Figure 6. For example, according to one aspect of this embodiment, the GUI 400 may include a feature portion that sends messages or notifications to the subject's friends to keep them informed of the subject's status regarding the meals consumed, a numerical score related to the meals consumed, or one or more indicators reflecting the impact of a particular meal consumed. Furthermore, in some embodiments, monetary incentives (e.g., membership discounts, discount codes, in-app rewards) can be offered for initial sign-up or for referring friends to sign up to use this GUI.

[0047] According to another aspect of this embodiment, the GUI 450 can also assist in the detection of prediabetes. As shown in Figure 8, chart 390 initially includes the following 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 prediabetic subject; and (3) analyte curve 398 representing the glucose response of a normal (neither diabetic nor prediabetic) subject. A dashed analyte curve 396 can represent the average glucose response over four weeks, which is superimposed on analyte curves 392, 394, and 398. In this regard, subjects can visually approximate their analyte tolerance to that of a normal subject, a prediabetic subject, and / or a diabetic subject. Furthermore, to provide further guidance regarding the information displayed in chart 390, a text display 395 can be provided below chart 390.

[0048] According to other embodiments of this embodiment, the GUI described herein may also receive and process data from other types of sensors to provide an assessment of a subject's health. For example, in some embodiments, other types of sensor data include physical activity level measurements from an accelerometer on the sensor control device 102. In other embodiments, other types of sensor data include data from a heart rate sensor, which can be incorporated into the sensor control device 102 or deployed separately from the sensor control device 102. Along with the GUI, instructions stored in the memory of the reader device 120 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 Embodiment of a Graphical User Interface for Monitoring Carbohydrates Figure 9 shows another example of a GUI for displaying analyte metrics on the reader device 120. In particular, Figure 9 shows a GUI 500 that includes a carbohydrate graph for monitoring a subject's carbohydrate intake. Generally, analyte metrics derived from raw glucose signals, such as signals acquired by the in vivo analyte sensor 104, can represent a subject's carbohydrate intake. For example, analyte metrics that reflect a rate of change or an integral value exceeding a certain threshold glucose value may better reflect raw carbohydrate intake than raw glucose values. In this regard, the carbohydrate graph in Figure 9 reflects a subject's carbohydrate intake by displaying a graph based on one or more analyte metrics received from the sensor control device 102. As shown in Figure 9, this graph can be a plot line 516. However, in other embodiments, this graph can be a discontinuous series of dots, extrapolations or curves, scatter shots, shaded areas, or other representations of the received analyte metrics. Furthermore, as can be seen in Figure 9, the horizontal axis 520 reflects increments of time units across a 72-hour time window (for example, from 0:00 on January 15, 2016 to 0:00 on January 18, 2016). It should be understood that other predetermined time windows, such as 8-10 hours or 5-15 hours, can also be used. The vertical axis is unlabeled and does not display values. GUI500 is more intuitive and easier to understand for subjects than, for example, graph 250 in Figure 5, because subjects may not have the numerical, analyte-level technical understanding necessary for interpreting raw analyte values.

[0050] Continuing to refer to Figure 9, the plot line 516 may include a series of plotted analyte values ​​associated with one or more metrics received from the sensor control device 102. Furthermore, each plotted analyte value may be assigned to one or more ranges, such as high, medium, or low, where each range can 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, the red band 514 may indicate analyte values ​​assigned to the high range, the yellow band 510 may indicate analyte values ​​assigned to the medium range, and the green band 512 may indicate analyte values ​​assigned to the low range. While Figure 9 shows three ranges (high, medium, and low) and corresponding three colors (red, green, and yellow), other ranges, indicators, colors, visual patterns, and gradients may also be used and are therefore within the scope of this disclosure.

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

[0052] According to another aspect of some embodiments, the assignment of plotted analytes to a particular range and / or colored band may also include the integral of several past analytes (or rates of change). For example, in some embodiments, if a predetermined number of preceding plotted analytes are all assigned to the high range, or similarly, if there is a predetermined period in which all plotted analytes are assigned to the high range, then the next plotted analyte can also be assigned to the high range. In contrast, according to the same exemplary embodiment, if a predetermined number of preceding plotted analytes are all assigned to the low range, or similarly, if there is a predetermined period in which all plotted analytes are assigned to the low range, then the next plotted analyte can also be assigned to the low range. In this regard, GUI500 may include an "inertial resistance" algorithm, in which, for example, if there is a long period of low carbohydrate consumption, GUI500 "remembers" the relatively long integral period of low carbohydrate consumption, so that the corresponding colored band does not necessarily turn red due to a short period of high carbohydrate consumption. In some embodiments, this "inertia resistance" algorithm can be started or stopped by the user via a GUI500 configuration interface (not shown). Similarly, in some embodiments, the level of inertia resistance, i.e., the threshold duration or number of preceding consecutive low-range (or high-range) analyte values, can be set by the user via an input device via a GUI500 configuration interface (not shown).

[0053] According to another aspect of this embodiment, the GUI 500 may include multiple user-selectable levels to control the "leniency" of the process of assigning plotted analyte values ​​to a particular and / or colored band. In particular, the subject can select an "easy," "medium," or "hard" level by a setting interface (not shown) accessible through the 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 the range of values ​​for the low, medium, or high range, respectively, based on the user-selectable level. In other words, if the easy level is selected, the low range may include a wider range of values ​​than the medium or high range. If the medium level is selected, the medium range may include a wider range of values ​​than the low or high range. If the hard level is selected, the high range may include a wider range of values ​​than the medium or high range. In this regard, the subject can start using the GUI 500 with the easy setting, where most of the plotted analyte values ​​are assigned to the low range and therefore have green. Subsequently, subjects can decide to proceed to a medium or hard setting, where there are fewer plotted analyte values ​​assigned to the low range and a greater predominance of "red" plotted analyte values. These settings allow users to determine the level of feedback that also provides the ability to distinguish between low-carbohydrate and high-carbohydrate diets while offering constructive incentives.

[0054] According to another aspect of this embodiment, periodic monitoring of carbohydrate intake (e.g., daily, weekly, monthly) can be performed to establish a carbohydrate intake profile for a particular user by having one or more processors in each device execute instructions stored in the memory on the sensor control device 102 and / or reader device 120. For example, in some embodiments, these instructions allow one or more processors to identify, store, and recognize the user's carbohydrate intake patterns. Based on the identified patterns of the user's carbohydrate intake related to specific foods and / or meals, a carbohydrate intake profile can be established. In some embodiments, these instructions can also detect excursions of analytes that contradict the established carbohydrate intake profile, and subsequently prompt the user to input additional information about the type and amount of food and meals consumed by the user. These instructions allow the processors to analyze the excursions and associated food intakes to determine the impact of various types and amounts of food and meals. In other embodiments, the instructions may prompt the user to input information about the type and / or amount of carbohydrates for each food or meal intake. According to one aspect of these embodiments, the command can identify and / or store a pattern of the user's carbohydrate intake profile after each intake of food or meal. Once a sufficient number of patterns have been detected, or after a predetermined period of time, the command can stop requesting user input for each intake of 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 Embodiment of an Interface for Modifying Daily Nutrient Recommendations In another exemplary embodiment, a GUI is provided for generating and modifying daily nutrient recommendations. The U.S. Department of Health and Human Services' National Health and Nutrition Board (NHSF) has issued Dietary Reference Intake (DRI) documents to establish principles and guidelines for appropriate food intake. In particular, the NHSF makes authoritative judgments regarding the relationship between food intake, nutrition, and health. Daily nutrient recommendations based on DRI can be calculated using several interactive tools, such as the Interactive DRI for Health Professionals provided by the USDA. These tools can calculate (1) estimated calorie requirements; (2) macronutrients such as carbohydrates, fiber, protein, fat, and water; and (3) vitamins and minerals, taking into account sex, age, BMI, and activity level.

[0056] The following table provides an example of daily nutrient recommendations for a 51-year-old male subject who is 6 feet 1 inch tall (185.42 cm) and weighs 170 pounds (77.1107 kg). In some embodiments, the daily nutrient recommendations shown below can be visually displayed on the reader device 120. In particular, when an instruction stored in the memory of the reader device is executed by one or more reader device processors, one or more reader device processors can be caused to output the following information to the 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 individual daily nutrient recommendations by monitoring the activity level of individual subjects; and (2) correlate with carbohydrate intake by monitoring the individual analytic response. As described above, in addition to the in vivo analytic sensor 104, the sensor control device 102 may 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 analytic metrics and physical activity level measurements to the reader device 120. Subsequently, in some embodiments, instructions stored in the memory of the reader device 120 can be executed by one or more reader device processors, causing the processors to decide whether to adjust the daily nutrient recommendations based on the one or more analytic metrics and physical activity level measurements.

[0062] According to another aspect of this embodiment, the subject's heart rate and daily heart rate pattern can be measured by a device, such as a heart rate monitor integrated into or separately deployed from the sensor control device 102, and transmitted wirelessly to the reader device 120. Subsequently, the recommended daily nutrient values ​​can be further modified based on one or more heart rate measurements, as well as 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 incorporated into or separately deployed from the sensor control device 102, and transmitted wirelessly to the reader device 120. Then, as with other physiological data, daily nutrient recommendations can be further modified based on one or more hydration level measurements.

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

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

[0066] Exemplary Embodiment of a Graphical User Interface for Ketone Monitoring In all embodiments disclosed herein, references to analyte-level measurements, analyte metrics, analyte curves and / or analyte curve profiles may refer to any of the numerous analytes found in the subject's bodily fluids and detectable by the in vivo analyte sensor 104. In some embodiments, analyte metrics related to ketone monitoring can be displayed to the subject on the reader device 120. In particular, a GUI can be provided to detect points in time when the ketosis threshold is not met, and to display one or more recommended values ​​to the subject in order to help the subject maintain a state of ketosis for dietary or medical reasons.

[0067] For example, several known diets involve reducing 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 producing energy using lipid stores. During this process, the body enters nutritional ketosis, a state in which ketone bodies containing three compounds (i.e., acetone, acetoacetate, and the most dominant β-hydroxybutyrate) can be detected in body fluids. It is generally understood that complex carbohydrates designed for slow metabolism can be introduced into this diet, which will continue to maintain the state of ketosis. Furthermore, ketone monitoring 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 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 in diabetic patients for identifying diabetic ketoacidosis. In both cases, ketone levels throughout the body can be determined using urine strips (e.g., Ketostix) or blood-based strips (e.g., Abbott Diabetes Care Ketone Test Strips). However, urine-based tests provide a historical view of ketone levels that is significantly delayed from the current state. While blood-based tests provide more near real-time results, the dataset is incidental and depends on how often the subject uses the test strips. This can lead to the loss of important information for understanding how diet and exercise affect a person's state of nutritional ketosis.

[0069] All of the above embodiments can be used for ketone monitoring, either alone or in combination with glucose monitoring. In some embodiments, a GUI can be provided, for example, to reach a target habit score for ketone metrics (Figure 6), or to determine a ketone response to a particular diet, food, supplement, or medication (Figure 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 the ketone levels in the subject's body fluids; and (2) physical activity level measurements from an accelerometer on the sensor control device 102. According to another aspect of one embodiment, a GUI can be provided for determining and displaying one or more times when the ketosis target threshold is not met, and for displaying one or more recommended values ​​for achieving the ketosis target threshold. With respect to these embodiments and other embodiments, instructions can be stored in the memory of the reader device 120, and when these instructions are executed by one or more reader device processors, they cause one or more processors to analyze and display specific ketone metrics to help the subject reach and maintain a state of ketosis.

[0070] It should be noted that all features, elements, components, functions, and steps described in relation to any embodiment provided herein are intended to be freely combined and substituted with features, elements, components, functions, and steps from any other embodiment. If a particular feature, element, component, function, or step is described in relation to only one embodiment, it should be understood that, unless otherwise specified, that feature, element, component, function, or step can be used with all other embodiments described herein. Therefore, this paragraph serves as prior foundation and codified support for introducing claims that combine features, elements, components, functions, and steps from multiple different embodiments, or replace features, elements, components, functions, and steps from one embodiment with those from another, even if such combinations or substitutions are not explicitly stated in the following description in a particular example. It is clearly acknowledged 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 permissible.

[0071] To the extent that the embodiments disclosed herein include, or operate in connection with, memory, storage, and / or computer-readable media, such memory, storage, and / or computer-readable media are non-transient. Accordingly, to the extent that memory, storage, and / or computer-readable media are encompassed by one or more claims, such memory, storage, and / or computer-readable media are solely non-transient.

[0072] These embodiments allow for various modifications and alternative forms, specific examples of which are illustrated in the drawings and described in detail herein. However, it should be understood that these embodiments are not limited to any particular form disclosed, but rather encompass all modifications, equivalents, and alternatives that fall within the spirit of this disclosure. Furthermore, any features, functions, steps, or elements of these embodiments may be enumerated or added to the claims, and negative limitations that define the scope of the claimed invention by features, functions, steps, or elements that fall outside that scope may also be enumerated or added to the claims. [Explanation of Symbols]

[0073] 100 Health and Nutrition Monitoring Systems 102 Sensor control devices 104 Sensor, Analytical Sensor 105 Adhesive Patches 120 Reader Devices 121 input 122 screens, displays 123 Power Ports Communication paths 140, 142, 143, 144 150 Sensor Applicators 160 Sensor Electronic Components 161 Semiconductor chips, ASICs 162 Analog Front-End, AFE, Semiconductor Chip 163, 223, 225 Non-temporary 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 Reliable Computer Systems 190 Networks 200, 250, 360 graphs 206 Processing Circuit Configuration 222 Communication Processors 224 Application Processors 226 Power supply 228 RF Transceiver 229 Antenna 230 Non-temporary memory 232 Multifunctional Transceiver 234 Antenna 238 Power Management Modules 300, 400, 450, 500 GUI 310 Date and time display 320 numerical score 330 Overview Metrics, Today's Score 340 Overview Metrics, This Week's Score 350 Overview Metrics, Target Weekly Score 362, 364, AC1, AC2, AC3 analyte curves 370, 375 Numerical scores 380 Text input boxes 385 Image Input Box 390 Chart 392, 394, 396, 398 analyte curve 395 Text display 510 Yellow Band 512 Green band 514 Red Band 516 Plot lines

Claims

1. A system for monitoring personal health and nutrition, the system comprising: (1) A sensor control device, comprising: (a) a glucose sensor including a portion configured to contact a bodily fluid of a user to sense a signal indicative of a glucose level of the user; and (b) sensor electronics coupled to the glucose sensor, (i) one or more processors; (ii) a memory coupled to the one or more processors, the memory holding instructions that, when executed by the one or more processors, cause the one or more processors to determine sensed glucose data based on the signal from the glucose sensor; and (iii) a wireless communication circuit configured to communicate the glucose data; Sensor electronics, A sensor control device comprising: (2) A reader device, (a) a display; (b) one or more processors; (c) a transceiver configured to receive the glucose data from the sensor electronics; (d) a memory coupled to the one or more processors of the reader device, the memory holding instructions that, when executed by the one or more processors of the reader device, cause the one or more processors of the reader device to calculate an accumulated score based on the glucose data and display the accumulated score and a target score on the display of the reader device; A reader device, Including, The system, wherein the accumulated score is a sum of a plurality of numerical scores over a predetermined period of time, each of the plurality of numerical scores representing an analyte curve for glucose associated with an ingested food and based on at least the area under the analyte curve and the length of the analyte curve.

2. The system of claim 1, wherein the glucose data includes one or more glucose metrics.

3. The system of claim 1, wherein the processor of the reader device is further configured to receive user input of information related to the ingested food.

4. The system of claim 1, wherein the processor of the reader device is configured to automatically detect the ingested food based on an excursion of the glucose data outside a target range.

5. The system of claim 1, wherein the accumulated score comprises the sum of the numerical scores for foods ingested in a day.

6. The system of claim 1, wherein each of the numerical scores is further based on the slope of the analyte curve for the glucose.

7. The system of claim 1, wherein each of the numerical scores includes an integer.

8. The system of claim 1, wherein the numerical score is within a predetermined range of scores.

9. The system of claim 1, wherein the processor of the reader device is further configured to output daily advice on the display to assist the user in reaching the target score.

10. The system of claim 1, wherein the accumulated score and the target score are displayed, and the analyte curve for glucose is not displayed.

11. A method for monitoring the health and nutrition of an individual using a sensor control device including a glucose sensor, the method comprising: determining an analyte curve for glucose associated with ingested food based on sensed glucose data obtained by the glucose sensor, the glucose sensor including a portion configured to contact a bodily fluid of the user to measure a glucose level in the bodily fluid of the user; determining a numerical score for the ingested food, the numerical score being based on at least the area under the analyte curve for glucose and the length of the analyte curve for glucose; calculating an accumulated score based on the glucose data, the accumulated score being a sum of the numerical scores of analyte curves for multiple glucose associated foods over a predetermined period of time; and displaying the accumulated score and the target score on a display of a reader device coupled to the glucose sensor and in wireless communication with the sensor electronics; A method comprising:

12. The method of claim 11, wherein the glucose data includes one or more glucose metrics.

13. The method of claim 11, further comprising the step of receiving user input of information related to the ingested food.

14. The method of claim 11, further comprising a step of automatically detecting the ingested food based on an excursion of the glucose data outside a target range.

15. The method of claim 11, wherein the accumulated score comprises the sum of the numerical scores for foods ingested on that day.

16. The method of claim 11, wherein each of the numerical scores is further based on the slope of the analyte curve for the glucose.

17. The method of claim 11, wherein each of the numerical scores comprises an integer.

18. The method of claim 11, wherein the numerical score is within a predetermined range of scores.

19. The method of claim 11, comprising the step of outputting daily advice on the display to assist the user in reaching the target score.

20. The method of claim 11, wherein the accumulated score and the target score are displayed on the display, and an analyte curve for the glucose is not displayed on the display.

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