Systems, devices and methods for dietary information collection, dietary assessment and analyte data correlation - Patents.com

Abstract

Systems, devices, and methods for detecting, measuring, and classifying an individual's diet based on analyte measurements. These results and related information can be presented to indicate to the individual which diet is causing the most severe analyte response. These results can be organized and classified based on preselected criteria or past diets and results, such that the results are organized and presented in a format with reference to glucose as the monitored analyte. Various embodiments disclosed herein relate to methods, systems, and software applications intended to engage individuals by providing direct and timely feedback regarding their meal-related glycemic response.

Description

Related Applications

[0001] This application claims priority to U.S. Provisional Application No. 63 / 144,782, filed February 2, 2021, and U.S. Provisional Application No. 63 / 046,849, filed July 1, 2020, both of which are expressly incorporated by reference herein in their entirety for all purposes. This application is also related to U.S. Application No. 15 / 206,095, filed July 8, 2016, which application claims priority to and the benefit of U.S. Provisional Application No. 62 / 191,218, filed July 10, 2015, U.S. Provisional Application No. 62 / 191,262, filed July 10, 2015, U.S. Provisional Application No. 62 / 307,344, filed March 11, 2016, and U.S. Provisional Application No. 62 / 307,346, filed March 11, 2016, all of which are expressly incorporated herein by reference in their entirety for all purposes. [Technical Field]

[0002] The present subject matter generally relates to systems, devices, and methods for collecting information about analyte levels of individuals and information about the diets consumed by those individuals. The subject matter further relates to correlating the dietary information with analyte levels and processing, analyzing, and / or presenting this information for the purpose of identifying and implementing adjustments to those individuals' diets, lifestyles, and / or medical treatment regimens. [Background technology]

[0003] The increasing prevalence of type 2 diabetes and metabolic syndrome over the past few decades has been attributed to changes in diet and activity levels. For example, the consumption of relatively readily available high-glycemic index (GI) foods causes rapid increases in postprandial blood glucose and insulin levels, which are clearly associated with weight gain and obesity. These conditions may further contribute to an increased risk of developing these and other diseases.

[0004] Most people generally understand the importance of their diet. However, in practice, many struggle to translate this general awareness into their specific food choices. These problems exist primarily because people cannot directly see the impact of their choices. This can lead to misconceptions about food portion sizes, misconceptions about which foods are relatively healthy, and a general lack of awareness about the duration and intensity of activity needed to maintain health. These problems are further exacerbated by recommendations based on advertising, habit, peer pressure, food preferences, and generalizations.

[0005] To address these issues, analyte monitoring systems can track and better understand an individual's physiological responses. Because elevated glucose levels are primarily caused by food intake, postprandial glucose levels can be related to the amount of carbohydrates and other dietary components an individual ingests, as well as the individual's physiological response to the meal. However, analyzing this vast amount of data presents a challenge: representing the data in a meaningful way that allows for efficient action. Data regarding dietary choices and their subsequent effects needs to be understood on both a clinical and individual basis to help individuals, dietary managers, and / or medical professionals understand and mitigate glucose excursions, such as hyperglycemic episodes.

[0006] Previous attempts to implement software for tracking a user's food intake and correlating it with the user's analyte data have suffered from numerous deficiencies. For example, some systems require individuals to take numerous, inconvenient and uncomfortable, discrete blood glucose measurements (e.g., fingerstick blood glucose tests). These solutions can also suffer from an insufficient number of data points to adequately determine the glycemic response to a meal. For example, an individual may take discrete blood glucose measurements at times before or after the user's peak glycemic response, making it difficult to accurately capture the glycemic response and to meaningfully compare meals based on the glycemic response. A lack of data points also makes it difficult to automatically detect the occurrence of meal events in the user's analyte data. Thus, some prior systems rely heavily on users' manual logging of meals.

[0007] Prior art systems that attempt to detect meal events based solely on the presence of elevated glucose levels, such as U.S. Patent No. 5,929,523, are inadequate because they fail to take into account a user's prior meal history and may therefore overestimate the number of meals a user has consumed.

[0008] Thus, there is a need for improved systems, devices, and methods for dietary information collection, dietary assessment and detection, and correlation with analyte levels. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] US Patent Application Publication No. 2003 / 0208113 Summary of the Invention

[0010] Provided herein are exemplary embodiments of systems, devices, and methods for detecting, measuring, and classifying a human individual's diet in relation to the individual's analyte measurements. These individuals may include those who exhibit or have been diagnosed with diabetes, those considered pre-diabetic, those with metabolic syndrome, and even those without symptoms of diabetes, pre-diabetes, or metabolic syndrome. These individuals may be anyone motivated to improve their health by adjusting their dietary and / or activity habits. As a result, the individual may be presented with information indicating which meals or aspects of their diet are most influencing analyte levels. These results may be organized and categorized based on pre-selected criteria, either selected directly by the individual or based on the individual's consultation with a medical professional.

[0011] In many embodiments, an individual's meal-related analyte responses collected by an analyte monitoring system, such as an in vivo analyte monitoring system, can be compared or linked to dietary information to discover common consistencies (or inconsistencies) along with trends based on associated historical glucose readings and associated algorithms, variables, weights, comparisons, and trends.

[0012] Many embodiments disclosed herein are intended to engage individuals by providing direct and timely feedback regarding their diet-related analyte responses, which in some embodiments can be provided to individuals to characterize the effects of dietary intake.

[0013] This embodiment is immediately beneficial and enjoyable for individuals to use, thereby encouraging them to experiment with it to better understand how their diet affects their body's analyte responses. Individuals can compare and contrast their current and past analyte data to see how their efforts relate to better eating habits and food categories, and how these choices directly impact their health.

[0014] Exemplary embodiments are also provided that can collect analyte data from a community population and link or associate that data with dietary information regarding the meals consumed by the community population. The aggregated information can be processed and presented to a user to identify common trends in analyte levels among the community population and determine whether any correlations exist with the population's common dietary habits. For example, a person responsible for dietary choices can use the presented information to determine whether dietary content, type, and / or administration time can be adjusted to mitigate undesirable trends in the aggregated analyte data, such as reducing the occurrence of hypoglycemic or hyperglycemic events. These exemplary embodiments are particularly suited to populations in common living environments, such as elderly care facilities, hospitals, rehabilitation centers, schools, dormitories, military installations or bases, family residences, prisons or penal colonies, and any other such environments where a group of individuals regularly share one or more meals.

[0015] In at least preferred embodiments, many of the embodiments provided herein are improved graphical user interfaces (GUIs) or GUI features for analyte monitoring systems that are intuitive, highly intuitive, and user-friendly, providing quick access to a user's physiological information. More specifically, these embodiments may enable a user (or HCP) to easily navigate through and between different user interfaces that quickly show the user various physiological states and / or actionable responses and can correlate analyte data with diet, exercise, stress, or other factors, without requiring the user (or HCP) to perform the arduous task of examining large amounts of analyte data. Furthermore, in preferred embodiments, at least some of the GUIs and GUI features enable users (and their caregivers) to better understand and improve their management of their diet, eating habits, and other stressors by viewing the correlation between these activities and glucose levels. Similarly, in preferred embodiments, at least improved digital interfaces and / or functionality for the dietary monitoring system may improve visualization of the impact of food choices on analyte (glucose) levels and the amount of time spent within a target analyte range (time in range, TIR), visualization of good and bad foods present in the user's current diet and their impact on glucose levels and TIR, correlation of dietary information with detected meal events, and motivating the user to maintain and / or increase TIR by informing the user of food options to eat while maintaining TIR goals, to name just a few. Other improvements and advantages are provided as well. Various configurations of these devices are described in detail in accordance with the embodiments, but are by way of example only.

[0016] The GUI improvements in various aspects described and claimed herein provide at least the technical effect of assisting device users in operating the device more accurately, more efficiently, and more safely. It will be appreciated that the information provided to a user on a GUI, the order in which that information is presented, and the clarity with which that information is organized can have a significant impact on how the user interacts with the system and how the system operates. Thus, the GUI guides the user in the technical task of operating the system to perform necessary readings and / or obtain information accurately and efficiently.

[0017] 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 figures and detailed description. All such additional systems, devices, methods, features, and advantages are intended to be included within this specification, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Features of the exemplary embodiments should not be construed in any way as limiting the scope of the appended claims unless those features are expressly recited in the claims.

[0018] Details of the subject matter described herein, both as to its structure and operation, may become apparent by examining the accompanying figures, in which like reference numerals refer to like parts. The components in the figures are not necessarily to scale, emphasis instead being placed on illustrating the principles of the subject matter. Moreover, all figures are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be shown diagrammatically, rather than literally or precisely. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a high-level diagram illustrating an exemplary embodiment of an analyte monitoring system for real-time analyte (eg, glucose) measurement, data acquisition, and / or processing. [Figure 2A] FIG. 2A is a block diagram illustrating an exemplary embodiment of a reader device configured as a smartphone. [Figure 2B] FIG. 2B is a block diagram illustrating an exemplary embodiment of a sensor control device. [Figure 3A] FIG. 3A is a flow diagram illustrating an exemplary embodiment of a method for dietary information collection and assessment. [Figure 3B] FIG. 3B is a flow diagram illustrating an exemplary embodiment of a method for performing automatic meal detection sensitivity setting adjustment. [Figure 3C] FIG. 3C illustrates an exemplary embodiment of a graphical display of a user's analyte data over time with an overlay of historical glycemic response. [Figure 3D] FIG. 3D shows an example of a graph of analyte data over time displaying an exemplary data set with four different meal events. [Figure 4A] FIG. 4A is a diagram illustrating an exemplary embodiment of a graphical user interface display screen. [Figure 4B] FIG. 4B illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 4C] FIG. 4C illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 4D] FIG. 4D illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 4E] FIG. 4E illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5A] FIG. 5A illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5B] FIG. 5B illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5C] FIG. 5C illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5D] FIG. 5D illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5E] FIG. 5E illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 5F] FIG. 5F illustrates an exemplary embodiment of a graphical user interface display screen. [Figure 6A] FIG. 6A is a block diagram illustrating an exemplary embodiment of an analyte monitoring system for use with multiple individuals within a community population. [Figure 6B] FIG. 6B is a flow diagram illustrating an exemplary embodiment of a method for using an analyte monitoring system with multiple individuals within a community population. [Figure 7] FIG. 7 shows exemplary aggregated analyte data for one or more (or all) individuals within a local population. [Figure 8A-1-1] FIG. 8A-1-1 illustrates an exemplary embodiment of a graphical user interface for displaying an analyte indicator. [Figure 8A-1-2] FIG. 8A-1-2 is a continuation of FIG. 8A-1-1. [Figure 8A-2-1] FIG. 8A-2-1 illustrates an exemplary embodiment of a graphical user interface for displaying an analyte indicator. [Figure 8A-2-2] FIG. 8A-2-2 is a continuation of FIG. 8A-2-1. [Figure 8B] FIG. 8B illustrates an exemplary embodiment of a graphical user interface for displaying an analyte indicator. [Figure 9A-1-1] FIG. 9A-1-1 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9A-1-2] FIG. 9A-1-2 is a continuation of FIG. 9A-1-1. [Figure 9A-2-1] FIG. 9A-2-1 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9A-2-2] FIG. 9A-2-2 is a continuation of FIG. 9A-2-1. [Figure 9B-1] FIG. 9B-1 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9B-2] FIG. 9B-2 is a continuation of FIG. 9B-1. [Figure 9B-3] FIG. 9B-3 is a continuation of FIG. 9B-2. [Figure 9C-1] FIG. 9C-1 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9C-2] FIG. 9C-2 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9C-3] FIG. 9C-3 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9C-4] FIG. 9C-4 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9C-5] FIG. 9C-5 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9D-1] FIG. 9D-1 illustrates an exemplary embodiment of a graphical user interface for logging meal information. [Figure 9D-2] FIG. 9D-2 is a continuation of FIG. 9D-1. [Figure 9D-3] FIG. 9D-3 is a continuation of FIG. 9D-2. [Figure 9E-1] FIG. 9E-1 illustrates an exemplary embodiment of a graphical user interface for displaying mismatch events. [Figure 9E-2] FIG. 9E-2 is a continuation of FIG. 9E-1. [Figure 10A-1-1] FIG. 10A-1-1 illustrates an exemplary embodiment of a graphical user interface for logging non-eating related events. [Figure 10A-1-2] FIG. 10A-1-2 is a continuation of FIG. 10A-1-1. [Figure 10A-1-3] FIG. 10A-1-3 is a continuation of FIG. 10A-1-2. [Figure 10A-2-1] FIG. 10A-2-1 illustrates an exemplary embodiment of a graphical user interface for logging non-eating related events. [Figure 10A-2-2] FIG. 10A-2-2 is a continuation of FIG. 10A-2-1. [Figure 10B-1] FIG. 10B-1 illustrates an exemplary embodiment of a graphical user interface for logging non-eating related events. [Figure 10B-2] FIG. 10B-2 is a continuation of FIG. 10B-1. [Figure 10C] FIG. 10C is a diagram illustrating an exemplary embodiment of an application icon. [Figure 11] FIG. 11 illustrates an exemplary embodiment of a representative flow chart for logging meal information. [Figure 12] FIG. 12 illustrates an exemplary embodiment of a graphical user interface for displaying a time-in-range indicator and a graph of analyte concentration data. [Figure 13A] FIG. 13A illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13B-1] FIG. 13B-1 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13B-2] FIG. 13B-2 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13B-3] FIG. 13B-3 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13B-4]FIG. 13B-4 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13B-5] FIG. 13B-5 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13C-1] FIG. 13C-1 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13C-2] FIG. 13C-2 is a continuation of FIG. 13C-1. [Figure 13C-3] FIG. 13C-3 is a continuation of FIG. 13C-2. [Figure 13D-1] FIG. 13D-1 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13D-2] FIG. 13D-2 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 13D-3] FIG. 13D-3 illustrates an exemplary embodiment of a graphical user interface for insight reports. [Figure 14A-1] FIG. 14A-1 illustrates an exemplary embodiment of a graphical user interface for displaying meal rankings. [Figure 14A-2] FIG. 14A-2 is a continuation of FIG. 14A-1. [Figure 14B-1] FIG. 14B-1 illustrates an exemplary embodiment of a graphical user interface for displaying meal rankings. [Figure 14B-2] FIG. 14B-2 is a continuation of FIG. 14B-1. [Figure 14B-3] FIG. 14B-3 is a continuation of FIG. 14B-2. [Figure 15A] FIG. 15A illustrates an exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 15B]FIG. 15B illustrates an exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 15C] FIG. 15C illustrates an exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16A-1] FIG. 16A-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16A-2] FIG. 16A-2 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16B-1-1] FIG. 16B-1-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16B-1-2] FIG. 16B-1-2 is a continuation of FIG. 16B-1-1. [Figure 16B-2-1] FIG. 16B-2-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16B-2-2] FIG. 16B-2-2 is a continuation of FIG. 16B-2-1. [Figure 16C-1-1] FIG. 16C-1-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16C-1-2] FIG. 16C-1-2 is a continuation of FIG. 16C-1-1. [Figure 16C-1-3] FIG. 16C-1-3 is a continuation of FIG. 16C-1-2. [Figure 16C-2-1] FIG. 16C-2-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16C-2-2] FIG. 16C-2-2 is a continuation of FIG. 16C-2-1. [Figure 16D-1-1] FIG. 16D-1-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16D-1-2] FIG. 16D-1-2 is a continuation of FIG. 16D-1-1. [Figure 16D-2-1] FIG. 16D-2-1 illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 16D-2-2] FIG. 16D-2-2 is a continuation of FIG. 16D-2-1. [Figure 17A] FIG. 17A illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 17B] FIG. 17B illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 18A] FIG. 18A illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 18B] FIG. 18B illustrates an additional exemplary embodiment of a graphical user interface for displaying in-range time indicators. [Figure 19A] FIG. 19A illustrates an exemplary embodiment for banking time within a goal or target range. [Figure 19B] FIG. 19B illustrates an exemplary embodiment for banking time within a goal or target range. [Figure 19C] FIG. 19C illustrates an exemplary embodiment for banking time within a goal or target range. [Figure 20A] FIG. 20A is a flow diagram of an exemplary embodiment of a method for analyzing meals and foods. [Figure 20B] FIG. 20B is a flow diagram of an exemplary embodiment of a method for analyzing meals and foods. [Figure 20C] FIG. 20C is a flow diagram of an exemplary embodiment of a method for analyzing meals and foods. [Figure 20D]FIG. 20D is a flow diagram of an exemplary embodiment of a method for analyzing meals and foods. [Figure 21] FIG. 21 is a flow diagram of an exemplary embodiment of a method for displaying rated meal and food counters. [Figure 22A] FIG. 22A is a flow diagram of an exemplary embodiment of a method for making food recommendations. [Figure 22B] FIG. 22B is a flow diagram of an exemplary embodiment of a method for making food recommendations. [Figure 23] FIG. 23 is a flow diagram of an exemplary embodiment of a method for reassessing a diet grade. DETAILED DESCRIPTION OF THE INVENTION

[0020] Provided herein are exemplary embodiments of systems, devices, and methods for detecting, measuring, and classifying a person's meals in relation to that individual's analyte levels. Based on the collected analyte data, meal-related events and their effects on an individual's analyte levels can be further understood and used to modify future meal choices and eating habits. Furthermore, administration schedules for insulin or other medications can be adjusted based on the analyte response to a particular meal.

[0021] Before describing this subject matter in more detail, it is worthwhile to describe exemplary embodiments of systems, devices, and methods in which this subject matter can be implemented.

[0022] Many systems have been developed for the automated monitoring of analytes, such as glucose, in bodily fluids, such as the bloodstream, interstitial fluid ("ISF"), skin fluid in the dermis layer, or other biological fluids. Some of these systems are configured such that at least a portion of the sensor is placed below the surface of the user's skin, for example, within the user's blood vessels or subcutaneous tissue, and to obtain information regarding at least one analyte in the body.

[0023] As such, these systems can be referred to as "in vivo" monitoring systems. In vivo analyte monitoring systems include "continuous analyte monitoring" systems (or "continuous glucose monitoring" systems), which allow data transmission from the sensor control device to the reader device to occur continuously and unprompted, e.g., automatically, according to a schedule. In vivo analyte monitoring systems also include "flash analyte monitoring" systems (or "flash glucose monitoring" systems or simply "flash" systems), which allow data to be transferred from the sensor control device upon scanning or requesting data by a reader device, such as using near-field communication (NFC) or radio frequency identification (RFID) protocols. In vivo analyte monitoring systems can also operate without the need for fingerstick calibration.

[0024] In vivo analyte monitoring systems can be distinguished from "in vitro" systems that contact biological samples outside the body (or, more accurately, "ex vivo"), and typically include a metering 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. While in many of the present embodiments, monitoring is accomplished in vivo, the embodiments disclosed herein can be used with in vivo analyte monitoring systems that incorporate in vitro capabilities, as well as purely in vitro or ex vivo analyte monitoring systems.

[0025] The sensor may be part of a sensor control device that resides on the user's body and houses the electronics and power supply that enable 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, "on-body" devices or units, or "sensor data communication" devices or units, to name a few.

[0026] In vivo monitoring systems can also include devices that receive sensed analyte data from the sensor control device and process and / or display the sensed analyte data in any number of forms for presentation to a user. These devices and variations thereof can be referred to as "reader devices" (or simply "readers"), "handheld electronic devices" (or handhelds), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply receivers), or "remote" devices or units, to name a few. Other devices, such as personal computers, have also been utilized in or incorporated into in vivo and in vitro monitoring systems.

[0027] In vivo monitoring system embodiments For purposes of illustration and not limitation, the graphical user interfaces and associated software described herein may be used in connection with an exemplary analyte monitoring system, such as that shown in FIG. 1. FIG. 1 is an exemplary diagram illustrating an exemplary in vivo analyte monitoring system 100 in which any and / or all of the embodiments described herein may be used. System 100 may have a sensor control device 102 and a reader device 120 that communicate with each other via a local communication path (or link) 140, which may be wired or wireless, and unidirectional or bidirectional. In embodiments in which local communication path 140 is wireless, any near-field communication (NFC) protocol, RFID protocol, Bluetooth or Bluetooth Low Energy protocol, Wi-Fi protocol, proprietary protocol, etc. may be used, including communication protocols existing as of the filing date of this application or subsequently developed variants thereof.

[0028] Bluetooth is a well-known, standardized short-range wireless communication protocol; Bluetooth Low Energy is the same version that requires less power to operate. Bluetooth Low Energy (Bluetooth LE, BTLE, BLE) is also known as Bluetooth Smart or Bluetooth Smart Ready. BTLE versions are described in the Bluetooth Specification, version 4.0, published June 30, 2010, and are expressly incorporated herein by reference for all purposes. The term "NFC" applies to numerous protocols (or standards) that define the operating parameters, modulation schemes, coding, transmission rates, frame formats, and command definitions of NFC devices. The following is a non-exhaustive list of examples of these protocols, each of which (together with all its subsections) is incorporated herein by reference in its entirety for all purposes: ECMA-340, ECMA-352, ISO / IEC 14443, ISO / IEC 15693, ISO / IEC 16000-3, ISO / IEC 18092, and ISO / IEC 21481.

[0029] Reader device 120 is also capable of bidirectional or unidirectional wired, wireless, or combined communication with any or all of drug delivery device 160 over communication path (or link) 143, local computer system 170 over communication path (or link) 141, and network 190 over communication path (or link) 142. The same wireless protocols described for link 140 may be used for all or some of links 141, 142, and 143 as well.

[0030] The reader device 120 can communicate with any number of entities via the network 190, which can be part of a telecommunications network such as a Wi-Fi network, a local area network (LAN), a wide area network (WAN), the Internet, or other data network for one-way or two-way communication. The trusted computer system 180 can be accessed via the network 190. In alternative embodiments, the communication paths 141 and 142 can be the same path, which can include the network 190 and / or additional networks. All communications on paths 140, 141, 142, and 143 can be encrypted, and the sensor control device 102, the reader device 120, the drug delivery device 160, the remote computer system 170, and the trusted computer system 180 can each be configured to encrypt and decrypt their transmitted and received communications.

[0031] Variations of devices 102 and 120, as well as other components of in vivo-based analyte monitoring systems suitable for use with embodiments of the systems, devices, and methods provided herein, are described in U.S. Patent Publication No. 2011 / 0213225 (the '225 publication), which is incorporated herein by reference in its entirety for all purposes.

[0032] The sensor control device 102 can include a housing 103 that houses an in vivo analyte monitoring circuit and a power source (not shown). The in vivo analyte monitoring circuit can be electrically coupled to an analyte sensor 104, which can extend through an adhesive patch 105 and protrude away from the housing 103. The adhesive patch 105 contains an adhesive layer (not shown) for attachment to the skin surface of a user's body. Other forms of attachment to the body can be used in addition to or instead of adhesive.

[0033] The sensor 104 is adapted to be at least partially inserted into a user's body, where it can be in fluid contact with the user's bodily fluids (e.g., interstitial fluid (ISF), skin fluid, or blood) and can be used, in conjunction with in vivo analyte monitoring circuitry, to measure analyte-related data of the user. Generally, the sensor control device 102 and its components can be applied to the body in one or more steps using a mechanical applicator 150, as described in the incorporated '225 publication, or in any other desired manner.

[0034] Upon activation, the sensor control device 102 wirelessly communicates collected analyte data (e.g., data corresponding to monitored analyte levels and / or monitored temperature data, and / or stored historical analyte-related data, etc.) to the reader device 120, which, in certain embodiments, algorithmically processes the data into data representative of the user's analyte levels, which can then be displayed to the user and / or otherwise incorporated into a diabetes monitoring regime.

[0035] Various embodiments disclosed herein relate to a reader device 120, which may have a user interface including one or more of a display 122, a keyboard, optional user interface components 121, etc., where the display 122 can output information to a user and / or receive input from a user (e.g., when configured as a touchscreen). The reader device 120 may include one or more optional user interface components 121, such as buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog wheels, etc. The reader device 120 may also include one or more data communication ports 123 for wired data communication with external devices, such as a local computer system 170. The reader device 120 may also include an integrated or attachable in vitro meter including an in vitro test strip port (not shown) for receiving in vitro analyte test strips for performing in vitro blood analyte measurements.

[0036] The drug delivery device 160 is capable of injecting or infusing a drug, such as, but not limited to, insulin, into the body of an individual wearing the sensor control device 102. Similar to the reader device 120, the drug delivery device may include a processing circuit, a non-transitory memory containing instructions executable by the processing circuit, a wireless or wired communication circuit, and a user interface including one or more of a display, a touch screen, a keyboard, input buttons or instruments, or the like. The drug delivery device 160 may include a drug reservoir configured for at least partial implantation within a user's body, a pump, an infusion tube, and an infusion cannula. The pump can deliver insulin from the reservoir through the tube and then through the cannula into the user's body. The drug delivery device 160 may include instructions executable by a processor for controlling the pump and the amount of insulin delivered. These instructions may also cause calculation of insulin delivery amount and duration (e.g., bolus infusion and / or basal infusion profile) based on analyte level measurements obtained directly or indirectly from the sensor control device 102. Alternatively, calculation of insulin delivery amount and duration, and control of the pump can be performed directly by reader device 120. The drug delivery device can be configured to communicate directly with reader device 120 in the form of a closed-loop or semi-closed-loop system. Alternatively, the drug delivery device can include the functionality of reader device 120 described herein, or vice versa, resulting in one integrated reader and drug delivery device.

[0037] Computer system 170 may be a personal or laptop computer, tablet, or other suitable data processing device. Computer 170 may be either local (e.g., accessible via a direct wired connection such as USB) or remote to reader device 120 and may be (or include) software for data management and analysis and communication with components within analyte monitoring system 100. Operation and use of computer 170 is further described in the '225 publication, which is incorporated herein by reference. Analyte monitoring system 100 may also be configured to operate with a data processing module (not shown), as described in the incorporated '225 publication.

[0038] The trusted computer system 180 may be used to perform authentication of the sensor control device 102 and / or the reader device 120, to store sensitive data received from the devices 102 and / or 120, to output sensitive data to the devices 102 and / or 120, or may be otherwise configured. The trusted computer system 180 may include one or more computers, servers, networks, databases, etc. The trusted computer system 180 may be within the ownership of the manufacturer or distributor of the sensor control device 102, or may be maintained and operated by a different party (e.g., a third party), either physically or virtually via a protected connection.

[0039] A trusted computer system 180 may be trusted in the sense that the system 100 may assume that the computer system 180 provides authentic data or information. A trusted computer system 180 may be trusted simply because it is within the ownership or control of its manufacturer, such as a typical web server. Alternatively, a trusted computer system 180 may be implemented in a more secure manner, such as by requiring additional passwords, encryption, firewalls, or other Internet access security enhancements that further protect against counterfeit attacks or attacks by computer hackers.

[0040] Data processing and software execution within system 100 may be performed by one or more processors in reader device 120, computer system 170, and / or sensor control device 102. For example, raw data measured by sensor 104 may be algorithmically processed into a value representative of an analyte level and readily suitable for display to a user, which may occur in sensor control device 102, reader device 120, or computer system 170. This information, and any other information derived from the raw data, may be displayed in any manner described above (with respect to display 122) on any display resident on either sensor control device 102, reader device 120, or computer system 170. This information may be utilized by a user to determine corrective action needed to ensure that analyte levels remain within acceptable and / or clinically safe ranges.

[0041] 2A-2B illustrate exemplary embodiments of the reader device 120 and the sensor control device 102, respectively. As mentioned above, the reader device 120 can be, for example, a mobile communication device such as a Wi-Fi or Internet-enabled smartphone, tablet, or personal digital assistant (PDA). Examples of smartphones can include, but are not limited to, mobile phones based on the WINDOWS® operating system, the ANDROID® operating system, the IPHONE® operating system, the PALM WEBOS® operating system, the BLACKBERRY® operating system, or the SYMBIAN® operating system, and having a network connection for data communication over the Internet or a local area network (LAN).

[0042] The reader device 120 may also be configured as a mobile smart wearable electronic assembly, such as an optical assembly (e.g., smart glasses or smart spectacles, such as GOOGLE GLASSES™) worn on or adjacent to a user's eyes. The optical assembly may have a transparent display that displays information (described herein) about the user's analyte levels to the user while allowing the user to see through the display so that the user's overall field of vision is minimally obstructed. The optical assembly may be capable of wireless communication similar to that of a smartphone. Other examples of wearable electronic devices include devices worn around or near a user's wrist (e.g., a watch, etc.), neck (e.g., a necklace, etc.), head (e.g., a headband, hat, etc.), chest, etc.

[0043] 2A is a block diagram of an exemplary embodiment of a reader device 120 according to various embodiments disclosed herein. In this example, the reader device 120 is in the form of a smartphone, on which various software, applications, and graphical user interfaces disclosed herein may reside. Here, the reader device 120 includes an input component 121, a display 122, and processing hardware 206, which may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or may be distributed among (and in part across) multiple different chips. Here, the processing hardware 206 includes a communications processor 222 having on-board non-transitory memory 223 and an application processor 224 having on-board non-transitory memory 225. The reader device 120 further includes an RF transceiver 228 coupled to an RF antenna 229, memory 230, a multifunction circuit 232 having one or more associated antennas 234, a power supply 226, and a power management circuit 238. FIG. 2A is a simplified diagram of the internal components of a smartphone, and may of course include other hardware and functionality (e.g., codecs, drivers, glue logic, etc.).

[0044] The communications processor 222 interfaces with the RF transceiver 228 and performs analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting the voice, video, and data signals into a format suitable for presentation to the RF transceiver 228 (e.g., in-phase and quadrature), which can then transmit the signals wirelessly. The communications processor 222 can also interface with the RF transceiver 228 to perform the inverse functions necessary to receive wireless transmissions and convert them into digital data, voice, and video.

[0045] The application processor 224 may be adapted to run the operating system and any software applications resident on the reader device 120 (e.g., any sensor interface or analyte monitoring applications, including SLL 304), process video and graphics, and perform other functions unrelated to processing communications sent and received via the RF antenna 229. Any number of applications may be running on the reader device 120 at any one time and will typically include one or more applications related to a diabetes monitoring regime, in addition to other commonly used applications unrelated to such regimes, e.g., email, calendar, weather, etc.

[0046] The memory 230 may be shared by one or more of the various functional units present in the reader device 120, or may be distributed among two or more of them (e.g., as separate memories present in different chips). The memory 230 may also be its own separate chip. The memory 230 may be non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0047] The multi-function circuitry 232 may be implemented as one or more chips and / or components that include communication circuitry to perform other functions, such as local wireless communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy) and determining the geographic location of the reader device 120 (e.g., Global Positioning System (GPS) hardware). One or more other antennas 234 are associated with the functional circuitry 232 as needed.

[0048] The power supply 226 can include one or more batteries, which can be rechargeable or disposable. The power management circuitry 238 can regulate battery charging and power supply monitoring, boost power, perform DC conversion, etc. As mentioned above, the reader device 120 can also include one or more data communication ports, such as a USB port (or connector) or an RS-232 port (or any other wired communication port), for data communication with a remote computer system 170 (see FIG. 1 ) or the sensor control device 102, to name a few.

[0049] FIG. 2B is a block schematic diagram illustrating an exemplary embodiment of a sensor control device 102 having an analyte sensor 104 and a sensor electronics device 250 (including analyte monitoring circuitry). While any number of chips can be used, here, most of the sensor electronics device 250 is integrated onto a single semiconductor chip 251, which may be, for example, a custom application-specific integrated circuit (ASIC). Shown within the ASIC 251 are several high-level functional units, including an analog front-end (AFE) 252, a power management circuit 254, a processor 256, and a communication circuit 258 (which may be implemented as a transmitter, receiver, transceiver, passive circuitry, or otherwise, depending on the communication protocol). In this embodiment shown in FIG. 2B, both the AFE 252 and the processor 256 are used as analyte monitoring circuitry, although in other embodiments, either circuitry may perform the analyte monitoring function. The processor 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers.

[0050] Non-transitory memory 253 is also included within ASIC 251 and may be shared by the various functional units present within ASIC 251 or distributed among two or more of those functional units. Memory 253 may be volatile and / or non-volatile memory. In this embodiment, ASIC 251 is coupled to power source 260, which may be a coin cell battery or the like. AFE 252 interfaces with and receives measurement data from in vivo analyte sensor 104 and outputs the data in digital form to processor 256, which in turn processes the data to obtain final result analyte discrete values ​​and trend values, etc. This data may then be provided by antenna 261 to communication circuitry 258 for transmission to reader device 120 (not shown), where further processing may be performed, for example, by a sensor interface application. Note that the functional components of ASIC 251 may also be distributed among two or more discrete semiconductor chips.

[0051] Performing data processing functions within the electronic device of the sensor control device 102 provides the system 100 with the flexibility to schedule communications from the sensor control device 102 to the reader device 120, thereby limiting the number of unnecessary communications and providing further power savings in the sensor control device 102.

[0052] The information may be automatically and / or continuously communicated from the sensor control device 102 to the reader device 120 as analyte information is available, or may not be automatically and / or continuously communicated but may be stored or logged in the memory of the sensor control device 102, for example, for later output.

[0053] Data may be transmitted from the sensor control device 102 to the reader device 120 at the initiative of either the sensor control device 102 or the reader device 120. For example, in many exemplary embodiments, the sensor control device 102 may communicate data periodically in an unprompted manner so that a qualified reader device 120 can receive the communicated data (e.g., sensed analyte data) if it is within range and listening. This occurs at the initiative of the sensor control device 102, as the reader device 120 does not first need to send a request or other transmission to prompt the sensor control device 102 to communicate. Transmissions may be performed, for example, using an active Wi-Fi, Bluetooth, or BTLE connection, and may occur according to a schedule programmed within the device 102 (e.g., approximately every 1 minute, approximately every 5 minutes, approximately every 10 minutes, etc.). Transmissions may also occur randomly or pseudo-randomly whenever the sensor control device 102 detects a change in the sensed analyte data. Furthermore, transmissions may occur in a repeated manner, regardless of whether each transmission is actually received by the reader device 120.

[0054] The system 100 may also be configured such that the reader device 120 sends a transmission prompting the sensor control device 102 to communicate its data to the reader device 120. This is commonly referred to as "on-demand" data transfer. On-demand data transfer may be initiated based on a schedule stored in the memory of the reader device 120 or by user request via the user interface of the reader device 120. For example, if a user wants to check their analyte levels, the user may perform a scan of the sensor control device 102 using an NFC, Bluetooth, BTLE, or Wi-Fi connection. Data exchange may be accomplished using only broadcast, only on-demand transfer, or any combination thereof.

[0055] Thus, when the sensor control device 102 is placed on the body such that at least a portion of the sensor 104 is in contact with bodily fluid and electrically coupled to the electronics within the device 102, sensor-derived analyte information can be communicated from the sensor control device 102 to the reader device 120 in an on-demand or unprompted (broadcast) manner. On-demand transfer can be performed by first powering on the reader device 120 (or it may be continuously powered on) and executing a software algorithm stored in and accessed from memory of the reader device 120 to generate one or more requests, commands, control signals, or data packets to send to the sensor control device 102. For example, the software algorithm, executing under control of the processing hardware 206 of the reader device 120, may include a routine that detects the position of the sensor control device 102 relative to the reader device 120 in order to initiate transmission of the generated request commands, control signals, and / or data packets.

[0056] Embodiments relating analyte levels to dietary information In many embodiments, the subject matter described herein is implemented by a software application program stored in memory of and executed by a processor-based device, such as any one of the reader devices, drug delivery devices, or other computing devices described herein. In certain embodiments, the software is implemented as one or more downloadable software applications (“apps”) on a reader device, such as a mobile communication device or smartphone.

[0057] The software may provide a mechanism for the user to define intakes (e.g., food types, drink types, or portions thereof) in any manner convenient to the user. These intakes will generally be referred to herein as meals or meals, and these terms are used broadly to refer to all types of food and drink.

[0058] This software can perform many functions related to collecting dietary information and correlating that dietary information with analyte information collected by the in vivo analyte sensors 104 or by the in vitro test strips and meters. This software will be generally referred to herein as the "dietary monitoring application."

[0059] A meal monitoring application may allow an individual to log information (including a photo of the meal) about each meal they consume (i.e., each "meal event"). The meal monitoring application can correlate analyte data from the same general time period in which the user's log entry indicated that the meal was consumed.

[0060] The meal monitoring application may also monitor the user's analyte data and identify when the analyte data changes in a way that indicates or suggests the occurrence of a potential meal event, and attempt to associate meal information throughout the same time period with the potential meal event. The meal monitoring application may prompt the individual with confirmation that a potential meal event has occurred and information describing the meal event. If the individual has already entered meal information, the system may prompt the user for additional information regarding the meal event that they have not yet entered. In some embodiments, if a meal is detected and the meal monitoring application determines that meal information has already been entered, the user may not be prompted.

[0061] The meal monitoring application can also associate the measured analyte response with each meal event, regardless of whether the analyte response is also classified as or includes an analyte excursion, and store the results in non-transitory memory or a database. The meal monitoring application can, for example, display each meal along with its associated analyte (e.g., glucose or other analyte) response to the user as a list sorted in descending order of the magnitude of the glycemic response, using glucose as an example. The meal monitoring application can display other lists, such as a "good" meal list consisting of meals with a glycemic response magnitude below a predefined magnitude and / or a "good" meal list consisting of the few meals with the lowest glycemic response magnitude of all recorded meals. Another example is a "bad" meal list consisting of meals with a glycemic response magnitude above a predefined magnitude and / or a "bad" meal list consisting of the few meals with the highest glycemic response magnitude of all recorded meals.

[0062] One exemplary embodiment of the magnitude of the glucose response is the peak glucose value detected from the postprandial glucose response. Methods for determining this peak glucose measurement are further described herein. Another exemplary embodiment of the magnitude of the glucose response is the difference between the postprandial peak glucose value and the detected glucose value at the start of the meal.

[0063] In yet another exemplary embodiment, the magnitude of the glucose response can be determined from a form of "area under the curve." The system can determine an area value with an upper limit set by a trace or curve that follows (or approximates) the values ​​of glucose data collected from the start of a detected meal through a) a fixed period, such as 6 hours, b) the start of the next detected meal, or c) the occurrence of when the glucose trace drops below the value at the start of the meal. In one exemplary embodiment, the endpoint is the first occurrence of any of the preceding events (a-c). The lower limit for the area determination can be the glucose value at the start of the detected meal. The area can be calculated by summing the glucose values ​​between the start and end (with or without the start and end values), multiplying this sum by the total time from start to end, and then subtracting the glucose value at the start of the detected meal multiplied by the total time from start to end. Other similar magnitude indicators along these lines can be implemented.

[0064] When a user repeatedly consumes the same or similar meals, a glycemic central tendency (e.g., mean or median glycemic response) can be determined for the meal and displayed. For example, if the peak of the glycemic response is the preferred indicator, when multiple identical meals are recorded by the system, the median of the peak values ​​can represent the glycemic response indicator for that meal. Other forms of glucose response indicators, such as glucose traces or parametric fits to glucose traces, may also be used. Alternatively, the glycemic responses of all meals may be displayed, regardless of whether each meal is the same or similar to another meal on the list or in the database. In yet another alternative embodiment, the meal monitoring application may generate a glycemic response representative of all similar meals. For example, if the glycemic response is displayed as a trace, the representative trace for all similar meals may consist of the median of all time periods of the individual's glycemic response, where time is relative to the start of the meal.

[0065] An exemplary embodiment of the meal monitoring application may utilize analyte data analysis software or software-implementable processes such as those disclosed in, for example, U.S. Patent Publication Nos. 2013 / 0085358, 2014 / 0350369, 2014 / 0088393, 2017 / 0185748, 2020 / 0105397, or any of International Publication Nos. 2015 / 153482 or PCT / US20 / 12134, all of which are incorporated herein by reference in their entirety for all purposes. This exemplary software embodiment is collectively referred to herein as a "meal event detector." A meal event detector may be an algorithm, routine, or other instruction set (part of or separate from the meal monitoring application) capable of detecting and / or quantifying the occurrence of actual or potential meal events in an individual's monitored analyte data.

[0066] Detection of a meal event may include detection of an analyte episode or excursion outside a desired, acceptable (e.g., medically recommended) target range in the user, and the user may be notified by the software that one or both have been detected. Examples of analyte excursions include violations of low glucose thresholds, violations of high glucose thresholds, violations of rate of change (e.g., increase or decrease) thresholds, violations of median glucose thresholds, violations of glucose variability thresholds, etc.

[0067] Some of the meal event detectors described in these incorporated documents are described solely with respect to identifying analyte excursions outside of desired target ranges. These embodiments can be extended to meal event detection based on teachings contained within others of these incorporated documents (e.g., WO 2015 / 153482). These embodiments can also be extended to meal event detection through designation of within-target episodes, in which glucose values ​​remain between upper and lower limits for a period of time. Detection of these episodes can be achieved through an extension of the threshold-based episode detection algorithm.

[0068] A relatively simple example of threshold-based logic involves grouping and associating all consecutive points above or below a threshold with a meal event, which in some cases may also be or include an analyte excursion outside the user's target tolerance range.

[0069] Very short episodes (e.g., trends or groupings in analyte data, or outliers) may not be clinically relevant. The embodiments described herein can manage this challenge by requiring a minimum number of readings and / or a minimum duration and / or a minimum area outside a threshold (e.g., integral) to consider an episode for analysis as either a meal event or an analyte excursion. Episodes that do not meet any of the requirements may be ignored. Software displays and applications for providing upload prompts to individuals are disclosed in the incorporated reference U.S. Patent Publication No. 2014 / 0088393, which also discloses software applications for analyzing and displaying data and providing heuristic meal announcements that can be used herein for meal selection.

[0070] A virtually limitless catalog of analyte episode types exists (e.g., analyte data occurrences with different characteristics), each of which, when independently clinically relevant, can be used to form the basis of dietary reporting, dietary effects, dietary analysis, diet-based treatments, medications, and dietary selection. Glycemic response and episode characteristics can also be used to categorize meals and ensure meal-related data collection. Such analyses and results can be presented to individuals, clinical diet supervisors, or physicians to make future dietary decisions.

[0071] Reference is now made to FIG. 3A, a flow diagram illustrating an exemplary embodiment of a method 300 for collecting meal information, associating it with analyte data, and determining the meal's impact on blood glucose levels. Method 300 includes operations that can be described as being performed by an electronic device, such as reader device 120, drug delivery device 160, or computer system 170 or 180, or a processor thereof. The user may be an individual or a diabetic patient, a clinical manager, a medical professional, a dietary professional, or another person. By way of example only, method 300 will be described with reference to a diabetic patient using meal monitoring software as an app downloaded on reader device 120 configured as a smartphone. For ease of explanation, the monitored analyte in this embodiment and other embodiments described below will be glucose, although, as noted herein, other analytes may be monitored as well.

[0072] 3A, a meal event may be logged by a user at 302. The user may enter meal information directly (via a user interface) into the reader device 120 at their discretion before, during, or after consuming a meal. In some embodiments, the user enters meal information according to a predetermined schedule, which may be set and / or modified by the user, and in response to reminders generated by the meal monitoring application. Exemplary embodiments of a user interface for receiving manual logs by a user are described with respect to FIGS. 4A and 4B.

[0073] The user's analyte data is monitored at 304. In most embodiments, this monitoring is a continuous process (e.g., a frequently repeated process, automatically, or at the user's discretion) as long as the user wears the sensor control device 102. Data collected by the sensor control device 102 can be transferred to the reader device 120 so that the meal monitoring application can access the data. Information indicating the time each analyte data measurement was collected (e.g., a timestamp) can also be transferred to the reader device 120. As previously described herein, this data transfer can occur on-demand (e.g., a user performing a scan), in a streaming manner, or in other periodically occurring manners. Analyte data collected by discrete blood glucose measurements (e.g., reading a test strip with a meter) can also be entered into the reader device 120, either manually or automatically.

[0074] The reader device 120 can algorithmically process the collected analyte data to determine whether a glucose excursion or meal event has occurred at 306. This can be done through the use of a meal event detector, as described herein, which examines the analyte data for one or more analyte values ​​that violate a threshold or other condition that indicates the occurrence of a glucose excursion or meal event. This algorithmic processing can also be repeated frequently, for example, whenever new analyte data is received from the sensor control device 102, such as in response to a user's NFC scan of the reader device 120 or otherwise of the sensor control device 102. Manual logging of meal information by the user (302) can occur contemporaneously with the monitoring (304) and processing (306) of the analyte data. Those skilled in the art will appreciate that these methods can be implemented in a system in which analyte data is autonomously and wirelessly transmitted from the sensor control device to the reader at predetermined intervals.

[0075] Each time new data is sent to reader 120, algorithmic processing may be applied to the new data, which may represent a multiple-hour period (e.g., the past 8 hours) to detect meal events. Steps 308-320 may be performed for each detected meal, starting with the most recently detected meal and repeated for each other detected meal event.

[0076] In some exemplary embodiments, the meal event detector may attempt to detect every meal in the analyte data using one or more sets of predefined glucose rate-of-change (e.g., rise) settings. If the analyte data exceeds a rate-of-change threshold, such as a series of data points over a predetermined time range where the average increase in value from one point to the next exceeds the threshold, the analyte data may be characterized as a glucose excursion.

[0077] One problem that arises particularly from the use of meal monitoring applications is that the meal monitoring application may be too sensitive or not sensitive enough to analyte data that is indicative of the occurrence of a meal event, which may result in too many or not enough meal events being identified.

[0078] In some embodiments, the settings used by the meal event detector to determine whether a potential meal event has occurred can be adjusted. For example, each setting or threshold used by the meal event detector can be individually set by the user. In another embodiment, the meal monitoring software can provide the ability to scale the sensitivity of a setting, for example, by providing the user with the option to select one of several different settings, each having a different magnitude.

[0079] The meal monitoring software can default to a "medium" (or "normal" or "default") mode using a predetermined group of settings or settings entered by the user. The meal monitoring software can provide the option to switch to different modes, such as a "low" mode or a "high" mode, where the sensitivity or magnitude of the settings is scaled to make it less or more likely, respectively, to consider a particular span of analyte data to qualify as a potential meal event compared to the "medium" mode. The "low" and "high" modes can each be a direct percentage scaling of the "medium" mode setting. For example, the "low" mode can use a "normal" mode setting adjusted by 5%, 10%, 15%, 20%, 25%, etc., to decrease the likelihood of detecting a meal event. Similarly, for example, the "high" mode can use a "normal" mode setting adjusted by 5%, 10%, 15%, 20%, 25%, etc., to increase the likelihood of detecting a meal event. Although three setting options are described herein, more than one setting option can be used. The meal monitoring software application may also provide the user with the option to scale the sensitivity of the meal event detector (at least from the user's perspective) in an analog or virtual analog manner, such as with a slide bar on a touch screen.

[0080] In some embodiments, the meal monitoring application is self-monitoring and optionally self-correcting such that it can continuously or repeatedly monitor the sensitivity of the meal event detector against a desired baseline and if the sensitivity of the meal event detector appears to need adjustment, it can notify the user to make an adjustment or the meal monitoring application can adjust automatically without input from the user.

[0081] 3B is a flow chart illustrating an example embodiment of a method 330 for automatically adjusting the sensitivity of a meal event detector. Here, method 330 may utilize a baseline integer or decimal value that indicates the typical number of meals a user consumes each day. This baseline value may be set by the user or may be a default value (e.g., 1, 2, 3, 4, 5, etc.) set within a meal monitoring application. Method 330 may also utilize a variable integer or decimal value that can account for typical variations in the number of meals a user consumes each day.

[0082] In this embodiment of method 330, the meal monitoring application runs a meal event detector on analyte data from a past time range (e.g., N days, hours, minutes) using a first set of sensitivity settings (e.g., analyte data magnitude threshold, duration threshold, rate of change threshold, or other) at 332. Here, method 330 looks at the analyte data over the past N days, where N can be any desired value. A determination is then made at 334 whether the number of meal events detected over those N days is less than or equal to a maximum value, which in this embodiment is (baseline value + variability value) multiplied by N. In an example where N is 3, the baseline value is 3 meals / day, and the variability value is 1 meal / day, the determination at 334 would evaluate whether the number of meal events detected is less than or equal to 12 (e.g., the maximum value).

[0083] A determination that the number of meal events is greater than the maximum value indicates that the sensitivity of the meal event detector is set too high. Therefore, the routine of method 330 proceeds to 336 where the sensitivity setting is reduced. The reduction in the sensitivity setting can be to the next lowest set of predetermined sensitivity settings, or can be by a scaling factor of 1%, 2%, 5%, 10%, etc. Then, at 338, the meal event detector is run on analyte data from a past time range using the new settings. Method 330 then proceeds to make a determination again at 334. This process can be repeated until the sensitivity setting is reduced sufficiently so that the condition of 334 is not violated.

[0084] If the condition at 334 is passed (e.g., the number of detected meal events is 12 or less), the routine proceeds to make a determination at 340 as to whether the number of detected meal events is greater than or equal to a minimum value, which in this embodiment is (baseline value minus variability value) multiplied by N. In an example where N is 3, the baseline value is 3 meals / day, and the variability value is 1 meal / day, the determination at 340 would evaluate whether the number of detected meal events is greater than or equal to 6 (e.g., the minimum value).

[0085] A determination that the number of meal events is less than the minimum value indicates that the sensitivity setting of the meal event detector is not high enough. Therefore, the routine of method 330 proceeds to 342 where the sensitivity setting is increased. The increase in the sensitivity setting can be to the next highest set of predetermined sensitivity settings, or by a scaling factor of 1%, 2%, 5%, 10%, etc. The routine then proceeds to 338 where the meal event detector is run on analyte data from a past time range using the new settings. Method 330 then proceeds to make a determination again at 334, and this process can be repeated until the sensitivity setting is sufficiently adjusted so as not to violate the conditions of 334 and 340. If the condition of 340 is met, the routine proceeds to 344 where the adjusted settings are deemed appropriate and are used in the subsequent normal course of operation of the meal event detector.

[0086] Method 330 can be repeated at regular intervals. In one exemplary embodiment, method 330 is run once a day using data from the past N days measured from midnight to midnight to prevent settings from being changed multiple times in a day.

[0087] In another embodiment, the meal monitoring application can perform an automatic sensitivity adjustment that analyzes data to identify settings that are too high, regardless of whether the settings are too low. For example, the self-tuning routine can iteratively evaluate whether each of a plurality of settings is lower than the maximum value by starting with the highest setting, determining whether the meal event detector uses the highest setting to output a number of detected meal events that is less than the maximum value, and, if so, using those highest settings for subsequent normal operation of the meal event detector. If the highest setting results in a number of detected meal events that exceeds the maximum value, the routine can proceed in decreasing order of magnitude through each iteratively lower setting and stop when a setting that does not exceed the maximum value is identified. That identified setting can then be used for subsequent normal operation. In yet another embodiment, the meal monitoring application can perform an automatic sensitivity adjustment that analyzes data to identify settings that are too low, regardless of whether the settings are too high, in a similar but reverse manner.

[0088] Returning to FIG. 3A , once a glucose excursion or meal event is detected at 306, the meal monitoring application evaluates at 308 whether meal information corresponding to the detected glucose excursion or meal event (collectively the “detected event”) has already been entered (e.g., by a user at 302). This evaluation may be performed by examining a period of time before the time the detected event occurred, and optionally a limited range of time after (e.g., to compensate for time-keeping or entry inaccuracies), to see whether any meal information was entered during that period. If so, the meal monitoring application may associate that meal information with the detected event at 314. If multiple meal information entries are found, the meal monitoring application may associate all with the detected event, or it may associate the detected event with only the meal information that occurred most recently in time.

[0089] If meal information that can be associated with the detected event has not been entered, the user is prompted to enter meal information at 310. Exemplary embodiments of user interfaces for prompting a user to log meals or meal information are described with respect to Figures 4C-E. If a meal event has not occurred, the user can decline or ignore the prompt. Otherwise, meal information can be entered at 312.

[0090] The prompt can take the form of an alarm notification, such as a vibration or sound, that cues the user to launch the meal monitoring application to acknowledge the prompt. Alternatively, the prompt can be a notification that appears the next time the user views the application.

[0091] Whether the user logs meal information at their own discretion (302) or in response to a prompt (310) (312), the meal information can include various levels of detail and can be entered in the same or similar manner.

[0092] A desirable aspect of the embodiments described herein is the ease with which meal information can be entered, so as to encourage use of the meal monitoring application to more intuitively understand the glycemic impact of meal intake, which in turn can improve the user's health. Meal information can be entered in any desired manner, for example, by manually entering text, by selecting a meal name from a list (e.g., a suggestion list or drop-down list), by selecting a meal image from a collection of images, by selecting a recognizable indicator of the meal (e.g., a tag or code), or any combination thereof.

[0093] The meal information can include the type of meal, for example, whether the meal is breakfast, lunch, snack, dinner, or dessert, etc. The meal information can include the time range over which the meal was consumed (e.g., start time and stop time). This can be an actual time (e.g., start time in hours:minutes) or can be an approximate time range (e.g., 6-7 AM, 5-6 PM, etc.) with a generalized or heuristic portion of the day (e.g., early morning, late afternoon, etc.) or any desired level of detail of the time range (e.g., 10 minutes, 15 minutes, 30 minutes, 60 minutes, etc.).

[0094] Meal information can also include meal contents and portion sizes. For example, a meal can be described to include calorie content, protein, carbohydrates, dairy products, meat, grains, vegetables, nuts, sugars, type of alcohol or other beverage, etc., along with the respective amount or size of each component. This can be done by portion, e.g., one serving of bread with one serving of peanut butter, where each portion has associated with it respective amounts of carbohydrates, sugars, protein, etc. Alternatively, a meal may be described solely on a nutritional category basis, e.g., 10 grams of carbohydrates, 5 grams of sugars, 8 grams of protein, etc. Heuristic categories (e.g., small portion, medium portion, large portion) as opposed to quantitative categories can also be used to facilitate data entry.

[0095] To further facilitate the entry of meal information, the meal monitoring application may present options from which the user can select meals or information about meals. For example, meals or meal information may be presented to the user in the form of a list or array of items from which the user can select the most appropriate entry that describes the meal the user has consumed.

[0096] In some embodiments, a user can create a list or array of common meals (and / or associated information) that the user has consumed for use in the meal monitoring application by entering the list directly into the application, by selecting from a list of options pre-programmed into the meal monitoring application, or by creating the list on a separate computing platform and uploading it to the host device on which the meal monitoring application is running. Intakes can be added or deleted at any time. As discussed herein, this data entry can be performed using a graphical user interface on the host device.

[0097] The meal monitoring application can be programmed to store information about any and all past meals the user has eaten in the past and related dietary information. That information can be presented to the user as options the user can select to identify specific meals or aspects thereof that were eaten more recently. For example, when the user is prompted to enter dietary information, the meal monitoring application can present a list of options the user can select from. This list can be ordered so that the most commonly eaten or selected meals are presented first or at the top of the list, with the remaining meals presented in descending order of frequency of consumption (e.g., the most commonly eaten meal is presented first, the second most commonly eaten meal is presented next, and so on, with the least commonly eaten meal being presented last).

[0098] Users may often need to adjust their selected meals from time to time. Thus, for any selected meal, the user is given the option to customize aspects of the meal, for example, by adding, removing, or substituting particular sides (e.g., broccoli instead of peas) or beverages, modifying portion sizes, modifying calorie or carbohydrate content, etc. In all instances where a user enters information about a selected meal, a similar approach can be taken in which the user is provided with a list of options to select from, which can be presented in order from most relevant to least relevant. For example, if a particular type of food is selected, the user may be presented with previously consumed portion sizes of that particular type of food. Again, this can be done in descending order based on frequency of consumption. For example, if chicken breast is selected as part of a meal, based on the user's past history stored on the device, a food monitoring application can present portion sizes to select in descending order of frequency of consumption (e.g., 8 ounces, 10 ounces, 6 ounces). Alternatively, the list may be presented in ascending (lowest to highest) or descending (highest to lowest) order of portion size. The same applies to, for example, carbohydrate amount, sugar amount, protein amount, and any other aspect of the dietary information described herein.

[0099] Thus, following selection of a particular meal, information about each type of food and beverage within that meal can be entered, where the user's history can be used to present the most relevant options first. Of course, if no customization is required, the user can end the data entry process after the initial selection of the meal itself, without further specifying variations such as portion sizes.

[0100] A meal monitoring application can be programmed to filter the presentation of options based on time periods. For example, if a user is prompted to enter information about meals consumed during the morning hours, the meal monitoring application can present a list of commonly consumed breakfast meals and / or morning snacks (in descending order of frequency of consumption) to provide the user with only the most relevant options from which to choose (e.g., dinner is excluded). Similarly, when entering information about meals consumed during the day, only the most commonly consumed lunches and / or afternoon snacks can be presented, and when entering information about meals consumed later in the day or evening, only the most commonly consumed dinners, desserts, and / or evening snacks can be presented.

[0101] The meal monitoring application can also be programmed to filter the presentation of options based on characteristics of the detected event (e.g., the magnitude and / or duration of the glucose excursion or meal event) and / or a correlation between the characteristics of the detected event and past meals associated with the detected event that have similar characteristics. For example, the meal monitoring application can have local access (e.g., stored in local memory) or remote access (e.g., by downloading from a server) to the user's historical glucose levels over a past amount of time, e.g., a day, a week, a month, etc., as well as meal information entered into system 100 over that same or similar amount of time.

[0102] When a meal event is detected, the meal monitoring application can identify previously detected events that have the same or similar characteristics in analyte data. This can be done by referencing a database of correlations stored locally or remotely to the meal monitoring application in system 100. When the user is prompted to enter meal information based on the detected event, the meal monitoring application can present meal options for selection based on a correlation between the analyte data of the recently detected event and the analyte data of previously detected events that already have meal information associated with them. Time of day information can also be used in the correlation. For example, if a hypoglycemic event is detected in the evening, and a previously detected hypoglycemic event occurring in the evening was previously associated with a particular high-carbohydrate meal (e.g., a spaghetti dinner), the user can be presented with that particular high-carbohydrate meal as the first option to select, followed by other options in order of decreasing potential relevance.

[0103] In other embodiments, once a meal event is detected, a list of all previous meals may be displayed to the user (or accessed, for example, by a virtual selectable button labeled "Previous Meals"). The user may be permitted to select one meal as corresponding to the detected meal event. The list of all previous meals may be sorted by the frequency of past selection of that meal and / or by the magnitude or severity of the glycemic response to that meal (which may be the average or median if the meal has been consumed multiple times in the past). The list may be ordered in any number of ways, but a preferred embodiment is to order the list first by the most frequently selected meal, then by the most recent meal. Each meal may be associated with a unique identification code. Meal names entered by the user with similar, but not identical, text may be assigned the same code. If the same meal is entered and selected more than once, that unique identification code may be stored multiple times, associated with different times of the same meal. When displayed on a list sorted by the magnitude of the glucose response, the meal monitoring application may detect when meals are repeated by detecting that they have the same unique code. The meal monitoring application can create a list of all glucose responses for each repeated meal, determine the mean or median of these responses, and associate this with a unique code. Once this is done for all unique codes, a final list for display can be generated for all unique meals, including meals entered only once and repeated meals, and the list can be sorted by the magnitude of the glucose response associated with each unique code.

[0104] If the user selects a meal event associated with an undesirable glycemic response, a real-time notification can be generated and output to the user to alert them to the potential glycemic impact of the meal just consumed.

[0105] Such real-time feedback (i.e., immediate feedback from the user's perspective) can help the user internalize the undesirable effects of the meal they ate. For example, if the type of detected meal event is a dinner meal, the real-time notification can display the average glycemic response of all other meals of that type (dinner), along with the average glycemic response of the meal type just ingested and an indication of the difference (e.g., the recently ingested meal has a typical glycemic response that is 20% higher than the average glycemic response of all other dinners). The real-time notification can also alert the user to reduce the amount of food eaten or take a counteracting medication, such as insulin.

[0106] In one exemplary embodiment, the notification may include a graphical display of the user's current analyte data over time, with a graph of the current analyte data synchronized to the meal start time overlaid with an average of all past glycemic responses for that meal type. One such example is shown in FIG. 3C , where a trace 343 of the user's current analyte data is displayed over time. The meal start time is indicated at 344, and the last collected analyte measurement is indicated at 345. Upon selecting a meal type associated with one or more undesirable past glycemic responses, the graph in FIG. 3C may display an overlaid trace 346 representing the user's average glycemic response for all past intakes of that meal type (or all past times at which undesirable excursions occurred for that meal type). The start of the overlaid trace 346 may be aligned with the analyte data at the meal start time 344. Thus, the user is given real-time visual feedback on the potential consequences of ingesting the problematic food. Alternatively, multiple traces may be overlaid, with each trace representing one past excursion from intake of that meal type.

[0107] Alternative outputs include reports available from menus provided by the meal monitoring application. Examples of reports include a list of harmful meals (e.g., a "bad meals" list) and a list of beneficial meals (e.g., a "good meals" list). The bad meals list may be a list of previous meals sorted in descending order of glucose response magnitude, while the good meals list may be a list of previous meals sorted in ascending order of glucose response magnitude. The lists may be further modified, for example, by including only meals in the bad list whose glucose response magnitude exceeds a predetermined or user-defined threshold. The good list may include only meals whose glucose response magnitude is below a predetermined or user-defined threshold. Additionally, for the list of previous meals used for selection when entering meal information, symbols may be used to identify meals as good, bad, or neutral, as described above. Good and bad meals may also be identified in other ways for this selection list, such as by being included in respective sublists.

[0108] A report and periodic notifications can also be provided that keeps tabulating the amount of good and bad meals over a certain period of time, such as one or two weeks. For example, each time a user selects this report, the meal monitoring application can count the number of good and bad meals and display the results. Additionally, the application can provide a user interface that allows the user or healthcare provider to set a goal for the maximum number of bad meals over a certain period of time. The report display can display the current number of bad meals next to the goal. Alternatively, the application can generate periodic notifications about these metrics, where the period is, for example, weekly.

[0109] The application can process new data (whenever available, either automatically or when manually queried) to detect meals, measure the glucose response to the detected meals, and perform meal execution calculations. Alternatively, the application can determine whenever a glucose response is needed for analysis or reporting; that is, the glucose response is retrieved from memory but recalculated as needed. For example, when the weekly bad meal execution calculation exceeds (or falls below) a threshold, a notification can be generated indicating a reversal in eating habits (or congratulating the user on achieving a goal). The notification can take the form of a standard lock screen notification on Android and iOS phones, or it can be presented in a specific display area of ​​a general application screen, such as the home screen or glucose results screen.

[0110] The user may input a photograph or image of the meal. The image may be input to supplement other information entered to describe the meal. Alternatively, the image may constitute the entire description of the meal and its contents. This streamlined data entry may greatly facilitate data entry when the user logs a meal or responds to prompts, thereby encouraging use of the meal monitoring application. If an image is input, the image may be presented along with or instead of the text description of the meal and / or its contents, such that the user need only recognize the image in a list of options presented to the user for data entry. Thus, when the user is presented with options for identifying a meal in response to detecting an event, the options may be presented solely in the form of text, solely in the form of icons, solely in the form of images, or the options may be presented as a combination of text, icons, and / or images.

[0111] In some embodiments, the meal image can be analyzed using image recognition techniques to identify meal attributes, such as recognizable components of the meal. Meal components can be recognized using meal component recognition algorithms based on spectral boundaries defined within the image. If recognized, the meal image can be named by the meal monitoring application to describe the contents of the meal; for example, an image of chicken and broccoli can be labeled as "chicken and broccoli." The meal monitoring application can then provide the user with options to specify further information, such as portion size, nutritional content, etc., about the particular type of meal detected from the image. The meal can also be linked to past instances in the user's meal history where the same type of meal occurred and can be used in correlating the detected event to a particular type of meal.

[0112] 4A-E show exemplary embodiments of graphical user interface (GUI) visual layouts or screens that may be displayed on any of the reader device 120, drug delivery device 160, or local computer system 170 embodiments described herein.

[0113] 4A shows an exemplary embodiment of a screen 402 for logging meals and associated meal information, such as may be used at 302 of method 300 (see FIG. 3A). Screen 402 may include displays 404 and 406 of analyte levels determined from data received from analyte sensor 104 (not shown). Display 404 is a numerical display (e.g., mg / dL) of the user's current or last received analyte level. Display 406 is a graphical display showing a trace or curve of the user's analyte level (y-axis) versus time (x-axis) matched with a shaded region 407 indicating a normal or desired analyte level range. Such a display 406 may show data over any desired period (e.g., 8 hours or other period).

[0114] Screen 402 can include visual indicators to indicate various attributes of the data. For example, graphical display 406 can include a highlighted or otherwise highlighted indicator or marker 408 of the data. Here, indicator 408 indicates a glucose rate of change that exceeds a desired maximum rate of change, e.g., a sudden rise (or, in other examples, a sudden fall) in analyte data over time. Specifically, indicator 408 is a highlighted area under the analyte level curve that corresponds in time to the occurrence of the sudden rise. Thus, an observer can more easily understand whether recent events or excursions have occurred and their nature.

[0115] Screen 402 may be the home screen of the meal monitoring application or may be accessed from the home screen or other higher level page, where screen 402 is accessed from the home screen and includes a selectable home screen back button 410 for returning thereto, which in turn may redirect back to other higher level pages.

[0116] Screen 402 also includes a meal log selectable button or field 412. Selection of meal log button 412 may direct the user to screen 420 shown in FIG. 4B. If the device on which the meal monitoring application is running includes a camera, screen 420 may include a selectable take photo or select photo button or field 422, which, when selected, may open a camera application that accesses the device's camera and captures a photo or image of the meal for storage and association therewith. Selection of button 422 may also give the user the option to select a photo from a gallery of photos previously used to log meals or from a gallery of photos stored in the device's general photo gallery. If the user captures or otherwise selects a photo of the meal, the photo may be displayed on screen 420. In some embodiments, selecting the meal log button 412 in FIG. 4A may cause the meal monitoring application to immediately open the device camera application to allow the user to take a photo, thereby further streamlining the image entry process.

[0117] Screen 420 also includes a text entry location 424 for the user to enter free text information describing the meal. Selecting this location 424 opens a text editor that can be used to enter text information about the meal. This information, along with a photo, can be associated with the meal and can form part of a data structure representing the meal. Although not shown here, screen 420 can also include a suggestion list or drop-down list that the user can select to choose from a list of previously saved options for the meal or meal information, as described in more detail herein.

[0118] Although not all variations are shown, it is recognized that all aspects of the meal information described herein can be entered via one or more screens similar to those described with respect to Figures 4A and 4B, including, but not limited to, inputting meal time information, meal portion information, nutritional information, representative meal icons, etc.

[0119] After the meal information has been entered, either as a photo, a textual description, or by selecting a predetermined option, the user may select a save button or field 426 to save the data in memory as a data structure associated with and digitally describing the meal.

[0120] 4C-E show exemplary embodiments of screens 428 and 430 for prompting a user to input meals and associated meal information, such as may be used, for example, at 310 of method 300 (see FIG. 3A). Like screen 402 of FIG. 4A, screens 428 and 430 may include displays 404 and 406 of analyte levels determined from data received from analyte sensor 104 (not shown). Also, like screen 402, indicator 408 indicates a glucose rate of change that exceeds a desired maximum rate of change, e.g., a sudden rise (or, in other examples, a sudden fall) in analyte data over time. Specifically, indicator 408 is a highlighted area under the analyte level curve that corresponds in time to the occurrence of the sudden rise.

[0121] This rapid increase can be a glucose excursion or a meal event detected at 306 of method 300 (see Figure 3A), which can trigger a prompt to the user to enter meal information at 310. The screen 428 of Figure 4C is an example of the home screen of a meal monitoring application. Here, when an excursion or event is detected, the home screen can display a pop-up window 429 that overlays the home screen to notify the user that one or more possible meals have been detected and optionally give the time at which each of those meals was detected. The pop-up window 429 can include selectable fields for the user to choose to log meals for each period corresponding to the detected excursion or event, and can also include a selectable field for the user to choose to ignore the notification of the window 429. Here, the screen 428 includes a graphical display 406 of the user's analyte levels with indicators 408 for each detected glucose excursion or meal event. When the pop-up screen 429 is displayed, the corresponding detected events for which meal information has not yet been entered can be graphically identified or distinguished from events for which meal information has already been entered or for which meal information is not required.

[0122] In an alternative embodiment, upon detection of a glucose excursion or a meal event, the meal monitoring application can initiate or display the screen 430 of Figure 4D to allow the user to identify the detected event (at least in part by the indicator 408) and prompt the user to enter meal information via a selectable event detection button or field 412. Alternatively, the meal monitoring application can also display the event detection button 412 on another screen currently being displayed to the user. The event detection button 412 can alternatively be labeled as a meal detection button.

[0123] Selecting the option to log a meal on pop-up screen 429 or selecting event detection button 412 can transition the meal monitoring application to screen 420 of FIG. 4B, reproduced for ease of illustration as FIG. 4E. There, as in the exemplary embodiment described with respect to FIGS. 4A and 4B, the user can enter meal information photographically via button 422 and / or in text form via free text field 424 and save the information via button 426. Because meal event detection occurs after ingestion of a meal, the user may choose to photograph the meal packaging or scan the barcode on the meal packaging instead of photographing the meal itself. All variations of the meal information entry process described with respect to FIGS. 4A and 4B (including, but not limited to, suggestion list and drop-down list variations), including all variations to the meal information entry process described herein but not shown in FIGS. 4A and 4B, can be applied here as well.

[0124] If a user logs a meal at their own discretion, for example, using screen 420 of FIG. 4B , the meal monitoring application can prompt the user to describe the time the meal occurred in any of the formats described herein. Once the meal monitoring application detects an event, the application already has access to time information about the event. Thus, when the user is prompted to enter information about a meal event, for example, using screen 430 to access screen 420, the determined time information can be displayed to the user, so the user has the option to edit the time information if it is not accurate. If rising analyte data is used to detect meal events, the time the rising episode begins can be the default displayed meal time, as described herein, to compensate for sensor- and digestion-based lags.

[0125] In some exemplary embodiments, the data entry process is streamlined to further minimize the burden on the user when entering data. The less burden, the more likely the user is to utilize, and thus enjoy, the benefits of, the meal monitoring application. In one exemplary embodiment, the meal monitoring application may be configured not to proactively prompt the user with any nutritional information about the meal or the type of meal (e.g., breakfast, lunch, or dinner) when receiving a log entry from the user (e.g., step 302 of method 300) and / or when receiving information from the user in response to a meal information prompt (e.g., step 310 of method 300). In another exemplary embodiment, the meal monitoring application may be configured to accept only an image of the meal and / or a free-text description of the meal and not allow entry of any other information about the meal when receiving a log entry from the user and / or when receiving information from the user in response to a prompt.

[0126] 3A, once the meal event information is entered at 312, the meal monitoring application will associate analyte data with that meal information in memory at 314. Specifically, analyte data occurring around the time of the meal event can be associated with the meal event information. The analyte data selected to be associated with the meal event can be selected based on the time the meal event occurred and optionally a period of time before the meal event occurred to reflect changes in analyte levels due to the digestion of a meal.

[0127] In some embodiments, analyte data occurring from the time a meal begins through a certain period after the meal has stopped can be associated with a meal event. Also, analyte data occurring from the time a meal begins through the end of a detected glucose excursion can be associated with a meal event. In some embodiments, analyte data collected during a fixed time range around a meal event is associated with the meal event, e.g., any combination of 1, 2, or 3 hours before the meal event (e.g., as measured by the start of the meal event, the midpoint of the meal event, or the end of the meal event) through 1, 2, 3, 4, 5, 6, 7, or 8 hours after the meal event. In each case, the time during which the meal event occurred can be identified based on information entered by a user or can be identified algorithmically through analysis of the analyte data.

[0128] Data collected from the analyte sensor 104 may lag in time from the user's actual blood glucose level, for example, if the sensor 104 is sensing data from within the user's interstitial fluid and, to a lesser extent, dermal fluid. Correlating analyte data to the time of meal ingestion must therefore account for this sensor-based lag, which can typically be on the order of 3 to 20 minutes depending on the type of fluid and sensor placement. This lag is in addition to the lag time due to food absorption through the digestive process. In other words, there is a time lag between when food is ingested and when the results of that ingestion are reflected in the user's blood glucose level. Thus, in some embodiments, analyte data associated with a meal event is selected as described above, further compensating for digestion lag and / or sensor-based lag. For example, the first analyte data point associated with a meal event may be based on the start of that meal event but may be delayed by 10 minutes (5 minutes to compensate for digestion lag + 5 minutes to compensate for sensor-based lag).

[0129] The association of the analyte data with the meal event can be used in determining the glycemic impact of the meal event at 316. In some embodiments, the determination of the glycemic impact of the meal event can be performed algorithmically with reference to analyte data contemporaneous with the meal event, and this algorithmic processing can constitute both steps 314 and 316. The determined glycemic impact can be determined quantitatively in terms of maximum (peak) or minimum glucose level, median or mean glucose level, change from minimum to maximum glucose level (delta (Δ) glucose value), percent change in glucose level, duration of glucose response, rate of change in glucose level, area of ​​glucose response, and any combination thereof.

[0130] The meal event detector outputs information about the glycemic response to a meal, which can be used to characterize the magnitude or severity of the response. For example, the meal event detector can output a start time and a peak time for each detected meal event. The meal start glucose can be the glucose value at the start time of the meal event, and the peak glucose can be the glucose value when the elevated episode of the detected meal event peaks. The difference between these glucose values ​​can be determined to provide a delta glucose measure of the glycemic response to the meal.

[0131] In other embodiments, the "area" of the glycemic response is determined in terms of glucose and time; for example, because it can be difficult to accurately assess where the glycemic response ends, a sum or integral of each glucose reading is determined from the meal start time to either a) the next meal start time, or b) when the glucose reading falls within a threshold (e.g., within 10 mg / dL) of the meal start glucose value. This sum or integral is proportional to glucose multiplied by the time area of ​​the glucose response. Each of the metrics described herein, as well as others, can be used as a basis for ranking and / or sorting the glycemic response to a meal output to the user, as described below.

[0132] Another problem that arises specifically from the use of meal monitoring and detection software is that in some cases, multiple meal events may be detected and / or logged close in time to one another, which, from a user's perspective, can result in too many meal events being identified when the events were part of the same meal.

[0133] In these cases, the meal monitoring application can cluster or group meal events into a single meal cluster. For example, meal events logged or detected within a time range condition (e.g., 1 hour, 90 minutes, etc.) of each other can be merged into a single meal cluster. The meal monitoring application can then analyze and output to the user blood glucose response data describing the meal cluster as a whole, distinct from each individual event therein, intended to correspond to the user's concept of a meal (e.g., dinner may be a cluster of different courses). Thus, each meal event or meal cluster analyzed and displayed to the user is separated by at least the value of the time range condition, which can result in a more natural information output that is easier to understand. To facilitate this, the meal monitoring application can avoid imposing a limit on the number of meal events considered per day.

[0134] 3D is an annotated graph 350 of analyte data over time displaying an exemplary dataset in which four different meal events (352-1, 352-2, 352-3, and 352-4) were automatically detected or manually logged. These four different meal events are considered a single meal cluster 354 because each event 352 falls within a time range condition, which may be a predetermined factory-set condition or a condition set by a user. In this example, the time range condition is one hour, and all four events 352 are grouped together because each event 352 is within one hour of at least one other event 352.

[0135] In some embodiments, a maximum time limit can be placed on the amount of time from the first event 352 that the meal monitoring application considers other events for clustering. For example, the time range condition can be one hour from the first event 352-1 with a 90 minute meal cluster maximum length (not shown), in which case meal events 352-1, 352-2, and 352-3 would be grouped together as one meal cluster, and meal event 352-4 would be considered a separate meal event from the cluster.

[0136] As described herein, the time of a meal event can be the time entered by the user in a manual logging process, the time when an analyte episode corresponding to a meal event is first detected (e.g., the start of the rise), the time when an analyte episode corresponding to a meal event is first detected plus a delay (e.g., 15 minutes, 25 minutes), the average or median time determined from the duration of the analyte episode (e.g., the average time from the start to the end of the rise or the median time of data measurements collected during the rise), or any other desired time.

[0137] In some embodiments, multiple different meal event detectors based on different algorithms can be used. Each individual meal event detector routine can be run independently on the collected analyte data to identify detected meal events and / or their start times. The meal monitoring application can then select which results to use for analysis and display to the user. For example, if each meal event detector outputs a different start time for the same meal event, the meal monitoring application can average the two and use the averaged meal start time for analysis and display. In another example, the meal monitoring application can consider an event to have been detected only if each meal event detector actually detected the event. In yet another example, the meal monitoring application can consider an event to have been detected if at least one meal event detector actually detected the event.

[0138] 3D, the pre-meal or meal start glucose value 356 may be the glucose value at the start of the first meal event 352-1 of the meal cluster 354. If desired, this value may be obtained from a data measurement occurring closest to the time of the meal event 352-1. If no data measurement exists within a maximum time range, such as 30 minutes, the meal monitoring application may consider the pre-meal glucose value unknown.

[0139] A meal peak glucose value may be the highest glucose value occurring in the analyte data collected during or after a meal cluster 354. Meal peak glucose time range 358 shows an exemplary time range window searched for the occurrence of the highest glucose value. Time range 358 can begin at the time of the first meal event 352-1 and end at the time of the last meal event 352-4, or, more likely, can end at a predetermined time after the last meal event 352-4. In the embodiment shown in FIG. 3D , time range 358 begins at the time of the first meal event 352-1 and ends two hours after the last meal event 352-4. Thus, a meal peak glucose value would be the magnitude of the value in region 360 where the analyte data peaks and remains at a constant level. If no readings are present in window 358, the meal peak glucose value may be considered unknown.

[0140] The meal delta glucose value can be determined by subtracting the pre-meal glucose value from the meal peak glucose value. If one or both of these values ​​are unknown, the meal delta glucose value may be considered unknown.

[0141] Returning to FIG. 3A, the determined glycemic response or effect can then be output to the user visually at 318, such as on a smartphone display, as described below with respect to FIGS. 5A-F. In many embodiments, this occurs immediately with the presentation of information about the corresponding meal event, allowing the user to immediately understand the effect of a particular meal on the user's analyte levels, making use of the application compelling. The determined glycemic response can be output in quantitative and / or qualitative terms. For example, the determined glycemic response can be output on the same quantitative scale as determined at 316. This can be done in textual and / or graphical formats. The determined glycemic response can also be output qualitatively, for example, described in text as low or minor, moderate or moderate, or severe or severe, or any synonym thereof. Magnitude grades such as low or major can also be used. The determined glycemic response can also or alternatively be output as an icon or other image (e.g., colored shapes of varying magnitude).

[0142] System 100 may also (or alternatively) issue a medication or other treatment output at 320. In certain embodiments, the output may notify an individual, such as a user or medical professional, that medication (e.g., insulin) or treatment may be warranted, for example, due to the risk or occurrence of high glucose excursions or hyperglycemia. This notification may be a visual and / or audio notification output by reader device 120.

[0143] The output issued at 320 can provide information directly to the user's medication or treatment program on the same device or a different device in communication with the device running the meal monitoring application. This information can be used by the treatment program in determining whether a change in the user's treatment profile (e.g., basal insulin delivery schedule or bolus doses) is warranted or to prompt the user as to whether a change in the user's treatment profile should be implemented.

[0144] The output issued at 320 can cause the user's drug delivery device 160 to administer a drug to treat a potential or actual high glucose condition, such as by bolus administration or by modifying a basal dosing profile or schedule. This can be done with user approval, as in semi-closed loop, or automatically without user approval, as in true closed loop or artificial pancreas.

[0145] The meal monitoring application can also issue any of the aforementioned medication or treatment outputs when the user indicates that the meal was previously or recently consumed as a meal that caused a glucose excursion, such as a sudden rise or high glucose level. In this way, the system 100 can proactively treat anticipated glucose changes without waiting for the sensor 104 to detect an actual change.

[0146] Outputting information from a dietary monitoring application to a medication or treatment program and / or drug delivery device improves the program or device's ability to administer accurate therapy for the user. More accurate information about the user's dietary history is obtained and available to the program or device, allowing it to make appropriate, sometimes subtle, adjustments to the user's treatment. This, in itself, can reduce the occurrence of errors in drug administration (e.g., overdosing or underdosing) and constitutes an improvement over the functionality of electronic and mechanical systems with drug delivery capabilities.

[0147] 5A-F show exemplary embodiments of GUI screens that may display output information to the user or any other individual during step 318 (see FIG. 3A) of method 300 or at other times during use of the meal monitoring application. These screens may be displayed on any of the reader device 120, drug delivery device 160, or local computer system 170 embodiments described herein.

[0148] FIG. 5A shows a screen 502 containing a series, list, or group of logged meals 505-1 through 505-N. The group 500 is generally referred to herein as a ranking report 500. The ranking report 500 shows all or a subset of the meal events 505 (and activities, if applicable) for which data was collected. If meal clusters are identified, the meal events within a cluster are grouped together and displayed as a single meal event 505. The ranking of the events 505 may be ordered by the magnitude of the glycemic response. When presented to the user, the ranking report 500 may start at the top of the list and show meals 505 that resulted in the largest glycemic response first, followed by meals 505 that resulted in glycemic responses of decreasing magnitude.

[0149] In another embodiment, the ranking report 500 may display the most recent meals that caused a glucose excursion, allowing the user to scroll (up or down) through a list, again sorted by magnitude of glucose excursion from highest to lowest. In yet another embodiment, the ranking report 500 may display meals causing glucose excursions from most recent to oldest. Toggle sort buttons may be provided that allow the user to change the criteria by which the ranking of the report 500 is determined (e.g., change the ranking by meal type, date and time, magnitude of glucose excursion, etc.).

[0150] 5A , for each meal 505 in the ranking report 500, an image of the meal can be displayed, if available, along with the date and time the meal was consumed, along with an indication of the magnitude of the glucose response, which can be presented in any of the output forms described herein. Alternatively or in addition to an image, the name of each meal 505 can be provided in text form. In addition to ranking meals by their glycemic response, the ranking report 500 can rank occurrences of a single specific meal type by their glycemic response. For example, the portion size, duration, and glycemic response for a commonly consumed spaghetti dinner can be ranked and displayed.

[0151] If a glucose excursion occurred but meal information was not entered, the time, date, and magnitude of the glucose excursion may be displayed in report 500 along with a selectable button or field that, when selected, prompts the user to enter information about the meal, such as by displaying screen 420 of FIG. 4B. If meal information is present but no photo is available, a missing photo indicator may be displayed so that the date and / or time is aligned with other items in report 500.

[0152] If the meal event detection sensitivity settings or glucose excursion sensitivity settings are adjusted while using the meal monitoring application, the data displayed in the ranking report 500 can be retroactively adjusted so that all data is displayed with one common ordering of sensitivity settings. Thus, each time a user views the ranking report 500 or otherwise views past meal or analyte information, the meal monitoring application can recalculate the displayed information to retroactively compensate for the sensitivity adjustment.

[0153] Selecting or clicking on a meal within the ranking report 500 can launch a screen that displays additional information about that particular meal. FIG. 5B shows an exemplary embodiment of a screen 512 that can include a graphical display 514 of historical glucose (e.g., 8 hours, 24 hours, 48 ​​hours, or other) surrounding a meal event. The graphical display 514 can include any time increment before, after, and surrounding the meal. In one exemplary embodiment, the graphical display 514 is an 8-hour period having 2 hours of data before the start of the meal event and 6 hours of data after the start of the meal event. The graphical display 514 can also include any time period of various ranges of analyte data associated with the meal event at 314 of method 300 (see discussion regarding FIG. 3A ).

[0154] An excursion indicator 408 (in this case, a shaded area under the glucose curve) may be displayed for each excursion detected in the analyte data in the graphical display 514. The occurrence of a selected meal event, along with all other meal events within a period of time in the graphical display 514, may be indicated by a food icon 516, a photo of the meal, and / or text describing the meal. Alternatively, meal events that are clustered together may be shown as only one event. Selection of a particular meal event may cause another screen 512 to be displayed that focuses instead on the newly selected meal event. Alternatively, selection of a meal event may cause a pop-up window with information about the particular meal event to be displayed.

[0155] In another embodiment, selecting an individual's meal from screen 502 of Figure 5A may display an embodiment of screen 512 that includes an image of the selected meal along with a graphical display 514 having a dietary summary of that meal (e.g., portion type, serving size, etc.) and / or any free text entered by the user regarding that meal. Thus, all or most of the information entered by the user to describe that meal is displayed along with historical analyte data surrounding that meal event.

[0156] In another embodiment, each meal displayed either in the ranking report or elsewhere may be displayed with an associated glucose trace. When displayed with other meals on the ranking report, the user can compare characteristics of different meals, such as relative peak glucose times and how long the glucose response lasts. The display may highlight some of these characteristics, such as showing the numerical time elapsed of peak glucose relative to the start of the meal.

[0157] 5C shows an exemplary embodiment of a screen 520 illustrating an organized layout of information to display to a user about a particular meal, such as may be displayed upon selecting a particular meal from ranking report 500. The date on which the time corresponding to the meal event first occurs is displayed in area 522, followed by the time of the meal event (or time range, if provided). Below one or more images 524 of the meal, for example, if the user has captured photos of each course of the meal, these may be displayed. The peak glucose value for the meal and the delta glucose value for the meal may be displayed to the right at 526 along with an optional free-text description of the meal at 528.

[0158] FIG. 5D shows an exemplary embodiment of screen 530 in which exemplary information about a particular meal has been entered. Screen 530 is similar to screen 520, but also includes a display of detected meal events 532-1 and 532-2 for which information has not yet been entered, e.g., omissions. Each meal event 532 is followed by a user-selectable log button 533-1 and 533-2 for logging information about the meal event, and a user-selectable ignore button 534-1 and 534-2 for selection if the user chooses not to enter information about the meal or if the meal did not actually occur. If the user chooses to log information about the meal, a logging information screen, such as screen 420 of FIG. 4E, can be displayed with the meal time pre-entered as the time of the omission. If the user returns to screen 530, the information will need to be recalculated to account for the newly logged data. If the user chooses to ignore the omission, the logging of the omission is updated, and the ignored entry can be removed from screen 530.

[0159] 5E shows an exemplary embodiment of screen 540, similar to screens 520 and 530. Here, screen 540 includes six photos of a meal cluster that occurred between 10:50 a.m. and 11:20 a.m. In any of the embodiments described herein, selection of a particular photo can cause a pop-up window to appear that includes a larger display of that image.

[0160] FIG. 5F shows an example embodiment of a screen 550 similar to the previous screen, but with logged or detected exercise or other activity 552 and the time the activity occurred in relation to the meal event listed on screen 550.

[0161] 3A, method 300 can repeat itself such that the meal monitoring application continues indefinitely to receive, monitor, and store analyte data (e.g., step 304 of method 300) and search for detected events 306. Recently entered information about detected or logged meals can be included in the options presented to the user for entering meal information each time a new event is detected.

[0162] The meal monitoring application can be used in conjunction with software that monitors and collects information about a user's activity level. Thus, for each embodiment described herein related to logging meals, prompting for information about meals, associating analyte data with meals, determining the glycemic impact of meals, and / or outputting information about the relationship between meals and a user's glucose level, it should be understood that these embodiments are equally applicable to activities undertaken by a user that are not the consumption of food. Common examples of such activities are exercise, work, and rest. All embodiments related to such activities are within the scope of this disclosure. To assist in the collection of activity data, each sensor control device 102 can include or otherwise be configured to operate with an activity monitor that continuously or repeatedly measures each wearer's activity level (e.g., calories burned by hour of day) and / or a heart rate monitor that monitors and enables system 100 to record the wearer's heart rate.

[0163] Information output by the meal monitoring application, such as the ranking report 500, provides the user with specific and immediate information regarding the impact of meals on glucose levels. This output information can help the user learn to avoid or minimize certain meals in their diet that they were unaware were affecting their glucose levels, e.g., that were causing their glucose levels to be so high. This output information can also help the user learn to control their portion sizes by seeing the relative impact of different portion sizes on glucose levels.

[0164] The dietary monitoring application can also encourage individuals to try different meals, times, and ingredients, making it compelling and enjoyable to use. This dietary monitoring application encourages the use of analyte sensors by many demographics, including those with type 2 diabetes, pre-diabetes, metabolic syndrome, and non-diabetics - essentially anyone motivated to improve their health by changing their dietary and / or activity habits.

[0165] The dietary and / or activity monitoring applications described herein can provide substantial benefits to individuals newly diagnosed with type 2 diabetes or prediabetes. Often, when individuals are faced with starting diabetes medication, they are highly motivated to attempt dietary and exercise modifications to avoid or delay the need for medication.

[0166] Thus, a clinical scenario for use of the various systems and software disclosed herein is one in which a newly diagnosed individual, based on, for example, a fasting glucose test and / or an A1c test, follows up by wearing a sensor control device 102 interfaced with a reader device 120 that can be blinded so that the user's current and past analyte levels are stored but not shown to the user. The user wears the sensor control device 102 for a period of time, e.g., two weeks, after which analyte data is collected by a medical professional and analyte patterns are revealed to the user. This blinded use minimizes the system's impact on the user's habits and tendencies, thereby allowing medical professionals to better understand specific glycemic issues and how best to address them with medication. At this point, a physician can use the software applications described herein to provide the individual with the option of combining nutritional training with an exercise plan to address their diabetic condition with diet and exercise. This can be followed up with another sensor control device 102 and reader device 120, allowing a medical professional to assess progress and determine whether medication or further diet / exercise intervention is still necessary.

[0167] Additional metrics and reports for healthcare professionals can be included. For example, a study of 14 normal glucose tolerance (NGT), 12 impaired glucose tolerance (IGT), and 16 non-insulin-dependent diabetes mellitus (NIDDM) patients showed that type 2 diabetes mellitus (T2DM) subjects lacked a first-phase insulin response to an intravenous glucose challenge. Adequate monitoring of dietary information paired with glucose excursion tracking allows healthcare professionals to track changes in first-phase insulin response.

[0168] Furthermore, one-quarter of adults worldwide have metabolic syndrome. These adults are at five times higher risk of developing type 2 diabetes. Furthermore, evidence indicates that most damage to a person's beta cells occurs before a diagnosis of prediabetes, demonstrating the need for prediagnostic intervention. Because progression to type 2 diabetes begins with impaired glucose tolerance (IGT) well before impaired fasting glucose (IFG), sensor-based hyperglycemia and postprandial excursion-based indicators that have been shown to reliably predict or diagnose the onset of type 1 diabetes may be applicable to individuals with metabolic syndrome. Thus, the software applications and systems described herein for newly diagnosed diabetic patients are therefore applicable to metabolic syndrome populations.

[0169] Furthermore, because of the known general correlation between glucose excursions and weight gain, the software applications and systems described herein are applicable to individuals wishing to lose weight.

[0170] To the extent information is entered by a user, device, or other software, that information may be received, read, and, if it includes instructions, executed by processing circuitry (which may be a single processor or may be distributed across multiple processors or devices having processing capabilities). Those instructions may be stored in the memory of, and executed by, the processing circuitry of any and all embodiments of a reader device, drug delivery device, meter, computer system, or other computing device described herein.

[0171] Population Analyte Monitoring Embodiments for Diet and Activity Planning Also provided herein are exemplary embodiments that enable analyte monitoring of a local population, population, community, or group of users. Data collected from this population monitoring can be used for many purposes, including providing guidance and dietary and activity plans. These embodiments are described with respect to one exemplary application, namely, the use of population monitoring in an elderly care residential facility or home. However, these embodiments are not limited to this exemplary application, and many other such applications exist and are within the scope of the present disclosure. Population monitoring embodiments can be used alone or in combination with dietary assessment and planning embodiments also described herein.

[0172] In senior living facilities, meals are prepared and served by residential facility staff. The resident population may be prone to diabetes or prediabetes due to age-related decreased insulin response, decreased physical activity, weight gain, and / or other factors. Generally speaking, the residents form a population, and each member of the population may be equipped with an in-vivo sensor 104, such as a sensor control device 102. The analyte data collected by each member's sensor control device 102 can be automatically read by the reader device while the member is within range of the reader device, for example, when all or most members of the population are approximately the same distance from each other in a common area (e.g., a dining room, etc.) during mealtimes (e.g., breakfast, lunch, or dinner). Alternatively, data can be read from each member of the population at different times relative to each other.

[0173] 6A is a block diagram illustrating an exemplary embodiment using an analyte monitoring system 600 to collect analyte data from N members of a population. Here, system 600 includes a reader device 620 that can communicate with multiple sensor control devices 102-1 through 102-N located at multiple device wearers 601-1 through 601-N, respectively. N can be any desired number. Reader device wearer 601 is physically located in a common area 610, such as a dining room.

[0174] The reader device 620 may be configured in a manner similar to all of the embodiments of the reader device 120 described herein, but also includes the ability to communicate with each of multiple sensor control devices 102 simultaneously, sequentially, or at scheduled or random intervals (e.g., as the wearer 601 cycles through a common area throughout the day). This communication may occur via a wireless or wired communication path 640, which may be configured similarly to the wireless communication path 140 described with respect to FIG. 1. For example, any of the wireless communication protocols described herein may be used, and the communication path may be unidirectional or bidirectional.

[0175] In one exemplary embodiment, the reader device 620 pairs with each sensor control device 102 using Bluetooth or Bluetooth Low Energy communication protocols. The reader device 620 can collect analyte data from each wearer 601, store the data locally, and / or upload the data to another computer system, such as a remote internet server. Here, the reader device 620 is adapted to communicate with a wireless hub 630, which itself can communicate data to a trusted computer system 180 via a network 190, via any number of intervening servers. The communication link (or path) 641 between the reader device 620 and the wireless hub 630 can operate according to any desired wireless protocol. In this embodiment, the wireless hub 630 is a Wi-Fi hub, and the link 640 is a Wi-Fi connection. Thus, the reader device 620 can function as a Bluetooth-to-Wi-Fi bridge.

[0176] In other embodiments, reader device 120 may include electronic circuitry hardware and software that enables reader device 620 to upload data directly to trusted computer system 180 via network 190, such that Wi-Fi hub 630 may be omitted. For example, reader device 620 may include cellular capability, satellite communication capability, cable modem, Ethernet capability, etc.

[0177] In many embodiments, trusted computer system 180 can be local to resident population 601-1 through 601-N, such that communication over network 190 is not necessary. For example, reader device 620 can upload collected data via communication link 641 to Wi-Fi hub 630, which can then pass the information directly to trusted computer system 180 via a local wired or wireless connection, or reader device 620 can upload collected data directly to trusted computer system 180 via a local wired or wireless connection.

[0178] The reader device 620 can also perform any amount of desired processing on the analyte data collected from the sensor control devices 102. For example, the reader device 620 can simply pass the raw data collected from each sensor control device 102 to the trusted computer system 180 via the wireless hub 630 and network 190. In another example, the reader device 620 can algorithmically process the collected raw data to determine the analyte levels for each wearer 601 and then upload the processed information to the trusted computer system 180.

[0179] The reader device 620 can be programmed to automatically recognize and pair with each sensor control device 102 within its communication range. However, greater selectivity may be desired to allow data to be collected only from specific sensor control devices 102. In such embodiments, the reader device 620 may be programmed to recognize and pair with only a subset of the sensor control devices 102. For example, the sensor control devices 102 may include a dedicated identification flag or code that is transmitted to the reader device 620 during the data collection or pairing procedure. The reader device 620 may be programmed to upload data only from sensor control devices 102 that transmit this flag or code.

[0180] In another example, the reader device 620 itself, or another reader device 120, can perform an identification procedure for each sensor control device 102 that reads the identification number (e.g., serial number) of that sensor control device 102 and associates that identification number with a subset of the sensor control devices 102 that communicate with the reader device 620. During this identification procedure, the name or identification of the wearer 601 of each sensor control device 102 can be associated with that device identification number so that data collected from each sensor control device 102 can be linked to a particular wearer 601. If the reader device 620 is used for this identification procedure, the list of sensor identification numbers can be stored locally in non-transitory memory of the reader device 620. If another reader device 120 is used for the recognition procedure, the list of sensor identification numbers can be uploaded to the trusted computer system 180 and then communicated to the reader device 620.

[0181] To enable communication with as many sensor control devices 102 as possible, the reader device 620 can be centrally located within the common area 610. For example, the reader device 620 can be mounted overhead in the center of the room, or placed on the dining table in the case of a dining room. In some embodiments, multiple reader devices 620 can be used, e.g., one reader device 620 on each table, to increase the overall communication area.

[0182] Each sensor control device 102 may include sufficient non-transitory memory to store analyte data for the entire period between readings by the reader device 620. This allows the reader device 620 to collect all or substantially all of the analyte data for each wearer 601 throughout a day or week. For example, the sensor control device 102 may include 12 hours of memory to record analyte data overnight from after a dinner meal until a breakfast meal. Other memory sizes corresponding to different lengths of time may also be used (e.g., 8 hours, 24 hours, 72 hours, etc.).

[0183] Although these embodiments are described with respect to the use of a common reader device 620 for the local population, each member of the local population may use their own reader device 120 to collect analyte data. In such cases, each reader device 120 may communicate directly to a trusted computer system 180 or to a data collection hub that, in turn, uploads the data to the appropriate computer system.

[0184] 6B is a flow diagram illustrating an exemplary embodiment of a method 650 for analyte monitoring of a population for dietary and / or activity planning. Similar to the system 600 of FIG. 6A, the method 650 is described with respect to collecting data from residents of an elderly care facility during mealtimes.

[0185] Shipped sensor control devices 102 are first received by the facility at 652. Facility personnel can optionally upload the identification number of each sensor control device 102 to the trusted computer system 180 at 654. Each wearer 601 to be monitored can then apply the sensor control device 102 at 656 such that the analyte sensor is positioned in vivo and ready to sense analyte data.

[0186] Each applied sensor control device 102 can then be initialized, activated, or recognized at 658 by the reader device 620 or another reader device 120, as previously described, to identify the subset of sensor control devices 102 that will transmit data to the reader device 620. This recognition procedure can be performed by each wearer 601 or a member of facility staff, and can optionally include associating the wearer's name or identification with their sensor control device 102. This data can be uploaded at 660 to the trusted computer system 180, which can in turn communicate at 662 the list of sensor control devices 102 (and optionally the associated wearers 601) to the reader device 620.

[0187] The reader device 620 may monitor 664 for the presence of sensor control devices 102 within the shared area 610 and then pair with each of the subset of sensor control devices 102 as they are discovered within the shared area 610. If the shared area 610 is a dining hall, many sensor control devices 102 are likely to be discovered within a short time span. After establishing a connection with a particular, recognized sensor control device 102, the reader device 620 may read or upload 667 the analyte data stored within that sensor control device 102, which may represent the series of analyte level measurements of the wearer 601 since the device 102 was last read, or the entire history of analyte level measurements stored in the device's 102 memory (in which case measurements that overlap with those already collected may be ignored). After collecting the analyte data, the reader device 620 may disconnect from that sensor control device 102 at 668, and if the reader device 620 determines at 669 that another sensor control device 102 is present and needs to be read, it may proceed to pair with and collect analyte data from the next recognized sensor control device 102 (returning to 664).

[0188] This process is repeated until analyte data is collected from all recognized sensor control devices 102 within range of the reader device 620. Because particular residents may have different meal schedules and their presence in the common area 610 may be correspondingly unpredictable, the reader device 620 may continuously monitor the sensor control devices 102 throughout the day. The reader device 620 may be programmed with a wait period so that after data from a particular wearer 601 has been collected, the reader device 620 does not attempt to re-pair with that wearer's sensor control device 102 until a sufficient amount of time has passed. For example, if the wearer temporarily leaves the common area 610, the reader device 620 will not attempt to collect data again upon re-entry. This wait period may be, for example, 30 minutes, 1 hour, 90 minutes, etc. After the expiration of this wait period, the next time the reader device 620 recognizes a sensor control device 102, the reader device 620 will begin the data collection procedure again. Thus, this process may be repeated continuously.

[0189] The data from all of the sensor control devices 102 may be stored 670 in non-transitory memory of the trusted computer system 180, and the aggregated data may be examined or processed 672 to identify trends among the residential facility population. An example set of aggregated data is shown in human-readable format in FIG. 7, where the X-axis represents time and the Y-axis represents analyte concentration, although each axis may be a different indicator. Each point (X) in the distribution represents an analyte concentration reading collected by the reader device 620 from a particular sensor control device 102. Thus, the distribution includes not only readings from the same wearer 601 at different times, but also readings from different wearers 601 (also at different times). Furthermore, the aggregated data may include readings collected from each wearer on different days, for example, allowing trends unrelated to the occurrence of a single meal to be identified.

[0190] Menu information, such as meal content (e.g., calorie content, carbohydrate content, sugar content, protein content, etc.), meal times throughout the day, and meal type (e.g., breakfast, lunch, snack, or dinner), can also be provided to the trusted computer system 180 and linked to the aggregate data so that correlations between analyte data and meal information can be identified. Meal events can be detected in the aggregate data using any of the meal event detectors described herein or by analysis of predefined periods each day, assuming scheduled meals are served. Analyte patterns of the resident population after previous meals can be compared to the meal information to identify factors causing undesirable analyte events, and meals can be adjusted to minimize these events. Even if a specific excursion has not occurred, meal content, type, and time can be adjusted to provide maximum stability or analyte levels across the resident facility population.

[0191] The trusted computer system 180 can be programmed to present aggregate data to the user in a format such as a graph, as shown in Figure 7, or a summary report. The trusted computer system 180 can also be programmed to determine or present analyte indicators for individual wearers and statistics for the population as a whole (e.g., analyte median or mean, standard deviation, or average maximum or minimum). Figure 7 shows a trend line 702 representing team analyte concentrations for the resident population during a portion of a day with indicators for a first meal 704 and a subsequent meal 706.

[0192] The trusted computer system 180 can indicate trends in the resident population and identify instances where a portion of the population (e.g., 5%, 10%, 20%, etc.) violates a threshold or the population is trending in an undesirable manner. The threshold can be, for example, a low analyte threshold, a high analyte threshold, a rate of change threshold, etc. A population can be trending in an undesirable manner, for example, by the occurrence of undesirably high or low mean, median, or minimum-to-maximum fluctuations, even if an excessive number of glucose excursions are not occurring. The trusted computer system 180 can be further programmed to reference dietary information (e.g., excessive carbohydrate content, excessive sugar content, meal frequency that is too high or not frequent enough) to provide potential causes of the trend and to provide recommendations on how aspects of the diet can be modified to mitigate the trend. These recommendations can include adjusting meal content, meal times, the introduction of snacks, the elimination of snacks, and adjustments to insulin doses and administration times (e.g., basal and bolus doses).

[0193] The trusted computer system 180 can also determine the analyte (e.g., glycemic) response or impact of each meal and provide a report identifying meals associated with the most desirable or undesirable analyte profile. This information can be used, for example, by facility administrators to modify meal plans to maintain improved analyte levels across the population. For example, the trusted computer system 180 can determine and store the glycemic response of each meal and output a ranking to assist in identifying meals that elicit the most severe response. Any of the metrics described herein can be used to quantify or otherwise indicate the severity of the glycemic response, including, but not limited to, peak glucose value, delta glucose value, and / or glucose area.

[0194] The statistical analysis can output, for example, mean, median, or variability glucose values ​​with standard deviation and / or interquartile range for each meal. The output can also include the results of threshold comparisons, such as outputting an indication of the mean or median of the population above a threshold, outputting the percentage of the population with a mean or median above a threshold, outputting the variability of the population above a threshold, etc.

[0195] In addition to those actions already mentioned, any number of actions can be taken on members of the population identified as exceeding a threshold for a given meal, such as reducing food portions, providing alternative foods, or increasing insulin or other medications. The trusted computer system 180 can also identify individuals experiencing low glucose levels that may indicate too much insulin is being administered for a particular meal.

[0196] Residences typically offer recreational activities and thus have the ability to select the types of activities and schedule these activities at times that are most effective for reducing glucose elevation. The embodiments described with respect to Figures 6A, 6B, and 7 can be used in combination with other activities experienced by the population, such as exercise.

[0197] For example, information regarding the population's exercise events can also be linked to the aggregate data such that the trusted computer system 180 can present the linked data and / or determine whether a correlation exists between analyte trends and the time, duration, and / or intensity of the exercise events, in much the same manner as described above for diet. Each sensor control device 102 can include or otherwise be configured to operate with activity monitoring that continuously measures each wearer's activity level (e.g., calories burned by hour of day).

[0198] Food monitoring application In another embodiment, in addition to the features described above, the dietary monitoring application categorizes food and beverage selections according to blood glucose (or other analyte) response. It can be used in conjunction with analyte monitoring system 100 to help people better understand the impact of their diet on their glucose levels. Users can see firsthand how their food and beverage choices, along with portion sizes, affect their glucose levels. Users can learn which foods have the greatest impact. This application also helps dispel myths about healthy eating (e.g., problematic high-carbohydrate foods such as orange juice and breakfast cereals, which may be mistakenly perceived as healthy). This application can be a standalone application or can be incorporated in whole or in part into other applications.

[0199] Meal monitoring applications can be used by diabetic patients who are fundamentally motivated to change their diet, including those with type 2 diabetes who are taking basal insulin or those with type 1 or type 2 diabetes who are on combination insulin therapy. They can also be used by prediabetic or non-diabetic patients who want to minimize glucose excursions by controlling their diet. Meal monitoring application users often have uncontrolled diabetes and want to get their diabetes under control, but they are unwilling to give up the foods they enjoy and find that following a prescribed "diabetic diet" over the long term is not sustainable. Meal monitoring application users also may be in control of their diabetes, but their usual diet may no longer be working because they are taking new medications, a new exercise plan, are pregnant, or are experiencing other life changes.

[0200] A meal monitoring application may allow a user to view the good and bad foods (or good and bad eating behaviors) present within their current diet to help them determine what modifications they can make to achieve glucose control and improve the time their glucose levels stay in the target range. The meal monitoring application may assist the user by providing data visualization of the glucose spike difference and time-in-range (TIR) ​​impact of different food choices. The meal monitoring application may also prompt the user to log a meal if a "less healthy" meal is detected (e.g., a meal that resulted in the user's blood glucose level falling outside the target or target range). The meal monitoring application may gamify TIR to motivate the user to make beneficial food choices and increase their TIR index. The meal monitoring application may also include encouraging and congratulatory messages to motivate the user to continue good eating behaviors.

[0201] The food monitoring application may present the user with an onboarding sequence to help customize the application based on the user's stated needs and goals so that the user has a targeted experience. The application may prompt the user to indicate their type of diabetes. The list of options may include type 1, type 2, prediabetes, gestational, other, and unknown. After the user selects an appropriate answer to the prompt, the application may prompt the user to specify the user's level of diabetes education. The list of possible answers may include beginner, intermediate, and experienced but still learning. The application may prompt the user to specify their goals for using the application. The list of possible answers may include eating for glucose control, maintaining glucose within range, and both (full guidance). A user who selects eating for glucose control may want to learn what and how much to eat based on the meals the user has logged and the impact those meals have on glucose. A user who selects maintaining glucose within range may want the application to understand how what they eat affects their glucose, be notified only when their glucose spikes, and help bring the user's glucose levels back within their target range. Users who choose both 1130 may want to learn what to eat and what to do to achieve their target glucose range.

[0202] During the onboarding process, the application may introduce various milestones for the user to meet to encourage the user to log more meals. The application may set milestones for the user to meet, which may include logging the first meal, staying within a goal range after eating a meal, and logging a certain number of meals (e.g., 5, 10, 15, 20, 25, 50 meals). Additionally, the application may display counters to track the user's progress in various milestones. For example, to show the user's progress toward logging a goal of 10 meals, a counter indicating the number of meals logged may be displayed along with a graphical progress indicator. The application may set milestones by default. Alternatively, the user can set or change the milestones.

[0203] There are many metrics that can be used to assess the glucose impact of meals (and other events). A meal monitoring application may utilize and evaluate any desired metric, and in some embodiments, may utilize and evaluate the amount of time a user exceeds an upper threshold, the amount of time the user is within a range of values, or the amount of time the user falls below a minimum threshold. For example, a meal monitoring application may evaluate the amount of time a user's glucose level exceeds a threshold (e.g., a value of about 180 mg / dL, alternatively about 250 mg / dL, alternatively about 120 mg / dL, or another value). Such a metric may be referred to as a time-above-threshold. A time-above-threshold episode may be triggered when the glucose level exceeds the threshold for a current duration. While not limited as such, embodiments are described in the context of using the time a user's glucose level exceeds a threshold of about 180 mg / dL for at least a preset period of time, which may be referred to as time-above-threshold 180 or TA180. A meal monitoring application may also evaluate the amount of time a user spends within a range, i.e., time in range, or "TIR" (e.g., between about 70 mg / dL and about 180 mg / dL, between about 70 mg / dL and about 120 mg / dL, or between about 140 mg / dL and about 250 mg / dL, or other ranges). Yet another is to evaluate the amount of time spent below a value, such as about 180 mg / dL. Such a metric is sometimes referred to as the time-below threshold.

[0204] These indices may be determined in percentage form by dividing the time the analyte level exceeded the threshold (or was in range) by the total time. For example, if glucose is within the TIR threshold for 6 hours over a 24-hour total period, the TIR index is 25%. Or, if the user's glucose level is above the threshold for 3 hours over a 24-hour total period, the time-over-threshold index is 12.5%. Alternatively, the index may be displayed in units of time increments instead of percentiles; using the example above, the TIR would be 6 hours (over a 24-hour total period). The total period may be a predetermined fixed length or an amount that varies over time. For example, the TIR to date for the current week may be the aggregate time glucose is in range from 12:00 AM on Sunday to the current time of the week divided by the total time elapsed since 12:00 AM on Sunday. The index may also be calculated according to the amount of in-range readings in a given elapsed time divided by the total number of readings in the elapsed time. For example, over a 24 hour period, glucose readings may be collected every 15 minutes. If the application is accessed, for example, from the home page, the TIR calculation can be performed according to the following formula: TIR = # Readings in range / # Readings from start of period In this example, if the time period starts at 12:00 AM, then at 12:00 PM (12 hours after 12:00 AM), the total number of readings in 15-minute intervals will be 48. However, at 11:45 PM (23.75 hours after 12:00 AM), the total number of readings in 15-minute intervals will be 95. Thus, the denominator changes over the course of a day and resets at the end of the configured time period, e.g., at 96 readings at 12:00 AM each night for a one-day period. A similar calculation can be made for the time exceeding threshold metric by substituting the amount of time or number of readings in range for the amount of time that a reading is above the threshold or the number of readings above the threshold.

[0205] In many embodiments, a user of an application has their glucose monitored, such as by a sensor control device 102 of system 100. As seen in FIGS. 8A-1-8A-2, analyte levels are (i) transferred from the sensor to a user interface application 802, then (ii) transferred from the user interface application 802 to a cloud including a server 804, and then (iii) transferred from the server 804 to a meal monitoring server 806, which may return results, if any, to the meal monitoring application. The cloud may include one or more servers 804, 806 with the same or different functionality. For example, analyte data may be uploaded to a first server or servers responsible for collecting the analyte data and then downloaded to the meal monitoring application by a second server or servers responsible for downloading the data for use by the meal monitoring application. As seen in the first screen, meal monitoring home screen 810, a graph 812 of glucose concentrations (in mg / dL or mmol / L) for a period of time, e.g., a day or portion of a day, is displayed. The time displayed on the graph 812 may represent measured glucose concentrations beginning at 12:00 AM on the current date. The goal range 818 is highlighted in the graph, for example, by shading it in a color different from the background color of the graph, e.g., green. As seen in FIG. 8B , when a meal event is detected, the meal monitoring application can mark the event with an indicator 820 at the time determined by the episode detector to be the start of the event. Notification to the user of the detected event may come at a time after the time determined by the episode detector to be the start of the event. The indicator may be a circle with a “?” indicating that meal information (or other information that can be logged, such as exercise, stress, etc.) is missing for this event, and the user is prompted to log the meal ingested that caused the elevated glucose level (see log entry screen 826).The meal monitoring application then returns to the home screen 810, which may display a representation 832 of the meal consumed (eg, an image of the food).

[0206] In an alternative embodiment, the home screen may include a graph of glucose concentrations (in mg / dL or mmol / L) displayed for a certain period of time, e.g., a day or portion of a day. The time displayed on the graph may represent measured glucose concentrations beginning at 12:00 AM on the current date. Target ranges may be highlighted in the graph, for example, by shading them in a different color than the background color of the graph, e.g., green shading. Also, upper glucose thresholds (e.g., approximately 250 mg / dL) and lower glucose thresholds (e.g., approximately 75 mg / dL) may be indicated on the graph by different colored lines to clearly indicate to the user whether they are above or below these thresholds. Additionally, detected events may be color-coded to indicate if the event is above the range (e.g., orange or red) or within the range (green). A time-to-date (TIR) ​​display for the day or week may also be displayed to allow the user to easily discern how well their glucose management is progressing. A graphic showing the current sensor life may also be displayed on the home screen 810.

[0207] The target range 818 (as well as the threshold for the episode detector) may be set by the user and / or HCP. For example, the target range may be set to about 80 mg / dL to about 170 mg / dL, alternatively about 70 mg / dL to about 180 mg / dL, or about 65 mg / dL to about 120 mg / dL.

[0208] According to another aspect of the embodiment, configurable goals may be used. The goal 854 itself may be set or configured by default by the patient or HCP. The goal is a threshold value that is compared to the aggregate amount of the glycemic response indicator (e.g., a TA180 not exceeding 30%, or a TIR of at least 70%) over a period of time (a week, or up to the current time in the current week, or up to the current time on the current date). The goal 854 may also be incrementally adjusted by the meal monitoring application if the user meets their currently assigned target or goal after a predetermined period of time. For example, if the user consistently achieves a performance goal of 30% of glucose readings within the target range, for example, over the past week, the meal monitoring application may set a new performance goal of maintaining 35% of the measured analyte levels within the user's target range. Similarly, if the user does not consistently achieve a target of 30% of glucose readings within the target range, the meal monitoring application may set a new performance goal for the user of maintaining 25% of the measured analyte levels within the target range.

[0209] The home screen 810 may also include a display of the amount of time the user spent within the target range ("Time in Range" or "TIR") for different time periods, e.g., two or more time periods, alternatively three or more time periods. The home screen 810 may display the TIR 850 percentage for the current date (e.g., measured from 12:00 AM to the current time), the TIR for the previous week, and the TIR so far for the current week (e.g., measured over a period of time, e.g., from 12:00 AM on Sunday to the current date and time, alternatively from 12:00 AM on Monday to the current date and time, alternatively from seven days ago to the current date and time). The value of these three TIR display methods is that they provide the user with an indication of how they are doing compared to the previous week in terms of the current week to date and the current date to date. With this information, the user can make dietary choices, such as avoiding certain foods if they are feeling worse than last week, or indulging in foods that may cause hyperglycemia if they are feeling better than last week. In addition to reporting the numerical percentage of time spent in the target range 818, the TIR display 850 may visually indicate the amount of time the user has spent in the target range 818. For example, for each time period on the TIR display 850, circles 852-1, 852-2, and 852-3 may be shown with a numerical percentage of TIR in the center of the circle. The circles 852-1, 852-2, and 852-3 may graphically display the amount of TIR (progress indicator) and the goal / target percentage of TIR. For example, with respect to an imaginary clock face on the circle, the indication of the TIR percentage may be displayed in the form of a colored line starting at the 12 o'clock position and extending to X% of the full circumference of the circle, indicating that the user has spent X% of TIR so far for that time period. Additionally, an indication of the target percentage 854 (e.g., a bull's-eye, circle, "x," or other delimiter) may be included around the circle to allow the user to easily see whether their TIR is above or below the target percentage. Each of the circles 852-1, 852-2, 852-3 for different time periods may include an indication such as (1) a numerical percentage of the TIR, (2) a graphical or visual indication of the amount of the TIR (progress indicator), or (3) an indication of the goal / target percentage.

[0210] Event Detection The meal monitoring application can have three configurable settings for episode detection: a high threshold, a low threshold, and a minimum duration. An analyte level greater than the high threshold for a duration greater than the duration threshold can be referred to as an "above range episode." For example, if the high threshold is set to approximately 180 mg / dL and the duration threshold is set to 30 minutes, a high episode is detected when glucose remains above 180 mg / dL for at least approximately 30 minutes, and the user is prompted to log a meal. Analyte levels, such as glucose readings, can be classified into one of three possible states: (1) an in-range episode, (2) a hyperglycemic episode, or (3) a hypoglycemic episode. A more detailed description of exemplary embodiments of software or software-implementable processes related to meal event detection is described in any of U.S. Patent Publication Nos. 2013 / 0085358, 2014 / 0350369, 2014 / 0088393, 2017 / 0185748, 2020 / 0105397, or WO 2015 / 153482 or PCT / US20 / 12134, all of which have been previously incorporated by reference in their entirety for all purposes.

[0211] For any embodiment of the application, as seen in FIG. 10C , when the episode detector detects an event, the meal monitoring application can increment a numeric badge 847 located on the meal monitoring application icon 849 by one. The meal monitoring application can also add an icon (e.g., a “?”) on the glucose graph at the time estimated by the episode detector to be the start of the event, offset at the glucose value or by a predetermined amount relative to the glucose value at that time. The numeric badge can be decremented by one when the user provides and saves information for the associated event, or after a certain period of time has passed, where the elapsed period may be a certain time value from the event or a quantized total day duration for the event. In further embodiments, the meal monitoring application can also issue a notification when a new event is detected. When the user selects the “?” icon, a log entry screen 826 may appear that allows the user to enter event information. When the screen 826 is opened in this manner, the event time may be set by default to the time estimated by the episode detector to be the start of the event, i.e., the time associated with the icon. However, this time is still editable by the user.

[0212] Food logging The meal monitoring application may prompt the user to log a meal, for example, when an event or episode is detected and / or when an out-of-goal-range event is detected. A user of the meal monitoring application may also log meals spontaneously without being prompted. As seen in FIG. 9A-1 , if a user wants to spontaneously add a meal, on the home screen 810, the user may select the option to add a meal, for example, by tapping the “+” icon 836, after which a log entry screen 826 pops up with multiple fields for the user to enter meal information. The log entry screen 826 may also include a “Description” field 828 in which the user may enter a description of the meal consumed. The log entry screen 826 may also include options 830 indicating the relative size of the consumed meal, for example, selectable buttons indicating different meal sizes, such as “Small,” “Regular,” and “Large.” The user may also add a photo of the meal to be associated with the meal. For example, the log entry screen 826 may include a camera icon 834 that, when tapped, opens the camera of the device on which the meal monitoring application is installed, allowing the user to take a photo of the meal or, alternatively, select a photo from a photo library. The log entry screen 826 may or may not include a field to include the carbohydrate content of the meal. After the meal information is entered, the user can click "Save" to log the meal, after which the user may be directed to a log details screen 840. As seen in the log details screen 840, a representation of the logged meal 832 appears on the graph 812, and a short description 842 of the logged details of the meal may be displayed below the graph in a short description window 842. The user may select to expand the short description window 842 to review the entire logged details, as seen in the log details screen 844.

[0213] As seen in FIGS. 9A-1 and 9B , instead of entering a meal description, the user can also select an item to be included in a list of frequently consumed meals by selecting or opening a “Frequent Items” drop-down menu 860. This alternative method for logging meals allows the user to quickly enter meal information if they have already logged meals in the past. As seen in FIG. 9B , the user can tap or otherwise open the “Frequent Items” pull-down menu 860 to open a list 864 of items the user has previously consumed. The user can select the meal they want to log by scrolling down the list 864 and, for example, tapping or otherwise selecting an entry. Tapping an entry will open the log entry screen 826 corresponding to the selected food item, with a description pre-populated. The user can then select the relative portion size of the consumed meal from the portion size options 830. After the meal information is entered, the user can click “Save” to log the meal. After the meal is logged, the user can access the same information as described in FIGS. 9A-1–9A-2 for manually entering a new meal.

[0214] In an alternative embodiment of the application, an alternative log entry screen 870 may be used in the meal monitoring application, as seen in FIGS. 9C-1 through 9C-5. The log entry screen 870 may include all of the options and fields as discussed with respect to the other log entry screens, including, but not limited to, a field to add a meal description 828, an option to select a meal size 830, an option to associate a photo 834 with the meal, a frequent food menu 860 (see FIG. 9C-2) that allows the user to select meals previously entered, and an option 846 to indicate no food was consumed (e.g., an Other (No Food / Drink) toggle switch). The user can also change or enter a time associated with the event, for example, by clicking the time 872 displayed on the log entry screen 870. As seen in FIG. 9C-5, a time screen 874 can be opened, where the user can select a time they want to associate with the event, for example, by turning a clock handle to a time or by entering a time in digital form.

[0215] In an alternative embodiment, a user can log a meal from the home screen by tapping the plus sign or other link and selecting "Food." A GUI can then appear, displaying a list of recent meals, frequently eaten meals, and all meals in different selectable tabs. If the user selects a meal that has already been entered, the meal entry can be pre-populated with information from the past meal and assigned the current date and time. Additionally, the user may be able to edit the meal entry as needed. An option to add a new meal can also appear. If the user selects the option to add a new meal, another GUI can appear that includes multiple tags to associate with the meal. The user can edit the entry to add a name for the meal. The user can also select a camera icon to add an image to associate with the meal entry. The user can also select a meal tag to specify the type of meal consumed. Meal tags can include breakfast, lunch, dinner, dessert, snack, or other suitable names. The user can also select one or more descriptive tags that describe the contents or characteristics of the meal. For example, a descriptive tag may relate to the carbohydrate content of the meal, e.g., low-carb or high-carb. Description tags may also relate to the specific contents of a meal and list food types, for example, vegetables, chicken, beef, pork, fish, salad, pasta, vegetarian. The user may also select portion size tags, which may list relative portion sizes. These tags may include regular, large, and small sizes compared to the user's usual portion. The user may also enter the amount of carbohydrates associated with the meal. The meal time and date may be automatically associated with the meal entry. The time and date may be edited by the user as needed. The user may also enter notes associated with the meal entry. The notes may include a more detailed description of the meal that can be used in natural language search to analyze the individual foods included in the meal.

[0216] After multiple meals have been logged, the food notes GUI can display a list of recent meals, frequently eaten meals, and all meals in different selectable tabs. If the user selects a meal that has already been entered, the meal entry is pre-populated with past meal information and assigned the current date and time. Additionally, the user can edit the meal entry as needed. A risk tag may be associated with each meal or food item, which can indicate that the meal is low risk, dangerous, or high risk, or alternatively low risk, medium risk (or moderate), or high risk. These risk tags may be determined in a manner similar to the determination of "good" and "bad" meals described elsewhere herein. For example, a meal that results in the user remaining above the goal / target range or about 180 mg / dL for a certain period of time, e.g., at least about one hour, may be classified as "bad" or "high risk," while a meal that results in the user remaining within the target range of about 70 mg / dL to about 180 mg / dL for a certain period of time may be classified as "good" or "low risk." Meals that result in the user remaining above the target range or about 180 mg / dl for a shorter period of time than that of a bad or high-risk meal, for example, about 15 minutes to about 60 minutes, alternatively about 30 minutes to about 60 minutes, may be classified as "risky" or "medium risk."

[0217] The user may also tap on a link to recommendations, which may result in an in-app modal GUI being displayed, which may include details of a suggested meal plan from the ADA or other organization.

[0218] The user may also be prompted to enter meal information when an event is detected. When an event is detected, the meal monitoring application increments a badge on the application icon and displays a “?” icon 820 on the analyte level graph at the time the meal is estimated by the episode detector to have started. As seen in the home screen 810 of FIG. 10A-1 , the user can add information to this event by tapping the “?” icon, which opens a log entry screen 826. Alternatively, the user can tap the “?” icon to open a window 824 prompting the user to add details about the meal. The user can click “Edit” 822, which opens a larger log entry screen 826. Once the user is in the log entry screen 826, either tapping “?” or clicking “Edit” allows the user to enter a meal description 828 and may also display the relative size 830 of the consumed meal, e.g., selectable buttons indicating different meal sizes such as “small,” “regular,” and “large.” The user may also associate a photo with the meal. For example, the log entry screen 826 may include a camera icon 834 that, when tapped, opens the camera of the device on which the meal monitoring application is installed, allowing the user to take a photo of the meal or, alternatively, select a photo from a photo library. The log entry screen 826 may or may not include a field for including the carbohydrate content of the meal. The log entry screen 826 may also include an option 846 for indicating that the increase in glucose was not caused by a meal, as seen in FIG. 10A-2. In the log entry screen 826, this option 846 is a selectable box labeled "Increase in glucose was not caused by a meal." In an alternative embodiment shown in FIG. 10B, this option 846 appears as a toggle switch labeled "Other (No Food / Drink)."If option 846 is selected, a symbol 848 (e.g., an "X") may be incorporated into the display space reserved for the photo to highlight that this event is not associated with a meal. After the information related to the event is entered, the user can click "Save" to log the information. After the event information is logged, a smaller version of the symbol 848 or meal may be associated with the event on the graph in the log details screen 840. Details of the logged event may be displayed in abbreviated form in a window 842 below the graph.

[0219] Thus, as summarized in the flowchart of FIG. 11 , there are two ways a user can enter a meal into the meal monitoring application. The user can either choose to log a meal 902 or be prompted 904 by the application to log a meal. In one method, the user can enter a description of the meal 910, select the size of the meal consumed (e.g., small, medium, large) 912, add a photo of the meal 914, and then save 916 the meal information and associate it with the meal event. The user can also edit the time associated with the meal entry. Alternatively, the user can choose to log a meal by opening a menu (e.g., a drop-down menu) 920 of frequent foods, selecting 922 the meal from the frequent food list, selecting the appropriate meal size 924, and saving 926 the meal information, which will then be associated with the meal event. Alternatively, the user can indicate 930 that the glucose increase was not due to a meal.

[0220] 9D , the meal monitoring application may also include a menu screen 880 that includes, but is not limited to, a meal log icon 882, a meal ranking icon 884, a mismatched excursions icon 886, and a settings icon 888. When a user selects the meal log icon 882, a meal log screen 890 opens, which may include the same details as described with respect to the log entry screen 826. The meal log screen 890 may include a field for the meal name 904, a field for comments 906, and an option to add an image of the meal. The meal log screen 890 may also include an option to select a meal from a list of previously entered meals 860 (e.g., see "Meal Selection").

[0221] In this alternative embodiment of the application, the user also has the option to view a list of detected events and associate meals with these detected events. As seen in FIG. 9E, when the user selects the unmatched excursions icon 886, an unmatched excursions screen 890 appears with a list of detected events. Each entry 892-1 through 892-4 may include a time or time range 894 at which the event / excursion occurred, a portion of a glucose concentration graph 896 showing the event, and a glucose concentration value 898 associated with the event. The user may select the option to log a meal associated with a particular event, whereupon a log entry or meal log screen, as described herein, is presented, allowing the user to enter meal information as described in connection with other embodiments.

[0222] Meal Monitoring Insights The food monitoring application is designed to provide users with multiple insights by grouping various analyses together. As seen in FIG. 12 , the home screen 810 provides insight into how successful the user was in reaching their goals over various time periods and which foods caused spikes in glucose levels. Small icons 820 on the graph 812 give the user insight into which foods spiked their glucose levels outside of the target range 818. The user can identify “bad foods” at a glance if an image is associated with that event, or the user can click on an icon to see the details of that food log. The current day TIR display 852-2 can inform the user how mindfully they need to eat for the remainder of the day to achieve their TIR goal 854, which is clearly displayed in the TIR circle 852-2. If the current week to date TIR display 852-3 indicates that they are above their TIR goal 854, the user can be informed that they can have a “cheat day” or “cheat meal” and still achieve their TIR goal 854. However, if the current week TIR to date display 852-3 indicates that they are below their TIR target 854, it may notify the user that they need to make better dietary choices to make up for poor choices earlier in the week. The week-past TIR display 852-1 may give the user insight into whether they are doing better so far in the current week compared to last week. If the current week TIR display 852-2 shows a higher percentage TIR compared to the previous week, the user may be encouraged by seeing that the better choices they made during the current week have succeeded in maintaining levels within the target range.

[0223] As seen in FIG. 13A , the weekly insights screen 930 can provide the user with additional insight into the impact of consuming certain foods. The weekly insights screen 930 can include a TIR display 932 of the week-to-date total, a trend statement 934, a display 940 of food groupings after which the user ate, causing glucose to rise above the goal / target range or having a TA 180 above a certain threshold, and a display 950 of food groupings after which the user ate, causing glucose to remain within range or having a TA 180 below a certain threshold. The food grouping displays 940, 950 can be displayed for different time periods 936 (e.g., all day, morning, afternoon, evening, and overnight) and may be expandable and collapsible. In an alternative insights report 930 seen in FIG. 13C , the screen may include a filter with an option to select the period 937 to be displayed. The TIR display 932 of the week-to-date total (“week total”) can indicate to the user whether they are reaching their goal for the week. The trend statement 934 can tell the user which periods of the day they had the most out-of-range readings (e.g., "When they spent the most time above range: Afternoon"). The display 940 of food groupings that contributed to the time the user spent out of range can give the user insight into which foods the user ate that consistently caused their glucose levels to go above the target range and / or above 180 mg / dl (TA180) (e.g., "bad" foods). From the food grouping display 950, the user can see if there were foods that consistently kept them within the target range (e.g., "good" foods). The display 950 of food groupings that contributed to the time the user spent within the target range can give the user insight into which foods they ate that consistently kept their glucose levels within the target range. From the display 950, the user can see which "good" foods consistently kept their glucose levels within the target range.The grouping of foods displayed and the display of periods may be programmed, for example, as a default, to first display the "bad" periods of the day in which the user spent the longest time above the goal range (e.g., highest TA180). The program may alternatively be programmed, for example, as a default, to first display the periods in which the user stayed the most within the goal / target range (highest percentage of TIR), and display the "good" foods associated with this period in an effort to enforce good behavior.

[0224] Meals can be associated with episodes according to several rules: all episodes can be classified into in-range and out-of-range episodes, with in-range episodes preceding and following each out-of-range episode.

[0225] Episode within range → Episode outside range → Episode within range (Ep1) (Ep2) (Ep3) Every logged meal can be characterized as belonging to an episode based on the following rules: - If a meal is logged within 30 minutes of the end of an episode, it will only belong to the subsequent episode. For example, if a meal in Ep1 is logged within 30 minutes before the start of Ep2, the logged meal will be assigned to Ep2, not Ep1.

[0226] - If a meal is logged more than 30 minutes but up to an hour before the end of an episode, it belongs to both the current episode and the next episode. For example, if a meal in Ep1 is logged one hour before the start of Ep2, the logged meal will be assigned to both Ep1 and Ep2.

[0227] - If a meal is logged more than 1 hour before the end of the current episode / start of the next episode, it will belong to the current episode only. For example, if a meal is logged in Ep1 1 hour and 1 minute before the start of Ep2, the logged meal will be assigned to Ep1 only.

[0228] So, for example, if a meal is logged in Episode 1 (an in-range episode) 15 minutes after Episode 1 ends and before Episode 2 (an out-of-range episode, e.g., above 180 mg / dL) begins, the meal will be assigned to Episode 2, not Episode 1.

[0229] 13B-1 through 13B-5 and 13C show how to access the Weekly Insights report. As seen in FIG. 13B-1, from the home screen 810, the user can tap or select the light bulb icon 952 to open the Weekly Insights Week Range screen 952, which contains a list of various weeks with associated date ranges. Each week entry, in addition to reporting the numerical value of the percentage of time spent in the goal / target range, can also include a TIR display 852, which includes a visual or graphic indication (e.g., a colored bar around the perimeter of a circle or other progress indicator) of the amount of time the user spent in the goal range. The time in range for the entire week can be calculated according to the following formula: TIR 週 = Range readings for # weeks / Overall readings for # weeks For example, if glucose readings are taken every 15 minutes, the total number of readings in a week is 672. The time in range to date for the current week can be calculated according to the following formula: TIR 現在の週の現時点までの時点 = ♯In-range reading / ♯Reading from start of week For example, if glucose readings are taken every 15 minutes, a week can be set to start at 12:00 AM on Sunday, with a TIR of 12:00 PM on Tuesday. 現在の週の現時点までの時点 The denominator of would be 240 readings.

[0230] The user can select a specific week to view that week's insight report 930, which opens the insight report screen 930 for the selected week. The TIR display can also be filtered to represent the entire week or a specific time of day (e.g., morning, afternoon, or evening). The insight report 930 can have a default setting that displays the period of the day in which the user stayed in the goal / target range the longest, including displaying "good" foods associated with this period in an effort to enforce good behavior. The background color of the screen can be colored to indicate that the user was within range; for example, the background color can be set to green for displaying "good" foods. As seen in FIGS. 13B-3 and 13B-4, the user can also select to display the longest period in which the user was above the goal / target range or approximately 180 mg / dl ("time spent in hyperglycemic zone") 940, which will display "bad" foods eaten before or during the time the user's glucose levels were above the target range. To be characterized as a "bad" food, the hyperglycemic zone 180 (time-above-180) may be greater than about 1 hour, alternatively greater than about 30 minutes, or alternatively greater than about 1.5 hours. The background color of the screen may be colored to indicate that the user was outside the target range; for example, the background color may be set to yellow, orange, or red for the indication of a "bad" food. Additionally, for any of the foods displayed on the insight report screen 930, the user may select a particular food entry icon to view episode details 955 associated with that meal.

[0231] The meal monitoring application can rank episodes to determine which meals to display in the insight report 930. All episodes, both within and outside the range, occurring weekly can be ranked in ascending order by duration. Episodes with the longest duration within the range (e.g., top 5, alternatively top 7, alternatively top 10, alternatively top 5-top 10) can be displayed along with their associated meals. Similarly, episodes with the longest duration outside the range (e.g., top 5, alternatively top 7, alternatively top 10, alternatively top 5-top 10) can be displayed along with their associated meals. Within the display of top episodes, the user can scroll to view meals related to that particular episode. The user can also scroll between episodes to identify recurring themes (e.g., chocolate bars are included in all of their top five episodes). The ranked episodes can also be filtered to include only episodes from a specific time period within a day, similar to the TIR display.

[0232] The user can also click on an episode to view the glucose trace for that particular episode. The episode details screen 955 can include a graph of the user's glucose levels for the relevant time period including that meal (as highlighted by a corresponding icon), and a simplified screen (expandable to reveal more meal details) can be displayed below the graph. The glucose concentration graph 812 can color-code target range concentrations green 818 and concentrations above the target range (e.g., area under the curve) yellow, orange, or red 958.

[0233] In an alternative embodiment, the weekly insights screen 960 may include the TIR display 852 for the week, the trend statement 934, and an indication 940 of “bad” foods that tend to raise the user’s glucose levels above the target range (see FIG. 13D-3 ). The weekly insights screen 960 may also include an indication 964 of how in-range and out-of-range the entered meals were. Similar to the TIR display 852, the in-range and above-range meal indication 964 may include a visual indication (e.g., a colored bar around the perimeter of a circle) of the number of entered meals for which the user remained within the target range, in addition to reporting a numerical value of the percentage of the number of meals for which the user remained within the target / target range. Another circle may display for meals that resulted in the user’s glucose levels rising above the target range. The weekly insights screen 960 may also include a graph 968 of the median or average glucose levels for the week displayed over a one-day period, with the day divided into segments (e.g., overnight, morning, afternoon, and evening). The graph 968 may be color coded to indicate which portions of the graph are within range and which are above range.

[0234] In an alternative embodiment, the weekly insights screen may include a TIR display for the week, a trend statement, an indication of "bad" foods that tend to raise the user's glucose levels above the target range, and an indication of "good" foods that the user ate after their glucose remained within range or had a TA180 below a certain threshold. In a simplified layout, meals may be grouped according to when they were above range or within range and may not be subdivided according to time of day or separated by meals eaten on different days.

[0235] In an alternative embodiment, the weekly insights screen may include a TIR display for the week, trend statements, an indication of "bad" foods that tend to raise the user's glucose levels above the target range, and an indication of "good" foods that the user ate after their glucose remained within range or had a TA180 below a certain threshold. The bad and good foods may be placed on separate tabs that the user can toggle between. Individual meals may be displayed on meal cards that may include a close-up photo of the meal, the name of the meal, the size of the meal, the date and time the meal was consumed, and a TIR display for the period before and after the meal (e.g., 2 hours after the meal).

[0236] In an alternative embodiment of the application, as seen in Figures 14A and 14B, a user can access a list of meals ranked according to the corresponding glucose levels recorded. The meal ranking screen 970 can be accessed from a meal ranking icon 884 on the menu screen 880. The meal ranking screen 970 can list all meals logged for different time periods 974 (e.g., daily, one week, two weeks, one month, all time). In the meal list 972, meals can be ordered from highest to lowest glucose level, or alternatively, from lowest to highest glucose level, where the glucose level may be the highest glucose concentration associated with that meal event. As seen in Figure 14B, a user can select a meal, and a meal details screen 978 or log details screen 840 can open to display all details of the meal, such as a photo, description, maximum glucose level, time over 180, and a glucose concentration graph including the associated period during which the meal was eaten.

[0237] As seen in FIGS. 13D-1 through 13D-3, in an alternative embodiment of the application, users can also have access to time-of-day (TOD) instance rankings instead of meal-specific rankings. Users can view meals, beverages, and snacks by each TOD instance. As seen in the alternative home screen 980, users can easily review meals and beverages associated with their glucose response curves along with the daily TIR display 852. By clicking on a meal icon, users can open a log details screen 984 that provides more detailed information about the meal, which can include a photo of the meal, a description, and the time above range resulting from that meal.

[0238] Alternative TIR displays are shown in Figures 15A-C, which use a circle to highlight time in range (Figure 15A), a partially shaded (filled bucket) circle to highlight percentage of time in range (Figure 15B), and a bar to highlight time in range (Figure 15C). As seen in Figure 15A, the TIR display 1002 displays a circle with a target (number of hours in TIR) inside. The current amount of time the user has spent in the target / goal range can be displayed in two ways: a numerical value may be listed inside the circle along with the target value, or the number of hours may be visually displayed as a band or progress indicator (e.g., colored green) around the perimeter of the circle that extends to X% of the circle's total circumference to indicate that the user has spent X% of TIR (where X% is the time spent in range divided by the target time (hours)). As seen in Figure 15B, the TIR display 1004 shows a circle with the target percentage listed near (e.g., above or below) the circle. The TIR percentage can be displayed in two ways: a numerical value may be listed inside the circle, or the number of hours may be visually displayed as a band or progress indicator (e.g., black) along the perimeter of the circle extending to X% of the total circumference of the circle to indicate that the user has spent X% of TIR. The inside of the circle may also be partially shaded (similar to filling a bucket with water) to reflect the current percentage of TIR, where the shaded percentage is proportional to the % of TIR to date for that period. For example, if a user has an 88% TIR, 88% of the circle's area would be shaded. As seen in FIG. 15C , the TIR display 1006 can also visually show the user's TIR against their TIR goal using a bar or other progress indicator. The TIR display 1006 displays both the TIR goal to date for the current day and the TIR goal to date for the current week. The length of the bar can represent the total goal, and the bar fills to represent the percentage of TIR the user has achieved for that particular period. The day and week TIR bars can be different colors.

[0239] In an alternative embodiment, as seen in FIGS. 16A-1-16A-2, the TIR display 1010 can display the elapsed time in range, with the in-range time and target time reported at the center of the circle. A band or progress indicator (e.g., colored green) can extend along the perimeter of the circle to X% of the circle's total circumference to indicate that the user has spent X% of the TIR (where X% is the time spent in range divided by the target time (hours)). For example, in FIG. 16A-1, the user has been in range for 11 hours so far, and the circle shows a visual representation of the percentage of time in range relative to the time goal (16 hours) with a band or progress indicator (e.g., colored green) extending along the perimeter to cover 11 / 16 of the circle's total circumference. FIG. 16A-2 shows the elapsed time in the goal / target range at the end of the day. FIGS. 16B-1 and 16B-2 illustrate alternative TIR displays 1012, 1014, 1016, and 1018 with a simple ring option. The TIR displays 1012, 1014, 1016, and 1018 display the current TIR percentage in a center or circle and, as described for the other TIR displays, include a band to visually indicate the TIR. Instead of listing the target percentage within the circle, the alternative TIR displays 1016 and 1018 can include markers that intersect with positions along the perimeter of the circle that correspond to the target percentage, allowing the user to quickly determine whether their TIR percentage is above or below the target percentage. FIGS. 16C-1 and 16C-2 illustrate alternative TIR displays 1020, 1022, 1024, 1026, and 1028 with a cumulative ring option. The TIR displays may compare two or more different time periods.For example, the TIR display may show a TIR display of the current week to date and the current date (1020), a TIR display of the current week to date and the previous week without the circle (1022), a TIR display of the current week to date and the previous week with the circle (1024), a TIR display of the current date to date and the current week (1026), and a TIR display of the previous week and the current week to date (1028). Figures 16D-1-16D-2 show alternative TIR displays 1030, 1032, 1034, 1036 with an alternative cumulative ring option that reports the percentage spent on the target / target area at the center of the circle and a colored band (progress indicator) along a portion of the perimeter of the circle indicating the relative percentage of TIR. The TIR display optionally includes a target percentage marker along the perimeter of the circle corresponding to the relative location of the target.

[0240] 17A and 17B illustrate further alternative TIR displays that use partially shaded (bucket-filled) circles to indicate the percentage of time in range. The target percentage or time can be listed near the circle. FIG. 17A reports the TIR as a percentage 1040, while FIG. 17B reports the TIR as a total number of hours 1044. For each TIR display, the center of the circle can be shaded to indicate the percentage of the target the user was in range of, where the shaded percentage is proportional to the % of TIR to date for that period.

[0241] 18A and 18B show a further alternative TIR display that uses a progress bar to indicate time in range. The progress bar, having a length equal to the goal time or percentage, can have a progress indicator (shaded or colored extension) to illustrate the user's TIR. For example, if the goal is to be in range 16 hours out of a day and the user was in range for 14 hours, the progress indicator (shaded or colored extension) can fill 14 / 16 of the total bar. The TIR display can show the TIR for the day (1050), or both the TIR for that day and that week (1054), or can report other periods (e.g., previous week, previous month, or current month).

[0242] TIR Banking As described above, the meal monitoring application may calculate a tracking index and display it to the user in a manner that indicates whether the user is ahead of or behind the target TIR goal. Specifically, the tracking index may be the average post-meal TA180 index across all of the logged meals for the previous week. The post-meal TA180 may not be the same as, but may be greater than, the TA180 index calculated over the entire 24-hour period. These two indices are correlated to some extent, and a linear correlation coefficient (or other functional fit) can be estimated based on either population data, per capita, or both.

[0243] The postprandial TA180 index can be displayed in a number of ways. The TA180 can be displayed as is or a correlation coefficient can be applied to convert it to a 24-hour equivalent. The meal monitoring application can provide a default 24-hour TA180 target value and / or require / allow the user to set a value. A typical TA180 target value considered to be under good control for a diabetic patient is about 30%, alternatively about 25% to about 35%, alternatively about 20% to about 40%.

[0244] According to some embodiments, the meal monitoring application can determine a target value set at a level that may be an achievable goal for the user based on historical glucose data collected for the user (e.g., one or two days). For example, if the measured TA 180 is substantially greater than 30%, the app can determine an achievable target TA 180 that is approximately 5% to 10% lower than the measured value. The meal monitoring application can also determine a new target value after the user has achieved or is close to achieving a previous goal. The meal monitoring application can also determine a new target value only after the user has maintained their current goal for a period of time.

[0245] A food monitoring application may display a tracking indicator as a progress bar. When a new goal is set, the tracking indicator may begin averaging subsequent meals and not include meals prior to the new goal being set. An example of a progress bar is shown in FIG. 19A.

[0246] The progress bar 1060 can indicate that the user is ahead of the goal, meaning that the user is making good food choices and avoiding overeating, thereby exceeding the goal. The shaded bar extension 1064 (e.g., shaded green) to the right of the midline 1062 is proportional to the difference between the average measurement TA 180 minus the goal, assuming the measurement is less than (better than) the goal.

[0247] In contrast, an example progress bar where the measured TA 180 is greater (worse) than the target is shown in FIG. 19B. The shaded bar extension 1066 to the left of the midline 1062 is proportional to the difference between the target and the average measured TA 180, assuming the measured value is greater (worse) than the target. This example progress bar indicates that the user is falling behind the target, meaning that the user is not making good food choices and avoiding overeating, and is not reaching the target goal. The progress bar 1060 may or may not actually show the measured TA 180 value and the target value, or the difference between them.

[0248] As seen in FIG. 19C , a meal monitoring application can help a user “bank” a TA 180 below the target, with the goal of feeling comfortable eating a “bad” meal without falling behind on the goal. The purpose of this feature is to help the user feel more in control by managing their glucose levels. For example, the meal monitoring application can list logged meals that the application considers “bad” based on the TA 180 recorded for the meal, as described with reference to other embodiments. This would be done by determining a “bad” post-meal TA 180 threshold, for example, by applying a correlation coefficient to the target TA 180 or to a fixed TA 180, such as 30%. In some embodiments, for example, the application can display “bad” meals and provide a user interface (UI) means for the user to identify bad meals that they actually want to continue eating (e.g., cannot live without).

[0249] An exemplary embodiment of a progress bar displaying additional information to promote the "banking concept" is shown in FIG. 19C. The width of this meal's impact indicator 1068 corresponds to the previously measured TA 180 for that meal, indicating how that meal may contribute / change the average TA 180 index. For example, assuming the average is a week's worth of meals, 7 x 3 = 21 meals. With a goal of 30%, the average measured TA 180 is 24%, and a "bad" meal has a TA 180 of 45%. This bad meal falls within the margin (green bar).

[0250] Impact = 45% / 21 = 2.1% Margin=30%-24%=6% FIG. 19C may show that the user is behaving so well that they can easily eat their favorite "bad" foods without jeopardizing their progress indicator. This encourages the user to try to behave well and reward themselves later. In some embodiments, the progress bar may rotate through all of the favorite foods shown each time it is displayed, or on a timer. Alternatively, or in addition, the application may provide a means for the user to select a favorite food or toggle all selections, and the app will calculate and display the "impact" (i.e., the percentage ahead of goal) required to cover that meal.

[0251] Alerts The application can also display various alerts and notifications reminding and / or encouraging the user to log meals to help the user improve or maintain glycemic control. For example, a lock screen notification may appear to notify the user that a high glucose meal has been detected and prompt the user to log good foods based on the detected glucose spike. Alternatively, the notification may be a congratulatory notification, for example, informing the user that they stayed within range after a meal or that they have logged a certain number of meals. The application can also present an in-app modal to remind and / or encourage the user in response to the user tapping the notification on the lock screen. The in-app modal may be a more vibrant, visual modal within the application that provides more context for the call to action. The in-app modal may present graphics and text that gently encourage the user to log meals by reminding the user that the cause of the detected glucose spike could be for many reasons and that logging meals can help identify the cause of the glucose spike. The in-app modal may include possible responses, such as indicating that the user did not eat anything, or allowing the user to select a link to add food, which may open a food logging screen (examples of which are described elsewhere in this application). Alternatively, the in-app modal may be a congratulatory notification, including, for example, encouraging words and graphics praising the user for staying within their goal.

[0252] guidance In an alternative embodiment, the user may have access to a guidance GUI that provides insight into a number of statistics and metrics about how the user is doing so far in the week. The guidance GUI may include a counter showing the user's progress in meeting milestones, e.g., how many meals have been logged for the week, and a progress indicator that graphically shows how many more meals need to be logged to meet a goal, e.g., logging 10 meals. The guidance GUI may also include a TIR display for the day or the week, which may be displayed to allow the user to easily discern how well they are managing their glucose. Groupings of above-range and within-range meals consumed for the week may be accessible in a separate tab. These groupings may include foods from different meal types grouped together. These groupings may allow the user to know after each logged meal whether it was a good or bad meal without having to wait to view the summary in the weekly insights report. Groupings of out-of-range meals may be color-coded red, and groupings of within-range meals may be color-coded green. The guidance GUI may also include a recommendations section, which includes a list of meal and / or food recommendations that may help the user increase their TIR to achieve their goal. The recommendations may include a list of ADA-approved meal suggestions and may also include other meal and food options that have been determined to have a positive effect on the user's TIR and improve glycemic control, or alternatively, have been determined to have a positive effect on glycemic control (e.g., associated post-meal glucose traces remained in the target range) of the user's population or subset of the population. The suggested meals or foods may include the name of the meal or food, a photo, and the suggested meal type (e.g., breakfast, lunch, dinner, and / or snack).

[0253] The GUIs associated with the various tabs of the logbook screen may also include information from the application. The log GUI may include a list of various events detected for a day, such as meals, alarms, exercise, and insulin doses. Each entry may include a glucose level, a trend arrow, a graphical display of the type of event, and a time. The logbook's graph GUI may include a glucose trace with a target or goal range highlighted in green. Event markers, which may be color-coded, corresponding to the various events listed in the log GUI may also be plotted on the glucose trace. For event markers corresponding to meals, a window may appear in association with the event marker listing the time of the event, the glucose level, and a graphical symbol. Additionally, the graph GUI may include a meal tile associated with the meal event marker. The meal tile may include the name of the meal, a photo of the meal, tags, a text description, the portion size, and the time / date of the meal. The logbook's insight GUI may include a TIR tile containing a TIR display for the day to date and the week to date, which may be displayed to allow the user to easily discern how well they are managing their glucose, along with their target TIR. The Insight GUI may also include a glucose tile that includes a graphical display of the glucose levels for that day, along with a listing of the highest glucose level recorded for that day along with the time it was recorded, the lowest glucose level recorded for that day along with the time it was recorded, and the percentage of time spent in range, above the high glucose threshold ("Very High"), and out of range (which may include time spent above the target range (e.g., above the lower glucose threshold than the high glucose threshold) and below the target range). The graphical display may be in the shape of a circle with color-coded sections corresponding to the percentage of time spent in range, above range, and out of range.The insight GUI may also include a food tile, which may list the amount of carbohydrates consumed that day, the daily carbohydrate goal, the number of meals logged, the number of meals within range, and the number of meals above range. The insight GUI may also include an exercise tile, which may list the amount of time exercised and / or the duration of exercise.

[0254] Evaluation and Recommendations The application may also be able to identify good and bad meals over time. In one exemplary method, as seen in FIG. 20A , a method 1300 for analyzing meal data may include, at step 1302, receiving an input log entry associated with a meal consumed. The log entry may include the meal content and meal size of the meal consumed.

[0255] In step 1304, the ingested meal may be associated with at least one post-prandial glucose trace. The post-prandial glucose trace may include analyte levels from a meal start time (or estimated meal start time) to an end time, which may be at least about 2 hours, alternatively at least about 4 hours, alternatively at least about 4.5 hours, alternatively until about the start time of the next meal.

[0256] At step 1306, a grade of the ingested meal may be determined based on at least one characteristic of the at least one post-prandial glucose trace.

[0257] At step 1308, the grade may be displayed in a graphical user interface in association with the meal consumed.

[0258] The steps described above are merely exemplary, and contemplated methods include methods in which the steps are in a different order, or in which certain steps are omitted or added.

[0259] The application may also be capable of identifying good and bad foods over time. In one exemplary method, as seen in FIG. 20B , a method 1320 for analyzing dietary data may include, in step 1322, receiving an input log entry associated with an ingested meal and, in step 1324, identifying at least one food item included in the ingested meal. In step 1326, the at least one food item may be associated with at least one postprandial glucose trace associated with the ingested meal. In step 1328, a grade for the food item may be determined based on the postprandial glucose trace. In step 1330, the grade for the food item may be displayed in a graphical user interface in association with the at least one food item. The steps described above are exemplary only, and contemplated methods include those in which the steps are in a different order or in which certain steps are omitted or added.

[0260] The application may also be able to identify good and bad foods for various populations. In one exemplary method, as seen in FIG. 20C , a method 1340 for analyzing dietary data may include, at step 1342, receiving an input log entry associated with an ingested meal and, at step 1344, identifying at least one food item included in the ingested meal. At step 1346, the at least one food item may be associated with at least one postprandial glucose trace associated with the ingested meal. At step 1348, the application may store the at least one food item and the at least one postprandial glucose trace associated with the ingested meal in a database. The database may include multiple foods and associated postprandial glucose traces for a population.

[0261] In step 1350, the application may determine a grade for at least one food item. The grade may be based on a subset of postprandial glucose traces associated with the at least one food item. The subset of postprandial glucose traces may be based on or associated with a population segment. A population segment may have a common disease state, such as prediabetes, type 1 diabetes, type 2 diabetes, gestational diabetes, etc. For example, a segment for users diagnosed with type 2 diabetes may have a high incidence of high-fat, high-calorie foods classified as very bad, while for other segments, such as prediabetes and type 1 diabetes users, this type of food is primarily classified as moderately bad. Thus, the system may report to users with type 2 diabetes that foods in this category are likely bad for them, while for other user segments, the system may report that these types of foods are only moderately bad.

[0262] In step 1352, the application may display a grade associated with the at least one food product in a graphical user interface.

[0263] The steps described above are merely exemplary, and contemplated methods include methods in which the steps are in a different order or in which certain steps are omitted or added.

[0264] The application may also categorize the user based on how they respond to various foods and recommend different foods based on the categorization. In one exemplary method, as seen in FIG. 20D , a method 1360 for recommending food selections may include, at step 1362, receiving an input log entry from a user associated with an ingested meal and, at step 1364, identifying at least one food item included in the ingested meal. At step 1366, the application may associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal. At step 1368, the application may determine a categorization of the at least one food item based on at least one characteristic of the at least one postprandial glucose trace.

[0265] In step 1370, the application may analyze the classification and at least one food item using a population model including a database of food classifications and associated foods for multiple population segments. The database may include meals, post-meal glucose traces, user demographics, and user preferences. The population model may use machine learning techniques to divide the population into multiple population segments. The population model may include a first model that assigns the user to a segment of the multiple population segments based on an analysis of the classification, the at least one food item, the at least one post-meal glucose trace associated with the at least one food item, the user demographics, and / or the user preferences. The population model may also include a second model that maps the segments of the multiple population segments to a plurality of good food items and a plurality of bad food items.

[0266] At step 1372, the application may associate the user with a segment of the plurality of population segments based on the classification and the at least one food item. A segment of the plurality of population segments may be associated with a plurality of foods having a positive classification. At step 1374, the application may display a recommended food item. The recommended food item may be one of a plurality of foods having a positive classification for a segment of the plurality of population segments.

[0267] The steps described above are merely exemplary, and contemplated methods include methods in which the steps are in a different order, or in which certain steps are omitted or added.

[0268] The application may also sort or classify meals and foods as good and bad, and display a count of the current number of meals or foods analyzed. As shown in FIG. 21 , in one exemplary method 1380, at step 1382, the application may classify a plurality of foods as good or bad based on the effect of each of the plurality of foods on the subject's postprandial glucose level. At step 1384, the application may display a plurality of foods determined to be good for the subject. At step 1386, the application may display a plurality of foods determined to be bad for the subject. At step 1388, the application may display a counter indicating the number or amount of foods sorted or classified as good and bad.

[0269] The application may also prioritize TODs with the best glucose patterns, e.g., the TOD periods with the most time above the threshold amount or the highest AUC. The system may also determine and display recommendations for food substitutions or meals the user should try to improve glycemic control.

[0270] 22A, in step 1392, the application may determine the TOD period having the highest glucose pattern based on the index determined with reference to an upper glucose threshold. As noted elsewhere herein, the upper glucose threshold may be about 180 mg / dL, alternatively about 175 mg / dL, alternatively about 170 mg / dL, alternatively about 190 mg / dL, or alternatively about 200 mg / dL, and may vary from individual to individual.

[0271] In step 1394, the application may determine the most frequently logged meal in the TOD period with the highest glucose pattern. The most frequently logged meal in the TOD period with the highest glucose pattern may be associated with a post-meal glucose trace with a high glucose pattern.

[0272] In step 1396, the application may display substitution meal suggestions to replace the most frequently logged meal in the TOD period with the highest glucose pattern.

[0273] The method may also include additional steps. For example, displaying a comparison of the postprandial glucose trace associated with the replacement meal suggestion with the postprandial glucose trace associated with the most frequently logged meal after the user ate the replacement meal suggestion. The method may also include displaying the replacement meal suggestion in a list of frequently logged foods. The replacement meal suggestion may be displayed at the top of the list. The method may also include determining the replacement meal suggestion by searching a database of healthy options for the TOD period with the highest glucose pattern.

[0274] The application may also encourage experimentation with other foods based on natural language learning. As seen in FIG. 22B , one exemplary method 1400 describes a method for recommending alternative food options. At step 1402, the application receives input log entries associated with ingested meals. The input log entries may include text descriptions of the ingested meals. At step 1404, the application may identify at least one food item included in the text descriptions of the at least one logged meal. At step 1406, the application may associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal. At step 1408, the application may determine a classification of the at least one food item as positive or negative, good or bad, or other equivalent classification based on the effect of the at least one food item on the postprandial glucose trace.

[0275] In step 1410, the application may store at least one food item, at least one post-prandial glucose trace associated with the ingested meal, and a classification in a database. The database may include multiple food items, associated post-prandial glucose traces, and classifications for a population and multiple alternative food options.

[0276] In step 1414, in response to classifying at least one food item as a bad food item, the application may determine a replacement food option from the plurality of replacement food options. The replacement food option may be classified as a good food item for the plurality of individuals. In step 1416, the application may display recommendations including the replacement food option as a replacement for the at least one food item classified as a bad food item.

[0277] The method may also include determining a TOD period having the highest glucose pattern for the user based on the indicator determined with reference to an upper glucose threshold, determining a food item most frequently logged in the TOD period having the highest glucose pattern, and displaying additional substitute food options to replace the most frequently logged food item.

[0278] The method may also prompt the user for input regarding food preferences and display additional substitute food options to replace at least one food item classified as a bad food item based on the database and the food preferences entered by the user.

[0279] The application may also assign a new grade to a meal or food product or remove a grade from its association with a meal or food product. As seen in Figure 23, one exemplary method 1430 describes a method for analyzing meal or food data. At step 1432, the application may receive an input log entry from a user associated with a meal or food product that was consumed.

[0280] In step 1434, the application may determine a grade for the ingested meal or food based on at least one characteristic of the at least one postprandial glucose trace associated with the ingested meal. In step 1436, the application may display the grade associated with the meal. For example, the grade may be changed by the user. The grade associated with the meal.

[0281] The grade may be changeable. The grade may be removed or no longer associated with a meal after a period of time after the grade is initially assigned or associated with the meal or food. Optionally, the method may include a step 1438 in which a new grade may be determined for the meal or food after a period of time after the initial grade was assigned, or the user may change the grade. This period may be about six months, alternatively three months, alternatively four months, or alternatively five months.

[0282] For the various methods described, meals and foods may be assigned grades based on various characteristics of the postprandial glucose traces associated with the meal. A meal or food may be assigned a poor grade under various circumstances, including, but not limited to, a determination that at least one postprandial glucose trace includes at least two postprandial glucose traces with an elevated glucose pattern, or a determination that at least one postprandial glucose trace does not include at least one postprandial glucose trace with an in-range postprandial pattern. A meal or food may be assigned a good grade under various circumstances, including, but not limited to, a determination that at least one postprandial glucose trace does not include at least one postprandial glucose trace with an in-range postprandial pattern. A meal or food may be assigned an inconclusive grade under various circumstances, including, but not limited to, a determination that at least one postprandial glucose trace does not include at least one postprandial glucose trace with an in-range postprandial pattern and does not include at least two postprandial glucose traces with an elevated glucose pattern.

[0283] The elevated glucose pattern can be based on the area under the curve above about 180 mg / dL, or alternatively, the amount of time above about 180 mg / dL. The target range for determining the amount of time in the range can be about 70 mg / dL to 180 mg / dL.

[0284] The grade may be displayed as a number or letter. Alternatively, the grade may be displayed as a number of stars, with more stars indicating a better grade and fewer stars indicating a lower grade. For example, the grading may be based on a model that maps input to a grade variable, e.g., a range of 0 to 4, where 0 corresponds to 1 star and 4 corresponds to 5 stars.

[0285] In one embodiment, the grade can also be based on a relationship between the start time of the ingested meal and at least one characteristic. In another embodiment, the grade can also be based on additional logged meals, where the additional logged meals are associated with at least one postprandial glucose trace. Additionally or alternatively, the grade can be further based on a model that maps multiple inputs to a grade variable.

[0286] Various aspects of the present subject matter are described below in discussion of and / or supplementary to the previously described embodiments, with emphasis placed on the interrelationships and interchangeability of the following embodiments. In other words, emphasis is placed on the fact that each feature of an embodiment can be combined with every other feature unless otherwise specified or logically impossible. The embodiments described herein are reproduced and expanded in the following paragraphs without explicit reference to the drawings.

[0287] In many ways, a method of processing analyte data is provided, the method including receiving sensed analyte data from an individual, detecting episodes in the received analyte data, displaying a first episode marker associated with the detected episode on a graph of the analyte data, providing a screen configured to receive information about the detected episode, and after receiving information about the detected episode, displaying a second episode marker associated with the detected episode on the graph of the analyte data.

[0288] In some embodiments, the method further includes displaying a notification that an episode has been detected. In some embodiments, displaying the notification includes displaying a number on the icon corresponding to the number of episodes detected. In some embodiments, the number is incremented when an episode is detected. In some embodiments, the number is decremented after information of the detected episode is received. In some embodiments, the number is decremented after a predetermined time has elapsed since the episode was detected.

[0289] In some embodiments, displaying the notification includes displaying an alert.

[0290] In some embodiments, the first episode marker is different from the second episode marker.

[0291] In some embodiments, the first episode marker comprises a question mark.

[0292] In some embodiments, the second episode marker includes an image associated with the received information.

[0293] In some embodiments, the second episode marker comprises X.

[0294] In some embodiments, the detected episode is an out-of-range episode.

[0295] In some embodiments, the first episode marker is placed on the graph of analyte data at the estimated start time of the detected episode.

[0296] In some embodiments, the analyte data is glucose data.

[0297] In many methods, a method of processing analyte data using an in vivo analyte monitoring system is provided, the method including receiving sensed analyte data from an individual; processing the analyte data to detect, with processing circuitry, a plurality of portions of the analyte data that meet a criterion over a period of time; and associating one or more meal events with one or more of the plurality of portions of analyte data, each of the one or more meal events having an associated time at which the associated one or more of the plurality of portions of analyte data met the criterion.

[0298] In some embodiments, this criterion requires that a portion of the analyte data remain above a threshold analyte concentration level for a period of time, hi some embodiments, the threshold analyte concentration level is about 180 mg / dL.

[0299] In some embodiments, the criteria require that a portion of the analyte data fall within a range over a period of time, hi some embodiments, the range is defined by a minimum analyte concentration level of about 80 mg / dL to a maximum analyte concentration level of about 170 mg / dL.

[0300] In some embodiments, the period is about 30 minutes.

[0301] In some embodiments, the method further includes ranking each of the one or more meal events according to their associated time. In some embodiments, the associated time of each of the one or more meal events is ranked in ascending order. In some embodiments, the method further includes displaying groups of one or more meal events, each of the groups having an associated time ranked as one of the top five longest associated times for the one or more meal events. In some embodiments, each of the displayed groups of one or more meal events is associated with a first period of a day. In some embodiments, the first period of a day is the period during which multiple portions of the analyte data met a criterion for the highest amount of time. In some embodiments, the criterion requires that a portion of the analyte data be within a range for a certain period of time. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, the method further includes displaying a graph of the analyte data over time, with the first period of a day highlighted. In some embodiments, the method further includes displaying a second group of one or more meal events, each having an associated time ranked as one of the top five longest associated times for the one or more meal events, and each of the displayed second group of one or more meal events is associated with a second time period of a day, the second time period of a day being different from the first time period of a day. In some embodiments, the criterion requires that a portion of the analyte data be within a range of values ​​for a certain period of time. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, the method further includes displaying a graph of the analyte data over time, with the first time period of a day being highlighted.

[0302] In many methods, a method is provided for displaying a graphical interface related to physiological analyte data and meal information on an electronic interface of a device, the method including receiving sensed analyte data from an individual; processing the analyte data to detect, by a processing circuit, a plurality of portions of the analyte data that meet a criterion over a period of time; generating a plurality of representations of meal events, each of the plurality of representations corresponding to when one of the plurality of portions of analyte data met the criterion over a period of time; associating meal information, an event time, and an associated time with each of at least some of the plurality of representations of meal events, the associated time being an amount of time during which a corresponding one or more of the plurality of portions of analyte data met the criterion; and displaying a first grouping of at least some of the plurality of representations of meal events with associated meal information, the event time of each of the representations of meal events in the first grouping occurring during a first time period during a day.

[0303] In some embodiments, the method further includes displaying a graphical element indicating the amount of time that the analyte data is within a target range for the second time period.

[0304] In some embodiments, the first time period is determined to have the greatest amount of time that a plurality of portions of the analyte data meets the criterion. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, the threshold analyte concentration is about 180 mg / dL and the period of time is about 30 minutes. In some embodiments, the criterion requires that a portion of the analyte data be within a range of values ​​for a certain period of time. In some embodiments, the range is defined by a minimum analyte concentration level of about 80 mg / dL to a maximum analyte concentration level of about 170 mg / dL.

[0305] In some embodiments, the method further includes displaying a statement indicating which period of the day has the highest amount of time that the plurality of portions of the analyte data met the criteria.

[0306] In some embodiments, the method further includes displaying a second grouping of at least a portion of the plurality of representations of meal events with associated meal information, wherein an event time of each representation of the meal events in the second grouping occurs during a second time period of a day, the second time period being different from the first time period.

[0307] In some embodiments, the associated times of each of at least a portion of the plurality of representations of the meal event are ranked in ascending order. In some embodiments, each of the first groupings has an associated time ranked as one of the top five longest associated times of each of at least a portion of the plurality of representations of the meal event. In some embodiments, the first period of the day is the period during which the plurality of portions of analyte data met the highest amount of time criterion.

[0308] In many embodiments, a method is described for displaying a graphical interface related to physiological analyte data on an electronic interface of a device, the method comprising the steps of receiving sensed analyte data from an individual; determining an amount of time the received analyte data is within a target range for a first time period; and displaying a first graphical element indicating the amount of time the analyte data is within the target range for the first time period, the first graphical element including a numerical value corresponding to the amount of time the received analyte data is determined to be within the target range for the first time period and a progress indicator having a length proportional to the amount of time the analyte data is determined to be within the target range for the first time period.

[0309] In some embodiments, the first period is the current date.

[0310] In some embodiments, the first time period is selected from the group consisting of the current date, the current week, and the previous week.

[0311] In some embodiments, the progress indicator is part of a progress bar.

[0312] In some embodiments, the progress indicator is part of a circular graph.

[0313] In some embodiments, the method further includes displaying a representation of the target objective associated with the progress indicator.

[0314] In some embodiments, the method further includes a second graphical element indicating an amount of time the analyte data is within the target range for a second time period, the second graphical element including a numerical value corresponding to an amount of time the received analyte data was determined to be within the target range for the second time period and a progress indicator having a length proportional to the amount of time the analyte data was determined to be within the target range for the second time period. In some embodiments, the method further includes a third graphical element indicating an amount of time the analyte data is within the target range for a third time period, the third graphical element including a numerical value corresponding to an amount of time the received analyte data was determined to be within the target range for the third time period and a progress indicator having a length proportional to the amount of time the analyte data was determined to be within the target range for the third time period. In some embodiments, each of the first, second, and third time periods is independently selected from the group consisting of the current date, the current week, and the previous week.

[0315] In many embodiments, a method for displaying a graphical interface related to physiological analyte data on an electronic interface of a device includes receiving sensed analyte data from an individual; determining an amount of time the received analyte data is within a target range for first, second, and third time periods; and displaying on the electronic interface a first graphical element indicating the amount of time the analyte data is within the target range for the first time period, a second graphical element indicating the amount of time the analyte data is within the target range for the second time period, and a third graphical element indicating the amount of time the analyte data is within the target range for the third time period, wherein the first, second, and third graphical elements each include first, second, and third progress indicators, the lengths of the progress indicators being proportional to the amount of time the analyte data is determined to be within the target range for the first, second, and third time periods, respectively.

[0316] In some embodiments, the first period is the current date.

[0317] In some embodiments, the second period is the previous week.

[0318] In some embodiments, the third period is the current week.

[0319] In some embodiments, each of the first, second, and third graphical elements comprises at least a portion of a circular shape, and the first, second, and third progress indicators extend along the perimeter of each of at least a portion of the circular shape of the first, second, and third graphical elements.

[0320] In some embodiments, each of the first, second, and third graphical elements further includes a goal indicator associated with a goal value, the goal value corresponding to an amount or percentage of time that the analyte data is within a target range. In some embodiments, the goal indicator is positioned at a position along the periphery of at least a portion of the circular shape, and the position of the goal indicator is proportional to the value of the goal.

[0321] In some embodiments, the method further includes displaying, in association with the first, second, and third graphical elements, numerical representations of the amount of time that the received analyte data was determined to be within the target range for the first, second, and third time periods, respectively. In some embodiments, the numerical representations of the amount of time that the received analyte data was determined to be within the target range are displayed at the center of at least a portion of the circular shape of the first, second, and third graphical elements. In many embodiments, an apparatus for processing analyte data is provided, the apparatus comprising: a non-volatile memory having a program stored therein; an input configured to receive measured analyte data; and a processor connected to the non-volatile memory and the input, the processor configured to execute the program, which execution of the program causes the processor to detect episodes in the received analyte data; display a first episode marker on a graph of the analyte data associated with the detected episode; provide a screen configured to receive information about the detected episode; and, after receiving information about the detected episode, display a second episode marker on the graph of the analyte data associated with the detected episode.

[0322] In some embodiments, execution of the program causes the processor to display a notification that an episode has been detected. In some embodiments, displaying the notification may include displaying a number on the icon corresponding to the number of episodes detected. In some embodiments, the number is incremented when an episode is detected. In some embodiments, the number is decremented after information is received about a detected episode. In some embodiments, the number is decremented after a predetermined time has elapsed since the episode was detected.

[0323] In some embodiments, the indication of notification is an indication of an alert.

[0324] In some embodiments, the first episode marker is different from the second episode marker.

[0325] In some embodiments, the first episode marker comprises a question mark.

[0326] In some embodiments, the second episode marker includes an image associated with the received information.

[0327] In some embodiments, the second episode marker comprises X.

[0328] In some embodiments, the detected episode is an out-of-range episode.

[0329] In some embodiments, the first episode marker is placed on the graph of analyte data at the estimated start time of the detected episode.

[0330] In some embodiments, the analyte data is glucose data.

[0331] In many embodiments, an apparatus for processing analyte data is provided, the apparatus comprising: a non-volatile memory having a program stored thereon; an input configured to receive measured analyte data; and a processor connected to the non-volatile memory and the input, the processor configured to execute the program, wherein execution of the program causes the processor to process the analyte data to detect a plurality of portions of the analyte data that satisfy a criterion over a period of time; and associate one or more meal events with one or more of the plurality of portions of analyte data, each of the one or more meal events having an associated time at which the associated one or more of the plurality of portions of analyte data satisfied the criterion.

[0332] In some embodiments, this criterion requires that a portion of the analyte data remain above a threshold analyte concentration level for a period of time, hi some embodiments, the threshold analyte concentration level is about 180 mg / dL.

[0333] In some embodiments, the criteria require that a portion of the analyte data fall within a range over a period of time, hi some embodiments, the range is defined by a minimum analyte concentration level of about 80 mg / dL to a maximum analyte concentration level of about 170 mg / dL.

[0334] In some embodiments, the period is about 30 minutes.

[0335] In some embodiments, execution of the program further causes the processor to rank each of the one or more meal events according to associated time. In some embodiments, the associated time of each of the one or more meal events is ranked in ascending order. In some embodiments, execution of the program further causes the processor to display a group of one or more meal events, each group having an associated time ranked as one of the top five longest associated times of the one or more meal events. In some embodiments, each of the displayed groups of one or more meal events is associated with a first time period of a day. In some embodiments, the first time period of a day is a time period during which multiple portions of the analyte data met a criterion for the highest amount of time. In some embodiments, the criterion requires that a portion of the analyte data be within a range for a certain period of time. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, execution of the program further causes the processor to display a graph of the analyte data over time, with the first time period of a day highlighted. In some embodiments, execution of the program further causes the processor to display a second group of one or more meal events, each having an associated time ranked as one of the top five longest associated times of the one or more meal events, and each of the displayed second group of one or more meal events is associated with a second time period of the day, the second time period of the day being different from the first time period of the day. In some embodiments, the criterion requires that a portion of the analyte data be within a range of values ​​for a certain period of time. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, execution of the program further causes the processor to display a graph of the analyte data over time, with the first time period of the day highlighted.

[0336] In many embodiments, an apparatus for displaying a graphical interface related to physiological analyte data and meal information on an electronic interface of a device includes: a non-volatile memory having a program stored therein; an input configured to receive measured analyte data; a display configured to visually present an analysis of the measured analyte data; and a processor connected to the non-volatile memory, the input, and the display, the processor configured to execute the program, wherein execution of the program causes the processor to: detect a plurality of portions of the analyte data that exceed a threshold analyte concentration level over a period of time; generate a plurality of representations of meal events, each representation corresponding to when one of the plurality of portions of the analyte data was above the threshold analyte concentration level for a minimum threshold time; associate meal information and event times with at least some of the plurality of representations of meal events; and control the display to visually present a first grouping of at least some of the plurality of representations of meal events with associated meal information, wherein the event times of each of the representations of meal events in the first grouping occur during a first time period during a day.

[0337] In some embodiments, execution of the program further causes the processor to display a graphical element indicating the amount of time that the analyte data is within a target range for the second time period.

[0338] In some embodiments, the first time period is determined to have the greatest amount of time that a plurality of portions of the analyte data meets a criterion. In some embodiments, the criterion requires that a portion of the analyte data be above a threshold analyte concentration level for a certain period of time. In some embodiments, the threshold analyte concentration is about 180 mg / dL, and the certain period of time is about 30 minutes. In some embodiments, the criterion requires that a portion of the analyte data be within a range of values ​​for a certain period of time. In some embodiments, the range of values ​​is defined by a minimum analyte concentration level of about 80 mg / dL to a maximum analyte concentration level of about 170 mg / dL.

[0339] In some embodiments, execution of the program further causes the processor to display a statement indicating which period of the day has the highest amount of time during which multiple portions of the analyte data met the criteria.

[0340] In some embodiments, execution of the program further causes the processor to display a second grouping of at least a portion of the plurality of representations of meal events having associated meal information, wherein an event time for each of the representations of meal events in the second grouping occurs during a second time period during the day, the second time period being different from the first time period.

[0341] In some embodiments, the associated times of each of at least a portion of the plurality of representations of the meal event are ranked in ascending order. In some embodiments, each of the first groupings has an associated time ranked as one of the top five longest associated times of each of at least a portion of the plurality of representations of the meal event. In some embodiments, the first period of the day is the period during which the plurality of portions of analyte data met the highest amount of time criterion.

[0342] In many embodiments, an apparatus for displaying a graphical interface related to physiological analyte data on an electronic interface of a device includes: a non-volatile memory having a program stored therein; an input configured to receive measured analyte data and meal information; a display configured to visually present an analysis of the measured analyte data; and a processor connected to the non-volatile memory, the input, and the display, the processor configured to execute the program, wherein execution of the program causes the processor to determine an amount of time the received analyte data is within a target range for a first time period; and control the display to visually present a first graphical element indicating the amount of time the analyte data is within the target range for the first time period, the first graphical element including a numerical value corresponding to the amount of time the received analyte data was determined to be within the target range for the first time period and a progress indicator having a length proportional to the amount of time the analyte data was determined to be within the target range for the first time period.

[0343] In some embodiments, the first period is the current date.

[0344] In some embodiments, the first time period is selected from the group consisting of the current date, the current week, and the previous week.

[0345] In some embodiments, the progress indicator is part of a progress bar.

[0346] In some embodiments, the progress indicator is part of a circular graph.

[0347] In some embodiments, execution of the program further causes the processor to display a representation of the target objective associated with the progress indicator.

[0348] In some embodiments, execution of the program causes the processor to determine an amount of time the received analyte data is within a target range for a second time period and control the display to visually present a second graphical element indicating the amount of time the analyte data is within the target range for the second time period, the second graphical element including a numerical value corresponding to the amount of time the received analyte data was determined to be within the target range for the second time period and a progress indicator having a length proportional to the amount of time the analyte data was determined to be within the target range for the second time period. In some embodiments, execution of the program causes the processor to determine an amount of time the received analyte data is within a target range for a third time period and control the display to visually present a third graphical element indicating the amount of time the analyte data is within the target range for the third time period, the third graphical element including a numerical value corresponding to the amount of time the received analyte data was determined to be within the target range for the third time period and a progress indicator having a length proportional to the amount of time the analyte data was determined to be within the target range for the third time period. In some embodiments, each of the first, second, and third time periods is independently selected from the group consisting of the current date, the current week, and the previous week.

[0349] In many embodiments, an apparatus for displaying a graphical interface related to physiological analyte data on an electronic interface of a device, the apparatus comprising: a non-volatile memory having a program stored therein; an input configured to receive measured analyte data; a display configured to visually present an analysis of the measured analyte data; and a processor connected to the non-volatile memory, the input, and the display, the processor configured to execute the program, wherein execution of the program causes the processor to generate a graphical interface for determining whether the received analyte data is within target ranges for first, second, and third time periods. determining the amount of time; and controlling the display to visually present a first graphical element indicating the amount of time the analyte data is within the target range for a first time period, a second graphical element indicating the amount of time the analyte data is within the target range for a second time period, and a third graphical element indicating the amount of time the analyte data is within the target range for a third time period, wherein the first, second, and third graphical elements each include first, second, and third progress indicators, the lengths of the progress indicators being proportional to the amount of time the analyte data is determined to be within the target range for the first, second, and third time periods, respectively.

[0350] In some embodiments, the first period is the current date.

[0351] In some embodiments, the second period is the previous week.

[0352] In some embodiments, the third period is the current week.

[0353] In some embodiments, each of the first, second, and third graphical elements defines at least a portion of a circle, and the first, second, and third progress indicators extend along the perimeter of each of at least a portion of the circular shape of the first, second, and third graphical elements.

[0354] In some embodiments, each of the first, second, and third graphical elements further includes a goal indicator associated with a goal value, the goal value corresponding to an amount or percentage of time that the analyte data is within a target range. In some embodiments, the goal indicator is positioned at a position along the periphery of at least a portion of the circle, and the position of the goal indicator is proportional to the value of the goal.

[0355] In some embodiments, execution of the program further causes the processor to display, in association with the first, second, and third graphical elements, numerical representations of the amount of time the received analyte data was determined to be within the target range for the first, second, and third time periods, respectively. In some embodiments, the numerical representations of the amount of time the received analyte data was determined to be within the target range are displayed at the center of at least a portion of the circular shape of the first, second, and third graphical elements.

[0356] In a number of ways, a method of analyzing meal data is described, the method including receiving input log entries associated with ingested meals, the log entries including meal content and meal size of the ingested meals, associating the ingested meals with at least one post-prandial glucose trace, determining a grade for the ingested meals based on at least one characteristic of the at least one post-prandial glucose trace, and displaying the grade associated with the ingested meals in a graphical user interface.

[0357] In some methods, the meal description includes a text description of the meal.

[0358] In some methods, the meal contents are selected from a list of frequent items.

[0359] In some methods, in response to determining that at least one post-prandial glucose trace includes at least two post-prandial glucose traces having a high glucose pattern, a poor grade is assigned to the ingested meal.

[0360] In some methods, in response to determining that the at least one postprandial glucose trace does not include at least one postprandial glucose trace having an in-range postprandial pattern, a poor grade is assigned to the ingested meal.

[0361] In some methods, in response to determining that the at least one postprandial glucose trace includes at least one postprandial glucose trace having an in-range postprandial pattern, a good grade is assigned to the ingested meal.

[0362] In some methods, in response to determining that the at least one postprandial glucose trace does not include at least one postprandial glucose trace having an in-range postprandial pattern and does not include at least two postprandial glucose traces having an elevated glucose pattern, an inconclusive grade is assigned to the ingested meal.

[0363] In some methods, the grade is a range of stars, with more stars indicating a better grade and fewer stars indicating a lower grade. In some methods, multiple gray stars indicate an inconclusive grade.

[0364] In some methods, the grade is based on at least one characteristic of the at least one postprandial glucose trace. In some methods, the at least one characteristic is the degree of an elevated glucose pattern. In some methods, the degree of an elevated glucose pattern is based on an area under the curve above about 180 mg / dL. In some methods, the degree of an elevated glucose pattern is based on the amount of time above about 180 mg / dL. In some methods, the at least one characteristic of the at least one postprandial glucose trace is the amount of time within a target range. In some methods, the target range is between about 70 mg / dL and about 180 mg / dL. In some methods, the grade is further based on a relationship between the start time of the ingested meal and the at least one characteristic.

[0365] In some methods, the grade is further based on additional logged meals, where the additional logged meals are associated with at least one post-prandial glucose trace.

[0366] In some methods, the grade is further based on a model that maps multiple inputs to a grade variable. In some methods, the grade variable ranges from 0 to 4, with 0 corresponding to a 1-star grade and 4 corresponding to a 5-star grade.

[0367] In a number of systems, a system for analyzing meal data is described that includes an input configured to receive input log entries associated with ingested meals, the log entries including meal content and meal size of the ingested meals, a display configured to visually present the meal data, and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to associate the ingested meals with at least one post-prandial glucose trace, determine a grade for the ingested meals based on at least one characteristic of the at least one post-prandial glucose trace, and display the grade associated with the ingested meals in a graphical user interface.

[0368] In some systems, the meal description includes a text description of the meal.

[0369] In some systems, the meal contents are selected from a list of frequently occurring items.

[0370] In some systems, a poor grade is assigned to the ingested meal in response to determining that the meal includes at least two post-prandial glucose traces, at least one of which has a high glucose pattern.

[0371] In some systems, in response to determining that the at least one post-prandial glucose trace does not include at least one post-prandial glucose trace having an in-range post-prandial pattern, a poor grade is assigned to the ingested meal.

[0372] In some systems, a good grade is assigned to the ingested meal in response to determining that at least one post-prandial glucose trace includes at least one post-prandial glucose trace having an in-range post-prandial pattern.

[0373] In some systems, an inconclusive grade is assigned to the ingested meal in response to a determination that at least one postprandial glucose trace does not include at least one postprandial glucose trace having an in-range postprandial pattern and does not include at least two postprandial glucose traces having an elevated glucose pattern.

[0374] In some systems, the grade is a range of stars, with more stars indicating a better grade and fewer stars indicating a lower grade. In some systems, multiple stars in gray indicate an inconclusive grade.

[0375] In some systems, the grade is based on at least one characteristic of the at least one postprandial glucose trace. In some systems, the at least one characteristic is a degree of high glucose pattern. In some systems, the degree of high glucose pattern is based on an area under the curve above about 180 mg / dL. In some systems, the degree of high glucose pattern is based on an amount of time above about 180 mg / dL. In some systems, the at least one characteristic of the at least one postprandial glucose trace is an amount of time within a target range. In some systems, the target range is from about 70 mg / dL to about 180 mg / dL. In some systems, the grade is further based on a relationship between the start time of the ingested meal and the at least one characteristic.

[0376] In some systems, the grade is further based on additional logged meals, which are associated with at least one post-prandial glucose trace.

[0377] In some systems, the grade is further based on a model that maps multiple inputs to a grade variable. In some methods, the grade variable ranges from 0 to 4, with 0 corresponding to a 1-star grade and 4 corresponding to a 5-star grade.

[0378] In many ways, a method for analyzing dietary data is described, the method including receiving an input log entry associated with an ingested meal, identifying at least one food item included in the ingested meal, associating the at least one food item with at least one postprandial glucose trace associated with the ingested meal, determining a grade for the at least one food item based on at least one characteristic of the at least one postprandial glucose trace, and displaying the grade associated with the at least one food item in a graphical user interface.

[0379] In some methods, the input log entry includes a text description of the meal consumed, and the identifying step includes using natural language processing.

[0380] In some methods, in response to determining that at least one postprandial glucose trace associated with the ingested meal includes at least two postprandial glucose traces having a high glucose pattern, a poor grade is assigned to at least one food item.

[0381] In some methods, in response to determining that the at least one post-prandial glucose trace does not include at least one post-prandial glucose trace having an in-range post-prandial pattern, a poor grade is assigned to the at least one food item.

[0382] In some methods, in response to determining that the at least one post-prandial glucose trace includes at least one post-prandial glucose trace having an in-range post-prandial pattern, a good grade is assigned to the at least one food item.

[0383] In some methods, in response to determining that the at least one postprandial glucose trace does not include at least one postprandial glucose trace having an in-range postprandial pattern and does not include at least two postprandial glucose traces having an elevated glucose pattern, a good grade is assigned to the at least one food item.

[0384] In some methods, the grade is a range of stars, with more stars indicating a better grade and fewer stars indicating a lower grade. In some methods, multiple gray stars indicate an inconclusive grade.

[0385] In some methods, the grade is based on at least one characteristic of the at least one postprandial glucose trace. In some methods, the at least one characteristic of the at least one postprandial glucose trace is the degree of an elevated glucose pattern. In some methods, the degree of an elevated glucose pattern is based on an area under the curve above about 180 mg / dL. In some methods, the degree of an elevated glucose pattern is based on the amount of time above about 180 mg / dL. The at least one characteristic of the at least one postprandial glucose trace is the amount of time within a target range. In some methods, the target range is about 70 mg / dL to 180 mg / dL.

[0386] In some methods, the grade is further based on a relationship between the start time of the ingested meal and at least one characteristic of the at least one postprandial glucose trace.

[0387] In some methods, the grade is further based on an additional logged meal that includes at least one food item, the additional logged meal being associated with at least one post-prandial glucose trace.

[0388] In some methods, the grade is further based on a model that maps multiple inputs to a grade variable. In some methods, the grade variable ranges from 0 to 4, with 0 corresponding to a 1-star grade and 4 corresponding to a 5-star grade.

[0389] In a number of systems, a system for analyzing dietary data is described, the system comprising: an input configured to receive input log entries associated with ingested meals; a display configured to visually present the food data; and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to identify at least one food item included in the ingested meal, associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal, determine a grade for the at least one food item based on at least one characteristic of the at least one postprandial glucose trace, and display the grade associated with the at least one food item in a graphical user interface.

[0390] In some systems, the input log entry includes a text description of the meal consumed, and the identifying step includes using natural language processing.

[0391] In some systems, a poor grade is assigned to at least one food item in response to determining that at least one postprandial glucose trace associated with the ingested meal includes at least two postprandial glucose traces having a high glucose pattern.

[0392] In some systems, in response to determining that the at least one post-prandial glucose trace does not include at least one post-prandial glucose trace having an in-range post-prandial pattern, a poor grade is assigned to the at least one food item.

[0393] In some systems, a good grade is assigned to the at least one food item in response to determining that the at least one post-prandial glucose trace includes at least one post-prandial glucose trace having an in-range post-prandial pattern.

[0394] In some systems, a good grade is assigned to the at least one food item in response to determining that the at least one postprandial glucose trace does not include at least one postprandial glucose trace having an in-range postprandial pattern and does not include at least two postprandial glucose traces having an elevated glucose pattern.

[0395] In some systems, the grade is a range of stars, with more stars indicating a better grade and fewer stars indicating a lower grade. In some systems, multiple stars in gray indicate an inconclusive grade.

[0396] In some systems, the grade is based on at least one characteristic of the at least one postprandial glucose trace. In some systems, the at least one characteristic of the at least one postprandial glucose trace is the degree of an elevated glucose pattern. In some systems, the degree of an elevated glucose pattern is based on an area under the curve above about 180 mg / dL. In some systems, the degree of an elevated glucose pattern is based on the amount of time above about 180 mg / dL. In some systems, the at least one characteristic of the at least one postprandial glucose trace is the amount of time within a target range. In some systems, the target range is about 70 mg / dL to 180 mg / dL.

[0397] In some systems, the grade is further based on a relationship between the start time of the ingested meal and at least one characteristic of at least one postprandial glucose trace.

[0398] In some systems, the grade is further based on additional logged meals that include at least one food item, and the additional logged meals are associated with at least one post-prandial glucose trace.

[0399] In some systems, the grade is further based on a model that maps multiple inputs to a grade variable. In some systems, the grade variable ranges from 0 to 4, with 0 corresponding to a 1-star grade and 4 corresponding to a 5-star grade.

[0400] In a number of ways, a method for analyzing dietary data is described, the method including receiving an input log entry associated with a meal ingested, identifying at least one food item included in the meal ingested, associating the at least one food item with at least one postprandial glucose trace associated with the meal ingested, storing the at least one food item and the at least one postprandial glucose trace associated with the meal in a database, the database including a plurality of food items and associated postprandial glucose traces for a population, determining a grade for the at least one food item, the grade based on a subset of the postprandial glucose traces associated with the at least one food item, the subset of the postprandial glucose traces based on a segment of the population, and displaying the grade associated with the at least one food item in a graphical user interface.

[0401] In some methods, the population segment has pre-diabetes.

[0402] In some methods, the population segment has type 1 diabetes.

[0403] In some methods, the population segment has type 2 diabetes.

[0404] In some methods, the grade is based on at least one characteristic of a subset of postprandial glucose traces associated with at least one food. In some methods, the at least one characteristic is a high pattern magnitude. In some methods, the high pattern magnitude is based on an area under the curve above about 180 mg / dL. In some methods, the high pattern magnitude is based on an amount of time above about 180 mg / dL. In some methods, the at least one characteristic of the at least one postprandial glucose trace is an amount of time within a target range. In some methods, the target range is between about 70 mg / dL and about 180 mg / dL. In some methods, the grade is further based on a relationship between a start time of the ingested meal and the at least one characteristic of the subset of postprandial glucose traces associated with the at least one food.

[0405] In some methods, the grade is further based on an additional entered log entry associated with a meal ingested that includes at least one food item, the entered log entry being associated with at least one post-prandial glucose trace.

[0406] In a number of systems, a system for analyzing dietary data is described, the system comprising: an input configured to receive input log entries associated with ingested meals; a display configured to visually present the dietary data; and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the system to: identify at least one food item included in the ingested meal; associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal; store the at least one food item and the at least one postprandial glucose trace associated with the ingested meal in a database, the database including a plurality of food items and associated postprandial glucose traces for a population; determine a grade for the at least one food item, the grade based on a subset of the postprandial glucose traces associated with the at least one food item, the subset of the postprandial glucose traces based on a segment of the population; and display the grade associated with the at least one food item in a graphical user interface.

[0407] In some systems, the population segment has pre-diabetes.

[0408] In some systems, the population segment has type 1 diabetes.

[0409] In some systems, the population segment has type 2 diabetes.

[0410] In some systems, the grade is based on at least one characteristic of a subset of postprandial glucose traces associated with at least one food. In some systems, the at least one characteristic is a high pattern magnitude. In some systems, the high pattern magnitude is based on an area under the curve above about 180 mg / dL. In some systems, the high pattern magnitude is based on an amount of time above about 180 mg / dL. In some systems, the at least one characteristic of the at least one postprandial glucose trace is an amount of time within a target range. In some systems, the target range is between about 70 mg / dL and about 180 mg / dL. In some systems, the grade is further based on a relationship between a start time of the ingested meal and the at least one characteristic of the subset of postprandial glucose traces associated with the at least one food.

[0411] In some systems, the grade is further based on an additional entered log entry associated with a meal ingested that includes at least one food item, and the entered log entry is associated with at least one post-prandial glucose trace.

[0412] In a number of ways, a method for recommending food options is described, the method including the steps of receiving an input log entry from a user associated with a meal consumed, identifying at least one food item included in the consumed meal, associating the at least one food item with at least one postprandial glucose trace associated with the consumed meal, determining a classification of the at least one food item based on at least one characteristic of the at least one postprandial glucose trace, analyzing the classification and the at least one food item using a population model including a database of food classifications and associated foods for a plurality of population segments, associating the user with a segment of the plurality of population segments based on the classification and the at least one food item, where the segment of the plurality of population segments is associated with a plurality of foods having a positive classification, and displaying a recommended food item, where the recommended food item is one of the plurality of foods having a positive classification for the segment of the plurality of population segments.

[0413] In some methods, the database further includes meals, postprandial glucose traces, user demographics, and user preferences. In some methods, the population model uses machine learning techniques to divide the population into a plurality of population segments. In some methods, the population model includes a first model that assigns the user to a segment of a plurality of population segments based on an analysis of the classification, the at least one food item, the at least one postprandial glucose trace associated with the at least one food item, the user demographic information, and the user preferences. In some methods, the population model includes a second model that maps the segments of the plurality of population segments to a plurality of good food items and a plurality of bad food items.

[0414] A system for recommending food options is described, comprising: an input configured to receive input log entries associated with ingested meals from a user; a display configured to visually present the foods; and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: identify at least one food item included in the ingested meal; associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal; determine a classification of the at least one food item based on at least one characteristic of the at least one postprandial glucose trace; analyze the classification and the at least one food item using a population model including a database of food classifications and associated foods for a plurality of population segments; associate the user with a segment of the plurality of population segments based on the classification and the at least one food item, wherein the segment of the plurality of population segments is associated with a plurality of foods having a positive classification; and display a recommended food item, wherein the recommended food item is one of the plurality of foods having a positive classification for the segment of the plurality of population segments.

[0415] In some systems, the database further includes meals, postprandial glucose traces, user demographics, and user preferences. In some systems, the population model uses machine learning techniques to divide the population into a plurality of population segments. In some systems, the population model includes a first model that assigns the user to a segment of a plurality of population segments based on an analysis of the classification, at least one food item, at least one postprandial glucose trace associated with the at least one food item, the user demographic information, and the user preferences. In some systems, the population model includes a second model that maps the segments of the plurality of population segments to a plurality of good food items and a plurality of bad food items.

[0416] In many ways, a method for classifying a meal is described, the method including receiving analyte data and meal information from a subject, classifying a plurality of foods as good or bad foods based on the effect each of the plurality of foods has on the subject's postprandial glucose level, displaying the plurality of foods determined to be good to the subject, displaying the plurality of foods determined to be bad to the subject, and displaying a counter indicating the number of foods classified as good and bad.

[0417] In some methods, the method further includes displaying a plurality of foods determined to have a neutral effect on the subject, and displaying a counter indicating the number of foods classified as good, bad, and neutral.

[0418] In some methods, the counter is displayed on the home screen.

[0419] In a number of systems, a system for categorizing meals is described, the system comprising: an input configured to receive analyte data and meal information from a subject; a display configured to visually present an indication of meal groupings; and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: categorize a plurality of foods as good or bad based on an effect each of the plurality of foods has on the subject's postprandial glucose level; display a plurality of foods determined to be good to the subject; display a plurality of foods determined to be bad to the subject; and display a counter indicating the number of foods classified as good and bad.

[0420] In some systems, the instructions further cause the one or more processors to display a number of foods determined to have a neutral effect on the subject and to display a counter indicating the number of foods classified as good, bad, and neutral.

[0421] In some systems, the instructions further cause the one or more processors to display the counter on a home screen.

[0422] In many ways, a method for recommending meals is described, the method including determining a daily period having a highest glucose pattern based on an index determined with reference to an upper glucose threshold, determining a most frequently logged meal during the daily period having the highest glucose pattern, and displaying replacement meal suggestions to replace the most frequently logged meal during the daily period having the highest glucose pattern.

[0423] In some methods, the upper glucose threshold is about 180 mg / dL.

[0424] In some methods, the indicator is an area under the curve greater than about 180 mg / dL.

[0425] In some methods, the indicator is time above about 180 mg / dL.

[0426] In some methods, the most frequently logged meal in the period of the day with the highest glucose pattern is associated with the post-prandial glucose trace with the high glucose pattern.

[0427] In some methods, the method further includes, after the user eats the replacement meal suggestion, displaying a comparison of the post-prandial glucose trace associated with the replacement meal suggestion and the post-prandial glucose trace associated with the most frequently logged meal.

[0428] In some methods, the method further includes displaying the substitution meal suggestions in a list of frequently logged foods. In some methods, the substitution meal suggestions are displayed at the top of the list.

[0429] In some methods, the method further comprises determining the replacement meal suggestions by searching a database of healthy options for the period of the day having the highest glucose pattern.

[0430] In some methods, a period of the day is breakfast, lunch, or dinner.

[0431] In a number of systems, a system for recommending meals is described, the system comprising: an input configured to receive analyte data and meal information from a subject, a display configured to visually present meals or foods, and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determine a daily period having a highest glucose pattern based on an index determined with reference to an upper glucose threshold; determine a most frequently logged meal during the daily period having the highest glucose pattern; and display substitution meal suggestions to replace the most frequently logged meal during the daily period having the highest glucose pattern.

[0432] In some systems, the upper glucose threshold is about 180 mg / dL.

[0433] In some systems, the indicator is an area under the curve above about 180 mg / dL.

[0434] In some systems, the indicator is time above about 180 mg / dL.

[0435] In some systems, the most frequently logged meal in the period of the day with the highest glucose pattern is associated with the post-prandial glucose trace with the highest glucose pattern.

[0436] In some systems, the instructions further cause the one or more processors to, after the user eats the replacement meal suggestion, display a comparison of the post-prandial glucose trace associated with the replacement meal suggestion and the post-prandial glucose trace associated with the most frequently logged meal.

[0437] In some systems, the instructions further cause the one or more processors to display the substitution meal suggestions in a list of frequently logged foods. In some systems, the substitution meal suggestions are displayed at the top of the list.

[0438] In some systems, the instructions further cause the one or more processors to determine replacement meal suggestions by searching a database of healthy options for the period of the day having the highest glucose pattern.

[0439] In some systems, a period of the day is breakfast, lunch, or dinner.

[0440] In a number of ways, a method for recommending substitute food options is described, the method including the steps of receiving input log entries associated with ingested meals, the input log entries including a text description of the ingested meals, identifying at least one food item included in the at least one text description of the logged meals, associating the at least one food item with at least one postprandial glucose trace associated with the ingested meals, determining a classification of the at least one food item as positive or negative (or as a good food item or a bad food item) based on an impact of the at least one food item on the postprandial glucose trace, storing the at least one food item, the at least one postprandial glucose trace associated with the ingested meals, and the classification in a database, the database including a plurality of foods, associated postprandial glucose traces, and classifications for a population and a plurality of substitute food options, determining a substitute food option from the plurality of substitute food options in response to classifying the at least one food item as a bad food item, and displaying a recommendation including the substitute food option as a substitute for the at least one food item classified as a bad food item.

[0441] In some methods, alternative food options are classified as foods that are good for multiple individuals.

[0442] In some methods, the method further includes determining a period of a day having the highest glucose pattern for the user based on the indicator determined with reference to an upper glucose threshold, determining the most frequently logged foods during the period of a day having the highest glucose pattern, and displaying additional substitute food options to replace the most frequently logged foods.

[0443] In some methods, the method further includes prompting the user to input food preferences and displaying additional substitute food options to replace the at least one food classified as a bad food based on the database and the food preferences input by the user.

[0444] A system for recommending substitute food options is described, comprising: an input configured to receive input log entries associated with ingested meals, the input log entries including text descriptions of the ingested meals; a display configured to visually present the meals or foods; and one or more processors coupled to the input, the display, and a memory storing instructions, the instructions, when executed by the one or more processors, causing the one or more processors to: identify at least one food item included in the text descriptions of the at least one logged meal; associate the at least one food item with at least one postprandial glucose trace associated with the ingested meal; and determining a classification of at least one food as positive or negative based on an effect of the at least one food on the glucose trace; storing the at least one food, at least one postprandial glucose trace associated with the ingested meal, and the classification in a database, the database including a plurality of foods, associated postprandial glucose traces, and classifications for a population and a plurality of alternative food options; determining an alternative food option from the plurality of alternative food options in response to classifying the at least one food as a bad food; and displaying a recommendation including the alternative food option as a replacement for the at least one food classified as a bad food.

[0445] In some systems, alternative food options are classified as being good for multiple individuals.

[0446] In some systems, the instructions further cause the one or more processors to determine a period of a day having the highest glucose pattern for the user based on an index determined with reference to an upper glucose threshold, determine the most frequently logged foods during the period of a day having the highest glucose pattern, and display additional substitute food options to replace the most frequently logged foods.

[0447] In some systems, the instructions further cause the one or more processors to prompt the user for input of food preferences and display additional substitute food options to replace the at least one food classified as an adverse food based on the database and the food preferences entered by the user.

[0448] In many ways, a method for recommending substitute food options is described, the method including the steps of identifying at least one food item included in a text description of at least one logged meal, associating the at least one food item with at least one postprandial glucose trace associated with the ingested meal, determining a classification of the at least one food item as positive or negative based on an impact of the at least one food item on the postprandial glucose trace, storing the at least one food item, the at least one postprandial glucose trace associated with the ingested meal, and the classification in a database, the database including a plurality of foods, associated postprandial glucose traces, and classifications for a population and a plurality of substitute food options, determining a substitute food option from the plurality of substitute food options in response to classifying the at least one food item as a bad food, and displaying a recommendation including the substitute food option as a substitute for the at least one food item classified as a bad food.

[0449] In some methods, alternative food options are classified as foods that are good for multiple individuals.

[0450] In some methods, the method further includes determining a period of a day having the highest glucose pattern for the user based on the indicator determined with reference to an upper glucose threshold, determining the most frequently logged foods during the period of a day having the highest glucose pattern, and displaying additional substitute food options to replace the most frequently logged foods.

[0451] In some methods, the method further includes prompting the user to input food preferences and displaying additional substitute food options to replace the at least one food classified as a bad food based on the database and the food preferences input by the user.

[0452] In a number of ways, a method of analyzing meal data is described, the method including receiving an input log entry from a user associated with an ingested meal, determining a grade for the ingested meal based on at least one characteristic of at least one postprandial glucose trace associated with the ingested meal, the grade being configurable, and displaying the grade associated with the ingested meal in a graphical user interface.

[0453] In some ways, the grade may be changed by the user.

[0454] In some methods, the input log entry is received at a first time, and the method further includes deleting the grade for the ingested meal a period of time after the first time, in some methods, the period of time is about six months.

[0455] In some methods, the input log entries are received at a first time, and the method further includes determining a new grade for at least one logged meal a period of time after the first time, in some methods, the period of time is about six months.

[0456] In a number of systems, a system for analyzing meals is described, the system comprising: an input configured to receive input log entries from a user associated with ingested meals, a display configured to visually present the meals or foods and grades, and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine a grade of the ingested meal based on at least one characteristic of at least one postprandial glucose trace associated with the ingested meal, where the grade is configurable, and display the grade associated with the ingested meal in a graphical user interface.

[0457] In some ways, the grade can be changed by the user.

[0458] In some methods, the input log entry is received at a first time, and the instructions further cause the one or more processors to delete the grade for the ingested meal a period of time after the first time, which period of time is about six months.

[0459] In some methods, the input log entries are received at a first time, and the method further includes determining a new grade for at least one logged meal a period of time after the first time, which period of time is about six months.

[0460] Systems, devices, and methods for detecting, measuring, and classifying an individual's diet based on analyte measurements. These results and related information can be presented to indicate to the individual which diet is causing the most severe analyte response. These results can be organized and classified based on preselected criteria or past diets and results, such that the results are organized and presented in a format with reference to glucose as the monitored analyte. Various embodiments disclosed herein relate to methods, systems, and software applications intended to engage individuals by providing direct and timely feedback regarding their meal-related glycemic response.

[0461] 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 interchangeable with those from any other embodiment. When a feature, element, component, function, or step is described with respect to only one embodiment, it should be understood that the feature, element, component, function, or step can be used in all other embodiments described herein unless expressly stated otherwise. Accordingly, this paragraph serves as a prerequisite and written support for the introduction of claims that combine features, elements, components, functions, and steps from different embodiments or substitute features, elements, components, functions, and steps from one embodiment with those from another embodiment, whenever the following description does not explicitly state that such combinations or substitutions are possible in a particular instance. It is expressly acknowledged that explicitly describing every possible combination and permutation would be unduly burdensome, especially given that the permissibility of each and every such combination and permutation would be readily recognized by those skilled in the art.

[0462] To the extent that the embodiments disclosed herein include or operate in conjunction with memory, storage, and / or computer-readable medium, that memory, storage, and / or computer-readable medium is non-transitory. Thus, to the extent that memory, storage, and / or computer-readable medium is covered by one or more claims, that memory, storage, and / or computer-readable medium is only non-transitory.

[0463] In many cases, entities are described herein as being coupled to other entities. It should be understood that the terms "coupled" and "connected" (or any of their forms) are used interchangeably herein and, in both cases, generally refer to the direct coupling of two entities (without significant (e.g., parasitic) intervening entities) and the indirect coupling of two entities (by one or more significant intervening entities). When entities are shown as being directly coupled or described as being coupled without any description of any intervening entities, it should be understood that those entities may also be indirectly coupled, unless the context clearly dictates otherwise.

[0464] The subject matter described in this specification and the accompanying drawings has been presented with sufficient detail and clarity to allow for the inclusion of mean-plus-function claims at any time pursuant to 35 U.S.C. § 112(f). However, a claim shall be construed as invoking this mean-plus-function form only if the phrase "means for" is expressly recited in the claim.

[0465] Aspects of the invention are set out in the independent claims and preferred features are set out in the dependent claims. Preferred features of the dependent claims may be provided in combination in a single embodiment and preferred features of one aspect may be provided in combination with other aspects.

[0466] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0467] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided may be different from the actual publication dates, which must be independently confirmed.

[0468] While the embodiments are susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and are described in detail herein. These embodiments are not limited to the particular forms disclosed; on the contrary, these embodiments are intended to cover all modifications, equivalents, and alternatives falling within the spirit of the disclosure. Furthermore, any feature, function, step, or element of the embodiments may be recited or added to the claims, as well as any negative limitations that define the scope of a claim by any feature, function, step, or element not recited in the claims.

[0469] Terms Exemplary embodiments are described in the following numbered clauses:

[0470] Clause 1. A method for processing analyte data, the method comprising: receiving sensed analyte data from an individual; detecting an episode of the received analyte data; displaying a first episode marker associated with the detected episode on a graph of the analyte data; providing a screen configured to receive information related to the detected episode; and, after receiving information related to the detected episode, displaying a second episode marker associated with the detected episode on the graph of the analyte data.

[0471] Clause 2. The method of clause 1, further comprising displaying a notification that the episode has been detected.

[0472] Clause 3. The method of clause 2, wherein displaying the notification includes displaying a number on the icon corresponding to the number of episodes detected.

[0473] Clause 4. The method of clause 3, wherein the number is incremented when an episode is detected.

[0474] Clause 5. The method of clause 3, wherein the value is decremented after information of a detected episode is received.

[0475] Clause 6. The method of clause 3, wherein the value is decremented after a predetermined time has elapsed since the episode was detected.

[0476] Clause 7. The method of clause 2, wherein the step of displaying the notification includes the step of displaying an alert.

[0477] Clause 8. The method of clause 1, wherein the first episode marker is different from the second episode marker.

[0478] Clause 9. The method of clause 1, wherein the first episode marker comprises a question mark.

[0479] Clause 10. The method of clause 1, wherein the second episode marker includes an image associated with the received information.

[0480] Clause 11. The method of clause 1, wherein said second episode marker comprises X.

[0481] Clause 12. The method of clause 1, wherein the detected episode is an out-of-range episode.

[0482] Clause 13. The method of clause 1, wherein the first episode marker is placed on the graph of analyte data at a time at which the detected episode is estimated to start.

[0483] Clause 14. The method of clause 1, wherein said analyte data is glucose data.

[0484] Clause 15. An apparatus for processing analyte data, the apparatus comprising: an input configured to receive measured analyte data; a display configured to visually present an analysis of the measured analyte data; and one or more processors coupled to the input, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: detect episodes in the received analyte data; display a first episode marker on a graph of analyte data associated with the detected episode; provide a screen configured to receive information related to the detected episode; and, after receiving information related to the detected episode, display a second episode marker on the graph of analyte data associated with the detected episode.

[0485] Clause 16. The apparatus of clause 15, wherein execution of the program causes the processor to display a notification that the episode has been detected.

[0486] Clause 17. The apparatus of clause 16, wherein the notification includes displaying a number on the icon corresponding to the number of episodes detected.

[0487] Clause 18. The apparatus of clause 17, wherein the value is incremented when an episode is detected.

[0488] Clause 19. The apparatus of clause 17, wherein the value is decremented after information of a detected episode is received.

[0489] Clause 20. The apparatus of clause 17, wherein the value is decremented after a predetermined time has elapsed since the episode was detected.

[0490] Clause 21. A method of processing analyte data using an in vivo analyte monitoring system, the method comprising: rec...

Claims

1. A method for processing analyte data by a computer, comprising: receiving sensed analyte data from the individual; detecting an episode of the received analyte data; displaying a first episode marker associated with the detected episode on a graph of analyte data; providing a screen configured to receive information regarding the detected episode; receiving information associated with the detected episode; after receiving information about the detected episode, displaying a second episode marker on the graph of analyte data related to the received information associated with the detected episode, the second episode marker replacing the first episode marker; A method comprising:

2. The method of claim 1 , further comprising displaying a notification that the episode has been detected.

3. The method of claim 2 , wherein displaying the notification comprises displaying a number on the icon corresponding to the number of episodes detected.

4. The method of claim 3 , wherein the value is incremented when an episode is detected.

5. The method of claim 3 , wherein the value is decremented after receiving information of a detected episode or after a predetermined time has elapsed since the episode was detected.

6. The method of claim 2 , wherein the step of displaying a notification comprises the step of displaying an alert.

7. The method of claim 1 , wherein the first episode marker is different from the second episode marker.

8. The method of claim 1 , wherein the first episode marker comprises a question mark.

9. 10. The method of claim 1, wherein the second episode marker includes an image associated with the received information, or the second episode marker includes an X to highlight that the episode is not associated with a meal.

10. The method of claim 1 , wherein the detected episode is an out-of-range episode in which the analyte data is out of range.

11. The method of claim 1 , wherein the first episode marker is placed on the graph of the analyte data at a time at an estimated start time of the detected episode.

12. The method of claim 1 , wherein the analyte data is glucose data.

13. 1. An apparatus for processing analyte data, comprising: wireless communication circuitry configured to receive the measured analyte data; a display configured to visually present an analysis of the measured analyte data; and one or more processors coupled to the wireless communication circuitry, the display, and a memory storing instructions; Equipped with The instructions, when executed by the one or more processors, cause the one or more processors to: detecting an episode of the received analyte data; and displaying a first episode marker associated with the detected episode on a graph of analyte data; providing a screen configured to receive information regarding the detected episode; receiving information associated with the detected episode; after receiving information about the detected episode, displaying a second episode marker on the graph of analyte data related to the received information associated with the detected episode, the second episode marker replacing the first episode marker; A device that performs the following.

14. 14. The apparatus of claim 13, wherein the instructions cause the processor to display a notice that the episode was detected.

15. The device of claim 14 , wherein the notification comprises displaying a number on an icon corresponding to the number of episodes detected.

16. The apparatus of claim 15 , wherein the value is incremented when an episode is detected.

17. 16. The apparatus of claim 15, wherein the value is decremented after information of a detected episode is received or after a predetermined time has elapsed since the episode was detected.

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