The subject matter described herein relates generally to systems, devices, and methods for improved meal and therapy interfaces in analyte monitoring systems. In particular, embodiments are provided for determining a medication dosage to be administered with the consumption of a meal, identifying meal start and meal peak response candidates, and recommending user-initiated analyte checks.
The increased prevalence of Type 2 diabetes and Metabolic Syndrome over the past few decades can be been attributed to changing diet and activity levels. For example, consumption of more readily available high glycemic index foods can cause rapid post-prandial increase of blood glucose and insulin levels, which has a positive association with weight gain and obesity. These conditions can be further traced to an increased risk of developing these and other diseases.
Most people generally understand the importance of their diet. However, in practice, many individuals struggle with translating this general awareness to their specific food choices. These problems exist primarily because people cannot directly see the impact of their choices. This can lead to misconceptions about portion size, misunderstandings about which foods are relatively healthy, and a general lack of awareness regarding the necessary duration and intensity of activity for maintaining good health. These problems are further exacerbated by advertisements, habits, peer pressure, food preferences, and recommendations based on overgeneralizations.
To address these issues, an individual's physiological responses can be tracked and better understood by analyte monitoring systems. Because high glucose levels are primarily driven by the consumption of food, the level of post-prandial glucose can relate to the amount of carbohydrates and other meal components consumed by the individual, as well as to the individual's physiological response to meals. However, a challenge for the analysis of this influx of data is to represent the data in a meaningful manner that enables efficient action. Data relating to meal selection, and the subsequent impact, should be understood on a clinical basis, as well as a personal basis for the individual, the meal administrator, and/or the medical professional to understand and moderate glucose excursions, such as episodes of hyperglycemia.
Prior solutions for correlating an individual's analyte data with meal consumption, as well as pre-prandial and post-prandial responses, suffer from numerous deficiencies. For example, some systems require that the individual perform numerous inconvenient and uncomfortable discrete blood glucose measurements (e.g., finger stick blood glucose tests). These solutions can also suffer from an insufficient number of data points to adequately determine a glycemic response to a meal. For example, an individual may perform a discrete blood glucose measurement at a time before or after the individual's glycemic response peaks, making it difficult to accurately ascertain the glycemic response, and to meaningfully compare meals based on the glycemic response. A deficiency in data points can also make it difficult to automatically detect the start of a meal event in the individual's analyte data.
Moreover, some prior and existing systems place significant reliance upon manual logging of meals by the individual, which can be unreliable. Another approach to determining pre-prandial and post-prandial meal responses involves collecting dense glucose measurements within a pre-determined time of day window, where glucose values within the window are assumed to represent pre-breakfast and/or post-breakfast times, for example. However, with respect to this approach, the reliability of the estimates will largely depend upon the consistency in the patient's meal timing routine, which can also be unreliable.
Other prior systems seek to detect meal events based simply on the existence of a rise in glucose levels, such as those described in U.S. Publication No. 2003/0208113. These systems, however, can be inadequate because they fail to take into account the individual's prior meal history, and can overestimate the number of meals that an individual has consumed.
A related challenge concerns the determination of a medication dosage (e.g., an insulin dosage) for diabetic individuals to compensate for an anticipated glycemic rise that occurs after consumption of a meal. This dose is often referred to as a meal bolus. Determining the appropriate amount of insulin to be administered can be difficult, and typically entails using a prior art bolus calculator that relies on parameters such as an individual's insulin sensitivity, the individual's insulin on-board, and the amount of carbohydrates in the meal. The carbohydrate content for home-cooked meals, for example, can be difficult to determine as it is often based on the amount of each individual ingredient in the recipe and may require the user to make estimates based on the weight of various portions of the meals. It also requires a carbohydrate determination to be made for each part of the meal. For example, in the case of a dinner including meat, casserole, and a vegetable, carbohydrate content must be determined for each component separately and then summed together for entry into the bolus calculator. The time and effort required in making such calculations can be particularly burdensome to diabetics and often result in the diabetic guessing as to the carbohydrate content.
For these and other reasons, needs exist for improved meal and therapy interfaces for analyte monitoring systems.
Example embodiments of systems, devices, and methods are described herein for improved meal and therapy interfaces for use in vivo analyte monitoring systems. These embodiments can provide for systems, devices, and methods for determining a medication dosage to be administered with consumption of a meal, identifying meal start and meal peak response candidates, and recommending user-initiated analyte checks.
According to one embodiment, for example, a computer-implemented method for determining a medication dosage for administration with the consumption of a meal includes the steps of receiving a user-inputted entry associated with a meal, referencing a first database to determine one or more nutrient parameters associated with the meal, identifying a closest-matched meal in a second database based on the nutrient parameters, and determining a medication dosage associated with the closest-matched meal.
According to another embodiment, a computer-implemented method for identifying a set of meal start candidates and meal peak response candidates includes the steps of determining time derivatives for data points corresponding to a monitored analyte level, creating a set of meal start candidates and meal peak response candidates by determining an optima of acceleration based on the time derivatives, retrieving multiple user-initiated checks and grouping the checks into time clusters, determining a time cluster start point, a time cluster end point, and a time cluster central tendency point for each time cluster, and removing a subset of meal start candidates from the set, wherein the subset includes one or more meal start candidates that are not within a predetermined temporal range of either a time cluster start point or a time cluster end point.
According to yet another embodiment, a computer-implemented method for recommending a user-initiated analyte check includes the steps of receiving a recorded action by a user, evaluating a historical log to determine if the recorded action corresponds to a historical user action associated with a glycemic risk, in response to determining that the recorded action corresponds to the historical user action associated with the glycemic risk, calculating an elapsed time until reaching an actionable time period associated with the glycemic risk, and outputting a notification to the user to perform a user-initiated analyte check after the elapsed time.
Numerous examples of algorithms and methods for performing combinations and/or variations of one or both of these detection mechanisms are provided, as well as example embodiments of systems and devices for performing the same.
Other systems, devices, methods, features and advantages of the subject matter described herein will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the subject matter described herein, and be protected by the accompanying claims. In no way should the features of the example embodiments be construed as limiting the appended claims, absent express recitation of those features in the claims.
The details of the subject matter set forth herein, both as to its structure and operation, may be apparent by study of the accompanying figures, in which like reference numerals refer to like parts. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the subject matter. Moreover, all illustrations are intended to convey concepts, where relative sizes, shapes and other detailed attributes may be illustrated schematically rather than literally or precisely.
Before the present subject matter is described in detail, it is to be understood that this disclosure is not limited to the particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.
The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present disclosure is not entitled to antedate such publications by virtue of prior disclosure. Furthermore, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.
Generally, embodiments of the present disclosure are used with systems, devices, and methods for detecting at least one analyte, such as glucose, in a bodily fluid (e.g., subcutaneously within the interstitial fluid (“ISF”) or blood, within the dermal fluid of the dermal layer, or otherwise). Accordingly, many embodiments include in vivo analyte sensors structurally configured so that at least a portion of the sensor is, or can be, positioned in the body of a user to obtain information about at least one analyte of the body. However, the embodiments disclosed herein can be used with in vivo analyte monitoring systems that incorporate in vitro capability, as well as purely in vitro or ex vivo analyte monitoring systems, including those systems that are entirely non-invasive.
Furthermore, for each and every embodiment of a method disclosed herein, systems and devices capable of performing each of those embodiments are covered within the scope of the present disclosure. For example, embodiments of electronic systems are disclosed, and these electronic systems can include non-transitory memory (e.g., for storing instructions), processing circuitry (e.g., for executing instructions), power sources, communication circuitry, transmitters, receivers, and/or controllers that can perform any and all method steps or facilitate the execution of any and all method steps.
A number of embodiments of the present disclosure are designed to improve upon the computer-implemented capabilities of analyte monitoring systems with respect to meal and therapy interfaces. In some embodiments, for example, a medication dosage for administration with the consumption of a meal can be determined by identifying a closest-matched meal in a database based on certain nutrient parameters. These embodiments can improve the accuracy of dosage determination software, for example, by referencing an individual's own historical glycemic responses and medication dosages, instead of relying upon an individual's guesswork as to the nutritional content of a meal.
According to other embodiments, data indicative of a monitored analyte level analyte is received from an analyte sensor and can be used by processing circuitry to identify a set of meal start and meal peak response candidates. These embodiments can improve upon the accuracy of software for determining meal start times and meal peak response times, without having to rely upon user estimation or strict adherence to daily meal routines. Further, these embodiments can present a limited and more accurate set of meal start and meal peak response candidates via a graphical interface, which allows the user to more efficiently navigate analyte data collected by an analyte monitoring system.
According to still other embodiments, if a current recorded action by a user is determined to have an associated glycemic risk, a recommendation to perform a user-initiated analyte check (e.g., a sensor scan) can be outputted to the user after an elapsed time. These embodiments evaluate a historical log of the user's past actions and associated glycemic risk to determine whether a future user-initiated analyte check is warranted. In this regard, these embodiments improve upon analyte monitoring systems by increasing and/or maintaining user engagement of the system through interactive user interfaces, as compared to known systems with passive interfaces.
Accordingly, the embodiments described herein reflect various computer-implemented improvements over prior analyte monitoring systems, and their respective user interfaces, in many respects. In particular, these embodiments improve upon the accuracy of analyte monitoring systems with respect to medication dosage determination, meal start and meal peak response detection, and glycemic risk determinations. Further, the embodiments described herein utilize specific types of data (e.g., user-initiated analyte check information) in a non-conventional way. Other features and advantages of the disclosed embodiments are further discussed below.
Before describing the embodiments in detail, however, it is first desirable to describe examples of devices that can be present within, for example, an in vivo analyte monitoring system, as well as examples of their operation, all of which can be used with the embodiments described herein.
There are various types of analyte monitoring systems. “Continuous Analyte Monitoring” systems (or “Continuous Glucose Monitoring” systems), for example, are in vivo systems that can transmit data from a sensor control device to a reader device repeatedly or continuously without prompting, e.g., automatically according to a schedule. “Flash Analyte Monitoring” systems (or “Flash Glucose Monitoring” systems or simply “Flash” systems), as another example, are in vivo systems that can transfer data from a sensor control device in response to a scan or request for data by a reader device, such as with a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol. In vivo analyte monitoring systems can also operate without the need for finger stick calibration.
In vivo monitoring systems can include a sensor that, while positioned in vivo, makes contact with the bodily fluid of the user and senses one or more analyte levels contained therein. The sensor can be part of a sensor control device that resides on the body of the user and contains the electronics and power supply that enable and control the analyte sensing. The sensor control device, and variations thereof, can also be referred to as a “sensor control unit,” an “on-body electronics” device or unit, an “on-body” device or unit, or a “sensor data communication” device or unit, to name a few. As used herein, these terms are not limited to devices with analyte sensors, and encompass devices that have sensors of other types, whether biometric or non-biometric. The term “on body” refers to any device that resides directly on the body or in close proximity to the body, such as a wearable device (e.g., glasses, watch, wristband or bracelet, neckband or necklace, etc.).
In vivo monitoring systems can also include one or more reader devices that receive sensed analyte data from the sensor control device. These reader devices can process and/or display the sensed analyte data, or sensor data, in any number of forms, to the user. These devices, and variations thereof, can be referred to as “handheld reader devices,” “reader devices” (or simply, “readers”), “handheld electronics” (or handhelds), “portable data processing” devices or units, “data receivers,” “receiver” devices or units (or simply receivers), “relay” devices or units, or “remote” devices or units, to name a few. Other devices such as personal computers have also been utilized with or incorporated into in vivo and in vitro monitoring systems.
In vivo analyte monitoring systems can be differentiated from “in vitro” systems that contact a biological sample outside of the body (or rather “ex vivo”) and that typically include a meter device that has a port for receiving an analyte test strip carrying a bodily fluid of the user, which can be analyzed to determine the user's analyte level. As mentioned, the embodiments described herein can be used with in vivo systems, in vitro systems, and combinations thereof.
The embodiments described herein can be used to monitor and/or process information regarding any number of one or more different analytes. Analytes that may be monitored include, but are not limited to, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, glycosylated hemoglobin (HbAlc), creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose, glucose derivatives, glutamine, growth hormones, hormones, ketones, ketone bodies, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. The concentration of drugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored. In embodiments that monitor more than one analyte, the analytes may be monitored at the same or different times.
Reader device 120 is also capable of wired, wireless, or combined communication with a computer system 170 (e.g., a local or remote computer system) over communication path (or link) 141 and with a network 190, such as the internet or the cloud, over communication path (or link) 142. Communication with network 190 can involve communication with trusted computer system 180 within network 190, or though network 190 to computer system 170 via communication link (or path) 143. Communication paths 141, 142, and 143 can be wireless, wired, or both, can be uni-directional or bi-directional, and 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. In some cases, communication paths 141 and 142 can be the same path. All communications over paths 140, 141, and 142 can be encrypted and sensor control device 102, reader device 120, computer system 170, and trusted computer system 180 can each be configured to encrypt and decrypt those communications sent and received.
Variants of devices 102 and 120, as well as other components of an in vivo-based analyte monitoring system that are suitable for use with the system, device, and method embodiments set forth herein, are described in U.S. Publication No. 2011/0213225 (the '225 Publication), which is incorporated by reference herein in its entirety for all purposes.
Sensor control device 102 can include a housing 103 containing in vivo analyte monitoring circuitry and a power source. In this embodiment, the in vivo analyte monitoring circuitry is electrically coupled with an analyte sensor 104 that extends through an adhesive patch 105 and projects away from housing 103. Adhesive patch 105 contains an adhesive layer (not shown) for attachment to a skin surface of the body of the user. Other forms of body attachment to the body may be used, in addition to or instead of adhesive.
Sensor 104 is adapted to be at least partially inserted into the body of the user, where it can make fluid contact with that user's bodily fluid (e.g., subcutaneous (subdermal) fluid, dermal fluid, or blood) and be used, along with the in vivo analyte monitoring circuitry, to measure analyte-related data of the user. Sensor 104 and any accompanying sensor control electronics can be applied to the body in any desired manner. For example, an insertion device (not shown) can be used to position all or a portion of analyte sensor 104 through an external surface of the user's skin and into contact with the user's bodily fluid. In doing so, the insertion device can also position sensor control device 102 with adhesive patch 105 onto the skin. In other embodiments, insertion device can position sensor 104 first, and then accompanying sensor control electronics can be coupled with sensor 104 afterwards, either manually or with the aid of a mechanical device. Examples of insertion devices are described in U.S. Publication Nos. 2008/0009692, 2011/0319729, 2015/0018639, 2015/0025345, and 2015/0173661, all which are incorporated by reference herein in their entireties and for all purposes.
After collecting raw data from the user's body, sensor control device 102 can apply analog signal conditioning to the data and convert the data into a digital form of the conditioned raw data. In some embodiments, sensor control device 102 can then algorithmically process the digital raw data into a form that is representative of the user's measured biometric (e.g., analyte level) and/or one or more analyte metrics based thereupon. For example, sensor control device 102 can include processing circuitry to calculate analyte metrics and algorithmically perform any of the method steps described herein. Sensor control device 102 can then encode and wirelessly communicate the calculated analyte metrics, processed sensor data, notifications, or any other data to reader device 120 and/or wearable electronics 120B, which in turn can format or graphically process the received data for digital display to the user. In other embodiments, in addition to, or in lieu of, wirelessly communicating sensor data to another device (e.g., reader device 120 and/or wearable electronics 120B), sensor control device 102 can graphically process the final form of the data such that it is ready for display, and display that data on a display of sensor control device 102. In some embodiments, the final form of the biometric data (prior to graphic processing) is used by the system (e.g., incorporated into a diabetes monitoring regime) without processing for display to the user.
In still other embodiments, the conditioned raw digital data can be encoded for transmission to another device, e.g., reader device 120 and/or wearable electronics 120B, which then algorithmically processes that digital raw data into a form representative of the user's measured biometric (e.g., a form readily made suitable for display to the user) and/or one or more analyte metrics based thereupon. Reader device 120 and/or wearable electronics 120B can include processing circuitry to calculate analyte metrics and algorithmically perform any of the method steps described herein. This algorithmically processed data can then be formatted or graphically processed for digital display to the user.
In other embodiments, sensor control device 102 and reader device 120 transmit the digital raw data to another computer system for algorithmic processing and display.
Reader device 120 can include a display 122 to output information to the user and/or to accept an input from the user, and an optional input component 121 (or more), such as a button, actuator, touch sensitive switch, capacitive switch, pressure sensitive switch, jog wheel or the like, to input data, commands, or otherwise control the operation of reader device 120. In certain embodiments, display 122 and input component 121 may be integrated into a single component, for example, where the display can detect the presence and location of a physical contact touch upon the display, such as a touch screen user interface. In certain embodiments, input component 121 of reader device 120 may include a microphone and reader device 120 may include software configured to analyze audio input received from the microphone, such that functions and operation of the reader device 120 may be controlled by voice commands. In certain embodiments, an output component of reader device 120 includes a speaker (not shown) for outputting information as audible signals. Similar voice responsive components such as a speaker, microphone and software routines to generate, process and store voice driven signals may be included in sensor control device 102. According to some embodiments, wearable electronics 120B can include components, including a display 122B (that can have a touch screen user interface), and an optional input component 121B, that function in a manner similar to like components of reader device 120.
Reader device 120 can also include one or more data communication ports 123 for wired data communication with external devices such as computer system 170 or sensor control device 102. Example data communication ports include USB ports, mini USB ports, USB Type-C ports, USB micro-A and/or micro-B ports, RS-232 ports, Ethernet ports, Firewire ports, or other similar data communication ports configured to connect to the compatible data cables. Reader device 120 may also include an integrated or attachable in vitro glucose meter, including an in vitro test strip port (not shown) to receive an in vitro glucose test strip for performing in vitro blood glucose measurements.
Reader device 120 and/or wearable electronics 120B can display the measured biometric data wirelessly received from sensor control device 102 and can also be configured to output alarms, alert notifications, glucose values, etc., which may be visual, audible, tactile, or any combination thereof. Further details and other display embodiments can be found in, e.g., U.S. Publication No. 2011/0193704, which is incorporated herein by reference in its entirety for all purposes.
Reader device 120 can function as a data conduit to transfer the measured data and/or analyte metrics from sensor control device 102 to computer system 170 or trusted computer system 180. In certain embodiments, the data received from sensor control device 102 may be stored (permanently or temporarily) in one or more memories of reader device 120 prior to uploading to system 170, 180 or network 190.
Computer system 170 may be a personal computer, a server terminal, a laptop computer, a tablet, or other suitable data processing device. Computer system 170 can be (or include) software for data management and analysis and communication with the components in analyte monitoring system 100. Computer system 170 can be used by the user or a medical professional to display and/or analyze the biometric data measured by sensor control device 102. In some embodiments, sensor control device 102 can communicate the biometric data directly to computer system 170 without an intermediary such as reader device 120, or indirectly using an internet connection (also optionally without first sending to reader device 120). Operation and use of computer system 170 is further described in the '225 Publication incorporated herein. Analyte monitoring system 100 can also be configured to operate with a data processing module (not shown), also as described in the incorporated '225 Publication.
Trusted computer system 180 can be within the possession of the manufacturer or distributor of sensor control device 102, either physically or virtually through a secured connection, and can be used to perform authentication of sensor control device 102, for secure storage of the user's biometric data, and/or as a server that serves a data analytics program (e.g., accessible via a web browser) for performing analysis on the user's measured data.
Reader device 120 can be a mobile communication device such as a dedicated reader device (configured for communication with a sensor control device 102, and optionally a computer system 170, but without mobile telephony communication capability) or a mobile telephone including, but not limited to, a Wi-Fi or internet enabled smart phone, tablet, or personal digital assistant (PDA). Examples of smart phones can include those mobile phones based on a Windows® operating system, Android™ operating system, iPhone® operating system, Palm® WebOS™, Blackberry® operating system, or Symbian® operating system, with data network connectivity functionality for data communication over an internet connection and/or a local area network (LAN).
Reader device 120 can also be configured as a mobile smart wearable electronics assembly, such as an optical assembly that is worn over or adjacent to the user's eye (e.g., a smart glass or smart glasses, such as Google glasses, which is a mobile communication device). This optical assembly can have a transparent display that displays information about the user's analyte level (as described herein) to the user while at the same time allowing the user to see through the display such that the user's overall vision is minimally obstructed. The optical assembly may be capable of wireless communications similar to a smart phone. Other examples of wearable electronics include devices that are worn around or in the proximity of the user's wrist (e.g., a watch, etc.), neck (e.g., a necklace, etc.), head (e.g., a headband, hat, etc.), chest, or the like. According to some embodiments, for example, wearable electronics can include a smart watch 120B, as shown in
Communications processor 202 can interface with RF communication circuitry 208 and perform analog-to-digital conversions, encoding and decoding, digital signal processing and other functions that facilitate the conversion of voice, video, and data signals into a format (e.g., in-phase and quadrature) suitable for provision to RF communication circuitry 208, which can then transmit the signals wirelessly. Communications processor 202 can also interface with RF communication circuitry 208 to perform the reverse functions necessary to receive a wireless transmission and convert it into digital data, voice, and video. RF communication circuitry 208 can include a transmitter and a receiver (e.g., integrated as a transceiver) and associated encoder logic.
Applications processor 204 can be adapted to execute the operating system and any software applications that reside on reader device 120, process video and graphics, and perform those other functions not related to the processing of communications transmitted and received over RF antenna 209. The smart phone operating system will operate in conjunction with a number of applications on reader device 120. Any number of applications (also known as “user interface applications”) can be running on reader device 120 at any one time, and may include one or more applications that are related to a diabetes monitoring regime, in addition to the other commonly used applications that are unrelated to such a regime, e.g., email, calendar, weather, sports, games, etc. For example, the data indicative of a sensed analyte level and in vitro blood analyte measurements received by the reader device can be securely communicated to user interface applications residing in memory 210 of reader device 120. Such communications can be securely performed, for example, through the use of mobile application containerization or wrapping technologies. In addition, according to some embodiments, reader device 120 can also include an application for communicating data indicative of a sensed analyte level with wearable electronics 120B.
Memory 210 can be shared by one or more of the various functional units present within reader device 120, or can be distributed amongst two or more of them (e.g., as separate memories present within different chips). Memory 210 can also be a separate chip of its own. Memories 203, 205, and 210 are non-transitory, and can be volatile (e.g., RAM, etc.) and/or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).
Multi-functional circuitry 212 can be implemented as one or more chips and/or components (e.g., transmitter, receiver, transceiver, and/or other communication circuitry) that perform other functions such as local wireless communications, e.g., with sensor control device 102 under the appropriate protocol (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy, Near Field Communication (NFC), Radio Frequency Identification (RFID), proprietary protocols, and others) and determining the geographic position of reader device 120 (e.g., global positioning system (GPS) hardware). One or more other antennas 214 are associated with the functional circuitry 212 as needed to operate with the various protocols and circuits.
Power supply 216 can include one or more batteries, which can be rechargeable or single-use disposable batteries. Power management circuitry 218 can regulate battery charging and power supply monitoring, boost power, perform DC conversions, and the like.
Reader device 120 can also include or be integrated with a drug (e.g., insulin, etc.) delivery device such that they, e.g., share a common housing. Examples of such drug delivery devices can include medication pumps having a cannula that remains in the body to allow infusion over a multi-hour or multi-day period (e.g., wearable pumps for the delivery of basal and bolus insulin). Reader device 120, when combined with a medication pump, can include a reservoir to store the drug, a pump connectable to transfer tubing, and an infusion cannula. The pump can force the drug from the reservoir, through the tubing and into the diabetic's body by way of the cannula inserted therein. Other examples of drug delivery devices that can be included with (or integrated with) reader device 120 include portable injection devices that pierce the skin only for each delivery and are subsequently removed (e.g., insulin pens). A reader device 120, when combined with a portable injection device, can include an injection needle, a cartridge for carrying the drug, an interface for controlling the amount of drug to be delivered, and an actuator to cause injection to occur. The device can be used repeatedly until the drug is exhausted, at which point the combined device can be discarded, or the cartridge can be replaced with a new one, at which point the combined device can be reused repeatedly. The needle can be replaced after each injection.
The combined device can function as part of a closed-loop system (e.g., an artificial pancreas system requiring no user intervention to operate) or semi-closed loop system (e.g., an insulin loop system requiring seldom user intervention to operate, such as to confirm changes in dose). For example, the diabetic's analyte level can be monitored in a repeated automatic fashion by sensor control device 102, which can then communicate that monitored analyte level to reader device 120, and the appropriate drug dosage to control the diabetic's analyte level can be automatically determined and subsequently delivered to the diabetic's body. Software instructions for controlling the pump and the amount of insulin delivered can be stored in the memory of reader device 120 and executed by the reader device's processing circuitry. These instructions can also cause calculation of drug delivery amounts and durations (e.g., a bolus infusion and/or a basal infusion profile) based on the analyte level measurements obtained directly or indirectly from sensor control device 102. In some embodiments sensor control device 102 can determine the drug dosage and communicate that to reader device 120.
A memory 253 is also included within ASIC 251 and can be shared by the various functional units present within ASIC 251, or can be distributed amongst two or more of them. Memory 253 can also be a separate chip. Memory 253 is non-transitory and can be volatile and/or non-volatile memory. In this embodiment, ASIC 251 is coupled with power source 260, which can be a coin cell battery, or the like. AFE 252 interfaces with in vivo analyte sensor 104 and receives measurement data therefrom and outputs the data to processing circuitry 256 in digital form, which in turn can, in some embodiments, process in any of the manners described elsewhere herein. This data can then be provided to communication circuitry 258 for sending, by way of antenna 261, to reader device 120 (not shown), for example, where minimal further processing is needed by the resident software application to display the data. Antenna 261 can be configured according to the needs of the application and communication protocol. Antenna 261 can be, for example, a printed circuit board (PCB) trace antenna, a ceramic antenna, or a discrete metallic antenna. Antenna 261 can be configured as a monopole antenna, a dipole antenna, an F-type antenna, a loop antenna, and others.
Information may be communicated from sensor control device 102 to a second device (e.g., reader device 120) at the initiative of sensor control device 102 or reader device 120. For example, information can be communicated automatically and/or repeatedly (e.g., continuously) by sensor control device 102 when the analyte information is available, or according to a schedule (e.g., about every 1 minute, about every 5 minutes, about every 10 minutes, or the like), in which case the information can be stored or logged in a memory of sensor control device 102 for later communication. The information can be transmitted from sensor control device 102 in response to receipt of a request by the second device. This request can be an automated request, e.g., a request transmitted by the second device according to a schedule, or can be a request generated at the initiative of a user (e.g., an ad hoc or manual request, or a “user-initiated analyte check”). In some embodiments, a manual request for data is referred to as a “scan” of sensor control device 102 or an “on-demand” data transfer from device 102. In some embodiments, the second device can transmit a polling signal or data packet to sensor control device 102, and device 102 can treat each poll (or polls occurring at certain time intervals) as a request for data and, if data is available, then can transmit such data to the second device. In many embodiments, the communication between sensor control device 102 and the second device are secure (e.g., encrypted and/or between authenticated devices), but in some embodiments the data can be transmitted from sensor control device 102 in an unsecured manner, e.g., as a broadcast to all listening devices in range.
Different types and/or forms and/or amounts of information may be sent as part of each communication including, but not limited to, one or more of current sensor measurements (e.g., the most recently obtained analyte level information temporally corresponding to the time the reading is initiated), rate of change of the measured metric over a predetermined time period, rate of the rate of change of the metric (acceleration in the rate of change), or historical metric information corresponding to metric information obtained prior to a given reading and stored in a memory of sensor control device 102.
Some or all of real time, historical, rate of change, rate of rate of change (such as acceleration or deceleration) information may be sent to reader device 120 in a given communication or transmission. In certain embodiments, the type and/or form and/or amount of information sent to reader device 120 may be preprogrammed and/or unchangeable (e.g., preset at manufacturing), or may not be preprogrammed and/or unchangeable so that it may be selectable and/or changeable in the field one or more times (e.g., by activating a switch of the system, etc.). Accordingly, in certain embodiments reader device 120 can output a current (real time) sensor-derived analyte value (e.g., in numerical format), a current rate of analyte change (e.g., in the form of an analyte rate indicator such as an arrow pointing in a direction to indicate the current rate), and analyte trend history data based on sensor readings acquired by and stored in memory of sensor control device 102 (e.g., in the form of a graphical trace). Additionally, an on-skin or sensor temperature reading or measurement may be collected by an optional temperature sensor 257. Those readings or measurements can be communicated (either individually or as an aggregated measurement over time) from sensor control device 102 to another device (e.g., reader 120). The temperature reading or measurement, however, may be used in conjunction with a software routine executed by reader device 120 to correct or compensate the analyte measurement output to the user, instead of or in addition to actually displaying the temperature measurement to the user.
Example Embodiments for Determining a Medication Dosage to be Administered with a Meal
Example embodiments of systems, devices, and methods for determining a medication dosage to be administered with the consumption of a meal will now be described. As described earlier, certain individuals, such as those with diabetes, need to compensate for an anticipated glycemic rise occurring after the consumption of a meal by administering medication, such as insulin. The medication dosage is often referred to as a meal bolus because it is an infusion of medication for the purpose of compensating for a meal.
Some prior systems and methods for determining a medication dosage to be administered with the consumption of a meal require an individual to manually count or estimate carbohydrates. These systems can lead to inaccurate and inconsistent medication dosages, as it can be difficult for individuals to accurately estimate the amount of carbohydrates and other nutritional components in their food. In addition, glycemic responses to nutrients can vary from individual to individual, as it is unlikely that different individuals all respond to the same nutrients the same way.
Other systems and methods have attempted to address this challenge by recording repeated instances of meal consumption in a database, along with descriptions of the meals and associated medication dosages. Corresponding analyte data (e.g., post-prandial glucose data) from an analyte monitoring system, such as an in vivo analyte monitoring system, can be also associated with records in the database based on a time period corresponding to the consumption of the meal. Association of the meal with analyte data from prior instances where medication dosages were administered can enable the individual or a health care provider (HCP) to readily identify beneficial medication titrations to improve future glycemic responses. These systems and methods are further described in U.S. patent application Ser. No. 15/863,279, now U.S. Publ. No. 2018/0197628, which is incorporated by reference herein in its entirety and for all purposes.
The embodiments described herein reflect improvements to the aforementioned systems and methods. For example, the embodiments described herein can determine a medication dosage to be administered with the consumption of a meal that an individual has not consumed before. At a general level, the example embodiments allow the individual to input meal information into an interface and, based on various nutritional parameters associated with the meal, a proper bolus amount for the meal is determined. More specifically, the example embodiment methods described herein include the steps of receiving a user-inputted entry associated with a new meal, referencing a first database to determine the nutritional content of the new meal, matching the new meal to a closest-matched meal in a second database based on the nutritional content, and determining a medication dosage associated with the closest-matched meal.
Because the embodiments are based in part on an individual's typical experience, they can be referred to herein as “experiential” tools. For ease of discussion, the example embodiments will be described in the context of insulin bolus dose determinations and will be generally referred to as the “experiential bolus assistant,” or “EBA” for short. However, it is stressed that these example embodiments can be used with all types of insulin (e.g., long-acting insulin, intermediate, short-acting insulin, etc.), and other types of diabetes medications other than insulin. The example embodiments can also be used to determine types of dosages other than bolus dosages, such as basal dosages or basal time-varying dosage profiles, etc.
Before describing the embodiments in detail, it will be understood by those of skill in the art that any one or more of the steps of the example methods described herein can be stored as software instructions in a non-transitory memory of a reader device, a remote computing device, a trusted computer system, such as those described with respect to
When used with an analyte monitoring system 100, these embodiments can capture, categorize, and index glucose responses to the meals and meal-time insulin doses (administered to compensate for the meal), and thus provide the user with additional data from which the user's insulin dosages can be perfected or “fine-tuned.” In addition, over time, the example embodiments can provide recommendations as to the titration of the bolus amount for each meal.
According to another aspect of the embodiment, EBA 402 sends a request through a resident application programming interface (API) to app 404 for glucose data recently collected from the user. App 404 processes the request and supplies the queried data back to EBA 402, as shown in the loop depicted in
A user can access this data, for example, using a web browser operating on a smartphone 120, or via a separate computing device such as personal computer system 170, as shown in
Referring still to
According to one aspect of the embodiments, nutrition database system 185 can include an interface through which meal information is received as input, and from which nutritional parameters associated with the inputted meal information are outputted. In some embodiments, the nutritional parameters can include a carbohydrate parameter, a fat parameter, and/or a protein parameter, where each of the nutritional parameters are associated with the nutritional content of the inputted meal. Those of skill in the art will recognize that other nutritional parameters can be utilized, and are fully within the scope of the present disclosure.
At Step 510, a first database is referenced to determine a plurality of nutrient parameters associated with the meal based on the user-inputted meal entry. According to one aspect of the embodiments, the first database can be a nutrition database system 185 (as shown in
Referring still to
According to one aspect of the embodiments, the closest-matched meal can be a historical meal record in the second database having a set of associated nutrient parameters that most closely resembles the nutrient parameters associated with the user-inputted meal. This can be determined, for example, by calculating a weighted set of differences between the nutrient parameters for each historical meal record and the nutrient parameters of the user-inputted meal entry, and selecting the historical meal record with the lowest total difference. To illustrate, in one example embodiment, the best-matched meal can be determined by calculating the lowest total difference resulting from the following equation: 0.5*(absolute % difference in carbohydrates)+0.25*(absolute % difference in fat)+0.25*(absolute % difference in protein), where the “absolute % difference” can be the absolute value of the percentage difference between the nutrient parameter of the historical meal record and the nutrient parameter of the user-inputted meal entry. Those of skill in the art will recognize that other weighting factors can be used for each of the nutrient parameters. Likewise, the lowest total difference can also be calculated without using any weighting factors. In some embodiments, after the closest-matched meal is identified in the second database, a new historical meal record can be created in the second database for the user-inputted meal entry and subsequently linked to the closest-matched meal.
At Step 520, a medication dosage associated with the closest-matched meal in the second database is determined. In some embodiments, the medication dosage can be, for example, the most recent insulin dosage administered with the consumption of the closest-matched meal (that was recorded in the second database). In other embodiments, the medication dosage can be an average of the prior insulin dosages administered for all or a predetermined number of past instances where the closest-matched meal was consumed. In still other embodiments, the medication dosage can be an insulin dosage that is flagged in the second database as an optimal medication dosage for the closest-matched meal. Optionally, the determined medication dosage and/or the associated nutrient parameters can be stored in the second database with a historical record associated with the user-inputted meal entry. Finally, at Step 525, the determined medication dosage can be visually outputted to, for example, a display of the reader device 120 and/or a display of computing device 170.
Some of the embodiments disclosed herein utilize analyte data from an analyte monitoring system, such as that described with respect to
Although
In accordance with the embodiments described herein, it is also desirable to describe certain characteristics of the analyte data of an analyte monitoring system, which can be utilized by the embodiments to identify a set of meal start candidates and meal peak response candidates.
Referring still to
According to one aspect of the embodiments, the forward time window associated with a meal start candidate does not necessarily have the same width as the forward time window associated with a meal peak response candidate. Similarly, the backward time window associated with a meal start candidate does not necessarily have the same width as the backward time window associated with a meal peak response candidate.
Referring back to graph 710 of
Referring still to
Referring still to lower graph 720 of
According to another aspect of the embodiments, if a time instance, k, has been previously identified as a meal peak response candidate, and is also identified as a meal start candidate, the meal start candidate tag is moved to the next instance k+1. Graph 720 illustrates the identification of local acceleration optima, i.e., the meal start candidates and meal peak response candidates, as indicated by “up” triangles 722 and “down” triangles 724, respectively.
Further details regarding the above calculations, including the determination of time derivatives and local optima of acceleration are described in U.S. Publication No. 2017/0185748A1 (“the '748 Publication”), the disclosure of which is incorporated herein by reference for all purposes.
Example embodiments of systems, devices, and methods for determining a set of meal start candidates and meal peak response candidates based on user-initiated analyte checks and analyte data from an analyte monitoring system will now be described.
It will be understood by those of skill in the art that any one or more of the steps of the example methods described herein can be stored as software instructions in a non-transitory memory of a sensor control device, a reader device, a remote computer, or a trusted computer system, such as those described with respect to
It will also be appreciated by those of skill in the art that the instructions can be stored in non-transitory memory on a single device (e.g., a sensor control device or a reader device) or, in the alternative, can be distributed across multiple discrete devices, which can be located in geographically dispersed locations (e.g., a cloud platform). Likewise, those of skill in the art will recognize that the representations of computing devices in the embodiments disclosed herein, such as those shown in
Next, at Step 810, time derivatives for the plurality of data points corresponding to the monitored analyte level are determined. The time derivatives for the plurality of data points can be determined according to the calculations previously described with respect to graphs 700 and 710 of
At Step 820, a plurality of user-initiated analyte checks is retrieved and grouped into a plurality of time clusters. The user-initiated analyte checks can comprise one or more of finger stick blood glucose tests, sensor scan instances from an analyte reader device, sensor viewer usage instances on a smartphone, or receiver display activation instances in a continuous glucose monitoring (CGM) system. According to some embodiments, the plurality of time clusters can comprise a subset of user-initiated analyte checks within a predetermined period of minutes.
At Step 825, a time cluster start point, a time cluster end point, and a time cluster central tendency point for each time cluster is determined. In some embodiments, the time cluster central tendency point can be a mean, a median, or a mode.
At Step 830, a subset of meal start candidates is removed from the initial set of meal start candidates and meal peak response candidates. According to one aspect of the embodiments, the subset of meal start candidates can include one or more meal start candidates that are not within a predetermined temporal range of either a time cluster start point or a time cluster end point.
At Step 835, the modified set of meal start candidates and meal peak response candidates is outputted to the individual user. In some embodiments, the output can be in the form of a graphical user interface on the display of a reader device, such as a smart phone. In other embodiments, the output can be an auditory or vibratory signal that is outputted to a sensor control device, a reader device, a local computer, or a trusted computer system.
It will be understood by those of skill in the art that, although method 800 shows the retrieval, grouping, and time cluster analysis of user-initiated analyte checks at Steps 820 and 825, these steps can be performed prior to or concurrently with any of the other steps of method 800.
Referring first to
Turning to
According to some embodiments, for example, a meal peak response candidate is removed from the initial set based on the following criteria: (1) the next instance in the set is also a meal peak response candidate; (2) the next instance in the set has a larger analyte value than the current instance; and (3) the rate from the forward peak calculation of the current instance is more than a non-negative noise floor v_min_rise (e.g. 0.5 mg/dL/min). Calculated rates of change whose absolute numbers are close to zero tend to contain a lot of noise. Additionally, in certain embodiments, a meal peak response candidate is also removed if the previous instance in the set is also a meal peak response candidate, and the previous instance in the set has a larger analyte value than the current instance.
Similarly, in certain embodiments, meal start candidates are removed because the previous instance of an adjacent meal start candidate has a smaller analyte value. That is, a meal start candidate is removed based on the following criteria: (1) the previous instance in the set is also a meal start candidate; (2) the previous instance in the set has a smaller analyte value than the current instance; and (3) the value a_start(m−1) is smaller than a_start(m), where m is the current instance. In addition, in certain embodiments, a meal start candidate is also removed if the next instance in the set is also a meal start candidate, and the next instance has an analyte value that is either equal to or less than the analyte value of the current instance.
Referring again to
At Step 940, another subset of meal start candidates and meal peak response candidates is removed from the set, where the subset includes meal start candidate and meal peak response candidate pairs, and where each pair has an amplitude difference that does not exceed a predetermined level. More specifically, in certain embodiments, meal peak response candidates in the set are analyzed to determine whether the change in analyte value from the previous instance, which would be a meal start candidate, to the current meal peak response candidate is sufficiently large. In other words, a current meal peak response candidate is removed from the set when the following criteria are met: (1) previous instance, m−1, in the set is a meal start candidate; (2) the current instance, m, is a meal peak response candidate; and (3) the difference between the amplitude of m and the amplitude of m−1 is less than or equal to a predetermined minimum amplitude. Moreover, in certain embodiments, when a meal peak response candidate is removed under these conditions, the corresponding meal start candidate, m−1, is also removed.
At Step 945, another subset of meal start candidates and meal peak response candidates is removed from the set, where the subset includes meal start candidate and meal peak response candidate pairs, and where each pair does not exceed a proximity threshold and an analyte level drop threshold. That is, in certain embodiments, meal start candidates that are too close in time to a prior meal peak response candidate, and whose value is not significantly lower than the value of its prior meal peak response candidate, are removed from the set. More specifically, in certain embodiments, a meal start candidate at instance, m, is removed when the following criteria are met: (1) the previous instance, m−1, is a meal peak response candidate; (2) the current instance, m, is a meal start candidate; (3) the next instance, m+1, is a meal peak response candidate; (4) the average value of v_start_bck(m) and v_peak_fwd(m−1) is greater than a maximum post-prandial recovery descent rate, v_max_descent (e.g., ¼ mg/dL/min); and (5) the difference between the value of the current instance, m, and the previous instance, m−1, is less than or equal to a minimum required drop from a previous peak, g_min_drop (e.g., 5-10 mg/dL). Moreover, when these criteria are met and a meal start candidate is removed, the meal peak response candidate at the previous instance, m−1, is also removed.
Turning to
At Step 955, the resulting set of meal start candidates and meal peak response candidates can be further refined. Occasionally, because of the magnitude and asymmetrical nature of the forward and backward time windows used to calculate the time derivatives, and because some post-prandial responses may be followed by a subsequent post-prandial response without sufficient time for the original post-prandial response to revert to a baseline, the identification of meal start candidates and meal peak response candidates may be slightly biased before or after the likely instances. Further refinement of the set, after removal of the aforementioned subsets, can be performed to address these circumstances.
According to one aspect of the embodiments, for each sampled analyte data at instance, k, g(k), an available sample that is as close to 30 minutes prior to k as possible, g_prev(k), is identified. Also, for each sampled analyte data at instance, k, g(k), an available sample that is as close to 30 minutes after k as possible, g_after(k), is identified. Then, forward and backward slopes, v_fwd(k) and v_bck(k) are determined by taking the difference, g_after(k)−g(k), and dividing it by their time interval (e.g., 30 minutes). Likewise, backward slope, v_bck(k), is calculated by taking the difference, g(k)−g_prev(k), and dividing it by their time interval. The difference in slope, dv(k), is determined by taking the difference v_fwd(k)−v_bck(k). Those of skill in the art will recognize that analyte data samples of different time durations besides 30 minutes can be utilized (e.g., 15 minutes, 60 minutes, 90 minutes, etc.), and are fully within the scope of the present disclosure.
Subsequently, meal start and meal peak response candidate pairs from the set are analyzed according to the following steps. For each start and peak pair, an analyte time series, g_array_start, up to 90 minutes prior to the start candidate, and up to 60 minutes after the start candidate is defined. The defined analyte time series, g_array_start, includes the meal start candidate. Similarly, a glucose time series, g_array_peak, up to 60 minutes prior to the peak candidate, and up to 180 minutes after the peak candidate is defined. The analyte time series, g_array_peak, includes the peak candidate. Next, g_array_peak is “trimmed” of any sampled analyte data whose timestamp overlaps the start time of the next pair. For each value in g_array_start and g_array_peak, the corresponding differences in slope values, dv, are determined, and the arrays for these values, dv_array_start and dv_array_peak, are defined. Those of skill in the art will recognize that other time durations for g_array_start and g_array_peak (e.g., 30 minutes prior, 30 minutes after, 45 minutes prior, 45 minutes after, etc.) can be utilized and are fully within the scope of the present disclosure.
Thereafter, in certain embodiments, a subset of time instances is determined such that (1) the measured analyte values at these instances are greater than or equal to the 75th percentile of g_array_peak, and (2) the dv values at these instances are less than or equal to the 25th percentile of dv_array_peak. If such a subset contains data, then the highest analyte value in this subset, g_max, and its corresponding instance, is stored. Similarly, another subset of time instances is determined such that (1) the measured analyte value at these instances are less than or equal to the 25th percentile of g_array_start, and (2) the dv values at these instances are greater than or equal to the 75th percentile of dv_array_start. If such a subset contains data, then the lowest glucose value in this subset, g_min, and its corresponding instance, is stored. According to the embodiments, the corresponding peak and start candidates for this pair are replaced by g_max and g_min, respectively, if the following criteria are met: (1) g_min, and g_max exist and are finite; (2) g_min occurs prior to g_max; and (3) g_min is less than g_max. Those of skill in the art will also understand that the 75th and 25th percentiles utilized to determine, respectively, g_max and g_min are not meant to be limiting, and that other percentiles (e.g., 80th percentile, 20th percentile) are fully within the scope of the present disclosure.
Further details regarding the refinement of the set of meal start candidates and meal peak response candidates are described in the '748 Publication, the disclosure of which is incorporated herein by reference for all purposes
Referring again to
Although
Some of the embodiments described herein can recommend a user-initiated analyte check in the future based on a current recorded action, if the recorded action corresponds to a historical user action associated with a glycemic risk. As previously described, analyte monitoring systems can provide for a more robust and convenient way of tracking an individual's physiological responses. For example, analyte monitoring systems can include a sensor control device that is worn on an individual's body, and which can continuously collect analyte measurements and transfer data in response to a scan by a reader device (such as by using a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol). One challenge with analyte monitoring systems, however, is that the increased influx of data may lead to user disengagement and, eventually, less frequent use by the individual patient. The embodiments described herein can increase engagement by the individual by suggesting useful instances to perform user-initiated analyte checks (e.g., scans). In this manner, the embodiments may help to mitigate certain glycemic risks, such as, for example, hypoglycemia or hyperglycemia.
Before describing the embodiments, as with many of the previous embodiments, it will be understood by those of skill in the art that any one or more of the steps of the example methods described herein can be stored as software instructions in a non-transitory memory of a sensor control device, a reader device, a remote computer, or a trusted computer system, such as those described with respect to
It will also be appreciated by those of skill in the art that the instructions can be stored in non-transitory memory on a single device (e.g., a reader device) or, in the alternative, can be distributed across multiple discrete devices, which can be located in geographically dispersed locations (e.g., a cloud platform). Likewise, those of skill in the art will recognize that the representations of computing devices in the embodiments disclosed herein, such as those shown in
By way of illustration, the recorded action can be a user utilizing a bolus calculator on a reader device, for example, to correct his or her blood glucose level to a target glucose value or range, where an insulin sensitivity factor is stored in the memory of the reader device. If the current insulin bolus target correction applied by the patient is equivalent to a significantly higher or lower insulin sensitivity factor than what had been previously used in the same meal period of the day (e.g., lunch), a higher risk of hypoglycemia or hyperglycemia is determined.
As another illustration, in some embodiments, trend uncertainty estimates can be used to determine if a trend-based insulin correction recommendation has a significant chance of resulting in hypoglycemia or hyperglycemia. If a trend estimate uncertainty exceeds a predetermined threshold, or if a risk calculation based on the trend uncertainty exceeds a predetermined threshold, then a glycemic risk is determined and a reminder to perform a user-initiated analyte check can be generated at some appropriate time in the future. The risk calculation may generally be dependent on one or more glucose readings and may not be explicitly dependent on a trend estimate.
As yet another example, another recorded action can be a user entering a carbohydrate amount that is abnormally large. In these circumstances, it is possible that the patient is adjusting the carbohydrate amount to account for extra macronutrients (e.g., protein and/or fat), or to account for a larger-than-usual meal. Because the post-prandial glucose excursion may be different from usual, a higher glycemic risk may be determined. As another example, another recorded action can be a user entering bolus insulin information or meal information into a bolus calculator or meal/medication logging application at a time of day that is significantly different from past logs. For example, due to unforeseen circumstances, the patient had a late lunch, or an earlier but smaller lunch. In those circumstances, it may be possible that the timing of the meal or insulin would result in a determination of a higher glycemic risk.
It should be noted, and will be apparent to those of skill in the art, that the above examples of evaluating a historical log to determine if a recorded action corresponds to a historical user action associated with a glycemic risk are merely illustrative and are not intended in any way to limit the scope of the embodiments.
More specifically, in some embodiments, the evaluation of the historical log can include retrieving an insulin sensitivity factor stored in memory, determining if an analyte trend uncertainty estimate exceeds a predetermined analyte trend threshold, or determining if a degree of glycemic risk exceeds a predetermined risk threshold.
At Step 1015, if it is determined that the recorded action does not correspond to a user action associated with a glycemic risk, then method 1000 returns to Step 1005. However, if the recorded action corresponds to a user action associated with a glycemic risk then, at Step 1020, a likely elapsed time until reaching an actionable time period associated with the glycemic risk is calculated. According to one aspect of the embodiments, the elapsed time can be a single instance in the near future (e.g., 65 minutes from now), or a set of instances (e.g., 65, 90, and 100 minutes from now). In some embodiments, after the elapsed time is calculated, the user can be prompted to confirm outputting a notification after the elapsed time.
Referring still to
If an indication of a user-initiated analyte check (e.g., sensor scan) is received before the elapsed time then, at Step 1130, data associated with the user-initiated analyte check is evaluated to determine whether the glycemic risk is still present. According to one aspect of the embodiments, the data associated with the user-initiated analyte check can be data indicative of a monitored analyte level, e.g., a blood glucose level.
If the glycemic risk is no longer present, then method 1100 returns to the beginning, or Step 1105. On the other hand, if the glycemic risk is determined to still be present at Step 1130, then, at Step 1135, the elapsed time until reaching the actionable time period is updated, if necessary. In some embodiments, for example, a second elapsed time until reaching a second actionable time period associated with the glycemic risk can be calculated. After the elapsed time (or second elapsed time) is reached then, at Step 1140, a notification to perform a user-initiated analyte check (or a second user-initiated analyte check) is outputted to the user. As with the previously described embodiments, outputting the notification to the user to perform a user-initiated analyte check can include outputting the notification multiple times at a predetermined interval, or in a single instance. In some embodiments, the output can be in the form of a graphical user interface on the display of a reader device, such as a smart phone, to remind the user to scan the sensor control unit. In other embodiments, the output can be an auditory or vibratory signal that is outputted to a sensor control device, a reader device, a local computer, or a trusted computer system.
Although the embodiments are described in terms of a monitored glucose level and glycemic risk, those of skill in the art will recognize that these embodiments can be utilized for other analytes, such as acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, HbAlc, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose derivatives, glutamine, growth hormones, hormones, ketones, ketone bodies, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin, as well as drugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored, and are fully within the scope of the present disclosure.
For each and every embodiment of a method disclosed herein, systems and devices capable of performing each of those embodiments are covered within the scope of the present disclosure. For example, embodiments of sensor control devices are disclosed and these devices can have one or more analyte sensors, analyte monitoring circuits (e.g., an analog circuit), memories (e.g., for storing instructions), power sources, communication circuits, transmitters, receivers, clocks, counters, times, temperature sensors, processors (e.g., for executing instructions) that can perform any and all method steps or facilitate the execution of any and all method steps. These sensor control device embodiments can be used and can be capable of use to implement those steps performed by a sensor control device from any and all of the methods described herein. Similarly, embodiments of reader devices are disclosed, and these devices can have one or more memories (e.g., for storing instructions), power sources, communication circuits, transmitters, receivers, clocks, counters, times, and processors (e.g., for executing instructions) that can perform any and all method steps or facilitate the execution of any and all method steps. These reader device embodiments can be used and can be capable of use to implement those steps performed by a reader device from any and all of the methods described herein. Embodiments of computer devices and servers are disclosed, and these devices can have one or more memories (e.g., for storing instructions), power sources, communication circuits, transmitters, receivers, clocks, counters, times, and processors (e.g., for executing instructions) that can perform any and all method steps or facilitate the execution of any and all method steps. These reader device embodiments can be used and can be capable of use to implement those steps performed by a reader device from any and all of the methods described herein.
Computer program instructions for carrying out operations in accordance with the described subject matter may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, JavaScript, Smalltalk, C++, C#, Transact-SQL, XML, PHP or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program instructions may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
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 substitutable with those from any other embodiment. If a certain feature, element, component, function, or step is described with respect to only one embodiment, then it should be understood that that feature, element, component, function, or step can be used with every other embodiment described herein unless explicitly stated otherwise. This paragraph therefore serves as antecedent basis and written support for the introduction of claims, at any time, that combine features, elements, components, functions, and steps from different embodiments, or that substitute features, elements, components, functions, and steps from one embodiment with those of another, even if the foregoing description does not explicitly state, in a particular instance, that such combinations or substitutions are possible. It is explicitly acknowledged that express recitation of every possible combination and substitution is overly burdensome, especially given that the permissibility of each and every such combination and substitution will be readily recognized by those of ordinary skill in the art.
To the extent the embodiments disclosed herein include or operate in association with memory, storage, and/or computer readable media, then that memory, storage, and/or computer readable media are non-transitory. Accordingly, to the extent that memory, storage, and/or computer readable media are covered by one or more claims, then that memory, storage, and/or computer readable media is only non-transitory.
As used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
While the embodiments are susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. It should be understood, however, that these embodiments are not to be limited to the particular form disclosed, but to the contrary, these embodiments are to cover all modifications, equivalents, and alternatives falling within the spirit of the disclosure. Furthermore, any features, functions, steps, or elements of the embodiments may be recited in or added to the claims, as well as negative limitations that define the inventive scope of the claims by features, functions, steps, or elements that are not within that scope.
This application is a continuation of International Patent Application No. PCT/US20/12134, filed Jan. 3, 2020, which claims priority to U.S. Provisional Application No. 62/788,310, filed Jan. 4, 2019, both of which are herein expressly incorporated by reference in their entireties for all purposes.
Number | Date | Country | |
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62788310 | Jan 2019 | US |
Number | Date | Country | |
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Parent | PCT/US20/12134 | Jan 2020 | US |
Child | 17363935 | US |