Dynamically adapting analyte rate-of-change determination to a current patient context

Abstract

In an embodiment, a method of dynamically adapting analyte rate-of-change determination to a current patient context includes receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient. The method also includes automatically determining a current context for the patient based on the plurality of analyte measurements. The method also includes automatically configuring an analyte rate-of-change determination based on the current context. The method also includes generating a context-adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

Description

DYNAMICALLY ADAPTING ANALYTE RATE-OF-CHANGE DETERMINATION TO A CURRENT PATIENT CONTEXTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 701,061 filed September 30, 2024, which application is hereby expressly incorporated by reference herein in its entirety as if fully set forth below and for all applicable purposes.INTRODUCTION

[0002] Diabetes is a metabolic condition affecting hundreds of millions of people. For these people, monitoring blood glucose levels and regulating those levels to be within an acceptable range is important not only to mitigate long-term issues such as heart disease and vision loss, but also to avoid the effects of hyperglycemia and hypoglycemia. Maintaining blood glucose levels within an acceptable range can be challenging, as glucose levels are almost constantly changing over time and in response to everyday events, such as eating or exercising. Advances in medical technologies have enabled development of various systems for monitoring blood glucose, including continuous glucose monitoring (CGM) systems, which measure and record glucose concentrations in substantially real-time. CGM systems are important tools for users of these systems to ensure that measured glucose values are within the acceptable range.SUMMARY

[0003] In an embodiment, one general aspect includes a method of dynamically adapting analyte rate-of-change determination to a current patient context. The method includes receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient. The method also includes automatically determining a current context for the patient based on the plurality of analyte measurements. The method also includes automatically configuring an analyte rate-of-change determination based on the current context. The method also includes generating a context-adapted rate-of-change measurement based on the automatically configured analyte rate- of-change determination and the plurality of analyte measurements.

[0004] In an embodiment, another general aspect includes a system for dynamically adapting analyte rate-of-change determination to a current patient context. The system includes a continuousanalyte monitoring system configured to generate measurements associated with an analyte level of a patient, one or more memories including executable instructions, and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories. The one or more processors are configured to execute the executable instructions to receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient. The one or more processors are further configured to execute the executable instructions to automatically determine a current context for the patient based on the plurality of analyte measurements and to automatically configure an analyte rate-of-change determination based on the current context. . The one or more processors are further configured to execute the executable instructions to generate a context- adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

[0005] In an embodiment, another general aspect includes a computer-program product that may include a non-transitory computer-usable medium having computer-readable program code embodied therein. The computer-program product is adapted to be executed to implement a method. The method includes receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient. The method also includes automatically determining a current context for the patient based on the plurality of analyte measurements. The method also includes automatically configuring an analyte rate-of-change determination based on the current context. The method also includes generating a context- adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] FIG. 1A illustrates an example of a therapy management system, in accordance with certain embodiments.

[0007] FIG. IB illustrates an example continuous analyte monitoring system including an example continuous analyte sensor(s) with sensor electronics, in accordance with certain embodiments.

[0008] FIG. 2 illustrates example inputs and example outputs that are generated based on the inputs, in accordance with certain embodiments.

[0009] FIG. 3 illustrates an example of a process for dynamically adapting an analyte ratc-of-changc determination to a current patient context, in accordance with certain embodiments.

[0010] FIG. 4 illustrates an example of a process for dynamically determining and using context- adapted rate-of-change coefficients, in accordance with certain embodiments.

[0011] FIG. 5 illustrates an example of a process for dynamically determining and using context-adapted sampling rates, in accordance with certain embodiments.

[0012] FIG. 6 illustrates an example of a process 600 for dynamically determining and using context-adapted window sizes, in accordance with certain embodiments.

[0013] FIG. 7 illustrates an example of using a lookup table to determine context-adapted rate-of-change settings, in accordance with certain embodiments.

[0014] FIG. 8 illustrates an example of using an adaptive linear estimator to determine context-adapted rate-of-change settings, in accordance with certain embodiments.

[0015] FIG. 9 illustrates an example of using a neural network, in accordance with certain embodiments.

[0016] FIG. 10 illustrates an example of using a linear combination of multiple methods to determine a context- adapted rate-of-change measurement, in accordance with certain embodiments.

[0017] FIG. 11 illustrates examples of different adaptive trend arrows that may be presented for different value ranges of a context- adapted rate-of-change measurement, in accordance with certain embodiments.

[0018] FIG. 12 illustrates example results of performing a context- adapted rate-of-change measurement, in accordance with certain embodiments.

[0019] FIG. 13 illustrates an example interface displaying results of performing a context- adapted rate-of-change measurement, in accordance with certain embodiments.

[0020] FIG. 14 is a block diagram depicting a computer system configured for dynamically adapting an analyte rate-of-change determination to a current patient context, in accordance with certain embodiments.DETAILED DESCRIPTION

[0021] In a continuous analyte monitoring (CAM) system, an analyte sensor measures analyte levels of a patient and communicates the raw sensor measurements to a transmitter, which can then transmit corresponding analyte values to the patient's device (e.g., a display device such as a mobile phone). The patient can use their device to monitor the health and collect health data (e.g., analyte data). An example of a CAM system is a continuous glucose monitoring (CGM) system. For illustrative purposes, examples will be periodically described herein relative to CGM systems.

[0022] For example, patients with diabetes, as well as other patients, may use a CGM system to continuously measure their glucose levels. Such patients often monitor their glucose levels via graphical information shown on a smartphone, smartwatch, or other display device. The graphical information can include, for example, a trend arrow. The trend arrow may be color-coded, and the angle at which the trend arrow is oriented may correspond to an actual rate of change (ROC) of the patient’s glucose levels. For example, a more horizontal arrow may indicate a low ROC, while a steeply sloping arrow may indicate a high ROC.

[0023] Currently, users typically wait a long period of time (e.g., 15 minutes or more) to see if corrective actions (e.g., ingestion of glucose or administration of insulin) are having an intended effect on their trending glucose value (e.g., shown via the trend arrow). This is due to the fact that, in current implementations, the trend arrow is determined in the same way, regardless of current context. For example, the trend arrow may be determined from a glucose ROC using fixed coefficients and a fixed time window and sampling rate.

[0024] The above-noted waiting period can cause anxiety for patients and supporters when their glucose values indicate a critical situation (e.g., urgent low, urgent low soon, rapid rise or fall, etc.), even if the patient has already taken measures to resolve the critical situation, for example, by ingesting glucose or administering insulin. This anxiety, combined with the lack of immediate feedback regarding the efficacy of the corrective action, may result in dangerous overcorrection (e.g., further administration of insulin that overcorrects a hyperglycemic situation to create a severe risk of hypoglycemia).

[0025] Furthermore, in general, traditional trend arrows do not adequately represent the dynamic, physical state of the patient in critical situations, such as when the patient is experiencing hypoglycemia or hyperglycemia, and the patient has taken decisive action to rapidly move glucoselevels in the opposite direction. Since there may he diametrically opposite trends within the same window, providing actionable information to the patient in such a situation is a technical challenge.

[0026] In response to the above problems, a therapy management system may be provided that dynamically adapts an analyte ROC determination to a current context for the patient. In certain aspects, the therapy management system can dynamically configure how the analyte ROC determination occurs based on a current context for the patient, such as the existence of dangerously high or low analyte levels (e.g., levels that satisfy hyperglycemic or hypoglycemic thresholds in the case of glucose levels). More particularly, in certain aspects, the therapy management system can automatically determine context-adapted ROC settings that configurably cause more weight or consideration to be given to more recent analyte measurements. Based on the context- adapted ROC settings, the therapy management system can generate a context-adapted ROC measurement for a current time.

[0027] In certain aspects, the therapy management system can present, for example, an adaptive trend arrow, or other graphical information, based on the context-adapted ROC measurement. In certain aspects, the adaptive trend arrow better reflects the patient’s physical state in the current context due to the greater weight or consideration given to more recent analyte measurements. More specifically, in certain aspects, by emphasizing or putting more weight on values closer to a current time, the effects of corrective actions taken in response to that critical time point may be weighted higher, which may result in a quicker adjustment or correction of the adaptive trend arrow during those critical time points, as compared to the traditional trend arrows discussed above.

[0028] Advantageously, in certain implementations, the approaches described herein can reduce a stress level of patients by more rapidly assuring the patients that the corrective actions they take are working. In addition, or alternatively, the approaches described herein can increase a level of trust in an analyte monitoring application, such as a CGM application, which may improve a responsiveness of a patient to suggestions made by the CGM system, thereby improving a health of the patient. Furthermore, in various aspects, reducing a stress level of the patient may also positively impact the analyte levels (e.g., blood glucose levels) of the patient.

[0029] Further, by dynamically adjusting an amount / type of processing that is performed by a computing device (such as a mobile computing device such as a smartphone or smartwatch) basedon a determined context, higher-resolution analysis (e.g., utilizing a dynamically adapted analyte ROC determination methodology) may be performed by the computing device only when needed (e.g., when a predetermined “urgent” context is determined to currently exist), and a lower- resolution analysis (such as a predetermined / static analyte ROC determination methodology) may be otherwise performed by the computing device as a default (e.g., when a predetermined “urgent” context is determined to currently not exist). This may improve a performance of the computing device implementing the software, and may also improve a battery life of the computing device as well (e.g., by implementing a higher-resolution, more resource-intensive analysis only when needed in a determined “urgent” context).

[0030] In addition, or alternatively, the approaches described herein can improve the meaningfulness of real-time health alerts by adapting graphical information, such as trend arrows, to a current context of the patient. This increases a likelihood that that the patient monitors whatever corrective action is taken (e.g., glucose ingestion or insulin administration), takes additional corrective action if needed, and avoids overcorrection due to anxiety, thereby improving their analyte time-in-range (e.g., glucose time-in-range) and overall health. Furthermore, such realtime health alerts can better reflect the dynamic, physical state of the patient in critical situations, such as when the patient is experiencing critical situations (e.g., hypoglycemia or hyperglycemia), after the patient has already taken decisive action to rapidly move analyte levels (e.g., glucose levels) in the opposite direction.

[0031] In addition, or alternatively, the approaches described herein can inform treatment. For example, in some aspects, if the therapy management system, as part of its context determination, has identified an urgent situation for the patient and, furthermore, has identified, via a context- adapted ROC measurement, that the urgent situation is worsening, the therapy management system can recommend that the patient take additional action (e.g., administer insulin for correction of hyperglycemia, ingest glucose for correction of hypoglycemia, etc.). In some cases, the therapy management system can automatically initiate treatment, or command initiation of such treatment. For example, in some aspects, if the therapy management system has identified a severe hyperglycemic situation, and the context-adapted ROC measurement indicates a worsening situation, the therapy management engine can command an insulin pump to administer insulin. Other examples will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0032] Although certain examples are periodically described below in relation to context- adapted ROC determinations for glucose levels, the embodiments herein arc likewise applicable and useful for context-adapted ROC determinations for other analyte levels (e.g., ketone levels, lactate levels, etc.).

[0033] As used herein, the term “continuous” analyte monitoring refers to monitoring one or more analytes in a fully continuous, semi-continuous, periodic manner, which results in a data stream of analyte values over time. A data stream of analyte values over time is what allows for meaningful data and insights to be derived using the algorithms described herein for dynamically adapting an analyte ROC determination to a current patient context. In other words, single pointin-time measurements collected as a result of a patient visiting their health care professional every few months results in sporadic data points (e.g., that are, at best, months apart in timing) that cannot form the basis of any meaningful data or insight to be derived. As such, without the continuous analyte monitoring system of the embodiments herein, it is simply impossible to continuously adapt an analyte ROC determination to a current patient context, as described herein.

[0034] Further, the data stream of analyte values collected over time, with the continuous analyte monitoring system presented herein, include real-time analyte values, which allows for deriving meaningful data and insight in real-time using the systems and algorithms described herein. The derived real-time data and insight in turn allows for providing real-time HT suggestions to prevent hypoglycemia. Real-time analyte values herein refer to analyte values that become available and actionable within seconds or minutes of being produced as a result of at least one sensor electronics module of the continuous analyte monitoring system (1) converting sensor current(s) (i.e., analog electrical signals) generated by the continuous analyte sensor(s) into sensor count values, (2) calibrating the count values to generate at least glucose and / or other analyte concentration values using calibration techniques described herein to account for the sensitivity of the continuous analyte sensor(s), and (3) transmitting measured glucose and / or other analyte concentration data, including glucose and / or other analyte concentration values, to a display device via wireless connection.

[0035] For example, the at least one sensor electronics module may be configured to sample the analog electrical signals at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, etc., and to transmit themeasured glucose and / or other analyte concentration data to a display device at a particular transmission period (or rate), which may be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, etc.

[0036] The real-time analyte data that is continuously generated by the continuous analyte monitoring system described herein, therefore, allows the therapy management system herein to dynamically adapt an analyte ROC determination to a current patient context, which is technically impossible to perform using existing or conventional techniques or systems. Further, because of the real-time nature of this data, it is also humanly impossible to continuously process a real-time data stream of analyte values over time to derive meaningful data and insight using the algorithms and systems described herein to dynamically adapt an analyte ROC determination to a current patient context. In other words, deriving meaningful data and insight from a stream of real-time data that is continuously generated, processed, calibrated, and analyzed, using the algorithms and systems described herein, is not a task that can be mentally performed. For example, executing the algorithms described in relation to FIGS. 3-10, in real-time and on a continuous basis, which would involve using a stream of real-time data that is continuously generated by a patient’s continuous analyte monitoring system and / or significantly large amount of population data (e.g., hundreds or thousands of data points for each one of thousands or millions of patients in the patient population) is not a task that can be mentally performed, especially in real-time at times.

[0037] Further, certain embodiments herein are directed to a technical solution to a technical problem associated with continuous analyte monitoring (CAM) systems. In particular, each CAM system that is manufactured by a CAM system manufacturer might perform slightly differently. As such, there might be inconsistencies between sensors and the measurements they generate once in use. Accordingly, certain embodiments herein are directed to determining the performance of a CAM system during a manufacturing calibration process (in vitro), which includes quantifying certain sensor operating parameters, such as a calibration slope (also known as calibration sensitivity), a calibration baseline, etc.

[0038] Generally, calibration sensitivity refers to the amount of electrical current produced by an analyte sensor of a CAM system when immersed in a predetermined amount of a measured analyte. The amount of electrical current may be expressed in units of picoAmps (pA) or counts. The amount of measured analyte may be expressed as a concentration level in units of milligramsper deciliter (mg / dL), and the calibration sensitivity may be expressed in units of pA / (mg / dL) or counts / (mg / dL). The calibration baseline refers to the amount of electrical current produced by the analyte sensor when no analyte is detected, and may be expressed in units of pA or counts.

[0039] The calibration sensitivity, calibration baseline, and other information related to the sensitivity profile for the CAM system may be programmed into the sensor electronics module of the CAM system during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, the calibration slope (calibration sensitivity) may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are programmed into the sensor electronics module and used to convert the analyte sensor electrical signals into measured analyte concentration levels.

[0040] In certain embodiments, during in vivo use, the sensor electronics module of a CAM system samples the analog electrical signals produced by the analyte sensor to generate analyte sensor count values, and then determines the measured analyte concentration levels based on the analyte sensor count values, the initial in vivo sensitivity (Mo), and the final in vivo sensitivity (Mf). For example, measured analyte concentration levels may be determined using a sensitivity function M(t) that is based on the initial in vivo sensitivity (Mo) and the final in vivo sensitivity (Mr). The sensitivity function M(t) may expressed in several different ways, such as a simple correction factor that is not dependent on elapsed time (ti) of in vivo use, a linear relationship between sensitivity and time (ti), an exponential relationship between sensitivity and time (ti), etc. Equation 1 presents one technique for determining a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time tpACL = count / M(ti) Eq. 1A calibration baseline (baseline) may also be used to determine a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time ti, and Equation 2 presents one technique:ACL = (count - baseline) I M(ti) Eq. 2

[0041] FIG. 1A illustrates an example of a therapy management system 100 for dynamically adapting an analyte ROC determination to a current patient context, in accordance with certain embodiments of the disclosure. The therapy management system 100 may be utilized forgenerating and presenting information related to user health, for example, using various user interfaces associated with system 100. Each user of system 100, such as user 102, may interact with a mobile health application, such as mobile health application (“application”) 106 (e.g., a diabetes intervention application that provides therapy management guidance), and / or a health monitoring device, such as a CAM system 104 (e.g., a CGM system). User 102, in certain embodiments, may be the patient or, in some cases, the patient’s caregiver. In the embodiments described herein, the user is assumed to be the patient for simplicity only, but is not so limited. As shown, system 100 may include a CAM system 104, a display device 107 that executes application 106, a therapy management engine 112, and a user database 110.

[0042] CAM system 104 may be configured to generate time-series data, such as analyte measurements (e.g., sensor data), for the user 102, e.g., on a continuous basis, and transmit the analyte measurements to the display device 107 for use by application 106. In some embodiments, the CAM system 104 may transmit the analyte measurements to the display device 107 through a wireless connection (e.g., Bluetooth connection). In certain embodiments, display device 107 is a smart phone. However, in certain embodiments, display device 107 may instead be any other type of computing device such as a laptop computer, a smartwatch, a tablet, or any other computing device capable of executing application 106.

[0043] Note that, while in certain examples the CAM system 104 is assumed to be a CGM system, CAM system 104 may operate to monitor one or more additional or alternative analytes. As discussed, the term “analyte” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a substance or chemical constituent in the body or a biological sample (e.g., bodily fluids, including, blood, serum, plasma, interstitial fluid, cerebral spinal fluid, lymph fluid, ocular fluid, saliva, oral fluid, urine, excretions, or exudates).

[0044] Analytes can include naturally occurring substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte measured and used by the devices and methods described herein may include albumin, alkaline phosphatase, alanine transaminase, aspartate aminotransferase, bilirubin, blood urea nitrogen, calcium, CO2, chloride, creatinine, glucose, gamma-glutamyl transpeptidase, hematocrit, lactate, lactate dehydrogenase, magnesium,oxygen, pH, phosphorus, potassium, ketones, sodium, total protein, uric acid, metabolic markers, and / or drugs.

[0045] Other analytes are contemplated as well, including but not limited to acetaminophen, dopamine, ephedrine, terbutaline, ascorbate, uric acid, oxygen, d-amino acid oxidase, plasma amine oxidase, xanthine oxidase, NADPH oxidase, alcohol oxidase, alcohol dehydrogenase, pyruvate dehydrogenase, diols, Ros, NO, bilirubin, cholesterol, triglycerides, gentisic acid, ibuprophen, L-Dopa, methyl dopa, salicylates, tetracycline, tolazamide, tolbutamide, acarboxyprothrombin; acylcamitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; camosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1 -0 hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1- antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, betathalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free 0-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose / gal- 1 -phosphate; galactose- 1 -phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase 1; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-l, 0); lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's diseasevirus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodcnalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi / rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain embodiments.

[0046] The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte can be introduced into the body, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbituates, methaqualone, tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), histamine, Advanced Glycation End Products (AGEs) and 5 -hydroxy indoleacetic acid (FHIAA).

[0047] Application 106 may be a mobile health application that is configured to receive and analyze timc-scrics data, including analyte measurements, from the CAM system 104 and / or other devices, as described in greater detail relative to FIGS. IB and 2. In some embodiments, application 106 may transmit analyte measurements received from the CAM system 104 to a user database 110 (and / or the therapy management engine 112), and the user database 110 (and / or the therapy management engine 112) may store the analyte measurements in a user profile 118 of user 102 for processing and analysis, for example, by the therapy management engine 112. In some embodiments, application 106 may store the analyte measurements in a user profile 118 of user 102 locally for processing and analysis, for example, by the therapy management engine 112.

[0048] In certain embodiments, application 106 is configured to provide various interfaces for receiving, from the user 102, data of the type discussed previously. In an example, the application 106 can provide a user interface that enables the user 102 to graphically respond to the therapy management engine 112 to confirm HTs (e.g., suggestions of CHO dosages) from the therapy management engine 112 for user 102, and / or that enables the application 106 to perform other functions such as indicating a CHO dosage.

[0049] In certain embodiments, therapy management engine 112 refers to a set of software instructions with one or more software modules, including a data analysis module (DAM) 111. In some embodiments, therapy management engine 112 executes entirely on one or more computing devices in a private or a public cloud. In some other embodiments, therapy management engine 112 executes partially on one or more local devices, such as display device 107 (e.g., via application 106) and / or CAM system 104, and partially on one or more computing devices in a private or a public cloud. In some other embodiments, therapy management engine 112 executes entirely on one or more local devices, such as display device 107 (e.g., via application 106) and / or CAM system 104. In certain embodiments, therapy management engine 112, via the application 106, may provide interfaces for suggesting and confirming recommended CHO dosages.

[0050] In certain embodiments, DAM 111 of therapy management engine 112 may be configured to receive and / or process a set of inputs 127 (described in more detail below) (also referred to herein as “input data”) to determine one or more outputs 130 (also referred to herein as “metrics data”). Inputs 127 may be stored in the user profile 118 in the user database 110. DAM 111 can fetch inputs 127 from the user database 110 and compute a plurality of outputs 130 whichcan then be stored as application data 126 in the user profile 118. Such outputs 130 may include health-related metrics.

[0051] In certain embodiments, application 106 is configured to take as input information relating to user 102 and store the information in a user profile 118 for user 102 in user database 110. For example, application 106 may obtain and record user 102 ’s demographic info 119, disease progression info 121, and / or medication info 122 in user profile 118. In certain embodiments, demographic info 119 may include one or more of the user’s age, body mass index (BM1), ethnicity, gender, etc. In certain embodiments, disease progression info 121 may include information about the user 102’ s disease, such as, for diabetes, whether the user is Type I, Type II, pre-diabetes, or whether the user has gestational diabetes. In certain embodiments, disease progression info 121 also includes the length of time since diagnosis, the level of disease control, level of compliance with disease management therapy, predicted pancreatic function, other types of diagnosis (e.g., heart disease, obesity) or measures of health (e.g., heart rate, exercise, stress, sleep, etc.), and / or the like. In certain embodiments, medication info 122 may include information about the amount and type of medication taken by user 102, such as insulin or non-insulin diabetes medications and / or non-diabetes medication taken by user 102.

[0052] In certain embodiments, application 106 may obtain demographic info 119, disease progression info 121, and / or medication info 122 from the user 102 in the form of user input or from other sources. In certain embodiments, as some of this information changes, application 106 may receive updates from the user 102 or from other sources. In certain embodiments, user profile 118 associated with the user 102, as well as other user profiles associated with other users are stored in a user database 110, which is accessible to application 106, as well as to the therapy management engine 112, over one or more networks (not shown).

[0053] In certain embodiments, application 106 collects inputs 127 through user 102 input and / or a plurality of other sources, including CAM system 104, other applications running on display device 107, and / or one or more other sensors and devices. In certain embodiments, such sensors and devices include one or more of, but are not limited to, an insulin pump, other types of analyte sensors, sensors or devices provided by display device 107 (e.g., accelerometer, camera, global positioning system (GPS), heart rate monitor, etc.) or other user accessories (e.g., a smartwatch), or any other sensors or devices that provide relevant information about the user 102.In certain embodiments, user profile 118 also stores application configuration information indicating the current configuration of application 106, including its features and settings.

[0054] User database 110, in some embodiments, refers to a storage server that may operate in a public or private cloud. User database 110 may be implemented as any type of data store, such as relational databases, non-relational databases, key-value data stores, file systems including hierarchical file systems, and the like. In some exemplary implementations, user database 110 is distributed. For example, user database 110 may comprise a plurality of persistent storage devices, which are distributed. Furthermore, user database 110 may be replicated so that the storage devices are geographically dispersed.

[0055] User database 110 may include other user profiles 118 associated with a plurality of other users served by therapy management system 100. More particularly, similar to the operations performed with respect to the user 102, the operations performed with respect to these other users may utilize an analyte monitoring system, such as CAM system 104, and also interact with the same application 106, copies of which execute on the respective display devices of the other users 102. For such users, user profiles 118 are similarly created and stored in user database 110.

[0056] FIG. IB is a diagram 150 conceptually illustrating an example CAM system 104 including example continuous analyte sensor(s) with sensor electronics, in accordance with certain aspects of the present disclosure. For example, CAM system 104 may be configured to continuously monitor one or more analytes of a patient, in accordance with certain aspects of the present disclosure.

[0057] CAM system 104 in the illustrated embodiment includes sensor electronics module 138 and one or more continuous analyte sensor(s) 140 (individually referred to herein as continuous analyte sensor 140 and collectively referred to herein as continuous analyte sensors 140) associated with sensor electronics module 138. Sensor electronics module 138 may be in wireless communication (e.g., directly or indirectly) with one or more of display devices 107a, 107b, 107c, and 107d. In certain embodiments, sensor electronics module 138 may also be in wireless communication (e.g., directly or indirectly) with one or more medical devices, such as medical devices 108 (individually referred to herein as medical device 108 and collectively referred to herein as medical devices 108), and / or one or more other non-analyte sensors 142 (individuallyreferred to herein as non-analyte sensor 142 and collectively referred to herein as non-analyte sensor 142).

[0058] In certain embodiments, a continuous analyte sensor 140 may comprise one or more sensors for detecting and / or measuring analyte(s). The continuous analyte sensor 140 may be a multi-analyte sensor configured to continuously measure two or more analytes or a single analyte sensor configured to continuously measure a single analyte as a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, and / or an intravascular device. In certain embodiments, the continuous analyte sensor 140 may be configured to continuously measure analyte levels of a patient using one or more techniques, such as enzymatic techniques, chemical techniques, physical techniques, electrochemical techniques, spectrophotometric techniques, polarimetric techniques, calorimetric techniques, iontophoretic techniques, radiometric techniques, immunochemical techniques, and the like. The term “continuous,” as used herein, can mean fully continuous, semi-continuous, periodic, etc. In certain aspects, the continuous analyte sensor 140 provides a data stream indicative of the concentration of one or more analytes in the patient. The data stream may include raw data signals, which are then converted into a calibrated and / or filtered data stream used to provide estimated analyte value(s) to the patient.

[0059] In certain embodiments, the continuous analyte sensor 140 may be a multi-analyte sensor, configured to continuously measure multiple analytes in a patient’s body. For example, in certain embodiments, the continuous multi-analyte sensor 140 may be a single sensor configured to measure lactate, glucose, ketones (e.g., 3-beta-hydroxybutyrate, acetoacetate, acetone, etc.), glycerol, and / or free fatty acids in the patient’s body.

[0060] In certain embodiments, one or more multi-analyte sensors may be used in combination with one or more single analyte sensors. As an illustrative example, a multi-analyte sensor may be configured to continuously measure lactate and glucose and may, in some cases, be used in combination with an analyte sensor configured to measure only ketones or only potassium. Information from each of the multi-analyte sensor(s) and single analyte sensor(s) may be combined to provide therapy management support using methods described herein. In further embodiments, other non-contact and or periodic or semi-continuous, but temporally limited, measurements for physiological information may be integrated into the system such as by including weight scaleinformation or non-contact heart rate monitoring from a sensor pad under the patient while in a chair or bed, through an infra-red camera detecting temperature and / or blood flow patterns of the patient, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0061] In certain embodiments, the continuous analyte sensor(s) 140 may comprise a percutaneous wire that has a proximal portion coupled to the sensor electronics module 138 and a distal portion with several electrodes, such as a measurement electrode and a reference electrode. The measurement (or working) electrode may be coated, covered, treated, embedded, etc., with one or more chemical molecules that react with a particular analyte, and the reference electrode may provide a reference electrical voltage. The measurement electrode may generate the analog electrical signal, which is conveyed along a conductor that extends from the measurement electrode to the proximal portion of the percutaneous wire that is coupled to the sensor electronics module 138. After the continuous analyte monitoring system 104 has been applied to epidermis of the patient, continuous analyte sensor(s) 140 penetrates the epidermis, and the distal portion extends into the dermis and / or subcutaneous tissue under epidermis. Other configurations of continuous analyte sensor(s) 140 may also be used, such as a multi-analyte sensor that includes multiple measurement electrodes, each generating an analog electrical signal that represents the concentration levels of a particular analyte.

[0062] Generally, a single-analyte sensor generates an analog electrical signal that is proportional to the concentration level of a particular analyte. Similarly, each multi-analyte sensor generates multiple analog electrical signals, and each analog electrical signal is proportional to the concentration level of a particular analyte. As an illustrative example, continuous analyte sensor 140 may include a single-analyte sensor configured to measure lactate concentration levels, and another single-analyte sensor configured to measure glucose concentration levels of the patient. As another illustrative example, continuous analyte sensor(s) 140 may include a single-analyte sensor configured to measure glucose concentration levels, and one or more multi-analyte sensors configured to measure lactate concentration levels, ketone concentration levels, creatinine concentration levels, etc. As yet another illustrative example, continuous analyte sensor(s) 140 may include a multi-analyte sensor configured to measure lactate concentration levels, glucose concentration levels, ketone concentration levels, creatinine concentration levels, etc. Accordingly, continuous analyte sensor(s) 140 is configured to generate at least one analogelectrical signal that is proportional to the concentration level of a particular analyte, and sensor electronics module 138 is configured to convert the analog electrical signal into an analyte sensor count values, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 140 to generate measured analyte concentration levels, and transmit the measured analyte concentration level data, including the measured analyte concentration levels, to a display device, such as display devices 107b, 107c, and / or 107d, via a wireless connection. For example, sensor electronics module 138 may be configured to sample the analog electrical signal at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, etc., and to transmit the measured analyte concentration data to the display device at a particular transmission period (or rate), which may be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, 30 minutes, at the conclusion of the wear period, etc. Depending on the sampling and transmission periods, the measured analyte concentration data transmitted to the display device include at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0063] In certain embodiments, continuous analyte sensor(s) 140 may incorporate a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 138, which may be used to correct the analog electrical signal or the measured analyte data for temperature. In other embodiments, the thermocouple may be incorporated into the sensor electronics module 138 above the adhesive pad, or, alternatively, the thermocouple may contact the epidermis of the patient through openings in the adhesive pad.

[0064] In certain embodiments, the sensor electronics module 138 includes, inter alia, processor 133, storage element or memory 134, wireless transmitter / receiver (transceiver) 136, one or more antennas coupled to wireless transceiver 136, analog electrical signal processing circuitry, analog to-digital (A / D) signal processing circuitry, digital signal processing circuitry, a power source for continuous analyte sensor(s) 140 (such as a potentiostat), etc.

[0065] Processor 133 may be a general-purpose or application- specific microprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., that executes instructions to perform control, computation, input / output, etc. functions for the sensor electronics module 138. Processor 133 may include a single integrated circuit, such as a microprocessing device, or multiple integrated circuit devices and / or circuit boards working in cooperation to accomplish the appropriate functionality. In certain embodiments, processor 133, memory 134, wireless transceiver 136, the A / D signal processing circuitry, and the digital signal processing circuitry may be combined into a system-on-chip (SoC).

[0066] Generally, processor 133 may be configured to sample the analog electrical signal using the A / D signal processing circuitry at regular’ intervals (such as the sampling instant or period) to generate analyte sensor count values based on the analog electrical signals produced by the continuous analyte sensor(s) 140, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 140 to generate measured analyte concentration levels, and generate measured analyte data from the measured analyte concentration levels, generate sensor data packages that include, inter alia, the measured analyte concentration level data. Processor 133 may store the measured analyte concentration level data in memory 134, and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by wireless transceiver 136 to a display device, such as display devices 107b, 107c, 107d, and / or 107a. Processor 133 may also add additional data to the sensor data packages, such as supplemental sensor information that includes a sensor identifier, a sensor status, temperatures that correspond to the measured analyte data, etc. The sensor data packages are then wirelessly transmitted over a wireless connection to the display device. In certain embodiments, the wireless connection is a Bluetooth or Bluetooth Low Energy (BLE) connection. In such embodiments, the sensor data packages are transmitted in the form of Bluetooth or BLE data packets to the display device

[0067] In various embodiments, memory 134 may include volatile and nonvolatile medium. For example, memory 134 may include combinations of random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), read only memory (ROM), flash memory, cache memory, and / or any other type of non-transitory computer-readable medium. Memory 134 may store one or more CAM system applications, modules, instruction sets, etc. for execution by processor 133, such as instructions to generate measured analyte data from the analyte sensor count values, etc.

[0068] Memory 134 may also store certain sensor operating parameters 135, such as a calibration slope (or calibration sensitivity), a calibration baseline, etc. In particular, the calibration sensitivity, calibration baseline, and other information related to the sensitivity profilefor the sensor electronics module 138 may be programmed into the sensor electronics module 138 during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, as discussed above, the calibration slope may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are stored in memory 134 and used to convert the analyte sensor electrical signals into measured analyte concentration levels. In certain embodiments, calibration sensitivity (Mcc) 146 and / or calibration baseline 147 may be stored in memory 134.

[0069] In certain embodiments, sensor electronics module 138 includes electronic circuitry associated with measuring and processing the continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the sensor data. Sensor electronics module 138 can be physically connected to continuous analyte sensor(s) 140 and can be integral with (non-releasably attached to) or releasably attachable to continuous analyte sensor(s) 140. Sensor electronics module 138 may include hardware, firmware, and / or software that enable measurement of levels of analyte(s) via continuous analyte sensor(s) 140. For example, sensor electronics module 138 can include a potentiostat, a power source for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to, e.g., one or more display devices. Electronics can be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0070] Display devices 107b, 107c, 107d, and / or 107a are configured for displaying displayable sensor data, including analyte data, which may be transmitted by sensor electronics module 138. Each of display devices 107b, 107c, 107d, or 107a may include a display such as a touchscreen display 109b, 109c, 109d, and / or 109a for displaying sensor data to a patient and / or for receiving inputs from the patient. For example, a graphical user interface (GUI) may be presented to the patient for such purposes. In certain embodiments, the display devices may include other types of user interfaces such as a voice user interface instead of, or in addition to, a touchscreen display for communicating sensor data to the patient of the display device and / or for receiving patient inputs. Display devices 107a, 107b, 107c, and 107d may be examples of display device 107 illustrated in FIG. 1 used to display sensor data to a patient of the system of FIG. 1 and / or to receive input from the patient.

[0071] In certain embodiments, one, some, or all of the display devices are configured to display or otherwise communicate (e.g., verbalize) the sensor data as it is communicated from the sensor electronics module (e.g., in a customized data package that is transmitted to display devices based on their respective preferences), without any additional prospective processing required for calibration and real-time display of the sensor data.

[0072] The plurality of display devices may include a custom display device specially designed for displaying certain types of display able sensor data associated with analyte data received from sensor electronics module. In certain embodiments, the plurality of display devices may be configured for providing alerts / alarms based on the displayable sensor data. Display device 107b is an example of such a custom device. In certain embodiments, one of the plurality of display devices is a smartphone, such as display device 107c which represents a mobile phone, using a commercially available operating system (OS), and configured to display a graphical representation of the continuous sensor data (e.g., including current and historic data). Other display devices can include other hand-held devices, such as display device 107d which represents a tablet, display device 107a which represents a smart watch or fitness tracker, medical device 108 (e.g., an insulin delivery device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0073] Because different display devices provide different user interfaces, content of the data packages (e.g., amount, format, and / or type of data to be displayed, alarms, and the like) can be customized (e.g., programmed differently by the manufacture and / or by an end user, such as the patient) for each particular display device. Accordingly, in certain embodiments, a plurality of different display devices can be in direct wireless communication with a sensor electronics module (e.g., such as an on-skin sensor electronics module 138 that is physically connected to continuous analyte sensor(s) 140) during a sensor session to enable a plurality of different types and / or levels of display and / or functionality associated with the display able sensor data.

[0074] As mentioned, sensor electronics module 138 may be in communication with a medical device 108. Medical device 108 may be a passive device in some example embodiments of the disclosure. For example, medical device 108 may be an insulin pump for administering insulin to a patient. For a variety of reasons, it may be desirable for such an insulin pump to receive and track lactate, glucose, ketones, glycerol and free fatty acid values transmitted from CAM systems104, where continuous analyte sensor 140 is configured to measure lactate, glucose, ketones, glycerol, and / or free fatty acids.

[0075] Further, as mentioned, sensor electronics module 138 may also be in communication with other non-analyte sensors 142. Non-analyte sensors 142 may include, but are not limited to, an altimeter sensor, an accelerometer sensor, a global positioning system (GPS) sensor, a temperature sensor, a respiration rate sensor, etc. Non-analyte sensors 142 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, caloric intake monitors, indirect calorimetry devices, continuous positive airway pressure machines, and medicament delivery devices. One or more of these non-analyte sensors 142 may provide data to therapy management engine 1 12 described further below. In some aspects, a patient may manually provide some of the data for processing by the therapy management engine 112 of FIG. 1.

[0076] In certain embodiments, non-analyte sensors 142 may further include sensors for measuring skin temperature, core temperature, sweat rate, and / or sweat composition.

[0077] In certain embodiments, the non-analyte sensors 142 may be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 140. As an illustrative example, a non-analyte sensor, e.g., a temperature sensor, may be combined with a continuous glucose sensor 140 to form a glucose / temperature sensor used to transmit sensor data to the sensor electronics module 138 using common communication circuitry. As another illustrative example, a non-analyte sensor, e.g., a temperature sensor, may be combined with a multi-analyte sensor 140 configured to measure lactate and glucose to form a lactate / glucose / temperature sensor used to transmit sensor data to the sensor electronics module 138 using common communication circuitry.

[0078] In certain embodiments, a wireless access point (WAP) may be used to couple one or more of CAM system 104, the plurality of display devices, medical device(s) 108, and / or non- analyte sensor(s) 142 to one another. For example, such WAP may provide Wi-Fi and / or cellular connectivity among these devices. Near Field Communication (NFC) and or Bluetooth may also be used among devices depicted in diagram 150 of FIG. IB.

[0079] FIG. 2 illustrates example inputs and example metrics that are generated based on the inputs in accordance with certain embodiments of the disclosure. In particular, FIG. 2 illustrates example inputs 127 on the left, application 106 and therapy management engine 112, with DAM111 , in the middle, and example outputs 130 on the right. In certain embodiments, application 106 may obtain inputs 127, in the form of time-series data, through one or more channels (e.g., continuous analyte sensor(s) 140, non-analyte sensor(s) 142, various applications executing on display device 107, etc.). Inputs 127 may be further processed by DAM 111 to output a plurality of metrics, such as outputs 130. Further, inputs (e.g., inputs 127) and metrics (e.g., outputs 130) may be used by the DAM 111 and / or any computing device in the system 100 to perform various processes. Any of inputs 127 may be used for computing any of outputs 130. In certain embodiments, each one of outputs 130 may correspond to one or more values, e.g., discrete numerical values, ranges, or qualitative values (high / medium / low or stable / unstable). In some embodiments, some or all of outputs 130 may include time-series data and / or be provided in the form of time- series data.

[0080] In certain embodiments, inputs 127 include food consumption information. Food consumption information may include information about one or more of meals, snacks, and / or beverages, such as one or more of the size, content (carbohydrate, fat, protein, etc.), sequence of consumption, and time of consumption. In certain embodiments, food consumption may be provided by the user through manual entry, by providing a photograph through an application that is configured to recognize food types and quantities, and / or by scanning a bar code or menu. In various examples, meal size may be manually entered as one or more of calories, quantity (e.g., 'three cookies'), menu items (e.g., 'Royale with Cheese'), and / or food exchanges (1 fruit, 1 dairy). In some examples, meals may also be entered with the user's typical items or combinations for this time or setting (e.g., workday breakfast at home, weekend brunch at restaurant). In some examples, meal information may be received via a convenient user interface provided by application 106.

[0081] In certain embodiments, inputs 127 include activity information. Activity information may be provided, for example, the one or more non-analyte sensors 142 of FIG. IB. In certain embodiments, activity information may additionally be provided through manual input by user 102. Activity information may include, for example, a time series for each of heart rate, activity minutes, step count, floors climbed, location information (e.g., GPS data), calories burned, sleep duration and / or quality, activity level (e.g., light, medium, or heavy), and / or similar- information. In addition, or alternatively, the activity information can include one or more time series for recorded activities of one or more defined activity types (e.g., walk, run, sprint, swim, weightlift etc.), where each activity is associated with a duration and / or time period.

[0082] In certain embodiments, inputs 127 include patient statistics, such as one or more of age, height, weight, body mass index, body composition (e.g., % body fat), stature, build, or other information. Patient statistics may be provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from measurement devices. The measurement devices may include one or more of a wireless, e.g., Bluetooth-enabled, weight scale and / or camera, which may, for example, communicate with the display device 107 to provide patient data.

[0083] In certain embodiments, inputs 127 include information relating to the user’s medication intake. For example, the user’s medication intake may include the user’s insulin delivery. Such information may be received, via a wireless connection on a smart pen, via user input, and / or from an insulin pump (e.g., medical device 108). Insulin delivery information may include one or more of insulin volume, time of delivery, etc. Other configurations, such as insulin action time or duration of insulin action, may also be received as inputs.

[0084] In certain embodiments, inputs 127 include physiological information received from non-analyte sensor(s) 142 , which may detect one or more of heart rate, respiration, oxygen saturation, body temperature, etc. (e.g., to detect illness, stress levels, etc.). In certain embodiments, inputs 127 include time, such as time of day, or time from a real-time clock.

[0085] In certain embodiments, inputs 127 include analyte data, which may be provided as input from CAM system 104, for example, in any of the ways described with respect to FIG. 1A. An example of analyte data is glucose data, which may be provided and / or stored as a time series corresponding to time-stamped glucose measurements over time. Other types of analyte data, such as ketone data, potassium data, lactate data, etc., may similarly be provided and / or stored as a time series.

[0086] As described above, in certain embodiments, DAM 111 generates, determines, and / or computes outputs 130 based on inputs 127 associated with user 102. An example list of outputs 130 is illustrated in FIG. 2. In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include metabolic rate. Metabolic rate is a metric that may indicate or include a basal metabolic rate (e.g., energy consumed at rest) and / or an active metabolism, e.g., energy consumed by activity, such as exercise or exertion. In some examples, basal metabolic rate and active metabolism may be tracked as separate metric. In certain embodiments, the metabolicrate may be calculated by DAM 111 based on one or more of inputs 127, such as one or more of activity information, sensor input, time, user input, etc.

[0087] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include an activity level metric. The activity level metric may indicate a level of activity of the user. In certain embodiments, the activity level metric may be determined, for example based on input from an activity sensor or other physiologic sensors. In certain embodiments, the activity level metric may be calculated by DAM 111 based on one or more of inputs 127, such as one or more of activity information, physiological information, analyte data, time, user input, etc. Activity level may indicate whether the user is exercising, at rest, sleeping, etc.

[0088] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include an insulin resistance metric (also referred to herein as an “insulin resistance”). The insulin resistance metric may be determined using historical data, real-time data, or a combination thereof, and may, for example, be based upon one or more inputs 127, such as one or more of food consumption information, blood glucose information, insulin delivery information, the resulting glucose levels, etc. In certain embodiments, the insulin on board metric may be determined using insulin delivery information, and / or known or learned (e.g., from patient data) insulin time action profiles, which may account for both basal metabolic rate (e.g., update of insulin to maintain operation of the body) and insulin usage driven by activity or food consumption.

[0089] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include a meal state metric. The meal state metric may indicate the state the user is in with respect to food consumption. For example, the meal state may indicate whether the user is in one of a fasting state, pre-meal state, eating state, post-meal response state, or stable state. In certain embodiments, the meal state may also indicate nourishment on board, e.g., meals, snacks, or beverages consumed, and may be determined, for example from food consumption information, time of meal information, and / or digestive rate information, which may be correlated to food type, quantity, and / or sequence (e.g., which food / beverage was eaten first.).

[0090] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include health and sickness metrics. Health and sickness metrics may be determined, for example, based on one or more of user input (e.g., pregnancy information or known sickness information), from non-analyte sensor(s) 142, such as physiologic sensors (e.g., temperature), activity sensors,or a combination thereof. In certain embodiments, based on the values of the health and sickness metrics, for example, the user’s state may be defined as being one or more of healthy, ill, rested, or exhausted. In certain embodiments, health and sickness metric may indicate the user’s heart rate, stress level, etc.

[0091] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include analyte level metrics. Analyte level metrics may be determined from analyte data (e.g., glucose measurements obtained from CAM system 104). In some examples, an analyte level metric may also be determined, for example, based upon historical information about analyte levels in particular situations, e.g., given a combination of food consumption, insulin, and / or activity. An analyte level metric may include a ROC of the analyte, time in range, time spent below a threshold level, time spent above a threshold level, or the like. In certain embodiments, an analyte trend may be determined based on the analyte level over a certain period of time. As described above, example analytes may include glucose, ketones, lactate, potassium and others described herein.

[0092] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include a disease stage. For example disease stages for Type II diabetics may include a pre-diabetic stage, an oral treatment stage, and a basal insulin treatment stage. In certain embodiments, degree of glycemic control (not shown) may also be determined as an outcome metric, and may be based, for example, on one or more of glucose levels, variation in glucose level, or insulin dosing patterns.

[0093] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include clinical metrics. Clinical metrics generally indicate a clinical state a user is in with respect to one or more conditions of the user, such as diabetes. For example, in the case of diabetes, clinical metrics may be determined based on glycemic measurements, including one or more of A1C, trends in A1C, time in range, time spent below a threshold level, time spent above a threshold level, and / or other metrics derived from glucose values. In certain embodiments, clinical metrics may also include one or more of estimated A 1C, glycemic variability, hypoglycemia, and / or health indicator (time magnitude out of target zone).

[0094] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 can include a current context for the patient. The current context can be, for example, an existence of an urgent situation for the patient, such as an urgent low or high based on low or high thresholds (e.g., hypoglycemic or hyperglycemic thresholds for glucose values), rapidly rising or fallinganalyte levels (e.g., based on suitable ROC thresholds), and / or the like. The current context can also be the non-existence of an urgent situation, such as any of the foregoing urgent situations.

[0095] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 can include context-adapted ROC settings, individual context-adapted ROC values for individual time points, and aggregate context- adapted ROC measurements for configurable windows of time. Equation 3 below illustrates an example of calculating a context-adapted ROC value (ROCVX) for an individual time point (Tx). ROCXcan correspond to an analyte level ROC at a time point Tx. with Axbeing a coefficient, or weight, given to ROCX. In some cases, Axmay represent a value between 0 and 1.ROCVX= AXROCXEq. 3

[0096] Equation 4 below illustrates an example of calculating an aggregate context-adapted ROC measurement ROCAGG) for a time window that is defined relative to a current time T. In certain aspects, the time window can have a size corresponding to a configurable amount of time preceding the current time T, such as 5 minutes, 10 minutes, 30 minutes, one hour, etc. Based on its size, the time window can include n time points Txcorresponding to a plurality of analyte measurements. As discussed above relative to Equation 3, each ROCVXcan be a context-adapted ROC value at an individual time point Tx. In the example of Equation 4, ROCAGGis shown to be an arithmetic mean of the context-adapted ROC values for n time points Tx, specifically, ROCvlto ROCvn. However, it should be appreciated that the aggregation can also occur via, for example, other statistical methods.

[0097] In certain aspects, the context- adapted ROC settings of the outputs 130 can include context-adapted coefficients. With reference to a time window including n time points Tx, the context-adapted coefficients can include, for example, a dynamically selected and / or adjusted set of coefficients A1to Anfor use, for example, in Equation 3 above. The context-adapted coefficients can be dynamically selected or adjusted based on the current context for the patient.In an example, in certain aspects, the DAM 111 can use coefficients or weights that are dynamically selected or adjusted based on the current context, for example, to provide greater weight to ROCs at time points closer to a current time. Determination and utilization of the context- adapted coefficients will be discussed in greater detail relative to FIG. 4.

[0098] In certain aspects, the context- adapted ROC settings of the outputs 130 can include a context-adapted sampling rate for analyte measurements. The DAM 111 can use dynamic sampling, such that the context-adapted sampling rate is dynamically determined or selected based on the current context for the patient. For example, the context- adapted sampling rate can be selectively increased so as to have a greater number of ROCs at time points that are in closer proximity to a current time, as compared to time points that are further from the current time, thereby emphasizing more recent analyte measurements. Determination and utilization of context- adapted sampling rates will be discussed in greater detail relative to FIG. 5.

[0099] In certain aspects, the context- adapted ROC settings of the outputs 130 can include a context-adapted window size. The context- adapted window size can be dynamically determined or selected based on the current context for the patient. For example, the context- adapted window size may be selectively shortened in certain urgent situations to provide more weight to ROCs at time points in closer proximity to a current time, as compared to time points that are further from the current time, thereby emphasizing more recent analyte measurements. In a more particular example, the context-adapted window size may be 30 minutes for a context indicative of a nonurgent situation, and 10 minutes for a context indicative of dangerously low analyte levels (e.g., a hypoglycemic situation). Determination and utilization of context- adapted window sizes will be discussed in greater detail relative to FIG. 6.

[0100] FIG. 3 illustrates an example of a process 300 for dynamically adapting an analyte ROC determination to a current patient context, in accordance with certain embodiments. In some aspects, the process 300 can be executed continuously (e.g., every minute, every 5 minutes, every 10 minutes, etc.). In addition, or alternatively, the process 300 can be executed with each new analyte measurement received, for example, from a CAM system such as the CAM system 104 of FIGS. 1A-B.

[0101] In some embodiments, the process 300 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 300 canbe executed, for example, by the application 106 of FIGS. 1 A-B and 2. In addition, or alternatively, the process 300 can be executed generally by any of the display devices 107 of FIGS. 1A-B and 2. Although any number of systems, in whole or in part, can implement the process 300, to simplify discussion, the process 300 will be described primarily in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0102] At block 302, the therapy management engine 112 receives one or more analyte measurements of a patient. The analyte measurements can be generated and received, for example, as discussed relative to FIGS. 1A-B and 2.

[0103] At block 304, the therapy management engine 112 automatically determines a current context for the patient based on the analyte measurements. For example, the therapy management engine 112 can determine whether the analyte measurements meet predetermined criteria associated with a predefined context, such as meeting a threshold value for a threshold period of time. As discussed previously, the current context can be, for example, an existence of an urgent situation for the patient, such as an urgent low or high based a current analyte measurement of the patient exceeding a low or high threshold (e.g., a hypoglycemic or hyperglycemic threshold for a patent’s glucose value), the existence of rapidly rising or falling analyte levels (e.g., based on a current analyte measurement ROC for the patient exceeding a predetermined ROC threshold), and / or the like. The current context can also be the non-existence of an urgent situation (such as the non-existence of any of the foregoing urgent situations). In some aspects, the current context can simply indicate whether a current situation for the patient is urgent or non-urgent.

[0104] At block 306, the therapy management engine 112 automatically configures an ROC determination based on the current context for the patient. In an example, if the current context matches criteria associated with one or more predefined contexts (e.g., urgent high, urgent low, rapidly rising, rapidly falling, etc.), the therapy management engine 112 can adjust how an ROC measurement is computed, such that the ROC measurement gives greater weight to more recent analyte measurements (e.g., by dynamically adjusting one or more of coefficients, time window size, and / or sampling rate based on the predefined context), as discussed previously. In another example, if the current context does not match criteria associated with one or more predefined contexts, the therapy management engine 112 can use a standard or default calculation for theROC measurement (e.g., by utilizing static / fixed coefficients, time window size, and sampling rate).

[0105] As discussed above relative to FIG. 2, the automatic configuration at the block 306 can include, for example, dynamically determining coefficients, a sampling rate, and / or a time window size. Examples of dynamically determining and using coefficients, sampling rates, and time window sizes will be described relative to FIGS. 4, 5, 6, respectively. In some aspects, multiple methods of configuring may be used together. For example, the therapy management engine 112 can use dynamically determined coefficients, dynamic sampling, and / or a dynamically resized time window in combination to give greater weight or consideration to more recent analyte measurements . Other examples will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0106] At block 308, the therapy management engine 112 generates a context- adapted ROC measurement based on the automatically configured ROC determination from the block 306. For example, the therapy management engine 112 can use Equations 3 and 4 discussed above to generate the context- adapted ROC measurement. In some aspects, the context-adapted ROC measurement can be an aggregate context-adapted ROC measurement as discussed above relative to FIG. 2 and Equation 4. Further examples of generating the context-adapted ROC measurement will be discussed relative to FIGS. 4, 5, and 6.

[0107] At block 310, the therapy management engine 112 presents graphical information related to the context- adapted ROC measurement to the patient, a supporter, or another user. The graphical information can include, for example, an adaptive trend arrow based on the context- adapted ROC measurement. In some aspects, the adaptive trend arrow can be shown in place of a standard or default trend arrow that is based on a standard or default ROC calculation method. In addition, or alternatively, the adaptive trend arrow can be shown in conjunction with the standard or default trend arrow (e.g., the adaptive trend arrow and the standard trend arrow can each have a different color, opacity, etc.). In addition, or alternatively, the adaptive trend arrow can be displayed upon zooming in on a standard trend arrow, or in response to user manipulation of another suitable user interface control. Examples of the adaptive trend arrow will be described relative to FIG. 11. After block 310, the process 300 ends.

[0108] As discussed above, in certain aspects, the process 300 can be executed continuously (c.g., every minute, every 5 minutes, every 10 minutes, etc.). In an example, the therapy management engine 112 can begin by using a default method of analyte ROC determination (e.g., including static coefficients, time window size, and sampling rate). According to this example, the therapy management engine 112 can continue to use the default method so long as, during each iteration of the process 300, the current patient context is determined to be indicative of a nonurgent situation. However, if, during an iteration of the process 300, the therapy management engine 112 determines that the current patient context is indicative of an urgent situation, the therapy management engine 112 can automatically configure the analyte ROC determination to deviate from the default method in any of the ways discussed previously (e.g., including dynamic coefficients, time window size, and / or sampling rate). Thereafter, according to this example, the therapy management engine 112 can continue to deviate from the default method of ROC determination, in the way configured, until the current patient context is determined to be indicative of a non-urgent situation, at which point the therapy management engine 112 returns to the default method. In addition, or alternatively, the therapy management engine 112 can continue to deviate from the default method, in the way configured, until the expiration of a predetermined period of time (e.g., 30 minutes), until the analyte ROC determination results in a predetermined value (or a predetermined corresponding adjustment of a displayed trend arrow is initiated / performed), until the expiration of a predetermined period of time in combination with the urgent situation no longer being present, in response to user selection of a predetermined option or performance of a predetermined action within a displayed UI, or until other suitable exit criteria is satisfied.

[0109] FIG. 4 illustrates an example of a process 400 for dynamically determining and using context-adapted ROC coefficients, in accordance with certain embodiments. In some aspects, the process 400 can be executed continuously (e.g., every minute, every 5 minutes, every 10 minutes, etc.). In addition, or alternatively, the process 400 can be executed with each new analyte measurement received, for example, from a CAM system such as the CAM system 104 of FIGS. 1A-B.

[0110] In some embodiments, the process 400 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 400 can be executed, for example, by the application 106 of FIGS. 1A-B and 2. In addition, or alternatively,the process 400 can be executed generally by any of the display devices 107 of FIGS. 1 A-B and 2. Although any number of systems, in whole or in part, can implement the process 400, to simplify discussion, the process 400 will be described primarily in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0111] At block 402, the therapy management engine 112 receives one or more analyte measurements of a patient. The analyte measurements can be generated and received, for example, as discussed relative to FIGS. 1A-B and 2. At block 404, the therapy management engine 112 automatically determines a current context for the patient based on the analyte measurements, for example, as discussed relative to the block 304 of FIG. 3.

[0112] At block 406, the therapy management engine 112 determines, for the current context, a set of context-adapted coefficients for an analyte ROC determination. In certain aspects, the block 406 can be performed as pail of automatically configuring the analyte ROC determination at the block 306 of FIG. 3. For example, with reference to the discussion above regarding FIG. 2 and Equations 3 and 4, for a time window that includes time points T}to Tn, the set of context- adapted coefficients can include corresponding coefficients A1to An.

[0113] As discussed above relative to FIG. 2, if the current context is indicative, for example, of an urgent situation, the set of context-adapted coefficients may provide greater weight to ROCs at time points Txcloser to a current time point T to emphasize those time points. Advantageously, in certain aspects, this emphasis may highlight (e.g., make more discernible) corrective actions taken close to the current time T (e.g., ingestion of glucose to correct hypoglycemia, administration of insulin to correct hyperglycemia, etc.).

[0114] In some aspects, the therapy management engine 112 can retrieve the set of context- adapted coefficients from a lookup table based on the current context for the patient. In addition, or alternatively, the therapy management engine 112 can use an adaptive linear estimator to determine the set of context-adapted coefficients. In addition, or alternatively, the therapy management engine 112 can use a neural network to determine the set of context-adapted coefficients. Other examples of how the set of context- adapted coefficients may be determined will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0115] At block 407, the therapy management engine 112 determines a context- adapted ROC value for each individual time point within a time window that is to be used for the analyte ROCdetermination. For example, with reference to Equation 3 above, for each time point Txin the time window, the therapy management engine 112 can multiply an analyte level ROC ROCX~) by a corresponding coefficient (Ax) to yield the context adapted ROC value (ROCVX). The block 407 can yield, for example, context- adapted ROC values ROCV1to ROCvnfor time points 7 to Tn.

[0116] At block 408, the therapy management engine 112 aggregates the context- adapted ROC values from the block 407 to determine a context- adapted ROC measurement for the current time T. In certain aspects, the blocks 407 and 408 can be performed together as all or part of generating the context-adapted ROC measurement at the block 308 of FIG. 3. For example, the aggregate context-adapted ROC measurement ROCAGG) can be computed from the context-adapted ROC values ROCV1to ROCvnusing Equation 4 above. However, it should be appreciated that the aggregation can also occur via, for example, other statistical methods.

[0117] At block 410, the therapy management engine 112 presents graphical information related to the context- adapted ROC measurement to the patient, a supporter, or another user. The graphical information can include, for example, an adaptive trend arrow based on the context- adapted ROC measurement, as discussed above relative to the block 310 of FIG. 3. After block 410, the process 400 ends.

[0118] FIG. 5 illustrates an example of a process 500 for dynamically determining and using context-adapted sampling rates, in accordance with certain embodiments. In some aspects, the process 500 can be executed continuously (e.g., every minute, every 5 minutes, every 10 minutes, etc.). In addition, or alternatively, the process 500 can be executed with each new analyte measurement received, for example, from a CAM system such as the CAM system 104 of FIGS. 1A-B.

[0119] In some embodiments, the process 500 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 500 can be executed, for example, by the application 106 of FIGS. 1A-B and 2. In addition, or alternatively, the process 500 can be executed generally by any of the display devices 107 of FIGS. 1A-B and 2. Although any number of systems, in whole or in part, can implement the process 500, to simplify discussion, the process 500 will be described primarily in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0120] At block 502, the therapy management engine 112 receives one or more analyte measurements of a patient. The analyte measurements can be generated and received, for example, as discussed relative to FIGS. 1A-B and 2. At block 504, the therapy management engine 112 automatically determines a current context for the patient based on the analyte measurements, for example, as discussed relative to the block 304 of FIG. 3.

[0121] At block 506, the therapy management engine 112 sets a context- adapted sampling rate based on the current context for the patient. In certain aspects, the block 506 can be performed as part of automatically configuring the analyte ROC determination at the block 306 of FIG. 3. For example, the therapy management engine 112 can determine, for a time window, a number and distribution of time points within the time window.

[0122] As discussed above relative to FIG. 2, if the current context is indicative, for example, of an urgent situation, the number of time points Txmay be increased in closer proximity to a current time T in order to provide more emphasis to that time period. Advantageously, in certain aspects, this emphasis may highlight (e.g., make more discernible) corrective actions taken close to the current time T (e.g., ingestion of glucose to correct hypoglycemia, administration of insulin to correct hyperglycemia, etc.).

[0123] In some aspects, setting the context-adapted sampling rate at the block 506 can involve changing the sampling rate for future analyte measurements generated, for example, by the CAM system 104. For example, the therapy management engine 112 can command the CAM system 104 to generate an analyte measurement every one minute instead of every five minutes, every 30 seconds instead of every two minutes, etc. In addition, or alternatively, setting the sample rate at the block 506 can involve selectively retrieving stored analyte measurements in correspondence to the number and distribution of time points Txwithin the time window. For example, for a 20- minute time window, the therapy management engine 112 could select twice as many analyte measurements from the most recent 10-minute segment as compared to the first 10-minute segment.

[0124] In some aspects, the therapy management engine 112 can set the context-adapted sampling rate according to a rate retrieved from a lookup table based on the current context for the patient. In addition, or alternatively, the therapy management engine 112 can use an adaptive linear estimator to set the context-adapted sampling rate. In addition, or alternatively, the therapymanagement engine 1 12 can use a neural network to set the context-adapted sampling rate. Other examples of how the context- adapted sampling rate may be set will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0125] At block 507, the therapy management engine 112 determines a context- adapted ROC value for each individual time point within a time window that is based on the sampling rate set at the block 506. For example, with reference to Equation 3 above, for each time point Txin the time window, the therapy management engine 112 can multiply an analyte level ROC (ROCX) by a corresponding coefficient (Ax) to yield the context adapted ROC value (ROCVX). The block 507 can yield, for example, context- adapted ROC values ROCV1to ROCvnfor time points 7 to Tn.

[0126] At block 508, the therapy management engine 112 aggregates the context- adapted ROC values from the block 507 to determine a context- adapted ROC measurement for the current time T. In certain aspects, the blocks 507 and 508 can be performed together as all or pail of generating the context-adapted ROC measurement at the block 308 of FIG. 3. For example, the aggregate context-adapted ROC measurement (ROCAGG) can be computed from the context-adapted ROC values ROCV1to ROCvnusing Equation 4 above. However, it should be appreciated that the aggregation can also occur via, for example, other statistical methods.

[0127] At block 510, the therapy management engine 112 presents graphical information related to the context-adapted ROC measurement to the patient, a supporter, or another user. The graphical information can include, for example, an adaptive trend arrow based on the context- adapted ROC measurement, as discussed above relative to the block 310 of FIG. 3. After block 510, the process 500 ends.

[0128] FIG. 6 illustrates an example of a process 600 for dynamically determining and using context-adapted window sizes, in accordance with certain embodiments. In some aspects, the process 600 can be executed continuously (e.g., every minute, every 5 minutes, every 10 minutes, etc.). In addition, or alternatively, the process 600 can be executed with each new analyte measurement received, for example, from a CAM system such as the CAM system 104 of FIGS. 1A-B.

[0129] In some embodiments, the process 600 can be executed, for example, by the therapy management engine 1 12 of FIGS. 1 A-B and 2. In addition, or alternatively, the process 600 can be executed, for example, by the application 106 of FIGS. 1A-B and 2. In addition, or alternatively,the process 600 can be executed generally by any of the display devices 107 of FIGS. 1 A-B and 2. Although any number of systems, in whole or in part, can implement the process 600, to simplify discussion, the process 600 will be described primarily in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0130] At block 602, the therapy management engine 112 receives one or more analyte measurements of a patient. The analyte measurements can be generated and received, for example, as discussed relative to FIGS. 1A-B and 2. At block 604, the therapy management engine 112 automatically determines a current context for the patient based on the analyte measurements, for example, as discussed relative to the block 304 of FIG. 3.

[0131] At block 606, the therapy management engine 112 determines a window size for a current time T based on the current context for the patient. In certain aspects, the block 606 can be performed as part of automatically configuring the analyte ROC determination at the block 306 of FIG. 3. For example, the therapy management engine 112 can determine the window size as an amount of time preceding the current time T (e.g., 5 minutes, 10, minutes, 30 minutes, etc.).

[0132] As discussed above relative to FIG. 2, if the current context is indicative, for example, of an urgent situation, the time window may be shorter to provide more weight, or emphasis, to time points Txin closer proximity to the current time T. Advantageously, in certain aspects, this emphasis may highlight (e.g., make more discernible) corrective actions taken close to the current time T (e.g., ingestion of glucose to correct hypoglycemia, administration of insulin to correct hyperglycemia, etc.). In some implementations, the size of the time window can be implemented via coefficients. For example, with reference to Equation 3 above, each analyte level ROC ROCX~) at a time point Txthat is outside the time window may be zeroed out, for example, by setting a corresponding coefficient (Ax) to zero.

[0133] In some aspects, the therapy management engine 112 can determine the context- adapted window size according to a size retrieved from a lookup table based on the current context for the patient. In addition, or alternatively, the therapy management engine 112 can use an adaptive linear estimator to determine the context-adapted window size. In addition, or alternatively, the therapy management engine 112 can use a neural network to determine the context-adapted window size. Other examples of how the context-adapted window size may be determined will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0134] At block 607, the therapy management engine 112 determines a context-adapted ROC value for each individual time point within a time window of the window size determined at the block 606. For example, with reference to Equation 3 above, for each time point Txin the time window, the therapy management engine 112 can multiply an analyte level ROC (ROCX~) by a corresponding coefficient Ax) to yield the context adapted ROC value (ROCVX). The block 607 can yield, for example, context-adapted ROC values ROCV1to ROCvllfor time points 7^ to Tn.

[0135] At block 608, the therapy management engine 112 aggregates the context- adapted ROC values from the block 607 to determine a context- adapted ROC measurement for the current time T. In certain aspects, the blocks 607 and 608 can be performed together as all or part of generating the context-adapted ROC measurement at the block 308 of FIG. 3. For example, the aggregate context-adapted ROC measurement (ROCAGG) can be computed from the context-adapted ROC values ROCV1to ROCvnusing Equation 4 above. However, it should be appreciated that the aggregation can also occur via, for example, other statistical methods.

[0136] At block 610, the therapy management engine 112 presents graphical information related to the context- adapted ROC measurement to the patient, a supporter, or another user. The graphical information can include, for example, an adaptive trend arrow based on the context- adapted ROC measurement, as discussed above relative to the block 310 of FIG. 3. After block 610, the process 600 ends.

[0137] FIG. 7 illustrates an example of using a lookup table to determine context- adapted ROC settings, in accordance with certain embodiments. The lookup table may be used, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. For illustrative purposes, context- adapted coefficients are shown in the example of FIG. 7. It should be appreciated, however, that lookup tables may similarly be used to determine sampling rates as discussed relative to FIG. 5 and / or window sizes as discussed relative to FIG. 6. Other examples of context- adapted ROC settings that may be determined via a lookup table will be apparent to one skilled in the art after a thorough study of the present disclosure.

[0138] FIG. 8 illustrates an example of using an adaptive linear estimator to determine context- adapted ROC settings, in accordance with certain embodiments. The adaptive linear estimator may be used, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. For illustrative purposes, context-adapted coefficients are shown in the example of FIG. 8. It should beappreciated, however, that an adaptive linear estimator may similarly be used to determine sampling rates as discussed relative to FIG. 5 and / or window sizes as discussed relative to FIG. 6. Other examples of context-adapted ROC settings that may be determined via an adaptive linear estimator will be apparent to one skilled in the art after a thorough study of the present disclosure.

[0139] More particularly, in the example of FIG. 8, the adaptive linear estimator can use a fixed-size lookback window and then linearly combine prior analyte measurements to calculate an underlying ROC. The adaptive linear estimator can update coefficient values of an averaging scheme after a new data point is received and / or after a batch of new data points has arrived. An initial coefficient and a coefficient after a reset (e.g. a long duration data loss) can be set based on a lookup table, as discussed above. The coefficients can be adjusted after each new data point arrives using a cost function that considers, for example, noise level and sensor faults. The cost function can minimize, for example, a minimum mean-square error or a least-square error of one- step-ahead or multiple-steps-ahead prediction. The updated coefficient at each time stamp can be used for calculation of a context- adapted ROC measurement from which an adaptive trend arrow, for example, may be derived as discussed above.

[0140] FIG. 9 illustrates an example of using a neural network, in accordance with certain embodiments. In various aspects, the therapy management engine 112 of FIGS. 1A-B and 2 can use the neural network to compute a context-adapted ROC measurement (as shown in FIG. 9), define contexts, establish context-adapted ROC settings for different contexts (e.g., coefficients, window size and / or sampling rate), and / or the like.

[0141] FIG. 10 illustrates an example of using a linear combination of multiple methods to determine a context- adapted ROC measurement, in accordance with certain embodiments. The linear combination can be used, for example, by the therapy management engine 112 of FIGS. 1 A-B and 2. More particularly, in the example of FIG. 9, the therapy management engine 112 of FIGS. 1A-B and 2 can use a linear combination of outputs from a lookup table (e.g., as shown in FIG. 7), an adaptive linear estimator (e.g., as shown in FIG. 7), and / or a neural network (e.g., as shown in FIG. 9). The outputs may be weighted, for example, based on an observed accuracy of each method.

[0142] Although certain examples of methods for determining a context-adaptive ROC measurement are described above, it should be appreciated that other methods are alsocontemplated. In certain aspects, certain implementations can use a three-state Kalman filter to determine a context- adapted ROC measurement. In an example, the three states of the three- state Kalman filter can be analyte signal, first derivation of the signal (i.e. , ROC), and second derivation of the analyte signal.

[0143] FIG. 11 illustrates examples of different adaptive trend arrows that may be presented for different value ranges of a context-adapted ROC measurement, in accordance with certain embodiments. In various aspects, one or more of the adaptive trend arrows shown in FIG. 11 may be presented, for example, as pail of the block 310 of FIG. 3, the block 410 of FIG. 4, the block 510 of FIG. 5, and / or the block 610 of FIG. 6.

[0144] FIG. 12 illustrates example results of performing a context-adapted ROC measurement, in accordance with certain embodiments. In various aspects, a measured glucose level 1210 for a patient over time may decrease and pass below a predetermined low threshold 1208. In response, at a first point in time 1202, the patient may consume glucose to address their declining glucose levels. Under circumstances where a static (e.g., default) ROC measurement is performed, it may take until a third point in time 1206 before the static ROC measurement results in a trend arrow tilting upward, indicating to the patient that their consumed glucose has addressed their low glucose levels.

[0145] However, under circumstances where a context- adapted ROC measurement is performed, such dynamic method may result in a trend arrow 1212 tilting upward at a second point in time 1204 that occurs significantly sooner than the third point in time 1206, indicating to the patient that their consumed glucose has addressed their low glucose levels. In this way, the positive results of corrective action may be presented to the patient at an earlier time, which may reduce a stress level of the patient (and thereby improve an overall health of the patient).

[0146] FIG. 13 illustrates an example interface displaying results of performing a context- adapted ROC measurement, in accordance with certain embodiments. In various aspects, the interface in FIG. 13 may be displayed on a device in response to (1) determining that a current context meets predetermined criteria associated with a predetermined context, as discussed relative to the block 304 of FIG. 3, and (2) determining that a context-adapted ROC measurement (as discussed in at least FIGS. 3-6) indicates that a patient’s glucose values are changing in a positive direction in response to some action. It should also be noted that all or a portion of the exampleresults of performing a context-adapted ROC measurement shown in FIG. 12 may be displayed in the interface of FIG. 13 (c.g., above, below, to the left or right of, or transluccntly or opaquely overlaid above or below the trend arrow and associated text).

[0147] For instance, in response to determining that the patient’s current glucose levels are below a threshold (e.g., in an “urgent low” situation), a context-adapted ROC measurement may be initiated. This dynamic ROC measurement may update a trend arrow to point upward at the first sign that a patient’s glucose is increasing, and may also display a notification to the patient (such as “blood sugar turning around”) to let the patient know at the earliest possible time that their “urgent low” situation is being resolved. Also, the interface may visually indicate when a context-adapted ROC measurement is initiated (e.g., by displaying an icon or text such as “high- fidelity mode activated”), and such visual indication may be removed from the interface when the system reverts back to a static ROC measurement.

[0148] FIG. 14 is a block diagram depicting a computer system 1400 configured for dynamically adapting an analyte ROC determination to a current patient context, for example, according to certain embodiments disclosed herein. Although depicted as a single physical device, in embodiments, the computer system 1400 may be implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment and / or via separate modules of portable or cloud devices. As illustrated, the computer system 1400 includes a processor 1405, a memory 1410, a storage 1415, a network interface 1425, and one or more I / O interfaces 1420. In the illustrated embodiment, the processor 1405 retrieves and executes programming instructions stored in the memory 1410, as well as stores and retrieves application data residing in the storage 1415. The processor 1405 is generally representative of a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like.

[0149] The memory 1410 is generally included to be representative of a random access memory (RAM). The storage 1415 may be any combination of disk drives, flash-based storage devices, and the like, and may include fixed and / or removable storage devices, such as fixed disk drives, removable memory cards, caches, optical storage, network attached storage (NAS), or storage area networks (SAN).

[0150] In some embodiments, the RO devices 1435 (such as keyboards, monitors, etc.) can be connected via the I / O interface(s) 1420. Further, via the network interface 1425, the computersystem 1400 can be communicatively coupled with one or more other devices and components, such as the user database 110. In certain embodiments, the computer system 1400 is communicatively coupled with other devices via a network, which may include the Internet, local network(s), and the like. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, the processor 1405, memory 1410, storage 1415, network interface(s) 1425, and the I / O interface(s) 1420 are communicatively coupled by one or more interconnects 1430. In certain embodiments, the computer system 1400 is representative of the display device 107 associated with the user. In certain embodiments, as discussed above, the display device 107 can include the user’s laptop, computer, smartphone, and the like. In another embodiment, the computer system 1400 is a server executing in a cloud environment.

[0151] In the illustrated embodiment, the storage 1415 includes the user profile 118. The memory 1410 includes the therapy management engine 112. The therapy management engine 112 can be executed by the computer system 1400 to perform operations, for example, of the process 300 of FIG. 3, the process 400 of FIG. 4, the process 500 of FIG. 5 and / or the process 600 of FIG. 6.Example Clauses

[0152] Implementation examples are described in the following numbered clauses:

[0153] Clause 1: A method of dynamically adapting analyte rate-of-change determination to a current patient context, the method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically configuring an analyte rate-of-change determination based on the current context; and generating a context- adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

[0154] Clause 2: The method of Clause 1, wherein the automatically configuring the analyte rate-of-change determination comprises determining one or more context-adapted rate-of-change settings.

[0155] Clause 3: The method of Clause 2, wherein the determining the one or more context- adapted ratc-of-changc settings comprises determining coefficients for the analyte ratc-of-changc determination based on the current context.

[0156] Clause 4: The method of Clause 3, wherein the generating the context-adapted rate-of- change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window using the determined coefficients; and aggregating the plurality of rate-of-change values to determine the context- adapted rate-of-change measurement.

[0157] Clause 5: The method of Clause 2, wherein the determining the one or more context- adapted rate-of-change settings comprises setting a sampling rate for the analyte measurements based on the current context.

[0158] Clause 6: The method of Clause 5, wherein the generating the context-adapted rate-of- change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window based on the sampling rate; and aggregating the plurality of rate-of- change values to determine the context-adapted rate-of-change measurement.

[0159] Clause 7: The method of Clause 6, wherein the setting the sampling rate comprises determining a number time points and a distribution of the time points within the time window.

[0160] Clause 8: The method of Clause 2, wherein the determining the one or more context- adapted rate-of-change settings comprises determining a window size based on the current context.

[0161] Clause 9: The method of Clause 8, wherein the generating the context-adapted rate-of- change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window of the determined window size; and aggregating the plurality of rate- of-change values to determine the context-adapted rate-of-change measurement.

[0162] Clause 10: The method of Clause 2, wherein the determining the one or more context- adapted rate-of-change settings comprises retrieving the one or more context-adapted rate-of- change settings from a lookup table based on the current context.

[0163] Clause 11: The method of Clause 2, wherein the one or more context-adapted rate-of- change settings are determined using an adaptive linear estimator.

[0164] Clause 12: The method of Clause 2, wherein the one or more context-adapted rate-of- changc settings arc determined using a linear combination of outputs from two or more of the following: a lookup table; an adaptive linear estimator; and a neural network.

[0165] Clause 13: The method of Clause 2, wherein the method comprises using a neural network for at least one of the following: the automatically determining the current context for the patient; the determining the one or more context-adapted rate-of-change settings; and the generating the context-adapted rate-of-change measurement.

[0166] Clause 14: The method of Clause 1, further comprising presenting graphical information related to the context-adapted rate-of-change measurement.

[0167] Clause 15: The method of Clause 14, wherein the presented graphical information comprises an adaptive trend arrow based on the context-adapted rate-of-change measurement.

[0168] Clause 16: The method of Clause 1, wherein the generating the context-adapted rate- of-change measurement comprises using a 3-state Kalman filter, such that a first state comprises an analyte signal, a second state comprises a first derivation of the analyte signal, and a third state comprises a second derivation of the analyte signal.

[0169] Clause 17: A method of dynamically adapting analyte rate-of-change determination to a current patient context, the method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically determining coefficients for an analyte rate-of-change determination based on the current context; determining a plurality of rate-of-change values for a plurality of time points of a time window based on the automatically determined coefficients and the plurality of analyte measurements; and aggregating the plurality of rate-of-change values to determine a context-adapted rate-of-change measurement.

[0170] Clause 18: The method of Clause 17, wherein the automatically determining the coefficients comprises retrieving the coefficients from a lookup table based on the current context.

[0171] Clause 19: The method of Clause 17, wherein the coefficients are automatically determined using an adaptive linear estimator.

[0172] Clause 20: The method of Clause 17, wherein the coefficients are automatically determined using a neural network.

[0173] Clause 21 : The method of Clause 17, further comprising presenting graphical information related to the context-adapted ratc-of-changc measurement.

[0174] Clause 22: The method of Clause 21, wherein the presented graphical information comprises an adaptive trend arrow based on the context-adapted rate-of-change measurement.

[0175] Clause 23: The method of Clause 17, wherein the context-adapted rate-of-change measurement is determined using a 3-state Kalman filter, such that a first state comprises an analyte signal, a second state comprises a first derivation of the analyte signal, and a third state comprises a second derivation of the analyte signal.

[0176] Clause 24: A method of dynamically adapting analyte rate-of-change determination to a current patient context, the method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically setting a sampling rate for the analyte measurements based on the current context, determining a plurality of rate-of-change values for a plurality of time points of a time window based on the sampling rate and the plurality of analyte measurements; and aggregating the plurality of rate-of-change values to determine a context- adapted rate-of-change measurement.

[0177] Clause 25: The method of Clause 24, wherein the setting the sampling rate comprises determining a number of time points and a distribution of the time points within the time window.

[0178] Clause 26: The method of Clause 24, wherein the automatically determining the sampling rate comprises retrieving the sampling rate from a lookup table based on the current context.

[0179] Clause 27: The method of Clause 24, wherein the sampling rate is automatically set using an adaptive linear estimator.

[0180] Clause 28: The method of Clause 24, wherein the sampling rate is automatically set using a neural network.

[0181] Clause 29: The method of Clause 24, further comprising presenting graphical information related to the context-adapted rate-of-change measurement.

[0182] Clause 30: The method of Clause 29, wherein the presented graphical information comprises an adaptive trend arrow based on the context-adapted rate-of-change measurement.

[0183] Clause 31 : The method of Clause 24, wherein the context-adapted rate-of-change measurement comprises is determined using a 3-statc Kalman filter, such that a first state comprises an analyte signal, a second state comprises a first derivation of the analyte signal, and a third state comprises a second derivation of the analyte signal.

[0184] Clause 32: A method of dynamically adapting analyte rate-of-change determination to a current patient context, the method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically determining a window size based on the current context; determining a plurality of rate-of-change values for a plurality of time points of a time window of the determined window size, wherein the plurality of rate-of-change values are determined based on the plurality of analyte measurements; and aggregating the plurality of rate-of-change values to determine a context-adapted rate-of-change measurement.

[0185] Clause 33: The method of Clause 32, wherein the automatically determining the window size comprises retrieving the window size from a lookup table based on the current context.

[0186] Clause 34: The method of Clause 32, wherein the window size is automatically determined using an adaptive linear estimator.

[0187] Clause 35: The method of Clause 32, wherein the window size is automatically determined using a neural network.

[0188] Clause 36: The method of Clause 32, further comprising presenting graphical information related to the context-adapted rate-of-change measurement.

[0189] Clause 37: The method of Clause 36, wherein the presented graphical information comprises an adaptive trend arrow based on the context-adapted rate-of-change measurement.

[0190] Clause 38: The method of Clause 32, wherein the context-adapted rate-of-change measurement comprises is determined using a 3-state Kalman filter, such that a first state comprises an analyte signal, a second state comprises a first derivation of the analyte signal, and a third state comprises a second derivation of the analyte signal.

[0191] Clause 39: A system for dynamically adapting analyte rate-of-change determination, comprising: a continuous analyte monitoring system configured to generate measurements associated with an analyte level of a patient; one or more memories comprising executable instructions; and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories, the one or more processors configured to execute the executable instructions to: receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determine a current context for the patient based on the plurality of analyte measurements; automatically configure an analyte rate- of-change determination based on the current context; and generate a context-adapted rate-of- change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

[0192] Clause 40: A system for dynamically adapting analyte rate-of-change determination, comprising: a continuous analyte monitoring system configured to generate measurements associated with an analyte level of a patient; one or more memories comprising executable instructions; and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories, the one or more processors configured to execute the executable instructions to: receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determine a current context for the patient based on the plurality of analyte measurements; automatically determine coefficients for an analyte rate-of-change determination based on the current context, determine a plurality of rate- of-change values for a plurality of time points of a time window based on the automatically determined coefficients and the plurality of analyte measurements; and aggregate the plurality of rate-of-change values to determine a context-adapted rate-of-change measurement.

[0193] Clause 41: A system for dynamically adapting analyte rate-of-change determination, comprising: a continuous analyte monitoring system configured to generate measurements associated with an analyte level of a patient; one or more memories comprising executable instructions; and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories, the one or more processors configured to execute the executable instructions to: receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determine a current context for the patient based on the plurality of analyte measurements; automatically set a sampling rate for theanalyte measurements based on the current context, determine a plurality of rate-of-change values for a plurality of time points of a time window based on the sampling rate and the plurality of analyte measurements; and aggregate the plurality of rate-of-change values to determine a context- adapted rate-of-change measurement.

[0194] Clause 42: A system for dynamically adapting analyte rate-of-change determination, comprising: a continuous analyte monitoring system configured to generate measurements associated with an analyte level of a patient; one or more memories comprising executable instructions; and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories, the one or more processors configured to execute the executable instructions to: receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determine a current context for the patient based on the plurality of analyte measurements; automatically determine a window size based on the current context, determine a plurality of rate-of-change values for a plurality of time points of a time window of the determined window size, wherein the plurality of rate-of-change values are determined based on the plurality of analyte measurements; and aggregate the plurality of rate-of-change values to determine a context-adapted rate-of-change measurement.

[0195] Clause 43: An apparatus, comprising: at least one memory comprising executable instructions; and at least one processor configured to execute the executable instructions and cause the apparatus to perform a method in accordance with any combination of Clauses 1 -38.

[0196] Clause 44: An apparatus, comprising means for performing a method in accordance with any combination of Clauses 1-38.

[0197] Clause 45: A non-transitory computer-readable medium comprising executable instructions that, when executed by at least one processor of an apparatus, cause the apparatus to perform a method in accordance with any combination of Clauses 1-38.

[0198] Clause 47: A computer program product embodied on a computer- readable storage medium comprising code for performing a method in accordance with any combination of Clauses 1-38.Additional Considerations

[0199] Each of these non-limiting examples can stand on its own or can be combined in various permutations or combinations with one or more of the other examples.

[0200] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or pennutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0201] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0202] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0203] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code caninclude computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0204] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to comply with 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

CLAIMS1. A method of dynamically adapting analyte rate-of-change determination to a current patient context, the method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically configuring an analyte rate-of-change determination based on the current context; and generating a context-adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

2. The method of claim 1, wherein the automatically configuring the analyte rate-of- change determination comprises determining one or more context- adapted rate-of-change settings.

3. The method of claim 2, wherein: the determining the one or more context-adapted rate-of-change settings comprises determining coefficients for the analyte rate-of-change determination based on the current context; and the generating the context-adapted rate-of-change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window using the determined coefficients; and aggregating the plurality of rate-of-change values to determine the context-adapted rate-of-change measurement.

4. The method of claim 2, wherein: the determining the one or more context- adapted ratc-of-changc settings comprises setting a sampling rate for the analyte measurements based on the current context; and the generating the context-adapted rate-of-change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window based on the sampling rate; and aggregating the plurality of rate-of-change values to determine the context-adapted rate-of-change measurement.

5. The method of claim 4, wherein the setting the sampling rate comprises determining a number time points and a distribution of the time points within the time window.

6. The method of claim 2, wherein: the determining the one or more context-adapted rate-of-change settings comprises determining a window size based on the current context; and the generating the context-adapted rate-of-change measurement comprises: determining a plurality of rate-of-change values for a plurality of time points of a time window of the determined window size; and aggregating the plurality of rate-of-change values to determine the context-adapted rate-of-change measurement.

7. The method of claim 2, wherein the determining the one or more context- adapted rate-of-change settings comprises retrieving the one or more context-adapted rate-of-change settings from a lookup table based on the current context.

8. The method of claim 2, wherein the one or more context- adapted rate-of-change settings are determined using an adaptive linear estimator.

9. The method of claim 2, wherein the one or more context-adapted rate-of-change settings arc determined using a linear’ combination of outputs from two or more of the following: a lookup table; an adaptive linear estimator; and a neural network.

10. The method of claim 2, wherein the method comprises using a neural network for at least one of the following: the automatically determining the current context for the patient; the determining the one or more context- adapted rate-of-change settings; and the generating the context-adapted rate-of-change measurement.

11. The method of claim 1 , further comprising presenting graphical information related to the context-adapted rate-of-change measurement.

12. The method of claim 11 , wherein the presented graphical information comprises an adaptive trend arrow based on the context- adapted rate-of-change measurement.

13. The method of claim 1, wherein the generating the context- adapted rate-of-change measurement comprises using a 3-state Kalman filter, such that a first state comprises an analyte signal, a second state comprises a first derivation of the analyte signal, and a third state comprises a second derivation of the analyte signal.

14. A system for dynamically adapting analyte rate-of-change determination, comprising: a continuous analyte monitoring system configured to generate measurements associated with an analyte level of a patient; one or more memories comprising executable instructions; and one or more processors in data communication with the continuous analyte monitoring system and the one or more memories, the one or more processors configured to execute the executable instructions to: receive, from the continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determine a current context for the patient based on the plurality of analyte measurements; automatically configure an analyte rate-of-change determination based on the current context; and generate a context- adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

15. A computer-program product comprising a non- transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising: receiving, from a continuous analyte monitoring system, a plurality of analyte measurements of a patient; automatically determining a current context for the patient based on the plurality of analyte measurements; automatically configuring an analyte rate-of-change determination based on the current context; and generating a context-adapted rate-of-change measurement based on the automatically configured analyte rate-of-change determination and the plurality of analyte measurements.

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