Analyte analysis session implementations

Abstract

The present disclosure describes an analyte monitoring system that establishes backup or alternative communication channels that may be used when certain components or connections in the analyte monitoring system become unavailable. The analyte monitoring system may form direct connections between components of the analyte monitoring system and / or select replacement devices when certain components or connections become unavailable.

Description

ANALYTE ANALYSIS SESSION IMPLEMENTATIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No.63 / 778,608, filed March 27, 2025, which is incorporated by reference herein in its entirety, and is hereby expressly made a part of this specification.INTRODUCTION

[0002] Diabetes mellitus is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy', or store glucose as fat.

[0003] When a person eats a meal that contains carbohydrates, the digestive system absorbs nutrients, ultimately depositing glucose in the person's blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high, or too low7. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0004] When the body does not produce enough insulin, or when the body is unable to effectively use insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher than normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), skin ulcers, and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis — a state in which the body becomes excessively acidic due to the production of excess ketones, or body acids. The state of having lower than normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to damage of the heart muscle, neurocognitive dysfunction, and in certain cases, acute crises that can result in seizures or even death.

[0005] A patient living with diabetes can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels.

[0006] Diabetes conditions are sometimes referred to as “Type 1” and “Type 2.” A Type 1 diabetes patient is typically able to use insulin when it is present, but the body is unable to produce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient's body to effectively regulate blood sugar levels.

[0007] Patients with diabetes can benefit from real-time diabetes management guidance, as determined based on a physiological state of the patient, in order to stay within a target glucose range and avoid physical complications. In certain cases, the physiological state of the patient is determined using monitoring systems that measure glucose levels, which inform the identification and / or prediction of adverse glycemic events, such as hyperglycemia and hypoglycemia, and the type of guidance provided to the patient.

[0008] For example, such monitoring systems may utilize a continuous glucose monitor (CGM) to measure a patient’s glucose levels over time. The measured glucose levels may then be processed by the monitoring system to identity' and / or predict adverse glycemic events, and / or to provide guidance to the patient for treatment and or actions to abate or prevent the occurrence of such adverse glycemic events. For example, trends, statistics, or other metrics may be derived from the glucose levels and used to identify and / or predict adverse glycemic events. Or, in certain cases, the glucose levels themselves may be used to identify and / or predict adverse glycemic events.

[0009] Even with the systems described above, however, the management of diabetes presents many challenges for patients, clinicians, and caregivers, as a confluence of various factors can impact a patient's glucose levels, thus affecting the accuracy of glycemic event prediction and the guidance provided by diagnostics systems.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] So that the manner in which the above-recited features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, may be had by reference to aspects, some of which are illustrated in the drawings. It is to be noted,however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.

[0011] FIG. 1 illustrates aspects of an example continuous analyte monitoring system used in connection with implementing embodiments of the present disclosure.

[0012] FIG.2 is a diagram conceptually illustrating example components of the continuous analyte monitoring system of FIG. 1, including example continuous analyte sensor(s) with sensor electronics, according to certain embodiments of the present disclosure.

[0013] FIG.3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the continuous analyte monitoring system of FIG. 1, according to certain embodiments of the present disclosure.

[0014] FIG. 4A illustrates an example operation for determining analysis sessions performed by the continuous analyte monitoring system of FIG. 1.

[0015] FIG.4B illustrates an example operation for determining a topic performed by the continuous analyte monitoring system of FIG. 1.

[0016] FIG. 4C illustrates an example operation for determining an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0017] FIG.4D is a flowchart of an example method for determining an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0018] FIG. 5A illustrates an example operation for determining an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0019] FIG. 5B illustrates an example operation for determining an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0020] FIG.5C is a flowchart of an example method for determining an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0021] FIG. 6A illustrates an example operation for implementing an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0022] FIG. 6B illustrates an example operation for implementing an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0023] FIG. 6C is a flowchart of an example method for implementing an analysis session performed by the continuous analyte monitoring system of FIG. 1.

[0024] FIGs. 7A through 7D illustrate example interfaces for determining and implementing analysis sessions in the continuous analyte monitoring system of FIG. 1.

[0025] FIG. 8 is a block diagram depicting a computer system, according to certain embodiments of the present disclosure.

[0026] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one aspect may be beneficially utilized on other aspects without specific recitation.DETAILED DESCRIPTION

[0027] In a continuous analyte monitoring system, a transcutaneous continuous analyte sensor that is inserted into the interstitial fluid is used to monitor a user’s analyte levels (e.g., glucose levels), thereby, providing analyte concentrations measurements reflective of the physiological state of the user. An analyte may be understood as any substance of interest that is to be measured or is being measured. Examples of such analytes include glucose, ketones, lactate, insulin, electrolytes, creatinine, as well as a number of other biomarkers including proteins, metabolites, and nucleic acids. The sensor electrodes of the transcutaneous continuous analyte sensor may interact with the desired analyte (e.g., through aptamers (singlestranded DNA or RNA molecules that bind to a specific analyte)). An analyte of particular interest may be referred to as a target analyte. For example, in a transcutaneous continuous glucose sensor, the target analyte may be glucose.

[0028] The concentration of the target analyte in the user may cause the continuous analyte sensor to generate an electric signal (e.g., an electric current or voltage). The continuous analyte sensor converts the electric signal into a signal that includes a time series of data points, with each data point indicating a concentration of the target analyte in the user at a particular time. The time series of data points may be presented on a display device of the continuous analyte monitonng system so the user can visualize the target analyte concentration in the user over time. When an analyte concentration (e.g., glucose concentration) changes rapidly orbeyond normal levels (e.g., spikes), the continuous analyte monitoring system may determine that a metabolic event is occurring.

[0029] In an existing system, the user may provide input to explain the cause of the changes in analyte concentration. The system may use these explanations to determine the user’s physiology, activities, diet, etc., which allows the system to provide the user more accurate insights. Initiating and maintaining user engagement with the analyte monitoring system, however, is difficult due to a variety of factors. Scientific evidence suggests that sustaining user engagement with meal logging is a challenge. Due to this lack of engagement, users often do not log supplemental contextual data (such as exercise and meal details) within the system, and in response, the system may have to infer, estimate, or otherwise guess the missing user context. These inferences, estimates, and / or guesses reduce the accuracy and relevance of insights and recommendations that the system determines and provides. Consequently, the insights and recommendations are often of limited value or are inapplicable to the user, and as a result, the system has wasted processing and power resources (e.g., logging data that was unhelpful, determining and presenting inaccurate recommendations and insights, etc.).

[0030] Additionally, the user may determine that the recommendations / insights are overgeneralized and inaccurate. For example, the user may feel that the user’s specific physiology, activities, and diet are insufficiently accounted for when computing and presenting the recommendations / insights. and the user is less likely to log data or follow provided guidance due to the perceived inaccuracy or insufficient contextual relevance of such guidance, which may have a negative impact on their overall health. As a result, the recommendations and insights further discourage the user from engaging w ith the system.

[0031] The present disclosure describes an analyte monitoring system in which a user can establish and / or initiate analysis sessions to test the effects of certain variables (e.g., meals, activities, medication, etc.) on the user's analyte levels (e.g., glucose levels). For example, the user may select an analysis session from a list of preset or predetermined analysis sessions, or the user may design an analysis session based on one or more user goals. The analysis session may occur according to a schedule, and during the analysis session, the system may monitor and log the user’s analyte levels with respect to particular parameters set for the analysis session (e.g.. specific activities to monitor, specific foods to monitor, specific analyte levels that trigger monitoring, etc.). The system may analyze the data from the analysis session to determine theeffects that certain variables have on the user’s analyte levels. The system may then present these determinations to the user.

[0032] In particular embodiments, the analyte monitoring system provides several technical advantages. For example, the analyte monitoring system improves the operation of a computer by reducing waste of processor and / or power resources. The system determines and implements analysis sessions, which may avoid wasting processor and power resources generating inaccurate or irrelevant insights and recommendations. Thus, the analyte monitoring system more efficiently uses the processor and power resources available to the analyte monitoring system, which improves the functioning and operation of the analyte monitoring system. As another example, the analyte monitoring system provides the technical improvement of more accurate and relevant insights to a user. Thus, by implementing the technical feature of analysis sessions, the analyte monitoring system provides the technical advantage of more accurate and relevant insights and recommendations. Additionally, the more accurate or relevant insights and recommendations may improve the health of the user.

[0033] As used herein, the term “continuous” analyte monitoring refers to monitoring one or more analytes in a fully continuous or 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 insight to be derived using the algorithms described herein. In other words, single point-in-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 meaningful data or insight to be derived.

[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. 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 analog electrical signals (e.g., electric current or voltage) generated by the continuous analyte sensor(s) into sensor count values, (2) calibrating the count values to generate analyte concentration values using calibration techniques described herein to account for the sensitivity of thecontinuous analyte sensor(s), and (3) transmitting measured analyte concentration data, including analyte concentration values, to a display device via wireless connection.

[0035] For example, the continuous analyte monitoring system may 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 transmit the measured 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] Because of the real-time nature of the data generated by the continuous analyte monitoring system, it is 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. In other words, deriving meaningful data and insight from a stream of realtime 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 herein in real-time and on a continuous basis, which would involve using a stream of real-time data that is continuously generated by a continuous analyte monitoring system and / or significantly large amount of data is not a task that can be mentally performed, especially in real-time.

[0037] FIG. 1 illustrates an example of a continuous analyte monitoring system 100, in accordance with certain embodiments of the disclosure. As seen in FIG. 1, the continuous analyte monitoring system 100 includes an analyte sensor 104, a display device 107 that executes an application 106, a host database 110, an historical records database 112, a training server system 140, and a management engine 114, each of which is described in more detail below.

[0038] The analyte sensor 104 generates time-series data, such as analyte measurements (e.g., sensor data or analyte sensor measurements), for the user 102 (e.g., on a continuous basis) and transmits the analyte measurements to the display device 107 for use by the application 106. The analyte sensor 104 may transmit the analyte measurements to the display device 107 through a wireless connection (e.g., Bluetooth connection). The display device 107 may be a smart phone or any other type of computing device capable of executing the application 106, such as a laptop computer, a smartwatch, and / or a tablet.

[0039] In certain examples, the analyte sensor 104 is assumed to be a glucose monitoring system, but the analyte sensor 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).

[0040] 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.

[0041] 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 hydroxycholic 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, beta-thalassemia,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 -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; glycochohc acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-l, P); lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, 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, Wuchereriabancrofti, 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.

[0042] 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. Tai win, 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 -hydroxy tryptamine (5HT), histamine. Advanced Glycation End Products (AGEs) and 5-hydroxyindoleacetic acid (FHIAA).

[0043] The application 106 may be a mobile health application that receives and analyzes time-series data, including analyte measurements, from the analyte sensor 104 and / or other devices. The application 106 may transmit analyte measurements received from the analyte sensor 104 to the host database 110 (and / or the historical records database 112). and the host database 110 (and / or the historical records database 112) may store the analyte measurements in a host profile 118 of the user 102 for processing and analysis, for example, by the training server system 140 or the management engine 114, based on contextual data supplied by the user 102. In some embodiments, the application 106 may store the analyte measurements in the host profile 118 of the user 102 locally for processing and analysis, for example, by the training server system 140 or the management engine 114, based on contextual data supplied by the user 102.

[0044] The application 106 may provide and present various interfaces for receiving, from the user 102, contextual data of the type discussed previously. In an example, the application 106 provides a user interface that allows the user 102 to log actions that the user 102 performed. In another example, the application 106 provides a user interface that allows the user 102 to link actions to detected metabolic events. The application 106 may also provide a user interfacethat presents information about detected metabolic events (e.g., the measured analyte concentrations during the events).

[0045] The application 106 may take as input information relating to the user 102 and store the information in a host profile 118 for the user 102 in the host database 110. For example, the application 106 may obtain and record the analyte concentration measurements for the user 102, the metabolic events detected in the user 102, and / or the actions logged by the user 102 in the host profile 118. The application 106, the training server system 140, and / or the management engine 114 may treat the information in the host profile 118 as historical data about the user 102. This historical data may provide insights about the user 102, such as the actions that cause certain metabolic events in the user 102 and the analyte concentrations or changes in the analyte concentrations that indicate that metabolic events are occurring in the user 102.

[0046] The application 106 collects inputs through user input and / or other sources, including the analyte sensor 104, other applications running on the display device 107, and / or one or more other sensors and devices. These sensors and devices may include one or more of, but are not limited to, an insulin pump, other types of analyte sensors, sensors or devices provided by the display device 107 (e.g., accelerometer, camera, global positioning system (GPS), heart rate monitor, etc.), other user accessories (e.g., a smartwatch), or any other sensors or devices that provide relevant information about the user 102. In certain embodiments, the host profile 118 also stores application configuration information indicating the cunent configuration of the application 106, including features and settings.

[0047] The host database 110 may be a storage server that operates in a public or private cloud. The host database 110 may be implemented as any t pe 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 implementations, the host database 110 is distributed. For example, the host database 110 may include persistent storage devices, which are distributed. Furthermore, the host database 110 may be replicated so that the storage devices are geographically dispersed.

[0048] The host database 110 may include other host profiles 118 for other users. 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 analyte sensor 104, andalso interact with the same application 106, copies of which execute on the respective display devices of the other users. For such users, the host profiles 118 are similarly created and stored in the host database 110.

[0049] Further, in certain embodiments, the host database 110 may store data for other users or people. For example, the host database 110 may include information (e.g., profile(s)) related to one or more users or hosts known to be metabolically unfit. Data stored in the host database 110 may be referred to herein as population data, which could include hundreds or thousands of data points for each one of thousands or millions of people in the population. In other words, data stored in the host database 110 and used in certain embodiments described herein could include gigabytes, terabytes, petabytes, exabytes, etc. of data.

[0050] The host profile 118 may include application data 128 collected about the user 102 from the application 106. For example, the application 106 provides a set of inputs 130, including the analyte measurements received from the analyte sensor 104. In certain embodiments, the inputs 130 provided by the application 106 include other data in addition to analyte measurements. For example, the application 106 may obtain additional inputs 130 through manual host input, one or more other non-analyte sensors or devices, other applications executing on the display device 107, etc.

[0051] The host profile 118 also includes demographic information 120, physiological information 122, disease information 124, medication information 126, and / or behavioral context information 127 for the user 102. In certain embodiments, such information may be provided through user input or obtained from certain data stores (e.g., electronic medical records (EMRs), etc.). The demographic information 120 may include one or more of the age, ethnicity, gender, etc. of the user 102. The physiological information 122 may include one or more of the height, weight, cardiovascular health, body mass index (BMI), pregnancy status, and / or due date of the user 102. The disease information 124 may include information about one or more diseases of the user 102, including relevant information pertaining to cardiovascular disease or congenital heart defects (CHDs), kidney disease, kidney dysfunction, and / or acute kidney injury, liver disease and / or liver dysfunction, diabetes, lung diseases, adrenal gland disorders, and / or other health conditions, syndromes, or diseases. The disease information 124 may also include the length of time since diagnosis, disease progression information, the level of disease control, level of compliance with disease management therapy,other types of diagnoses (e.g., obesity, hormone imbalances, hyperkalemia or hypokalemia, etc.), and the like. The disease information 124 may include hospitalizations and / or surgical history. In some instances, the disease information 124 may include other measures of health (e.g., heart rate, heart rhythm, blood pressure, stress, sleep, etc.) or fitness (e.g., cardiovascular endurance, metabolic state, muscular endurance, and other measures of fitness), and / or the like. The behavioral context information 127 may include user-provided data that covers attitudes, perceptions, routines, activity levels, and other lifestyle habits, all of which may influence metabolic health and analyte (e.g., glucose) levels.

[0052] The medication information 126 may include information about the amount, frequency, and / or type of a medication taken by, or prescribed to, the user 102. The amount, frequency, and type of a medication taken by the user 102 is time-stamped and correlated with the analyte levels of the user 102, thereby, indicating the impact the amount, frequency , and type of the medication had on the analyte levels. The medication information 126 may include information about the consumption of one or more drugs, including renin-angiotensin-aldosterone system inhibitors (RAASi), mineral corticoid receptor antagonists (MRAs), angiotensin-converting enzy me inhibitors (ACE inhibitors), angiotensin-receptor blockers (ARBs), other heart drugs such as amiodarone and clopidogrel, diuretics (which may be used to treat excessive fluid accumulation caused by, for example, heart failure (HF), liver failure, and / or nephritic syndrome) such as loop diuretics, thiazide and thiazide-like diuretics, and potassium-sparing diuretics, antibiotics such as amoxicillin / clavulanate, clindamycin, erythromycin, nitrofurantoin, rifampin, sulfonamides, tetracyclines, trimethoprim / sulfamethoxazole, vancomycin, and drugs used to treat tuberculosis (isoniazid and pyrazinamide), anticonvulsants such as tarbamazepine, thenobarbital, phenytoin, and valproate, antidepressants such as bupropion, fluoxetine, mirtazapine, paroxetine, sertraline, trazodone, and tricyclic antidepressants such as amitriptyline, antifungal drugs such as ketoconazole and terbinafine, antihypertensive drugs (e.g., drugs used to treat high blood pressure or sometimes kidney or heart disorder) such as captopril, enalapril, irbesartan, lisinopril, losartan, and verapamil, antipsychotic drugs such as phenothiazines (e.g., such as chlorpromazine) and risperidone, hormone regulation drugs such as anabolic steroids, birth control pills (oral contraceptives), and estrogens, pain relievers such as acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), and other drugs such as acarbose (e.g.. used to treat diabetes), allopurinol (e.g., used to treat gout), antiretroviral therapy (ART) drugs (e.g.,used to treat human immunodeficiency virus (HIV) infection), baclofen (e.g., a muscle relaxant), cyproheptadine (e.g., an antihistamine), azathioprine (e.g., used to prevent rejection of an organ transplant), methotrexate (e.g.. used to treat cancer), omeprazole (e.g., used to treat gastroesophageal reflux), PD-1 / PD-L1 inhibitors (e.g., anticancer drugs), statins (e.g., used to treat high cholesterol levels), ademetionine, avatrombopag, dehydroemetine, entecavir, glecaprevir and pibrentasvir, lamivudine, lithium, metadoxine, methionine, sofosbuvir, velpatasvir, and voxilaprevir, telbivudine, tenofovir. trientine, tacrolimus and other calcineurin inhibitors, ursodeoxycholic acid, many types of chemotherapies, including immune checkpoint inhibitors, drugs for treatment of acute hyper- and / or hypokalemia, such as insulin, dextrose, and glucose, and the like.

[0053] The host profile 118 may be dynamic because at least part of the information that is stored in the host profile 118 may be revised or updated over time and / or new information may be added to the host profile 118 by the management engine 114 and / or the application 106. Accordingly, information in the host profile 118 stored in the host database 110 provides an up-to-date repository of information related to the user 102.

[0054] The host profile 118 stored in the host database 110 may also be stored in the historical records database 112. The historical records database 112 may provide a repository of up-to-date information and historical information for the user 102. Thus, the historical records database 112 essentially provides data related to the user 102, where data is stored using timestamps. The timestamp associated with any piece of information stored in the historical records database 112 may identify, for example, when the piece of information was obtained and / or updated.

[0055] Further, the historical records database 112 may include data collected for one or more users over a period of time, including users who are hosts of the analyte sensor 104 and / or the application 106, as well as users who are not hosts of the analyte sensor 104 and / or the application 106. For example, the historical records database 112 may include information (e.g., user profile(s)) related to one or more hosts analyzed by, for example, a healthcare physician (or other known method), and not previously diagnosed with heart failure, kidney disease, and / or other indications, as well as information (e.g., user profile(s)) related to one or more hosts who were analyzed by, for example, a healthcare physician (or other knownmethod) and were previously diagnosed with (varying types and stages of) heart failure, kidney disease, and / or other indications.

[0056] Data stored in the historical records database 112 may be referred to herein as population data, which could include hundreds or thousands of data points for each one of thousands or millions of hosts in the host population. In other words, data stored in the historical records database 112 could include gigabytes, terabytes, petabytes, exabytes, etc. of data.

[0057] Data related to each host stored in the historical records database 112 may provide time series data collected over the lifetime of the host, a period of the lifetime of the host, and / or a disease lifetime of the host. For example, the data may include information about the host prior to being diagnosed with HF, kidney disease, and / or other indications, and information associated with the patient during the lifetime of the disease, including information related to each stage of the disease as it progressed and / or regressed in the patient, as well as information related to other diseases, such as hy perkalemia, hypokalemia, diabetes, or similar diseases that are co-morbid in relation thereto. The data may also include physiological information (e g., height and weight), as well as non- analyte sensor data (e g., heart rate, respiratory rate, etc.). Such data may indicate physiological states of the host, potassium levels of the host, glucose levels of the host, lactate levels of the host, insulin levels of host, other hormone levels of the host, states / conditions of one or more organs of the host, habits of the host (e.g., activity levels, food consumption, etc.), medication prescribed throughout the lifetime of the disease, as well as progress of outcomes such as weight loss and cardiovascular health over time, etc.

[0058] Although depicted as separate databases for conceptual clarity', in certain embodiments, the host database 110 and the historical records database 112 may operate as a single database. That is. historical and current data related to hosts, as well as historical data related to hosts that were not previously hosts, may be stored in a single database. The single database may be a storage server that operates in a public or private cloud.

[0059] The management engine 114 includes a set of software instructions with one or more software modules, including a data analysis module (DAM) 116. The management engine 114 may execute entirely on one or more computing devices in a private or a public cloud. The application 106 communicates with the management engine 114 over a network(e.g., the Internet). The management engine 114 may execute partially on one or more local devices, such as the display device 107, and partially on one or more computing devices in a private or a public cloud. The management engine 114 may execute entirely on one or more local devices, such as the display device 107. As discussed in more detail herein, the management engine 114 may provide therapy management support recommendations to the user 102 via the application 106. The management engine 114 provides therapy management support recommendations based on information included in the host profile 118.

[0060] The DAM 116 of the management engine 114 may process the set of inputs 130 to determine one or more metrics 132. The metrics 132 may generally indicate the health or state of the user 102, such as one or more of the physiological state, trends associated with the health or state, etc. In certain embodiments, the metrics 132 may then be used by the management engine 114 as input for providing guidance to the user 102. As shown, the metrics 132 are also stored in host profile 118.

[0061] The management engine 114 may (1) provide real-time and or non-real-time therapy management guidance (e.g.. guidance) to the user 102 and or others, including but not limited, to healthcare providers, family members of the host, caregivers of the host, etc., and / or (2) provide real-time instructions to an automated medication deliver}' device for automatically adjusting medication administration, including dose and timing parameters, for the user 102. Therapy management support may be intended to provide optimal medication administration guidance to treat the indication (e.g., prevent development and / or progression of the indicated disease state) of the administered medication.

[0062] In particular, the management engine 114 may collect information associated with the user 102 in the host profde 118 stored in the host database 110 and to perform analytics thereon to determine a risk or presence of a disease. Based on the determination, the management engine 114 may optimize therapy for a disease state of the user 102. Optimizing therapy, as described above, may include providing optimized therapy management guidance to the user 102 and / or automatically controlling the operations of a medication pump, such as by adjusting a flow rate, dose, etc. of the therapy, as well as the timing of the therapy. The host profile 118 may be accessible to the management engine 114 over one or more networks (not shown) for performing such analytics.

[0063] The management engine 114 may utilize one or more rule-based algorithms or trained machine learning models capable of performing analytics on information that the management engine 114 has collected / received from the host profile 118. In the illustrated embodiment of FIG. 1, the management engine 114 may utilize a trained machine learning model provided by the training server system 140. Although depicted as a separate server for conceptual clarity', in certain embodiments, the training ser er system 140 and the management engine 114 may operate as a single server or system. That is, the model may be trained and used by a single server, or may be trained by one or more servers and deployed for use on one or more other servers or systems. In certain embodiments, the model may be trained on one or many virtual machines (VMs) running, at least partially, on one or many physical sendees in relational and or non-relational database formats.

[0064] The training server system 140 trains the machine learning model(s) using training data, which may include data (e.g.. from host profiles) associated one or more hosts (e.g., hosts or non-hosts) previously diagnosed with, for example, varying stages of disease, as well as hosts not previously diagnosed with disease. The training data may be stored in the historical records database 112 and may be accessible to the training server system 140 over one or more networks (not shown) for training the machine learning model(s).

[0065] The training data refers to a dataset that has been featurized and labeled. For example, the dataset may include a plurality of data records, each including information corresponding to a different host profile stored in the host database 110, where each data record is featurized and labeled. In machine learning and pattern recognition, a feature is an individual measurable property or characteristic. Generally, the features that best characterize the patterns in the data are selected to create predictive machine learning models. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. As an illustrative example, each relevant characteristic of a host, which is reflected in a corresponding data record, may be a feature used in training the machine learning model.

[0066] Such features may include demographic information (e.g., age, gender, ethnicity, etc.), analyte information (e.g., analyte levels (e.g., analyte baseline, analyte threshold, analyte clearance rate, and / or analyte rate of change during and after administration of a medication, etc.)), non-analyte sensor information (e g., heart rate, temperature, etc ), cardiovascular healthinformation (e.g., cardiovascular disease diagnosis (e.g., HF) and staging), co-morbidities (e.g., kidney disease), and / or any other information relevant to optimizing therapy for the hosts. In addition, the data record is labeled with information the corresponding model is being trained to predict. In one example, if a model is being trained to output optimized therapy parameters, then the data records in the training dataset are labeled with one or more of such parameters. Note that, in one example, such a model may be a multi-input single-output (MISO) model that predicts one optimized therapy parameter (e.g., dosage), in which case additional MISO models may be trained, each predicting one of other therapy parameters (e.g., timing, etc.). In another example, such a model may be a multi-input multi-output (MIMO) model that predicts multiple optimized therapy parameters (e.g., dosage and time, etc.).

[0067] The model(s) are then trained by the training server system 140 using the featurized and labeled training data. In particular, the features of each data record may be used as input into the machine learning model(s), and the generated output may be compared to label(s) associated with the corresponding data record. The model(s) may compute a loss based on the difference between the generated output and the provided label(s). This loss is then used to modify the internal parameters or weights of the model. By iteratively processing each data record corresponding to each historical host, the model(s) may be iteratively refined to generate accurate determinations of a host’s risk or presence of potassium imbalance, optimized therapy parameters to treat a disease state of the host and simultaneously stabilize or maintain potassium levels, etc.

[0068] As illustrated in FIG. 1, the training sen' er system 140 deploys these trained model(s) to the management engine 114 for use during runtime. For example, the management engine 114 may obtain the host profile 118 associated with a host and stored in the host database 110, use information in the host profile 118 as input into the trained model(s), and output a determination indicative of the host’s risk or presence of disease, and / or suggested therapy parameters to treat a disease state of the host (e.g., shown as output 144 in FIG. 1).The output 144 generated by the management engine 114 may also indicate improvement in the host’s health over time. The output 144 may be provided to the host (e.g., through the application 106), to a caretaker of the host (e.g., a parent, a relative, a guardian, a teacher, a physical therapist, a fitness trainer, a nurse, etc.), to a physician or healthcare provider of the host, or any other individual that has an interest in the w ellbeing of the host for purposes ofimproving the health of the host, such as, in some cases by effectuating recommended therapy. The output 144 generated by the management engine 114 is stored in the host database 110 and is utilized to train or re-train the trained model(s).

[0069] In certain embodiments, the output 144 generated by the management engine 114 may be stored in the host profile 118. The output 144 stored in the host profile 118 may be continuously updated by the management engine 114. Accordingly, for example, suggested therapy parameters, originally stored as the outputs 144 in the host profile 118 in the host database 110 and then passed to the historical records database 112, may provide an indication of the progression of the physiological status (e.g., disease state) of a host over time, as well as provide an indication as to the effectiveness of different recommendations.

[0070] In certain embodiments, a host’s own historical data may be used by the training server system 140 to train a personalized model for the host that provides therapy management support and insight around the host’s disease. For example, in certain embodiments, a model trained based on population data may be used to provide optimized therapy parameters to the host. However, after collecting personalized information (e.g., analyte sensor information, non-analyte sensor information, etc.) associated with the host during one or more administrations of therapy, the personalized information may be used for further personalizing the model. For example, information obtained during a prior administration of therapy for the host may be used to optimize therapy parameters for future administrations of such therapy.

[0071] In certain embodiments, a model may be trained to provide food, lifestyle, and other types of therapy management support recommendations based on the host’s historical data, including how different types of food and / or activities impacted the host’s health in the past. In certain embodiments, a model may be trained to predict the underlying cause of certain improvements or deteriorations in the host’s analyte levels. For example, the application 106 may display a user interface with a graph that shows the host's analyte levels with trend lines and indicate, e.g., retrospectively, how analyte levels were affected at certain points in time.

[0072] The continuous analyte monitoring system 100 also includes one or more devices 146 that serve as extra or backup devices. The device 146 may connect to the management engine 114 and receive, from the management engine 114, data from the analyte sensor 104 and / or the display device 107. The device 146 may then present the received data. The device146 may be used by another user (e.g., family member, healthcare provider, etc.) to monitor the analyte measurements of the user 102.

[0073] FIG. 2 is a diagram 200 conceptually illustrating example components of the continuous analyte monitoring system 100 of FIG. 1, including example continuous analyte sensor(s) with sensor electronics, according to certain embodiments of the present disclosure. For example, the analyte sensor 104 may continuously monitor one or more analytes of a host, in accordance wi th certain aspects of the present disclosure.

[0074] The analyte sensor 104 in the illustrated embodiment includes sensor electronics module 204 and one or more sensor(s) 202 (individually referred to herein as sensor 202 or continuous analyte sensor 202. and collectively referred to herein as sensors 202 or continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 may be in wireless communication (e.g., directly or indirectly) with one or more display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also be in wireless communication (e.g., directly or indirectly) with one or more medical devices, such as medical devices 208 (individually referred to herein as medical device 208 and collectively referred to herein as medical devices 208), and / or one or more other nonanalyte sensors 206 (individually referred to herein as non-analyte sensor 206 and collectively referred to herein as non-analyte sensor 206).

[0075] The sensor 202 may include one or more sensors for detecting and / or measuring analyte(s). The sensor 202 may be a multi-analyte sensor that continuously measures two or more analytes or a single analyte sensor that continuously measures a single analyte as a non-invasive device, a subcutaneous device, a transcutaneous device, atransdermal device, and / or an intravascular device. In certain embodiments, the sensor 202 may continuously measure analyte levels of a host 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 sensor 202 provides a data stream indicative of the concentration of one or more analytes in the host. 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 host.

[0076] The sensor 202 may be a multi-analyte sensor that continuously measures multiple analytes in a host's body. For example, the continuous multi-analyte sensor 202 may be a single sensor that measures glucose, lactate, calcium, creatinine, ketones (e.g., 3-beta-hydroxybutyrate, acetoacetate, acetone, etc.), glycerol, and / or free fatty acids in the host’s body.

[0077] 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 multianalyte sensor may continuously measure potassium, glucose, and / or lactate, and may, in some cases, be used in combination with an analyte sensor that measures only calcium, ketones, creatinine, or another analyte. 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. 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 scale information or non-contact heart rate monitoring from a sensor pad under the host while in a chair or bed, through an infra-red camera detecting temperature and / or blood flow7patterns of the host, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0078] The sensor 202 may include a percutaneous wire that has a proximal portion coupled to the sensor electronics module 204 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 an 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 204. After the analyte sensor 104 has been applied to the epidermis of the host, the sensor 202 penetrates the epidermis, and the distal portion extends into the dermis and / or subcutaneous tissue under epidermis. Other configurations of the sensor 202 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.

[0079] 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, the sensor 202 may include a single-analyte sensor that measures glucose concentration levels, and another single-analyte sensor that measures lactate concentration levels of the patient. As another illustrative example, the sensor 202 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, the sensor 202 may include a multi-analyte sensor that measures glucose concentration levels, lactate concentration levels, calcium concentration levels, ketone concentration levels, creatinine concentration levels, etc.

[0080] Accordingly, the sensor 202 generates at least one analog electrical signal that is proportional to the concentration level of a particular analyte, and the sensor electronics module 204 converts the analog electrical signal into analyte sensor count values, calibrates the analyte sensor count values based on the sensitivity profile of the sensor 202 to generate measured analyte concentration levels, and transmits the measured analyte concentration level data, including the measured analyte concentration levels, to a display device, such as display devices 210, 220, and / or 230, via a wireless connection. For example, the sensor electronics module 204 may 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, 60 minutes, 4 hours. 8 hours, 12 hours, 24 hours, etc., and 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, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, 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 may include at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0081] The sensor 202 may include a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 204, which may be used to correct the analog electrical signal or the measured analyte data for temperature. Inother embodiments, the thermocouple may be incorporated into the sensor electronics module 204 above the adhesive pad, or, alternatively, the thermocouple may contact the epidermis of the host through openings in the adhesive pad.

[0082] The sensor 202 may be or include an aptamer sensor, such as an electrochemical aptamer sensor, that continuously measures one or more analytes in a host’s body. Aptamers are peptides or oligonucleotides with high sensitivity and selectivity for the detection of various types of analytes ranging from nucleotides, peptides, proteins, and small molecules to cells. If multiple aptamers are combined into a single sensor 202, the sensor 202 can measure large numbers of different analytes in the host's body.

[0083] One or more aptamer sensors can be used to directly detect and / or measure an amount of medication(s) administered to a host (e.g., a medication that has entered the body of the host), or can be used to detect and / or measure an amount of protein, enz me, or other metabolite of the medication(s) to indirectly detect and / or measure an amount of the medication(s). Accordingly, in such embodiments, the analyte monitoring system 100 may utilize the one or more aptamer sensors to monitor a host's medication compliance. For example, HF patients are often prescribed multiple medications for treating their disease state, and each medication can have a different administration schedule. One or more aptamer sensors may therefore enable the analy te monitoring system 100 to monitor one or more of the prescribed medications and determine if, and when, each of the one or more medications was taken, and the doses thereof. Information gathered about the medication(s) and the host’s medical compliance can, in turn, inform therapy management guidance (e.g., a suggested timing and / or dose of medication(s) and / or a recommendation for administration of an additional medication) provided by the analyte monitoring system 100.

[0084] The analyte monitoring system 100 may utilize one or more aptamer sensors to assist in titration of one or more medications taken by a host. For example, the one or more aptamer sensors can be used to continuously monitor the effects of the medications on the host (e.g., via measuring one or more levels of the medication itself or a metabolite of the medication). Based on the monitored effects, the analyte monitoring system 100 can provide real-time recommendations to the host and / or a healthcare provider to up-titrate or down- titrate one or more medications to reach a target, or optimal (most effective), medication dose for the host. The recommendations may be based on a measured absolute concentration of amedication detected in a host. In certain embodiments, the measured concentration of the medication can be compared to one or more threshold concentrations to identify a risk or presence of potential adverse events associated with threshold concentrations based on historical population data. If the measured concentration of the medication is determined to be above or below a threshold concentration of the one or more threshold concentrations, the host can be directed to up-titrate or down-titrate their medication dose as appropriate to avoid the potential adverse effects. In certain embodiments, one or more aptamer sensors can monitor a relative change in medication concentration relative to a target metric. The target metric may be based on a desired change in medication concentration when the measured concentration of the medication is associated, or mapped, with one or more negative symptoms (e.g., shortness of breath, potassium imbalance, etc.).

[0085] The analyte monitoring system 100 may utilize one or more aptamer sensors, in combination with other analyte and / or non- analyte sensors as described above, to prevent hospital readmissions of a host. For example, upon discharge from a hospital, the host may be provided a sensor, such as an aptamer sensor, to monitor medication adherence of the host and the efficacy of the prescribed dosage. The analyte monitoring system 100 or a healthcare provider can then monitor the host's medication adherence, as well as other analyte and / or nonanalyte data, to then identify and predict potential adverse events, and recommend therapy management guidance to the host to prevent the adverse health events from occurring without requiring hospital readmission.

[0086] In certain embodiments, the sensor electronics module 204 includes a processor 233, a storage element or memory 234, a wireless transmitter / receiver (transceiver) 236, one or more antennas coupled to wireless transceiver 236, 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) 202 (such as a potentiostat), etc.

[0087] The processor 233 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 204. The processor 233 may include a single integrated circuit, such as a micro processing device, or multiple integrated circuit devices and / or circuit boards working in cooperation to accomplish the appropriate functionality. Incertain embodiments, the processor 233, the memory 234, the wireless transceiver 236, the A / D signal processing circuitry, and the digital signal processing circuitry may be combined into a system-on-chip (SoC).

[0088] Generally, the processor 233 may sample the analog electrical signal using the A / D signal processing circuitry at regular intervals (such as the sampling period) to generate analyte sensor count values based on the analog electrical signals produced by the continuous analyte sensor(s) 202, calibrate the analyte sensor count values based on the sensitivity profile of the sensor 202 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. The processor 233 may store the measured analyte concentration level data in the memory 234 and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by the wireless transceiver 236 to a display device, such as display devices 210, 220, 230, and / or 240. The processor 233 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

[0089] The memory 234 may include volatile and nonvolatile media. For example, the memory 234 may include combinations of random-access memory7(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. The memory 234 may store one or more analyte sensor system applications, modules, instruction sets, etc. for execution by the processor 233, such as instructions to generate measured analyte data from the analyte sensor count values, etc.

[0090] The memory7234 may also store certain sensor operating parameters 235, 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 profile for the sensor electronics module 204 may be programmed into the sensor electronicsmodule 204 during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, the calibration slope may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mr), which are stored in the memory 234 and used to convert the analyte sensor electrical signals into measured analyte concentration levels. The calibration sensitivity (Mcc) 246 and / or calibration baseline 247 may also be stored in the memory 234.

[0091] The sensor electronics module 204 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. The sensor electronics module 204 can be physically connected to the sensor 202 and can be integral with (non-releasably attached to) or releasably attachable to the sensor 202. The sensor electronics module 204 may include hardware, firmware, and / or software that enable measurement of levels of analyte(s) via the sensor 202. For example, the sensor electronics module 204 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.

[0092] The display devices 210, 220, 230, and / or 240 display displayable sensor data, including analyte data, which may' be transmitted by the sensor electronics module 204. Each of the display devices 210, 220, 230, or 240 may include a display such as a touchscreen display 212, 222. 232, and / or 242 for displaying sensor data to a host and / or for receiving inputs from the host. For example, a graphical user interface (GUI) may be presented to the host 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 host of the display device and / or for receiving host inputs. The display devices 210, 220, 230, and 240 may be examples of the display device 107 illustrated in FIG. 1 used to display sensor data to a host of the system of FIG. 1 and / or to receive input from the host.

[0093] In certain embodiments, one, some, or all of the display devices 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.

[0094] The display devices 210, 220, 230, and / or 240 may display received sensor data at a particular display rate, such as every' 1 second, 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, etc. The display rate of the display devices 210, 220. 230, and / or 240 may be less than, substantially equal to, or longer than the sampling period (or rate) and / or transmission period (or rate) of the sensor electronics module 204. Generally, the display devices 210, 220, 230, and / or 240 can display individual sensor measurements, measurement trends, and / or aggregate measurement statistics such as hourly, daily, weekly, and monthly aggregate values and deviations from trends.

[0095] The display devices may include a custom display device specially designed for displaying certain types of displayable sensor data associated with analyte data received from the sensor electronics module. In certain embodiments, the display devices may provide alerts / alarms based on the displayable sensor data. The display device 210 is an example of such a custom device. In certain embodiments, one of the display devices is a smartphone, such as the display device 220, 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 the display device 230, which represents a tablet or phablet, the display device 240, which represents a smart watch or fitness tracker, the medical device 208 (e.g., a medication administration device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0096] 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 host) for each particular display device. Accordingly, in certain embodiments, different display devices can be in direct wireless communication with a sensor electronics module (e.g., such as an on-skin sensor electronics module 204 that is physically connected to the sensor 202) during asensor session to enable a plurality of different types and / or levels of display and / or functionality associated with the displayable sensor data.

[0097] The sensor electronics module 204 may be in communication with the medical device 208. The medical device 208 may be a passive device. For example, the medical device 208 may be a medication pump for administering one or more medications to a host, such as one or more medications for treating diabetes. For a variety of reasons, it may be desirable for such a medication pump to receive and track potassium, lactate, glucose, calcium, creatinine, and / or other analytes transmitted from the continuous analyte monitoring systems, where the sensor 202 measures glucose, lactate, , calcium, creatinine, and / or other analytes. In certain embodiments, the medical device 208 may include an insulin pump.

[0098] Further, as mentioned, the sensor electronics module 204 may also be in communication with other non-analyte sensors 206. The non-analyte sensors 206 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. The non-analyte sensors 206 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, cardiovascular implantable electronic devices (CIEDs) such as implantable cardioverter defibrillators (ICDs), pacemakers (PMs), cardiac resynchronization therapy (CRT) devices, implantable loop recorders (ILRs), and implantable hemodynamic monitors (IHMs), caloric intake monitors, indirect calorimetry devices and medicament administration / delivery devices. One or more of these non-analyte sensors 206 may provide data to the management engine 114 described further below. In some aspects, a host may manually provide some of the data for processing by the training server system 140 and / or the management engine 114 of FIG. 1. The non-analyte sensors 206 may further include sensors for measuring skin temperature, core temperature, sweat rate, and / or sweat composition.

[0099] In certain embodiments, the non-analyte sensors 206 may be combined in any other configuration, such as, for example, combined with one or more sensors 202. As an illustrative example, anon-analyte sensor, e.g., a temperature sensor, may be combined with a sensor 202 to form an analyte / temperature sensor used to transmit sensor data to the sensor electronics module 204 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 202 tomeasure glucose and / or lactate to form a glucose / lactate / temperature sensor used to transmit sensor data to the sensor electronics module 204 using common communication circuitry.

[0100] In certain embodiments, a wireless access point (WAP) may be used to couple the analyte sensor 104, the display devices, the medical device(s) 208, and / or the non-analyte sensor(s) 206 to one another. For example, the WAP 138 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 200 of FIG. 2.

[0100] FIG.3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the analyte monitoring system 100 of FIG. 1, according to some embodiments disclosed herein. In particular, FIG. 3 provides a more detailed illustration of example inputs and example metrics introduced in FIG. 1.

[0101] FIG.3 illustrates example inputs 130 on the left, the application 106 and the DAM 116 in the middle, and the metrics 132 on the right. In certain embodiments, each one of the metrics 132 may correspond to one or more values, e.g., discrete numerical values, ranges, or qualitative values (high / medium / low, stable / unstable, etc.). The application 106 obtains inputs 130 through one or more channels (e.g., manual host input, sensors, other applications executing on display device 107, an EMR system, etc.). As mentioned previously, in certain embodiments, the inputs 130 may be processed by the DAM 116 to output a plurality of metrics, such as the metrics 132. The inputs 130 and the metrics 132 may be used by the training server system 140 and the management engine 114 to both train and deploy one or more machine learning models for determining disease progression, providing therapy management guidance or treatment, and other functionalities described herein.

[0102] Starting with the inputs 130, host statistics, such as one or more of age, height, weight, BMI, body composition (e.g., % body fat or % muscle from a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, dual-energy7X-ray absorptiometry (DEXA) scan, etc.), stature, build, or other information may also be provided as an input. In certain embodiments, the host statistics are provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from measurement devices. In certain embodiments, the measurement devices include one or more wireless devices, e.g., Bluetooth-enabled, weight scale and / or camera, which may, for example, communicate with the display device 107 to provide the host data.

[0103] The medication / treatment information may also be provided as an input. Medication information may include information about the type, dose, and / or timing of when one or more medications are to be taken by the host. As mentioned elsewhere herein, the medication information may include information about one or more medications prescribed to the host for treating one or more symptoms of cardiovascular disease (e.g., HF), kidney disease, diabetes, and / or other conditions. In certain embodiments, the medication information includes information about administration of insulin, dextrose, and / or glucose, and / or other drugs presenbed for treatment of acute hyper- and / or hypokalemia or other acute conditions. Treatment information may further include information regarding different lifestyle habits, surgical procedures, dialysis, and / or other invasive or non-invasive procedures recommended by the host’s physician. For example, the host’s physician may recommend a user increase / decrease their potassium intake, or exercise for a minimum of thirty minutes a day, to reduce hyper- and / or hypokalemic episodes, etc. The medication / treatment information may be provided through manual host input.

[0104] In certain embodiments, the analyte sensor data may also be provided as input, for example, through the continuous analyte sensor 104. The analyte sensor data may include analyte data measured by at least an analyte sensor (or multi-analyte sensor). For example, the analyte sensor data may include glucose data measured by at least a glucose sensor (or multianalyte sensor) in the continuous analyte sensor 104. For hosts undergoing intensive insulin therapy, glucose data can be used as a predictor of how and when the host would be dosing insulin, and such data may then also be used as an input 130 to forecast other metrics. In certain embodiments, the analyte sensor data may include lactate data measured by at least a lactate sensor (or multi-analyte sensor) in the continuous analyte sensor 104. The analyte sensor data may include other analyte data, such as calcium data, creatinine data, BUN data, ammonia data, C-peptide data, or cystatin C-data, measured by a sensor (or multi-analyte sensor) in the continuous analyte sensor 104.

[0105] The input may also be received from one or more non-analyte sensors, such as nonanalyte sensors 206 described with respect to FIG.2. Input from such non-analyte sensors 206 may include information related to heart rate, heart rate variability, electrocardiogram (ECG) data, respiration rate, oxygen saturation, blood pressure, blood volume, blood volume / host weight, accelerometer data, urine output, or a body temperature (e.g., to detect illness, physicalactivity, etc.) of a host. In certain embodiments, electromagnetic sensors may also detect low-power radio frequency (RF) fields emitted from objects or tools touching or near the object, which may provide information about host activity or location.

[0106] The input received from non-analyte sensors may include input relating to a user’s medication administration / delivery. In particular, input related to the user’s medication administration may be received, via a wireless connection on a smart pen, via user input, and / or from a medication pump or other device. Medication administration information may include one or more of medication volume, time of delivery, etc. Other parameters, such as medication action time or duration of medication action, may also be received as inputs.

[0107] The inputs 130 may also include food consumption information, including 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. The food consumption information may be provided by a host 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 (“three cookies”), menu items (“Royale with Cheese”), and / or food exchanges (1 fruit, 1 daily'). In some examples, meal information may be received via a convenient user interface provided by the application 106.

[0108] The food consumption information (the type of food (e.g., liquid or solid, snack or meal, etc.) and / or the composition of the food (e g., carbohydrate, fat, protein, etc.)) may be determined automatically based on information provided by one or more sensors. Some example sensors may include body sound sensors (e g., abdominal sounds may be used to detect the types of meal, e.g., liquid / solid food, snack / meal, etc.), radio-frequency sensors, cameras, hyperspectral cameras, and / or analyte (e.g., potassium, insulin, glucose, lactate, calcium, creatinine, etc.) sensors to determine the type and / or composition of the food.

[0109] Medical history and / or disease diagnoses (e.g., cardiovascular disease, kidney disease, diabetes, liver disease, hypertension, etc.) may be provided as an input. For example, the host may have an existing diagnosis of diabetes and this diagnosis may be provided through manual host input. In certain embodiments, disease diagnoses are also provided by interfacing with an electronic source such as an EMR.

[0110] Exercise / activity information may also be provided as an input. Exercise information may be any information surrounding activities requiring physical exertion by the host. For example, exercise information may range from information related to low intensity (e.g., walking a few steps) and high intensity (e.g., five-mile run) physical exertion. In certain embodiments, the exercise information may include information related to high intensity interval training (HIIT), resistance training, or Zone 2 training. Exercise information may also be provided through manual host input suggesting the host will begin a specific exercise type and / or with certain exercise parameters. The exercise information may be provided or determined based on information provided, for example, by non-analyte sensors 206 (e.g., a temperature sensor, a heart rate monitor, a wearable blood pressure monitor, an accelerometer sensor on a wearable device such as a watch, fitness tracker, and / or patch, etc.). The exercise information may be provided or determined based on information provided, for example, by continuous analyte sensor 104 (e.g., it may be deduced that the host engaged in exercise based on their glucose, lactate, and / or ketone data). The exercise information provided by analyte and non-analyte sensors may be used as input into a model trained for predicting whether the host is engaging in exercise and / or predicting the types and / or parameters of such exercise.[OHl] Environmental information may be provided as an input. The environmental information may include weather and / or environmental temperature (indoor and / or outdoor) information, as such environmental factors can influence metabolic activity and cause metabolic variability of the host.

[0112] Date / time information may also be provided as an input. The date and / or time information may be processed by the DAM 116 independently of other inputs 130, or may be dependent upon (e.g., associated with) another input 130. Time information may include time of day or time from a real-time clock. For example, in certain embodiments, input analyte data may be timestamped to indicate a date and time when the analyte measurement was taken for the host.

[0113] Information related to pregnancy may also be provided as an input. For example, the pregnancy information may include pregnancy status and expected due date for delivery'. The pregnancy information may be provided through manual host input.

[0114] Host input of any of the above-mentioned inputs 130 may be provided through continuous analyte sensor 104, non-analyte sensors 206, and / or a user interface, such a userinterface of display device 107 of FIG. 1. As described above, in certain embodiments, the DAM 116 determines or computes the host’s metrics 132 based on inputs 130. An example list of metrics 132 is shown in FIG. 3.

[0115] In certain embodiments, analyte metrics (e.g., glucose metrics) may be calculated by the DAM 116 based on the inputs 130. Analyte metrics may include analyte levels, analyte baselines, maximum and minimum analyte levels, analyte rates of change, and / or analyte baseline rates of change.

[0116] In certain embodiments, analyte levels may be determined from sensor data (e.g., glucose measurements obtained from a CPM of continuous analyte sensor 104). For example, analyte levels refer to time-stamped analyte measurements or values that are continuously generated and stored over time.

[0117] In certain embodiments, an analyte baseline may be determined from sensor data (e.g., analyte measurements obtained from the continuous analyte sensor 104). An analyte baseline represents a host’s normal analyte levels during periods where significant fluctuations in analyte levels are typically not expected. A host's analyte baseline is generally expected to remain constant or within a narrow “normal range” over time, unless challenged through an action such as by the consumption of foods (e.g., diet), performance of exercise, or administration of a medicament (e.g., insulin). Generally, increasing fluctuation from the host’s analyte baseline may indicate a loss of analyte regulation (e.g., glycemic control), which may put the host at an increased risk.

[0118] Further, each host may have a different potassium baseline. In certain embodiments, a host’s potassium baseline may be determined by calculating an average of potassium levels of the user over a specified amount of time where significant fluctuations are not expected. For example, the baseline potassium for a host may be determined over a period of time when the host is sleeping, sitting in a chair, or other periods of time where the host is sedentary and not consuming food or medication which would reduce or increase potassium levels (e.g., where no external conditions exist that would affect the potassium baseline exist). In certain embodiments, DAM 116 may continuously calculate a potassium baseline, timestamp the calculated potassium baseline, and store the corresponding information in the host profile 118.

[0119] In certain embodiments, an absolute maximum analyte level may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 100). health / sickness metrics (e.g.. described in more detail below), and / or disease stage metrics (e.g., described in more detail below). The absolute maximum analyte level represents a host’s maximum analyte level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute maximum analyte level may be consistent across all hosts (e.g., based on current medical guidelines). In certain other embodiments, each host may have a different absolute maximum analyte level. In certain embodiments, the absolute maximum analyte level per host may change over time. For example, a host may be initially assigned an absolute maximum analyte level based on clinical input. This assigned absolute maximum analyte level may be adjusted over time based on other sensor data, disease stages, comorbidities, etc. for the host.

[0120] For example, a host’s absolute maximum analyte level may vary over time as a user’s disease progresses and / or improves. In certain embodiments, a first absolute maximum analyte level may be determined for periods of time where no external conditions exist that would affect the analyte level, and a second absolute maximum analyte level may be determined for periods of time where external conditions do exist that would affect the analyte level (e.g., during periods of time when the host is eating, exercising, taking medication that affects analyte levels, etc.).

[0121] In certain embodiments, an absolute minimum analyte level may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 100), medication / treatment metrics, and / or medical history / disease diagnosis metrics. The absolute minimum analyte level represents a host’s minimum analyte level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute minimum analyte level may be consistent across all hosts (e g., set based on current medical guidelines). In certain other embodiments, each host may have a different absolute minimum analyte level. In certain embodiments, the absolute minimum analyte level per host may change over time. For example, a host may be initially assigned an absolute minimum analyte level based on clinical input. This assigned absolute minimum analyte level may be adjusted over time based on other sensor data, disease stages, comorbidities, etc. for the host.

[0122] For example, a host’s absolute minimum analyte level may vary over time as a host’s diseases progress and / or improves. In certain embodiments, a first absolute minimum analyte level may be determined for periods of time where no external conditions exist that would affect the analyte level, and a second absolute minimum analyte level may be determined for periods of time where external conditions do exist that would affect the analyte level (e.g., during periods of time when the host is eating, exercising, taking medication that affects analyte levels, etc.).

[0123] In certain embodiments, analyte level rates of change may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 100 over time). For example, an analyte level rate of change refers to a rate that indicates how one or more time-stamped analyte measurements or values change in relation to one or more other time-stamped analyte measurements or values. Analyte level rates of change may be determined over one or more seconds, minutes, hours, days, etc.

[0124] In certain embodiments, determined analyte level rates of change may be marked as "increasing rapidly” or "decreasing rapidly.” As used herein, “rapidly” may describe analyte level rates of change that are clinically significant and pointing towards a trend of the analyte level of the patient likely breaching the absolute maximum analyte level or the absolute minimum analyte level within a next period of defined time. In other words, a predictive trend (e.g., produced by the management engine 114 using one or more trained models) may, in some cases, indicate that a host is likely to hit, for example, the absolute maximum analyte level within a specified time period (e.g., one or two hours) based on the determined analyte level rate of change. Accordingly, such an analyte level rate of change may be marked as “increasing rapidly.” Similarly, a predictive trend (e.g., produced by the management engine 114 using one or more trained models) may, in some cases, indicate that a host is likely to hit the absolute minimum analyte level within a specified time period (e.g., one or two hours) based on the analyte level rate of change determined. Accordingly, such an analyte level rate of change may be marked as “decreasing rapidly.”

[0125] In certain embodiments, analyte baseline rates of change may be determined from analyte baselines determined for a host over time. For example, an analyte baseline rate of change refers to a rate that indicates how one or more time-stamped analyte baselines for a host change in relation to one or more other time-stamped analyte baselines for the same host.Analyte baseline rates of change may be determined over one or more seconds, minutes, hours, days, etc.

[0126] The host’s metrics 132 may further include metrics for other analytes. For example, in certain embodiments, metrics 132 may include lactate levels, lactate baselines, maximum and minimum lactate levels, lactate rates of change, lactate baseline rates of change, lactate clearance rates, lactate trends, calcium levels, calcium baselines, maximum and minimum calcium levels, calcium rates of change, calcium baseline rates of change, calcium clearance rates, calcium trends, ketone levels, ketone baselines, maximum and minimum ketone levels, ketone rates of change, ketone baseline rates of change, ketone clearance rates, ketone trends, creatinine levels, creatinine baselines, maximum and minimum creatinine levels, creatinine rates of change, creatinine baseline rates of change, creatinine clearance rates, creatinine trends, and / or levels, baselines, maximum and minimum levels, rates of change, baseline rates of change, clearance rates, and / or trends of one or more other analytes of the host.

[0127] In certain embodiments, meal state metrics may indicate the state the host is in with respect to food consumption. For example, the meal state may indicate whether the host 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).

[0128] In certain embodiments, meal habits metrics are based on the content and the timing of a host’s meals. For example, if a meal habit metric is on a scale of 0 to 1, the better / healthier meals the host eats the higher the meal habit metric of the host will be to 1, in an example. Also, the more the host's food consumption adheres to a certain time schedule or a recommended diet, the closer their meal habit metric will be to 1, in the example.

[0129] In certain embodiments, body temperature metrics may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a temperature sensor. Heart rate metrics (e.g., including heart rate and heart rate variability) may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensordata from a heart rate sensor. Respiratory metrics may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a respiratory' rate sensor.

[0130] Health and sickness metrics may be determined, for example, based on one or more of host input (e.g., pregnancy information or known sickness information), from 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, a host’s state may be defined as being one or more of healthy, ill, rested, or exhausted.

[0131] Medication habit metrics are based on the host’s prescribed medications and a determination of whether the prescribed medications may have an effect on the host's analyte levels. For example, by analyzing a host’s medication habits, the DAM 116 may determine whether the host’s medications may impact the host’s analyte measurements at a particular time. Based on the host’s medication habits, the DAM 116 may determine whether the host’s analyte levels are a result of medication consumption or another cause, such as worsening organ function, for example. The medication habit metrics may be time-stamped so that they can be correlated with the host’s analyte levels at the same time.

[0132] Treatment habit metrics are based on the host’s lifestyle habits, surgical procedures, and / or other non-invasive procedures recommended by the host’s physician, and a determination of whether the treatment habits may have an effect on the host’s analyte levels. For example, by analyzing a host’s treatment habits, the DAM 116 may determine whether the host’s treatment habits may impact the host’s analyte measurements at a particular time. Based on the host’s treatment habits, the DAM 116 may determine whether the host’s analyte levels are a result of treatment habits or another cause. The treatment habit metrics may be time-stamped so that they can be correlated with the host’s analyte levels at the same time.

[0133] Medication adherence is measured by one or more metrics that are indicative of how committed the host is towards their medication regimen. In certain embodiments, medication adherence metrics are calculated based on one or more of the timing of when the host takes medication (e.g., whether the host is on time or on schedule), the type of medication (e.g., is the host taking the right type of medication), and the dosage of the medication (e.g., is the host taking the right dose).

[0134] Similarly, treatment adherence is measured by one or more metrics that are indicative of how committed the host is towards their treatment regimen. In certainembodiments, treatment adherence metrics are calculated based on one or more of the timing of when the host performs certain treatment habits and / or the type of treatment.

[0135] The activity' level metric may indicate the host’s level of activity. In certain embodiments, the activity level metric be determined, for example based on input from an activity sensor or other physiologic sensors, such as non-analyte sensors 206. In certain embodiments, the activity level metric may be calculated by the DAM 116 based on one or more of inputs 130, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, host input, etc. In certain embodiments, the activity level may be expressed as a step rate of the host. Activity level metrics may be time-stamped so that they can be correlated with the host’s potassium levels at the same time.

[0136] The DAM 116 may determine a glycemic control level for a host by considering the inputs 130 and / or the analyte metrics over time. For example, by tracking the analyte levels (e.g., glucose levels) for the host over a period of time, the DAM 116 may determine how well the host is controlling the host’s glucose levels. The DAM 116 or management engine 114 may determine the glycemic control level using other factors, such as the disease (e.g.. diabetes) progression of the host, the pregnancy status and / or due date for the host, the meal times for the host, the exercise times for the host, etc. In this manner, the DAM 116 or management engine 114 may accurately determine the glycemic control level for each host over time. The management engine 114 may then administer particular medical treatments for the disease progression.

[0137] FIGs. 4A through 4D. 5A through 5C, and 6A through 6C depict example operations performed by an analyte monitoring system (e.g., the continuous analyte monitoring system 100 of FIG. 1). One or more components of the analyte monitoring system may perform the operations. For example, a management engine (e.g., the management engine 114 shown in FIG. 1) and / or a display device (e.g., the display device 107 shown in FIG. 1) may perform some or all of the steps of these operations. Although the management engine and the display device will be described as performing certain steps of the operations, it is understood that these steps may be performed by either of the management engine or the display device (or any other component of the analyte monitoring system 100). Generally, by performing these operations, the analyte monitoring system determines and implements analysis sessions in which the analyte monitoring system may determine how certain activities (e.g., exercise,meal consumption, sleep, etc.) affect a user's analyte levels. In certain embodiments, the analysis sessions allows the analyte monitoring system to provide the user more accurate and relevant insights and / or recommendations.

[0138] User-centric analysis sessions are implemented or designed within the analyte monitoring system. Analysis sessions may involve lifestyle modifications aimed at supporting the user’s health goals. Lifestyle modifications are voluntary actions performed by the user. Some analysis sessions may not involve lifesty le modifications. Rather, these analysis sessions may analyze and output the results of the user’s current habits and lifestyle. The analysis sessions can be manually created (e.g., by a PCP or user), or analysis sessions can be dynamically generated and presented to the user based on a generally specified goal and / or historical user characteristics.

[0139] Upon selection of an analysis session, the user may be prompted to create a datalogging schedule / event performance schedule (or confirm an existing schedule) where, in accordance with the schedule, one or more actions are performed and / or one or more parameters (analyte data, meal data, exercise data, heart rate data, blood pressure, etc.) are logged by the user (and / or automatically logged via the system). Upon deployment, the analysis session presents these action remin ders / data logging opportunities to the user according to the schedule and / or according to data collected by the system (e.g., from the user or from another device).

[0140] At the termination of the analysis session, the obtained data is analyzed and userspecific insights are presented. A comparison of the user’s results with earlier analysis sessions, a larger user population, etc. may also be performed. There is additional opportunity for the system to generate insights and recommendations on how to interpret the results or implement findings to optimize future analyte management.

[0141] By engaging a user in a customized analysis session, the user is much more likely to contribute contextual data as required by the analysis session. This increases the amount of contextual data that is obtained from the user, which may decrease an amount of contextual data that needs to be inferred, estimated, or guessed by the system. Reducing the amount of inferred, estimated, or guessed contextual data may increase an accuracy of such data, as well as an accuracy and relevance of insights derived from such data (additional insights may also be derived from this additional data, where these additional insights were not previouslypossible without the corresponding data). Moreover, reducing the amount of inferred, estimated, or guessed contextual data may also decrease an amount of processing or power resources wasted by one or more devices implementing the system (mobile device, sensor, backend cloud), which may improve the performance of such devices.

[0142] Further, by tailoring analysis sessions to a fit a user’s needs and interests, as well as by providing a more streamlined visualization of analysis session results, the user may be more engaged with the system and may be more receptive to recommendations made by the system. This may increase a likelihood that the user integrates guidance presented by the system into the user’s lifestyle, which may improve the overall health of the user.

[0143] The system may present analysis sessions for selection when the user selects a tab (e.g., a learn, lab. experiments, or analysis sessions tab) via an interface of an application. The user may select an analysis session and then set a schedule for the analysis session. The schedule may set when the analysis session begins and ends and when data is collected and / or logged during the analysis session. The schedule may also indicate when the user should perform certain activities during the analysis session (e.g., eat, sleep, exercise, etc.).

[0144] The system may determine the analysis session presented to the user in different ways. For example, analysis sessions may be predetermined or preset analysis sessions. These analysis sessions may be popular analysis session choices that are most frequently chosen by users. As another example, analysis sessions may be dynamically generated based on past user data or behavior. These analysis sessions may be determined based on previous analysis sessions selected by the user, topics of interest to the user, health goals of the user, health conditions of the user, etc. The system may also analyze existing user data to determine trends in the user data and / or insights from the user data. The system may then design the analysis session in view of the determined trends and / or insights to collect or log additional data used to derive another insight. As another example, analysis sessions may be custom designed by the user. The user may select or input topics that interest the user to generate the analysis session based on that topic. The system may use artificial intelligence to process input from the user to design the analysis session. The system may also design the analysis session based on the types of user data available to the system and the amount of time allowed for the analysis session according to the schedule.

[0145] After the user selects an analysis session and sets the schedule for the analysis session, the system implements events according to the schedule. For example, the system may present reminders or notifications for the activities set by the analysis session. As another example, the system may present alerts when activities are logged that deviate from the schedule.

[0146] The system may conduct the analysis session by collecting user data (e.g., analyte levels) before, during, or after certain behavior or activities (e.g., eating, sleeping, exercising, etc.) during the analysis session. When the analysis session has concluded, the system may analyze the user data to determine insights about the user. For example, the insights may reveal the effects of certain behavior or activities on the analyte levels (or other health goals) of the user. The system may then present these insights along with the user data to the user.

[0147] One or more aspects of the analyte monitoring system (e g., an application) may be adjusted based on the conducted analysis session. For example, if the results of an analysis session indicate that a predetermined meal type is an optimal meal for a user at a predetermined time (e.g., lunchtime), the predetermined meal type may be suggested to the user. As another example, the system may also adjust monitoring algorithms based on the results of analysis sessions (e.g., results relating to user-centric sensitivities, etc.).

[0148] The system provides two overarching features. First, the system determines and / or designs analysis sessions for a user. Some analysis sessions may be preset or predetermined analysis sessions. Some analysis sessions may be determined based on past user data or historical activity’. Some analysis sessions may be custom experiments designed by the user.

[0149] Second, after the user selects an analysis session, the system implements or performs the analysis session according to the schedule for the analysis session. After the analysis session concludes, the system analyzes the analyte sensor measurements collected for the analysis session and the activities or behaviors that occurred during the analysis session to generate insights or recommendations about the activities or behaviors.

[0150] FIG. 4A illustrates an example operation 400 for determining analysis sessions performed by the continuous analyte monitoring system 100 of FIG. 1. Generally, by performing the operation 400, the analyte monitoring system 100 may determine a set of analysis sessions for selection. The analyte monitoring system 100 may determine the set ofanalysis sessions using a variety of information, such as population data 402, historical user data 406, and / or a topic 408.

[0151] For example, a management engine of the analyte monitoring system 100 may begin by receiving population data 402. The population data 402 may include information about a population of users, which may include the user of the analyte monitoring system 100. As an example, the population data 402 may indicate analysis sessions 404 that were popular or selected the most by the population of users. The management engine may review the analysis sessions 404 to determine a number of analysis sessions 410 for selection. For example, the management engine may select some of the analysis sessions 404 to include as part of the analysis sessions 410. The selected analysis sessions 404 may include analysis sessions that are most frequently selected by the population of users, most often selected by the population of users, and / or that have been selected by the population of users the most number of times. By selecting these analysis sessions from the analysis sessions 404 for inclusion in the analysis sessions 410, the management engine selects preset or predetermined analysis sessions for presentation to the user. As an example, some of the selected analysis sessions may be statically generated and may include analysis sessions involving food eating order, the impact of modifying dietary choices including macronutrient composition, impact of timing of physical activity on post-meal glucose levels, timing of meal intake on overnight and future glucose levels, anaerobic versus aerobic exercise, etc.

[0152] As another example, the management engine may receive historical user data 406 that may indicate previous actions taken by the user and / or previous analyte measurements for the user. The management engine may determine one or more analysis sessions to include in the analysis sessions 410 using the historical user data 406. For example, the historical user data 406 may indicate previous analysis sessions selected by the user. The management engine may include some of these previously selected analysis sessions in the analysis sessions 410. As another example, the historical user data 406 may indicate previous analyte measurements for the user, medical conditions of the user, or lifestyle choices of the user. The management engine may compare the information in the historical user data 406 with information in the population data 402 to determine a subset of the population with matching or similar data or characteristics. The management engine may then determine previous analysis sessionsselected by the subset of the population and include some of those analysis sessions in the analysis sessions 410.

[0153] As another example, the management engine may receive a topic 408, which may indicate a topic of interest to the user. In some instances, the user may provide input to select the topic 408. As another example, the management engine may analyze the historical user data 406 to determine the topic 408. For example, the management engine may determine the topic 408 may be a topic of interest based on the user’s health conditions or lifestyle choices indicated in the historical user data 406.

[0154] The management engine may determine an analysis session to include in the analysis sessions 410 based on the topic 408. For example, the management engine may determine that the analysis session is relevant or applicable to the topic 408, and in response, the management engine may include the analysis session in the analysis sessions 410. The management engine may determine an analysis session to include in the analysis sessions 410 based on the historical user data 406. For example, one or more initial insights may be determined based on the historical user data 406. Historical user analyte values over time, combined with user-logged meal consumption details (including one or more of type of meal, timing of meal, size of meal, etc.) and exercise details over the same time period, may be analyzed to determine initial insights such as a progression toward a disease state (e.g., prediabetes), an impact of lifestyle choices on overall analyte levels, a progression towards one or more predetermined health goals, etc. an impact of certain activities on certain analyte levels (e.g., late-night snacking on overall glucose control and overnight glucose levels), declining exercise effectiveness because of exercise type, etc.

[0155] These initial insights may in turn be analyzed to determine an appropriate analysis session. For example, a predetermined mapping may be established between a predetermined set of initial insights and a predetermined set of analysis sessions. An initial insight indicating a progression towards prediabetes may be mapped to an analysis session concerning an impact of behavior modification (food consumed, exercise performed) to reduce resting blood glucose levels. An initial insight indicating recurring late-night meal consumption may be mapped to an analysis session concerning an impact of late-night snacking on overall glucose control and overnight glucose levels. An initial insight indicating recurring exercise of a specific type may be mapped to an analysis session covering declining exercise effectiveness because of exercisetype. Determined initial insights may be compared to these predetermined mappings to identify corresponding analysis sessions for the initial insights. A machine learning model may also be trained to take one or more initial insights as input and to output one or more appropriate analysis sessions.

[0156] The management engine may communicate the analysis sessions 410 to the user for selection. For example, the management engine may communicate the analysis sessions 410 to a display device of the user. The display device may then present the analysis sessions 410 to the user, and the user may use the display device to select one or more of the analysis sessions 410. The management engine may also include and the display device may present high-level details of each analysis session (e.g., topic, insights, duration, frequency, estimated time to complete logging, etc.).

[0157] Analysis sessions determined using one or more of the above techniques may also be conditionally included in the analysis sessions 410 that are communicated to the user based on one or more requirements for the analysis sessions. For example, data needed as input may be identified for determined analysis sessions. The management engine may determine whether the user is capable of providing this required input data. For example, the management engine may identity7available data inputs to the user (e.g., one or more analyte sensors, an accelerometer, a smart watch and / or smart ring, etc.) and may determine if such data inputs are sufficient to provide the data needed to implement an analysis session. If the available inputs are insufficient to implement the analysis session, such analysis session may be removed from the analysis sessions 410 that are communicated to the user.

[0158] In some embodiments, the user may create a custom analysis session. For example, the user may select a topic 408, which may indicate a topic of interest to the user. The user may then set certain parameters for the analysis session (e.g., analytes to measure, duration, activity to be monitored, etc.). After setting the parameters of the analysis session, the management engine may implement the custom analysis session.

[0159] FIG. 4B illustrates an example operation 420 for determining a topic performed by the continuous analyte monitoring system 100 of FIG. 1. In some instances, the analyte monitoring system 100 may use artificial intelligence to determine the topic of interest. As seen in FIG.4B, the management engine of the analyte monitoring system 100 receives input422 from the user. The input 422 may be any type of input, such as textual input, voice input, etc.

[0160] The management engine directs the input 422 to a machine learning model 424. The machine learning model 424 may be any suitable artificial intelligence model that determines the topic 408 of interest based on the input 422. For example, the machine learning model 424 may include a large language model that processes natural language text or input in the input 422 to determine a meaning of the input 422 and / or a response to the input 422. As another example, the machine learning model 424 may include a neural network that predicts the topic 408 of interest to the user based on the input 422 provided by the user.

[0161] As an example operation, the user may provide voice input that explains the health condition of the user and / or the user’s health goals. The management engine may process the voice input to convert the voice input to text. The machine learning model 424 may analyze the text to determine a meaning of the user’s speech. The machine learning model 424 may then determine the user’s health condition and / or health goals. The machine learning model 424 may then determine (e.g., from population data) one or more topics of interest that are relevant to the health condition or health goals. In this manner, the management engine may determine the topic 408 for the user.

[0162] FIG. 4C illustrates an example operation 440 for determining an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. In some instances, the analyte monitoring system 100 may determine the analyte session for presentation to the user according to certain rules.

[0163] The management engine may begin by receiving or determining the topic 408, which may have been provided by the user or may have been determined based on input from the user. The management engine may determine from the topic 408 one or more measurements 442 that may be relevant to the topic 408. For example, the management may determine certain analytes that may be relevant to the topic 408.

[0164] The management engine then references rules 444 to determine which measurements 442 or which data may be available or may be gathered or collected. The rules 444 may indicate what data the analyte monitoring system 100 may collect from the user and / or what data may be available to the analyte monitoring system 100 (e g., due to consent requirements, hardware requirements, etc.). For example, the rules 444 may prohibit theanalyte monitoring system 100 from measuring the levels of certain analytes. As another example, the rules 444 may specify that the user has not collected data about certain analytes. In this manner, the rules 444 may indicate the types of data that are or will be available to the management engine. The management engine references the rules 444 to determine which of the measurements 442 are or will be available to the management engine.

[0165] The management engine may determine the analysis session 410 based on the data that is available or allowed / permitted to be collected, as indicated by the rules 444. For example, the management engine may determine that certain analysis sessions may use measurements or data that is not available to the management engine or not allowed to be collected. In response, the management engine may exclude these analysis sessions from being presented to the user. As another example, the management engine may determine that an analysis session uses or requires data or measurements that is available to the management engine and that is allowed to be collected. In response, the management engine may determine that the analysis session may be presented to the user.

[0166] FIG. 4D is a flowchart of an example method 460 for determining an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. In certain embodiments, a management engine (e.g., the management engine 114 shown in FIG. 1) performs the method 460. By performing the method 460, the management engine determines an analysis session for presentation to a user.

[0167] At 462, the management engine begins by receiving population data, historical user data, and / or a topic (e.g.. a selected topic or data indicating a topic of interest). The population data may indicate analysis sessions that are popular or that are most commonly selected by a population of users. The historical user data may indicate information about a user, such as previous analyte level measurements for the user, health conditions of the user, health goals of the user, etc. The topic may indicate a topic of interest to the user. The topic may relate to the user’s health condition or health goals. The user may have provided input that indicates the topic of interest. Additionally or alternatively, the management engine may analyze information about the user (e.g., the historical user data) to determine the topic of interest.

[0168] At 464, the management engine determines one or more analysis sessions using the population data, historical user data, and / or topic. The analysis sessions may each relate to a question to be answered or an insight to be provided to the user. The question or insight mayrelate to health conditions of the user and / or health goals of the user. Certain analytes may be measured as part of answering the question or providing the insight. As an example, some of the analysis sessions may be frequently selected analysis sessions as indicated by the population data. As another example, some of the analysis sessions may be related to the health conditions or health goals of the user as indicated by the historical user data. As another example, some of the analysis sessions may relate to the topic of interest provided by the user.

[0169] At 466, the management engine presents the analysis sessions to the user. For example, the management engine may communicate the determined analysis sessions to a display device of the user. The display device may then present or display the analysis sessions to the user. The user may then use the display device to select one or more of the analysis sessions to direct the analyte monitoring system to begin implementing or conducting the selected analysis sessions.

[0170] FIG. 5A illustrates an example operation 500 for determining an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. The analyte monitoring system 100 performs the operation 500 to determine aspects of a selected analysis session (e.g., activities to be tracked, schedules to be followed, etc.), which may be used to implement the analysis session. Generally, after the user selects an analysis session, the analyte monitoring system 100 determines a schedule for the analysis session. The schedule may set when the analysis session begins and ends. The schedule may also set when certain activities should occur during the analysis session (e.g., when certain foods are eaten, when sleep / exercise occurs, etc.). The analyte monitoring system 100 may pre-populate the schedule, and / or the user may populate the schedule. When the schedule is finalized, the analyte monitoring system 100 may add, to the user’s calendar, events that correspond with the scheduled activities.

[0171] The management engine begins by receiving a selection 502 from the user. The selection 502 may indicate a selected analysis session 503. For example, the user may have reviewed several analysis sessions using the display device, and the user may have used the display device to select one of the analysis sessions. The display device then communicates the selection 502 to the management engine to indicate the selected analysis session 503. The management engine then determines certain information for implementing the selected analysis session 503.

[0172] The management engine may determine an activity 504 to be monitored during the analysis session 503. The activity 504 may be related to the topic of interest for the selected analysis session 503. For example, if the user wants to determine the effect of aerobic vs anaerobic exercise on the user’s glucose levels, then the activity 504 may include aerobic exercise and anaerobic exercise. As another example, if the user wants to determine the effect of portion size on the user's analyte levels, then the activity 504 may include meals with different portion sizes. As another example, if the user wants to determine the effect of carbohydrates on the user’s analyte levels, then the activity 504 may include meals with different amounts of carbohydrates.

[0173] The management engine may determine a schedule 506 for the analysis session 503. The schedule 506 may control different aspects of timing for the analysis session. For example, the schedule 506 may indicate a start time 508 and an end time 510 for the analysis session. The analysis session may start at the start time 508 and end at the end time 510. As another example, the schedule 506 may indicate a duration 512 for the analysis session, which may indicate a period of time for which the analysis session should last. As another example, the schedule 506 may indicate one or more activity times 514, which control when the activity7504 should occur.

[0174] In some embodiments, the management engine may determines the timing for the analysis session (e.g., the start time 508. the end time 510, the duration 512, the activity times 514, etc.) according to histoneal data. For example, the management engine may determine the timing according to the timing for similar analysis sessions that were implemented previously for similar users. The management engine may present the timing information to the user (e.g., on the display device of the user), and the user may edit or change the timing information to fit better with the user’s 1 i Testy 1 e or schedule. The user may then confirm the timing information to begin the analysis session.

[0175] In some instances, the user may change (e.g., add, remove, edit, etc.) the activity 504 for the analysis session. For example, the user may select one or more of the activities 504 to be included in the analysis session. As another example, the user may remove or change one or more of the activities 504 that the management engine may have selected to include in the analysis session 503. In this manner, the user may design or set the parameters of the analysis session.

[0176] By determining the activity 504 and the schedule 506, the management engine determines the contours of the analysis session 503. The management engine may then conduct the analysis session 503 according to the schedule 506. During the analysis session 503, the management engine may measure analyte levels (or collect other information) with respect to the activity 504. The management engine may then analyze the measured or collected information to determine insights or recommendations for the activity' 504.

[0177] As an example, if the user wants to analyze the effects of aerobic and anaerobic exercise on the user’s analyte levels (e.g., glucose levels), the analysis session 503 may indicate activities 504 of aerobic exercise and anaerobic exercise. The schedule 506 may indicate when the analysis session 503 begins and ends and / or the duration of the analysis session 503. Additionally, the schedule 506 may indicate times when aerobic exercise should occur and times when anaerobic exercise should occur. The user may also provide input that sets certain times in the schedule 506 when aerobic exercise or anaerobic exercise will occur.

[0178] As another example, if the user wants to analyze the effects of portion sizes on the user’s analyte levels (e.g., glucose levels), the analysis session 503 may indicate activities 504 of eating meals of certain sizes (e.g., small meals, medium meals, large meals, etc.). The schedule 506 may indicate when the analysis session 503 begins and ends and / or the duration of the analysis session 503. Additionally, the schedule 506 may indicate times when meals of certain sizes should be eaten. The user may also provide input that sets certain times in the schedule 506 when meals of certain sizes will be eaten.

[0179] FIG. 5B illustrates an example operation 520 for determining an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. The analyte monitoring system 100 performs the operation 520 to set calendar alerts for an analysis session.

[0180] The management engine begins with the analysis session 503 that the user selected, as seen in FIG. 5B, the analysis session 503 includes the activity' 504 and the schedule 506. The user may engage in the activity 504 during the analysis session 503 according to times indicated by the schedule 506. The management engine may add entries into a calendar 522 (e.g., a calendar application) to remind the user to perform or engage in the activity 504. The entries may be set for times according to the times indicated in the schedule 506. When those times approach, the calendar 522 may generate an alert 524 (e.g., on a display device of theuser) that reminds the user to engage in the activity 504 soon. In this manner, the management engine assists in ensuring that the analysis session 503 proceeds as scheduled.

[0181] FIG. 5C is a flowchart of an example method 540 for determining an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. In certain embodiments, a management engine (e.g., the management engine 114 shown in FIG. 1) performs the method 540. By performing the method 540, the management engine determines characteristics or information for an analysis session.

[0182] At 542, the management engine receives a selection of an analysis session. The management engine may have directed multiple analysis sessions to a display device of a user. The user may view the analysis sessions on the display device and select one of the analysis sessions to implement. The display device may then communicate the selected analysis session to the management engine.

[0183] At 544, the management engine determines an activity and a schedule for the selected analysis session. The activity for the analysis session may be determined according to a topic of interest related to the analysis session. The schedule may indicate the start time and end time for the analysis session and / or a duration of the analysis session. The schedule may also indicate times when the activity is scheduled to occur during the analysis session.

[0184] At 546, the management engine adds calendar entries to a calendar of the user. The entries may indicate the activity for the analysis session, and the entries may indicate times when the activity7is scheduled to occur according to the schedule for the analysis session. At 548, the management engine may present an alert when the time approaches a time indicated in a calendar entry. The alert may be presented on a display device of the user, and the alert may remind the user that the user should engage in the activity for the analysis session soon.

[0185] FIG.6A illustrates an example operation 600 for implementing an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. The analyte monitoring system 100 performs the operation 600 to implement an analysis session. Generally, the analyte monitoring system 100 retrieves the schedule for the analysis session and coordinates actions according to the schedule. For example, the analyte monitoring system 100 may collect user data (e.g., analyte measurements) at times indicated in the schedule. The analyte monitoring system 100 may determine when certain user activities or behaviors should occur according to the schedule. The analyte monitoring system 100 may then set reminders or alertsfor these activities to notify the user when these activities should occur and / or to notify the user when these activities do not occur according to the schedule. The analyte monitoring system 100 collects data about activities, behaviors, and analyte levels during the analysis session. During the analysis session or after the analysis session ends, the analyte monitoring system 100 analyzes the data collected during the analysis session and determines insights or recommendations about the activities or behavior involved in the analysis session, which may¬ aid future decision-making. The analyte monitoring system 100 may present a timeline of the activities or behaviors that occurred during the analysis session along with the corresponding user data.

[0186] The management engine begins by receiving input 602 from a user. For example, the user may enter the input 602 into a display device of the user. As another example, a sensor (e.g., a sensor worn by the user) may provide the input 602 to the display device. The displaydevice may then communicate the input to the management engine. The input 602 may indicate that an activity indicated by the analysis session is occurring or has occurred. For example, the input 602 may indicate that the user is exercising or eating a meal. As another example, the input 602 may indicate that the user is sleeping. The management engine may determine, from the input 602, that a scheduled activity for the analysis session is occurring or has occurred.

[0187] The management engine may also receive measurements 604. A sensor worn by the user may provide the measurements 604. The measurements 604 may indicate measured analyte levels (e.g., glucose levels) of the user before, during, and / or after the activity indicated by the input 602. Each of the measurements 604 may be accompanied with a timestamp that indicates when the measurement 604 was taken.

[0188] The management engine correlates the input 602 indicating that the activity is occurring or has occurred with the measurements 604 using the schedule 506 to determine insights about the user. For example, the management engine may determine from the timestamps accompanying the measurements 604 which of the measurements 604 were taken before the activity occurred, when the activity was occurring, and / or after the activity had concluded. The management engine may then evaluate the measurements 604 to determine the effects of the activity on the measured analyte levels. For example, the management engine may determine whether the activity increased or reduced analyte levels. As another example.the management engine may determine whether one activity increased or reduced analyte levels more than another activity.

[0189] The management engine may then generate a recommendation 606 that indicates the effects of the activity on the measured analyte levels. For example, the recommendation 606 may indicate the measured analyte levels before, during, and / or after the activity. Additionally, the recommendation 606 may provide the user guidance as to how to approach the activity moving forward with respect to the user’s analyte levels. For example, the recommendation 606 may indicate that the user should perform aerobic exercise more than anaerobic exercise to further reduce the user’s glucose levels. As another example, the recommendation 606 may indicate that the user should eat smaller meals throughout the day as opposed to a few large meals to better regulate the user’s glucose levels. The recommendation 606 may also include a graph that shows the analyte level of the user in relation to when the activity occurred. The management engine communicates the recommendation 606 to the user (e.g., to the display device of the user) so that the user can view the recommendation 606 and implement changes to the user’s lifestyle or diet based on the recommendation 606.

[0190] In some embodiments, the management engine generates the recommendation 606 by correlating measurements 604 that were taken across multiple instances of the activity. For example, an activity may be scheduled to occur multiple times during an analysis session. Each time the activity occurs, the input 602 may be provided to the management engine indicating that the activity occurred. The management engine may then correlate the measurements 604 taken during that instance of the activity. The management engine may repeat this process each time the activity occurs. After the analysis session concludes, the management engine may generate the recommendation 606 based on the correlations of the measurements 604 with their respective activity occurrences.

[0191] FIG.6B illustrates an example operation 620 for implementing an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. Generally, during each analysis session, the analyte monitoring system 100 tracks analyte levels after certain activities or behaviors occur. The analyte monitoring system 100 may then provide analysis and insights on the impact of these activities or behaviors on the analyte levels of the user. The analyte monitoring system 100 may also flag certain analyte levels as being outliers or being contrary to established trends for the user to explain.

[0192] The management engine begins by comparing the measurements 604 against a threshold 622. The threshold 622 may be set based on population data and / or historical user data. For example, the threshold 622 may be set to reflect maximum values for the measurements 604 that were produced by a population of users. As another example, the threshold 622 may be set to reflect maximum values for the measurements 604 that were previously produced by the user. By comparing the measurements 604 to the threshold 622, the management engine may determine whether certain measurements 604 are erroneous or unrealistic. For example, if a measured glucose level exceeds the threshold 622, which reflects the maximum glucose levels produced by a population of users or by the user, then the management engine may determine that the measured glucose level is an outlier.

[0193] The management engine may apply or set a flag 624 for a measurement 604 that exceeds or falls below the threshold 622. By setting the flag 624, the management engine marks the measurement 604 as being an outlier according to the threshold 622. During or after an analysis session, the management engine may present the flagged measurement to the user to indicate to the user that an outlier measurement was detected. If the user believes that the measurement is legitimate, the user may provide an explanation 626 (e.g., using the display device) to explain what caused the measurement to be an outlier. Based on the explanation 626, the management engine may consider the measurement when correlating measurements to generate recommendations. The management engine may also adjust the threshold 622 based on the explanation 626 being provided. If the user does not provide the explanation 626, then the management engine may disregard or discard the measurement.

[0194] FIG. 6C is a flowchart of an example method 640 for implementing an analysis session performed by the continuous analyte monitoring system 100 of FIG. 1. In certain embodiments, a management engine (e.g.. the management engine 114 shown in FIG. 1) performs the method 640. By performing the method 640, the management engine generates recommendations using information from an analysis session.

[0195] At 642, the management engine receives input indicating an activity occurred. For example, the user may provide input through the display device when the user is about to begin the activity. As another example, the user may provide input through the display device when the user has completed the activity. The display device may communicate the input to the management engine. The activity may be one of the scheduled activities during the analysissession. By providing the input, the management engine may determine when the activity began and / or ended.

[0196] At 644, the management engine receives measurements during the analysis session. The measurements may be measured analyte levels (e.g., measured glucose levels) of the user. Each measurement may be accompanied by a timestamp that indicates when the measurement was taken.

[0197] At 646, the management engine correlates the measurements with the activity. For example, the management engine may determine, from the timestamps accompanying the measurements, whether the measurements were taken before, during, and / or after the activity. The management engine may then determine, from the measurements, the effect that the activity has on the measured analyte levels of the user. For example, the management engine may determine whether the activity increased or reduced the analyte levels of the user. As another example, the management engine may determine whether the activity increased or reduced the analyte levels of the user relative to other activities.

[0198] At 648, the management engine generates a recommendation for the user concerning the effects of the activity on the user’s analyte levels. For example, the recommendation may indicate whether the activity tends to increase or reduce the analyte levels. As another example, the recommendation may indicate whether the user should engage in the activity' more or less to help manage the user’s analyte levels. The recommendation may also include a graph that shows the analyte levels of the user in relation to when the activity occurred. The management engine may communicate the recommendation to the display device of the user. The user may then view the recommendation on the display device.

[0199] In some embodiments, the management engine also updates an application (e.g., the application 106 shown in FIG. 1) based on the results of the analysis session. For example, the management engine may cause additional interfaces to be displayed in the application. Some of these interfaces may show the results of the analysis session, including recommendations and insights generated based on the analysis session. The interfaces may also show graphs that indicate the analyte measurements during the analysis session as well as the timing or occurrence of events.

[0200] Generally, the management engine may implement any analysis session to study the effects of any activity on the measured analyte levels of the user. Below are some examples of analysis sessions that the management engine may implement.

[0201] Aerobic vs. Anaerobic Exercise: The analysis session may determine the impact of aerobic and anaerobic exercise on the analyte levels of the user. During the analysis session, the user may indicate when aerobic exercise or anaerobic exercise occurred along with the duration of the exercise. The system may use artificial intelligence to analyze the user input to ensure that the exercise matches criteria for the analysis session. The system analyzes the analyte levels of the user before, during, and after the indicated exercises. The system may provide a graph indicating when certain exercises occurred along with the relevant analyte levels. The system may also provide insights indicating how aerobic and anaerobic exercise impacted analyte levels.

[0202] Postprandial Exercise: The analysis session may determine the impact of exercise that occurs after meals on the analyte levels of the user. During the analysis session, the user may indicate when meals are eaten and exercises that occur after the user eats a meal. The system may analyze the analyte levels of the user after meals when no postprandial exercise occurs and after meals when postprandial exercise occurs. The system may also analyze analyte levels after meals when an activity' other than exercise (e.g., house chores) occurs. The system may provide a graph indicating when meals were eaten and / or when exercise occurred along with the relevant analyte levels. The system may also provide insights indicating how postprandial exercise impacted analyte levels.

[0203] Portion Size: The analysis session may determine impact of portion size of a meal on analyte levels of the user. During the analysis session, the user may indicate portion sizes of foods that the user ate and when the foods were eaten. The system may analyze the analyte levels of the user after eating the foods. The system may provide a graph indicating when the foods were eaten, the portion sizes of the food, and the relevant analyte levels. The system may also provide insights indicating how portion size impacted analyte levels.

[0204] Macronutrient Consumption Order: The analysis session may determine the impact of macronutrient (e.g., proteins, fibers, carbs, etc.) consumption order on analyte levels of the user. During the analysis session, the user may indicate when meals are eaten and the order in which macronutrients were eaten during the meal. The system may analyze the analytelevels of the user after the meals. The system may provide a graph indicating when the meals were eaten, the macronutrient consumption order during the meals, and the relevant analyte levels. The system may also provide insights indicating how macronutrient consumption order impacted analyte levels.

[0205] Carb Amount: The analysis session may determine the impact of consuming carbs on analyte levels of the user. During the analysis session, the user may indicate when carbs are eaten and the amount or level of the carbs (e.g., high, medium, low). The user may also input the food that was eaten, and the system may assign a carb amount or carb level. The system may analyze the analyte levels of the user after consuming carbs. The system may provide a graph indicating when the carbs were eaten, the amount or level of carbs eaten, and the relevant analyte levels. The system may also provide insights indicating how carb consumption impacted analyte levels.

[0206] Carb Type: The analysis session may determine the impact of consuming different types of carbs (e.g., simple vs. complex carbs) on analyte levels of the user. During the analysis session, the user may indicate when carbs are eaten and the type of carbs eaten. The user may also input the food that was eaten, and the system may assign a carb type. The system may analyze the analyte levels of the user after consuming carbs. The system may provide a graph indicating when carbs were eaten, the type of carbs eaten, and the relevant analyte levels. The system may also provide insights indicating how the type of carb consumed impacted analyte levels.

[0207] Macronutrient Proportions: The analysis session may determine the impact of macronutrient proportions (e.g., more fiber, less fiber, etc.) on analyte levels of the user. During the analysis session, the user may indicate when meals are eaten and whether the meals have or lack a particular macronutrient. The system may analyze the analyte levels of the user after consuming the meals. The system may provide a graph indicating when certain macronutrients were eaten or not each and the relevant analyte levels. The system may also provide insights indicating how macronutrient proportions impacted analyte levels.

[0208] Meal Timing: The analysis session may determine the impact of meal timing on analyte levels of the user. During the analysis session, the user may indicate when meals (e.g., breakfasts and dinners) are eaten, and the user may vary the time of day when the meals are eaten. The system may analyze the analyte levels of the user after eating the meals. The systemmay provide a graph indicating when the meals were eaten and the relevant analyte levels. The system may also provide insights indicating how meal times impacted analyte levels.

[0209] Food / Drink Before Eating: The analysis session may determine the impact of consuming certain foods or drinks (e.g.. coffee) before eating a meal on analyte levels of the user. During the analysis session, the user may indicate when certain foods or drinks are consumed or not consumed prior to a meal. The system may analyze the analyte levels of the user after consuming the meals. The system may provide a graph indicating when the meals occurred, whether certain foods or drinks were consumed before the meals, and the relevant analyte levels. The system may also provide insights indicating how consuming the foods or drinks before a meal impacted analyte levels.

[0210] Sleep: The analysis session may determine the impact of sleep on analyte levels of the user. During the analysis session, the user may indicate when sleep occurred. The system may analyze the analyte levels of the user relative to how much sleep the user had the previous night. The system may also provide a graph indicating how much sleep occurred and the relevant analyte levels. The system may also provide insights indicating how sleep impacted analyte levels.

[0211] FIGs. 7A through 7D illustrate example interfaces for determining and implementing analysis sessions in the continuous analyte monitoring system 100 of FIG. 1.Generally, these interfaces may be presented on a display device (e.g., the display device 107 shown in FIG. 1). The user may interact with the interfaces on the display device to view and select analysis sessions and to view recommendations (e.g.. from the management engine).

[0212] FIG.7A illustrates example interfaces that may be presented on the display device. As seen in FIG. 7A, an interface 702 may be labelled the ‘'Learn” interface. The interface 702 includes a portion 704 that shows an analysis session option that explores the effects of simple and complex carbohydrates on analyte levels (e.g., blood sugar levels) of the user. If the user interacts with the portion 704 (e g., by touching the portion 704), then the display device may interpret the interaction as the user having selected the analysis session for testing the effects of simple and complex carbohydrates.

[0213] After selecting the portion 704. the display device may present an interface 706 that shows information about the analysis session. The interface 706 shows that the analysis session has two parts. In the first part, the effects of simple carbohydrates are tested, and in thesecond part, the effects of complex carbohydrates are tested. The interface includes a portion 708 for a button to begin the first part of the analysis session. When the user interacts with the portion 708, then the display device may interpret the interaction as the user beginning the first part of the analysis session.

[0214] After selecting the portion 708. the display device may present an interface 710 where the user can set a schedule for an activity. The activity is the consumption of a snack that contains simple carbohydrates. The user may input when the snack will occur each day of the first part of the analysis session. In the example of FIG.7A, the user has set the snack time to be at 10:00 AM each day.

[0215] After setting the schedule for the snack time, the display device may present an interface 712 where the user can provide information about the activity. For example, the user may provide details about the snacks being consumed during the analysis session (e.g., veggie sticks with guac) and some nutritional information (e.g., carbohydrate content, protein content, fat content, etc ). After providing this information, the analysis session may begin.

[0216] FIG.7B illustrates example interfaces that may be presented on the display device. The display device shows an interface 722 that shows measured analyte levels (e g., glucose levels) of the user over time. The interface 722 also includes a portion 724 that shows an ongoing analysis session. In the example of FIG. 7B, the analysis session tests the effects of snacks and other foods on the analyte levels of the user.

[0217] The display device may also show an interface 726 when the user interacts with the portion 724. In the interface 726, the user may make changes to the analysis session. For example, the interface 726 includes a portion 728 that allows the user to add a snack (e.g., strawberry yogurt) to the snacks planned during the analysis session. If the user adds the snack to the analysis session, then the display device may show an interface 730 that shows that the snack has been added to the analysis session. The interface 730 includes a portion 732 that allows the user to see the progress of the analysis session.

[0218] If the user interacts with the portion 732, then the display device may show an interface 734. The interface 734 includes a graph that shows analyte levels over time during the analysis session. Additionally, the interface 734 shows information about the measured analyte levels during the analysis session so far. For example, the interface 734 showsinformation about the analyte levels (e.g., time in range, average glucose level, average glucose change) two hours after consuming a snack during the analysis session.

[0219] The display device may also show an interface 736 after the first part of the analysis session is complete. The interface 736 may include a graph showing the measured analyte levels over time during the first part of the analysis session. The interface 736 may also include a portion 738. The user may interact with the portion 738 to begin the second part of the analysis session to test the effects of complex carbohydrates on the analyte levels of the user.

[0220] FIG.7C illustrates example interfaces that may be presented on the display device. As seen in FIG.7C, the display device may show an interface 742 that is similar to the interface 702 shown in FIG.7A. The user may interact with part of the interface 742 to select an analysis session to test the effects of simple and complex carbohydrates on the analyte levels of the user.

[0221] The display device may also show an interface 744 if the user interacts with the interface 742. The interface 744 may be similar to the interface 710 show n in FIG. 7A. In the interface 744, the user may set a schedule for when snacks are consumed. In the example of FIG. 7C, the user has set snack time to 10:00 AM.

[0222] After setting the schedule, the display device may show an interface 746 that allows the user to add information about the activity (e g., the snacks to be eaten). The interface 746 is similar to the interface 712 shown in FIG. 7A. The user may provide details about the snacks being consumed during the analysis session (e.g., veggie sticks with guac) and some nutritional information (e.g., carbohydrate content, protein content, fat content, etc.). The user may change or add snacks to be consumed during the analysis session. In the interface 748, the user has added or changed the snack to fruit juice.

[0223] FIG.7D illustrates example interfaces that may be presented on the display device. As seen in FIG.7D, the display device may show an interface 762 that is similar to the interface 722 shown in FIG. 7B. The interface 762 may allow the user to add a snack to the analysis session.

[0224] The display device may also show an interface 764 that is similar to the interface 734 show n in FIG. 7B. The interface 764 includes a graph showing analyte levels measured over time during the analysis session. The interface 764 also shows information about the analyte levels (e.g., time in range, average glucose level, average glucose change) during aperiod of time (e.g., between one to four hours) after consuming a snack during the analysis session.

[0225] The display device may allow the user to transition to results of different parts of the analysis session. For example, an interface 766 shows the display device transitioning to the results of another part of the analysis session. These results may also include a graph showing analyte levels measured during another portion of the analysis session as well as information about the analyte levels.

[0226] The display device may also include an interface 768 when the user scrolls down on the results of the analysis session. In the interface 768, the display device shows recommendations and insights generated from the analysis session. In the example of FIG.7D. the insight indicates that snacks with simple carbohydrates spike glucose levels, while snacks with complex carbohydrates slowly increase glucose levels.

[0227] FIG. 8 is a block diagram depicting a computer system 800, according to certain embodiments of the present disclosure. The computer system 800 may execute various components of the analyte monitoring system 100 shown in FIG. 1, such as the management engine 114 and / or the display device 107 shown in FIG. 1. Although depicted as a single physical device, in embodiments, the computer system 800 may be implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment. As illustrated, the computer system 800 includes a processor 805, a memory7810, a storage 815, a network interface 825, and one or more input / output (I / O) interfaces 820. In the illustrated embodiment, the processor 805 retrieves and executes programming instructions stored in the memory 810, as well as stores and retrieves application data residing in the storage 815. The processor 805 is generally representative of a single central processing unit (CPU) and / or graphics processing unit (GPU), multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like. The memory 810 is generally included to be representative of a randomaccess memory7. The storage 815 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 memory7cards, caches, optical storage, network attached storage (NAS), or storage area networks (SAN).

[0228] In some embodiments, the input and output (I / O) devices 835 (such as keyboards, monitors, etc.) can be connected via the I / O interface(s) 820. Further, via network interface825, the computer system 800 can be communicatively coupled with one or more other devices and components, such as the host database 110. In certain embodiments, the computer system 800 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 805, the memory 810, the storage 815, the network interface(s) 825, and the I / O interface(s) 820 are communicatively coupled by one or more interconnects 830. In certain embodiments, the computer system 800 is representative of a display device (e.g., the display device 107 shown in FIG. 1) associated with the host. In certain embodiments, as discussed above, the display device can include the host’s laptop, computer, smartphone, and the like. In another embodiment, the computer system 800 is a server executing in a cloud environment.

[0229] In the illustrated embodiment, the storage 815 includes the host profile 118. The memory 810 includes the management engine 114, which itself includes DAM 116.

[0230] According to an embodiment, an analyte monitoring system includes an analyte sensor and one or more devices. The analyte sensor includes a sensor and sensor electronics. The sensor generates a sensor current. The sensor electronics generates analyte sensor measurements based on the sensor current. The one or more devices include one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively, perform an operation that includes determining an analysis session that includes a schedule and an activity and receiving the analyte sensor measurements from the analyte sensor according to the schedule. The operation also includes receiving, based on the schedule, input indicating that the activity7occurred, correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity, and presenting the recommendation to a user of the analyte sensor.

[0231] Determining the analysis session may include determining a plurality of analysis sessions based on at least one of (i) population data indicating a set of analysis sessions that are selected the most by a population or (ii) historical data for the user and receiving a selection of the analysis session from the plurality of analysis sessions.

[0232] Determining the analysis session may include receiving, from the user, a selection of a topic and generating the analysis session based on the topic. Determining the analysissession may include determining that the analyte sensor measurements should be collected based on the topic and determining that the analyte sensor measurements are permitted to be collected. Determining the analysis session may include providing input from the user to a machine learning model to determine the topic.

[0233] Determining the analysis session may include determining a duration for the analysis session and wherein the schedule indicates the duration.

[0234] The schedule may indicate a time when the activity should occur during the analysis session. The operation may include presenting an alert indicating the time and the activity.

[0235] The operation may include flagging, based on a threshold, an analyte sensor measurement of the analyte sensor measurements and receiving, from the user, a message providing an explanation for the analyte sensor measurement.

[0236] The activity may include at least one of (i) exercise, (ii) meal consumption, or (iii) sleep.

[0237] According to another embodiment, a method includes generating, by an analyte sensor, a sensor cunent and generating, by the analyte sensor, analyte sensor measurements based on the sensor current. The method also includes determining an analysis session that includes a schedule and an activity7and receiving the analyte sensor measurements from the analyte sensor according to the schedule. The method further includes receiving, based on the schedule, input indicating that the activity occurred, correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity7, and presenting the recommendation to a user of the analyte sensor.

[0238] Determining the analysis session may include determining a plurality of analysis sessions based on at least one of (i) population data indicating a set of analysis sessions that are selected the most by a population or (ii) historical data for the user and receiving a selection of the analysis session from the plurality of analysis sessions.

[0239] Determining the analysis session may include receiving, from the user, a selection of a topic and generating the analysis session based on the topic. Determining the analysis session may include determining that the analyte sensor measurements should be collected based on the topic and determining that the analyte sensor measurements are permitted to becollected. Determining the analysis session may include providing input from the user to a machine learning model to determine the topic.

[0240] Determining the analysis session may include determining a duration for the analysis session and wherein the schedule indicates the duration.

[0241] The schedule may indicate a time when the activity should occur during the analysis session. The operation may include presenting an alert indicating the time and the activity.

[0242] The operation may include flagging, based on a threshold, an analyte sensor measurement of the analyte sensor measurements and receiving, from the user, a message providing an explanation for the analyte sensor measurement.

[0243] According to another embodiment, a device includes one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively, perform an operation that includes determining an analysis session comprising a schedule and an activity and receiving analyte sensor measurements from an analyte sensor according to the schedule. The operation also includes receiving, based on the schedule, input indicating that the activity occurred, correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity, and presenting the recommendation to a user of the analyte sensor.

[0244] The phrases "analyte-measuring device,” “analyte-monitoring device,” “analytesensing device,” and / or “multi -analyte sensor device” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to an apparatus and / or system responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. For example, these phrases may refer without limitation to an instrument responsible for detection of a particular analyte or combination of analytes. In one example, the instrument includes a sensor coupled to circuitry- disposed within a housing, and configure to process signals associated with analyte concentrations into information. In one example, such apparatuses and / or systems are capable of providing specific quantitative, semi-quantitative, qualitative, and / or semi qualitativeanalytical information using a biological recognition element combined with a transducing (detecting) element.

[0245] The terms “biosensor"’ and / or “sensor” as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a part of an analyte measuring device, analyte-monitoring device, analyte sensing device, and / or multianalyte sensor device responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. In one example, the biosensor or sensor generally comprises a body, a working electrode, a reference electrode, and / or a counter electrode coupled to body and forming surfaces configured to provide signals during electrochemical reactions. One or more membranes can be affixed to the body and cover electrochemically reactive surfaces. In one example, such biosensors and / or sensors are capable of providing specific quantitative, semi-quantitative, qualitative, semi qualitative analytical signals using a biological recognition element combined with a transducing (detecting) element.

[0246] The term “continuous” 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 an uninterrupted or unbroken portion, domain, coating, or layer.

[0247] The phrases “continuous analyte sensing” and “continuous multi-analyte sensing” as used herein are broad phrases, and are to be given their 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 the period in which monitoring of analyte concentration is continuously, continually, and / or intermittently (but regularly) performed, for example, from about every second or less to about one week or more. In further examples, monitoring of analyte concentration is performed from about every 2, 3, 5, 7,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds to about every 1.25, 1.50, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25, 4.50, 4.75, 5.00, 5.25, 5.50, 5.75, 6.00, 6.25, 6.50, 6.75, 7.00, 7.25, 7.50, 7.75, 8.00, 8.25, 8.50, 8.75, 9.00, 9.25, 9.50 or 9.75 minutes. In further examples, monitoring of analyte concentration is performed from about 10, 20, 30, 40 or 50 minutes to about every 1, 2, 3, 4, 5, 6, 7 or 8 hours. In further examples, monitoring of analyte concentration is performedfrom about every78 hours to about every 12, 16, 20, or 24 hours. In further examples, monitoring of analyte concentration is performed from about every day to about every 1.5, 2, 3, 4, 5, 6, or 7 days. In further examples, monitoring of analyte concentration is performed from about every week to about every 1.5, 2, 3 or more weeks.

[0248] The term “coupled” 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 two or more system elements or components that are configured to be at least one of electrically, mechanically, thermally, operably, chemically or otherwise attached. For example, an element is “coupled” if the element is covalently, communicatively, electrostatically, thermally connected, mechanically connected, magnetically connected, or ionically associated with, or physically entrapped, adsorbed to or absorbed by another element. Similarly, the phrases “operably connected”, “operably linked”, and “operably coupled” as used herein may refer to one or more components linked to another component(s) in a manner that facilitates transmission of at least one signal between the components. In some examples, components are part of the same structure and / or integral with one another as in covalently, electrostatically, mechanically, thermally, magnetically, ionically associated with, or physically entrapped, or absorbed (i.e., “directly coupled” as in no intervening element(s)). In other examples, components are connected via remote means. For example, one or more electrodes can be used to detect an analyte in a sample and convert that information into a signal; the signal can then be transmitted to an electronic circuit. In this example, the electrode is “operably linked” to the electronic circuit. The phrase “removably coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached and detached without damaging any of the coupled elements or components. The phrase “permanently coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached but cannot be uncoupled without damaging at least one of the coupled elements or components, covalently, electrostatically, ionically associated with, or physically entrapped, or absorbed.

[0249] The term “distal” as used herein is a broad term, and is to be given its ordinary and customary7meaning to a person of ordinary' skill in the art (and is not to be limited to a specialor customized meaning), and refers without limitation to a region spaced relatively far from a point of reference, such as an origin or a point of attachment.

[0250] The term “in vivo’ as used herein is a broad term, and is to be given its ordinary7and customary meaning to a person of ordinary7skill in the art (and is not to be limited to a special or customized meaning), and without limitation is inclusive of the portion of a device (for example, a sensor) adapted for insertion into and / or existence within a living body of a host.

[0251] The term “membrane” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary7skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a structure configured to perform functions including, but not limited to, protection of the exposed electrode surface from the biological environment, diffusion resistance (limitation) of the analyte, service as a matrix for a catalyst (e.g., one or more enzymes) for enabling an enzymatic reaction, limitation or blocking of interfering species, provision of hydrophilicity at the electrochemically reactive surfaces of the sensor interface, service as an interface between host tissue and the implantable device, modulation of host tissue response via drug (or other substance) release, and combinations thereof. When used herein, the terms “membrane” and “matrix” are meant to be interchangeable.

[0252] The term “proximal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary7skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference.

[0253] The term “sensitivity'” 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 an amount of signal (e.g., in the form of electrical current and / or voltage) produced by a predetermined amount (unit) of the measured analyte. For example, in one example, a sensor has a sensitivity (or slope) of from about 1 to about 100 picoAmps of current for every 1 mg / dL of analyte.Example Embodiments

[0254] Implementation examples are described in the following numbered clauses:

[0255] Clause 1 : An analyte monitoring system comprising: an analyte sensor comprising: a sensor configured to generate a sensor current; and sensor electronics configured to generate analyte sensor measurements based on the sensor current; and one or more devices comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories, the one or more processors configured to, individually or collectively, perform an operation comprising: determining an analysis session comprising a schedule and an activity; receiving the analyte sensor measurements from the analyte sensor according to the schedule; receiving, based on the schedule, input indicating that the activity occurred; correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity; and presenting the recommendation to a user of the analyte sensor.

[0256] Clause 2: The analyte monitoring sy stem of Clause 1, wherein determining the analysis session comprises: determining a plurality of analysis sessions based on at least one of (i) population data indicating a set of analysis sessions that are selected the most by a population or (ii) historical data for the user; and receiving a selection of the analysis session from the plurality of analysis sessions.

[0257] Clause 3: The analyte monitoring system of any of Clauses 1-2, wherein determining the analysis session comprises: receiving, from the user, a selection of a topic; and generating the analysis session based on the topic.

[0258] Clause 4: The analyte monitoring system of Clause 3, wherein determining the analysis session further comprises: determining that the analyte sensor measurements should be collected based on the topic; and determining that the analyte sensor measurements are permitted to be collected.

[0259] Clause 5: The analyte monitoring system of Clause 3, wherein determining the analysis session further comprises providing input from the user to a machine learning model to determine the topic.

[0260] Clause 6: The analyte monitoring system of any of Clauses 1-5, wherein determining the analysis session further comprises determining a duration for the analysis session and wherein the schedule indicates the duration.

[0261] Clause 7: The analyte monitoring system of any of Clauses 1-6, wherein the schedule indicates a time when the activity' should occur during the analysis session.

[0262] Clause 8: The analyte monitoring system of Clause 7, wherein the operation further comprises presenting an alert indicating the time and the activity.

[0263] Clause 9: The analyte monitoring system of any of Clauses 1-8, wherein the operation further comprises: flagging, based on a threshold, an analyte sensor measurement of the analyte sensor measurements; and receiving, from the user, a message providing an explanation for the analyte sensor measurement.

[0264] Clause 10: The analyte monitoring system of any of Clauses 1-9, wherein the activity' comprises at least one of (i) exercise, (ii) meal consumption, or (iii) sleep.

[0265] Clause 11: A method for performing operations performed by7the analyte monitoring system in Clauses 1-10.

[0266] Clause 12: A device configured to perform operations performed by the analyte monitoring system in Clauses 1-10.

[0267] Clause 13: An analyte monitoring system comprising: an analyte sensor comprising: a sensor configured to generate a sensor current; and sensor electronics configured to generate analyte sensor measurements based on the sensor current; and one or more devices comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories, the one or more processors configured to, individually or collectively, perform an operation comprising: determining a plurality' of analysis sessions; presenting the plurality of analysis sessions to a user of the analyte sensor for selection; receiving a selection of an analysis session from the plurality of analysis sessions; and performing the analysis session to generate an insight about the user.

[0268] Clause 14: The analyte monitoring system of Clause 13, wherein the operation further comprises presenting details for the plurality' of analysis sessions.

[0269] Clause 15: The analyte monitoring system of any of Clauses 13-14, wherein the analysis session of the plurality' of analysis sessions is determined based on population data indicating that the analysis session is selected the most by a population.

[0270] Clause 16: The analyte monitoring system of any of Clauses 13-15, wherein the analysis session of the plurality of analysis sessions is determined based on population data indicating that the analysis session was previously selected by a population.

[0271] Clause 17: The analyte monitoring system of any of Clauses 13-16, wherein the analysis session of the plurality of analysis sessions is determined based on historical data for the user.

[0272] Clause 18: The analyte monitoring system of any of Clauses 13-17, wherein the operation further comprises receiving, from the user, a selection of a topic; and wherein the analysis session of the plurality' of analysis sessions is determined based on the topic.

[0273] Clause 19: The analyte monitoring system of any of Clauses 13-18, wherein the operation further comprises: determining, based on previous analysis sessions, to determine that user data is needed to generate the insight, and wherein performing the analysis session collects the user data.

[0274] Clause 20: The analyte monitoring system of any of Clauses 13-19, wherein operation further comprises determining, based on data about previously performed analysis sessions, a duration of the analysis session.

[0275] Clause 21: The analyte monitoring system of any of Clauses 13-20, wherein the operation further comprises: determining a schedule for the analysis session, and wherein the analysis session is performed according to the schedule.

[0276] Clause 22: A method for performing operations performed by the analyte monitoring system in Clauses 13-21.

[0277] Clause 23: A device configured to perform operations performed by the analyte monitoring system in Clauses 13-21.

[0278] Clause 24: An analyte monitoring system comprising: an analyte sensor comprising: a sensor configured to generate a sensor current; and sensor electronics configured to generate analyte sensor measurements based on the sensor current; and one or more devices comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories, the one or more processors configured to. individually or collectively, perform an operation comprising: receiving, from a user of the analyte sensor, a selection of a topic; determining a plurality of analysis sessions based on the topic; presentingthe plurality of analysis sessions to the user for selection; receiving a selection of an analysis session from the plurality' of analysis sessions; and performing the analysis session to generate an insight about the user.

[0279] Clause 25: The analyte monitoring system of Clause 24, wherein the operation further comprises: determining, based on previous analysis sessions, to determine that user data is needed to generate the insight, and wherein performing the analysis session collects the user data.

[0280] Clause 26: The analyte monitoring system of any of Clauses 24-25, wherein operation further comprises determining, based on data about previously performed analysis sessions, a duration of the analysis session.

[0281] Clause 27: The analyte monitoring system of any of Clauses 24-26, wherein the operation further comprises: determining a schedule for the analysis session, and wherein the analysis session is performed according to the schedule.

[0282] Clause 28: A method for performing operations performed by the analyte monitoring system in Clauses 24-27.

[0283] Clause 29: A device configured to perform operations performed by the analyte monitoring system in Clauses 24-27.

[0284] Clause 30: An analyte monitoring system comprising: an analyte sensor comprising: a sensor configured to generate a sensor current; and sensor electronics configured to generate analyte sensor measurements based on the sensor current; and one or more devices comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories, the one or more processors configured to, individually or collectively, perform an operation comprising: determining a plurality of analysis sessions based on historical data for a user of the analyte sensor; presenting the plurality of analysis sessions to the user for selection; receiving a selection of an analysis session from the plurality of analysis sessions; and performing the analysis session to generate an insight about the user.

[0285] Clause 31 : The analyte monitoring system of Clause 30. wherein the historical data indicates a previous analysis session selected by the user, and wherein the plurality of analysis sessions is determined based on the previous analysis session.

[0286] Clause 32: The analyte monitoring system of any of Clauses 30-31, wherein the historical data indicates at least one of a health goal or health condition of the user, and wherein the plurality of analysis sessions is determined based on at least one of the health goal or the health condition.

[0287] Clause 33: The analyte monitoring system of any of Clauses 30-32, wherein the operation further comprises comparing the historical data to population data to determine a population with population data similar to the historical data; determining a previous analysis session selected by the population, and wherein the plurality of analysis sessions is determined based on the previous analysis session.

[0288] Clause 34: A method for performing operations performed by the analyte monitoring system in Clauses 30-33.

[0289] Clause 35: A device configured to perform operations performed by the analyte monitoring system in Clauses 30-33.Additional Considerations

[0290] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0291] As used herein, a phrase referring to ‘"at least one of’ a list of items refers to any combination of those items, including single members. As an example, ’‘at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b. a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0292] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean "one and only one” unlessspecifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. §112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”

[0293] While various examples of the invention have been described above, it should be understood that they have been presented by way of example only, and not by way of limitation. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosure, which is done to aid in understanding the features and functionality that can be included in the disclosure. The disclosure is not restncted to the illustrated example architectures or configurations, but can be implemented using a variety of alternative architectures and configurations. Additionally, although the disclosure is described above in terms of various example examples and aspects, it should be understood that the various features and functionality described in one or more of the individual examples are not limited in their applicability to the particular example with which they are described. They instead can be applied, alone or in some combination, to one or more of the other examples of the disclosure, whether or not such examples are described, and whether or not such features are presented as being a part of a described example. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described examples.

[0294] All references cited herein are incorporated herein by reference in their entirety. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0295] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meaning to a person of ordinary skill in the art, and are not to be limited to a special or customized meaning unless expressly so defined herein.

[0296] Terms and phrases used in this application, and variations thereof, especially in the appended claims, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. As examples of the foregoing, the term ‘including’ should be read to mean ‘including, without limitation,’ ‘including but not limited to,’ or the like; the term ‘comprising’ as used herein is synonymous with ‘including,’ ‘containing,’ or ‘characterized by,’ and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term ‘having’ should be interpreted as ‘having at least;’ the term ‘includes’ should be interpreted as ‘includes but is not limited to;’ the term ‘example’ is used to provide example instances of the item in discussion, not an exhaustive or limiting list thereof; adjectives such as ’known', ‘normal’, ‘standard’, and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass known, normal, or standard technologies that may be available or known now or at any time in the future; and use of terms like ‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words of similar meaning should not be understood as implying that certain features are critical, essential, or even important to the structure or function of the invention, but instead as merely intended to highlight alternative or additional features that may or may not be utilized in a particular example of the invention. Likewise, a group of items linked with the conjunction ‘and’ should not be read as requiring that each and every' one of those items be present in the grouping, but rather should be read as ‘and / or’ unless expressly stated otherwise. Similarly, a group of items linked with the conjunction ‘or’ should not be read as requiring mutual exclusivity among that group, but rather should be read as ‘and / or’ unless expressly stated otherwise.

[0297] The term ‘‘comprising as used herein is synonymous with “including.” “containing,” or “characterized by” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0298] All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the term ‘about.’ Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to the presentapplication, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0299] Furthermore, although the foregoing has been described in some detail by way of illustrations and examples for purposes of clarity’ and understanding, it is apparent to those skilled in the art that certain changes and modifications may be practiced. Therefore, the description and examples should not be construed as limiting the scope of the invention to the specific examples and examples described herein, but rather to also cover all modification and alternatives coming with the true scope and spirit of the invention.

Claims

CLAIMS1. An analyte monitoring system comprising:an analyte sensor comprising:a sensor configured to generate a sensor current; andsensor electronics configured to generate analyte sensor measurements based on the sensor current; andone or more devices comprising:one or more memories; andone or more processors communicatively coupled to the one or more memories, the one or more processors configured to, individually or collectively, perform an operation comprising:determining an analysis session comprising a schedule and an activity; receiving the analyte sensor measurements from the analyte sensor according to the schedule;receiving, based on the schedule, input indicating that the activity occurred;correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity; and presenting the recommendation to a user of the analyte sensor.

2. The analyte monitoring system of Claim 1, wherein determining the analysis session comprises:determining a plurality of analysis sessions based on at least one of (i) population data indicating a set of analysis sessions that are selected the most by a population or (ii) historical data for the user; andreceiving a selection of the analysis session from the plurality' of analysis sessions.

3. The analyte monitoring system of any of Claims 1 and 2, wherein determining the analysis session comprises:receiving, from the user, a selection of a topic; andgenerating the analysis session based on the topic.

4. The analyte monitoring system of Claim 3, wherein determining the analysis session further comprises:determining that the analyte sensor measurements should be collected based on the topic; anddetermining that the analyte sensor measurements are permitted to be collected.

5. The analyte monitoring system of any of Claims 3 and 4, wherein determining the analysis session further comprises providing input from the user to a machine learning model to determine the topic.

6. The analyte monitoring system of any of Claims 1 through 5, wherein determining the analysis session further comprises determining a duration for the analysis session and wherein the schedule indicates the duration.

7. The analyte monitoring system of any of Claims 1 through 6, wherein the schedule indicates a time when the activity should occur during the analysis session.

8. The analyte monitoring system of Claim 7, wherein the operation further comprises presenting an alert indicating the time and the activity.

9. The analyte monitoring system of any of Claims 1 through 8, wherein the operation further comprises:flagging, based on a threshold, an analyte sensor measurement of the analyte sensor measurements; andreceiving, from the user, a message providing an explanation for the analyte sensor measurement.

10. The analyte monitoring system of any of Claims 1 through 9. wherein the activity comprises at least one of (i) exercise, (ii) meal consumption, or (iii) sleep.

11. A method comprising :generating, by an analyte sensor, a sensor current;generating, by the analyte sensor, analyte sensor measurements based on the sensor current;determining an analysis session comprising a schedule and an activity ;receiving the analyte sensor measurements from the analyte sensor according to the schedule:receiving, based on the schedule, input indicating that the activity occurred; correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity; andpresenting the recommendation to a user of the analyte sensor.

12. The method of Claim 11, wherein determining the analysis session comprises: determining a plurality of analysis sessions based on at least one of (i) population data indicating a set of analysis sessions that are selected the most by a population or (ii) historical data for the user; andreceiving a selection of the analysis session from the plurality of analysis sessions.

13. The method of any of Claims 11 and 12, wherein determining the analysis session comprises:receiving, from the user, a selection of a topic; andgenerating the analysis session based on the topic.

14. The method of Claim 13, wherein determining the analysis session further comprises:determining that the analyte sensor measurements should be collected based on the topic; anddetermining that the analyte sensor measurements are permitted to be collected.

15. A device comprising:one or more memories; andone or more processors communicatively coupled to the one or more memories, the one or more processors configured to, individually or collectively, perform an operation comprising:determining an analysis session comprising a schedule and an activity7; receiving analyte sensor measurements from an analyte sensor according to the schedule;receiving, based on the schedule, input indicating that the activity occurred;correlating the analyte sensor measurements with the input indicating that the activity occurred to generate a recommendation about the activity; and presenting the recommendation to a user of the analyte sensor.

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