Glucose reporting and visualization with best days

Abstract

Certain aspects of the present disclosure provide techniques for processing and presenting analyte data. Some example aspects may describe techniques for generating and providing user interface views of a user's performance report for display. Some example aspects may describe techniques for providing one or more user interface views for display on one or more widgets.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 153,524, filed February 25, 2021, which is assigned to the assignee hereof and is expressly incorporated by reference in its entirety as if fully set forth below and for all applicable purposes.

[0002] FIELD OF THEINVENTION Aspects of the present disclosure relate generally to continuous analyte monitoring, and more specifically to analyte data processing, reporting, and visualization. [Background technology]

[0003] Diabetes mellitus is a disease in which the pancreas cannot produce enough insulin. In the diabetic state, individuals suffering from hyperglycemia may experience a series of physiological side effects related to the deterioration of microvessels. These side effects may include, for example, kidney failure, skin ulcers, bleeding into the vitreous of the eye, etc. Hypoglycemic reactions, such as hypoglycemic events, may be triggered by inadvertent overdosing of insulin or after normal doses of insulin or glucose-lowering drugs. Severe hypoglycemic reactions may be associated with a high risk of headaches, seizures, loss of consciousness, and coma.

[0004] Individuals with diabetes may carry self-monitoring blood glucose (SMBG) monitors that typically require users to prick their finger to measure their glucose levels. Given the inconvenience associated with traditional finger-stick methods, individuals with diabetes are unlikely to take timely SMBG measurements and, as a result, may not realize that their blood glucose levels indicate a dangerous situation.

[0005] A variety of non-invasive, transdermal (e.g., transcutaneous), and / or implantable electrochemical sensors have been developed to detect and / or quantify blood glucose levels. These devices generally transmit raw or minimally processed data for subsequent analysis in a remote device. This remote device may have a display that presents information to the user hosting the sensor. In some systems, patients can check their glucose levels on a handheld computing device. Presenting this information discreetly and with a high degree of confidence is a challenge. Furthermore, efficiently analyzing this information so that reports and insights can be presented to the diabetic user for the ongoing management of the diabetic condition is a challenge. Summary of the Invention [Means for solving the problem]

[0006] The systems, methods, and devices of the present disclosure each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of the present disclosure as expressed by the claims that follow, certain features will now be briefly discussed. After considering this discussion, and particularly after reading the section entitled "Description of the Preferred Embodiments," one will understand how the features of the present disclosure provide advantages, including improved analysis and presentation of analyte data.

[0007] Methods, means, apparatus, processors, and / or processing systems, as well as computer readable media and / or computer program products, for the processing and visualization of analyte data are provided.

[0008] Certain aspects of the present disclosure provide a method for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host. The method can include accessing sensor data, the sensor data can include a plurality of blood glucose readings associated with the host during a plurality of analysis periods in a current week. Each blood glucose reading can indicate a blood glucose concentration value of the host at a respective time. The method can include determining an average blood glucose concentration value of the host for the current week based on the plurality of blood glucose readings. The method can include generating a performance report. The performance report can include, but is not limited to, an average blood glucose concentration value of the host for a first time period and a comparison of the average blood glucose concentration value of the host for the first time period with the average blood glucose concentration value of the host for at least two previous time periods of similar duration, an average blood glucose concentration value per day of the host, a percentage of the blood glucose concentration value of the host in one or more blood glucose concentration value ranges per day, or combinations thereof. Information in the report can be customizable by a user. The method can include generating a user interface view of the performance report. The method can include providing a user interface view of the performance report for display.

[0009] Certain aspects of the present disclosure provide a method for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host. The method can include accessing first data associated with blood glucose concentration values ​​of the host, the first data associated with a first time period. The method can include analyzing the first data to generate first one or more user interface views associated with the first data for display on one or more widgets. The method can include providing the first one or more user interface views for display on the one or more widgets. The method can include automatically updating the first one or more user interface views for display on the one or more widgets. Automatically updating the first one or more user interface views can include accessing second data associated with blood glucose concentration values ​​of the host, the second data associated with a second time period, analyzing the second data to generate second one or more user interface views associated with the second data for display on the one or more widgets, and providing the second one or more user interface views for display on the one or more widgets. The one or more widgets can be customizable by a user.

[0010] It should be understood that both the general description above and the following Detailed Description are merely exemplary and explanatory, and not restrictive. Additional features and / or variations may be provided in addition to those described herein. For example, the embodiments described herein may be directed to various combinations and subcombinations of the disclosed features and / or some additional feature combinations and subcombinations disclosed in the Detailed Description below.

[0011] So that the above-listed features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, can be made by reference to embodiments, some of which are illustrated in the drawings. It should be noted, however, that the attached drawings merely illustrate certain exemplary embodiments of the present disclosure and therefore should not be considered as limiting its scope, since the description may be allowed for other equally valid embodiments. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 conceptually illustrates an exemplary system including an exemplary continuous analyte sensor with sensor electronics, in accordance with certain exemplary aspects of the present disclosure. [Diagram 2] FIG. 2 is a block diagram conceptually illustrating a sensor electronics module in communication with multiple sensors, in accordance with certain exemplary aspects of the disclosure. [Figure 3A] 1 illustrates different views of a sensor system including an on-board unit and sensor electronics attached thereto, according to some exemplary aspects of the present disclosure. [Figure 3B] 1 illustrates different views of a sensor system including an on-board unit and sensor electronics attached thereto, according to some exemplary aspects of the present disclosure. [Figure 4] FIG. 1 is a block diagram conceptually illustrating an analyte processing system according to some exemplary aspects of the present disclosure. [Diagram 5] 1 illustrates an example user interface view associated with sensor data representing glucose concentration values ​​in a host, according to certain example aspects of the present disclosure. [Figure 6] FIG. 11 is a flow diagram illustrating example operations for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host, according to some example aspects of the present disclosure. [Figure 7]7 illustrates an example wireframe 700 of a user interface view in a vertical layout, according to some example aspects of the present disclosure. [Figure 8A] 8 illustrates an example wireframe 800a of a user interface view in a horizontal layout, according to some example aspects of the present disclosure. [Figure 8B] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 8C] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 8D] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 8E] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 8F] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 8G] 7A and 7B illustrate close-up views of features 1-6 depicted in vertical and horizontal wire frames 700 and 800a, respectively, according to some exemplary embodiments of the present disclosure. [Figure 9] 9 illustrates an example user interface view 900 associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to the wireframe 700, according to some example aspects of the present disclosure. [Figure 10A] 10 illustrates an example user interface view 1000a associated with sensor data representing glucose concentration values ​​of a host in a horizontal layout corresponding to a wireframe 800a, according to some example aspects of the present disclosure. [Figure 10B]9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 10C] 9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 10D] 9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 10E] 9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 10F] 9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 10G] 9A and 9B illustrate expanded views of features 1-6 shown in vertical and horizontal user interface views 900 and 1000a, respectively, according to some exemplary aspects of the present disclosure. [Figure 11A] 11 illustrates an example wireframe 1100a of another example user interface view in a vertical layout, according to some example aspects of the present disclosure. [Figure 11B] 11 illustrates an example wireframe 1100b of another user interface view in a horizontal layout, according to some example aspects of the present disclosure. [Figure 11C] 11A and 11B illustrate close-up views of features 1-4 depicted in vertical and horizontal wire frames 1100a and 1100b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 11D] 11A and 11B illustrate close-up views of features 1-4 depicted in vertical and horizontal wire frames 1100a and 1100b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 11E]11A and 11B illustrate close-up views of features 1-4 depicted in vertical and horizontal wire frames 1100a and 1100b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 11F] 11A and 11B illustrate close-up views of features 1-4 depicted in vertical and horizontal wire frames 1100a and 1100b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 12A] 12 illustrates an example user interface view 1200a associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to the wireframe 1100a, according to some example aspects of the present disclosure. [Figure 12B] 12 illustrates an example user interface view 1200b associated with sensor data representing glucose concentration values ​​of a host in a horizontal layout corresponding to the wireframe 1100b, according to some example aspects of the present disclosure. [Figure 12C] 12A and 12B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of the present disclosure. [Figure 12D] 12A and 12B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of the present disclosure. [Figure 12E] 12A and 12B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of the present disclosure. [Figure 12F] 12A and 12B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of the present disclosure. [Figure 12G] 12A and 12B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of the present disclosure. [Figure 13A]13 illustrates an example wireframe 1300a of another example user interface view in a vertical horizontal layout, according to some example aspects of the present disclosure. [Figure 13B] 13 illustrates an example wireframe 1300b of another example user interface view in a horizontal layout, according to some example aspects of the present disclosure. [Figure 13C] 13A and 13B illustrate close-up views of features 1-3 depicted in vertical and horizontal wire frames 1300a and 1300b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 13D] 13A and 13B illustrate close-up views of features 1-3 depicted in vertical and horizontal wire frames 1300a and 1300b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 13E] 13A and 13B illustrate close-up views of features 1-3 depicted in vertical and horizontal wire frames 1300a and 1300b, respectively, according to some exemplary embodiments of the present disclosure. [Figure 14A] 1 illustrates an example user interface view 1400a associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to the wireframe 1300a, according to some example aspects of the present disclosure. [Figure 14B] 14 illustrates an example user interface view 1400b associated with sensor data representing glucose concentration values ​​of a host in a horizontal layout corresponding to the wireframe 1300b, according to some example aspects of the present disclosure. [Figure 14C] 14A and 14B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of the present disclosure. [Figure 14D] 14A and 14B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of the present disclosure. [Figure 14E]14A and 14B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of the present disclosure. [Figure 14F] 14A and 14B illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of the present disclosure. [Figure 15] FIG. 2 is a block diagram conceptually illustrating a software architecture for implementing widget functionality in accordance with certain example aspects of the present disclosure. [Figure 16] 1 illustrates an example dashboard including several user interface elements, also referred to herein as "widgets," in accordance with some example aspects of the present disclosure. [Figure 17] FIG. 11 is a flow diagram illustrating example operations for generating a user interface view associated with sensor data representing glucose concentration values ​​of a host, according to some example aspects of the disclosure. [Figure 18A] FIG. 18A is a table categorizing exemplary analyte data widgets according to some exemplary embodiments of the present disclosure. [Figure 18B] FIG. 18B is a table categorizing exemplary analyte data widgets, according to some exemplary embodiments of the present disclosure. [Figure 18C] FIG. 18C is a table categorizing exemplary analyte data widgets, according to some exemplary embodiments of the present disclosure. [Figure 19] 1 illustrates a wireframe of an exemplary summary widget, according to some exemplary aspects of the present disclosure. [Figure 20] 13 illustrates a wireframe of another example summary widget, according to some example aspects of the present disclosure. [Figure 21] 1 illustrates another example summary widget, according to some example aspects of the present disclosure. [Figure 22] 1 illustrates another example summary widget, according to some example aspects of the present disclosure. [Diagram 23] 1 illustrates another example summary-type widget, according to some example aspects of the present disclosure. [Figure 24] 1 illustrates another example summary-type widget, according to some example aspects of the present disclosure. [Figure 25A] 1 illustrates another example summary-type widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 25B] 1 illustrates another example summary-type widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 26A] 1 illustrates another example summary-type widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 26B] 1 illustrates another example summary-type widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 27] 1 illustrates a wireframe of another example summary type widget, according to some example aspects of the present disclosure. [Figure 28] 1 illustrates a wireframe of another example summary type widget, according to some example aspects of the present disclosure. [Figure 29] 1 illustrates a wireframe of another example summary type widget, according to some example aspects of the present disclosure. [Diagram 30] 1 illustrates a wireframe of another example summary type widget, according to some example aspects of the present disclosure. [Diagram 31] 1 illustrates a wireframe of another example summary type widget, according to some example aspects of the present disclosure. [Diagram 32] 1 illustrates another example summary-type widget, according to some example aspects of the present disclosure. [Figure 33A] 1 illustrates an example motivational widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 33B] 1 illustrates an example motivational widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 33C] 1 illustrates an example motivational widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 33D] 1 illustrates an example motivational widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Figure 33E] 1 illustrates an example motivational widget and its corresponding wireframe, respectively, according to some example aspects of the present disclosure. [Diagram 34] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Figure 35A] 1 illustrates another example motivation widget and its corresponding wireframe, respectively, in accordance with certain example aspects of the present disclosure. [Figure 35B] 1 illustrates another example motivation widget and its corresponding wireframe, respectively, in accordance with certain example aspects of the present disclosure. [Diagram 36] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Figure 37] 1 illustrates a wireframe of another example motivation widget, according to some example aspects of the present disclosure. [Figure 38] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Figure 39] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Diagram 40] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Diagram 41] 1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Diagram 42]1 illustrates another example motivation widget, according to some example aspects of the present disclosure. [Diagram 43] 1 illustrates a wireframe of another example motivation widget, according to some example aspects of the present disclosure. [Diagram 44] 1 illustrates a wireframe of an exemplary event-based widget, according to some exemplary aspects of the present disclosure. [Diagram 45] 1 illustrates a wireframe of another exemplary event-type widget, according to some exemplary aspects of the present disclosure. [Figure 46] 1 illustrates another example event-based widget, according to some example aspects of the present disclosure. [Figure 47A] 1 illustrates another example event type widget and its corresponding wireframe, respectively, in accordance with certain example aspects of the present disclosure. [Figure 47B] 1 illustrates another example event type widget and its corresponding wireframe, respectively, in accordance with certain example aspects of the present disclosure. [Figure 48] 13 illustrates example wireframes of other types of widgets, according to some example aspects of the present disclosure. [Figure 49] 1 illustrates another exemplary type of widget, according to some exemplary aspects of the present disclosure. [Figure 50] 1 illustrates another exemplary type of widget, according to some exemplary aspects of the present disclosure. [Figure 51] 13 illustrates another exemplary wireframe of another type of widget, according to some exemplary aspects of the present disclosure. [Figure 52] 1 is a flow diagram illustrating example operations for activating, customizing, and using a dashboard having widgets in accordance with certain example aspects of the present disclosure. [Figure 53A] 1 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. [Figure 53B]1 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. [Figure 54] 1 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. [Figure 55] 1 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. [Figure 56] 1 is a flow diagram illustrating example operations for activating, customizing, and using a dashboard having widgets in accordance with certain example aspects of the present disclosure. [Figure 57] 1 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] For ease of understanding, wherever possible, the same reference numerals have been used to designate identical elements that are common to each of the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized in other embodiments without specific recitation. Data visualization is the process of transforming large data sets and measurements into charts, graphs, and other visual aids. The resulting visual representation of the data makes it easier to identify and share new insights about real-time trends, outliers, and information represented within the data. Perhaps one of the most important benefits of data visualization is that it facilitates easy and rapid assimilation of large amounts of data. Visualization allows analysts and end users to recognize patterns and relationships within large amounts of data that are not easily visible in the raw data or reports. This can help identify emerging trends, enabling users to address, for example, health issues before they become a bigger problem. The ultimate goal is to provide actionable insights that help drive change.

[0014] Health data visualization is becoming an increasingly important focus in health analytics. Visualizing health data is a powerful way to quickly and effectively share urgent health information. If implemented correctly, health data visualization can provide significant benefits to end users. For example, data visualization tools can encourage improved absorption of health information, provide quick access to meaningful health insights, communicate findings in a constructive manner to engage and inform users about future and / or current health issues, and facilitate user interaction with data to make informed lifestyle decisions and changes, inspiring a healthier lifestyle.

[0015] There is an increasing emphasis on self-monitoring applications that allow patients to measure their own physical health parameters. Data visualization techniques and tools play an important role in the continuous management of a person's health. For example, data visualization techniques and tools can be used to efficiently analyze, report, and provide insights to diabetic users for the continuous management of their diabetic condition.

[0016] Aspects of the present disclosure provide apparatus, methods, processing systems, and computer-readable media for processing, reporting, and visualizing analyte data.

[0017] As used herein, the term "analyte" is a broad term used in its ordinary sense, including, but not limited to, referring to a substance or chemical component within a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymphatic fluid, or urine) that can be analyzed. Analytes can include naturally occurring substances, man-made substances, metabolites, and / or reaction products. Analytes measured by the present device and method may include, but are not limited to, glucose, acarboxyprothrombin; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; α-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine A; d-pep-terin; Nicilamine; Deethylchloroquine; Dehydroepiandrosterone sulfate; DNA (acetylation polymorphism, alcohol dehydrogenase, α1-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, β-thalassemia, hepatitis B virus, HCMV , HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free beta-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 peroxidase;glycocholate;glycosylated hemoglobin;halofantrine;hemoglobin variants;hexosaminidase A;human erythrocyte carbonic anhydrase I;17-α-hydroxyprogesterone;hypoxanthine phosphoribosyltransferase;immunoreactive trypsin;lactate;lead;lipoproteins ((a), B / A-1, β);lysozyme;mefloquine;netilmicin;phenobarbitone;phenytoin;phytanic acid / pristanic acid;progesterone;prolactin;prolidase;purine nucleoside phosphorylase;quinine;reverse triiodothyronine (reverse tri-iodothyronine, rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies (adenovirus, antinuclear antibodies, anti-zeta antibodies, arbovirus, pseudorabies virus, dengue virus, guinea worm, tapeworm, ameba histolytica, enterovirus, giardiasis, 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, malaria parasite , poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, Rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / langeri, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus);Specific antigens (Hepatitis B virus, HIV-1);Succinylacetone;Sulfadoxine;Theophylline;Thyrotropin (TSH);Thyroxine (T4);Thyroxine-binding globulin;Trace elements;Transferrin;UDP-galactose-4-epimerase;Urea;Uroporphyrinogen I synthase;Vitamin A;Leukocytes;and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain embodiments. Analytes can be naturally occurring in biological fluids, such as metabolites, hormones, antigens, antibodies, and the like. Alternatively, analytes can be introduced into the body or can be exogenous, such as contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, or drugs or pharmaceutical compositions, including insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Silurt, Preludine, Didrex, Prestate, Boranil, Sandrek, antidepressants (barbiturates, tranquilizers such as methaqualone, valium, librium, miltuna, serax, equanil, tranxine); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, percocet, percodan, tasionex, fentanyl, darvon, talwin, lomotil); synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and phencyclidine analogues, e.g. ecstasy); anabolic steroids;Analytes that can be analyzed include, but are not limited to, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA), as well as intermediates of the citric acid cycle, and other chemicals produced in the body.

[0018] The analyte for measurement and visualization by the devices and methods described herein is glucose, but other analytes listed above may be considered, including but not limited to: body temperature, heart rate, metabolic function, respiratory rate, and other biological parameters.

[0019] Aspects provide reports that can be generated by a processor based on analyte data, e.g., continuous glucose monitoring (CGM) data. The reports can also be generated based on information input from a user and / or other information. The reports can include information such as the host's average analyte concentration value for the current week and a comparison of the host's average analyte concentration value for the current week to the host's analyte concentration value for at least two weeks prior, and / or the host's average analyte concentration value per day, the percentage of the host's analyte concentration value per day in one or more analyte concentration value ranges, etc. The information in the reports can be customizable by a user. Aspects of the present disclosure provide various data associated with continuous analyte monitoring, which data can be processed and used to generate one or more user views that can be presented to a user on one or more widgets. The information and user views can be updated based on the continuously monitored data and / or based on other parameters. The widgets can include summary widgets, motivation widgets, event widgets, and / or other widgets. The widgets can also be customizable by a user.

[0020] 1 is a diagram conceptually illustrating an exemplary system 100 including an exemplary continuous analyte sensor with sensor electronics according to certain embodiments of the present disclosure. For example, the system 100 can be configured to provide reports based on the continuously monitored analyte data, according to certain embodiments discussed in more detail with respect to FIGS. 5-14. The system 100 can be configured to provide one or more widgets based on the continuously monitored analyte data, according to certain embodiments discussed in more detail herein with respect to FIGS. 15-53.

[0021] System 100 includes a continuous analyte sensor system 8, which includes sensor electronics 12 and a continuous analyte sensor 10. System 100 may include other devices and / or sensors, such as a drug delivery pump 2 and a glucose meter 4. The continuous analyte sensor 10 may be physically connected to the sensor electronics 12, which may be integral to (e.g., attached and non-releasable) or releasably attachable to the continuous analyte sensor 10. The sensor electronics 12, drug delivery pump 2, and / or glucose meter 4 may be coupled to one or more devices, such as display devices 14, 16, 18, and / or 20.

[0022] In some embodiments, system 100 may include a cloud-based analyte processor 490 configured to analyze analyte data (and / or other patient related data) provided over network 406 (e.g., via wired, wireless, or combinations thereof) from the continuous analyte sensor system 8 and other devices, such as display devices 14, 16, 18, and / or 20 associated with a host (also referred to as a patient), to generate reports providing high value information, such as statistics about analytes measured over a particular time frame.

[0023] In some embodiments, the analyte processor 490 or a report generator therein can generate a view for display in a user interface and / or for display on one or more widgets in the user interface. The user interface view can include one or more graphical representations that include multiple different graphically distinct elements representing the processed analyte data and / or other information.

[0024] In some embodiments, the system 100 can dynamically generate performance reports and / or user interface views. For example, the analyte processor 490 can receive a request to generate a report or user interface view. In response to the request, the analyte processor 490 can then select a report and / or interface view to provide. This selection can be performed based on metadata. This metadata can include information describing the host, the type of device used to measure the analyte concentration value, rules, etc. The selection can be considered dynamic in the sense that the report and / or user interface view selection changes from request to request based on the metadata. A report or user interface view can then be generated to include at least one selected report and / or user interface view and then provided to the user interface for presentation.

[0025] In some aspects, the sensor electronics 12 can include electronic circuitry associated with measuring and processing data generated by the continuous analyte sensor 10. This generated continuous analyte sensor data can also include algorithms that can be used to process and calibrate the continuous analyte sensor data, although these algorithms can be provided in other ways. The sensor electronics 12 can include hardware, firmware, software, or a combination thereof for providing measurements of analyte values ​​via a continuous analyte sensor, such as a continuous glucose sensor. An exemplary implementation of the sensor electronics 12 is further described below with respect to FIG. 2.

[0026] Sensor electronics 12, as described above, can be coupled (e.g., wirelessly, etc.) with one or more devices, such as display devices 14, 16, 18, and / or 20. Display devices 14, 16, 18, and / or 20 can be configured to present (and / or alert) information, such as sensor information transmitted by sensor electronics 12, for display on display devices 14, 16, 18, and / or 20.

[0027] The display devices may include a relatively small key fob-like display device 14, a relatively large handheld display device 16, a mobile phone (e.g., smartphone, tablet, etc.), a computer 20, and / or any other user equipment configured to present at least information (e.g., medication delivery information, individual self-monitoring glucose readings, heart rate monitor, calorie intake monitor, etc.).

[0028] In some embodiments, a relatively small key fob-like display device 14 may include a watch, belt, necklace, pendant, jewelry, adhesive patch, pager, key fob, plastic card (e.g., credit card), identification (ID) card, and / or the like. This small display device 14 may include a relatively small display portion (e.g., smaller than a larger display device) and may be configured to display certain types of displayable sensor information, such as numbers and arrows.

[0029] In some embodiments, the relatively large handheld display device 16 may comprise a handheld receiver device, a palmtop computer, and / or the like. This large display device may include a relatively larger display portion (e.g., larger than a small display device) and may be configured to display information such as a graphical representation of the continuous sensor data, including current and historical sensor data, output by the continuous analyte sensor system 8.

[0030] In some embodiments, the continuous analyte sensor 10 can include a sensor for detecting and / or measuring an analyte, and the continuous analyte sensor 10 can be configured to continuously detect and / or measure an analyte as a non-invasive, subcutaneous, transdermal, and / or intravascular device. In some exemplary embodiments, the continuous analyte sensor 10 can analyze multiple intermittent blood samples, although other analytes can be used as well.

[0031] In some embodiments, the continuous analyte sensor 10 can include a glucose sensor configured to measure glucose in blood using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, etc. In embodiments in which the continuous analyte sensor 10 includes a glucose sensor, the glucose sensor can include any device capable of measuring the concentration of glucose and can provide data, such as a data stream indicative of the concentration of glucose in a host, using a variety of techniques for measuring glucose, including invasive, minimally invasive, and non-invasive sensing techniques (e.g., fluorescence monitoring). This data stream can be a raw data signal that is converted into a calibrated and / or filtered data stream that is used to provide glucose values ​​to a host, such as a user, patient, or caregiver (e.g., a parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the health of the host). Additionally, the continuous analyte sensor 10 may be implanted as at least one of the following types of sensors: an implantable glucose sensor, a transcutaneous glucose sensor that is implanted within a host's blood vessel or externally, a subcutaneous sensor, a refillable subcutaneous sensor, and an intravascular sensor.

[0032] Although the description herein refers to some examples including a continuous analyte sensor 10 comprising a glucose sensor, the continuous analyte sensor 10 can include other types of analyte sensors as well. Additionally, while some embodiments may refer to the glucose sensor as an implantable glucose sensor, other types of devices capable of detecting glucose concentrations and providing an output signal representative of the glucose concentration can be used as well. Additionally, while the description herein refers to glucose as the analyte that is measured, processed, etc., other analytes can be used as well, including, for example, ketone bodies (e.g., acetone, acetoacetate and beta-hydroxybutyrate, lactate esters, etc.), glucagon, acetyl coenzyme A, triglycerides, fatty acids, intermediates in the citric acid cycle, choline, insulin, cortisol, testosterone, etc.

[0033] FIG. 2 is a block diagram conceptually illustrating a sensor electronics module in communication with multiple sensors, according to certain aspects of the disclosure. As shown in FIG. 2, the sensor electronics module 212 can communicate with multiple sensors, including a glucose sensor 220, an altimeter sensor 222, an accelerometer sensor 224, a temperature sensor 226, and a location determination module 269 (e.g., a global positioning system processor or other location determination information source), according to some exemplary aspects. Although FIG. 2 illustrates the sensor electronics module 212 in communication with certain sensors, other sensors and / or devices can be used. The other devices and / or sensors can include, for example, a heart rate monitor, a blood pressure monitor, a pulse oximeter, caloric intake, and a medication delivery device. Additionally, one or more of these sensors can provide data to the analyte processing system 400 and / or the analyte processor 490, which are described further below. In some aspects, a user can manually provide some of the data to the analyte processing system 400 and / or the analyte processor 490. For example, a user may provide calorie consumption information to the analyte processing system 400 and / or analyte processor 490 via a user interface.

[0034] 2, each of the glucose sensors 220, 222, 224, and / or 226 can wirelessly communicate sensor data to the sensor electronics module 212. In some examples, the sensor electronics module 212 can include one or more of the glucose sensors 220, 222, 224, and / or 226. In some examples, the sensors can be combined in any other configuration, such as, for example, a combined glucose / temperature sensor used to transmit the sensor data to the sensor electronics module 212 using a common communication circuit. Depending on the example, fewer or additional sensors can be in communication with the sensor electronics module 212. In some examples, one or more of the glucose sensors 220, 222, 224, and / or 226 can be directly coupled to the sensor electronics module 212 (e.g., coupled via one or more electrical communication lines).

[0035] The sensor electronics 212 can generate a data package and transmit it to a device such as the display device 250. The display device 250 may be any electronic device configured to receive, store, retransmit, and / or display displayable sensor data. The sensor electronics module 212 can analyze sensor data from multiple sensors and determine which displayable sensor data should be transmitted based on characteristics of one of the host, the display device 250, the user of the display device 250, and / or characteristics of the sensor data. Thus, the customized displayable sensor information transmitted to the display device 250 can be displayed on the display device with minimal processing by the display device 250.

[0036] 3A and 3B are perspective and side views 300A and 300B, respectively, of a sensor system including a mounting unit 314 and sensor electronics 12 attached thereto. In the example shown in a functional position, the mounting unit 314 can be mated and engaged with the sensor electronics 12. In some examples, the mounting unit 314 can include a base 334, also referred to as a housing or sensor pod, adapted for fastening to the skin of a host. The base 334 can be formed from a variety of rigid or flexible materials and can include a low profile to minimize protrusion of the device from the host during use. The base 334 can be at least partially formed from a flexible material, which in some aspects is believed to provide numerous advantages over other transcutaneous sensors, but unfortunately can be subject to motion related artifacts associated with host movement when the host is using the device. The mounting unit 314 and / or sensor electronics 12 can be positioned over the sensor insertion site to protect the site and / or provide a minimal footprint (utilization of the surface area of ​​the host's skin).

[0037] In some exemplary embodiments, a removable connection can be provided between the onboard unit 314 and the sensor electronics 12. This removable connection can allow for improved manufacturability. That is, the relatively inexpensive onboard unit 314 can be disposed of when replacing the sensor system after its useful life, while the relatively more expensive sensor electronics 12 can be reusable with multiple sensor systems. In some exemplary embodiments, the sensor electronics 12 can be configured with signal processing. For example, the sensor electronics 12 can be configured to provide other algorithms, filtering, calibration, and / or other functions useful for calibrating and / or displaying sensor information.

[0038] In some exemplary embodiments, the contacts 338 may be mounted on or in a subassembly (hereafter referred to as the contact subassembly 336) configured to fit within the base 334 of the mounting unit 314, and a hinge 348 that allows the contact subassembly 336 to pivot between a first position (for insertion) and a second position (for use) relative to the mounting unit 314. The hinge may provide a pivoting, articulating, and / or hinge mechanism, such as a bonded hinge, slip joint, or the like. The hinge action may be implemented in some embodiments without a fulcrum or fixed point about which the articulation occurs. In some exemplary embodiments, the contacts 338 may be formed from a conductive elastomeric material, such as a carbon black elastomer, through which the continuous analyte sensor 10 extends, although the contacts may be formed in other manners as well.

[0039] In some exemplary aspects, the mounting unit 314 can be provided with an adhesive pad 308 disposed on the back of the mounting unit and including a peelable backing layer. The backing layer can then be removed to allow the mounting unit 314 to adhere to the host's skin by pressing the mounting unit's base 334 onto the host's skin. Additionally or alternatively, an adhesive pad can be placed over some or all of the sensor system to ensure adhesion and, optionally, an air-tight or water-tight seal around the wound exit site (or sensor insertion site) after sensor insertion is complete. A suitable adhesive pad can be selected and designed to stretch, expand, conform to, and / or vent the area (e.g., the host's skin). Configurations and arrangements that provide water-resistant, waterproof, and / or air-tight sealing properties can be provided with some of the mounting unit / sensor electronics aspects described herein.

[0040] 4 is a block diagram conceptually illustrating an analyte processing system according to some exemplary aspects of the present disclosure. As shown in FIG. 4, the analyte data processing system 400 can include one or more user interfaces 410A-C, such as browsers, applications, widgets, and / or any other type of user interface configured to allow access to and / or interaction with the analyte processor 490 via, for example, the network 406 and the load balancer 412. The analyte processor 490 can be further coupled to a repository 475.

[0041] The analyte data processing system 400 can also receive data from source systems such as health management systems, patient management systems, prescription management systems, electronic medical record systems, personal health record systems, etc. This source system information can provide metadata for dynamic report generation.

[0042] The analyte data processing system 400 can be implemented in a variety of configurations, including standalone, distributed, and / or cloud-based frameworks. The analyte data processing system 400 can be implemented within a cloud-based framework, such as a software-as-a-service (SaaS) arrangement, in which the analyte processor 490 is hosted on computing hardware, such as servers and data repositories, maintained remotely from the location of the entity (e.g., remotely from hosts, medical service providers, and similar end users), and accessed over a network 406 by authenticated users via user interfaces, such as user interfaces 410A, B, and / or C, and / or data retrievers 465.

[0043] In addition to the example illustrated in FIG. 4, the analyte data processing system 400 can be implemented as a SaaS-based system including multiple servers, each of which can be virtualized to provide one or more analyte processors 490. Furthermore, each of the virtualized analyte processors 490 can serve different tenants, such as end users, clinics, hosts wearing sensors, etc. To more efficiently use the computing resources of the software as a service (SaaS) provider and provide significant redundancy and / or high reliability, in some embodiments it may be advantageous to host multiple tenants (e.g., hosts, users, clinics in the user interfaces 410A-C and / or data searcher 465, etc.) on a single analyte data processing system 400 that includes multiple servers and maintains all data of the multiple tenants in a secure manner in the repository 475 while also providing customized solutions specific to each tenant.

[0044] Referring again to FIG. 4, in some exemplary embodiments, the analyte data processing system 400 can provide a cloud-based diabetes data management framework configured to receive patient-related data from various devices. The various devices may include, but are not limited to, medical devices, glucose meters, continuous glucose monitors, continuous analyte sensor systems 8, display devices 14, 16, 18, and / or 20, source systems, devices providing food consumption information (e.g., carbohydrates, etc.) associated with food consumed by the host or patient, medication delivery data, time of day, temperature sensors, and / or motion / activity sensors. In some exemplary embodiments, the cloud-based diabetes data management can programmatically receive data with little (or no) intervention on the part of the user. The data received from devices, source systems, etc. may be in various formats and may be structured or unstructured. For example, in some exemplary embodiments, the analyte data processing system 400 can receive raw sensor data that has been minimally processed or analyzed. The received data can then be formatted, processed (e.g., analyzed), and / or stored to enable visualization of the analyte data.

[0045] For example, the data searcher 465 can be implemented in one or more devices, such as computer 20, coupled to the continuous analyte sensor system 8. In this example, the data searcher 465 can format the sensor data into one or more common formats compatible with the analyte processor 490 and provide the formatted data to the analyte processor 490 such that the analyte processor 490 can analyze the formatted data. Although FIG. 4 illustrates a single data searcher 465, in some example embodiments, multiple data searchers 465 can be used to format data from multiple devices and / or systems.

[0046] In some exemplary embodiments, the data searcher 465 may be accessed through a kiosk that includes a processor, such as a dedicated computer configured with a user interface, or may be accessed via a secure web-based interface residing on a non-dedicated computer.

[0047] In some exemplary embodiments, the data searcher can be programmatically installed on the processor by downloading software for the data searcher into the processor's memory when the processor (e.g., a computer, a smartphone, and any other device) first accesses the analyte data processing system 400. The downloaded software can then be programmatically installed on the processor, and the data searcher can then generate a view that can be presented on a user interface (e.g., a user interface view for display within an email, or for display on one or more widgets, as described below).

[0048] In some embodiments, the user interface may allow a user to select an icon, such as a retrieve icon, to programmatically initiate data transfer to the analyte processor 490. For example, a user selects a retrieve icon in a user interface on a processor, such as computer 20, which initiates data transfer from the continuous analyte sensor system 8 coupled to the data retriever 465 and the analyte processor 490. In some embodiments, the retrieve icon may be implemented as a software widget. Additionally, the software widget may be placed on a web page such that when selected, the retrieve process is initiated for a registered user.

[0049] Additionally, the software associated with the data retriever 465 may include a self-update mechanism such that, when retrieval is selected in the user interface, the data retriever programmatically checks for updates (e.g., software, drivers, data, etc.) on the analyte processor 490 (or another designated computer) and installs the updates. Updates may be performed programmatically with little or no user intervention. Data download from the device or system to the data retriever 465 may be performed using a wired connection, such as a device-specific download cable, or wirelessly if the device and processor are equipped for wireless data transfer.

[0050] The analyte processor 490 can check the data downloaded by the data retriever 465 for transmission related errors, data formatting, device related errors, data validity, duplicate data points, and / or other aspects of the data. Additionally, if out of range data points or device errors are found, the analyte processor 490 can identify those data points. For example, the analyte processor 490 can flag those data points, correct the identified data points programmatically or by a system administrator, and store the corrected data points. Additionally, the analyte processor 490 can be configured by a user, such as a clinician, physician, etc., to perform additional data processing steps, such as correcting the time, correcting the date, and analyzing the data by specific populations, groups, and / or relationships (e.g., demographics such as age, city, state, gender, ethnicity, type I diabetes, type II diabetes, age at diabetes diagnosis, lab results, prescription medications in use, patient self-reported status, patient diagnostic status, responses to questions posed to the patient, and any other metadata describing the host / patient). Once the analyte processor 490 has performed the initial data processing (eg, checking, cleaning, and analysis), the processed data and / or raw data provided by the data retriever may be stored in the repository 475.

[0051] Processing in the analyte processor 490 may also include associating metadata with the data received from the devices and / or sensors. Examples of metadata may include, but are not limited to, patient information, a key used to encrypt the data, the patient accelerometer, location data (e.g., the patient's location, or the patient's clinic location), time, date, and the type of device used to generate the associated sensor data. Patient information may include the patient's age, weight, sex, home address, and / or any past health-related information, such as whether the patient has been diagnosed as having type 1 or type 2 diabetes, high blood pressure, or any other health condition. Processing may also include analysis, such as determining one or more descriptive measures and / or generating one or more user interface views based on the received information and the descriptive measures. These descriptive measures may include statistics (e.g., median, inner and outer interquartile range, mean, sum, sample size n, standard deviation, and coefficient of variation). Examples of user interface views are illustrated in Figures 7A-14F and 19-49.

[0052] In the example of FIG. 4, user interfaces 410A-C may be used by one or more entities, such as an end user, a host, a healthcare provider, a clinic, a patient, a research group, a health system, a medical device manufacturer, etc. These entities may remotely access analyte data processing system 400 via user interfaces 410A-C to request operations, such as retrieving analyte data, providing analyte data, requesting analysis of the analyte data, requesting generation of a report including a module having views presenting descriptive measures of the analyte data, presenting the analyte data and the report, etc. Other examples of operations include providing sensor data, such as glucose data, carbohydrate data, insulin pump data, etc., to analyte processor 490, initiating processing of the sensor data, initiating analysis of the sensor data, and storing the data in repository 475. In some exemplary embodiments, the computing resources provided by analyte processor 490 may include one or more physical servers that are virtualized to provide the analyte processing services disclosed herein.

[0053] The data searcher 465 can acquire (e.g., receive, retrieve, etc.) data from one or more sources and provide any acquired data in a compatible format for use within the analyte processor 490. In some embodiments, the data searcher 465 can be implemented in one or more of the source systems and / or devices that provide data to the analyte processor 490. For example, the data searcher 465 can be implemented in one or more devices, such as the continuous analyte sensor system 8, the continuous analyte sensor 10, the display devices 14, 16, 18, and / or 20, the medication delivery pump 2, the glucose meter 4, computers / processors coupled to these devices, and any other devices that can provide data to the analyte data processing system 400. In these embodiments, the data searcher 465 can receive data from a host device and format the data in a format compatible with the analyte processor 490. The data searcher 465 can also be implemented on source systems, such as disease management systems, weight management systems, prescription management systems, electronic medical record systems, personal health record systems, etc. In these embodiments, the data retriever 465 can obtain data from the source system and format the data in a format compatible with the analyte processor 490 .

[0054] In some exemplary embodiments, the data searcher 465 may be automatically downloaded and / or provided to a device, computer, system, etc., as described above. For example, when a user on a computer first accesses the analyte data processing system 400, the analyte data processing system 400 may automatically install and configure the data searcher 465 on the user's computer. Once installed, the data searcher 465 may begin retrieving data for the analyte data processing system 400 and, if necessary, format the data to enable processing of the retrieved data by the analyte processor 490. To further illustrate by way of example, the data searcher 465 may be downloaded onto a device, such as the computer 20. In this example, when the computer 20 receives sensor data from the sensor electronics module 12, the data searcher 465 may provide the sensor data and / or metadata in a format compatible with the analyte processor 490.

[0055] In some exemplary embodiments, the analyte processor 490 can process the received data by performing one or more of the following: associating metadata with the data received from devices, sensors, source systems, and / or data retrievers; determining one or more descriptive measures such as statistics (e.g., median, inner and outer interquartile range, mean, sum, sample size n, standard deviation, and coefficient of variation); verifying and validating the completeness of the data received from devices, sensors, source systems, and / or data retrievers; processing the received data based on the metadata (e.g., to select a particular patient, device, condition, type of diabetes, etc.); and / or correlating the data received from devices, sensors, source systems, and / or data retrievers so that the data may be compared and combined for processing and analysis.

[0056] Additionally, the results of any processing performed by analyte processor 490 can be used to generate views (e.g., the user interface views illustrated in Figures 7A-14F and 19-49) that present descriptive measures and / or comparisons of the analyte data. The descriptive measures and / or comparisons can be presented, for example, as graphs, bar graphs, static charts, charts, badges, tables, diagrams, maps, plots, and / or other visualizations.

[0057] Additionally, the output generated by the analyte data processing system 400 can be provided via one or more delivery mechanisms, such as a report delivery module 420K. For example, the report delivery module 420K can provide the output generated by the analyte data processing system 400 via email (e.g., as illustrated in FIGS. 7A-14F), secure email, print, text, a representation for display on a user interface (such as user interfaces 410A-C hosted on a tablet, phone (e.g., as illustrated in FIG. 51), or other processor), machine-to-machine communication (e.g., via a third party interface 420J), and any other communication mechanism.

[0058] In some exemplary aspects, the views can be dynamically customized for use by entities such as, for example, a host, an end user, a clinician, a healthcare provider, or a device manufacturer. Additionally, the views can be customized based on the type and / or amount of sensors and systems providing data to the analyte data processing system 400, as well as the type of metadata available to the analyte data processing system 400. This customization can be performed by a user, programmatically by the analyte data processing system 400, or a combination of both.

[0059] The analyte processor 490, in some example embodiments, can include an authenticator / authorizer 420A for authenticating access to the analyte processor 490, a data parser 420B for parsing requests sent to the analyte processor 490, a calculation engine 420H for receiving data from the sensors and processing the received data into numbers for use with the histograms, logic 420C, a data filter 420D, a data formatter 420E, a report generator 420G, a pattern detector 420I, a report delivery module 420K for delivering views in a format for the destination, and a third party access application programming interface to allow other systems and devices to access and / or interact with the analyte processor 490.

[0060] Analyte processor 490 may receive requests from a user interface, such as user interfaces 410A-C, to perform an action (e.g., provide data, store data, analyze / process data, request a report, etc.). Before analyte processor 490 fulfills a request, analyte processor 490 may process the request to determine whether the request is authorized and authenticated. For example, authenticator and authorizer 420A may determine whether the source of the request is authorized by requesting a user to provide security credentials (e.g., a user identifier, a password, a stored security token, and / or a verification identifier provided by text message, phone call, or email) in a user interface presented on a computer. If authorized, the authenticator and authorizer 420A authenticates the sender of the request and checks whether the security credentials associated with the sender of the request indicate that the sender (e.g., a user at the user interface 410A) is actually authorized to access a particular resource of the analyte data processing system 400, so that actions can be performed such as storing (or uploading) data in the repository 475, performing analysis / processing of the data, and / or requesting user interface view generation.

[0061] To further illustrate, a data retriever 465 associated with the continuous analyte sensor system 8 and computer 20 may be authorized and authenticated by the authenticator and authorizer 420A to access the analyte processor 490 to write data to a buffer or other storage mechanism, such as the repository 475. In contrast, an entity, such as a user, at the user interface 410A may be authorized and authenticated by the authenticator and authorizer 420A to access the analyte processor 490, but may only be permitted to access certain information. In this second example, a user at the user interface 410A may be authorized and authenticated to access the repository 475, view certain information corresponding to the user's own analyte data (e.g., glucose data), and access reports generated for the analyte data, but the user would not be authorized and authenticated to access another user's data.

[0062] Once authorized and / or authenticated, the request received at the analyte processor 490 may then be parsed by the data parser 420B to separate any data from the request, such as sensor data, metadata, etc. In some embodiments, the data parser 420B may perform checks on data formatting, device related error codes, validity of the data, duplicate data points, and / or other aspects of the data. Additionally, the data parser 420B may associate additional metadata with the separated data. The metadata may include any of the metadata described herein, including the owner of the data, a key for tracking the data, a cryptographic key unique per user, time, date information, one or more locations where the data resides (or will be stored), etc. In some exemplary embodiments, the data parser 420 may provide the data to a calculation engine 420H for formatting the data into counts and histograms, as described further below.

[0063] In some example aspects, the request (or the parsed data therein) can be processed by a calculation engine 420H, which can pre-process data received from devices, sensors, etc. to form a "count." The count can represent measurements such as an analyte value measured by the sensor, a glucose value measured by the sensor, continuous glucose values ​​measured by the sensor, and / or other diabetes-related information such as carbohydrates consumed, temperature, physical activity values, and the frequency with which the measurements occurred.

[0064] The computation engine 420H can then use the count 508 to perform additional processing. This additional processing can include storing the count in a repository 475, which can include one or more databases for storing the count. Additionally, the count can be stored with metadata, such as time / date information. Additionally, the count can be encrypted prior to storage in the repository 475, as described above.

[0065] The calculation engine 420H can also use the counts to update one or more histograms. For example, rather than continuing to track and process analyte values ​​for a host over a particular period of time using raw sensor data values, the calculation engine 420H can convert the data values ​​into counts. The counts can be added to a histogram for a given host.

[0066] In some example aspects, the calculation engine 420H can generate multiple histograms for a given host over multiple given time periods.

[0067] In some exemplary aspects, the calculation engine 420H can also update other histograms that represent aggregate count information.

[0068] Although the description of the calculation engine 420H refers to a histogram, as used herein, a histogram refers to a data structure that includes one or more values ​​associated with one or more time intervals. For example, a histogram can represent one or more values, such as frequencies of occurrence, associated with bins that correspond to one or more time intervals. Moreover, this data structure can be stored in a database so that it is easily accessed by reads, such as at the rows (or columns, if, for example, a column database is used) of the database.

[0069] In some exemplary embodiments, repository 475 stores histograms including the counts in a database. For example, repository 475 can store data for patients covering time frames such as 1 day, 2 days, 7 days, 14 days, 30 days, or more. In this example, the days can be broken down into epochs, each of which has a corresponding histogram stored in repository 475. Additionally, each histogram can be stored as a row in repository 475's database to facilitate fast data access.

[0070] Logic 420C of FIG. 4 may also process requests to perform actions (e.g., data storage, retrieval, processing, analysis, reporting, etc.) in analyte processor 490. Logic 420C may also determine one or more descriptive measures, such as statistics (e.g., median, inner and outer quartile range, mean, sum, standard deviation, etc.) based on the counts, histograms, and / or received sensor data. Logic 420C may provide these descriptive measures to report generator 420G to enable report generation (e.g., generation of a view for presentation in user interfaces 410A-C). For example, an average may be determined by summing the products of the counts and bin values ​​and dividing the sum by the sum of the counts.

[0071] 4 may perform pattern detection on data (e.g., sensor data representing blood glucose data, analytes, insulin pump data, carbohydrate consumption data, etc.) processed by analyte processor 490 and stored in repository 475. Additionally, pattern detector 420I may detect patterns retrospectively over a predetermined period of time defined by analyte data processing system 400 and / or a user.

[0072] In some exemplary embodiments, pattern detector 420I can receive input data from repository 475. This input data can include, for example, analyte concentration data from a continuous analyte sensor, other analyte data, such as, for example, rate of change, predicted concentration, etc. In some exemplary embodiments, the input data can also include other data, such as temperature data, accelerometer data, insulin pump data, carbohydrate consumption data, food intake data, nutrient intake or breakdown information, time of day, movement and / or activity data, wake / sleep intervals, medication information, or other similar data related to the user's activities that can affect one or more biological parameters of the user.

[0073] Additionally, the input data may include historical data obtained over a time frame such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days, and / or any other time period. For example, the input data may include "counts" representing monitored analyte detection values ​​(e.g., glucose concentration values) received and stored in the analyte data processing system 400 over a period covering a 4 week (or greater) time frame. As discussed above, the "counts" may be stored in the repository 475 along with metadata such as time / date information to be used as input data at a later time. In another example, the input data may include a histogram updated with a user's "counts." The histogram may include an x-axis of analyte concentration values ​​and a y-axis of number of occurrences per analyte concentration value. A histogram associated with a given user / patient may be an example of input data used by the pattern detector 420I.

[0074] The pattern detector 420I can analyze the input data for patterns. For example, a pattern can be recognized based on one or more predefined rules (also called criteria or triggers). Furthermore, the one or more predefined rules can be variable and adjustable based on user input. For example, some types of patterns and the rules that define the patterns can be selected, turned on and off, and / or modified by a user, the user's physician, or the user's guardian, although the analyte data processing system 400 can also programmatically select, adjust, and / or otherwise modify the rules. In another exemplary embodiment, the one or more patterns can be based on predefined rules set by factory or device settings.

[0075] The pattern detector 420I can detect patterns and generate an output that can be provided to the report generator 420G, which can further include a retrospective analysis of the input data and any patterns determined by the pattern detector 420I.

[0076] The data filter 420D can be used to check whether output generated by the analyte processor 490, such as responses to particular types of data, reports, etc., violates data rules. For example, the data filter 420D can include data rules to check whether a response includes data, such as PII, to a destination that is not authorized or permitted to receive the response (e.g., based on the authorization and authentication of the user making the request and their corresponding role).

[0077] Data formatter 420E can format data for delivery based on the type of destination. For example, data formatter 420E can format a view based on whether it is being sent to a printer, a user interface, secure email, another processor, and / or any other similar device or platform.

[0078] The report generator 420G can generate one or more reports and / or user interface views. The reports / views can provide descriptive information, such as statistics, describing the sensor data received at the analyte processor 490. Additionally, the reports / views can provide retrospective analysis of the sensor data stored in the repository 475. For example, the reports / views can provide statistics based on the sensor data (and / or corresponding histograms including counts) over a time frame, such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days, and any other time frame. Additionally, the reports / views can allow a user, such as a patient, host, or clinician, to view the information to identify trends and other health-related issues.

[0079] In some example embodiments, the report generator 420G generates reports and / or views (e.g., using sensor data, metadata, counts, histograms, etc.) based on data received and / or stored in the analyte data processing system 400. Examples of reports and / or user interface views are illustrated in FIGS. 7A-14F and 19-49.

[0080] According to certain embodiments, logic 420C and pattern detector 420I can be used to determine one or more descriptive measures, patterns, or relationships for effective visualization. As previously described, logic 420C can determine medians, inner and outer quartile ranges, means, sums, standard deviations, and other statistical measures based on counts, histograms, and / or received sensor data. Pattern detector 420I can analyze relationships between data to determine patterns. Input data relationships that may result in identified patterns may include, for example, analyte values ​​above a target analyte range (which may be defined, for example, by a user, a healthcare provider, the analyte data processing system 400, or a combination thereof), analyte values ​​below a target analyte range, an abrupt change in analyte value from low to high (or vice versa), the time of day when a low, high, within range, or abrupt analyte value change event occurs, and the day when a low, high, within range, and / or abrupt analyte value event occurs.

[0081] Additional examples of types of relationships in the input data that can be considered patterns include very high analyte events and / or very low analyte events by time of day. As an example, in an embodiment where the analyte for measurement is glucose, a pattern can be identified in situations where a user has a low analyte concentration at approximately the same time of day (e.g., a hypoglycemic event). Another type of pattern that can be identified is a "rebound high" situation. For example, a rebound high can be defined as a situation where a user overcorrects a hypoglycemic event by excessively increasing glucose intake, leading to a hyperglycemic event. These events can be detected based on one or more predefined rules. Patterns that can be detected include hyperglycemic patterns, hypoglycemic patterns, patterns associated with time of day or week, and scoring of different patterns weighted based on frequency, sequelae, and severity.

[0082] In some embodiments, the patterns may be based on the individual sensitivity of the user / patient, transitions from very low to very high patterns, amount of time spent in severe events, and a combination of analyte changes and time information. The detected patterns may also be patterns of large fluctuations in analyte data. Additionally, the patterns may be based on a combination of past pattern data and currently detected conditions, whereby the combined information generates a predictive alert.

[0083] 5 illustrates an example user interface view 500 associated with sensor data representing analyte values ​​in a host, specifically glucose concentration values ​​in a host, according to some example aspects of the disclosure. Patterns and statistics identified by logic 420C and pattern detector 420I can be presented in a performance report. As shown in FIG. 5, weekly reports can be provided to a user of a diabetes management application to provide insight into the user's retrospective glucose values, patterns, and trends over time.

[0084] A first feature of the user interface view 500 may provide a time in range stacked bar graph representing the percentage of time the host was in the target glucose range, the very high or high glucose range, and the very low or low glucose range over a particular time period (e.g., any consecutive seven days). The target glucose ranges may be defined as different ranges for daytime (e.g., in the example shown, 6:00 AM to 10:00 PM) and nighttime (e.g., in the example shown, 10:00 PM to 6:00 AM). The user's percentage of time in range may also be compared to the percentage of time in range for the previous week. In some examples, the stacked bar graph may be presented using different colors to distinguish the percentage of time the host was in the target glucose range, the very high or high glucose range, and the very low or low glucose range over a particular time period. In some examples, the stacked bar graph may be presented using different sized blocks (stacked within the stacked bar graph) for each range. The different sizes may correlate to the amount of time the user spent in each range. For example, the largest block size in the stacked bar graph can represent the glucose range in which the host spent the greatest amount of time over a specified period of time, whereas the smallest block size in the stacked bar graph can represent the glucose range in which the host spent the least amount of time over a specified period of time.

[0085] In a second feature of user interface view 500, average glucose and standard deviation statistics (e.g., as determined by logic 420C) can be presented to the user. The average glucose and standard deviation can be calculated based on a specified time period (e.g., any consecutive seven day period).

[0086] A third feature of the user interface view 500 may report the user's patterns of daytime lows / highs and nighttime lows / highs. A daytime or nighttime low pattern may be identified in situations where the user has a pattern of low glucose concentration values ​​at approximately similar times each day within a specified time period (e.g., any consecutive seven days). A daytime or nighttime high pattern may be identified in situations where the user has a pattern of high glucose concentration values ​​at approximately similar times each day within a specified time period (e.g., any consecutive seven days).

[0087] In a fourth feature of the user interface view 500, a summary of the user's times in range can be presented in a scatter plot with a best fit line. The best fit line represents the relationship between the data points and identifies the trend of the host's times in range over a 12 hour period (e.g., in the illustrated example, 12:00 AM - 12:00 PM) for a specified time period (e.g., any consecutive 7 days). In addition, target glucose ranges can be provided on the graph for both daytime and nighttime hours. The target glucose ranges can be defined as different ranges for daytime and nighttime hours. For example, as illustrated in the fourth feature, the graph can use a sun shape to identify the daytime target glucose range (e.g., the daytime range illustrated in that feature is 80 - 180 mg / dL) and a moon shape to identify the nighttime target glucose range (e.g., the nighttime range illustrated in that feature is 90 - 200 mg / dL). A fourth feature may also provide bar graphs of different colors to distinguish between the host's time in the high or very high glucose range, the host's time in the low or very low glucose range, and the host's time in the glucose range over a specified 12 hour period.

[0088] A fifth feature of the user interface view 500 may provide the user with a hyperlink to access a more detailed continuous glucose monitoring (CGM) report on a website.

[0089] In some examples, features 1-5 may be presented to the user in a vertical format, such that feature 1 may be at the top of the page (e.g., an email) and feature 5 may be at the bottom of the page.

[0090] Aspects of the present disclosure provide improvements in data visualization of analyte data, including identifying new patterns and relationships to improve communication of data-based insights and trends to a user / host. For example, the present disclosure provides improved methods, such as operation 600 shown in FIG. 6, for generating improved user interface views shown in FIGS. 7-14F. The user interface views shown in FIGS. 7-14F are improved as compared to the user interface view illustrated in FIG. 5. For example, additional information is provided that a user can use to adjust the user's behavior, e.g., to control the user's blood glucose concentration value. The analyte for measurement and visualization by the devices and methods described herein is glucose, although other biological parameters and / or analytes may be considered as well.

[0091] Exemplary Analyte Daily Processing and Weekly Reporting 6 is a flow diagram illustrating example operations 600 for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host, according to some example aspects of the disclosure. The operations 600 may be performed by a processing system, such as the analyte processor 490. In some examples, the operations 600 may be used to generate one or more of the reports illustrated in FIGS. 7-14F and described in more detail below.

[0092] The operations 600 may begin at 602 by accessing sensor data including multiple blood glucose readings associated with the host during multiple analysis periods for a current week. Each blood glucose reading indicates a blood glucose concentration value for the host at a respective time. In some examples, the multiple blood glucose readings may be collected by a continuous glucose monitor (CGM) worn by the host. In some examples, the multiple blood glucose readings may be stored in the repository 475 and accessed by the analyte processor 490.

[0093] At 604, the operations 600 may include determining an average blood glucose concentration value for the host for the current week. For example, the calculation engine 420H in the analyte processor 490 may calculate the average blood glucose concentration value based on multiple blood glucose readings.

[0094] At 606, the operations 600 may include generating a performance report. The performance report may include the host's average blood glucose concentration value for the current week, and a comparison of the host's average blood glucose concentration value for the current week to the host's average blood glucose concentration value for at least the previous two weeks, the host's average daily blood glucose concentration value, the host's daily percentage of blood glucose concentration values ​​in one or more blood glucose concentration value ranges, or a combination thereof. For example, the performance report may be a weekly performance report (e.g., one of the reports shown in Figures 7-14F). The performance report may be generated by the report generator 420G of the analyte processor 490. In some examples, the calculation engine 420H, the pattern detector 420I, and / or the logic 420C may determine (e.g., calculate, process, and / or generate) information to be included in the performance report.

[0095] At 608, the operations 600 may include generating a user interface view of the performance report.

[0096] At 610, the operations 600 may include providing a user interface view of the performance report for display. In some examples, the user interface view may be provided to a user device via email for display in a user interface. In some examples, the user interface view may be provided to a display within an application executing on the user device for display in a user interface.

[0097] Figure 7 illustrates an example wireframe 700 of a user interface view in a vertical layout, and Figure 8A illustrates an example wireframe 800a of a user interface view in a horizontal layout, according to some example aspects of the present disclosure. Figures 8B-8G illustrate expanded views of features 1-6 shown in the vertical and horizontal wireframes 700 and 800a, respectively, and are further described in more detail below.

[0098] Figure 9 illustrates an example user interface view 900 associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to wireframe 700, and Figure 10A illustrates an example user interface view 1000a in a horizontal layout corresponding to wireframe 800a, according to some example aspects of the present disclosure. Figures 10B-10G illustrate expanded views of features 1-6 shown in the vertical and horizontal user interface views 900 and 1000a, respectively, and are further described in more detail below.

[0099] The horizontal or vertical display of a user interface view may be based on the type or configuration of the user device. For example, a horizontal layout may be useful for a desktop or laptop computer, while a vertical layout may be useful for a phone, tablet, or other smaller user device. Although both horizontal and vertical orientations are shown, only one orientation may be displayed to the user. In some aspects, the orientation of the user interface view may be automatically selected and displayed based on the type of user device.

[0100] As shown in the example user interface views of Figures 9-10G and the corresponding wireframes of Figures 7-8G, weekly reports can be provided to a user of a diabetes management application to provide relevant insight into the user's retrospective glucose values, patterns, and trends over time.

[0101] The report can provide average weekly glucose in one or more time ranges (e.g., weeks). The time ranges and number of time ranges can be configurable. The average weekly glucose can be calculated by the calculation engine 420H and / or logic 420C of FIG. 4. As shown in FIG. 8B and FIG. 10B, the wireframe 700, 800a and first feature 800b, 1000b of the user interface view 900, 1000a can provide a visualization comparing the host's average blood glucose concentration value for the current week with the host's average blood glucose concentration value for at least two weeks prior (e.g., average blood glucose concentration values ​​for three weeks prior, two weeks prior, and last week).

[0102] As shown in FIG. 8B and FIG. 10B, the wireframe 700, 800a and first feature 800b, 1000b of the user interface view 900, 1000a can further provide the dates associated with the time range (e.g., September 6, 2020 - September 11, 2020), the user's average glucose (e.g., in the example of FIG. 10B, 156 mg / dL). To provide context for this glucose average, the user's average glucose can be compared to an average glucose calculated for one or more previous weeks (e.g., in the example of FIG. 10B, "last week," "2 weeks ago," "3 weeks ago"). Visualization of this comparison can provide insight into the host's glycemic consistency, improvement, and / or poor performance over multiple weeks.

[0103] As shown in FIG. 8D and FIG. 10D, the third feature 800d, 1000d of the wireframe 700, 800a and user interface view 900, 1000a can provide a visualization comparing the host's average daily blood glucose concentration value and the host's daily percentage of blood glucose concentration values ​​in one or more blood glucose concentration value ranges. The average daily blood glucose concentration value can be calculated by the calculation engine 420H and / or logic 420C of FIG. 4 based on the continuously monitored blood glucose data. The report can also provide the host's daily percentage of blood glucose concentration values ​​in one or more blood glucose concentration value ranges. For example, the pattern detector 420I, calculation engine 420H, and / or logic 420C of FIG. 4 can compare the blood glucose concentration value data to predetermined target or "normal" blood glucose concentration value values ​​or ranges over the course of a 24-hour period to identify periods when the host's blood glucose concentration values ​​are "in range" or "out of range". The analyte processor can then determine the percentage of time during a 24 hour period that the host's blood glucose concentration value was "in range."

[0104] As shown in FIG. 8D and FIG. 10D, a daily bar graph may be presented to illustrate the host's blood glucose concentration values. The daily breakdown may allow the host to understand their problem days throughout the week and adjust their lifestyle, diet, or other related factors accordingly. The visualization may also provide the percentage of time in range per day to provide additional context to the host's average daily glucose concentration values. For example, as shown in FIG. 10D, the host may experience their highest average glucose for the week on Saturday, at 234 mg / dL. With the corresponding 66% time in the target glucose range, the host may be able to conclude that the majority of their time outside the target range was spent in the high or very high glucose range (rather than the low or very low glucose range).

[0105] As shown in FIG. 8G and FIG. 10G, a sixth feature 800g, 1000g of the wireframe 700, 800a and user interface view 900, 1000a may be a button provided to redirect the user to a website or application (app) on the user's device that provides a more detailed continuous glucose monitoring (CGM) report for the user and / or host. Whether the button redirects the user to a website or to an app may depend on the type or configuration of the user device used to display the button. Unlike the hyperlink of feature 5 of FIG. 5, the button may be larger than the hyperlink, which may capture the user's attention and encourage further interaction between the user and the user interface.

[0106] The second feature 800c, 1000c shown in Figures 8C and 10C, the fourth feature 800e, 1000e shown in Figures 8E and 10E, and the fifth feature 800f, 1000f shown in Figures 8F and 10F of the wireframes 700, 800a and user interface views 900, 1000a correspond to stacked bar graphs of time in range, pattern summary, and trend summary and may be similar to features 1, 3, and 4 shown in Figure 5.

[0107] In some examples, features 1-6 in wireframes 700, 800a and user interface views 900, 1000a may be presented chronologically, from top to bottom as shown in the vertical layouts of Figures 7 and 9, or from left to right as shown in Figures 8A and 10A. In some examples, feature 1 may be stacked on top of feature 2 in a horizontal layout. In some examples, feature 4 may be stacked on top of feature 5 in a horizontal layout.

[0108] Figure 11A illustrates an example wireframe 1100a of another example user interface view in a vertical layout, and Figure 11B illustrates an example wireframe 1100b of a user interface view in a horizontal layout, according to some example aspects of the present disclosure. Figures 11C-11F illustrate expanded views of features 1-4 shown in the vertical and horizontal wireframes 1100a and 1100b, respectively, and are further described in more detail below.

[0109] Figure 12A illustrates an example user interface view 1200a associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to wireframe 1100a, and Figure 12B illustrates an example user interface view 1200b in a horizontal layout corresponding to wireframe 1100b, according to some example aspects of the present disclosure. Figures 12C-12G illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, and are further described in more detail below.

[0110] Although both horizontal and vertical orientations are shown, only one orientation may be displayed to the user, in some aspects the orientation of the user interface view may be automatically selected and displayed based on the type of user device.

[0111] As shown in the example user interface views of Figures 12A and 12B and the corresponding wireframes of Figures 11A and 11B, the weekly reports may be feature-based, starting with glucose and adding other interchangeable elements to provide pertinent insight into the host's retrospective glucose values, patterns, and trends over time with respect to insulin, meals, and / or other factors that may affect the host's glucose values. In some examples, the user / host may be able to switch between categories presented on the user interface by interacting with buttons provided on the user interface view (e.g., the "Switch Up!" button provided in Figure 12G).

[0112] As shown in Figures 11C and 12C, a first feature 1100c, 1200c of the wireframe 1100a, 1100b and user interface view 1200a, 1200b can provide information related to the host's glucose levels over a specified period of time. The glucose information may include the current week's average glucose (e.g., as determined by logic 420C of FIG. 4) compared to the previous week's average glucose and the month's average glucose; a time in range stacked bar graph broken down into blocks (stacked on top of each other) correlating with the host's time spent in the target glucose range, the very high glucose range, the high glucose range, the low glucose range, and the very low glucose range; a summary of the number of times the host spent over (e.g., hyperglycemia if the host's blood glucose is too high) and under (e.g., hypoglycemia if the host's blood glucose is too low) during a specified period (e.g., one week); a daily bar graph of the host's average glucose value compared to the weekly average; and a button in the user interface to redirect the user to the website housing detailed CGM report.

[0113] As shown in FIG. 11D and FIG. 12D, the second feature 1100d, 1200d of the wireframe 1100a, 1100b and user interface view 1200a, 1200b can provide time related insulin values ​​of the host over a specified time period. This insulin information can include the average number of insulin units taken by the host. For example, the average number of insulin units for the current week can be compared to the average insulin units for the previous week and the average insulin units for the month. Additionally, a stacked bar graph illustrating the host's basal insulin intake versus bolus insulin intake, as well as the host's average daily basal and bolus units can be provided in the second feature. As used herein, basal is the amount of insulin infused per hour in terms of long-lasting insulin, and bolus is the additional dose given with each meal in case of hyperglycemia and to correct blood glucose. The analyte processor 490 can determine the average insulin amount based on information entered by the user, automatically uploaded and collected information, and the like.

[0114] As shown in Figures 11E and 12E, the third feature 1100e, 1200e of the wireframe 1100a, 1100b and user interface view 1200a, 1200b may provide information related to the host's carbohydrate intake over a specified time period. The carbohydrate intake information (i.e., calculated based on the user's input of events, including meals logged by the user) may include the average number of carbohydrates for the current week compared to the previous week's average number of carbohydrates and the monthly average number of carbohydrates (i.e., determined by logic 420C of Figure 4), a daily bar graph of the host's average carbohydrate intake, and a daily breakdown of the host's percentage of time within the target glucose range. The percentage of time within the target glucose range may be provided in the third feature to assist the user / host in identifying correlations, if any, between carbohydrate intake and its effect on the host's glucose values.

[0115] In some examples, the third feature of the wireframes 1100a, 1100b and user interface views 1200a, 1200b may also include information related to the host's activity over a specified time period (e.g., additional third feature 1200f shown in FIG. 12F). The activity information (i.e., calculated based on user / host input or event tracker and including the number of steps taken by the host) may include a current week's average number of steps (determined by logic 420C of FIG. 4) compared to the previous week's average number of steps and the monthly average number of steps, a daily bar graph of the host's average number of steps taken, and a daily breakdown of the host's percentage of time within the target glucose range. The percentage of time within the target glucose range may be provided in the third feature to assist the user in identifying any correlation, if any, between the number of steps taken and its effect on the host's glucose values.

[0116] As shown in FIG. 11F and FIG. 12G, a fourth feature 1100f, 1200g of the wireframe 1100a, 1100b and user interface view 1200a, 1200b can provide information related to the host's "best day" over a specified time period. The logic 420C can determine the host's "best day" based on the day the host spent the most time in the target glucose range. In some examples, the host's glucose, insulin, carbohydrates, and steps can be given to better understand the factors that contributed to and led to the host's "best day". A summary of the contributing factors can also be given to motivate the user to experience more days similar to the host's "best day". The user can also receive prizes (also referred to herein as badges) to reward the user for their "best day" behavior. In some examples, the prizes can include a "steady carb prize" used to reward the host for not having a day with any carbohydrate spikes. In some examples, the prizes can include a "Bas / Bol Balancer Award" used to reward the host for keeping their basal / bolus ratio at a target ratio (e.g., 60 / 40) or better. In some examples, the prizes can include a "Time in Range Award" used to reward the host for spending more than 80% of their week within the target glucose range. In some examples, the most prizes won by the host can be identified.

[0117] In some examples, features 1-4 of wireframes 1100a, 1100b and user interface views 1200a, 1200b may be presented in chronological order, from top to bottom as shown in the vertical layout of Figures 11A and 12A, or from left to right as shown in the horizontal layout of Figures 11B and 12B.

[0118] Figure 13A illustrates an example wireframe 1300a of another example user interface view in a vertical horizontal layout, and Figure 13B illustrates an example wireframe 1300b of the user interface view in a horizontal layout, according to some example aspects of the present disclosure. Figures 13C-13E illustrate expanded views of features 1-3 shown in the vertical and horizontal wireframes 1300a and 1300b, respectively, and are further described in more detail below.

[0119] FIG 14A illustrates an example user interface view 1400a associated with sensor data representing glucose concentration values ​​of a host in a vertical layout corresponding to wireframe 1300a, and FIG 14B illustrates an example user interface view 1400b in a horizontal layout corresponding to wireframe 1300b, according to some example aspects of the disclosure. FIG 14C-F illustrate expanded views of features 1-4 shown in vertical and horizontal user interface views 1400a and 1400b, respectively, and are further described in more detail below.

[0120] Wireframe 1300a and user interface view 1400a show a vertical orientation of the wireframe and user interface view, respectively, while wireframe 1300b and user interface view 1400b show a horizontal orientation of the wireframe and user interface view, respectively. Although both horizontal and vertical orientations are shown, only one orientation may be displayed to the user. In some aspects, the orientation of the user interface view may be automatically selected and displayed based on the type of user device.

[0121] As shown in the example user interface views of Figures 14A and 14B and the corresponding wireframes of Figures 13A and 13B, the weekly reports may be on a single day basis, referring to reports that compare an "average day" to a "problem day" and a "best day." A host's "best day" may be determined based on the day on which the host spent the most time within the target glucose range (e.g., as may be determined by logic 420C). Alternatively, a host's "problem day" may be determined based on the day on which the host spent the least time within the target glucose range.

[0122] As shown in FIG. 13C and FIG. 14C, the first feature 1300c, 1400c of the wireframe 1300a, 1300b and user interface view 1400a, 1400b may provide a time in range stacked bar graph representing the percentage of time the user was in the target glucose range, the very high or high glucose range, and the very low or low glucose range over a specified time period (e.g., any consecutive 7 days). In some examples, the target glucose ranges may be defined as different ranges during daytime (e.g., 6:00 AM to 10:00 PM) and nighttime (e.g., 10:00 PM to 6:00 AM) times. The user's percentage of time in range may also be compared to the percentage of time in range for the previous week. In some examples, the stacked bar graphs may be presented using different colors to distinguish the percentage of time the host was in the target glucose range, the very high or high glucose range, and the very low or low glucose range over a particular time period. In some examples, the stacked bar graph can be presented using different sized blocks (stacked within the stacked bar graph) for each range. The different sizes can be correlated to the amount of time the user spent within each range. For example, the largest block size in the stacked bar graph can represent the glucose range in which the host spent the greatest amount of time over a specified time period, whereas the smallest block size in the stacked bar graph can represent the glucose range in which the host spent the least amount of time over a specified time period.

[0123] Additionally, the first feature of the user interface view can provide a summary overview of the host's recorded average glucose, average insulin units, average carbohydrate intake, and average steps for a specified time period (e.g., any consecutive seven day period). The first feature can also provide trend and insight graphs. The trend graph can include a summary of the user's in-range time presented in a scatter plot with a best fit line. The best fit line represents the relationship between the data points and identifies a trend of the user's host's in-range time over a 12 hour period (e.g., in the example shown, 12:00 AM to 12:00 PM) for the specified time period (e.g., any consecutive seven day period). Additionally, target glucose ranges can be provided within the graph for both daytime and nighttime hours. Target glucose ranges can be defined as different ranges for daytime and nighttime hours. For example, the graph can identify the daytime target glucose range using a sun shape on the side of the trend graph and the nighttime target glucose range using a moon shape on the side of the trend graph (sun and moon shapes not shown). The trend graph can also provide bar graphs of different colors to differentiate between the host's time in high or very high glucose ranges, the host's time in low or very low glucose ranges, and the host's time in glucose range over a 12 hour period in a specified time period. The trend graph can also be a bar graph broken down into a breakdown of average insulin units, average carbohydrate intake, and average steps recorded over a 12 hour period in a specified time period (e.g., any consecutive 7 day period). The insights can include a summary of high and low periods and recommendations to mitigate these undesirable spikes.

[0124] As shown in FIGS. 13D and 14D, the wireframes 1300a, 1300b and the second feature 1300d, 1400d of the user interface views 1300a, 1300b may provide information regarding the host's "problem days" over a specified time period. In some examples, the host's glucose, insulin, carbohydrates, and steps may be given to better understand the contributing factors that resulted in the host's "problem days." Additionally, a trend graph for the host's "problem days" may be provided. This trend graph may present a summary of the host's recorded glucose values, insulin units, carbohydrate intake, steps, and sleep time over a 12-hour period (e.g., 12:00 AM - 12:00 PM). This trend graph may reveal relationships that the user may not otherwise know. For example, as shown in the example Sunday trend graph, between approximately 6:00 AM and approximately 9:00 AM, the host experienced a first drop in his glucose values, and between approximately 3:00 PM and approximately 6:00 PM, a second drop. During each of these periods, the user logged multiple carbohydrates. While not the only factor contributing to the glucose value, there may be an inverse relationship between the glucose value presented in the trend graph and carbohydrate intake that the user was not previously aware of. Thus, the user can take appropriate action to prevent future intraday glucose drops.

[0125] As shown in Figures 13E and 14E, a third feature 1300e, 1400e of the wireframe 1300a, 1300b and corresponding user interface view 1400a, 1400b may provide similar information as the first feature, except that the data may be customized to represent the host's "best day."

[0126] As shown in FIG. 14F, in a fourth feature 1400f of the user interface views 1300a, 1300b, the host can receive trophies (also referred to herein as badges) to reward the host for his / her weekly behavior. In some examples, the trophies can include a "Step Master Award" used to reward the host for having a day during the week in which the host walked at least 10,000 steps. In some examples, the trophies can include a "Time in Range Award" used to reward the host for spending more than 80% of his / her week within the target glucose range. In some examples, the most awards the host has earned can be reviewed. A button (e.g., a "My Badge Collection" button) can also be provided for the host to initiate a view of all the trophies (also referred to herein as badges) that he / she has earned.

[0127] In some examples, features 1-3 of wireframes 1300a, 1300b and features 1-4 of user interface views 1400a, 1400b may be presented in chronological order, from top to bottom as shown in the vertical layout of Figures 13A and 14A, or from left to right as shown in the horizontal layout of Figures 13B and 14B.

[0128] While the above is directed to visualizing a host's analyte data in a specified format for presentation, other formats can be customized and / or designed to best meet the needs of each individual user.

[0129] Exemplary Analyte Data Processing and Visualization of Analyte Data on Widgets Aspects of the present disclosure provide techniques for providing one or more user interface views for display on one or more widgets. More specifically, aspects of the present disclosure provide techniques for visualization of analyte data on a widget. In some aspects, with continuous glucose monitoring, information associated with a host's blood glucose data can be processed and presented to a user and updated in real-time or periodically via a widget. The analyte for measurement and visualization on a widget by the devices and methods described herein is glucose, although other biological parameters and / or analytes may be considered as well.

[0130] A distinctive feature of modern graphical user interfaces is that they allow a vast number of items to be displayed on the screen simultaneously. Widgets are the user's portal to various information and quick functions. A widget can communicate with a remote server to provide information to the user (e.g., weather reports), provide commonly needed functions (e.g., calculators), or act as an information repository (e.g., notepad or calendar). Some widgets can provide a combination of these functions. Some widgets can interact with a remote source of information, such as a server, to provide information. For example, a weather feature can retrieve raw weather data from a remote server. Widgets may be interactive, so that a user can utilize the widget's functionality by performing common input operations (such as clicking a mouse or typing on a keyboard). Additionally, widgets can act as links to programs, such as applications (apps), running on the user's device. For example, by engaging with a widget (e.g., clicking on the widget), the user can be associated back to the app, thereby allowing the user to enjoy all the features, services, and / or information provided by the app.

[0131] Widgets can be "pinned" to the user interface in various spots and sizes, allowing for many different layouts. Widgets can be placed in a view that is accessible with a swipe (e.g., from left to right) on the user device's home screen, or can be accessible on the user device's home screen (without requiring a swipe). In some exemplary aspects, widgets cannot overlap one another, and if a user attempts to move one widget into a position occupied by another widget, one of the widgets can automatically move out of the way to make space. In some exemplary aspects, the widgets' positions, configurations, and sizes can be saved when the user interface is released, so that the same state can be restored the next time the dashboard is invoked.

[0132] FIG. 15 is a block diagram conceptually illustrating a software architecture for implementing widget functionality according to some exemplary aspects of the disclosure. As shown in FIG. 15, the software architecture 1500 can include an operating system that executes a dashboard server 1501, a dashboard client 1502, and widgets 1503. Dashboard configuration information 1504 can be used by the server 1501 and / or the client 1502 to specify configuration options (e.g., access levels, etc.) for displaying the widgets 1503. The client 1502 can display the widgets 1503 by rendering a web page into views, and the size of each view can be defined as metadata associated with the corresponding widget 1503. The server 1501 can provide data for rendering a user interface layer that can be overlaid on a desktop or home screen of the user interface. The widgets 1503 can be rendered into a separate layer that can then be fetched on top of a regular desktop or home screen, thus partially or completely obscuring the desktop or home screen while the dashboard is active. In some aspects, widgets associated with a user's analyte data may be provided.

[0133] According to certain aspects, a widget may be provided by a user device (e.g., by an application running on the user device), in which case information displayed on the widget may be provided by the user device (e.g., rather than provided by a server). For example, some widgets may operate without a network connection. In some cases, the information may be provided by another user device that has a connection to the user device, such as a wearable.

[0134] 16 illustrates an example dashboard (also referred to as a "unified interest layer") including several user interface elements, also referred to herein as "widgets," according to some example aspects of the present disclosure. These user interface elements or widgets generally include software accessories for performing useful and commonly required functions. Examples of widgets may include, but are not limited to, a calendar, a calculator, an address book, a package tracker, a weather feature, a health tracker, and the like.

[0135] A user may interact with and / or configure a widget as desired. For example, a user may move a widget around the screen and / or resize the widget as appropriate. Some widgets may be resizable, some widgets may be of a fixed size, and the widget creator may specify whether the widget can be resized. Some widgets may automatically resize themselves based on the amount or nature of the data being displayed.

[0136] FIG. 17 is a flow diagram illustrating example operations for generating a user interface view associated with sensor data representing glucose concentration values ​​of a host, according to some example aspects of the disclosure.

[0137] The operations 1700 may begin, at 1702, by accessing first data associated with blood glucose concentration values ​​of a host during a first time period. For example, the analyte processor 490 of FIG. 4 may access data stored in the repository 475 and / or may receive data from one or more sources.

[0138] At 1704, the operations 1700 may include analyzing the first data to generate a first one or more user interface views associated with the first data for display on one or more widgets. For example, one or more components of the analyte processor 490 of FIG. 4 may process the analyte data and / or other information associated with the host to generate information provided to a user in one or more widgets.

[0139] At 1706, the operations 1700 may include providing the first one or more user interface views for display on one or more widgets. For example, the analyte processor 490 may provide one or more widgets for display on the UI 410, the computer 20, and / or other user interfaces.

[0140] At 17, operations 1700 can include automatically updating a first one or more user interface views for display on the one or more widgets. For example, the automatically updating can include accessing second data associated with blood glucose concentration values ​​of the host during a second time period, analyzing the second data to generate second one or more user interface views associated with the second data for display on the one or more widgets, and providing the second one or more user interface views for display on the one or more widgets.

[0141] 18A, 18B, and 18C are tables categorizing example analyte data widgets according to some example aspects of the present disclosure. As shown in FIG. 18A, 18B, and 18C, the widgets can be categorized as either summary type widgets, motivation type widgets, event type widgets, or another type of widget according to some example aspects of the present disclosure.

[0142] A summary widget may be a widget that provides a short, clear description of key facts and / or insights regarding the host's collected data over a specified period (e.g., 8 hours, 3 days, 7 days, 14 days, 30 days, or another defined period). For example, a summary widget may provide the host's average glucose from sensor data measured over a 7-day period. A motivation widget may be a widget that provides a comparison of data and / or messages used to encourage the host to take action (e.g., maintain glucose stability, change lifestyle, etc.). For example, a motivation widget may provide the host with a display of their "best day" over the past 7 days along with a summary of factors that contributed to their "best day." Such a widget may be used to motivate the host to achieve similar results on other days (e.g., encouraging the host to keep their average glucose at the same average glucose value as their "best day" for the next few days). An event widget may be a widget that tracks the host's health based on events logged by the host. For example, a recorded event may include a recorded meal consumed (e.g., input of calories, carbohydrates, etc.), or a recorded period of time when the host engaged in some form of exercise (e.g., input of type of exercise, calories burned, etc.). Thus, an event type widget may show the host's glucose values ​​at the start of the event, one hour after the event, and / or two hours after the event. Other kinds of widgets may encompass widgets that do not fit into the other three classifications. For example, another type of widget may be a widget that displays a trend graph of the host's average glucose values ​​over a 24 hour period over the past 14 days.

[0143] 19 illustrates an example summary widget 1900, according to some example aspects of the present disclosure. As shown in FIG. 19, the summary widget 1900 can provide a summary of the host's percentage of time spent within the target glucose range and the host's average glucose over a seven day period.

[0144] 20 illustrates another example summary widget 2000, according to some example aspects of the present disclosure. As shown in FIG. 20, the summary widget 2000 can provide a summary of the host's average glucose, the host's standard deviation, and the host's glucose management indicator (GMI) percentage over a 14 day period.

[0145] 21 illustrates another example summary widget 2100, according to some example aspects of the present disclosure. As shown in FIG. 21, the summary widget 2100 can provide a summary of the host's percentage of time spent within the target glucose range, the host's average glucose, and the host's standard deviation over a 14-day period.

[0146] 22 illustrates another example summary widget 2200, according to some example aspects of the present disclosure. As shown in FIG. 22, the summary widget 2200 can provide a summary of the host's percentage of time spent within the target glucose range, the host's average glucose, and the host's GMI percentage over a 14-day period.

[0147] 23 illustrates another example summary widget 2300, according to some example aspects of the disclosure. As shown in FIG. 23, the summary widget 2300 can provide a summary of the percentage of the host's time spent in the target glucose range, the very high and high glucose ranges, and the very low and low glucose ranges over a 14 day period. As shown, the summary widget 2300 can provide both a numerical value representing the percentage, and a stacked bar graph. The stacked bar graph can be associated with a color coding representing the severity of the ranges.

[0148]

[00136] Figure 24 illustrates another example summary widget 2400 according to some example aspects of the disclosure. As shown in Figure 24, the summary widget 2400 can provide the information of the summary widget 2300 of Figure 23 as well as a summary of the percentage of the host's time spent within the target glucose range, the very high and high glucose ranges, and the very low and low glucose ranges over a 14 day period, and can also provide an indication of the positive or negative percentage of change compared to the previous 14 day period.

[0149] 25A and 25B illustrate another example summary widget 2500b and its corresponding wireframe 2500a according to some example aspects of the disclosure. As shown in FIG. 25A and 25B, the summary widget 2500b can provide a summary of the information of the summary widget 2300 of FIG. 23 as well as the percentage of the host's time spent in the target glucose range, the very high and high glucose ranges, and the very low and low glucose ranges over a 14 day period, as well as the host's average glucose and the host's GMI percentage over the 14 day period.

[0150] 26A and 26B illustrate another exemplary summary widget 2600b and its corresponding wireframe 2600a according to some exemplary aspects of the disclosure. As shown in FIG. 26A and 26B, the summary widget 2600b can provide the information of the summary widget 2500b of FIG. 25A and 25B, a summary of the percentage of the host's time spent in the target glucose range, very high and high glucose ranges, and very low and low glucose ranges over a 30-day period, as well as an indication of the positive or negative percentage of change compared to the previous 30-day period, the host's average glucose, the host's GMI percentage, and even an indication of the pattern (e.g., the host's high glucose pattern during the day or low glucose pattern overnight) over the 30-day period. As shown in the figures, the indication can be provided as a legend.

[0151] 27 illustrates another example summary widget 2700 according to some example aspects of the disclosure. As shown in FIG. 27, the summary widget 2700 can provide a summary of the host's percentage of time spent within the target glucose range comparing percentages for the current week, previous week, this month, and year to date (YTD).

[0152] 28 illustrates another example summary widget 2800 according to some example aspects of the disclosure. As shown in FIG. 28, the summary widget 2800 can provide a display of the host's average glucose and standard deviation over the previous 7 days, 14 days, and 30 days, in addition to a summary of the host's percentage of time spent within the target glucose range comparing the previous 7 days, 14 days, and 30 days. As shown, the percentage of time within range can be provided as a stacked bar graph.

[0153] 29 illustrates another example summary widget 2900, according to some example aspects of the disclosure. As shown in FIG. 29, the summary widget 2900 can provide a summary of the percentage of the host's time spent in the target glucose range, very high and high glucose ranges, and very low and low glucose ranges over a seven day period, as well as an indication of the hours / minutes the host spent in each of these ranges. As shown, the percentage of time spent in the ranges can be represented by stacked bar graphs, which can be associated with values ​​for the number of hours spent in the various ranges associated with the stacked bar graph.

[0154] 30 illustrates another example summary widget 3000 according to some example aspects of the present disclosure. As shown in FIG. 30, the summary widget 3000 can provide a summary of the time / minutes the host spent in the target glucose range, high glucose range, and low glucose range over a seven day period.

[0155] 31 illustrates another example summary widget 3100, according to some example aspects of the present disclosure. As shown in FIG. 31, the summary widget 3100 can provide a summary of a number of high and low events in which a host participated over a seven day period.

[0156] 32 illustrates another example summary widget 3200 according to some example aspects of the disclosure. As shown in FIG. 32, the summary widget 3200 can provide a summary of the time within the host, which can be presented in a graph with a best fit line showing the trend of the time within the host over a 12 hour period (e.g., 12:00 AM to 12:00 PM) over a three day period. An indication of the data included in the summary graph can also be included in the summary widget 3200.

[0157] 33A and 33B illustrate an example motivation widget 3300b and its corresponding wireframe 3300a, according to some example aspects of the present disclosure. As shown in FIG. 33A and 33B, the motivation widget 3300b can provide a horizontal bar graph illustrating the percentage of the host's time spent within the daily target glucose range over a seven day period compared to an end goal percentage of time spent within the daily target glucose range.

[0158] 34 illustrates another example motivation widget 3400 according to some example aspects of the present disclosure. As shown in FIG. 34, the motivation widget 3400 can provide a vertical bar graph illustrating the percentage of the host's time spent within the target glucose range per day over a seven day period compared to an end goal percentage of time spent within the target glucose range per day (e.g., 70%).

[0159] 35A and 35B illustrate another example motivation widget 3500b and its corresponding wireframe 3500a, according to some example aspects of the present disclosure. As shown in FIG. 35A and 35B, the motivation widget 3500b can provide a vertical bar graph illustrating the percentage of the host's time spent within the target glucose range today compared to an end goal percentage of time spent within the target glucose range per day.

[0160] 36 illustrates another example motivation widget 3600 according to some example aspects of the disclosure. As shown in FIG. 36, the motivation widget 360 can provide a horizontal bar graph illustrating the number of hours the host spent within the target glucose range today compared to an end goal number of hours spent within the target glucose range per day.

[0161] 37 illustrates another example motivation widget 3700, according to some example aspects of the disclosure. As shown in FIG. 37, the motivation widget 3700 can provide an indication of the host's "best day" over the previous seven day period. This motivation widget 3700 can also include the host's average glucose, the host's percentage of time spent within the target glucose range, and the host's standard deviation on the indicated "best day."

[0162] 38 illustrates another example motivation widget 3800, according to some example aspects of the disclosure. As shown in FIG. 38, the motivation widget 3800 can provide an indication of the host's "best day" over the previous seven day period. The widget can also provide a summary of the host's average glucose and the percentage of the host's time spent within the target glucose range during the displayed "best day."

[0163] 39 illustrates another example motivation widget 3900 according to some example aspects of the disclosure. As shown in FIG. 39, the motivation widget 3900 can provide an indication of the host's "best day" over the previous seven day period, along with a summary of the host's average glucose, the host's standard deviation, and the percentage of the host's time spent within the target glucose range on the indicated "best day." This motivation widget 3900 can also include an indication of the percentage of the host's time spent within the target glucose range on the host's "2nd best day" and "3rd best day."

[0164] 40 illustrates another example motivation widget 4000 according to some example aspects of the disclosure. As shown in FIG. 40, the motivation widget 4000 can provide a comparison of the host's "best days," "average days," and "problem days" over the past week. This motivation widget 4000 can also include a summary of the host's average glucose, the host's standard deviation, and the percentage of the host's time spent within the target glucose range on each of the indicated "best days," "average days," and "problem days."

[0165] 41 illustrates another example motivation widget 4100 according to some example aspects of the disclosure. As shown in FIG. 41, the motivation widget 4100 can also provide a comparison of the host's "best days", "average days", and "problem days" over the previous seven day period with a summary of the host's average glucose and the host's percentage of time spent within the target glucose range on each of the indicated "best days", "average days", and "problem days".

[0166] 42 illustrates another example motivation widget 4200 according to some example aspects of the disclosure. As shown in FIG. 42, the motivation widget 4200 can also include a comparison of the host's percentage of time spent within the target glucose range in the previous four week period. This motivation widget 4200 can also include a comparison of the host's percentage of time spent within the target glucose range in the previous four week period to an end goal percentage of time spent within the target glucose range per day.

[0167] 43 illustrates another example motivation widget 4300 according to some example aspects of the disclosure. As shown in FIG. 43, the motivation widget 4300 can provide the number of consecutive days the host has spent within the target glucose range along with a notification to lower or increase the target glucose range. The notification can include explanatory text.

[0168] 44 illustrates an example event type widget 4400 according to some example aspects of the disclosure. As shown in FIG. 44, the event type widget 4400 can provide a summary of the host's glucose values ​​at the start of each recorded event and two hours after the start of the recorded event.

[0169] 45 illustrates another example event-based widget 4500, according to some example aspects of the disclosure. As shown in FIG. 45, the event-based widget 4500 can provide an indication of the host's glucose value at the start of each recorded event, and a trend of the host's glucose fluctuations following each recorded event (e.g., at 1 hour after the recorded event, 2 hours after the recorded event, 3 hours after the recorded event, etc.).

[0170] 46 illustrates another example event type widget 4600 according to some example aspects of the disclosure. As shown in FIG. 46, the event type widget 4600 can provide a summary of the host's glucose values ​​per day and per event at the start of each recorded event, one hour after the recorded event started, and two hours after the recorded event started.

[0171] 47A and 47B illustrate another example event type widget 4700b and its corresponding wireframe 4700a, according to some example aspects of the present disclosure. As shown in FIG. 47A and 47B, the event type widget 4700b can provide trend graphs of the host's glucose values, insulin units, recorded carbohydrates, recorded steps, and recorded sleep over a specified period of time (e.g., a 24-hour period from 12:00 AM to 12:00 AM).

[0172] 48 illustrates another example type of widget 4800 according to some example aspects of the present disclosure. As shown in FIG. 48, the widget 4800 can provide a summary of the host's average glucose for each day of the week over the previous 30 days (or previous week) compared to the end goal average glucose per day. As shown, the summary can include both a bar graph and a numerical value for the average glucose.

[0173] 49 illustrates another example type of widget 4900 according to some example aspects of the present disclosure. As shown in FIG. 49, the widget 4900 can provide a trend of the host's average glucose values ​​in a 24-hour period over the previous 14-day period.

[0174] 50 illustrates another example type of widget 5000 according to some example aspects of the disclosure. As shown in FIG. 50, the widget 5000 can provide a trend of the best time within a 24-hour period of a day, where the host has the highest percentage of time spent within the target glucose range, over the previous 14 day period. The widget can also include a notification indicating how long the host must stay within the target glucose range to reach a final goal percentage of time in range. The notification can include explanatory text.

[0175] 51 illustrates another example type of widget 5100 according to some example aspects of the disclosure. As shown in FIG. 51, the widget 5100 can provide a summary of the host's percentage of time spent in the target glucose range, very high and high glucose ranges, and very low and low glucose ranges over a 14 day period, the host's average glucose over the 14 day period, and the host's GMI percentage over the 14 day period. This widget 5100 can also include a trend of the host's average glucose values ​​over the previous 14 day period.

[0176] It should be understood that the foregoing Detailed Description is merely exemplary and explanatory, and not limiting. Further features and / or variations of the widget for analyte data visualization may be provided in addition to those described above.

[0177] According to certain aspects, the widgets described herein may be customizable by a user. For example, a user may customize various goals, ranges, and information provided for one or more widgets.

[0178] FIG. 52 is a flow diagram illustrating example operations 5200 for activating and using a dashboard with widgets, according to some example aspects of the disclosure. A dashboard layer (also referred to as a "unified interest layer" or "dashboard") can be used to manage and display widgets. A user can invoke the dashboard in a number of ways, including but not limited to, hitting a designated function key or key combination, clicking an icon, selecting a command from an on-screen menu, or moving an on-screen cursor to a designated screen corner, at 5202. In response to such user input, at 5203, the current state of the user interface can be saved, at 5204, the user interface can be temporarily deactivated (e.g., faded), at 5205, an animation or effect can be played or presented to introduce the dashboard, and at 5206, the dashboard can be displayed with one or more widgets. At 5206, the widgets displayed on the dashboard can include one or more of the widgets previously described in FIGS. 19-51.

[0179] Optionally, the previous state of the dashboard may be retrieved so that the dashboard may be displayed in its previous configuration. In some example aspects, the user interface and the dashboard may be active simultaneously.

[0180] At 5207, the user can interact with and / or configure the widgets as desired. In some exemplary aspects, the user can move the widgets around the screen and resize them accordingly. As previously mentioned, some widgets may be resizable and some widgets may have a fixed size. At 5208, the user can hide the dashboard by invoking a hide command, which may cause the normal user interface to return to the display screen by itself or reappear. In some exemplary aspects, the dashboard may be hidden when the user presses a function key or key combination, clicks a negative space in the dashboard (e.g., a space between widgets), or moves an on-screen cursor to a predefined screen corner.

[0181] In some exemplary aspects, the dashboard may be hidden automatically (i.e., without user input) after some predetermined period of time or in response to a triggering event. At 5209, an animation or other effect may be played or displayed to provide a transition when the dashboard is hidden. When the dashboard is hidden, the current configuration or state of the widgets (e.g., position, size, etc.) may be saved so that it can be retrieved the next time the dashboard is activated. In some exemplary aspects, the animation or effect may be played or presented when reintroducing the user interface. At 5210, the user interface is restored to its previous state so that the user can resume interacting with the software application and / or computer operating system.

[0182] In some example aspects, the dashboard may be configurable. A user may select some widgets to be displayed, for example, by dragging the widget from a configuration bar (or other user interface element) onto the dashboard. For example, FIG. 53 illustrates an example user interface that may include various widgets, according to some example aspects of the present disclosure. The widgets selected for display on the dashboard may include one or more of the widgets previously described in FIGS. 19-51.

[0183] Beyond those aspects described herein, there are many ways in which dashboards and widgets can be displayed. For example, dashboards and widgets can be displayed on any user interface or user interface element, including, but not limited to, the desktop, a browser or application window, a menu system, a tray, a multi-touch sensitive display, and other widgets. Furthermore, widgets and dashboards can be displayed on any surface on which widgets and dashboards can be displayed, such as projections onto the surface, holograms, the surface of a consumer device (e.g., a refrigerator door), and the like.

[0184] Additional Considerations The methods disclosed herein include one or more steps or actions for achieving the method. These 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 specifically specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0185] 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. By way of example, "at least one of a, b, or c" is intended to cover a, b, c, ab, ac, bc, and abc, as well as any combination of multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other permutation of a, b, and c).

[0186] The foregoing description is provided to enable those skilled in the art to practice the various embodiments described herein. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments. Thus, the claims are not intended to be limited to the embodiments shown herein, but are to be given the full scope consistent with the claim language, in which reference to an element in the singular is not intended to mean "one and only one" unless specifically stated otherwise, but conversely is intended to mean "one or more". The term "some" refers to one or more, unless specifically stated otherwise. All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are now known or that will become known in the future to those skilled in the art are expressly incorporated herein by reference and are intended to be encompassed within the scope of the claims. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly 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."

[0187] While various examples of the present invention have been described above, it should be understood that the examples are presented by way of example only and not by way of limitation. Similarly, various diagrams may depict example architectural or other configurations for the present disclosure, which configurations are implemented to aid in understanding features and functionality that may be included in the present disclosure. The present disclosure is not limited to the illustrated example architecture or configurations, but may be implemented using various alternative architectures and configurations. Furthermore, while the present disclosure has been described above with respect to various illustrative examples and aspects, it should be understood that various features and functions described in one or more of the individual examples are not limited in their applicability to the particular example for which they are described. On the contrary, those features and functions may be applied, alone or in any combination, to one or more of the other examples of the present disclosure, whether or not such an example is described, and whether or not such features are presented as part of a described example. Thus, the breadth and scope of the present disclosure should not be limited by any of the illustrative examples described above.

[0188] All references cited herein are incorporated herein by reference in their entirety. To the extent that publications and patents or patent applications incorporated by reference conflict with the present disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material.

[0189] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meanings to those of ordinary skill in the art and are not to be limited to any special or customized meaning unless expressly defined as such herein.

[0190] Terms and phrases used in this application, and variations thereof, unless expressly stated otherwise, particularly in the appended claims, are to be construed as open ended, as opposed to limiting. As an example of the foregoing, the term "including" should be read to mean "including without limitation," "including, but not limited to," and 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 "including" should be interpreted as "including, but not limited to." The term "examples" is used to provide illustrative examples of items in the description, not an exhaustive or exclusive listing of such examples. Adjectives such as "known," "usual," "standard," and terms of similar import should not be construed as limiting the items being described to a given period of time or to items available at a given time, but rather should be read to embrace known, conventional, or standard technology that may be available or known at any time now or in the future, and words such as "preferably," "preferred," "desired," or "desirable," and similar imports should not be understood to imply that a particular feature is critical, essential, or even essential to the structure or function of the invention, but rather are merely intended to highlight alternative or additional features that may or may not be utilized in a particular example of the invention. Similarly, a group of items connected with the conjunction "and" should not be read as requiring that any and all of those items be present in the group, but rather should be read as "and / or" unless expressly stated otherwise.Similarly, a group of items joined with the conjunction "or" should not be read as requiring mutual exclusivity among the groups, but rather as "and / or" unless expressly stated otherwise.

[0191] As used herein, "comprising" is synonymous with "including," "containing," or "characterized by" and is inclusive or open-ended and does not exclude additional or unrecited elements or method steps.

[0192] All numerical values ​​expressing amounts of raw materials, reaction conditions, and the like used herein should be understood to be modified in all instances by the term "about." Accordingly, unless otherwise indicated, 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 this application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0193] Moreover, although the foregoing has been described in some detail using illustrations and examples for purposes of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be practiced. Thus, the descriptions and examples should not be construed as limiting the scope of the invention to the specific examples and embodiments described herein, but rather as covering all modifications and alternatives that come within the true scope and spirit of the invention. [Explanation of symbols]

[0194] 2. Drug delivery pumps 4. Glucose Meter 8. Continuous Analyte Sensor Systems 10 Continuous Analyte Sensors 12 Sensor Electronics 14, 16, 18, 20 Display Devices 100 Systems 406 Network 490 Cloud-Based Analyte Processors

Claims

1. 1. A method for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host, comprising: accessing first data associated with a blood glucose concentration value of the host, the first data being associated with a first time period; analyzing the first data to generate a first one or more user interface views associated with the first data for display on one or more widgets; providing the first one or more user interface views for display on the one or more widgets; and automatically updating the first one or more user interface views for display on the one or more widgets, wherein the automatically updating comprises: accessing second data associated with blood glucose concentration values ​​of the host, the second data associated with a second time period; analyzing the second data to generate one or more second user interface views associated with the second data for display on the one or more widgets; and providing the second one or more user interface views for display on the one or more widgets.

2. the first one or more user interface views for display on one or more widgets; a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; an average blood glucose concentration value of the host based on the first data; or a summary of an average blood glucose concentration value of the host based on the first data; and A standard deviation of the blood glucose concentration value of the first data; a glucose management indicator (GMI) percentage based on the first data; or a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; an average blood glucose concentration value of the host based on the first data; a standard deviation of the blood glucose concentration values ​​of the first data; or a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; an average blood glucose concentration value of the host based on the first data; and a glucose management index (GMI) percentage based on the first data.

3. 2. The method of claim 1 , wherein the first one or more user interface views for display on one or more widgets include a summary of a percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period, a very high blood glucose range associated with the first time period, a high blood glucose range associated with the first time period, a low blood glucose range associated with the first time period, and a very low blood glucose range associated with the first time period.

4. the first one or more user interface views for display on one or more widgets; a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period, a very high blood glucose range associated with the first time period, a high blood glucose range associated with the first time period, a low blood glucose range associated with the first time period, and a very low blood glucose range associated with the first time period; a percentage change by which the summary of the percentage of time that the blood glucose concentration values ​​of the first data were within target, very high, high, low, and very low blood glucose ranges associated with the first time period compared to a summary of the percentage of time that the blood glucose concentration values ​​of the host in a third time period were within target, very high, high, low, and very low blood glucose ranges associated with the third time period; or a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period, a very high blood glucose range associated with the first time period, a high blood glucose range associated with the first time period, a low blood glucose range associated with the first time period, and a very low blood glucose range associated with the first time period; an average blood glucose concentration value of the host based on the first data; a glucose management index (GMI) percentage based on the first data; or a summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period, a very high blood glucose range associated with the first time period, a high blood glucose range associated with the first time period, a low blood glucose range associated with the first time period, and a very low blood glucose range associated with the first time period; a percentage change in the summary of the percentage of time that the blood glucose concentration values ​​of the first data were within a target, very high, high, low, and very low blood glucose range associated with the first time period compared to a summary of the percentage of time that the blood glucose concentration values ​​of the host in a third time period were within a target, very high, high, low, and very low blood glucose range associated with the third time period; an average blood glucose concentration value of the host based on the first data; a glucose management index (GMI) percentage based on the first data; and and an indication of periods during the first time period during which the blood glucose concentration values ​​of the first data were within the high and very high ranges associated with the first time period or within the low and very low ranges associated with the first time period.

5. the first one or more user interface views for display on one or more widgets; A comparison, the comparison comprising: the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; and a percentage of time that the host's blood glucose concentration value associated with a third time period was within a target blood glucose range associated with the third time period; and a percentage of time that a blood glucose concentration value of the host associated with a fourth time period was within a target blood glucose range associated with the fourth time period, the fourth time period being greater than and including the first time period; and a percentage of time that the host's blood glucose concentration value associated with a fifth time period was within a target blood glucose range associated with the fifth time period, the fifth time period being longer than the fourth time period and including the first time period; or A comparison, the comparison comprising: the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; and a percentage of time that a blood glucose concentration value of the host associated with a third time period was within a target blood glucose range associated with the third time period, the third time period being greater than and including the first time period; and comparing a percentage of time that the host's blood glucose concentration value associated with a fourth time period was within a target blood glucose range associated with the fourth time period, the fourth time period being greater than and including the third time period; a first average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the first time period, a second average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the third time period, and a third average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the fourth time period; 2. The method of claim 1, comprising: a first standard deviation of the blood glucose concentration values ​​corresponding to the first period of time, a second standard deviation of the blood glucose concentration values ​​corresponding to the third period of time, and a third standard deviation of the blood glucose concentration values ​​corresponding to the fourth period of time.

6. the first one or more user interface views for display on one or more widgets include a summary of the percentage, minutes, and number of hours that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period, a very high blood glucose range associated with the first time period, a high blood glucose range associated with the first time period, a low blood glucose range associated with the first time period, and a very low blood glucose range associated with the first time period; or the blood glucose concentration values ​​of the first data include a summary of the number of minutes and hours that were within a target blood glucose range associated with the first time period, an upper blood glucose range associated with the first time period, and a lower blood glucose range associated with the first time period; or The method of claim 1 , comprising a summary based on the first data of a number of high blood glucose concentration events and a number of low blood glucose concentration events that the host had during the first time period.

7. 2. The method of claim 1 , wherein the first one or more user interface views for display on one or more widgets include a summary of the blood glucose concentration values ​​of the first data presented as a trend in a graph over a second period of time with a best fit line.

8. the blood glucose concentration values ​​of the first data are divided into a plurality of equal data sets having corresponding blood glucose concentration values; the first one or more user interface views for display on one or more widgets include a percentage of time that the blood glucose concentration values ​​corresponding to each of the plurality of equal data sets are within a target blood glucose range associated with the first time period presented in a horizontal bar graph, compared to an end goal percentage of time that it is desired that the blood glucose concentration values ​​are within a target blood glucose range associated with the first time period; or the blood glucose concentration values ​​of the first data are divided into a plurality of equal data sets having corresponding blood glucose concentration values; 2. The method of claim 1 , wherein the first one or more user interface views for display on one or more widgets include a percentage of time during which the blood glucose concentration values ​​corresponding to each of the plurality of equal data sets are within a target blood glucose range associated with the first time period presented in a vertical bar graph, compared to an end goal percentage of time during which it is desired that the blood glucose concentration values ​​are within a target blood glucose range associated with the first time period.

9. the first one or more user interface views for display on one or more widgets include a summary of the percentage of time that the first data was within a target blood glucose range associated with the first time period presented in a vertical bar, and of the subset of blood glucose concentration values ​​compared to an end goal percentage of time that it is desired that the blood glucose concentration values ​​be within the target blood glucose range associated with the first time period; or 2. The method of claim 1, comprising a summary of the percentage of time that the first data is within a target blood glucose range associated with the first time period presented in a horizontal bar, and of the subset of blood glucose concentration values ​​compared to an end goal percentage of time that it is desired that the blood glucose concentration values ​​be within the target blood glucose range associated with the first time period.

10. the first one or more user interface views for display on one or more widgets; an indication of a third time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the third time period and have a highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; the percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within a target blood glucose range associated with the first time period; and an average blood glucose concentration value for the host based on the blood glucose concentration values ​​corresponding to the third time period; a standard deviation of the blood glucose concentration values ​​corresponding to the third period of time; or an indication of a third time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the third time period and have a highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; the percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within a target blood glucose range associated with the first time period; and and an average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the third period of time.

11. the first one or more user interface views for display on one or more widgets; an indication of a third time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the third time period and have a highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fourth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the fourth time period and have a second highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fifth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing a consecutive blood glucose concentration value that corresponds to the fifth time period and has a third highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; a first percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period, a second percentage of time that the blood glucose concentration values ​​corresponding to the fourth time period were within the target blood glucose range associated with the first time period, and a third percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period; an average blood glucose concentration value for the host based on the blood glucose concentration values ​​corresponding to the third time period; and a standard deviation of the blood glucose concentration values ​​corresponding to the third period of time.

12. the first one or more user interface views for display on one or more widgets; an indication of a third time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the third time period and have a highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fourth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the fourth time period and have an average percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fifth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the fifth time period and have a lowest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; a first percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period, a second percentage of time that the blood glucose concentration values ​​corresponding to the fourth time period were within the target blood glucose range associated with the first time period, and a third percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period; a first average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the third time period, a second average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the fourth time period, and a third average blood glucose concentration value of the host based on the blood glucose concentration values ​​corresponding to the third time period; 2. The method of claim 1, comprising: a first standard deviation of the blood glucose concentration values ​​corresponding to the third period of time; a second standard deviation of the blood glucose concentration values ​​corresponding to the third period of time; and a third standard deviation of the blood glucose concentration values ​​corresponding to the third period of time.

13. the first one or more user interface views for display on one or more widgets; an indication of a third time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the third time period and have a highest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fourth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the fourth time period and have an average percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; an indication of a fifth time period within the first time period, the subset of blood glucose concentration values ​​associated with the first time period representing consecutive blood glucose concentration values ​​that correspond to the fifth time period and have a lowest percentage of correlation to a target blood glucose range associated with the first time period when compared to other consecutive blood glucose concentration values ​​of the host within the first time period; a first percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period, a second percentage of time that the blood glucose concentration values ​​corresponding to the fourth time period were within the target blood glucose range associated with the first time period, and a third percentage of time that the blood glucose concentration values ​​corresponding to the third time period were within the target blood glucose range associated with the first time period; a first average blood glucose concentration value for the host based on the blood glucose concentration values ​​corresponding to the third time period, a second average blood glucose concentration value for the host based on the blood glucose concentration values ​​corresponding to the fourth time period, and a third average blood glucose concentration value for the host based on the blood glucose concentration values ​​corresponding to the third time period; or 1. A bar graph, the bar graph comprising: the percentage of time that the blood glucose concentration values ​​of the first data were within a target blood glucose range associated with the first time period; and a percentage of time that a blood glucose concentration value of the host associated with a third time period was within a target blood glucose range associated with the third time period, the third time period being prior to the first time period; and a percentage of time that a blood glucose concentration value of the host associated with a fourth time period was within a target blood glucose range associated with the fourth time period, the fourth time period being prior to the third time period; and a percentage of time that a blood glucose concentration value of the host associated with a fifth time period was within a target blood glucose range associated with the fifth time period, the fifth time period being prior to the fourth time period; 2. The method of claim 1, including a bar graph comparing the blood glucose concentration values ​​for the first, third, fourth, and fifth time periods to an end goal percentage of time that it is desired for the blood glucose concentration values ​​to be within a target blood glucose range associated with each of the first, third, fourth, and fifth time periods.

14. The first time period is a time period in a current week, and the method further comprises: determining an average blood glucose concentration value of the host for the current week; generating a performance report including the average blood glucose concentration value of the host during the first time period and a comparison of the average blood glucose concentration value of the host during the first time period to average blood glucose concentration values ​​of the host during at least two previous time periods of similar duration; generating a user interface view of the performance report; The method of claim 1 , further comprising: providing the user interface view of the performance report for display.

15. The method of claim 14, wherein the performance report further includes the host's average daily blood glucose concentration value, the host's daily percentage of blood glucose concentration values ​​in one or more blood glucose concentration value ranges, or a combination thereof.

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