Glucose reporting and visualization with best day data

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 Feb. 25, 2021, which is assigned to the assignee of this specification and is hereby expressly incorporated by reference in its entirety as if fully set forth herein and for all applicable purposes.

[0002] (Field of the Invention) Aspects of the present disclosure generally relate to continuous analyte monitoring, and more specifically, to analyte data processing, reporting, and visualization.

Background Art

[0003] Type 1 diabetes is a disease in which the pancreas cannot produce sufficient insulin. In a diabetic state, a person suffering from hyperglycemia may experience a series of physiological side effects related to the deterioration of microvessels. These side effects may include, for example, renal failure, skin ulcers, bleeding into the vitreous of the eye, etc. Hypoglycemic reactions such as hypoglycemic events may be induced by inadvertent over - ingestion of insulin or after normal doses of insulin or glucose - lowering agents. In severe hypoglycemic reactions, there may be a high risk of headache, seizure, loss of consciousness, and coma.

[0004] Diabetics can carry a self - monitoring blood glucose (SMBG) monitor, which typically requires the user to prick their finger with a needle to measure their glucose level. Considering the inconvenience associated with conventional finger - prick methods, diabetics are less likely to perform timely SMBG measurements, and as a result, may not notice whether their blood glucose level indicates a dangerous situation.

[0005] Various 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 the information to the user hosting the sensor. In some systems, patients can check their glucose levels on a handheld computing device. Presenting this information carefully and with high reliability is a challenge. Furthermore, efficiently analyzing this information so that reports and insights can be presented to diabetic users for the continuous management of their diabetic condition is also a challenge. [Overview of the Initiative] [Means for solving the problem]

[0006] Each of the systems, methods, and devices of this disclosure has several embodiments, and none of them alone play a role in its desired attributes. Without limiting the scope of this disclosure as expressed in the subsequent claims, several features will be briefly considered here. After considering this consideration, and in particular after reading the section entitled “Modes for Carrying Out the Invention,” it will be understood how the features of this disclosure provide advantages, including improved analysis and presentation of analyte data.

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

[0008] Certain aspects of this disclosure provide a method for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host. The method may include accessing sensor data, which may include multiple blood glucose readings associated with a host over multiple analysis periods in the current week. Each blood glucose reading may represent the host's blood glucose concentration value at a given time. The method may include determining the average blood glucose concentration value of the host for the current week based on the multiple blood glucose readings. The method may include generating a performance report. This performance report may include, but is not limited to, the average blood glucose concentration value of the host for a first period, a comparison of the average blood glucose concentration value of the host for the first period with the average blood glucose concentration value of the host for at least two preceding periods of similar duration, the average daily blood glucose concentration value of the host, the daily percentage of the host's blood glucose concentration value within one or more blood glucose concentration ranges, or a combination thereof. The information in the report may be customizable by the user. The method may include generating a user interface view of the performance report. The method may include providing a user interface view of the performance report for display.

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

[0010] It should be understood that both the general description above and the following "Modes for Carrying Out the Invention" are merely illustrative and descriptive, and not restrictive. Further features and / or variations may be provided in addition to those described herein. For example, the embodiments described herein may cover various combinations and secondary combinations of the disclosed features, and / or some of the further features disclosed in the following "Modes for Carrying Out the Invention."

[0011] To enable a more detailed understanding of the features of this disclosure listed above, a more specific description, which is briefly summarized above, may be provided by reference to embodiments, some of which are illustrated in the drawings. However, it should be noted that the accompanying drawings merely illustrate specific typical embodiments of this disclosure and should not be considered limiting in scope, as their description may be applicable to other equally valid embodiments. [Brief explanation of the drawing]

[0012] [Figure 1] This figure conceptually illustrates an exemplary system, including an exemplary continuous analyte sensor having sensor electronic equipment, according to some exemplary embodiments of the present disclosure. [Figure 2] This is a block diagram conceptually illustrating a sensor electronics module that communicates with multiple sensors, according to some exemplary embodiments of the present disclosure. [Figure 3A] Different diagrams of a sensor system, including a mounting unit and sensor electronics attached thereto, are illustrated according to some exemplary embodiments of this disclosure. [Figure 3B] Different diagrams of a sensor system, including a mounting unit and sensor electronics attached thereto, are illustrated according to some exemplary embodiments of this disclosure. [Figure 4] This is a block diagram conceptually illustrating an analyte processing system according to some exemplary embodiments of the present disclosure. [Figure 5] The present disclosure illustrates exemplary user interface views associated with sensor data representing glucose concentration values ​​in a host, as described in several exemplary embodiments of this disclosure. [Figure 6] This flowchart illustrates exemplary operations for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host, according to some exemplary embodiments of the present disclosure. [Figure 7]An exemplary wireframe 700 of a user interface view in a vertical layout, according to several exemplary embodiments of this disclosure, is provided. [Figure 8A] An exemplary wireframe 800a of a user interface view in a horizontal layout, according to several exemplary embodiments of this disclosure, is provided. [Figure 8B] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 8C] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 8D] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 8E] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 8F] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 8G] Enlarged views of features 1-6 shown in vertical and horizontal wireframes 700 and 800a, respectively, according to some exemplary embodiments of this disclosure are provided below. [Figure 9] The present disclosure illustrates an exemplary user interface view 900 associated with sensor data representing a host glucose concentration value in a vertical layout corresponding to a wireframe 700, according to several exemplary embodiments of this disclosure. [Figure 10A] The present disclosure illustrates an exemplary user interface view 1000a associated with sensor data representing a host glucose concentration value in a horizontal layout corresponding to a wireframe 800a, according to several exemplary embodiments of this disclosure. [Figure 10B]Illustrate enlarged 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] Illustrate enlarged 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] Illustrate enlarged 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] Illustrate enlarged 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] Illustrate enlarged 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] Illustrate enlarged 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] Illustrate an exemplary wireframe 1100a of another exemplary user interface view in a vertical layout according to some exemplary aspects of the present disclosure. [Figure 11B] Illustrate an exemplary wireframe 1100b of another user interface view in a horizontal layout according to some exemplary aspects of the present disclosure. [Figure 11C] Illustrate enlarged views of features 1 - 4 shown in vertical and horizontal wireframes 1100a and 1100b, respectively, according to some exemplary aspects of the present disclosure. [Figure 11D] Illustrate enlarged views of features 1 - 4 shown in vertical and horizontal wireframes 1100a and 1100b, respectively, according to some exemplary aspects of the present disclosure. [Figure 11E]Enlarged views of features 1-4 shown in vertical and horizontal wireframes 1100a and 1100b, respectively, are illustrated in some exemplary embodiments of this disclosure. [Figure 11F] Enlarged views of features 1-4 shown in vertical and horizontal wireframes 1100a and 1100b, respectively, are illustrated in some exemplary embodiments of this disclosure. [Figure 12A] The present disclosure illustrates an exemplary user interface view 1200a associated with sensor data representing a host glucose concentration value in a vertical layout corresponding to a wireframe 1100a, according to several exemplary embodiments of this disclosure. [Figure 12B] The present disclosure illustrates an exemplary user interface view 1200b associated with sensor data representing a host glucose concentration value in a horizontal layout corresponding to a wireframe 1100b, according to several exemplary embodiments of this disclosure. [Figure 12C] The following are illustrative examples of enlarged views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of this disclosure. [Figure 12D] The following are illustrative examples of enlarged views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of this disclosure. [Figure 12E] The following are illustrative examples of enlarged views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of this disclosure. [Figure 12F] The following are illustrative examples of enlarged views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of this disclosure. [Figure 12G] The following are illustrative examples of enlarged views of features 1-4 shown in vertical and horizontal user interface views 1200a and 1200b, according to some exemplary aspects of this disclosure. [Figure 13A]An exemplary wireframe 1300a of another exemplary user interface view in a vertical and horizontal layout, according to some exemplary embodiments of this disclosure, is provided. [Figure 13B] An exemplary wireframe 1300b of another exemplary user interface view in a horizontal layout, according to some exemplary embodiments of this disclosure, is provided. [Figure 13C] Enlarged views of features 1-3 shown in vertical and horizontal wireframes 1300a and 1300b, respectively, are illustrated in some exemplary embodiments of this disclosure. [Figure 13D] Enlarged views of features 1-3 shown in vertical and horizontal wireframes 1300a and 1300b, respectively, are illustrated in some exemplary embodiments of this disclosure. [Figure 13E] Enlarged views of features 1-3 shown in vertical and horizontal wireframes 1300a and 1300b, respectively, are illustrated in some exemplary embodiments of this disclosure. [Figure 14A] The present disclosure illustrates an exemplary user interface view 1400a associated with sensor data representing a host glucose concentration value in a vertical layout corresponding to a wireframe 1300a, according to several exemplary embodiments of this disclosure. [Figure 14B] The present disclosure illustrates an exemplary user interface view 1400b associated with sensor data representing a host glucose concentration value in a horizontal layout corresponding to a wireframe 1300b, according to several exemplary embodiments of this disclosure. [Figure 14C] The following are illustrative examples of enlarged views of features 1-4 shown in the vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of this disclosure. [Figure 14D] The following are illustrative examples of enlarged views of features 1-4 shown in the vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of this disclosure. [Figure 14E]The following are illustrative examples of enlarged views of features 1-4 shown in the vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of this disclosure. [Figure 14F] The following are illustrative examples of enlarged views of features 1-4 shown in the vertical and horizontal user interface views 1400a and 1400b, respectively, according to some exemplary aspects of this disclosure. [Figure 15] This is a block diagram conceptually illustrating a software architecture for implementing widget functionality according to some exemplary aspects of the present disclosure. [Figure 16] Examples of exemplary dashboards, including several user interface elements, also referred to herein as “widgets,” are illustrated by some exemplary aspects of this disclosure. [Figure 17] This flowchart illustrates exemplary operations for generating a user interface view associated with sensor data representing a host glucose concentration value, according to some exemplary embodiments of the present disclosure. [Figure 18A] Figure 18A is a table classifying exemplary analyte data widgets according to several exemplary embodiments of the present disclosure. [Figure 18B] Figure 18B is a table classifying exemplary analyte data widgets according to several exemplary embodiments of the present disclosure. [Figure 18C] Figure 18C is a table classifying exemplary analyte data widgets according to several exemplary embodiments of the present disclosure. [Figure 19] Wireframes of exemplary summary widgets according to several exemplary embodiments of this disclosure are provided below. [Figure 20] Wireframes of other exemplary summary widgets are shown, according to some exemplary embodiments of this disclosure. [Figure 21] An example of another exemplary summary widget is provided, according to several exemplary embodiments of this disclosure. [Figure 22] An example of another exemplary summary widget is provided, according to several exemplary embodiments of this disclosure. [Figure 23] Examples of other exemplary summary widgets are provided according to some exemplary embodiments of this disclosure. [Figure 24] Examples of other exemplary summary widgets are provided according to some exemplary embodiments of this disclosure. [Figure 25A] Examples of other exemplary summary widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 25B] Examples of other exemplary summary widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 26A] Examples of other exemplary summary widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 26B] Examples of other exemplary summary widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 27] Wireframes of other exemplary summary widgets are shown in some exemplary embodiments of this disclosure. [Figure 28] Wireframes of other exemplary summary widgets are shown in some exemplary embodiments of this disclosure. [Figure 29] Wireframes of other exemplary summary widgets are shown in some exemplary embodiments of this disclosure. [Figure 30] Wireframes of other exemplary summary widgets are shown in some exemplary embodiments of this disclosure. [Figure 31] Wireframes of other exemplary summary widgets are shown in some exemplary embodiments of this disclosure. [Figure 32] Examples of other exemplary summary widgets are provided according to some exemplary embodiments of this disclosure. [Figure 33A] Examples of exemplary motivational widgets and their corresponding wireframes are provided for several exemplary embodiments of this disclosure. [Figure 33B] Examples of exemplary motivational widgets and their corresponding wireframes are provided for several exemplary embodiments of this disclosure. [Figure 33C] Examples of exemplary motivational widgets and their corresponding wireframes are provided for several exemplary embodiments of this disclosure. [Figure 33D] Examples of exemplary motivational widgets and their corresponding wireframes are provided for several exemplary embodiments of this disclosure. [Figure 33E] Examples of exemplary motivational widgets and their corresponding wireframes are provided for several exemplary embodiments of this disclosure. [Figure 34] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 35A] Examples of other exemplary motivational widgets and their corresponding wireframes are provided in some exemplary embodiments of this disclosure. [Figure 35B] Examples of other exemplary motivational widgets and their corresponding wireframes are provided in some exemplary embodiments of this disclosure. [Figure 36] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 37] Wireframes of other exemplary motivational widgets are illustrated in some exemplary embodiments of this disclosure. [Figure 38] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 39] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 40] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 41] Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 42]Some exemplary embodiments of this disclosure illustrate other exemplary motivational widgets. [Figure 43] Wireframes of other exemplary motivational widgets are illustrated in some exemplary embodiments of this disclosure. [Figure 44] Wireframes of exemplary event-type widgets according to several exemplary embodiments of this disclosure are provided below. [Figure 45] Wireframes of other exemplary event-type widgets are illustrated according to some exemplary embodiments of this disclosure. [Figure 46] Some exemplary aspects of this disclosure illustrate other exemplary event-type widgets. [Figure 47A] Examples of other exemplary event-type widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 47B] Examples of other exemplary event-type widgets and their corresponding wireframes are provided, according to several exemplary embodiments of this disclosure. [Figure 48] Wireframes of other exemplary types of widgets are illustrated in some exemplary embodiments of this disclosure. [Figure 49] Some exemplary embodiments of this disclosure illustrate other exemplary types of widgets. [Figure 50] Some exemplary embodiments of this disclosure illustrate other exemplary types of widgets. [Figure 51] Wireframes of other exemplary types of widgets are illustrated in some exemplary embodiments of this disclosure. [Figure 52] This flowchart illustrates exemplary actions for activating, customizing, and using a dashboard with widgets, according to some exemplary aspects of the present disclosure. [Figure 53A] This disclosure illustrates exemplary user interfaces that may include various widgets, as described in several exemplary embodiments of this disclosure. [Figure 53B]This disclosure illustrates exemplary user interfaces that may include various widgets, as described in several exemplary embodiments of this disclosure. [Figure 54] This disclosure illustrates exemplary user interfaces that may include various widgets, as described in several exemplary embodiments of this disclosure. [Figure 55] This disclosure illustrates exemplary user interfaces that may include various widgets, as described in several exemplary embodiments of this disclosure. [Figure 56] This flowchart illustrates exemplary actions for activating, customizing, and using a dashboard with widgets, according to some exemplary aspects of the present disclosure. [Figure 57] This disclosure illustrates exemplary user interfaces that may include various widgets, as described in several exemplary embodiments of this disclosure. [Modes for carrying out the invention]

[0013] For ease of understanding, the same reference numerals are used to designate identical elements common to each figure where possible. Elements disclosed in one embodiment are intended to be usefully utilized in other embodiments without specific description. Data visualization is the process of transforming large datasets and metrics into charts, graphs, and other visual materials. The resulting visual representation of data makes it easier to identify and share new insights into real-time trends, outliers, and information expressed within the data. Perhaps one of the most important benefits of data visualization is that it facilitates the easy and rapid assimilation of large amounts of data. Visualization enables analysts and end-users to recognize patterns and relationships within large datasets that are not readily apparent in raw data or reports. This can help identify emerging trends, enabling users to address issues before, for example, health problems escalate. The ultimate goal is to provide actionable insights that help drive change.

[0014] Visualizing health data is becoming an increasingly important focus in health analytics. Visualizing health data is a powerful way to quickly and effectively share urgent health information. When properly implemented, health data visualization can provide significant benefits to end users. For example, data visualization tools can encourage improved absorption of health information, provide rapid access to meaningful health insights, communicate findings in a constructive way to engage and inform users about future and / or current health issues, and promote data-user interaction to stimulate healthy lifestyles by enabling informed lifestyle decisions and changes.

[0015] There is a growing emphasis on self-monitoring applications that enable patients to measure their own physical health parameters. Data visualization technologies and tools play a crucial role in the continuous management of a person's health. For example, data visualization technologies 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 this 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, substances or chemical components in a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine) that is analyzable. Analytes may include naturally occurring substances, artificial substances, metabolites, and / or reaction products. The analytes measured by this device and method include, but are not limited to, glucose, acarboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; α-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomer; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β-hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine A; d-pe Nishiramine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylated polymorphisms, 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); desbutylhalofantrin; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycine; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3);Fumaryl acetase; galactose / gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycocholic acid; glycosylated hemoglobin; halofantrin; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-α-hydroxyprogesterone; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; lactate; lead; lipoprotein ((a), B / A-1, β); lysozyme; mefloquine; netylmycin; phenobarbiton; phenytoin; phytanic acid / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine Tri-iodothyronine, rT3); selenium; serum pancreatic lipase; shisomycin; somatomedin C; specific antibodies (adenovirus, antinuclear antibody, antizeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea pig, tapeworm, amoeba histolytica, enterovirus, giardiasis, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Donovan leishmania, leptospirosis, measles / mumps / rubella, Mycoplasma leprae, myoglobin, trichomoniasis, parainfluenza virus, malaria parasite , poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (tsutsugushi disease), Schistosomiasis mansoni, Toxoplasma, 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; white blood cells;and zinc protoporphyrin may be included. Naturally occurring salts, sugars, proteins, fats, vitamins, and hormones in blood or interstitial fluid can also constitute the analyte in certain embodiments. The analyte may be naturally occurring in biological fluids, such as metabolites, hormones, antigens, antibodies, etc. Alternatively, the analyte may be introduced into the body or be exogenous, for example, contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, or drugs or pharmaceutical compositions, such as insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Silulto, Preludin, Didrex, Prestate, Boranil, Sandrex) (S, Pregin); Antidepressants (barbiturates, tranquilizers such as methacaron, Valium, Librium, Miltown, Serax, Equanil, and Tranxine); Hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); Narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tasionex, fentanyl, Dalvon, Talwin, Romotil); Synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and analogs of phencyclidine, e.g., ecstasy); Anabolic steroids;Examples include, but are not limited to, nicotine. Metabolites of drugs and pharmaceutical compositions are also intended analytes. For example, analytes such as neurochemicals and other chemicals produced in the body, including ascorbic acid, uric acid, dopamine, norepinephrine, 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, can also be analyzed.

[0018] The analytes for measurement and visualization by the devices and methods described herein are glucose, but other analytes listed above may be considered, but are not limited to them. Biological parameters such as body temperature, heart rate, metabolic function, and respiratory rate may also be considered.

[0019] The embodiment provides a report that can be generated by a processor based on analyte data, such as continuous glucose monitoring (CGM) data. This report can also be generated based on user-inputted information and / or other information. This report may include information such as the average analyte concentration value of the host for the current week, a comparison of the average analyte concentration value of the host for the current week with the host's analyte concentration value at least two weeks prior, and / or the average daily analyte concentration value of the host, and the daily percentage of the host's analyte concentration value within one or more analyte concentration ranges. The information in the report may be customizable by the user. The embodiment of this disclosure provides various data associated with continuous analyte monitoring, which can be processed and used to generate one or more user views that can be presented to the user on one or more widgets. This information and user views can be updated based on the continuously monitored data and / or other parameters. The widgets may include summary widgets, motivational widgets, event widgets, and / or other widgets. The widgets may also be customizable by the user.

[0020] Figure 1 is a conceptual illustration of an exemplary system 100, which includes an exemplary continuous analyte sensor having sensor electronic equipment, according to a particular aspect of this disclosure. For example, the system 100 can be configured to provide reports based on continuously monitored analyte data, according to a particular aspect which will be considered in more detail with respect to Figures 5 to 14. The system 100 can be configured to provide one or more widgets based on continuously monitored analyte data, according to a particular aspect which will be considered in more detail herein with respect to Figures 15 to 53.

[0021] System 100 includes a continuous analyte sensor system 8, which includes a sensor electronic device 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 can be physically connected to the sensor electronic device 12, which may be integrated with the continuous analyte sensor 10 (e.g., mounted and not detachable) or detachably attached to it. The sensor electronic device 12, the drug delivery pump 2, and / or the glucose meter 4 may be coupled with one or more devices such as display devices 14, 16, 18, and / or 20.

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

[0023] In some embodiments, the analyte processor 490 or the report generator therein may generate a view for display in the user interface and / or for display on one or more widgets in the user interface. The user interface view may include one or more graphic representations, each containing distinctly different elements, that represent 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 and generate a report or user interface view. In response to that request, the analyte processor 490 can then select a report and / or interface view to provide. This selection can be made based on metadata, which may include information representing the host, the type of device used to measure analyte concentration values, rules, etc. The selection can be considered dynamic in the sense that the report and / or user interface view selection changes on a per-request basis based on the metadata. The 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 embodiments, the sensor electronics 12 may include electronic circuits associated with measuring and processing data generated by the continuous analyte sensor 10. This generated continuous analyte sensor data may also include algorithms that can be used to process and calibrate the continuous analyte sensor data, although these algorithms may be provided in other ways. The sensor electronics 12 may include hardware, firmware, software, or a combination thereof for providing measurement of analyte values ​​via a continuous analyte sensor such as a continuous glucose sensor. Exemplary embodiments of the sensor electronics 12 are described further below with reference to Figure 2.

[0026] As described above, the sensor electronic device 12 can be coupled (for example, wirelessly) with one or more devices such as display devices 14, 16, 18, and / or 20. The display devices 14, 16, 18, and / or 20 can be configured to display (and / or warn) information such as sensor information transmitted by the sensor electronic device 12 for display on the display devices 14, 16, 18, and / or 20.

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

[0028] In some embodiments, the relatively small key fob-like display device 14 may include a wristwatch, belt, necklace, pendant, jewelry, adhesive patch, pager, key fob, plastic card (e.g., credit card), identification (ID) card, and / or equivalent. This small display device 14 may include a relatively small display area (e.g., smaller than that of a large 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 equivalent. This large display device may include a relatively larger display area (e.g., larger than that of the small display device) and may be configured to display information such as a graphic representation of continuous sensor data, including current and past sensor data output by the continuous analyte sensor system 8.

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

[0031] In some embodiments, the continuous analyte sensor 10 may include a glucose sensor configured to measure glucose in blood using one or more measurement techniques such as enzyme, chemical, physical, electrochemical, spectrophotometric, optical rotation, calorimetry, ion electrophoresis, radiometric analysis, and immunochemistry. In embodiments where the continuous analyte sensor 10 includes a glucose sensor, the glucose sensor may include any device capable of measuring glucose concentration and may provide data such as a data stream indicating glucose concentration 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 may be a raw data signal that is converted into a calibrated and / or filtered data stream used to provide glucose values ​​to a host such as a user, patient, or caregiver (e.g., parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the host's health). Furthermore, the continuous analyte sensor 10 can be implanted as at least one of the following types of sensors: an implantable glucose sensor, a transcutaneous glucose sensor implanted intravascularly or extravascularly in a host, a subcutaneous sensor, a refillable subcutaneous sensor, or an intravascular sensor.

[0032] The description herein refers to several examples including a continuous analyte sensor 10 equipped with a glucose sensor, but the continuous analyte sensor 10 may also include other types of analyte sensors. Furthermore, although some embodiments may refer to the glucose sensor as an embedded glucose sensor, other types of devices capable of detecting glucose concentration and providing an output signal representing glucose concentration may also be used. Furthermore, the description herein refers to glucose as the analyte being measured, processed, etc., but other analytes, such as ketone bodies (e.g., acetone, acetoacetic acid, and β-hydroxybutyrate, lactate esters, etc.), glucagon, acetyl coenzyme A, triglycerides, fatty acids, intermediates in the citric acid cycle, choline, insulin, cortisol, testosterone, etc., may also be used.

[0033] Figure 2 is a block diagram conceptually illustrating a sensor electronics module communicating with multiple sensors according to a particular aspect of the present disclosure. As shown in Figure 2, the sensor electronics module 212 can communicate with multiple sensors, including, in several exemplary aspects, a glucose sensor 220, an altimeter sensor 222, an accelerometer sensor 224, a temperature sensor 226, and a location module 269 (e.g., a global positioning system processor or other location information source). While Figure 2 illustrates a sensor electronics module 212 communicating with specific sensors, other sensors and / or devices can be used. Other devices and / or sensors may include, for example, heart rate monitors, blood pressure monitors, pulse oximeters, calorie intake monitors, and drug delivery devices. Furthermore, one or more of these sensors can provide data to the analyte processing system 400 and / or analyte processor 490, which are further described below. In some aspects, the user can manually provide some of the data to the analyte processing system 400 and / or analyte processor 490. For example, a user can provide calorie consumption information to the analyte processing system 400 and / or analyte processor 490 via a user interface.

[0034] In the example illustrated in Figure 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 may 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 a composite glucose / temperature sensor used to transmit sensor data to the sensor electronics module 212 using, for example, a common communication circuit. Depending on the example, fewer or additional sensors may communicate 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 (for example, they can be coupled via one or more telecommunication lines).

[0035] The sensor electronics module 212 can generate a data package and transmit it to a device such as a 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 one characteristic of the host, the display device 250, the user of the display device 250, and / or the characteristics of the sensor data. Thus, 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] Figures 3A and 3B are perspective view 300A and side view 300B of a sensor system, respectively, including a mounting unit 314 and a sensor electronic device 12 attached thereto. In the example shown in a functional position, the mounting unit 314 can mate and engage with the sensor electronic device 12. In some examples, the mounting unit 314 may also be called a housing or sensor pod and may include a base 334 adapted for fixation to the host's skin. The base 334 may be formed from a variety of rigid or flexible materials and may have a low profile to minimize the protrusion of the device from the host during use. The base 334 may be formed at least partially from a flexible material, which in some embodiments is thought to offer a great many advantages over other transcutaneous sensors, but unfortunately may be susceptible to motion artifacts associated with the host's movement when the host is using the device. The mounting unit 314 and / or sensor electronic device 12 may be positioned above the sensor insertion site to protect that site and / or provide minimal occupancy (utilizing the surface area of ​​the host's skin).

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

[0038] In some exemplary embodiments, the contact 338 may be mounted on or within a hinge 348 configured to fit into the base 334 of the mounting unit 314 (hereinafter referred to as the contact subassembly 336), and the contact subassembly 336 may be mounted on or within 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. This hinge may provide pivoting, articulating, and / or hinge mechanisms such as adhesive hinges and sliding joints. The operation of the hinge may be implemented in some embodiments without a pivot or fixed point through which articulating motion occurs around a center. In some exemplary embodiments, the contact 338 may be formed from a conductive elastomer material such as carbon black elastomer through which the continuous analyte sensor 10 extends, although the contact may be formed in other ways as well.

[0039] In some exemplary embodiments, the mounting unit 314 may be provided with an adhesive pad 308 disposed on the back of the mounting unit and including a peelable backing layer. Thus, by removing the backing layer and pressing the base 334 of the mounting unit onto the host's skin, adhesion of the mounting unit 314 to the host's skin can be enabled. In addition, or alternatively, the adhesive pad may be positioned to cover part or all of the sensor system after sensor insertion is complete to ensure adhesion and, optionally, to ensure an airtight or watertight seal around the wounded exit site (or sensor insertion site). A suitable adhesive pad may be selected and designed to stretch, elongate, conform to and / or allow ventilation over its area (e.g., the host's skin). Configurations and arrangements that provide water resistance, waterproofing, and / or airtight sealing properties may be provided for some of the mounting unit / sensor electronic device embodiments described herein.

[0040] Figure 4 is a block diagram conceptually illustrating an analytic processing system according to some exemplary embodiments of the present disclosure. As shown in Figure 4, the analytic data processing system 400 may include one or more user interfaces 410A-C, such as a browser, application, widget, and / or any other type of user interface configured to allow access to and / or interaction with the analytic processor 490 via, for example, a network 406 and a load balancer 412. The analytic processor 490 may 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, and personal health record systems. This source system information can provide metadata for dynamic report generation.

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

[0043] In addition to the example illustrated in Figure 4, the analytic 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 analytic processors 490. Furthermore, each of the virtualized analytic processors 490 can serve different tenants, such as end users, clinics, and hosts equipped with sensors. In some embodiments, it may be advantageous to host multiple tenants (e.g., hosts, users, clinics, etc. in user interfaces 410A-C and / or data retrieval 465) on a single analytic data processing system 400, which includes multiple servers and securely maintains all data from multiple tenants in a repository 475, while also providing customized solutions specific to each tenant.

[0044] Referring again to Figure 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. These 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) associated with food consumed by the host or patient, drug delivery data, time and temperature sensors, and / or exercise / activity sensors. In some exemplary embodiments, the cloud-based diabetes data management can programmatically receive data with little (or no) intervention from some of the users. 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 minimally processed or analyzed raw sensor data. 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 retrieval unit 465 can be implemented in one or more devices, such as a computer 20 coupled to the continuous analyte sensor system 8. In this example, the data retrieval unit 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 so that the analyte processor 490 can analyze the formatted data. Figure 4 illustrates a single data retrieval unit 465, but in some exemplary embodiments, multiple data retrieval units 465 can be used to format data from multiple devices and / or systems.

[0046] In some exemplary embodiments, the data retrieval unit 465 may be accessible through a kiosk including a processor such as a dedicated computer configured with a user interface, or through a secure web-based interface residing on a non-dedicated computer.

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

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

[0049] Furthermore, the software associated with the data retrieval unit 465 may include a self-update mechanism such that, when retrieval is selected in the user interface, the data retrieval unit programmatically checks for updates (e.g., software, drivers, data, etc.) on the analyte processor 490 (or another designated computer) and installs the updates. The updates can be performed programmatically with little (or no) intervention from the user. Data download from the device or system to the data retrieval unit 465 can be performed using a wired connection such as a device-specific download cable, or wirelessly if the device and processor are equipped with 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. Furthermore, 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. In addition, the analyte processor 490 can be configured by a user such as a clinician or physician 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, sex, ethnicity, type 1 diabetes, type 2 diabetes, age at diabetes diagnosis, test results, prescribed medications in use, patient self-report status, patient diagnostic status, responses to questions presented to the patient, and any other metadata representing the host / patient). Once the analyte processor 490 performs initial data processing (e.g., checking, cleaning, and analysis), the processed data and / or raw data provided by the data retriever can be stored in the repository 475.

[0051] Processing in the analyte processor 490 may also include associating metadata with data received from devices and / or sensors. Examples of metadata may include, but are not limited to, patient information, keys used to encrypt data, patient accelerometer data, location data (e.g., patient's location or the location of the patient's clinic), 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 historical health-related information such as whether the patient has been diagnosed with type 1 or type 2 diabetes, hypertension, or any other health condition. Processing may also include analysis, such as determining one or more descriptive measurements and / or generating one or more user interface views based on the received information and descriptive measurements. These descriptive measurements may include statistics (e.g., median, internal and external quartile ranges, 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 shown in Figure 4, user interfaces 410A-C may be used by one or more entities such as end users, hosts, healthcare providers, clinics, patients, research groups, healthcare schemes, and medical device manufacturers. These entities can remotely access the analyte data processing system 400 via user interfaces 410A-C to request actions such as retrieving analyte data, providing analyte data, requesting analysis of analyte data, requesting the generation of a report including a module having a view presenting descriptive measurements of analyte data, and presenting the analyte data and report. Other examples of actions include providing sensor data such as glucose data, carbohydrate data, and insulin pump data to the analyte processor 490, initiating processing of the sensor data, initiating analysis of the sensor data, and storing the data in the repository 475. In some exemplary embodiments, the computing resources provided by the analyte processor 490 may include one or more virtualized physical servers to provide the analyte processing services disclosed herein.

[0053] The data retriever 465 can retrieve data (e.g., receive, retrieve, etc.) from one or more sources and provide any retrieved data in a compatible format for use within the analyte processor 490. In some embodiments, the data retriever 465 can be implemented in one or more source systems and / or devices that provide data to the analyte processor 490. For example, the data retriever 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 drug delivery pump 2, the glucose meter 4, a computer / processor coupled to these devices, and any other devices that can provide data to the analyte data processing system 400. In these embodiments, the data retriever 465 can receive data from the host device and format the data in a format compatible with the analyte processor 490. The data retriever 465 can also be implemented on source systems such as disease management systems, weight management systems, prescription management systems, electronic medical record systems, and personal health record systems. In these embodiments, the data retriever 465 can retrieve 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 retrieval unit 465 can 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 analytic data processing system 400, the analytic data processing system 400 can automatically install and configure the data retrieval unit 465 on the user's computer. Once the installation is complete, the data retrieval unit 465 can begin retrieving data for the analytic data processing system 400 and, if necessary, format the data to enable processing of the retrieved data by the analytic processor 490. As a further example, the data retrieval unit 465 can be downloaded to a device such as a computer 20. In this example, when the computer 20 receives sensor data from the sensor electronics module 12, the data retrieval unit 465 can provide the sensor data and / or metadata in a format compatible with the analytic processor 490.

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

[0056] Furthermore, using the results of any processing performed by the analyte processor 490, views presenting descriptive measurements and / or comparisons of the analyte data can be generated (e.g., user interface views illustrated in Figures 7A to 14F and Figures 19 to 49). Descriptive measurements and / or comparisons can be presented, for example, as graphs, bar graphs, static charts, charts, badges, tables, figures, maps, plots, and / or other visualizations.

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

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

[0059] In some exemplary embodiments, the analyte processor 490 may 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 computing engine 420H for receiving data from sensors and processing the received data into numbers for use with 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 a destination, and a third-party access application programming interface for enabling other systems and devices to access and / or interact with the analyte processor 490.

[0060] The analyte processor 490 can receive requests from user interfaces such as user interfaces 410A-C and perform actions (e.g., provide data, store data, analyze / process data, request reports, etc.). Before the analyte processor 490 fulfills a request, it can process the request and determine whether the request is authorized and authenticated. For example, the authenticator and authorizer 420A can determine whether the source of a request is authorized by requesting the user to provide security certificates (e.g., user identifier, password, stored security token, and / or verification identifier provided by text message, telephone, or email) in a user interface presented on the computer. If authorized, the authenticator and authorizer 420A authenticate the source of the request and check whether the security certificate associated with the source of the request indicates that the source (e.g., a user in the user interface 410A) is actually authorized to access a specific resource of the analyte data processing system 400. As a result, they can perform actions such as storing (or uploading) data from the repository 475, performing data analysis / processing, and / or requesting user interface view generation.

[0061] To illustrate further, a data retriever 465 associated with the continuous analyte sensor system 8 and computer 20 is 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 in the user interface 410A is authorized and then authenticated by the authenticator and authorizer 420A to access the analyte processor 490, but may only be permitted to access specific information. In this second example, a user in the user interface 410A is authorized and authenticated to access the repository 475, view specific information corresponding to their own analyte data (e.g., glucose data), and access reports generated for the analyte data, but that user is not authorized and authenticated to access another user's data.

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

[0063] In some exemplary embodiments, a request (or the parsed data therein) can be processed by a computation engine 420H. This computation engine 420H can preprocess data received from devices, sensors, etc., to form a "count." This count may represent measured values ​​such as analyte values ​​measured by sensors, glucose values ​​measured by sensors, continuous glucose values ​​measured by sensors, and / or other diabetes-related information such as carbohydrates consumed, temperature, physical activity values, and the frequency with which these measurements occurred.

[0064] Next, the computing engine 420H can perform additional processing using the count 508. This additional processing may include storing the count in the repository 475, which may contain one or more databases for storing the count. Furthermore, the count may be stored along with metadata such as time / date information. In addition, the count may be encrypted before being stored in the repository 475, as described above.

[0065] The 420H computing engine can also use counts to update one or more histograms. For example, instead of continuously tracking and processing host analyte values ​​over a specific period using raw sensor data values, the 420H computing engine can convert the data values ​​into counts. These counts can then be added to the histogram for a given host.

[0066] In some exemplary embodiments, the computing engine 420H can generate multiple histograms for a given host over multiple given time periods.

[0067] In some exemplary embodiments, the calculation engine 420H can also update other histograms representing total count information.

[0068] The description of the 420H computing engine mentions histograms, which, as used herein, refer to a data structure containing one or more values ​​associated with one or more time intervals. For example, a histogram can represent one or more values, such as frequency of occurrence, associated with bins corresponding to one or more time intervals. Furthermore, this data structure can be stored in a database, and thus is readily accessible by reading from the database rows (or, for example, columns if a column database is used).

[0069] In some exemplary embodiments, the repository 475 stores histograms containing counts in a database. For example, the repository 475 can store data about patients covering timeframes such as 1 day, 2 days, 7 days, 14 days, 30 days, or more. In this example, those days can be subdivided into epochs, and each of those epochs has a corresponding histogram stored in the repository 475. Furthermore, each histogram can be stored as a row in the database of the repository 475 to facilitate fast data access.

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

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

[0072] In some exemplary embodiments, the pattern detector 420I may receive input data from the repository 475. This input data may include, for example, analyte concentration data from continuous analyte sensors such as rate of change, predicted concentration, and other analyte data. In some exemplary embodiments, the input data may 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, exercise and / or activity data, wake / sleep intervals, medication information, or other similar data related to the user's activities that may affect one or more of the user's biological parameters.

[0073] Furthermore, the input data may include historical data acquired over timeframes such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days, and / or any other period. For example, the input data may include a “count” 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 timeframe of 4 weeks (or more). As mentioned above, the “count” may be stored in the repository 475 along with metadata such as time / date information, which will be used as input data at a later time. In another example, the input data may include a histogram updated by the user's “count.” This histogram may include the analyte concentration values ​​on the x-axis and the number of occurrences for each analyte concentration value on the y-axis. 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 input data about patterns. For example, patterns may be recognized based on one or more predefined rules (also called criteria or triggers). Furthermore, these one or more predefined rules may be variable and adjustable based on user input. For example, several types of patterns and the rules defining those patterns may be selected, switched on and off, and / or modified by the user, the user's physician, or the user's guardian, but the analyte data processing system 400 may also programmatically select, adjust, and / or modify the rules in other ways. In another exemplary embodiment, one or more patterns may be based on predefined rules set by factory settings or device settings.

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

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

[0077] The data formatter 420E can format data for delivery based on the type of destination. For example, the 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. These reports / views can provide descriptive information, such as statistics, representing the sensor data received by the analyte processor 490. Furthermore, 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 sensor data (and / or corresponding histograms including counts) across timeframes such as 8 hours, 1 day, 2 days, 7 days, 14 days, 30 days, and any other timeframe. In addition, the reports / views can enable users, such as patients, hosts, or clinicians, to view the information and identify trends and other health-related issues.

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

[0080] In certain embodiments, the logic 420C and pattern detector 420I can be used to determine one or more descriptive measurements, patterns, or relationships for effective visualization. As previously described, the logic 420C can determine the median, inside and outside quartile ranges, mean, sum, standard deviation, and other statistical measurements based on counts, histograms, and / or received sensor data. The pattern detector 420I can analyze relationships between data to determine patterns. Relationships in the input data that may result in identified patterns may include, for example, analyte values ​​above a target analyte range (which may be defined by, for example, the user, healthcare provider, analyte data processing system 400, or a combination thereof), analyte values ​​below a target analyte range, abrupt changes in analyte values ​​from low to high (or vice versa), the time of day when low, high, within range, or abrupt analyte value change events occur, and the day when low, high, within range, and / or abrupt analyte value events occur.

[0081] Examples of additional types of relationships in input data that can be considered patterns include very high analyte events and / or very low analyte events based on time. For example, in a case where the analyte for measurement is glucose, a pattern can be identified in situations where the user has low analyte concentrations at roughly the same time of day (e.g., hypoglycemic events). 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 the user overcompensates for 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, sequence, and severity.

[0082] In some embodiments, patterns may be based on individual user / patient sensitivity, transitions from very low to very high patterns, the amount of time spent on severe events, and a combination of analyte changes and temporal information. Detected patterns may also be patterns of large fluctuations in analyte data. Furthermore, patterns may be based on a combination of historical pattern data and currently detected circumstances, thereby generating predictive alerts based on the combined information.

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

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

[0085] A second feature of the user interface view 500 is that statistics of mean glucose and standard deviation (determined, for example, by logic 420C) can be presented to the user. This mean glucose and standard deviation can be calculated based on a specified period (for example, any seven consecutive days).

[0086] A third feature of the user interface view 500 allows reporting of the user's low / high daytime and nighttime low / high patterns. Low daytime or nighttime patterns can be identified when the user has a pattern of low glucose concentration values ​​at approximately the same time each day within a specified period (e.g., any 7 consecutive days). High daytime or nighttime patterns can be identified when the user has a pattern of high glucose concentration values ​​at approximately the same time each day within a specified period (e.g., any 7 consecutive days).

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

[0088] The fifth feature of User Interface View 500 is the ability to provide users with hyperlinks to access more detailed continuous glucose monitoring (CGM) reports on the website.

[0089] In some examples, features 1-5 can 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 this disclosure provide improvements in data visualization of analyte data, including identifying new patterns and relationships to improve the communication of data-driven insights and trends to the user / host. For example, this disclosure provides improved methods, such as operation 600 shown in Figure 6, for generating improved user interface views shown in Figures 7–14F. The user interface views shown in Figures 7–14F are improved compared to the user interface view illustrated in Figure 5. For example, additional information is provided that the user can use to adjust their behavior, such as controlling their blood glucose concentration values. While the analyte for measurement and visualization by the devices and methods described herein is glucose, other biological parameters and / or analytes can be considered as well.

[0091] Exemplary daily analysis and weekly reporting of analytes. Figure 6 is a flowchart illustrating an exemplary operation 600 for generating a user interface view associated with sensor data representing glucose concentration values ​​in a host, according to some exemplary embodiments of this disclosure. Operation 600 can be performed by a processing system such as an analyte processor 490. In some examples, operation 600 can be used to generate one or more reports illustrated in Figures 7 to 14F and described in more detail below.

[0092] Operation 600 can be initiated in 602 by accessing sensor data, including multiple blood glucose readings associated with the host over multiple analysis periods in the current week. Each blood glucose reading represents the host's blood glucose concentration value at a given time. In some examples, multiple blood glucose readings can be collected by a continuous glucose monitor (CGM) worn by the host. In some examples, multiple blood glucose readings can be stored in a repository 475 and accessed by the analyte processor 490.

[0093] In 604, operation 600 may include determining the average blood glucose concentration value of 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] In 606, operation 600 may include generating a performance report. This performance report may include the average blood glucose concentration of the host for the current week, a comparison of the average blood glucose concentration of the host for the current week with the average blood glucose concentration of the host for at least the previous two weeks, the average daily blood glucose concentration of the host, the daily percentage of the host's blood glucose concentration within one or more blood glucose concentration 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 to 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 logic 420C may determine (e.g., calculate, process, and / or generate) the information to be included in the performance report.

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

[0096] In 610, operation 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 the user device via email for display in the user interface. In some examples, the user interface view may be provided for display in the user interface for display within an application running on the user device.

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

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

[0099] The horizontal or vertical orientation of the user interface view may depend on the type or configuration of the user device. For example, a vertical layout may be useful for phones, tablets, or other smaller user devices, while a horizontal layout may be useful for desktop or laptop computers. Both horizontal and vertical orientations may be shown, but only one orientation may be displayed to the user. In some embodiments, 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 illustrative user interface views in Figures 9-10G and the corresponding wireframes in Figures 7-8G, weekly reports can be provided to users of the diabetes management application to offer relevant insights into the user's retrospective glucose levels, patterns, and trends over time.

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

[0102] As shown in Figures 8B and 10B, the first feature 800b, 1000b of wireframes 700, 800a and user interface views 900, 1000a can further provide the date associated with the time range (e.g., September 6, 2020 to September 11, 2020) and the user's average glucose (e.g., 156 mg / dL in the example in Figure 10B). To provide context for this average glucose, the user's average glucose can be compared to the average glucose calculated for one or more previous weeks (e.g., "Last Week", "Two Weeks Ago", "Three Weeks Ago" in the example in Figure 10B). Visualization of this comparison can provide insights into the consistency, improvement, and / or poor performance of the host's blood glucose over multiple weeks.

[0103] As shown in Figures 8D and 10D, the third feature 800d, 1000d of wireframes 700, 800a and user interface views 900, 1000a can provide visualizations comparing the host's average daily blood glucose concentration value with the host's daily percentage of the host's blood glucose concentration value within 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 in Figure 4 based on continuously monitored blood glucose data. The report can also provide the host's daily percentage of the host's blood glucose concentration value within one or more blood glucose concentration value ranges. For example, the pattern detector 420I, calculation engine 420H, and / or logic 420C in Figure 4 can compare blood glucose concentration value data over a course of a 24-hour period with a predetermined target or "normal" blood glucose concentration value or range to identify periods in which the host's blood glucose concentration value is "within range" or "outside range". Next, the analyte processor can determine the percentage of time during a 24-hour period in which the host's blood glucose concentration was "within range."

[0104] As shown in Figures 8D and 10D, daily bar graphs may be presented to illustrate the host's blood glucose concentration values. Daily breakdowns can allow the host to understand their problem days throughout the week and adjust their lifestyle, diet, or other relevant factors accordingly. Visualization can also provide additional context to the host's average daily glucose concentration values ​​by providing the percentage of time spent within the range per day. For example, as shown in Figure 10D, the host may experience their highest average glucose of the week on Saturday at 234 mg / dL. Using the corresponding 66% of time within the target glucose range, the host may conclude that the majority of their time outside their target range was spent within the high or very high glucose range (rather than the low or very low glucose range).

[0105] As shown in Figures 8G and 10G, the sixth feature 800g, 1000g of wireframes 700, 800a and user interface views 900, 1000a may be a button that directs the user back to a website or application (app) on the user's device that provides more detailed continuous glucose monitoring (CGM) reports for the user and / or host. Whether this button directs the user back to a website or an app may depend on the type or configuration of the user device used to display the button. Unlike the hyperlink in feature 5 of Figure 5, the button may be larger than the hyperlink, thereby capturing the user's attention and facilitating further interaction between the user and the user interface.

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

[0107] In some examples, features 1-6 of wireframes 700, 800a and user interface views 900, 1000a can 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 the horizontal layout. In some examples, feature 4 may be stacked on top of feature 5 in the horizontal layout.

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

[0109] Figure 12A illustrates an exemplary user interface view 1200a associated with sensor data representing a host glucose concentration value in a vertical layout corresponding to wireframe 1100a, according to some exemplary embodiments of the present disclosure, and Figure 12B illustrates an exemplary user interface view 1200b in a horizontal layout corresponding to wireframe 1100b. Figures 12C to 12G illustrate enlarged views of features 1 to 4 shown in the vertical and horizontal user interface views 1200a and 1200b, which are 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 embodiments, 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 exemplary user interface views of Figures 12A and 12B, and the corresponding wireframes of Figures 11A and 11B, the weekly report may be feature-based, starting with glucose and adding other interchangeable elements to provide appropriate insights into the host's retrospective glucose values, patterns, and trends over time with respect to insulin, diet, and / or other factors that may influence the host's glucose levels. 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, the first features 1100c and 1200c of the wireframes 1100a and 1100b and the user interface views 1200a and 1200b can provide information related to the host's glucose values ​​over a specified period of time. Glucose information may include the average glucose for the current week (e.g., determined by logic 420C in Figure 4) compared to the average glucose for the previous week and the average glucose for the month, a stacked bar graph of range time broken down into interrelated blocks (stacked on top of each other) showing the host's time spent within the target glucose range, very high glucose range, high glucose range, low glucose range, and very low glucose range, respectively, a summary of the number of hours the host spent in excess (e.g., hyperglycemia if the host's blood glucose is too high) and insufficient (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 direct the user back to the website housing details CGM report.

[0113] As shown in Figures 11D and 12D, the wireframes 1100a and 1100b and the second feature 1100d and 1200d of the user interface views 1200a and 1200b can provide time related to the host's insulin levels over a specified period. This insulin information may include the average number of insulin units ingested by the host. For example, the average number of insulin units for the current week can be compared to the average number of insulin units for the previous week and the average number of insulin units for the month. Furthermore, stacked bar graphs illustrating the host's basal insulin intake relative to bolus insulin intake, as well as the host's average daily basal and bolus units, may be provided in the second feature. As used herein, basal refers to the hourly infusion of insulin converted to long-lasting insulin, and bolus refers to an additional dose given with each meal in cases of hyperglycemia to correct blood glucose levels. The analyte processor 490 can determine the average insulin amount based on information entered by the user, automatically uploaded and collected information, etc.

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

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

[0116] As shown in Figures 11F and 12G, the wireframes 1100a, 1100b and the fourth feature 1100f, 1200g of user interface views 1200a, 1200b can provide information related to the host's "best day" over a specified period of time. Logic 420C can determine the host's "best day" based on the day the host spent the most time within the target glucose range. In some examples, the host's glucose, insulin, carbohydrates, and steps are given to better understand the factors that contributed to and resulted in the host's "best day." A summary of the contributing factors is also provided to motivate the user to experience more days similar to the host's "best day." The user can also receive rewards (also referred to herein as badges) to reward the user for their "best day" behavior. In some examples, these rewards may include a "steady carbohydrate award" used to reward the host for having no days with any carbohydrate spikes. In some examples, the prize could include a "Bas / Bol Balancer Award" used to reward the host for maintaining their base / bolus ratio at a target ratio (e.g., 60 / 40) or a better ratio. In some examples, the prize could include a "Timed Within the Time Limit Award" used to reward the host for spending more than 80% of their week within their target glucose range. In some examples, it would be possible to see which prizes the host has won.

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

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

[0119] Figure 14A illustrates an exemplary user interface view 1400a associated with sensor data representing the host glucose concentration value in a vertical layout corresponding to wireframe 1300a, according to some exemplary embodiments of the present disclosure, and Figure 14B illustrates an exemplary user interface view 1400b in a horizontal layout corresponding to wireframe 1300b. Figures 14C to 14F illustrate enlarged views of features 1 to 4 shown in the vertical and horizontal user interface views 1400a and 1400b, respectively, and are described in more detail below.

[0120] Wireframe 1300a and user interface view 1400a show the vertical orientation of the wireframe and user interface view, respectively, while wireframe 1300b and user interface view 1400b show the 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 embodiments, 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 exemplary user interface views of Figures 14A and 14B, and the corresponding wireframes of Figures 13A and 13B, the weekly report may be on a single-day basis, referring to a report comparing the “average day” to “problem days” and “best days.” A host’s “best day” can be determined based on the day the host spent the most time within the target glucose range (e.g., by logic 420C). Alternatively, a host’s “problem day” can be determined based on the day the host spent the least time within the target glucose range.

[0122] As shown in Figures 13C and 14C, the wireframes 1300a, 1300b and the first feature 1300c, 1400c of user interface views 1400a, 1400b can provide a stacked bar chart of range time representing the percentage of time the user was within the target glucose range, the very high or high glucose range, and the very low or low glucose range over a specified period (e.g., any seven consecutive days). In some examples, the target glucose range can be defined as different ranges between daytime (e.g., 6:00 AM to 10:00 PM) and nighttime (e.g., 10:00 PM to 6:00 AM). The user's percentage of range time can also be compared to the percentage of range time in the previous week. In some examples, the stacked bar chart can be presented using different colors to distinguish the percentage of time the host was within the target glucose range, the very high or high glucose range, and the very low or low glucose range over a particular period. In some examples, stacked bar charts can be presented using blocks of different sizes (stacked within the bar chart) for each range. The varying sizes can correlate with the amount of time the user spent within each range. For example, the largest block size in a stacked bar chart might represent the glucose range where the host spent the most time over a given period, while the smallest block size in a stacked bar chart might represent the glucose range where the host spent the least time over a given period.

[0123] In addition, the first feature of the user interface view can provide a summary overview of the host's average glucose, average insulin units, average carbohydrate intake, and average steps recorded over a specified period (e.g., any 7 consecutive days). The first feature can also provide trend and insight graphs. This trend graph may include an aggregation of the user's in-range time presented in a scatter plot with a best-fit line. This best-fit line represents the relationship between data points and identifies the trend of the user's host in-range time over a 12-hour period (e.g., 12:00 AM to 12:00 PM in the example shown) over a specified period (e.g., any 7 consecutive days). Furthermore, the target glucose range can be provided within the graph for both daytime and nighttime hours. The target glucose range 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 diagram on the side of the trend graph, and the nighttime target glucose range can identify the nighttime target glucose range using a moon shape diagram on the side of the trend graph (sun and moon shape diagrams are not shown). Trend graphs can also provide bar charts of different colors to distinguish between periods of the host in the high or very high glucose range, periods of the host in the low or very low glucose range, and periods of the host within the glucose range, over 12-hour periods within a specified timeframe. Trend graphs can also be bar charts broken down into categories of average insulin units, average carbohydrate intake, and average steps recorded over 12-hour periods within a specified timeframe (e.g., any 7-day consecutive period). Insights may include summaries of high and low periods, as well as recommendations for mitigating these undesirable spikes.

[0124] As shown in Figures 13D and 14D, the wireframes 1300a and 1300b and the second features 1300d and 1400d of the user interface views 1300a and 1300b can provide information about the host's "problem days" over a specified period of time. In some examples, the host's glucose, insulin, carbohydrates, and steps are given to better understand the contributing factors that resulted in the host's "problem days." Furthermore, a trend graph for the host's "problem days" can be provided. This trend graph can 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 to 12:00 PM). This trend graph can highlight relationships that the user would otherwise not be aware of. For example, as shown in the example Sunday trend graph, between approximately 6:00 AM and 9:00 AM, the host experienced a first decline in their glucose levels, and between approximately 3:00 PM and 6:00 PM, they experienced a second decline. During each of these periods, the user recorded multiple carbohydrate intakes. While not the only factor contributing to glucose levels, there may be an inverse relationship between glucose levels and carbohydrate intake presented in the trend graph that the user was previously unaware of. Therefore, the user can take appropriate action to prevent future daytime glucose drops.

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

[0126] As shown in Figure 14F, in the fourth feature 1400f of user interface views 1300a and 1300b, the host can receive trophies (also referred to herein as badges) to reward the host for their weekly activity. In some examples, the trophy may include a “Step Master Award” used to reward the host for having at least one day during the week in which they walked 10,000 steps. In some examples, the trophy may include a “Range Time Award” used to reward the host for spending more than 80% of their week within their target glucose range. In some examples, the host can view most of the awards they have earned. A button (e.g., “My Badge Collection” button) may also be provided to allow the host to begin browsing all the trophies (also referred to herein as badges) they have earned.

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

[0128] The above focuses on visualizing host analytic data in a specified format for representation, but other formats can be customized and / or designed to best meet the needs of individual users.

[0129] Exemplary analyte data processing and visualization of analyte data on a widget. Aspects of this disclosure provide techniques for providing one or more user interface views for display on one or more widgets. More specifically, aspects of this disclosure provide techniques for visualizing analyte data on widgets. In some aspects, using continuous glucose monitoring, information associated with host blood glucose data may be processed, presented to the user, and updated in real time or periodically via a widget. The analyte for measurement and visualization on widgets by the devices and methods described herein is glucose, but other biological parameters and / or analytes may also be considered.

[0130] A prominent feature of modern graphical user interfaces (GUIs) is their ability to display a vast number of items simultaneously on the screen. Widgets serve as a user gateway to various information and quick functions. Widgets can communicate with remote servers to provide users with information (e.g., weather reports), commonly needed functions (e.g., a calculator), or function as an information repository (e.g., a notepad or calendar). Some widgets can offer a combination of these functions. Some widgets can interact with remote sources of information, such as servers, to provide information. For example, weather features can retrieve raw weather data from a remote server. Widgets can be interactive, allowing users to utilize their functions by performing common input operations (such as clicking the mouse or typing on the keyboard). Furthermore, widgets can function as links to programs, such as applications (apps), running on the user's device. For example, by engaging with a widget (e.g., clicking the widget), a user can be reassociated with an app, thereby enabling them to enjoy all the features, services, and / or information provided by the app.

[0131] Widgets can be “pinned” to the user interface in various locations and sizes, enabling many different layouts. Widgets can be placed within a view accessible by swiping (e.g., from left to right) on the user's device's home screen, or they can be accessible on the user's device's home screen (without requiring swiping). In some exemplary embodiments, widgets cannot overlap each other, and if a user attempts to move one widget to a location occupied by another widget, one of the widgets may automatically move out of the way to make space. In some exemplary embodiments, the position, configuration, and size of widgets may be saved when the user interface is released, and as a result, that same state can be restored the next time the dashboard is invoked.

[0132] Figure 15 is a block diagram conceptually illustrating a software architecture for implementing widget functionality according to some exemplary aspects of the present disclosure. As shown in Figure 15, the software architecture 1500 may include a dashboard server 1501, a dashboard client 1502, and an operating system running a widget 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) for displaying the widget 1503. The client 1502 can display the widget 1503 by rendering a web page into a view, 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 the desktop or home screen of the user interface. The widget 1503 can be rendered into a separate layer, which can then be placed on top of the normal desktop or home screen, thus partially or completely covering the desktop or home screen while the dashboard is active. In some embodiments, a widget associated with the user's analytic data may be provided.

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

[0134] Figure 16 illustrates an exemplary dashboard (also called a “Unified Concern Layer”) that includes several user interface elements, also referred to herein as “widgets,” according to some exemplary aspects of this disclosure. These user interface elements, or widgets, generally include software accessories for performing useful and commonly required functions. Examples of widgets include, but are not limited to, calendars, calculators, address books, package trackers, weather features, and health trackers.

[0135] Users can interact with and / or configure widgets as they wish. For example, users can move widgets around the screen and / or resize them as appropriate. Some widgets may be resizable, some may be fixed in size, and widget creators can specify whether a widget is resizable or not. Some widgets can automatically resize themselves based on the amount or nature of the data they are displaying.

[0136] Figure 17 is a flowchart illustrating exemplary operations for generating a user interface view associated with sensor data representing a host glucose concentration value, according to some exemplary embodiments of this disclosure.

[0137] Operation 1700 can be initiated in 1702 by accessing first data associated with the host's blood glucose concentration value during a first period. For example, the analyte processor 490 in Figure 4 can access data stored in the repository 475 and / or receive data from one or more sources.

[0138] In 1704, operation 1700 may include analyzing first data to generate one or more first 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 in Figure 4 may process the analyte data and / or other information associated with the host to generate information to be provided to the user in one or more widgets.

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

[0140] In 17, operation 1700 may include automatically updating one or more first user interface views for display on one or more widgets. For example, the automatic updating includes accessing second data associated with the host's blood glucose concentration values ​​during a second period, analyzing the second data to generate one or more second user interface views associated with the second data for display on one or more widgets, and providing one or more second user interface views for display on one or more widgets.

[0141] Figures 18A, 18B, and 18C are tables classifying exemplary analyte data widgets according to several exemplary embodiments of the present disclosure. As shown in Figures 18A, 18B, and 18C, the widgets can be classified as summary widgets, motivational widgets, event widgets, or other types of widgets according to several exemplary embodiments of the present disclosure.

[0142] A summary widget may provide a short, clear explanation of key facts and / or insights about 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 could provide the host's average glucose from sensor data measured over a 7-day period. A motivational widget may provide a comparison of data and / or messages used to encourage the host to take action (e.g., maintain glucose stability, change lifestyle). For example, a motivational widget could provide the host with a display of their "best days" over the past 7 days, along with a summary of the factors that contributed to their "best day." Such a widget can be used to motivate the host to achieve similar results on other days (e.g., encourage the host to maintain their average glucose at the same average glucose value as their "best day" for the next few days). An event-based widget may track the host's health status based on events recorded by the host. For example, recorded events may include recorded meals consumed (e.g., inputs such as calories and carbohydrates) or recorded periods when the host engaged in any form of exercise (e.g., inputs such as type of exercise and calories burned). Therefore, an event-type widget could show the host's glucose values ​​at the start of an event, one hour after the event, and / or two hours after the event. Other types of widgets may encompass widgets that do not fit into the other three categories. For example, another type of widget might be one that displays a trend graph of the host's average glucose values ​​over 24-hour periods over the past 14 days.

[0143] Figure 19 illustrates an exemplary summary widget 1900 according to some exemplary embodiments of the present disclosure. As shown in Figure 19, the summary widget 1900 can provide a summary of the percentage of time a host spent within the target glucose range and the host's average glucose over a 7-day period.

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

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

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

[0147] Figure 23 illustrates another exemplary summary widget 2300 according to some exemplary embodiments of the present disclosure. As shown in Figure 23, the summary widget 2300 can provide a summary of the percentage of time a host spent in the target glucose range, the very high and high glucose range, and the very low and low glucose range over a 14-day period. As shown in the figure, the summary widget 2300 can provide both numerical values ​​representing the percentages and a stacked bar chart. This stacked bar chart can be associated with color coding representing the severity of its range.

[0148] Figure 24 illustrates another exemplary summary widget 2400 according to some exemplary embodiments of the present disclosure. As shown in Figure 24, the summary widget 2400 can provide a summary of the information from the summary widget 2300 of Figure 23, as well as the percentage of time the host spent in the target glucose range, the very high and high glucose range, and the very low and low glucose range over a 14-day period, and can further provide an indication of the positive or negative percentage of change compared to the previous 14-day period.

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

[0150] Figures 26A and 26B illustrate another exemplary summary widget 2600b and its corresponding wireframe 2600a according to some exemplary embodiments of the present disclosure. As shown in Figures 26A and 26B, the summary widget 2600b can provide, in addition to the information of the summary widget 2500b in Figures 25A and 25B, a summary of the percentage of time the host spent in the target glucose range, the very high and high glucose range, and the very low and low glucose range over a 30-day period, 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 further an indication of the pattern over the 30-day period (e.g., the host's daytime high glucose pattern or nighttime low glucose pattern). As shown in the figures, the indication can be provided as explanatory text.

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

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

[0153] Figure 29 illustrates another exemplary summary widget 2900 according to some exemplary embodiments of the present disclosure. As shown in Figure 29, the summary widget 2900 can provide a summary of the percentage of time the host spent in the target glucose range, the very high and high glucose ranges, and the very low and low glucose ranges over a period of seven days, as well as an indication of the time / minute the host spent in each of these ranges. As shown in the figure, the percentage of time spent in the ranges can be represented by a stacked bar chart, and the stacked bar chart can be associated with the values ​​of the number of hours spent in the various ranges associated with that stacked bar chart.

[0154] Figure 30 illustrates another exemplary summary widget 3000 according to some exemplary embodiments of the present disclosure. As shown in Figure 30, the summary widget 3000 can provide a summary of the time / minutes a host spent within the target glucose range, high glucose range, and low glucose range over a 7-day period.

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

[0156] Figure 32 illustrates another exemplary summary widget 3200 according to some exemplary embodiments of the present disclosure. As shown in Figure 32, the summary widget 3200 can provide a summary of time within a host's range, which can be presented graphically with a best-fit line showing the trend of time within the host's range over a 12-hour period over three days (e.g., 12:00 AM to 12:00 PM). Instructions for the data contained within the summary graph can also be included within the summary widget 3200.

[0157] Figures 33A and 33B illustrate an exemplary motivational widget 3300b and its corresponding wireframe 3300a according to some exemplary embodiments of the present disclosure. As shown in Figures 33A and 33B, the motivational widget 3300b can provide a horizontal bar graph illustrating the percentage of time a host spent within the target glucose range per day over a 7-day period, compared to the final target percentage of time spent within the target glucose range per day.

[0158] Figure 34 illustrates another exemplary motivational widget 3400 according to some exemplary embodiments of the present disclosure. As shown in Figure 34, the motivational widget 3400 can provide a vertical bar graph illustrating the percentage of time a host spent within the target glucose range per day over a 7-day period, compared to the final target percentage of time spent within the target glucose range per day (e.g., 70%).

[0159] Figures 35A and 35B illustrate another exemplary motivational widget 3500b and its corresponding wireframe 3500a according to some exemplary embodiments of the present disclosure. As shown in Figures 35A and 35B, the motivational widget 3500b can provide a vertical bar graph illustrating the percentage of time spent by the host today within the target glucose range, compared to the final target percentage of time spent within the target glucose range per day.

[0160] Figure 36 illustrates another exemplary motivational widget 3600 according to some exemplary embodiments of the present disclosure. As shown in Figure 36, the motivational widget 360 can provide a horizontal bar graph illustrating the number of hours a host has spent within the target glucose range today, compared to the final target number of hours spent within the target glucose range per day.

[0161] Figure 37 illustrates another exemplary motivational widget 3700 according to some exemplary embodiments of the present disclosure. As shown in Figure 37, the motivational widget 3700 can provide indication of a host's “best day” over a period of seven days prior to the present. The motivational widget 3700 may also include the host’s average glucose, the percentage of time the host spent within the target glucose range, and the host’s standard deviation on the indicated “best day.”

[0162] Figure 38 illustrates another exemplary motivational widget 3800 according to some exemplary embodiments of the present disclosure. As shown in Figure 38, the motivational widget 3800 can provide indication of the host's “best day” over a period of seven days. The widget can also provide a summary of the host’s average glucose and the percentage of time the host spent within the target glucose range during the indicated “best day.”

[0163] Figure 39 illustrates another exemplary motivational widget 3900 according to some exemplary embodiments of the present disclosure. As shown in Figure 39, the motivational widget 3900 can provide indications for the host's "best day" over a period of seven days, along with a summary of the host's mean glucose, host standard deviation, and the percentage of time the host spent within the target glucose range on the indicated "best day". The motivational widget 3900 may also include indications for the percentage of time the host spent within the target glucose range on the host's "second best day" and "third best day".

[0164] Figure 40 illustrates another exemplary motivational widget 4000 according to some exemplary embodiments of the present disclosure. As shown in Figure 40, the motivational widget 4000 can provide a comparison of a host's “best day,” “average day,” and “problem day” for the previous week. The motivational widget 4000 may also include a summary of the host’s average glucose, the host’s standard deviation, and the percentage of time the host spent within the target glucose range for each of the indicated “best day,” “average day,” and “problem day.”

[0165] Figure 41 illustrates another exemplary motivational widget 4100 according to some exemplary embodiments of the present disclosure. As shown in Figure 41, the motivational widget 4100 can also provide a comparison of the host's “best day,” “average day,” and “problem day” for the previous seven-day period with a summary of the host’s average glucose and the percentage of time the host spent within the target glucose range on each of the indicated “best day,” “average day,” and “problem day.”

[0166] Figure 42 illustrates another exemplary motivational widget 4200 according to some exemplary embodiments of the present disclosure. As shown in Figure 42, the motivational widget 4200 may also include a comparison of the percentage of time the host spent within the target glucose range over the previous four-week period. The motivational widget 4200 may also include a comparison of the percentage of time the host spent within the target glucose range over the previous four-week period with the final target percentage of time spent within the target glucose range per day.

[0167] Figure 43 illustrates another exemplary motivational widget 4300 according to some exemplary embodiments of the present disclosure. As shown in Figure 43, the motivational widget 4300 may provide the number of consecutive days the host has been within the target glucose range, along with a notification to lower or increase the target glucose range. This notification may include an explanatory text.

[0168] Figure 44 illustrates an exemplary event widget 4400 according to some exemplary embodiments of the present disclosure. As shown in Figure 44, the event widget 4400 can provide the host glucose value at the start of each recorded event and a summary of the host glucose value two hours after the start of the recorded event.

[0169] Figure 45 illustrates another exemplary event widget 4500 according to some exemplary embodiments of the present disclosure. As shown in Figure 45, the event widget 4500 can provide indications of the host glucose value at the start of each recorded event, and trends in the changes in the host glucose following each recorded event (e.g., 1 hour, 2 hours, 3 hours after the recorded event, etc.).

[0170] Figure 46 illustrates another exemplary event widget 4600 according to some exemplary embodiments of the present disclosure. As shown in Figure 46, the event 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 start of the recorded event, and two hours after the start of the recorded event.

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

[0172] Figure 48 illustrates another exemplary type of widget 4800 according to some exemplary embodiments of the present disclosure. As shown in Figure 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-target average glucose per day. As shown in the figure, the summary may include both bar graphs and numerical values ​​for the average glucose.

[0173] Figure 49 illustrates another exemplary type of widget 4900 according to some exemplary embodiments of the present disclosure. As shown in Figure 49, the widget 4900 can provide a trend of the host's average glucose value over 24 hours over a period of 14 days prior to the present.

[0174] Figure 50 illustrates another exemplary type of widget 5000 according to some exemplary embodiments of the present disclosure. As shown in Figure 50, the widget 5000 may provide a trend for the best time within a 24-hour day over a period of 14 days, in which case the host has the highest percentage of time spent within the target glucose range. The widget may also include a notification indicating how long the host must remain within the target glucose range in order to reach the final target percentage of time within the range. This notification may include an explanatory text.

[0175] Figure 51 illustrates another exemplary type of widget 5100 according to some exemplary embodiments of the present disclosure. As shown in Figure 51, the widget 5100 can provide a summary of the host's time spent in the target glucose range, the very high and high glucose range, and the very low and low glucose range 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. The widget 5100 can also include the trend of the host's average glucose value over the previous 14-day period.

[0176] It should be understood that the aforementioned "modes for carrying out the invention" are merely illustrative and descriptive, and not limiting. Further features and / or variations of the widget for visualizing analyte data can be provided in addition to those described above.

[0177] In certain embodiments, the widgets described herein may be customizable by the user. For example, the user may customize various target values, ranges, and information provided for one or more widgets.

[0178] Figure 52 is a flowchart illustrating exemplary operations 5200 for activating and using a dashboard having widgets, according to some exemplary embodiments of the present disclosure. Widgets can be managed and displayed using a dashboard layer (also referred to as the “Unified Concern Layer” or “Dashboard”). Users can invoke the dashboard in 5202 by many ways, including, but not limited to, pressing 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 corner of the screen. In response to such user input, 5203 the current state of the user interface may be saved, 5204 the user interface may be temporarily deactivated (e.g., faded), 5205 animations or effects may be played or presented to introduce the dashboard, and 5206 the dashboard may be displayed with one or more widgets. In 5206, the widgets displayed on the dashboard may include one or more of the widgets previously described in Figures 19 to 51.

[0179] The previous state of the dashboard may be retrieved as appropriate, and as a result, the dashboard may be displayed in its previous configuration. In some exemplary embodiments, the user interface and the dashboard may be active simultaneously.

[0180] In 5207, the user may, as desired, interact with and / or configure a widget. In some exemplary embodiments, the user may move a widget to the edges of the screen and resize it as appropriate. As previously stated, some widgets may be resizable, while others may have a fixed size. In 5208, the user may hide the dashboard by calling a hide command, which may either return the normal user interface to the display screen on its own or redisplay it. In some exemplary embodiments, the dashboard may be hidden when the user presses a function key or key combination, clicks a negative space within the dashboard (e.g., a space between widgets), or moves the cursor on the screen to a predefined corner of the screen.

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

[0182] In some exemplary embodiments, the dashboard may be configurable. A user can select several widgets to display by, for example, dragging widgets from a configuration bar (or other user interface element) onto the dashboard. For example, Figure 53 illustrates an exemplary user interface that may include various widgets according to some exemplary embodiments of this disclosure. The widgets selected to be displayed on the dashboard may include one or more of the widgets described in Figures 19 to 51.

[0183] Beyond the embodiments described herein, there are many other 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, desktops, browser or application windows, menu systems, trays, multi-touch sensitive displays, and other widgets. Furthermore, widgets and dashboards can be displayed on any surface on which they can be displayed, such as projections onto surfaces, holograms, and the surface of consumer devices (e.g., refrigerator doors).

[0184] Additional considerations The methods disclosed herein include one or more steps or actions for achieving the methods. These method steps and / or actions are interchangeable without departing from the claims. In other words, unless a particular order of steps or actions is specifically designated, the order and / or use of any particular steps and / or actions can be modified without departing from the claims.

[0185] As used herein, the phrase “at least one of” the list of items refers to any combination of those items, including individual members. For example, “at least one of a, b, or c” is intended to encompass a, b, c, ab, ac, bc, and abc, as well as any combination of multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other order of a, b, and c).

[0186] With the prior explanation provided, a person skilled in the art will be able to implement the various embodiments described herein. Various modifications to these embodiments will be readily apparent to a person skilled in the art, and the general principles defined herein can be applied to other embodiments. Accordingly, the claims are not intended to be limited to the embodiments shown herein, but should cover the entire scope consistent with the language of the claims, where, unless otherwise specified, references to singular elements are not intended to mean "one and only one," but rather "one or more." Unless otherwise specifically stated, the term "several" refers to one or more. All structural and functional equivalents to elements of the various embodiments described throughout this disclosure, which are currently known or will be known to a person skilled in the art, are expressly incorporated by reference herein and are intended to be included within the claims. Furthermore, nothing disclosed herein is intended to be made available to the public, whether such disclosure is expressly enumerated in the claims or not. The elements of a claim should not be construed under Section 112(f) of the United States Patent Act unless they are explicitly enumerated using the phrase “means for” or, in the case of a method claim, enumerated using the phrase “steps for.”

[0187] While various examples of the present invention are described above, it should be understood that these examples are presented merely as illustrations and not for limitation. Similarly, various figures may depict exemplary architectural configurations or other configurations for the present disclosure, which are performed to help understand the features and functions that may be included in the present disclosure. The present disclosure is not limited to the exemplary architectures or configurations described, but can be implemented using various alternative architectures and configurations. Furthermore, while the present disclosure is described above with respect to various exemplary examples and embodiments, it should be understood that the various features and functions described in one or more of the individual examples are not limited in their applicability to the specific example in which they are described. Conversely, those features and functions can be applied individually or in any combination to one or more of the other examples of the present disclosure, regardless of whether such examples are described or whether such features are presented as part of the example in which they are described. Therefore, the scope and breadth of the present disclosure should not be limited by any of the exemplary examples described above.

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

[0189] Unless otherwise explicitly defined, all terms (including technical and scientific terms) should be given their common and customary meanings to those skilled in the art, and should not be limited to any special or customized meanings unless explicitly defined herein.

[0190] The terms and phrases used in this application, and their variations thereof, should be interpreted as open-ended, as opposed to limiting, unless otherwise expressly stated, particularly in the appended claims. For example, the term “including” should be read as meaning “including without limitation,” “including but not limited,” etc. As used herein, “comprising” is synonymous with “including,” “containing,” or “characterized by,” and is comprehensive or open-ended, not excluding additional unlisted 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.” The term “example” is used to provide illustrative examples of items described and is not an exhaustive or limiting list of such examples. Adjectives such as “known,” “usual,” and “standard,” and similar terms should not be interpreted as limiting the items being described to a given period or to items available at a given time, but rather as encompassing known, ordinary, or standard techniques that are available or may be known at any point in the present or future. Words such as “preferred,” “desired,” or “desirable,” and similar terms should not be understood as implying that certain features are important, essential, or even crucial to the structure or function of the invention, but rather as merely intended to highlight alternative or additional features that may or may not be used in a particular example of the invention. Similarly, groups of items linked with the conjunction “and” should not be interpreted as requiring all of these items to be present in the group, but rather as “and / or” unless otherwise explicitly stated.Similarly, groups of items linked using the conjunction "or" should not be interpreted as requiring mutual exclusivity within the group; on the contrary, unless explicitly stated otherwise, they should be interpreted as "and / or."

[0191] As used herein, “comprising” is synonymous with “including,” “containing,” or “characterized by,” and is comprehensive or open-ended, not excluding additional or non-enumerated elements or method steps.

[0192] All numerical values ​​used herein to express quantities of raw materials, reaction conditions, and other similar matters should be understood in all cases to be modified by the term "approximately." Therefore, unless otherwise indicated, numerical parameters described herein are approximations that may vary depending on the desired properties to be obtained. At a minimum, each numerical parameter should be interpreted with regard to significant figures and common rounding techniques, not as an attempt to limit the application of the doctrine of equivalents to the scope of any claim in any application claiming priority to this application.

[0193] Furthermore, although the above has been described in some detail using examples and embodiments for the purpose of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be made. Therefore, the description and embodiments should not be interpreted as limiting the scope of the invention to specific embodiments and the embodiments described herein, but rather as covering all modifications and alternatives that come with the true scope and spirit of the invention. [Explanation of symbols]

[0194] 2. Drug delivery pump 4. Glucose meter 8. Continuous Analytical Sensor System 10 Continuous Analytical Sensors 12 Sensor Electronic Devices 14, 16, 18, 20 Display devices 100 Systems 406 Network 490 Cloud-based Analytical Processors

Claims

1. A method for generating a user interface view associated with sensor data representing analyte concentration values ​​in a host, Receiving first data associated with the analyte concentration value of a host, wherein the first data is associated with a first period, the first period includes multiple analysis periods of the same duration, and the sensor data associated with the host during the first period is Analyte data showing the analyte level of the host during the first period, Insulin data showing insulin intake during the first period, Carbohydrate data showing carbohydrate intake during the first period, Physical activity data showing the level of physical activity during the first period, Including the aforementioned receiving, The first data is analyzed to generate one or more first user interface views associated with the first data for display on one or more widgets, wherein the one or more first user interface views include a performance report. To provide the first one or more user interface views for display on the one or more widgets, This includes automatically updating the first one or more user interface views for display on the one or more widgets, and the automatic updating is Receiving second data associated with the analyte concentration value of the host, wherein the second data is associated with a second period, Analyzing the second data to generate one or more second user interface views associated with the second data for display on one or more widgets, The provision of one or more second user interface views for display on one or more widgets includes, To generate one or more first user interface views associated with the first data, Determining a first analysis period within the first period in which the host spends the minimum amount of time within the target analyte range, wherein the first analysis period includes a single continuous period of 24 hours or less. Determining the period within the first analysis period during which the host was outside the range of the target analyte, To generate a performance report, wherein the performance report is A first feature comprising a first aggregate of first factors that contributed to the host spending the least amount of time within the target analyte range, wherein the first factors include the average analyte level, carbohydrate intake, insulin intake, and physical activity level of the host during the first analysis period. A method comprising: a second feature including a second aggregate of second factors that contributed to a host spending the least amount of time within the target analyte range, wherein the second factor includes trends in the host's analyte levels, carbohydrate intake, insulin intake, and physical activity levels during the first analysis period, and at least one of the first aggregate or the second aggregate generates such that the first factor or the second factor is associated with the period within the first analysis period.

2. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, Includes a summary representing the average blood glucose concentration value of the host based on the first data, or Based on the first data, the average blood glucose concentration value of the host, The standard deviation of the blood glucose concentration values ​​of the first data, Includes a summary representing the glucose management indicator (GMI) percentage based on the first data, or The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, Based on the first data, the average blood glucose concentration value of the host, The first data includes a summary representing the standard deviation of the blood glucose concentration values, or The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, Based on the first data, the average blood glucose concentration value of the host, The method according to claim 1, comprising a glucose management index (GMI) percentage based on the first data, and a summary representing the above.

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

4. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, A summary representing the percentage of time the blood glucose concentration values ​​of the first data were within the target blood glucose range associated with the first period, the very high blood glucose range associated with the first period, the high blood glucose range associated with the first period, the low blood glucose range associated with the first period, and the very low blood glucose range associated with the first period, The summary representing the percentage of time the blood glucose concentration values ​​of the first data were within the target, very high, high, low, and very low blood glucose ranges associated with the first period has changed compared to the summary representing the percentage of time the host's blood glucose concentration values ​​in the third period were within the target, very high, high, low, and very low blood glucose ranges associated with the third period, or A summary representing the percentage of time the blood glucose concentration values ​​of the first data were within the target blood glucose range associated with the first period, the very high blood glucose range associated with the first period, the high blood glucose range associated with the first period, the low blood glucose range associated with the first period, and the very low blood glucose range associated with the first period, Based on the first data, the average blood glucose concentration value of the host, A glucose management index (GMI) percentage based on the first data, including, or A summary representing the percentage of time the blood glucose concentration values ​​of the first data were within the target blood glucose range associated with the first period, the very high blood glucose range associated with the first period, the high blood glucose range associated with the first period, the low blood glucose range associated with the first period, and the very low blood glucose range associated with the first period, The percentage change in the summary representing the percentage of time during which the blood glucose concentration values ​​of the first data were within the target, very high, high, low, and very low blood glucose ranges associated with the first period compared to the summary representing the percentage of time during which the host's blood glucose concentration values ​​in the third period were within the target, very high, high, low, and very low blood glucose ranges associated with the third period, Based on the first data, the average blood glucose concentration value of the host, The glucose management index (GMI) percentage based on the first data mentioned above, The method according to claim 1, further comprising: an indication of a period during the first period in which the blood glucose concentration value of the first data was within the high and very high range associated with the first period, or within the low and very low range associated with the first period.

5. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, It is a comparison, The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, The percentage of time during which the host's blood glucose concentration value associated with the third period was within the target blood glucose range associated with the third period, The percentage of time during which the host's blood glucose concentration value associated with the fourth period was within the target blood glucose range associated with the fourth period, wherein the fourth period is longer than the first period and includes the first period. The comparison includes the percentage of time during which the host's blood glucose concentration value associated with the fifth period was within the target blood glucose range associated with the fifth period, wherein the fifth period is longer than the fourth period and includes the first period, or It is a comparison, The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, The percentage of time during which the host's blood glucose concentration value associated with the third period was within the target blood glucose range associated with the third period, wherein the third period is longer than the first period and includes the first period. A comparison between the percentage of time during which the host's blood glucose concentration value associated with the fourth period was within the target blood glucose range associated with the fourth period, where the fourth period is longer than the third period and includes the third period, and the percentage of time during which the fourth period is longer than the third period and includes the third period. A first average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the first period, a second average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period, and a third average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the fourth period, The method according to claim 1, comprising: a first standard deviation of the blood glucose concentration value corresponding to the first period; a second standard deviation of the blood glucose concentration value corresponding to the third period; and a third standard deviation of the blood glucose concentration value corresponding to the fourth period.

6. The analyte concentration value includes the blood glucose concentration value, The first user interface view for display on one or more widgets includes a summary of the first data, representing the percentage, minutes, and hours of time the blood glucose concentration values ​​were within the target blood glucose range associated with the first period, the very high blood glucose range associated with the first period, the high blood glucose range associated with the first period, the low blood glucose range associated with the first period, and the very low blood glucose range associated with the first period, or The first data includes a summary representing the number of minutes and hours in which the blood glucose concentration values ​​were within the target blood glucose range associated with the first period, the high blood glucose range associated with the first period, and the low blood glucose range associated with the first period, or The method according to claim 1, comprising a summary representing the number of high blood glucose events and low blood glucose events that the host had during the first period, based on the first data.

7. The analyte concentration value includes the blood glucose concentration value, The method according to claim 1, wherein one or more first user interface views for display on one or more widgets include a summary representing the blood glucose concentration values ​​of the first data presented as trends in a graph over a second period, along with a best-fit line.

8. The analyte concentration value includes the blood glucose concentration value, The blood glucose concentration value of the first data is divided into a plurality of equal datasets having corresponding blood glucose concentration values. The first user interface view for display on one or more widgets includes a percentage of time, compared to the desired percentage of time during which the blood glucose concentration value corresponding to each of the plurality of equal datasets was within the target blood glucose range associated with the first period presented in the horizontal bar graph, or The blood glucose concentration value of the first data is divided into a plurality of equal datasets having corresponding blood glucose concentration values. The method according to 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 datasets were within the target blood glucose range associated with the first period presented in a vertical bar graph, compared to an end-target percentage of time during which it is desirable for the blood glucose concentration values ​​to be within the target blood glucose range associated with the first period.

9. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views for display on one or more widgets are: The first data includes a summary representing a percentage of time corresponding to a subset of the blood glucose concentration values, where the first data was within the target blood glucose range associated with the first period presented on the vertical bar, compared to the final target percentage of time during which it is desirable for the blood glucose concentration values ​​to be within the target blood glucose range associated with the first period, or The method according to claim 1, comprising a summary representing a percentage of time corresponding to a subset of blood glucose values, where the first data was within the target blood glucose range associated with the first period presented on a horizontal bar, compared to an end-target percentage of time during which it is desirable for the blood glucose values ​​to be within the target blood glucose range associated with the first period.

10. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, A representation of a third period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the third period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the highest percentage correlation to the target blood glucose range associated with the first period. The percentage of time during which the blood glucose concentration value corresponding to the third period was within the target blood glucose range associated with the first period, The average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period, The standard deviation of the blood glucose concentration value corresponding to the third period, and, or A representation of a third period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the third period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the highest percentage correlation to the target blood glucose range associated with the first period. The percentage of time during which the blood glucose concentration value corresponding to the third period was within the target blood glucose range associated with the first period, The method according to claim 1, comprising: the average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period.

11. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, A representation of a third period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the third period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the highest percentage correlation to the target blood glucose range associated with the first period. A representation of a fourth period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the fourth period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the second highest percentage correlation to the target blood glucose range associated with the first period. A representation of a fifth period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the fifth period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the third highest percentage correlation to the target blood glucose range associated with the first period. The first percentage of time during which the blood glucose concentration value corresponding to the third period was within the target blood glucose range associated with the first period, the second percentage of time during which the blood glucose concentration value corresponding to the fourth period was within the target blood glucose range associated with the first period, and the third percentage of time during which the blood glucose concentration value corresponding to the fifth period was within the target blood glucose range associated with the first period. The average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period, The method according to claim 1, comprising: the standard deviation of the blood glucose concentration value corresponding to the third period.

12. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, A representation of a third period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the third period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the highest percentage correlation to the target blood glucose range associated with the first period. A representation of a fourth period within the first period, wherein a subset of the blood glucose concentration values ​​associated with the first period corresponds to the fourth period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, have an average percentage correlation to the target blood glucose range associated with the first period. A representation of a fifth period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the fifth period and represents the consecutive blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, have the lowest percentage correlation to the target blood glucose range associated with the first period. The first percentage of time during which the blood glucose concentration value corresponding to the third period was within the target blood glucose range associated with the first period, the second percentage of time during which the blood glucose concentration value corresponding to the fourth period was within the target blood glucose range associated with the first period, and the third percentage of time during which the blood glucose concentration value corresponding to the fifth period was within the target blood glucose range associated with the first period. A first average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period, a second average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the fourth period, and a third average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the fifth period, The method according to claim 1, comprising: a first standard deviation of the blood glucose concentration value corresponding to the third period; a second standard deviation of the blood glucose concentration value corresponding to the fourth period; and a third standard deviation of the blood glucose concentration value corresponding to the fifth period.

13. The analyte concentration value includes the blood glucose concentration value, The first one or more user interface views, for display on one or more widgets, A representation of a third period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the third period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, has the highest percentage correlation to the target blood glucose range associated with the first period. A representation of a fourth period within the first period, wherein a subset of the blood glucose concentration values ​​associated with the first period corresponds to the fourth period and represents a sequence of blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, have an average percentage correlation to the target blood glucose range associated with the first period. A representation of a fifth period within the first period, wherein the subset of blood glucose concentration values ​​associated with the first period corresponds to the fifth period and represents the consecutive blood glucose concentration values ​​that, when compared with other consecutive blood glucose concentration values ​​of the host within the first period, have the lowest percentage correlation to the target blood glucose range associated with the first period. The first percentage of time during which the blood glucose concentration value corresponding to the third period was within the target blood glucose range associated with the first period, the second percentage of time during which the blood glucose concentration value corresponding to the fourth period was within the target blood glucose range associated with the first period, and the third percentage of time during which the blood glucose concentration value corresponding to the fifth period was within the target blood glucose range associated with the first period. A first average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the third period, a second average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the fourth period, and a third average blood glucose concentration value of the host based on the blood glucose concentration value corresponding to the fifth period, or A bar graph, wherein the bar graph is The percentage of time during which the blood glucose concentration value of the first data was within the target blood glucose range associated with the first period, The percentage of time during which the host's blood glucose concentration value associated with the third period was within the target blood glucose range associated with the third period, wherein the third period precedes the first period. The percentage of time during which the host's blood glucose concentration value associated with the fourth period was within the target blood glucose range associated with the fourth period, wherein the fourth period precedes the third period. The percentage of time during which the host's blood glucose concentration value associated with the fifth period was within the target blood glucose range associated with the fifth period, wherein the fifth period precedes the fourth period. The method according to claim 1, comprising a bar graph comparing the blood glucose concentration values ​​for the first, third, fourth, and fifth periods with the final target percentage of time during which it is desirable that the blood glucose concentration values ​​for each of the first, third, fourth, and fifth periods be within the target blood glucose range associated with each of the first, third, fourth, and fifth periods.

14. The analyte concentration value includes the blood glucose concentration value, The first period is the period in the current week, and the method is To determine the average blood glucose concentration value of the host during the current week, To generate a performance report that includes the average blood glucose concentration value of the host during the first period, and a comparison of the average blood glucose concentration value of the host during the first period with the average blood glucose concentration value of the host during at least two preceding periods of similar duration, To generate a user interface view of the aforementioned performance report, The method according to claim 1, further comprising providing a user interface view of the performance report for display.

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

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