Dynamic presentation of inter-feature correlation insights for continuous analyte data

Abstract

Systems, devices, and methods are provided for dynamic determination and presentation of inter-feature correlation insights. In one embodiment, a non-transitory computer-readable storage medium storing a program is provided, the program including instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including: identifying at least one analyte feature using an analyte feature selection user interface (UI), identifying at least one correlated feature using a correlated feature selection UI, determining an analyte feature trend for the at least one analyte feature, determining a correlation feature trend for the at least one correlated feature, determining at least one inter-feature correlation insight based on the analyte features and the correlated feature trend, determining a correlation magnitude profile for the at least one inter-feature correlation, and displaying the at least one inter-feature correlation insight using the at least one insight UI.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 476,921, filed December 22, 2022, which is hereby assigned to the assignee herein and expressly incorporated by reference in its entirety as if fully set forth below and for all applicable purposes.

[0002] FIELD OF THE INVENTION This application relates generally to medical devices (e.g., analyte sensors), and more particularly to systems, devices, and methods that provide insights to improve patient health outcomes. [Background technology]

[0003] Diabetes is a metabolic disease related to the body's production or use of insulin, a hormone that allows the body to use glucose for energy or store it as fat.

[0004] When a person eats a meal containing carbohydrates, the food is processed by the digestive system, producing glucose in the blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels within a range that provides enough energy to support bodily functions and avoids problems that can arise from glucose levels that are too high or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0005] When the body does not produce enough insulin or is unable to effectively use the insulin that is present, blood glucose levels can rise above the normal range. Higher-than-normal blood glucose levels are called "hyperglycemia." Chronic hyperglycemia can lead to many health problems, including cardiovascular disease, cataracts and other eye diseases, neuropathy, and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis (a condition in which the body becomes excessively acidic due to the presence of blood glucose and ketone bodies, which are produced when the body can no longer use glucose). Lower-than-normal blood glucose levels are called "hypoglycemia." Severe hypoglycemia can lead to an acute crisis that can result in seizures or death.

[0006] Diabetics can receive insulin to manage blood glucose levels. Insulin can be received, for example, through manual injection with a needle. Wearable insulin pumps can also be used to receive insulin. Diet and exercise also affect blood glucose levels.

[0007] Diabetes can be referred to as "type 1" and "type 2." Type 1 diabetics are typically able to use insulin when it is available, but because of problems with the insulin-producing beta cells in the pancreas, they are unable to produce sufficient amounts of insulin. Type 2 diabetics are able to produce some insulin, but due to reduced sensitivity to insulin, they are "insulin resistant." As a result, even though insulin is present in the body, it is not used effectively and blood glucose levels are not effectively regulated.

[0008] This background is provided to introduce a brief context for the Summary and Detailed Description that follow. This background is not intended to aid in determining the scope of the claimed subject matter, nor is it intended to limit the claimed subject matter to implementations that solve any or all of the disadvantages or problems presented above. Summary of the Invention [Means for solving the problem]

[0009] Various embodiments of the present systems, devices, and methods for dynamic determination and presentation of inter-feature correlation insights to improve patient health outcomes comprise several features, no single one of which is solely responsible for their desirable attributes. Without limiting the scope of the present embodiments, their more prominent features are discussed below. After reviewing this discussion, and particularly after reading the section entitled "Detailed Description of the Invention," you will understand how the features of the present embodiments provide the advantages described herein.

[0010] In a first aspect, a non-transitory computer-readable storage medium is provided that, when executed by at least one processor of a computing device, causes the at least one processor to provide an analyte feature selection user interface. identifying at least one correlation feature using a correlation feature selection UI; determining an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; determining a correlation feature trend for the at least one correlation feature, the correlation feature trend corresponding to a variation in the at least one correlation feature over the period of time; determining at least one inter-feature correlation insight based on the analyte feature trend and the correlation feature trend, the at least one inter-feature correlation insight being based on a correlation between the at least one analyte feature and the at least one correlation feature; determining a correlation magnitude profile for the at least one inter-feature correlation, the correlation magnitude profile including a correlation magnitude for the at least one inter-feature correlation insight over a correlation period; and displaying the at least one inter-feature correlation insight using the at least one insight UI.

[0011] In one embodiment of the first aspect, the at least one analyte feature is identified by displaying an analyte feature selection UI and receiving user input selecting the at least one analyte feature.

[0012] In another embodiment of the first aspect, the at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

[0013] In another embodiment of the first aspect, the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag the inter-feature correlation insight.

[0014] In another embodiment of the first aspect, at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, which provides UI elements for a user to select a correlation period and displays a correlation magnitude profile of the flagged inter-feature correlation insights for the user-selected correlation period.

[0015] In another embodiment of the first aspect, the operations further include receiving a user-selected correlation period using a mutual time insight interactive UI, updating correlation magnitudes for the flagged mutual feature correction insights, and displaying an updated correlation magnitude profile for the flagged mutual feature correlation insights using the interactive UI.

[0016] In another embodiment of the first aspect, at least one inter-feature correlation insight is displayed using a set of flagged insight engagement UIs, the set of insight engagement UIs providing UI elements for a user to edit the flagged inter-feature correlation insights and assigning metadata fields to the flagged inter-feature correlation insights.

[0017] In another embodiment of the first aspect, the at least one inter-feature correlation insight is displayed using a set of content engagement UIs that display curated educational content based on the flagged inter-feature insights and provide UI elements for a user to engage with the educational content.

[0018] In a second aspect, a method is provided for dynamic determination and presentation of inter-feature correlation insights, the method including: identifying at least one analyte feature using an analyte feature selection user interface (UI); identifying at least one correlated feature using a correlated feature selection UI; determining an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; and determining a correlated feature trend for the at least one correlated feature, the correlated feature trend corresponding to a variation in the at least one correlated feature over the period of time. determining at least one inter-feature correlation insight based on the analyte feature trend and the correlation feature trend, the at least one inter-feature correlation insight being based on a correlation between the at least one analyte feature and the at least one correlation feature; determining a correlation magnitude profile for the at least one inter-feature correlation, the correlation magnitude profile including a correlation magnitude for the at least one inter-feature correlation insight over a correlation period; and displaying the at least one inter-feature correlation insight using the at least one insight UI.

[0019] In one embodiment of the second aspect, the at least one analyte feature is identified by displaying an analyte feature selection UI and receiving user input selecting the at least one analyte feature.

[0020] In another embodiment of the second aspect, the at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

[0021] In another embodiment of the second aspect, the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag the inter-feature correlation insight.

[0022] In another embodiment of the second aspect, at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, which provides UI elements for a user to select a correlation period and displays a correlation magnitude profile of the flagged inter-feature correlation insights for the user-selected correlation period.

[0023] In another embodiment of the second aspect, at least one inter-feature correlation insight is displayed using a set of flagged insight engagement UIs, the set of insight engagement UIs providing UI elements for a user to edit the flagged inter-feature correlation insights and assigning metadata fields to the flagged inter-feature correlation insights.

[0024] In another embodiment of the second aspect, the at least one inter-feature correlation insight is displayed using a set of content engagement UIs that display curated educational content based on the flagged inter-feature insights and provide UI elements for a user to engage with the educational content.

[0025] In a third aspect, there is provided a computing device for dynamic determination and presentation of inter-feature correlation insights, the computing device comprising: a network interface; a memory containing executable instructions; and a processor in data communication with the memory, the processor executing the instructions to identify at least one analyte feature using an analyte feature selection user interface (UI); identify at least one correlated feature using the correlated feature selection UI; determine an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; and determine a correlated feature trend for the at least one correlated feature, determine a correlation feature trend corresponding to a variation in the at least one correlation feature over the time period; determine at least one inter-feature correlation insight based on the analyte feature trend and the correlation feature trend, where the at least one inter-feature correlation insight is based on a correlation between the at least one analyte feature and the at least one correlation feature; determine a correlation magnitude profile for the at least one inter-feature correlation, where the correlation magnitude profile includes a correlation magnitude for the at least one inter-feature correlation insight over the correlation time period; and display the at least one inter-feature correlation insight using at least one insight UI.

[0026] In one embodiment of the third aspect, the at least one analyte feature is identified by displaying an analyte feature selection UI and receiving user input selecting the at least one analyte feature.

[0027] In another embodiment of the third aspect, the at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

[0028] In another embodiment of the third aspect, the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag the inter-feature correlation insight.

[0029] In another embodiment of the third aspect, at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, which provides UI elements for a user to select a correlation period and displays a correlation magnitude profile of the flagged inter-feature correlation insights for the user-selected correlation period. [Brief explanation of the drawings]

[0030] [Figure 1A] 1 illustrates an exemplary health monitoring and assistance system, according to certain embodiments of the present disclosure. [Figure 1B] 1 illustrates a continuous analyte monitoring system according to certain embodiments of the present disclosure. [Figure 2] 1 illustrates example inputs and example metrics generated based on the inputs, according to certain embodiments of the present disclosure. [Figure 3] 1 is a flow diagram illustrating an exemplary user interface (UI) for use with dynamic determination and presentation of inter-feature correlation insights, in accordance with certain embodiments of the present disclosure. [Figure 4] 1 is a flow chart illustrating an exemplary process for dynamic determination and presentation of inter-feature correlation insights, according to certain embodiments of the present disclosure. [Figure 5] 5 illustrates an analyte feature selection UI for identifying at least one analyte feature used in the process of FIG. 4 according to certain embodiments of the present disclosure. [Figure 6] 5 illustrates a correlation feature selection UI for identifying at least one correlation feature used in the process of FIG. 4 according to certain embodiments of the present disclosure. [Figure 7] 5 illustrates an insight flagging UI for presenting inter-feature correlation insights as part of the process of FIG. 4 in accordance with certain embodiments of the present disclosure. [Figure 8] 5 illustrates a cross-time insight interactive UI for presenting cross-feature correlation insight as part of the process of FIG. 4 according to certain embodiments of the present disclosure. [Figure 9] 5 illustrates a flagged insight engagement UI for presenting inter-feature correlation insights as part of the process of FIG. 4 in accordance with certain embodiments of the present disclosure. [Figure 10] 10 illustrates another flagged insight engagement UI for assigning metadata to the flagged inter-feature correlation insight of FIG. 9 in accordance with certain embodiments of the present disclosure. [Figure 11] 10 illustrates another flagged insight engagement UI for modifying the flagged inter-feature correlation insight of FIG. 9 in accordance with certain embodiments of the present disclosure. [Figure 12] 5 illustrates a content engagement UI for presenting inter-feature correlation insights determined as part of the process of FIG. 4 in accordance with certain embodiments of the present disclosure. [Figure 13] 13 illustrates another content engagement UI for engaging with the educational content of FIG. 12 in accordance with certain embodiments of the present disclosure. [Figure 14] FIG. 1 is a block diagram depicting a computing device configured for dynamic determination and presentation of inter-feature correlation insights in accordance with certain embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0031] Portable and / or wearable health monitoring devices (also referred to herein as "health monitoring devices") and mobile health applications (also referred to herein as "applications") are rapidly becoming renowned for their ability to support user-centered care. For example, managing diabetes can pose complex challenges to patients, clinicians, and caregivers, as a large constellation of factors can affect a patient's glucose levels and glucose trends. To assist patients in better managing this condition, health monitoring devices (e.g., sensors and other types of monitoring and diagnostic devices) and a variety of mobile health applications (e.g., diabetes intervention software applications) have been developed. The widespread adoption of such health monitoring devices and the increased development and distribution of mobile health applications have improved health management, and more specifically, chronic disease management, in the healthcare field. In particular, the use of mobile health applications in conjunction with these health monitoring devices represents a more scalable and potentially more cost-effective alternative to traditional interventions, providing a means to improve health and chronic disease management by expanding the reach of healthcare services and improving users' access to health-related information and interventions.

[0032] Mobile health applications enable users to become more involved in their own medical care by granting them access to and control of their health information. In particular, mobile health applications allow users to access, monitor, record, and update their health information regardless of physical constraints such as time and location. In particular, a variety of intervention applications have been developed to provide guidance that may assist patients, caregivers, healthcare providers, or other users in improving their lifestyles or clinical / patient outcomes by addressing a variety of issues, such as analyte management, exercise, and / or other health factors. For example, diabetes intervention applications may assist patients, caregivers, healthcare providers, or other users in overnight glucose management (e.g., reducing the incidence of hypoglycemic events or hyperglycemic excursions), mealtime and post-meal glucose management (e.g., using historical information and trends to increase glycemic control), hyperglycemic correction (e.g., increasing time in the target zone while avoiding hypoglycemic events due to over-correction), and / or hypoglycemic treatment (e.g., addressing hypoglycemia while avoiding “rebound” hyperglycemia), to name a few.

[0033] Unfortunately, many health-related applications suffer from high user attrition rates due to their inability to provide health management support features that enable users to holistically manage their health conditions (e.g., their type 2 diabetes status) by setting their lifestyle choices and treatment plans according to their monitored health conditions. For example, many health-related applications are unable to provide personalized health management insights based on observed correlations between various health trends of users. As another example, many health-related applications are unable to enable users to better prepare for medical appointments based on personal health initiatives or to receive curated educational content based on their monitored health conditions.

[0034] Accordingly, certain embodiments described herein provide a software application configured to run on a computing device (e.g., a mobile device) in communication with an analyte monitoring system (e.g., a glucose sensor system, such as a continuous glucose sensor system used for continuous glucose monitoring), where the software application provides a variety of functions and user interface (UI) views to enable a user to holistically manage their health status based on how the sensor data (also referred to herein as “analyte data”) reported by the analyte monitoring system correlates with the user's other health indicators. In particular, certain embodiments described herein detect “cross-feature correlation insights” between one or more analyte features of the user and one or more correlated features of the user.

[0035] Cross-feature correlation insights are determined correlations between a user's analyte features and a user's correlation features. A user's analyte features are features determined based on sensor data reported by an analyte monitoring system, and correlation features are features analyzed for potential correlation with the analyte features. Examples of analyte features include a time-in-range feature, an A1C analyte feature, a high glucose feature, and a low glucose feature. Examples of correlation features include a sleep level feature, a medication intake feature, a food intake feature, an exercise level feature, a stress level feature, a heart rate feature, etc. An example of cross-feature correlation insight is a determined correlation between a time-in-range feature and a user's sleep level feature, such as the detected observation that a 20% increase in the amount of sleep by a user is correlated with a 40% increase in the user's weekly time-in-range.

[0036] In some embodiments, after the software application detects inter-feature correlation insights, the software application presents the insights using various UIs that allow, for example, a user to flag the insight for follow-up discussion at an upcoming medical appointment, engage with educational content curated based on the insight, and see how the magnitude of the underlying correlation changes when the "correlation period" of the insight is modified (e.g., when the period used to determine the magnitude of the correlation is changed from weekly to monthly).

[0037] In some embodiments, to enable the functionality described herein, the software application uses (i) a feature selection UI (e.g., an analyte feature selection UI and a correlation feature selection UI) that allows a user to select one or more analyte features and one or more correlation features for analysis, (ii) an insight flagging UI that allows a user to flag cross-feature correlation insights for discussion in follow-up medical appointments, (iii) a cross-time insight interaction UI that allows a user to view correlation magnitude data for cross-feature correlation insights over various correlation time periods, (iv) a set of flagged insight engagement UIs that allow a user to edit cross-feature correlation insights that were previously flagged using the insight flagging UI and assign various metadata fields to flagged cross-feature correlation insights, and (v) a set of content engagement UIs that allow a user to engage with content data curated based on the cross-feature correlation insights. The insight flagging UI, the cross-time insight interaction UI, the cross-time insight interaction UI, the set of flagged insight engagement UIs, and the set of content engagement UIs may be collectively referred to herein as “insight UIs.”

[0038] The systems, devices, and methods of the embodiments described herein can be used in conjunction with any type of analyte sensor for any measurable analyte. As used herein, the term "analyte" refers to, but is not limited to, a substance or chemical constituent in a body or biological sample. Furthermore, the systems, devices, and methods of the embodiments described herein can be used in conjunction with any health-related application provided to a user to improve the user's health. For example, a health-related application may help a user treat a particular disease or help improve the health of a user who has not necessarily been diagnosed with a disease.

[0039] Exemplary system having a cross-feature correlation engine for dynamic determination and presentation of cross-feature correlation insights - Patent Application 20070122999 1A illustrates an exemplary health monitoring and assistance system according to certain embodiments of the present disclosure. Health monitoring and assistance system 100 may be utilized to monitor a user's health and display cross-feature correlation insights using various UIs to users associated with system 100. Each user of system 100, such as user 102, may interact with a mobile health application, such as mobile health application (“application”) 106 (e.g., a diabetes intervention application providing decision support guidance), and / or a health monitoring device, such as analyte monitoring system 104 (e.g., a glucose monitoring system). User 102 may, in certain embodiments, be a patient or, in some cases, a caregiver for a patient. In embodiments described herein, the user is assumed to be a patient solely for simplicity, but is not so limited. As shown, system 100 may include analyte monitoring system 104, a mobile device 107 running application 106, a cross-feature correlation engine 112 (including a Data Analysis Module (DAM) 111), and a user database 110.

[0040] The analyte monitoring system 104 may be configured to, for example, continuously generate analyte measurements (e.g., sensor data) for the user 102 and transmit the analyte measurements to the mobile device 107 for use by the application 106. In some embodiments, the analyte monitoring system 104 may transmit the analyte measurements to the mobile device 107 via a wireless connection (e.g., a Bluetooth connection). In particular embodiments, the mobile device 107 is a smartphone. However, in particular embodiments, the mobile device 107 may instead be any other type of computing device, such as a laptop computer, a smartwatch, a tablet, or any other computing device capable of running the application 106.

[0041] In particular examples, the analyte monitoring system 104 is assumed to be a glucose monitoring system, although it should be noted that the analyte monitoring system 104 may operate to monitor one or more additional or alternative analytes. As discussed, the term "analyte" as used herein is a broad term and is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a substance or chemical constituent in a body or a biological sample (e.g., bodily fluids, including blood, serum, plasma, interstitial fluid, cerebrospinal fluid, lymphatic fluid, ocular fluid, saliva, oral fluid, urine, feces, or exudates). Analytes may include naturally occurring substances, man-made substances, metabolites, and / or reaction products. In some embodiments, the analytes for measurement by the sensing regions, devices, and methods are albumin, alkaline phosphatase, alanine transaminase, aspartate aminotransferase, bilirubin, blood urea nitrogen, calcium, CO2, chloride, creatinine, glucose, gamma-glutamyl transpeptidase, hematocrit, lactate, lactate dehydrogenase, magnesium, oxygen, pH, phosphorus, potassium, sodium, total protein, uric acid, metabolic markers, and drugs.

[0042] Other analytes are contemplated, including acetaminophen, dopamine, ephedrine, terbutaline, ascorbate, uric acid, oxygen, d-amino acid oxidase, plasma amine oxidase, xanthine oxidase, NADPH oxidase, alcohol oxidase, alcohol dehydrogenase, pyruvate dehydrogenase, diols, Ros, NO, bilirubin, cholesterol, triglycerides, gentisic acid, ibuprofen, L-dopa, methyldopa, salicylate, tetracycline, tolazamide, tolbutamide, acarboxylate, riboflavin ... Prothrombin; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; α-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasia amine; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β-hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine A; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphisms, alcohol dehydrogenase, α1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin Globin C, Hemoglobin D, Hemoglobin E, Hemoglobin F, D-Punjab, beta-thalassemia, Hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol); Desbutylhalofantrine; Dihydropteridine reductase; Diphtheria / tetanus antitoxin; Erythrocyte arginase; Erythrocyte protoporphyrin; Esterase D; Fatty acids / acylglycines; Free beta-human chorionic gonadotropin; Free erythrocyte porphyrin;Free thyroxine (FT4); free triiodothyronine (free tri-iodothyronine, FT3); fumarylacetoacetase; galactose / gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycocholate; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-α-hydroxyprogesterone; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic acid / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine tri-iodothyronine, rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea worm, Echinococcus granulosus, Entamoeba histolytica, enterovirus, giardiasis, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, Leptospirosis, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, myoglobin, Onchocerciasis volvulus, parainfluenza) Viruses, malaria parasites, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / rangeli, vesicular stomatitis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; leukocytes;and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones naturally present in blood or interstitial fluid can also constitute analytes in certain embodiments.

[0043] The analyte may be naturally occurring in a biological fluid, e.g., a metabolite, hormone, antigen, antibody, etc. Alternatively, the analyte may be introduced into the body, e.g., a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including, but not limited to, insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Silurt, Preludine, Didrex, Prestate, Boranil, Sandrex, Pregin); depressants (barbiturates, methaqualone, valium tranquilizers such as benzodiazepines, ... Analytes such as neurochemicals and other chemicals produced in the body, such as ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), histamine, advanced glycation end products (AGEs), and 5-hydroxyindoleacetic acid (FHIAA), can also be analyzed.

[0044] The application 106 may be a mobile health application configured to receive and analyze analyte measurements from the analyte monitoring system 104. In some embodiments, the application 106 transmits the analyte measurements received from the analyte monitoring system 104 to the user database 110 (and / or the cross-feature correlation engine 112), which may store the analyte measurements in the user profile 118 of the user 102 for processing and analysis and for use by the cross-feature correlation engine 112, determine cross-feature correlation insights, and provide the cross-feature correlation insights to the user 102 using a UI via the application 106. In some embodiments, the application 106 may store the analyte measurements locally in the user profile 118 of the user 102 for processing and analysis and for use by the cross-feature correlation engine 112, determine cross-feature correlation insights using a UI, and present the cross-feature correlation insights to the user 102.

[0045] In particular embodiments, the mutual feature correlation engine 112 refers to a set of software instructions having one or more software modules, including a data analysis module (DAM) 111. In some embodiments, the mutual feature correlation engine 112 executes entirely on one or more computing devices in a private or public cloud. In some other embodiments, the mutual feature correlation engine 112 executes partially on one or more local devices, such as the mobile device 107, and partially on one or more computing devices in a private or public cloud. In some other embodiments, the mutual feature correlation engine 112 executes entirely on one or more local devices, such as the mobile device 107.

[0046] As discussed in more detail herein, the cross-feature correlation engine 112 may determine cross-feature correlation insights and present the cross-feature correlation insights to the user via the application 106. For example, the cross-feature correlation engine 112 may identify one or more analyte features and one or more correlation features, determine analyte feature trends and correlation feature trends, and determine cross-feature correlation insights based on the analyte feature trends and correlation feature trends, as described further below. In some embodiments, the cross-feature correlation engine 112 may determine and present the cross-feature correlation insights based on information, including, but not limited to, information contained in a user profile 118 stored in the user database 110. In some embodiments, the user profile 118 may include information collected about the user from the application 106, as described further below.

[0047] In particular embodiments, the DAM 111 of the decision support engine 112 is configured to receive and / or process a set of inputs 127 (described in more detail below) (hereinafter also referred to as "input data") to determine one or more metrics 130 (hereinafter also referred to as "metric data"), which the decision support engine 112 then uses to determine and display inter-feature correlation insights. The inputs 127 may be stored in a user profile 118 of the user database 110. The DAM 111 may fetch the inputs 127 from the user database 110 and calculate multiple metrics 130, which may be stored in the user profile 118 as application data 126. Such metrics 130 may include health-related metrics.

[0048] In certain embodiments, application 106 is configured to receive information about user 102 as input and store the information in user profile 118 for user 102 in user database 110. For example, application 106 may obtain and record demographic information 119, disease progression information 121, and / or medication information 122 for user 102 in user profile 118. In certain embodiments, demographic information 119 may include one or more of the user's age, body mass index (BMI), ethnicity, gender, etc. In certain embodiments, disease progression information 121 may include information about the user's 102 disease, such as, for diabetes, whether the user has type 1, type 2, pre-diabetes, or whether the user has gestational diabetes, etc. In certain embodiments, disease progression information 121 also includes length of time since diagnosis, level of disease control, level of adherence to disease management treatment, predicted pancreatic function, other types of diagnoses (e.g., heart disease, obesity), or health measures (e.g., heart rate, exercise, stress, sleep, etc.), etc. In certain embodiments, medication regimen information 122 may include information regarding the amount and type of medication taken by user 102, such as insulin or non-insulin diabetic and / or non-diabetic medications taken by user 102.

[0049] In certain embodiments, application 106 may obtain demographic information 119, disease progression information 121, and / or medication information 122 from user 102 in the form of user input or from other sources. In certain embodiments, application 106 may receive updates from user 102 or other sources as some of this information changes. In certain embodiments, user profile 118 associated with user 102, as well as other user profiles associated with other users, are stored in user database 110, which is accessible to application 106, as well as decision support engine 112, via one or more networks (not shown). In certain embodiments, application 106 collects input 127 from user 102 and / or through multiple other sources, including analyte monitoring system 104, other applications running on mobile device 107, and / or one or more other sensors and devices. In certain embodiments, such sensors and devices include, but are not limited to, one or more of an insulin pump, other types of analyte sensors, sensors or devices provided by the mobile device 107 (e.g., an accelerometer, camera, global positioning system (GPS), heart rate monitor, etc.), or other user accessories (e.g., a smart watch), or any other sensors or devices that provide relevant information about the user 102. In certain embodiments, the user profile 118 also stores application configuration information that indicates the current configuration of the application 106, including its features and settings.

[0050] User database 110, in some embodiments, refers to a storage server that may operate in a public or private cloud. User database 110 may be implemented as any type of data store, such as a relational database, a non-relational database, a key-value data store, or a file system, including a hierarchical file system. In some example implementations, user database 110 is distributed. For example, user database 110 may comprise multiple distributed persistent storage devices. Furthermore, user database 110 may be replicated so that the storage devices are geographically distributed.

[0051] User database 110 may include other user profiles 118 associated with multiple other users serviced by health monitoring system 100. More specifically, similar to the operations performed with respect to user 102, operations performed with respect to these other users may utilize analyte monitoring systems, such as analyte monitoring system 104, and may interact with the same application 106, copies of which are running on the respective mobile devices of the other users 102. For such users, user profiles 118 are similarly created and stored in user database 110.

[0052] FIG. 1B illustrates a continuous analyte monitoring system according to certain embodiments of the present disclosure. Diagram 150 illustrates an example of analyte monitoring system 104. In the example of FIG. 1B, analyte monitoring system 104 is a glucose monitoring system. However, as described above, analyte monitoring system 104 may be configured to measure any other analyte or combination of analytes. FIG. 1B illustrates several mobile devices 107a, 107b, 107c, and 107d (individually referred to as mobile device 107 and collectively referred to as mobile devices 107). Note that mobile device 107 in FIG. 1A can be any one of mobile devices 107a, 107b, 107c, or 107d. In other words, any one of mobile devices 107a, 107b, 107c, or 107d may be configured to execute application 106. The analyte monitoring system 104 can be communicatively coupled to mobile devices 107a, 107b, 107c, and / or 107d.

[0053] By way of overview and example, analyte monitoring system 104 may be implemented as an encapsulated microcontroller that performs sensor measurements, generates analyte data (e.g., by calculating values ​​of continuous glucose monitoring data), and engages in wireless communication (e.g., via Bluetooth and / or other wireless protocols) to transmit such data to a remote device, such as mobile device 107. Paragraphs

[0137] -

[0140] and Figures 3A, 3B, and 4 of U.S. Patent Application Publication No. 2019 / 0336053 further describe on-skin sensor assemblies that, in certain embodiments, may be used in connection with analyte monitoring system 104. Paragraphs

[0137] -

[0140] and Figures 3A, 3B, and 4 of U.S. Patent Application Publication No. 2019 / 0336053 are incorporated herein by reference.

[0054] In certain embodiments, the analyte monitoring system 104 includes an analyte sensor electronics module 138 and a continuous analyte sensor 140 (e.g., a glucose sensor) associated with the analyte sensor electronics module 138. In certain embodiments, the analyte sensor electronics module 138 includes electronic circuitry associated with measuring and processing analyte sensor data or information, including algorithms associated with processing and / or calibrating the analyte sensor data / information. The analyte sensor electronics module 138 may be physically / mechanically connected to the analyte sensor 140 and may be integral with (i.e., non-releasably attached to) the analyte sensor 140 or may be releasably attachable.

[0055] The analyte sensor electronics module 138 may also be electrically coupled to the analyte sensor 140 such that the components may be electromechanically coupled to one another. The analyte sensor electronics module 138 may include hardware, firmware, and / or software that enable measurement and / or estimation of the level of an analyte in a user via the analyte sensor 140 (e.g., which may be / may include a glucose sensor). For example, the analyte sensor electronics module 138 may include one or more potentiostats, a power supply for providing power to the analyte sensor 140, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to various devices, including, but not limited to, one or more display devices (e.g., the user's mobile device 107), a user database 110, a decision support engine 112, etc. The electronics may be mounted on a printed circuit board (PCB), platform, or the like within the analyte monitoring system 104 and may take a variety of forms. For example, the electronics may take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, a processor, and / or a state machine.

[0056] The analyte sensor electronics module 138 may include sensor electronics configured to process sensor information, such as sensor data, and generate transformed sensor data and displayable sensor information. Examples of systems and methods for processing analyte data are described in more detail herein, as well as in U.S. Pat. Nos. 7,310,544 and 6,931,327, and U.S. Patent Application Publication Nos. 2005 / 0043598, 2007 / 0032706, 2007 / 0016381, 2008 / 0033254, 2005 / 0203360, 2005 / 0154271, 2005 / 0192557, 2006 / 0222566, 2007 / 0203966, and 2007 / 0208245, all of which are incorporated herein by reference in their entireties.

[0057] The analyte sensor 140 is configured to measure the concentration or level of an analyte in the user 102. The term analyte is further defined in paragraph

[0117] of U.S. Patent Application No. 2019 / 0336053. Paragraph

[0117] of U.S. Patent Application No. 2019 / 0336053 is incorporated herein by reference. In some embodiments, the analyte sensor 140 includes a continuous analyte sensor, such as a subcutaneous, transcutaneous (e.g., transdermal), or intravascular device. In some embodiments, the analyte sensor 140 can analyze multiple intermittent blood samples. The analyte sensor 140 can use any analyte measurement method, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, or immunochemical method. Additional details regarding continuous analyte sensors, such as continuous glucose sensors, are provided in paragraphs

[0072] -

[0076] of U.S. Patent No. 9,445,445. Paragraphs

[0072] -

[0076] of U.S. Patent No. 9,445,445 are incorporated herein by reference.

[0058] 1B , the mobile devices 107 may be configured to display (and / or alert) displayable sensor information that may be transmitted by the sensor electronics module 138 (e.g., in customized data packages transmitted to a display device based on their respective preferences). Each of the mobile devices 107a, 107b, 107c, and / or 107d may include a display, e.g., touchscreen display 109a, 109b, 109c, and / or 109d, respectively, for displaying a graphical user interface (e.g., application 106), for presenting sensor information and / or analyte data to the user 102 and / or for receiving input from the user 102. In certain embodiments, the mobile device 107 may include other types of user interfaces, such as a voice user interface, instead of or in addition to a touchscreen display for communicating sensor information to the user 102 of the mobile device 107 and / or receiving user input. In particular embodiments, one, some, or all of the mobile devices 107 may be configured to display or otherwise communicate sensor information as communicated from the sensor electronics module 138 (e.g., within a data package sent to a respective display device) without any additional anticipated processing required for calibration and / or real-time display of the sensor data.

[0059] The mobile devices 107 may include custom or proprietary display devices, such as an analyte display device 107b, specifically designed to display a particular type of displayable sensor information (e.g., in certain embodiments, numerical values ​​and / or arrows) associated with analyte data received from the sensor electronics module 138. In certain embodiments, one of the mobile devices 107 includes a mobile phone, such as a smartphone using Android, iOS, or another operating system configured to display a graphical representation of continuous sensor data (e.g., including current and / or historical data). As described further herein, the mobile devices 107 may be configured for dynamic determination and presentation of cross-feature correlation insights for the continuous analyte data.

[0060] Exemplary inputs and exemplary metrics generated based on the inputs according to certain embodiments of the present disclosure are illustrated in FIG. 2. FIG. 2 illustrates exemplary inputs 127 on the left, application 106 and DAM 111 in the center, and exemplary metrics 130 on the right. In certain embodiments, application 106 obtains inputs 127 via one or more channels (e.g., manual user input, sensors, other applications running on mobile device 107, etc.). Inputs 127 may be further processed by DAM 111 to output multiple metrics, such as metric 130. Furthermore, the inputs (e.g., inputs 127) and metrics (e.g., metrics 130) may be used by DAM 111 and / or any computing device in system 100 to perform various processes in determining and displaying inter-feature correlation insights to a user, as described further below. Any of inputs 127 may be used to calculate any of metrics 130. In particular embodiments, each of the metrics 130 may correspond to one or more values, for example, a discrete numeric value, a range, or a qualitative value (high / medium / low, or stable / unstable).

[0061] In certain embodiments, input 127 includes food consumption information. Food consumption information may include information about one or more of meals, snacks, and / or beverages, such as one or more of the amount, content (carbohydrates, fat, protein, etc.), order of consumption, and time of consumption. In certain embodiments, food consumption may be provided by a user through manual input, by providing a photograph through an application configured to recognize food types and amounts, and / or by scanning a barcode or menu. In various examples, meal amounts may be manually entered as one or more of calories, amount (e.g., "3 cookies"), menu items (e.g., "Royale with Cheese"), and / or food exchanges (1 fruit, 1 dairy product). In some examples, meals may also be entered along with the user's typical items or combinations for this time or context (e.g., weekday breakfast at home, weekend brunch at a restaurant). In some examples, meal information may be received through a convenient user interface provided by application 106.

[0062] In particular embodiments, input 127 includes activity information. Activity information may be provided, for example, by an accelerometer sensor on a wearable device such as a watch, fitness tracker, and / or patch. In particular embodiments, activity information may also be provided through manual input by user 102. Activity information may include exercise-related information, sleep information, and other types of information related to the user's activity or lack thereof.

[0063] In certain embodiments, input 127 includes patient demographics such as one or more of age, height, weight, body mass index, body composition (e.g., body fat percentage), build, body type, or other information. Patient demographics may be provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from a measurement device. The measurement device may include, for example, one or more of a wireless, e.g., Bluetooth-enabled, scale, and / or camera that may communicate with mobile device 107 to provide patient data.

[0064] In certain embodiments, input 127 includes information regarding a user's medication intake. For example, a user's medication intake may include a user's insulin administration. Such information may be received via a wireless connection on the smart pen, via user input, and / or from an insulin pump. Insulin administration information may include one or more of insulin amount, administration time, etc. Other configurations, such as insulin action time or duration of insulin action, may also be received as input.

[0065] In particular embodiments, input 127 includes information received from a sensor, such as a physiological sensor, which may detect one or more of heart rate, respiration, oxygen saturation, body temperature, etc. (e.g., to detect illness, stress levels, etc.).

[0066] In certain embodiments, input 127 includes glucose information. Such information may be provided as an input, for example, through analyte monitoring system 104. In certain embodiments, blood glucose information may be received from one or more of a smart pill dispenser that tracks when a user takes medication, a blood ketone meter, laboratory-measured or estimated AlC, other measures of long-term management, or a sensor that measures peripheral neuropathy using a tactile response, such as by using the tactile features of a smartphone or specialized device.

[0067] In certain embodiments, input 127 includes a time, for example, the time of day or the time from a real-time clock.

[0068] As described above, in certain embodiments, the DAM 111 determines or calculates metrics 130 based on inputs 127 associated with the user 102. An exemplary list of metrics 130 is illustrated in FIG. 2. In certain embodiments, the metrics 130 determined or calculated by the DAM 111 include metabolic rate. Metabolic rate is a metric that may indicate or include basal metabolic rate (e.g., energy expended at rest) and / or active metabolism, e.g., energy expended by activity such as exercise or exertion. In some examples, basal metabolic rate and active metabolism may be tracked as separate metrics. In certain embodiments, metabolic rate may be calculated by the DAM 111 based on one or more of inputs 127, such as one or more of activity information, sensor input, time, user input, etc.

[0069] In particular embodiments, the metrics 130 determined or calculated by the DAM 111 include an activity level metric. The activity level metric may indicate a level of activity of a user. In particular embodiments, the activity level metric is determined based on input from, for example, an activity sensor or other physiological sensor. In particular embodiments, the activity level metric may be calculated by the DAM 111 based on one or more of the inputs 210, such as one or more of activity information, sensor input, time, user input, etc. The activity level may indicate whether the user is exercising, resting, sleeping, etc.

[0070] In certain embodiments, metrics 130 determined or calculated by the DAM 111 include an insulin sensitivity metric (also referred to hereinafter as "insulin resistance"). The insulin sensitivity metric may be determined using historical data, real-time data, or a combination thereof and may be based on one or more inputs 127, such as, for example, one or more of food consumption information, blood glucose information, insulin administration information, resulting glucose levels, etc. In certain embodiments, the insulin on-board metric may be determined using insulin administration information and / or a known or learned (e.g., from patient data) insulin time-action profile that may account for both basal metabolic rate (e.g., insulin updates to maintain physical activity) and insulin use driven by activity or food consumption.

[0071] In certain embodiments, the metrics 130 determined or calculated by the DAM 111 include meal state metrics. The meal state metrics may indicate a user's state with respect to food consumption. For example, the meal state may indicate whether the user is in one of a fasting state, a pre-meal state, a fed state, a post-meal reaction state, or a stable state. In certain embodiments, the meal state may also indicate remaining nutrients, e.g., meals, snacks, or beverages consumed, which may be determined from food consumption information, mealtime information, and / or digestibility information, which may be correlated to food type, amount, and / or order (e.g., which food / drink was eaten first).

[0072] In particular embodiments, the metrics 130 determined or calculated by the DAM 111 include health and illness metrics. The health and illness metrics may be determined, for example, from physiological sensors (e.g., body temperature), activity sensors, or a combination thereof, based on one or more of user inputs (e.g., pregnancy information or known illness information). In particular embodiments, based on the values ​​of the health and illness metrics, for example, the user's state may be defined as one or more of healthy, sick, rested, or fatigued. In particular embodiments, the health and illness metrics may indicate the user's heart rate, stress level, etc.

[0073] In certain embodiments, the metrics 130 determined or calculated by the DAM 111 include a glucose level metric. The glucose level metric may be determined from sensor information (e.g., blood glucose information obtained from the analyte monitoring system 104). In some examples, the glucose level metric may also be determined, for example, based on historical information regarding glucose levels in particular situations, for example, taking into account a given combination of food consumption, insulin, and / or activity. In certain embodiments, a blood glucose trend may be determined based on glucose levels over a particular period of time.

[0074] In certain embodiments, the metrics 130 determined or calculated by the DAM 111 include a disease stage. For example, disease stages for type 2 diabetes may include a prediabetes stage, an oral treatment stage, and a basal insulin treatment stage. In certain embodiments, the degree of glycemic control (not shown) may also be determined as an outcome metric and may be based, for example, on one or more of glucose levels, glucose level variability, or insulin dosing patterns.

[0075] In certain embodiments, the metrics 130 determined or calculated by the DAM 111 include clinical metrics. Clinical metrics generally indicate the clinical state a user is in with respect to one or more of the user's conditions, such as diabetes. For example, in the case of diabetes, clinical metrics may be determined based on blood glucose measurements including one or more of A1C, A1C trend, time in range, time spent below a threshold level, time spent above a threshold level, and / or other metrics derived from blood glucose levels. In certain embodiments, clinical metrics may also include one or more of estimated A1C, blood glucose variability, hypoglycemia, and / or health indicators (amount of time out of target zone).

[0076] In certain embodiments, metrics 130 determined or calculated by DAM 111 include inter-feature correlation insights. Inter-feature correlation insights provide a user with information based on correlations determined between analyte features and correlation features. As described further below, certain embodiments described herein relate to processes that include identifying one or more analyte features and one or more correlation features, identifying analyte feature trends and correlation feature trends, and identifying inter-feature correlation insights. In certain embodiments, the analyte features and / or correlation features may include or be based on any input data (e.g., input 127) and / or metrics (e.g., metrics 130). As described further below, inter-feature correlation insights are determined correlations between a user's analyte features and the user's correlation features. Examples of analyte features include time in range, A1C analyte features, high glucose features, low glucose features, etc. In certain embodiments, the analyte features may be based on a variety of data, including, but not limited to, input 127. An analyte feature trend is any trend associated with an analyte feature, as described further below. In certain embodiments, the analyte feature trend may be based on a variety of data, including, but not limited to, metrics 130.

[0077] Additionally, a correlation feature is a feature that may be analyzed for potential correlation with an analyte feature. Examples of correlation features include sleep level, medication intake, food intake, and activity level, stress, heart rate, etc. A correlation feature trend is any trend associated with a correlation feature, as described further below. In certain embodiments, a correlation feature trend may be based on various data, including, but not limited to, metrics 130. Additionally, inter-feature correlation insights may be determined between one or more analyte features of a user and one or more correlation features of a user, as described further below. In certain embodiments, the inter-feature correlation insights may be stored as metrics 130. In various embodiments, dynamic determination and presentation of inter-feature correlation insights may utilize various UIs, as described further below.

[0078] Exemplary user interface for use with dynamic determination and presentation of inter-feature correlation insights 3 is a flow chart illustrating an example user interface (UI) for use with the dynamic determination and presentation of inter-feature correlation insights in accordance with certain embodiments of the present disclosure. Flow chart 300 includes five types of UIs that may be utilized in the dynamic determination and presentation of inter-feature correlation insights. In certain embodiments, the five types of UIs may include: (1) a set of feature selection UIs; (2) an insight flagging UI; (3) a cross-time insight interaction UI; (4) a set of flagged insight engagement UIs; and (5) a set of content engagement UIs. The various UIs may be generated and presented using a software application (e.g., application 106) executing on a computing device (e.g., mobile device 107). In certain embodiments, flow chart 300 may include an insight reporting configuration UI 301 for configuring insight reporting settings, such as the frequency at which insights are reported to a user and other aspects of the reporting settings. However, in some embodiments, insight reporting configuration UI 301 may be omitted. In such an embodiment, the flowchart 300 may begin with a set of feature selection UIs, as further described below.

[0079] Using the insight reporting configuration UI 301, the computing device may present insight reporting settings for the user to configure. For example, the insight reporting configuration UI 301 includes a prompt text element that presents a general question regarding how frequently the user wants to receive cross-feature correlation insights. The insight reporting configuration UI 301 may also include one or more checkbox elements that allow the user to select an answer to the presented question. In the depicted example, the user is presented with a first checkbox and first text element (e.g., “all day”), a second checkbox and second text element (e.g., “several times a day”), a third checkbox and third text element (e.g., “once a day”), a fourth checkbox and fourth text element (e.g., “every other day”), and a first checkbox and first text element (e.g., “once a week”). The computing system may then configure the frequency with which cross-feature correlation insights are reported to the user based on the user selection.

[0080] The computing device may then present a set of feature selection UIs (e.g., analyte feature selection UI 302 and correlation feature selection UI 303) that enable user selection of analyte features and correlation features. For example, analyte feature selection UI 302 enables user selection of one or more analyte features for which inter-feature correlation insights are to be determined. Analyte feature selection UI 302 includes a prompt text element that presents a question related to the analyte feature. Additionally, analyte feature selection UI 302 also includes one or more checkbox elements that enable selection of analyte features as well as other user goals (e.g., in the depicted example, the user goal of obtaining assistance with the user's efforts). In the depicted example, the user is presented with (1) a first checkbox element and a first text element associated with a combination of a time-in-range analyte feature and an A1C analyte feature, (2) a second checkbox element and a second text element associated with a combination of a high glucose analyte feature and a low glucose analyte feature, and (3) a third checkbox element and a third text element associated with the user goal of obtaining assistance with the user's efforts. Additionally, the analyte feature selection UI 302 may also include a button element (eg, a "Next" button element) that allows transition from the analyte feature selection UI 302 to a subsequent UI (eg, the correlation feature selection UI 303).

[0081] Using the correlated feature selection UI 303, the computing device may enable user selection of one or more correlated features for which inter-feature correlation insights are to be determined. For example, the correlated feature selection UI 303 includes a prompt text element that presents a question related to the correlated feature. Furthermore, the correlated feature selection UI 303 also includes checkbox elements that enable selection of the correlated feature. In the depicted example, the correlated feature selection UI 303 includes (1) a first checkbox element and a first text element associated with a food intake-related correlated feature, (2) a second checkbox element and a second text element associated with a medication intake-related correlated feature, (3) a third checkbox element and a third text element associated with a physical activity-related correlated feature, (4) a fourth checkbox element and a fourth text element associated with a stress level-related correlated feature, and (5) a fifth checkbox element and a first text element associated with a sleep habit-related correlated feature. In addition, the correlated feature selection UI 303 also includes a button element (e.g., an “Exit” button element) that enables transition from the correlated feature selection UI 303 to a subsequent UI, such as the insight flagging UI 304.

[0082] In certain embodiments, displaying the set of feature selection UIs may occur during a sensor warm-up session, which refers to the period after the software application 106 and the continuous analyte monitoring system 104 initially connect. During this period, the continuous analyte monitoring system 104 may be referred to as acclimating. In such embodiments, analyte data may be measured and transmitted to the software application 106 by the continuous analyte monitoring system 104 during this period, but the measurements may not be displayed or reported to the user.

[0083] After determining the inter-feature correlation insights between the analyte features and the correlation features using the insight flagging UI 304, the computing device may display the determined inter-feature correlation insights to the user, allowing the user to flag the inter-feature correlation insights and initiate engagement with educational content associated with the inter-feature correlation insights, as described further below. For example, the insight flagging UI 304 includes a text box element that depicts a notification for content data other than the inter-feature correlation insight (e.g., "Sleep"). Additionally, the insight flagging UI 304 includes an insight interaction element that allows the user to engage with the inter-feature correlation insights. As depicted, the insight interactive elements depict an insight text box element that provides information including a correlation magnitude profile of the cross-feature correlation insight, an upper button element (e.g., an "Add to Follow-Up" button element) that allows the user to flag the depicted cross-feature correlation insight for discussion in a follow-up medical appointment, and a lower button element (e.g., a "Learn How Sleep Affects TIR" button element) that allows the user to engage with educational content related to the depicted cross-feature correlation insight.

[0084] Using the inter-time insight interactive UI 306, the computing device may allow a user to view correlation magnitude data for flagged inter-feature correlation insights for various user-selected correlation periods, as described further below. For example, the inter-time insight interactive UI 306 includes a trend line depiction element that depicts a set of variation trend lines for a selected set of features. Additionally, the inter-time insight interactive UI 306 includes a first layer of button elements that allow a user to select a correlation period from a set of available correlation periods. In particular embodiments, a default value for the selected correlation period for the depicted inter-feature correlation insight may be determined based on the correlation score associated with the depicted inter-feature correlation insight over the set of correlation periods. The inter-time insight also includes a second layer of button elements that allow a user to select a feature whose variation trend line for the selected correlation period is depicted within the trend line depiction element.

[0085] Using the flagged insight engagement UI 308, the computing device may enable a user to edit inter-feature correlation insights that were previously flagged using the insight flagging UI and assign various metadata fields to the flagged inter-feature correlation insights, as described further below. For example, the flagged insight engagement UI 308 includes navigation elements (e.g., a selected “Follow Up” navigation element) that enable a user to navigate between various UIs, as described herein. The flagged insight engagement UI 308 also includes a top button element (e.g., a “Select Appointment Date” button element) that enables a user to assign a future timestamp (e.g., appointment date) to the flagged inter-feature correlation insight displayed by the flagged insight engagement UI 308.

[0086] The flagged insight engagement UI 308 also includes a modification button element (e.g., an “Edit” button element) for each depicted inter-feature correlation insight that enables modifying the description of the inter-feature correlation insight and adding explanatory metadata to the inter-feature correlation insight. For example, the flagged insight engagement UI 308 may include (1) a first inter-feature correlation insight (e.g., “Nighttime glucose spiking”) and a first modification button element, (2) a second inter-feature correlation insight (e.g., “Daily gym with no impact on A1C”) and a second modification button element, and (3) a third inter-feature correlation insight (e.g., “Sleep 10% less this month”) and a third modification button element. Furthermore, the flagged insight engagement UI 308 also includes a bottom button element (e.g., an “Add to Follow-Up” button element) that enables flagging the depicted inter-feature correlation insight for discussion at a follow-up medical appointment.

[0087] Using the content engagement UI 310, the computing device may enable the user to engage with educational content selected for the user, including educational content selected for the user based on flagged inter-feature correlation insights, as described further below. For example, the content engagement UI 310 includes an educational content text element that is the title of the educational content being presented (e.g., "How Sleep Habits Affect Glucose Levels"). The user may engage with the educational content using a start element, and the user may rate particular educational content using a rating element (e.g., 1 to 5 stars).

[0088] Exemplary Process for Dynamically Determining and Presenting Inter-Feature Correlation Insights Using a User Interface FIG. 4 is a flow diagram illustrating an example process 400 for dynamic determination and presentation of cross-feature correlations, according to certain embodiments of the present disclosure.

[0089] At block 402, process 400 includes identifying at least one analyte feature and at least one correlation feature. As further described above, the analyte feature is a feature (e.g., a time-in-range feature, an A1C analyte feature, a high glucose feature, a low glucose feature, etc.) determined based on sensor data generated by analyte sensor 140. The correlation feature is a feature that may have a potential correlation to the analyte feature (e.g., a sleep level feature, a medication intake feature, a food intake feature, an exercise level feature, an activity feature, a stress level feature, a heart rate feature, etc.).

[0090] In certain embodiments, the at least one feature and the at least one correlation feature may be identified by providing a feature selection UI to the user. For example, a computing device (e.g., a mobile device 107 running the software application 106) may be configured to generate and display one or more feature selection UIs, such as the feature selection UIs of FIG. 3 (e.g., the analyte feature selection UI 302 and / or the correlation feature selection UI 303). The feature selection UIs allow the user to select analyte features and correlation features, which are then used to determine inter-feature correlation insights, as described further below. In some embodiments, two different feature selection UIs, an analyte feature selection UI and a correlation feature selection UI, are presented to the user, as described further below with reference to FIGS. 5 and 6, respectively.

[0091] At block 404, process 400 includes determining an analyte feature trend for each of the at least one identified analyte feature. In some embodiments, application 106 and / or DAM 11 may determine the analyte feature trend by determining the change in the value of the analyte feature over time. In various embodiments, the analyte feature trend describes the variation of the analyte feature over a period of time. For example, if the identified analyte feature (from block 402) is a time-in-range feature, the analyte feature trend includes the trend of time-in-range variation over a period of time. In another example, if the identified analyte feature (from block 402) is an A1C feature, the analyte feature trend includes the trend of A1C variation over a period of time.

[0092] In yet another example, if the identified analyte feature (from block 402) is a high glucose feature, the analyte feature trend includes a trend of high glucose variability over a period of time, where high glucose variability refers to a change in the user's maximum glucose reading over a series of periods of time. In a further example, if the identified analyte feature (from block 402) is a low glucose feature, the analyte feature trend includes a trend of low glucose variability over a period of time, where low glucose variability refers to a change in the user's minimum glucose reading over a series of periods of time. In some embodiments, determining the analyte feature trend includes receiving sensor data from the analyte monitoring system 104 indicative of the user's analyte levels, such that the sensor data can be utilized to determine the variability of the analyte over a period of time.

[0093] At block 406, process 400 includes determining a correlated feature trend for each of the at least one identified correlated feature. In some embodiments, application 106 and / or DAM 11 may determine the correlated feature trend by determining a change in the value of the correlated feature over time. In various embodiments, the correlated feature trend describes the variation of the correlated feature over a period of time. For example, if the identified correlated feature (from block 402) is a sleep level feature, the correlated feature trend may include trends in the variation of sleep length over a period of time, the variation of sleep quality over a period of time, etc. In another example, if the identified correlated feature (from block 402) is a medication intake feature (e.g., insulin intake feature), the correlated feature trend may include a trend in medication (e.g., insulin) intake over a period of time. In a further example, if the identified correlated feature (from block 402) is a food intake feature, the correlated feature trend may include a trend in food intake over a period of time.

[0094] At block 408, process 400 includes determining at least one inter-feature correlation insight based on the identified analyte feature trends and the identified correlated feature trends. In certain embodiments, the computing device may determine the one or more inter-feature correlation insights by determining one or more correlations between the determined analyte feature trends and the determined correlated feature trends, as further described above. In various embodiments, the inter-feature correlation insights represent correlations between each analyte feature from the set of analyte features and each correlated feature from the set of correlated features.

[0095] In some embodiments, determining that a correlation exists between each analyte feature and each correlation feature includes determining that the analyte feature trend for each analyte feature and the correlation feature trend for each correlation trend are correlated over at least T correlation periods (where T may be a threshold that may be predetermined, set by a user, set by an application, etc.). For example, a computing device (e.g., a mobile device or backend server) determines correlation scores for the analyte feature trend and the correlation feature trend over different correlation periods until the determined correlation scores include T correlation scores that meet the correlation score threshold or until correlation scores have been determined over all available correlation periods without identifying correlation scores that meet the T threshold. In such an example, if correlation scores that meet the T thresholds are determined, the computing device determines that a correlation exists between each analyte feature and each correlation feature. However, if correlation scores are determined over all available correlation periods without identifying correlation scores that meet the T thresholds, the computing device determines that no correlation exists between each analyte feature and each correlation feature.

[0096] In other embodiments, determining at least one inter-feature correlation insight may include a computing device determining whether a correlation exists between each analyte feature and each correlation feature by determining a set of correlation scores for each analyte feature trend and each correlation feature trend over a set of correlation periods. In such embodiments, the computing device determines that each analyte feature and each correlation feature are correlated if a central tendency measure (e.g., mean measure, median measure, etc.) for the correlation scores meets (e.g., exceeds) a first threshold and a statistical deviation measure (e.g., variance measure, standard deviation measure, etc.) for the correlation scores does not meet (e.g., does not exceed) a second threshold.

[0097] In further embodiments, determining at least one inter-feature correlation insight may include a computing device determining whether a correlation exists between each analyte feature and each correlated feature by determining a set of correlation scores for each analyte feature trend and each correlated feature trend over a set of correlation periods and providing the correlation scores as input data to a machine learning model. In such embodiments, the machine learning model may be configured to determine an output score for each analyte feature trend and each correlated feature. For example, the output score may describe a predicted / calculated likelihood that each analyte feature and each correlated feature are statistically correlated. In another example, the output score may describe a predicted / calculated likelihood that each analyte feature and each correlated feature are statistically correlated and that the statistical correlation is likely to be of interest to a user. In such an example, the computing device may determine that each analyte feature and each correlated feature are correlated if the output score generated by the machine learning model meets a threshold.

[0098] In further embodiments, determining at least one inter-feature correlation insight may include a computing device determining whether a correlation exists between each analyte feature and each correlation feature by determining a set of analyte feature trend segments for each analyte feature trend over the set of available correlation periods. In such embodiments, the computing device may also determine a set of correlation feature trend segments for each correlation feature trend over the set of available correlation periods. Further, the computing device may use a time series comparison model to compare the analyte feature trend segments and correlation feature trend segments for each correlation period to determine a correlation score for the correlation period. In such embodiments, the computing device may determine that each analyte feature and each correlation feature are correlated if the determined correlation scores include at least T correlation scores.

[0099] In further embodiments, determining at least one inter-feature correlation insight may include a computing device determining whether a correlation exists between each analyte feature and each correlation feature by determining a set of analyte feature trend segments for each analyte feature trend over a set of available correlation periods. The computing device may then determine a set of correlation feature trend segments for each correlation feature trend over the set of available correlation periods. In such embodiments, the computing device may use a time series comparison model to compare the analyte feature trend segments and the correlation feature trend segments for each correlation period to determine a correlation score for the correlation period. In such embodiments, the computing device may determine that each analyte feature and each correlation feature are correlated if a central tendency measure (e.g., mean measure, median measure, etc.) for the associated correlation score meets (e.g., exceeds) a first threshold and a statistical deviation measure (e.g., variance measure, standard deviation measure, etc.) for the correlation score does not meet (e.g., does not exceed) a second threshold.

[0100] At block 410, process 400 includes determining a correlation magnitude profile for each inter-feature correlation insight over the available time period. In this step, the computing device determines how the correlation magnitude for each insight changes as the user modifies the correlation period of interest (e.g., from a weekly correlation period to a monthly correlation period). In various embodiments, each correlation magnitude profile is associated with a respective inter-feature correlation insight and a respective correlation period and describes correlation magnitude data for each inter-feature correlation insight over the respective correlation period. For example, each correlation magnitude profile describes (i) the magnitude of change of each analyte feature for each inter-feature correlation insight between a current time period whose length corresponds to the respective correlation period and a previous (e.g., immediately preceding) time period whose length corresponds to the respective correlation period, and (ii) the magnitude of change of each correlation feature between a current time period whose length corresponds to the respective correlation period and a previous (e.g., immediately preceding) time period whose length corresponds to the respective correlation period.

[0101] In some embodiments, the correlation magnitude profile may describe correlation scores for analyte feature trend segments and correlation feature trend segments, where the analyte feature trend segments are segments of analyte feature trends associated with respective inter-feature correlation insights associated with respective correlation periods, and the correlation feature trend segments are segments of correlation feature trends associated with respective inter-feature correlation insights associated with respective correlation periods.

[0102] In some embodiments, the correlation magnitude profile may describe correlation scores of analyte feature trend segments and correlation feature trend segments, where the analyte feature trend segments are segments of analyte feature trends associated with each inter-feature correlation insight associated with each correlation period, and the correlation feature trend segments are segments of correlation feature trends associated with each inter-feature correlation insight associated with each correlation period, in such embodiments, the correlation scores of each inter-feature correlation insight are normalized.

[0103] At block 412, process 400 may include displaying inter-feature correlation insights using an insight UI. For example, one or more inter-feature correlation insights may be provided using an insight flagging UI, and a correlation magnitude profile may be provided using an inter-time insight interaction UI. Further, user engagement with the inter-feature correlation insights may be provided using a flagged insight engagement UI. Additionally, educational content associated with the inter-feature correlation insights may be provided using a content engagement UI, as described further below.

[0104] Block 402 will now be described in more detail with reference to the following Figures 5-6, which illustrate feature selection UIs. In particular, block 402 will be described in more detail with reference to the analyte feature selection UI (illustrated in Figure 5) and the correlation feature selection UI (illustrated in Figure 6). Additionally, block 412 will be described in more detail with reference to the following Figures 7-13, which illustrate insight UIs. In particular, block 412 will be described in more detail with reference to the insight flagging UI (illustrated in Figure 7), the inter-temporal insight interaction UI (illustrated in Figure 8), the flagged insight engagement UI (illustrated in Figures 9-11), and the content engagement UI (Figures 12-13).

[0105] 1. Block 402: Identify analytes and correlation features using the feature selection UI 4, at block 402, process 400 may include identifying at least one analyte feature and at least one correlation feature. In some embodiments, the at least one analyte feature may be identified using an analyte feature selection UI. In some embodiments, the at least one correlation feature may be identified using a correlation feature selection UI.

[0106] a. Analyte Feature Selection UI FIG. 5 illustrates an analyte feature selection UI for identifying at least one analyte feature (block 402) according to certain embodiments of the present disclosure. The analyte feature selection UI 500 allows a user to select one or more analyte features for which inter-feature correlation insights are to be determined. The analyte feature values ​​are determined based on sensor data provided by the analyte monitoring system 104. Referring to FIG. 5, the analyte feature selection UI 500 includes a prompt text element 502 that presents a question related to the analyte feature. Additionally, the analyte feature selection UI 500 may also include one or more checkbox elements that allow the user to select an analyte feature as well as other user goals (e.g., in the depicted example, the user goal of obtaining assistance with the user's efforts). In the depicted example, the user is presented with a first checkbox element 504 and a first text element 510 associated with a combination of a time-in-range analyte feature and an A1C analyte feature, a second checkbox element 506 and a second text element 512 associated with a combination of a high glucose analyte feature and a low glucose analyte feature, and a third checkbox element 508 and a third text element 514 associated with a user goal of obtaining assistance with the user's efforts. Further, the analyte feature selection UI 500 may also include a button element 516 (e.g., a "Next" button element) that enables transitioning from the analyte feature selection UI 500 to a subsequent UI (e.g., a correlation feature selection UI).

[0107] b. Correlation feature selection UI FIG. 6 illustrates a correlation feature selection UI for identifying at least one correlation feature (block 402) according to certain embodiments of the present disclosure. The correlation feature selection UI 600 allows a user to select one or more correlation features for which cross-feature correlation insights are determined. The correlation feature may be a feature whose correlation with an analyte feature is determined and presented using the cross-feature correlation insights. In some embodiments, the correlation feature may be based on data manually or automatically entered from another application (e.g., diet manually logged into a My Fitness Pal application, prescription information from an online pharmacy application, etc.). In some embodiments, the correlation feature may be based on sensor data provided by one or more sensor devices (e.g., wearable sensor devices, smartphone sensor devices, etc.) that report a user's condition. For example, a wearable sensor device (e.g., a Fitbit device) may provide data about a user's activity level. In this example, data received from the wearable sensor device may be used to determine the correlation feature.

[0108] 6 , the correlated feature selection UI 600 includes a prompt text element 602 that presents a question related to the correlated feature. Additionally, the correlated feature selection UI 600 may also include checkbox elements that enable selection of the correlated feature. In the depicted example, the correlated feature selection UI 600 includes a first checkbox element 604 and a first text element 614 associated with a food intake-related correlated feature, a second checkbox element 606 and a second text element 616 associated with a medication intake-related correlated feature, a third checkbox element 618 and a third text element 618 associated with a physical activity-related correlated feature, a fourth checkbox element 610 and a fourth text element 620 associated with a stress level-related correlated feature, and a fifth checkbox element 612 and a first text element 622 associated with a sleep habit-related correlated feature. Additionally, the correlated feature selection UI 600 also includes a button element 624 (e.g., an “Exit” button element) that enables transition from the correlated feature selection UI to a subsequent UI, such as one or more home page UIs that display inter-feature correlation insights, as described further below.

[0109] 2. Block 412: Display inter-feature correlation insights using insight UI As described above with reference to FIG. 4 , at block 412, process 400 may include displaying the inter-feature correlation insights using an insight UI. In some embodiments, the computing device may generate and display an insight flagging UI that allows a user to flag inter-feature correlation insights for user interaction and / or engagement. Further, the computing device may generate and display a cross-time insight interaction UI that allows a user to select a correlation period and visualize a correlation magnitude profile of the flagged inter-feature correlation insights for the user-selected correlation period. In addition, the computing device may generate and display a set of flagged insight engagement UIs that allow a user to edit the flagged inter-feature correlation insights and assign metadata fields. Further, the computing device may generate and display a set of content engagement UIs that allow a user to engage with curated educational content based on the flagged inter-feature insights.

[0110] a. Insight Flagging UI FIG. 7 illustrates an insight flagging UI for displaying inter-feature correlation insights (block 412) as part of process 400 of FIG. 4 , according to certain embodiments of the present disclosure. The insight flagging UI 700 is used to display determined inter-feature correlation insights and enable a user to flag inter-feature correlation insights for interaction and / or engagement (e.g., for discussion at an upcoming medical appointment). As further described above, inter-feature correlation insights represent inferred correlations between analyte feature trends for an analyte feature and correlation feature trends for a correlation feature. Each feature trend describes the variation of a feature over time. Thus, an analyte feature trend describes the variation of the corresponding analyte feature over time, while a correlation feature trend describes the variation of the corresponding correlation feature over time. For example, an analyte feature trend for a time-in-range feature describes the variation of a user's time-in-range feature over time. As another example, a correlation feature trend for a sleep-correlated feature describes the variation of a user's sleep-correlated feature over time. In some embodiments, the cross-feature correlation insights may be associated with a correlation magnitude profile that describes the estimated magnitude of the correlation between the analyte feature trend and the correlation feature trend. For example, a correlation magnitude profile of cross-feature correlation insights associated with a time-in-range feature and a sleep correlation feature may describe that a 20 percent increase in weekly sleep resulted in a 10 percent increase in weekly time-in-range.

[0111] 7 , the insight flagging UI 700 includes an add button element 702 (i.e., a “+” button element) that allows additional sensor devices and / or additional applications to be connected to the computing device's application 106. The insight flagging UI 700 also includes a navigation element (i.e., a selected “General” navigation element 704) that allows the user to navigate between various UIs, as described herein. The insight flagging UI 700 also includes text box elements that depict notifications for content data other than inter-feature correlation insights. For example, the insight flagging UI 700 includes a first text box element 706 for activity-related content, a second text box element 708 for sleep-related content, and a third text box element 710 for content related to displaying previous events. Additionally, the insight flagging UI 700 includes an insight interaction element 712 for each inter-feature correlation insight that allows the user to interact with the inter-feature correlation insight. As depicted, the insight interactive element 712 for the inter-feature correlation insight depicts an insight text box element 714 depicting a correlation magnitude profile of the inter-feature correlation insight, an upper button element 716 (i.e., "Add to Follow-Up" button element) that allows the user to flag the depicted inter-feature correlation insight for discussion in a follow-up medical appointment, and a lower button element 718 (i.e., "Learn How Sleep Affects TIR" button element) that allows the user to engage with educational content related to the depicted inter-feature correlation insight. In some embodiments, when a user flags a inter-feature correlation insight, the software application adds an indication of the inter-feature correlation insight to the flagged insight engagement UI, as described further below.

[0112] b. Interactive time insight dialogue UI FIG. 8 illustrates a cross-time insight interactive UI for displaying cross-feature correlation insights (block 412) as part of process 400 of FIG. 4 in accordance with certain embodiments of the present disclosure. The cross-time insight interactive UI 800 allows a user to select a correlation period for a cross-feature correlation insight and display a correlation magnitude profile for the cross-feature correlation insight in relation to the selected correlation period. In some embodiments, given a cross-feature correlation insight between an analyte feature and a correlation feature, all of the correlation magnitude profiles for the cross-feature correlation insight across all of the available correlation periods are pre-calculated. Pre-calculating the correlation magnitude profiles increases the runtime responsiveness and real-time speed of the cross-time insight interactive UI. In some embodiments, the cross-time insight interactive UI 800 may be associated with a corresponding one of one or more cross-feature correlation insights, allow user selection of a selected correlation period from among the available correlation periods, and display a correlation magnitude profile for the corresponding cross-feature correlation insight across the selected correlation period in response to user selection of the selected correlation period.

[0113] 8 , the cross-time insights interactive UI 800 includes a navigation element (i.e., a selected “insight” navigation element 802) that allows a user to navigate between various UIs as described herein. The cross-time insights interactive UI 800 also includes a trend line depiction element 804 that depicts a set of variation trend lines for a selected set of features. Additionally, the cross-time insights interactive UI 800 includes a first layer of button elements 806 that allows for selection of a correlation period from a set of available correlation periods. In particular embodiments, a default value for the selected correlation period for a depicted cross-feature correlation insight may be determined based on the correlation score associated with the depicted cross-feature correlation insight over the set of correlation periods.

[0114] For example, in such embodiments, given a set of A analyte features, B correlation features, and C correlation periods, the computing device determines an A*B*C correlation score. In these embodiments, each correlation score (i) is associated with a respective one of the analyte features, a respective one of the correlation features, and a respective one of the correlation periods, and (ii) describes the degree of correlation between the analyte feature trend for the respective analyte feature and the correlation feature trend for the respective correlation feature over the respective correlation period. For example, one correlation score may describe the degree of correlation between the time-within-range feature trend and the sleep feature trend over a week correlation period.

[0115] In some embodiments, given a depicted inter-feature correlation insight associated with each analyte feature and each correlated feature, the default correlation period initially selected for displaying correlation magnitude data for the depicted inter-feature correlation insight may be the period having the highest correlation score among the correlation scores for each analyte feature trend and each correlated feature trend. In some embodiments, given a depicted inter-feature correlation insight associated with each analyte feature and each correlated feature, the default correlation period initially selected for displaying correlation magnitude data for the depicted inter-feature correlation insight may be the period having the median correlation score among the correlation scores for each analyte feature trend and each correlated feature trend.

[0116] 8 , the inter-time insight interactive UI 800 also includes a second layer of button elements 808 that allow a user to select a feature whose variation trend line for a selected correlation period is to be depicted within the trend line depiction element. In some embodiments, the inter-time insight interactive UI 800 may include a text box element depicted above the trend line depiction element 804 that displays the depicted inter-feature correlation insight and correlation magnitude data associated with the selected correlation period. The text box element may be a dynamic element of the inter-time insight interactive UI 800 that changes as the user-selected correlation period changes via interaction with the first layer of button elements 806.

[0117] c. Flagged Insights Engagement UI 9 illustrates a flagged-insight engagement UI for displaying inter-feature correlation insights (block 412) as part of process 400 of FIG. 4 in accordance with certain embodiments of the present disclosure. The flagged-insight engagement UI 900 allows a user to view inter-feature correlation insights previously flagged by the user, edit flagged inter-feature correlation insights, and assign metadata fields (e.g., future timestamps such as future appointment dates) to the inter-feature correlation insights. As depicted in FIG. 9, the flagged-insight engagement UI 900 includes a navigation element (i.e., a selected “follow-up” navigation element 902) that allows a user to navigate between various UIs as described herein. The flagged-insight engagement UI 900 is a top button element (i.e., a “select appointment date” button element 904) that allows a user to assign a future timestamp (e.g., appointment date) to the flagged inter-feature correlation insight displayed by the flagged-insight engagement UI 900.

[0118] For example, user selection of top button element 904 may cause the display of a timestamp selection element (e.g., date picker element 1002 depicted in FIG. 10 ) to select a future timestamp from a set of available timestamps. Specifically, FIG. 10 illustrates another flagged insight engagement UI for assigning a future timestamp to the flagged inter-feature correlation insight of FIG. 9 in accordance with certain embodiments of the present disclosure. The flagged insight engagement UI 1000 may include a date picker element 1002 that allows a user to assign a future timestamp (e.g., appointment date) to the flagged inter-feature correlation insight. In some embodiments, a default value for the future timestamp may be set to the future medical appointment date indicated by data received from a calendar software application.

[0119] 9 , the flagged-insight engagement UI 900 also includes a modify button element (e.g., an “Edit” button element) for each depicted inter-feature correlation insight that allows for modifying the description of the inter-feature correlation insight and adding explanatory metadata to the inter-feature correlation insight. For example, the flagged-insight engagement UI 900 may include a first inter-feature correlation insight 906 (e.g., “Nighttime glucose spiking”) and a first modify button element 912, a second inter-feature correlation insight 908 (e.g., “Daily gym with no impact on A1C”) and a second modify button element 914, and a third inter-feature correlation insight 910 (e.g., “Sleep 10% less this month”) and a third modify button element 916. User selection of a modify button element (e.g., the first, second, and third modify buttons 906, 908, 910) for a inter-feature correlation insight may cause the display of another flagged-insight engagement UI for modifying the flagged inter-feature correlation insight. 11 illustrates another flagged-insight engagement UI for modifying the flagged inter-feature correlation insight of FIG. 9 in accordance with certain embodiments of the present disclosure. The flagged-insight engagement UI 1100 allows for editing metadata fields associated with the selected inter-feature correlation insight and / or adding new metadata fields (e.g., new multimedia content files) for the selected inter-feature correlation insight. For example, the flagged-insight engagement UI 1100 may include a text element 1102 that describes an item related to the flagged inter-feature correlation insight and a confirmation element 1104 that allows a user to confirm the information in the text element 1102.

[0120] 9, the flagged insight engagement UI 900 includes an add button element 920 (i.e., a "+" button element) that allows for adding new items to the flagged insight engagement UI (e.g., other unflagged inter-feature correlation insights, user-entered follow-up items, etc.). Additionally, the flagged insight engagement UI 900 also includes a bottom button element 918 (i.e., an "add to follow-up" button element) that allows for flagging the depicted inter-feature correlation insight for discussion in a follow-up medical appointment.

[0121] d. Content Engagement UI The content engagement UI enables user engagement with educational content curated for the user. In various embodiments, the content engagement UI enables displaying curated educational content based on determined inter-feature correlation insights for the user, based on analyte features and correlation features selected by the user, and / or based on any other user input (e.g., application objectives selected by the user).

[0122] 12 illustrates a content engagement UI for displaying inter-feature correlation insights determined as part of process 400 of FIG. 4, in accordance with certain embodiments of the present disclosure. Content engagement UI 1200 includes an educational content text element 1202 that is the title of the educational content (i.e., "How Sleep Habits Affect Glucose Levels"). A user may engage with the educational content using a start element 1204, and a user may rate the particular educational content using a rating element 1206 (e.g., 1-5 stars).

[0123] FIG. 13 illustrates another content engagement UI for engaging with the educational content of FIG. 12 , in accordance with certain embodiments of the present disclosure. When a user selects start element 1204, the user may be presented with a content engagement UI 1300 that includes an educational content text element 1302. Additionally, the user may be presented with an engagement prompt element 1304 (i.e., “How many hours of sleep do you aim to achieve each night?”), and the user may provide an answer using a first checkbox element 1306 and a first text element 1314 (i.e., “0-4 hours”), a second checkbox element 1308 and a second text element 1316 (i.e., “4-6 hours”), a third checkbox element 1310 and a third text element 1318 (i.e., “6-8 hours”), and a fourth checkbox element 1312 and a fourth text element 1320 (i.e., “8+ hours”). In some embodiments, the content engagement UI 1300 may include a next element 1322 that enables the user to view and engage with more educational content.

[0124] Exemplary Apparatus for Dynamic Determination and Presentation of Inter-Feature Correlation Insights 14 is a block diagram depicting a computing device 1400 configured for dynamic determination and presentation of cross-feature correlation insights according to certain embodiments disclosed herein. While depicted as a single physical device, in embodiments, computing device 1400 may be implemented using virtual devices and / or across several devices, such as in a cloud environment. Computing device 1400 may be a mobile device 107, a server, multiple services, or any combination thereof.

[0125] As illustrated, computing device 1400 includes one or more processors 1402, memory 1404 (e.g., volatile or non-volatile memory), a network interface 1410, and one or more input / output (I / O) interfaces 1408. In the illustrated embodiment, processor 1402 retrieves and executes programming instructions stored in memory 1404 and stores and retrieves data resident in memory 1404. In particular embodiments, memory 1404 is configured to store instructions (e.g., computer-executable code, device applications 1416) that, when executed by processor 1402, cause processor 1402 to perform the processes and / or operations described herein and illustrated in FIGS. 3-13. In particular embodiments, memory 1404 stores code for performing functions of DAM 111, decision support engine 112, and / or applications 106. It should be noted that the computing device 1400 may be configured to perform only one function of the DAM 111, the decision support engine 112, and / or the application 106, in which case additional systems may be used to perform other functions.

[0126] Processor 1402 generally represents a single central processing unit (CPU) and / or graphics processing unit (GPU), multiple CPUs and / or GPUs, a single CPU and / or GPU with multiple processing cores, etc. In particular embodiments, memory 1404 may be volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory. For example, volatile memory may include random access memory (RAM). Non-volatile memory may be any combination of disk drives, flash-based storage devices, etc., and may include fixed and / or removable storage devices such as fixed disk drives, removable memory cards, cache, optical storage, network attached storage (NAS), or storage area networks (SAN).

[0127] In some embodiments, I / O devices 1414 (e.g., keyboard, monitor, etc.) can be connected via I / O interface 1408. Additionally, via network interface 1410, computing device 1400 may be communicatively coupled to one or more other devices and components, such as user database 110. In particular embodiments, computing device 1400 is communicatively coupled to other devices via a network, which may include the Internet, a local network, etc. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, processor 1402, memory 1404, network interface 1410, and I / O interface 1408 are communicatively coupled by one or more bus interconnects 1412. In particular embodiments, computing device 1400 is a server running in an on-premises data center or a cloud environment. In particular embodiments, computing device 1400 is a user's mobile device.

[0128] In the illustrated embodiment, the memory 1404 may include a device application 1416 that configures the processor 1402 to perform various processes and / or operations in dynamically determining and presenting inter-feature correlation insights, as described above. In some embodiments, the device application 1416 may perform the functions of the DAM 111, the decision support engine 112, and / or the application 106. As described above with reference to FIGS. 3-13 , the computing device 1400 may be configured to identify analyte features and correlation features using a feature selection UI 1422, such as, but not limited to, an analyte feature selection UI and a correlation feature selection UI. In some embodiments, the analyte features and / or correlation features may be identified based on various data, such as, but not limited to, input data 1418 (e.g., input 127), as further described above. In some embodiments, the analyte features and related data may be stored as analyte feature data 1434. In some embodiments, the correlation features and related data may be stored as correlation feature data 1432. Additionally, computing device 1400 may be configured to determine analyte feature trends and correlated feature trends based on various data, such as, but not limited to, monitoring data 1420 (e.g., metrics 130), as further described above. In some embodiments, analyte feature trends and associated data may be stored as analyte feature trend data 1436. In some embodiments, correlated feature trends and associated data may be stored as correlated feature trend data 1438.

[0129] With further reference to the illustrated embodiment, the computing device 1400 may be configured to determine the inter-feature correlation insights 1440 and the correlation magnitude profile 1444, as further described above. For example, the computing device 1400 may be configured to determine the inter-feature correlation insights 1440 and / or the correlation magnitude profile 1444 using the correlation score 1442, the threshold 1450, the correlation period 1452, the central tendency data 1446, the statistical deviation data 1448, the analyte feature trend segments 1454, and the correlation feature trend segments 1456. Furthermore, the computing device 1400 may be configured to generate and display the inter-feature correlation insights 1432 using insight UIs such as, but not limited to, the insight flagging UI 1424, the inter-time insight interaction UI 1426, the flagged insight engagement UI 1428, and the content engagement UI 1430, as further described above. In some embodiments, computing device 1400 may be configured to store educational content data 1458, which may be used to display curated educational content using content engagement UI 1430, as further described above. In some embodiments, correlation magnitude profile 1444 may be stored in a high-speed storage medium (e.g., volatile memory) to further improve the real-time responsiveness of cross-temporal insight interaction UI 1426.

[0130] Furthermore, although certain operations (e.g., the operations of Figures 3-13) and data are described with respect to Figure 14 as being performed and / or stored by particular computing devices described above, in certain embodiments, a combination of computing devices may be utilized instead.

[0131] Each of these non-limiting examples can stand alone or can be combined in various permutations or combinations with one or more of the other examples. The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are also referred to herein as "examples." Such examples may include elements in addition to those shown or described. However, the inventors also contemplate examples in which only those elements shown or described are provided. Furthermore, the inventors also contemplate examples that use any combination or permutation of those elements (or one or more aspects thereof) shown or described with respect to a particular example (or one or more aspects thereof), or with respect to any other example (or one or more aspects thereof) shown or described herein.

[0132] In the event of a conflict of usage between this document and any document incorporated by reference, the usage in this document shall take precedence.

[0133] In this document, the terms "a" or "an" are used to include one or more, as is common in patent documents, regardless of other instances or uses of "at least one" or "one or more." In this document, the term "or" is used to refer to a non-exclusive inclusion, such that "A or B" includes "A but not B," "B but not A," and "A and B," unless otherwise indicated. In this document, the terms "including" and "in which" are used as the plain English equivalents of "comprising" and "wherein," respectively. In this document, the term "set" or "set of" a particular item is used to refer to one or more of the particular items.

[0134] Also, in the following claims, the terms "including" and "comprising" are open-ended, i.e., systems, devices, articles, compositions, formulations, or processes that include elements in addition to those listed after such terms in a claim are still deemed to be within the scope of that claim. Furthermore, in the following claims, terms such as "first," "second," and "third" are used merely as labels and are not intended to impose numerical requirements on their objects.

[0135] Geometric terms such as "parallel," "perpendicular," "circular," or "square" are not intended to require absolute mathematical precision unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as "circular" or "approximately circular," components that are not exactly round (e.g., slightly elliptical or multi-sided polygonal) are still encompassed by this description.

[0136] Embodiments of the methods described herein may be at least partially machine- or computer-implemented. Some embodiments may include a computer-readable or machine-readable medium encoded with instructions operable to configure an electronic device to perform the methods described in the embodiments. Such method implementations may include code, such as microcode, assembly language code, high-level language code, etc. Such code may include computer-readable instructions for performing various methods. This code may form part of a computer program product. Furthermore, in embodiments, the code may be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memory (RAM), read-only memory (ROM), etc.

[0137] The above description is intended to be illustrative, not limiting. For example, the above examples (or one or more aspects thereof) could be used in combination with each other. Other embodiments may be used, such as those of ordinary skill in the art upon reviewing the above description. The Abstract is provided to comply with 37 CFR §1.72(b) to enable the reader to quickly grasp the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or spirit of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be construed as intending that any unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in fewer than all features of a particular disclosed embodiment. Thus, the following claims are incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. [Explanation of symbols]

[0138] 100 Health Monitoring and Support System 102 users 104 Analyte Monitoring System 106 Mobile Health Applications 107, 107a, 107b, 107c, 107d Mobile Devices 109a, 109b, 109c, 109d Touchscreen Displays 110 User Database 111 Data Analysis Module (DAM) 112 Mutual feature correlation engine, decision support engine 118 User Profile 119 Demographic Information 121 Disease progression information 122 Drug Information 126 Application Data 127 inputs 130 metric 138 Analyte Sensor Electronics Module 140 Analyte Sensor

Claims

1. A non-transitory computer-readable storage medium storing a program including instructions that, when executed by at least one processor of a computing device, cause the at least one processor to: identifying at least one analyte feature using an analyte feature selection user interface (UI); Identifying at least one correlation feature using a correlation feature selection UI; determining an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; determining a correlation feature trend for the at least one correlation feature, the correlation feature trend corresponding to a variation of the at least one correlation feature over the time period; determining at least one inter-feature correlation insight based on the analyte feature trend and the correlated feature trend, wherein the at least one inter-feature correlation insight is based on a correlation between the at least one analyte feature and the at least one correlated feature; determining a correlation magnitude profile for the at least one inter-feature correlation, the correlation magnitude profile comprising a correlation magnitude for the at least one inter-feature correlation insight over a correlation period; and displaying the at least one inter-feature correlation insight using at least one insight UI.

2. 10. The non-transitory computer-readable storage medium of claim 1, wherein the at least one analyte feature is identified by displaying the analyte feature selection UI and receiving user input selecting the at least one analyte feature.

3. 3. The non-transitory computer-readable storage medium of claim 2, wherein at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

4. 2. The non-transitory computer-readable storage medium of claim 1, wherein the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag inter-feature correlation insights.

5. The at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, the inter-time insight interactive UI comprising: providing a UI element for the user to select a correlation period; The non-transitory computer-readable storage medium of claim 4 , displaying the correlation magnitude profile for flagged inter-feature correlation insights for a user-selected correlation period.

6. The operation is receiving a user-selected correlation period using the Inter-Time Insight Interaction UI; updating the correlation magnitude for the flagged inter-feature correction insights; and displaying updated correlation magnitude profiles for the flagged inter-feature correlation insights using the interactive UI.

7. The at least one inter-feature correlation insight is displayed using a set of flagged insight engagement UIs, the set of insight engagement UIs comprising: providing UI elements for the user to edit the flagged inter-feature correlation insights; The non-transitory computer-readable storage medium of claim 5 , further comprising assigning a metadata field to the flagged inter-feature correlation insight.

8. The at least one inter-feature correlation insight is displayed using a set of content engagement UIs, the set of content engagement UIs comprising: Displaying curated educational content based on the flagged interaction insights; The non-transitory computer-readable storage medium of claim 7 , providing UI elements for the user to engage with the educational content.

9. 1. A method for dynamic determination and presentation of inter-feature correlation insights, comprising: identifying at least one analyte feature using an analyte feature selection user interface (UI); Identifying at least one correlation feature using a correlation feature selection UI; determining an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; determining a correlation feature trend for the at least one correlation feature, the correlation feature trend corresponding to a variation of the at least one correlation feature over the time period; determining at least one inter-feature correlation insight based on the analyte feature trend and the correlated feature trend, wherein the at least one inter-feature correlation insight is based on a correlation between the at least one analyte feature and the at least one correlated feature; determining a correlation magnitude profile for the at least one inter-feature correlation, the correlation magnitude profile comprising a correlation magnitude for the at least one inter-feature correlation insight over a correlation period; and displaying the at least one inter-feature correlation insight using at least one insight UI.

10. 10. The method of claim 9, wherein the at least one analyte feature is identified by displaying the analyte feature selection UI and receiving user input selecting the at least one analyte feature.

11. The method of claim 10 , wherein at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

12. 10. The method of claim 9, wherein the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag an inter-feature correlation insight.

13. The at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, the inter-time insight interactive UI comprising: providing a UI element for the user to select a correlation period; The method of claim 12 , further comprising displaying the correlation magnitude profile for flagged inter-feature correlation insights for a user-selected correlation period.

14. The at least one inter-feature correlation insight is displayed using a set of flagged insight engagement UIs, the set of insight engagement UIs comprising: providing UI elements for the user to edit the flagged inter-feature correlation insights; The method of claim 13 , further comprising assigning metadata fields to the flagged inter-feature correlation insights.

15. The at least one inter-feature correlation insight is displayed using a set of content engagement UIs, the set of content engagement UIs comprising: Displaying curated educational content based on the flagged interaction insights; The method of claim 14 , further comprising providing UI elements for the user to engage with the educational content.

16. 1. A computing device for dynamic determination and presentation of inter-feature correlation insights, comprising: A network interface; a memory containing executable instructions; a processor in data communication with the memory; The processor executes the instructions to identifying at least one analyte feature using an analyte feature selection user interface (UI); Identifying at least one correlation feature using a correlation feature selection UI; determining an analyte feature trend for the at least one analyte feature, the analyte feature trend corresponding to a variation in the at least one analyte feature over a period of time; determining a correlation feature trend for the at least one correlation feature, the correlation feature trend corresponding to a variation of the at least one correlation feature over the time period; determining at least one inter-feature correlation insight based on the analyte feature trend and the correlated feature trend, wherein the at least one inter-feature correlation insight is based on a correlation between the at least one analyte feature and the at least one correlated feature; determining a correlation magnitude profile for the at least one inter-feature correlation, the correlation magnitude profile comprising a correlation magnitude for the at least one inter-feature correlation insight over a correlation period; and displaying the at least one inter-feature correlation insight using at least one insight UI.

17. 17. The computing device of claim 16, wherein the at least one analyte feature is identified by displaying the analyte feature selection UI and receiving user input selecting the at least one analyte feature.

18. 20. The computing device of claim 17, wherein at least one corresponding feature is identified by displaying a corresponding feature selection UI and receiving user input selecting the at least one corresponding feature.

19. 17. The computing device of claim 16, wherein the at least one inter-feature correlation insight is displayed using an insight flagging UI, the insight flagging UI providing UI elements for a user to flag inter-feature correlation insights.

20. The at least one inter-feature correlation insight is displayed using an inter-time insight interactive UI, the inter-time insight interactive UI comprising: providing a UI element for a user to select a correlation period; 20. The computing device of claim 17, wherein the computing device displays the correlation magnitude profile for flagged inter-feature correlation insights for a user-selected correlation period.

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