System, device, and method of dynamic glucose profile response to physiological parameters

Abstract

To provide a system of dynamic glucose profile response to physiological parameters for providing consistent and reliable glucose response information to physiological changes and activities.SOLUTION: A glucose response data analysis system, the system of dynamic glucose profile response to physiological parameters: receives activity metric information and glucose level information in a plurality of periods; classifies glucose level information relating to a period having significant activity metric into a first dataset and glucose level information other than that into a second dataset; determines a correlation between the glucose level information and the activity metric in respective periods of the first dataset; and uses a prescribed function fitted to a level of the activity metric in a third period to output a treatment recommendation based on a glucose level in a fourth period relating to the third period.SELECTED DRAWING: Figure 1

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is related to U.S. Provisional Patent Application No. 62 / 307,346, filed March 11, 2016, U.S. Provisional Patent Application No. 62 / 191,218, filed July 10, 2015, and U.S. Provisional Patent Application No. 62 / 307,344, filed March 11, 2016, all entitled "Systems, Devices, and Methods For Meal Information Collection, Meal Assessment, and Analyte Data Correlation," the disclosures of each of which are incorporated herein by reference for all purposes.

[0002] Citation by reference The patents, patent applications, and / or publications mentioned herein, including the following patents, patent applications, and / or publications, are hereby incorporated by reference for all purposes: U.S. Patent Nos. 4,545,382; 4,711,245; 5,262,035; 5,262,305; 5,264,104; 5,320,715; 5,356,786; 5,509,410; 5,543,326; 5,593,852; 5,601,435; 5,628,890; 5,8 No. 20,551; No. 5,822,715; No. 5,899,855; No. 5,918,603; No. 6,071,391; No. 6,071,391; No. 6,103,033; No. 6,120,676; No. 6,121,009; No. 6,134,461; No. 6,143,164; Same No. 6,144,837; Same No. 6,161,095; Same No. 6,175,752; Same No. 6,270,455; Same No. 6,284,47 No. 8; No. 6,299,757; No. 6,338,790; No. 6,377,894; No. 6,461,496; No. 6,503, No. 381; No. 6,514,460; No. 6,514,718; No. 6,540,891; No. 6,560,471; No. 6,5 No. 79,690; No. 6,591,125; No. 6,592,745; No. 6,600,997; No. 6,605,200; No. 6 , 605,201; 6,616,819; 6,618,934; 6,650,471; 6,654,625; Same No. 6,676,816; Same No. 6,730,200; Same No. 6,736,957; Same No. 6,746,582; Same No. 6,749,740 Nos. 6,764,581; 6,773,671; 6,881,551; 6,893,545; 6,932,892; 6,932,894; 6,942,518; 7,041,468; 7,167,818; and 7,299,082; U.S. Patent Application Publication Nos. 2004 / 0186365 (now U.S. Patent No. 7,811,231); 2005 / 0182306 (now U.S. Patent No. 8,771,183); 2006 / 0025662 (now U.S. Patent No. 7,740,581);Nos. 2006 / 0091006; 2007 / 0056858 (now U.S. Pat. No. 8,298,389); 2007 / 0068807 (now U.S. Pat. No. 7,846,311); 2007 / 0095661; 2007 / 0108048 (now U.S. Pat. No. 7,918,975); 2007 / 0199818 (now U.S. Pat. No. 7,811,430); 2007 / 0227911 (now U.S. Pat. No. Nos. 7,887,682; 2007 / 0233013; 2008 / 0066305 (now U.S. Pat. No. 7,895,740); 2008 / 0081977 (now U.S. Pat. No. 7,618,369); 2008 / 0102441 (now U.S. Pat. No. 7,822,557); 2008 / 0148873 (now U.S. Pat. No. 7,802,467); 2008 / 0161666; 2008 / 0267 No. 823; and No. 2009 / 0054748 (now U.S. Pat. No. 7,885,698); U.S. Patent Application Nos. 11 / 461,725 ​​(now U.S. Pat. No. 7,866,026); 12 / 131,012; 12 / 393,921, 12 / 242,823 (now U.S. Pat. No. 8,219,173); 12 / 363,712 (now U.S. Pat. No. 8,346,335); 12 / 495,709; 12 / 698 ,124; 12 / 698,129; 12 / 714,439; 12 / 794,721 (now U.S. Pat. No. 8,595,607); and 12 / 842,013; and U.S. Provisional Patent Applications Nos. 61 / 238,646; 61 / 246,825; 61 / 247,516; 61 / 249,535; 61 / 317,243; 61 / 345,562; and 61 / 361,374. [Background technology]

[0003] Detecting and / or monitoring glucose levels or other analytes, such as lactate, oxygen, A1C, etc., in certain individuals is critical to the health of the individual. For example, monitoring glucose levels is particularly important for individuals with diabetes and individuals with conditions that suggest the onset of diabetes. Diabetic patients generally monitor their glucose levels to determine whether their glucose levels are maintained within a clinically safe range, and can also use this information to determine whether and / or when insulin is needed to reduce their body glucose levels or when additional glucose is needed to raise their body glucose levels. Summary of the Invention [Problem to be solved by the invention]

[0004] With the development of glucose monitoring devices and systems that provide real-time glucose level information in a convenient and painless manner, there is a desire to integrate such monitoring devices and systems into daily life and activities to improve glycemic control. More particularly, there is a strong desire to identify the impact of daily activities, such as exercise, medication management, and food intake, on glucose level fluctuations and to provide actionable, personalized health-related information to tightly control glycemic fluctuations. Furthermore, there is a strong desire to bring precision to medication dosage determination by considering parameters that affect medication in daily activities, including exercise and food intake, thereby accurately assessing the determination of correct medication dosages while reducing errors in such determinations. [Means for solving the problem]

[0005] Embodiments of the present disclosure include the determination and dynamic adjustment or modification of multiphasic glucose response patterns to personalize the glycemic response to specific activities and external parameters for a particular patient or user. In certain embodiments, the analysis module is provided as a software application ("App") executable by any processor-controlled device, particularly a smartphone with communications capabilities to receive, analyze, transfer, transmit, display, or output actionable information, including, for example, treatment recommendations based on the determined glucose response pattern. In certain embodiments, the glucose response pattern determined in light of a particular activity or combination of activity, food intake, drug intake, or other external parameters specific to a user's or patient's daily activity is intelligently and dynamically adjusted on an ongoing, real-time basis as additional activity-specific or external parameter-specific data is received and analyzed by the App.

[0006] Embodiments of the present disclosure include an overall network having sensor-based devices in communication with smartphones configured to run apps, and optionally a data communications network having one or more back-end server terminals providing a network cloud configuration, configured to, for example, run the functionality of the apps when in direct data communication with the sensor-based devices and provide analysis results to the smartphones for analysis, or configured to operate in a more passive role, such as performing data backup or data repository functions for the smartphones and / or sensor-based devices. Also included in the overall network are one or more medication delivery devices, such as insulin pumps or insulin injection pens, configured to receive analysis data from the smartphones, from the one or more back-end server terminals, or directly from the sensor-based devices, as needed.

[0007]

[0004] Embodiments of the present disclosure include a data collection phase, during which user- or patient-specific information is collected, for example, over a predetermined period of time, from one or more sensor-based devices or from a drug delivery device via manual user input. Once a sufficient amount of information about the patient or user (e.g., at least 5 days, 6 days, 1 week, 10 days, 14 days, or any combination of at least one of a number of days or fractions of a day) has been determined, as it relates to glucose response and glycemic variability, an app running on a smartphone in certain embodiments can prompt the user or patient that a specific glycemic response pattern has been determined or identified and is ready for user input for response analysis. To reach this point, in certain embodiments, the app analyzes data or information from the sensor-based devices and other received user- or patient-specific parameters, and as part of the data analysis, classifies the received data to determine a glucose response pattern, and then continuously and dynamically updates the response pattern with additional real-time information received from one or more sensor-based devices or other user- or patient-specific parameters. Thus, in certain embodiments, when a user inputs an activity or parameters in which the user wishes to engage (e.g., a 90-minute run including an incline of approximately 1,000 feet (305 m), or the number of steps walked during an established period, such as 12 hours, 18 hours, 24 hours, or other suitable period), the App is configured to use its dynamic glucose response pattern recognition capabilities to notify the user or patient that such activity will result in a particular glucose response (e.g., a drop in glucose levels of approximately 25 mg / dL following the activity).

[0008] Furthermore, in certain embodiments, in addition to performing a physical activity-driven analysis, the app may be configured to provide recommendations, such as providing a list of types and amounts of food to be consumed at specific times before engaging in an activity and / or within a certain period of time after the activity, to minimize blood glucose fluctuations beyond a predetermined range over a set period of time spanning before, during, and after the activity. In certain embodiments, the app is configured to perform a similar analysis as described above, but with recommendations regarding the amount of medication, food, beverage, or one or more combinations thereof to be consumed instead of the physical activity to be performed. Thus, in certain embodiments, a user or patient can take action before consuming food and / or beverages or before taking medication.

[0009] These and other features, objects, and advantages of the present disclosure will become apparent to those skilled in the art upon reading the details of the present disclosure, which is more fully described below. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an overall glucose response data analysis system according to one embodiment of the present disclosure. [Figure 2A] FIG. 2 is a block diagram of the analysis module of FIG. 1 according to one embodiment of the present disclosure. [Figure 2B] 1. Information flow diagram in combination with the analysis module of FIG. 1 for data classification, pattern recognition, and dynamic updates according to one embodiment of the present disclosure. [Figure 3] An exemplary screenshot of the data entry interface 111 (FIG. 2A) according to one embodiment of the present disclosure. [Figure 4] 1 is a flowchart illustrating a routine for determining the effect of daytime activity on overnight glucose levels according to one embodiment of the present disclosure. [Figure 5] 1 is a flowchart illustrating another routine for determining the effect of daytime activity on overnight glucose levels according to an embodiment of the present disclosure. [Figure 6]1 is a flowchart illustrating the identification and characterization of glucose response patterns for specific activities based on absolute nocturnal glucose levels according to one embodiment of the present disclosure. [Figure 7] 1 is a flowchart illustrating the identification and characterization of glucose response patterns for specific activities based on day-night variations in glucose levels according to one embodiment of the present disclosure. [Figure 8] 1 is a flowchart illustrating the identification and characterization of glucose response patterns for specific activities based on day-night ratios of glucose levels according to one embodiment of the present disclosure. [Figure 9] Process flow diagram for training and notification according to one embodiment of the present disclosure [Figure 10] FIG. 1 is a process flow diagram for training and notification according to another embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] Before describing the present disclosure in detail, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

[0012] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value between the upper and lower limits of that range, to the tenth of the unit of the lower limit, and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these narrower ranges may independently be included in the narrower ranges, and are also encompassed within the disclosure, subject to any explicitly excluded limit in the stated range. When the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included within the disclosure.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described. All publications mentioned herein are incorporated by reference for the purpose of disclosing and describing the methods and / or materials in connection with which the publications are cited.

[0014] It must be noted that as used in this specification and the appended claims, nouns refer to plural referents unless the context clearly dictates otherwise.

[0015] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0016] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has individual components and features that can be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the disclosure.

[0017] The figures shown herein are not necessarily drawn to scale, and some components and features may be exaggerated for clarity.

[0018] 1 is an overall glucose response data analysis system according to one embodiment of the present disclosure. Referring to the figure, glucose response data analysis system 100 includes mobile phone 110, which in one particular embodiment includes user interface 110A and analysis module 110B programmed into mobile phone 110 as an App installed as an executable file downloaded from server 150 over data network 140, for example. As discussed in further detail below, in one particular embodiment, data conditioning, analysis, and dynamic glucose response pattern recognition and / or updating of said glucose response pattern recognition are implemented as one or more routines executable by the App.

[0019] 1, activity monitor 130A, heart rate monitor 130B, and glucose monitor 130C are also shown each in data communication with mobile phone 110, either instead of or in addition to their respective data communication with server 150 via data network 140. As such, each monitor 130A, 130B, 130C is programmed, in certain embodiments, to communicate monitored information to server 150 for storage and / or analysis, or to communicate one or both of raw data received from each monitor 130A, 130B, 130C and / or processed data or information from each monitor 130A, 130B, 130C to mobile phone 110 for storage, analysis, and subsequent communication via the data network to server 150 for storage and / or further analysis.

[0020] 1 , glucose response data analysis system 100 is shown with drug delivery device 120 in data communication with mobile phone 110, server 150, or one or more of monitors 130A, 130B, 130C via data network 140. Although not shown, in certain embodiments, operation of the app's routines and functions may be embodied within drug delivery device 120, which receives data or information directly from one or more of monitors 130A, 130B, 130C, recognizes and analyzes glucose response patterns, and modifies a drug delivery profile (e.g., bolus insulin dose determined by basal insulin delivery rate) based on the glucose response pattern determined from monitored data (e.g., physiologically monitored conditions, and / or food and / or beverage consumption, and drug intake), e.g., taking into account proposed physical activity and / or food or beverage consumption.

[0021] In certain embodiments, mobile phone 110 includes one or more monitors 130A, 130B, 130C integrated within mobile phone 110. For example, mobile phone 110 includes an accelerometer and / or gyroscope that can monitor the movements of a user of mobile phone 110 (while holding mobile phone 110 on or near the body, such as using an armband), such as tracking or recording the number of steps taken or physical activity engaged in, such as walking, running, jogging, sprinting, etc. In certain embodiments, mobile phone 110 is provided in a wristwatch configuration, in which case mobile phone 110 also includes a heart rate monitor in addition to an accelerometer or gyroscope. In certain embodiments using mobile phone 110 configured as a wristwatch, mobile phone 110 incorporates a glucose sensor, whether in vivo, dermal, transcutaneous, or visual, such that real-time glucose level monitoring functionality is incorporated into mobile phone 110.

[0022] Referring again further to glucose response data analysis system 100, in certain embodiments, a hub device (not shown) may be incorporated within system 100 that is configured to communicate with one or more of monitors 130A, 130B, 130C, or configured to communicate directly with other devices in system 100, such as, for example, mobile phone 110 and / or drug delivery device 120, for data reception, storage, and subsequent communication to other devices in system 100 via data network 140. The hub device, in certain embodiments, is configured as a pass-through relay device or adapter that collects information from one or more of monitors 130A, 130B, 130C, and communicates or transmits the collected data to server 150, to mobile phone 110, and / or to drug delivery device 120, either in real time or after a specified period of data collection. In certain embodiments, the hub device is physically embodied as a small, unobtrusive key fob or dongle-type device that is held near the user's or patient's body and communicates directly with the body-worn monitors 130A, 130B, 130C. Moreover, while three monitors 130A, 130B, 130C are shown in glucose response data analysis system 100, it is within the scope of this disclosure that additional sensors are provided to monitor other or related parameters of the user. For example, parameters monitored or measured by one or more sensors may include, but are not limited to, sweat level, temperature level, heart rate variability (HRV), neural activity, eye movement, speech, etc. Each one or more of these monitored parameters in certain embodiments of glucose response data analysis system 100 may be monitored by a sensor, as discussed in further detail below. It is used as an input parameter to the analysis module 110B of the mobile phone 110.

[0023] 2A is a block diagram of analysis module 110B of FIG. 1 according to one embodiment of the present disclosure. As shown in a particular embodiment, analysis module 110B of mobile phone 110 includes a data input interface 111 for coupling to or receiving data input from one or more monitors 130A, 130B, and 130C external to or internal to mobile phone 110. Data and / or information received via the data input interface is provided to glucose response training unit 112. In a particular embodiment, glucose response training unit 112 classifies the received input data into respective categories according to the type of data and one or more types of parameters associated with the data. For example, if the type of data is related to a physical activity such as a 90-minute run, the parameters associated with the data may include, in addition to duration, an intensity level of the run (running, jogging, sprinting), which in a particular embodiment may be determined using monitored heart rate information (if available) or the pace of the run, aerobic or anaerobic running, competitive or non-competitive (training) running, or any other suitable category related to the physical activity (e.g., running). In certain embodiments, other types of data related to physical activity may be used, such as the number of steps taken during an established period of time.

[0024] Using the classification data received from one or more monitors 130A, 130B, 130C (FIG. 1), time-correlated glucose level information is retrieved (or received from glucose monitor 130C (FIG. 1)), and glucose response training unit 112 performs dynamic glucose response pattern recognition, for example, based on analysis tools provided in an app for execution on mobile phone 110. Furthermore, in certain embodiments, glucose response training unit 112 is configured to dynamically and continuously update the determined glucose response pattern based on real-time information from one or more monitors (FIG. 1).

[0025] In certain embodiments, the accuracy of the glucose response pattern increases as the data set grows over a longer period of time (and / or as the resolution / monitoring frequency increases). However, a person's glycemic response to input data may change over time. Certain embodiments address this by “resetting” or erasing the data set after some predetermined period of time. In other embodiments, the app recognizes that exceeding a set data collection duration potentially introduces errors in the accuracy of the glucose response pattern; in this case, when this point is reached, the app is configured to reset and enter a data collection period, preventing user-driven analysis of the glucose response feedback for at least the minimum number of days or hours for which monitored data is needed to analyze and determine a new glucose response pattern. As described in more detail below, in certain embodiments, the app is configured to establish a “forget” window, during which user-driven analysis of the glucose response feedback is continuously updated. The “forget” window, in certain embodiments, includes one or more predetermined periods set by the app or based on user input, or is dynamically modified based on the glucose response feedback.

[0026] Returning to FIG. 2A , in certain embodiments, the output of the glucose response training unit 112 is provided to a data output interface 113 operably coupled to the user interface 110A of the mobile phone 110 for display, output, or other notification, or the App is operable to analyze the glucose response to inputs such as steps walked, cycling, running, hiking, eating, etc., for which the user or patient wishes to identify a corresponding glucose response so that timely (corrective or proactive) action can be taken to maintain glycemic control and minimize undesirable glucose excursions.

[0027]

[0023] Figure 2A illustrates an information flow in conjunction with the analysis module 110B of Figure 1 for data classification, pattern recognition, and dynamic updating according to one embodiment of the present disclosure. Referring to Figure 2A, in one particular embodiment, the analysis module 110B of the mobile phone 110 running the App (Figures 1 and 2A) is configured to classify (220) received input data (210), such as, for example, activity type, intensity level, duration, location, altitude information, glucose level, heart rate information, heart rate variability (HRV) information, oxygen saturation level, sweat level, temperature level, medication intake information, medication type, medication duration, time of day information corresponding to medication, carbohydrate intake information, alcohol consumption information, or any other relevant metric for a particular monitored condition corresponding to the received input data.

[0028] Using the received information, in certain embodiments, glucose response training unit 112 (FIG. 2A) performs dynamic glucose response pattern recognition and updates to the pattern as new or additional data is received (220). As discussed in more detail below, in certain embodiments, glucose response training unit 112 of analysis module 110B in mobile phone 110 ensures that sufficient input data has been analyzed before outputting a glucose response profile based on the determined pattern (230). Once this point is reached, and monitored information has been received and analyzed for at least a minimum period of time, the app, in certain embodiments, is configured to generate a notification to the user (e.g., as an output prompt on user interface 110A of mobile phone 110) when information that may be useful to the user is determined. The notification may be automatic, such as an alarm notification; or may be retrieved by the user when using the app, such as by accessing information from a menu; or may be displayed the next time the user interacts with the app. An example of useful information is that a user's glucose levels are typically 20% lower overnight after they exercised the previous day. Users can use this information to ensure they do not experience nocturnal hypoglycemia, for example, by reducing their insulin coverage during this period or by eating a snack before bed.

[0029] In another aspect of the present disclosure, the app prompts the user to input contextual information when it detects certain conditions, ensuring that more information is entered. The input information is used in a routine that analyzes the input data and determines glycemic response patterns. The app includes routines that detect conditions, such as when a meal is eaten or an activity is performed, and notifies the user when these conditions are detected. Embodiments that notify the user include one or more of an icon display, an audio or text output notification, or a vibration notification configured to prompt the user to provide more information about the detected condition. Examples of the one or more conditions include detected movement, a detected rate of change in glucose increase or decrease that exceeds or accelerates beyond a set threshold, or a detected spike or change in heart rate, sweat, or temperature level. Alternatively, rather than an alarm-type notification, the app may provide a notification the next time the user interacts with the app or the smartphone.

[0030] Still referring back to the figures, glucose response training unit 112 of analysis module 110B, in certain embodiments, is configured to perform dynamic glucose response pattern recognition based on glucose metrics that characterize the effects of particular activities or events (e.g., meals or drug intake) for a particular user or patient, such as the effects of a particular activity or event for a particular time period, occurring during and after the activity. Different glucose metrics, such as mean or median glucose levels, can be used as glucose metrics. In certain embodiments, the use of median glucose information is less susceptible to outlier glucose data compared to mean glucose levels.

[0031] In certain embodiments, the glucose response training unit 112 determines a median value of continuously monitored glucose levels during the night after a particular activity, such as from 10 pm to 3 am, or from 3 am to 8 am, or from 10 pm to 8 am. In certain embodiments, the glucose response training unit 112 uses median glucose levels determined during the day, such as from 8 am to 10 pm, 8 am to 6 pm, 9 am to 5 pm, 5 pm to 10 pm, or any other suitable daytime range. In certain embodiments, median glucose information is determined for a particular activity, such that the median glucose level is determined a certain period after the start of the activity (e.g., 2 hours after the start of the activity) for a particular time period (e.g., 12 hours). In certain embodiments, the relative start time and duration for determining the median glucose level vary depending on the type of activity and / or other parameters related to the activity or related to the user or patient.

[0032] While the disclosed embodiments focus on daytime activity that impacts overnight glucose levels, within the scope of this disclosure, similar analyses apply to any period defined by a fixed time period within a day, such as morning (e.g., 5 AM to 12 PM) activity that impacts post-dinner (e.g., 6 PM to 10 PM) glucose levels. Alternatively, the analyses disclosed herein within the scope of this disclosure apply to periods defined by regularly occurring events. For example, an activity dataset may be generated from a period defined daily from 5 AM to breakfast, where breakfast is at a different time each day and is determined by user-entered or generated instructions, by an algorithm processing glucose data to determine meal onset, or by recorded fast-acting insulin infusions. Exemplary embodiments for algorithmically detecting meal onset are disclosed in International Publication No. WO 2015 / 153482 (International Application No. PCT / US2015 / 023380, filed March 30, 2015), which is assigned to the assignee of the present application and is incorporated herein by reference in its entirety for all purposes.

[0033] Additionally, the affected period may similarly be defined as the period beginning when a meal is detected, such as from the start of dinner to midnight. Also provided within the scope of the present disclosure are hybrid approaches in which the activity period is determined as a fixed time period and the affected period is determined by a specific meal start time. Impacts over multiple time periods, such as after breakfast, after lunch, after dinner, and overnight, are included within the scope of the present disclosure. Furthermore, the analysis can be extended to periods spanning multiple days, such as determining how activity occurring in the morning of day 1 affects glucose levels for the remainder of the day.

[0034] Additionally, within the scope of the present disclosure, two or more activity types can be used for analysis. Non-limiting examples include: a) requiring a user to input contextual information about their activities into the app's user interface (UI) (e.g., data input interface 111 (FIG. 2A) of analysis module 110B); b) using one or more sensors to distinguish between different types of activities; or c) using alternative detection technologies to distinguish between different types of activities. For the above method (a) in which the user inputs information, the app is configured to present a user interface (e.g., as shown in FIG. 3) to allow the user to input activity information. In certain embodiments, the user can input information via a checklist or free text entry. Additionally, the app is configured to detect when the measured activity exceeds a predetermined threshold and prompt the user to input this information. For the method (b) in which one or more sensors are used to detect different activities, a combination of a pedometer, a heart rate sensor, and a location sensor can be used, where one or more thresholds and defined logic are configured to identify changes in body movement, intensity, and speed, as well as elevation. Finally, for an approach using alternative detection techniques (approach (c)), for example, a position sensor can be used to detect when a user is in a weightlifting gym, thereby correlating the measured activity with anaerobic activity.

[0035] If an activity type attribute is associated with a measured activity metric, the analysis described below can be performed for each activity type. For example, if two activity types are used, such as aerobic and anaerobic, the analysis described below can be used to determine the impact of aerobic activity on future glucose levels and separately determine the impact of anaerobic activity on future glucose levels. Within the scope of this disclosure, one or more combinations of activity and analysis period can be achieved that indicate new types of activity, such as days with both types of activity.

[0036] In a particular embodiment, the glucose response training unit 112 determines median glucose levels, activity, and other related parameters for a plurality of daytime periods, and median glucose levels are determined for associated nighttime periods following the daytime periods. In a particular embodiment, the glucose response training unit 112 determines median glucose levels for time periods of the day that have no activity. More particularly, in a particular embodiment, the glucose response training unit 112 is configured to confirm with the user or patient that no significant activity (e.g., exercise events, steps taken during a time period of the day (12, 18, 24, or other suitable period), running, cycling, hiking, etc.) occurred during these days that have no significant activity. Using the separated periods between days with significant activity and days without significant activity, the glucose response training unit 112, in one particular embodiment, analyzes the received input data (see FIG. 2A ) and characterizes the impact of specific activities on overnight glucose levels to generate a dynamic glucose response pattern—i.e., evaluates how the user's or patient's body responds to specific activities—and generates or provides appropriate treatment recommendations to the user or patient if the user decides to engage in the same activity with the same or similar parameters, such as duration, intensity level, etc.

[0037] 3 is an example screenshot of the data entry interface 111 (FIG. 2A) according to one embodiment of the present disclosure. Referring to FIG. 3, in one particular embodiment, a customized data entry screen is presented to the user for inputting information for analysis by the App. In a non-limiting example, a set of radio buttons on a user interface (e.g., on a mobile phone running the App) is seeded with one or more default activity-related parameters, such as steps, running, jogging, hiking, cycling, swimming, sleep, and / or parameters related to food / drinks, such as coffee, sugared alcohol, sugarless alcohol, cereal, bacon, toast, etc., and an option to modify over time is added by the user as a new habit answer / feedback or response. This allows the user to quickly enter the most common or most used types of activity without losing the flexibility to enter other types of habit data.

[0038] Within the scope of this disclosure, the App provides the user or patient with multiple means to input information about meals and activities. The patient can actively input this information. This is particularly useful for inputting meals, and can input photos of meals. This can be an even more convenient and enjoyable way for the user or patient to input and view meal information. Further details can be found in co-filed U.S. Provisional Patent Application No. 2006 / 0109994, entitled "Systems, Devices, and Methods For Meal Information Collection, Meal Assessment, and Analyte Data Correlation." [Attorney Docket No. A0130.0134.P2]. As discussed above, in certain embodiments, the app can detect meal or activity episodes and prompt the patient for more information, as disclosed in WO 2015 / 153482, which is incorporated herein by reference in its entirety for all purposes.

[0039] For users or patients who use insulin or take other glucose modifying drugs, the App may be configured to automatically collect user / patient specific data regarding the use of these drugs or to allow manual entry into the system by the patient.

[0040] Within the scope of the present disclosure, the app is configured to facilitate experimentation and understanding by providing a meal / activity analysis output. In certain embodiments, the output is presented as one or more reports on the smartphone or retrieved from a server and presented on a web browser. The one or more reports list meal episodes defined by glucose excursions. The list of meal episodes can be sorted by the date and time of the episode or by the severity of the glucose excursion, measured, for example, by peak glucose level, by glucose change during the excursion, or by an area defined by glucose and the duration of the excursion. Each column of the analysis output report contains information related to a meal episode. In certain embodiments, the report includes one or more of a photo or other text entry associated with the meal episode, a date and time, and one or more meal severity metrics. In certain embodiments, the report also includes associated activity information within a certain period of the meal. Too much information on this list can be too cluttered and impractical. Thus, the app, in certain embodiments, provides the user or patient with the ability to manipulate the presentation of information, such as by selecting a column and presenting a pop-up window with a more detailed information screen. Such detailed information screen also provides a glucose plot associated with the meal episode. In this manner, meals with the greatest impact on glucose levels can be highlighted to facilitate viewing the presentation to provide the user or patient with a better understanding of the impact of certain foods on their glucose levels so that they can avoid or limit foods that are harmful to their health.

[0041] The app, in certain embodiments, is also configured to learn how food and activity may affect future glucose levels. As food and activity selections are made in the customizable checklist described above, glucose data is associated with these selections, and multiple glucose datasets can be associated with a single entry type. Multiple glucose datasets can also be associated with a combination of one or more meal entry types and one or more activity entry types. The glucose datasets can be processed in one or more different ways to characterize the impact of the episode on glucose levels.

[0042] In one specific embodiment, the median glucose level from all of the data sets is determined and compared to the median value for all periods of captured glucose data. Alternatively, this approach can be applied to individual time periods, such as before breakfast, after breakfast, after lunch, after dinner, and after bedtime. Over time, the app is configured to estimate the glycemic impact for a given entry type or combination of entry types with a certain degree of confidence. For example, a specific activity type of "uphill cycling" for an hour or more of activity may be associated with a 20% increase in the patient's insulin sensitivity for the next 24 hours—changes in insulin resistance are easily associated with changes in median glucose. This association can be made by the system when it detects that the statistical level of confidence exceeds some predetermined amount. This information can modify the parameters used for bolus calculation over the next 24 hours. Alternatively, the app can detect cycling-related activities and, for example, alert the patient at bedtime so that they can eat a snack to avoid hypoglycemia that night.

[0043] Another type of output report presented by the app includes a list of activities sortable by median glucose level over a period of time after the activity, such as 24 hours. The list can illustrate activities that have the greatest impact on future glucose levels. Yet another type of report can present a list of food and activity combinations in the same manner as described above. These approaches can be easily extended to other sensor data and other contextual inputs, such as illness, alcohol consumption, coffee consumption, etc.

[0044] FIG. 4 is a flowchart illustrating a routine for determining the effect of daytime activity on overnight glucose levels according to one embodiment of the present disclosure. Referring to FIG. 4, in one embodiment, determining the effect of daytime activity on overnight glucose levels includes generating (410) a metric to determine overnight glucose levels for all days without significant activity over a predetermined period (e.g., two weeks, one month, or any other suitable period). A metric is then generated (420) to define an overnight glucose level for each day with significant activity within the predetermined period. Within the scope of the present disclosure, determining a day with or without significant activity is based on one or more activity metrics exceeding a defined threshold (e.g., number of steps exceeding a threshold within a 24-hour period). Returning to FIG. 4, after generating the metric to define overnight glucose levels for all days without significant activity and the multiple metrics to define overnight glucose levels for each day with significant activity, each of the multiple metrics to define overnight glucose levels for each day with significant activity is modified (430) using the metric for all days without significant activity. Next, a correlation is determined between each modified metric for days with significant activity and the metric for all days without significant activity (440), and then, given the activity level, the effect of the activity level on overnight glucose levels is determined and presented to the user (450) based on the determined correlations.

[0045] FIG. 5 is a flow chart illustrating another routine for determining the effect of daytime activity on overnight glucose levels according to one embodiment of the present disclosure. Referring to FIG. 5, in one embodiment, determining the effect of daytime activity on overnight glucose levels includes generating (510) a metric for defining day-night variations in glucose levels for all days without significant activity over a predetermined period (e.g., two weeks, one month, or other suitable period). Then, multiple metrics are generated (520) to define day-night variations in glucose levels for each corresponding day with significant activity. Using the metric for day-night variations in glucose levels for each day with significant activity and the metric for day-night variations in glucose levels for all days without significant activity, each daily metric defining day-night variations in glucose levels for days with significant activity is corrected (530) with the metric for day-night variations in glucose levels for days without significant activity. Next, a correlation is determined between each corrected metric for days with significant activity and the metric for all days without significant activity (540). Using the determined correlation, for a given activity level, the effect of the activity level on overnight glucose levels based on the determined correlation is determined and presented to the user (550).

[0046] 6 is a flowchart illustrating the identification and characterization of glucose response patterns for a particular activity based on absolute overnight glucose levels according to one embodiment of the present disclosure. Referring to FIG. 6, based on input data received from one or more of monitors 130A, 130B, 130C, glucose response training unit 112 (FIG. 2A) of analysis module 110B determines whether a sufficient amount of data has been received via data input interface 111 (FIG. 2A). In a particular embodiment, the sufficient amount of data to perform glucose response pattern and characterization is based on data received over a predetermined number of days with significant activity and a predetermined number of days without significant activity (collectively "X"). In a particular embodiment, whether a particular activity qualifies as a significant activity is determined based on one or more of the duration of the activity, the calories burned during the activity duration, the intensity level of the activity, whether the activity is aerobic or anaerobic, or the type of activity (e.g., competitive activity or non-competitive training activity). For example, in one particular embodiment, the glucose response training unit 112 determines that input data from one or more of the monitors 130A, 130B, 130C for three days with significant activity and three days without significant activity provides a sufficient amount of data for analysis.

[0047] In an alternative embodiment, the determination of data sufficiency is based on the degree of certainty of the estimated blood glucose pattern, rather than on a predetermined number of days or amount of data.

[0048] 6, using the determination of the number of days of input data required for analysis (610), the glucose response training unit 112 (FIG. 2A) determines (620) the median glucose level (Gwo) of all overnight median glucose levels for the determined number of days without significant activity. In a particular embodiment, the number of days without significant activity (Gwo) is defined as the number of days for which the activity measurement falls below a predetermined threshold, such as 10,000 steps, during a predetermined daytime period (12 hours, 18 hours, or other suitable period). In a particular embodiment, the median glucose level (Gwo) of all overnight median glucose levels for the days without significant activity varies depending on the type of activity.

[0049] Then, as shown in FIG. 6, for each day (day X) with significant activity, a delta median glucose level (G delta(day X)) is determined (630), where the delta median glucose level (G delta(day X)) is the difference between the nighttime median glucose value G(day X) for the particular day with significant activity and the median glucose level (Gwo) of all nighttime median glucose levels for the determined number of days without significant activity, i.e.: (G delta(day X)) = G(day X) - (Gwo).

[0050] In certain embodiments, the median glucose level (Gwo) (620) of all overnight median glucose levels for the determined number of days without significant activity and the delta median glucose level (Gdelta(Day X)) (630) for each day are similarly determined. In other words, steps 620 and 630 can be performed sequentially or in parallel with each other.

[0051] 6, a correlation between the median glucose level for a day with significant activity (day X) (G delta(day X)) and the activity metric for that day (Act(day X)) is determined (640), and the correlation is fit (650) to a predetermined function. In certain embodiments, the correlation comprises a linear function, where the delta median glucose level for a day with significant activity (G delta(day X)) is a linear function of the activity metric (Act(day X)). Within the scope of this disclosure, correlations include a constant offset relationship, an exponential relationship, a logarithmic relationship, or a polynomial relationship between the delta median glucose level for a day with significant activity (G delta(day X)) and the activity metric (Act(day X)).

[0052] In certain embodiments, the activity metric (Act(Day X)) is predetermined for a particular activity engaged in by a user or patient, for example, based on input data classification 220 (FIG. 2B) performed by glucose response training unit 112 (FIG. 2A) of analysis module 110B. In certain embodiments, the activity metric (Act(Day X)) varies depending on one or more parameters related to the activity, including, for example, duration of the activity, intensity level, type of activity, heart rate data related to the activity, among others. In certain embodiments, the activity metric (Act(Day X)) includes a "walking rate," such as steps per hour or steps over a given or fixed period of time.

[0053] In certain embodiments, a least-squares method is applied to fit the correlation to the data set. For example, a least-squares approach can be applied to the data set to determine the slope and offset for a linear relationship defining the correlation between delta median glucose levels (G delta(day X)) for days with significant activity and the activity metric (Act(day X)). In certain embodiments, the linear relationship is then applied by the app to predict or anticipate the effect of significant exercise on overnight glucose levels. In other words, using a known or determined activity metric (Act(day X)), the app estimates the resulting delta median glucose levels (G delta(day X)) for days with significant activity by multiplying the activity metric (Act(day X)) by the slope of the linear correlation and adding an offset, where the slope and offset are parameters determined, for example, by a best-fit analysis. In certain embodiments, the best-fit analysis is updated with each modification or addition of the data set collected or received from the monitor (130A-130C in FIG. 1 ). Alternatively, in certain embodiments, the best-fit analysis is updated after a predetermined period of data set collection.

[0054] In certain embodiments, a set of ratios (R) is determined for each day with significant activity. The ratios are calculated by dividing the delta median glucose level (G Delta(Day X)) for the days with significant activity by the activity metric (Act(Day X)). The median or average value of the set of ratios is then calculated. The effect of activity is then determined by multiplying the median value of the set of ratios (R) by the current activity metric (Act(Day X)). Alternatively, within the scope of the present disclosure, curve fitting techniques are applied, such as using least squares to fit the set of ratios (R) to a least-squares fit line.

[0055] Returning to Figure 6, in certain embodiments, the number of days required for analysis (610) can be determined by the quality of the correlation (650). For example, in certain embodiments, a linear line fit analysis provides a metric indicative of such a line fit (e.g., the correlation coefficient (R) of delta median glucose levels (G delta (day X)) for days with significant activity). 2 ) or estimate of standard error). The dataset may, in certain embodiments, be generated using a software program such as (but not limited to) R 2 If a metric of the quality of the line fit exceeds a certain value, such as a value greater than 0.9, or if the standard error of the delta median glucose level (G delta (day X)) for days with significant activity relative to the line fit is less than 10%, it is determined to be sufficient (610). If the line fit is determined to be invalid, in certain embodiments, the app is configured to continue analyzing the data set (i.e., continue training) and update for each day to determine whether the line fit is valid. If the line fit is determined to be valid, then the analysis results are presented to the user, in certain embodiments, for example, in data output interface 113 (FIG. 2A) of analysis module 110B.

[0056] By way of non-limiting example, Table 1 below shows a data set collected for identifying and characterizing glucose response patterns using steps as activity in accordance with certain embodiments of the present disclosure.

[0057] [Table 1]

[0058] From Table 1 above, it can be seen that over the two-week period, there were six days with activity (determined as steps above a threshold level, e.g., 10,000 steps walked within a 24-hour period), including days 1, 4, 5, 6, 9, and 13. It can also be seen that during the two-week period, there were eight days with no activity (determined as steps below the 10,000 step threshold level within a 24-hour period), including days 2, 3, 7, 8, 10, 11, and 12.

[0059] Given the daytime median glucose levels for each of the 14 days and the corresponding nighttime median glucose levels for each of the 14 days, the median glucose level (Gwo) of all nighttime median glucose levels for days without significant activity is determined by taking the median value of the nighttime median glucose levels for days 2, 3, 7, 8, 10, 11, and 12 in Table 1, which is 143.5 mg / dL. Furthermore, for each day with activity (e.g., days 1, 4, 5, 6, 9, and 13), the delta median glucose (Gdelta(Day X)) is determined by subtracting the median glucose level (Gwo) of all nighttime median glucose levels for days without significant activity, which was determined to be 143.5 mg / dL, from the corresponding nighttime median glucose level (G(Day X)). For example, for Day 1 (with activity), delta median glucose (G Delta (Day 1)) is 117 mg / dL minus 143.5 mg / dL (the median glucose level (Gwo) of all overnight median glucose levels for days without significant activity), resulting in a delta median glucose (G Delta (Day 1)) of -26.5. Similarly, for Day 4 (with activity), delta median glucose (G Delta (Day 4)) is -18.5 (125 mg / dL minus 143.5 mg / dL). For Day 5 (with activity), delta median glucose (G Delta (Day 5)) is -32.5 (111 mg / dL minus 143.5 mg / dL). For Day 6 (with activity), the delta median glucose (G Delta (Day 6)) is -23.5 (120 mg / dL minus 143.5 mg / dL). For Day 9 (with activity), the delta median glucose (G Delta (Day 9)) is -12.5 (131 mg / dL minus 143.5 mg / dL). Finally, for Day 13 (with activity), the delta median glucose (G Delta (Day 13)) is -38.5 (105 mg / dL minus 143.5 mg / dL).

[0060] Using the delta median glucose (G delta(day X)) for each day of activity determined as described above, the corresponding R value for each day of activity is determined by dividing the determined delta median glucose (G delta(day X)) by the activity metric for the corresponding day of activity (Act(day X)). For example, the R value for day 1 is -0.002 (-26.5 divided by 12,503 steps (the activity metric for day 1)). In this way, the R values ​​for the days of activity are determined, and the resulting values ​​(along with the corresponding delta median glucose levels (G delta(day X))) are shown in Table 2 below.

[0061] [Table 2]

[0062] Based on the data set determined as shown in Table 2 above, a line fit analysis is performed for days with activity against the corresponding R values, as shown in Graph 1 below: TIFF2025156571000004.tif86114

[0063] Alternatively, the median or mean R value can be used to represent the glycemic pattern. Additionally, a line fit analysis can be performed on the delta median glucose (G delta (day X)) with respect to activity level (number of steps), as shown in Graph 2 below: TIFF2025156571000005.tif103129

[0064] Here, the correlation value (R 2 ) was found to be 0.9125, demonstrating an acceptable correlation, and line fit analysis gave an offset of 10.811 with a slope of -0.0026. This line represents the blood glucose pattern.

[0065] Using Graph 2, if a user decides to undertake a particular activity that results in 15,000 steps, a line fit analysis shows that such activity will result in a drop in glucose levels of approximately 28 mg / dL. With this information, if the user desires to maintain tighter glycemic control and it is known that taking 15,000 steps will result in a drop in glucose levels of approximately 28 mg / dL, the user can take proactive action to counter the effects of the activity (e.g., 15,000 steps) by, for example, consuming additional food and / or beverages either before or during the activity.

[0066] In an alternative embodiment, the activity metric is converted to two values: significant activity or insignificant activity. In this case, the nighttime median glucose level is associated with either a day with or without significant activity, where significant activity is defined as when the activity measurement exceeds a predetermined threshold (e.g., more than 10,000 steps for that day). More specifically, with reference to Table 1, the median glucose level for all nights associated with significant activity days (days 1, 4, 5, 6, 9, and 13) is determined to be 118.5 mg / dL, and the median glucose level for all nights associated with insignificant activity days (days 2, 3, 7, 8, 10, 11, 12, and 14) is determined to be 143.5 mg / dL. The median activity decline is then determined by dividing 143.5 mg / dL (the median glucose level for all nights associated with days of insignificant activity) from 118.5 mg / dL (the median glucose for all nights associated with days of significant activity), resulting in -25 mg / dL. Therefore, the median percentage decline is 17.42% (-25 mg / dL divided by 143.5 mg / dL). In this manner, whether a sufficient number of data sets have been collected can be determined using standard statistical tests to determine whether the means of two different populations are different. For example, by verifying that the standard deviation of each median nighttime glucose determination (with and without activity) is below a predetermined threshold, such as 20 mg / dL. Referring to Table 1, the standard deviation for days with significant activity (days 1, 4, 5, 6, 9, and 13) is 8.864 mg / dL, while the standard deviation for days without significant activity (days 2, 3, 7, 8, 10, 11, 12, and 14) is 7.08 mg / dL.

[0067] Referring again to the Figures, using the glucose response pattern identification and characterization described above, the App is configured in one particular embodiment to output the following to the user when subsequent significant activity is detected: "Days with significant activity tend to have overnight glucose levels 25 mg / dL lower than days without significant activity." Alternatively, this result may be displayed as a percentage, in this example, 17% lower. Within the scope of this disclosure, the above-described techniques can be extended to any level of quantization, such as three or four levels.

[0068] In one specific embodiment, using the above-described routines in combination with Figure 6, the glucose response training unit 112 (Figure 2A) of analysis module 110B identifies consistent glucose responses to specific activities using specific parameters. The user or patient then uses this information to modify or adjust their treatment protocol, meals consumed, or type of activity to maintain tight glycemic control and improve their health, given their underlying physiological condition.

[0069] 7 is a flowchart illustrating the identification and characterization of glucose response patterns for specific activities based on day-night glucose level variations according to one embodiment of the present disclosure. Referring to FIG. 7, similar to step 510 of FIG. 5, based on input data received from one or more of monitors 130A, 130B, 130C, glucose response training unit 112 (FIG. 2A) of analysis module 110B determines (710) whether a sufficient amount of data has been received via data input interface 111 (FIG. 2A). Next, glucose response training unit 112 of analysis module 110B determines (720) the median value (Gwo(delta)) of all day-night variations in glucose median (Gd2n(day X)) for days without significant activity (a number of days determined to provide a sufficient amount of data).

[0070] More specifically, the day-night change in each glucose median without significant activity (Gd2n(Day X)) is determined by subtracting the median glucose level over a first predetermined time period (e.g., 8 AM - 10 PM) (GDay(Day X)) from the median glucose level over a second predetermined time period (e.g., 10 AM - 6 PM) (GNight(Day X)) (720), i.e.: (Gd2n(day X)) = G night (day X) - G day (day X).

[0071] Within the scope of the present disclosure, the duration and range for the first and second predetermined time periods may be varied, such that one is longer than the other, or the two periods are the same length. In certain embodiments, the first and second predetermined time periods for each day are determined based on a particular event, such as a meal event or other indicator related to the patient.

[0072] Returning to FIG. 7, using the determined median value of all day-night changes in median glucose for days without significant activity (Gwo(delta)) (720), the glucose response training unit 112, in one particular embodiment, determines the delta median glucose level (Gdelta(day X)) by subtracting the median value of all day-night changes in median glucose for days without significant activity (Gwo(delta)) from the day-night changes in median glucose for days without significant activity (Gd2n(day X)) (730). In certain embodiments, the median value of all day-night changes in median glucose for days without significant activity (Gwo(Delta)) (720) and the delta median glucose level for each day with significant activity (Gdelta(Day X)) (730) are determined simultaneously, rather than sequentially. In alternative embodiments, the delta median glucose level for each day with significant activity (Gdelta(Day X)) (730) may be determined before the median value of all day-night changes in median glucose for days without significant activity (Gwo(Delta)) (720).

[0073] A correlation between delta median glucose (G delta(day X)) and an activity metric (Act(day X)) for each day (day X) with significant activity is then determined (740). Similar to the routine performed in conjunction with FIG. 6, in certain embodiments, the activity metric (Act(day X)) is predetermined for the particular activity in which the user or patient engages, and as such may be based on input data classification (FIG. 2B) performed by glucose response training unit 112 (FIG. 2A) of analysis module 110B. Similarly, in certain embodiments, the activity metric (Act(day X)) varies depending on one or more parameters related to the activity, including, for example, the duration of the activity, the intensity level, the activity type, and heart rate data related to the activity.

[0074] Again, similar to the routine performed in conjunction with FIG. 6, and referring to FIG. 7, once the correlation between the delta median glucose level (G delta(day X)) for a day with significant activity (day X) and the activity metric for that day (Act(day X)) is determined (740), for example, where the delta median glucose level for a day with significant activity (G delta(day X)) is expressed as a linear function of the activity metric (Act(day X)), the correlation is used to generate an estimate of the delta median glucose level (G delta(day X)) for the next night's day with significant activity for the day of significant activity, and the analysis is displayed to the user. That is, the correlation is fit (750) to a predetermined function, and the resulting relationship is output to the user.

[0075] For example, referring to the data set shown in Table 1, the median value of all day-night changes in glucose median for days without significant activity (Gwo(delta)) is −1.5. This is derived by determining the median value of all day-night changes in glucose median for days without significant activity (Gd2n(Day X)). That is, from Table 1, for each day without significant activity (Days 2, 3, 7, 8, 10, 11, 12, and 14), the median day-night change in glucose median (Gd2n(Day X)) is determined by subtracting the daytime median glucose level from the nighttime glucose level. For example, the median day-night change in glucose median for Day 2 (Gd2n(Day 2)) is −14 mg / dL (142 mg / dL − 156 mg / dL). The median day-night variation in median glucose for day 3 (Gd2n(Day 3)) is 8 mg / dL (150 mg / dL-142 mg / dL). The median day-night variation in median glucose for day 7 (Gd2n(Day 7)) is 17 mg / dL (160 mg / dL-143 mg / dL). The median day-night variation in median glucose for day 8 (Gd2n(Day 8)) is 6 mg / dL (151 mg / dL-145 mg / dL). The median day-night variation in median glucose for day 10 (Gd2n(Day 10)) is 1 mg / dL (140 mg / dL-139 mg / dL). The median value of the day-night variation in median glucose for day 11 (Gd2n(Day 11)) is -22 mg / dL (139 mg / dL - 161 mg / dL). The median value of the day-night variation in median glucose for day 12 (Gd2n(Day 12)) is -11 mg / dL (144 mg / dL - 155 mg / dL). Finally, the median value of the day-night variation in median glucose for day 14 (Gd2n(Day 14)) is -4 mg / dL (143 mg / dL - 147 mg / dL). This is shown in Table 3 below.

[0076] [Table 3]

[0077] Using the median value of all day-night changes in median glucose (Gwo(delta)) for days without significant activity determined to be -1.5, delta median glucose (Gdelta(Day X)) can be determined for each day with significant activity by subtracting the median value of all day-night changes in median glucose (Gwo(delta)) for days without significant activity from the median value of day-night changes in median glucose for each day. This is shown in Table 4 below.

[0078] [Table 4]

[0079] As can be seen from Table 4, for each day with significant activity, the corresponding R value is determined by dividing the determined delta median glucose (G Delta(Day X)) by the activity metric for the corresponding day with activity (Act(Day X)).

[0080] Additionally, in certain embodiments, rather than a linear function, a set of ratios (R) is generated for each day with significant activity. The ratio R is determined by dividing the delta median glucose (G delta(day X)) for each day with significant activity by the corresponding activity metric (Act(day X)). The median or mean value of the set of ratios R is then determined (in this example, the median R value for days with significant activity is −0.00199553198802936). The impact of activity can then be determined by multiplying the median value R by the current activity metric (Act(day X)). Alternatively, a curve fitting technique can be applied, for example, using least squares, to fit the set of ratios (R) to a line.

[0081] Graph 3 below shows the R values ​​plotted against days with activity. TIFF2025156571000008.tif65114

[0082] Alternatively, the median or mean R value can be used to represent the glycemic pattern. Furthermore, delta median glucose (G delta(day X)) can be plotted against the activity metric (Act(day X)) and a line fit analysis can be performed, resulting in the plot shown in Graph 4 below. TIFF2025156571000009.tif74114

[0083] From the line fit analysis shown in Graph 4, the correlation coefficient R 2 is approximately 0.86, the offset is 2.687 for the line fit, and the slope is -0.0022. Using the analysis shown in Graph 4, a user who wishes to engage in an activity involving 15,000 steps can determine from Graph 4 that such activity will result in a glucose level drop of approximately 30 mg / dL. Alternatively, the App includes a routine that estimates subsequent overnight G-delta (day X) by inputting daytime activity into a linear equation. The user can then decide to take appropriate action (e.g., consume more food / drinks during or before the activity) to better control the predicted glucose level drop that will occur as a result of the activity.

[0084] In another embodiment, the activity metric (Act(Day X)) can be categorized into two values: significant activity or insignificant activity. In such cases, the nighttime median glucose value is associated with either a day of significant activity or a day without significant activity, and significant activity is determined if the activity measurement exceeds a predetermined threshold (e.g., greater than 10,000 steps during a daytime period). The median day-night change in median glucose levels (Gd2n(Day X)) for all nights associated with days with significant activity is determined, as well as the median day-night change in median glucose levels (Gd2n(Day X)) for all nights associated with days with insignificant activity, and then the median activity decline is determined. Data sufficiency, in certain embodiments, is determined using statistical methods, for example, by verifying that the standard error of calculation for each median value is less than a predetermined threshold, such as 20 mg / dL.

[0085] For example, the median day-night change in median glucose levels for all nights associated with days with significant activity (Gd2n(day X)) is determined to be −28.5 mg / dL (taking the median values ​​of day-night change in median glucose levels for days 1, 4, 5, 6, 9, and 13, respectively, which are −26, −25, −35, −31, −18, and −39), and the median day-night change in median glucose levels for all nights associated with days with non-significant activity (Gd2n(day X)) is determined to be −1.5 mg / dL (taking the median values ​​of day-night change in median glucose levels for days 2, 3, 7, 8, 10, 11, 12, and 14, respectively, which are −14, 8, 17, 6, 1, −22, −11, and −4). From this, the median decrease in glucose levels can be determined to be -27 mg / dL (-28.5 mg / dL minus -1.5 mg / dL).

[0086] In this example, the analysis result, if subsequent significant activity is detected, is displayed by the App to the user as follows: "For days with significant activity, glucose levels tend to be 27 mg / dL lower than days without significant activity." Within the scope of this disclosure, the analysis can be extended to any level of quantization, such as three or four levels.

[0087] 8 is a flowchart illustrating the identification and characterization of glucose response patterns for a particular activity based on day-night glucose level ratios according to one embodiment of the present disclosure. Referring to FIG. 8, the difference between the routine performed by glucose response training unit 112 (FIG. 2A) of analysis module 110B in combination with FIG. 7 compared to the routine shown in FIG. 8 is that instead of using the median value (Gwo(delta)) of all day-night changes in median glucose levels (Gd2n(day X)) for days without significant activity (step 720 in FIG. 7), the routine in FIG. 8 determines the median value (Gwod2nr) of all day-night ratios in median glucose levels (Gd2nr(day X)) for days without significant activity (820) after the number of days of data needed for analysis has been determined (810). In certain embodiments, the day-night ratio in median glucose levels for days without significant activity (Gd2nr(Day X)) is determined by dividing the median glucose level over a second predetermined time period (e.g., 10 pm to 6 am) (Gnight(Day X)) by the median glucose level over a first predetermined time period (e.g., 8 am to 10 pm) (GDay(Day X)), i.e.: (Gd2nr(day X)) = G nighttime(day X) / G daytime(day X).

[0088] 8, the median value (Gwo(delta)) of all day-night ratios at the median glucose levels for days without significant activity (Gd2nr(day X)) is determined. Glucose response training unit 112 of analysis module 110B then determines (830) the delta median glucose (Gdelta(day X)) for each day with significant activity by subtracting the median value (Gwo(delta)) of all day-night ratios at the median glucose levels for days without significant activity from each of the day-night ratios (Gd2nr(day X)) for each day with significant activity. In certain embodiments, after determining the number of days of data needed for analysis (810), the median value (Gwo(Delta)) of all day-night ratios of glucose medians for days without significant activity (Gd2nr(Day X)) (820) and the delta median glucose (G Delta(Day X)) for each day with significant activity (830) are determined simultaneously, rather than sequentially.

[0089] Referring again to FIG. 8, similar to step 740 of FIG. 7, a correlation between delta median glucose (G delta(day X)) and the activity metric (Act(day X)) for each day is determined (840). This correlation suggests a decrease in the ratio of day-night glucose levels during nights following significant activity. The correlation of delta median glucose (G delta(day X)) against the activity metric (Act(day X)) for days with significant activity is fit (850) to a predetermined function, and the resulting correlation information is output to the user.

[0090] Referring again to the data set shown in Table 1 above, the analysis described in conjunction with FIG. 8 results in the median value of all day-night ratios at median glucose levels (Gwod2nr) being 0.989991680125287, based on the median value of day-night ratios at median glucose levels for days with no significant activity as shown in Table 5 below:

[0091] [Table 5]

[0092] Next, as shown in Table 6 below, the ratio of median level glucose for each day with significant activity (Gactd2nr(Day X)) can be determined by dividing the day-to-night ratio at the median glucose value for each day with significant activity (Gd2nr(Day X)) by the median value of each day-to-night ratio at the median glucose level of 0.989991680125287 (Gwod2nr).

[0093] [Table 6]

[0094] From Table 6, the median value of the median glucose ratio (Gactd2nr(Day X)) for days with significant activity can be determined to be 0.814595. Alternatively, a line fit analysis can be performed by plotting the median glucose ratio (Gactd2nr(Day X)) against the activity metric (Act) for days with significant activity, as shown in Graph 5 below. TIFF2025156571000012.tif87129

[0095] The correlation coefficient R2 from Graph 5 is found to be approximately 0.89, the offset is approximately 1.03, and the slope is -0.00002 (2E-05).

[0096] 9 illustrates a process flow for training and notification according to one embodiment of the present disclosure. Referring to FIG. 9, in one particular embodiment, data analysis training, for example, as described above in conjunction with FIGS. 4-8, is performed 910 on the received input data set at predetermined intervals, such as once per day. Each time a routine is run, the acquired new data set is added to the data set maintained and used for data analysis training, for example, to determine correlations between activity and future glucose levels (e.g., overnight glucose levels).

[0097] Returning to FIG. 9 , in addition to adding new data sets to the training data set (910), each time the data analysis training routine is run, old data, such as data older than 90 days, older than 180 days, or any other suitable period, is removed from the training set (920). This allows the data analysis training routine to adapt to the changing physiology of the user delivering the data sets ("forgetting"). In certain embodiments, the "forgetting" subroutine may be performed or may be optional. Once the data analysis training process concludes (930), training sufficiency is checked (940), as described above in conjunction with FIGS. 4-8, e.g., so that an uncertainty metric associated with the "fit" of the correlation is below a predetermined threshold. If the training is determined to be sufficient (940), then a notification of the results is generated and output (950). However, if the training is determined to be insufficient, no notification is generated or output. Alternatively, in certain embodiments, no notification may be provided when the app determines that the training was insufficient, but rather a notification indicating that the training is still insufficient may be provided.

[0098] FIG. 10 illustrates a process flow for training and notification according to another embodiment of the present disclosure. As shown in FIG. 10, the data analysis training and notification routine is similar to the routine shown and described in FIG. 9, with the "forget" feature (920) replaced by resetting or clearing the training data set (1010 and 1020). Referring to FIG. 10, the initiation of the reset routine (1010) and the clearing of the training data set (1020) are performed in certain embodiments in response to the activation of an input button, for example, on the user interface of the app, to reset the training routine. In certain embodiments, the user initiates the reset routine (1010) and clears the training data set (1020) so that the app updates the correlation it has learned between activity and future glucose levels.

[0099] 10, once the reset is initiated, the data training and notification routine is thereafter periodically invoked, similar to the routine shown in FIG. 9, where new data sets are added to the training data set (1030), and after the training process is completed (1040), it is determined whether the training is sufficient (1050). If the training is determined to be sufficient, the app, in one particular embodiment, generates and outputs a notification to the user (1060). If the training is determined to be insufficient (1060), either no notification is presented to the user, or a notification indicating that the training was insufficient is generated and presented to the user by the app.

[0100] Modifications to the dataset training and notification routines described in conjunction with FIGS. 9 and 10 are contemplated within the scope of this disclosure that include the reset / clear training dataset (1010-1020 in FIG. 10) feature and the "forget" feature (920 in FIG. 9) within the same analysis routine. Also, in certain embodiments, the reset occurs periodically, such as once a year. Alternatively, in certain embodiments, the reset occurs after training has provided effective notification (i.e., when it is determined that training has been sufficient).

[0101] In the described aspects, in accordance with embodiments of the present disclosure, type 1 diabetes patients, type 2 diabetes patients, and pre-diabetic patients are provided with tools to monitor their physiological condition while they go about their daily routines, and over time, an app executable on, for example, the user's or patient's mobile phone provides a consistent glucose response to various types of activities and parameters that may affect the user's or patient's glucose level fluctuations. Such tools enable the user or patient to know how specific foods, exercise, or activities affect glucose level fluctuations and to modify their food, exercise routine, or other daily activities and take proactive action to maintain desired glycemic control and avoid harmful blood sugar spikes.

[0102] Embodiments of the present disclosure include aspects of data collection that include detecting certain activities and prompting the user or patient to input additional information about the detected activity to make data collection more robust. For example, if an app running on mobile phone 110 detects continuous movement for a predetermined period of time using activity monitor 130A, the app, in certain embodiments, is configured to generate and output a query to user interface 110A to instruct the user or patient to either confirm that the detected activity is occurring and / or add additional information about the detected activity (which may, in certain embodiments, be generated and output to user interface 110A upon detection of the end of the activity).

[0103] Thus, according to embodiments of the present disclosure, a robust physiological parameter monitoring system and dynamic glucose response pattern is provided to provide consistent and reliable glucose responses to physiological or other parameters and activities.

[0104] Various other modifications and variations in the structure and method of operation of the present disclosure will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments of the present disclosure. Although the present disclosure has been described in connection with specific embodiments, it should be understood that the present disclosure as claimed should not be unduly limited to such specific embodiments. The following claims define the scope of the present disclosure, and are intended to cover structures and methods that come within the scope of the claims and their equivalents. [Explanation of symbols]

[0105] 100 Glucose Response Data Analysis System 110 Mobile Phone 110A User Interface 110B Analysis Module 130A Activity Monitor 130B Heart Rate Monitor 130C glucose monitor 140 Data Network 150 servers 111 Data Entry Interface 112 Glucose Response Training Units 113 Data Output Interface 210 Received input data 220 Classification / Update 230 Glucose response profile output

Claims

1. A system for predicting a glucose response to an event, wherein the system A glucose monitor configured to collect user glucose data; A portable device configured to communicate with the glucose monitor and display information related to the user's glucose data, and A glucose response training unit that communicates with the aforementioned mobile device, Includes, The glucose response training unit, A step of receiving information about multiple events performed by the user, A step of receiving glucose data collected by the glucose monitor during a certain period following each of the aforementioned multiple events, A step of determining a dynamic glucose response pattern based on information regarding the multiple events and glucose data corresponding to each of the multiple events. A step of receiving information about the current event, A step of determining a predicted glucose response based on the information regarding the current event and the dynamic glucose response pattern. The step of outputting the predicted glucose response to the portable device, A system configured to perform [a specific action].

2. The system according to claim 1, wherein the plurality of events include the event of eating.

3. The system according to claim 1, wherein the plurality of events include events related to medication management.

4. The step of determining the dynamic glucose response pattern is: A step of determining a first glucose metric based on the glucose data at a certain time on each of the multiple days on which information about the event is received; The system according to claim 1, comprising the steps of: determining a second glucose metric based on the glucose data at a certain time on each of several days on which no information of the aforementioned event was received; and determining a delta glucose which is the difference between the first glucose metric and the second glucose metric.

5. The system according to claim 1, wherein the certain period is a nighttime period.

6. The system according to claim 1, wherein the glucose response training unit is further configured to perform the step of outputting a treatment recommendation based on the predicted glucose response.

7. The system according to claim 1, wherein the information about the predicted glucose response includes a notification that draws the user's attention to the predicted glucose response.

8. The system according to claim 1, wherein the glucose response training unit is further configured to perform the step of updating the dynamic glucose response based on event information relating to the current event and glucose data for a predetermined period following the current event.

9. The system according to claim 1, wherein the glucose response training unit is further configured to perform a step of determining the dynamic glucose response pattern based on events that occurred within a predetermined period of time prior to the present.

10. The system according to claim 1, wherein the glucose response training unit is further configured to perform the step of prompting the user to input information about the event when one of the plurality of events is detected.

11. A method for predicting a glucose response to an event, wherein the method is: Steps to collect user glucose data using a glucose monitor; A mobile device communicating with the glucose monitor receives information about multiple events performed by the user; The steps include: using the portable device to receive the glucose data collected by the glucose monitor during a certain period following each of the multiple events; A glucose response training unit communicating with the portable device determines a dynamic glucose response pattern based on information about the plurality of events and glucose data corresponding to each of the plurality of events; The mobile device receives information about the current event; A step of determining a predicted glucose response based on the information regarding the current event and the dynamic glucose response pattern; and The step of outputting the predicted glucose response to the portable device, Methods that include...

12. The method according to claim 11, wherein the plurality of events include the event of eating.

13. The method according to claim 11, wherein the plurality of events include events related to medication management.

14. The step of determining the dynamic glucose response pattern is: A step of determining a first glucose metric based on the glucose data at a certain time on each of the multiple days on which information about the event is received; The method according to claim 11, comprising the steps of: determining a second glucose metric based on the glucose data at a certain time on each of several days on which no information of the aforementioned event was received; and determining a delta glucose which is the difference between the first glucose metric and the second glucose metric.

15. The method according to claim 11, wherein the certain period is a nighttime period.

16. The method according to claim 11, further comprising the step of outputting a treatment recommendation based on the predicted glucose response.

17. The method according to claim 11, wherein the information about the predicted glucose response includes a notification to draw the user's attention to the predicted glucose response.

18. The method according to claim 11, further comprising the step of updating the dynamic glucose response based on event information relating to the current event and glucose data for a predetermined period following the current event.

19. The method according to claim 11, wherein the glucose response training unit is further configured to perform a step of determining the dynamic glucose response pattern based on events that occurred within a predetermined period of time prior to the present.

20. The method according to claim 11, further comprising the step of outputting a prompt to the user to input information about the event when one of the plurality of events is detected.

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