EP2603133A1

EP2603133A1 - Method and system for improving glycemic control

Info

Publication number: EP2603133A1
Application number: EP11748893.2A
Authority: EP
Inventors: Ofer Yodfat; Gali Shapira
Original assignee: F Hoffmann La Roche AG; Roche Diagnostics GmbH
Current assignee: F Hoffmann La Roche AG; Roche Diabetes Care GmbH
Priority date: 2010-08-10
Filing date: 2011-08-08
Publication date: 2013-06-19
Also published as: WO2012019746A1

Abstract

There are disclosed systems and methods of estimating a nutritional parameter of a food intake, comprising the steps of obtaining input values indicative of a measured postprandial blood parameter value and a blood parameter value associated with a medication dose value; determining from at least said obtained input values an estimate of the nutritional parameter of the food, and generating an output indicative of the determined estimate. The output can be an image, an audio signal or a vibration signal. One or more images associated with the food intake can be displayed. The history of past determined estimates and a nutrition recommendation can be displayed. Contents of the images can be analyzed. The postpandrial blood parameter value can be measured by a continuous monitoring device, at different delays. The nutritional parameter can be a carbohydrates load value, or a glycemic index value or a fat load value for example.

Description

METHOD AND SYSTEM FOR IMPROVING GLYCEMIC CONTROL

Examples are described of estimating a nutritional parameter of a food intake. Systems, devices and methods of improving glycemic control are provided. Insulin dispensing systems and devices configured to measure glucose levels are detailed.

BACKGROUND

For persons with diabetes, the appropriate assessment of the amount of carbohydrates contained in a meal is important for maintaining normoglycemia , i.e. glycemic control.

Patent Application US20100280329 entitled "Estimating a nutritional parameter for assisting insulin administration" discloses a device for estimating a nutritional parameter of a meal consumed by an individual The apparatus comprises processing means adapted to obtain input values of at least a physiological parameter of the user measured prior to and after intake of a meal by the user, and of at least a dose of medication administered to the user. Based on the input values the apparatus is adapted to determine from at least the obtained input values, an estimate of a nutritional parameter of the meal and to generate an output to a user indicative of the determined estimate.

This device presents limitations. There is a need for improved methods and systems for improving glycemic control .

SUMMARY

Unfortunately, substantial inaccuracies in the estimation of carbohydrates load of meals are common. It has been established that the estimation error even increases when the load of carbohydrates increases (Journal of Diabetes Science and Technology 2010, vol 4 (4) , 893-902) .

There are disclosed systems and methods of estimating a nutritional parameter of a food intake, comprising the steps of obtaining input values indicative of a measured postprandial blood parameter value, a blood parameter value associated with a medication dose value; determining from at least said obtained input values an estimate of the nutritional parameter of the food, and generating an output indicative of the determined estimate. The output can be one or more images, an audio signal or a vibration signal. One or more images associated with the food intake can be displayed. The history of past determined estimates and a nutrition recommendation also can be displayed. Images can be extracted from a video stream. Contents of the images can be analyzed. The postpandrial blood parameter value can be measured by a continuous monitoring device, at different delays. The nutritional parameter can be a slow/fast carbohydrates load value or a fat load value for example. Direct or indirect effects or advantages or objectives of disclosed methods and systems relate to better carbohydrates ("carbs") estimations, better glycemic control, improved user experience, increased ease of use and increased medical safety.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 a, b and c illustrate the fluid delivery device including an insulin dispensing unit and a remote control unit. The carbs load estimation feedback feature is located in the remote control unit and/or in the dispensing unit. FIG. 2 is a flow chart of a method for providing carbs load estimation feedback.

FIG. 3 is an exemplary flow chart of a method for providing carbs load estimation feedback. DETAILED DESCRIPTION

Diabetes mellitus is a disease of major global importance, increasing in frequency at almost epidemic rates, such that the worldwide prevalence in 2006 is 170 million people and predicted to at least double over the next 10-15 years. Diabetes is characterized by a chronically raised blood glucose concentration (hyperglycemia) , due to a relative or absolute lack of the pancreatic hormone, insulin. Within the healthy pancreas, beta cells, located in the islets of Langerhans, continuously produce and secrete insulin according to the blood glucose levels, maintaining near constant glucose levels in the body.

Much of the burden of the disease is due to the long-term tissue complications, which affect both small blood vessels {microangiopathy, causing eye, kidney and nerve damage) and large blood vessels (causing accelerated atherosclerosis, with increased rates of coronary heart disease, peripheral vascular disease and stroke) . The Diabetes Control and Complications Trial (DCCT) demonstrated that development and progression of the chronic complications of diabetes are greatly related to the degree of altered glycaemia as quantified by determinations of glycohemoglobin (HbAlc) . [DCCT Trial, N Engl. J Med 1993; 329: 977-986, UKPDS Trial, Lancet 1998; 352: 837-853. BMJ 1998; 317, (7160): 703-13 and the EDIC Trial, N Engl. J Med 2005; 353, (25): 2643-53]. Thus, maintaining normoglycemia by frequent glucose measurements and adjustment of insulin delivery accordingly can be of utmost importance. Insulin pumps deliver rapid acting insulin (e.g. Lispro, Aspart, etc.) 24 hours a day through a catheter placed under the skin. The total daily insulin dose (TDD) can be divided into basal and bolus doses. Basal insulin is delivered continuously over 24 hours, and keeps the blood glucose levels in range between meals and overnight. Diurnal basal rates can be pre-programmed or manually changed according to various daily activities. Insulin bolus doses are delivered before or after meals to counteract carbohydrates loads or during periods of high blood glucose levels . The dose of the delivered bolus typically depends on the following parameters (there may be other parameters) :

• Amount of carbohydrates to be consumed

• Carbohydrate-to-insulin ratio (CIR) - amount of carbohydrates balanced by one unit of insulin measured in grams per one unit of insulin.

• Insulin sensitivity (IS) - amount of blood glucose lowered by one unit of insulin measured in mg/dL (milligrams/deciliter) per one unit of insulin.

• Current blood glucose levels (CBG) , measured in mg/dL.

• Target blood glucose levels (TBG) desired blood glucose level measured in mg/dL.

• Residual insulin (RI) amount of stored active insulin remaining in the body of the patient after a recent bolus delivery (also known as bolus on board or BOB) .

Currently existing insulin pumps provide bolus dose recommendations that are based on the above mentioned parameters according to the following formula (hereinafter the "formula"):

Recommended bolus = (Carbs/CIR) + (CBG-TBG) / IS - RI

"Food estimate" "Correction estimate" wherein Carbs - total amount of carbohydrates; CIR - carbohydrate-to- insulin ratio; TBG - target blood glucose; CBG - current blood glucose ; IS -insulin sensitivity ; RI -residual insulin.

This bolus recommendation feature may comprise, according to some embodiments, sets of grids of ranges of carbohydrate and blood glucose level. Each grid corresponds to a different combination of IS, CIR, and TBG. Additional grids correspond to selected bolus doses and residual insulin values. The final recommended dose is related to a value that is substantially equivalent to the selected bolus dose minus the RI .

In one embodiment a drug delivery device is provided and comprises a drug delivery unit, a processor adapted to receive postprandial BG level and to determine the carbs load of a consumed meal based on the received postprandial glucose level, and a user interface adapted for displaying the determined carbs load to the user. In some embodiments, the device includes a glucose monitor (e.g. glucometer) or a continuous glucose monitor (CGM) .

In some embodiments the measured postprandial glucose is compared to a predicted postprandial blood glucose level, and the carbs load misestimating is deduced from the compared glucose levels.

In one embodiment the postprandial glucose is measured 2 hours after the consumed meal. According to one such embodiment the predicted blood glucose (BG) level is the target BG as BG has been shown to return to normal levels in healthy subjects 2 hours after consumption of meals of different glycemic index (The Journal of Nutrition 1996,126, 2807-2812). Alternatively, the predicted BG level may be in the range of the TBG and 140 mg/dL (as impaired glucose tolerance is defined by 2 hours postprandial plasma glucose between 140 and 200mg/dL) .

In one optional embodiment, the carbs estimation feedback feature can be optionally applied if no significant physical activity has been performed between the meal and the postprandial glucose measurement. This estimation is assessed by an accelerometer and/or a pedometer and/or by application of a predefined threshold.

In one embodiment, if the postprandial BG level is substantially equal to the predicted BG level (e.g. the TBG range) , the carbs estimation feedback feature indicates appropriate carbs load estimation. Otherwise, the correct carbs load is displayed and optionally compared to the misestimated carbs load.

In one embodiment, if a normal bolus has been delivered, than the bolus that should have been delivered is calculated according to the following formula:

Bolus' = Carb'/CIR + (CBG-TBG) /IS = Bolus + (CBG ¹ - PBG) /IS wherein:

Bolus' - bolus that should have been delivered (bolus based on appropriate carbs load estimation) ,

Carb' - the appropriate carbs load estimation,

CIR - carbohydrate to insulin ratio (assumed to be correct) ,

CBG - BG level at time of bolus administration,

TBG - target BG,

IS - insulin sensitivity (assumed to be correct) ,

Bolus - the normal bolus that has been delivered to cover the consumed meal ,

CBG' - the postprandial BG level (e.g. measured 2 hours after the meal) , and

PBG - the predicted BG level (e.g. TBG) .

The appropriate carbs load estimation is thus calculated according to the following formula:

Carb' = CIR* [Bolus + (CBG' - PBG) /IS - (CBG-TBG) /IS]

For example, if the user's PBG is lOOmg/dL, CIR = lOg/U, IS = 50mg/dL/U, Bolus = 2U and CBG' = 200mg/dL, the bolus that should have been delivered is:

Bolus' = 2 + (200 - 100)/ 50 = 4U. Assuming that the "correction estimate" of the Bolus was correct, than the "food estimate" (Carb'/CIR) should have received an extra 2U (4-2) → Carb'= Carb +20g.

If, for example, the correction bolus was 0 (CBG=TBG) , it can be assumed that the estimated carbs load (Carb) was 20 (2U = Carb/CIR= 20/10), and that 4U = Carb'/ 10 → Carb' = 40g.

The appropriate carbs load is therefore 40g and not 20g.

If for example the CBG ¹ = 50 mg/dL, then

Bolus' = 2 + (50 - 100)/ 50 = 1U. Assuming that the "correction estimate" of the Bolus was correct, than the "food estimate" (Carb'/CIR) should have received 1U less (2-1) → Carb'= Carb -lOg.

If, for example, the correction bolus was 0 (CBG=TBG) , than 1U = Carb'/ 10 → Carb' = lOg. The appropriate carbs load is thus lOg and not 20g.

The figures 1 a, b and c illustrate some embodiments of the device/system 1000. The device 1000 can be used for dispensing therapeutic fluids (e.g., insulin) to the body of the patient. The device/system can comprise a dispensing unit 1010, a remote control unit 1008, and blood glucose (BG) monitor 90. The dispensing unit is connected to a cannula 6 that penetrates the skin 5 to deliver insulin to the subcutaneous tissue. The dispensing unit can comprise a single part having a single housing 1003, as shown in figures la and lb, or two parts having two housings 1001, 1002. In some embodiments, a first part can be reusable (1) (reusable part) and a second part can be disposable (2) (disposable part), as shown in figure lc. Flow programming and data acquisition can be done by a remote control unit 1008 or directly by one or more operating buttons/switches 1004 located on the dispensing unit housing. A blood glucose monitor or a continuous glucose monitor (CGM) can be located at the remote control unit and/or the dispensing unit. In some embodiments, the remote control unit may be implemented in a Personal Data Assistance (PDA) , a cellular phone, a watch, a media player, a smartphone, a tablet device, a laptop and/or a PC. In some embodiments, the carbs load estimation feedback feature can be located in the dispensing unit 1010 (see FIG. la) , in the remote control unit 1008 (see figures lb to lc) or shared between the two units 1010 and 1008. Such a device/system is disclosed, for example, in US2007/106218 and in PCT/IL09 /000388. In some embodiments (not shown) , the remote control unit 1008 and/or dispensing unit 1010 containing the carbs load estimation feedback feature 10 may comprise a memory, a keypad or any other input means (e.g. buttons, switches, touch-screen, voice commander), a display/screen to show the user the appropriate bolus and carbs estimation, accordingly.

According to some embodiments, continuous glucose readings can be transmitted to the remote control and/or patch units from a standalone CGM apparatus. Alternatively a CGM apparatus can be contained within the patch unit and can be divided between the reusable part and the disposable part. The dispensing apparatus can be connected to a cannula and the CGM apparatus can be connected to a separate probe (not shown) or both apparatuses can be connected to a single cannula/probe as described in detail in US11706606. The CGM can be a separate unit or incorporated within the dispensing unit and a feature for dose dependent RI (Residual Insulin) time adjustment and RI calculation can be provided within the remote control unit.

The figure 2 is a flow chart of a method for providing carbs load estimation feedback, according to some embodiments of the present disclosure. At step 200, the predicted BG (PBG) is determined. In some embodiments, the PBG can be determined to be a range of values (e.g. the user's TBG range), a discrete value (e.g. user's discrete TBG value), or a combination of values (e.g. TBG - 140mg/dL) . In other embodiments, it can be a mathematical function (e.g. analytic or numeric) or a lookup table for example that correlates between a bolus dose and the PBG. The PBG can further be a function of one or more of the following parameters: type of bolus (e.g. normal, dual wave, extended bolus) , insulin type (e.g. rapid acting insulin, regular insulin) , site of cannula insertion and insulin delivery, RI time, physical activity, body temperature, insulin sensitivity, and glycemic index of consumed meals. In some embodiments, the PBG may be determined based on history information and/or via machine-learning algorithms (e.g. neural networks, fuzzy logic).

In some embodiments the PBG may be configurable by the user or caregiver. In other embodiments, the PBG may be determined by the carbs estimation feature. In further embodiments, the PBG may retrieved from a memory or communicated (e.g. wirelessly) from another device.

At step 201, the postprandial glucose is measured. According to one preferred embodiment it is measured 2 hours after meal consumption. According to other embodiments it is measured after a certain percentage of the RI time (or DIA) has elapsed (e.g. 50%) . The measured postprandial glucose level may be received via input means or for example may be communicated (e.g. wirelessly or via wires) from a glucose measuring device.

At step 202, the PBG and the measured glucose level are compared. In some embodiments, the comparison may include computational correlation. At step 203, the carbs load misestimating is deduced from the difference between PBG and measured postprandial BG. At step 204, a feedback is provided (e.g. to the user) regarding his/her carbs load estimation and/or misestimating.

At step 205, data associated with correction bolus delivery (if measured postprandial BG > PBG) or correction carbs/basal adjustment (e.g. reduction) (if measured postprandial BG < PBG) may be provided, e.g. to the user. The figure 3 is an exemplary flow chart of one embodiment of a method for providing carbs load estimation feedback. At 300, user's physiological parameters (IS = 50mg/dL/U, CIR = lOg/U, TBG = 80-120 mg/dL) are disclosed. At 301, an exemplary bolus meant to cover a meal is provided - the BG level at the time of bolus administration (CBG) is lOOmg/dL, the estimated carbs load (Carbs) is 20g and the bolus dose is therefore 2U (Carbs/CIR + (CBG-TBG) /IS = 20/10 +0= 2), assuming RI=0. At 302, the predicted BG level at 2 hours after the meal consumption is assumed to be the user's TBG ( 80-120mg/dL) and the measured postprandial BG level (CBG') is 200mg/dL. At 303 the appropriate bolus dose (Bolus¹) is calculated based on the difference between the PBG and CBG' . The appropriate bolus dose should have been 4U and not 2U, thus the appropriate estimated carbs load (Carbs') is 40g and not 20g. At 304 the user is notified of his/her carbs load misestimating. At 305 the user may receive a recommendation to deliver a correction bolus of 2U.

In a first development, there is disclosed a system for estimating a nutritional parameter of a food intake, comprising devices adapted to obtain input values indicative of a measured postprandial blood parameter value, a blood parameter value associated with a medication dose value; determine from at least said obtained input values an estimate of the nutritional parameter of the food, and generate an output indicative of the determined estimate. The blood parameter value can be a predicted value, i.e. as computed by the bolus calculator. The blood parameter value can be a desired target. Predicted value and desired value are not necessarily the same values. The predicted value can be associated with the medication dose, such as the insulin bolus dose for example. The term refers to an underlying metabolic model . The desired value may correspond to a broader understanding, for example including the consequences of other physiological parameters (such as physical exercise for example) . The food intake can be a regular meal or a snack. The food intake can thus occur at fixed time intervals or at random times (some moments may present higher probability, for example 11 am) . The medication dose value can be an insulin dose value for example, but not necessarily. It can be any drug dose value. A drug is any substance that, when absorbed into the body of a living organism, alters normal bodily function. The postpandrial blood parameter value can be obtained directly from a glucometer for example, when the blood parameter being considered is glucose concentration. Measurements can be done by pricking a finger and extracting a drop of blood, which is applied to a test strip including chemicals sensitive to the glucose in the blood sample. An optical or electrochemical detector called a glucometer is used to analyze the blood sample and gives a numerical glucose reading. Recently, non-invasive glucose measuring devices that monitor BG through infrared technology and optical sensing have become available; the obtained value can be directly communicated to the one or more devices determining the estimate of the nutritional content of the food.

Display devices adapted to display the generated output indicative of the estimate of the nutritional parameter of the food can be : a screen of an insulin deliver device (e.g., insulin pump, insulin pen), a screen of a continuous glucose monitoring CGM-based device, a screen of a remote controller, a screen on a watch, a television, a screen of the smart phone, a tablet PC, a PC screen, a headed-mounted display, a retinal projector display, a display projected on a car windscreen, a traditional projector, a projector beaming an image on a wall or on any other surface with appropriate geometrical corrections in response to the deformations of the projecting surface. A combination of screens or display means can be used simultaneously to display the output indicative of the nutritional value or carbs load. In other words, data displayed to the user can be distributed across several different devices.

Other means for communicating the generated output indicative of the estimate of the nutritional parameter of the food can be implemented alone or in combination with the display. Such means may be, for example, audible means (e.g. buzzer, speaker, media player) .

The determination of the estimate of the nutritional parameter of the food can be based on several data. The accuracy can be increased by using more data or by using different data. In one embodiment, the determination is based on the underlying physiological model which is associated with the bolus calculation. In another embodiment, the determination is based on the analysis of the images associated with the meal. This will be explained later. In one other embodiment, the past discrepancies, i.e. the user meal, bolus and glycemic history are leveraged to correct the estimate. In other embodiments, one or more of the preceding techniques are combined. The system can comprise a collection of networked devices. Architectural choices are numerous. In other words, the physical implementation of the disclosed methods and systems can occur by many different ways. In a preferred embodiment, the display device is located in the remote control or in the pump. The input means or devices are located in the remote control , the input values are provided wirelessly.

The networked devices or elements of the system which perform the disclosed steps can comprise one or more of the following :

- A network; any kind of networks, such as Internet, Intranet, Wi-Fi, or a mesh network or ad-hoc network, a network enabling a system of networked medical devices) ; - Computing and storage resources (processors and storage or memory- units for example) ; these resources can be local (physical implementation of said resources in the drug infusion device for example) or remotely accessed (through the network, in the "cloud" for example) . An insulin pump today comprises a processor and a memory unit, for example. In the future, such an insulin pump may correspond to a thin client which is controlled in/by the cloud; these resources are used by the data management means, in order to determine from the obtained input values an estimate of the nutritional parameter of the food.

- Data management means (pieces of software code provided through the network according to firmware and/or software and/or hardware embodiments) . Data can be shared by communities of users of patients (leveraging social features and sharing of experiences through one or more networks) . Data management means can be updated with firmware updates of any one of the networked medical devices (the insulin pump for example) . Other embodiments may correspond to hardware embodiments (i.e. storage in ROM or USB sticks for example) .

- Optional body sensors can comprise an accelerometer/gyroscope ; a blood pressure sensor ; a C02 gas sensor ; an ECG sensor ; an EEG sensor ; an EMG sensor ; a Pulse Oximetry sensor ; humidity and temperature sensors ; image or video sensors ; these sensors can contribute to the assessment of the physiological state of the user and modulate bolus and/or nutrition recommendation ;

Input devices adapted to obtain input values indicative of a measured postprandial blood parameter value can be one or more physical buttons, and/or a touchscreen or a portion thereof, and/or a device adapted to voice recognition. A wide range of haptic devices can also be used. Such devices also include motion gestures analysis or interpretation. Said devices can be combined with one another (multimodal interaction) . For example, a voice command can be confirmed or modulated by an action on a touch sensitive interface. In another development, there is disclosed a system wherein the output comprises a display of one or more images and/or an audio signal and/or a vibration signal. The feedback of the nutritional content of the meal can be multimodal. In a preferred embodiment, the estimate is visually displayed. The nutritional parameter may be inputted or outputted as a single value, or a range of values, or a plurality of values (slow/fast carbs for example) and/or a graph and/or an icon or a symbol .

In another development, there is disclosed that the output further comprises the display of one or more images associated with the food intake and/or the display of a history of past determined estimates and/or the display of information such as a nutrition recommendation. The user may not remember well his previous meal. Often patients remember the nature of their previous meal but they forgot the volumes, details and arrangement thereof. To improve the glycemic control, it is efficient to associate a visual representation of the meal to the determined estimate of the nutritional value. Said estimate is the "real" one, i.e. the one which should have been entered in the system given the associated glycemic model. In one embodiment, one image (a photography) is shown to the user. In one other embodiment, several images are shown, disclosing the internal structure of the food at different moments. In another embodiment, a collection of images are shown to the user. A video sequence would be long but a video summary can comprise selected snapshots. With visual representations; it becomes easier to visually assess trends and a possible systematic misestimating of the nutritional value of the food (fat load for cholesterol, carbs load for diabetes, etc). The misestimating' s (made by the user) may be recorded in memory. An analysis (e.g., statistical, trend) may be provided to the user and/or caregiver. For example, over the past week the user misestimated carbs with an average of minus 15 grams per estimate. This kind of observation may result in a change of the medication, such as an automatic delivery or an automatic suggestion provided to the user to compensate for his estimation. Other analysis may be also provided, such as the misestimating' s during lunches, the over-estimations when dealing with beverages or sweets. For example, the user can be advised not to consume certain food intakes which he cannot estimate. As indicated, this analysis may be aggregated and serve as a basis for adapting the medication dose and/or the communication model with the user.

In another development, there is disclosed that said image (s) associated with the food intake can be a frame of a video stream. With head-mounted camera or with any device capturing what the user is eating for example, it is possible to extract images of the video stream and to use such images for the described purpose. The selection of the frames can be statistical (around midday) , triggered by the user, or performed by image or sound recognition and analysis techniques .

In another development, there is disclosed that the determination of the estimate comprises the analysis of said one or more said images. For example, one image taken before the meal (image of the plate) and one after the meal, taken approximately in the same conditions (position in space) enable to isolate the meal by subtracting the two images; following image similarity techniques and libraries of correspondence between ingredients and nutritional values allow a probabilistic assessment of the content of the considered meal. In another development, there is disclosed that the postpandrial blood parameter value can be measured by the patient or can be measured automatically by a continuous monitoring device. Optionally, a reminder can help the patient to have appropriate measures, i.e. at optimal times for the physiological model (for example the bolus calculation model) . A reminder to the user to measure his postprandial blood glucose can be configurable (by the user or by the caregiver) . Such a reminder can state that one should measure postprandial blood glucose after 2 hours, another that one should measure it after 3 hours, etc. This reminder can be also implemented in a "machine- learning" process, i.e. what is the optimal time that one should wait in order to obtain a postprandial blood glucose measurement.

In another development, there is disclosed that the measurement of said postpandrial blood parameter value occurs at a predefined delay, such as a fixed delay of 2 hours . The delay of 2 hours corresponds to the duration of action of the insulin for example (in such fast acting insulin it is considered that there is no further effect after 2 hours) . Such a fixed delay can be 3 hours or any other predefined value. The delay also can be defined by computation, or by machine- learning, or according to delays associated with "structured testing" or with the calibration of a continuous monitoring device for example.

In another development, there is disclosed that the output indicative of the determined estimate is provided if no significant physical activity has been detected. This significant physical activity can be determined by an accelerometer and/or a pedometer and/or by application of a predefined threshold. In another development, there is disclosed that the nutritional parameter can be a carbohydrates load value, and/or a glycemic index, and/or fast carbohydrates load value, and/or a fat load value, and/or a vitamin load value, and/or fiber load value and a mineral load value According to ikipedia, the glycemic index or GI is a measure of the effects of carbohydrates on blood sugar levels. Carbohydrates that break down quickly during digestion and release glucose rapidly into the bloodstream have a high GI; carbohydrates that break down more slowly, releasing glucose more gradually into the bloodstream, have a low GI . A lower glycemic index suggests slower rates of digestion and absorption of the foods ¹ carbohydrates and may also indicate greater extraction from the liver and periphery of the products of carbohydrate digestion. A lower glycemic response usually equates to a lower insulin demand but not always, and may improve long-term blood glucose control and blood lipids. The insulin index is also useful for providing a direct measure of the insulin response to a food. Fast and slow carbohydrates are most of the time present indicated on the packaging of food or can be deduced from it. The glycemic index assessed by the present disclosure may relate to one particular food element, or to the entire meal (composed of several food elements) . In such latter case, the glycemic index is the global glycemic index of the meal. The disclosed methods and systems handle both carbs load and global meal glycemic index. In other developments, there are disclosed methods, computer programs and computer-readable medium. In particular, the disclosed methods can be integrated into medical infusion systems.

In the present disclosure, a blood glucose measurement corresponds to the measure of the concentration of glucose in the blood. This measure can be handled by sampling superficial blood from a finger for example But the blood sampling can also be interstitial, i.e. from subcutaneous tissue (where interstitial fluid exists) , and even intravenous or measured by an device implemented deep in the body (internal blood)

In one embodiment, the method results in the determination of a correction bolus which is scheduled by the system and not by the user (like in conventional insulin pumps) . In other words, the machine sets a predefined time (still configurable by the user or caregiver) to check postprandial BG and administer a correction dose (which may be a bolus or adjustment of the basal rate) . The misestimating of carbs is stored in memory. It will be used in the next time to validate or correct user's new input. According to this embodiment, misestimating' s of carbs are stored in memory and analyzed, resulting in trends or other conclusions, e.g., an average under-dose of percentage of units of insulin which may be recommended to the user or be automatically corrected. The result would be an improved glycemic control system. A further non-technical effect will be a better clinical state of the user. According to another embodiment, there is disclosed a method implemented in a system comprising an insulin delivery device, a glucose monitoring device adapted to measure concentration of blood glucose of a user, a processor adapted to receive glucose readings and insulin delivery doses ; said method comprising:

1. A method for improving glycemic control, comprising the steps of: receiving physiological parameters of a user, said physiological parameters comprising:

- a Carbohydrate to Insulin Ratio;

- an Insulin Sensitivity Factor;

- a Target Blood Glucose;

- Residual Insulin;

- remaining Duration of Insulin Action;

- postprandial Predicted Blood Glucose. receiving, at a time such as a meal time, an insulin bolus dose value associated with a carbohydrate load estimation, a blood glucose concentration, a Carbohydrate to Insulin Ratio and an Insulin Sensitivity Factor ; receiving a measured postprandial blood glucose concentration level and calculating the delta by subtracting the Target Blood Glucose to the post-prandial Predicted Blood Glucose; determining the error in carbohydrates load estimation, wherein the error equals said delta divided by the Carbohydrate to Insulin Ratio;

2. The method of claim 1 wherein the step of receiving the measured postprandial blood glucose concentration level comprises a step of providing, at a predefined time, a reminder for measuring a postprandial blood glucose concentration level, said predefined time being associated with the remaining Duration of Insulin Action. 3. The method of any preceding claim, further comprising the step of displaying an indication of the carbohydrate estimation error.

According to another embodiment, there is disclosed a method for carbohydrates load estimation and advising such estimation. In more details :

1. A method for advising a correction of a carbohydrates load estimation, the method comprising the steps of: receiving one or more meal parameters associated with a meal to be ingested by a user; determining a first insulin bolus dose value based, at least in part, on the one or more meal parameters ; receiving a postprandial blood glucose concentration value, and determining a carbohydrates load value based on at least said postprandial blood glucose concentration value.

2. The method of claim 1, wherein the one or more meal parameters comprises one or more of an estimated carbohydrate load value to be ingested by the user and a pre-prandial blood glucose concentration value . 3. The method of claim 1, wherein the one or more meal parameters comprises a meal time.

4. The method of claim 1, wherein the step of determining the first insulin bolus dose value comprises a step of receiving an amount of insulin. 5. The method of claim 1, wherein the step of determining of the first insulin bolus dose value comprises the step of: receiving meal parameters comprising at least an estimated carbohydrates amount of insulin and a preprandial blood glucose concentration value; receiving physiological parameters of the user comprising at least a Carbohydrate to Insulin Ratio value ; an Insulin Sensitivity Factor value ; a Target Blood Glucose value ; a Residual Insulin value, and determining the first insulin bolus dose based on the received meal parameters and the received physiological parameters of the user. 6. The method of claim 5, wherein the RI value equals to zero.

7. The method of claim 1, wherein the physiological parameters received from a user are selected from the group comprising a Carbohydrate to Insulin Ratio ("CIR"), an Insulin Sensitivity Factor ("ISF") and a Target Blood Glucose ( "TBG" ) . 8. The method of claim 1, further comprising the step of notifying the user to measure a postprandial blood glucose concentration value.

9. The method of claim 1, further comprising the step of setting a predicted blood glucose concentration value, and comparing the predicted blood glucose concentration value with the received postprandial blood glucose concentration value.

10. The method of claim 9, wherein the predicted blood glucose concentration value corresponds to the Target Blood Glucose value.

11. The method of claim 10, wherein the predicted blood glucose concentration value corresponds to a percentage of the residual insulin value.

12. The method of claim 1, wherein the postprandial blood glucose concentration value is received at least two hours after receiving a preprandial blood glucose concentration value.

13. The method of claim 9, wherein the step of comparing comprises a step of subtracting the postprandial blood glucose value to the preprandial blood glucose value.

14. The method of claim 13, further comprising the step of determining a correction bolus based on said comparison and the Insulin Sensitivity Factor value. 15. The method of claim 14, wherein the determination of a correction bolus includes the step of subtracting the postprandial blood glucose value to the preprandial blood glucose value and dividing the result by the Insulin Sensitivity Factor 16. The method of claim 14, wherein the step of determining a carbohydrates load corresponds to the multiplication of the correction bolus by the Carbohydrate to Insulin Ratio value.

17. The method of claim 2 , further comprising the step of calculating the difference between the determined carbohydrates load value and the estimated carbohydrates load value.

18. The method of claim 17, further comprising storing one or more of the determined and estimated carbohydrate load values and associated differences .

19. The method of any preceding claim, comprising the further step of advising the user on corrected carbohydrates estimation, said estimation for example being based on historical data.

20. The method of any preceding claim, wherein the postprandial blood glucose concentration value is measured at a predefined time, said predefined time being configurable by the user and/or caregiver and/or wherein said predefined time corresponds to a time when the postprandial blood glucose value is substantially equal to the Target Blood Glucose value and/or wherein said predefined time corresponds to a time when the residual insulin value is substantially equal to a predetermined percentage of the residual insulin and/or wherein said predefined time is superior or equal to two hours from meal time.

21. A system implementing one or more steps of any one of claims 1 to 20, wherein the system comprises: a glucometer adapted to measure the concentration of glucose in the blood of a user; an insulin delivery device adapted to deliver insulin to said user, and a device adapted to deliver time information. 22. The system of claim 21 further comprising a continuous monitoring device adapted to measure blood glucose concentration values .

The present invention also encompasses the following items:

1. A method of estimating a nutritional parameter of a food intake, comprising the steps of:

- obtaining input values indicative of a measured postprandial blood parameter value, a blood parameter value associated with a medication dose value;

- determining from at least said obtained input values an estimate of the nutritional parameter of the food, and

- generating an output indicative of the determined estimate.

2. The method of claim 1, wherein the output comprises a display of one or more images and/or an audio signal and/or a vibration signal.

3. The method of any preceding claim, wherein the output further comprises the display of one or more images associated with the food intake and/or the display of a history of past determined estimates and/or the display of information such as a nutrition recommendation.

4. The method of claim 3, wherein at least one image associated with the food intake is a frame of a video stream. 5. The method of any one of claims 3 to 4, wherein the determination of the estimate comprises the analysis of said one or more said images

6. The method of any preceding claim, further comprising the step of measuring or triggering a measurement or reminding to measure the postpandrial blood parameter value. 7. The method of claim 6, wherein the measurement of said postpandrial blood parameter value occurs at a predefined delay, such as a fixed delay of 2 hours or such as a machine-learning defined delay. 8. The method of any preceding claim, wherein the output indicative of the determined estimate is provided if no significant physical activity has been detected.

9. The method of claim 8, wherein said significant physical activity is determined by an accelerometer and/or a pedometer and/or by application of a predefined threshold.

10. The method of any preceding claim, wherein the nutritional parameter comprises one or more values selected from the group consisting of a glycemic index value, a slow carbohydrates load value, a fast carbohydrates load value, a fat load value, a vitamin load value, a fiber load value and a mineral load value.

11. A system comprising means adapted to carry out the steps of the method according to any one of claims 1 to 10. 12. A computer program comprising instructions for carrying out the steps of the method according to any one of claims 1 to 10 when said computer program is executed on a suitable computer device.

13. A computer readable medium having encoded thereon a computer program according to claim 12. The invention can take form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. Furthermore, the invention can take the form of a computer program product accessible from a computer- usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer- readable can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. In particular the method can be implemented on one or more processors adapted to execute instructions for estimating the nutritional parameter of a food intake.

Claims

1. A system for estimating a nutritional parameter of a food intake, comprising devices adapted to:

- obtain input values indicative of a measured postprandial blood parameter value, a blood parameter value associated with a medication dose value; - determine from at least said obtained input values an estimate of the nutritional parameter of the food, and

- generate an output indicative of the determined estimate.

2. The system of claim 1, wherein the output comprises a display of one or more images and/or an audio signal and/or a vibration signal.

3. The system of any preceding claim, wherein the output further comprises the display of one or more images associated with the food intake and/or the display of a history of past determined estimates and/or the display of information such as a nutrition recommendation.

4. The system of claim 3, wherein at least one image associated with the food intake is a frame of a video stream.

5. The system of any one of claims 3 to 4, wherein the determination of the estimate comprises the analysis of said one or more said images

6. The system of any preceding claim, wherein the postpandrial blood parameter value is obtained by a measurement handled by a continuous monitoring device.

7. The system of claim 6, wherein the measurement of said postpandrial blood parameter value occurs at a predefined delay, such as a fixed delay of 2 hours or such as a machine-learning defined delay.

8. The system of any preceding claim, wherein the output indicative of the determined estimate is provided if no significant physical activity has been detected.

9. The system of claim 8, wherein said significant physical activity is determined by an accelerometer and/or a pedometer and/or by application of a predefined threshold.

10. The system of any preceding claim, wherein the nutritional parameter comprises one or more values selected from the group consisting of a glycemic index value, a slow carbohydrates load value, a fast carbohydrates load value, a fat load value, a vitamin load value, a fiber load value and a mineral load value.

11. A method comprising one or more steps implemented in any one of the devices or systems of the claims 1 to 10.

12. A computer program comprising instructions for carrying out the steps of the method according to claim 10 when said computer program is executed on a suitable computer device.

13. A computer readable medium having encoded thereon a computer program according to claim 12.