Patent Application
20190320976
Monitoring glucose levels is critical for diabetes patients. Continuous glucose monitoring (CGM) sensors are a type of device in which fluid is sampled from just under the skin multiple times a day. CGM devices typically involve a small housing in which the electronics are located and which is adhered to the patient's skin to be worn for a period of time. A CGM sensor, which is often electrochemical, is delivered subcutaneously by a small needle within the device.
Electrochemical glucose sensors operate by using electrodes which detect an amperometric signal caused by oxidation of enzymes during conversion of glucose to gluconolactone. The amperometric signal can then be correlated to a glucose concentration. Two-electrode (also referred to as two-pole) designs use a working electrode and a reference electrode, where the reference electrode provides a reference against which the working electrode is compared. Three-electrode (or three-pole) designs have a working electrode, a reference electrode and a counter electrode. The counter electrode replenishes ionic loss at the reference electrode and is part of the ionic circuit.
Glucose readings taken by the sensor can be tracked and analyzed by a monitoring device, such as by scanning the sensor with a customized receiver or by transmitting signals to a smartphone or other device that has a specific software application. Software features that have been included in CGM systems include viewing glucose levels over time, indicating glucose trends, and alerting the patient of high and low glucose levels.
In some embodiments, a method for metabolic monitoring includes a processor receiving glucose data associated with an individual from a metabolic sensor and food intake information associated with the individual. The processor calculates a plurality of global metrics. Each global metric is based on a glucose variability, a glucose load, and a post-prandial peak. The glucose variability is calculated from the glucose data associated with the individual. The processor determines an individualized metric by correlating the food intake information associated with the individual to the plurality of global metrics, and recommends a behavior modification based on the individualized metric.
In some embodiments, a metabolic monitoring system includes a metabolic sensor configured to measure glucose data associated with an individual and a processor configured to receive glucose data associated with the individual from the metabolic sensor. Food intake information associated with the individual is received. A plurality of global metrics are calculated. Each global metric is based on a glucose variability, a glucose load, and a post-prandial peak. The glucose variability is calculated from the glucose data associated with the individual. An individualized metric is determined by correlating the food intake information associated with the individual to the plurality of global metric. A behavior modification is recommended based on the individualized metric.
The present embodiments uniquely use direct real-time metabolic data to encourage a user to change or modify behavior related to eating, exercise and subsequent weight loss. Methods and systems are disclosed in which continuous glucose monitoring is used to provide real-time feedback on the impact of eating various foods on post-prandial (post-meal) glucose levels in an individual. A goal of the present methods and systems is to encourage patients to lower the amplitude and number of glucose spikes following eating. While individuals eat and drink according to their likes and dislikes and until a feeling of satiety, the impact of the food and drinks on their body chemistry is unknown. The higher the glucose spike the higher the production of insulin, which leads to fat formation, a crescendo and decrescendo in glucose values, and a greater likelihood of eating more frequently. Each individual reacts to different foods in a unique manner. In the present embodiments, providing information that correlates food intake to glucose metrics can dramatically alter food choices and improve health. Furthermore, weight, exercise, and stress can change these reactions, requiring frequent recalibration. The present embodiments can account for the fact that the body is always changing.
Embodiments disclose a sensor-based system for weight loss, treatment of insulin resistance and its related diseases such as polycystic ovary syndrome (PCOS), non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), and reducing the likelihood of cancer recurrence. These diseases are related to excess glucose in the body. The present systems include a monitoring system with feedback and professional advice via calculated metrics to help treat the person's obesity or weight management regime, and then creating actionable goals that are communicated on a software application for the patient to utilize and modify their behavior.
Although embodiments shall be described in terms of providing weight loss recommendations for a user, the concepts can be applied to providing other user-derived behavior modifications or correlations. For example, the present methods and systems can recommend behavior modifications such as training programs for athletes, or health management recommendations in relation to a medical condition of a user.
The system includes a software application and a sensor/transceiver that wirelessly communicates to a device which can be, for example, a smartphone, tablet, smart watch, or the like. In some embodiments, all the information is sent to a cloud-based server for analysis. In other embodiments, the information and processing of the data can be performed on the device or by an electronics unit connected to the metabolic sensor. In further embodiments, the sensor/transceiver can be sent to a non-mobile device, such as a desktop computer or a kiosk that may be located in a facility such as a doctor's office or hospital. Analytics, which can be cloud-based or can be included in the software application of the device, create personalized advice with personalized metrics based directly on the individual's data and can be distributed back to the patient and/or sent to a physician, dietician, trainer or family member.
The metrics are based upon glucose variability and total glucose load, uniquely combining multiple glucose indicators to form a global metric that serves as a metabolic index. The glucose information is sent to artificial intelligence (AI) programs, which may be cloud-based, to correlate the global metrics with food intake. The metrics and food intake information may also be correlated with numerous other measures such as heart rate (HR), location, activity, etc., to provide a more complete picture of the person's individual metabolic profile.
Many embodiments are included for displaying data on a user interface device. The data may be information such as the mean glucose level or another metric that is calculated or obtained from the sensor of the continuous glucose monitoring. The recommended behavior modifications may also be displayed which includes eating a food, drinking a beverage, taking medication, or performing an exercise. These are specific activities and quantities based on the historical food intake information received and historical metabolic index calculations for the specific individual. In some embodiments, the processor is part of a device such as a mobile phone, having a lock screen, home screen or wallpaper feature. The lock screen, home screen or wallpaper may be modified based on the glucose data associated with an individual from a metabolic sensor. In other words, the lock screen, home screen and/or wallpaper of the device may be modified based on the glucose data from the sensor.
Clinical evidence data from various trials and studies show a correlation between weight loss and glucose variation, a link between obesity and glucose variation, and a correlation between food intake and glucose variation.
Evidence of a correlation between weight loss and glucose variation was first shown in the FLAT-SUGAR trial. This trial compared insulin to GLP-1 Agonist (a drug that controls glucose variation) in the hope of improving A1c levels (a measure of glycosolated hemoglobin linked to long-term health risks). The study showed no marked change in A1c even when marked reductions in glucose variations were shown. However, an unexpected observation from the study was a dramatic and sustained weight loss (4.5 kg or 10.6 lbs over 26 weeks) in the group with dramatically reduced glucose variation.
Evidence of a link between obesity and glucose variation was demonstrated in a paper by Salkind et al. This study showed that higher glucose variability exists in overweight, pre-diabetic and obese patients compared to non-diabetic adult controls. It remains to be demonstrated whether this is a cause or an effect. A study by Trico et al. showed that by simple changes in the order of food intake, glucose response can be altered. In other words, glucose variation can be reduced through changing the food intake sequence. This study, which focused on post-prandial (meal) peaks in glucose, demonstrates that simple advice can be used to modify glucose response to foods. A further consideration in correlating food intake to glucose response is that every individual responds to food differently. For example,
The present embodiments personalize a program to control blood glucose variations and overall glucose load for a consumer by using global metrics that are a unique combination of multiple glucose indicators. The global metrics are essentially metabolic indices and are utilized to determine an individualized metric, where the individualized metric is customized for that particular consumer. By controlling blood glucose levels, the consumer can improve their health which is beneficial for managing diseases such as diabetes and for weight loss.
To derive the unique global metrics of the present disclosure, a pre-existing data set was first examined to calculate concepts and to determine whether continuous calculation of variation would provide new data or insights to use in a weight loss product.
Next, a new concept of tracking glucose variability in real-time was investigated.
The present embodiments uniquely use the concept of monitoring real-time glucose variation to formulate metrics for a weight loss program, where the metrics are personalized for an individual's specific characteristics. Terminology used in calculating the metrics is listed below:
The global metrics are unique indices of the present embodiments that use weighting factors to combine GV, GL and PPP. The weighting factors are tailored for the data of an individual person, such as by curve-fitting the data for the individual.
Listed below are example formulas for the metrics, where A, B and C are weighting factors that can be either constants or derived functions. Other formulas for global metrics may be used, and one or more of these derived metrics may be shown on-screen in the software application used by the patient.
The derived functions for weighting factors A, B and C can be, for example, a polynomial function, exponential function, logarithmic function or power-law function. In some embodiments, a rate of change may be used as part of the functions, such as a rate of change of metabolic sensed values where rapid rise or rapid decrease of these values correspond to certain behaviors such as eating or exercise. For example, during rapid rates of change these weighting factors may increase how portions of the index (global metric) may be weighted, such as the post prandial peak value. The five examples of global metric calculations above use different additive and multiplicative combinations.
Each variable in the overall (global) metric is likely to be weighted differently for each individual. For example, GV is known to rise as a person goes from normal weight to overweight to obese, and higher values of GV are known to be correlated to those who are overweight. Thus, for higher weight individuals, the weighting factor “A” for GV in the global metric of the present embodiments may be higher than for people with lower or normal weight. in another example, the PPP is often more muted in the morbidly obese population than people in the overweight category, and thus the weighting factor “C” of PPP in the global metric of the present embodiments may be lower in value for morbidly obese patients compared to overweight individuals. In yet another example, the GL may be more correlative for weight gain or loss in the normal population rather than in the overweight or obese populations. Consequently, GL may have a higher weighting factor “B” for individuals in lower weight categories. Note that these examples describe general trends, which may not apply to every case since the actual weighting factors for each situation is highly individualized. Furthermore, although these examples show how a person's weight category can be used to affect the derived functions or rate of change for the weighting ors, other aspects may be used to tailor the weighting factors.
The rate of change can also differ widely in different cohorts. For example, for PPP a fast rate of change of may potentially result in the PPP being more correlated to weight gain even though the PPP value is low. The rate of decline from a PPP may be especially relevant to long term weight gain with a slow decline more likely to be correlated to weight gain.
The present glucose variation monitoring and weight loss systems and methods integrate continuous glucose monitoring with, in some embodiments, image and auditory recognition software to provide information in a single displayed screen that predicts an individual's post-prandial glucose and guide food selection. The system receives meal inputs from the user to input what is being eaten. Then using analytics, which may be cloud-based, the system generates a series of parameters for the meal. Based on the meal parameters and the CGM measurements, the system calculates and displays actionable targets for the patient that are communicated back to the patient and displayed as behavior modification recommendations.
The device 520 receives food intake information (e.g., food eaten during or between meals) from the patient 550, and the food information and glucose readings are transmitted to the server 530. The transmission may be accomplished through a variety of paths, communication access systems or networks. The networks may be the Internet, a variety of carriers for telephone services, third-party communication service systems, third-party application cloud systems, third-party customer cloud systems, cloud-based broker service systems (e.g., to facilitate integration of different communication services), on-premises enterprise systems, or other potential data communication systems. The server 530 can represent a cloud-based processing system. In other embodiments, the meal and glucose data can be stored and processed on the device 520 itself, such that the server 530 is not required.
In the illustrated embodiment, the server 530 generally includes at least one processor 532, a main electronic memory 533, a data storage 534, a user input/output (I/O) 536, and a network I/O 537, among other components not shown for simplicity, connected or coupled together by a data communication subsystem 538. A non-transitory computer readable medium 535 includes instructions that, when executed by the processor 532, cause the processor 532 to perform operations including calculations of global metrics, determining of an individualized metric, and providing behavior modification recommendations as described herein.
In accordance with the description herein, the various components of the system or method generally represent appropriate hardware and software components for providing the described resources and performing the described functions. The hardware generally includes any appropriate number and combination of computing devices, network communication devices, and peripheral components connected together, including various processors, computer memory (including transitory and non-transitory media), input/output devices, user interface devices, communication adapters, communication channels, etc. The software generally includes any appropriate number and combination of conventional and specially-developed software with computer-readable instructions stored by the computer memory in non-transitory computer-readable or machine-readable media and executed by the various processors to perform the functions described herein.
In step 640 metabolic data including glucose data is provided by the metabolic (CGM) sensor. In step 650 the system, in some embodiments, the server or the device, analyzes the data—that is, the food information and glucose readings from the CGM. The food information can include the types of food, amounts, and sequence in which the food items were eaten. Individual metrics may be generated and displayed for the patient and are based on the calculated global metrics described herein. The global metrics are based on glucose variability using formulas that combine GV, GL and PPP using weighting factors depending on each individual. Displayed metrics may also include possible rates of metabolic change.
The system then begins to predict the patient's PPG and whether the meal will be in a high, borderline, or safe zone for the particular patient. These PPG zones may be indicated visually on the display of the device by, for example, red, yellow, and green colors, respectively. Determination of which global metric to use as the individualized metric to display for a patient can be based on factors such as their weight category, the presence of a diabetic condition, or their individual historical trends. For example, the determining of the individualized metric may include learning from the received historical food intake information associated with the patient and historical metabolic index calculations. A behavior modification recommendation is generated by analyzing correlations between the global metric and food intake, where the analysis may use artificial intelligence (AI) in some embodiments.
An example of a behavior modification recommendation based on the metrics is suggesting an order of eating foods in a meal, such as eating protein or fat first to produce lower GV and PPP for a particular person. In another example, an individual may have high glucose responses to certain foods (i.e., carbohydrates), and recommendations can be made by the system to substitute foods that result in a lower response. These substitutions could be alternative types of food items or could be another food within the same food type, based on the individual's own data on how they respond to each type of food eaten. Over time, the system's response database (e.g., data storage 534 of
The cycle of steps 620, 640 and 650 then repeats so that the system can learn the patient's typical metabolic responses. Once there is a reasonable match between the predicted and actual results the learning phase is complete. The learning phase can also be used to train the analysis system on voice recognition of audio input of food intake information from the individual.
The patient continues to use the application in a monitoring phase 630 and the system receives pre-eating information in step 632 (e.g., by receiving an uploaded picture of what is eaten), and receives post-eating information in step 636 after the food is eaten. As described in relation to step 620, in some embodiments the system uses a mobile device input (e.g., by smart phone) of food intake via photos and/or voice-driven inputs to obtain caloric estimates. However, receiving food information from other devices is also possible, such as by a desktop computer that can then send the information to a mobile device or to a computer server that has the metabolic readings.
For calculations during the monitoring phase, in step 640 glucose data is again provided to the system by the metabolic (CGM) sensor 510 via the electronics unit 515. In step 650 the system analyzes the food information (e.g., a meal, drink, or snack) and the glucose readings from the CGM to correlate the food intake information to the global metrics. The system can then calculate a prediction of a glucose level zone the patient will be in. If there is a spike in glucose level without food data entry, the system requests entry of the information. The predictions can be performed in real-time, thus providing useful information for the user to monitor their metabolic and alter their behavior immediately as needed. Metabolic sensors continuously measure and track the patient for a desired time period, such as several days (e.g., up to 14 days). The process of analyzing the data in step 650 continues during this time period, using the meal information from monitoring phase 630 and CGM data in step 640.
The analysis during the monitoring phase 630 may continue to use the individualized metric that was determined during the learning phase 620 or may change the individualized metric to adapt to changes in the user's response. Changing the individualized metric may involve adjusting the weighting factors and/or changing which global metric to use for the individualized metric. In some embodiments the behavior in the monitoring phase 630 may be different than in the learning phase 620 due to information regarding meals not being received. In such cases, the system sends reminders to the patient that data is not received and suggests repeat CGMs.
In step 660 a report is generated periodically (daily, for example), that provides information such as the mean glucose level, number of spikes, highest spike, foods that caused spikes, and the like. The displayed information may be generated as an aggregate value (day by day, weekly, etc.) or for each individual meal or activity. This information can be presented visually, such as percentages of meals in red, yellow, or green zones, where the zone categories are based on which global metric is used to serve as the individualized metric. The reports may include a daily predicted mean glucose and other metrics that a user may want to monitor. The reports may convey a behavior modification recommendation based on the individualized metric. Behavior modification recommendations can include at least one of a type of food to eat, a sequence in which to eat different food types, a timing of meals during a day, a timing of exercise in relation to a meal or exercise. In step 670, at regular intervals recalibration of the entire system can be suggested with repeat glucose monitoring.
In some embodiments, other quantities can be measured in addition to glucose. For example, sensors for lactate, ketone, etc., can be utilized. These additional sensors can be separate sensors from the glucose sensor or can be combined into a single device with the glucose sensor to provide the metabolic data in step 640 to be used in the analyses. The additional sensors can help indicate further aspects of a person's metabolic response, such as during exercise. For example, higher ketone levels indicate more fat burning, and lactate levels indicate a shift between aerobic and anaerobic activity. Accordingly, additional metrics calculated and displayed to the patient may also include direct ratios of multiple metabolites such as glucose to ketone/lactate/free fatty acid or calculated metabolic indices such as glucose indices to ketone/lactate/free fatty acid indices. Correlations can be created between meal input ratios and these indices to generate individualized expert advice. In some embodiments, tracking of these additional aspects may be useful for athletes in determining a training program.
The meal parameters used in the analyses can include ratios of estimated carbohydrates, proteins, and fat content, as well as approximate caloric portion size. The system may request several mixed meals like a protein bar to sample a broad array and to provide better machine learning. These meals are then indexed along with metabolic sensor metrics and tracked (e.g., in a cloud-based infrastructure) for the individual patient.
Metabolic sensor data in step 640 may be augmented by additional sensor data such as heart rate, blood pressure, steps, weight, and/or accelerometer activity and sleep monitors, all of which may be transmitted to the mobile device, such as wirelessly. These measurements can be used to create correlations to the overall metrics as well. Aggregate data from the metabolic sensor (e.g., glucose, ketone, free fatty acid, etc.) and response to all activities (e.g., meals, sleep, exercise, general levels of activity) can have additional cross-correlations with heart rate, blood pressure, activity and other physical sensors included in the system and recorded in the databases.
Displays of meaningful indices/correlates, such as those in the reports of step 660, may be displayed in a simple, graphical format. As described earlier, each individual has different metrics and correlates based on analysis of their data. Weekly or monthly data can be aggregated and trended along with expert advice or reports that can be given via a patient caregiver or consultation. Displaying data or the behavior modification recommendation in a simple, graphical format keeps the user up-to-date in real-time so an immediate action can be performed based on the individualized metrics.
In some embodiments, the display of data may be a simple, visual cue for the user of the individualized metric such as the glucose level of the individual.
In
There are benefits to the user by displaying metrics on the user interface in a simple format in real-time. This reduces the burden of use on the user because the user can quickly understand if an action needs to be performed to modify their glucose level. In some embodiments, the processor is part of a device having a display screen with a lock screen, home screen or wallpaper feature. The lock screen, home screen or wallpaper may be modified based on the glucose data associated with an individual from a metabolic sensor. In other words, the lock screen, home screen and/or wallpaper of the device may be changed based on the glucose data from the sensor. For example, when a high glucose level (high load or high variation metric) is detected, the screen may change to a yellow screen. When a normal glucose level (normal load and variation metric) is detected, the screen may change to a green screen. When a low glucose level (low load and high variation metric) is detected, the screen may change to a red screen. A metric such as the glucose level and/or the behavior modification recommendation such as an action may also be communicated.
The described embodiment is a fast, discreet, convenient method for the user to understand the metric by merely glancing at the device, such as the mobile phone, without opening a software application. Moreover, this is different than receiving a notification on the home screen of the mobile phone because the present embodiments work with the operating system of the mobile phone and change the image of the lock screen, home screen and/or wallpaper based on the sensor data monitoring the user without user input. This occurs automatically and in real-time. For example, the user may opt-in to this feature in the software application. The software application may trigger a “software flag” based upon the user's data via the sensor. The software flag is transmitted to interact with the operating system or home screen setting of the operating system, and the lock screen, home screen and/or wallpaper then changes color and/or displays an image based on the user settings. This may be similar to settings or software applications that change the lock screen, home screen and/or wallpaper based on time.
The behavior modification recommendation may be displayed in a banner 1204 on the display screen 1202 that is the lock screen, home screen and/or wallpaper of the device. For example, based on the individual data of the user,
In some embodiments, more information or a deeper level of data may be needed to help guide the user. Referring to
A dashboard 1410 in
In another embodiment,
The banner 1504 repeats the action from the banner on the lock screen, home screen and/or wallpaper and additionally, recommends an amount of insulin to the consumer. This amount is what is needed in order to bring the user's blood glucose level back to in-range. In section 1506, a visual of the medication is shown. A dashboard 1510 may list other metrics such as blood glucose statistics for the present time and an estimate for in 15 minutes based on performing the recommendation such as consuming the insulin. A footer 1512 may include a snooze function that can be selected by the user so that the user is reminded in a future amount of time such as 5 minutes, or an option to confirm the action such as “I just did”. After the snooze amount of time, a visual or audio alert may be seen or heard.
Some embodiments involve a system where understanding the individual's metabolic response to certain foods is used to guide the person to a metabolic data-driven weight loss program. For example, in some embodiments glucose (variation, total load and post-meal peaks) can be used for guiding weight loss. Further embodiments may use lactate sensing coincidently to distinguish exercise or other rises from food-related changes. Some embodiments may use a rate of decline in lactate levels from post-meal peaks to indicate fat storing.
Some embodiments may use global positioning system (GPS) data to provide supplemental information to the user. For example, based on an individual's GPS location, the system may provide locations of suggested restaurants and food recommendations served that the suggested restaurants. The system may also use location to encourage behavior modification, such as sending proactive texts or messages to not eat certain foods when the individual is identified as being in a location where that food is offered. GPS location data may also be used to map prior behaviors (e.g., food, others), or for “nagging” to prevent night binging. Another example of using GPS information in the present glucose monitoring and weight loss system includes using glucose peaks for retrospective understanding and behavioral monitoring.
Although embodiments have been described in relation to enhancing weight loss, the present glucose monitoring systems and methods can also be used to treat other diseases. For example, the present glucose monitoring systems and methods can be used for patients with cancer, from early stage to late stage, where the global metrics can be used to monitor food intake to reduce glucose variability. Glucose variability can uniquely serve as a proxy for insulin production and the presence of insulin-binding globulins and other potential growth-inducing factors that encourage cancer proliferation and increase the potential for recurrence or further metastases. In another example, the glucose monitoring can be used to address polycystic ovary syndrome, where modification of food intake can reduce glucose variability and insulin resistance, consequently improving fertility by increasing the chance of ovulation. Another example is the treatment of non-alcoholic fatty liver disease where glucose monitoring can prevent the elevation of glucose, which increases the deposition of fat in the liver that can then lead to, for instance, cirrhosis and liver failure.
Reference has been made in detail to embodiments of the disclosed invention, one or more examples of which have been illustrated in the accompanying figures. Each example has been provided by way of explanation of the present technology, not as a limitation of the present technology. In fact, while the specification has been described in detail with respect to specific embodiments of the invention, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily conceive of alterations to, variations of, and equivalents to these embodiments. For instance, features illustrated or described as part of one embodiment may be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present subject matter covers all such modifications and variations within the scope of the appended claims and their equivalents. These and other modifications and variations to the present invention may be practiced by those of ordinary skill in the art, without departing from the scope of the present invention, which is more particularly set forth in the appended claims. Furthermore, those of ordinary skill in the art will appreciate that the foregoing description is by way of example only, and is not intended to limit the invention.
This application claims priority to U.S. Provisional Patent Application No. 62/659,537 filed on Apr. 18, 2018 and entitled “Metabolic Monitoring System,” which is hereby incorporated by reference in full.
| Number | Date | Country | |
|---|---|---|---|
| 62659537 | Apr 2018 | US |
