Abstract: Embodiments include methods and systems for automated eating detection. Systems comprise a continuous glucose monitor (CGM), accelerometer, and processing unit. During a first time period, the processing unit receives glucose readings and a first set of acceleration readings. The processing unit identifies an eating episode if the glucose readings satisfy one or more criteria. Using the identified eating episode and first set of acceleration readings, the processing unit generates an individual model that identifies eating episodes using acceleration readings without using glucose readings. During a second time period, a processing unit uses the individual model and a second set of acceleration readings to identify a second eating episode. Some embodiments may use additional sensor types (for example, PPG or heart rate) to identify eating episodes in the first or second time period, generate the individual model, or any combination thereof.

Abstract: Embodiments include methods and systems for automated eating detection. Systems comprise a continuous glucose monitor (CGM), accelerometer, and processing unit. During a first time period, the processing unit receives glucose readings and a first set of acceleration readings. The processing unit identifies an eating episode if the glucose readings satisfy one or more criteria. Using the identified eating episode and first set of acceleration readings, the processing unit generates an individual model that identifies eating episodes using acceleration readings without using glucose readings. During a second time period, a processing unit uses the individual model and a second set of acceleration readings to identify a second eating episode. Some embodiments may use additional sensor types (for example, PPG or heart rate) to identify eating episodes in the first or second time period, generate the individual model, or any combination thereof.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for predictive data analysis. Certain embodiments utilize systems, methods, and computer program products that perform predictive metabolic intervention by utilizing at least one of activity recommendation machine learning models and prediction window encoding machine learning models.

Abstract: Embodiments include methods and systems for regulating a user's glucose levels. Systems comprise a processing unit in communication with a glucose monitor, an accelerometer, and a user interface. The glucose monitor generates glucose readings corresponding to the user's glucose levels and the accelerometer generates acceleration readings corresponding to the user's activity. The processing unit determines if the glucose readings indicate a glucose state and whether the acceleration readings indicate an activity state. If the glucose state and the activity state coincide, the processing unit sends a message to the user interface recommending a behavior that would influence the user's glucose level.

Abstract: Embodiments include methods and systems for automated eating detection. Systems comprise a continuous glucose monitor (CGM), accelerometer, and processing unit. During a first time period, the processing unit receives glucose readings and a first set of acceleration readings. The processing unit identifies an eating episode if the glucose readings satisfy one or more criteria. Using the identified eating episode and first set of acceleration readings, the processing unit generates an individual model that identifies eating episodes using acceleration readings without using glucose readings. During a second time period, a processing unit uses the individual model and a second set of acceleration readings to identify a second eating episode. Some embodiments may use additional sensor types (for example, PPG or heart rate) to identify eating episodes in the first or second time period, generate the individual model, or any combination thereof.

Abstract: Embodiments include methods and systems for automated eating detection. Systems comprise a continuous glucose monitor (CGM), accelerometer, and processing unit. During a first time period, the processing unit receives glucose readings and a first set of acceleration readings. The processing unit identifies an eating episode if the glucose readings satisfy one or more criteria. Using the identified eating episode and first set of acceleration readings, the processing unit generates an individual model that identifies eating episodes using acceleration readings without using glucose readings. During a second time period, a processing unit uses the individual model and a second set of acceleration readings to identify a second eating episode. Some embodiments may use additional sensor types (for example, PPG or heart rate) to identify eating episodes in the first or second time period, generate the individual model, or any combination thereof.

Abstract: A coating composition comprising a coating agent, a high intensity sweetener, and an acid.

Abstract: Pervious filters incorporating sweeteners for providing sweetness to liquids passing though the filter and devices incorporating such pervious filters. The filters may be impregnated with sweeteners such as sucrose, sucralose, saccharin, acesulfame-K, stevia, neotame, alitame, asp artame and combinations thereof. The filters may be designed for single use or for multiple uses, and may be used for sweetening any of a variety of beverages, such as, coffee and tea.

Abstract: This invention is directed to an apparatus and method for dispensing precise, pre-calibrated amounts of powdered material, such as, a high intensity sweetener, a flavor, a nutritional supplement, a vitamin, a mineral, a food grade bulking material, or mixtures thereof, in a controlled manner into a food or beverage composition. The apparatus of this invention includes a container that is capable of holding the powdered material, and a series of adjacent plates located at the lower end of the container. Each plate has a plurality of openings therein, and at least one plate is rotatable in relation to the other plates. Rotation of the plate or plates between various positions enables the user to deliver a precisely controlled volume of powdered material into the food or beverage from the container.

Abstract: Compositions for ameliorating the symptoms associated with lactase deficiency, the composition including a lactose reduced dairy product, and an effective amount of a probiotic, a prebiotic, or a mixture thereof. The lactose reduced dairy product is selected from a fluid milk, a smoothie, a liquado, ice cream, yogurt, and a yogurt drink. Methods for treating lactose intolerance in a patient in need thereof, the method includes providing a composition having a lactose reduced dairy product, and an effective amount of a probiotic, a prebiotic, or a mixture thereof. The lactose reduced dairy product is selected from a fluid milk, a smoothie, a liquado, ice cream, yogurt, and a yogurt drink.

Abstract: Methods for achieving weight loss goals and maintaining weight loss including selecting an appropriate weight loss program that provides a caloric deficit of not more than 350 kilocalories per day, and concurrently ingesting a composition comprising effective amounts of a combination of an anionic soluble fiber and a multivalent cation source during the weight loss program. Also, a method for producing weight loss including identifying an individual in need, providing the individual a weight loss program that provides a caloric deficit of not more than 350 kilocalories per day, and directing the individual to ingest a composition comprising an effective amount of a combination of anionic soluble fiber and a multivalent cation.

Abstract: Ingestible compositions for weight management having an soluble anionic fiber and a multivalent cation and an in vitro gel strength of from about 2000 to about 3000 cps. Methods of weight management and caloric reduction by orally administering the ingestible compositions are included.

Abstract: Compositions and methods for reducing weight, improving weight loss and for providing satiety are provided. Such compositions include at least one soluble anionic fiber and at least one monovalent cation.

Abstract: A method of reducing body weight, the method by orally administering a fluid ingestible composition at regular intervals, the fluid ingestible composition having water, about 0.5 g to about 3.0 g of a soluble anionic fiber and participating in an exercise program.

Abstract: Methods for weight management by ingesting or orally administering ingestible compositions between meals. The compositions include viscosity building fibers and have between about 25 to about 95 kcal per serving The ingestible composition can provide an SE from about 1.0 to about 3.0.

Abstract: Methods for inducing satiety, reducing food intake, and reducing weight in an animal by ingesting at least one soluble anionic fiber in the presence of a milk source.

Abstract: Methods of reducing calorie are disclosed. The methods include ingestible compositions having at least one soluble anionic fiber from about 0.25 g to about 5.0 g per serving, optionally in the presence of an effective amount of a cation. The fiber and optional cation componentscan be consumed together or separately.