Method and apparatus for insulin dosing guidance and decision support for exercise in diabetic patients

Abstract

To provide computerized methods and apparatus for insulin dosing guidance and decision support for diabetic patients.SOLUTION: Methods recommend one or more exercises to a diabetic patient, recommend adjustments to a diabetes treatment plan based on the diabetic patient's planned exercise, provide recommendations to the diabetic patient while exercising, and customize a computerized exercise planning tool that is used to develop an exercise plan for the diabetic patient, based on user preference data of the diabetic patient, data indicative of a treatment aspect of the diabetic patient, data indicative of a physiological aspect of the diabetic patient, or some combination thereof.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] [Background technology]

[0002] The pancreas regulates a person's glucose levels, but people with diabetes usually have a reduced ability to regulate their own glucose levels. If glucose levels fall too low, the patient can fall into a dangerous condition called hypoglycemia. If glucose levels rise too high, the patient can fall into another dangerous condition called hyperglycemia. Therefore, people with diabetes need to keep their glucose levels within an ideal target range by administering insulin (to lower glucose levels), ingesting carbohydrates, and / or administering glucagon (to raise glucose levels). Insulin can be administered in a variety of forms, including injections and / or using a pump. For example, insulin can be administered as an individual dose injected all at once (e.g., a long-acting basal dose, or a bolus dose) or through a steady trickle infused over minutes or hours using a pump. Too much insulin can cause glucose levels to fall too low, causing the patient to fall into hypoglycemia. Too little insulin can cause glucose levels to remain high, causing the patient to fall into hyperglycemia. Therefore, people with diabetes need to administer the right amount of insulin at the right time.

[0003] Exercise is important for diabetics, but it can affect the glucose levels of diabetics in complex ways. For example, aerobic exercise (e.g., light jogging) can lower the glucose levels of a patient. Exactly how much of a drop is expected and when this drop is expected to occur can depend on various factors, such as the intensity and type of exercise. Conversely, anaerobic exercise (e.g., weightlifting, sprinting) can increase the glucose levels of a patient in the short term. Exactly how much of a rise occurs can also depend on various factors, such as the intensity and type of exercise. The effect of exercise on the glucose levels of a patient can occur both during and after the end of the exercise, sometimes hours later. Thus, for a patient with diabetes, exercising can include complex decisions such as when and how to adjust insulin dosage, whether and when to take additional carbohydrates to offset the drop in glucose levels, and / or when to check glucose levels. Summary of the Invention

[0004] The present disclosure relates to techniques for planning and executing exercises for users with diabetes (e.g., referred to herein interchangeably as "user" and / or "patient"). The techniques may include recommending one or more exercises, planning the exercise through a series of guided check-ins and responding to recommendations at or hours before the exercise, and guiding and / or monitoring the user through the execution of the exercise. The techniques may be customized for each user, such as based on the user's preferences (e.g., goals) and based on the user's responses to previous exercises.

[0005] In one embodiment, the present technology provides a method for recommending one or more exercise types to a patient with diabetes using a computing device. The method includes receiving, by the computing device, input data indicating (i) a future exercise start time at which the patient intends to begin exercising, and (ii) the patient's current glucose value. The computing device determines an amount of time between the current time and the future exercise start time. The computing device determines one or more recommended exercise types based on the patient's current glucose value and the amount of time. The computing device displays the one or more recommended exercise types via a display of the computing device.

[0006] In one embodiment, the present technology provides a method for using a computing device to recommend adjustments to a treatment of a patient with diabetes based on a planned exercise session. The method includes receiving, by the computing device, input data indicating (i) a future exercise start time at which the patient intends to start exercising, (ii) an exercise type the patient intends to perform, and (iii) an initial glucose value of the patient. The computing device presents, via a display of the computing device, a first recommendation to the user including at least one of an adjustment to a planned insulin bolus dose and an adjustment to a planned insulin basal rate, the first recommendation being based on at least one of the received exercise type and the received initial glucose value. When the current time is within a first period of the exercise start time, the computing device prompts, via the display, the user to provide an input indicating a first scheduled glucose value of the patient. The computing device receives the input data indicating the first scheduled glucose value of the patient. The computing device determines a second recommendation for the patient based on the received first scheduled glucose value. The computing device presents the second recommendation via the display.

[0007] In one embodiment, the present technology provides a method for customizing a computerized exercise planning tool for creating an exercise plan for a patient with diabetes using a computing device. The method includes storing, by the computing device, a default rule set associated with the exercise planning tool for creating an exercise plan for a patient with diabetes. The computing device receives input data indicating user preferences for the exercise planning tool. The computing device modifies aspects of the exercise planning tool by modifying the default rule set to customize the exercise planning tool for the patient based on the input data. The computing device generates an exercise plan for the patient based on the modified aspects of the exercise planning tool, where the exercise plan differs from a second exercise plan that would have been generated using the unmodified default rule set.

[0008] In one embodiment, the present technology provides a method for customizing a computerized exercise planning tool for creating an exercise plan for a patient with diabetes using a computing device. The method includes storing, by the computing device, a default rule set associated with the exercise planning tool for creating an exercise plan for a patient with diabetes. The computing device uses the exercise planning tool to plan a set of exercise plans for the patient, each exercise plan being associated with an exercise. The computing device monitors, for each exercise plan in the exercise plan set, data indicative of (i) a therapeutic aspect of the patient, (ii) a physiological aspect of the patient, or both. Based on the monitored data, the computing device modifies the default rule set to customize the exercise planning tool for the patient. The computing device generates a new exercise plan for the patient based on the modified default rule set, the new exercise plan being different from the exercise plan that would have been generated using the unmodified default rule set.

[0009] In one embodiment, the present technology provides a method for providing recommendations to a patient with diabetes while exercising using a computing device. The method includes receiving, by the computing device, input data indicative of (i) an exercise being performed by the patient, and (ii) a current glucose value of the patient while performing the exercise. The computing device determines one or more recommendations based on the current glucose value. The computing device displays the one or more recommendations via a display of the computing device.

[0010] Further embodiments of the present disclosure, as well as its features and advantages, will become more apparent by reference to the following description taken in conjunction with the accompanying drawings, in which components are not necessarily drawn to scale. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the different views. [Brief description of the drawings]

[0011] [Figure 1] 1 is an exemplary computerized method for recommending one or more exercise types to a user, according to some embodiments. [Diagram 2] 1 illustrates an exemplary grouping of exercise types into aerobic, anaerobic, and mixed aerobic and anaerobic exercises, according to some embodiments. [Figure 3A] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3B] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3C] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3D] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3E] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3F] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3G] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3H] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 3I] 1 illustrates a series of exemplary screenshots that may be shown on a display of a computing device according to some embodiments. [Figure 4] 1 illustrates an exemplary computerized method for checking in with a user over one or more time periods, according to some embodiments. [Figure 5A] 1 illustrates an exemplary computerized method for providing a first recommendation for aerobic exercise, according to some embodiments. [Figure 5B] 1 illustrates an exemplary computerized method for providing a first recommendation for aerobic exercise, according to some embodiments. [Figure 5C] 1 shows a table illustrating example logic that may be used to determine a first recommendation of a basal adjustment for an aerobic exercise, according to some embodiments. [Figure 5D] 1 shows a table illustrating example logic that may be used to determine a first recommendation of a basal adjustment for an aerobic exercise, according to some embodiments. [Figure 5E] 1 shows a table illustrating example logic that may be used to determine a first bolus adjustment recommendation for an aerobic exercise, according to some embodiments. [Figure 6]1 shows a table illustrating example logic that may be used to determine and provide a first recommendation for anaerobic exercise, according to some embodiments. [Figure 7A] 1 illustrates an example computerized method for providing a first recommendation for a mix of aerobic and anaerobic exercise, according to some embodiments. [Figure 7B] 1 illustrates an example computerized method for providing a first recommendation for a mix of aerobic and anaerobic exercise, according to some embodiments. [Figure 7C] 1 shows a table illustrating an example logic for determining a first recommendation for basal adjustments for a mix of aerobic and anaerobic exercise, according to some embodiments. [Figure 7D] 1 shows a table illustrating an example logic for determining a first recommendation for basal adjustments for a mix of aerobic and anaerobic exercise, according to some embodiments. [Figure 7E] 1 shows a table illustrating an example logic for determining a first recommendation for basal adjustments for a mix of aerobic and anaerobic exercise, according to some embodiments. [Figure 7F] 13 shows a table illustrating an example logic for determining bolus adjustments for mixed aerobic and anaerobic exercise, according to some embodiments. [Figure 8A] 1 shows an example table illustrating logic that may be used to determine and provide carbohydrate intake recommendations one hour prior to aerobic exercise, according to some embodiments. [Figure 8B] 1 shows an example table illustrating logic that may be used to determine and provide recommendations one hour prior to anaerobic exercise, according to some embodiments. [Figure 8C] 1 shows an example table illustrating logic that may be used to determine and provide carbohydrate intake recommendations one hour prior to mixed aerobic and anaerobic exercise, according to some embodiments. [Figure 9A] 1 provides an example of a 15 minute status check before a cardio workout, according to some embodiments. [Figure 9B]1 provides an example of a 15 minute status check before a cardio workout, according to some embodiments. [Figure 9C] 1 provides an example of a 15 minute status check before a cardio workout, according to some embodiments. [Figure 9D] 1 provides an example of a 15 minute status check before a cardio workout, according to some embodiments. [Figure 10] 1 provides an example of a 15 minute status check before an anaerobic workout, according to some embodiments. [Figure 11A] 1 provides an example of a 15 minute context check before a workout for mixed aerobic and anaerobic exercise, according to some embodiments. [Figure 11B] 1 provides an example of a 15 minute context check before a workout for mixed aerobic and anaerobic exercise, according to some embodiments. [Figure 11C] 1 provides an example of a 15 minute context check before a workout for mixed aerobic and anaerobic exercise, according to some embodiments. [Figure 12A] 1 shows an example table illustrating logic that may be used to provide recommendations based on a user's glucose levels for aerobic exercise, according to some embodiments. [Figure 12B] 1 shows an example table illustrating logic that may be used to provide recommendations based on a user's glucose levels for anaerobic exercise, according to some embodiments. [Figure 12C] 1 shows an example table illustrating logic that may be used to provide recommendations based on a user's glucose level for mixed exercise, according to some embodiments. [Figure 13A] 13 shows another example sequence of displays for planning a jogging workout more than an hour away, according to some embodiments. [Figure 13B] 13 shows another example sequence of displays for planning a jogging workout more than an hour away, according to some embodiments. [Figure 13C]13 shows another example sequence of displays for planning a jogging workout more than an hour away, according to some embodiments. [Figure 13D] 13 shows another example sequence of displays for planning a jogging workout more than an hour away, according to some embodiments. [Figure 14] 1 illustrates an exemplary computerized method for monitoring a user during exercise, according to some embodiments. [Figure 15] 1 illustrates an exemplary computerized method for customizing an exercise plan for a patient with diabetes, according to some embodiments. [Figure 16] 1 illustrates an exemplary computerized method for customizing an exercise planning tool based on user-specific information, according to some embodiments. [Figure 17] 1 illustrates an exemplary implementation of a computer system that may be used to implement any of the aspects of the embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same, it being understood, however, that no limitation of the scope of the invention is intended.

[0013] The present disclosure relates to computer-implemented techniques for planning and executing exercise for diabetic patients. Exercise can be an important part of the lifestyle management of a person with diabetes (e.g., type 1 diabetes) due to, for example, the various cardiometabolic and other benefits it can bring to the patient. However, maintaining normal glucose during and after exercise can be challenging, complicating safe exercise participation. For example, patients may be at higher risk for hypoglycemia or hyperglycemia during exercise, hypoglycemia 24 hours after exercise, etc.

[0014] Guidelines are available to guide people with diabetes with exercise, but such guidelines are typically complex, difficult to implement, and require individualization to be used effectively.

[0015] The technology described herein provides computer-implemented technology (e.g., computer applications such as mobile phone applications) that provide diabetic patients with the ability to prepare for and perform workouts. The technology can adapt otherwise complex and difficult to follow manual guidelines for exercise and tailor them to each user based on the user's goals, preferences, current metabolic state, personal physiology, past successful treatments, etc. The technology enables users to plan and / or participate in exercise while limiting bothersome and sometimes dangerous conditions such as hypoglycemia and hyperglycemia.

[0016] In some embodiments, the technology can be used to plan anticipated exercises that will occur in the future. In some embodiments, the user can interact with the planning tool throughout the day. For example, the user can provide information regarding current glucose and insulin status, which the technology can use to provide tailored recommendations to the user throughout the day, for example, to enable the user to begin exercise at optimal glucose levels and insulin states and participate in the user's preferred form of exercise. Recommendations can be tailored, for example, to exercise type (e.g., anaerobic or aerobic), intensity of exercise, duration of exercise, user history and / or monitored historical data, adjustments to insulin administration, carbohydrate supplementation, and / or the like.

[0017] In some embodiments, the technology can be used to identify and / or recommend preferred or athletic activities based on the user. For example, the system can determine an athletic activity based on the user's glucose status, insulin status, and / or the like. The system can determine and recommend a particular activity based on the time the user plans to exercise, the user's current glucose level, and / or the like, thereby allowing the user to exercise in a way that minimizes glucose fluctuations during that exercise.

[0018] In some embodiments, the technology may be used during exercise. The technology may monitor data indicative of current glucose levels, glucose trends, heart rate, and / or the like while the patient is exercising, and provide notification of potential interventions during exercise (e.g., to limit exercise-related hypoglycemia or hyperglycemia). The technology may obtain glucose information manually and / or in combination with glucose monitoring, such as continuous glucose monitoring (CGM). Examples of such notifications include changing activity type, eating carbohydrates, taking additional insulin, and / or the like.

[0019] In some embodiments, the technology monitors and analyzes the user's history to further customize the technology for each user. For example, the technology can include monitoring the patient's therapeutic aspects (e.g., bolus doses, basal rates, etc.), the patient's physiological aspects (e.g., heart rate, glucose levels, activity data measured by one or more accelerometers or gyroscopes, etc.), preferred exercise activities, heart rate response to activity, glucose response to exercise type, how a person feels after exercise, and / or the like to customize the technology for each user. The technology can monitor such aspects of each user's unique history to improve future recommendations (e.g., exercises, exercise plans, etc.). These and other features described herein can work together in a complementary manner to allow patients with diabetes to incorporate their current best training and exercise into their lifestyle in a manner that personalizes guidelines based on the user's unique physiology, preferences, history, and / or the like.

[0020] While various embodiments have been described, it will be apparent to those skilled in the art that many more embodiments and implementations are possible. Thus, the embodiments described herein are examples and are not the only possible embodiments and implementations. Moreover, the advantages described above are not necessarily the only advantages, and it is not necessarily expected that all of the advantages described will be achieved in each embodiment.

[0021] In some embodiments, the technology recommends to a user (e.g., a diabetic) which exercises and / or exercise types to perform for a planned workout. In general, as described further herein, a device (e.g., a computer, a mobile phone, or other computing device) can request and / or receive information from a user related to planning an exercise, and determine one or more recommended activities that are optimal for the user based on the input data, exercise duration, and / or other relevant information.

[0022] 1 is an exemplary computerized method 100 for recommending one or more types of exercise to a user, according to some embodiments. At step 102, a computing device receives input data for planning an exercise. At step 104, the computing device determines an amount of time until a future exercise start time. At step 106, the computing device determines one or more recommended exercise types based on the user input data. At step 108, the computing device displays the one or more recommended exercise types, for example, via a display of the computing device.

[0023] Referring to step 102, the input data may include data indicating a future exercise start time when the user intends to start the exercise, the patient's current glucose value, a desired exercise category, and / or other data related to planning the exercise. In some embodiments, the computing device may prompt the user for one or more inputs. For example, the computing device may ask the user when the user plans to train (e.g., after 3 p.m., 3 hours later, etc.). As another example, the computing device may ask the user for the user's current blood glucose reading. In some embodiments, the user is connected to a monitoring device that provides input data to the computing device. For example, the user may be connected to a connected glucose meter (CGM), which provides the user's blood glucose reading to the computing device. In some embodiments, the input data may also include data indicating the patient's insulin on-board (IOB) amount, which may indicate the amount of active insulin the patient has previously taken and that is still circulating in his / her body. The patient's IOB may be used to plan the exercise. The patient's IOB may be estimated or calculated from previously taken insulin doses, or the patient may manually enter the amount of IOB.

[0024] Referring to step 104, the computing device can determine the amount of time until the future exercise start time, for example, by determining the difference between the current time received by the user and the future exercise start time. The computing device can use, for example, an on-board clock to determine the time, to interact with a timer server, and / or for similar purposes. As another example, the computing device can recommend an exercise time and determine the difference between the current time and the recommended exercise time.

[0025] Referring to step 106, the computing device may determine one or more recommended exercise types based on user input data, such as based on the patient's current glucose value, the amount of time until exercise, the desired exercise type, the amount of IOB, and / or the like. For example, the computing device may determine one or more recommended exercises taking into account how much the patient plans to exercise in the future and the patient's current blood glucose level.

[0026] In some embodiments, the computing device is capable of distinguishing between different exercise groups, storing exercises according to different exercise categories, and / or the like. FIG. 2 illustrates an exemplary grouping 200 of exercise types 202 into aerobic exercises 204, anaerobic exercises 206, and mixed aerobic and anaerobic exercises 208, according to some embodiments. A non-limiting exemplary list of aerobic exercises 204 may include one or more of walking, hiking, cycling, jogging, swimming, rowing, cardio classes, using an elliptical machine, using a stair climber, dancing, cross-country skiing, skipping rope, golfing, and / or the like. A non-limiting exemplary list of anaerobic exercises 206 may include high-intensity resistance training (e.g., free weights, weight machines, resistance bands, and / or the like), bodyweight resistance training (e.g., push-ups, lunges, squats), sprinting, yoga, pilates, rock climbing, and / or the like. A non-limiting, exemplary list of blended aerobic and anaerobic exercises 208 may include racquet sports, basketball, soccer, circuit or interval training, boxing, martial arts training, and / or the like.

[0027] The computing device may also store, present, review, or apply exercise criteria, exercise descriptions, injection information, exercise hints, and / or the like. For example, walking may be categorized or described as walking at a fast pace but where the user can talk while walking. Injection information associated with walking may include recommending administration of an injection into the user's abdomen. Exercise hints for walking may include a hint that the user should ensure that they bring hypoglycemic treatment measures (e.g., glucose tablets, or glucagon) for the walk because walking may cause the user's glucose to drop rapidly due to the lack of an adrenaline response. Hiking may be categorized or described as walking a long distance over difficult terrain (e.g., through a forest). Injection information associated with hiking may include recommending administration of an injection into either the user's abdomen or arm. Exercise tips for hiking may include: (a) if the user plans to hike or for more than an hour, be sure to bring a hearty snack and water; (b) the user may need to eat a meal beforehand (e.g., reduce the bolus); (c) if the user is hiking up a hill or mountain, the user should watch for dropping glucose levels along the way to avoid tripping; and (d) as the user becomes more tired, it may be more difficult to recognize symptoms.

[0028] Cycling can be categorized or described as riding a stationary, road, track, or trail bike. Injection information associated with cycling can include a recommendation to administer an injection into the arm. Exercise tips for cycling can include a tip to eat a meal with slowly digested carbohydrates (and reduce the bolus, for example) before riding. Jogging can be categorized or described as a slow run at a steady pace. Injection information associated with jogging can include a recommendation to administer an injection into the abdomen. Exercise tips for jogging can include a tip to consider in advance how to incorporate hip bands or pockets into exercise equipment, as it is difficult to carry food while jogging. Swimming can be categorized or described as pacing back and forth in water or other constant repetitive activity. Injection information associated with swimming can include a recommendation to administer an injection into the abdomen. Exercise tips for swimming can include a tip that frequent swimmers may need to consider applying an additional layer of sports or medical tape over the transmitter if the user is wearing a CGM. Rowing can be categorized or described as the act of propelling oneself forward using a rowing oar or machine. Injection information associated with rowing can include a recommendation to administer an injection into the user's upper back. Exercise tips for rowing can include a tip that if the user is rowing outdoors, consider obtaining a waterproof carrier for supplies and hypoglycemia treatment measures.

[0029] A cardio class may be categorized or described as a group class with up to 90 minutes of constant sustained exercise. Injection information associated with a cardio class may include a recommendation to administer an injection into the user's upper back. Exercise tips for a cardio class may include a tip to begin with a more conservative approach to reducing insulin and / or snacking in preparation for exercise, as classes may vary by instructor. An elliptical machine activity may be categorized or described as an activity using an exercise machine, such as an exercise bike without a seat. Injection information associated with an elliptical machine activity may include a recommendation to administer an injection into the user's arm. Exercise tips for an elliptical machine activity may include a tip for the user to keep their feet flat on the pedals to prevent numbness in the feet or toes while using the machine. A stair climber may be categorized or described as an exercise machine that allows a user to perform the motion of climbing stairs at an adjustable speed. Injection information associated with a stair climber may include a recommendation to administer an injection into the arm. Exercise tips for stair climbers can include tips for utilizing stair climbers to create a low impact workout that burns calories quickly. Dance can be categorized or described as sustained movement to music of many different speeds and styles. Injection information associated with dance can include recommending administration of an injection in the upper back. Exercise tips for dance can include tips on selecting music and styles to improve the user's mood and physical health.

[0030] Cross-country skiing can be categorized or described as skiing on relatively flat terrain with ski boots that lift back when the user takes a step. Injection information associated with cross-country skiing can include recommending administration of an injection into the user's arm. Exercise tips for cross-country skiing can include tips to ensure the user monitors the temperature of the insulin, especially if the user is outside in sub-freezing temperatures. Skipping rope can be categorized or described as jumping over a rope as the rope is swung in a constant, regular pattern. Injection information associated with skipping rope can include recommending administration of an injection into the arm. Exercise tips for skipping rope can include tips that skipping rope is a great optional choice for exercising in a hotel room or while traveling. Golf can be categorized or described as a game played on a large outdoor course that involves a group of people trying to hit a ball with a club as close as possible to a small hole. Injection information associated with golf can include recommending administration of an injection into the upper back. Exercise tips for golf may include tips to estimate in advance how many hours the user will be on the course and plan snacks accordingly.

[0031] Weight-based resistance training can be categorized or described as using high resistance from free weights, machines, or bands to work specific muscle groups. Injection information associated with weight-based strength training can include a recommendation to administer injections away from the muscles the user is targeting. Exercise tips for weight-based resistance training can include a tip that glucose levels may be elevated through pure anaerobic exercise. Bodyweight resistance training can be categorized or described as activities that use one's own body weight to challenge specific muscle groups, such as push-ups, lunges, and squats. Injection information associated with weight-based resistance training can include a recommendation to administer injections away from the muscles the user is targeting. Exercise tips for bodyweight resistance training can include a tip that adding a small portion of these exercises to a regular aerobic routine can stabilize glucose levels and make the routine both aerobic and anaerobic. Sprinting can be categorized or described as running as fast as possible for 400 meters or less. Injection information associated with sprinting may include a recommendation to administer an injection in the upper back. Exercise tips for sprinting may include the tips that (a) glucose levels may drop if sprinting changes to jogging, and (b) anaerobic sprints should be limited to short bursts of intense running.

[0032] Yoga may be categorized or described as an attempt to build muscle strength and flexibility through various body postures and conscious deep breathing. Injection information associated with yoga may include a recommendation to administer an injection in any location. Exercise tips for yoga may include a tip to the user, if participating in a hot yoga class, to remember to hydrate and eat a small meal before class to avoid nausea. Pilates may be categorized or described as exercise that focuses on core muscle strength and flexibility, sometimes using special equipment. Injection information associated with Pilates may include a recommendation to administer an injection in the arm or leg. Exercise tips for Pilates may include a tip to consider attaching your CGM and / or injection site in a location that would be comfortable while lying on the floor, if the user is doing Pilates on a mat. Rock climbing may be categorized or described as using the hands and feet to climb a series of rock stairs (e.g., indoors or outdoors). Injection information associated with rock climbing may include a recommendation to administer an injection in the upper back. Exercise tips for rock climbing can include a tip to consider checking fingertip glucose levels immediately prior to climbing. Racquet sports can be categorized or described as tennis, racquetball, squash, etc., that include short bursts of sprints and more constant movement throughout the course of the game. Injection information associated with racquet sports can include a recommendation to administer an injection in the upper back. Exercise tips for racquet sports can include a tip that the glucose lowering effects of aerobic exercise may not be seen if the user is playing a particularly intense game.

[0033] Basketball can be categorized or described as a sustained jog and sprint while shooting the ball at the opponent's basket. Injection information associated with basketball can include a recommendation to administer an injection into the user's upper back. Exercise tips for basketball can include a tip to remember that there may be different glucose effects for playing indoors and outdoors. Soccer can be categorized or described as a game that involves sustained running, including kicking the ball into the opponent's goal. Injection information associated with soccer can include a recommendation to administer an injection into the user's arm. Exercise tips for soccer can include a tip to remember different glucose effects when playing on grass, sand, pavement, etc.

[0034] Circuit training or interval training may be categorized or described as completing a series of high intensity exercises lasting between 30 seconds and 5 minutes, respectively. Injection information associated with circuit training or interval training may include a recommendation to administer an injection at any location. Exercise tips for circuit training or interval training may include a tip for the user to stagger aerobic and anaerobic activity to stabilize glucose levels. Boxing may be categorized or described as a sport that involves attacking and defending with fists. Injection information associated with boxing may include a recommendation to administer an injection in the legs or upper back. Exercise tips for boxing may include a tip to note differences in glucose levels between training and sparring, as sparring involves adrenaline and may increase the user's level. Martial arts training may be categorized or described as several disciplines of attack and defense. Injection information associated with martial arts training may include a recommendation to administer an injection at any location. A practice tip for martial arts training may include the tip that if the user's class is in the evening, consider having a light snack before bed to prevent nighttime hypoglycemia.

[0035] Referring back to step 106 of FIG. 1, the computing device may be configured to recommend one or more exercises based on the amount of time determined in step 104, the user input data from step 102, and / or some combination thereof. In some embodiments, the computing device may be configured to determine the one or more exercises to recommend based on a threshold amount of time. For example, if the user plans to work out for more than a threshold amount of time (e.g., more than one hour from the current time), the computing device may recommend a first set of one or more exercise types from a first exercise category and a second set of one or more exercise types from a second exercise category. In some embodiments, the computing device may also optionally recommend a third set of one or more exercise types from a third exercise category in addition to the first and second sets of exercise types. In some embodiments, the first exercise category may be aerobic exercise, the second exercise category may be anaerobic exercise, and the optional third exercise category may be a mix of aerobic and anaerobic exercise. The computing device may also consider and / or recommend other athletic categories, such as more detailed categories determined based on the user's previous athletic activity, as further described herein.

[0036] The technology may be configured to accommodate a user's exercise preferences, but if the desired exercise is not a safe choice depending on the time (e.g., as the time to exercise approaches), the technology may be configured to provide the user with one or more alternative exercises. For example, if the user plans to work out for less than a threshold amount of time (e.g., if the user plans to exercise within an hour of the current time), the computing device may present various exercises based on the user's blood glucose (BG) readings. In some embodiments, the system may use one or more thresholds to determine which exercise to recommend to the user. For example, if the user's glucose is below a first threshold, the computing device may be configured to provide an exercise from a first category, and if the user's glucose is equal to or greater than the first threshold, the computing device may be configured to provide an exercise from a second (different) category. In some embodiments, the first threshold is in a range between 130 mg / dL and 160 mg / dL, between 140 mg / dL and 150 mg / dL, and / or similar.

[0037] In some embodiments, the computing device may use one or more ranges between the thresholds to determine which exercises to recommend to the user. For example, if the user's glucose is between a first threshold range (e.g., between 144 mg / dL and 350 mg / dL), the computing device may recommend one or more aerobic exercises. As another example, if the user's glucose is between a second threshold range (e.g., between 90 mg / dL and 144 mg / dL), the computing device may recommend one or more anaerobic exercises. As a further example, if the user's glucose is between a third threshold range (e.g., between 100 mg / dL and 160 mg / dL), the computing device may recommend one or more mixed aerobic and anaerobic exercises. As another example, the lower threshold of the threshold range may be in the range of 80 mg / dL to 120 mg / dL, in the range of 95 mg / dL to 105 mg / dL, and / or similar values. The upper threshold of the threshold range may be between 140 mg / dL and 180 mg / dL, between 155 mg / dL and 165 mg / dL, and / or similar values.

[0038] At step 108, the computing device may present the user with one or more determined exercises. For example, the computing device may present the user with a menu of recommended physical exercise types. The user may then select or otherwise input the type of physical exercise the user intends to perform to plan using the techniques discussed herein. In some embodiments, the computing device may provide one or more safety responses (e.g., instead of or in addition to exercise). For example, if the user's glucose is below 50 mg / dL, the computing device may determine that the user's glucose is too low (e.g., the user is in severe hypoglycemia). The computing device may not suggest any exercise and / or may present a warning message. For example, the computing device may warn the user to be careful because their glucose levels are too low for safe physical activity, and that the user should immediately treat with fast-acting glucose or glucagon as advised by their medical professional. As another example, if the user's glucose is above 270 mg / dL, the computing device may not suggest any exercise and / or may present a warning message. For example, the computing device can prompt the user to check for ketones and, if ketones are not present or only at low concentrations, can initiate gentle exercise. Otherwise, if the user's ketone levels are high, the user should follow procedures as advised by their medical professional. As another example, if the user is unable to perform a ketone test, the user can correct high blood glucose levels over time (e.g., as advised by their medical professional) and reschedule the desired exercise activity. The computing device can set a reminder for the user to recheck glucose levels in the future (e.g., 15 minutes, 30 minutes, or 60 minutes) to continue with the exercise recommendation and / or plan.

[0039] 3A-3I show a series of example screenshots of a display of a computing device (e.g., a mobile application in this example) according to some embodiments. FIG. 3A shows an example display 300 prompting a user to schedule their activity. The current time 302 is 1:30 PM, and the patient has scheduled an exercise session from 2:00 PM to 2:45 PM, as shown at 304. The patient can select a "set time" button 306, which transitions the computing device to a display 310 shown in FIG. 3B. The display 310 prompts the user to enter a current glucose level. As discussed herein, in some embodiments, if the user is wearing a CGM, the screen 310 may be omitted (e.g., since the user's glucose level may be provided automatically). FIG. 3C shows a display 320 requesting the user to press a button "check glucose" 322 to have the user confirm a current glucose level of 150 mg / dL (e.g., entered by the user using the display 310 or received from the CGM).

[0040] After confirming the glucose level using screen 320, the user is presented with screen 330, shown in FIG. 3D. Screen 330 allows the patient to select an exercise type. Screen 330 may present one or more exercises, for example, determined using computerized method 100 discussed in conjunction with FIG. 1. In this example, only six exercises 332 are presented to the user on display 330, so the user may scroll to reveal more exercise types (e.g., if the system determines more than six exercises for the user). As discussed herein, the exercise types presented may change depending, for example, on how soon the patient plans to work out, the patient's current glucose level, and / or the like. "Schedule" button 334 is unavailable (e.g., grayed out) because the user has not selected an exercise 332. FIG. 3E shows display 340 with exercise 332A (swimming) highlighted because the user selected exercise 332A. A "Schedule" button 334 can now be selected by the user which, when selected, selects the exercise 332A for planning, as discussed further herein.

[0041] In some embodiments, the technology can check in with the user at one or more times (e.g., when the exercise is scheduled, one hour before the scheduled exercise, 15 minutes before the scheduled exercise, 15 minutes after the scheduled exercise, and / or the like) to guide the user in preparation for and / or after the completion of the exercise. At each checkpoint, the user can provide data such as glucose levels at each checkpoint. The system can use the input data to make one or more recommendations to the user, such as recommendations for food intake, bolus rates, recommendations for adjusting basal rates, and / or the like. The recommendations provided to the user can be dynamically changed throughout the day based on the checks with the user. For example, as described further herein, if the user initially plans to run 5 miles at 5 p.m., if the user is unlikely to be able to safely run 5 miles at 5 p.m., the technology can indicate to the user to engage in other activities. As another example, the results of one check can affect the recommendations provided in response to that check and / or recommendations for other checks.

[0042] FIG. 4 illustrates an exemplary computerized method 400 for contextualizing a user for one or more time periods to provide one or more recommendations for planning an exercise, according to some embodiments. At step 402, the computing device receives input data for planning an exercise (e.g., a future exercise start time, an exercise type, and / or an initial glucose value, according to the techniques discussed herein). At step 404, the computing device presents an initial recommendation to the user via a display of the computing device. At step 406, the computing device determines whether it is time to perform an updated analysis for the exercise (e.g., to determine whether to display further recommendations, etc.). If it is time to perform an updated analysis, the method 400 proceeds to step 408; if not, the method 400 waits to perform another time check. At step 408, the computing device requests and receives additional input data from the user. At step 410, the computing device determines a second recommendation for the patient based on the received additional input data (e.g., what the computing device presents to the user via a display). The method 400 may return to step 406 to provide an additional contextualization.

[0043] Method 400 can be used to provide one or more planned recommendations so that a user can plan an athletic activity for later in the day (e.g., after work) and the computing device (e.g., phone application) can guide the user throughout the day with multiple glucose level status checks and multiple recommendations (e.g., bolus or basal rate adjustments, carbohydrate intake, etc.) at multiple points leading up to, during, and / or after the athletic activity. As discussed herein, the recommendations can be adjusted based on the exercise, time, the user's goals, and / or the like. For example, depending on a particular period before the exercise, the computing device can status check the user and request further information to provide additional recommendations. As discussed further herein, the recommendations can be used to make iterative adjustments. For example, as the user's glucose level fluctuates, the computing device can adjust the recommendation value at the status check to remind the user of the target range when exercising. As discussed further herein, the technology can recommend a particular activity among either aerobic, anaerobic, or mixed type exercise to accommodate the glucose level at the start of the activity.

[0044] With reference to step 402, the input data may include data indicative of a future exercise start time at which the patient intends to begin exercise, the type of exercise the patient intends to perform, the patient's initial glucose value, and / or the like. In some embodiments, the computing system receives some (or all) of the input data when the user determines the exercise they intend to perform, as discussed in conjunction with method 100 of FIG. 1. Thus, in some embodiments, the computing device may have already received some of the data and therefore does not need to obtain it again (e.g., as long as the data is still current).

[0045] Referring to step 404, the first recommendation may include an adjustment to a planned insulin bolus dose, an adjustment to a planned insulin basal rate, a carbohydrate recommendation, and / or other recommendations. In some embodiments, the computing device determines the first recommendation based on an exercise type, an initial glucose value of the user, and / or the like. In some embodiments, the first recommendation is determined when scheduling the exercise.

[0046] 5A-5B illustrate an exemplary computerized method 500 for providing a first recommendation of aerobic exercise, according to some embodiments. When the following discussion refers to the computerized method 500 or computing device "determining" a fact, amount, or some other piece of data, the determination may be made by prompting a user and receiving user input in response to the query. In some cases, the determination may also be made by the computerized method or computing device by referencing pre-stored parameters or rules related to the user's preferences or treatment regimen. In some cases, the determination may be by the computing device by communicating with an external sensor or device, such as a CGM sensor or a body-worn infusion pump worn by the user. In some cases, the determination may be based, at least in part, on a patient's treatment log or physiological data, such as a record of the user's recent glucose levels or insulin doses.

[0047] For example, if exercise is scheduled within a threshold of the exercise duration (e.g., within an hour), the method 500 begins at step 502 of FIG. 5A. At step 502, the computing device determines whether the user takes a bolus insulin within 3 hours before starting the exercise. If no, the method proceeds to step 504, otherwise the method proceeds to step 506. At step 504, the computing device determines whether the user is wearing an insulin pump. If yes, the method proceeds to step 508 and provides a recommendation for basal reduction (A), which will be discussed further herein in conjunction with FIG. 5C. If no, the method proceeds to step 510 and provides a carbohydrate intake recommendation.

[0048] Returning to step 506, if the user has not yet received a bolus dose, the method proceeds to step 512 and provides a bolus reduction recommendation, as further discussed in conjunction with FIGURE 5E. If the user has already received a bolus dose, the method proceeds to step 514 and determines whether the user is wearing an insulin pump. If the user is wearing an insulin pump, the method proceeds to step 516 and recommends a basal reduction (A). If the user is not wearing an insulin pump, the method proceeds to step 518 and recommends a carbohydrate intake.

[0049] For example, if exercise is not scheduled within a threshold of the exercise period (e.g., within an hour), the method 500 starts at step 520 of FIG. 5B. At step 520, the computing device determines whether the user takes a bolus insulin within three hours before starting the exercise. If no, the method proceeds to step 522, where it determines whether the user is wearing an insulin pump. If no, the method does not provide a first recommendation. If not, the method proceeds to step 524, where it recommends a basal reduction (C), which will be further discussed in conjunction with FIG. 5D. Returning to step 520, if yes, the method proceeds to step 526, where it determines whether the user has already received a bolus dose. If no, the method proceeds to step 528, where it determines whether the user has a bolus scheduled within an hour of the workout. If no, the method proceeds to step 530, where it recommends a bolus reduction. If yes, the method proceeds to step 532, where it recommends a bolus reduction (which may vary depending on the amount of time until the exercise, for example, as discussed in conjunction with FIG. 5E). Referring back to step 526, if the user has already received a bolus dose, in step 534 the system checks or determines whether the user is wearing an insulin pump. For example, the patient may have provided the computing device with information indicating whether the patient is wearing an insulin pump, and / or the computing device may request such information from the user. If no, the computing device does not provide a first recommendation. If yes, the computing device proceeds to step 536 and recommends a basal reduction (B), which will be further discussed in conjunction with FIG. 5D.

[0050] FIG. 5C shows a table 540 illustrating an example logic that may be used to determine a first recommendation of basal adjustment (A). Basal adjustment (A) is determined based on the user's glucose 542 to provide an associated recommendation 544. For example, if the user's glucose is below 90 mg / dL, the computing device provides a first recommendation to take a 16 g glucose tablet and reduce the basal rate by 80% from now through the duration of the exercise. FIG. 5D shows a table 560 illustrating an example logic that may be used to determine to indicate basal adjustment (B), and a table 570 illustrating an example logic that may be used to determine basal adjustment (C). FIG. 5E shows a table 580 illustrating an example logic that may be used to determine a bolus adjustment, determined based on exercise intensity 582 and whether the exercise is within 30-60 minutes (584) or more than 60 minutes (586) from the current time.

[0051] In some embodiments, the computing device is configured to determine and provide a first recommendation for anaerobic exercise. For example, the computing device can determine and provide the recommendation using the exemplary logic illustrated in table 600 of FIG. 6. The computing device can determine the recommendation based on the user's glucose reading 602 and whether the user is wearing (604) or not wearing (606) an insulin pump. For example, if the user's glucose is between 151-250 mg / dL and the user is wearing a pump, the computing device recommends temporarily increasing the user's basal rate by 20% until the user's glucose is between 120-150 mg / dL, otherwise, if the user is not wearing a pump, the computing device does not provide a recommendation.

[0052] In some embodiments, the computing device is configured to determine and provide a first recommendation for a mix of aerobic and anaerobic exercise. Figures 7A-7B show an exemplary computerized method 700 for providing a first recommendation for a mix of aerobic and anaerobic exercise, according to some embodiments. For example, if exercise is scheduled within a threshold of exercise duration (e.g., within an hour), the method 700 starts at step 702 of Figure 7A. At step 702, the computing device determines whether the user takes a bolus insulin within 3 hours before starting the exercise. If no, the method proceeds to step 704, otherwise the method proceeds to step 706. At step 704, the computing device determines whether the user is wearing an insulin pump. If yes, the method proceeds to step 708 and provides a recommendation for basal reduction (B), which is further described herein in conjunction with Figure 7D. If no, the method proceeds to step 710 and provides a recommendation for carbohydrate intake.

[0053] Returning to step 706, if the user has not yet received a bolus dose, the method proceeds to step 712 and provides a bolus reduction recommendation, as further discussed in conjunction with Figure 7F. If the user has already received a bolus dose, the method proceeds to step 714 and determines whether the user is wearing an insulin pump. If the user is wearing an insulin pump, the computing device proceeds to step 716 and recommends a basal reduction (A), as further discussed in conjunction with Figure 7C. If the user is not wearing an insulin pump, the method proceeds to step 718 and recommends carbohydrate intake.

[0054] For example, if exercise is not scheduled within a threshold of the exercise period (e.g., within an hour), the method 700 begins at step 720 of FIG. 7B. At step 720, the computing device determines whether the user will take a bolus insulin within three hours before starting the exercise. If no, the method proceeds to step 722, where it determines whether the user is wearing an insulin pump. If no, the method does not provide a first recommendation. If not, the method proceeds to step 724, where it recommends a basal reduction (D), which will be further discussed in conjunction with FIG. 7E. Returning to step 720, if yes, the method proceeds to step 726, where it determines whether the user has already received a bolus dose. If no, the method proceeds to step 728, where it determines whether the user has a bolus scheduled within an hour of the workout. If no, the method proceeds to step 730, where it recommends a bolus reduction. If yes, the method proceeds to step 732, where it recommends a bolus reduction (which may vary depending on the amount of time until the workout, as discussed, for example, in FIG. 7F). Referring back to step 726, if the user is already receiving a bolus dose, in step 734 the system determines whether the user is wearing an insulin pump. If no, the computing device does not provide a first recommendation. If yes, the computing device proceeds to step 736 and recommends a basal reduction (C), which will be discussed further in conjunction with FIG. 7E.

[0055] FIG. 7C shows a table 740 illustrating an example logic that may be used to determine a first recommendation of basal adjustment (A). Basal adjustment (A) is determined based on the user's glucose 742 to provide an associated recommendation 744. For example, if the user's glucose is below 90 mg / dL, the computing device provides a first recommendation to take a 16 g glucose tablet and reduce the basal rate by 50% from now through the duration of the exercise. FIG. 7D shows a table 750 illustrating an example logic that may be used to determine basal adjustment (B). Basal adjustment (B) is determined based on the user's glucose 752 to provide an associated recommendation 754. FIG. 7E shows a table 760 illustrating an example logic that may be used to determine basal adjustment (C), and a table 770 illustrating an example logic that may be used to determine basal adjustment (D). FIG. 7F shows a table 780 illustrating example logic that may be used to determine bolus adjustments based on exercise intensity 782 and whether the exercise is within 30-60 minutes (784) or more than 60 minutes (786).

[0056] 4, at step 406, the computing device determines whether it is time to perform an updated analysis for the exercise (e.g., to determine whether to display additional recommendations, etc.). For example, the computing device may be configured to determine whether to provide additional recommendations for a particular period of time before and / or after exercise (e.g., 1 hour before exercise, 15 minutes before exercise, 15 minutes after exercise, and / or the like).

[0057] The computing device can be configured to provide additional recommendations based on the time prior to exercise and / or exercise type. For example, FIG. 8A illustrates an example table 800 illustrating logic that can be used to determine and provide a carbohydrate intake recommendation one hour prior to an aerobic exercise, according to some embodiments. The computing device determines the recommendation based on the user's glucose level 802 and provides a corresponding recommendation 804. For example, if the user's glucose is between 91-150 mg / dL, the computing device does not provide a recommendation (e.g., because the user's glucose is on track for the workout). FIG. 8B illustrates an example table 810 illustrating logic that can be used to determine and provide a recommendation one hour prior to an anaerobic exercise, according to some embodiments. The computing device determines the recommendation based on the user's glucose level 822 and whether the user is wearing a pump (814) or not wearing a pump (816). For example, if the user's glucose is between 151-250 mg / dL and the user is wearing a pump, the computing device recommends that the user temporarily increase the basal rate by 20% until the glucose is between 120-150 mg / dL. FIG. 8C shows an example table 820 illustrating logic that may be used to determine and provide a carbohydrate intake recommendation one hour prior to mixed aerobic and anaerobic exercise, according to some embodiments. The computing device determines the recommendation based on the user's glucose level 822 and provides a corresponding recommendation 824. For example, if the user's glucose is less than 90 mg / dL, the computing device recommends that the user immediately consume 16 g of carbohydrates.

[0058] In some embodiments, the system can be configured to provide further context checks and potential new recommendations, such as 15 minutes before exercise. As discussed herein, the recommendations may depend on the exercise type and / or other factors. FIGS. 9A-9D provide an example of a 15-minute context check before a workout for an aerobic exercise, according to some embodiments. Each of FIGS. 9A-9D represents a different example logic for implementing a 15-minute context check before a workout for an aerobic exercise. In some cases, the system can select one of the logic schemes depicted by one of FIGS. 9A-9D depending on characteristics of the patient or treatment. Such characteristics may include (but are not limited to) whether the user is wearing an insulin pump, whether the user administered a reduced bolus dose or reduced his / her basal rate leading up to the exercise, and / or whether the user ingested food or carbohydrates leading up to the exercise. The system can make the selection of the logic scheme to apply based on previous decisions about the patient and / or his / her treatment leading up to the exercise, according to the techniques discussed herein. For example, the system may make a selection when providing a first recommendation to the patient based on decisions made during the steps depicted and described in Figures 5A-5B. Figure 10 provides an example of a 15 minute context check prior to an anaerobic workout, according to some embodiments.

[0059] 11A-11C provide examples of a 15-minute pre-workout status check for a mixed aerobic and anaerobic exercise, according to some embodiments. Similar to FIGS. 9A-9D, FIGS. 11A-11C represent different example logic for implementing a 15-minute pre-workout status check for a mixed aerobic and anaerobic exercise. The system can also select one of the logic schemes depicted by one of FIGS. 11A-11C depending on the aforementioned patient or treatment characteristics. For example, the system can make a selection based on the decisions regarding the patient and / or his / her treatment made during the steps depicted and described in FIGS. 7A-7B when providing the patient with a first recommendation.

[0060] 9A-9D, FIG. 9A illustrates a table 900 showing example logic that may be used to determine recommendations based on a user's glucose level 902 and whether the exercise is light (904), medium (906) or vigorous (908). The logic represented by table 900 may be arrived at based on previous determinations, for example, by techniques discussed herein. For example, the logic may be arrived at from steps 516, 518 and 510 of FIG. 5A (and thus the resulting logic that would cause method 500 to perform those steps of FIG. 5A).

[0061] 9B shows a table 910 illustrating example logic that may be used to determine recommendations based on a user's glucose level 912 and whether the exercise is light (914), moderate (916) or vigorous (918). The logic represented by table 910 may be arrived at based on previous determinations, for example, by techniques discussed herein. For example, this logic may be arrived at from step 536 of FIG. 5B.

[0062] 9C illustrates a table 920 showing example logic that may be used to determine a recommendation 924 based on a user's glucose level 922. The logic represented by table 920 may be arrived at based on previous decisions, such as by techniques discussed herein. For example, the logic may be arrived at from steps 508, 512, 524, 530, 532, and / or a "No" at step 522 of FIGS. 5A-5B.

[0063] 9D shows a table 930 illustrating example logic that may be used to determine recommendations based on a user's glucose level 932 and whether the exercise is light (934), moderate (936) or vigorous (938). The logic represented by table 930 may be arrived at based on previous decisions, for example, by techniques discussed herein. For example, the logic may be arrived at from a "no" at step 534 of FIG. 5B.

[0064] Referring to FIG. 10, a table 1000 is shown illustrating example logic that may be used to determine recommendations based on a user's glucose level 1002 and whether the user is wearing a pump (1004) or not wearing a pump (1006).

[0065] 11A-11C, Fig. 11A shows a table 1100 illustrating exemplary logic that may be used to determine a recommendation 1104 based on a user's glucose level 1102, and Fig. 11B shows a table 1110 illustrating exemplary logic that may be used to determine a recommendation 1114 based on a user's glucose level 1112. Fig. 11C shows a table 1120 illustrating exemplary logic that may be used to determine a recommendation 1124 based on a user's glucose level 1122. The logic represented by tables 1100, 1110, and 1120 may be arrived at based on previous determinations, for example, according to the techniques discussed herein. For example, table 1100 may be reached from "no" at step 710, 716, or step 722 of Figures 7A-7B, table 1110 may be reached from "no" at step 718 or step 734 of Figures 7A-7B, and table 1120 may be reached from steps 708, 712, 724, 730, 732, or 736 of Figures 7A-7B.

[0066] In some embodiments, as described herein, the computing device can be configured to provide further post-exercise status checks, such as 15 minutes after exercise, and potential new recommendations (e.g., aerobic cool-down). For example, the computing device can monitor the user (e.g., heart rate) to determine the end of the exercise, prompt the user to indicate the end of the exercise, and / or prompt the user to input an estimated end time of the exercise. Figures 12A, 12B, and 12C show example tables 1200, 1210, and 1220, respectively, illustrating logic that may be used to provide recommendations based on the user's glucose levels for aerobic, anaerobic, and mixed exercise.

[0067] In some embodiments, this post-exercise status check and potential recommendations can be provided to the user in the form of a push notification. For example, if the user has been active during the day (e.g., upon completing an exercise session or upon completing an exercise session that meets a minimum intensity or duration threshold), the user can be provided with a push notification since the user may need to make some adjustments due to being active. In some cases, this push notification may provide the user with recommendations to avoid hypoglycemia while sleeping after the exercise session. This push notification may be displayed as a message or dialog box on the user's smartphone screen and may be provided to the user at a specified time interval after the exercise session (e.g., immediately after the exercise session or 15 minutes, 30 minutes, or 1 hour after the exercise session) or at a specified time (e.g., at 9 p.m. when the user is expected to be preparing for sleep). In some embodiments, the push notification can provide tailored recommendations for the user, such as the amount of carbohydrates to ingest, dosing recommendations (e.g., to cut insulin), and / or the like. As another example, the push notification may inform the user that the user's insulin sensitivity may increase after the exercise session, such that the user may need less insulin compared to other days when the user did not exercise. As another example, if the user is wearing a CGM, the push notification may recommend adjusting the CGM alarm to increase sensitivity to ensure that the alarm is triggered in the event of hypoglycemia after exercise or overnight. This may be done, for example, by recommending that the user increase the glucose level threshold at which the CGM alerts the user to a possible hypoglycemic episode. As another example, if the user is using a fingerstick, the push notification may recommend that the user set an alarm to take a blood glucose measurement late at night after an exercise session.As yet another example, the push notification may recommend that the user consume protein and / or fat before bed (e.g., drink a glass of milk before bed) to mitigate or reduce the likelihood of a hypoglycemic episode while the user is asleep.

[0068] In some embodiments, the user may be presented with a visual display guiding the user through the context check process. In some embodiments, the user may be presented with a visual timeline including visual indicators indicating each of one or more times when the user is prompted to provide input data (e.g., for scheduled glucose readings) and a separate visual indicator indicating a future exercise start time. For example, the timeline may include a visual indicator indicating a time when the user is prompted to provide a first scheduled glucose value (e.g., 1 hour after exercise), a visual indicator indicating a second time when the user is prompted to provide another scheduled glucose value (e.g., 15 minutes after exercise), a visual indicator indicating an exercise start time, and a visual indicator indicating a time when the user is prompted to provide a post-exercise glucose value (e.g., 15 minutes after exercise).

[0069] 3A-3I, FIG. 3F includes a display 350 showing a first recommendation of the computing device. In this example, the patient's glucose is on target, so the patient does not need to take any action. For example, if the patient's glucose was high or low, the computing device may recommend adjusting the patient's bolus or basal rate, as discussed herein. Display 350 includes a "Show Timeline" button 352, which when selected, directs the user to display 360 of FIG. 3G, which may show a timeline 362 of status check events leading up to the exercise session, the session itself (as shown by the highlighted portion of the timeline curve), and status check-in events after the session, as described herein. The user can see all the events that take place throughout the day leading up to the scheduled exercise. In this example, the exercise sessions are only about 30 minutes apart (cell phone time is 1:32 PM and the exercise is scheduled for 2 PM), so there is only one status check 364, 15 minutes before the start of the exercise, as shown at 366. If the exercise sessions are further apart, such as more than an hour apart, additional status checks may occur, such as another status check shown on the timeline one hour before the start of the exercise (e.g., as described with respect to FIG. 13A). The user can touch each symbol on the timeline to view details at the bottom of the display. For example, touching status check 364 indicates that a glucose status check at 1:45 PM is required before the activity begins.

[0070] FIG 3H shows display 370 illustrating how display 360 changes when the user touches exercise session symbol 366, indicating an activity scheduled to begin at 2:00 PM and end at 2:45 PM (again indicated by the highlighted portion of the timeline curve), and that the user has a target glucose value of 125-160 mg / dL for the exercise. FIG 3I shows display 380 illustrating how the display updates when the user selects post-exercise status icon 382, ​​causing the display to show post-activity feedback scheduled for 3:00 PM.

[0071] 13A-13D show another exemplary series of displays for planning jogging workouts that are more than an hour apart, according to some embodiments. FIG. 13A shows a display 1300 with a timeline 1302 including a 1-hour status check icon 1304, a 15-minute status check icon 1306, and an exercise icon 1308. In display 1300, the 1-hour status check icon 1304 is selected, so the display summarizes at the bottom of the screen that a glucose status check is required 1 hour after the workout. FIG. 13B shows a display 1310 when the 15-minute status check icon 1306 is selected, and the text is updated to indicate that a glucose status check is required 15 minutes after the workout. FIG. 13C shows a display 1320 when the exercise icon 1308 is selected for a scheduled activity. FIG. 13D shows a display 1330 when a post-exercise icon 1332 is selected, updating the display to indicate that a post-workout status check is scheduled at that time.

[0072] In some embodiments, the techniques described herein provide for monitoring a patient during exercise, such as to keep track of medical and / or physiological data for customization, to provide further recommendations to the user, etc. FIG. 14 illustrates an exemplary computerized method 1400 for monitoring a user during exercise, according to some embodiments. At step 1402, a computing device receives input data regarding the exercise. At step 1404, the computing device determines one or more recommendations based on the input data. At step 1406, the computing device displays the one or more recommendations via a display of the computing device.

[0073] Referring to step 1402, the input data may include data indicative of the exercise being performed by the patient, one or more current glucose values ​​of the patient while performing the exercise, heart rate data, and / or other information regarding the patient and / or the exercise. As described herein, the data may be entered manually by the patient, received from another device (e.g., from a CGM or transmitted from a wearable sensor), and / or the like.

[0074] Referring to step 1404, the computing device determines one or more recommendations based on the input data. These recommendations are displayed to the user in step 1406. In some embodiments, the computing device can recognize when the user's glucose level is outside of an ideal glucose range or derive a trend in the user's glucose values ​​during exercise and determine one or more recommendations accordingly. The technology can be configured to maintain the user's glucose level within a target range throughout the activity. For example, if the user is performing an aerobic exercise and the user's glucose level is observed to have dropped below a certain minimum threshold or is trending downward such that the patient may experience hypoglycemia in the near future, the computing device can recommend that the user ingest carbohydrates, administer glucagon, or perform an anaerobic activity to raise the user's glucose level. As another example, if the user is performing an anaerobic exercise and the user's glucose level is observed to have increased above a certain maximum threshold or is trending upward such that the patient may experience hyperglycemia in the near future, the computing device can suggest that the user administer insulin to the user or perform a new aerobic exercise to lower the user's glucose level. As a further example, if a user is engaged in mixed activities and the user's glucose levels begin to rise or fall, the computing device may make appropriate new recommendations accordingly.

[0075] In some embodiments, the technology can use heart rate data to monitor the intensity of a user's exercise. The technology can be configured to take into account both the user's heart rate and glucose level to determine recommendations. For example, the computing device can correlate the user's heart rate level with the user's glucose level, such as determining how the user's glucose value drops upon reaching a particular heart rate level. As another example, the computing device can determine the amount of time between the onset of the user's peak heart rate and a glucose drop.

[0076] A user's heart rate can be correlated with the nature of the activity (e.g., aerobic and / or anaerobic activity) and used to draw inferences regarding the user's glucose level. For example, if a person sprints, their heart rate will rise quickly and their glucose level may also rise. In contrast, if a user slows down the pace of the activity, their heart rate should similarly drop and glucose values ​​may also drop. In the present technology, heart rate can be used to monitor and / or make recommendations (e.g., to maintain glucose levels using a mix of jogging and sprinting) based on the user's performance of the activity. For example, if the system detects that the user's glucose is dropping at a heart rate of 120 beats per minute, the computing device can recommend that the user speed up to a faster pace to increase the user's heart rate, thereby performing anaerobic exercise and raising the user's glucose level. As another example, the system can encourage the user to perform weight training exercises to increase the user's heart rate and thereby raise the user's glucose level. Thus, heart rate information can be used to determine the type of activity (e.g., aerobic and / or anaerobic) to provide custom recommendations to the user.

[0077] In some embodiments, the thresholds can be adjusted for user-based recommendations during exercise. For example, there may be user-specific issues with exercise, such as not noticing hypoglycemia (e.g., the user does not exhibit typical symptoms that lead to hypoglycemia, such as sweating, turning pale, etc.). Thus, the thresholds used to trigger exercise recommendations can be modified on a per-user basis. For example, for users who are unaware of hypoglycemia, the technology can be designed to detect signs of hypoglycemia, taking into account potentially higher minimum glucose thresholds and / or shorter detection time windows. Modifying a computing device to provide custom recommendations to a user is further discussed below in conjunction with FIG. 16.

[0078] In some embodiments, the technology is configured to customize the exercise planning features discussed herein based on user preferences, such as user goals for exercise (e.g., lose weight, build muscle, and / or the like). FIG. 15 illustrates an exemplary computerized method 1500 for customizing an exercise plan for a diabetic patient, according to some embodiments. At step 1502, a computing device stores a default rule set associated with an exercise planning tool. At step 1504, the computing device receives input data indicating user preferences for the exercise planning tool. At step 1506, the computing device modifies aspects of the exercise planning tool by modifying the default rule set to customize the exercise planning tool for the patient based on the input data. At step 1508, the computing device generates an exercise plan for the patient based on the modified aspects of the exercise planning tool. By customizing the exercise planning tool, the exercise plan is customized to the user preferences, as the exercise plan may otherwise differ from what would have been generated using an unmodified set of default rules.

[0079] Referring to step 1502, the default rules may include one or more rules and / or associated configuration data used to implement one or more aspects of the technology described herein, such as for creating an exercise plan for an individual with diabetes.

[0080] Referring to step 1504, the user preferences may include the user's goals for performing exercise. Examples of goals include losing weight, maintaining the user's weight, building muscle, maintaining muscle, training for a particular event (e.g., half marathon, marathon, etc.), performing an exercise (e.g., an exercise that the user must commit to performing safely), improving flexibility, maintaining flexibility, etc. The user preferences may also include an indication from the user that a particular type of exercise is preferred over another type of exercise. For example, the user may provide input indicating that aerobic exercise is preferred over anaerobic exercise (or vice versa), or that a particular type of exercise is preferred over another type of exercise in the same category (e.g., the aerobic exercise of walking is preferred over the aerobic exercise of elliptical training, etc.).

[0081] With reference to step 1506, the computing device may monitor the exercise planning tool, e.g., by modifying one or more aspects of the tool used to plan and / or monitor the exercise. For example, the computing device may monitor one or more rules or configuration data (e.g., used by rules and / or other aspects of the planning tool, and / or the like). The modifications may modify recommendations provided to the user, as described in connection with step 1508.

[0082] Referring to step 1508, the custom exercise plan may include customized recommendations that differ from default recommendations, such as recommendations discussed herein that may be provided before, during, and / or after exercise. The computing device may use the input data to plan a custom exercise plan, such as exercise type, exercise duration, glucose levels, heart rate, and / or the like, as described herein. In some embodiments, the user's desired customization may be used to control the overall decision-making of the algorithms of the techniques discussed herein. For example, if the user desires to use exercise to lose weight, the computing device may modify the tool to not recommend activities that may reduce the user's ability to lose weight. For example, rather than recommending carbohydrate intake to avoid hypoglycemia, the application may instead suggest performing anaerobic exercise, mixed activity exercise, reducing bolus insulin doses before or during exercise further than the application would have otherwise recommended, reducing the user's basal rate before or during exercise further than the application would have otherwise recommended, and / or other similar recommendations to avoid hypoglycemia. As another example, the system may be configured to sort the original order of the set of recommended exercises to provide a set of preferred exercises before other, less preferred exercises based on user preferences (whereas, for example, the computing device may otherwise provide the original order of the recommended exercises).A preferred exercise may be identified based on the user's previously expressed preferences (e.g., if the user indicates that he or she prefers running over weightlifting), based on the user's expressed goals (e.g., if the user indicates that he or she is trying to lose weight, then running or jogging may be identified as a preferred exercise), based on the user's feedback to previous exercise sessions (e.g., if the user indicates that he or she had a good exercise session while swimming, then swimming may be identified as a preferred exercise), or based on the user's glucose levels during previous exercise sessions (e.g., if the user's glucose levels are observed to remain more consistently within the ideal range while running than while weightlifting, then running may be identified as a preferred exercise, or alternatively, if the user's glucose levels are observed to remain within the ideal range during one or more swimming sessions, then swimming may be identified as a preferred exercise).

[0083] In some embodiments, the technology may include customizing the exercise planning function based on user-specific information, such as based on user-specific therapeutic aspects, physiological aspects, etc. For example, the technology may include a set of predetermined general recommendations and / or guidelines (e.g., "reduce basal rate by 80%" or "reduce bolus dose by 50%) designed as a one-size-fits-all initial set of recommendations designed to be broadly applicable to most users. Over time, the computing device may adapt the technology to the user, such as based on the user's past glucose responses to such interventions. For example, if the user experienced hyperglycemia the last time the user reduced their insulin basal rate or bolus dose by 80% before exercising, the computing device may instead recommend reducing the insulin basal rate or bolus dose (e.g., by 70%) during the next exercise session. Conversely, if the user experienced hypoglycemia the last time the user reduced their insulin basal rate or bolus dose by 80% before exercising, the computing device may instead recommend increasing the insulin basal rate or bolus dose (e.g., by 90%) during the next exercise session. By maintaining a log of the user's past glucose responses to interventions, the computing device can customize a general set of recommendations and / or guidelines for the user's particular glucose responses to past interventions. Thus, in some embodiments, the technology can be designed to allow a user to easily select activities that the user is likely to enjoy in addition to being likely to have safe glucose levels.

[0084] FIG. 16 illustrates an exemplary computerized method 1600 for customizing an exercise planning tool based on user-specific information, according to some embodiments. At step 1602, the computing device stores a default rule set associated with the exercise planning tool for creating an exercise plan. At step 1604, the computing device creates an exercise plan set for the user. At step 1606, for each exercise plan, the computing device monitors data indicative of user-specific data. At step 1608, the computing device modifies aspects of the exercise planning tool by modifying the default rule set to customize the exercise planning tool for the patient based on the monitored data.

[0085] Referring to steps 1604-1606, the user-specific data monitored for each exercise plan may include patient therapeutic aspects, patient physiological aspects, and / or other user-specific data. Therapeutic aspects may include monitoring one or more aspects related to the user's diabetes treatment, such as monitoring bolus insulin doses, basal rates, and / or the like. Physiological aspects may include monitoring a heart rate measurement set, a glucose measurement set, a food intake set, sweating, the user's body temperature (e.g., of the user's skin), environmental temperature (e.g., of the exercise location), the user's hydration (e.g., as reported by the user or based on the user's heart rate), the user's brain or heart activity (e.g., via an electroencephalogram (EEG) or electrocardiogram (EKG), etc.), the user's activity or movement (e.g., as measured by an accelerometer or gyroscope worn on the user's body), how well the user slept the previous night, cortisol levels, ketone levels, and / or the like. In some embodiments, the technology can monitor the user's starting glucose level (e.g., at the start of the exercise), the duration of the exercise, the intensity of the exercise (e.g., based on heart rate information, exercise category, exercise time, and / or the like), the user's glucose level during the exercise, the ending glucose level at the end of the user's exercise, the user's feedback after the exercise (e.g., to understand how the user felt after the exercise, such as "good," "bad," and / or a numerical rating indicating the user's satisfaction with the exercise, and / or his or her glucose level during or after the exercise, and / or the like). In some embodiments, the technology can monitor whether the user is taking medication, the user's menstrual cycle phase, whether the user is taking steroids (e.g., for asthma, other health reasons), whether the user is ill, whether the user is stressed, whether the user is suffering from a physical injury, and / or other health factors.

[0086] 15, the computing device may modify the rules in various ways, such as by modifying the rules, changing configuration data, and / or the like. Thus, when the next exercise is planned, the exercise plan is customized for the patient based on the modifications implemented in step 1608. As discussed herein, the exercise plan may include various recommendations, such as recommendations to adjust bolus doses, basal rates, the amount of carbohydrates to be ingested, exercise recommendations as discussed herein, and / or the like as discussed herein.

[0087] In some embodiments, recommendations can be customized based on information obtained during various phases leading up to, during, and / or after exercise. For example, the user can be monitored and / or provided with recommendations during the period leading up to the start of exercise (e.g., the time leading up to exercise). This period can be monitored to customize recommendations to the user on how to manage insulin and / or carbohydrate intake leading up to exercise to ensure that he or she begins the exercise with glucose levels within a target ideal range. The user can also be monitored and / or provided with recommendations during a first phase of exercise (e.g., during the first 10 minutes, first 30 minutes, etc.). For example, because during some exercises the user's glucose levels may drop rapidly during the first phase, that period can be monitored and / or used to provide recommendations to the user. The user can also be monitored during a second phase of exercise (e.g., after the first 10 minutes, or after the first 30 minutes, until the exercise ends). Because the user's glucose levels may behave differently during this second phase of exercise, the second phase of exercise can be monitored and / or analyzed using a separate set of rules or processes to provide recommendations. As another example, the user may be monitored and / or provided with recommendations for a first period of time after exercise, such as the first 30 minutes after exercise, 40 minutes after exercise, etc. For example, the period may be monitored because the user's glucose levels may increase rapidly during the post-exercise phase. As a further example, the user may be monitored and / or provided with recommendations for a longer period of time after exercise (e.g., 8-12 hours, or 24 hours after exercise). For example, during such longer post-exercise periods, the user may be more insulin sensitive and therefore need to take less insulin (e.g., may be provided with recommendations regarding basal adjustments or carbohydrate intake). For example, for the post-exercise period, recommendations may be presented to reduce the next bolus dose by a percentage, reduce the next basal amount by a percentage, reduce the post-dinner dose by a percentage, and / or similar.

[0088] In some embodiments, exercise data, associated recommendations, and / or other usage data described herein can be collected from the user as they interact with the application to further develop the technology described herein. For example, the recommendations described herein (e.g., pre-exercise recommendations, recommendations presented during exercise, recommendations presented after exercise, etc.) can be based on an initial rule set established as a starting baseline for the technology described herein. This initial rule set can be determined based on baseline recommendations expected to apply to an average user in a population of expected users, but these initial rules can be customized to better suit a particular user by analyzing the data described above. Data from each of the periods discussed above (e.g., the period leading up to exercise, the first phase of exercise, the second phase of exercise, the first period after exercise, and the longer period after exercise) can be analyzed to better customize future recommendations to a particular user. For example, user data can be accumulated for recommendations made leading up to exercise (e.g., basal recommendations, bolus recommendations, glucose checks, carbohydrate intake, etc.). The system can build a database and / or report set of user interactions including activity data, glucose measurements, heart rate, etc., and responses as people exercise (e.g., regarding whether the user exercised successfully, had issues, etc.) The information can be analyzed and used to make the technology more robust given realistic exercise data.

[0089] Various ways are possible to modify aspects of the exercise planning tool in order to customize the tool for a particular user. Some non-limiting examples of how this customization can be performed are described below.

[0090] In some embodiments, the system may be configured to sort the original order of the set of recommended exercises to provide a set of preferred exercises before other less preferred exercises based on monitored user therapy or physiological data. For example, if the system observes that the user's glucose levels are more consistently within the ideal range when performing a particular type of exercise (perhaps with minimal or no therapeutic intervention during the exercise, i.e., no insulin or glucagon administration required), the system may be configured to identify those exercises as preferred exercises and sort the recommended exercise set to prioritize those preferred exercises. Alternatively, if the system observes that the user exhibited good glucose performance (i.e., the user's glucose levels remained within the ideal range) while performing a particular type of exercise, that exercise may be identified as a preferred exercise. As yet another alternative, if the system observes that the user exhibited poor glucose performance while performing a particular type of exercise, that exercise may be identified as a less preferred exercise.

[0091] In some embodiments, the computing device may be configured to classify and / or further classify the patient's exercise. For example, the computing device may store an original set of exercise classifications (e.g., aerobic, anaerobic, and / or mixed). The computing device may generate a new set of exercise classifications, such as subclassifications and / or different classifications of exercise. For example, the computing device may be configured to classify running as aerobic exercise by default. However, after several exercise sessions, the computing device may observe that the user's heart rate consistently increased above a certain maximum threshold for aerobic exercise and / or that the user's glucose level increased rather than decreased while running. Based on this experience, the computing device may reclassify running as anaerobic exercise to customize the exercise tool to the particular user's preferences or fitness level. As another example, the computing device may be configured to subdivide the default set of classifications into finer subclassifications. For example, the exercise tool may observe that both swimming and jogging are classified as aerobic exercise by default, but that the user's glucose level tends to drop significantly more while swimming than while jogging. As a result, the exercise tool may classify swimming as a separate sub-classification from jogging. Both exercise types remain classified as aerobic exercise, but the exercise tool may then recognize and take into account the fact that swimming may lead to a greater decrease in glucose values ​​than jogging. In response, the exercise tool may recommend a greater decrease in insulin dose when preparing to swim rather than jog. Similar reclassifications, or finer subdivisions of other default exercise categories, are possible.

[0092] In some embodiments, the computing device can modify the bolus / basal rate recommendation and / or carbohydrate intake recommendation based on monitored data indicative of the therapeutic or physiological aspects of the user in previous exercise sessions. For example, an exercise tool may be configured to recommend reducing the insulin basal rate or bolus dose by 50% under certain circumstances (e.g., given a particular planned exercise session and at a particular starting glucose level). However, if the tool observes that a previous time a user reduced the basal rate or bolus dose by 50% under such circumstances and the user became hypoglycemic, the tool may instead recommend reducing the basal rate or bolus dose further (e.g., by 80% or 90%) under future similar circumstances. The tool may also recommend carbohydrate intake (or more or less carbohydrate intake than the default recommendation) under future similar circumstances. Similarly, if the tool observes that the user became hyperglycemic, the tool may instead recommend reducing the basal rate or bolus dose less (e.g., by 20%) under future similar circumstances.

[0093] In some embodiments, the computing device can customize its recommendations based on the user's heart rate data monitored during previous exercise sessions. For example, the computing device may observe that, over the course of one or more exercise sessions, the user's glucose level increases when the user's heart rate exceeds a certain level (e.g., in the case of anaerobic exercise) or decreases when the user's heart rate is within a certain range (e.g., in the case of aerobic exercise). Based on these observed heart rate levels and / or heart rate ranges, the computing device can be configured to predict the user's glucose response to a particular exercise based on the user's heart rate. Then, based on these predictions, the computing device can be configured to recommend at least one of administering a bolus dose of insulin, ingesting an amount of carbohydrates, and / or exercising modified based on this predicted glucose response before any actual glucose response is observed from a glucose sensor (e.g., CGM). This can improve the response time of the computing device to potential fluctuations in glucose levels due to exercise.

[0094] In yet other embodiments, the computing device can customize post-exercise status checks or push notifications based on the user's therapeutic or physiological data after exercise. For example, the computing device can be configured to instruct the patient to reduce their basal or bolus insulin dose by a certain amount (e.g., 50%) after exercise. This amount may be based on default guidelines that generally apply to an average user in the expected user population. However, the computing device may observe, after one or more exercise sessions, that the user would benefit from reducing their post-exercise insulin by more or less based on observations of the user's glucose levels several hours after the exercise session. Thus, the computing device can be configured to adjust the post-exercise recommendation based on the observed therapeutic or physiological aspects of the user.

[0095] 17 illustrates an exemplary implementation of a computer system 1700 that may be used to implement any of the aspects of the techniques and embodiments disclosed herein. The computer system 1700 may include one or more processors 1710 and one or more non-transitory computer-readable storage media (e.g., memory 1720 and one or more non-volatile storage media 1730), as well as a display 1740. The processor 1710 may control the writing of data to and reading of data from the memory 1720 and the non-volatile storage device 1730 in any suitable manner, as the aspects of the invention described herein are not limited in this respect. To implement the functions and / or techniques described herein, the processor 1710 may execute one or more instructions stored in one or more computer-readable storage media (e.g., memory 1720, storage media, etc.), which may function as a non-transitory computer-readable storage medium that stores instructions for execution by the processor 1710.

[0096] In connection with the techniques described herein, for example, code used to provide a tool for a diabetic patient to plan exercises may be stored in one or more computer-readable storage media of the computer system 1700. The processor 1710 may execute such code to provide any technique for planning exercises as described herein. Other software, programs, or instructions described herein may also be stored and executed by the computer system 1700. It will be understood that computer code may be applied to any aspect of the methods and techniques described herein. For example, computer code may be applied to interact with an operating system and plan exercises for a diabetic user through conventional operating system processes.

[0097] The various methods or processes outlined herein may be coded as software executable on one or more processors using any one of a variety of operating systems or platforms. Further, such software may be written using any of a number of suitable programming languages ​​and / or programming or scripting tools, and may be compiled as executable machine code or intermediate code that runs on a virtual machine or suitable framework.

[0098] In this regard, the various inventive concepts may be embodied as at least one non-transitory computer-readable storage medium (e.g., a computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memories, circuitry in a field programmable gate array or other semiconductor device, etc.) encoded with one or more programs that, when executed on one or more computers or other processors, perform various embodiments of the invention. The non-transitory computer-readable medium or media may be portable such that the one or more programs stored thereon may be loaded into any computer resource to implement various aspects of the invention as discussed above.

[0099] The terms "program," "software," and / or "application" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions employed to program a computer or other processor to perform various aspects of the embodiments as discussed above. It should further be understood that, according to one aspect, one or more computer programs that, when executed, perform the methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular manner among different computers or processors to perform various aspects of the present invention.

[0100] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0101] Also, the data structures may be stored in the non-transitory computer-readable storage medium in any suitable format. The data structures may include fields that are related by location within the data structure. Such relationships may be achieved by allocating storage to the fields with locations in the non-transitory computer-readable medium that similarly convey the relationship between the fields. However, any suitable mechanism may be used to establish relationships between information in fields of the data structures, including through the use of pointers, tags, or other mechanisms that establish relationships between data elements.

[0102] Various inventive concepts may be embodied as one or more methods, examples of which are provided. The acts performed as part of a method may be ordered in any suitable manner. Thus, while shown as sequential acts in the exemplary embodiments, embodiments may be constructed in which acts are performed in an order different from that illustrated, which may include performing some acts simultaneously.

[0103] The indefinite articles "a" and "an" as used herein in the specification and claims should be understood to mean "at least one" unless clearly indicated to the contrary. When used herein in the specification and claims, the phrase "at least one" in connection with a list of one or more elements should be understood to mean at least one selected from any one or more of the elements in the list of elements, and need not include at least one of each and every element specifically listed in the list of elements, nor exclude any combination of elements in the list of elements. This allows elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether related or unrelated to those specifically identified elements, to be optionally present.

[0104] The phrase "and / or" as used herein in the specification and claims should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be interpreted in the same manner, i.e., "one or more" of the elements so conjoined. Other elements other than the elements specifically identified by the "and / or" clause may optionally be present, whether related to those elements specifically identified or not. Thus, as a non-limiting example, a reference to "A and / or B", when used in combination with open-ended language such as "comprising", may in one embodiment be a reference to A only (optionally including elements other than B), in another embodiment a reference to B only (optionally including elements other than A), in yet another embodiment a reference to both A and B (optionally including other elements), and so forth.

[0105] As used herein and in the claims, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" should be interpreted as inclusive; that is, to include at least one of, but not only more than one, of a plurality of elements or a list of elements, and, optionally, to include additional unlisted items. Only terms clearly indicated to the contrary, such as "only one of" or "exactly one of," or, when used in the claims, "consisting of," refer to the inclusion of exactly one element of a plurality of elements or a list of elements. In general, as used herein, the term "or" should be interpreted as indicating an exclusive choice (i.e., "one or the other but not both") when followed by an exclusive term, such as "either," "one of," "only one of," or "exactly one of,". "Consisting essentially of," when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0106] The use of ordinal terms such as "first," "second," "third," etc. to modify claim elements in the claims does not, by itself, imply any priority, precedence, or ordering of one claim element relative to another claim element or chronological order (the order in which the acts of a method are performed). Such terms are used solely as labels to distinguish one claim element having a particular name from other elements having the same name (but only through the use of ordinal terms).

[0107] The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "comprising," "having," "containing," "involving," and variations thereof are meant to encompass the items listed thereafter as well as additional items.

[0108] Although several embodiments of the present invention have been described in detail, various modifications and improvements will readily occur to those skilled in the art. Such modifications and improvements are intended to be within the spirit and scope of the present invention. Accordingly, the foregoing description is by way of example only and is not intended to be limiting.

[0109] Various aspects are described in this disclosure, including but not limited to the following aspects.

[0110] 1. A method for recommending one or more types of exercise to a diabetic patient using a computing device, the method including receiving, by the computing device, input data indicating (i) a future exercise start time that the patient plans to begin and (ii) the patient's current glucose value; determining, by the computing device, an amount of time between the current time and the future exercise start time; determining, by the computing device, one or more recommended exercise types based on the patient's current glucose value and the amount of time; and displaying, via a display of the computing device, the one or more recommended exercise types.

[0111] 2. The method of aspect 1, wherein displaying one or more recommended exercise types includes determining whether the current glucose value is less than a first glucose threshold if the amount of time is less than a minimum duration threshold, displaying a first plurality of exercise types from a first exercise category if the current glucose value is less than the first glucose threshold, and displaying a second plurality of exercise types from a second category if the current glucose value is greater than the first glucose threshold, wherein the first plurality of exercise types is different from the second plurality of exercise types.

[0112] 3. The method of aspect 2, further comprising displaying via the display a third plurality of exercise types from a third exercise category when the current glucose value is between the second glucose threshold and a third glucose threshold when the amount of time is less than a minimum duration threshold.

[0113] 4. The method of aspect 2 or 3, further comprising displaying via the display both the first plurality of exercise types from the first exercise category and the second plurality of exercise types from the second exercise category when the amount of time is equal to or greater than a minimum duration threshold.

[0114] 5. The method of aspect 3, further comprising displaying via the display a first plurality of exercise types from a first exercise category, a second plurality of exercise types from a second exercise category, and a third plurality of exercise types from a third exercise category when the amount of time is equal to or greater than a minimum duration threshold.

[0115] 6. The method of any one of aspects 2-5, wherein the minimum duration threshold is 1 hour.

[0116] 7. The method of any one of aspects 2-6, wherein the first exercise category includes anaerobic exercise and the second exercise category includes aerobic exercise.

[0117] 8. The method of any one of aspects 3-7, wherein the third exercise category comprises a mix of aerobic and anaerobic exercise.

[0118] 9. The method of any one of aspects 2-8, wherein the first glucose threshold is between 130 mg / dL and 160 mg / dL.

[0119] 10. The method of any one of aspects 2-9, wherein the first glucose threshold is between 140 mg / dL and 150 mg / dL.

[0120] 11. The method of any one of aspects 3-10, wherein the second glucose threshold is between 80 mg / dL and 120 mg / dL and the third glucose threshold is between 140 and 180 mg / dL.

[0121] 12. The method of any one of aspects 3-11, wherein the second glucose threshold is between 95 mg / dL and 105 mg / dL and the third glucose threshold is between 155 mg / dL and 165 mg / dL.

[0122] 13. The method of any one of aspects 1-12, wherein the computing device receives the patient's current glucose value from at least one of a connected glucose meter and manual user input.

[0123] 14. The method of any one of aspects 1-13, further comprising receiving second input data indicative of the patient's insulin on-board (IOB) amount, and wherein the one or more recommended exercise types are determined based at least in part on the second input data.

[0124] 15. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform a method according to any one of aspects 1-14.

[0125] 16. A system comprising: a memory storing instructions; and a processor configured to execute the instructions to implement a method according to any one of aspects 1-14.

[0126] 17. A method for recommending adjustments to therapy for a patient having diabetes based on a planned exercise session using a computing device, the method including: receiving, by the computing device, input data indicating (i) a planned future exercise start time when the patient intends to start exercising, (ii) a planned type of exercise the patient intends to perform, and (iii) an initial glucose value of the patient; presenting, via a display of the computing device, a first recommendation to a user including at least one of an adjustment to a planned insulin bolus dose and an adjustment to a planned insulin basal rate, the first recommendation being based on at least one of the received exercise type and the received initial glucose value; prompting, by the computing device, via the display, the user to provide an input indicating the patient's first planned glucose value when a current time is within a first time period of the exercise start time; receiving, by the computing device, input data indicating the patient's first planned glucose value; determining, by the computing device, a second recommendation for the patient based on the received first planned glucose value; and presenting, via the display, the second recommendation.

[0127] 18. The method of aspect 17, wherein determining the second recommendation includes determining at least one of a recommended amount of carbohydrates for the patient to consume based on the received first scheduled glucose value, an adjustment to a planned insulin bolus dose, an adjustment to a planned insulin basal rate, and a new insulin bolus dose for an unplanned bolus administration.

[0128] 19. The method of aspect 17 or 18, further comprising monitoring an on-board clock of the computing device to determine a current time.

[0129] 20. The method of any one of aspects 17-19, further comprising: prompting a user via the display to provide an input indicating a second scheduled glucose value for the patient when a current time is within a second time period of the exercise start time, the second time period being less than the first time period; receiving, at the computing device, an input indicating the second scheduled glucose value for the patient; and upon receipt of the second scheduled glucose value, determining a third recommendation for the patient based on the received second scheduled glucose value, wherein the third recommendation includes at least one of a recommended amount of carbohydrates for the patient to consume, an adjustment to a planned insulin bolus dose, an adjustment to a planned insulin basal rate, and a new insulin bolus dose for an unplanned bolus administration; and presenting the third recommendation to the patient via the display.

[0130] 21. The method of any one of aspects 17-20, further comprising: receiving, at the computing device, an input indicating an exercise end time when the patient plans to stop exercising; and if the current time is at or after a third period of time after the exercise end time, prompting a user via the display to provide an input indicating a third planned glucose value for the patient; receiving, at the computing device, an input indicating the patient's third planned glucose value; and upon receipt of the third planned glucose value, presenting via the display at least one of a recommended amount of carbohydrates for the patient to consume based on the third planned glucose value, a recommended adjustment to a planned insulin bolus dose, and a recommended adjustment to a planned insulin basal rate.

[0131] 22. The method of embodiment 21, wherein the first period of time is 1 hour, the second period of time is 15 minutes, and the third period of time is 15 minutes.

[0132] 23. The method of any one of aspects 17-22, further comprising presenting via the display a visible timeline including a visual indicator indicating a time when the user will be prompted to provide an input indicating a first scheduled glucose value, and a separate visual indicator indicating a future exercise start time.

[0133] 24. The method of any one of aspects 21-23, further comprising presenting via the display a visual timeline including a visual indicator indicating a time when the user will be prompted to provide an input indicating a first scheduled glucose value, a visual indicator indicating a time when the user will be prompted to provide an input indicating a second scheduled glucose value, a visual indicator indicating an exercise start time, and a visual indicator indicating a time when the user will be prompted to provide an input indicating a third scheduled glucose value.

[0134] 25. The method of any one of aspects 17-24, further comprising providing a push notification to the user after the exercise session providing recommendations to the user for avoiding hypoglycemia during sleep after the exercise session.

[0135] 26. The method of aspect 25, wherein the push notification includes at least one of a recommendation to increase the sensitivity of a hypoglycemia alarm of the glucose sensor during sleep, a recommendation to consume at least one of protein and fat, and a recommendation to consume less insulin after an exercise session.

[0136] 27. The method of any one of aspects 17-26, further comprising, upon receipt of the first scheduled glucose value, presenting via the display a recommendation based on the received first scheduled glucose value that the patient limit their exercise to aerobic exercise.

[0137] 28. The method of any one of aspects 21-27, further comprising, upon receipt of the third scheduled glucose value, presenting via the display a recommendation based on the received third scheduled glucose value that the patient perform an aerobic cool down.

[0138] 29. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform a method according to any one of aspects 17-28.

[0139] 30. A system comprising: a memory storing instructions; and a processor configured to execute the instructions to implement a method according to any one of aspects 17 to 28.

[0140] 31. A method for customizing a computerized exercise planning tool for creating an exercise plan for a patient with diabetes using a computing device, the method including: storing, by the computing device, a default rule set associated with the exercise planning tool for creating an exercise plan for a patient with diabetes; receiving, by the computing device, input data indicating user preferences for the exercise planning tool; modifying, by the computing device, aspects of the exercise planning tool, including modifying the default rule set to customize the exercise planning tool for the patient based on the input data; and generating, by the computing device, an exercise plan for the patient based on the modified aspects of the exercise planning tool, wherein the exercise plan differs from a second exercise plan that would have been generated using an unmodified default rule set.

[0141] 32. The method of aspect 31, wherein generating an exercise plan for the patient includes receiving second input data indicating one or more of (i) a future exercise start time when the patient intends to start exercising, (ii) an exercise type the patient intends to perform, and (iii) an initial glucose value for the patient, where the input data indicating user preferences does not include any of (i), (ii), or (iii); and generating the exercise plan based on the received second input data and the modified aspect of the exercise planning tool.

[0142] 33. The method of aspect 31 or 32, wherein receiving input data includes receiving data indicative of a user goal for the exercise plan.

[0143] 34. The method of aspect 33, wherein the user goal includes one or more of a goal to lose weight, a goal to maintain weight, a goal to build muscle, a goal to maintain muscle, a goal to train for a particular event, a goal to perform an exercise, a goal to improve flexibility, a goal to maintain flexibility, or any combination thereof.

[0144] 35. The method of any one of aspects 31-34, wherein the modifying includes modifying the default rule set to provide customized recommendations during preparation for exercise, the customized recommendations differing from the default recommendations.

[0145] 36. The method of any one of aspects 31 to 35, wherein the modifying includes modifying the default rule set to provide customized recommendations during performance of the exercise, the customized recommendations differing from the default recommendations.

[0146] 37. The method of aspect 36, wherein providing the customized recommendation includes receiving second input data indicating the patient's glucose value during performance of the exercise, determining a customized recommendation for the patient based on the second input data and a modified aspect of the exercise planning tool, and presenting the customized recommendation via a display of the computing device.

[0147] 38. The method of aspect 37, wherein determining the customized recommendation includes determining that the patient is at risk for hypoglycemia based on the second input data, the customized recommendation includes an exercise modification, and the default recommendation includes a recommendation to consume carbohydrates.

[0148] 39. The method of embodiment 37, wherein the default recommendation includes a default bolus dose, a default basal rate, or some combination thereof, and the customized recommendation includes a customized bolus dose that is different from the default bolus dose, a customized basal rate that is different from the default basal rate, or some combination thereof.

[0149] 40. The method of any one of aspects 31-39, wherein generating an exercise plan for the patient based on a modified aspect of the exercise planning tool includes sorting an original order of the recommended exercise sets and providing a preferred exercise set at a beginning of the sorted recommended set such that the preferred exercise is presented to the patient before other exercises in the recommended exercise set, and a second exercise plan that would have been generated using an unmodified default rule set includes providing recommended exercise sets according to the original order of the recommended exercise sets.

[0150] 41. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform a method described in any one of aspects 31-40.

[0151] 42. A system comprising: a memory for storing instructions; and a processor configured to execute the instructions to implement a method according to any one of aspects 31 to 40.

[0152] 43. A method for customizing a computerized exercise planning tool for creating an exercise plan for a patient with diabetes using a computing device, the method comprising: storing, by the computing device, a default rule set associated with the exercise planning tool for creating an exercise plan for a patient with diabetes; planning, by the computing device, a set of exercise plans for the patient using the exercise planning tool, each exercise plan being associated with an exercise; monitoring, by the computing device, for each exercise plan in the exercise plan set, data indicative of (i) a therapeutic aspect of the patient, (ii) a physiological aspect of the patient, or both, based on the monitored data, modifying, by the computing device, the default rule set to customize the exercise planning tool for the patient; and generating, by the computing device, a new exercise plan for the patient based on the modified default rule set, wherein the new exercise plan differs from the exercise plan that would have been generated using an unmodified default rule set.

[0153] 44. The method of embodiment 43, wherein the monitoring data indicative of the patient's therapeutic behavior includes monitoring data indicative of an insulin dose set.

[0154] 45. The method of aspect 43 or 44, wherein the monitoring data indicative of a physiological aspect of the patient includes monitoring a set of heart rate measurements, a set of glucose measurements, a set of activity measurements, a set of food intake, or any combination thereof.

[0155] 46. ​​The method of any one of aspects 43-45, wherein the new exercise plan includes at least one of a bolus dose that is different from the bolus dose of the exercise plan that would have been generated using an unmodified default rule set, and a basal rate that is different from the basal rate of the exercise plan that would have been generated using an unmodified default rule set.

[0156] 47. The method of any one of aspects 43-46, wherein generating a new exercise plan includes generating a recommended amount of carbohydrates for the patient to consume that differs from the amount of carbohydrates recommended by the exercise plan that would have been generated using an unmodified default rule set.

[0157] 48. The method of any one of aspects 43-47, wherein generating a new exercise plan includes predicting a patient's glucose response to a current exercise currently being performed by the patient based on the patient's heart rate during the current exercise, and recommending at least one of an insulin bolus administration, a carbohydrate intake, and a modified exercise that differs from the current exercise based on the predicted glucose response.

[0158] 49. A method according to any one of aspects 43 to 48, wherein generating a new exercise plan includes storing an original classification set of exercises; generating a new classification set of exercises, the new classification set including more classifications than the original classification set; and generating a new exercise plan based on the new classification set.

[0159] 50. The method of any one of aspects 43-49, wherein generating the new exercise plan includes sorting an original order of the recommended exercise sets and providing the preferred exercise set at the beginning of the sorted recommended set such that the preferred exercise is presented to the patient before other exercises in the recommended exercise set.

[0160] 51. The method of any one of aspects 43 to 50, wherein generating a new exercise plan includes selecting recommended exercises from a group of available exercises based on the monitored data.

[0161] 52. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform a method described in any one of aspects 43-51.

[0162] 53. A system comprising: a memory for storing instructions; and a processor configured to execute the instructions to perform a method according to any one of aspects 43 to 51.

[0163] 54. A method for providing recommendations to a patient having diabetes during exercise using a computing device, the method including receiving, by the computing device, input data indicative of (i) an exercise being performed by the patient, and (ii) a current glucose value of the patient while performing the exercise; determining, by the computing device, one or more recommendations based on the current glucose value; and displaying, via a display of the computing device, the one or more recommendations.

[0164] 55. The method of aspect 54, wherein determining the one or more recommendations includes determining that the exercise is an aerobic exercise based on the input data, determining that the patient's glucose level is below a minimum threshold based on the current glucose value, and generating a recommendation to the patient to perform one or more anaerobic exercises.

[0165] 56. The method of aspect 54, wherein determining the one or more recommendations includes determining that the exercise is anaerobic exercise based on the input data, determining that the patient's glucose level is greater than a maximum threshold based on the current glucose value, and generating a recommendation to the patient to perform one or more aerobic exercises.

[0166] 57. The method of aspect 54, wherein determining the one or more recommendations includes determining that the exercise is a mix of aerobic and anaerobic exercise based on the input data, determining a change in the patient's glucose level compared to a previous glucose value based on the current glucose value, and generating a recommendation to the patient based on the determined change in the patient's glucose level.

[0167] 58. A non-transitory computer-readable medium containing instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform a method described in any one of aspects 54 to 57.

[0168] 59. A system comprising: a memory storing instructions; and a processor configured to execute the instructions to perform a method according to any one of aspects 54 to 57.

Claims

1. 1. A method of a computing device implementing recommendations for adjustments to a therapy for a user with diabetes based on a planned exercise session, the method comprising: receiving, by the computing device, input data indicating (i) a future exercise start time at which the user intends to begin exercise, (ii) an exercise type that the user intends to engage in, and (iii) an initial glucose value of the user; presenting a first recommendation to the user, the first recommendation including at least one of an adjustment to a planned insulin bolus dose and an adjustment to a planned insulin basal rate, the first recommendation being based on at least one of the exercise type and the initial glucose value; and prompting a user via the display, by the computing device, to provide an input indicating a first scheduled glucose value for the user when a current time is within a first period of the exercise start time; receiving, by the computing device, input data indicative of the first scheduled glucose value; determining, by the computing device, a second recommendation for the user based on the first scheduled glucose value, the second recommendation comprising: A recommended amount of carbohydrates consumed by the user; Adjustments to planned insulin bolus doses, and and adjusting the scheduled insulin basal rate. presenting the second recommendation via the display.

2. 2. The method of claim 1, wherein determining the second recommendation includes determining at least one of a recommended amount of carbohydrates for the user to consume based on the first scheduled glucose value, an adjustment to a planned insulin bolus dose, an adjustment to a planned insulin basal rate, and a new insulin bolus dose for an unplanned bolus administration.

3. The method of claim 1 or 2, further comprising monitoring an on-board clock of the computing device to determine the current time.

4. prompting a user via the display to provide an input indicating a second scheduled glucose value for the user when the current time is within a second time period of the exercise start time, the second time period being less than the first time period; receiving, at the computing device, an input indicating the second scheduled glucose value; Upon receipt of the second scheduled glucose value, determining a third recommendation for the user based on the second scheduled glucose value, the third recommendation comprising: A recommended amount of carbohydrates consumed by the user; Adjustments to planned insulin bolus doses; Adjustments to scheduled insulin basal rates, and determining a new insulin bolus dose for an unscheduled bolus administration; The method of claim 1 , further comprising: presenting the third recommendation to the user via the display.

5. receiving, at the computing device, an input indicating an end-of-exercise time when a user plans to stop exercising; if the current time is at or after a third period of time after the end of exercise, displaying on the display a prompt to provide an input indicating a third scheduled glucose value of the user; receiving, at the computing device, an input indicating the third scheduled glucose value; 5. The method of claim 4, further comprising, upon receipt of the third scheduled glucose value, presenting via the display at least one of a recommended amount of carbohydrates for the user to consume based on the third scheduled glucose value, a recommended adjustment to a planned insulin bolus dose, and a recommended adjustment to a planned insulin basal rate.

6. 6. The method of claim 5, wherein the first period of time is 1 hour, the second period of time is 15 minutes, and the third period of time is 15 minutes.

7. 7. The method of claim 5, wherein the prompting for providing an input indicating the first scheduled glucose value via the display further comprises presenting a visual timeline including a visual indicator indicating a time of day, and a separate visual indicator indicating the future exercise start time.

8. Via the display, a visual indicator indicating a time when the user will be prompted to provide an input indicating the first scheduled glucose value; a visual indicator indicating a time when the user will be prompted to provide an input indicating the second scheduled glucose value; a visual indicator indicating the start time of the exercise; and a visual indicator indicating a time when the user will be prompted to provide an input indicating the third scheduled glucose value.

9. providing a push notification to the user after an exercise session to avoid hypoglycemia during sleep following the exercise session; The method of any of claims 1-8, wherein the push notification includes at least one of a recommendation to increase the sensitivity of a hypoglycemia alarm of a glucose sensor during sleep, a recommendation to consume at least one of protein and fat, and a recommendation to consume less insulin after the exercise session.

10. The method of any one of claims 1-9, further comprising, upon receipt of the first scheduled glucose value, presenting via the display a recommendation based on the first scheduled glucose value that the user limit their exercise to aerobic exercise.

11. The method of any one of claims 5-8, further comprising, upon receipt of the third scheduled glucose value, presenting via the display a recommendation based on the third scheduled glucose value that the user perform an aerobic cool down.

12. A non-transitory computer-readable medium comprising instructions, the instructions being operable, when executed by one or more processors on a computing device, to cause the one or more processors to perform the method of any one of claims 1-11.

13. A system comprising a memory for storing instructions and a processor configured to execute said instructions to perform a method according to any one of claims 1-11.

14. 1. A method executed by a computing device for providing recommendations to a user with diabetes during exercise, the method comprising: receiving input data, the computing device indicative of (i) an exercise being performed by the user, and (ii) a current glucose value of the user while performing the exercise; determining, by the computing device, one or more recommendations based on the current glucose value; displaying, via a display, the one or more recommendations by the computing device; Determining the one or more recommendations includes: determining that the exercise is aerobic exercise based on the input data; determining that the user's glucose level is below a minimum threshold based on the current glucose value; and generating a recommendation to the user to perform one or more anaerobic exercises.

15. 1. A method executed by a computing device for providing recommendations to a user with diabetes during exercise, the method comprising: receiving input data, the computing device indicative of (i) an exercise being performed by the user, and (ii) a current glucose value of the user while performing the exercise; determining, by the computing device, one or more recommendations based on the current glucose value; displaying, via a display, the one or more recommendations by the computing device; Determining the one or more recommendations includes: determining that the exercise is anaerobic exercise based on the input data; determining that the user's glucose level is greater than a maximum threshold based on the current glucose value; and generating a recommendation to the user to perform one or more aerobic exercises.

16. 1. A method executed by a computing device for providing recommendations to a user with diabetes during exercise, the method comprising: receiving input data, the computing device indicative of (i) an exercise being performed by the user, and (ii) a current glucose value of the user while performing the exercise; determining, by the computing device, one or more recommendations based on the current glucose value; displaying, via a display, the one or more recommendations by the computing device; Determining the one or more recommendations includes: determining that the exercise is a mix of aerobic and anaerobic exercise based on the input data; determining a change in the user's glucose level based on the current glucose value as compared to a previous glucose value; and recommending carbohydrate intake based on the determined change in the user's glucose level.

17. A non-transitory computer-readable medium comprising instructions, which, when executed by one or more processors on a computing device, are operable to cause the one or more processors to perform the method of any one of claims 14-16.

18. A system comprising a memory for storing instructions and a processor configured to execute said instructions to perform the method of any one of claims 14-16.

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