Method and system for automated activity recommendations in diabetes treatment planning - Patents.com

Abstract

A method for generating activity recommendations in a diabetes treatment plan includes receiving physiological data, preferences, and proposed activities for a person with diabetes (PwD), generating a physiological profile for the PwD, providing the physiological profile to a virtual physiological model, receiving a projection from the virtual physiological model, generating weighted values ​​based on the proposed activities and the preference data, each weighted value corresponding to a likelihood that the PwD will adhere to the proposed activity, ranking each activity based on an estimated change in a physiological characteristic of a projection associated with the activity relative to a baseline physiological projection, the estimated change scaled by the weighted value corresponding to each activity, and generating an output including a predetermined number of proposed activities ordered based on a ranking of activities that result in the greatest change in the physiological characteristic, taking into account the likelihood that the PwD will adhere to the proposed activity.

Description

[Technical field]

[0001] Claiming priority This application claims the benefit of U.S. Provisional Patent Application No. 63 / 181,863, entitled "METHOD AND SYSTEM FOR AUTOMATED ACTIVITY RECOMMENDATION IN DIABETES TREATMENT PLANS," filed April 29, 2021, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates generally to the field of treatment for individuals with diabetes, and more specifically to a system for assisting in the selection of activity goals in a treatment plan for individuals with diabetes. [Background technology]

[0003] Diabetes mellitus, commonly referred to as diabetes, is a category of chronic diseases that reduces or eliminates the human body's ability to metabolize dietary glucose due to the inability of the pancreas to produce the hormone insulin, insulin resistance, or a combination of insulin deficiency and insulin resistance. In particular, the development of type 2 diabetes often occurs in patients whose bodies still produce some levels of insulin but have acquired some resistance to insulin, which results in elevated blood glucose levels that, if left untreated, can result in ketoacidosis and other comorbidities. For purposes of explanation, reference to type 2 diabetes also includes the disease known as "prediabetes," which is a form of mild insulin resistance that causes elevated average blood glucose levels that may progress to type 2 diabetes. Some persons (PwD) with diabetes, particularly type 2 diabetes, are able to make changes to their diet, exercise, and sleep hygiene regimens that slow or sometimes reverse the progression of diabetes. For example, proper management of type 2 diabetes allows some PwD to delay or prevent the need to receive external insulin, the need to take other diabetic medications, and reduce the likelihood of diabetic comorbidities. Improvements in diet, exercise, sleep hygiene, and medication adherence may also be beneficial for PwD dependent on exogenous insulin or other diabetes-related medications in avoiding the need for increasing insulin doses due to ongoing insulin resistance and reducing the likelihood of diabetic comorbidities.

[0004] Diabetes treatment plans that incorporate improvements in diet, exercise, sleep hygiene, and medication adherence provide benefits to PwDs that are well known in the art, but many challenges remain in getting PwDs to consistently implement these plans over time for effective diabetes management. Diabetes educators are professionals who provide advice and plans to help PwDs commit to and adhere to these plans, which often include setting goals to achieve improvements in one or more areas of diet, exercise, sleep hygiene, and medication adherence for PwDs who have been prescribed medications. Although educators provide valuable assistance to PwDs, in practice educators face several obstacles in devising diabetes treatment plans with goals for PwDs that provide benefits to them and that PwDs can consistently adhere to. Educators often see a large number of PwDs in a relatively short educating session and may not have enough time and resources to provide each PwD with a highly individualized plan. These constraints often lead to a "one size fits all" approach to providing plans to many PwDs, especially those without extensive experience in diabetes management. Such plans may not provide optimal outcomes for each PwD, and some PwDs may not adhere to the plan even if following the plan would benefit their diabetes care. Given these challenges, improved technology that provides customized goal recommendations for each PwD would be beneficial. Summary of the Invention

[0005] In one embodiment, a method for generating activity recommendations in a diabetes treatment plan includes receiving, with a processor, physiological data for a person with diabetes (PwD), preference data for the PwD, and a plurality of suggested activities for the PwD, and generating, with the processor, a plurality of physiological profiles for the PwD, the plurality of physiological profiles including a baseline physiological profile based on the physiological data for the PwD and a plurality of activity physiological profiles, each activity physiological profile corresponding to one of the plurality of suggested activities, each activity physiological profile based on the physiological data for the PwD and a modification of physiological data associated with the one of the plurality of suggested activities corresponding to the activity physiological profile. The method includes providing, with a processor, a plurality of physiological profiles to a virtual physiological model; receiving, with the processor, a plurality of projections of the PwD from the virtual physiological model, where each projection of the plurality of projections provides an estimated change in a physiological characteristic in the PwD during a predetermined time period corresponding to a physiological profile of the plurality of physiological profiles; and generating, with the processor, a plurality of weighted values ​​based on the plurality of suggested activities and preference data, where each weighted value corresponds to a likelihood that the PwD will adhere to a corresponding one of the suggested activities. generating a plurality of weighted values ​​based on the data; ranking, with a processor, each activity among the plurality of suggested activities based on an estimated change in a physiological characteristic of a projection among the plurality of projections associated with the activity relative to a baseline physiological projection among the plurality of projections corresponding to the baseline physiological profile, scaled by the weighted value corresponding to the activity; and generating, with the processor, an output including a predetermined number of the plurality of suggested activities in an order based on the ranking, taking into account a likelihood that the PwD will adhere to the proposed activity, to identify one or more suggested activities that result in the greatest change in the physiological characteristic.

[0006] In another embodiment, a system for generating activity recommendations is developed. The system includes a memory, a network interface device, and a processor operably connected to the memory and the network interface device. The processor is configured to store physiological data for a person with diabetes (PwD), preference data for the PwD, and a plurality of suggested activities for the PwD in the memory, and generate a plurality of physiological profiles for the PwD. The plurality of physiological profiles includes a baseline physiological profile based on the physiological data for the PwD and a plurality of activity physiological profiles, each activity physiological profile corresponding to one of the plurality of suggested activities, and each activity physiological profile is based on the physiological data for the PwD and a modification of physiological data associated with the one of the plurality of suggested activities corresponding to the activity physiological profile.The processor includes: transmitting, using the network interface device, a plurality of physiological profiles to a virtual physiological model service; receiving, using the network interface device, a plurality of projections of the PwD from the virtual physiological model service, where each projection of the plurality of projections provides an estimated change in a physiological characteristic in the PwD during a predetermined time period corresponding to a physiological profile of the plurality of physiological profiles; and generating a plurality of weighted values ​​based on the plurality of proposed activities and the preference data, where each weighted value represents a likelihood that the PwD will adhere to a corresponding one of the proposed activities. generating a plurality of weighted values ​​based on the plurality of proposed activities and the preference data corresponding to the plurality of proposed activities; ranking each activity among the plurality of proposed activities based on an estimated change in a physiological characteristic of a projection among the plurality of projections associated with the activity relative to a baseline physiological projection among the plurality of projections corresponding to the baseline physiological profile, scaled by the weighted value corresponding to each activity; and generating an output including a predetermined number of the plurality of proposed activities in an order based on the ranking to identify one or more proposed activities that result in the greatest change in the physiological characteristic, taking into account a likelihood that the PwD will adhere to the proposed activity.

[0007] Further advantages, benefits, features and objects will become more readily apparent from a consideration of the following detailed description, which refers to the following drawings, in which: [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram of a system that provides automated generation of suggested activities to reduce average blood glucose or body weight of a person with diabetes based on projections and preference data of a virtual physiological model of the person with diabetes. [Diagram 2]FIG. 13 is a block diagram of a process for generating suggested activities that result in changes in physiological characteristics, such as reducing average blood glucose or body weight, in a person with diabetes based on projections and preference data from a virtual physiological model of the person with diabetes. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] These and other benefits, advantages, features and objects will be better understood from the following description, in which reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of example, and not by way of limitation, embodiments of the inventive concepts. Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings.

[0010] While the inventive concept is susceptible to various modifications and alternative forms, exemplary embodiments thereof are shown by way of example in the drawings and described in detail herein. However, it should be understood that the following description of exemplary embodiments is not intended to limit the inventive concept to the particular form disclosed, but rather, the intention is to cover all advantages, effects, and features that fall within the spirit and scope defined by the embodiments described herein and the following embodiments. Therefore, reference should be made to the embodiments described herein and the following embodiments to interpret the scope of the inventive concept. Therefore, it should be noted that the embodiments described herein may have advantages, effects, and features that are useful in solving other problems.

[0011] The devices, systems, and methods will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the inventive concepts are shown. Indeed, the devices, systems, and methods may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0012] Similarly, many modifications and other embodiments of the devices, systems, and methods described herein will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. It is therefore to be understood that the devices, systems, and methods are not limited to the particular embodiments disclosed, and that modifications and other embodiments are intended to be included within the scope of the embodiments. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure pertains. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the methods, the preferred methods and materials are described herein.

[0014] Moreover, the reference to an element by the indefinite article "a" or "an" does not exclude the possibility that more than one element is present, unless the context clearly requires that only one element is present. Thus, the indefinite article "a" or "an" usually means "at least one". Similarly, the terms "have", "comprise", or "include", or any grammatical variants thereof, are used in a non-exclusive manner. Thus, these terms may refer both to the situation where there are no further features present in the entity described in this context other than the features introduced by these terms, and to the situation where one or more further features are present. For example, the expressions "A has B", "A comprises B", and "A includes B" may refer both to the situation where no other elements are present in A other than B (i.e., the situation where A consists solely and exclusively of B), or the situation where, other than B, one or more further elements are present in A, such as element C, elements C and D, or further elements.

[0015] The description herein refers to computer systems using various components including, but not limited to, processors, memory, and network interfaces. The term "processor" as used herein refers to one or more digital logic devices that execute stored program instructions within a computing system to perform digital logic operations. Examples of processors include one or more central processing units (CPUs), graphics processing units (GPUs), neural network processors (NPUs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), digital logic devices implementing application specific integrated circuits (ASICs), and any other suitable digital logic devices within an integrated device or as a combination of devices working together to implement a processor. In operation, each processor executes stored program instructions and accesses data stored in memory. The term memory as used herein refers to both non-volatile and volatile data storage devices. Non-volatile data storage devices include magnetic disks, optical disks, solid state NAND and phase change memory devices, and any other suitable data storage devices that do not require active power to maintain the state of the stored data. Volatile data storage devices refer to static and dynamic random access memory (RAM), as well as any other data storage device that stores data while receiving an active power supply to maintain the state of the stored data. As used herein, the term "network" refers to any communication system that enables two or more computing systems to transmit and receive data during operation, with common examples including local area networks (LANs) and wide area networks (WANs), including the Internet.Each computing system accesses the network using one or more network interface devices that the corresponding processor uses to send and receive data; common examples of network interface devices include an Ethernet network interface card for a wired network connection, or a Wireless Local Area Network (WLAN) device or a Wireless Wide Area Network (WWAN) device for a wireless network connection.

[0016] The following description refers to a diabetes educator, or more simply, a "educator." A diabetes educator is a person qualified to provide advice to a person with diabetes regarding changes in daily life, such as diet, exercise, sleep hygiene, or medication adherence for PWDs who have been prescribed medication. A diabetes educator is not necessarily a medical professional, such as a doctor, nurse, or Certified Diabetes Educator (CDE), although any of these professionals may act as a diabetes educator. Illustrative examples of educators and people with diabetes provide context for the operation of the embodiments described herein.

[0017] FIG. 1 illustrates a system 100 that automatically generates one or more activity recommendations for a person with diabetes (PwD) 104 based on physiological and preference data for the PwD 104. For illustrative purposes, assume that the PwD 104 is diagnosed with either type 2 diabetes or prediabetes (collectively hereafter referred to as "diabetes"), which the PwD can manage by meeting a goal to perform one or more activities. As used herein, the term "activity" refers to modification of diet, exercise, sleep hygiene, or medication adherence for a PwD who has been prescribed medication to improve at least one physiological characteristic associated with diabetes. Two non-limiting examples of physiological characteristics include achieving a desired blood glucose or weight level for the PwD. A goal refers to a recommended activity that the PwD sets as a target to consistently perform to achieve an improved physiological characteristic. In the configuration of FIG. 1, a diabetes coach 102 and a PwD 104 use the system 100 to generate one or more activity recommendations for the PwD 104. In the embodiment of FIG. 1, the system 100 includes an activity recommendation service 120. The activity recommendation service 120 is communicatively connected to the virtual physiological model service 160, and in an alternative embodiment, the system 100 incorporates both the activity recommendation service 120 and the virtual physiological model service 160. The trainer accesses the system 100 using the terminal 112, and the PwD 104 uses the electronic device 116 to provide input data to the system 100 and, optionally, receive activity recommendations directly from the system 100 or communicate with the trainer 102 via the terminal 112. The activity recommendation service 120, the virtual physiological model service 160, the terminal 112, and the electronic device 116 use a network 118 for communication.

[0018] In the system 100, the terminal 112 is, for example, a personal computer (PC), a tablet computing device, a smartphone, or other suitable computing device that implements client software that allows the instructor 102 to access the system 100 and, in some embodiments, to communicate with the PwD electronic device 116. The PwD electronic device 116 is another PC, a tablet computing device, a smartphone, or other suitable computing device that is typically owned by or available to the PwD 104. The PwD electronic device 116 implements client software that allows the PwD 104 to provide answers to diagnostic questions and provide at least a portion of relevant physiological data about the PwD 104 to an activity recommendation service 120 in the system 100. The PwD electronic device 116 also allows the PwD 104 to provide preference data to the activity recommendation service 120. An example of a client software program in the terminal 112 and the PwD electronic device 116 is a commercially available web browser that acts as a client to one or more web services provided by the activity recommendation service 120 to enable the terminal 112 and the PwD electronic device 116 to function as a user interface to the system 100. In some configurations, the terminal 112 and the PwD electronic device 116 further include audio or audio / visual devices that enable direct communication between the mentor 102 and the PwD 104 to conduct a remote mentoring session, although the mentoring session may also be conducted in person.

[0019] In the system 100, the activity recommendation service 120 is a computing system further including a processor 124, a network interface device 128, and a memory 132. The activity recommendation service 120 receives physiological data and preference data corresponding to the PwD. The activity recommendation service 120 further receives a projection from a virtual physiological model service 160 that estimates a change in at least one physiological characteristic of the PwD 104 in response to the proposed activity. The activity recommendation service 120 generates a ranked output of suggested activities based on both the projections from the virtual physiological model service 160 and the preference data. In the activity recommendation service 120, the memory 132 stores the PwD physiological data 136, the PwD physiological profile 138, the PwD preference data 140, the activity database 144, the virtual physiological model projections 148, stored program instructions for the activity recommendation service software 152, and an output of the ranked activities 156.

[0020] In the memory 132, the physiological data 136 is medical data including age, sex, height, weight, average blood glucose level, sleep schedule, metabolic data including diet and exercise, current medication data, diagnosed medical conditions other than diabetes, and any other medically related parameters of the PwD 104 that the activity recommendation service 120 uses as part or all of the input data to generate the PwD physiological profile 138. As described in more detail below, each PwD physiological profile 138 includes all or a portion of the physiological data 136 required as input to a virtual physiological model 176 in the virtual physiological model service 160. In some configurations, the system 100 receives all or a portion of the PwD physiological data 136 from an external electronic medical record (EMR) system via the network 118, a data storage device, or from entry via the instructor terminal 112 or the PwD electronic device 116. One of the PwD physiological profiles 138 is referred to as a “baseline physiological profile” that the activity recommendation service 120 generates based solely on the PwD's 104 actual physiological data 136 to represent the PwD's 104 current physiology and activity at the time of the coaching session. The other PwD physiological profiles 138 are referred to as “activity physiological profiles.” Each activity physiological profile incorporates both the PwD physiological data 136 and modifications to the PwD's 104 physiological data that occur in response to the PwD's 104 performing one of the suggested activities from the activity database 144. As described in further detail below, the activity recommendation service 120 identifies the effect of the proposed activities to alter the physiological parameters of the PwD 104 in each PwD physiological profile 138 based on the data associated with each activity in the activity database 144. During operation, the activity recommendation service 120 sends the PwD physiological profiles 138 to the virtual physiological model service 160, which uses the PwD physiological profiles 138 as inputs to a virtual physiological model 176 that generates projections to estimate changes in physiological characteristics of the PwD 104 corresponding to the physiological data in each of the physiological profiles 138.

[0021] In the memory 132, the PwD preference data 140 includes information provided by the PwD 104 regarding the types of activities the PwD prefers to perform to manage their diabetes. In one configuration, the PwD 104 provides numerical data of survey responses to predefined questions measuring preferences for performing different activities using a numerical range, such as a scale of 1 to 10 or other suitable scale. The numerical data allows the activity recommendation service 120 to quantify the preferences of the PwD 104. In the embodiment of FIG. 1, the PwD 104 submits responses to the survey using the PwD electronic device 116, which transmits the survey question responses to the activity recommendation service 120 prior to a meeting with the mentor 102. In another configuration, the mentor 102 elicits responses to the survey questions from the PwD 104 during a mentoring session and enters the responses using the terminal 112. In some configurations, the preference data includes geographic data corresponding to the home location of the person with diabetes, and further implied preference data regarding the PwD 104 providing demographic information of the person with diabetes. Geographic data influences the applicability of various types of activities for PwDs. For example, indoor exercise activities may be more suitable for PwDs living in urban or cold climates, while outdoor activities may be more suitable for PwDs living in rural or warm climates. PwDs 104 provide other demographic information, such as income, work schedule, education level, transportation access, and similar parameters, as implied preference data used to identify PwDs 104 preferences for various recommended activities. For example, PwDs 104 who do not own a car may only access activities located within a relatively short distance from their home compared to PwDs who have access to a car. In another example, PwDs who work at night may benefit from certain sleep hygiene activities that are less important to PwDs with diurnal sleep patterns.

[0022] In the memory 132, the activity database 144 includes a predefined set of dietary, exercise, sleep hygiene, and medication adherence activities each linked to a metabolic characteristic of the PwD. For example, dietary activities correspond to changes in calorie intake, and exercise activities are associated with changes in calorie expenditure. The activity database 144 also stores one or more metabolic characteristics associated with sleep hygiene activities, since improved sleep hygiene can directly increase metabolism of calories, reduce calorie intake by reducing overeating due to lack of sleep, and increase energy levels that enable the PwD to perform other activities, such as exercise. The activity database 144 also stores medication adherence activities that apply to PWDs taking medications that directly or indirectly affect diabetes, and the physiological impact of increased medication adherence may include a direct reduction in average blood glucose levels or other improvements to metabolism. In operation, the activity recommendation service 120 identifies metabolic changes that occur in response to a selected activity based on the data stored in the activity database 144. For example, a dietary activity recommendation may reduce calorie consumption by a large number of calories (e.g., 130 calories) if the PwD 104 drinks water or unsweetened tea instead of soft drinks, reducing the consumption of soft drinks. Other activities may increase calorie intake with foods that are less likely to have a harmful effect on the PwD 104, such as a recommendation to consume nuts (e.g., 200 calories) instead of another food that may have fewer calories but a higher proportion of carbohydrate calories compared to protein calories or fat calories. The activity database 144 stores the calorie content of various foods and beverages from public nutrition databases in association with each dietary activity, and the activity recommendation service 120 calculates the change in calories, carbohydrates consumed, fat, and protein compared to baseline calorie information contained in the PwD physiological data 136, or other dietary information for performing the activity. The activity database 144 also stores baseline calorie information for the number of calories metabolized during different athletic activities and different intensity levels of each activity, such as baseline calorie metabolism data for walking at 3 miles per hour compared to running at 7 miles per hour.The activity recommendation service 120 calculates a final estimate of calories burned based on the selected activity, the weight of the PwD 104, and the intended duration of the activity. The number of calories metabolized for an activity increases with weight, intensity, and duration of any given activity. In some cases, the activity recommendation service 120 also identifies changes in calorie consumption or calorie metabolism based on how frequently the PwD 104 performs a recommended activity, such as a daily activity or an activity performed one or more times per week.

[0023] The memory 132 also stores a virtual physiological model projection 148 and an activity recommendation service software 152. The virtual physiological model projection 148 provides an estimate of changes in physiological characteristics of the PwD 104 if the PwD 104 performs one or more of the suggested activities in the activity database 144. As described in further detail below, the virtual physiological model service 160 generates and transmits the physiological model projection 148 to the activity recommendation service 120. The activity recommendation service software 152 includes any stored program instructions that the processor 124 executes to perform the operations of the activity recommendation service 120 described herein. In operation, the processor 124 executes the activity recommendation service software 152 to perform a ranking algorithm of suggested activities from the activity database 144 based on the estimated changes in physiological characteristics from the physiological model projection 148 scaled by the PwD preference data 140, and generates ranked activities 156 as output provided to the instructor terminal 112 or the PwD electronic device 116. The ranked activities 156 are ordered based on the activities with the greatest estimated benefit and likelihood of adherence to the PwD 104. The activity recommendation service software 152 also provides a communication interface with the virtual physiological model service 160 over the network 118 to enable transmission of the PwD physiological data 136 to the virtual physiological model service 160 and to receive the virtual physiological model projections 148. The activity recommendation service software 152 further implements a user interface to receive input and provide output data to either or both of the instructor terminal 112 and the PwD electronic device 116. In the configuration of FIG. 1, the system 100 includes a networked activity recommendation service 120 that implements a remote user interface, such as a web server or other suitable server program, to enable access from either or both of the instructor terminal 112 and the PwD electronic device 116 using a web browser or other suitable client software program that communicates with the system 100 over the network 118.

[0024] In the embodiment of FIG. 1, the virtual physiological model service 160 is a computing system further including a processor 164, a network interface device 168, and a memory 172. The virtual physiological model service 160 receives physiological data about the PwD and uses a virtual physiological model 176 to project the effect of proposed activities that the PwD performs over time in changing at least one physiological characteristic, such as changing average blood glucose levels or body weight. The virtual physiological model service 160 does not directly process information related to activities. Instead, the activity recommendation service 120 generates different physiological profiles 138 for the baseline of the PwD 104 and each proposed activity of the PwD 104. The virtual physiological model service 160 receives from the activity recommendation service 120 different sets of physiological profiles 138 including different levels of any or all of calorie expenditure, calorie metabolism, sleep hygiene, or medication adherence for different activities to generate the projections. Each projection includes an estimate of the change in at least one physiological characteristic over a predetermined time period given the activity, and the virtual physiological model service 160 also generates a baseline projection having an estimate of the at least one physiological characteristic if PwD 104 maintains his / her current activity without performing any of the newly proposed activities.

[0025] In the virtual physiological model service 160, memory 172 stores PwD physiological profile 138, one or more virtual physiological models 176, stored program instructions implementing virtual physiological model service software 180, and one or more virtual physiological model projections 148. PwD physiological profile 138 includes the same data as described above in connection with activity recommendation service 120, and in the embodiment of FIG. 1, virtual physiological model service 160 receives PwD physiological profile 138 from activity recommendation service 120 via network 118.

[0026] In memory 172, virtual physiological model 176 refers to one or more digital models of the human body that simulate physiological processes within the PwD's body to generate projections of estimated changes to physiological characteristics over time. One non-limiting example of a commercially available virtual physiological modeling service that provides virtual physiological models is the Bodylogical Digital Twin service available from PricewaterhouseCoopers, London, UK. In operation, processor 164 executes virtual physiological model service software 180 to run simulations using different sets of PwD physiological profiles 138 as inputs to virtual physiological model 176 to generate virtual physiological model projections 148. PwD physiological profiles 138 provide parameter information for virtual physiological model 176 to use to provide customized projections for PwD 104 or other PwDs, each with different physiological data. During operation, the virtual physiological model service 160 uses the PwD physiological profile 138 as input to the virtual physiological model 176 to generate projections having estimates of changes for at least one physiological characteristic, such as average blood glucose or weight. Each projection corresponds to an estimated state of the PwD generated by the virtual physiological model service software 180 over a given time horizon, such as expected changes in blood glucose, weight, or other physiological characteristic over 30 days, 3 months, 6 months, 1 year, or other selected time period. Additionally, the virtual physiological model service 160 uses the virtual physiological model 176 to generate baseline projections based on the baseline physiological profile. The baseline projections include estimates of at least one physiological characteristic of the PwD 104 based on existing physiological data, assuming that the PwD 104 makes no changes to his / her current activity. In the embodiment of FIG. 1, the virtual physiological model service 160 transmits the generated virtual physiological model projections 148, including the baseline projections and one or more projections generated based on different recommended activities, to the activity recommendation service 120.The activity recommendation service 120 uses the baseline projection and the projections of the different selected activities to identify estimated changes in physiological characteristics relative to the baseline projection and identifies the activities with the greatest potential benefit for the PwD 104.

[0027] Although the system 100 includes an activity recommendation service 120 having a processor 124, those skilled in the art will recognize that the system 100 may be implemented using a single computing system having a single processor or multiple computing systems incorporating multiple processors. For example, alternative implementations of the activity recommendation service 120 use a single computing system or split the functionality described herein across more computing systems. Furthermore, in many practical embodiments, the activity recommendation service 120 is implemented using a cluster of multiple individual computing devices with redundant data storage devices to provide fault tolerance and scalability using clustering techniques commonly known in the art. In an alternative configuration, the system 100 incorporates the functionality of the activity recommendation service 120 and the virtual physiological model service 160 into a single computing system using a single computing device or a cluster of multiple individual computing devices. In FIG. 1, the activity recommendation service 120 stores copies of the virtual physiological model projections 148 that the virtual physiological model service 160 generates and transmits to the activity recommendation service 120 in memory 132, although a single memory may store the physiological model projections 148 in alternative system configurations combining the activity recommendation service 120 and the virtual physiological model service 160. Additionally, while the system 100 is shown as a networked service for illustrative purposes, those skilled in the art will recognize that the system 100 may be implemented entirely within the instructor terminal 112, the PwD electronic device 116, or another individual computing device. As such, any reference to the operation of separate processors performing some of the functions in the system 100 should be understood to be interchangeable with a reference to the operation of a single processor, and vice versa.

[0028] 2 illustrates a process 200 for generating suggested activities that result in a change in a physiological characteristic, such as reducing average blood glucose or weight, in a person with diabetes based on projections and preference data from a virtual physiological model of the person with diabetes. In the following description, references to a process that performs a function or operation refer to the operation of one or more digital processors that execute stored program instructions to perform the function or operation. Process 200 is described with reference to system 100 of FIG. 1 for illustrative purposes.

[0029] Process 200 begins when activity recommendation service 120 receives physiological data for PwD 104 (block 204) and preference data for PwD 104 (block 208), which may be received in any order or simultaneously during process 200. In one configuration, system 100 receives physiological data from an external electronic medical record (EMR) service (not shown) using a standardized medical record system, such as the Fast Healthcare Interoperability Resources (FHIR) standard, the HL7 standard, or another commonly accepted standard exchange system for medical record data. Activity recommendation service 120 stores the EMR data in memory 132 as PwD physiological data 136. Although not required, transfer of EMR data to activity recommendation service 120 often occurs prior to an initial coaching session, and activity recommendation service 120 is configured to store PwD physiological data 136 between coaching sessions to track the history and progress of PwD 104 over time. The automated transfer of EMR data reduces the need for manual data entry, and the PwD 104 provides consent for the data transfer before the system 100 receives any EMR data to comply with applicable medical data privacy regulations. The activity recommendation service 120 may receive EMR data for multiple PwDs to enable identification and prioritization of PwDs to receive coaching services. In another configuration, the system 100 receives at least a portion of the physiological data from the PwD 104 directly via the PwD electronic device 116. In this configuration, the PwD 104 accesses a website or other remote user interface provided by the activity recommendation service 120 to elicit a particular piece of physiological data from the PwD 104. The PwD 104 enters the piece of physiological information using the electronic device 116 and submits answers to survey questions providing preference data prior to the coaching session to reduce the time required to collect preliminary physiological data during the coaching session.In another configuration, the trainer 102 collects either or both of the physiological and preference data during the training session, and the activity recommendation service 120 provides a similar website or remote interface to the trainer terminal 112 to receive the physiological and preference data from the PwD 104. In some configurations, the physiological data 136 includes both EMR data and physiological data received from the PwD electronic device 116 and the trainer terminal 112.

[0030] The process 200 resumes when the system 100 identifies potential activities by receiving a list of suggested activities from the trainer 102 and the PwD 104 or by selecting suggested activities from the activity database 144 (block 212). In one configuration, the trainer 102 and the PwD discuss the potential activities and the trainer 102 enters the selected suggested activities into a user interface of the trainer terminal 112, where each activity corresponds to one of the activities in the activity database 144. In another configuration, the activity recommendation service 120 identifies potential suggested activities for the PwD instead of or in addition to receiving a specific suggested activity from the trainer 102 and the PwD 104 by selecting all activities in the activity database 144 or selecting activities from categories of activities such as diet, exercise, sleep hygiene, and medication adherence categories. As described above, the activity selected from the activity database 144 includes data corresponding to both the type of activity and, if appropriate, the intensity and frequency of performing the activity.

[0031] During process 200, the activity recommendation service 120 generates a physiological profile 138 of the PwD 104 including a baseline physiological profile of the PwD 104 based on the physiological data 136 and the activity physiological profiles each corresponding to the PwD 104 performing one of the suggested activities (block 216). To generate the baseline physiological profile, the activity recommendation service 120 processes the physiological data 136 that corresponds to the PwD 104's current medical condition and incorporates metabolic data about the PwD 104's current dietary and exercise activity, sleep patterns, and medication adherence.

[0032] To generate an activity physiological profile 138 for a proposed exercise and diet activity in the activity database 144, the activity recommendation service 120 identifies either an increase in calorie metabolism in response to the exercise activity or a change in calories burned in response to the diet activity. In some cases, the activity recommendation service 120 further identifies a change in the overall percentage of calories from different macronutrients consumed by the diet, such as identifying a decrease in the percentage of carbohydrate calories consumed compared to protein and fat calories. The activity recommendation service 120 also calculates the total calorie consumption of different exercise activities based on the weight of the PwD 104, as well as the duration, intensity, frequency, and type of each exercise. Thus, in addition to identifying activities corresponding to different exercise types, the activity recommendation service 120 further identifies different activities, each corresponding to a single type of exercise having various levels of intensity, duration, and frequency that affect a corresponding change in calorie metabolism. The activity recommendation service 120 generates each activity physiological profile as a modification of the baseline physiological profile that includes modified parameters that reflect the impact of the proposed activity on the physiology of the PwD 104. Examples of altered physiological parameters in the activity physiological profile 138 include, for example, changes in total calorie metabolism, resting metabolism for motor activity, and total calorie expenditure, along with changes in the proportion of dietary derived macronutrients for dietary activity.

[0033] The activity recommendation service 120 further modifies one or more physiological parameters related to sleep hygiene or medication adherence to generate an activity physiological profile 138 corresponding to the proposed sleep hygiene and medication adherence activities. A sleep hygiene activity recommendation refers to a proposed change in sleep duration, sleep pattern, or sleep quality that the PwD 104 should implement to reduce the negative metabolic effects of insufficient or poor quality sleep. For example, one sleep hygiene activity suggests that the PwD set a regular bedtime and wake-up time each day to establish a consistent sleep pattern. The activity recommendation service 120 modifies the sleep parameters in the baseline physiological profile to include the new sleep hygiene parameters from the activity database 144 to generate an activity physiological profile 138 for the proposed sleep hygiene activity. A medication adherence activity corresponds to a technique for the PwD 104 to follow formal on-label instructions to consistently take a given medication, which is applicable to some PwDs who consume prescribed diabetes medications. By way of non-limiting example, at least some forms of metformin specify meal intake, and the activity database 144 stores activity suggestions for scheduling regular meal times to encourage the habit of taking metformin with scheduled meals to improve medication adherence. The activity recommendation service 120 modifies the medication adherence parameters in the baseline physiological profile, which includes data regarding how frequently the PwD 104 actually uses the medication as indicated on the label, to include the new medication adherence parameters from the activity database 144 to generate an activity physiological profile 138 for the suggested medication adherence activities.

[0034] The process 200 resumes when the activity recommendation service 120 provides the PwD physiological profiles 138 to the virtual physiological model service 160, which generates a projection of change in at least one physiological characteristic of the PwD 104 over time in each of the PwD physiological profiles 138 using the virtual physiological models 176 (block 220). In the system 100, the activity recommendation service 120 transmits the PwD physiological profiles 138 for the PwD 104 to the virtual physiological model service 160 over the network 118 using the network interface 128. The memory 172 of the virtual physiological model service 160 stores the PwD physiological profiles 138 as input to the virtual physiological models 176. As described above, the PwD physiological profile 138 includes a baseline profile that the system 100 collects for the PwD 104 using current diet, exercise, sleep hygiene, and medication adherence activities without any modifications to the activities for the PwD 104. Each of the other PwD physiological profiles 138 includes modified physiological data corresponding to each activity, including changes to metabolic or other physiological parameters that would occur if the PwD 104 were to perform one of the suggested activities. A processor 164 within the virtual physiological model service 160 executes the virtual physiological model service software 180 to apply each PwD physiological profile 138 to a corresponding virtual physiological model 176 to generate a virtual physiological model projection 148. The virtual physiological model service 160 transmits the virtual physiological model projection 148 to the activity recommendation service 120 over the network 118 using the network interface device 168. As shown in FIG. 1, the corresponding network interface 128 of the activity recommendation service 120 receives the virtual physiological model projection 148 and the processor 124 stores a copy of the virtual physiological model projection 148 in the memory 132 .

[0035] More specifically, the virtual physiological model service 160 runs a simulation for each PwD physiological profile 138 to generate a baseline projection including estimates of PwD 104's physiological characteristics over time for the baseline physiological projection and an activity projection for each of the PwD activity physiological profiles 138. For example, the virtual physiological model service 160 runs a simulation that generates a baseline projection that estimates PwD 104's HbA1c at 6.8% after 6 months given the baseline PwD physiological profile 138 for PwD 104. However, if PwD 104 performs a proposed exercise activity, the virtual physiological model service 160 runs another simulation with a corresponding activity physiological profile including a different set of physiological parameters with increased caloric metabolism due to the activity, which results in a different estimated HbA1c level, for example, 6.3%. The virtual physiological model service 160 runs similar simulations based on the different PwD physiological profiles 138 for each of the proposed activities. The activity recommendation service 120 measures an estimated change in the physiological characteristic for each activity based on the difference in the physiological characteristic in the baseline projection compared to its estimated counterpart in each activity projection corresponding to the proposed activity. In addition to generating an estimate of PwD's 104 average blood glucose level that correlates to HbA1c level, the virtual physiological model service 160 also generates estimates of changes in weight and other physiological characteristics of interest in assisting PwD 104 in managing his / her diabetes.

[0036] The process 200 resumes when the activity recommendation service 120 generates weight values ​​based on the PwD preference data 140 (block 224). Generally, each weight value is a numeric value in a predetermined range (e.g., 0.0 to 1.0, or any other suitable range) that corresponds to the likelihood that the PwD 104 will consistently perform a given activity. For example, activities that are more likely to be performed may be assigned a higher numeric value corresponding to a higher weighting of the selected activity. In one configuration, the activity recommendation service 120 generates the numeric weight values ​​from the preference data using an empirical weighting system based on the post-mortem outcomes of a large number of PwDs with similar preferences to the PwD 104. For example, the activity recommendation service 120 uses a clustering algorithm or other suitable classification algorithm to find groups of PwDs whose historical data is stored in the preference data 140 in the memory 132 or in an external database that represents a cohort of PwDs with similar preferences to the PwD 104. The activity recommendation service 120 then identifies the levels of adherence to different activities in the PwD physiological data 136 and other records for the cohort of PwDs, which provides a record with a posteriori outcomes of how consistent PwDs with similar preferences as PwD 104 actually perform different activities. In this example, the numerical weighting values ​​may be generated directly from the percentage of PwDs who successfully adhered to each activity based on the posteriori data, although other numerical weighting systems may be used as well. The generation of the projections described above with reference to the processing of block 220 and the generation of the weighting values ​​based on PwD preference data 140 may occur in any order or simultaneously during the process 200.

[0037] The process 200 resumes when the activity recommendation service 120 ranks the selected activities based on the estimated change in at least one physiological characteristic from the virtual physiological projection data 148 and the weight value corresponding to the user preference data 140 of the PwD 104 (block 228). The activity recommendation service 120 identifies the change in the at least one physiological characteristic by comparing a baseline projection corresponding to the PwD baseline physiological profile with a corresponding activity projection of the activity physiological profile associated with each proposed activity. In one configuration, the activity recommendation service 120 generates the ranking by multiplying the weight value by a numerical amount of change in the physiological characteristic of the activity relative to the baseline projection to generate a ranking score that considers both the potential benefit of performing the activity and the likelihood that the PwD 104 will actually perform the activity consistently. For example, consider activities A and B related to losing weight as physiological characteristics. Activity A results in an estimated weight loss of 5 kg relative to the baseline projection with a user preference weight score of 0.7, and activity B results in an estimated weight loss of 7 kg relative to the baseline projection with a user preference weight score of 0.4. Using the multiplicative scaling factors, activity A has a ranking score of 0.7 x 5 kg = 3.5 kg (approximately) and activity B has a ranking score of 0.4 x 7 kg = 2.8 kg (approximately). In this example, even if activity B was predicted to have a greater weight loss if PwD 104 was actually performed consistently, activity A has a higher ranking score due to the scaling of the preference weight values. Although the embodiment described above multiplies the weight values ​​by a numerical value corresponding to the change in the physiological characteristic to generate the ranking score, other scaling operations may be used as well. For example, in another scaling configuration, the preference weight score of an activity must exceed a predetermined threshold to be considered for recommendation to PwD 104. If multiple activities exceed the predetermined weight threshold, the ranking algorithm ranks the remaining activities based on the largest estimated improvement to the physiological characteristic to generate the ranked result.Yet other embodiments perform different scaling operations to rank activities based on the estimated changes in physiological characteristics from the virtual physiological model data 148 and weight values ​​from the preference data 140 .

[0038] In some embodiments, the activity recommendation service 120 optionally filters any activities from the ranking process where the estimated change in the physiological characteristic would result in an estimated outcome that is undesirable for the PwD 104. For example, if an activity is determined to lower HbA1c beyond a maximum threshold that is considered healthy for the PwD 104, the activity recommendation service 120 filters the activity and does not generate a recommendation for the activity even if the process 200 resulted in a high ranking score for the activity. Similarly, the activity recommendation service 120 filters activities that result in weight loss that is considered too great to be healthy for the PwD 104. The filtering process may also be applied to remove any activities that are estimated to result in a worse outcome for the physiological characteristic of the PwD 104 compared to the estimate in the baseline projection, such as an undesirable increase in HbA1c or weight gain. Although physiological parameters for many PwDs may decline from optimal levels over time as their diabetes progresses, even when the PwD 104 performs an activity, the activity recommendation service 120 still filters activity recommendations using projections that yield worse estimated outcomes than the baseline projections.

[0039] The process 200 resumes when the system 100 generates an output including one or more of the suggested activities starting with the highest ranked activity identified as the one or more suggested activities that will result in the greatest change in physiological characteristics, taking into account the likelihood of the person with diabetes being adherent to the activity (block 232). The suggested activities provide a basis for the trainer 102 to consult with the PwD 104 to set goals for performing activities that the PwD is likely to consistently perform to achieve improvements in average blood glucose levels and body weight. In the configuration of FIG. 1, the activity recommendation service 120 transmits one or more of the potential activities to the trainer terminal 112, the PwD electronic device 116, or both, which function as output devices operatively connected to a processor 124 in the activity recommendation service 120 via a network 118. In one configuration, the activity recommendation service 120 generates only an output having the single highest ranked recommended activity. In another configuration, the activity recommendation service 120 generates an output including the highest ranked activity recommendations in two or more categories, such as generating an output having the highest ranked exercise activity and the highest ranked dietary activity recommendation. In yet another embodiment, the activity recommendation service 120 provides an output having a number of activity recommendations ranked from best to worst for the trainer 102 and the PwD 104 to incorporate into a diabetes treatment plan. In addition to ranking the activities, the output of each ranked activity optionally includes an estimated change in physiological characteristic from the virtual physiological model projection data 148 and a weighting value or other metric that provides an estimate of the likelihood that the PwD 104 will adhere to the activity based on the PwD preference data 140.

[0040] While the embodiments of the system 100 and method 200 described above provide activity recommendations to the instructor 102 and the PwD 104 as part of a coaching session, one skilled in the art will recognize that the PwD 104 may utilize the system 100 and method 200 directly using the PwD electronic device 116. For example, in one embodiment, the PwD 104 uses the PwD electronic device 116 to execute a web browser or other client program that accesses the system 100. The PwD electronic device 116 provides physiological and preference data to the system 100 to enable the PwD 104 to receive ranked recommendations for one or more activities from the system 100.

[0041] The present disclosure will be described in connection with what are believed to be the most practical and preferred embodiments. However, these embodiments are presented by way of example and are not limited to the disclosed embodiments. Accordingly, those skilled in the art will understand that the present disclosure encompasses all modifications and alternative arrangements within the spirit and scope of the present disclosure and as set forth in the following claims.

Claims

1. 1. A method for generating activity recommendations in a diabetes care plan, comprising: receiving, with a processor, physiological data for a person with diabetes (PwD), preference data for the PwD, and a plurality of suggested activities for the PwD; generating, with the processor, a plurality of physiological profiles for the PwD, the plurality of physiological profiles comprising: a baseline physiological profile based on the physiological data for the PwD; and Multiple activity physiological profiles and generating a plurality of physiological profiles for the PwD, each activity physiological profile corresponding to one of the plurality of suggested activities, each activity physiological profile based on the physiological data for the PwD and a modification of the physiological data associated with the one of the plurality of suggested activities corresponding to the activity physiological profile; providing, with the processor, the plurality of physiological profiles to a virtual physiological model; receiving, with the processor, a plurality of projections for the PwD from the virtual physiological model, each projection of the plurality of projections providing an estimated change in a physiological characteristic of the PwD during a predetermined time period corresponding to a physiological profile of the plurality of physiological profiles; generating, with the processor, a plurality of weighted values ​​based on the plurality of suggested activities and the preference data, each weighted value corresponding to a likelihood that the PwD will adhere to a corresponding one of the suggested activities; ranking, with the processor, each activity among the plurality of proposed activities based on the estimated change in the physiological characteristic of a projection among the plurality of projections associated with the activity relative to a baseline physiological projection among the plurality of projections corresponding to the baseline physiological profile, the estimated change scaled by the weighted value corresponding to each activity; generating, with the processor, an output including a predetermined number of the plurality of suggested activities in an order based on the ranking, taking into account the likelihood that the PwD will adhere to the suggested activities, to identify one or more suggested activities that result in the greatest change in the physiological characteristic; A method comprising:

2. 10. The method of claim 1, wherein the plurality of suggested activities further comprises at least one suggested exercise, at least one suggested dietary change, at least one suggested sleep hygiene change, or at least one recommendation for medication compliance.

3. The preference data includes: Numerical data corresponding to responses to predetermined survey questions received from said PwD; geographic data corresponding to the PwD's home location; demographic information of said PwD; The method of claim 1 or 2, further comprising:

4. 3. The method of claim 1 or 2, wherein the estimated change in the physiological characteristic is an estimated change in the percentage of hemoglobin A1c (HbA1c) in the blood of the PwD.

5. 5. The method of claim 4, further comprising: using the processor to filter certain activities of the plurality of suggested activities such that the certain activities are not generated in the output in response to an estimated HbA1c decline for the activity that exceeds a maximum HbA1c decline threshold for the PwD.

6. The method of claim 1 or 2, wherein the estimated change in the physiological characteristic is an estimated change in body weight.

7. The method of claim 1 or 2, wherein the plurality of suggested activities for the PwD are received over a network from at least one of a trainer's terminal or an electronic device of the PwD.

8. The method of claim 1 or 2, wherein the plurality of suggested activities for the PwD is received from a database that stores all recognized suggested activities.

9. The method of claim 1 or 2, wherein the preference data for the PwD is received over a network from at least one of a trainer's terminal or an electronic device of the PwD.

10. 3. The method of claim 1 or 2, wherein the predetermined period of time is one of 30 days, 3 months, 6 months, or 1 year.

11. 1. A system for generating activity recommendations, comprising: Memory, A network interface device; a processor operatively connected to the memory and to the network interface device, storing in the memory physiological data for a person with diabetes (PwD), preference data for the PwD, and a plurality of suggested activities for the PwD; generating a plurality of physiological profiles for the PwD, the plurality of physiological profiles comprising: a baseline physiological profile based on the physiological data for the PwD; and Multiple activity physiological profiles and generating a plurality of physiological profiles for the PwD, each activity physiological profile corresponding to one of the plurality of suggested activities, each activity physiological profile based on the physiological data for the PwD and a modification of the physiological data associated with the one of the plurality of suggested activities corresponding to the activity physiological profile; transmitting, using the network interface device, the plurality of physiological profiles to a virtual physiological model service; receiving, with the network interface device, a plurality of projections for the PwD from the virtual physiological model service, each projection of the plurality of projections providing an estimated change in a physiological characteristic of the PwD during a predetermined time period corresponding to a physiological profile of the plurality of physiological profiles; generating a plurality of weighted values ​​based on the plurality of suggested activities and the preference data, each weighted value corresponding to a likelihood that the PwD will adhere to a corresponding one of the suggested activities; ranking each activity among the plurality of proposed activities based on the estimated change in the physiological characteristic of a projection among the plurality of projections associated with the activity relative to a baseline physiological projection among the plurality of projections corresponding to the baseline physiological profile, the estimated change scaled by the weighted value corresponding to each activity; generating an output including a predetermined number of the plurality of suggested activities in an order based on the ranking, taking into account the likelihood that the PwD will adhere to the suggested activities, to identify one or more suggested activities that result in the greatest change in the physiological characteristic; a processor configured to: A system comprising:

12. The system of claim 11 , wherein the processor is further configured to transmit the output to at least one of an instructor's terminal or an electronic device of the PwD using the network interface device.

13. 13. The system of claim 11 or 12, wherein the plurality of suggested activities further comprises at least one suggested exercise, at least one suggested dietary change, at least one suggested sleep hygiene change, or at least one recommendation for medication compliance.

14. The preference data includes: Numerical data corresponding to responses to predetermined survey questions received from said PwD; geographic data corresponding to the PwD's home location; demographic information of said PwD; The system of claim 11 or 12, further comprising:

15. 13. The system of claim 11 or 12, wherein the estimated change in the physiological characteristic is an estimated change in the percentage of hemoglobin A1c (HbA1c) in the blood of the PwD.

16. 16. The system of claim 15, wherein the processor is further configured to filter certain activities of the plurality of suggested activities such that the certain activity is not generated in the output in response to an estimated HbA1c decline for the activity that exceeds a maximum HbA1c decline threshold for the PwD.

17. The system of claim 11 or 12, wherein the estimated change in the physiological characteristic is an estimated change in body weight.

18. The system of claim 11 or 12, wherein the plurality of suggested activities for the PwD are received over a network from at least one of a trainer's terminal or an electronic device of the PwD.

19. 13. The system of claim 11 or 12, wherein the memory is further configured to store a database of all recognized suggested activities for the PwD.

20. The system of claim 11 or 12, wherein the preference data for the PwD is received over a network from at least one of a trainer's terminal or an electronic device of the PwD.

21. 13. The system of claim 11 or 12, wherein the predetermined period of time is one of 30 days, 3 months, 6 months, or 1 year.

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