Patent Publication Number: US-11653860-B2

Title: Recommendations based on continuous glucose monitoring

Description:
RELATED APPLICATION 
     Any and all priority claims identified in the Application Data Sheet, or any correction thereto, are hereby incorporated by reference under 37 CFR 1.57. This application claims the benefit of U.S. Provisional Patent Application No. 62/940,715, filed Nov. 26, 2019. The aforementioned application is incorporated by reference herein in its entirety, and is hereby expressly made a part of this specification. 
    
    
     BACKGROUND 
     Diabetes is a metabolic condition affecting hundreds of millions of people, and is one of the leading causes of death worldwide. For people living with diabetes, access to treatment is critical to their survival. With proper treatment, serious damage to the heart, blood vessels, eyes, kidneys, and nerves, due to diabetes can be largely avoided. Proper treatment for a person with Type I diabetes oftentimes involves monitoring glucose levels throughout the day and regulating those levels—with some combination of insulin, eating, and exercise—so that the levels stay within a desired range. With advances in medical technologies a variety of systems for monitoring glucose levels have been developed. 
     Some of these systems include assemblies for pricking a body part of a person (e.g., the person&#39;s finger in many cases) to draw blood and also sensors for detecting analytes in the drawn blood indicative of a glucose level. Other systems detect analytes indicative of glucose levels with sensors in substantially real-time and produce measurements of those glucose levels over a period of time—referred to as continuous glucose monitoring (CGM). Both types of systems are configured to output (e.g., display) these measurements so that users can decide how to regulate their glucose levels, if necessary and based on a plan formulated with a qualified caregiver. The sheer volume of glucose measurements produced and output by CGM systems shows users how their glucose levels have been trending and enables them to make better informed decisions regarding treatment. 
     SUMMARY 
     Recommendations based on continuous glucose monitoring (CGM) are described herein. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a CGM platform that provides a CGM system with a sensor for detecting glucose levels, and maintains measurements produced by such a system may have an enormous amount of data, e.g., tens of millions of patient days&#39; worth of measurements. However, this amount of data is practically, if not actually, impossible for a human to process to reliably identify patterns not only in the glucose measurements but also in connection with a wealth of additional data, which can be correlated with the glucose measurements to accurately predict various conditions, e.g., health indicators. 
     In one or more implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system worn by a user. The data analytics platform also obtains additional data associated with the user. However, the data analytics platform obtains the additional data from one or more sources different from a sensor of the CGM system, such as from a computing device that processes the glucose measurements before communication to the CGM platform or a third party that provides a device or service capable of producing health-related information, e.g., insulin data, exercise data, diet data, and so on. 
     The data analytics platform processes these glucose measurements and the additional data to predict a health indicator for the user by using one or more models, e.g., a statistical model, a machine learning model configured as a neural network, or other machine learning models. The data analytics platform generates these models based on historical glucose measurements and historical additional data of a user population, e.g., a plurality of users that also wear or have worn the CGM system. Based on the predicted health indicator, the data analytics platform generates a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition. The data analytics platform then communicates at least one of the prediction or the recommendation over a network to one or more computing devices for output, such as a computing device associated with the user (e.g., a mobile phone or smart watch), a computing device associated with a guardian (e.g., parent) of the user, a computing device associated with a validation service (accessible to health care professionals authorized to validate the recommendations), and so on. 
     This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description. As such, this Summary is not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. 
         FIG.  1    is an illustration of an environment in an example implementation that is operable to employ techniques described herein. 
         FIG.  2    depicts an example of the continuous glucose monitoring (CGM) system of  FIG.  1    in greater detail. 
         FIG.  3    depicts an example implementation in which CGM device data, including glucose measurements, is routed to different systems to enable provision of CGM-related services. 
         FIG.  4    depicts an example implementation of the data analytics platform of  FIG.  1    in greater detail. 
         FIG.  5    depicts an example of an implementation in which at least one of predictions or recommendations produced by the data analytics platform are routed to at least one of a validation service or decision support platform. 
         FIG.  6    depicts an example implementation of a user interface of the CGM platform displayed on a computing device coupled to a CGM system. 
         FIG.  7    depicts an example implementation of the user interface outputting an updated prediction and an updated recommendation. 
         FIG.  8    depicts another example implementation of a user interface outputting a prediction and recommendation for supporting diabetes treatment decisions. 
         FIG.  9    depicts an example implementation of the user interface outputting information about a health trend. 
         FIG.  10    depicts an example implementation of a user interface of a validation service with which an approved user can interact to validate recommendations generated by a CGM platform. 
         FIG.  11    depicts an example implementation of a user interface that outputs information about detected faults and system configuration issues in connection with use of the CGM platform. 
         FIG.  12    depicts a procedure in an example implementation in which a prediction and a recommendation are generated based on both glucose measurements and additional data of a user. 
         FIG.  13    depicts a procedure in an example implementation in which a recommendation to use a particular application is communicated to one or more devices of a similar user. 
         FIG.  14    illustrates an example system including various components of an example device that can be implemented as any type of computing device as described and/or utilized with reference to  FIGS.  1 - 13    to implement embodiments of the techniques described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Recommendations based on continuous glucose monitoring (CGM) are described herein. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a CGM platform that provides a CGM system with a sensor for detecting glucose levels, and maintains measurements produced by such a system may have an enormous amount of data, e.g., tens of millions of patient days&#39; worth of measurements. However, this amount of data is practically, if not actually, impossible for a human to process to reliably identify patterns not only in the glucose measurements but also in connection with a wealth of additional data, which can be correlated with the glucose measurements to accurately predict various conditions, e.g., health indicators. 
     To overcome these problems, prediction generation with CGM is leveraged. A CGM platform obtains glucose measurements from various CGM systems and computing devices of users in a user population. In accordance with the described techniques, a CGM system is configured to monitor blood glucose of a person continuously. The CGM system may be configured with a CGM sensor, for instance, that is inserted subcutaneously into skin of a person and detects analytes indicative of the person&#39;s blood glucose. The CGM system can generate glucose measurements based on the detected analytes continuously. As used herein, the term “continuously” means near-continuously, such that continuous glucose monitoring produces measurements at intervals of time that are supported by resources of a CGM system (e.g., battery life, processing capabilities, communication capabilities, etc.) and without requiring manual interaction of a user such as finger pricks. By monitoring glucose levels continuously, the CGM system not only allows users to make better informed decisions about their treatment but also continues to monitor glucose levels while allowing them to participate in activities where manually pricking a finger could be dangerous, e.g., driving a car. 
     The CGM system transmits glucose measurements to a computing device that is communicatively coupled to the CGM system, such as a smart watch worn by the person, the person&#39;s smartphone, or a dedicated device associated with the CGM system. The CGM system may communicate the glucose measurements in real-time, at set time intervals, or responsive to a request from the computing device. The computing device then provides the glucose measurements to the CGM platform, such as by communicating the glucose measurements over a network to a cloud-based service that hosts the CGM platform. 
     The CGM platform may also obtain additional data of users in the user population which originate from various devices, sensors, applications, or services. The additional data may include, by way of example and not limitation, health-related data, application interaction data, environmental data, demographic data, device data in addition to the glucose measurements (e.g., sensor identification data, incident reports), supplemental data added by the computing device, third party data, and so forth. Health-related data may include activity data (e.g., steps, exercise frequency, sleep data), biometric data (e.g., insulin level, ketone levels, heart rate, temperature, stress), nutrition data (e.g., food and drink logs, scanned restaurant receipts, carbohydrate consumption, fasting), medical records (e.g., A1C, cholesterol, electrocardiogram results, and data related to other medical tests or history), to name just a few. Application interaction data may include data extracted from application logs describing user interactions with particular applications, clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the computing device, gaze data describing where a user is looking (e.g., in relation to a display device associated with the computing device or when the user is looking away from the device), voice data describing audible commands and other spoken phrases of the user or other users (e.g., including passively listening to users), and so forth. Environmental data may include data describing various environmental aspects associated with the user, such as the user&#39;s location, a temperature and/or weather at the user&#39;s location, altitude of the user, barometric pressure, and so forth. Demographic data may include data describing the user, such as age, sex, height, weight, and so forth. The above-discussed types of additional data are merely examples and the additional data may include more, fewer, or different types of data without departing from the spirit or scope of the techniques described herein. 
     The CGM platform stores and aggregates glucose measurements and additional data collected from the various respective users of the user population. In some cases, the glucose measurements and the additional data may be time stamped which enables the glucose measurements and additional data of a respective users to be stored in a way which maintains a time-based relationship, or sequence, between the various pieces of data. This allows the CGM platform to make a variety of different predictions and inferences based on distinct data sets which have simply not been analyzed together at such a massive scale by conventional systems. 
     In order to generate predictions and inferences using the aggregated data, the CGM platform leverages the wealth of aggregated data maintained by the CGM platform to build various models, such as statistical model, a machine learning model configured as a neural network, and/or other statistical model. For instance, the system can build statistical models, build other machine learning models, train the other machine learning models (or otherwise learn a policy deployed by such machine learning models), and update these models using the glucose measurements and the additional data of the user population. 
     Notably, unlike conventional systems, the CGM platform may have access to glucose measurements obtained using the CGM system for hundreds of thousands of users of the user population (e.g., 500,000 or more). Moreover, these measurements are taken by sensors of the CGM system at a continuous rate. As a result, the glucose measurements available to the system for model building and training may number in the millions, or even billions. With such a robust amount of data, the system can build and train the models to accurately mimic real-life effects of different behaviors on glucose levels. Absent the robustness of this aggregated data, conventional systems simply cannot build or train models to cover state spaces in a manner that suitably represents how various user behaviors and actions affect glucose levels. Failure to suitably cover these state spaces can result in glucose predictions or predictions of other health indicators that are inaccurate, which can lead to recommending unsafe actions or behaviors that could cause death. Given the gravity of generating inaccurate predictions, it is important to build the models using an amount of glucose measurements that is robust against rare events. 
     The CGM platform uses the models built and/or trained using the aggregated data in order to generate various predictions for users wearing the CGM system, as well as recommendations to improve predicted health condition. The predictions may correspond to or otherwise include health indicators. As used herein, the term “health indicator” may refer to a predicted health condition, which can be “negative” or “positive.” Examples of negative health conditions, for example, include pre-diabetes, Type I diabetes, Type II diabetes, neuropathy, Alzheimer&#39;s disease, and heart disease, to name just a few. In contrast, examples of “positive” health conditions, may include improved bloodwork, body composition, cardiovascular capacity, and so forth. 
     Moreover, predictions generated by the system may include generalized predictions or trends for the user population as a whole (e.g., drinking soda causes high blood glucose spikes which results in long term neuropathy, or eating a low carb diet lowers A1C), as well as specific predictions for individual users. For example, the system can apply a trained machine learning model to an individual user&#39;s glucose measurements and additional data over a particular time period in order to generate a user-specific prediction of a health indicator or event for the user, such as by predicting that the user will develop Type II diabetes or heart disease in the future. The system may generate an accuracy or probability associated with the prediction, as well as a time period associated with the prediction (e.g., 75% chance of developing Type II diabetes within 40 months). In some cases, the system may also generate predictions for individual users based on real-time data in order to generate short-term predictions. For example, a trained model may be applied to glucose measurements, heart rate, insulin level, and the like in real-time as the data is being captured in order to generate a predicted blood glucose level of the user in the near future (e.g., the next thirty minutes). 
     Based on these predictions, the CGM platform generates various recommendations. In some cases, a recommendation is generated based on logic that associates a predicted negative health condition with one or more actions or behaviors that mitigate the predicted negative health condition (e.g., reduce the probability of occurrence of the negative health condition). As such, the recommendation may include the one or more actions or behaviors intended to mitigate the predicted negative health condition. The recommendation, for instance, may instruct a user to perform an action (e.g., download an app to the computing device, seek medical attention immediately, dose insulin, go for a walk, consume a particular food or drink), continue a behavior (e.g., continue eating a certain way or exercising a certain way), change a behavior (e.g., change eating habits or exercise habits), and so on. 
     For example, based on the prediction that the user&#39;s blood glucose level will rise to a hyperglycemic level in the next 30 minutes, the CGM platform may generate a recommendation that includes actions intended to lower the user&#39;s blood glucose level, such as by recommending that the user dose insulin or go for a brisk walk. Conversely, based on a prediction that the user&#39;s glucose will decrease to a hypoglycemic level overnight, the CGM platform may generate a recommendation that the user eat a banana before going to sleep in order to keep the user&#39;s blood sugar level above the hypoglycemic level. As another example, based on a prediction that the user will develop Type II diabetes within 40 months, the CGM platform may generate a recommendation to adjust the user&#39;s diet or increase activity levels. 
     The predictions and recommendations generated by the CGM platform may be provided directly to the user, or to other parties or platforms associated with the user, such as a health care provider, a family member, third party services, and so forth. Such predictions and recommendations, for example, may be communicated to the user or other parties as electronic communications (e.g., email messages or text messages), notifications (e.g., in-app or on-device notifications), or uploaded to secure platforms or websites accessible via credentials. 
     In accordance with various implementations, the CGM platform includes one or more application programming interfaces (API&#39;s) to enable the communication of glucose measurements and additional data back-and-forth between the CGM platform and one or more third parties. Such API&#39;s may include an “egress” API which enables glucose measurements to be communicated from the CGM platform to various third parties which provide applications and services that utilize the glucose measurements collected by the CGM system. For example, users may be able to download such third party applications, and authorize these third party applications to access the user&#39;s glucose measurements. Doing so enables third party applications to leverage the glucose measurements in a variety of different ways to improve the user&#39;s health. In this way, third party service providers may be able to provide various services that use the glucose measurements, even though such third party service providers may not manufacture and deploy their own CGM systems. 
     The CGM platform may also include an “ingress” API which enables the CGM platform to receive “third party” data from the third party service providers. Such third party data may include application interaction data describing user interactions with third party services or applications. The CGM platform can aggregate the application interaction data, along with the user&#39;s glucose measurements and other data in order to determine whether the interaction with a particular application is improving the user&#39;s health. Based on this, the CGM platform may recommend that other users of the user population also utilize the particular application. 
     As part of this, the system may collect demographic data of a particular user, such as age, gender, location, and so forth. The glucose measurements collected from the user can be combined with the demographic data and additional data in order to generate a similarity score with other users in the user population. For example, a user who is 22 years old, female, has a mean glucose of 162 mg/dL, and experiences patterns of nighttime low glucose measurements, may have a high similarity score with other users in that age, gender, mean glucose measurement, and pattern experience. In this scenario, recommendations to utilize a particular application may be based on the user&#39;s similarity to other users in the population. For instance, if use of the particular application improves the glycemia of a subset of users in the user population, then the CGM platform can recommend use of the particular application to similar users in the user population. 
     In the following discussion, an example environment is first described that may employ the techniques described herein. Example implementation details and procedures are then described which may be performed in the example environment as well as other environments. Performance of the example procedures is not limited to the example environment and the example environment is not limited to performance of the example procedures. 
     Example Environment 
       FIG.  1    is an illustration of an environment  100  in an example implementation that is operable to employ recommendations based on continuous glucose monitoring (CGM) as described herein. The illustrated environment  100  includes person  102 , who is depicted wearing a CGM system  104 , insulin delivery system  106 , and computing device  108 . The illustrated environment  100  also includes other users in a user population  110  of the CGM system, CGM platform  112 , and Internet of Things  114  (IoT  114 ). The CGM system  104 , insulin delivery system  106 , computing device  108 , user population  110 , CGM platform  112 , and IoT  114  are communicatively coupled, one to another, via a network  116 . 
     Alternately or additionally, one or more of the CGM system  104 , the insulin delivery system  106 , and the computing device  108  may be communicatively coupled in other ways, such as using one or more short range communication protocols or techniques. For example, the CGM system  104 , the insulin delivery system  106 , and the computing device  108  may communicate with one another using one or more of Bluetooth, near-field communication (NFC), 5G, and so forth. The CGM system  104 , the insulin delivery system  106 , and the computing device  108  may leverage these types of communication to form a closed-loop system between one another. In this way, the insulin delivery system  106  may deliver insulin based on glucose predictions computed in real-time (e.g., by the computing device  108 ) as glucose measurements are obtained by the CGM system  104 . 
     In accordance with the described techniques, the CGM system  104  is configured to monitor glucose of the person  102  continuously. The CGM system  104  may be configured with a CGM sensor, for instance, that continuously detects analytes indicative of the person  102 &#39;s glucose and enables generation of glucose measurements. In the illustrated environment  100  these measurements are represented as glucose measurements  118 . This functionality along with further aspects of the CGM system  104 &#39;s configuration are discussed in more detail in relation to  FIG.  2   . 
     In one or more implementations, the CGM system  104  transmits the glucose measurements  118  to the computing device  108 , such as via Bluetooth. The CGM system  104  may communicate these measurements in real-time, e.g., as they are produced using a CGM sensor. Alternately or in addition, the CGM system  104  may communicate the glucose measurements  118  to the computing device  108  at set time intervals, e.g., every 30 seconds, every minute, every hour, every 6 hours, every day, and so forth. Further still, the CGM system  104  may communicate these measurements responsive to a request from the computing device  108 , e.g., communicated to the CGM system  104  when the computing device  108  causes display of a user interface having information about the person  102 &#39;s glucose level, updates such a display, predicts the person  102 &#39;s upcoming glucose level for the purpose of delivering insulin, and so forth. Accordingly, the computing device  108  may maintain the glucose measurements  118  of the person  102  at least temporarily, e.g., in computer readable storage media of the computing device  108 . 
     Although illustrated as a wearable device (e.g., a smart watch), the computing device  108  may be configured in a variety of ways without departing from the spirit or scope of the described techniques. By way of example and not limitation, the computing device  108  may be configured as a different type of mobile device (e.g., a mobile phone or tablet device). In one or more implementations, the computing device  108  may be configured as a dedicated device associated with the CGM platform  112 , e.g., with functionality to obtain the glucose measurements  118  from the CGM system  104 , perform various computations in relation to the glucose measurements  118 , display information related to the glucose measurements  118  and the CGM platform  112 , communicate the glucose measurements  118  to the CGM platform  112 , and so forth. In contrast to implementations where the computing device  108  is configured as a mobile phone, however, the computing device  108  may not include some functionality available with mobile phone or wearable configurations when configured as a dedicated CGM device, such as the ability to make phone calls, camera functionality, the ability to utilize social networking applications, and so on. 
     Additionally, the computing device  108  may be representative of more than one device in accordance with the described techniques. In one or more scenarios, for instance, the computing device  108  may correspond to both a wearable device (e.g., a smart watch) and a mobile phone. In such scenarios, both of these devices may be capable of performing at least some of the same operations, such as to receive the glucose measurements  118  from the CGM system  104 , communicate them via the network  116  to the CGM platform  112 , display information related to the glucose measurements  118 , and so forth. Alternately or in addition, different devices may have different capabilities that other devices do not have or that are limited through computing instructions to specified devices. In the scenario where the computing device  108  corresponds to a separate smart watch and a mobile phone, for instance, the smart watch may be configured with various sensors and functionality to measure a variety of physiological markers (e.g., heartrate, breathing, rate of blood flow, and so on) and activities (e.g., steps) of the person  102 . In this scenario, the mobile phone may not be configured with these sensors and functionality or may include a limited amount of that functionality—although in other scenarios a mobile phone may be able to provide the same functionality. Continuing with this particular scenario, the mobile phone may have capabilities that the smart watch does not have, such as an amount of computing resources (e.g., battery and processing speed) that enables the mobile phone to more efficiently carry out computations in relation to the glucose measurements  118 . Even in scenarios where a smart watch is capable of carrying out such computations, computing instructions may limit performance of those computations to the mobile phone so as not to burden both devices and to utilize available resources efficiently. To this extent, the computing device  108  may be configured in different way and represent different numbers of devices than discussed herein without departing from the spirit and scope of the described techniques. 
     As mentioned above, the computing device  108  communicates the glucose measurements  118  to the CGM platform  112 . In the illustrated environment  100 , the glucose measurements  118  are shown stored in storage device  120  of the CGM platform  112  as part of CGM data  122 . The storage device  120  may represent one or more databases and also other type of storage capable of storing the CGM data  122 . The CGM data  122  also includes user profile  124 . In accordance with the described techniques, the person  102  corresponds to a user of at least the CGM platform  112  and may also be a user of one or more other, third party service providers. To this end, the person  102  may be associated with a username and be required, at some time, to provide authentication information (e.g., password, biometric data, and so forth) to access the CGM platform  112  using the username. This information may be captured in the user profile  124 . The user profile  124  may also include a variety of other information about the user, such as demographic information describing the person  102 , information about a health care provider, payment information, prescription information, determined health indicators, user preferences, account information for other service provider systems (e.g., a service provider associated with a wearable, social networking systems, and so on), and so forth. The user profile  124  may include different information about a user within the spirit and scope of the described techniques. 
     Further, the CGM data  122  not only represents data of a user that corresponds to the person  102 , but also represents data of the other users in the user population  110 . Given this, the glucose measurements  118  in the storage device  120  include the glucose measurements from a CGM sensor of the CGM system  104  worn by the person  102  and also include glucose measurements from CGM sensors of CGM systems worn by persons corresponding to the other users in the user population  110 . It follows also that the glucose measurements  118  of these other users are communicated by their respective devices via the network  116  to the CGM platform  112  and that these other users have respective user profiles  124  with the CGM platform  112 . 
     The data analytics platform  126  represents functionality to process the CGM data  122  to generate a variety of predictions, such as by using various machine learning models. Based on these predictions, the CGM platform  112  may provide recommendations and/or other information about the predictions. For instance, the CGM platform  112  may provide the recommendations or other information directly to the user, to a medical professional associated with the user, and so forth. The specific types of predictions, recommendations, and other information are described in more detail below. Although depicted as separate from the computing device  108 , portions or an entirety of the data analytics platform  126  may alternately or additionally be implemented at the computing device  108 . The data analytics platform  126  is also configured to generate these predictions using data in addition to the glucose measurements  118 , such as additional data obtained via the IoT  114 . 
     It is to be appreciated that the IoT  114  represents various sources capable of providing data that describes the person  102  and the person  102 &#39;s activity as a user of one or more service providers and activity with the real world. By way of example, the IoT  114  may include various devices of the user, e.g., cameras, mobile phones, laptops, and so forth. To this end, the IoT  114  may provide information about interaction of the user with various devices, e.g., interaction with web-based applications, photos taken, communications with other users, and so forth. The IoT  114  may also include various real-world articles (e.g., shoes, clothing, sporting equipment, appliances, automobiles, etc.) configured with sensors to provide information describing behavior, such as steps taken, force of a foot striking the ground, length of stride, temperature of a user (and other physiological measurements), temperature of a user&#39;s surroundings, types of food stored in a refrigerator, types of food removed from a refrigerator, driving habits, and so forth. The IoT  114  may also include third parties to the CGM platform  112 , such as medical providers (e.g., a medical provider of the person  102 ) and manufacturers (e.g., a manufacturer of the CGM system  104 , the insulin delivery system  106 , or the computing device  108 ) capable of providing medical and manufacturing data, respectively, that can be leveraged by the data analytics platform  126 . Certainly, the IoT  114  may include devices and sensors capable of providing a wealth of data in connection with recommendations based on CGM without departing from the spirit or scope of the described techniques. In the context of measuring glucose, e.g., continuously, and obtaining data describing such measurements, consider the following discussion of  FIG.  2   . 
       FIG.  2    depicts an example implementation  200  of the CGM system  104  of  FIG.  1    in greater detail. In particular, the illustrated example  200  includes a top view and a corresponding side view of the CGM system  104 . 
     The CGM system  104  is illustrated to include a sensor  202  and a sensor module  204 . In the illustrated example  200 , the sensor  202  is depicted in the side view having been inserted subcutaneously into skin  206 , e.g., of the person  102 . The sensor module  204  is depicted in the top view as a dashed rectangle. The CGM system  104  also includes a transmitter  208  in the illustrated example  200 . Use of the dashed rectangle for the sensor module  204  indicates that it may be housed or otherwise implemented within a housing of the transmitter  208 . In this example  200 , the CGM system  104  further includes adhesive pad  210  and attachment mechanism  212 . 
     In operation, the sensor  202 , the adhesive pad  210 , and the attachment mechanism  212  may be assembled to form an application assembly, where the application assembly is configured to be applied to the skin  206  so that the sensor  202  is subcutaneously inserted as depicted. In such scenarios, the transmitter  208  may be attached to the assembly after application to the skin  206  and via the attachment mechanism  212 . Additionally or alternately, the transmitter  208  may be incorporated as part of the application assembly, such that the sensor  202 , the adhesive pad  210 , the attachment mechanism  212 , and the transmitter  208  (with the sensor module  204 ) can all be applied at once to the skin  206 . In one or more implementations, this application assembly is applied to the skin  206  using a separate applicator (not shown). This application assembly may also be removed by peeling the adhesive pad  210  off of the skin  206 . It is to be appreciated that the CGM system  104  and its various components as illustrated are simply one example form factor, and the CGM system  104  and its components may have different form factors without departing from the spirit or scope of the described techniques. 
     In operation, the sensor  202  is communicatively coupled to the sensor module  204  via at least one communication channel which can be a “wireless” connection or a “wired” connection. Communications from the sensor  202  to the sensor module  204  or from the sensor module  204  to the sensor  202  can be implemented actively or passively and these communications can be continuous (e.g., analog) or discrete (e.g., digital). 
     The sensor  202  may be a device, a molecule, and/or a chemical which changes or causes a change in response to an event which is at least partially independent of the sensor  202 . The sensor module  204  is implemented to receive indications of changes to the sensor  202  or caused by the sensor  202 . For example, the sensor  202  can include glucose oxidase which reacts with glucose and oxygen to form hydrogen peroxide that is electrochemically detectable by the sensor module  204  which may include an electrode. In this example, the sensor  202  may be configured as or include a glucose sensor configured to detect analytes in blood or interstitial fluid that are indicative of glucose level using one or more measurement techniques. 
     In another example, the sensor  202  (or an additional sensor of the CGM system  104 —not shown) can include a first and second electrical conductor and the sensor module  204  can electrically detect changes in electric potential across the first and second electrical conductor of the sensor  202 . In this example, the sensor module  204  and the sensor  202  are configured as a thermocouple such that the changes in electric potential correspond to temperature changes. In some examples the sensor module  204  and the sensor  202  are configured to detect a single analyte, e.g., glucose. In other examples, the sensor module  204  and the sensor  202  are configured to detect multiple analytes, e.g., sodium, potassium, carbon dioxide, and glucose. Alternately or additionally, the CGM system  104  includes multiple sensors to detect not only one or more analytes (e.g., sodium, potassium, carbon dioxide, and glucose) but also one or more environmental conditions (e.g., temperature). Thus, the sensor module  204  and the sensor  202  (as well as any additional sensors) may detect the presence of one or more analytes, the absence of one or more analytes, and/or changes in one or more environmental conditions. 
     In one or more implementations, the sensor module  204  may include a processor and memory (not shown). The sensor module  204 , by leveraging the processor, may generate the glucose measurements  118  based on the communications with the sensor  202  that are indicative of the above-discussed changes. Based on these communications from the sensor  202 , the sensor module  204  is further configured to generate CGM device data  214 . The CGM device data  214  is a communicable package of data that includes at least one glucose measurement  118 . Alternately or additionally, the CGM device data  214  includes other data, such as multiple glucose measurements  118 , sensor identification  216 , sensor status  218 , and so forth. In one or more implementations, the CGM device data  214  may include other information such as one or more of temperatures that correspond to the glucose measurements  118  and measurements of other analytes. It is to be appreciated that the CGM device data  214  may include a variety of data in addition to at least one glucose measurement  118  without departing from the spirit or scope of the described techniques. 
     In operation, the transmitter  208  may transmit the CGM device data  214  wirelessly as a stream of data to the computing device  108 . Alternately or additionally, the senor module  204  may buffer the CGM device data  214  (e.g., in memory of the sensor module  204 ) and cause the transmitter  208  to transmit the buffered CGM device data  214  at various intervals, e.g., time intervals (every second, every thirty seconds, every minute, every hour, and so on), storage intervals (when the buffered CGM device data  214  reaches a threshold amount of data or a number of instances of CGM device data  214 ), and so forth. 
     In addition to generating the CGM device data  214  and causing it to be communicated to the computing device  108 , the sensor module  204  may include additional functionality in accordance with the described techniques. This additional functionality may include generating predictions of glucose levels of the person  102  in the future and communicating notifications based on the predictions, such as by communicating warnings when the predictions indicate that the person  102 &#39;s level of glucose is likely to be dangerously low in the near future. This computational ability of the sensor module  204  may be advantageous especially where connectivity to services via the network  116  is limited or non-existent. In this way, a person may be alerted to a dangerous condition without having to rely on connectivity, e.g., to the Internet. This additional functionality of the sensor module  204  may also include calibrating the sensor  202  initially or on an ongoing basis as well as calibrating any other sensors of the CGM system  104 . 
     With respect to the CGM device data  214 , the sensor identification  216  represents information that uniquely identifies the sensor  202  from other sensors, such as other sensors of other CGM systems  104 , other sensors implanted previously or subsequently in the skin  206 , and so on. By uniquely identifying the sensor  202 , the sensor identification  216  may also be used to identify other aspects about the sensor,  202  such as a manufacturing lot of the sensor  202 , packaging details of the sensor  202 , shipping details of the sensor  202 , and so on. In this way, various issues detected for sensors manufactured, packaged, and/or shipped, in a similar manner as the sensor  202  may be identified and used in different ways, e.g., to calibrate the glucose measurements  118 , to notify users to change defective sensors or dispose of them, to notify manufacturing facilities of machining issues, and so forth. 
     The sensor status  218  represents a state of the sensor  202  at a given time, e.g., a state of the sensor at a same time one of the glucose measurements  118  is produced. To this end, the sensor status  218  may include an entry for each of the glucose measurements  118 , such that there is a one-to-one relationship between the glucose measurements  118  and statuses captured in the sensor status  218  information. Generally speaking, the sensor status  218  describes an operational state of the sensor  202 . In one or more implementations, the sensor module  204  may identify one of a number of predetermined operational states for a given glucose measurement  118 . The identified operational state may be based on the communications from the sensor  202  and/or characteristics of those communications. 
     By way of example, the sensor module  204  may include (e.g., in memory or other storage) a lookup table having the predetermined number of operational states and bases for selecting one state from another. For instance, the predetermined states may include a “normal” operation state where the basis for selecting this state may be that the communications from the sensor  202  fall within thresholds indicative of normal operation, e.g., within a threshold of an expected time, within a threshold of expected signal strength, an environmental temperature is within a threshold of suitable temperatures to continue operation as expected, and so forth. The predetermined states may also include operational states that indicate one or more characteristics of the sensor  202 &#39;s communications are outside of normal activity and may result in potential errors in the glucose measurements  118 . 
     For example, bases for these non-normal operational states may include receiving the communications from the sensor  202  outside of a threshold expected time, detecting a signal strength of the sensor  202  outside a threshold of expected signal strength, detecting an environmental temperature outside of suitable temperatures to continue operation as expected, detecting that the person  102  has rolled (e.g., in bed) onto the CGM system  104 , and so forth. The sensor status  218  may indicate a variety of aspects about the sensor  202  and the CGM system  104  without departing from the spirit or scope of the described techniques. 
     Having considered an example environment and example CGM system, consider now a discussion of some example details of the techniques for recommendations based on CGM in a digital medium environment in accordance with one or more implementations. 
     Recommendations Based on CGM 
       FIG.  3    depicts an example implementation  300  in which CGM device data, including glucose measurements, is routed to different systems to enable provision of CGM-related services. 
     The illustrated example  300  includes from  FIG.  1    the CGM system  104  and examples of the computing device  108 . The illustrated example  300  also includes the data analytics platform  126  and the storage device  120 , which, as discussed above, stores the CGM data  122 , including the glucose measurements  118 . In this example  300 , the CGM system  104  is depicted transmitting the CGM device data  214  to the computing device  108 . As discussed above in relation to  FIG.  2   , the CGM device data  214  includes the glucose measurements  118  along with other data. The CGM system  104  may transmit the CGM device data  214  to the computing device  108  in a variety of ways. 
     The illustrated example  300  also includes CGM package  302 , which includes the CGM device data  214  and supplemental data  304 . In this example  300 , the CGM package  302  is depicted being routed from the computing device  108  to the storage device  120  of the CGM platform  112 . Broadly speaking, the computing device  108  includes functionality to generate the supplemental data  304  based, at least in part, on the CGM device data  214 , package this data together in the CGM package  302 , and communicate the CGM package  302  to the CGM platform  112  for storage in the storage device  120 , e.g., via the network  116 . 
     With respect to the supplemental data  304 , the computing device  108  may generate a variety of supplemental data to supplement the CGM device data  214 . In accordance with the described techniques, the supplemental data  304  may describe one or more aspects of a user&#39;s context, such that correspondences of the user&#39;s context with CGM device data  214  (e.g., glucose measurements  118 ) can be identified. By way of example, the supplemental data  304  may describe user interaction with the computing device  108 , and include, for instance, data extracted from application logs describing interaction (e.g., selections made, operations performed) for particular applications. The supplemental data  304  may also include clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the computing device  108 . As another example, the supplemental data  304  may include gaze data describing where a user is looking (e.g., in relation to a display device associated with the computing device  108  or when the user is looking away from the device), voice data describing audible commands and other spoken phrases of the user or other users (e.g., including passively listening to users), device data describing the device (e.g., make, model, operating system and version, camera type, apps the computing device  108  is running), and so on. The supplemental data  304  may also describe other aspects of a user&#39;s context, such as environmental aspects including, for example, a location of the user, a temperature at the location (e.g., outdoor generally, proximate the user using temperature sensing functionality), weather at the location, an altitude of the user, barometric pressure, context information obtained in relation to the user via the IoT  114  (e.g., food the user is eating, a manner in which a user is using sporting equipment, clothes the user is wearing), and so forth. The supplemental data  304  may also describe health-related aspects detected about a user including, for example, steps, heart rate, perspiration, a temperature of the user (e.g., as detected by the computing device  108 ), and so forth. To the extent that the computing device  108  may include functionality to detect, or otherwise measure, some of the same aspects as the CGM system  104 , the data from these two sources may be compared, e.g., for accuracy, fault detection, and so forth. The above-discussed types of the supplemental data  304  are merely examples and the supplemental data  304  may include more, fewer, or different types of data without departing from the spirit or scope of the techniques described herein. 
     Regardless of how robustly the supplemental data  304  describes a context of a user, the computing device  108  may communicate CGM packages  302  to the CGM platform  112  for processing at various intervals. In one or more implementations, the computing device  108  may stream CGM packages  302  to the CGM platform  112  substantially in real-time, e.g., as the CGM system  104  provides the CGM device data  214  continuously to the computing device  108 . The computing device  108  may alternately or additionally communicate one or more of the CGM packages  302  to the CGM platform  112  at a predetermined interval, e.g., every second, every 30 seconds, every hour, and so on. 
     Although not depicted in the illustrated example  300 , the CGM platform  112  may process these CGM packages  302  and cause at least some of the CGM device data  214  and the supplemental data  304  to be stored in the storage device  120 . From the storage device  120 , this data may be provided to, or otherwise accessed by, the data analytics platform  126 , e.g., to generate various predictions and provide recommendations, as described in more detail below. Alternately or additionally, the data may be provided to a third party  306 , such as a third party service provider. In this way, third party service providers may be able to provide various services that use the glucose measurements  118 , even though such third party service providers may not manufacture and deploy their own CGM systems. 
     In the illustrated example  300 , the glucose measurements  118  are depicted being communicated from the storage device  120  of the CGM platform  112  to storage device  308  (or other types of storage) of the third party  306  over the network  116 . In particular, the glucose measurements  118  are depicted being communicated across CGM platform application programming interface (API)  310 . In this type of scenario, the CGM platform API  310  may be considered an “egress” for data, such as the glucose measurements  118 . By “egress” it is meant that a flow of data is generally outward from the CGM platform  112  to the third party  306 . 
     In one or more implementations, the CGM platform  112  provides access to data from the storage device  120  via the CGM platform API  310 . In the context of data provision, the CGM platform API  310  may expose one or more “calls” (e.g., specific formats for data requests) to the third party  306 . By way of example, the CGM platform API  310  may expose calls to the third party  306  after the third party  306  enters into an agreement, e.g., with a business corresponding to the CGM platform  112 , that allows the third party  306  to obtain data from the storage device  120  via the CGM platform API  310 . As part of this agreement, the third party  306  may agree to exchange payment in order to obtain data from the CGM platform  112 . Alternately or additionally, the third party  306  may agree to exchange data that it produces, e.g., via an associated device, in order to obtain data from the CGM platform  112 . Parties that enter into agreements to obtain data (e.g., the glucose measurements  118 ) from the CGM platform  112  via the CGM platform API  310  may be referred to as “data partners.” 
     Broadly speaking, the CGM platform API  310  allows the third party  306  to make a request for data (e.g., glucose measurements  118 ) in a specific request format, and if the request is made in the specific format, then the CGM platform API  310  provides the requested data in a specific response format. In other words, the CGM platform API  310  is configured to receive requests for the glucose measurements  118  in a specific request format from the third party  306 , obtain the requested glucose measurements  118  from the storage device  120 , and provide the requested glucose measurements  118  in a formatted response to the third party  306 . The CGM platform API  310  may expose calls that enable the third party  306  to request one or more periods of time of the glucose measurements  118  (e.g., the last 10 days), the glucose measurements  118  for particular users or segments of users, the glucose measurements  118  for a number of users (e.g., 10,000 users) and over a specified period of time (e.g., the last 10 days), and so on. The CGM platform  310  may expose a variety of calls that enable third parties to request glucose measurements  118  meeting specified criteria in a variety of ways without departing from the spirit or scope of the described techniques. In operation, the CGM platform API  310  may limit which data is accessible to different third parties depending on terms of a corresponding agreement, such as to limit a frequency at which the glucose measurements  118  can be obtained, introduce latency to provision of the glucose measurements  118  after those measurements are obtained from the CGM system  104  and the computing devices  108 , and so forth. 
     Once the third party  306  obtains the glucose measurements  118 , the third party  306  may generate one or more third party recommendations  312  based on the obtained glucose measurements  118 . By way of example, the third party  306  may provide a lifestyle application to users and use the glucose measurements  118  to provide the third party recommendation  312  in relation to one or more lifestyle behaviors tracked via such an application, such as a recommendation to exercise more, a recommendation to exercise less, a recommendation to continue certain behaviors (e.g., steps, eating certain foods, sleeping), recommendations to decrease or eliminate certain behaviors (e.g., eating certain foods, drinking alcohol, sleeping), and so forth. Examples of lifestyle applications may include exercise applications, health measurement applications, food tracking applications, sport-specific applications, and so forth. 
     As noted above, the third party  306  may produce its own, additional data, such as via devices that the third party  306  manufactures and/or deploys, e.g., wearable devices. Given this, the third party  306  may generate the third party recommendation  312  based not only on the glucose measurements  118  but also on the additional data the third party  306  produces. For instance, the third party  306  may provide the obtained glucose measurements  118  and this additional data as input to one or more machine learning models trained using historical glucose measurements  118  and historical additional data. Responsive to this input, the third party  306  obtains at least one prediction generated by the one or more models as output. The third party  306  may use such predictions as a basis for the third party recommendation  312 . The third party recommendations  312  are illustrated being output by the third party  306 . This represents that the third party  306  may deliver a third party recommendation  312  by communicating it over the network  116  to the computing device  108  or to other computing devices, e.g., computing devices of the user population  110 . The third party recommendation  312  may then be output by a receiving computing device, such as by displaying the recommendation, outputting the recommendation audibly, and so forth. 
     The illustrated example  300  also includes third party data  314 , which is shown being communicated from the third party to the data analytics platform  126 . As mentioned above, the third party  306  may manufacture and/or deploy associated devices. Additionally or alternately, the third party  306  may obtain data through other sources, such as corresponding applications. This data may thus include user-entered data entered via corresponding third party applications, e.g., social networking applications, lifestyle applications, and so forth. Given this, the data produced by the third party  306  may be configured in various ways, including as proprietary data structures, text files, images obtained via mobile devices of users, formats indicative of text entered to exposed fields or dialog boxes, formats indicative of option selections, and so forth. The third party data  314  may describe various aspects related to one or more services provided by a third party without departing from the spirit or scope of the described techniques. The third party data  314  may include, for instance, application interaction data which describes usage or interaction by users with a particular application provided by the third party  306 . Generally, the application interaction data enables the data analytics platform  126  to determine usage, or an amount of usage, of a particular application by users of the user population  110 . Such data, for example, may include data extracted from application logs describing user interactions with a particular application, clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the application, and so forth. In one or more implementations, the data analytics platform  126  may thus receive the third party data  314  produced or otherwise obtained by the third party  306 . 
     In the illustrated example  300 , the third party data  314  is depicted being communicated across the CGM platform API  310 . In this type of scenario, the CGM platform API  310  may be considered an “ingress” for the third party data  314 . By “ingress” it is meant that a flow of data is generally inward to the CGM platform  112  from the third party  306 . Although the CGM platform API  310  is illustrated as supporting both egress and ingress data flows, in one or more implementations, the functionality to allow egress of data from the CGM platform  112  and ingress of data to the CGM platform  112  may be handled by different APIs. For example, the ingress functionality may be handled by an API that corresponds to the third party  306  rather than the CGM platform  112 &#39;s API. Regardless, in addition to the data of the CGM platform  112 —the glucose measurements  118  and the supplemental data  304 —the data analytics platform  126  may utilize third party data  314  in one or more scenarios. 
     The data analytics platform  126  is illustrated with prediction system  316 . In accordance with the described systems, the prediction system  316  is configured to generate predictions  318  based on at least the glucose measurements  118 . In one or more implementations, for instance, the prediction system  316  generates predictions  318  based on both the glucose measurements  118  and additional data, where the additional data may include one or more portions of the CGM device data  214  additional to the glucose measurements  118 , the supplemental data  304 , the third party data  314 , data from the IoT  114 , and so forth. As discussed below, the prediction system  316  may generate such predictions  318  by using one or more machine learning models. These models may be trained or otherwise built using the glucose measurements  118  and additional data obtained from the user population  110 . 
     In one or more implementations, the predictions  318  may correspond to or otherwise include health indicators. As used herein, the term “health indicator” may refer to a predicted health condition, which can be “negative” or “positive.” Examples of negative health conditions, for example, include pre-diabetes, Type I diabetes, Type II diabetes, neuropathy, Alzheimer&#39;s disease, and heart disease, to name just a few. In contrast, examples of “positive” health conditions, may include predicting a decreased risk of developing a negative health condition, or a positive health condition related to bodyfat, cardiovascular capability, and so forth. In some cases, the health indicator may refer to a predicted medical state, such as a predicted A1C. Notably, the predictions  318  are based on glucose measurements  118  and additional data collected during a certain time period. Thus, in some cases, the predictions  318  predict that the user currently has the predicted health condition based on the aggregated data. Alternately, the predicted health condition may correspond to a time period that occurs after the certain time period during which the aggregated data is collected (e.g., a prediction of Type II diabetes in 40 months). Some additional types of predictions and the specific types of information used to generate these predictions is also discussed in further detail below. 
     Based on the generated predictions  318 , the data analytics platform  126  generates recommendation  320 . The recommendation  320 , for instance, may instruct a user to perform an action (e.g., download an app to the computing device  108 , seek medical attention immediately, dose insulin, go for a walk, consume a particular food or drink), continue a behavior (e.g., continue eating a certain way or exercising a certain way), change a behavior (e.g., change eating habits or exercise habits), and so on. In such scenarios, the prediction  318  and/or the recommendation  320  is communicated from the data analytics platform  126  and output via the computing device  108 . In the illustrated example  300 , the prediction  318  is also illustrated being communicated to the computing device  108 . It is to be appreciated that either or both of the prediction  318  and the recommendation  320  may be communicated to the computing device  108 . Additionally or alternately the prediction  318  and/or the recommendation  320  may be routed to a decision support platform and/or a validation platform, e.g., before the prediction and/or recommendation are allowed to be delivered to the computing device  108 . In the context of generating one or more predictions which serve as a basis for the recommendation  320 , consider the following discussion of  FIG.  4   . 
       FIG.  4    depicts an example implementation  400  of the data analytics platform  126  in greater detail. As in  FIG.  3   , the data analytics platform  126  includes the prediction system  316 . 
     In the illustrated example  400 , the prediction system  316  includes model manager  402 , which manages models  404 , including a statistical model  406  and an additional machine learning model  408 , e.g., a neural network. It is to be appreciated that the models  404  may include different models without departing from the spirit or scope of the described techniques, such as multiple, different statistical models, multiple machine learning models configured as neural network, and/or multiple other types of machine learning models. These different machine learning models may be built or trained (or the model otherwise learned), respectively, using different data, according to different statistical modeling techniques, having different architectures, according to different algorithms, and so on. Accordingly, it is to be appreciated that the following discussion of the model manager  402 &#39;s functionality is applicable to a variety of machine learning models. For explanatory purposes, however, the functionality of the model manager  402  will be described generally in relation to the statistical model  406  and the additional machine learning model  408 . 
     In general, the model manager  402  is configured to manage the models  404 . This model management includes, for example, building the statistical model  406 , building the machine learning model  408 , training the machine learning model  408 , updating these models, and so on. Specifically, the model manager  402  is configured to carry out this model management using, at least in part, the wealth of data maintained in the storage device  120  of the CGM platform  112 . As illustrated, this data includes the glucose measurements  118  and additional data  410  of the user population  110 . Said another way, the model manager  402  builds the statistical model  406 , builds the machine learning model  408 , trains the machine learning model  408  (or otherwise learns a policy deployed by it), and updates these models using the glucose measurements  118  and the additional data  410  of the user population  110 . 
     Generally, the CGM platform  112  obtains the additional data  410  of the user population from various devices, sensors, applications, or services. Thus, the additional data may be obtained from one or more “sources” that are different from the CGM system  104  from which the glucose measurements  118  are detected. In one or more implementations, this additional data  410  may include at least one or more portions of the CGM device data  214  additional to the glucose measurements  118  (e.g., the sensor identification  216  and sensor status  218  data), the supplemental data  304 , the third party data  314 , data from the IoT  114 , and so forth. 
     The additional data  410  may include, by way of example and not limitation, health-related data, application interaction data, environmental data, demographic data, device data in addition to the glucose measurements (e.g., sensor identification data, incident reports), supplemental data added by the computing device, third party data, and so forth. Health-related data may include activity data (e.g., steps, exercise frequency, sleep data), biometric data (e.g., insulin level, ketone levels, heart rate, temperature, stress, temperature), nutrition data (e.g., food and drink logs, scanned restaurant receipts, carb consumption, fasting), medical records (e.g., A1C, cholesterol, electrocardiogram results, and data related to other medical tests or history), to name just a few. Application interaction data may include data extracted from application logs describing user interactions with a particular applications, clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the computing device, gaze data describing where a user is looking (e.g., in relation to a display device associated with the computing device or when the user is looking away from the device), voice data describing audible commands and other spoken phrases of the user or other users (e.g., including passively listening to users), and so forth. Environmental data may include data describing various environmental aspects associated with the user, such as the user&#39;s location, a temperature and/or weather at the user&#39;s location, altitude of the user, barometric pressure, and so forth. Demographic data may include data describing the user, such as age, sex, height, weight, and so forth. The above-discussed types of the additional data are merely examples and the additional data may include more, fewer, or different types of data without departing from the spirit or scope of the techniques described herein. 
     Unlike conventional systems, the CGM platform  112  stores (e.g., in the storage device  120 ) or otherwise has access to glucose measurements  118  obtained using the CGM system  104  for hundreds of thousands of users of the user population  110  (e.g., 500,000 or more). Moreover, these measurements are taken by sensors of the CGM system  104  at a continuous rate. As a result, the glucose measurements  118  available to the model manager  402  for model building and training numbers in the millions, or even billions. With such a robust amount of data, the model manager  402  can build and train the models  404  to accurately mimic real-life effects of different behaviors on glucose levels. Absent the robustness of the CGM platform  112 &#39;s glucose measurements  118 , conventional systems simply cannot build or train models to cover state spaces in a manner that suitably represents how various behaviors affect glucose levels. Failure to suitably cover these state spaces can result in glucose predictions or predictions of other health indicators that are inaccurate, which can lead to recommending unsafe actions or behaviors that could cause death. Given the gravity of generating inaccurate predictions, it is important to build models  404  using an amount of glucose measurements  118  that is robust against rare events. 
     In one or more implementations, the model manager  402  builds the statistical model  406  by extracting from the glucose measurements  118  and the additional data  410  observed values corresponding to at least one attribute. Once built, the statistical model  406  is configured to predict values of this at least one attribute and output them—values of the at least one attribute do not serve as input to the model. In scenarios where the statistical model  406  is a regression model, for instance, these values may correspond to one or more dependent variables of the statistical model  406 . The values of these attributes—corresponding to the statistical model  406 &#39;s dependent variables—may be referred to as a first set of values in the following discussion. Also, the model manager  402  extracts from the glucose measurements  118  and the additional data  410  observed values corresponding to at least one other attribute. Once built, values of this at least one other attribute are to serve as input to the statistical model  406 , e.g., as a vector of such values. In scenarios where the statistical model  406  is a regression model, the at least one other attribute may correspond to one or more explanatory (or independent) variables. The extracted values of these independent variables may be referred to as a second set of values in the following discussion. 
     Given the first set of values and the second set of values, the model manager  402  uses one or more known approaches for “fitting” these values to an equation so that it produces values of the first set from the values of the second set within some tolerance. Examples of such fitting approaches include using a least squares approach, using a least absolute deviations regression, minimizing a penalized version of the least squares cost function (e.g., ridge regression or lasso), and so forth. By “fitting” it is meant that the model manager  402  estimates model parameters for the equation using the one or more approaches and these sets of data. The estimated parameters include, for instance, weights to apply to values of the independent variables when the values are input to the statistical model  406  during operation. The model manager  402  incorporates these parameters estimated from the observed values into the equation to generate the statistical model  406 . In operation, the prediction system  316  inputs values of the independent variables into the statistical model  406  (e.g., as one or more vectors or a matrix), the statistical model  406  applies the estimated weights to these input values, and then outputs values for the one or more dependent variables. This output is represented as prediction  318 . 
     In one statistical-model building scenario, the model manager  402  uses the glucose measurements  118  of the user population  110  having timestamps before a particular timestamp and also uses corresponding additional data  410  (e.g., with timestamps that correspond to the glucose measurements  118  and are associated with users corresponding to the glucose measurements) as values of the independent variables for the statistical model  406 . In this scenario, the model manager  402  may use the glucose measurements  118  of the user population  110  having timestamps after the particular timestamp as values of the dependent variables for the statistical model  406 . Here, the model manager  402  uses one or more known approaches for fitting an equation to the pre- and post-timestamp data. In so doing, the model manager  402  estimates parameters of the equation so that by inputting the pre-timestamp data values, the post-timestamp glucose measurements  118  (or values within some tolerance of those measurements) are output. 
     The model manager  402  then incorporates the estimated parameters into the equation and persists this incorporation as the statistical model  406 , such that the statistical model  406  preserves the estimated parameters with the equation. In this way, the model manager  402  builds a statistical model  406  capable of generating a prediction  318  of glucose measurements after a particular time when it receives as input glucose measurements before the particular time and also corresponding additional data. In operation and continuing with this scenario, the prediction system  316  may thus obtain a subset of the glucose measurements  118  of the person  102  before a particular time (e.g., a current time) along with the additional data  410  of the person  102  that corresponds to the independent variable used to train the statistical model  406 . The prediction system  316  may then provide this data of the person  102  to the statistical model  406  as input. In the continuing scenario, the statistical model  406  generates the prediction  318  as glucose measurements of the person  102  after the particular time, e.g., the current time. 
     Although prediction of glucose measurements after a particular time (e.g., a current time) is discussed in relation to building and actually using the statistical model  406 , the model manager  402  may build the statistical model  406  to predict different aspects from patterns in the observed glucose measurements  118  and additional data  410 . By way of example, the model manager  402  may build the statistical model  406  to predict upward or downward trends in health indicators of the person  102 , maintained health indicators of the person  102  over some period of time, and so forth—and use the glucose measurements  118  and the additional data  410  of the user population  110  to build the model to persist correlations with these health indicators and trends among the user population  110 . 
     Returning now to a discussion of the additional machine learning model  408  (e.g., configured as a neural network) in accordance with the described techniques. In a similar manner as with the statistical model  406 , the model manager  402  extracts a first set of observed values corresponding to at least one attribute and a second set of the values corresponding to at least one other attribute—both sets extracted from the glucose measurements  118  and the additional data  410  of the user population  110 . The model manager  402  uses these sets of values to train the machine learning model  408  or provide feedback to the machine learning model  408  about its predictions so that it learns a policy for generating the predictions. 
     Also similar to the statistical model  406 , once the additional machine learning model  408  is trained or learns at least an initial policy to deploy, the machine learning model  408  is configured to predict values of the at least one attribute corresponding to the first set and output those values. Further, the machine learning model  408 , once trained or used to deploy at least an initial policy, is configured to receive values of the at least one other attribute of the second set as input, e.g., as a vector of such values. In scenarios where the machine learning model  408  is a neural network, for instance, the machine learning model  408  during operation may thus receive as input one or more vectors (e.g., feature vectors) that represent values of the at least one other attribute. In such scenarios, the machine learning model  408  during operation may also output one or more vectors (e.g., feature vectors) that represent values of the at least one attribute. 
     In the context of training, the model manager  402  may train the machine learning model  408  by providing an instance of data from the second set of values as input to the machine learning model  408 . Responsive to this, the machine learning model generates the prediction  318 , e.g., a prediction of a value for the at least one attribute corresponding to the first set. The model manager  402  obtains this training prediction from the machine learning model  408  as output and compares the training prediction to the actual extracted value of the first set that corresponds to the instance of data input. By way of example, the model manager  402  compares the training prediction to the actual extracted value using a cost function. Based on this comparison, the model manager  402  adjusts internal weights of the machine learning model  408  so that the machine learning model can substantially reproduce the actual extracted value when the instance of data is provided as input in the future. 
     This process of inputting instances of observed data into the machine learning model  408 , receiving training predictions from the machine learning model  408 , comparing the training predictions to expected output values (observed) that correspond to the input instances (e.g., using a cost function), and adjusting internal weights of the machine learning model  408  based on these comparisons, can be repeated for hundreds, thousands, or even millions of iterations—using an instance of training data per iteration. 
     The model manager  402  may perform such iterations until the machine learning model  408  is able to generate predictions  318  that consistently and substantially match an expected output, e.g., that substantially match the observed values of the first set of data. The capability of a machine learning model to consistently generate predictions that substantially match an expected output may be referred to as “convergence.” Given this, it may be said that the model manger  402  trains the machine learning model  408  until it “converges” on a solution, e.g., the internal weights of the model have been suitably adjusted due to training iterations so that the model consistently generates predictions that substantially match expected output. 
     It is to be appreciated that this is just one additional example of a machine learning model  408  and how it may be trained. Indeed, machine learning models may be configured according to various paradigms (e.g., supervised learning, unsupervised learning, reinforcement learning, and so on) and trained using different approaches without departing from the spirit or scope of the described techniques. By way of example, the machine learning model  408  may be initially trained on the glucose measurements  118  and the additional data  410  of the user population and then the training may be further updated using training instances from the glucose measurements  118  and the additional data  410  of the person  102 , e.g., to further tune various parameters of the machine learning model  408   
     Regardless, once the machine learning model  408  is trained using, at least in part, the glucose measurements  118  and the additional data  410  of the user population  110 , the machine learning model  408  may be used in operation to generate the predictions  318  for a user corresponding to the person  102 . Consider the following implementation example, which parallels the above-discussed statistical-model building scenario and use, but instead of leveraging the statistical model  406  leverages the machine learning model  408 . 
     In this machine learning example, the model manager  402  uses the glucose measurements  118  of the user population  110  which have timestamps before a particular timestamp and also uses corresponding additional data  410  (e.g., with timestamps that correspond to the glucose measurements  118  and are associated with users corresponding to the glucose measurements) as training input to the machine learning model  408 . In this scenario, the model manager  402  may use the glucose measurements  118  of the user population  110  which have timestamps after the particular timestamp as expected output (target or label) of the machine learning model  408 . Here, the model manager  402  uses one or more known approaches for adjusting parameters of the model to predict the post-timestamp data given the pre-timestamp data as input. Examples of these approaches include supervised learning approaches such as gradient descent, stochastic gradient descent, and so on. Certainly, other approaches may be used without departing from the spirit or scope of the described techniques. 
     By using these approaches, the model manager  402  adjusts internal weights of the machine learning model  408  so that by inputting the pre-timestamp data values, the post-timestamp glucose measurements  118  (or values within some tolerance of those measurements) are output. Further, the machine learning model  408  preserves these internal weights, e.g., in connection with particular nodes of the model. In this way, the model manager  402  builds a machine learning model  408  capable of generating a prediction  318  of glucose measurements after a particular time when it receives as input glucose measurements before the particular time and also corresponding additional data. 
     In operation and continuing with this scenario, the prediction system  316  may thus obtain a subset of the glucose measurements  118  of the person  102  before a particular time (e.g., a current time) along with the additional data  410  of the person  102  that corresponds to the input data used to train the machine learning model  408 . The prediction system  316  may then provide this data of the person  102  to the machine learning model  408  as input. In the continuing scenario, the machine learning model  408  generates the prediction  318  as glucose measurements of the person  102  after the particular time, e.g., the current time. In one or more implementations, the machine learning model  408  outputs this prediction in the form of a vector. 
     Although prediction of glucose measurements after a particular time (e.g., a current time) is discussed in relation to training and actually using the machine learning model  408 , the model manager  402  may build the machine learning model  408  to predict different aspects from patterns in the observed glucose measurements  118  and additional data  410 . By way of example, the model manager  402  may build the machine learning model  408  to predict upward or downward trends in health indicators of the person  102 , maintained health indicators of the person  102  over some period of time, and so forth—and use the glucose measurements  118  and the additional data  410  of the user population  110  to build the model to persist correlations with these health indicators and trends among the user population  110 . Due, in part, to training the machine learning model  408  with vast amounts of training data, the machine learning model  408  is capable of capturing latent features in the data, which may include hidden relationships and spurious correlations within the data, which are virtually impossible for human analysts to uncover absent them randomly happening upon the relationship. 
     Regardless of whether the statistical model  406 , the additional machine learning model  408 , or some combination (ensemble) of statistical and/or additional machine learning models is used to generate the prediction  318 , it may be obtained by the recommendation system  412 . The recommendation system  412  is configured to generate the recommendation  320  based on the prediction  318 . The recommendation system  412  may be implemented using, or otherwise have access, to logic, which configures the recommendation  320  according to the prediction. By way of example, if the prediction  318  indicates a positive health trend for the person  102  (e.g., her A1C is lower), the recommendation system  412  can generate the recommendation  320  to recommend continuing various behaviors. 
     The logic used by the recommendation system  412  to generate the recommendation  320  may vary in complexity without departing from the spirit or scope of the described techniques, such as from a heuristic manually coded to one or more additional machine learning models for configuring recommendations based on receiving the prediction  318  as input. Further implementation examples of the types of predictions and recommendations that may be produced by the models  404  and the recommendation system  412 , respectively, are discussed in further detail below. Now, though, consider the following discussion of  FIG.  5    in relation to a validation service and decision support platform in accordance with the described techniques. 
       FIG.  5    depicts an example  500  of an implementation in which at least one of predictions or recommendations produced by the data analytics platform are routed to at least one of a validation service or decision support platform. 
     The illustrated example  500  includes the computing device  108  and the data analytics platform  126  having the prediction system  316 . In this example  500 , the data analytics platform is depicted communicating the prediction  318  and the recommendation  320 . Here, the recommendation  320  relates to a user of the computing device  108 , e.g., the person  102 . By way of example, the prediction  318  includes information about the person (e.g., a predicted glucose level over an upcoming time period, a predicted health trend over an upcoming time period, and so on), and the recommendation  320  includes one or more suggestions intended for the user (e.g., one or more actions to perform or to eliminate, behaviors to adopt or eliminate, and so on). 
     In contrast to the illustrated example  300  of  FIG.  3   , the illustrated example  500  includes a validation service  502  and a decision support platform  504  as intermediaries between the data analytics platform  126  and the computing device  108 . Accordingly, the prediction  318  and/or the recommendation  320  may be routed to either one or both of the validation service  502  or the decision support platform  504 . Although the validation service  502  and the decision support platform  504  are not depicted in  FIG.  3    it is to be appreciated that the predictions  318  and recommendations  320  generated in scenarios discussed in relation to  FIG.  3    may also be routed through the validation service  502  and/or the decision support platform  504 . 
     In accordance with the described techniques, the validation service  502  is configured to validate the recommendation  320 . This means determining whether the recommendation is valid (e.g., safe) and can be further communicated to the decision support platform  504  and/or directly to the computing device  108 . The validation service  502  may expose the recommendation  320  to a user that has been authenticated by the validation service  502  as authorized to validate recommendations, e.g., a clinician. By way of example, the validation service  502  may email the recommendation  320  to the clinician, provide the recommendation  320  through a clinician portal (e.g., where the clinician can review multiple recommendations and validate them or not), provide a notification of the recommendation  320  on a screen of a mobile device—allowing the clinician to approve, decline, or obtain additional information with mere gestures, just to name a few. The validation service  502  may surface recommendations to users that are allowed to validate the recommendations (e.g., clinicians) in a variety of ways without departing from the spirit or scope of the techniques described herein. 
     Responsive to a recommendation being validated (e.g., by a clinician or logic of the validation service  502 ), the recommendation may be further routed to the decision support platform  504  or directly to the computing device  108 . When a recommendation is not validated (i.e., it is rejected), the recommendation may not be further routed to the decision support platform  504  or to the computing device  108 . Instead, the validation service  502  may modify the recommendation (e.g., according to clinician input) and/or provide a notification back to the data analytics platform  126  that the recommendation is not validated. In this scenario, the data analytics platform  126  may be able to add an indication of non-validation as input to the prediction system and initiate generation of a different prediction  318  and/or recommendation  320 . 
     Indeed, the models  404  may be updated based on validations and non-validations received from the validation service  502 . In scenarios where the validation service  502  validates the recommendation  320  and consequently allows the recommendation  320  to be forwarded directly to the computing device  108 , the computing device  108  may output the recommendation  320 , such as via a display device, via an audio device (e.g., speakers, headphones, ear buds), via tactile feedback, and so on, as described above and below. An example of how recommendations may be surfaced by the validation service  502  to users that are allowed to validate recommendations (e.g., clinicians) is discussed in more detail below in relation to  FIG.  10   . 
     As mentioned above, the prediction  318  and/or the recommendation  320  may be communicated to the decision support platform  504  by the validation service  502  or alternately may be communicated to the decision support platform  504  directly from the data analytics platform  126 , bypassing the validation service  502 . The decision support platform  504  is configured to provide support to users of the CGM platform  112  for managing one or more health conditions, e.g., diabetes. Responsive to receiving the recommendation  320 , for example, the decision support platform  504  may provide the recommendation to a customer support specialist, e.g., via email, a support-specialist portal, and so on. 
     Based on the recommendation  320 , as well as based on other information accessible about the corresponding user, the customer service specialist may determine how to support the user. By way of example, the customer service specialist may determine to call the user to provide voice support during a phone call, to select (e.g., via a support-specialist portal) one or more pre-configured messages to send to the user (e.g., text message, mobile phone notifications, email messages, and so on), to build one or more messages to send to the user from pre-configured message components, to simply forward the recommendation  320  to the computing device  108 , to contact a clinician or other medical professional associated with the user, to contact emergency services, to contact a caregiver or other guardian (e.g., parent) of the user, and so forth. The decision support platform  504  may provide tools, content, and services for supporting users&#39; management of their health conditions based on the prediction  318  and the recommendation  320  in a variety of ways without departing from the spirit or scope of the described techniques. 
     Having discussed the CGM system  104  as well as how data collected from various sources is used with glucose measurements to generate predictions related to user health and provide recommendations, consider the following implementation examples of recommendations based on CGM. 
     Generating Predictions and Recommendations Using a Model 
       FIG.  6    depicts an example  600  of a user interface of the CGM platform displayed on a computing device coupled to a CGM system. 
     The illustrated example  600  includes a CGM user interface  602  displayed by the computing device  108 . In this example, the CGM interface  602  is depicted as displaying a prediction  604  along with a recommendation  606 . As described throughout, the prediction  604  is generated by the prediction system  316  to predict a health indicator of the user by processing the glucose measurements  118  and additional data  410  of a user using one or more models, such as statistical model  406  or additional machine learning model  408 . 
     For purposes of this example, assume that a user begins using a CGM system  104  to measure glucose levels after receiving A1C and fasting glucose test results indicating that the user has “pre-diabetes,” which puts the user at risk for developing Type II diabetes in the near future. In view of this, the user begins wearing the CGM system  104 , which automatically provides the glucose measurements  118  to the prediction system  316 . The CGM platform  112  processes the glucose measurements  118  collected for the user to determine that the user&#39;s blood glucose is increasingly in the “high” range (e.g., &gt;250 mg/dl). 
     Along with the glucose measurements  118 , the prediction system  316  obtains nutrition data of the user, such as food and drink purchase data provided by various third parties  306  (e.g., grocery stores, restaurants, liquor stores) and/or user provided nutrition data (e.g., food logs, captured images of foods and drinks consumed, scanned restaurant or grocery store receipts). This nutrition data indicates that, on average, the user consumes a one-liter bottle of soda each week, eats at fast food restaurants three times per week, and drinks beer and eats potato chips most weekends. The prediction system  316  may also make various inferences based on food or drinks which are not consumed by the user. In this case, the prediction system  316  analyzes the nutrition data to determine that the user rarely purchases “whole foods,” such as fruit, vegetables, or meat. Additionally, the prediction system  316  obtains activity data for the user which includes steps data indicating that the user rarely walks more than 5,000 steps per day and does not work out, and also includes sleep data indicating that the user sleeps an average of just five hours each night. 
     The prediction system applies the models  404  to the glucose measurements  118  and the additional data  410 , which in this example, includes the above-discussed nutrition data and activity data of the user. In view of the user&#39;s increasing blood glucose measurements, poor diet choices, and lack of activity, the prediction system  316  generates prediction  604  which indicates that the user has a 76% chances of developing Type II diabetes in 40 months. 
     Along with the prediction  604 , the CGM user interface  602  displays recommendation  606  generated by the recommendation system  412  of the data analytics platform  126 . The recommendation  606  includes one or more actions or behaviors that the user can take to improve the user&#39;s predicted negative health condition. In this case, the recommendation  606  includes a recommendation for a customized eating plan, a recommendation for a customized exercise plan, and a recommendation to get a coach that can help the user stay on track with the recommended nutrition and exercise plan. In  FIG.  6   , the user is shown selecting the customized exercise plan recommendation in order to obtain more detailed information regarding the recommended exercise plan. 
     Continuing with this example, assume that the user follows the actions and behaviors recommended by the prediction system  316 , such as by switching to a whole food diet and tracking the diet using an online food log, walking 10,000 steps per day and tracking steps using a smart watch with a pedometer, and working out three times per week and logging each work out with an online workout log. In this case, the prediction system  316  continuously gathers the glucose measurements  118 , nutrition data, and activity data for the user, and determines that the user&#39;s improved nutrition choices and exercise frequency is correlated with a decrease in the user&#39;s average blood glucose measurements  118 . 
     Based on this continuous analysis of the user&#39;s updated glucose measurements  118  from the CGM sensor and the additional data  410  indicating the user is making better food choices (as indicated by the user&#39;s nutrition log) and exercising more frequently (as indicated by the steps data and exercise log), the prediction system  316  generates an updated prediction, which is communicated to the computing device  108  for display as a notification  702  as shown in  FIG.  7   . In this example, the updated prediction of notification  702  indicates that the user is now “unlikely” to develop Type II diabetes in the next 40 months. Notably, the updated prediction of notification  702  provides positive feedback to the user, which may further incentivize the user to continue eating healthy and exercising. Conversely, in cases where an updated prediction indicates a worsening health condition, the updated prediction may be helpful motivation to get the user back on track. 
     Generating Application Recommendations for Similar Users 
     As described throughout, the CGM platform  112  leverages one or more CGM platform API&#39;s  310  to enable the communication of glucose measurements  118  from the CGM platform  112  to various third parties  306 , as well as the communication of third party data  314  from the third parties  306  to the CGM platform  112 . Due to this, applications and services provided by such third parties  306  which leverage the glucose measurements  118  are increasingly becoming available and can often be downloaded via an “App Store”. Once downloaded to computing device  108 , the user can authorize the third party  306  to access the user&#39;s glucose measurements  118  via the API  310 . Doing so enables third parties  306  to leverage the glucose measurements  118  in a variety of different ways to improve the user&#39;s health. In this way, third parties  306  may be able to provide various applications and services that use the glucose measurements  118 , even though such third parties  306  may not manufacture and deploy their own CGM systems. As the number of third party “apps” and services increase, it becomes increasingly difficult for users of the population to discover the apps and services that would work best for their individual situation. 
     The CGM platform  112  may include an “ingress” API  310  which enables the CGM platform  112  to receive third party data  314  from the various third parties  306  (e.g., via third party servers of the third party  306 ). The third party data  314  may include application interaction data describing user interactions with third party services or applications. Such data, for example, may include data extracted from application logs describing user interactions with a particular applications, clickstream data describing clicks, taps, and presses performed in relation to input/output interfaces of the computing device, and so forth. 
     The CGM platform  112  can aggregate the application interaction data, along with the glucose measurements  118  and additional data in order to determine whether a user&#39;s interaction with a particular application or service correlates to improvement of the user&#39;s health. For example, based on the glucose measurements  118 , the CGM platform  112  can objectively determine an improvement in a health condition of a user. The CGM platform  112  can consider a variety of different factors, based on the glucose measurements, when determining whether application usage improves a user&#39;s health, including improvements in average blood glucose level, time-in-range, mitigation of specific undesired patterns, or any combination thereof. Additionally, the CGM platform  112  may provide various controls to account for differences in the glucose measurements  118 , such as sensor utilization and calibration frequency. The CGM platform  112  may also consider data provided by the third parties  306  when determining an improvement in a user&#39;s health. The CGM platform  112  can then correlate the improvement or decline in the user&#39;s health with usage of a particular application based on the application interaction data. For example, if the CGM platform  112  detects an improvement with the user&#39;s health that coincides with heavy usage of a particular application, then the CGM platform  112  may determine that the particular application is correlated with the improvement. Based on the amount of data available to the CGM platform  112 , the correlation of a particular application with improvement in a health condition may be determined for a subset of users of the user population  110 . 
     The recommendation system  412  can then identify a similar user having the health condition, and generate a recommendation to the similar user to utilize the particular application that helped to improve the health condition of the subset of similar users. To do so, the recommendation system  412  can predict a probability of a similar improvement in a health condition through usage of a particular application by other users in the user population and recommend applications with a high probability of improving health to other target users. Such application recommendations may be targeted to individual users who are similar to the subset of users with an improved health condition that correlates to the usage of the particular application. For instance, if use of a particular application is correlated with improving the glycemia of a subset of users in the user population, then the CGM platform  112  can recommend use of the particular application to similar users in the user population  110 . 
     Identification of a similar user may be based on at least one of demographics or observed patterns in glucose measurements of the similar user. For example, a similar user may be identified as having a same health condition based, in part, on glucose measurements  118  provided by a CGM system  104  worn by the user. The CGM platform  112  may first generate a user profile for new users who begin wearing the CGM system  104  that includes demographic data, such as age, gender, location, existing medical records, and so forth. As the CGM platform  112  collects glucose measurements  118  and additional data  410  from the user, the CGM platform  112  refines the user&#39;s similarity scores with other users. For example, a user who is 22 years old, female, has a mean glucose of 162 mg/dL, and experiences patterns of nighttime low glucose measurements, may have a high similarity score with other users in that age, gender, mean glucose measurement, and pattern experience. 
     Then, to determine application recommendations, the similarity score is combined with the prior application success of similar users. For instance, if users that are similar to a target user downloaded and used a particular application, and then saw their glycemia improve (e.g., as evidenced by reduced mean glycemia and reduced nighttime lows), then the recommendation system  412  can be configured to generate a high recommendation score for the particular application for the target user. Conversely, if other applications did not show such improvement, these applications would have lower recommendation scores for the target user. The application recommendations can be communicated to the computing device  108  for output. 
     In the context of generating application recommendations, consider  FIG.  8    which depicts an additional example  800  of a user interface of the CGM platform displayed on a computing device coupled to a CGM system. The illustrated example  800  includes a CGM user interface  802  displayed by computing device  108 . In this example, the CGM user interface  802  is depicted as displaying recommended applications  804 . As discussed throughout, the recommended applications  804  may be determined by the recommendation system  412  to improve a health condition of a target user based on the similarity of the user to other users in the user population  110  whose health condition improved based on usage of at least one of the recommended applications  804 . In this case, the recommended applications  804  correspond to various third party applications. In  FIG.  8   , a user is depicted as selecting the application “Nutrition by Neha” in order to download this application to the user&#39;s smartphone. 
     Notably, the recommendation system  412  can further reinforce the application recommendations as the target user downloads and uses applications, thus reinforcing or negating prior recommendations and leading to improved follow-on recommendations in the future. For example, if the recommendation system  412  obtains glucose measurements  118  from the similar user indicating an improvement in the health condition of the similar user, then this feedback will positively reinforce the correlation between the improvement to the health condition of the subset of users and usage of the particular application. Conversely, if the glucose measurements  118  from the similar user indicates no improvement in the health condition (or a worsening of the health condition) of the similar user, then this feedback will negatively reinforce the correlation between the improvement to the health condition of the subset of users and usage of the particular application 
     With regards to  FIG.  8   , for example, as the user begins interacting with the “Nutrition by Neha” application, application interaction data may be communicated to the CGM platform  112  via the API  310  and correlated with the user&#39;s glucose measurements  118  and additional data. In this way, the CGM platform  112  may continually update the models  404  used to recommend applications based on feedback received from application usage by the user population  110 . In configurations where the machine learning model  408  is updated based on feedback, for instance, the model may be configured as a reinforcement learning model. The updated models  404  are then used to generate improved application recommendations. 
     Moreover, if the CGM platform  112  detects that usage of a particular application is improving the health condition of the user based on the updated glucose measurements  118 , then the CGM platform  112  may communicate a notification of the improvement to the computing device  108  for output. In  FIG.  9   , for example, a notification  902  is displayed by computing device  108  and indicates that usage of the “Nutrition by Neha” application has caused the user&#39;s neuropathy to improve. It is to be appreciated that this positive notification may incentivize the user to continue to use the application. 
     Validation Service 
       FIG.  10    depicts an example implementation  1000  of a user interface of a validation service with which an authorized user can interact to validate recommendations generated by a CGM platform. 
     In the illustrated example  1000 , display device  1002  is depicted displaying a user interface  1004  of the validation service  502 . Broadly speaking, interface elements of the user interface  1004  enable an authorized user to interact with those elements to validate or reject recommendations that are provided by the data analytics platform  126  and are intended for delivery to users, e.g., the person  102 . Responsive to receiving input via a user interface element validating a recommendation (e.g., recommendation  320 ), the validation service  502  may route the recommendation to the computing device  108  of the respective user. As discussed above, the validation service  502  may also route the recommendation to the decision support platform  504 . Responsive to receiving input via user interface element of the user interface  1004  rejecting a recommendation, the validation service  502  does not communicate the recommendation to the computing device  108 . Instead, the validation service may communicate a notification to the data analytics platform  126  indicating that the recommendation is rejected. Additionally or alternately, an approved user of the validation service  502  may modify the recommendation and then send the modified recommendation to the computing device  108  of the user. As mentioned above, users that are authorized to validate recommendations provided by the data analytics platform  126  may include clinicians or other health care professionals, qualified to deliver health instruction to patients. 
     In the illustrated example  1000 , the user interface  1004  displays stubs  1006  of recommendations provided to the validation service  502  by the data analytics platform  126 . These stubs  1006  are configured as interactive elements with which a user can interact to review, validate, reject, and/or take some other course of action (e.g., modify) in relation to a respective recommendation. Although recommendation stubs are depicted in this example, other user interface elements may be used that enable authorized users to review, validate, reject, and/or take some other course of action (e.g., modify) in relation to a respective recommendation without departing from the spirit or scope of the described techniques. 
     In this example  1000 , each of the stubs  1006  includes a user or patient name, an indication of the prediction  318  on which the respective recommendation  320  is based, and also an indication of the recommendation  320 . User  1008  is depicted performing a gesture in relation to one of the stubs  1006 —in this case a right-to-left swiping gesture—to expose further interface elements that are selectable to validate the respective recommendation  320  or reject it. It is to be appreciated that elements to validate or reject a respective recommendation may be exposed in other ways such as displayed as part of each of the stubs without requiring interaction such as the swiping gesture, displayed as part of a menu launched responsive to some interaction (e.g., right click with a mouse) on a stub, and so on. Although not illustrated, the stubs  1006  may also be selectable to expose a recommendation-specific user interface that outputs an entirety of the prediction and an entirety of the recommendation for review as well as a plurality of options for handling the recommendation—including the options to validate and reject the recommendation and other options. 
     Fault Detection and System Configuration Issues 
       FIG.  11    depicts an example implementation  1100  of a user interface that outputs information about detected faults and system configuration issues in connection with use of the CGM platform. 
     In the illustrated example, display device  1102  is depicted displaying a user interface  1104  of a fault detection and system configuration service. In one or more implementations, the fault detection and system configuration service may be included as part of or otherwise accessible to the CGM platform  112 . It is also to be appreciated that portions of the user interface  1102  may be provided and displayed to other entities through respective portals, such as to manufacturers or service providers that provide devices or services, respectively, that can be used in connection with the CGM system  104 . These devices may include one or more of the computing devices  108 , the insulin delivery system  106 , myriad physiological marker measuring devices, various components of the CGM system  104 , and so forth. 
     The user interface  1104  displays stubs  1106  for a plurality of detected faults and system configuration issues. Although a variety of faults and issues are displayed in the user interface  1104 , the faults and/or issues displayed to a given entity (e.g., a particular manufacturer or a regulatory body such as the U.S. Food and Drug Administration (FDA)) may be limited (e.g., to faults or issues involving the particular manufacturer&#39;s device or to information required by law to be exposed to the regulatory body). By way of contrast, users authenticating to the CGM platform  112  as employee or similar users (e.g., engineers, quality assurance, development partners, and so on) may have authorization to be shown all faults and/or issues related to the CGM platform  112 . 
     In this example  1100 , the stubs  1106  include stubs for events (e.g., faults) reported by one or more devices communicably coupled or otherwise related to the CGM platform  112 , such as reported by the CGM system  104  about the sensor  202 , the computing device  108 , the insulin delivery system  106 , third parties  306 , just to name a few. The stubs  1106  also include stubs that relate to issues that arise in connection with particular system configurations which include the CGM system  104  but different combinations of other devices, such as configurations having a particular computing device  108  (e.g., particular manufacturer), a particular ensemble of computing devices  108  (e.g., a mobile phone corresponding to a first manufacturer and a smart watch corresponding to a second manufacturer), particular insulin delivery systems (e.g., insulin pen versus insulin pump and from different manufacturers), particular firmware and software versions, and so on. 
     The stubs  1106  also include stubs that convey one or more measures of confidence, e.g., confidence of data obtained by various components (e.g., a manufacturing lot of sensors  202 ), system configurations, in connection with users having various demographics, and so forth. In one or more implementations, the measures of confidence are confidence intervals. Additionally, the stubs  1106  include stubs indicating use of platform features (e.g., functionality and/or user interface elements of applications corresponding to the CGM platform  112 ). This information can be used by system developers to determine whether to continue developing and/or providing support in relation to the different features. 
     In relation to stubs that describe events reported by one or more devices, it is difficult if not impossible for developers corresponding to the CGM platform  112  to test, prior to deployment, all combinations of devices that may be used in connection with the CGM system  104  and applications of the CGM platform  112 , e.g., mobile phone and smart watch applications. Instead, those developers may limit testing to combinations of devices most likely to be used (e.g., most popular mobile devices or insulin delivery systems  106 ) and/or the combinations recommended in content dispersed by the CGM platform  112 , e.g., via publications, webpages, packaging, emails, advertising, and so forth. To this extent, developers may be aware of only a subset of issues with the tested combinations of devices, and have fixed them, but not issues with untested combinations. 
     By collecting and maintaining vast amounts of the glucose measurements  118 , the CGM device data  214 , and the supplemental data  304  in the storage device  120 , the data analytics platform  126  can train and then leverage the models  404  to identify issues (e.g., faults) with the different combinations of devices used by the user population  110 , such as issues observed with both tested and untested combinations during real world use. Indeed, the tested combinations of devices may not have been tested in the various ways in which they are actually used by the user population  110  in the real world. Accordingly, by using the models  404  to identify these issues, the data analytics platform  126  can notify developers of the issues, so that the developers can develop fixes for the issues and then deploy them, e.g., as firmware or software updates, as updates to the models  404 , and so on. 
     Alternately or in addition, the data analytics platform  126  may use identification of these issues to adjust predictions and recommendations for the combinations experiencing the issues. By way of example, if a combination of devices used by a small subset of the user population (e.g., 1%) provides glucose measurements  118  to the CGM platform  112  that are consistently (and predictably) lower than glucose measurements  118  provided by other combinations, this information can be used to update real-time glucose measurements  118  presented to users via their computing devices  108 . 
     This information can also be used by the models  404  to predict that a user having the combination of devices will experience the same issues as the subset of the population. Notably, this information can be used to ensure that valid (e.g., safe) recommendations are generated and provided to these users. The data analytics platform  126  may use the capability to identify issues, such as faults, with different combinations of devices in a variety of ways without departing from the spirit or scope of the described techniques. 
     In relation to stubs that describe use of platform features, this information may be used to determine whether to continue developing and/or providing support in relation to the different features, as noted above. In one or more implementations, the data analytics platform  126  can analyze the data maintained in the storage device  120  to determine a cost to a company corresponding to the CGM platform  112  of supporting various features of the CGM platform  112 , such as various functionality provided by its CGM system  104  and applications. As part of this, the data analytics platform  126  is configured to measure a variation, co-variation, and statistical dependence between each functionality deployed by the CGM platform  112 , such as variation, co-variation, and statistical dependence between functionality of the CGM system  104  to measure the person  102 &#39;s temperature, functionality of the CGM platform  112 &#39;s mobile phone application to identify likely occurrence of hypoglycemia during the night, functionality of the CGM platform  112 &#39;s smart watch application to use tactile feedback (e.g., vibrations) in connection with outputting notifications, and so on. 
     In one or more implementations, the data analytics platform  126  determines the variation and co-variation by generating a matrix with dimensions that correspond to a predetermined number of users sampled from the user population  110  (e.g., 50,000 users) and to a number of features (e.g., functionalities) deployed by the CGM platform  112 —or the number of features under consideration for potentially discontinuing development or support. In the following discussion, the predetermined number of users is represented by the term m and the number of features is represented by the term n. To this end, the data analytics platform  126  constructs an m×n matrix, where each cell of the matrix indicates whether a sampled user uses the corresponding feature. The data analytics platform  126  may determine that a user uses a feature in a variety of ways, and use may be defined differently for different features. For example, the data analytics platform  126  may determine that a user has used a feature if the data from the storage device  120  indicates that the user has ever used the feature, has spent more than a threshold amount of time using the feature or with the feature being active, has used the feature more than a threshold number of times, has allowed the feature to be active (e.g., allows notifications), and so on. 
     Using the m×n matrix, the data analytics platform  126  computes a variation score of a given feature i as a function of a number of users a that “use” the given feature i from the number of sampled users m. In one example, the data analytics platform  126  computes the variation according to the following: 
     
       
         
           
             
               φ 
               ⁡ 
               ( 
               i 
               ) 
             
             = 
             
               log 
               ⁢ 
               
                 a 
                 m 
               
             
           
         
       
     
     Here, the term φ(i) represents the variation score. The data analytics platform  126  is also configured to compute a co-variation score of the given feature i and another feature j as a function of the number of users a that use the given feature i and as a function of a second number of users b that use the other given feature j from the number of sampled users m. The data analytics platform  126  also computes the co-variation as a function of a third number of users c that use the given feature i and the other feature j, concurrently. In one example, the data analytics platform  126  computes the co-variation score according to the following: 
     
       
         
           
             
               φ 
               ⁡ 
               ( 
               
                 i 
                 , 
                 j 
               
               ) 
             
             = 
             
               
                 log 
                 ⁢ 
                 
                   c 
                   m 
                 
               
               - 
               
                 φ 
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                 ( 
                 i 
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               - 
               
                 φ 
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                 j 
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     Here, the term φ(i, j) represents the co-variation score and the term φ(j) represents the variation score of the other given feature j. 
     The data analytics platform  126  measures the statistical independence between the different features by constructing a contingency table for each pair of the features, such that each feature is paired with m−1 features and contingency tables are generated for every pair. The data analytics platform  126  performs a known independence test on each table. In one or more implementations, the known independence test comprises a Chi-Square Test for Independence, the output of which is a P-value, e.g., the probability of observing a sample statistic as extreme as a test statistic. The data analytics platform  126  then compares the output of the known independence test (e.g., the P-value) for each table to a significance level threshold. If the output satisfies the significance level threshold, then the data analytics platform identifies the pair of features as dependent features. 
     The data analytics platform  126  then determines the cost of the given feature i as a function of the given feature&#39;s variation φ(i) plus the pairwise scores φ(i, j) for other features that are statistically dependent with the given feature i. The data analytics platform  126  may then present these scores to authorized users (e.g., engineers, marketing persons, and so on) corresponding to the CGM platform  112 , such as by display via the user interface  1104  or some other interface. It is to be appreciated that a cost of the CGM platform  112 &#39;s features may also be determined in other ways. 
     Example Procedures 
     This section describes example procedures for recommendations based on continuous glucose monitoring (CGM). Aspects of the procedures may be implemented in hardware, firmware, or software, or a combination thereof. The procedures are shown as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. In at least some implementations the procedures are performed by a data analytics platform, such as data analytics platform  126  of CGM platform  112  that makes use of a prediction system  316  and a recommendation system  412 . 
       FIG.  12    depicts an example procedure  1200  in which a prediction and a recommendation are generated based on both glucose measurements and additional data of a user. 
     Glucose measurements provided by a CGM system worn by a user are obtained (block  1202 ). By way of example, the CGM platform  112  obtains glucose measurements  118  detected by the CGM system  104  worn by person  102 . As discussed throughout, the CGM system  104  is configured to monitor glucose of the person  102  continuously. For instance, the CGM system  104  may be configured with sensor  202  that is inserted subcutaneously into skin  206  of the person  102 , and continuously measures analytes indicative of the person  102 &#39;s glucose for generating glucose measurements. In one or more implementations, the CGM platform  112  obtains the glucose measurements  118  from a computing device  108  that is communicatively coupled to the CGM system  104 , such as a mobile phone or wearable device of the user. 
     Additional data associated with the user is obtained (block  1204 ). By way of example, the CGM platform  112  obtains additional data  410  from various devices, sensors, applications, or services. Thus, in accordance with the principles discussed herein, the additional data may be obtained from one or more “sources” that are different from the CGM system  104  from which the glucose measurements  118  are provided. The additional data  410  may include least one or more portions of the CGM device data  214  additional to the glucose measurements  118 , the supplemental data  304 , the third party data  314 , data from the IoT  114 , and so forth. 
     A health indicator of the user is predicted by processing the glucose measurements and the additional data using one or more models (block  1206 ). In accordance with the principles discussed herein, the one or more models are generated based on historical glucose measurements and historical additional data of a user population. By way of example, the prediction system  316  of the data analytics platform  126  generates a prediction  318  that includes a health indicator by processing the glucose measurements  118  and additional data  410  of the person  102  using one or more models  404 . The one or more models  404  are generated based on the glucose measurements  118  and the additional data  410  of the user population  110 . The one or more models  404  may include, by way of example and not limitation, a statistical model  406  and/or an additional machine learning model  408 . 
     A recommendation is generated based on the health indicator of the user (block  1208 ). By way of example, regardless of whether the statistical model  406 , the machine learning model  408 , or some combination of statistical and/or machine learning models is used to generate the prediction  318 , the prediction  318  is obtained by the recommendation system  412  of the data analytics platform  126 . The recommendation system  412  is configured to generate the recommendation  320  based on the prediction  318 . In some cases, the health indicator corresponds to a predicted negative health condition, such as a prediction that the user will develop Type II diabetes in the next 40 months. In this scenario, the recommendation system  412  can generate the recommendation  320  based on logic that associates the predicted negative health condition with one or more actions or behaviors that mitigate the predicted negative health condition. As such, the recommendation  320  may include the one or more actions or behaviors intended to mitigate the predicted negative health condition. 
     At least one of the prediction or the recommendation is communicated, over a network, to one or more computing devices for output (block  1210 ). By way of example, the data analytics platform  126  communicates the prediction  318  and/or the recommendation  320  to the computing device  108  for output. The computing device  108  can then display the prediction  318  and/or the recommendation  320  in a CGM interface. As shown in  FIG.  6   , for example, CGM user interface  602  displays a prediction  604  along with a recommendation  606 . In this example, the prediction  604  indicates that the user has a 76% chance of developing Type II diabetes in 40 months. The recommendation  606  includes one or more actions or behaviors that the user can take to improve the user&#39;s predicted negative health condition. In  FIG.  6   , for example, the recommendation  606  includes a recommendation for a customized eating plan, a recommendation for a customized exercise plan, and a recommendation to get a coach that can help the user stay on track with the recommended nutrition and exercise plan. 
     In one or more implementations, the prediction or the recommendation can be communicated to a validation service  502  and/or a decision support platform  504  prior to, or instead of, being communicated to the computing device  108  of the user. In this way, the validation service  502  and the decision support platform  504  may act as intermediaries between the data analytics platform  126  and the computing device  108 . In the scenario in which the prediction or recommendation is communicated to the validation service  502 , the validation service  502  can validate the recommendation  320 . This means determining whether the recommendation is valid (e.g., safe) and can be further communicated to the decision support platform  504  and/or directly to the computing device  108 . The validation service  502  may expose the recommendation  320  to a user that has been authorized by the service  502 , such as a clinician, to validate recommendations. 
     Responsive to a recommendation being validated (e.g., by a clinician or logic of the validation service  502 ), the recommendation may be further routed to the decision support platform  504  or directly to the computing device  108 . When a recommendation is not validated (i.e., it is rejected), the recommendation may not be further routed to the decision support platform  504  or directly to the computing device  108 . Instead, the validation service  502  may modify the recommendation (e.g., according to clinician input) and/or provide a notification back to the data analytics platform  126  that the recommendation is rejected. In this scenario, the data analytics platform  126  may be able to add an indication of rejection as input to the prediction system and initiate generation of a different prediction  318  and/or recommendation  320 . Indeed, the models  404  may be updated based on validations and rejections received from the validation service  502 . In scenarios where the validation service  502  validates the recommendation  320  and consequently allows the recommendation  320  to be forwarded directly to the computing device  108 , the computing device  108  may output the recommendation  320 , such as via display, via speakers, via tactile feedback, and so on, as described above and below. 
     As mentioned above, the recommendation  320  may also be communicated to the decision support platform  504  by the validation service  502  or alternately may be communicated to the decision support platform  504  directly from the data analytics platform  126 , bypassing the validation service. Based on the recommendation  320 , as well as based on other information accessible about the corresponding user, the customer service specialist may determine how to support the user. By way of example, the customer service specialist may determine to call the user to provide voice support during a phone call. 
     An updated health indicator is predicted by processing updated glucose measurements and additional data of the user using the one or more models, and a notification based on the updated health indicator is communicated, over the network, to the one or more computing devices for output (block  1212 ). By way of example, the data analytics platform  126  continuously gathers the glucose measurements  118  and additional data  410  for the user. As such, the prediction system  316  can predict an updated health indicator by processing the updated glucose measurements and additional data using the one or more models  404 . As shown in  FIG.  7   , for example, based on the continuous analysis of the user&#39;s updated glucose measurements  118  from the CGM system  104  and the additional data  410  indicating the user is making better food choices (as indicated by the user&#39;s nutrition log) and exercising more frequently (as indicated by the steps data and exercise log), the prediction system  316  generates an updated prediction, which is communicated to the computing device  108  for display as notification  702 . 
       FIG.  13    depicts an example procedure  1300  in which a recommendation to use a particular application is communicated to one or more devices of a similar user. 
     Glucose measurements of a user population and application interaction data associated with users of a user population are maintained in one or more storage devices (block  1302 ). In accordance with the principles discussed herein, the application interaction data describes usage of applications (e.g., usage of “apps” by various users of the user population). By way of example, the CGM platform  112  obtains glucose measurements  118  detected by the CGM system  104  worn by person  102  and maintains the glucose measurements  118  in storage device  120 . Additionally, the CGM platform obtains application interaction data from various applications, such as applications provided by third parties  306 . 
     An improvement to a health condition of a subset of the users of the user population is identified based at least in part on the glucose measurements (block  1304 ), and the improvement to the health condition of the subset of users is correlated with usage of a particular application based on the application interaction data (block  1306 ). By way of example, the CGM platform  112  can aggregate the application interaction data, along with the glucose measurements  118  and additional data in order to determine whether a user&#39;s interaction with a particular application or service correlates to improvement of the user&#39;s health. For example, based on the glucose measurements  118 , the CGM platform  112  can objectively determine an improvement in a health condition of a user. The CGM platform  112  can then correlate the improvement or decline in the user&#39;s health with usage of a particular application based on the application interaction data. For example, if the CGM platform detects an improvement with the user&#39;s health that coincides with heavy usage of a particular application, then the CGM platform may determine that the particular application is correlated with the improvement. 
     A similar user having the health condition is identified (block  1308 ), and a recommendation to use the particular application is communicated to one or more devices associated with the similar user (block  1310 ). By way of example, the recommendation system  412  can identify a similar user having the health condition, and generate a recommendation to the similar user to utilize the particular application that helped to improve the health condition of the subset of similar users. To do so, the recommendation system  412  can predict a probability of a similar improvement in a health condition through usage of a particular application by other users in the user population and recommend applications with a high probability of improving health to other similar users. 
     The application recommendations can be communicated to computing device  108  for output. By way of example, as shown in  FIG.  8   , the CGM user interface  802  is depicted as displaying recommended applications  804 . In this case, the recommended applications  804  correspond to various third party applications. In  FIG.  8   , a user is depicted as selecting the application “Nutrition by Neha” in order to download this application to the user&#39;s smartphone. 
     Having described example procedures in accordance with one or more implementations, consider now an example system and device that can be utilized to implement the various techniques described herein. 
     Example System and Device 
       FIG.  14    illustrates an example system generally at  1400  that includes an example computing device  1402  that is representative of one or more computing systems and/or devices that may implement the various techniques described herein. This is illustrated through inclusion of the CGM platform  112 . The computing device  1402  may be, for example, a server of a service provider, a device associated with a client (e.g., a client device), an on-chip system, and/or any other suitable computing device or computing system. 
     The example computing device  1402  as illustrated includes a processing system  1404 , one or more computer-readable media  1406 , and one or more I/O interfaces  1408  that are communicatively coupled, one to another. Although not shown, the computing device  1402  may further include a system bus or other data and command transfer system that couples the various components, one to another. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control and data lines. 
     The processing system  1404  is representative of functionality to perform one or more operations using hardware. Accordingly, the processing system  1404  is illustrated as including hardware elements  1410  that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements  1410  are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically-executable instructions. 
     The computer-readable media  1406  is illustrated as including memory/storage  1412 . The memory/storage  1412  represents memory/storage capacity associated with one or more computer-readable media. The memory/storage component  1412  may include volatile media (such as random access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory/storage component  1412  may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media  1406  may be configured in a variety of other ways as further described below. 
     Input/output interface(s)  1408  are representative of functionality to allow a user to enter commands and information to computing device  1402 , and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality (e.g., capacitive or other sensors that are configured to detect physical touch), a camera (e.g., which may employ visible or non-visible wavelengths such as infrared frequencies to recognize movement as gestures that do not involve touch), and so forth. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, tactile-response device, and so forth. Thus, the computing device  1402  may be configured in a variety of ways as further described below to support user interaction. 
     Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors. 
     An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. The computer-readable media may include a variety of media that may be accessed by the computing device  1402 . By way of example, and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media.” 
     “Computer-readable storage media” may refer to media and/or devices that enable persistent and/or non-transitory storage of information in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal bearing media. The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage device, tangible media, or article of manufacture suitable to store the desired information and which may be accessed by a computer. 
     “Computer-readable signal media” may refer to a signal-bearing medium that is configured to transmit instructions to the hardware of the computing device  1402 , such as via a network. Signal media typically may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier waves, data signals, or other transport mechanism. Signal media also include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. 
     As previously described, hardware elements  1410  and computer-readable media  1406  are representative of modules, programmable device logic and/or fixed device logic implemented in a hardware form that may be employed in some embodiments to implement at least some aspects of the techniques described herein, such as to perform one or more instructions. Hardware may include components of an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), and other implementations in silicon or other hardware. In this context, hardware may operate as a processing device that performs program tasks defined by instructions and/or logic embodied by the hardware as well as a hardware utilized to store instructions for execution, e.g., the computer-readable storage media described previously. 
     Combinations of the foregoing may also be employed to implement various techniques described herein. Accordingly, software, hardware, or executable modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage media and/or by one or more hardware elements  1410 . The computing device  1402  may be configured to implement particular instructions and/or functions corresponding to the software and/or hardware modules. Accordingly, implementation of a module that is executable by the computing device  1402  as software may be achieved at least partially in hardware, e.g., through use of computer-readable storage media and/or hardware elements  1410  of the processing system  1404 . The instructions and/or functions may be executable/operable by one or more articles of manufacture (for example, one or more computing devices  1402  and/or processing systems  1404 ) to implement techniques, modules, and examples described herein. 
     The techniques described herein may be supported by various configurations of the computing device  1402  and are not limited to the specific examples of the techniques described herein. This functionality may also be implemented all or in part through use of a distributed system, such as over a “cloud”  1414  via a platform  1416  as described below. 
     The cloud  1414  includes and/or is representative of a platform  1416  for resources  1418 . The platform  1416  abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud  1414 . The resources  1418  may include applications and/or data that can be utilized while computer processing is executed on servers that are remote from the computing device  1402 . Resources  1418  can also include services provided over the Internet and/or through a subscriber network, such as a cellular or Wi-Fi network. 
     The platform  1416  may abstract resources and functions to connect the computing device  1402  with other computing devices. The platform  1416  may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources  1418  that are implemented via the platform  1416 . Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system  1400 . For example, the functionality may be implemented in part on the computing device  1402  as well as via the platform  1416  that abstracts the functionality of the cloud  1414 . 
     CONCLUSION 
     Although the systems and techniques have been described in language specific to structural features and/or methodological acts, it is to be understood that the systems and techniques defined in the appended claims are not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed subject matter.