Monitoring devices

Abstract

The mobile device includes a memory and a processor, the memory storing instructions that, when executed by the processor, cause the device to receive dosage information including a previous dose emitted by the injection device; determine at least one of: a measure of the patient's well-being based on the received well-being data; a measure of the patient's fitness based on physiological data received from a fitness tracking device and a measure of the patient's weight based on weight data received from a scale; determine an adjusted medication dose based at least in part on the dosage information, the measure of well-being, and at least one of the measure of fitness and the measure of weight; and output the adjusted medication dose.

Description

Technical Field

[0001] The present invention relates to monitoring patients during drug treatment. In particular, the present invention relates to devices and systems for monitoring patients during drug treatment, as well as methods for operating the devices.

Background Art

[0002] There are various diseases that require regular treatment, for example, by injection of drugs. Such injections can be performed using an injection device utilized by a healthcare professional (HCP) or the patient themselves. As an example, type 1 and type 2 diabetes can be treated, for example, once or several times a day, by the patient themselves injecting insulin doses. Similarly, in the pre-diabetes stage, overweight or obese patients can be treated by injection for continuous weight management.

[0003] Unfortunately, during the process of drug treatment with some drugs, patients may experience the drug itself or side effects that may result from the symptoms being treated. These side effects can include, for example, weight changes, susceptibility to other diseases, or an overall deterioration of general health.

[0004] Therefore, when patients require regular or long-term treatment, there is a need to provide a system that can support the treatment and monitor the overall well-being of the patient in response to that treatment. In particular, it is necessary to determine changes in the patient's health according to the treatment plan and encourage good patient habits in compliance with the treatment plan. This is important because changes in the patient's response to treatment may require, for example, adjustments to the nature of the treatment, such as the administration area, or the introduction of additional treatment.

[0005] A significant proportion of patients with type 2 diabetes are overweight or obese. Many of the available treatment options are associated with promoting weight gain, which is a distressing side effect for patients already struggling with excess weight and can negatively impact treatment adherence. SAR425899 is a dual agonist of glucagon-like peptide-1 and glucagon receptor (GLP-1 / GCR agonist) developed for simultaneous diabetes management and sustained weight management in overweight or obese individuals. The glucagon-like peptide increases satiety in patients, leading to reduced subsequent food intake, thereby promoting weight loss. Therefore, a SAR425899 injection regimen can be used to control a patient's glucagon levels and support weight management.

[0006] Aspects of the present invention were conceived with the above in mind. [Overview of the project] [Means for solving the problem]

[0007] According to a first aspect of this disclosure, a mobile device comprising memory and a processing unit, wherein the memory stores instructions, and when the instructions are executed by the processing unit, the device: The injection device receives dose information, including previous doses released; Based on the received well-being data, determine the patient's well-being measurement; Measurements of a patient's fitness based on physiological data received from a fitness tracking device; and Patient weight measurement based on weight data received from a scale. Determine at least one of them; Determine the adjusted drug dose based at least in part on dosage information, well-being measurements, and at least one of the fitness and weight measurements; A mobile device is provided that outputs adjusted drug dosages.

[0008] Mobile devices can be used for performing titrations.

[0009] Determining an adjusted drug dose may include adjusting a previous dose included in the received dose information. Determining an adjusted drug dose may include adjusting a previous dose based on well-being measurements and at least one of fitness measurements and weight measurements. Determining an adjusted drug dose may include increasing, decreasing, or maintaining a previous dose based on well-being measurements, and increasing, decreasing, or maintaining a previous dose based on at least one of fitness measurements and weight measurements.

[0010] When the instruction is executed by the processing unit, the device can further output dose information, including at least well-being data and previous doses, to a server device for storage.

[0011] Physiological data can include pulse data, and when an instruction is executed by the processing unit, the device can be instructed to determine fitness measurements based on the pulse data.

[0012] Physiological data can include blood pressure data, and when an instruction is executed by the processing unit, the device can be instructed to determine a fitness measurement based on the blood pressure data.

[0013] Physiological data may include step count data, and when an instruction is executed by the processing unit, the device can be instructed to determine fitness measurements based on the step count data.

[0014] When the instruction is executed by the processing unit, the device may further output at least one of the following for display: well-being measurements, fitness measurements, and weight measurements.

[0015] At least one of the well-being measurements, fitness measurements, and weight measurements output for display can be displayed together with one or more previous well-being measurements, fitness measurements, and weight measurements.

[0016] When the instruction is executed by the processing unit, the device may further: receive adverse event data indicating one or more adverse events the patient may experience; and determine an adjusted drug dose based at least in part on this adverse event data.

[0017] When the instruction is executed by the processing unit, the device may further: determine whether the fitness measurement meets a first predetermined threshold; determine whether the weight measurement meets a second predetermined threshold; and, in response to determining that the fitness measurement meets the first predetermined threshold or the weight measurement meets the second predetermined threshold, output a virtual award for display on the mobile device.

[0018] When the command is executed by the processing unit, the device can also receive and store food intake information entered by the user.

[0019] When executed by the processing unit, the command can also cause the device to publish data corresponding to at least one of the following: well-being measurements, fitness measurements, and weight measurements, to a social media platform.

[0020] According to a second aspect of the present invention, a system is provided which includes a mobile device according to the first aspect, and the system is Fitness tracking devices configured to transmit physiological data to mobile devices; A scale configured to transmit weight data to a mobile device, It further includes, The mobile device is configured to determine an adjusted drug dosage based at least in part on physiological data and weight data received by the mobile device.

[0021] The system can further include an injection device configured to send dosage information to the mobile device, and the mobile device is configured to determine an adjusted drug dosage based at least in part on the dosage information.

[0022] According to a third aspect of the present disclosure, a computer-implemented method is provided, the method comprising: receiving dosage information including previous dosages released by an injection device; determining a measure of the patient's well-being based on the received well-being data; a measure of the patient's fitness based on physiological data received from a fitness tracking device; and a measure of the patient's weight based on weight data received from a scale determining at least one of; determining an adjusted drug dosage based at least in part on the dosage information, the measure of well-being, and at least one of the measure of fitness and the measure of weight; outputting the adjusted drug dosage; and including.

[0023] The method can be a method for performing titration.

[0024] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium including instructions that, when executed by a computer, cause the computer to perform the method according to the third aspect.

[0025] Here, embodiments of the present invention are described by way of example only with reference to the accompanying drawings.

Brief Description of the Drawings

[0026] [Figure 1] This is a schematic diagram showing the components of a mobile device according to an embodiment of the present invention. [Figure 2] This is a schematic diagram of a system including a mobile device according to an embodiment of the present invention, as shown in Figure 1. [Figure 3] Figure 2 is a schematic diagram showing the data flow in the system. [Figure 4] Figure 3 is a schematic diagram showing the data output as multiple graphs on the device's display. [Figure 5] This flowchart shows a titration method according to an embodiment of the present invention. [Figure 6] This is a flowchart showing a method of drug therapy according to an embodiment of the present invention. [Modes for carrying out the invention]

[0027] The following disclosure describes a computer program having machine-readable instructions that, when executed by a processing unit, cause the processing unit to perform various operations. In the following exemplary embodiments, the computer program is implemented on a mobile device, and the computer program takes the form of an application. According to the following exemplary embodiments, the mobile device is a mobile phone such as a smartphone. Advantageously, the monitoring application may be a separate application. The monitoring application may be provided on the mobile device at the time of manufacture, or it may be downloaded by the user to the mobile device from, for example, an application marketplace or application store. However, the mobile device may take different forms, such as a tablet, laptop, or smart glasses.

[0028] In other words, a computer program, such as one implemented on a mobile device, provides a means of monitoring a patient during drug therapy. The drug may be SAR425899 and the therapy may be for continuous weight management, but the drug and therapy are not limited to such examples, and instead, aspects of the present invention can be applied to other drugs and / or therapies.

[0029] Monitoring applications can be provided to support a wide range of clinical research. These may include, for example, clinical research on diabetes, sustained weight management, cardiovascular conditions, rheumatism, or psoriasis. The terms user and patient may be used interchangeably throughout this disclosure.

[0030] Aspects of the present invention provide support for patient treatment. Aspects of the present invention can encourage patient adherence to drug treatment plans, leading to more efficient, faster, and potentially safer treatment outcomes. In an example of a treatment plan involving the use of SAR425899, etc., aspects of the present invention can support further weight loss in the patient. Improved patient adherence can be achieved through improvements in the titration of dose titrations associated with the present invention. Aspects of the present invention can help encourage patient adherence by enabling patients to visualize their medical statistics and monitor treatment-related data such as fitness level and body mass index (BMI) over time. Patients can also be encouraged through sharing statistics on social media platforms for motivation and / or social feedback and encouragement.

[0031] Figure 1 is a schematic diagram of some of the components of a mobile device 100 on which a computer program can run. The mobile device 100 includes a processing unit 102. The processing unit 102 may include one or more processors, logic circuits, etc. The processing unit 102 controls the operation of other hardware components of the mobile device 100. The processing unit 102 and the other hardware components can be connected via a system bus (not shown). Each hardware component can be connected to the system bus directly or via an interface. The various method steps disclosed throughout this specification can be performed by or made to be performed by the processing unit 102 unless otherwise indicated.

[0032] The mobile device 100 includes a display 112, which can be any suitable type of display, such as a liquid crystal display (LCD), a thin-film transistor (TFT) display, an organic light-emitting diode (OLED) display, or ePaper. The processing unit 102 is configured to provide the display 112 with commands to cause the display 112 to provide visual information to the user. In some cases, the processing unit 102 provides commands to the display 112 via a display driver (not shown).

[0033] Device 100 further includes a user input interface 114. The user input interface 114 is a means by which a user can provide input to the mobile device 100, in particular to the processing unit 102. In some examples, the user input interface 114 may include a haptic user interface for receiving haptic input from the user. For example, the haptic user interface may include one or more buttons or touch-sensitive panels. The touch-sensitive panel may be, for example, any type of resistive touch-sensitive panel or capacitive touch-sensitive panel. In some cases, the display 112 may be a touch-sensitive display that also includes the user input interface 114. This is the case when device 100 is a smartphone. In some examples, the user input interface 114 may include an audio user interface configured to receive voice commands, for example, via a microphone, as input from the user. In this way, the user can provide input to the user input interface 114 via voice commands. The voice commands can be parsed by the processing unit 102 or different processing components of device 100 and converted into machine learning instructions.

[0034] As described below, the mobile device 100 also includes a communication interface 116 for sending data to and receiving data from other electronic devices or systems. The communication interface 116 can take any suitable form for providing wireless and / or wired communication. For example, the communication interface 116 may include at least one of the following: a Bluetooth interface 116a, a WiFi interface 116b, a Near Field Communication (NFC) interface 116c, and a wired interface 116d such as a USB (e.g., USB-C) interface, but other suitable types of wired or wireless communication interfaces 116 may also be used.

[0035] The mobile device 100 may optionally include a camera device 117, which may include one or more cameras. Any suitable camera can be used. The processing unit 102 may be configured to process one or more images captured by the camera device 117 and, for example, extract data from one or more images.

[0036] The mobile device 100 also includes a power supply 118 for supplying power to the various electronic components of the mobile device 100. The power supply 118 may include a battery 119 or may receive power through other means such as wireless power transfer or a power cable.

[0037] The processing unit 102 is configured to send and receive signals to and from other components of device 100 in order to control the operation of those components. For example, the processing unit 102 controls the display of content on the display 112 and receives signals as a result of user input via the user input interface 114.

[0038] The mobile device 100 includes memory 104, which includes working memory or volatile memory such as random access memory (RAM), and non-volatile memory. The processing unit 102 can access the RAM to process data and can control the storage of data in memory 104. The RAM can be any type of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), or flash memory. The non-volatile memory stores the operating system 108 and applications 110, as well as data files and associated metadata. Memory 104 can include any type of non-volatile memory such as read-only memory (ROM), flash memory, and magnetic drive memory.

[0039] The processing unit 102 operates under the control of the operating system 108. The operating system 108 may include code for hardware such as the display 112 and the communication interface 116, as well as code for the basic operation of the mobile device 100. In addition to the application 110, the operating system 108 can also trigger the launch of other software modules stored in memory 104.

[0040] Other standard or optional components of the mobile device 100, such as transceivers and audio transducers, may be present but are omitted for simplicity.

[0041] Figure 2 shows a mobile device 100 as part of a monitoring system 200. The system 200 can be used by patients to monitor their treatment plan. As should be apparent throughout this disclosure, the monitoring system 200 can be used to collect and store patient and treatment plan-related data, such as fitness-related data, weight data, dose information, patient well-being, and adverse event data. The data can be presented to the user so that they can track their progress during the treatment plan and be encouraged to adhere to the plan. The data can be used to provide improved titration of drug doses to the patient, thereby improving treatment outcomes, encouraging patient adherence, and enabling patients to perform titrations themselves rather than relying entirely on healthcare professionals.

[0042] System 200 includes an injection device 210, a fitness tracking device 220, a scale 230, and a server device 240. The mobile device 200 is capable of communicating with one or more of the injection device 210, fitness tracking device 220, electronic scale 230, and server device 240, as discussed below, and transmitting and / or receiving data via the communication interface 116.

[0043] The injection device 210 can be any suitable form of device for administering a drug dose to a patient. For example, the injection device 210 can be an injection pen, an auto-injector, a pump patch, an infusion device, etc. The injection device 210 is used to inject a drug dose into the patient / user. Therefore, the injection device 210 is used by the patient / user as part of a treatment plan.

[0044] The injection device 210 has an electronic circuit section (not shown) that can transmit dose information received via the communication interface 116 of the mobile device 100 to the mobile device 100. The dose information includes the dose of drug released or to be released from the injection device 210. In other words, the dose can be the dose actually injected into the patient by the injection device 210, or the dose programmed into the injection device 210 when it is ready for subsequent release. The dose can be expressed as a dose value such as the number of units of drug.

[0045] In addition to the dose, the dose information may include further information such as the time and / or date the dose was injected, as determined by the injection device 210.

[0046] Dosage information can be transmitted wirelessly from the injection device 210 to the communication interface 116 of the mobile device 100 using, for example, Bluetooth, NFC, or WiFi protocol, or any other suitable wireless protocol. In other examples, dose information can be transmitted to the communication interface 116 of the mobile device 100 via a wired link. In some examples, the injection device 210 can transmit dose information to the mobile device 100 in response to receiving a signal transmitted to the injection device 210 by the mobile device 100, as discussed below. In other examples, the injection device 210 can transmit dose information to the mobile device 100 in response to receiving user input in the injection device 210, for example, in response to user operation of the injection mechanism of the injection device 210 causing drug dispensing, or in response to the user pressing a button on the user interface of the injection device 210. In some examples, the injection device 210 can automatically transmit dose information, for example, according to a predetermined schedule (for example, daily).

[0047] Dosage information can be received by the mobile device 100 by means other than through the communication interface 116. For example, the camera device 117 of the mobile device 100 can capture an image of the injection device 210, and the processing device 102 of the mobile device 100 can extract dose information from the image. For example, the image may include a visible representation of the dose emitted by the injection device 210 or dialed into the injection device, such as the display of the injection device 210 showing the dose on the dial sleeve. The processing device 102, or a different device, can identify the dose from the image of the dial sleeve.

[0048] System 200 may also include a fitness tracking device 220. While the fitness tracking device 220 is shown as a fitness tracking smartwatch in Figure 2, any suitable form of fitness tracking device 220 can be used instead. In some examples, the functions of the fitness tracking device 220 can be performed by multiple devices such as multiple fitness bands, smartwatches, or monitors. The fitness tracking device 220 is configured to monitor one or more physiological characteristics of a patient, particularly a patient receiving treatment from the injection device 210. These physiological characteristics may indicate the patient's fitness level. These physiological characteristics may include one or more of heart rate (pulse), blood pressure, or step count, but other physiological characteristics may also be used.

[0049] The fitness tracking device 220 includes one or more sensors for measuring the patient's physiological characteristics and determining physiological data that reflects the measured characteristics. For example, the fitness tracking device 220 may include one or more of the following: a heart rate sensor 221 for measuring the patient's heart rate, a blood pressure sensor 222 for measuring the patient's blood pressure, and / or a pedometer 223 for measuring the patient's step count.

[0050] The fitness tracking device 220 can continuously measure one or more physiological characteristics. For example, the fitness tracking device can continuously measure a patient's heart rate and / or step count. The fitness tracking device 220 can measure one or more physiological characteristics at predetermined discrete intervals. For example, the fitness tracking device can measure a patient's blood pressure once a day. The fitness tracking device 220 can measure one or more physiological characteristics in response to receiving input such as user input on the fitness tracking device 220, or in response to receiving a signal transmitted from the mobile device 100. For example, the fitness tracking device can measure a patient's heart rate in response to a user pressing a button on the fitness tracking device 220.

[0051] Physiological data measured by the fitness tracking device 220 can be stored in the memory of the fitness tracking device 220 and / or transmitted to another electronic device for storage, such as the mobile device 100, a different electronic device, or a server. The physiological data can be processed by the fitness tracking device 220, the mobile device 100, a different electronic device, and / or a server.

[0052] The fitness tracking device 220 can transmit physiological data to the mobile device 100, which will be received via the mobile device 100's communication interface 116. For example, the fitness tracking device 220 can transmit one or more of the following to the mobile device 100: heart rate data, blood pressure data, and step count data. The fitness tracking device 220 can also transmit physiological data associated with one or more physiological characteristics wirelessly to the communication interface 116 using, for example, Bluetooth, NFC, or WiFi protocol, or any other suitable wireless protocol. In another example, physiological data can be transmitted to the mobile device 100's communication interface 116 via a wired link.

[0053] In some cases, the fitness tracking device 220 can transmit physiological data to the mobile device 100 in response to receiving a request sent to the fitness tracking device 220 by the mobile device 100, as discussed below. In other cases, the fitness tracking device 220 can transmit physiological data to the mobile device 100 in response to receiving user input in the fitness tracking device 220, for example, in response to a user providing input in the user interface of the fitness tracking device 220. In some cases, the physiological data can be transmitted automatically from the fitness tracking device 220 to the mobile device 100, rather than being transmitted in response to a request such as input from a user or a request from the mobile device 100. For example, the fitness tracking device 220 can transmit physiological data indicating one or more of the physiological characteristics hourly, daily, or weekly. Physiological data associated with some of the physiological characteristics can be transmitted more frequently than physiological data associated with other physiological characteristics. For example, data indicating heart rate can be transmitted every minute, while blood pressure data can be transmitted daily.

[0054] In addition to data indicating one or more physiological characteristics, the fitness tracking device 220 may also transmit time and / or data information associated with one or more physiological characteristics to the mobile device 100. For example, the information may include the time and / or date when the physiological characteristic was measured by the fitness tracking device 220.

[0055] Physiological data associated with one or more physiological characteristics can be received by the mobile device 100 by means other than through the communication interface 116. For example, the camera device 117 of the mobile device 100 can capture images of the fitness tracking device 220, and the processing unit 102 of the mobile device 100 can extract physiological data from the images. For example, the images may include a visible representation of physiological data associated with one or more physiological characteristics, such as measured heart rate, blood pressure, and / or step count, displayed on the display of the fitness tracking device 220.

[0056] System 200 may also include a scale 230, also known as a weighing scale, used to measure the weight of a patient. Mobile device 100 can receive weight data from the scale 230, which represents the patient's weight. The scale 230 may be an electronic scale 230 that includes an electronic circuit capable of measuring the patient's weight and wirelessly transmitting the weight data to the mobile device 100 using, for example, Bluetooth, NFC or WiFi protocol, or any other suitable wireless protocol. In other examples, the weight data may be transmitted to the communication interface 116 of the mobile device 100 via a wired link. In some examples, the scale 230 may transmit weight data to the mobile device 100 in response to receiving a request sent to the scale 230 by the mobile device 100. In other examples, the scale 230 may transmit weight data to the mobile device 100 in response to receiving user input on the scale 230, for example, detection of a user standing on the scale 230, or detection of a user pressing a button on the user interface of the scale 230.

[0057] In addition to the weight value, it may include further information such as weight data, the time and / or date when the weight was measured by the scale 230.

[0058] Weight data can be received by the mobile device 100 by means other than through the communication interface 116. For example, the camera device 117 of the mobile device 100 can capture an image of the scale 230, and the processing unit 102 of the mobile device 100 can extract weight data from the image. For example, the image may include a visible representation of the weight measured by the scale 230, such as the weight of a patient displayed on the scale 230's display.

[0059] System 200 may include a server device 240 containing one or more servers. One or more servers may be part of a distributed computing device such as a cloud computing device. The mobile device 100 can communicate with the server device 240 via the communication interface 116 using any suitable means of wired or wireless communication, for example, over the internet, a cellular network, or a local area network (LAN), but other devices may also be used. In some examples, the server device 240 may instead include another mobile device, a personal computing device such as a laptop, a desktop computer, or a tablet. As will be discussed below, the mobile device 100 can send data to and / or receive data from the server device 240. The mobile device 100 can send data to the server device 240 for storage or processing.

[0060] In some examples, one or more embodiments disclosed herein as being performed by the mobile device 100 may be further or alternatively performed by the server device 240. For example, instead of performing a processing step on the mobile device 100 itself, the mobile device 100 may instead send data to the server device 240 for processing, and the mobile device 100 then receives the processing results from the server device 240. This may allow the mobile device 100 to use the improved processing capabilities of the server device 240 to process data faster and reduce the processing load on the mobile device 100. Alternatively or in addition, it is disclosed herein that one or more of the injection device 210, fitness tracking device 220, and scale 230 send or receive data or information to the mobile device 100, which may not be through direct communication with the mobile device 100, but rather through an intermediary such as the server device 240.

[0061] In some cases, the mobile device 100 may be able to communicate with a social media platform 250, such as a social media platform 250 provided by Facebook or Twitter. The mobile device 100 may communicate with the social media platform 250 via a server device 240 or through any other suitable type of network device. As discussed below, the mobile device 100 may be able to post data to the social media platform 250, which can then be viewed by one or more other users of the social media platform 250. The mobile device 100 may also be able to receive and view information sent to the mobile device 100 from the social media platform 250.

[0062] Figure 3 is a schematic diagram of System 200 from Figure 2, showing various exemplary data flows between the components of System 200.

[0063] As shown in Figure 3, device 100 can receive dose information from injection device 210, as discussed below. The dose information may include the dose value measured by injection device 210, and in some examples, the time and / or date when the dose was injected.

[0064] As shown in Figure 3, device 100 can receive physiological data associated with one or more physiological characteristics from the fitness tracking device 220, such as the patient's heart rate, step count, or blood pressure. Each time physiological data is received, it can be stored in the device 100's memory 104 and / or transmitted to the server device 240 for storage. The physiological data can be stored associated with a timestamp representing the time and / or date the physiological data was received, or the time and / or date one or more of the measurements performed by the fitness tracking device 220 were performed. The timestamp may include timestamp information transmitted to the mobile device 100 by the fitness tracking device 220.

[0065] Device 100 can determine a measure of the patient's fitness based at least in part on physiological data received from the injection device 210. The fitness measure provides a quantitative indication of the patient's physiological fitness and can be, for example, a fitness score. For example, device 100 can determine a fitness measure based at least in part on one or more of the heart rate, step count, and blood pressure received from the fitness tracking device 220. The fitness measure can be normalized. For example, the fitness measure can be calculated as a score from 0 to 100, where 0 indicates the patient has the lowest level of fitness and 100 indicates the patient has the highest level of fitness. New fitness measures can be calculated periodically. For example, the fitness measure can be calculated according to a predetermined schedule (e.g., daily) in response to the reception of new physiological data from the fitness tracking device 220 (e.g., in response to the reception of new blood pressure data), in response to the reception of user input in the user input interface 114, or as part of a method such as the method discussed in relation to Figures 5 and 6. Therefore, fitness measurements may change over time; for example, they may increase as the patient's fitness improves and decrease when the patient's fitness declines.

[0066] When determining fitness measurements, various types of physiological data can be weighted. Some types of physiological data can be weighted more heavily than others to have a greater impact on the determined fitness measurement. For example, blood pressure data can be weighted more heavily than step count data, thereby making it more likely that an improvement in a patient's blood pressure will lead to a greater improvement in fitness measurement than an improvement in their step count.

[0067] Fitness measurements can be determined based on the most recent physiological data received by the mobile device 100, or at least partially based on historical physiological data. For example, fitness measurements can be determined using a moving average of heart rate data. In some cases, the moving average can be based on a predetermined number of most recent physiological data measurements (the last 10 heart rate measurements received by the mobile device 100). In other cases, the moving average can be based on an average over a predetermined period, for example, the average heart rate over the last 7 days. Physiological data can be weighted such that the most recent physiological data is given more weight than older physiological data.

[0068] Each time a fitness measurement is calculated, this data can be stored in the memory 104 of device 100 and / or transmitted to the server device 240 for storage. The fitness measurement can be stored associated with a timestamp representing the time and / or date when the fitness measurement was determined, or the time and / or date when one or more of the measurements performed by the fitness tracking device 220 were performed. The stored fitness measurement and associated timestamp information can provide a record of the patient's changing physiological fitness. The record can be used to evaluate how the patient's physiological health has changed over time, for example, whether it has improved or worsened as the treatment plan progresses. The record of fitness measurements can be output to the user as a visualization, as discussed below, or transmitted to the server device 240 for analysis.

[0069] As shown in Figure 3, device 100 can receive weight data from scale 230. Each time weight data is received, this data can be stored in the memory 104 of device 100 and / or sent to the server device 240 for storage. The weight data can be stored associated with a timestamp representing the time and / or date the weight data was acquired, or the time and / or date the weight data was received by the mobile device 100. The timestamp may include timestamp information sent from scale 230 to the mobile device 100.

[0070] Device 100 can determine a patient's weight measurement based at least part of the weight data received from the scale 230. The weight measurement provides a quantitative indication of the patient's weight, which can be, for example, a Body Mass Index (BMI) value. When Device 100 determines the patient's BMI, it can do so based on the weight data and the patient's height. The patient's height can be pre-stored in Device 100, for example in the device's memory 104, or Device 100 can prompt the user for height input via the user input interface 114. In some examples, height can be received by Device 100 from a server device 240, a different server, etc.

[0071] New weight measurements can be calculated periodically. For example, weight measurements can be calculated according to a predetermined schedule (e.g., daily) in response to the reception of new weight data from the scale 230, in response to the reception of user input in the user input interface 114, or as part of a method such as the method discussed in relation to Figures 5 and 6. Therefore, weight measurements may change over time and may decrease during the course of treatment.

[0072] Fitness measurements can be determined based on the most recent weight data received by the mobile device 100, or at least partially based on historical weight data. For example, a moving average of weight data can be used to determine weight measurements. In some cases, the moving average can be based on a predetermined number of most recent weight data measurements received (e.g., the last five weight measurements received by the mobile device 100). In other cases, the moving average can be based on an average over a predetermined period, e.g., the average weight over the last seven days. Historical weight data can be weighted such that the most recent weight data is given more weight than older weight data.

[0073] Each time a weight measurement is calculated, this data can be stored in the memory 104 of device 100 and / or transmitted to the server device 240 for storage. The weight measurement can be stored associated with a timestamp representing the time and / or date when the weight measurement was determined, or the time and / or date when one or more of the weight measurements performed by the scale 230 were carried out. The stored weight measurement and associated timestamp information can provide a record of the patient's changing weight. Using the record, it is possible to evaluate how the patient's weight has changed over time, for example, whether it has increased or decreased as the treatment plan progresses. The record of weight measurements can be output to the user as a visualization, as discussed below, or transmitted to the server device 240 for analysis.

[0074] Device 100 can also receive well-being data and adverse event data from user 350. User 350 inputs well-being data and adverse event data to device 100, for example, via the user input interface 114.

[0075] Well-being data includes data indicating the patient's current well-being, such as the patient's happiness level or mood. Each time well-being data is received, this data can be stored in the memory 104 of device 100 and / or transmitted to the server device 240 for storage. Well-being data can be stored associated with a timestamp representing the time and / or date the well-being data was received.

[0076] Device 100 can determine a measure of the patient's well-being based at least in part on well-being data received from user 350. The measure of well-being provides a quantitative indication of the patient's well-being (i.e., happiness or mood) and can be, for example, a well-being score. The measure of well-being can be normalized. For example, the measure of well-being can be calculated as a score from 0 to 100, where 0 indicates the patient has the lowest sense of well-being and 100 indicates the patient has the highest sense of well-being. New measures of well-being can be calculated periodically. For example, the measure of well-being can be calculated according to a predetermined schedule (e.g., daily) in response to the receipt of new well-being data entered by user 350, in response to the receipt of different user inputs in the user input interface 114, or as part of a method such as the method discussed in relation to Figures 5 and 6. Thus, the measure of well-being may change over time and may increase as the treatment plan progresses.

[0077] Well-being measurements can be determined based on the most recent well-being data received by the mobile device 100, or based on at least some historical well-being data. For example, a moving average of well-being data can be used to determine fitness measurements. In some cases, the moving average can be based on a predetermined number of most recent well-being data inputs (e.g., the last 10 well-being data inputs received by the mobile device 100). In some cases, the moving average can be based on an average over a predetermined period, for example, the average well-being over the last 7 days. The well-being data can be weighted such that the most recent well-being data is weighted more heavily than older well-being data.

[0078] Each time a well-being measurement is calculated, this data can be stored in the memory 104 of device 100 and / or transmitted to the server device 240 for storage. The well-being measurement can be stored associated with a timestamp representing the time and / or date the well-being measurement was determined, or the time and / or date the well-being data used to calculate the well-being measurement was entered by user 350. The stored well-being measurement and associated timestamp information can provide a record of the patient's changing well-being. Using this record, it is possible to evaluate how the patient's well-being has changed over time, for example, whether it has improved or worsened as the treatment plan progresses. The well-being measurement record can be output to the user as a visualization, as discussed below, or transmitted to the server device 240 for analysis.

[0079] Adverse event data includes data showing any adverse events experienced by the patient, and these adverse events are associated with the medications administered during the treatment plan. Illustrative adverse events may include headache, drowsiness, fatigue, and visual disturbances, but adverse events are not limited to such examples, and other adverse events are possible.

[0080] Device 100 can provide a graphical user interface (GUI) on the display 112 to assist user 350 in providing side effect data via a user input interface 114. For example, the GUI can request specific side effect data from user 350 and provide means for the user to input the requested data. For example, the GUI could include a prompt asking user 350 if they have experienced any migraines in the past seven days. The user can respond to the prompt by inputting data via the user input interface 114. For example, the user can use a keypad to select a "yes" or "no" option displayed on the display 112, or, if the display is a touchscreen display, the user can select a user-selectable GUI element provided on the display 112 by device 100. Device 100 can request user 350 to respond with a choice (i.e., a "yes" / "no" answer) or a free response (i.e., a numerical value).

[0081] Device 100 can transmit data to server device 240. The data may include the aforementioned information and data received by the device, such as physiological data, dose information, weight data, well-being data, adverse event data, or any other data derived therefrom, such as fitness measurements (e.g., fitness score), weight measurements (e.g., BMI), or any of the well-being scores. The data can be transmitted to server device 240 for processing and / or storage. The data can be added to patient records, thereby allowing for monitoring of the data over time. The data can also be compared with corresponding data from other patients to modify or improve treatment plans. Acquisition and sharing of patient well-being and adverse event data can further support the study and improvement of treatment plans.

[0082] Some of the data, or data derived therefrom, can also be transmitted from the mobile device 100 to the social media platform 250, via a server device 240 in some examples. The patient can choose whether or not to post (i.e., make public) some of the data on the social media platform 250. Posted data can be made visible to other users on the social media platform 250. Other users on the social media platform 250 may be able to post their own information, such as comments, virtual "likes," or messages of encouragement to the user, in response to the data posted by the user of the mobile device 100. The user may be able to view messages posted on the social media platform 250 by other users, which can encourage the user to continue with their treatment plan.

[0083] Some of the data collected and / or determined by device 100 can be output for display to the user so that the user can monitor changes in the data over time. The data can be output for display on the display 112 of the mobile device 100, or on another display, such as the display of a different laptop or tablet.

[0084] Figure 4 shows patient data output to the user using the display 112 of the mobile device 100. The data is output in the form of one or more graphs 412, 414, 416, and 418. One of the graphs 412 may show the dose over time. For example, graph 412 in Figure 4 shows the dose that increases up to 4 weeks, then decreases, and then remains constant. This may show the dose that is titrated to find the optimal dose for the patient, as will be discussed below. One of the graphs 414 may show the weight measurement over time. For example, graph 414 in Figure 4 shows the patient's weight decreasing over time as treatment progresses. One of the graphs 416 may show the fitness measurement over time. For example, graph 416 in Figure 4 shows the patient's fitness increasing over time as treatment progresses. One of the graphs 418 may show the well-being measurement over time. For example, graph 418 in Figure 4 shows the patient's well-being increasing up to 4 weeks, then slightly decreasing, and then increasing again. From Graphs 418 and 412, it can be seen that the decrease in patient well-being in week 4 coincides with the dose reduction. This can be a result of taking patient well-being into consideration when determining the new adjusted drug dose to administer, as discussed elsewhere in this disclosure. Patient well-being decreased, and as a result, the dose was adjusted to be slightly lower compared to the previously administered dose. Subsequently, it can be seen that patient well-being increased in the following weeks after the dose reduction.

[0085] Figure 4 shows four separate graphs 412, 414, 416, and 418, but in other examples, two or more datasets can be shown in the same graph. Other forms of visualization can also be used to present the data. Patients may be encouraged to adhere to their treatment plan in response to viewing the data visualizations provided, for example, by graphs 412, 414, 416, and 418. Patients may be encouraged by seeing fitness measurements that increase overall over time, weight measurements that decrease overall over time, and / or well-being scores that increase overall over time.

[0086] Device 100 can provide graphical user interface (GUI) elements 422, 424, 426, and 428 on the display 112, and the user's selection allows device 100 to share relevant data with server device 240 and / or social media platform 250. For example, a user can interact with user input interface 114 to select GUI element 426, thereby causing device 100 to share fitness data, for example, by posting fitness data to social media platform 250. Similarly, selecting GUI element 422 allows for the sharing of dosage data, selecting GUI element 424 allows for the sharing of weight data, and selecting GUI element 428 allows for the sharing of well-being data. In some examples, selecting one GUI element allows for the sharing of two or more types of data.

[0087] In some examples, a user can receive a virtual reward 430 on a mobile device 100 based on data collected or determined by the system 200. The virtual reward 430 can be made visible on the display 112 of the device 100 as a GUI element to encourage the user to continue with their treatment plan. A patient can earn a reward 430 when a predetermined goal is achieved, for example, when a fitness measurement or weight measurement meets predetermined criteria, such as exceeding or falling below a threshold. A user can receive a virtual reward 430 based on a trend in their fitness measurement or weight measurement, for example, when their fitness measurement increases over two or more consecutive judgments, or when their weight measurement decreases over two or more judgments. In the example in Figure 4, a user received a virtual reward 430 in relation to their fitness measurement. This is indicated by the reward 430 displayed in association with the fitness graph 426. The reward 430 may have been received in response to the user's fitness measurement exceeding a predetermined threshold.

[0088] The virtual award 430 can be posted by the user to the social media platform 250. For example, device 100 can provide a notification on display 112 encouraging the user to post the award 430. The award 430 can be posted in response to a user selection of a graphical user interface (GUI) element 432 on display 112. Other users on the social media platform 250 can comment on the award 430 or otherwise interact with the award 430, thereby providing encouragement to the patient.

[0089] Figures 5 and 6 are flowcharts illustrating the operation of the monitoring application 110 when a machine-readable instruction is executed by the processing unit 102 of the mobile device 100. The flowcharts show how the monitoring application 110 and the mobile device 100 interact and operate to provide the monitoring device. The steps are executed by the processing unit 102 of the mobile device 100 under the control of the monitoring application 110 stored in memory 104.

[0090] The dosage of medication administered to a patient may need to be adjusted over time to find the optimal dose for that patient. This is known as titration. Figure 5 shows a titration method performed by a mobile device 100 according to an aspect of the present invention. The titration method allows the patient to determine a new adjusted dose of medication to be administered based on previously administered doses, well-being data, adverse event data, and statistical data such as fitness and weight measurements.

[0091] The titration is initiated in step 510. Step 510 can be performed in response to user input provided in the user input interface 114, for example, a user selection of a graphical user interface (GUI) element provided on the display 112. In some examples, the titration may be initiated without user input. For example, the titration method may be started according to a schedule, such as 10 a.m. every day. In some examples, the titration is performed each time a dose of the drug is administered, while in other examples, the titration may be performed during a "setup" period, for example, during the first few weeks when the patient begins the treatment plan.

[0092] In step 520, as discussed above, the device receives dose information. The dose information may indicate previous doses administered to the patient during the treatment plan, for example, using the injection device 210. The dose information may include the dose value, and in some examples, the time and / or date the dose was injected. The dose information may be received from the injection device 210 or entered by the user, for example, through the user input interface 114. In some examples, the dose information may already be stored in the device 100, for example in the device 100's memory 104, and the device 100 retrieves the stored dose information from memory 104. In other examples, the device 100 may receive dose information from another source, such as a server device 240. The device 100 may send a request to the injection device 210 and / or the server device 240, thereby the injection device 210 and / or the server device 240 sending the dose information to the device 100.

[0093] In step 530, device 100 receives well-being data associated with the patient. The well-being data can be entered by the user in response to prompts from the device as discussed above, or it can be retrieved from the device's memory 104 or the server device 240.

[0094] In step 540, device 100 receives adverse event data. The adverse event data can be entered by the user in response to prompts from the device as discussed above, or it can be retrieved from the device's memory 104 or the server device 240.

[0095] In step 550, the device receives statistical data, which includes at least one of the following: fitness measurements and weight measurements.

[0096] When a device receives fitness measurements, this may involve the mobile device 100 requesting physiological data from the fitness tracking device 220 and determining the fitness measurements as discussed above. In other examples, the mobile device 100 may not need to request physiological data from the fitness tracking device 220 and can determine the fitness measurements based on physiological data already stored in the device 100. In other examples, the mobile device 100 can retrieve fitness measurements that have already been determined and stored in the device 100, such as the most recent fitness measurement.

[0097] When the device receives a weight measurement, this may involve the mobile device 100 requesting the patient's weight data from the scale 230 and determining the weight measurement as discussed above. In other examples, the mobile device 100 may not need to request weight data from the scale 230 and can determine the weight measurement based on weight data already stored in the device 100. In other examples, the mobile device 100 can retrieve a weight measurement that has already been determined and stored in the device 100, for example, the most recent weight measurement.

[0098] In step 560, the device determines the adjusted dose to be administered to the patient. This is the next dose of medication the patient should receive from the injection device 210. The adjusted dose is determined based at least in part on dose information, well-being data, and statistical data. The adjusted dose is determined using an algorithm that takes into account each of the dose information, well-being data, adverse event data, and statistical data. The algorithm can determine the adjusted dose by adjusting the previous dose indicated by the dose information based on the well-being data, adverse event data, and statistical data.

[0099] The well-being measurement is determined based on the well-being data discussed above, and this is the well-being measurement used to determine the adjusted dose. The previous dose can be increased, decreased, or maintained to produce the adjusted dose based on the well-being measurement. For example, if the well-being measurement exceeds a given threshold, the algorithm can determine the adjusted dose by increasing the previous dose (or decreasing the dose, if appropriate for the drug). However, if the well-being measurement falls below a given threshold, the algorithm can determine the adjusted dose by decreasing the previous dose (or increasing the dose, if appropriate for the drug). If the well-being measurement is within a given range, the algorithm may not increase or decrease the previous dose to determine the adjusted dose.

[0100] Fitness measurements are determined based on the physiological data discussed above, and these are the fitness measurements used to determine the adjusted dose. The previous dose can be increased, decreased, or maintained to produce the adjusted dose based on the fitness measurement. For example, if the fitness measurement exceeds a given threshold, the algorithm can determine the adjusted dose by increasing the previous dose (or decreasing the dose, if appropriate for the drug). However, if the fitness measurement falls below a given threshold, the algorithm can determine the adjusted dose by decreasing the previous dose (or increasing the dose, if appropriate for the drug). If the fitness measurement is within a given range, the algorithm may not increase or decrease the previous dose to determine the adjusted dose.

[0101] The algorithm can also determine an adjusted dose based on adverse event data, for example, by increasing, decreasing, or maintaining the previous dose to obtain an adjusted dose based on adverse event data. If the adverse event data indicates no adverse events, or in some cases, that the patient experiences very few or relatively minor adverse events, the previous dose can be increased (or decreased, if appropriate for the drug) to provide an adjusted dose, or in some cases, the previous dose can be maintained (i.e., not adjusted). If the adverse event data indicates that the patient experienced an adverse event, for example, that the adverse event is not insignificant, the previous dose can be decreased (or increased, if appropriate for the drug) to provide an adjusted dose. The size of the adjustment made to the previous dose to determine the adjusted dose can be weighted depending on the type of adverse event. For example, serious adverse events such as vomiting can be weighted more heavily than less serious adverse events such as minor headaches to have a greater impact on the dose adjustment determination. The size of the adjustment made to the previous dose to determine the adjusted dose can be weighted depending on the frequency and / or severity of the adverse event. For example, if a patient provides adverse event data showing frequent headaches, this can be weighted more heavily to have a greater impact on dose determination than if the headaches were less frequent. Similarly, adverse event data showing severe headaches can be weighted more heavily than adverse event data showing minor headaches.

[0102] In step 570, the determined dose is output. This may include the device 100 displaying the determined adjusted dose value on the device 100's display 112 in the relevant drug units, etc. The patient can then see the outputted adjusted dose value and dial that dose on the injection device 210, which is ready for the next injection. In some examples, outputting the determined dose may include the device 100 sending a signal to the injection device 210 indicating the determined dose, which may cause the injection device 210 to display the determined dose on its display and / or cause the injection device 210 to automatically dial the adjusted dose value. The patient can then inject the determined dose of the drug.

[0103] Figure 6 shows a titration method performed using a mobile device 100 according to an embodiment of the present invention.

[0104] In an optional step 610, device 100 may cause the user to output an alarm indicating that an injection is scheduled. The alarm may include, for example, an audible alarm using the speaker of device 100, a visual alarm using the display 112 of device 100, and / or a tactile alarm using the tactile transducer of device 100. In some examples, device 100 may cause another device, such as an injection device 210, a fitness tracking device 220, or a scale 230, to output the alarm. This can be done in addition to or instead of outputting the alarm from device 100 itself. Device 100 may be caused to output the alarm according to a predetermined schedule, for example, daily, at predetermined intervals, or at predetermined times and / or dates.

[0105] In step 612, device 100 requests dose information, and in step 614, the device receives dose information, for example, as discussed below. In some examples, device 100 does not need to request dose information, and therefore step 612 is not performed. As discussed above with respect to step 520 in Figure 5, the dose information may indicate a dose previously administered to the patient using, for example, the injection device 210. The dose information may include the dose value, and in some examples, the time and / or date the dose was injected.

[0106] Dosage information can be received from the injection device 210 in step 614 in response to the device 100 sending a request for information to the injection device 210 in step 612. However, in some cases, a request is not sent to the injection device 210, and instead, the injection device 210 automatically sends the dosage information, for example, in response to an injection being performed.

[0107] In some examples, step 614 includes receiving dosage information from the user, for example, via a user input interface 114. In this case, step 612 may include displaying a prompt on the device 100's display 112 requesting the user to provide dosage information. The user can then enter the dosage information via the user input interface 114 in response to the prompt or otherwise.

[0108] In some cases, the dosage information can already be stored in device 100, for example in the memory 104 of device 100, and therefore, in step 614, device 100 retrieves the stored dosage information from memory 104. Therefore, step 612 may not be necessary.

[0109] In another example, device 100 may receive dosage information from another source, such as a server device 240. In step 612, device 100 may send a request to the server device 240, which then sends dosage information to device 100, which is received by device 100 in step 614.

[0110] As will be discussed below, in step 616, device 100 requests physiological data from fitness tracking device 220. In step 618, device 100 receives physiological data from fitness tracking device 220. The physiological data may include at least one of the user's heart rate, blood pressure, and step count. In some examples, step 618 is performed without step 616. In this case, fitness tracking device 220 can automatically send the physiological data to mobile device 100.

[0111] As will be discussed below, in step 620, device 100 requests weight data from scale 230. In step 622, device 100 receives weight data from scale 230. In some examples, step 622 is performed without step 620. In this case, scale 230 can automatically send the weight data to mobile device 100.

[0112] In step 624, as discussed above, the device requests well-being data. Requesting well-being data may include providing a graphical user interface element on the display of device 100 to indicate to the user that well-being data is being requested. However, in some cases, step 624 is not performed. In step 626, the device receives well-being data. Well-being data can be received via user input to the user input interface 114 of the mobile device 100.

[0113] In step 628, as discussed above, the device requests adverse event data. Requesting adverse event data may include providing a graphical user interface element on the display of device 100 to indicate to the user that adverse event data is being requested. However, in some cases, step 628 is not performed. In step 630, the device receives adverse event data. Adverse event data can be received via user input to the user input interface 114 of the mobile device 100.

[0114] In step 632, the device determines fitness measurements, such as a fitness score, based on the received physiological data, as discussed above.

[0115] In step 634, the device determines a measure of the patient's weight, such as BMI, based on the received weight data, as discussed above. In some examples, the measure of weight can simply be the patient's mass in kilograms or pounds, for example.

[0116] In step 636, the device determines a measure of well-being based on the received weight data, as discussed above. The measure of well-being provides a quantitative indication of the patient's well-being and may be, for example, a well-being score. As discussed below, the measure of well-being may be based on the most recent well-being data, or it may be based on the most recent well-being data in addition to historical well-being data, for example by calculating a moving average of the well-being data.

[0117] In step 638, device 100 can store any of the data, information, or statistics derived therefrom as described above. The data can be stored in the device's memory 104 and / or transmitted by the device to server device 240 for storage in the cloud or elsewhere. The data can be stored along with associated point-in-time and / or date information. The data can form part of a record for a particular patient so that changes in the data can be monitored over time.

[0118] In the optional step 640, the device 100 determines a new adjusted drug dose to be injected by the patient, for example, as described below with respect to step 560 in Figure 5. Step 640 may include any of the steps in Figure 5 required to determine the adjusted dose. Some of these steps may have already been performed in the method of Figure 6 and therefore do not need to be repeated in the method of Figure 6. In some examples, the patient may have already injected a dose of the drug using the injection device before step 612 and therefore step 640 is not required.

[0119] In step 642, device 100 outputs data. The data output by device 100 may include one or more of the following: well-being measurements, weight measurements, fitness measurements, previous doses indicated in the dose information, newly determined doses, and any associated time / date data. As described above in relation to Figure 4, the data may be output to display 112 in the form of data visualizations such as graphs. Alternatively or in addition, the data may be output to server device 240 and / or social media platform 250 as discussed above.

[0120] Although the steps described above were discussed in a specific order, the present invention is not limited to that specific order, and various steps may be performed in a different order or simultaneously with other steps. Similarly, some steps may be omitted, or additional steps may be performed. For example, various data may be requested and / or received in an order different from that discussed above and shown in Figures 5 and 6.

[0121] Device 100 can also enable tracking of user food intake. Device 100 can provide a graphical user interface on the display 112 that allows the user to input food intake information. The graphical user interface can be provided in response to the user selecting a graphical user interface element on the display 112 of Device 100 using the user input interface 114, or as part of the method shown in Figure 5 or Figure 6. Food intake information may include indications of the food consumed by the user and / or nutritional data associated with that food, such as the carbohydrate content in grams. Food intake information can be stored by Device 100 along with timestamp information indicating when and / or the date the food was consumed. The timestamp information can be entered by the user through the user input interface 114 or determined by the mobile device 100. Food intake information can be stored in the memory 104 of Device 100 and / or transmitted to a server device 240 for storage and / or processing. Thus, the patient's food intake can be tracked over time. The mobile device 100 or server device 240 can determine food intake recommendations for the patient based on historical food intake information. These recommendations can be displayed on the mobile device 100 via the display 122.

[0122] Similarly, the mobile device 100 and / or server device 240 can determine fitness recommendations based on historical physiological data received by the mobile device 100 and display these recommendations on the mobile device 100's display 112. For example, the mobile device 100 or server device 240 may determine, based on historical physiological data (or fitness measurements), that a patient's fitness is declining and therefore can provide the user with recommendations such as a request for the user to try to increase their step count. The mobile device 100 may also output encouragement to the user, for example, by providing a message on the display 112 encouraging the user to "continue" their treatment plan, based on fitness measurements or weight measurements.

[0123] When well-being data and determined dose data are output to the server device 240, this data can be used by medical professionals to improve treatment plans, for example, by improving algorithms.

[0124] While this disclosure is described with reference to diabetes and sustainable weight management, this is not intended to be limiting, and the teachings herein can be applied equally well to other diseases or health conditions.

[0125] This disclosure is described in the context of a computer program run on a mobile device 100, but this is not intended to limit the computer program to run equally well on another suitable device. For example, the device can run equally well on any type of PDA, tablet computer, or other mobile device 100, or on a medical device such as a blood glucose monitoring device. Alternatively, the computer program can run on another suitable device such as a PC.

[0126] The terms “drug” or “pharmaceutical” are used synonymously herein to describe a pharmaceutical preparation comprising one or more active pharmaceutical ingredients or pharmaceutically acceptable salts or solvates thereof, and optionally a pharmaceutically acceptable carrier. An active pharmaceutical ingredient ("API") is, in its broadest sense, a chemical structure that has a biological effect on humans or animals. In pharmacology, a drug or pharmaceutical is used to treat, cure, prevent or diagnose a disease, or otherwise improve physical or mental well-being. Drugs or pharmaceuticals may be used for a limited duration or, in the case of chronic disorders, regularly.

[0127] As described below, drugs or pharmaceuticals may contain at least one API or a combination thereof in various types of formulations for the treatment of one or more diseases. Examples of APIs include small molecules, polypeptides, peptides, and proteins with molecular weights of 500 Da or less (e.g., hormones, growth factors, antibodies, antibody fragments, and enzymes), carbohydrates and polysaccharides, as well as nucleic acids, double-stranded or single-stranded DNA (including naked and cDNA), RNA, antisense nucleic acids (e.g., antisense DNA and RNA), small interfering RNA (siRNA), ribozymes, genes, and oligonucleotides. Nucleic acids can be incorporated into molecular delivery systems such as vectors, plasmids, or liposomes. Mixtures of one or more drugs are also intended.

[0128] Drugs or pharmaceuticals can be contained in a primary package or “drug container” adapted for use in a drug delivery device. A drug container may be, for example, a cartridge, syringe, reservoir, or other rigid or flexible vessel configured to provide chambers suitable for storing one or more drugs (e.g., short-term or long-term storage). For example, in some cases, a chamber may be designed to store a drug for at least one day (e.g., one day to at least 30 days). In some cases, a chamber may be designed to store a drug for about one month to about two years. Storage may be carried out at room temperature (e.g., about 20°C) or refrigerated temperature (e.g., about -4°C to about 4°C). In some cases, a drug container may be or include a dual-chamber cartridge configured to store two or more components of the pharmaceutical formulation to be administered (e.g., an API and a diluent, or two different drugs) one in each chamber individually. In such cases, the two chambers of a dual-chamber cartridge can be configured to allow mixing of two or more components before and / or during administration to a human or animal body. For example, the two chambers can be configured to be in fluid communication with each other (e.g., via a conduit between the two chambers) and, optionally, to allow mixing of the two components by the user before administration. Alternatively or additionally, the two chambers can be configured to allow mixing of the components at the time of administration to a human or animal body.

[0129] The drugs or agents contained in the drug delivery devices described herein can be used for the treatment and / or prevention of many different types of medical disorders. Examples of disorders include, for example, diabetes or complications associated with diabetes, such as diabetic retinopathy, and thromboembolic disorders, such as deep vein thromboembolism or pulmonary thromboembolism. Further examples of disorders include acute coronary syndrome (ACS), anguina, myocardial infarction, cancer, macular degeneration, inflammation, hay fever, atherosclerosis, and / or rheumatoid arthritis. Examples of APIs and drugs are listed in handbooks such as Rote Liste 2014 (for example, main group 12 (antidiabetic drugs) or 86 (oncology drugs), but not limited to) and Merck Index, 15th edition.

[0130] Examples of APIs for the treatment and / or prevention of type 1 or type 2 diabetes or complications associated with type 1 or type 2 diabetes include insulin, e.g., human insulin, or human insulin analogs or derivatives; glucagon-like peptides (GLP-1), GLP-1 analogs or GLP-1 receptor agonists, their analogs or derivatives; dipeptidyl peptidase-4 (DPP4) inhibitors; or pharmaceutically acceptable salts or solvates thereof; or mixtures thereof. As used herein, the terms “analog” and “derivative” refer to polypeptides having a molecular structure that can be formally derived from the structure of a naturally occurring peptide, such as the structure of human insulin, by the deletion and / or replacement of at least one amino acid residue present in the naturally occurring peptide and / or the addition of at least one amino acid residue. The added and / or replaced amino acid residue may be any of the coding amino acid residues, other naturally occurring residues, or purely synthetic amino acid residues. Insulin analogs are also called “insulin receptor ligands.” In particular, the term “derivative” refers to a polypeptide having a molecular structure that can be formally derived from the structure of a naturally occurring peptide, such as a polypeptide having the molecular structure of human insulin in which one or more organic substituents (e.g., fatty acids) are bonded to one or more amino acids. In some cases, one or more amino acids present in a naturally occurring peptide are deleted and / or replaced by other amino acids, including non-coding amino acids, or amino acids are added to a naturally occurring peptide, including non-coding ones.

[0131] Examples of insulin analogs include Gly(A21), Arg(B31), Arg(B32) human insulin (insulin glargine); Lys(B3), Glu(B29) human insulin (insulin glulisine); Lys(B28), Pro(B29) human insulin (insulin lispro); Asp(B28) human insulin (insulin aspart); human insulin in which proline at position B28 may be replaced with Asp, Lys, Leu, Val or Ala and Lys at position B29 may be replaced with Pro; Ala(B26) human insulin; Des(B28~B30) human insulin; Des(B27) human insulin and Des(B30) human insulin.

[0132] Examples of insulin derivatives include, for example, B29-N-myristoyl-des(B30) human insulin, Lys(B29)(N-tetradecanoyl)-des(B30) human insulin (insulin detemir, Levemir (registered trademark)); B29-N-palmitoyl-des(B30) human insulin; B29-N-myristoyl human insulin; B29-N-palmitoyl human insulin; B28-N-myristoylLysB28ProB29 human insulin; B28-N-palmitoyl-LysB28ProB29 human insulin; and B30-N-myristoyl-ThrB29LysB30 human insulin. These are B30-N-palmitoyl-ThrB29LysB30 human insulin; B29-N-(N-palmitoyl-gamma-glutamyl)-des(B30) human insulin, B29-N-omega-carboxypentadecanoyl-gamma-L-glutamyl-des(B30) human insulin (insulin degludec, Tresiba (registered trademark)); B29-N-(N-litocoryl-gamma-glutamyl)-des(B30) human insulin; B29-N-(ω-carboxyheptadecanoyl)-des(B30) human insulin and B29-N-(ω-carboxyheptadecanoyl) human insulin.

[0133] Examples of GLP-1, GLP-1 analogs, and GLP-1 receptor agonists include, for example, lixisenatide (Lyxumia®), exenatide (Exsendin-4, Byetta®, Bydureon®, a 39-amino acid peptide produced by the salivary glands of Hiramonster), liraglutide (Victoza®), semaglutide, taspoglutide, albiglutide (Syncria®), dulaglutide (Trulicity®), r-exsendin-4, CJC-1134-PC, PB-1023, TTP-054, and langrenatide / HM-11260C (efpeglenatide). HM-15211, CM-3, GLP-1 Erigen, ORMD-0901, NN-9423, NN-9709, NN-9924, NN-9926, NN-9927, Nodexen, Biador-GLP-1, CVX-096, ZYOG-1, ZYD-1, GSK-2374697, DA-3091, MAR-701, MAR709, ZP-2929, ZP-3022, ZP These include -DI-70, TT-401 (Pegapamodtide), BHM-034, MOD-6030, CAM-2036, DA-15864, ARI-2651, ARI-2255, Chilzepatide (LY3298176), Bamadutide (SAR425899), Exenatide-XTEN, and Glucagon-XTEN.

[0134] Examples of oligonucleotides include mipomersen sodium (Kynamro®), a cholesterol-lowering antisense therapy for the treatment of familial hypercholesterolemia, or RG012 for the treatment of Alport syndrome.

[0135] Examples of DPP4 inhibitors include linagliptin, vidagliptin, sitagliptin, denagliptin, saxagliptin, and berberine.

[0136] Examples of hormones include pituitary hormones or hypothalamic hormones or regulatory active peptides and their antagonists, such as gonadotropins (follitropin, lutropin, choriongonadotropin, menotropin), somatropin (somatropin), desmopressin, terlipressin, gonadrelin, triptorelin, leuprorelin, buserelin, nafarelin, and goserelin.

[0137] Examples of polysaccharides include glucosaminoglycans, hyaluronic acid, heparin, low molecular weight heparin or very low molecular weight heparin or their derivatives, or sulfated polysaccharides, such as the polysulfated forms of the aforementioned polysaccharides, and / or pharmaceutically acceptable salts thereof. An example of a pharmaceutically acceptable salt of polysulfated low molecular weight heparin is enoxaparin sodium. Examples of hyaluronic acid derivatives are Hylan G-F20 (Synvisc®) and sodium hyaluronate.

[0138] As used herein, the term “antibody” refers to an immunoglobulin molecule or its antigen-binding portion. Examples of antigen-binding portions of immunoglobulin molecules include F(ab) and F(ab')2 fragments that retain the ability to bind to antigens. Antibodies may be polyclonal antibodies, monoclonal antibodies, recombinant antibodies, chimeric antibodies, deimmunized or humanized antibodies, fully human antibodies, non-human (e.g., mouse) antibodies, or single-chain antibodies. In some embodiments, antibodies may have effector function and be complement-immobilized. In some embodiments, antibodies may have reduced or no ability to bind to Fc receptors. For example, antibodies may be isotypes or subtypes, antibody fragments, or mutants that do not support binding to Fc receptors, for example, having mutations or deletions in the Fc receptor-binding region. The term “antibody” also includes antigen-binding molecules based on quadrivalent bispecific tandem immunoglobulins (TBTIs) and / or bivariable-region antibody-like binding proteins (CODVs) with crossover binding region orientation.

[0139] The terms “fragment” or “antibody fragment” refer to polypeptides derived from antibody polypeptide molecules (e.g., antibody heavy chain and / or light chain polypeptides) that do not contain the full-length antibody polypeptide but still contain at least a portion of a full-length antibody polypeptide capable of binding to an antigen. Antibody fragments may include cleavage portions of the full-length antibody polypeptide, but the term is not limited to such cleavage fragments. Examples of antibody fragments useful in the present invention include, for example, Fab fragments, F(ab')2 fragments, scFv (single-stranded Fv) fragments, linear antibodies, monospecific or multispecific antibody fragments, e.g., bispecific, trispecific, quadrispecific and multispecific antibodies (e.g., diabodies, triabodies, tetrabodies), monovalent or multivalent antibody fragments, e.g., bivalent, trivalent, quadrivalent and multivalent antibodies, minibodies, chelated recombinant antibodies, tribodies or vibodies, intrabodies, nanobodies, small module immunopharmaceuticals (SMIPs), binding domain immunoglobulin fusion proteins, camelized antibodies, and VHH-containing antibodies. Additional examples of antigen-binding antibody fragments are known in the art.

[0140] The term "complementarity-determining region" or "CDR" refers to a short polypeptide sequence within the variable region of both heavy and light polypeptides that primarily mediates specific antigen recognition. The term "framework region" refers to an amino acid sequence within the variable region of both heavy and light polypeptides that is not a CDR sequence and primarily maintains the proper arrangement of the CDR sequence to enable antigen binding. While the framework region itself is typically not directly involved in antigen binding, as is known in the art, certain residues within the framework region of a particular antibody may directly participate in antigen binding or influence the interaction ability of one or more amino acids in the CDR with the antigen.

[0141] Examples of antibodies include anti-PCSK-9 mAbs (e.g., alirocumab), anti-IL-6 mAbs (e.g., sarilumab), and anti-IL-4 mAbs (e.g., dupilumab).

[0142] Any pharmaceutically acceptable salt of any API described herein is intended for use in drug delivery devices with drugs or pharmaceuticals. Examples of pharmaceutically acceptable salts include acid addition salts and basic salts.

[0143] It will be understood by those skilled in the art that modifications (additions and / or deletions) can be made to various components of the APIs, substances, formulations, apparatus, methods, systems, and embodiments described herein without departing from the full scope and spirit of the present invention, and that the present invention encompasses such modifications and all equivalents.

[0144] Exemplary drug delivery devices may include needle-based injection systems, such as those described in Table 1 of Section 5.2 of ISO 11608-1:2014(E). As described in ISO 11608-1:2014(E), needle-based injection systems can be broadly distinguished into multi-dose container systems and single-dose container systems (with partial or complete discharge). Containers may be replaceable or integrated, non-replaceable containers.

[0145] As further described in ISO 11608-1:2014(E), a multi-dose container system may include a needle-based injection device with replaceable containers. In such a system, each container may hold multiple doses, and its size may be fixed or variable (pre-set by the user). Another multi-dose container system may include a needle-based injection device with an integrated, non-replaceable container. In such a system, each container may hold multiple doses, and its size may be fixed or variable (pre-set by the user).

[0146] As further described in ISO 11608-1:2014(E), a single-dose container system may include a needle-based injection device having replaceable containers. In one example of such a system, each container holds a single dose, thereby discharging the entire deliverable amount (total discharge). In a further example, each container holds a single dose, thereby discharging a portion of the deliverable amount (partial discharge). As also described in ISO 11608-1:2014(E), a single-dose container system may include a needle-based injection device having an integrated, non-replaceable container. In one example of such a system, each container holds a single dose, thereby discharging the entire deliverable amount (total discharge). In a further example, each container holds a single dose, thereby discharging a portion of the deliverable amount (partial discharge).

Claims

1. A mobile device including memory and a processing unit, wherein the memory stores instructions, and when the instructions are executed by the processing unit, the mobile device: The injection device receives dose information, including previous doses released; Based on the received well-being data, determine the patient's well-being measurement; Measurements of a patient's fitness based on physiological data received from a fitness tracking device; and Patient weight measurement based on weight data received from a scale. Determine at least one of the following: As part of an adjustment process to increase, decrease, or maintain a previous dose, the adjusted drug dose is determined based on dose information, well-being measurements, and fitness or weight measurements; The mobile device that outputs the adjusted drug dosage.

2. The mobile device according to claim 1, wherein when an instruction is executed by a processing unit, the mobile device further causes the mobile device to output dose information, including at least well-being data and previous doses, to a server device for storage.

3. The mobile device according to claim 1 or 2, wherein physiological data includes pulse data, and when executed by a processing unit, the mobile device causes the mobile device to determine a fitness measurement based on the pulse data.

4. The mobile device according to claim 1, 2, or 3, wherein the physiological data includes blood pressure data, and when an instruction is executed by a processing unit, the mobile device causes the mobile device to determine a fitness measurement based on the blood pressure data.

5. The mobile device according to any one of claims 1 to 4, wherein the physiological data includes step count data, and the instruction, when executed by the processing unit, causes the mobile device to determine a fitness measurement based on the step count data.

6. When the instruction is executed by the processing unit, the mobile device will also receive: A mobile device according to any one of claims 1 to 5, which outputs for display at least one of well-being measurements, fitness measurements, and weight measurements.

7. The mobile device according to claim 6, wherein at least one of the well-being measurements, fitness measurements, and weight measurements output for display is displayed together with one or more previous well-being measurements, fitness measurements, and weight measurements.

8. When the instruction is executed by the processing unit, the mobile device will also receive: Provide adverse event data showing one or more adverse events experienced by the patient; A mobile device according to any one of claims 1 to 7, which causes a modified drug dose to be determined based on at least a portion of adverse event data.

9. When the instruction is executed by the processing unit, the mobile device will also receive: Determine whether the fitness measurement meets a first predetermined threshold; Determine whether the measured weight meets a second predetermined threshold; A mobile device according to any one of claims 1 to 8, which outputs a virtual award for display on the mobile device in response to the determination that a fitness measurement meets a first predetermined threshold or a weight measurement meets a second predetermined threshold.

10. The mobile device according to any one of claims 1 to 9, wherein when an instruction is executed by a processing unit, the mobile device further causes the mobile device to receive food intake information entered by a user and to store the received food intake information.

11. The mobile device according to any one of claims 1 to 10, wherein, when executed by a processing unit, the mobile device further causes the mobile device to publish data corresponding to at least one of well-being measurements, fitness measurements, and weight measurements to a social media platform.

12. A system including a mobile device according to any one of claims 1 to 11: A fitness tracking device configured to transmit physiological data to a mobile device; A scale configured to transmit weight data to a mobile device, It further includes, Herein, the mobile device is configured to determine an adjusted drug dose based on at least a portion of the physiological data and weight data received by the mobile device, in the system.

13. The system according to claim 12, further comprising an injection device configured to transmit dose information to a mobile device, the mobile device configured to determine an adjusted drug dose based on at least a portion of the dose information.

14. A computer implementation method: Receiving dose information, including previous doses released by the injection device; Based on the well-being data received, determine the patient's measure of well-being; Patient fitness based on physiological data received from fitness tracking devices Measurements of; and Patient weight measurement based on weight data received from a scale. To determine at least one of the following: As part of the dosage adjustment process, the adjusted drug dose is determined based at least in part on dosage information, on well-being measurements, and on fitness measurements or body weight measurements; Outputting the adjusted drug dosage, The computer implementation method, including the above.

15. A computer-readable storage medium that, when executed by a computer, includes instructions causing the computer to perform the method described in claim 14.

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