Digital Biomarkers

Abstract

Currently, the assessment of symptom severity and progression in subjects diagnosed with muscle disorders, particularly SMA, involves monitoring and testing of subjects at a clinic every 6 to 12 months. However, while more frequent monitoring and testing of subjects is preferable, increasing the frequency of clinic monitoring and testing can be costly and inconvenient for subjects. Therefore, as described herein, assessment of symptom severity and progression by remote monitoring and testing of subjects outside the clinic environment offers advantages in terms of cost, ease of monitoring, and convenience for subjects. The systems, methods, and apparatus of this disclosure provide a diagnosis for assessing lung volume in subjects with muscle disorders, particularly SMA, by active testing of the subject.

Description

[Technical Field]

[0001] Field

[0002] Embodiments described herein relate to medical devices for improved subject testing and subject analysis. More specifically, embodiments described herein provide diagnostic devices, systems, and methods for assessing the severity and progression of symptoms of muscle disorders, particularly spinal muscular atrophy (SMA), in a subject through active testing of the subject. [Background technology]

[0002] background Spinal muscular atrophy (SMA), also known as proximal spinal muscular atrophy and 5q spinal muscular atrophy, is an autosomal recessive disorder that is a rare but life-threatening neuromuscular disorder associated with motor neuron loss and progressive muscle wasting.

[0003] SMA has become a health problem and a significant economic burden on the health care system. Because SMA is a clinically heterogeneous disease of the CNS, there is a need for diagnostic tools that allow reliable diagnosis and differentiation of the current disease state and symptom progression, thus supporting accurate treatment.

[0004] Several standardized methods and tests exist for measuring the severity and progression of symptoms in subjects diagnosed with SMA. These tests involve a physician measuring the subject's ability to perform physical functions. These standardized tests can provide an assessment of various symptoms, particularly lung volume, by measuring the pitch variability associated with the subject's forced vital capacity (FVC), which can help track changes in these symptoms over time. Therefore, assessing the severity and progression of symptoms using standardized methods and tests can help guide treatment and therapy options.

[0005] Currently, assessment of symptom severity and progression in subjects diagnosed with muscle disorders, particularly SMA, involves monitoring and testing subjects in a clinic every 6 to 12 months. While it would be ideal to monitor and test subjects more frequently, increasing the frequency of clinic monitoring and testing can be costly and inconvenient for subjects. Summary of the Invention

[0006] Quick Overview The following provides a simplified summary of various embodiments described herein. This summary is not an extensive overview and is not intended to identify key or critical elements or to delineate the claims. The following summary merely presents some concepts in a simplified form as a prelude to the more detailed description provided below. The embodiments described herein describe a particular medical device for assessing the severity and progression of symptoms in subjects diagnosed with muscle disorders, particularly SMA. The ability to perform testing and monitoring remotely and outside of a clinic setting can provide lower cost, greater frequency, and simplified ease and convenience for subjects, resulting in improved detection of symptom progression and better treatment.

[0007] According to one aspect, the present disclosure relates to a diagnostic device for assessing lung volume in a subject with a myopathy, particularly SMA, including at least one processor, one or more sensors associated with the device, and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the device to: receive a first plurality of first sensor data via the one or more sensors associated with the device; extract a first plurality of features from the received first sensor data associated with the myopathy, particularly SMA, FVC in the subject; and determine a first assessment of the myopathy, particularly SMA, FVC based on the extracted first plurality of features.

[0008] 1) A diagnostic device for assessing lung volume in a subject with a muscle disorder, particularly SMA, comprising: at least one processor; one or more sensors associated with the device; a memory for storing computer readable instructions, The computer-readable instructions, when executed by the at least one processor, cause the apparatus to: receiving a plurality of first sensor data via the one or more sensors associated with the device; Extracting a first plurality of features from the received first sensor data, the first plurality of features being related to lung volume of the subject with a muscle disorder, particularly SMA; and determining a first assessment of the subject's lung volume based on the extracted first plurality of features; Execute Memory and An apparatus comprising:

[0009] 2) The computer-readable instructions, when executed by at least one processor, cause the apparatus to: prompting the subject to perform a diagnostic task in which they make a long "aaah" sound; receiving a plurality of second sensor data via the one or more sensors associated with the device in response to the subject performing the diagnostic task; extracting a second plurality of features related to the subject's lung volume from the received second sensor data; and determining a second assessment of the subject's pitch variability based on the extracted second plurality of features; Further execute the E1 device.

[0010] 3) Any one of devices E1 to E2 that is a smartphone.

[0011] 4) Any one of the devices E1 to E3, wherein the diagnostic task is associated with at least one of forced vital capacity tests.

[0012] 5) A computer-implemented method for assessing lung volume in a subject with a muscle disorder, particularly SMA, comprising: receiving a plurality of first sensor data via one or more sensors associated with the device; extracting a first plurality of features from the received first sensor data, the first plurality of features being associated with lung volume in a subject with a muscle disorder, particularly an SMA; determining a first assessment of lung volume for the subject with a muscle disorder, particularly SMA, based on the extracted first plurality of features; 11. A computer-implemented method comprising:

[0013] 6) prompting the subject to perform one or more diagnostic tasks; and receiving a plurality of second sensor data via the one or more sensors in response to the subject performing the one or more diagnostic tasks; extracting a second plurality of features from the received second sensor data, the second plurality of features being related to lung volume in the subject with a muscle disorder, particularly SMA; determining a second assessment of lung volume for the subject with a muscle disorder, particularly an SMA, based on at least the extracted second sensor data; The computer-implemented method of E5 further comprising:

[0014] 7) The computer-implemented method of any one of E5-6, wherein the subject's lung volume is assessed based on active testing, particularly based on the duration the subject makes a long "aaah" sound, and more particularly, the sound is made while the subject forcefully exhales from full inspiration to full expiration.

[0015] 8) Any one of the devices E1 to E4 or any one of the computer-implemented methods E5 to E7, wherein the subject is a human.

[0016] 9) A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for assessing lung volume in a subject with a muscle disorder, particularly SMA, the method comprising: receiving a plurality of sensor data via one or more sensors associated with the device; extracting from the received sensor data a plurality of features related to lung volume of the subject with muscle disorders, particularly SMA; Determining the lung volume assessment of subjects with muscle disorders, particularly SMA, based on the extracted features; Including, A non-transitory machine-readable storage medium.

[0017] 10) A computer-implemented method for assessing muscle disorders, particularly SMA, in a subject, comprising: i) measuring the duration of the subject's "aaah" sound daily, particularly at least five times a week, more particularly at least once a week; ii) comparing the determined scores with the reference scores of the clinical anchors; iii) To determine the severity of muscle disorders, especially SMA; 11. A computer-implemented method comprising:

[0018] 11) A computer-implemented method for identifying whether a subject has a muscle disorder, particularly SMA, comprising: i) scoring the subject on a diagnostic task in which the subject makes a long "aaah" sound; ii) comparing the determined score with a standard, thereby assessing muscle disorders, particularly SMA; and 11. A computer-implemented method comprising:

[0019] 12) further comprising administering to the subject a pharmaceutically active agent to reduce the likelihood of progression of a muscle disorder, particularly SMA; In particular, the pharmaceutically active agent is suitable for treating SMA in a subject, and is in particular an m7GpppX diphosphatase (DCPS) inhibitor, a survival motor neuron protein 1 regulator, an SMN2 expression inhibitor, an SMN2 splicing regulator, an SMN2 expression enhancer, a survival motor neuron protein 2 regulator, or an SMN-AS1 (SMN1-derived long non-coding RNA) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branapram; E11 method.

[0020] 13) The method of E13, wherein the drug is risdiplam.

[0021] 14) Methods E10-13, in which the subject is a human.

[0022] 15) The invention as described herein. [Brief explanation of the drawings]

[0023] A more complete understanding of the aspects described herein and their advantages can be obtained by reference to the following description in consideration of the accompanying drawings, in which like reference numerals indicate like features, and in which:

[0024] [Figure 1] 1 is a diagram of an exemplary environment in which a diagnostic device for assessing lung volume in a subject with muscle disorders, particularly SMA, is provided in accordance with an exemplary embodiment. FIG. [Figure 2] FIG. 1 is a flow diagram of a method for assessing muscle disorders, particularly SMA pitch variability, in a subject based on active testing of the subject's lung volume according to an exemplary embodiment. [Figure 3] 1 illustrates an example of a network architecture and data processing device that can be used to implement one or more exemplary aspects described herein. [Figure 4] 1 illustrates an example illustrating a diagnostic application according to one or more exemplary aspects described herein. [Figure 5]10 is a plot showing the results of the sensor characteristics according to Example 1. DETAILED DESCRIPTION OF THE INVENTION

[0025] Detailed Description In the following description of various aspects, reference is made to the accompanying drawings, which form a part hereof, and which show, by way of illustration, various embodiments in which the aspects described herein may be practiced. It is to be understood that other aspects and / or embodiments may be utilized and structural and functional changes may be made without departing from the scope of the described aspects and embodiments. The aspects described herein are capable of other embodiments and of being practiced or carried out in various ways. It is also to be understood that the phraseology and terminology used herein are for purposes of description and should not be regarded as limiting. Rather, the words and terms used herein should be accorded the broadest interpretation and meaning. The use of "including" and "comprising" and variations thereof is meant to encompass the items listed thereafter and their equivalents, as well as additional items and their equivalents. The use of the terms "mounted," "connected," "coupled," "disposed," "engaged," and similar terms is meant to include both direct and indirect mounting, connecting, coupling, disposing, and engaging.

[0026] The systems, methods, and devices described herein provide diagnostics for assessing lung volume in a subject with muscle disorders, particularly SMA. In some embodiments, the diagnostics may be provided to the subject as a software application installed on a mobile device, particularly a smartphone.

[0027] In some embodiments, the diagnosis involves acquiring or receiving sensor data from one or more sensors associated with a mobile device as the subject performs activities of daily living. In some embodiments, the sensors may be in a mobile device, such as a smartphone, or a wearable sensor, such as a smartwatch. In some embodiments, sensor features related to symptoms of myopathy, particularly SMA, are extracted from the received or acquired sensor data. In some embodiments, an assessment of the severity and progression of symptoms of myopathy, particularly SMA, in the subject is determined based on the extracted sensor features.

[0028] In some embodiments, systems, methods, and devices according to the present disclosure provide a diagnosis for assessing myopathy, particularly SMA pitch variability, in a subject based on active testing of the subject. In some embodiments, the diagnosis prompts the subject to perform a diagnostic task. In some embodiments, the diagnostic task is anchored or modeled after established methods and standardized tests. In some embodiments, in response to the subject performing the diagnostic task, the diagnosis acquires or receives sensor data via one or more sensors. In some embodiments, the sensors may be in a mobile device or in a wearable sensor worn by the subject. In some embodiments, sensor features related to myopathy, particularly SMA, symptoms are extracted from the received or acquired sensor data. In some embodiments, an assessment of the severity and progression of myopathy, particularly SMA, symptoms in the subject is determined based on the extracted features of the sensor data.

[0029] Assessment of the severity and progression of symptoms of myopathies, particularly SMA, using a diagnostic according to the present disclosure correlates well with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing. Exemplary diagnostics according to the present disclosure can be used outside of a clinic environment, thus offering advantages in cost, ease of subject monitoring, and convenience for subjects. This facilitates frequent subject monitoring and testing, particularly daily, resulting in a better understanding of disease stages and providing insights into the disease that are useful to both the clinical and research communities. Exemplary diagnostics according to the present disclosure can detect even small changes in the pitch variability of myopathies, particularly SMA, in subjects early, and can therefore be used for better disease management, including personalized treatment.

[0030] FIG. 1 is a diagram of an exemplary environment in which a diagnostic device 105 for assessing myopathy, particularly SMA lung volume, in a subject 110 may be provided. In some embodiments, the device 105 may be a smartphone, smartwatch, or other mobile computing device. The device 105 includes a display screen 160. In some embodiments, the display screen 160 may be a touchscreen. The device 105 includes at least one processor 115 and a memory 125 that stores computer instructions for a symptom monitoring application 130 that, when executed by the at least one processor 115, causes the device 105 to assess myopathy, particularly SMA lung volume. The device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. In some embodiments, the one or more sensors associated with the device are at least one of sensors disposed within the device or sensors worn by the subject and configured to communicate with the device. In FIG. 1, the sensors associated with the device 105 include a first sensor 120a disposed within the device 105 and a second sensor 120b that can be worn by the subject 110. The device 105 receives a plurality of first sensor data via the first sensor 120a and a plurality of second sensor data via the second sensor 120b as the subject 110 performs an activity.

[0031] The device 105 extracts features related to myopathy, particularly the lung volume of the SMA, in the subject 110 from the received first sensor data and second sensor data. In some embodiments, the symptoms of myopathy, particularly the SMA, in the subject 110 can include symptoms indicative of the FVC of the subject 110, symptoms indicative of the lung volume of the subject 110.

[0032] In some embodiments, the sensor 120 associated with the device 105 can include a sensor associated with Bluetooth and WiFi functionality, and the sensor data can include information associated with Bluetooth and WiFi signals received by the sensor 120. In some embodiments, the device 105 extracts data from the received first and second sensor data corresponding to the density of the Bluetooth and WiFi signals received or transmitted by the device 105 or the sensor. In some embodiments, an assessment of the subject's 110 lung volume due to pitch variability can be based on the extracted Bluetooth and WiFi signal data (e.g., an assessment of the subject's sociability can be based in part on the density of the picked-up Bluetooth and WiFi signals).

[0033] The device 105 determines an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 transmits the extracted features to the server 150 over the network 180. The server 150 includes at least one processor 155 and a memory 161 that stores computer instructions for a symptom assessment application 170 that, when executed by the server's processor 155, causes the processor 155 to determine an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features from the device 105 received by the server 150. In some embodiments, the symptom assessment application 170 can determine an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the sensor data received from the device 105 and a subject database 175 stored in the memory 160. In some embodiments, the subject database 175 can include subject data and / or clinical data. In some embodiments, subject database 175 can include clinic-based sensor-based measurements of baseline long-term pitch variation lung volume from subjects with myopathy, particularly SMA. In some embodiments, subject database 175 can be independent of server 150. In some embodiments, server 150 transmits the determined assessment of myopathy, particularly SMA, lung volume in subject 110 to device 105. In some embodiments, device 105 can output the myopathy, particularly SMA, lung volume assessment. In some embodiments, device 105 can communicate information to subject 110 based on the assessment. In some embodiments, the myopathy, particularly SMA, lung volume assessment can be communicated to a clinician, who can determine an individualized treatment for subject 110 based on the assessment.

[0034] In some embodiments, the computer instructions for the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to assess lung volume for myopathies, particularly SMA, in the subject 110 based on active testing of the subject 110. The device 105 prompts the subject 110 to perform one or more tasks. In some embodiments, prompting the subject to perform one or more diagnostic tasks includes prompting the subject to transcribe pre-specified statements or prompting the subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored or modeled after well-established methods and standardized tests for diagnosing and assessing myopathies, particularly SMA.

[0035] In response to subject 110 performing one or more diagnostic tasks, diagnostic device 105 receives a plurality of sensor data via one or more sensors associated with device 105. As described above, the sensors associated with device 105 may include a first sensor 120a disposed within device 105 and a second sensor 120b worn by subject 110. Device 105 receives a plurality of first sensor data via first sensor 120a and a plurality of second sensor data via second sensor 120b. In some embodiments, one or more diagnostic tasks may be associated with measuring pitch variability, particularly measuring the longest "aaah" sound.

[0036] The device 105 extracts features related to myopathy, particularly the lung volume of the SMA, in the subject 110 from the received plurality of first sensor data and the received plurality of second sensor data. Symptoms of the myopathy, particularly the SMA, in the subject 110 may include symptoms representative of the lung volume of the subject 110. In some embodiments, pitch variations of the myopathy, particularly the SMA, in the subject 110 are representative of the lung volume.

[0037] The device 105 determines an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 transmits the extracted features to the server 150 over the network 180. The server 150 can include at least one processor 155 and a memory 161 that stores computer instructions for a symptom assessment application 170 that, when executed by the server's processor 155, causes the processor 155 to determine an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features from the device 105 received by the server 150. In some embodiments, the symptom assessment application 170 can determine an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the sensor data received from the device 105 and a subject database 175 stored in the memory 160. In some embodiments, the subject database 175 can include subject data and / or clinical data. In some embodiments, subject database 175 may include baseline long-term pitch variability measurements from subjects with myopathy, particularly SMA. In some embodiments, subject database 175 may include data from subjects with other stages of myopathy, particularly SMA. In some embodiments, subject database 175 may be independent of server 150. In some embodiments, server 150 transmits the determined assessment of myopathy, particularly SMA lung volume, in subject 110 to device 105. In some embodiments, device 105 may output the myopathy, particularly SMA lung volume assessment. In some embodiments, device 105 may communicate information to subject 110 based on the assessment. In some embodiments, the myopathy, particularly SMA lung volume assessment may be communicated to a clinician, who may determine an individualized treatment for subject 110 based on the assessment.

[0038] FIG. 2 illustrates an exemplary method for assessing myopathy, particularly SMA lung volume, in a subject based on active testing of the subject using the exemplary device 105 of FIG. 1. FIG. 3 is described with reference to FIG. 1, but it should be noted that the steps of the method of FIG. 3 may be performed by other systems. The method includes prompting the subject to perform one or more diagnostic tasks (205). The method includes receiving a plurality of sensor data via one or more sensors in response to the subject performing the one or more tasks (step 210). The method includes extracting a plurality of features associated with myopathy, particularly SMA lung volume, from the received sensor data (215). The method includes determining an assessment of myopathy, particularly SMA lung volume, based at least on the extracted sensor data (step 220).

[0039] Figure 2 illustrates an exemplary method for assessing lung volume in muscle disorders, particularly SMA, based on active testing of a subject 110 using the exemplary device 105 of Figure 1. In some embodiments, active testing of a subject 110 using the device 105 may be selected via a user interface of a symptom monitoring application 130.

[0040] The method begins by proceeding to step 205, which includes prompting the subject 110 to perform one or more diagnostic tasks. The device 105 prompts the subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform the one or more diagnostic tasks includes prompting the subject to perform one or more movements. In some embodiments, the diagnostic tasks are anchored or modeled after well-established methods and standardized tests for diagnosing and assessing muscle disorders, particularly SMA.

[0041] In some embodiments, the diagnostic task may include making the loudest "aaah" sound possible to encourage the monster to cross the finish line.

[0042] In certain embodiments of the invention, a long or loud "aaah" sound allows the time spent by the subject blowing forcefully from full inspiration to full expiration while making the sound to be measured.

[0043] In another embodiment of the invention, the invention involves measuring the time a subject takes to forcefully breathe from full inspiration to full expiration while emitting a long or loud "aaah."

[0044] As used herein, the term "test" refers to a test in which a subject is asked to perform a diagnostic task as described herein.

[0045] The method proceeds to step 210, which includes receiving a plurality of second sensor data via one or more sensors in response to the subject 110 performing the one or more diagnostic tasks. In response to the subject 110 performing the one or more diagnostic tasks, the diagnostic device 105 receives the plurality of sensor data via one or more sensors associated with the device 105. As described above, the sensors associated with the device 105 include a first sensor 120a disposed within the device 105 and a second sensor 120b worn by the subject 110. The device 105 receives the plurality of first sensor data via the first sensor 120a and the plurality of second sensor data via the second sensor 120b.

[0046] The method proceeds to step 215 with extracting a second plurality of features from the received sensor data related to myopathy, particularly SMA lung volume. The device 105 extracts features from the received first and second sensor data related to myopathy, particularly SMA lung volume, in the subject 110. Symptoms of myopathy, particularly SMA, in the subject 110 may include symptoms representative of the lung volume of the subject 110. In some embodiments, the extracted features of the first and second plurality of sensor data may be representative of myopathy, particularly SMA, symptoms such as pitch fluctuations.

[0047] The method proceeds to step 220, which includes determining an assessment of myopathy, particularly SMA lung volume, based on at least the extracted sensor data. The device 105 determines an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 can transmit the extracted features to the server 150 via the network 180. The server 150 includes at least one processor 155 and a memory 160 that stores computer instructions for a symptom assessment application 170 that, when executed by the processor 155, determines an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features from the device 105 received by the server 150. In some embodiments, the symptom assessment application 170 can determine an assessment of myopathy, particularly SMA lung volume, in the subject 110 based on the extracted features of the sensor data received from the device 105 and a subject database 175 stored in the memory 160. The subject database 175 can include various clinical data. In some embodiments, the second device may be one or more wearable sensors. In some embodiments, the second device may be any device including a motion sensor with an inertial measurement unit (IMU). In some embodiments, the second device may be several devices or sensors. In some embodiments, the subject database 175 may be independent of the server 150. In some embodiments, the server 150 transmits the determined assessment of myopathy, particularly SMA lung volume, in the subject 110 to the device 105. In some embodiments, as shown in FIG. 1 , the device 105 may output the assessment of myopathy, particularly SMA lung volume, on the display 160 of the device 105.

[0048] As described above, the assessment of symptom severity and progression of myopathies, particularly SMA, using the diagnostics disclosed herein correlates well with clinical outcome-based assessments and can therefore replace clinical subject monitoring and testing. The diagnostics disclosed herein were studied in a group of subjects with myopathies, particularly SMA subjects. Subjects were provided with a smartphone application that included a lung volume test, specifically a test called "Cheer the Monster."

[0049] FIG. 3 illustrates an example of a network architecture and data processing devices that can be used to implement one or more exemplary aspects described herein, such as those described in FIGS. 1 and 2. Various network nodes 303, 305, 307, and 309 may be interconnected via a wide area network (WAN) 301, such as the Internet. Other networks, including private intranets, corporate networks, LANs, wireless networks, and personal networks (PANs), may also or instead be used. Network 301 is illustrative and may be replaced by fewer or additional computer networks. The local area network (LAN) may have one or more of any known LAN topologies and may use one or more of a variety of protocols, such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) may be connected to one or more of the networks via twisted pair wire, coaxial cable, optical fiber, radio waves, or other communication media.

[0050] The term "network," as used herein and depicted in the drawings, refers not only to a system in which remote storage devices are coupled together through one or more communication paths, but also to stand-alone devices that may be coupled to such a system at any time with storage capabilities. Thus, the term "network" includes not only a "physical network" but also a "content network" consisting of data—belonging to a single entity—that resides across all physical networks.

[0051] The components may include a data server 303, a web server 305, and client computers 307, 309. The data server 303 provides overall access, control, and management of the databases and control software for implementing one or more exemplary embodiments described herein. The data server 303 may be connected to a web server 305 with which users interact and obtain requested data. Alternatively, the data server 303 may itself operate as a web server and be connected directly to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet) through a direct or indirect connection, or through some other network. Users may interact with the data server 303 using remote computers 307, 309, for example, by using a web browser to connect to the data server 303 through one or more publicly accessible websites hosted by the web server 305. The client computers 307, 309 may be used in conjunction with the data server 303 to access data stored therein, or may be used for other purposes. For example, from client device 307, a user may access web server 305 using an internet browser as known in the art or by executing a software application that communicates with web server 305 and / or data server 303 over a computer network (such as the internet). In some embodiments, client computer 307 may be a smartphone, smartwatch, or other mobile computing device and may implement a diagnostic device such as device 105 shown in FIG. 1. In some embodiments, data server 303 may implement a server such as server 150 shown in FIG. 1.

[0052] The servers and applications may be combined on the same physical device, maintain separate virtual or logical addresses, or may reside on separate physical devices. Figure 1 shows only one example of a network architecture that may be used, and those skilled in the art will appreciate that the particular network architecture and data processing devices used may vary and are secondary to the functionality they provide, as described further herein. For example, the services provided by web server 305 and data server 303 may be combined on a single server.

[0053] Each of the components 303, 305, 307, and 309 may be any type of known computer, server, or data processing device. The data server 303 may include, for example, a processor 311 that controls the overall operation of the rate server 303. The data server 303 may further include RAM 313, ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.), and memory 321. The I / O 319 may include various interface units and drivers for reading, writing, displaying, and / or printing data or files. The memory 321 may further store operating system software 323 for controlling the overall operation of the data processing device 303, control logic 325 for directing the data server 303 to perform aspects described herein, and other application software 327 that provides secondary support and / or other functions that may or may not be used in combination with other aspects described herein. The control logic is sometimes referred to herein as data server software 325. The functionality of the data server software can refer to actions or decisions that are made automatically based on rules coded into the control logic, actions or decisions that are made manually by a user by providing input to the system, and / or a combination of automated processing based on user input (e.g., queries, data updates, etc.).

[0054] Additionally, memory 321 can store data used to perform one or more aspects described herein, including first database 329 and second database 331. In some embodiments, the first database may include a second database (e.g., as another table, report, etc.). That is, information may be stored in a single database or separated into different logical, virtual, or physical databases, depending on the system design. Devices 305, 307, and 309 can have an architecture similar to or different from that described with respect to device 303. Those skilled in the art will appreciate that the functionality of data processing device 303 (or devices 305, 307, and 309) described herein can be distributed across multiple data processing devices, for example, to distribute processing load across multiple computers, segregate transactions based on geographic location, user access level, quality of service (QoS), etc.

[0055] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions, such as in one or more program modules executed by one or more computers or other devices described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that, when executed by a processor of a computer or other device, perform particular tasks or implement particular abstract data types. Modules may be written in source code programming languages ​​that are subsequently compiled for execution, or in scripting languages, such as, but not limited to, HTML or XML. Computer-executable instructions may be stored in computer-readable media, such as hard disks, optical disks, removable storage media, solid-state memory, RAM, etc. Those skilled in the art will appreciate that functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, functionality may be embodied, in whole or in part, in firmware or hardware equivalents, such as integrated circuits, field programmable gate arrays (FPGAs), etc. Certain data structures may be used to more efficiently implement one or more aspects, and such data structures are contemplated within the scope of the computer-executable instructions and computer-usable data described herein.

[0056] 4 depicts an exemplary screenshot and progression of a diagnostic test according to one or more exemplary embodiments described herein. The user must select "Start" to begin the task.

[0057] Figure 5 is a plot showing sensor signature results from the diagnostic tests shown in Figures 1A-1B. It shows the correlation between forced vital capacity (FVC) in milliliters and results from the Monster Cheer Test. The sensor signature results are consistent with the clinical anchor (FCV) in both studies.

[0058] Although the present subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the particular features or acts described above. Rather, the specific features and acts described above are disclosed merely as exemplary forms of implementing the claims. [Example]

[0059] Example 1 Characteristics of the analyzed patient populations collected in the two different studies. i) OLEOS study (https: / / clinicaltrials.gov / ct2 / show / NCT02628743) Participants analyzed: 20 Data analysis period: Smartphone data between the last two visits (176 days) TIFF0007770928000001.tif48128

[0060] ii) JEWELFISH study (https: / / clinicaltrials.gov / ct2 / show / NCT03032172?term=BP39054) Participants analyzed: 19 TIFF0007770928000002.tif48128TIFF0007770928000003.tif30170Covariates: FVC, SD = standard deviation ICC: Intraclass correlation coefficient

[0061] A test for measuring finger strength by pressure measurement was conducted on a mobile phone (iPhone). Patients were to return monsters to their nests by tapping them with their index finger. The phone was to be placed on a table. The monsters were to be tapped as quickly as possible. The patient had to select a preferred hand to use. Patients were required to play the game for 30 seconds with the goal of obtaining the maximum pressure of a single tap, the median time from monster appearance to tapping the monster, and the total number of monsters tapped within the 30-second period. The standard deviation of maximum pressure, median maximum pressure, maximum single tap pressure, median time from monster appearance to hitting the monster, and total number of monster hits obtained within the 30-second period were determined. True monster hits were protocoled events. This data was transferred, and the timestamps of the monster hits were used to calculate the median time to hit the monster.

Claims

1. 1. A diagnostic device for assessing lung volume in a subject with a muscle disorder, comprising: at least one processor; one or more sensors associated with the diagnostic device; a memory for storing computer readable instructions, The computer readable instructions, when executed by the at least one processor, cause the diagnostic device to: receiving a plurality of first sensor data via the one or more sensors associated with the diagnostic device; extracting a first plurality of features from the received first sensor data to identify a condition indicative of lung volume in a subject with a muscle disorder; obtaining recorded subject and / or clinical data regarding the subject, the subject and / or clinical data including a baseline long-term pitch variability measurement from the subject; determining a first assessment of the lung volume of the subject based on the extracted first plurality of features and the subject data and / or clinical data; Execute a memory; The computer readable instructions, when executed by the at least one processor, cause the diagnostic device to: prompting the subject to perform a diagnostic task of emitting a long predetermined sound, the diagnostic task measuring the time the subject spends emitting the predetermined sound; receiving a plurality of second sensor data via the one or more sensors associated with the diagnostic device in response to the subject performing the diagnostic task; extracting a second plurality of features from the received second sensor data to identify a condition indicative of the lung volume of the subject; determining a second assessment of pitch variability for the subject based at least on the extracted second plurality of features with respect to performance of the diagnostic task; and A diagnostic device that further performs the above.

2. 1. A diagnostic device for assessing lung volume in a subject with a muscle disorder, comprising: at least one processor; one or more sensors associated with the diagnostic device; a memory for storing computer readable instructions, The computer readable instructions, when executed by the at least one processor, cause the diagnostic device to: receiving a plurality of first sensor data via the one or more sensors associated with the diagnostic device; extracting a first plurality of features from the received first sensor data to identify a condition indicative of lung volume in a subject with a muscle disorder; obtaining recorded subject and / or clinical data regarding the subject, the subject and / or clinical data including a baseline long-term pitch variability measurement from the subject; determining a first assessment of the lung volume of the subject based on the extracted first plurality of features and the subject data and / or clinical data; Execute a memory; The computer readable instructions, when executed by the at least one processor, cause the diagnostic device to: prompting the subject to perform a diagnostic task in which the subject makes a long predetermined sound while forcibly breathing from full inspiration to full expiration, the diagnostic task measuring the time the subject takes to make the predetermined sound; receiving a plurality of second sensor data via the one or more sensors associated with the diagnostic device in response to the subject performing the diagnostic task; extracting a second plurality of features from the received second sensor data to identify a condition indicative of the lung volume of the subject; determining a second assessment of pitch variability for the subject based at least on the extracted second plurality of features with respect to performance of the diagnostic task; and A diagnostic device that further performs the above.

3. The diagnostic device according to claim 1 or 2, which is a smartphone.

4. The diagnostic device according to any one of claims 1 to 3, wherein the diagnostic task is associated with at least one of a forced vital capacity test.

5. 1. A computer-implemented method for assessing lung volumes in a subject with a muscle disorder, comprising: receiving a plurality of first sensor data via one or more sensors associated with the device; extracting a first plurality of features from the received first sensor data to identify a condition indicative of lung volume in a subject with a muscle disorder; obtaining recorded subject and / or clinical data regarding the subject, the subject and / or clinical data including a baseline long-term pitch variability measurement from the subject; determining a first assessment of lung volume for the subject with a muscle disorder based on the extracted first plurality of features and the subject data and / or clinical data; Including, A computer-implemented method, wherein the subject's lung volume is assessed based on the duration for which the subject makes a long, predetermined sound during an active test.

6. prompting the subject to perform one or more diagnostic tasks, the diagnostic tasks measuring the time the subject spends making the predetermined sounds; receiving a plurality of second sensor data via the one or more sensors in response to the subject performing the one or more diagnostic tasks; extracting a second plurality of features from the received second sensor data to identify a condition indicative of the lung volume of the subject with a muscle disorder; determining a second assessment of the lung volume of the subject with a muscle disorder based at least on the extracted second plurality of features; The computer-implemented method of claim 5 further comprising:

7. 7. The computer-implemented method of claim 5 or 6, wherein the duration during which the subject makes a long predetermined sound comprises the time the subject spends forcefully blowing from full inspiration to full expiration while making a long or loud predetermined sound.

8. The diagnostic device according to any one of claims 1 to 4, wherein the subject is a human being.

9. 1. A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for assessing lung volume in a subject with a muscle disorder, the method comprising: The method comprises: receiving a plurality of sensor data via one or more sensors associated with the device; extracting a plurality of features from the received sensor data to identify symptoms indicative of lung volume in a subject with a muscle disorder; obtaining recorded subject and / or clinical data regarding the subject, the subject and / or clinical data including a baseline long-term pitch variability measurement from the subject; determining an assessment of the lung volume of the subject with myopathy based on the extracted features and the subject data and / or clinical data; Including, 10. A non-transitory machine-readable storage medium, wherein the subject's lung volume is assessed based on the duration of the subject uttering a long, predetermined sound during an active test.

10. 10. The non-transitory machine-readable storage medium of claim 9, wherein the duration during which the subject makes a long predetermined sound comprises the time the subject spends forcefully blowing from a full inspiration to a full expiration while making a long or loud predetermined sound.

11. and the muscle disorder is SMA, and a pharmaceutically active agent is administered to the subject to reduce the likelihood of progression of SMA; the pharmaceutically active agent is suitable for treating SMA in a subject; the pharmaceutically active agent is an m7GpppX diphosphatase (DCPS) inhibitor, a survival motor neuron protein 1 regulator, an SMN2 expression inhibitor, an SMN2 splicing regulator, an SMN2 expression enhancer, a survival motor neuron protein 2 regulator, or an SMN-AS1 (SMN1-derived long non-coding RNA) inhibitor; A computer-implemented method according to any one of claims 5 to 7.

12. 12. The computer-implemented method of claim 11, wherein the pharmaceutically active agent is risdiplam.

13. The computer-implemented method of any one of claims 5 to 7, wherein the subject is a human.

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