Digital Biomarkers

Abstract

The embodiments described herein relate to the field of disease tracking and diagnosis.Specifically, the method for assessing myopathy and especially spinal muscular atrophy (SMA) of a subject comprises: using a mobile device to determine at least one parameter from a dataset of sensor measurements of the subject; and comparing the determined at least one parameter with a reference, thereby assessing myopathy and especially SMA.The embodiments described herein also relate to a mobile device, the mobile device comprising a processor, at least one pressure sensor, a database, and software that is tangibly embedded in the device and that, when executed on the device, performs the method of the present invention, and the use of the mobile device for assessing myopathy and especially SMA.

Description

Technical Field

[0001] Field Aspects described herein relate to the field of assisting in the disease tracking and diagnosis process, and in particular to the evaluation of a subject's muscular disorder, particularly spinal muscular atrophy (SMA). Aspects described herein also relate to a mobile device comprising a processor, at least one sensor, a database, and software tangibly embedded in the device and which, when executed on the device, implements a method as described herein, and to the use of such a device for evaluating muscular disorders and particularly SMA. Aspects described herein also relate to a computer-implemented method using machine learning to predict the clinical anchor score of a subject, particularly a patient suffering from a muscular disorder and particularly SMA.

Background Art

[0002] Background Spinal muscular atrophy (SMA) represents, in its broadest sense, a group of hereditary and acquired central nervous system (CNS) disorders characterized by progressive motor neuron loss in the spinal cord and brainstem, which causes muscle weakness and atrophy. SMA can be characterized by the degeneration of alpha motor neurons from the anterior horn of the spinal cord, which leads to muscle atrophy and can result in paralysis. Thus, this alpha motor neuron degeneration significantly impairs the patient's life prognosis. In healthy subjects, these neurons transmit messages from the brain to the muscles, which results in muscle contraction. Without such stimulation, the muscles atrophy. Subsequently, in addition to muscle weakness and atrophy spreading throughout the body, more specifically to the trunk, upper arms, and thighs, these disorders can be associated with severe respiratory problems.

[0003] Infantile SMA is the most severe form of this neurodegenerative disorder. Symptoms include muscle weakness, low muscle tone, weak cry, floppiness or tendency to fall, difficulty sucking or swallowing, accumulation of secretions in the lungs or throat, feeding difficulties, and increased susceptibility to respiratory infections. The legs tend to be weaker than the arms and cannot reach developmental milestones such as holding the head up or sitting. Generally, the earlier the symptoms appear, the shorter the lifespan. When motor neuron cells deteriorate, symptoms appear soon after. The severe forms of the disease are fatal and not all forms have known treatments. The course of SMA is directly related to the rate of motor neuron cell deterioration and the severity of the resulting weakness. Infants with severe SMA often die from respiratory complications due to weakness of the muscles that assist breathing. Children with milder SMA survive much longer but may require extensive medical support, especially on the severe end of the spectrum. The clinical spectrum of SMA disorders is divided into the following five groups. 1) Type 0 SMA (intrauterine SMA) is the most severe form of the disease and begins before birth. Usually, the first symptoms of type 0 SMA are decreased fetal movement, which can first be observed at 30 - 36 weeks of gestation. After birth, these newborns move little, have difficulty swallowing and breathing, and die soon after birth. 2) Type I SMA (infantile SMA or Werdnig - Hoffman disease) presents symptoms between 0 - 6 months and this form of SMA is very severe. Patients do not achieve the ability to sit and usually die within the first 2 years. 3) Type II SMA (intermediate SMA) presents at 7 - 18 months of age. Patients achieve the ability to sit unsupported but do not stand or walk independently. The prognosis for this group depends greatly on the degree of respiratory complications. 4) Type III SMA (juvenile SMA or Kugelberg - Welander disease) is generally diagnosed after 18 months. Individuals with type 3 SMA can walk independently at some point during the course of the disease but often become wheelchair - bound during adolescence or adulthood. 5) Type IV SMA (Adult-onset SMA). Weakness usually begins in the tongue, hands, or feet in late adolescence and then progresses to other areas of the body. The course of adult SMA is much slower and has little or no effect on life expectancy.

[0004] All forms of spinal muscular atrophy are associated with progressive muscle weakness and atrophy following the degeneration of neurons from the anterior horn of the spinal cord. SMA currently constitutes one of the most common causes of infant death. It affects girls and boys equally in all regions of the world, with a prevalence of 1 / 6000 - 1 / 10,000. Although classified as a rare disease, spinal muscular atrophy is the second most common hereditary disease with an autosomal recessive pattern.

[0005] Nusinersen (Spinraza™, FDA approved in 2017), Onasemnogene abeparvovec (Zolgensma®, FDA approved in 2019), Risdiplam (CAS1825352 - 65 - 5), and Branaplam (CAS1562338 - 42 - 4) are well-known drugs for the treatment of SMA. A small amount of survival motor neuron protein (SMN) plays a role as a cause of the pathogenesis of SMA. As a result, new therapies have been developed to increase the amount of this protein, for example, by replacing or correcting the abnormal SMN1 gene or by regulating the expression of SMN2. Further routes include strategies targeting neuroprotection and improving muscle strength and function. Since the SMN protein plays an important role in the early neonatal period (when the neuromuscular junction develops), especially in the case of type I SMA patients, the putative window for intervention is very early and short. By frequently and dynamically measuring clinically relevant features, objective and highly sensitive precise measurements can be obtained, and ultimately a more complete picture of the patient's disease state can be obtained. This reduces the burden of evaluating the patient and provides support for diagnosis.

[0006] In addition to drug treatment, SMA patients generally require special medical care, particularly with regard to orthopedics, motor support, respiratory management, nutrition, cardiology, and mental health. Data from the U.S. Department of Defense's military health care system (2003 - 2012) were studied by Armstrong et al. to determine the medical costs incurred by patients with spinal muscular atrophy. The median total expenditure over the 10-year study period for SMA patients was more than $83,000 above the median of about $4,500 for the corresponding control group. In the subgroup of patients diagnosed early, the median cost was about $170,000 (J Med Econ. 2016 Aug;19(8):822 - 6 (Non-Patent Document 1)).

[0007] Currently, the assessment of the severity and progression of symptoms in subjects diagnosed with muscle disorders, particularly SMA, involves clinic visits every few weeks or even months, sometimes with in-hospital monitoring and testing of the subjects. For the clinical anchor measurement of muscle disorders, particularly SMA (MFM score), it can be found below (http: / / www.motor-funciton-measure.org / user-s-manual.aspx (Non-Patent Document 2)).

[0008] Since SMA is a clinically heterogeneous disease of the CNS, there is a need for diagnostic tools that can enable reliable diagnosis and identification of the current disease state and symptom progression, and thus assist in accurate treatment.

[0009] US2014 / 163426 (Patent Document 1) relates to tests for assessing a patient's neurological and cognitive functions. Merlini et al. MUSCLE AND NERVE, vol. 26, no, 1, July 2002 (Non-Patent Document 3) relates to the reliability of a handheld dynamometry method for SMA. International Patent Application PCT / EP2018 / 086192 (Patent Document 2) describes characteristic tests for assessing SMA.

Prior Art Documents

Patent Documents

[0010]

Patent Document 1

Patent Document 2

Non-Patent Document

[0011]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Summary of the Invention

[0012] Summary One technical problem underlying the embodiments described herein can be found in providing means and methods in accordance with the above-mentioned needs. One technical problem is characterized in the claims and is solved by the embodiments described in the following specification.

[0013] E1 A method for evaluating spinal muscular atrophy (SMA) of a subject, comprising: a) determining at least one parameter from a dataset of sensor measurements from the subject using a mobile device; and b) comparing the determined at least one parameter with a reference, whereby SMA is evaluated from the result of the comparison. A method comprising the steps of:

[0014] E2 The method of E1, wherein the at least one parameter is a parameter indicating distal motor function, central motor function, and axial motor function.

[0015] E3 A dataset of sensor measurements of individual motor functions includes measurements of the maximum pressure that can be applied by the subject with individual fingers, or measurements of the ability to apply pressure with individual fingers over time, measurements of the maximum duration of the sound "ah", the maximum amount of screen touches within a given period, especially within 30 seconds, the maximum non-simultaneity of double touches, fluctuations in acceleration after the wind blows, the number of items collected, especially the number of coins collected, and / or data from the maximum turning speed of the hand, any one of the methods of E1 - E2.

[0016] E4 A dataset of sensor measurements of individual motor functions includes data from the following characteristic measurements, any one of the methods of E1 - E3. i. Average applied pressure, ii. Pitch fluctuations, iii. Median time until hitting the screen, iv. Non-simultaneity of double touches, v. Time taken to draw a shape, vi. Maximum turning speed of the phone, vii. Fluctuations in acceleration (after the wind blows), and / or, viii. Number of coins collected.

[0017] E5 A dataset of sensor measurements of individual motor functions includes data from the following characteristic tests, any one of the methods of E1 - E4. i. Ring a bell, ii. Cheer for a monster, iii. Tap a monster, iv. Crush a tomato, v. Walk a trajectory, vi. Change the orientation of the phone, vii. Do a tightrope walk, and / or, viii. Collect coins.

[0018] A data set of sensor measurements of six individual motor functions includes data from daily or at least every other day measurements, and in particular, the data set of sensor measurements of individual motor functions includes data from sensor measurements obtained in the morning, according to any one of E1 to E5 methods.

[0019] E7 Any one of the methods of E1 to E6, wherein the mobile device is adapted to perform one or more of the sensor measurements according to any one of claims 3 to 6 on the subject.

[0020] E8 Any one of the methods of E1 to E7, wherein at least one parameter determined to be essentially the same as a reference indicates a subject having SMA.

[0021] E9 A mobile device comprising a processor, at least one pressure sensor, and a database, and software tangibly embedded in the device and, when executed on the device, implementing any one of the methods of E1 to E8 A mobile device.

[0022] E10 A mobile device comprising at least one pressure sensor, and a remote device comprising a processor and a database, and software tangibly embedded in the device and, when executed on the device, implementing any one of the methods of E1 to E8 Comprising The mobile device and the remote device are operably linked to each other. A system.

[0023] E11 Use of the mobile device according to E9 or the system according to E10 for evaluating SMA in a data set of sensor measurements of individual subjects.

[0024] A method by any one of E12 E1 to E8, and a pharmaceutical agent suitable for treating the target SMA, in particular, an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam, in combination.

[0025] E13 A pharmaceutical agent suitable for treating the target SMA, in particular, an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam, wherein the subject to be treated monitors the subject's disease using a method by any one of E1 to E8.

[0026] E14 A method for treating SMA, comprising administering to a subject an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam, and including a method by any one of E1 to E8 for monitoring the subject's disease.

[0027] E15 A combination of the method according to E13, wherein at least one determined parameter is better as compared to the reference parameter of the patient before the subject receives treatment with the pharmaceutical agent.

[0028] A computer-implemented method of using machine learning to predict the MFM32 score of a subject suffering from E16 SMA.

[0029] A computer-implemented method of using machine learning to predict the FVC score of a subject suffering from E17 SMA.

[0030] E18 The methods as described according to the aspects described herein include methods consisting essentially of the steps described above, or methods that may include additional steps. [The present invention 1001] A method for evaluating a target spinal muscular atrophy (SMA), comprising: a) determining at least one parameter from a dataset of sensor measurements from the subject using a mobile device; and b) comparing the determined at least one parameter with a reference, whereby SMA is evaluated from the result of the comparison. A method comprising the above steps. [The present invention 1002] The method of the present invention 1001, wherein the at least one parameter is a parameter indicating distal motor function, central motor function, and axial motor function. [The present invention 1003] The dataset of sensor measurements of each of the motor functions includes measurements of the maximum pressure that can be applied by the subject with each individual finger or the ability to apply pressure with each individual finger over time, measurements of the maximum duration of the sound "ah", the maximum amount of touching the screen within a predetermined period, particularly within 30 seconds, the maximum non-simultaneity of double-touch, the variation of acceleration after the wind blows, the number of items collected, particularly the number of coins collected, and / or data from the maximum turning speed of the hand. The method of the present invention 1001 or 1002. [The present invention 1004] The dataset of sensor measurements of each of the motor functions includes the following characteristic measurements: i. Average applied pressure, ii. Variation in pitch, iii. Central time until hitting the screen, iv. Non-simultaneity of double-touch, v. Time taken to draw a shape, vi. Maximum turning speed of the phone, vii. Variation of acceleration (after the wind blows), and / or viii. Number of coins collected. The method according to any one of the present inventions 1001 to 1003. [The present invention 1005] The dataset of sensor measurements of each of the motor functions includes the following characteristic tests: i. Ringing a bell, ii. Cheering for a monster, iii. Tapping a monster, iv. Crushing a tomato, v. Walking a trajectory, vi. Changing the orientation of the phone, vii. Performing a tightrope walk, and / or viii. Collecting coins. The method according to any one of the present inventions 1001 to 1004. [The present invention 1006] ​ ​ The data set of the sensor measurements of the individual motor functions includes data from daily or at least every other day measurements, in particular, the data set of the sensor measurements of the individual motor functions includes data from sensor measurements taken in the morning, according to any of the methods of the present invention 1001 to 1005. [The present invention 1007] According to any of the methods of the present invention 1001 to 1006, the mobile device is adapted to perform one or more of the sensor measurements of any of the present inventions 1003 to 1006 on the subject. [The present invention 1008] According to any of the methods of the present invention 1001 to 1007, at least one determined parameter that is essentially identical compared to the reference indicates a subject having SMA. [The present invention 1009] A mobile device, a processor, at least one pressure sensor, and a database, and software tangibly embedded in the device that, when executed on the device, performs any of the methods of the present invention 1001 to 1008 comprising a mobile device. [The present invention 1010] A mobile device comprising at least one pressure sensor, a processor and a database, and a remote device comprising software tangibly embedded in the device that, when executed on the device, performs any of the methods of the present invention 1001 to 1008 comprising, the mobile device and the remote device being operably linked to each other, a system. [The present invention 1011] Use of the mobile device of the present invention 1009 or the system of the present invention 1010 for evaluating SMA in the data set of the sensor measurements of the individual subjects. [The present invention 1012] Any of the methods of the present invention 1001 to 1008 in combination with a pharmaceutical agent suitable for treating SMA of a subject, in particular, an m7GpppX diphosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam. [The present invention 1013] A pharmaceutical agent suitable for treating the target SMA, in particular, an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam, wherein the said target to be treated monitors the disease of the said target using any one of the methods E1 to E8. [Invention 1014] A method for treating SMA, comprising administering to a subject an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 modulator, an SMN2 expression inhibitor, an SMN2 splicing modulator, an SMN2 expression enhancer, a survival motor neuron protein 2 modulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam, and including any one of the methods E1 to E8 for monitoring the disease of the said subject. [Invention 1015] A combination of the methods of Invention 1012, wherein at least one determined parameter is better as compared with the said reference parameter of the said patient before the said subject receives treatment with the said pharmaceutical agent. [Invention 1016] A computer-implemented method using machine learning to predict the MFM32 score of a subject suffering from SMA. [Invention 1017] A computer-implemented method using machine learning to predict the FVC score of a subject suffering from SMA.

DETAILED DESCRIPTION OF THE INVENTION

[0031] When used hereinafter, the terms "have", "comprise", or "include", or any grammatical variations thereof, are used in a non-exclusive sense. Accordingly, these terms can refer to both situations where there are no additional features in the entity described in this context other than the features introduced by these terms, and situations where one or more additional features exist. As an example, the expressions "A has B", "A comprises B", and "A includes B" can refer to both situations where there are no other elements in A other than B (i.e., situations where A consists exclusively of only B), and situations where one or more additional elements such as element C, elements C and D, or still other elements exist in entity A in addition to B.

[0032] Furthermore, it should be noted that terms such as "at least one", "one or more", or similar expressions indicating that a feature or element can exist one or more times are generally used only once when introducing each feature or element. Hereinafter, in most cases, when referring to each feature or element, the expressions "at least one" or "one or more" are not repeated, regardless of the fact that each feature or element can exist one or more times.

[0033] Furthermore, when used hereinafter, the terms "in particular", "more particularly", "specifically", "more specifically", "generally", and "more generally", or similar terms, are used in conjunction with additional / alternative features without limiting the possibility of alternatives. Accordingly, the features introduced by these terms are additional / alternative features and are not intended to limit the claims in any way. The present invention can be practiced, as will be recognized by those skilled in the art, by using alternative features. Similarly, features introduced by expressions such as "in one embodiment of the present invention" or similar expressions are additional / alternative features that have no limitation regarding alternative embodiments of the present invention, no limitation regarding the scope of the present invention, and no limitation regarding the possibility of combining such introduced features with other additional / alternative or non-additional / alternative features of the present invention.

[0034] The method can be carried out on a mobile device by the subject or on a different device when a dataset of pressure measurement values is acquired. Thus, the mobile device and the device acquiring the dataset can be physically identical, for example the same device, or different, for example remotely located devices. Such a mobile device may have a data acquisition unit, which generally includes means for acquiring data, i.e., detecting or measuring physical and / or chemical parameters quantitatively or qualitatively and converting them into an electronic signal that is transmitted to the assessment unit of the mobile device used to carry out the method according to the invention, including software and / or hardware. The data acquisition unit may further or alternatively include hardware and / or software for detecting or measuring physical and / or chemical parameters quantitatively or qualitatively and converting them into an electronic signal that is transmitted to a device located remotely from the mobile device and used to carry out the method according to the aspects described herein. Generally, data acquisition is carried out by at least one sensor. It will be understood that more than one sensor, for example at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, or even more different sensors, can be used on the mobile device. Common sensors used for data acquisition include sensors such as gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, touch sensors, voice recorders, light sensors, pressure sensors, position data detectors, cameras, sweat analysis sensors, etc. The assessment unit generally includes a processor and a database, and software embodied on the device and, when executed on the device, for carrying out one or more methods as described herein. Such a mobile device may also be provided with a user interface, such as a screen, for enabling the results of the analysis carried out by the assessment unit to be provided to the user.If a separate device is used, the mobile device can communicate and / or transmit data with and to the device used to implement the analysis method by any means. Such data transmission can be achieved by a permanent or temporary physical connection such as coaxial, fiber, fiber optic, or twisted pair 10BASE-T cable. Alternatively, it can be achieved by a temporary or permanent wireless connection, for example using radio waves such as Wi-Fi, 3G, 4G, LTE, LTE Advanced, 5G, and / or Bluetooth. Thus, the only requirement for implementing the method as described herein is the existence of a dataset of input measurements obtained from the subject using a mobile device. The above dataset may be transmitted or stored from the mobile device on the acquisition side to a permanent or temporary memory device, and then the memory device may be used to transfer the data to a second device for analysis. The remote device implementing the method of the present invention in this setup generally includes a processor and a database, and is embedded in the above device and, when executed on the above device, includes software for implementing the method of the present invention. More generally, the above device may also include a user interface such as a screen that enables the user to be provided with the results of the analysis performed by the assessment unit.

[0035] As used herein, the term "evaluate" refers to determining or providing assistance for diagnosing whether a subject has a muscular disorder, particularly SMA. As will be understood by those skilled in the art, such an evaluation is preferably appropriate for 100% of the subjects being investigated, but it may not be so. However, this term requires appropriately evaluating a statistically significant portion of the subjects and thus being able to identify those having a muscular disorder or SMA. Whether a portion is statistically significant can be determined without further ado by those skilled in the art using various well-known statistical evaluation tools, such as determination of confidence intervals, p-values, Student's t-tests, Mann-Whitney tests, etc. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Generally, the contemplated confidence intervals are at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%. The p-values are generally 0.2, 0.1, 0.05. Thus, the method of the present invention generally aids in identifying muscular disorders or SMA by assessing a dataset of pressure measurements. This term also encompasses any type of diagnosis, monitoring, or staging of SMA, particularly with respect to the evaluation, diagnosis, monitoring, and / or staging of any symptoms or the progression of any symptoms associated with muscular disorders and particularly SMA. Once an appropriate diagnosis or evaluation is made, appropriate treatment can be administered or prescribed. These include, without limitation, drugs, gene therapy, strategies targeting the improvement of muscle strength and function, orthopedics, exercise assistance, respiratory management, nutrition, cardiology, and mental health interventions.

[0036] As used herein, the term "muscle disorder" refers to a condition associated with impaired muscle function. Generally, such muscle disorders may be caused by diseases or disorders such as muscle atrophy, and more commonly, may be neuromuscular diseases such as spinal muscular atrophy. The term "spinal muscular atrophy (SMA)" as used herein generally relates to a neuromuscular disease characterized by the loss of motor neuron function in the spinal cord. As a result of the loss of motor neuron function, generally, muscle atrophy occurs, leading to the early death of the affected subject. The disease is caused by a hereditary genetic abnormality of the SMN1 gene. The SMN protein encoded by the above gene is necessary for the survival of motor neurons. The disease is inherited in an autosomal recessive manner.

[0037] As used herein, the term "subject" relates to animals, generally mammals. In particular, the subject is a primate, and most commonly, a human. The subject according to the present invention is considered to have or be suspected of having a muscle disorder and in particular SMA, i.e., may already exhibit some or all of the symptoms associated with the above disease.

[0038] The term "at least one" means that one or more parameters, i.e., at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten, or even more different parameters, can be determined according to the present invention. Thus, there is no upper limit to the number of different parameters that can be determined according to the method of the present invention. However, generally, there will be one to four different parameters for each dataset of sensor measurements determined. The parameters may be selected from the group consisting of peak pressure, integrated pressure, pressure profile over time, and pressure oscillation.

[0039] As used herein, the term "parameter" can refer to a parameter that indicates the ability of an object to apply pressure with a finger. More generally, a parameter is selected from the group consisting of peak pressure, integrated pressure, a pressure profile over time, and pressure oscillations. Depending on the type of activity being measured, a parameter can be derived from a data set obtained by a pressure measurement performed on an object. Specific parameters used in accordance with the present invention are listed elsewhere in this specification in more detail.

[0040] As used herein, the term "data set of sensor measurements" refers to all of the data obtained by a mobile device from an object during measurement by sensors of the mobile device, particularly a smartphone, or any subset of the above data that is useful for deriving a parameter.

[0041] As used herein, the term "individual finger strength" refers to the level of force that can be applied by a finger. This includes the ability to apply a pressure peak, the ability to apply a specific pressure level (integrated pressure) over time, and / or the ability to maintain pressure over time.

[0042] Specific contemplated pressure tests and means for measurement by a mobile device in accordance with the method of the present invention are identified below.

[0043] In one embodiment, thus, the mobile device is configured to perform or be adapted to obtain data from a pressure test (so-called "ringing the bell test") that measures the maximum pressure that can be applied by the finger of interest. Further, the test is generally also configured to measure the duration of the maximum pressure application. The data set obtained from such a test makes it possible to identify the peak pressure, the integrated pressure, as well as the pressure profile over time. The test may require calibration against the maximum force that can initially be applied by the finger of interest. Further, there are sensor-specific limitations to consider. To measure pressures within the range below saturation specific to the sensor, the test can be configured to avoid applying the maximum pressure.

[0044] The above pressure measurement can be performed by a mobile device such as a smartphone by using a pressure-sensitive touch technology or a 3D touch technology. The pressure-sensitive touch technology uses force-sensing electrodes that border the edge of the screen of the mobile device. The electrodes determine the pressure applied to the screen. Thus, the test can display on the screen a specific task that requires pressing the screen with a finger and thereby applying force at a specific intensity or over a specific time. The measured parameters are then relayed from the electrodes to an electromagnetic linear actuator that vibrates back and forth. The actuator generates data for a data set of force measurement values according to the present invention. The 3D touch technology works by using capacitive sensors directly integrated into the screen. When a press is detected, these capacitive sensors measure minute changes in the distance between the backlight and the cover glass. These data are then combined with accelerometer data and touch sensor data to complete the data for a data set of force measurement values, which can be used to determine at least one parameter by an appropriate algorithm executed, for example, on an assessment unit. Further details regarding the pressure-sensitive touch sensors commonly included in mobile devices used to generate the data set of force measurement values used in the method of the present invention are described in U.S. Patent No. 8,633,916. The force sensors of the 3D touch technology commonly included in mobile devices used to generate the data set of force measurement values used in the method of the present invention are described in International Application Publication No. WO2015 / 106183. Further suitable force measurement sensors used in mobile devices are described in any one of European Patent No. 2 368 170, U.S. Patent No. 9,116,569, European Patent No. 2 635 957, U.S. Patent No. 8,952,987, or U.S. Application Publication No. US2015 / 0097791.

[0045] In another embodiment, the mobile device is configured to perform or be adapted to obtain data from a further pressure test that measures the ability to sustain a controlled amount of pressure via a finger over a given period of time. The data set obtained from such a test makes it possible to identify pressure oscillations and pressure profiles over time. The test can obtain calibration regarding a comfortable pressure level, i.e., it may be necessary to first identify a threshold for the comfortable pressure level. Further, the test is configured such that measurements are made below sensor-specific saturation for pressure measurements. The above-described pressure measurements can be performed by a mobile device, such as a smartphone, by using a pressure-sensitive touch technology or 3D touch technology as defined anywhere in this specification, or a similar technology that enables measurement of force or pressure on a touch screen.

[0046] Both tests can be implemented on the mobile device by computer program code that requires a subject user to perform certain tasks that enable potential calibration and actual pressure measurement. Generally, such tasks can be masked within entertainment exercises or games that require the subject to perform the tasks in an enjoyable and thus comfortable manner on the device. By using the above game setup, the tasks can also be performed, in particular, by children or subjects with cognitive impairments. Further, the gaming character of the test can also improve the overall motivation of the subject to perform the test. Examples of generally contemplated pressure measurement tests are described in more detail in the following attached examples.

[0047] It will be understood that a mobile device applied in accordance with the present invention can be adapted to perform one or more of the above-described force measurement tests. In particular, it can be adapted to perform both tests.

[0048] Depending on the mobile device, the ability to measure peak pressure, apply a specific pressure level (integrated pressure) over time, and / or maintain pressure over time (pressure profile) can also be performed during other uses of the mobile device where an operation is performed that enables the user to record the pressure measurement (passive test) without focusing on it. Generally, when a smartphone is used as the mobile device, the subject (user) will typically perform various touch control tasks involving finger pressure-driven interaction with the screen. Generally, tapping is done when other standard activities are performed, such as when a phone number is dialed or, for example, an Internet query is made. The pressure applied by the finger during the performance of such tasks can be analyzed over a specific period of time to provide calibration purposes or a reference. Generally, peak pressure measurements can be performed, for example, during a tapping task such as dialing, or the applied pressure can be integrated over a specific time window to yield an integrated pressure. Subsequently, changes in the peak force, integrated pressure, or task-specific pressure profile relative to a reference can be used in the method according to the invention, which is applied to investigate the data set obtained from the above (passive) pressure measurements.

[0049] Furthermore, tapping and other pressure application activities may be performed during further tests described below. The pressure measurement can also be performed as a passive test during the above further tests.

[0050] Furthermore, the mobile device may be adapted to perform further tests that may be related to muscular disorders such as SMA. Thus, further data can be similarly processed by the method of the present invention. These further data are generally suitable for further enhancing the evaluation of SMA or muscular disorders in the subject. Specific contemplated tests for investigating distal motor function (e.g., the ability to tap, draw, and pinch fingers), axial motor function (e.g., the ability of the subject to lift, twist, walk on a rope, and pour water), and / or central motor function (e.g., vocalization ability) will be described in more detail later. In addition, tests for overall well-being and cognitive ability can also be considered.

[0051] Specific contemplated further tests implemented in the mobile device for obtaining data that can generally be included in the data set investigated by the method of the present invention are selected from the following tests.

[0052] (1) Tests for distal motor function: Tapping the monster, walking the track, and squeezing the tomato.

[0053] The mobile device can further perform or be adapted to obtain data from a further test regarding distal motor function (so-called "tapping the monster") configured to measure finger dexterity and distal weakness. The data set obtained from such a test enables the identification of finger speed, finger movement accuracy, and finger movement time and distance.

[0054] The mobile device can further perform or be adapted to obtain data from a further test regarding distal motor function (so-called "walking the track") configured to measure finger dexterity and distal weakness. The data set obtained from such a test enables the identification of finger movement accuracy, pressure profile, and speed profile.

[0055] The purpose of the "walk the path" test is to evaluate fine finger control and stroke sequencing. The test is considered to cover aspects of the motor function of the affected hand such as tremors and spasms, as well as hand-eye coordination disorders. The patient holds a mobile device with the non-tested hand and is instructed to use the middle finger of the tested hand to draw different alternating shapes (straight lines, rectangles, circles, sine curves, and spirals (see below)) that are pre-written on the touch screen of the mobile device and gradually become more complex, "as accurately and quickly as possible" within a maximum time of, for example, 30 seconds. To draw the shapes well, the patient's finger must continuously slide on the touch screen, pass through all the indicated checkpoints, and connect the indicated starting and ending points that fall within the boundaries of the writing path. The patient has a maximum of two attempts to successfully complete each of the six shapes. The test may be performed alternately with the right and left hands. The user may be instructed to perform it alternately every day. Each of the two straight line shapes may have a specific number "a" of checkpoints, that is, connect "(a - 1)" segments. The square shape may have a specific number "b" of checkpoints, that is, connect "(b - 1)" segments. The circular shape may have a specific number "c" of checkpoints, that is, connect "(c - 1)" segments. The figure-eight shape may have a specific number "d" of checkpoints, that is, connect "(d - 1)" segments. The spiral shape may have a specific number "e" of checkpoints, that is, connect "(e - 1)" segments. Completing the six shapes suggests success in drawing a total of "(2a + b + c + d + e - 6)" segments. One or more of the shapes may optionally be given a greater weight than others, for example, drawing the number "8".

[0056] Parameters of interest in the test of drawing common shapes Based on the complexity of the shape, straight lines and square shapes are associated with a weighting factor (Wf) of 1, circular and sine curve shapes are associated with a weighting factor of 2, and spiral shapes are associated with a weighting factor of 3. Shapes successfully completed in the second trial can be associated with a weighting factor of 0.5. These weighting factors are numerical examples that can be changed within the context of the present invention.

[0057] 1. Shape Completion Score i. The number of shapes successfully completed per test (0 - 6) (ΣSh). ii. The number of shapes successfully completed in the first trial (0 - 6) (ΣSh 1 ). iii. The number of shapes successfully completed in the second trial (0 - 6) (ΣSh 2 ). iv. The number of shapes that failed / were not completed in all trials (0 - 12) (ΣF). v. Shape Completion Score (0 - 10) (Σ[Sh×Wf]), which reflects the number of shapes successfully completed adjusted by the weighting factors for different complexity levels of each shape. vi. Shape Completion Score (0 - 10) (Σ[Sh 1 ×Wf] + Σ[Sh 2 ×Wf×0.5]), which reflects the number of shapes successfully completed adjusted by the weighting factors for different complexity levels of each shape and for success in the first versus second trials. vii. The shape completion score as defined in #1e, and #1f can consist of the speed at the completion of the test when multiplied by 30 / t (t represents the time in seconds to complete the test). viii. Overall and first - trial completion rates for each of the six individual shapes based on multiple tests within a specific period: (ΣSh 1 ) / (ΣSh 1 +ΣSh 2 +ΣF) and (ΣSh 1 +ΣSh 2 ) / (ΣSh 1 +ΣSh 2 +ΣF).

[0058] 2. Segment Completion and Phase Velocity Score / Index (Analysis based on the best of two trials for each shape [maximum number of completed segments], if applicable) i. Number of successfully completed segments per test (0 to [2a + b + c + d + e - 6]) (ΣSe). ii. Average phase velocity of successfully completed segments ([C], segments / second): C = ΣSe / t (t represents the time in seconds to complete the test (maximum 30 seconds)). iii. Segment Completion Score (Σ[Se × Wf]), reflecting the number of successfully completed segments adjusted by weighting factors for different complexity levels of each shape. iv. Velocity - adjusted and weighted Segment Completion Score (Σ[Se × Wf] × 30 / t) (t represents the time in seconds to complete the test). v. Number of successfully completed segments specific to the straight - line and square shapes (ΣSe LS ) vi. Number of successfully completed segments specific to the circular and sine - curve shapes (ΣSe CS ) vii. Number of successfully completed segments specific to the spiral shape (ΣSe S ) viii. Shape - specific average linear phase velocity for successfully completed segments in the straight - line and square shape tests: C L = ΣSe LS / t (t represents the cumulative epoch time in seconds from the start point to the end point in the corresponding successfully completed segments within these specific shapes). ix. Shape - specific average circular phase velocity for successfully completed segments in the circular and sine - curve shape tests: C C = ΣSe CS / t (t represents the cumulative epoch time in seconds from the start point to the end point in the corresponding successfully completed segments within these specific shapes). x. Shape - specific average spiral phase velocity for successfully completed segments in the spiral shape test: CS =ΣSe S / t (where t represents the cumulative epoch time elapsed in seconds from the start point to the end point in the corresponding successfully completed segment within this specific shape).

[0059] 3. Drawing accuracy score / metric (Analysis based on the best of two trials [maximum number of completed segments] for each shape, if applicable) i. Deviation (Dev) calculated as the sum of the metrics of the total area under the curve (AUC) of the integrated surface deviation between the drawn trajectory and the target drawing path from the start checkpoint to the end checkpoint stretched for each of these shapes, divided by the total cumulative length of the corresponding target path (stretched from the start checkpoint to the end checkpoint) within the specific shape. ii. Dev calculated as in #3a, specifically the linear deviation (Dev L ) from the linear and square shape test results. iii. Dev calculated as in #3a, specifically the circular deviation (Dev C ) from the circular and sine curve shape test results. iv. Dev calculated as in #3a, specifically the helical deviation (Dev S ) from the helical shape test results. v. Dev calculated as in #3a, but specifically applicable only to the shapes in which at least 3 segments were successfully completed in the best trial from each of the 6 individual shape test results, the shape-specific deviation (Dev 1-6 ) vi. Continuous variable analysis of any other arbitrary method for calculating the overall deviation from the shape-specific or shape-independent target trajectory.

[0060] 4) Pressure profile measurement (1) Average applied pressure. (2) Deviation (Dev) calculated as the standard deviation of the pressure.

[0061] The mobile device can further be configured to perform or adapt to acquire data from further tests regarding distal motor function (so-called "squeezing a tomato"), which is configured to measure finger dexterity and distal weakness. The data set obtained from such tests makes it possible to identify the accuracy and speed of finger movements, as well as the associated pressure profiles. The test may initially require calibration regarding the ability of movement accuracy in the subject.

[0062] One purpose of the squeezing a tomato test is to evaluate fine distal motor operations (grasping and pinching) and control by assessing the accuracy of the movement of the fingers when pinched and closed. The test is considered to cover aspects of the impaired hand motor function such as impaired grasping / pinching function, muscle strength decline, and hand-eye coordination disorder. The patient is instructed to hold the mobile device with the non-tested hand and touch the screen with two fingers (thumb + middle finger, or preferably thumb + ring finger) of the same hand to squeeze / pinch as many round shapes (i.e., tomatoes) as possible within 30 seconds. The impaired fine motor operation affects the_number pinched_. The test is performed alternately with the right and left hands. The user is instructed to perform it alternately every day.

[0063] Parameters of interest in the squeezing a general shape test 1. Number of squeezed shapes a) Total number of tomato shapes squeezed in 30 seconds (ΣSh). b) Total number of tomatoes squeezed in the first trial in 30 seconds (ΣSh 1 )(The first trial is detected as the first double contact on the screen following a successful squeeze if it is not the very first trial of the test).

[0064] 2. Accuracy index of the pinching action a) The success rate of the pinching action (PSR), defined as ΣSh divided by the total number of pinching action trials (ΣP) (measured as the total number of double finger contacts on the screen detected separately) within the total duration of the test. b) The double - touch asynchrony (DTA), measured as the latency time between the screen touches by the first and second fingers for all detected double - contacts. c) The pick - target precision (PTP), measured as the distance from an equidistant point between the start - touch points of the two fingers in the double - contact to the center of the tomato shape, for all detected double - contacts. d) The pick - finger movement asymmetry (PFMA), measured as the ratio (shortest / longest) between the respective distances that the two fingers slide from the start point of the double - contact until reaching the pick - gap, for all double - contacts that succeed in the pick operation. e) The pick - finger velocity (PFV), measured as the velocity (mm / sec) of one and / or both fingers sliding on the screen from the time of the double - contact until reaching the pick - gap, for all double - contacts that succeed in the pick operation. f) The pick - finger asynchrony (PFA), measured as the ratio (lowest speed / highest speed) between the velocities of the individual fingers sliding on the screen from the time of the double - contact until reaching the pick - gap, for all double - contacts that succeed in the pick operation. g) Analysis of the continuous variables of 2a - 2f over time, as well as their analysis by epochs of variable duration (5 - 15 seconds). h) Analysis of continuous variables that integrally measure the deviation from the drawn trajectory of the target for all tested shapes (especially helix and square).

[0065] 3) Pressure profile measurement a) The average pressure applied. b) The deviation (Dev) calculated as the standard deviation of the pressure.

[0066] (2) Tests for measuring axial movement function: Changing the orientation of the phone, tightrope - walking, and collecting coins.

[0067] The mobile device can further be adapted to perform or obtain data from further tests on axial and proximal motor function (so-called "changing the orientation of the telephone"), configured to measure upper limb movement (e.g., by twisting the mobile device), weakness and fatigue, reduction of proximal tension, joint contracture, and tremors. In this test, the patient must hold the telephone in their palm and repeatedly turn the telephone screen up and down.

[0068] The data set obtained from such tests makes it possible to identify the accuracy, speed, and number of twists (wrist rotations). The test may initially require calibration regarding the subject's ability to move accurately.

[0069] The mobile device can further be adapted to perform or obtain data from further tests on axial motor function (so-called "tightrope walking"), configured to measure reduction of proximal tension in the upper limb. The data set obtained from such tests makes it possible to identify the proper number, size, and speed of movements. The test may initially require calibration regarding the subject's ability to balance and imbalance.

[0070] The mobile device can further be adapted to perform or obtain data from further tests on axial motor function (so-called "collecting coins"), configured to measure upper limb movement (by moving the mobile device), weakness and fatigue. The data set obtained from such tests makes it possible to identify the range of axial rotational movement, the speed and number of movements over time, and the reaction time as a response to the ongoing game situation (i.e., the ball needs to be moved back and forth by the user between opposing parts of the screen). The test may initially require calibration regarding the subject's ability to move accurately.

[0071] (3) Test of central motor function: Cheering for the monster

[0072] The mobile device can further be adapted to perform and obtain data from further tests regarding central motor function (so-called "cheering for the monster"), configured to measure proximal central motor function by measuring vocalization ability.

[0073] In general, the above tests can similarly be implemented on a mobile device by computer program code that requires a subject user to perform specific tasks that enable calibration and force measurement. In general, such tasks can be masked within a game that requires the subject to perform the task in a fun, and thus comfortable and relaxed, manner on the device. By using the above game setup, the tasks can also be performed, in particular, by children or subjects with cognitive impairments. Furthermore, the gaming characters of the tests can also improve the overall motivation of the subject to perform the tests. Examples of the above tests that are generally contemplated are described in more detail in the following attached examples.

[0074] In a further embodiment of the method of the present invention, the mobile device from which the data set is obtained is configured to at least provide data from at least one of the tests regarding distal motor function, axial motor function, and / or central motor function, more generally any one of these types of data, in addition to the data set of pressure measurements.

[0075] As used herein, the term "mobile device" refers to any portable device that includes at least a pressure sensor and a data recording device, an accelerometer, and a gyroscope, suitable for obtaining a data set of pressure measurements. This may also require a data processor and a storage unit, as well as a display, to electronically simulate pressure measurement tests in the mobile device. Further, data is to be recorded and compiled from the activity of interest to result in a data set that is evaluated by the method of the present invention, either on the mobile device itself or on a second device. Depending on the particular setup envisioned, the mobile device may need to include a data transmission device to transfer the acquired data set from the mobile device to a further device. Particularly well-suited as a mobile device according to the present invention are smartphones, portable multimedia devices, or tablet computers. Alternatively, a portable sensor with data recording and processing capabilities can be used. Further, depending on the type of activity test being performed, the mobile device is to be adapted to display an instruction regarding the activity to be performed for the test to the subject. Specific envisioned activities to be performed by the subject are described elsewhere in this specification and include distal tension reduction tests as well as other tests described herein.

[0076] Determining at least one parameter can be accomplished by directly deriving the desired measured value as a parameter from the data set. Alternatively, the parameter can integrate one or more measured values from the data set and thus can be derived from the data set by a mathematical operation such as a calculation. Generally, the parameter is derived from the data set by a computer program that automatically derives the parameter from the data set of activity measurements when tangibly embedded in, for example, a data processing device feed by the data set by an automated algorithm.

[0077] As used herein, the term "reference" refers to a discriminator that enables the evaluation of a subject's muscular disorder and particularly SMA. Such a discriminator can be a value of a parameter that indicates a subject suffering from a muscular disorder and particularly SMA, or a subject not suffering from a muscular disorder and particularly SMA.

[0078] Such a value can be derived from one or more parameters of a subject known to be suffering from a muscular disorder and particularly SMA. Generally, an average or a median can be used as the discriminator in such cases. If the determined parameter from the subject is the same as the reference or exceeds a threshold derived from the reference, the subject can be identified as suffering from a muscular disorder and particularly SMA in such an example. If the determined parameter is different from the reference, particularly less than the above-mentioned threshold, the subject shall be identified as not suffering from a muscular disorder and particularly SMA.

[0079] Similarly, the value can be derived from one or more parameters of a subject known not to be suffering from a muscular disorder and particularly SMA. Generally, an average or a median can be used as the discriminator in such cases. If the determined parameter from the subject is the same as the reference or is below a threshold derived from the reference, the subject can be identified as not suffering from a muscular disorder and particularly SMA in such an example. If the determined parameter is different from the reference, particularly exceeding the above-mentioned threshold, the subject shall be identified as suffering from a muscular disorder and particularly SMA.

[0080] As an alternative, the reference can be a parameter previously determined from a dataset of pressure measurement values obtained from the same subject prior to the actual dataset. In such an example, a parameter determined from the actual dataset, which is different from the previously determined parameter, shall indicate either an improvement or a deterioration depending on the previous state of the disease or its associated symptoms and the type of activity represented by the parameter. A person skilled in the art would know how to use the above parameter as a reference based on the type of activity and the previous parameter.

[0081] The comparison of at least one determined parameter with a reference can be achieved by an automated comparison algorithm implemented in a data processing device such as a computer. What are compared with each other are the determined parameter and the value of the reference for the determined parameter as specified in detail anywhere in the present invention. As a result of the comparison, it can be evaluated whether the determined parameter is identical to, different from, or in a specific relationship with (for example, greater than or less than) the reference. Based on the above evaluation, the subject can be identified as having ( "ruled in") or not having ( "ruled out") a muscular disorder and particularly SMA. Regarding the evaluation, the type of reference is taken into account as described anywhere in connection with suitable references according to the present invention.

[0082] Furthermore, it is assumed that a quantitative evaluation of the muscular disorder and particularly SMA in the subject is possible by determining the degree of difference between the determined parameter and the reference. It should be understood that an improvement, worsening, or unchanged state of the disease state or its symptoms can be determined by comparing the actual determined parameter with what was previously determined and used as a reference. Based on the quantitative difference in the value of the above parameter, an improvement, worsening, or unchanged state can be determined and optionally quantified. When other references such as references from subjects with SMA are used, it will be understood that the quantitative difference is meaningful when a specific disease stage can be assigned to a collective reference. For this disease stage, a worsening, improving, or unchanged disease state can be determined and optionally quantified in such an example.

[0083] The above diagnosis, for example, the evaluation of a muscular disorder or SMA in a subject, is presented to the subject or another person such as a healthcare provider or a clinical analyst. Generally, this is achieved by displaying it on a mobile device or an assessment device.

[0084] Furthermore, one or more parameters can also be stored on the mobile device or generally presented to the subject in real time. The stored parameters can be combined to serve as a measure of time elapsed or a similar metric. Such measured parameters can be provided to the subject as feedback on the mobility investigated according to the method of the present invention. Generally, such feedback can be provided in electronic form on a suitable display of the mobile device and can be linked to therapy recommendations or rehabilitation metrics as described above.

[0085] Furthermore, the measured parameters can be provided to healthcare providers such as medical staff in a hospital or clinic, as well as developers of diagnostic tests or drug developers related to clinical trials, health insurance providers, or other stakeholders in the public or private healthcare system.

[0086] Exemplarily, the method of the present invention for assessing the subject's SMA can be implemented as follows.

[0087] First, at least one parameter is determined from an existing dataset of sensor measurements obtained from the subject using the mobile device. The dataset can be transmitted from the mobile device to an assessment device such as a computer, or can be processed on the mobile device to derive at least one parameter from the dataset.

[0088] Second, at least one determined parameter is compared to a reference, for example, using a computer-implemented comparison algorithm implemented by a data processor of the mobile device or an assessment device such as a computer. The result of the comparison is evaluated against the reference used for the comparison, and based on the evaluation, the subject is identified as a subject with or without SMA.

[0089] Thirdly, the above diagnosis, i.e., the identification of the subject as a subject with or without SMA, is presented to the subject or to other persons such as healthcare providers. However, it will be understood that additional factors or parameters may be taken into account by the clinician with respect to the final clinical diagnosis or evaluation.

[0090] As used herein, the term "identification" refers to evaluating the likelihood that a subject has SMA. Thus, it will be understood that the evaluation may not be appropriate for all. However, it is generally contemplated that a statistically significant portion of the subjects investigated can be evaluated, i.e., identified, as having SMA. How statistical significance can be determined is described elsewhere in the present invention. Identification, as used herein, generally refers to providing a hint rather than a final conclusion.

[0091] Further, alternatively or in addition, at least one parameter underlying the diagnosis is stored in a mobile device. Generally, it is to be evaluated in conjunction with other stored parameters by a suitable assessment tool, such as a time-course assembly algorithm implemented on a mobile device, that can electronically assist in the recommendation of rehabilitation or therapy as specified elsewhere in this specification.

[0092] Advantageously, in the research underlying the present invention, it has been found that parameters obtained from a dataset of sensor measurements of SMA patients can be used as digital biomarkers for evaluating the SMA of those patients, i.e., for identifying those patients suffering from SMA. Said dataset can be conveniently obtained from SMA patients by using a mobile device such as a smartphone, a portable multimedia device, or a tablet computer, in which the subject performs an active or passive pressure test. In particular, it has been found in the research underlying the present invention that even a dataset obtained by passive pressure measurements performed while other activities are being carried out on a smartphone is of sufficient quality for a meaningful evaluation of SMA patients. The acquired dataset can then be appraised by the method of the present invention with respect to parameters suitable as digital biomarkers. Said appraisal can be carried out on the same mobile device or on a separate remote device. Furthermore, by using such a mobile device, recommendations regarding lifestyle or therapy can be provided directly to the patient, i.e., without the advice of medical personnel in a clinic or an ambulance. Thanks to the present invention, by using the parameters actually determined by the method of the present invention, the living conditions of SMA patients can be adjusted more precisely according to the actual disease state. Thereby, a more effective drug treatment can be selected or the dosing schedule can be adapted to the patient's current state. It should be understood that the method of the present invention is generally a data appraisal method that requires an existing dataset of activity measurements from a subject. Within this dataset, the method determines at least one parameter that can be used for evaluating SMA, i.e., that can be used as a digital biomarker for SMA. Furthermore, it will be understood that the method of the present invention, which uses parameters from a dataset of pressure measurements, can also be applied to the evaluation of muscle disorders other than SMA. For such an evaluation, the same principle as for SMA shall apply.

[0093] Therefore, the method of the present invention can be used for the following - Evaluating a disease state - Monitoring a patient in real life - Monitoring a patient daily - Investigating the efficacy of a drug, especially during a clinical trial - Facilitating and / or assisting in treatment decision-making

[0094] The explanations and definitions regarding the above terms, with any necessary changes, apply to the embodiments described later in this specification.

[0095] The present invention also recalls a computer program, a computer program product, or a computer-readable storage medium having the above computer program tangibly embedded therein, wherein the computer program includes instructions for implementing the method of the present invention as described above when executed on a data processing device or a computer. Specifically, the present disclosure further includes the following. - A computer or computer network comprising at least one processor, the processor being adapted to implement a method according to one of the embodiments described herein - A computer loadable data structure adapted to implement a method according to one of the embodiments described herein while the data structure is being executed on a computer - A computer script adapted such that a computer program implements a method according to one of the embodiments described herein while the program is being executed on a computer - A computer program comprising program means for implementing a method according to one of the embodiments described herein while the computer program is being executed on a computer or computer network - A computer program comprising program means according to the above embodiments, wherein the program means are stored on a computer-readable storage medium - A storage medium in which a data structure is stored and which, after the data structure has been loaded into the main and / or working storage of a computer or computer network, is adapted to carry out a method according to one of the embodiments described herein - A computer program product having program code means which, when executed on a computer or computer network, can store the program code means in a storage medium or is stored in a storage medium for carrying out a method according to one of the embodiments described herein - A generally encrypted data stream signal comprising a dataset of pressure measurement values obtained from a subject using a mobile device - A generally encrypted data stream signal comprising at least one parameter derived from a dataset of pressure measurement values obtained from a subject using a mobile device

[0096] A system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database, the software being tangibly embedded in the device and, when executed on the device, carrying out any of the methods of the invention, the mobile device and the remote device being operably linked to each other

[0097] "Operably linked to each other" should be understood to mean that the devices are connected so that data can be transferred from one device to the other. Generally, it is contemplated that a mobile device that acquires data from a subject is connected to a remote device that performs the steps of the method of the present invention such that at least the acquired data can be transmitted to the remote device for processing. However, the remote device can also transmit data to the mobile device, such as signals that control or monitor its proper functioning. The connection between the mobile device and the remote device can be achieved by a permanent or temporary physical connection, such as a coaxial, fiber, optical fiber, or twisted pair 10BASE-T cable. Alternatively, it can be achieved by a temporary or permanent wireless connection that uses, for example, radio waves, such as Wi-Fi, cellular, 3G, 4G, LTE, LTE-Advanced, 5G, Bluetooth, etc., but is not limited thereto. Further details can be found elsewhere in this specification. For data acquisition, the mobile device can comprise a user interface such as a screen or other data acquisition device. Generally, it will be understood that the activity measurement can be performed on a screen comprised by the mobile device, which can have different sizes, for example, including a 5.1-inch screen.

Brief Description of the Drawings

[0098] [Figure 1A] A diagram showing an exemplary screenshot and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 1B] A diagram showing an exemplary screenshot and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 2]A plot showing the results of various sensor characteristics from the diagnostic tests shown in FIGS. 1A - B. The results of the sensor characteristic (the duration of the longest "ah" in seconds during the test) are consistent with the clinical anchor (forced vital capacity) in both studies. [Figure 3A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 3B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 3C] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 4] A plot showing the results of sensor characteristics from the "Tap the Monster" diagnostic test of Example 2 shown in FIGS. 3A - C. The results of the sensor characteristic (the median time until tapping the monster) are consistent with the clinical anchor (circling the edge of the CD without compensatory movements) in both studies. [Figure 5A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 5B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 6]A plot showing the results of sensor characteristics from the "crush a tomato" diagnostic test of Example 3 shown in FIGS. 5A - B. The results of the sensor characteristics (time difference in seconds when a finger touches the screen) are consistent with the clinical anchors (MFM004, MFM017, MFM018, MFM019, MFM020, MFM021, MFM022) in both studies. [Figure 7A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 7B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 7C] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 7D] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 7E] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 8] A plot showing the results of sensor characteristics from the "walk a trajectory" diagnostic test of Example 4 shown in FIGS. 7A - E. The results of the sensor characteristics (duration in seconds to draw the shape) are consistent with the clinical anchor (pick up 10 coins with one hand in 20 seconds) in both studies. [Figure 9A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary aspects described herein. The user needs to select the "Start" button to begin the task. [Figure 9B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 9C] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 10] A plot showing the results of sensor characteristics from the "Changing the orientation of the phone" diagnostic test of Example 5 shown in FIGS. 9A - C. The results of the sensor characteristics (duration in seconds of changing the orientation of the phone) are consistent with the clinical anchor (picking up a tennis ball and then the duration of changing the hand orientation) in both studies. [Figure 11A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 11B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 12] A plot showing the results of sensor characteristics from the "Walking on a tightrope" diagnostic test of Example 6 shown in FIGS. 11A - B. The results of the sensor characteristics (standard deviation of the magnitude of acceleration in response to wind) are consistent with the clinical anchor (MFM32) in both studies. [Figure 13A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 13B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 13C]A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 14] A plot showing the results of sensor characteristics from the "Collect Coins" diagnostic test of Example 7 shown in FIGS. 13A - C. The results of the sensor characteristics (number of coins collected in 30 seconds) are consistent with the clinical anchor (pick up a tennis ball and then change the hand orientation) in both studies. [Figure 15A] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 15B] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 15C] A diagram showing exemplary screenshots and progress of a diagnostic test according to one or more exemplary embodiments described herein. The user needs to select the "Start" button to begin the task. [Figure 16] A plot showing the results of sensor characteristics from the "Ring the Bell" diagnostic test of Example 8 shown in FIGS. 15A - C. The results of the sensor characteristics (average touch pressure over 10 seconds) are consistent with the clinical anchor (pick up 10 coins with one hand in 20 seconds) in both studies. [Figure 17A] A plot comparing five different machine learning (ML) methods. The top row shows the results in the test set (i.e., here the left - out patient as leave - one - out cross - validation was applied). The results are calculated for the patients of the OLEOS study. The results show that random forest and boosting tree models based on features from all tests have the potential to predict the MFM32 total score. [Figure 17B]A plot comparing five different machine learning (ML) methods. The upper columns show the results in the test set (i.e., here the left-out patient as leave-one-out cross-validation was applied). The y-axis has the same units as shown in Figure 17A. The results were calculated for the patients of the OLEOS study. The results indicate that random forest and boosting tree models based on features from all tests have the potential to predict the MFM32 total score. [Figure 17C] A plot comparing five different machine learning (ML) methods. The upper columns show the results in the test set (i.e., here the left-out patient as leave-one-out cross-validation was applied). The y-axis has the same units as shown in Figure 17A. The results were calculated for the patients of the OLEOS study. The results indicate that random forest and boosting tree models based on features from all tests have the potential to predict the MFM32 total score. [Figure 18A] A plot comparing five different ML methods. The upper columns show the results in the test set (i.e., here the left-out patient as leave-one-out cross-validation was applied). The results were calculated for the patients of the OLEOS study. The results indicate that linear regression and partial least squares regression have the potential to predict FVC. [Figure 18B] A plot comparing five different ML methods. The upper columns show the results in the test set (i.e., here the left-out patient as leave-one-out cross-validation was applied). The y-axis has the same units as shown in Figure 17A. The results were calculated for the patients of the OLEOS study. The results indicate that linear regression and partial least squares regression have the potential to predict FVC. [Figure 18C] A plot comparing five different ML methods. The upper columns show the results in the test set (i.e., here the left-out patient as leave-one-out cross-validation was applied). The y-axis has the same units as shown in Figure 17A. The results were calculated for the patients of the OLEOS study. The results indicate that linear regression and partial least squares regression have the potential to predict FVC. [Figure 19] FIG. 1 is an exemplary schematic diagram of an interconnected computing system that may be used in whole or in part to implement one or more of the exemplary embodiments described herein. EXAMPLE

[0099] In addition to the detailed description and algorithms described above, which are provided for many of the various exemplary embodiments described herein, the following examples merely illustrate various embodiments and should not be construed as limiting the scope of the present invention.

[0100] Characteristics of the analyzed patient cohort were collected in two different studies.

[0101] i) OLEOS study (https: / / clinicaltrials.gov / ct2 / show / NCT02628743) Participants analyzed: 20 Data analysis period: Smartphone data between the last two visits (176 days) TABLE 1

[0102] ii) JEWELFISH study (https: / / clinicaltrials.gov / ct2 / show / NCT03032172?term=BP39054) Participants analyzed: 19 TABLE 2

[0103] Example 1: Acquisition of a dataset using a central motor function test, which is a computer-implemented test (test: cheer for the monster) for determining vital capacity TABLE 3

[0104] A test for measuring lung volume was implemented using a mobile phone (iPhone). Refer to Figures 1 - 2. The patient is required to make a loud "ah" sound such that the monster reaches the goal line in 30 seconds. The phone needs to be placed on the table with the arm extended in front of the patient. The louder the "ah" sound, the faster the monster runs. A voice detector was used to detect the continuous vocalization and segment the vocalization each time the "ah" stops. The patient needs to play a game aimed at obtaining the maximum vocalization duration. The result of the test is expressed as the above maximum duration in seconds. The standard pitch variation was determined.

[0105] Figure 2 shows the correlation between the forced vital capacity (FVC) in millimeters and the results from the test of cheering for the monster. The results of the sensor characteristics are consistent with the clinical anchor (FCV) in both studies.

[0106] Example 2: Acquisition of a dataset using a central motor function test, which is a computer - implemented test for determining finger strength by pressure measurement (test: tapping the monster) [Table 4]

[0107] A test for measuring finger strength by pressure measurement was implemented on a mobile phone (iPhone). Refer to Figures 3 - 4. The patient is to tap the monster with the index finger so that the monster returns to its nest. The phone must be placed on the table. The patient must tap the monster as quickly as possible. The patient must choose the hand they prefer to use. The patient needs to play the game for 30 seconds for the purpose of obtaining the maximum pressure of a single tap, the median time from when the monster appears until tapping, and the total number of monsters tapped within a 30 - second period. The standard deviation of the maximum pressure, the median of the maximum pressure, the maximum pressure of a single tap, the median time from when the monster appears until tapping, and the total number of monster taps obtained within 30 seconds were determined. True monster tapping was an event protocolized by the test. This data was transferred, and the monster - tapping time stamp was used to calculate the median time until tapping the monster.

[0108] Figure 4 shows the correlation between the results from the clinical anchor test and the results (time to hit_50%) from the monster - cheering test. The results of the sensor characteristics are consistent with the clinical anchor (twirling around the edge of the CD using the finger) in both studies.

[0109] Example 3: Acquisition of a dataset using a distal motor function test, which is a computer - implemented test (test: crush a tomato) for determining the synchronization of two fingers (punch and index finger of the same hand) by measuring the delay time between screen touches by the first and second fingers for all detected double contacts. [Table 5]

[0110] A test regarding the asynchrony of double touch (DTA) was implemented on a mobile phone (iPhone). Refer to Figures 5 - 6. The patient is to crush as many tomatoes as possible within 30 seconds by pinching between the thumb and index finger of the designated hand. The phone needs to be placed on the table. It is necessary to select the relevant hand. The patient needs to play the game for 30 seconds.

[0111] Figure 6 shows the correlation (DTA) between the clinical anchor test and the results by crushing tomatoes. The results of the sensor characteristics are consistent with the clinical anchor in both studies.

[0112] Example 4: Acquisition of a dataset using a central motor function test, which is a computer - implemented test (test: walking a trajectory) determined by measuring the time required to draw the number '8'.

Table 6

[0113] The test was implemented on a mobile phone (iPhone). Refer to Figures 7 - 8. The patient is to progress along the shape as accurately as possible using the index finger of the preferred hand. The phone must be placed on the table. The preferred hand must be selected. The patient must start from the largest point. One of the shapes is the number '8'. One of the shapes is a bar. One of the shapes is a square. One of the shapes is a circle. One of the shapes is a helix. The patient needs to play the game for 30 seconds and progress along the shape as quickly as possible without losing accuracy.

[0114] Figure 8 shows the correlation between the clinical anchor test and the results of the walking - trajectory test (time to draw '8'). The results of the sensor characteristics are not clearly related to the clinical anchor (pick up 10 coins with one hand in 20 seconds) in both studies.

[0115] Example 5: Acquisition of a dataset using an axial motor function test, which is a computer-implemented test (test: changing the orientation of a telephone) determined by measuring the time required to change the orientation of the telephone [Table 7]

[0116] The test was implemented using a mobile phone (iPhone). Refer to FIGS. 9-10. The patient should repeatedly turn the phone up and down with their preferred hand for 10 seconds. The phone should be held with the preferred hand. The arm should be extended as much as possible in front of the patient. The patient should indicate the position of the arm, i.e., fully extended, bent with the elbow floating in the air, resting the elbow on the armrest, or placing the hand on the table. The turning speed for each change in orientation and the number of orientation changes in 10 seconds are measured.

[0117] FIG. 10 shows the correlation between the results of the clinical anchor test and the test of changing the orientation of the telephone (maximum speed per orientation change in seconds). The results of the sensor characteristics are clearly related to the clinical anchor (picking up a tennis ball and then changing the orientation of the hand) in both studies. For the clinical anchor, there is no unit. It is on a scale of 0, 1, 2, 3, or 4. A value between 2 and 3 indicates the average of the clinical measurements for two consecutive visits. The selected feature is the average maximum turning speed per turn as an index in angular velocity (rad / s). The feature (maximum speed per second for one orientation change) was calculated based on the detected and segmented turns.

[0118] Example 6: Acquisition of a dataset using an axial motor function test, which is a computer-implemented test (test: tightrope walking) determined by measuring the acceleration variations that occur when changing the orientation of the telephone while reacting / compensating to sudden wind movements [Table 8]

[0119] The test was implemented on a mobile phone (iPhone). Refer to Figures 11 - 12. Assume that the patient is to balance the monster on the rope while the wind is trying to disrupt the balance of the monster. The phone should be held with both hands. To balance the monster, the phone needs to be tilted left and right. To further counter the influence of the wind, the phone can be rotated. Assume that the patient shows the position of the arm, i.e., fully extended, bent with the elbow floating in the air, rested on the elbow, or the hand placed on the table. The test lasts for 30 seconds.

[0120] Figure 12 shows the correlation with the results of the clinical anchor test and the tightrope - walking test (standard deviation of the magnitude of acceleration with respect to the wind reaction in m / s 2 units). In the test, when balancing the monster, the wind may interfere, which is the reaction in the first 2 seconds thereafter. Also, the degree to which the hand movement varies is the average with respect to the overall interference of the wind in one test run. The results of the sensor characteristics are clearly related to the clinical anchor (MFM32) in both studies.

[0121] Example 7: Obtaining a dataset using an axial movement function test, which is a computer - implemented test (test: collecting coins) determined by measuring the number of coins the patient has to collect by quickly tilting the phone left and right.

Table 9

[0122] The test was implemented on a mobile phone (iPhone). Refer to Figures 13 - 14. The phone should be held with both hands. The patient is to collect as many coins as possible by tilting the phone rapidly from side to side. The patient is to indicate the position of the arm, i.e., fully extended, bent with the elbow floating in the air, rested on the elbow, or the hand placed on the table. The test lasts for 30 seconds. The characteristic (the maximum number of coins collected) is the number of coins collected in the test.

[0123] Figure 14 shows the correlation between the results of the clinical anchor test and the test of collecting coins (the maximum number of coins collected). The results of the sensor characteristics are clearly related to the clinical anchor (pick up a tennis ball and then change the orientation of the hand) in both studies.

[0124] Example 8: Acquisition of a pressure dataset using a distal motor function test, which is a computer - implemented test (test: ring a bell) for determining finger strength [Table 10]

[0125] A test for measuring the pressure applied by the fingers was implemented on a mobile phone (iPhone). Refer to Figures 15 - 16. The phone must be placed on the table. The patient is to apply the maximum pressure to the surface of the display so that the bell rings. This means that the tap button on the screen must be pressed as hard as possible with the index finger of the preferred hand for at least 10 seconds. The wrist and other fingers must be placed on the table. The test is adapted to measure the pressure application by the patient's fingers. The patient needs to play a game for the purpose of obtaining the maximum pressure and the duration of the maximum pressure application. The test required calibration for the maximum pressure that could be initially applied by the subject's finger. The results of the test of ringing the bell are presented as a percentage of the above - mentioned maximum pressure. The test lasts for 10 seconds.

[0126] FIG. 16 shows the correlation between the results of the clinical anchor test and the test of ringing the bell (average touch pressure applied during the game). The results of the sensor characteristics are clearly related to the clinical anchor (picking up 10 coins with one hand in 20 seconds) in both studies.

[0127] FIG. 19 shows an example of a network architecture and a data processing device that may be used to implement one or more of the exemplary aspects described herein. Various network nodes 303, 305, 307, and 309 may be interconnected via a wide area network (WAN) 301 such as the Internet. Other networks may also be added or used instead, including, but not limited to, a private intranet, a corporate network, a LAN, a wireless network, a personal area network (PAN), etc. The network 301 is for illustrative purposes only and may be replaced with 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 various different 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, fiber optic, radio wave, or other communication media.

[0128] As used herein and when illustrated in the drawings, the term "network" refers not only to a system in which remote storage devices are coupled to each other via one or more communication paths, but also to stand-alone devices that may sometimes be coupled to a system having storage capabilities. In conclusion, the term "network" includes not only "physical networks" but also "content networks" composed of data that resides (can belong to a single entity) across all physical networks.

[0129] 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 database and control of software that implements one or more exemplary aspects described herein. The data server 303 may be connected to the web server 305 through which a user can interact with and obtain data as needed. Alternatively, the data server 303 may act as the web server itself and may be directly connected to the Internet. The data server 303 may be connected to the web server 305 through the network 301 (e.g., the Internet), directly or through an indirect connection, or through some other network. A user may use the remote computers 307, 309 to interact with the data server 303, for example, using a web browser that connects to the data server 303 through one or more externally exposed websites hosted by the web server 305. The client computers 307, 309 may be used in cooperation with the data server 303 to access the stored data or for other purposes. For example, a user may access the web server 305 from the client device 307 by using an Internet browser as known in the art or by executing a software application that communicates with the web server 305 and / or the data server 303 through a computer network (such as the Internet). In some embodiments, the client computer 307 may be a smartphone, a smartwatch, or other mobile computing device and may implement a diagnostic device. In some embodiments, the data server 303 may implement a server.

[0130] The server and the application may be combined on the same physical machine, may hold separate virtual or logical addresses, or may reside on separate physical machines. For example, the services provided by web server 305 and data server 303 may be combined on a single server.

[0131] Each component 303, 305, 307, 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 rate server 303. The data server 303 may further include a RAM 313, a ROM 315, a network interface 317, an input / output interface 319 (e.g., keyboard, mouse, display, printer, etc.), and a memory 321. The I / O 319 may include various interface units and drives for reading, writing, displaying, and / or printing data or files. The memory 321 may further store an operating system software 323 that controls the overall operation of the data processing device 303, a control logic 325 that instructs the data server 303 to implement the aspects described herein, and other application software 327 that provides secondary, auxiliary, and / or other functionality that may or may not be used in conjunction with other aspects described herein. The control logic may sometimes be referred to herein as data server software 325. The functionality of the data server software may refer to actions or decisions that are automatically performed based on rules encoded in the control logic, actions or decisions that are manually performed by a user who provides input to the system, and / or a combination of automated processing based on user input (e.g., queries, data updates, etc.).

[0132] Memory 321 may also store data used to implement one or more aspects described herein, including a first database 329 and a second database 331. In some embodiments, the first database may include the second database (e.g., as a separate table, report, etc.). That is, information can be stored in a single database or divided among different logical, virtual, or physical databases, depending on the system design. Devices 305, 307, 309 may have similar or different architectures as described for device 303. One of ordinary skill in the art will understand that the functionality of data processing device 303 (or devices 305, 307, 309) as described herein may be spread across multiple data processing devices, for example, to distribute the processing load across multiple computers, to distinguish transactions based on geographical location, user access level, quality of service (QoS), etc.

[0133] One or more aspects described herein may be embodied in the form of one or more program modules, such as in the form of computer-usable or readable data and / or computer-executable instructions, to be executed by one or more computers or other devices as described herein. Generally, a program module includes routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor of a computer or other device. The modules may be written in a source code programming language and subsequently compiled for execution, or may be written in a scripting language such as, but not limited to, HTML or XML. The computer-executable instructions may be stored on a computer-readable medium such as a hard disk, optical disk, removable storage medium, solid state memory, RAM, etc. As will be appreciated by those skilled in the art, the functionality of the program modules may be combined or distributed as desired in various embodiments. Additionally, the functionality may be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits, field programmable gate arrays (FPGAs), etc. Particular data structures may be used to more effectively 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.

[0134] Figure 20 illustrates an example method for assessing the motor function of a subject with a muscular disorder, particularly SMA, based on an active examination of the subject. The method begins by proceeding to step 205, which includes instructing the subject to perform a diagnostic task. In some embodiments, the diagnostic task is fixed or modeled after well-established methods and standardized tests for assessing and evaluating a muscular disorder, particularly SMA.

[0135] The method proceeds to step 210, which includes receiving, via one or more sensors, a plurality of second sensor data in response to a subject performing one or more diagnostic tasks. In response to a subject performing one or more diagnostic tasks, the diagnostic device receives a plurality of sensor data via one or more sensors associated with the device. The method proceeds to step 215, which includes extracting, from the received sensor data, a second plurality of features associated with the axial motor function of a muscular disorder, particularly SMA.

[0136] The method proceeds to step 220, which includes determining, based at least on the extracted sensor data, an assessment of the axial motor function of a muscular disorder, particularly SMA.

[0137] As described above, the assessment of the severity and progression of the symptoms of a muscular disorder, particularly SMA, using the diagnosis according to the present disclosure, is well correlated with the assessment based on clinical outcomes, and thus can replace the clinical monitoring and examination of subjects. The diagnosis according to the present disclosure was studied in a group of subjects with a muscular disorder, particularly subjects with SMA. The subjects were provided with a smartphone application that included one or more motor function tests.

Claims

1. 1. A mobile device comprising: A processor; At least one pressure sensor; A database; software tangibly embedded in said device, said software executing on said device: a) determining at least one parameter from a dataset of sensor measurements from a subject using the mobile device, the mobile device being a smartphone; the sensor measurements include pressure measurements measured by the pressure sensor; The at least one parameter determined is selected from the group consisting of peak pressure, integrated pressure, pressure profile over time, and pressure oscillations. Steps and b) comparing the determined at least one parameter with a reference, whereby the result of the comparison assesses spinal muscular atrophy (SMA); and software implementing a method for assessing SMA in said subject, comprising: A mobile device comprising:

2. The mobile device of claim 1 , wherein the at least one parameter is a parameter indicative of distal motor function, central motor function, and axial motor function.

3. 3. The mobile device of claim 2, wherein the dataset of sensor measurements of individual motor functions comprises data from measurements of maximum pressure that can be exerted by a subject with an individual finger or measurements on the ability to exert pressure with an individual finger over time, measurements of maximum duration of an "ahh" sound, maximum amount of touching a screen within a defined period, in particular within 30 seconds, maximum double-touch asynchronicity, variation in acceleration after wind blows, number of objects collected, in particular number of coins collected, and / or maximum hand turning speed.

4. The dataset of sensor measurements of the individual motor functions includes the following feature measurements: i. average applied pressure; ii. Pitch variation; iii. Median time to screen tap; iv. double touch asynchrony; v. the time it takes to draw a shape; vi. The maximum turning speed of the phone; vii. Acceleration fluctuations, and / or viii. Number of coins collected The mobile device of claim 3, comprising data from

5. 5. The mobile device of claim 3 or 4, wherein the dataset of sensor measurements of the individual motor functions comprises data from measurements from every day or at least every other day, in particular the dataset of sensor measurements of the individual motor functions comprises data from sensor measurements taken in the morning.

6. The mobile device according to any one of claims 1 to 5, wherein the mobile device is adapted to perform one or more of the sensor measurements according to claims 4 or 5 on the object.

7. The mobile device according to any one of claims 1 to 6, wherein at least one determined parameter being essentially identical compared to the reference is indicative of the subject having SMA.

8. a mobile device comprising at least one pressure sensor; A remote device comprising a processor and a database, and software tangibly embedded in said device, said software executing on said device comprising: a) determining at least one parameter from a dataset of sensor measurements from a subject using the mobile device, the mobile device being a smartphone; the sensor measurements include pressure measurements measured by the pressure sensor; The at least one parameter determined is selected from the group consisting of peak pressure, integrated pressure, pressure profile over time, and pressure oscillations; b) comparing the determined at least one parameter with a reference, whereby the result of the comparison assesses spinal muscular atrophy (SMA); and software implementing a method for assessing SMA in said subject, comprising: the remote device comprising: Equipped with The system, wherein the mobile device and the remote device are operatively linked to each other.

9. The system of claim 8 , wherein the at least one parameter is a parameter indicative of distal motor function, central motor function, and axial motor function.

10. 10. The system of claim 9, wherein the dataset of sensor measurements of individual motor functions includes data from measurements of maximum pressure that can be exerted by the subject with individual fingers or measurements on the ability to exert pressure with individual fingers over time, measurements of maximum duration of an "ahh" sound, maximum amount of touching the screen within a defined period, in particular within 30 seconds, maximum double-touch asynchronicity, variation in acceleration after wind blows, number of objects collected, in particular coins collected, and / or maximum hand turning speed.

11. The dataset of sensor measurements of the individual motor functions includes the following feature measurements: i. Mean applied pressure; ii. Pitch variation; iii. Median time to screen tap; iv. double touch asynchrony; v. the time it takes to draw a shape; vi. The maximum turning speed of the phone; vii. Acceleration fluctuations, and / or viii. Number of coins collected The system of claim 10, comprising data from

12. 12. The system of claim 10 or 11, wherein the dataset of sensor measurements for the individual motor functions includes data from measurements from every day or at least every other day, in particular the dataset of sensor measurements for the individual motor functions includes data from sensor measurements taken in the morning.

13. The system according to any one of claims 8 to 12, wherein the mobile device is adapted to perform one or more of the sensor measurements according to claims 11 or 12 on the object.

14. The system of any one of claims 8 to 13, wherein at least one determined parameter that is essentially identical compared to the reference is indicative of the subject having SMA.

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