Means and methods for assessing Huntington's disease in the asymptomatic stage

Abstract

The present invention relates to the field of diagnosis. In particular, the present invention relates to a method for assessing the subclinical stage of Huntington's disease in a subject, the method comprising the steps of determining at least one performance parameter from a dataset of fine motor measurements from the subject, comparing the determined at least one performance parameter with a reference standard, and assessing the subclinical stage of Huntington's disease in the subject based on the comparison. The present invention also contemplates devices and systems for carrying out the above method, and the use of such devices or systems for assessing the subclinical stage of Huntington's disease in a subject.

Description

Technical Field

[0001] The present invention relates to the field of diagnosis. Specifically, the present invention includes the steps of determining at least one performance parameter from a dataset of fine movement measurements from a subject, comparing the determined at least one performance parameter with a reference, and assessing the premanifest stage of Huntington's disease in the subject based on the comparison. The present invention also relates to a method for assessing the premanifest stage of Huntington's disease in a subject. Further, the present invention contemplates a device and system for implementing the above method, and the use of the above device or system for assessing the premanifest stage of Huntington's disease in a subject.

Background Art

[0002] Huntington's disease is a hereditary neurological disorder accompanied by nerve cell death in the central nervous system. Most notably, the basal ganglia of the brain are affected by cell death. There are also other areas of the brain that are affected, such as the substantia nigra, cerebral cortex, hippocampus, and Purkinje cells. Typically, all areas play a role in motor and behavioral control.

[0003] This disease is caused by a genetic mutation in the gene encoding huntingtin. Huntingtin is a protein involved in various cell functions and interacts with over 100 other proteins. Mutant huntingtin is thought to be cytotoxic to certain types of nerve cells.

[0004] The symptoms of this disease most commonly become prominent in middle age, but can occur at any age from infancy to old age. In the initial stage, the symptoms include slight changes in personality, cognition, and physical skills. In the initial stage, cognitive and behavioral symptoms are generally not as severe as to be recognized alone, so physical symptoms are usually recognized first.

[0005] The most characteristic initial physical symptom is a spasm-like, random, uncontrollable movement called chorea. Chorea may initially present as general restlessness, slight, involuntary, or incomplete movements, dyscoordination, or slow, impulsive eye movements. These minor movement abnormalities usually appear at least three years before the more obvious signs of movement disorder. As the disease progresses, clear symptoms such as rigidity, torsion movements, or abnormal postures appear.

[0006] Additional symptoms of Huntington's disease include physical instability, abnormal facial expressions, and difficulty chewing, swallowing, and speaking. As a result, eating and sleep disorders also occur in association with this disease. Cognitive abilities are also gradually impaired. What is impaired are executive function, cognitive flexibility, abstract thinking, acquisition of behavioral rules, and normal activity / reaction function. At a more obvious stage, memory impairment tends to appear, including short-term memory impairment to long-term memory impairment. Cognitive problems worsen over time and ultimately result in dementia. The psychiatric complications associated with Huntington's disease are anxiety, depression, blunted affect (emotional blunting), egocentricity, aggression, and compulsive behaviors, the latter of which can cause or exacerbate addictions, including alcohol dependence, gambling, and hypersexuality.

[0007] There is no cure for Huntington's disease. There are symptomatic treatments in disease management according to the symptoms to be addressed. Also, several drugs are used to improve the disease, its progression, or the symptoms associated with this disease.

[0008] This disease can be diagnosed by genetic testing. In addition, the severity of this disease can be staged according to the Unified Huntington’s Disease Scale (UHDRS) (The Huntington Group, 1996, Rao 2009). This scale system addresses four components, namely, motor function, cognition, behavior, and functional ability. Motor function assessment includes assessments of eye tracking, saccade initiation, saccade velocity, dysarthria, tongue protrusion, maximal dystonia, maximal chorea, backward pull test, finger tapping, pronation / supination of the hand, Luria, arm rigidity, bradykinesia, gait, and tandem gait, and can be summarized as a total motor score (TMS). Motor function needs to be reviewed and judged by a physician in a teaching hospital.

[0009] A computer-implemented examination for assessing Huntington's disease is described in particular in WO2019 / 081640.

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0010] However, in order to enable appropriate care and / or accurate treatment, there is a need for a diagnostic tool that enables a reliable diagnosis and identification of premanifest Huntington's disease in patients. The technical problem underlying the present invention may become apparent in providing means and methods that meet the above needs. This technical problem is characterized in the claims and is solved by the embodiments described hereinafter in this specification.

MEANS FOR SOLVING THE PROBLEM

[0011] The present invention is a method for assessing premanifest Huntington's disease in a subject, comprising: a) determining at least one performance parameter from a dataset of fine motor measurements from the subject; b) comparing at least one determined performance parameter with a reference; c) assessing a subclinical stage of Huntington's disease in a subject based on said comparison.

[0012] This method is typically a computer-implemented method, i.e., steps a) to c) are performed in an automated manner using a data processing device. Details are provided in the following description of the present specification and the attached examples.

[0013] In some embodiments, the method may also include, prior to step (a), using a mobile device to obtain, from the subject, a dataset of fine motor measurements from the subject during a predetermined activity performed by the subject or during a period of a predetermined time window. However, typically, this method is an in vitro method performed on an existing dataset of measurements from the subject that does not require any physical interaction with the subject.

[0014] The method referred to by the present invention basically includes a method consisting of the above steps or a method that may include additional steps. As used hereinafter, the terms "having", "comprising", "including" or any grammatical variations thereof are used non-exclusively. Thus, these terms can refer to both situations where there are no additional features in the entity described in the context other than the features presented by these terms, and situations where one or more additional features are present. 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 solely of B) and situations where there are one or more additional elements in entity A, such as element C, elements C and D, or more elements.

[0015] Also, it should be noted that expressions such as "at least one", "one or more", or similar expressions indicating that a feature or element may be present one or more times are typically only used once when initially presenting each feature or element. In the following, in most cases, when referring to each feature or element, the expressions "at least one" or "one or more" are not repeated even though each feature or element may be present one or more times.

[0016] Also, terms such as "specifically", "more specifically", "in detail", "more in detail", "typically", and "more typically" or similar terms used hereinafter are used with additional / alternative features without limiting the possibility of alternatives. Accordingly, the features presented by these terms are additional / alternative features and are not intended to limit the scope of the claims in any way. As will be understood by those skilled in the art, the present invention may be practiced using alternative features. Similarly, features presented by the expression "in one embodiment of the present specification" or similar expressions are additional / alternative features without any limitation regarding alternative embodiments of the present invention, without any limitation regarding the scope of the present invention, and without any limitation regarding the possibility of combination with other additional / alternative, or non-additional / non-alternative features of the present invention as so presented.

[0017] This method may be carried out on a mobile device by the subject after a dataset of fine movement measurements has been acquired. Thus, the mobile device and the device for acquiring the dataset may be physically identical, i.e., the same device. Such a mobile device typically has a data acquisition unit including means for detecting or measuring physical and / or chemical parameters quantitatively or qualitatively and converting them into an electronic signal that is transmitted to an evaluation unit within the mobile device used to carry out the method according to the invention. The data acquisition unit includes means 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 remote from the mobile device and used to carry out the method according to the invention. Typically, said data acquisition means includes at least one sensor. It should be understood that a plurality of sensors, 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 sensors, can be used in the mobile device. Typical sensors used as data acquisition means are sensors such as gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, tactile sensors, voice recorders, light sensors, pressure sensors, position data detectors, cameras, sweat analysis sensors, etc. The evaluation unit typically includes a processor, a database, and software that is tangibly incorporated into the device and, when executed on the device, implements the method of the invention. More typically, such a mobile device may also include a user interface, such as a screen, that enables the results of the analysis carried out by the evaluation unit to be provided to the user.

[0018] Alternatively, this method may be carried out on a device remote from the mobile device used to obtain the dataset. In this case, the mobile device only needs to include data acquisition means, that is, means for quantitatively or qualitatively detecting or measuring physical and / or chemical parameters and converting them into an electronic signal transmitted from the mobile device to a device remote from it and used to carry out the method according to the invention. Typically, the data acquisition means includes at least one sensor. It should be understood that a plurality of sensors, that is, 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 more different sensors can be used in the mobile device. Typical sensors used as data acquisition means are sensors such as gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, tactile sensors, voice recorders, light sensors, pressure sensors, position data detectors, cameras, sweat analysis sensors, GPS, etc. Therefore, the mobile device and the device used to carry out the method of the present invention may be physically different devices. In this case, the mobile device can communicate with the device used to carry out the method of the present invention by any data transmission means. Such data transmission may be realized by a permanent or temporary physical connection such as a coaxial cable, a fiber optic cable, an optical fiber cable or a twisted pair 10BASE-T cable. Alternatively, the data transmission may be realized by a temporary or permanent wireless connection using radio waves, such as Wi-Fi, LTE, LTE-advanced or Bluetooth. Therefore, the only requirement for carrying out the method of the present invention is the existence of a dataset of measurements obtained from the subject using a mobile device. The dataset may be transmitted or stored in a permanent or temporary memory device that can be used to transfer data from the acquisition mobile device to the device used later to carry out the method of the present invention.A remote device implementing the method of the present invention in this setting typically includes a processor, a database, and software that is tangibly incorporated into the device and, when executed on the device, implements the method of the present invention. More typically, the device may also include a user interface, such as a screen, that enables the results of the analysis performed by the evaluation unit to be provided to the user.

[0019] As used herein, the term "assessing" refers to assessing whether a subject has asymptomatic Huntington's disease. Thus, the assessment as used herein includes the diagnosis, staging, classification and / or prediction of asymptomatic Huntington's disease, or the recommendation of treatment methods for asymptomatic Huntington's disease. As will be understood by those skilled in the art, such an assessment is preferably accurate for 100% of the subjects being investigated, but usually may not be the case. However, this term requires that a statistically significant proportion of the subjects can be accurately assessed. Whether the proportion is statistically significant can be determined by those skilled in the art without further effort using various well-known statistical evaluation tools, such as confidence interval determination, p-value determination, Student's t-test, Mann-Whitney test, etc. Details can be found in Downy and Wearden, Statistics for Research, John Wiley & sons, New York 1983. Typically assumed confidence intervals are at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%. The p-value is typically 0.2, 0.1, 0.05. Thus, the method of the present invention typically aids in the assessment of Huntington's disease by providing means for evaluating a dataset of fine motor measurements.

[0020] As used herein, the term "Huntington's disease (HD)" refers to a hereditary neurological disorder associated with nerve cell death in the central nervous system. Most notably, the basal ganglia of the brain are affected by cell death. There are also other areas of the brain that are affected, such as the substantia nigra, cerebral cortex, hippocampus, and Purkinje cells. Typically, all areas play a role in motor and behavioral control. This disease is caused by a genetic mutation in the gene that encodes huntingtin. Huntingtin is a protein involved in various cell functions and interacts with over 100 other proteins. Mutant huntingtin appears to be cytotoxic to certain types of nerve cells. Mutant huntingtin is characterized by a polyglutamine region caused by a trinucleotide repeat in the huntingtin gene. As a result of repeats of more than 36 glutamine residues in the polyglutamine region of the protein, a disease-causing huntingtin protein is produced. Since Huntington's disease is autosomal dominant, genomic testing for CAG repeats in the huntingtin (HTT) allele is recommended for genetically at-risk individuals, i.e., patients with a corresponding family history of this disease. Also, the diagnosis of this disease requires not only DNA analysis but also imaging diagnostic methods such as CT, MRI, PET, or SPECT scans for the determination of brain atrophy and neurological assessment by a physician.

[0021] The symptoms of this disease most commonly become prominent in middle age, but can occur at any age from infancy to old age. In the early stages, the symptoms include slight changes in personality, cognition, and physical skills. In the initial stage, cognitive and behavioral symptoms are generally not severe enough to be recognized alone, so usually physical symptoms are recognized first. Most people with Huntington's disease eventually exhibit similar physical symptoms, but the onset, progression, and degree of cognitive and behavioral symptoms vary significantly among individuals. The most characteristic early physical symptom is choreiform, random, and uncontrollable movements called chorea. Chorea may initially appear as restlessness throughout the body, slight, involuntary movements or incomplete movements, dyscoordination, or slow, impulsive eye movements. These minor movement abnormalities usually appear at least three years before the more obvious signs of movement disorder. As the disease progresses, distinct symptoms such as rigidity, dystonic movements, or abnormal postures appear. These are signs indicating that the systems in the brain that control movement are affected. Psychomotor function is gradually impaired, and as a result, any behavior requiring muscle control is affected. Common consequences include physical instability, abnormal facial expressions, as well as difficulty chewing, swallowing, and speaking. As a result, eating disorders and sleep disorders also occur in association with this disease. Cognitive abilities are also gradually impaired. What is impaired are executive function, cognitive flexibility, abstract thinking, acquisition of behavioral rules, and normal activity / reaction function. At a more obvious stage, there is a tendency for memory impairment, including short-term memory impairment to long-term memory impairment, to appear. Cognitive problems worsen over time and eventually result in dementia. The psychiatric complications associated with Huntington's disease are anxiety, depression, blunted affect, egocentricity, aggression, and compulsive behavior, the latter of which can cause or exacerbate addictions, including alcohol dependence, gambling, and hypersexuality.

[0022] There is no cure for Huntington's disease. There are symptomatic treatments in disease management according to the symptoms to be addressed. Also, several drugs are used to improve this disease, its progression, or the symptoms associated with this disease. Tetrabenazine is approved for the treatment of Huntington's disease, includes neuroleptics, benzodiazepines are used as drugs useful for reducing chorea, amantadine or remacemide are still under investigation but have shown positive intermediate results. In particular, motor function decline and rigidity, especially in the case of minors, can be treated with anti-Parkinson drugs, and myoclonic hyperkinesia can be treated with valproic acid. Ethyl eicosapentaenoic acid has been found to improve the motor symptoms of patients, but its long-term effects need to be clarified.

[0023] This disease can be diagnosed by genetic testing. Also, the severity of this disease can be staged according to the Unified Huntington's Disease Rating Scale (UHDRS). This scale system addresses four components, namely, motor function, cognition, behavior, and functional ability. Motor function assessment includes the assessment of eye tracking, saccade initiation, saccade velocity, dysarthria, tongue protrusion, maximum dystonia, maximum chorea, backward propulsion pull test, finger tapping, pronation / supination movement of the hand, Luria, arm rigidity, bradykinesia, walking, and tandem gait, and can be summarized as the Total Motor Score (TMS). Motor function needs to be reviewed and judged by a physician.

[0024] The "asymptomatic stage" of Huntington's disease is the stage in which the subject shows no clinical symptoms of Huntington's disease. However, certain motor functions may be weaker than those of subjects who do not have asymptomatic-stage Huntington's disease. Typically, the subject has a TMS of 15 or less, 12 or less, 10 or less, 8 or less, or 5 or less. Also, the subject will be a carrier of the HTT gene mutation (CAG repeat).

[0025] The term "fine motor measurement" refers to the accuracy, dexterity, and / or speed of fine movements of the limbs, more typically, the accuracy and / or dexterity and / or movement speed of finger movements. The measurement of the accuracy and / or dexterity and / or movement speed of finger movements is performed during tapping movements and / or drawing movements as described below. At least one performance parameter can typically be determined from a dataset of measurements collected from a subject during the performance of the following activities regarding fine motor ability. The following tests are typically computer-implemented on a data acquisition device such as a mobile device as described elsewhere in this specification.

[0026] Examination of fine motor ability: Shape drawing examination The mobile device may be configured to perform or obtain data from a further examination of the terminal motor function (so-called "shape drawing examination") configured to measure finger dexterity and terminal fine movements. The dataset obtained from such an examination enables the identification of the accuracy, pressure profile, and speed profile of finger movements.

[0027] The purpose of the "Shape Drawing" test is to evaluate precise finger control and stroke sequences. The patient holds a mobile device in the hand not being tested and is instructed to draw six pre-written, increasingly complex alternating shapes (linear, rectangular, circular, sine curve, and spiral, see below) on the touch screen of the mobile device with the index finger of the hand being tested, "as quickly and accurately as possible" within a maximum time of, for example, 30 seconds. To successfully draw a shape, the patient's finger must slide continuously on the touch screen, pass through all the indicated checkpoints, stay within the boundaries of the writing trajectory as much as possible, and connect the indicated starting and ending points. The patient can have a maximum of two attempts to successfully complete each of the six shapes. The test is performed alternately with the right and left hands. The user is instructed alternately every day. Each of the two linear shapes has a specific number "a" of checkpoints to be connected, i.e., "a - 1" segments. The square shape has a specific number "b" of checkpoints to be connected, i.e., "b - 1" segments. The circular shape has a specific number "c" of checkpoints to be connected, i.e., "c - 1" segments. The figure-eight shape has a specific number "d" of checkpoints to be connected, i.e., "d - 1" segments. The spiral shape has a specific number "e" of checkpoints to be connected, i.e., "e - 1" segments. In this case, completing the six shapes means successfully drawing a total of "(2a + b + c + d + e - 6)" segments.

[0028] Typical performance parameters for the shape drawing test are as follows. Based on the complexity of the shapes, a weight factor (Wf) of 1 can be associated with the linear and square shapes, a weight factor of 2 with the circular and sine curve shapes, and a weight factor of 3 with the spiral shape. A weight factor of 0.5 can be associated with a shape successfully completed on the second attempt. These weight factors are numerical examples that can be changed within the context of the present invention.

[0029] 1. Shape Completion Performance Score: a. Number of successfully completed shapes per inspection (from 0 to 6) (ΣSh) b. Number of successfully completed shapes in the first trial (from 0 to 6) (ΣSh1) c. Number of successfully completed shapes in the second trial (from 0 to 6) (ΣSh2) d. Number of shapes that failed / were not completed in all trials (from 0 to 12) (ΣF) e. Shape completion score (from 0 to 10) (Σ[Sh*Wf]) that reflects the number of successfully completed shapes adjusted using weight coefficients for different complexities of each shape f. Shape completion score (from 0 to 10) (Σ[Sh1*Wf]+Σ[Sh2*Wf*0.5]) that reflects the number of successfully completed shapes adjusted using weight coefficients for different complexities of each shape and takes into account whether it was a success in the first trial or the second trial g. The shape completion scores defined in items 1e and 1f may consider the inspection completion speed when multiplied by 30 / t, where t represents the time in seconds to complete the inspection

[0030] h. Overall and first-trial completion rates for each of the six individual shapes based on multiple inspections within a specific period: (ΣSh1) / (ΣSh1+ΣSH2+ΣF) and (ΣSh1+ΣSh2) / (ΣSh1+ΣSh2+ΣF)

[0031] 2. Segment completion and speed performance scores / metrics: (Analysis based on the highest value [maximum number of completed segments] out of two trials for each shape, if applicable) a. Number of segments successfully completed per inspection (from 0 to [2a+b+c+d+e-6]) (ΣSe) b. Average speed of successfully completed segments ([C], number of segments / second): C = ΣSe / t, where t represents the time in seconds to complete the inspection (maximum 30 seconds)

[0032] c. The segment completion score (Σ[Se*Wf]) that reflects the number of successfully completed segments adjusted using weight coefficients due to different complexities for each shape d. The speed-adjusted weighted segment completion score (Σ[Se*Wf]*30 / t), where t represents the time expressed in seconds until the inspection is completed.

[0033] e. The number of segments successfully completed specific to the linear and square shapes (ΣSe LS ) f. The number of segments successfully completed specific to the circular and sinusoidal shapes (ΣSe CS ) g. The number of segments successfully completed specific to the spiral shape (ΣSe S ) h. The shape-specific average linear speed C of the segments successfully completed in the linear and square shape inspections: L = ΣSe LS / t, where t represents the cumulative epoch time expressed in seconds elapsed from the start point to the end point of the corresponding successfully completed segments within these specific shapes.

[0034] i. The shape-specific average circular speed C of the segments successfully completed in the circular and sinusoidal shape inspections: C = ΣSe CS / t, where t represents the cumulative epoch time expressed in seconds elapsed from the start point to the end point of the corresponding successfully completed segments within these specific shapes.

[0035] j. The shape-specific average spiral speed C of the segments successfully completed in the spiral shape inspection: S = ΣSe S / t, where t represents the cumulative epoch time expressed in seconds elapsed from the start point to the end point of the corresponding successfully completed segments within this specific shape.

[0036] 3. Drawing accuracy performance score / metric: (If applicable, analysis based on the highest value [maximum number of completed segments] out of two trials for each shape) a. The deviation (Dev) calculated as the sum of the area under the curve (AUC) measures of the integrated surface deviation between the drawn trajectory from the starting point to the reached end - point checkpoint and the target drawn trajectory for each specific shape, divided by the total cumulative length of the corresponding (from the starting point to the reached end - point checkpoint) target trajectories within these shapes.

[0037] b. The Dev of item 3a, but specifically the linear deviation (Dev L ) calculated from the linear and square shape inspection results. c. The Dev of item 3a, but specifically the circular deviation (Dev C ) calculated from the circular and sine - curve shape inspection results.

[0038] d. The Dev of item 3a, but specifically the spiral deviation (Dev S ) calculated from the spiral shape inspection results. e. The Dev of item 3a, but the shape - specific deviation (Dev 1-6 ) calculated separately from each of the six different shape inspection results, applicable only to shapes in which at least three segments were successfully completed within the best trial. f. Continuous variable analysis of any other method for calculating the shape - specific or shape - independent overall deviation from the target trajectory.

[0039] 4. Pressure profile measurement i) The applied average pressure ii) The deviation (Dev) calculated as the standard deviation of the pressure More typical performance parameters for fine - motor ability in shape drawing inspection are the coefficient of variation of spiral drawing speed, the coefficient of variation of circular drawing speed, and / or the average spiral drawing speed. More typically, all of these performance parameters may be determined. Further typical performance parameters are also described in the attached examples below.

[0040] Fine motor ability test: High-speed tapping test The purpose of the "high-speed tapping" test is to evaluate the speed of finger motor ability. The patient holds a mobile device in the hand that is not being tested and is instructed to tap on the touch screen of the mobile device at various speeds. The tapping speed may be controlled by showing the subject symbols such as buttons or circles that need to be touched during the tapping movement.

[0041] More typical performance parameters for fine motor ability in the high-speed tapping test are the standard deviation of the time the finger is raised between two taps, the maximum time the finger is raised between two taps, the coefficient of variation of the time the finger is raised between two taps, the coefficient of variation of the distance between two taps, the standard deviation of the time the finger is touching the screen during the tap, the coefficient of variation of the time the finger is touching the screen during the tap, and / or the maximum time the finger is touching the screen during the tap. More typically, all of these performance parameters may be determined. Further typical performance parameters are also described in the examples attached below.

[0042] Further tests that can be performed together with the above tests are described in WO2019 / 081640. As used herein, the term "subject" refers to an animal, typically a mammal. In particular, the subject is a primate, most typically a human. The subject according to the present invention is assumed to have or be suspected of having premanifest Huntington's disease. As described elsewhere in this specification, several risk factors may determine an increased prevalence of Huntington's disease, and a subject exposed to such risk factors may be considered a subject suspected of having premanifest Huntington's disease.

[0043] The term "at least one" means that, according to the present invention, one or more performance parameters can be determined, that is, at least two, at least three, at least four, or even more different performance parameters. Therefore, there is no upper limit to the number of different performance parameters that can be determined by the method of the present invention. Typically, the parameters are selected from a data set of fine motor measurements and include measurements of finger movement accuracy and / or dexterity and / or movement speed, and more typically, the performance parameters specifically mentioned elsewhere in this specification. The measurement of finger movement accuracy and / or dexterity and / or movement speed is carried out during finger tapping movement and / or drawing movement. Such movements are more typically carried out using a mobile device such as a smartphone, smartwatch, wearable sensor, portable multimedia device, or tablet computer that implements a test that requires input from the subject being tested so that finger movement accuracy and / or dexterity and / or movement speed can be determined. Appropriate tests implemented on mobile devices are described elsewhere in this specification.

[0044] As used herein, the term "performance parameter" refers to a parameter that indicates the ability of a subject to perform a specific activity during the measurement of fine motor activities such as finger movement. Typically, the performance parameter may be the accuracy or speed at which a fine motor task, such as finger movement on a screen, can be performed. For example, the performance parameter can be determined from a data set of measurements of tapping movement on a screen using a finger. In such a case, the tapping speed can be determined as a performance parameter. Alternatively, or in addition, under the condition that finger movement, such as drawing a specific predefined shape on a screen, is the task during the test, the accuracy and / or speed of finger movement can be determined as a performance parameter.

[0045] The term "measurement data set" refers to the entirety of the data obtained from a subject by a mobile device during the measurement of the fine motor ability of the subject, or a subset of such data. A data set according to the method of the present invention may be derived from a fine motor examination performed by the subject being examined. The subject may perform a computer-implemented examination of fine motor ability as described elsewhere in this specification. Details are described in the examples attached below.

[0046] The determination of at least one performance parameter can be made by directly deriving a desired measured value as the performance parameter from the data set. Alternatively, the parameter may incorporate one or more measured values from the data set and thus may be derived from the data set by a mathematical technique such as calculation. Typically, the performance parameter is derived from the data set by an automated algorithm, for example, by a computer program that automatically derives the performance parameter from the measurement data set when the data set is tangibly incorporated into a data processing device to which the data set is supplied.

[0047] The term "criterion" as used herein refers to a discrimination value that enables the assessment of premanifest Huntington's disease based on at least one determined performance parameter. Such a discrimination value may be a value of a performance parameter indicative of a normal (i.e., healthy) subject or a subject suffering from premanifest Huntington's disease.

[0048] Such values may be derived from one or more contrast vision parameters of a subject known to have premanifest Huntington's disease. Typically, in such cases, the mean or median value of the parameter may be used as the discrimination value. If the performance parameter determined from the subject is the same as the reference or exceeds the threshold value derived from the reference, the subject can be identified as having premanifest Huntington's disease in such cases. If the determined performance parameter is different from the reference, particularly if it is below the threshold value, the subject shall be identified as not having premanifest Huntington's disease.

[0049] Similarly, values may be derived from one or more performance parameters of a subject known not to have premanifest Huntington's disease. Typically, the mean or median value of the parameter may be used as the discrimination value in such cases. If the performance parameter determined from the subject is the same as the reference or below the threshold value derived from the reference, the subject can be identified as not having premanifest Huntington's disease in such cases. If the determined performance parameter is different from the reference, particularly if it exceeds the threshold value, the subject shall be identified as having premanifest Huntington's disease.

[0050] Alternatively, the reference may be a previously determined performance parameter from a dataset of measurements obtained from the same subject prior to the actual dataset. In such cases, the performance parameter determined from an actual dataset different from the previously determined parameter will indicate either an improvement or a deterioration depending on the previous state of premanifest Huntington's disease. Those skilled in the art will understand how such a parameter can be used as a reference based on the type of activity and the previous parameter.

[0051] The comparison of the determined at least one performance parameter with a criterion can be performed by an automatic comparison algorithm implemented on a data processing device such as a computer. As described in detail elsewhere in this specification, the value of the determined performance parameter is compared with the criterion of the determined parameter. As a result of the comparison, it is possible to evaluate whether the determined performance parameter is the same as, different from, or in a particular relationship with the criterion (e.g., greater than or less than the criterion). Based on the evaluation, it is possible to identify whether the subject has Huntington's disease ("definitive diagnosis") or does not have it ("excluded diagnosis"). For this evaluation, the type of criterion will be taken into account as described elsewhere in connection with the appropriate criteria according to the present invention.

[0052] Also, by determining the degree of difference between the determined performance parameter and the criterion, a quantitative assessment of premanifest Huntington's disease in the subject becomes possible. It should be understood that by comparing the actually determined performance parameter with a previously determined performance parameter used as a criterion, it is possible to determine an improvement, deterioration, or no change state. Based on the quantitative difference in the value of the performance parameter, an improvement, deterioration, or no change state can be determined, and furthermore, quantification can optionally also be performed. When other criteria are used, such as a reference value from a subject with premanifest Huntington's disease, the quantitative difference will be meaningful if it is possible to assign a particular stage of dysfunction to a reference population. In such a case, relative to this stage of dysfunction, a deterioration, improvement, or no change state can be determined, and furthermore, quantification can optionally also be performed.

[0053] The assessment performed by the method of the present invention is presented to the subject or another person such as a physician. Typically, this is done by displaying the diagnosis on the display of a mobile device or an assessment device. Alternatively, a treatment method such as drug therapy, or a recommendation for a specific lifestyle, such as rehabilitation means, is automatically provided to the subject or other person. For this purpose, the determined assessment is compared with the recommendations assigned to various assessments in the database. After the determined assessment matches one of the stored and assigned assessments, the appropriate recommendation can be identified by the assignment of the recommendation to the stored assessment that matches the determined assessment. Thus, typically, it is assumed that the recommendations and assessments exist in the form of a relational database. However, other configurations that enable the identification of appropriate recommendations are also possible and are known to those skilled in the art.

[0054] Also, one or more performance parameters may be stored in the mobile device or typically presented to the subject in real time. The stored performance parameters may be summarized over time or as a similar evaluation scale. Such evaluated performance parameters may be provided to the subject as feedback on the course of Huntington's disease investigated by the method of the present invention. Typically, such feedback can be provided in electronic form on the appropriate display of the mobile device and can be linked to recommendations for treatment methods or rehabilitation means as described above.

[0055] Also, the evaluated performance parameters may be provided to other healthcare providers such as, in addition to physicians in clinics or hospitals, developers of diagnostic tests or drug developers in the context of clinical trials, health insurance providers, or other stakeholders in public or private healthcare systems.

[0056] Typically, the method of the present invention for assessing premanifest Huntington's disease in a subject can be implemented as follows. First, using a mobile device, at least one performance parameter is determined from an existing dataset of fine movement measurements from the subject. The dataset may be transmitted from the mobile device to an evaluation device, such as a computer, or processed on the mobile device, in order to derive at least one performance parameter from the dataset.

[0057] Second, the at least one determined performance parameter is compared to a criterion using a computer-implemented comparison algorithm, for example, implemented by a data processor of the mobile device or by an evaluation device, such as a computer. The result of the comparison is rated in light of the criterion used in the comparison, and the presence of Huntington's disease in the subject during the presymptomatic period is rated based on the comparison, such that, for example, the subject is identified as having or not having Huntington's disease during the presymptomatic period.

[0058] Third, the rating, such as the identification of whether the subject has or does not have Huntington's disease during the presymptomatic period, is presented to the subject or another person, such as a physician, on a suitable display, such as a screen connected to or implemented on the mobile device or the evaluation device.

[0059] Alternatively, a treatment method, such as drug therapy, or recommendations for a specific lifestyle are automatically provided to the subject or another person. For this purpose, the determined rating is compared to the recommendations assigned to the various ratings in the database. After the determined rating matches one of the stored and assigned ratings, appropriate recommendations can be identified by the assignment of the recommendation to the stored rating that matches the determined rating.

[0060] As a further alternative form, or in addition to the above, at least one performance parameter on which the assessment is based will be stored in the mobile device. Typically, the performance parameter will be evaluated together with other stored performance parameters by an appropriate evaluation tool, such as an aggregation algorithm over time implemented on the mobile device, which can electronically assist in the recommendations for rehabilitation or therapy as described elsewhere in this specification.

[0061] In light of the above, the present invention particularly contemplates a method for assessing subclinical Huntington's disease in a subject, the method comprising: a) obtaining a dataset of fine motor measurements from the subject using a mobile device; b) determining at least one performance parameter determined from the dataset obtained from the subject using the mobile device; c) comparing the determined at least one performance parameter with a reference; d) assessing subclinical Huntington's disease based on the comparison performed in step c).

[0062] Advantageously, in the research underlying the present invention, it has been found that performance parameters obtained from a dataset of fine motor measurements from said subjects can be used as digital biomarkers for assessing premanifest Huntington's disease. According to the present invention, it has been found that the accuracy and / or dexterity and / or movement speed of finger movements, such as fine motor measurements and in particular finger tapping movements and / or drawing movements, enables the identification of patients suffering from premanifest Huntington's disease. Such measurements can be conveniently performed using computer-implemented tests, for example tests implemented on mobile devices. Thanks to the use of mobile devices, patients can perform the tests at any time and anywhere. There is no need for a doctor's examination in a clinic or ambulance to perform the measurements. As a result of the present invention, by using the performance parameters actually determined by the method of the present invention, the living conditions of Huntington's disease patients, especially in the initial stages, can be adjusted more precisely according to the actual disease state. Thereby, a more efficient drug treatment can be selected, or the dosing schedule can be adjusted according to the patient's current condition. It should be understood that the method of the present invention is typically a data evaluation method that requires an existing dataset of activity measurements from subjects.

[0063] Therefore, the method of the present invention - assesses the disease state, - monitors patients extensively in their daily situations, especially in real life, - supports patients through lifestyle and / or treatment recommendations, - investigates the effectiveness of drugs, for example also during clinical trials, - facilitates and / or aids in treatment decision-making, - supports hospital management, - supports rehabilitation treatment management, - improves the disease state as a rehabilitation means for simulating higher-density cognitive, motor, and walking activities, - To assist in health insurance evaluation and management and / or - To assist in making decisions in public health management It can be used for.

[0064] Unless otherwise specified, the definitions and explanations of the above terms are applied to the following embodiments with necessary modifications. In one embodiment of the method of the present invention, the aforementioned fine motor measurement includes measurement of the accuracy and / or dexterity and / or movement speed of finger movement. More typically, the aforementioned measurement of the accuracy and / or dexterity and / or movement speed of finger movement is performed during finger tapping movement and / or drawing movement.

[0065] In one embodiment of the method of the present invention, the measurement is performed using a mobile device. More typically, the mobile device includes a smartphone, smartwatch, wearable sensor, portable multimedia device or tablet computer.

[0066] In one embodiment of the present invention, the method is computer-implemented. In a further embodiment of the method of the present invention, the reference is at least one performance parameter from a dataset of fine motor measurements from the subject, and the dataset has been acquired prior to the dataset of step a).

[0067] In one embodiment of the method of the present invention, the reference is at least one performance parameter from a dataset of fine motor measurements from at least one subject known to have Huntington's disease in the presymptomatic stage. More typically, at least one performance parameter that is essentially the same as the reference indicates a subject with Huntington's disease in the presymptomatic stage.

[0068] In one embodiment of the method of the present invention, the reference is at least one performance parameter from a dataset of fine motor measurements from at least one subject known not to have Huntington's disease in the presymptomatic stage. More typically, at least one performance parameter different from the reference indicates a subject with presymptomatic Huntington's disease.

[0069] In a further embodiment of the method of the present invention, said assessment of presymptomatic Huntington's disease includes diagnosing and / or predicting presymptomatic Huntington's disease, or recommending a treatment method for presymptomatic Huntington's disease.

[0070] The present invention also includes a method for determining the effectiveness of a treatment method for presymptomatic Huntington's disease, which includes steps of the method of the present invention (i.e., a method for assessing presymptomatic Huntington's disease), determining a treatment response when improvement of presymptomatic Huntington's disease occurs in a subject during treatment, and determining a failure of response when deterioration of presymptomatic Huntington's disease occurs in the subject during treatment, or when presymptomatic Huntington's disease remains unchanged.

[0071] As used herein, the term "treatment method for presymptomatic Huntington's disease" refers to any type of medical treatment including drug-based treatment methods, respiratory assistance, etc. This term also includes lifestyle recommendations and rehabilitation measures. Typically, this method includes a drug-based treatment method and, in particular, recommendations for treatment with drugs known to be useful for treating presymptomatic Huntington's disease. Such drugs may be tetrabenazine, neuroleptics, benzodiazepines, amantadine, remacemide, anti-Parkinson's drugs, valproic acid, or ethyl eicosapentaenoic acid. Also, in a further embodiment, the above method may include an additional step of applying the treatment method recommended for the subject.

[0072] Also, according to the present invention, there is included a method for determining the effectiveness of a treatment method for premanifest Huntington's disease, including the steps of the above method of the present invention (i.e., a method for assessing premanifest Huntington's disease), and further steps of determining a treatment response when improvement of premanifest Huntington's disease occurs in a subject during treatment, and determining a failure of response when deterioration of premanifest Huntington's disease occurs in the subject during treatment or when premanifest Huntington's disease remains unchanged.

[0073] The term "improvement" as referred to by the present invention relates to the overall condition or individual symptoms of the condition, specifically any improvement in contrast vision. Similarly, "deterioration" means any deterioration of the overall condition or individual symptoms of the condition, specifically contrast vision. Since Huntington's disease as a progressive disease is typically accompanied by deterioration of the overall condition and symptoms of the condition, the deterioration referred to in relation to the above method is an unexpected or atypical deterioration beyond the normal course of this disease. Premanifest Huntington's disease that does not change means that the overall condition and accompanying symptoms are within the normal course of this disease.

[0074] Also, the present invention relates to a method for monitoring premanifest Huntington's disease in a subject, including determining whether the disease in the subject improves, deteriorates, or remains unchanged by performing the steps of the method of the present invention (i.e., a method for assessing premanifest Huntington's disease) at least twice during a predetermined monitoring period.

[0075] The present invention relates to a mobile device including a processor, at least one sensor, a database, and software that is tangibly incorporated into the device and, when executed on the device, implements the method of the present invention.

[0076] As used herein, the term "mobile device" refers to any portable device that includes at least a sensor and a data recording device suitable for acquiring the above-described measurement data set. This may also require a data processor, a storage unit, and a display for electronically simulating a fine motor ability test on the mobile device. The data processor may include a central processing unit (CPU) and / or one or more graphics processing units (GPUs) and / or one or more application-specific integrated circuits (ASICs) and / or one or more tensor processing units (TPUs) and / or one or more field-programmable gate arrays (FPGAs), etc. Also, it is assumed that data is recorded from the activities of the subject and compiled into a data set that is evaluated by the method of the present invention on the mobile device itself or on a second device. Depending on the specific settings envisaged, the mobile device may need to include a data transmission device for transferring the acquired data set from the mobile device to a further device. Particular mobile devices that are well-suited as mobile devices according to the present invention are smartphones, portable multimedia devices or tablet computers. Alternatively, a portable sensor equipped with a data recording and processing device may be used. Also, depending on the type of activity test to be performed, the mobile device is to be made to display instructions for the subject regarding the activity to be performed for the test. The specific activities envisaged to be performed by the subject are described elsewhere in this specification and include the fine motor ability tests described herein.

[0077] The present invention contemplates a system that includes a mobile device including at least one sensor and a remote device including a processor and a database, the remote device including software that is tangibly incorporated into the device and that, when executed on the device, implements the method of the present invention, the mobile device and the remote device being operatively linked to each other.

[0078] The phrase "operably linked to each other" should be understood to mean that these devices are connected so as to enable data transfer from one device to the other. Typically, it is assumed that at least a mobile device that acquires data from a subject is connected to a remote device that executes the steps of the method of the present invention so that the acquired data can be transmitted to the remote device for processing. However, the remote device may also transmit data, such as signals for controlling or monitoring the normal functions of the mobile device, to the mobile device. The connection between the mobile device and the remote device may be realized by a permanent or temporary physical connection, such as a coaxial cable, a fiber cable, an optical fiber cable, or a twisted pair 10BASE-T cable. Alternatively, the connection may be realized by a temporary or permanent wireless connection using radio waves, such as Wi-Fi, LTE, LTE-advanced, or Bluetooth. Further details may be described elsewhere in this specification. For data acquisition, the mobile device may include a user interface, such as a screen, or other devices for data acquisition. Typically, the activity measurement can be performed on a screen provided in the mobile device, and in this case, it should be understood that the screen may have various sizes, including, for example, a 12.95 cm (5.1 inch) screen.

[0079] The present invention also relates to the use of a mobile device or system of the present invention for assessing Huntington's disease in a subject using at least one performance parameter from a dataset of measurement values of fine movement measurements from the subject.

[0080] The present invention also contemplates the use of a mobile device or system according to the present invention for patient monitoring, specifically for widely monitoring a patient in daily situations in real life. The present invention further encompasses the use of a mobile device or system according to the present invention for assisting a patient by means of lifestyle and / or treatment recommendations. Furthermore, it should be understood that the present invention contemplates the use of a mobile device or system according to the present invention for investigating the safety and efficacy of a drug, for example also during a clinical trial. The present invention also contemplates the use of a mobile device or system according to the present invention for facilitating and / or assisting in treatment decision-making. Furthermore, the present invention also provides the use of a mobile device or system according to the present invention for improving a medical condition as a rehabilitation means and for assisting in decision-making in hospital management, rehabilitation means management, health insurance evaluation and management, and / or public health management.

[0081] The present invention also contemplates, in principle, a computer program, a computer program product, or a computer-readable storage medium in which the computer program is tangibly incorporated, the computer program including 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 relates to - a computer or computer network including at least one processor, the processor being adapted to execute a method according to one of the embodiments described herein, - a computer-loadable data structure adapted to perform a method according to one of the embodiments described herein when executed on a computer, - A computer script, wherein a computer program is configured to perform a method according to one of the embodiments described herein when executed on a computer, and the computer script; - A computer program including program means for performing a method according to one of the embodiments described herein when the computer program is executed on a computer or a computer network; - A computer program including program means according to the embodiment, wherein the program means is stored in a computer-readable storage medium; - A storage medium on which a data structure is stored, and the data structure is configured to perform a method according to one of the embodiments described herein after being loaded into the main storage and / or working storage of a computer or a computer network; - A computer program product having program code means, wherein the program code means is capable of being stored in a storage medium or is stored in a storage medium for performing a method according to one of the embodiments described herein when the program code means is executed on a computer or a computer network; - A typically encrypted data stream signal including a dataset of measurement values of fine movement measurements obtained from a subject using a mobile device; - Further including a typically encrypted data stream signal including at least one performance parameter derived from a dataset of measurement values of fine movement measurements obtained from a subject using a mobile device.

[0082] The present invention further provides a method for determining at least one performance parameter from a dataset of measurement values of fine movement measurements from the subject using a mobile device, a) Deriving at least one parameter from a dataset of measurement values of fine motor measurements from the subject using a mobile device, b) Comparing the determined at least one performance parameter with a reference, typically related to a method by which the at least one performance parameter can assist in the assessment of premanifest Huntington's disease in the subject.

[0083] The following lists further specific embodiments of the present invention. Embodiment 1: A method for assessing premanifest Huntington's disease in a subject, comprising: a) Determining at least one performance parameter from a dataset of fine motor measurements from the subject; b) Comparing the determined at least one performance parameter with a reference; and c) Assessing premanifest Huntington's disease in the subject based on the comparison.

[0084] Embodiment 2: The method of Embodiment 1, wherein the fine motor measurements include measurement of finger movement accuracy and / or dexterity and / or movement speed. Embodiment 3: The method of Embodiment 2, wherein the measurement of finger movement accuracy and / or dexterity and / or movement speed is performed during finger tapping movement and / or drawing movement.

[0085] Embodiment 4: The method according to any one of Embodiments 1 to 3, wherein the measurement is performed using a mobile device. Embodiment 5: The method of Embodiment 4, wherein the mobile device is included in a smartphone, smartwatch, wearable sensor, portable multimedia device, or tablet computer.

[0086] Embodiment 6: The method according to any one of Embodiments 1 to 5, implemented on a computer. Embodiment 7: The method according to any one of Embodiments 1 to 6, wherein the reference is at least one performance parameter from a data set of fine movement measurements from the subject, and the data set has been acquired prior to the data set of step a).

[0087] Embodiment 8: The method according to any one of Embodiments 1 to 6, wherein the reference is at least one performance parameter from a data set of fine movement measurements from at least one subject known to have Huntington's disease in the premanifest stage.

[0088] Embodiment 9: The method of Embodiment 8, wherein at least one performance parameter that is essentially the same as the reference indicates a subject with Huntington's disease in the premanifest stage. Embodiment 10: The method according to any one of Embodiments 1 to 6, wherein the reference is at least one performance parameter from a data set of fine movement measurements from at least one subject known not to have Huntington's disease in the premanifest stage.

[0089] Embodiment 11: The method of Embodiment 10, wherein at least one performance parameter different from the reference indicates a subject with Huntington's disease in the premanifest stage. Embodiment 12: The method according to any one of Embodiments 1 to 11, wherein the step of assessing Huntington's disease includes diagnosing and / or predicting premanifest Huntington's disease or recommending a treatment method for premanifest Huntington's disease.

[0090] Embodiment 13: A mobile device including a processor, at least one sensor, and a database, and including software that is tangibly incorporated into the device and, when executed on the device, implements the method according to any one of Embodiments 1 to 12.

[0091] Embodiment 14: A system including a mobile device including at least one sensor, and a remote device including a processor and a database, the software being tangibly incorporated in the device and, when executed on the device, implementing the method of any one of Embodiments 1 to 12, and the mobile device and the remote device being operably linked to each other. Embodiment 15: Use of the mobile device according to Embodiment 13 or the system according to Embodiment 14 for assessing subclinical Huntington's disease in a subject using at least one performance parameter from a dataset of measurement values of fine movement measurements from the subject.

[0092] All references cited throughout this specification are hereby incorporated by reference in their entirety with respect to the entire disclosure of those references and with respect to the specific disclosure referred to herein.

Brief Description of the Drawings

[0093]

Figure 1-1

Figure 1-2

Figure 1-3

Figure 2

Figure 3

Mode for Carrying Out the Invention

Examples

[0094] The following examples are merely illustrative of the present invention. In any case, they should not be construed as limiting the scope of the present invention. Example 1: Identification of Novel Characteristics Differentiating Healthy Controls from Asymptomatic Subjects Investigation and Research In an observational study (Digital HD) of participants with premanifest Huntington's disease (HD), manifest HD, and healthy controls (HC), a smartphone application was deployed that included seven active tests (Symbol Digit Modalities Test [SDMT], Stroop Word Reading Test [SWRT], rapid tapping, chorea, balance, U-turn, 2-minute walk) and continuous passive monitoring.

[0095] Here, we present the results of 79 participants who were enrolled at the data cutoff. To minimize practice effects, predefined active test measurements were aggregated across the 5th and 6th week post-screening.

[0096] This 1-year observational study was conducted on 80 participants (manifest HD: 40, premanifest HD: 20, HC: 20). Data were collected from continuous passive monitoring and daily active tests. Passive monitoring required the wearing of a smartwatch and a GPS-enabled smartphone, both equipped with triaxial accelerometers and gyroscopes. Active tests measured motor and non-motor symptoms of HD and included motor tasks, cognitive tests, QoL, and mood questions.

[0097] Patient demographic details are shown in Table 1 below.

[0098]

Table 1

[0099] Participants were trained on the active tests at the on-site Equipment In Vivo (EIV) visit and performed these tests at home as instructed. At EIV, participants received a clinical assessment using the motor, cognitive, and functional subscales of the Unified HD Rating Scale and items selected from the Timed Up and Go test and the Berg Balance Scale, together with a Kinect sensor.

[0100] The encrypted telephone data was securely transferred via the Internet and analyzed to extract clinically meaningful measurements for group discrimination and correlation with clinical parameters.

[0101] The Mann-Whitney test was used to calculate the differences in characteristics between groups. The Spearman correlation coefficient was used to calculate the correlation with the within-clinic control. Results The SDMT and SWR tests showed excellent correlation with their within-clinic controls. The chorea test showed good correlation with the UHDRS maximum chorea upper limb item. There were significant differences between the dominant group and the healthy control group for all eight pre-defined characteristics, and all were p < 0.001 except for the gait tests (U-turn: p = 0.009, 2-minute walk: p = 0.004) (see Figure 1). The results are also shown in Table 2 below.

[0102]

Table 2-1

[0103]

Table 2-2

[0104]

Table 2-3

[0105] Two promising candidate characteristics (see Figures 2 and 3) were identified in the discrimination between the subclinical group and the healthy control group using the rapid tapping and shape drawing tests.

[0106] References Cited The Huntington Group, 1996, Movement Disorders, 11(2): 136 Rao 2009, Gait Posture. 29 (3): 433-6 WO 2019 / 081640

Claims

1. A method for providing data for assessing premanifest Huntington's disease in a subject, comprising: a) determining at least one performance parameter from a dataset of fine motor measurements from the subject, wherein the fine motor measurements are measurements of finger movement accuracy, dexterity, or speed, and the fine motor measurements are performed by a shape drawing test or a rapid tapping test; b) comparing the determined at least one performance parameter with a reference; and c) providing the result of the comparison as data for assessing premanifest Huntington's disease in the subject.

2. The method according to claim 1, wherein the measurement is performed using a mobile device.

3. The method according to claim 2, wherein the mobile device is included in a smartphone, smartwatch, wearable sensor, portable multimedia device, or tablet computer.

4. The method according to any one of claims 1 to 3, implemented by a computer.

5. The method according to any one of claims 1 to 4, wherein the reference is at least one performance parameter from a dataset of fine motor measurements from the subject, and the dataset is obtained prior to the dataset of step a).

6. The method according to any one of claims 1 to 4, wherein the reference is at least one performance parameter from a dataset of fine motor measurements from at least one subject known to have premanifest Huntington's disease.

7. The method according to claim 6, wherein if at least one performance parameter of the subject to be assessed is essentially the same as the reference, it indicates that the subject is a subject suffering from premanifest Huntington's disease.

8. The method according to any one of claims 1 to 4, wherein the reference is at least one performance parameter from a dataset of fine motor measurements from at least one subject known not to have premanifest Huntington's disease.

9. The method according to claim 8, wherein at least one performance parameter different from the reference indicates a subject suffering from premanifest Huntington's disease.

10. The method according to any one of claims 1 to 9, wherein the step of assessing the asymptomatic stage of Huntington's disease includes diagnosing and / or predicting the asymptomatic stage of Huntington's disease, or recommending a treatment method for the asymptomatic stage of Huntington's disease.

11. A mobile device comprising a processor, at least one sensor, a database, and software, wherein the software, when executed on the mobile device, implements the method according to any one of claims 1 to 10.

12. A mobile device including at least one sensor, A remote device including a processor, a database, and software, wherein the software, when executed on the remote device, implements the method according to any one of claims 1 to 10. A system comprising the mobile device and the remote device, wherein the mobile device and the remote device are operably linked to each other.

13. Use of the mobile device according to claim 11 or the system according to claim 12 for providing data for assessing the asymptomatic stage of Huntington's disease in a subject using at least one performance parameter from a dataset of measurement values of fine motor measurements from the subject.

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