Apparatus and methods for assessing premanifest huntington's disease

Abstract

The present invention relates to the field of diagnostics. In particular, it relates to a method for assessing a pre-manifest stage of Huntington's disease in a subject, comprising the steps of determining at least one performance parameter from a data set of fine motor measurements from said subject, comparing said determined at least one performance parameter to a reference, and assessing a pre-manifest stage of Huntington's disease in said subject based on said comparison. The present invention is furthermore directed to a device and a system for carrying out the above-mentioned method and to the use of such a device or system for assessing a pre-manifest stage of Huntington's disease in said subject.

Description

Technical Field

[0001] This invention relates to the field of diagnostics. Specifically, it relates to a method for assessing pre-manifest stage Huntington's disease in a subject, 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; and assessing pre-manifest stage Huntington's disease in the subject based on the comparison. The invention also contemplates apparatus and systems for performing the above-described method, and the use of such apparatus or systems for assessing pre-manifest stage Huntington's disease in a subject. Background Technology

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

[0003] The disease is caused by a genetic mutation in the gene encoding huntingtin, a protein involved in various cellular functions and interacting with over 100 other proteins. The mutated huntingtin appears to be cytotoxic to certain neuronal cell types.

[0004] The symptoms of this disease most commonly become significant in middle age, but can begin at any age from childhood to old age. In the early stages, symptoms involve subtle changes in personality, cognition, and physical skills. Physical symptoms are usually the first to be noticed because cognitive and behavioral symptoms are often not severe enough to be identified independently in the early stages.

[0005] The most distinctive initial physical symptom is chorea, characterized by unsteady, random, and uncontrollable movements. Chorea may initially manifest as general restlessness, small, unconscious initiation or incomplete movements, uncoordinated or slowed eye movements. These milder motor abnormalities usually precede more obvious motor dysfunctions by at least three years. As the condition progresses, more pronounced symptomatic phenomena appear, such as stiffness, contortionist movements, or abnormal postures.

[0006] Further symptoms of Huntington's disease include physical instability, abnormal facial expressions, and difficulties with chewing, swallowing, and speaking. Consequently, eating difficulties and sleep disturbances also accompany the disease. Cognitive abilities are also progressively impaired. Impaired functions include executive function, cognitive flexibility, abstract thinking, rule acquisition, and appropriate action / response. In more pronounced stages, memory deficits often occur, ranging from short-term memory loss to long-term memory difficulties. Cognitive problems worsen over time and eventually lead to dementia. Psychotic complications accompanying Huntington's disease include anxiety, depression, reduced emotional expression (emotional blunting), egocentrism, aggression, and compulsive behaviors, the latter of which can lead to or exacerbate addictions, including alcoholism, gambling, and hypersexuality.

[0007] There is no cure for Huntington's disease. Supportive measures exist in disease management, depending on the symptoms being treated. In addition, many medications are used to improve the disease, its progression, or the accompanying symptoms.

[0008] The disease can be diagnosed through genetic testing. Furthermore, the severity of the disease can be graded using the Unified Huntington's Disease Rating Scale (UHDRS) (The Huntington Group, 1996, Rao 2009). This scale system addresses four components: motor function, cognition, behavior, and functional ability. Motor function assessment includes ocular pursuit, saccade initiation, saccade speed, dysarthria, tongue protrusion, maximal dystonia, maximal chorea, retropulsion pull test, finger taps, pronation / supination of the hand, luria, rigidity arms, slow movement of the body, gait, and tandem walking, and can be summarized into a total motor score (TMS). Motor function assessment must be conducted and judged by a resident physician at a medical practice hospital.

[0009] The computer-based tests used to assess Huntington's disease have been described, among other things, in WO 2019 / 081640.

[0010] However, diagnostic tools are needed to allow for reliable diagnosis and identification of pre-manifestation stages of Huntington's disease in patients in order to facilitate appropriate care and / or accurate treatment.

[0011] The technical problem that forms the basis of this invention can be seen in the apparatus and method provided according to the above-described needs. This technical problem is solved by embodiments characterized in the claims and described below. Summary of the Invention

[0012] This invention relates to a method for assessing pre-symptom stages of Huntington's disease in a subject, comprising the following steps: a) Determine at least one performance parameter from a dataset of fine motion measurements from the subject; b) Compare the determined at least one performance parameter with a reference; and c) Evaluate pre-existing stages of Huntington's disease in the subject based on the comparison.

[0013] The method is generally a computer-implemented method, i.e., steps a) through c) are performed automatically using a data processing device. Details can also be found below and in the appended embodiments.

[0014] In some embodiments, the method may further include a step prior to step (a) of obtaining a dataset of fine motion measurement results from the subject using a mobile device during a predetermined activity performed by the subject or within a predetermined time window. However, generally the method is an in vitro-to-in vivo approach performed on an existing dataset of measurement results from the subject, which does not require any physical interaction with the subject.

[0015] The methods mentioned in this invention include methods that consist essentially of the steps described above or methods that may include additional steps.

[0016] As used below, the terms “have,” “contain,” or “include,” or any of their arbitrary grammatical variations, are used in a non-exclusive manner. Thus, these terms can refer either to a situation where no further features exist in the entity described in the context, in addition to the features introduced by these terms, or to a situation where one or more further features exist. For example, the expressions “A has B,” “A contains B,” and “A includes B” can refer either to a situation where no other elements exist in A besides B (i.e., A is entirely and exclusively composed of B), or to a situation where entity A contains one or more further elements besides B, such as elements C, C and D, or even further elements.

[0017] Furthermore, it should be noted that the terms "at least one," "one or more," or similar wording indicating that a feature or element may appear once or more will generally be used only once when introducing the corresponding feature or element. In the following text, in most cases, the wording "at least one" or "one or more" will not be repeated when referring to the corresponding feature or element, even though it is true that the corresponding feature or element may appear once or more.

[0018] Furthermore, as used below, the terms “particularly,” “more particularly,” “specifically,” “more specifically,” “generally,” and “more generally,” or similar terms, are used with additional / alternative features without limiting the possibility of alternatives. Therefore, features introduced by these terms are additional / alternative features and are not intended to limit the scope of the claims in any way. As those skilled in the art will recognize, the invention can be practiced by using alternative features. Similarly, features introduced by “in an embodiment of the invention” or similar wording are intended to be additional / alternative features, without any limitation on alternative embodiments of the invention, without any limitation on the scope of the invention, and without any limitation on the possibility of combining features introduced in this way with other additional / alternative or non-additional / alternative features of the invention.

[0019] Once a dataset of fine motion measurement results has been obtained, the method can be performed by the subject on a mobile device. Therefore, the mobile device and the device for obtaining the dataset can be physically identical, i.e., the same device. This mobile device should have a data acquisition unit, which generally includes means for data acquisition, i.e., means for quantitatively or qualitatively detecting or measuring physical and / or chemical parameters and converting them into electronic signals transmitted to an evaluation unit in the mobile device for performing the method according to the invention. The data acquisition unit includes means for data acquisition, i.e., means for quantitatively or qualitatively detecting or measuring physical and / or chemical parameters and converting them into electronic signals transmitted to a device remote from the mobile device and for performing the method according to the invention. Generally, the means for data acquisition includes at least one sensor. It will be understood that more than one sensor can be used in the mobile device, 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. Typical sensors used as devices for data acquisition include gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, tactile sensors, voice recorders, light sensors, pressure sensors, location data detectors, cameras, sweat analysis sensors, and the like. The evaluation unit generally includes a processor and a database, as well as software tangibly embedded in the device, which, when run on the device, performs the methods of the present invention. More generally, such mobile devices may also include a user interface, such as a screen, that allows the results of the analysis performed by the evaluation unit to be provided to the user.

[0020] On the other hand, it can be performed on a device remote from the mobile device already used to acquire the dataset. In this case, the mobile device should only include means for data acquisition, i.e., means for quantitatively or qualitatively detecting or measuring physical and / or chemical parameters and converting them into electronic signals transmitted to a device remote from the mobile device for performing the method according to the invention. Generally, the means for data acquisition includes at least one sensor. It will be understood that more than one sensor can be used in the mobile device, 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. Typical sensors used as means for data acquisition 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, location data detectors, cameras, sweat analysis sensors, GPS, etc. Therefore, the mobile device and the device for performing the method of the invention can be physically different devices. In this case, the mobile device can communicate with the device for performing the method of the invention through any means for data transmission. This data transfer can be achieved through permanent or temporary physical connections, such as coaxial, fiber, optical fiber, or twisted-pair, 10 BASE-T cables. Alternatively, it can be achieved through temporary or permanent wireless connections using, for example, 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 measurement results obtained from the subject using a mobile device. This dataset can also be transferred or stored from the acquiring mobile device on a permanent or temporary storage device, which can then be used to transfer the data to a device for carrying out the method of the present invention. The remote device carrying out the method of the present invention in this configuration generally includes a processor and a database, as well as software tangibly embedded in the device, which performs the method of the present invention when running on the device. More generally, the device may also include a user interface, such as a screen, which allows the results of the analysis performed by the evaluation unit to be provided to the user.

[0021] As used herein, the term "assessment" refers to assessing whether a subject has pre-manifestation stage Huntington's disease. Therefore, assessment as used herein includes diagnosis, staging, classification, and / or prediction of pre-manifestation stage Huntington's disease or recommendation of treatment for pre-manifestation stage Huntington's disease. As those skilled in the art will understand, such assessment, while preferred, may not typically be accurate for 100% of the subjects surveyed. However, the terminology requires that statistically significant subject fractions be accurately assessed. Those skilled in the art can immediately determine whether a fraction is statistically significant using various well-known statistical evaluation tools, such as confidence interval determination, p-value determination, Stokes t-test, Mann-Whitney test, etc. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Generally, confidence intervals are assumed to be at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, and at least 95%. p-values ​​are generally 0.2, 0.1, or 0.05. Therefore, the method of the present invention generally helps to assess Huntington's disease by providing a means of evaluating a dataset of fine motor measurement results.

[0022] As used herein, the term "Huntington's disease (HD)" refers to a hereditary neurological disorder characterized by the death of neurons in the central nervous system. Most notably, the basal ganglia are affected by cell death. Further areas of the brain, such as the substantia nigra, cerebral cortex, hippocampus, and Purkinje cells, are also involved. Generally, all these areas play a role in motor and behavioral control. The disease is caused by a genetic mutation in the gene encoding the huntingtin protein. Huntington proteins are proteins involved in a wide range of cellular functions and interact with over 100 other proteins. Mutated huntingtin proteins appear to be cytotoxic to certain neuronal cell types. The mutated huntingtin protein is characterized by a polyglutamine region resulting from a trinucleotide repeat sequence in the huntingtin protein gene. Repeats of more than 36 glutamine residues in the polyglutamine region of the protein result in the disease-causing huntingtin protein. Because Huntington's disease is inherited in a dominant-autosomal manner, genomic testing for CAG repeats in the huntingtin protein (HTT) allele is recommended for individuals at genetic risk, i.e., those with a family history of the disease. In addition, the diagnosis of the disease involves DNA analysis, but also imaging methods such as CT, MRI, PET or SPECT scans to determine brain atrophy, as well as neurological evaluations performed by a medical practitioner.

[0023] Symptoms of the disease most commonly become prominent in middle age, but can begin at any age from childhood to old age. In the early stages, symptoms involve subtle changes in personality, cognition, and physical skills. Physical symptoms are usually the first to be noticed because cognitive and behavioral symptoms are often not severe enough to be recognizable on their own in the early stages. Almost everyone with Huntington's disease eventually develops similar physical symptoms, but the onset, progression, and severity of cognitive and behavioral symptoms vary significantly between individuals. The most characteristic initial physical symptom is unsteady, random, and uncontrollable movements called chorea. Chorea may initially manifest as general restlessness, small, unconscious initiation or incomplete movements, uncoordinated or slowed eye saccades. These milder motor abnormalities usually precede more obvious signs of motor dysfunction by at least three years. As the condition progresses, more pronounced symptomatic phenomena appear, such as stiffness, contorted movements, or abnormal postures. These are signs that the systems in the brain responsible for movement have been affected. Conscious motor function becomes increasingly impaired, to the point that any action requiring muscle control is affected. Common consequences include body instability, abnormal facial expressions, and difficulty chewing, swallowing, and speaking. Therefore, eating difficulties and sleep disturbances also accompany the disease. Cognitive abilities are also impaired in a progressive manner. Impaired functions include executive function, cognitive flexibility, abstract thinking, rule acquisition, and appropriate action / response. In more pronounced stages, memory deficits often occur, ranging from short-term memory loss to long-term memory difficulties. Cognitive problems worsen over time and eventually develop into dementia. Psychotic complications accompanying Huntington's disease include anxiety, depression, reduced emotional expression (emotional dulling), egocentrism, aggression, and compulsive behaviors, the latter of which can lead to or exacerbate addictions, including alcoholism, gambling, and hypersexuality.

[0024] There is no cure for Huntington's disease. Supportive measures exist in disease management, depending on the symptoms being treated. In addition, many medications are used to improve the disease, its progression, or accompanying symptoms. Bubenazine is approved for the treatment of Huntington's disease, and neuroleptics and benzodiazepines are used to help alleviate chorea. Amantadine or remdesivir are still under investigation but have shown preliminary positive results. Motor dysfunction and stiffness, especially in juvenile cases, can be treated with anti-Parkinsonian drugs, and myoclonic hyperkinesis can be treated with valproic acid. Ethyl-eicosapentoic acid has been found to enhance motor symptoms in patients; however, its long-term effects need to be elucidated.

[0025] The disease can be diagnosed through genetic testing. Furthermore, the severity of the disease can be graded using the Unified Huntington's Disease Rating Scale (UHDRS). This scale system addresses four components: motor function, cognition, behavior, and functional ability. Motor function assessment includes eye tracking, skipping initiation, skipping speed, dysarthria, tongue protrusion, maximal dystonia, maximal chorea, backward traction test, finger tapping, pronation / supination of the hand, luria, rigid arms, bradykinesia, gait, and tandem walking assessment, which can be summarized as a total motor score (TMS). Motor function must be investigated and assessed by a practicing physician.

[0026] The "pre-phenotype stage" of Huntington's disease is a stage in which the subject does not exhibit any clinical symptoms of Huntington's disease. However, some motor functions may be weaker than in subjects without Huntington's disease in the pre-phenotype stage. Generally, the subject has 15 or fewer, 12 or fewer, 10 or fewer, 8 or fewer, or 5 or fewer TMS. In addition, the subject should be a carrier of the HTT genetic mutation (CAG repeat).

[0027] The term "fine motor movement measurement results" refers to the accuracy, dexterity, and / or speed of motor movements of the limbs, and more generally, to the accuracy and / or dexterity and / or speed of finger movements. The accuracy and / or dexterity and / or speed of finger movements are measured during the tapping and / or drawing movements described below. At least one performance parameter can generally be determined from a dataset of measurement results collected from the subject during the following activities demonstrating fine motor skills. These tests are generally performed by a computer on a data acquisition device, such as a mobile device as specifically defined elsewhere herein.

[0028] Fine motor skills test: shape drawing test Mobile devices can be adapted to perform or acquire data from further tests of distal motor functions (so-called "shape drawing tests"), which are configured to measure finger dexterity and distal fine motor skills. Data sets obtained from such tests allow for the assessment of the accuracy of finger movements, pressure distribution maps, and velocity distribution maps.

[0029] The purpose of the “Drawing Shapes” test is to assess fine finger control and stroke sequencing. Patients are instructed to hold the mobile device with their untested hand and, using the middle finger of the tested hand, to draw six pre-written alternating shapes of increasing complexity (linear, rectangular, circular, sine, and spiral; see below) on the device's touchscreen "as quickly and accurately as possible" within a maximum time of, say, 30 seconds. To successfully draw a shape, the patient's finger must slide continuously across the touchscreen, passing through all indicated checkpoints and connecting the start and end points of the indications as close as possible to the boundaries of the writing path. Patients have a maximum of two attempts to successfully complete each of the six shapes. The test will be conducted alternately with the right and left hands. Users will be instructed to alternate daily. Each of the two linear shapes has a specific number of checkpoints to connect, i.e., "a-1" segments. The square shape has a specific number of checkpoints to connect, i.e., "b-1" segments. The circular shape has a specific number of checkpoints to connect, i.e., "c-1" segments. The figure-eight shape has a specific number of checkpoints to be connected, "d", which is "d-1" segments. The spiral shape has a specific number of checkpoints to be connected, "e-1" segments. Completing all 6 shapes means successfully drawing a total of "(2a+b+c+d+e-6)" segments.

[0030] Typical drawing shape test performance parameters of interest: Based on the complexity of the shape, linear and square shapes are associated with a weight factor (Wf) of 1, circular and sinusoidal shapes with a weight factor of 2, and spiral shapes with a weight factor of 3. Shapes that are successfully completed on the second attempt can be associated with a weight factor of 0.5. These weight factors are numerical examples that can be changed within the context of this invention.

[0031] 1. Shape completion performance score: a. The number of shapes successfully completed in each test (0 to 6) (ΣSh) b. Number of shapes successfully completed on the first attempt (0 to 6) (ΣSh1) c. Number of shapes successfully completed on the second attempt (0 to 6) (ΣSh2) d. Number of shapes that failed / were not completed in all attempts (0 to 12) (ΣF) e. Shape completion score (0 to 10) reflecting the number of successfully completed shapes, adjusted by weighting factors for different complexity levels of each shape. (Σ[Sh*Wf]) f. For each shape with different complexity levels, a weighted factor is used to reflect the number of successfully completed shapes and to take into account the first successful shape completion score for the second attempt (0 to 10) (Σ[Sh1*Wf]+Σ[Sh2*Wf*0.5]). g. The speed at which the test is completed can be calculated by multiplying the shape completion score as defined in #1e and #1f by 30 / t, where t will represent the time (in seconds) to complete the test.

[0032] h. The total first attempt completion rate for each of the 6 individual shapes tested multiple times within a specific time period: (ΣSh1) / (ΣSh1+ΣSh2+ΣF) and (ΣSh1+ΣSh2) / (ΣSh1+ΣSh2+ΣF).

[0033] 2. Segment completion and celerity performance rating / measurement: (Based on the analysis of the best result [maximum number of segments completed] from two attempts for each shape, if applicable) a. The number of segments successfully completed in each test (from 0 to [2a+b+c+d+e-6]) (ΣSe) b. Average speed of movement of successfully completed segments ([C], segments / second): C = ΣSe / t, where t will represent the time to complete the test (in seconds) (maximum 30 seconds). c. Segment completion score (Σ[Se*Wf]) reflecting the number of successfully completed segments, adjusted by weighting factors for different complexity levels of each shape. d. Speed ​​adjustment and weighted segment completion score (Σ[Se*Wf]*30 / t), where t will represent the time to complete the test (in seconds).

[0034] e. The number of shape-specific segments that are successfully completed for both linear and square shapes (ΣSe) LS ) f. The number of shape-specific segments that are successfully completed for circular and sinusoidal shapes (ΣSe) CS ) g. The number of shape-specific segments that successfully complete a spiral shape (ΣSe) S ) h. The shape-specific average linear motion velocity of successfully completed segments in linear and square shape tests: C L = ΣSe LS / t, where t represents the cumulative epoch time (in seconds) from the start to the end of the corresponding successfully completed segment within these specific shapes.

[0035] i. Shape-specific average circular motion velocity of successfully completed segments in circular and sinusoidal shape tests: C C = ΣSe CS / t, where t will represent the cumulative epoch time (in seconds) from the start to the end of the corresponding successfully completed segment within these specific shapes.

[0036] j. The shape-specific average spiral motion speed of a successfully completed segment in the spiral shape test: C S = ΣSe S / t, where t will represent the cumulative epoch time (in seconds) from the start to the end of the corresponding successfully completed segment within the specific shape.

[0037] 3. Drawing accuracy performance rating / measurement: (Based on the analysis of the best result [maximum number of segments completed] from two attempts for each shape, if applicable) a. Deviation, which is calculated as the sum of the total area under the curve (AUC) measure of the integrated surface deviation between the drawn trajectory and the target drawing path from the start to the end checkpoint reached for each specific shape, divided by the total cumulative length of the corresponding target path within those shapes (from the start to the end checkpoint reached).

[0038] b. Linear deviation (Dev) L ), which is calculated as Dev in #3a, but specifically from the results of linear and square shape tests.

[0039] c. Circular Deviation (Dev) C ), which is calculated as Dev in #3a, but specifically from the test results of circular and sinusoidal shapes.

[0040] d. Spiral Deviation (Dev) S ), which is calculated as Dev in #3a, but specifically from the results of the spiral shape test.

[0041] e. Shape specificity deviation (Dev) 1-6 ), which is calculated as Dev in #3a, but from each of the 6 different shape test results, and only applies to those shapes that successfully complete at least 3 segments in the best attempt.

[0042] f. Continuous variable analysis for any other method of calculating the total deviation from the target trajectory that is shape-specific or shape-agnostic.

[0043] 4.) Pressure distribution map measurement results i) Average applied pressure ii) Deviation (Dev), which is calculated as the standard deviation of pressure. More typical performance parameters for fine motion capability in shape rendering tests are the coefficient of variation of spiral rendering speed, the coefficient of variation of circular rendering speed, and / or the average rendering speed of spirals. More generally, all of these performance parameters can be determined. Further typical performance parameters are also described in the accompanying embodiments below.

[0044] Fine motor skills test: speed tapping test The purpose of the "rapid tapping" test is to assess finger movement speed ability. The patient is instructed to hold the mobile device with the untested hand and tap the touchscreen of the device at various speeds. The tapping speed can be controlled by displaying the subject symbols such as buttons or circles that need to be touched during the tapping motion.

[0045] More typical performance parameters for fine motor skills in a rapid tap test include the standard deviation of the finger-lift time between two taps, the maximum finger-lift time between two taps, the coefficient of variation of the finger-lift time between two taps, the coefficient of variation of the distance between two taps, the standard deviation of the finger-touch screen time during the tap, the coefficient of variation of the finger-touch screen time during the tap, and / or the maximum finger-touch screen time during the tap. More generally, all of these performance parameters can be determined. Further typical performance parameters are also described in the accompanying embodiments below.

[0046] Further tests that can be performed in conjunction with the above tests are those described in WO 2019 / 081640.

[0047] As used herein, the term "subject" refers to an animal, and generally to a mammal. In particular, a subject is a primate, and most generally a human. A subject according to the invention should have or be suspected of having the pre-symptom stage of Huntington's disease. As discussed elsewhere herein, several risk factors can identify an increased prevalence of Huntington's disease, and subjects affected by such risk factors can be considered subjects suspected of having the pre-symptom stage of Huntington's disease.

[0048] The term "at least one" means that one or more performance parameters can be determined according to the invention, i.e., 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 according to the invention. Generally, one or more parameters are selected from a dataset of fine motion measurement results and include measurements of finger movement accuracy and / or dexterity and / or movement speed, and more generally, those performance parameters specifically mentioned elsewhere herein. The measurement of finger movement accuracy and / or dexterity and / or movement speed is performed during the tapping and / or drawing movements of the finger. More generally, such movements are performed using mobile devices such as smartphones, smartwatches, wearable sensors, portable multimedia devices, or tablets, which have implemented tests requiring input from the subject to be tested, thereby enabling the determination of finger movement accuracy and / or dexterity and / or movement speed. Suitable tests to be implemented on mobile devices are described elsewhere herein.

[0049] As used herein, the term "performance parameter" refers to a parameter that indicates a subject's ability to perform an activity, such as fingering, during a measurement of fine motor activities. Generally, a performance parameter can be the accuracy or speed at which a fine motor task can be performed, such as fingering on a screen. For example, a performance parameter can be determined from a dataset of measurement results using the tapping motion of a finger on a screen. In this case, the tapping speed can be determined as a performance parameter. Alternatively, or further, fingering accuracy and / or speed can be determined as performance parameters under the condition that fingering is the task being tested, such as drawing a predetermined shape on a screen.

[0050] The term "measurement result dataset" refers to all or a subset of the data obtained by a mobile device from the subject during a measurement of the subject's fine motor abilities. The dataset according to the method of the invention can be obtained from a fine motor test performed by the subject to be tested. As described elsewhere herein, the subject can perform a computer-implemented test of its fine motor abilities. Further details can be found in the following appended embodiments.

[0051] Determining at least one performance parameter can be achieved either by directly obtaining the desired measurement value from the dataset or by integrating one or more measurements from the dataset, and thus, by obtaining the parameter from the dataset through mathematical operations such as calculation. Generally, performance parameters are obtained from the dataset using automated algorithms, such as computer programs that automatically derive performance parameters from the measurement result dataset when tangibly embedded in a data processing device input from said dataset.

[0052] As used herein, the term "reference" refers to a discriminator that allows for the evaluation of pre-performance stage Huntington's disease based on at least one identified performance parameter. Such a discriminator may be a value of a performance parameter that indicates whether a subject is normal (i.e., healthy) or has pre-performance stage Huntington's disease.

[0053] This value can be derived from one or more contrast vision parameters of a subject known to have pre-manifestation stage Huntington's disease. Generally, in this case, the mean or median of the parameters can be used as a discriminator. If the performance parameter determined from the subject is the same as or higher than the threshold derived from the reference, then in this case, the subject can be identified as having pre-manifestation stage Huntington's disease. If the determined performance parameter differs from the reference and is particularly below the threshold, the subject should be identified as not having pre-manifestation stage Huntington's disease.

[0054] Similarly, values ​​can be derived from one or more performance parameters of a subject known not to have pre-manifestational Huntington's disease. Generally, in this case, the mean or median of the parameters can be used as a discriminator. If the performance parameter determined from the subject is the same as or lower than the threshold derived from the reference, then in this case, the subject can be identified as not having pre-manifestational Huntington's disease. If the determined performance parameter differs from the reference and is particularly higher than the threshold, then the subject should be identified as having pre-manifestational Huntington's disease.

[0055] Alternatively, the reference could be a previously determined performance parameter from a dataset of measurement results obtained from the same subject prior to the actual dataset. In this case, depending on the previous state of Huntington's disease in the preceding stage, a different performance parameter determined from the actual dataset than the previously determined parameter should indicate improvement or deterioration. Those skilled in the art know how to use said parameter as a reference based on the type of activity and previous parameters.

[0056] Comparing at least one determined performance parameter with a reference can be accomplished using an automated comparison algorithm implemented on a data processing device such as a computer. The comparison is between the value of the determined performance parameter and a reference for said determined parameter, as detailed elsewhere herein. As a result of the comparison, it can be assessed whether the determined performance parameter is the same as or different from the reference, or whether there is some relationship between them (e.g., greater than or less than the reference). Based on this assessment, a subject can be identified as having Huntington's disease (“rule-in”) or not (“excluded”). For the assessment, the type of reference will be considered, as described elsewhere in conjunction with suitable references according to the invention.

[0057] Furthermore, quantitative assessment of pre-manifestation stage Huntington's disease in subjects should be possible by determining the degree of difference between the determined performance parameters and references. It should be understood that the state of improvement, deterioration, or no change can be determined by comparing the actually determined performance parameters with earlier determined performance parameters used as references. Based on the quantitative differences in the values ​​of said performance parameters, the state of improvement, deterioration, or no change can be determined, and optionally also quantified. If other references are used, such as those from subjects with pre-manifestation stage Huntington's disease, it will be understood that quantitative differences are meaningful if a certain stage of impairment can be assigned to a set of references. In this case, the state of deterioration, improvement, or no change can be determined relative to said stage of impairment, and optionally, it can also be quantified.

[0058] The assessment performed by the method of the present invention is displayed to a subject or another person, such as a medical practitioner. Generally, this is achieved by displaying the diagnosis on a display of a mobile device or evaluation device. Alternatively, recommendations for treatments such as medication or lifestyle changes such as recovery measures are automatically provided to the subject or another person. For this purpose, the established assessment is compared with recommendations assigned to different assessments in a database. Once the established assessment matches one of the stored and assigned assessments, a suitable recommendation can be identified by assigning the recommendation to the stored assessment that matches the established assessment. Therefore, it is generally envisioned that recommendations and assessments exist in the form of a relational database. However, other arrangements that allow for the identification of suitable recommendations are also possible and are known to those skilled in the art.

[0059] Furthermore, one or more performance parameters can also be stored on a mobile device or displayed to the subject, generally in real time. The stored performance parameters can be assembled into a time-based process or similar evaluation measure. Such evaluated performance parameters can be provided to the subject as feedback on the course of Huntington's disease studied according to the method of the present invention. Generally, this feedback can be provided in electronic format on a suitable display of a mobile device and can be linked to recommendations for therapies or recovery measures as specifically described above.

[0060] Furthermore, the performance parameters of the evaluation can also be provided to medical practitioners in doctors' offices or hospitals, as well as other healthcare providers, such as diagnostic test developers or drug developers in clinical trial settings, health insurance providers, or other stakeholders in public or private healthcare systems.

[0061] Generally, the method of the present invention for assessing pre-symptom stages of Huntington's disease in a subject can be performed as follows: First, at least one performance parameter is determined from an existing dataset using fine motion measurements from the subject via a mobile device. The dataset may be transferred from the mobile device to an evaluation device, such as a computer, or it may be processed within the mobile device to obtain at least one performance parameter.

[0062] Secondly, at least one performance parameter is determined and compared with a reference using a computer-implemented comparison algorithm, for example, executed by a data processor of a mobile device or an evaluation device such as a computer. The result of the comparison is evaluated relative to the reference used in the comparison, and based on the comparison, pre-stage Huntington's disease in the subject is assessed; for example, the subject will be identified as having pre-stage Huntington's disease or not.

[0063] Third, the assessment, such as the identification of whether the subject has pre-existing Huntington's disease or not, is displayed to the subject or another person, such as a medical practitioner, on a suitable display, such as a screen connected or implemented in a mobile device or evaluation device.

[0064] On the other hand, recommendations for treatments such as medication or lifestyle modifications are automatically provided to the subject or others. To do this, the established assessment is compared with recommendations assigned to different assessments in a database. Once the established assessment matches one of the stored and assigned assessments, an appropriate recommendation can be identified by assigning the recommendation to the stored assessment that matches the established assessment.

[0065] However, as an alternative or additional option, at least one performance parameter constituting the basis of the evaluation will be stored on the mobile device. Generally, it should be evaluated, along with other stored performance parameters, using a suitable evaluation tool implemented on a mobile device that can electronically assist in recovery or therapy recommendations, such as a time-process assembly algorithm, as specifically described elsewhere in this document.

[0066] In view of the above, the present invention also specifically contemplates a method for assessing pre-symptom stages of Huntington's disease in a subject, comprising the following steps: a) A dataset of fine motion measurement results obtained from the subject using a mobile device; b) Determine at least one performance parameter from the dataset obtained from the subject using a mobile device; c) Compare the determined at least one performance parameter with a reference; and d) Based on the comparative assessment performed in step c), evaluate the pre-stage Huntington's disease in the subject.

[0067] Advantageously, it has been found that performance parameters obtained from datasets of fine motor measurement results from the subject can be used as digital biomarkers for assessing pre-manifestational Huntington's disease in the research that forms the basis of this invention. According to the invention, fine motor measurements, and particularly finger motor accuracy and / or dexterity and / or speed, such as finger motor accuracy and / or dexterity and / or speed during tapping and / or drawing movements of the fingers, have been found to allow for the identification of patients with pre-manifestational Huntington's disease. Such measurements can be conveniently performed using computer-implemented tests, for example, tests implemented on mobile devices. Due to the use of mobile devices, patients can be tested anytime, anywhere. Consultation with a physician in a doctor's office or hospital ambulance is not required for the measurement. Because of this invention, and because the method of the invention uses practically determined performance parameters, the life condition of Huntington's disease patients, especially in the early stages, can be more precisely adjusted according to the actual disease state. Therefore, more effective drug treatments can be selected, or dosage regimens can be adapted to the patient's current condition. It should be understood that the method of the invention is generally a data evaluation method that requires an existing dataset of activity measurement results from the subject.

[0068] Therefore, the method of the present invention can be used for: - Assess the disease condition; - Monitor patients, especially in real-life, routine, and large-scale situations; - Provide patients with lifestyle and / or treatment recommendations and support; - Researching drug efficacy, for example, during clinical trials; - To facilitate and / or assist in making treatment decisions; - Support hospital management; - Support recovery measures management; - Improves disease condition and serves as a restorative tool to stimulate higher-density cognitive, motor, and walking activities. - Support health insurance assessment and management; and / or - Support decision-making in public health management.

[0069] Unless otherwise specified, the definitions and interpretations of the terms made above, with the necessary modifications, apply to the following implementation scheme.

[0070] In embodiments of the method of the present invention, the fine motor measurement results include measurements of finger movement accuracy and / or dexterity and / or movement speed. More generally, the measurements of finger movement accuracy and / or dexterity and / or movement speed are performed during tapping and / or drawing movements of the fingers.

[0071] In embodiments of the method of the present invention, the measurement is performed using a mobile device. More generally, the mobile device includes smartphones, smartwatches, wearable sensors, portable multimedia devices, or tablet computers.

[0072] In an embodiment of the method of the present invention, the method is implemented by a computer.

[0073] In another embodiment of the method of the present invention, the reference is at least one performance parameter of a dataset of fine motion measurement results from the subject, wherein the dataset was obtained prior to the dataset in step a).

[0074] In embodiments of the method of the present invention, the reference is at least one performance parameter from a dataset of fine motor measurements of at least one subject known to have pre-manifestational Huntington's disease. More generally, at least one performance parameter substantially the same as the reference indicates that the subject has pre-manifestational Huntington's disease.

[0075] In embodiments of the method of the present invention, the reference is at least one performance parameter from a dataset of fine motor measurements of at least one subject known not to have pre-manifestational Huntington's disease. More generally, at least one performance parameter different from the reference indicates that the subject has pre-manifestational Huntington's disease.

[0076] In another embodiment of the method of the present invention, the assessment of pre-manifestation stage Huntington's disease includes diagnosing and / or predicting pre-manifestation stage Huntington's disease or recommending treatment for pre-manifestation stage Huntington's disease.

[0077] The present invention also includes a method for determining the efficacy of a treatment for pre-emergent stage Huntington's disease, comprising the steps of the method of the present invention (i.e., a method for evaluating pre-emergent stage Huntington's disease) and further steps of determining a treatment response if an improvement in pre-emergent stage Huntington's disease occurs in the subject during treatment, or a response failure if an exacerbation of pre-emergent stage Huntington's disease occurs in the subject during treatment, or if pre-emergent stage Huntington's disease remains unchanged.

[0078] As used herein, the term "therapeutic treatment for pre-manifestation stage Huntington's disease" refers to all types of medical treatment, including drug-based therapies, respiratory support, etc. The term also includes lifestyle recommendations and recovery measures. Generally, methods include recommendations for drug-based therapies, and particularly the use of medications known to be used to treat pre-manifestation stage Huntington's disease. Such medications may be buphenazine, neuroleptics, benzodiazepines, amantadine, remdesivir, anti-Parkinson's drugs, valproic acid, or ethyl eicosapentaenoic acid. Furthermore, in yet another embodiment, the above methods may include the additional step of applying the recommended therapy to the subject.

[0079] Furthermore, the present invention includes a method for determining the efficacy of a treatment for pre-symptom stage Huntington's disease, comprising the steps of the above-described method of the present invention (i.e., a method for evaluating pre-symptom stage Huntington's disease) and further steps of determining a treatment response if an improvement in pre-symptom stage Huntington's disease occurs in the subject during treatment, or determining a response failure if an exacerbation of pre-symptom stage Huntington's disease occurs in the subject during treatment, or if pre-symptom stage Huntington's disease remains unchanged.

[0080] As used in this invention, the term "improvement" refers to any improvement in the overall disease condition or its individual symptoms, and in particular, contrast visual acuity. Similarly, "deterioration" means any worsening of the overall disease condition or its individual symptoms, and in particular, contrast visual acuity. Because Huntington's disease, as an ongoing disease, is generally associated with a worsening of the overall disease condition and its symptoms, the worsening referred to in the above-described manner is an unexpected or atypical worsening that deviates from the normal course of the disease. Unchanged pre-stage Huntington's disease means the overall disease condition and its accompanying symptoms are within the normal course of the disease.

[0081] Furthermore, the present invention relates to a method for monitoring pre-symptom stage Huntington's disease in a subject, comprising determining whether the disease in the subject has improved, worsened, or remained unchanged by performing the method of the present invention (i.e., the method for assessing pre-symptom stage Huntington's disease) at least twice during a predetermined monitoring period.

[0082] The present invention relates to a mobile device, including a processor, at least one sensor and a database, and software tangibly embedded in the device, which, when run on the device, performs the method of the invention.

[0083] 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 aforementioned dataset of measurement results. This may also require a data processor and storage unit, as well as a display for electronically simulating tests for fine motor skills 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. Furthermore, from the moment of the subject's activity, data should be recorded and compiled into a dataset, which will be evaluated on the mobile device itself or on a second device using the method of the present invention. Depending on the specific configuration envisioned, the mobile device may have to include a data transfer device to transfer the acquired dataset from the mobile device to a further device. Smartphones, portable multimedia devices, or tablet computers are particularly suitable as mobile devices according to the present invention. Alternatively, portable sensors with data recording and processing devices can be used. Furthermore, depending on the type of activity test to be performed, the mobile device should be adapted to display instructions to the subject regarding the activities to be performed for the test. Specific envisioned activities to be performed by the subject are described elsewhere herein, and include tests of fine motor skills as described in this specification.

[0084] The present invention is contemplated to include a system comprising a mobile device and a remote device, the mobile device including at least one sensor, the remote device including a processor and a database and software tangibly embedded in the device and, when running on the device, performing the method of the present invention, wherein the mobile device and the remote device are operatively connected to each other.

[0085] Under the phrase "operably connected to each other," it is understood that devices are connected to allow data transfer from one device to another. Generally, it is envisioned that a mobile device, at least acquiring data from a subject, is connected to a remote device performing the steps of the method of the invention, thereby enabling the acquired data to be transferred to the remote device for processing. However, the remote device may also send data to the mobile device, such as signals to control or monitor its proper functioning. The connection between the mobile device and the remote device can be achieved through a permanent or temporary physical connection, such as coaxial, fiber, optical fiber, or twisted-pair, 10BASE-T cable. Alternatively, it can be achieved through a temporary or permanent wireless connection using, for example, radio waves such as Wi-Fi, LTE, LTE-evolution, or Bluetooth. Further details can be found elsewhere in the specification. For data acquisition, the mobile device may include a user interface, such as a screen or other device for data acquisition. Generally, activity measurements can be performed on a screen included in the mobile device, where it will be understood that the screen can have different sizes, including, for example, a 5.1-inch screen.

[0086] The present invention also relates to the use of the mobile device or system of the present invention for evaluating Huntington's disease in the subject using at least one performance parameter of a dataset of measurement results from fine motion measurements of the subject.

[0087] The present invention also contemplates the use of the mobile device or system according to the invention for monitoring patients, particularly in real-life, daily, and large-scale situations. Furthermore, the invention includes the use of the mobile device or system according to the invention for supporting patient lifestyle and / or treatment recommendations. It will also be understood that the invention contemplates the use of the mobile device or system according to the invention for studying drug safety and efficacy, for example, also during clinical trials. Further, the invention contemplates the use of the mobile device or system according to the invention for facilitating and / or assisting in treatment decision-making. In addition, the invention provides the use of the mobile device or system according to the invention as a recovery tool for improving disease conditions and for supporting decision-making in hospital management, recovery measures management, health insurance assessment and management, and / or supporting public health management.

[0088] In principle, the present invention also contemplates computer programs, computer program products, or computer-readable storage media tangibly embedded with said computer programs, wherein said computer programs include instructions that, when run on a data processing device or computer, perform the methods of the present invention as specifically defined above. Specifically, this disclosure further includes: - A computer or computer network, including at least one processor, wherein said processor is adapted to perform a method according to one of the embodiments described in the specification. - A computer-loadable data structure suitable for performing a method according to one of the embodiments described in the specification when the data structure is executed on a computer. - A computer script, wherein a computer program is adapted to perform a method according to one of the embodiments described in the specification when the program is executed on a computer. - A computer program, including program means for performing a method according to one of the embodiments described in the specification when the computer program is executed on a computer or computer network. - A computer program, including program means according to the foregoing embodiments, wherein the program means are stored on a computer-readable storage medium. - A storage medium on which a data structure is stored, and wherein the data structure is adapted to perform a method according to one of the embodiments described in the specification after being loaded into the main memory and / or working memory of a computer or computer network. - A computer program product having program code means, wherein the program code means can be stored or stored on a storage medium for performing a method according to one of the embodiments described in the specification, if the program code means are executed on a computer or computer network. - Generally encrypted data stream signals, including datasets of measurement results obtained from the subject using a mobile device for fine motion measurements. - A generally encrypted data stream signal, including at least one performance parameter derived from a dataset of measurement results obtained from a subject using a mobile device for fine motion measurements.

[0089] The present invention further relates to a method for determining at least one performance parameter using a mobile device from a dataset of measurement results from fine motion measurements of a subject. a) Obtain at least one performance parameter from a dataset of measurement results from fine motion measurements of the subject using a mobile device; and b) Compare the determined at least one performance parameter with a reference. Generally, the at least one performance parameter can help assess pre-existing Huntington's disease in the subject.

[0090] Further specific embodiments of the invention are listed below: Implementation Plan 1. A method for assessing pre-manifestation stages of Huntington's disease in a subject, comprising the following steps: a) Determine at least one performance parameter from a dataset of fine motion measurements from the subject; b) Compare the determined at least one performance parameter with a reference; and c) Evaluate pre-existing stages of Huntington's disease in the subject based on the comparison.

[0091] Implementation Scheme 2. According to the method of Implementation Scheme 1, the fine motion measurement results include measurement results of motion accuracy and / or dexterity and / or motion speed.

[0092] Implementation Scheme 3. The method according to Implementation Scheme 2, wherein the accuracy and / or dexterity and / or speed of the finger movement are measured during the tapping and / or drawing movements of the finger.

[0093] Implementation Scheme 4. The method according to any one of Implementation Schemes 1 to 3, wherein the measurement is performed using a mobile device.

[0094] Implementation Scheme 5. The method according to Implementation Scheme 4, wherein the mobile device includes a smartphone, smartwatch, wearable sensor, portable multimedia device or tablet computer.

[0095] Implementation Scheme 6. The method according to any one of Implementation Schemes 1 to 5, wherein the method is implemented by a computer.

[0096] Implementation Scheme 7. The method according to any one of Implementation Schemes 1 to 6, wherein the reference is at least one performance parameter of a dataset of fine motion measurement results from the subject, wherein the dataset was obtained prior to the dataset in step a).

[0097] Implementation Scheme 8. The method according to any one of Implementation Schemes 1 to 6, wherein the reference is at least one performance parameter from a dataset of fine motor measurement results from at least one subject known to have pre-manifestation stage Huntington's disease.

[0098] Implementation Scheme 9. The method according to Implementation Scheme 8, wherein at least one performance parameter substantially the same as the reference indicates that the subject suffers from pre-exhibition stage Huntington's disease.

[0099] Implementation Scheme 10. The method according to any one of Implementation Schemes 1 to 6, wherein the reference is at least one performance parameter from a dataset of fine motor measurement results from at least one subject known not to have pre-manifestation stage Huntington's disease.

[0100] Implementation Scheme 11. The method according to Implementation Scheme 10, wherein at least one performance parameter different from the reference indicates that the subject suffers from pre-manifestation stage Huntington's disease.

[0101] Implementation Scheme 12. The method according to any one of Implementation Schemes 1 to 11, wherein the assessment of Huntington's disease includes diagnosing and / or predicting pre-manifestation stage Huntington's disease or recommending therapy for pre-manifestation stage Huntington's disease.

[0102] Implementation Scheme 13. A mobile device, including a processor, at least one sensor and a database, and software tangibly embedded in the device, and performing a method according to any one of Implementation Schemes 1 to 12 when running on the device.

[0103] Implementation Scheme 14. A system comprising a mobile device and a remote device, the mobile device including at least one sensor, the remote device including a processor and a database and software tangibly embedded in the device, and performing a method according to any one of Implementation Schemes 1 to 12 when running on the device, wherein the mobile device and the remote device are operatively connected to each other.

[0104] Implementation Scheme 15. The use of a mobile device according to Implementation Scheme 13 or a system according to Implementation Scheme 14 for evaluating pre-performance stage Huntington's disease in the subject using at least one performance parameter of a dataset of measurement results from fine motor measurement results of the subject.

[0105] All references cited throughout this specification are incorporated herein by reference for the full disclosure thereof and for the specific disclosure referred to herein. Attached Figure Description

[0106] Figure 1: Boxplots comparing predetermined features of the tests achieved by each mobile device across groups. Captions: *: p < 0.05; **: p < 0.01; ***: p < 0.001. (A) Cognitive tests SDMT and SWR, (B) Stability and gait tests balance, 2-minute walk, and turning, (C) Upper body movement tests shape drawing, chorea, and speeded tapping. “Dominant” refers to the dominant hand, and “non-dominant” refers to the non-dominant hand.

[0107] Figure 2: Exploratory analysis of characteristics distinguishing pre-performance and healthy control groups. Caption: *: p < 0.05; **: p < 0.01; ***: p < 0.001. Shows characteristics from tests performed with hands that had p < 0.01 for at least one hand and p < 0.05 for both hands. The standard deviation of normal operating time from the rapid tapping test is defined as the standard deviation of the time it takes for the finger to lift off the screen between consecutive taps; p = 0.002 (dominant hand); p < 0.001 (non-dominant hand).

[0108] Figure 3: Exploratory analysis of characteristics distinguishing pre-performance and healthy control groups. Caption: *: p < 0.05; **: p < 0.01; ***: p < 0.001. Shows characteristics from tests performed on hands with p < 0.01 for at least one hand and p < 0.05 for both hands. The coefficient of variation for shape drawing test characteristics was the same as that for spiral drawing speed; p = 0.04 (dominant hand); p = 0.007 (non-dominant hand).

[0109] Example: The following examples are merely illustrative of the invention. In any case, they should not be interpreted as limiting the scope of the invention.

[0110] Example 1: Identifying new features that distinguish healthy controls from pre-existing subjects Research A smartphone app was used in an observational study of participants with pre-exhibition Huntington's disease (HD), manifest HD, and healthy controls (HC) (digital-HD). The app included seven active tests (Symbol DigitModalities Test [SDMT], Stroop Word Reading test [SWRT], rapid tapping, chorea, balance, turning, and 2-minute walk) and continuous passive monitoring.

[0111] This section shows the results for the 79 participants who registered at the data cutoff. Results from planned active testing were aggregated in weeks 5 and 6 after screening to minimize the impact of the practice.

[0112] The 1-year study aimed to enroll 80 participants (high performance (HD): 40, pre-HD: 20, high performance (HC): 20). Data was captured from continuous passive monitoring and daily active testing. Passive monitoring involved wearing a smartwatch and carrying a GPS-enabled smartphone, both containing a three-axis accelerometer and gyroscope. Active testing measured HD's motor and non-motor performance and included questions on motor tasks, cognitive tests, QoL, and mood.

[0113] The patient's demographic details are summarized in Table 1 below.

[0114] Table 1: HC Performance before HD HD performance total N 20 20 39 79 Age, mean (standard deviation [SD]), years 47.8 (14.2) 44.4 (10.0) 56.2 (11.2) 51.1 (12.7) female,% 35.0 50.0 46.2 44.3 Right-hand advantage, % 85.0 100.0 74.4 83.5 Total functional capabilities, average (SD) 13 (0.0) 12.9 (0.3) 10.6 (2.2) 11.8 (2.0) Total athletic performance score, mean (SD) 1.4 (2.5) 4.9 (3.9) 32.9 (16.8) 17.8 (19.2) Participants were trained on proactive testing during the equipment issue visit (EIV) and conducted these tests at home when prompted.

[0115] During the EIV, participants underwent clinical assessments using the Unified HD Rating Scale subscales for motor, cognitive, and functional functions, the Timed-up-and-go test, and selected items from the Berg Balance Scale, which is used in conjunction with the Kinect sensor.

[0116] Encrypted telephone data is securely transmitted over the internet and analyzed to extract clinically meaningful measurements for group differentiation and correlation with clinical parameters.

[0117] 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 in-clinic counterparts.

[0118] result The SDMT and SWR tests showed excellent correlations with their clinical counterparts. The chorea test showed good correlations with the UHDRS Maximum Chorea Upper Limb item. Except for the gait test (turning over: p=0.009; 2-minute walk: p=0.004), all eight predetermined characteristics showed significant differences between the performing and healthy control groups with all p < 0.001 (see Figure 1). The results are also shown in Table 2 below.

[0119] Two promising candidate features were identified when using rapid tapping and drawing shape tests to differentiate between pre-performance and healthy control groups (see Figures 2 and 3).

[0120] References cited

Claims

1. A computer-implemented method for evaluating pre-manifestation stages of Huntington's disease in a subject, comprising the following steps: a) Determine at least one performance parameter from a dataset of fine motion measurements from the subject; b) Compare the determined performance parameter with a reference; as well as c) Display the evaluation on a suitable display to the subject or another person. The fine motor measurement results include measurements of finger movement accuracy, dexterity, or speed, and the at least one performance parameter is determined from a dataset of measurement results collected from the subject during a rapid tap test of fine motor ability, and the performance parameter of fine motor ability in the rapid tap test is the standard deviation of the finger lift-off time between two taps, the longest finger lift-off time between two taps, the coefficient of variation of the finger lift-off time between two taps, the coefficient of variation of the distance between two taps, the standard deviation of the finger touch-screen time during the tap, the coefficient of variation of the finger touch-screen time during the tap, and / or the longest finger touch-screen time during the tap.

2. The computer-implemented method of claim 1, wherein the measurement is performed using a mobile device.

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

4. The computer-implemented method of claim 1, wherein the reference is at least one performance parameter of a dataset of fine motion measurement results from the subject, wherein the dataset was obtained prior to the dataset in step a).

5. A mobile device, comprising a processor, at least one sensor, a database, and software tangibly embedded in the device, and performing the method of claim 1 when running on the device.

6. A system comprising a mobile device and a remote device, the mobile device including at least one sensor, the remote device including a processor and a database and software tangibly embedded in the device, and performing the method of claim 1 when running on the device, wherein the mobile device and the remote device are operatively connected to each other.

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