阅读说明：本技术 用于认知和移动疾病或障碍的数字质量计量生物标记 (Digital quality metric biomarkers for cognitive and mobility diseases or disorders ) 是由 M.巴克 S.M.贝拉丘 C.戈森斯 M.林德曼 J.施普伦格尔 于 2018-10-25 设计创作，主要内容包括：本发明涉及诊断的领域。更具体地,其涉及一种用于在被怀疑遭受认知和移动疾病或障碍的主体中评定它的方法,包括下述步骤：根据使用移动设备从所述主体获得的认知和/或精细运动活动测量结果的数据集确定针对认知和/或精细运动活动测量结果的至少一个质量计量活动参数；以及将所确定的至少一个质量计量活动参数与参考进行比较,由此,将评定认知和移动疾病或障碍。本发明还涉及一种用于识别主体是否将受益于针对认知和移动疾病或障碍的疗法的方法,包括上面提及的发明的方法的步骤和下述进一步步骤：如果认知和移动疾病或障碍被评定,则将主体识别为受益于疗法的主体。本发明想到了：一种移动设备,包括处理器、至少一个传感器和数据库以及软件,该软件被有形地嵌入到所述设备,且当在所述设备上运行时实施本发明的方法；一种包括移动设备和远程设备的系统,该移动设备包括至少一个传感器,该远程设备包括处理器和数据库以及软件,该软件被有形地嵌入到所述设备,且当在所述设备上运行时实施该方法；以及根据本发明的移动设备或系统用于在主体中评定认知和移动疾病或障碍的用途。(The present invention relates to the field of diagnostics. More specifically, it relates to a method for assessing cognitive and mobility diseases or disorders in a subject suspected of suffering therefrom, comprising the steps of: determining at least one quality metric activity parameter for cognitive and/or fine motor activity measurements from a dataset of cognitive and/or fine motor activity measurements obtained from the subject using a mobile device; and comparing the determined at least one quality metric activity parameter to a reference, whereby cognitive and mobility diseases or disorders will be assessed. The present invention also relates to a method for identifying whether a subject would benefit from a therapy for a cognitive and movement disease or disorder, comprising the steps of the above-mentioned method of the invention and the following further steps: identifying a subject as one who benefits from therapy if cognitive and movement diseases or disorders are assessed. The invention thinks of: a mobile device comprising a processor, at least one sensor and a database, and software that is tangibly embedded in said device and that when run on said device implements the method of the present invention; a system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database and software tangibly embedded in said device and implementing the method when run on said device; and the use of a mobile device or system according to the invention for assessing cognitive and mobility diseases or disorders in a subject.)

1. a computer-implemented method for assessing cognitive and mobility diseases or disorders in a subject suspected of suffering therefrom, comprising the steps of:

a) determining at least one quality metric activity parameter for cognitive and/or fine motor activity from a pre-existing dataset of cognitive and/or fine motor activity measurements from the subject using a mobile device; and

b) the determined at least one quality metric activity parameter is compared to a reference, whereby cognitive and movement diseases or disorders will be assessed.

2. The method of claim 1, wherein the cognitive and movement disease or disorder is a disease or disorder affecting the central and/or peripheral nervous system of the pyramidal, extrapyramidal, sensory or cerebellar systems, or a neuromuscular disease, or a muscular disease or disorder.

3. The method of claim 1 or 2, wherein the cognitive and mobility disease or disorder is selected from the group consisting of: multiple Sclerosis (MS), neuromyelitis optica (NMO) and NMO spectrum disorders, stroke, cerebellar disorders, cerebellar ataxia, spastic paraplegia, essential tremor, muscle weakness and myasthenia syndrome or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, peripheral neuropathy, cerebral palsy, extra-pyramidal syndrome, parkinson's disease, huntington's disease, alzheimer's disease, other forms of dementia, leukodystrophy, autism spectrum disorders, attention deficit disorder (ADD/ADHD), intellectual disability as defined by DSM-5, impairment of cognitive performance and stores associated with aging, parkinson's disease, huntington's disease, polyneuropathy, motor neuron disease, and Amyotrophic Lateral Sclerosis (ALS).

4. The method of any of claims 1 to 3, wherein the at least one quality metric activity parameter is a cognitive quality metric activity parameter indicative of fluctuations in neurocognitive function, a hand/arm functional quality metric activity parameter indicative of fluctuations in manual agility, or a walking quality metric activity parameter indicative of movement fluctuations.

5. The method of any one of claims 1 to 4, wherein the dataset of cognitive and/or fine motor activity measurements comprises data from pinch tests performed on a sensor surface of a mobile device and/or from U-turn tests (UTT), 2-minute walk tests (2 MWT), Static Balance Tests (SBT), or continuous gait analysis (CAG) from passive monitoring.

6. The method of any of claims 1 to 5, wherein the dataset of cognitive activity measurements comprises data from an Information Processing Speed (IPS) test on a sensor surface of a mobile device.

7. The method of any one of claims 1 to 6, wherein the mobile device has been adapted for conducting one or more of the tests mentioned in claim 4 or 5, preferably all of the tests mentioned in claims 4 and 5, on a subject.

8. The method of claim 7, wherein the mobile device is included in a smartphone, a smart watch, a wearable sensor, a portable multimedia device, or a tablet computer.

9. The method of any one of claims 1 to 8, wherein the reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a dataset of cognitive and/or fine motor activity measurements obtained from a subject at a point in time before the point in time when the dataset of cognitive and/or fine motor activity measurements mentioned in step a) has been obtained from the subject.

10. The method of claim 9, wherein a deterioration between the determined at least one quality metric activity parameter and a reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

11. The method of any one of claims 1 to 8, wherein the reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a dataset of cognitive and/or fine motor activity measurements obtained from a subject or group of subjects known to suffer from cognitive and motor diseases or disorders.

12. The method of claim 11, wherein the determined at least one quality metric activity parameter being substantially the same as compared to a reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

13. The method of any one of claims 1 to 8, wherein the reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a dataset of cognitive and/or fine motor activity measurements obtained from a subject or group of subjects known not to suffer from cognitive and motor diseases or disorders.

14. The method of claim 13, wherein deterioration of the determined at least one quality metric activity parameter compared to a reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

15. A method for recommending therapy for a cognitive and movement disease or disorder, comprising the steps of the method of any one of claims 1-14 and the further step of recommending therapy if the cognitive and movement disease or disorder is assessed.

16. A method for determining the efficacy of a therapy against cognitive and movement diseases or disorders, comprising the steps of the method of any one of claims 1 to 14 and the following further steps: determining a therapy response if an improvement in cognitive and mobility diseases or disorders occurs in the subject at the time of therapy; or determining a response failure if a worsening of cognitive and movement disease or disorder occurs in the subject at the time of therapy or if the cognitive and movement disease or disorder remains unchanged.

17. A method of monitoring cognitive and mobility diseases or disorders in a subject, comprising: determining whether a cognitive and mobility disease or disorder improves, worsens or remains unchanged in a subject by carrying out the steps of the method of any one of claims 1 to 14 at least twice during a predefined monitoring period.

18. A mobile device comprising a processor, at least one sensor and database, and software tangibly embedded in the device and when run on the device implementing the method of any one of claims 1 to 17.

19. A system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database and software tangibly embedded in said device and when run on said device implementing the method of any of claims 1 to 17, wherein said mobile device and said remote device are operatively linked to each other.

20. The mobile device of claim 18 or the system of claim 19 for use in identifying a subject suffering from cognitive and mobility diseases or disorders.

21. The mobile device of claim 18 or the system of claim 19 for use in monitoring subjects suffering from cognitive and mobility diseases or disorders, in particular in real-life daily situations and on a large scale, for studying drug efficacy in subjects suffering from cognitive and mobility diseases or disorders, for facilitating and/or assisting therapy decision making for subjects suffering from cognitive and mobility diseases or disorders, for supporting hospital management, rehabilitation measures management, health insurance assessment and management and/or supporting decisions in public health management about subjects suffering from cognitive and mobility diseases or disorders, for example also during clinical trials, or for supporting subjects suffering from cognitive and mobility diseases or disorders with lifestyle and/or therapy recommendations.

Technical Field

The present invention relates to the field of diagnostics. More specifically, it relates to a method for assessing cognitive and mobility diseases or disorders in a subject suspected of suffering therefrom, comprising the steps of: determining at least one quality metric activity parameter for cognitive and/or fine motor activity from a dataset of cognitive and/or fine motor activity measurements obtained from the subject using a mobile device; and comparing the determined at least one quality metric activity parameter to a reference, whereby cognitive and mobility diseases or disorders will be assessed. The present invention also relates to a method for identifying whether a subject would benefit from a therapy for a cognitive and movement disease or disorder, comprising the steps of the above-mentioned method of the invention and the following further steps: identifying a subject as one who benefits from therapy if cognitive and movement diseases or disorders are assessed. The invention thinks of: a mobile device comprising a processor, at least one sensor and a database, and software that is tangibly embedded in said device and that when run on said device implements the method of the present invention; a system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database and software tangibly embedded in said device and implementing the method when run on said device; and the use of a mobile device or system according to the invention for assessing cognitive and mobility diseases or disorders in a subject.

Background

Cognitive and movement diseases and disorders are typically characterized by impaired cognitive and/or motor function. Diseases and disorders are less frequent but, nevertheless, are typically accompanied by serious complications in daily life for the affected patient. Various cognitive and mobility disorders can lead to life-threatening conditions and are ultimately fatal.

Diseases and disorders have in common: impaired function of the central nervous system, peripheral nervous system, and/or muscular system leads to cognitive and mobility disabilities. The movement disabilities may be major disabilities due to direct damage to muscle cells and function, or may be minor disabilities resulting from damage to muscle control by the peripheral and/or central nervous system (particularly, pyramidal, extrapyramidal, sensory or cerebellar systems). The damage may involve damage, degradation, poisoning, or injury to nerve and/or muscle cells.

Typical cognitive and movement diseases and disorders include, but are not limited to, Multiple Sclerosis (MS), neuromyelitis optica (NMO) and NMO spectrum disorders, stroke, cerebellar disorders, cerebellar ataxia, spastic paraplegia, essential tremor, muscle weakness and myasthenia syndrome or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, peripheral neuropathy, cerebral palsy, extra-pyramidal syndrome, parkinson's disease, huntington's disease, alzheimer's disease, other forms of dementia, cerebral dystrophy, autism spectrum disorders, attention deficit disorder (ADD/ADHD), intellectual disability as defined by DSM-5, impairment of cognitive performance and stores associated with aging, polyneuropathy, motor neuron disease, and Amyotrophic Lateral Sclerosis (ALS).

Among the most commonly known and serious diseases and disorders, there are MS, stroke, alzheimer's disease, parkinson's disease, huntington's disease and ALS.

Multiple Sclerosis (MS) is a serious neurodegenerative disease that cannot be cured at present. Affected by the disease are approximately 2 to 3 million individuals worldwide. It is the most prevalent disease of the Central Nervous System (CNS) leading to long-term and severe disability in young adults. Evidence exists to support the following concepts: diseases result from B and T cell mediated inflammatory processes that antagonize self molecules within the white matter of the brain and spinal cord. However, the etiology is still not clearly understood. Myelin reactive T cells have been found to be present in both MS patients and healthy individuals. Accordingly, major abnormalities in MS may be more likely to involve impaired regulatory mechanisms leading to enhanced T cell activation states and less stringent activation requirements. The pathogenesis of MS includes: extracns encephalitogenic (i.e., autoimmune) myelination is accompanied by T cell activation followed by opening of the blood-brain barrier, T cell and macrophage infiltration, microglial cell activation and depigmentation. The latter results in irreversible neuronal damage (see, e.g., Aktas 2005, Neuron 46, 421-.

More recently it has been shown that: in addition to T cells, B lymphocytes (which display the CD20 molecule) can play a central role in MS and influence underlying pathophysiology through at least four specific functions:

1. antigen presentation: b cells can present self-neural antigens to T cells and activate them (Crawford A, et al. J Immunol 2006;176(6): 3498-;

2. cytokine production: b cells in patients with MS produce aberrant proinflammatory cytokines, which can activate T cells and other immune cells (Bar-Or A, et al, Ann Neurol 2010;67(4): 452-61; LisakRP, et al, J neuroumnol 2012;246(1-2): 85-95);

3. autoantibody production: b cells produce autoantibodies that can lead to tissue damage and activate macrophages and Natural Killer (NK) cells (Weber MS, et al Biochim Biophys Acta 2011;1812(2): 239-45);

4. formation of vesicular aggregates: b cells are present in ectopic lymphoid capsule aggregates that are associated with microglial activation, local inflammation and neuronal loss in the nearby cortex (Serafini B, et al. brain Pathol 2004;14(2): 164-74; Magliozzi R, et al. Ann Neurol 2010;68(4): 477-93).

Despite the reasonable knowledge about the mechanisms responsible for encephalitogenic effects, it is far from known about the control mechanisms used to regulate adverse lymphocyte responses into and within the CNS in a subject.

MS diagnosis is currently based on clinical studies conducted by medical practitioners. Such studies involve testing of a patient's ability to target certain physical activities. Several tests have been developed and routinely applied by medical practitioners. These tests are intended to assess walking, balance and other motor abilities. Examples of currently applied tests are the extended disability status scale (EDSS, www.neurostatus.net) or multiple sclerosis functional synthesis (MSFC). These tests require the presence of a medical practitioner for evaluation and assessment purposes, and are currently performed on-the-fly at the doctor's office or hospital. Very recently, there has been some effort to monitor MS patients using smart phone devices in order to collect data of MS patients in a natural setting (Bove 2015, neuro unflamm 2 (6): e 162).

Further, diagnostic tools are used in MS diagnostics. Such tools include neuroimaging, cerebrospinal fluid analysis, and evoked potentials. Magnetic Resonance Imaging (MRI) of the brain and spinal cord can visualize demyelination (lesions or plaques). A contrast agent comprising gadolinium may be administered intravenously to label active plaques and distinguish acute inflammation from the presence of older lesions that are not associated with symptoms at the time of evaluation. Analysis of cerebrospinal fluid obtained from lumbar puncture can provide evidence of chronic inflammation of the central nervous system. Cerebrospinal fluid can be analyzed for oligoclonal immunoglobulin bands, which are markers of inflammation present in 75-85% of people with MS (Link 2006, J neuro-immumunal. 180 (1-2): 17-28). However, none of the above mentioned techniques is dedicated to the MS. Therefore, the ascertainment of diagnosis may require repeated clinical and MRI studies to demonstrate the spatial and temporal spread of the disease as a prerequisite for MS diagnosis.

There are several treatments approved by regulatory agencies for relapsing-remitting multiple sclerosis that should modify the course of the disease. These treatments include interferon beta-1 a, interferon beta-1 b, glatiramer acetate, mitoxantrone, natalizumab, fingolimod, teriflunomide, dimethylbutyrate, alemtuzumab, and daruzumab. Interferon and glatiramer acetate are first line treatments that reduce recurrence by approximately 30% (see, e.g., Tsang 2011, Australian family physics 40(12): 948-55). Natalizumab reduces the relapse rate more than interferon, however, due to the problem of adverse effects, it is a second line of agents reserved for those that do not respond to other treatments or patients for severe disease (see, e.g., Tsang 2011, loc. cit.). Treatment of Clinically Isolated Syndrome (CIS) with interferon reduces the chances of progressing to clinically definite MS (Compston 2008, Lancet 372(9648): 1502-17). The efficacy of interferon and glatiramer acetate in children has been estimated to be approximately equivalent to that of adults (Johnston 2012, Drugs 72 (9): 1195-.

Recently, new monoclonal antibodies such as ocrelizumab, alemtuzumab and darlizumab have shown potential as therapies against MS. Monoclonal antibodies, ocrelizumab, targeting anti-CD 20B cells have shown beneficial effects in both relapsed and primary progressive (progressive) forms of MS in a stage 2 and 3 III assay (NCT 00676715, NCT01247324, NCT01412333, NCT 01194570).

MS is a clinical heterogeneous inflammatory disease of the CNS. Therefore, the following diagnostic tools are needed: it allows reliable diagnosis and identification of current disease states and can thus help accurate treatment, particularly for those patients suffering from progressive forms of MS. Improvements in the monitoring of disease progression are also highly desirable.

Stroke may occur as an ischemic stroke, in which blood support is impaired due to occlusion of blood vessels, or as a hemorrhagic stroke, which is caused by damage to blood vessels and hemorrhage.

The signs and symptoms of stroke may include movement/movement or sensory impairment of typically one side, walking, speaking, hearing problems, dizziness or visual abnormalities (Donnan 2008, lancet. 371 (9624): 1612-23). The signs and symptoms often appear immediately or shortly after a stroke has occurred. If the symptoms persist for less than an hour or two, it is called a transient ischemic attack. Hemorrhagic stroke may also be accompanied by severe headache. The symptoms of stroke may be permanent. Long-term co-complications may include pneumonia or loss of bladder control.

Early diagnosis and treatment of stroke is crucial for the outcome. Current stroke diagnosis requires imaging techniques such as Magnetic Resonance Imaging (MRI) scans, doppler ultrasound or angiography, and neurological examinations by medical practitioners (see, e.g., Harbison 1999, Lancet. 353 (9168): 1935; Kidwell 1998, prehospital emergency care. 2 (4): 267-73; Nor 2005, Lancet neurology. 4 (11): 727-34).

There are more than 1 million people affected by stroke each year. In the developed world, stroke management has simultaneously become quite efficient due to the stroke unit. However, these specialized centers are not present in less developed parts of the world other than urban areas. Early detection of a disorder has a major impact on the outcome of stroke in a patient. Accordingly, there is a need for early detection of signs and symptoms of stroke even beyond qualified stroke units and hospitals. In addition to stroke detection, there is a critical need for appropriate assessment of the consequences of intermediate to long-term disability associated with acute stroke treatment intervention and spontaneous and rehabilitation program-related recovery.

Alzheimer's disease is a serious and fatal neurodegenerative disease accompanied by dementia and associated problems. In fact, alzheimer's disease is responsible for 60 to 70% of all dementia cases. Early symptoms of the disease are reduced short-term memory. Subsequent symptoms include: social symptoms, such as withdrawal from family and society; and physical symptoms such as loss of physical function (Burns 2009, The bmj. 338: b 158).

The diagnosis of alzheimer's disease is based on imaging techniques such as CT, MRI, SPECT or PET. In addition, neurological assessments are performed by practitioners, including tests for assessing cognitive function (Pasquier 1999, Journal of neurology 246 (1): 6-15). Typical tests include the following: in this test, a person was instructed to copy a picture, to note, to read and to subtract a serial number similar to the picture shown in the picture. Usually, a caregiver is required for diagnosis, since an alzheimer's patient does not know his/her own deficiency. There is no efficient disease modifying treatment or course of treatment for alzheimer's disease. However, for efficient disease management, a reliable and early diagnosis is helpful.

Alzheimer's disease affects about 5 million people worldwide and is probably one of the most frequent neurodegenerative diseases among the elderly. Accordingly, there is a need for early detection of signs and symptoms for proper management of disease and for monitoring of disease progression.

Parkinson's disease is a neurodegenerative disease that critically affects the central nervous system of the motor system. Typical symptoms are resting tremor, postural instability, trembling, rigidity, slow movement and difficulty walking. Dementia and depression and sensory, autonomic nervous system and sleep problems may also occur at more severe stages of the disease. Motor problems result from degeneration of neurons in the substantia nigra of the midbrain, resulting in significant alterations in dopaminergic neurotransmission. There is no available course of treatment for parkinson's disease.

The diagnosis of parkinson's disease is based on neurological assessment together with imaging methods such as CT, MRI, PET or SPECT scans. Neurological criteria for diagnosing disease include assessing bradykinesia, rigidity, resting tremor, and postural instability (Jankovic 2008, Journal of Neurology, Neurosurgery, and Psychiatry. 79 (4): 368-.

More than 5 million people are affected by parkinson's disease. There is a need for an early and reliable diagnosis of this neurodegenerative disease as well as monitoring the progression of the disease.

Huntington's disease is a genetic disorder that leads to the death of neurons in the central nervous system and in particular in the brain. The earliest symptoms are often subtle problems with emotional or mental capacity. However, general coordination impairment and unstable gait typically ensues (Dayalu 2015, Neurologic clinics 33 (1): 101-14). In its later stages, uncoordinated body movements become apparent and physical abilities gradually deteriorate until coordinated movements become difficult and the person cannot speak. Cognitive abilities are also impaired and may decline to dementia (Frank 2014, The joural of The American Society for Experimental neurological therapeutics, 11(1): 153-60). However, the specific symptoms may vary individually. There is no course of treatment available for huntington's disease.

Since huntington's disease is inherited in a dominant autosomal manner, genomic testing of CAG repeats in the Huntington's (HTT) allele is recommended for individuals at risk genetically (i.e., patients with a corresponding family history of the disease). In addition, the diagnosis of disease involves DNA analysis, but also imaging methods such as CT, MRI, PET or SPECT scans to determine brain atrophy and neurological assessments by medical practitioners. In particular, the neurological assessment may be conducted according to criteria for a unified huntington's disease rating scale (Rao 2009, goal post. 29 (3): 433-6).

Huntington's disease is less frequent than alzheimer's disease and parkinson's disease. However, it remains a cognitive and mobility disease or disorder affecting a significant proportion of people with serious and life-threatening complications. There is a need for an early and reliable diagnosis of this neurodegenerative disease and monitoring of disease progression.

ALS is a neurodegenerative disease that involves the control of cell death of lower and upper motor neurons that voluntarily contract muscle (Zarei 2015, Surgical Neurology International 6: 171). ALS is characterized by stiff muscles, muscle twitches, muscle atrophy, and progressively worsening weakness due to muscle reduction in size, resulting in difficulty walking, speaking, swallowing, and breathing. Respiratory failure is often the cause of death in patients suffering from ALS. There is no course of treatment available for this fatal disease.

Diagnosis of ALS is difficult and requires exclusion of other possible causes of symptoms and signs, such as muscle weakness, muscle atrophy, impaired swallowing or breathing, spasms, or stiffness of the affected muscles, and/or loss of mouth and teeth and the presence of nasal sounds. In addition to neurological assessments by medical practitioners, diagnosis typically involves EMG, measuring nerve conduction velocity or MRI. Laboratory tests including muscle biopsies are also available.

However, there is a need for early and reliable diagnosis of this neurodegenerative disease and monitoring of disease progression.

The above mentioned cognitive and mobility diseases and disorders are prominent examples that should illustrate the need for early and reliable diagnosis of a disease or disorder condition (especially in daily life situations) and monitoring of the disease condition and/or progression. However, such reliable and efficient diagnosis currently requires the presence of a medical practitioner for neurological assessment or application of expensive and time-consuming imaging methods, e.g. in hospitals. These deficits apply mutatis mutandis to other cognitive and mobility diseases and disorders. Thus, there is a need for less expensive, reliable and effective diagnostic tools and metrics that can be implemented in a simple manner by affected patients during daily life situations.

Disclosure of Invention

The technical problem underlying the present invention can be seen in the provision of means and methods that meet the above-mentioned needs. The technical problem is solved by the embodiments characterized in the claims and described hereinafter.

The present invention relates to a method for assessing cognitive and mobility diseases or disorders in a subject suspected of suffering therefrom, comprising the steps of:

a) determining at least one quality metric activity parameter for cognitive and/or fine motor activity from a typically pre-existing dataset of cognitive and/or fine motor activity measurements from the subject using a mobile device; and

b) comparing the determined at least one quality metric activity parameter with a reference, whereby the cognitive and mobility diseases or disorders will be assessed.

Typically, the method further comprises the steps of: c) assessing cognitive and movement diseases or disorders in the subject based on the comparison conducted in step (b).

In some embodiments, prior to step (a), the method may further comprise the steps of: during a predetermined activity performed by a subject, a data set of activity measurements is obtained from the subject using a mobile device. Typically, however, the method is an in vitro method implemented on an existing dataset of cognitive or fine motor activity measurements of a subject that does not require any physical interaction with the subject, i.e. a method of data analysis and evaluation performed on an existing dataset. Typically, the method is a computer-implemented method.

A method as referred to according to the invention comprises a method essentially consisting of the steps referred to above or may comprise additional steps.

Once the dataset of activity measurements has been acquired, the method may be implemented by the subject on a mobile device. Thus, the mobile device acquiring the data set and the device evaluating the data set may be physically the same, i.e. the same device. Such a mobile device should have a data acquisition unit which typically comprises means for data acquisition, i.e. means to detect or measure quantitative or qualitative physical and/or chemical parameters and transform them into electronic signals which are transmitted to an evaluation unit in the mobile device for implementing the method according to the invention. The data acquisition unit comprises means for data acquisition, i.e. means for detecting or measuring quantitative or qualitative physical and/or chemical parameters and transforming them into electronic signals that are transmitted to a device remote from the mobile device and used for implementing the method according to the invention. Typically, the means for data acquisition comprises at least one sensor. It should be understood that more than one sensor, 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 may be used in the mobile device. Typical sensors used as means for data acquisition are sensors such as the following: gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, touch sensors, voice recorders, light sensors, pressure sensors, position data detectors, cameras, time recorders, sweat analysis sensors, and the like. The evaluation unit typically comprises a processor and a database and software that is tangibly embedded in the device and that when run on the device implements the inventive method. More typically, such a mobile device may further comprise a user interface, such as a screen, allowing the results of the analysis performed by the evaluation unit to be provided to a user.

Alternatively, the method of the present invention may be implemented on a device that is remote with respect to the mobile device that has been used to acquire the data set. In this case, the mobile device should only comprise means for data acquisition, i.e. means to detect or measure quantitative or qualitative physical and/or chemical parameters and transform them into electronic signals that are transmitted to a device remote from the mobile device and used for implementing the method according to the invention. Typically, the means for data acquisition comprises at least one sensor. It should be understood that more than one sensor, 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 may be used in the mobile device. Typical sensors used as means for data acquisition are sensors such as the following: gyroscopes, magnetometers, accelerometers, proximity sensors, thermometers, humidity sensors, pedometers, heart rate detectors, fingerprint detectors, touch sensors, voice recorders, light sensors, pressure sensors, position data detectors, cameras, time recorders, sweat analysis sensors, and the like. Thus, the mobile device and the device for implementing the method of the invention may be physically different devices. In this case, the mobile device may correspond to the device for implementing the method of the invention by any means for data transmission. Such data transmission may be accomplished by permanent or temporary physical connections, such as coaxial, fiber optic or twisted pair, 10BASE-T cables. Alternatively, it may be implemented by a temporary or permanent wireless connection (such as Wi-Fi, LTE advanced, or bluetooth) using, for example, radio waves. Accordingly, to implement the method of the present invention, the only requirement is that there is a data set of activity measurements obtained from the subject using the mobile device. The data set may also be transmitted or stored from the mobile device from which the retrieval is made on a permanent or temporary memory device which may then be used to transfer the data to a device for carrying out the method of the invention. The remote device in the arrangement implementing the method of the invention typically comprises a processor and a database and software tangibly embedded in the device and implementing the method of the invention when run on the device. More typically, the device may further comprise a user interface, such as a screen, allowing the results of the analysis performed by the evaluation unit to be provided to a user. Thus, the mobile device and the remote device in the set-up form a system for implementing the method of the invention.

The term "assessing" as used herein refers to assessing whether a subject suffers from a cognitive and movement disease or disorder or whether a disease or disorder as mentioned herein or individual symptoms thereof worsen or improve over time or depending on a certain stimulus. Accordingly, assessment as used herein includes: identifying progression of the cognitive and movement disease or disorder or one or more symptoms associated therewith; identifying an improvement in the cognitive and movement disease or disorder or one or more symptoms associated therewith; monitoring the cognitive and movement disease or disorder or one or more symptoms associated therewith; determining the efficacy of a therapy of the cognitive and movement disease or disorder or one or more symptoms associated therewith; and/or diagnosing the cognitive and mobility disease or disorder or one or more symptoms associated therewith. As will be appreciated by those skilled in the art, although preferred, such an assessment may often not be correct for 100% of subjects studied. However, this term requires that statistically significant portions of subjects can be correctly assessed and thus identified as suffering from cognitive and mobility diseases or disorders. One skilled in the art can use various well-known statistical evaluation tools (e.g., determination of confidence intervals, p-value determination, student's t-test, man-wheaty test, etc.) to determine whether a portion is statistically significant without a cost-effectiveness. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Typically, the confidence interval envisaged is at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%. Typically, p values are 0.2, 0.1, 0.05. Thus, typically, the methods of the present invention help assess cognitive and mobility diseases or disorders by providing a means for evaluating a data set of activity measurements.

The term "cognitive and movement disease or disorder" as used herein relates to a disease accompanied by impaired cognitive and/or movement disability. Typically, these diseases or disorders result from impaired function of the central nervous system, peripheral nervous system, or muscular system. The damage may involve damage or injury to nerve and/or muscle cells, such as damage caused by neurodegenerative diseases (such as multiple sclerosis, alzheimer's disease, huntington's chorea, parkinson's disease, or others). Typically, the cognitive and movement disorders are diseases or disorders affecting the central and/or peripheral nervous system of the pyramidal, extrapyramidal, sensory or cerebellar systems, or neuromuscular diseases, or muscular diseases or disorders. More typically, the disease or disorder is selected from the group consisting of: multiple Sclerosis (MS), neuromyelitis optica (NMO) and NMO spectrum disorders, stroke, cerebellar disorders, cerebellar ataxia, spastic paraplegia, essential tremor, muscle weakness and myasthenia syndrome or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, peripheral neuropathy, cerebral palsy, extra-pyramidal syndrome, parkinson's disease, huntington's disease, alzheimer's disease, other forms of dementia, leukodystrophy, autism spectrum disorders, attention deficit disorder (ADD/ADHD), intellectual disability as defined by DSM-5, impairment of cognitive performance and stores associated with aging, parkinson's disease, huntington's disease, polyneuropathy, motor neuron disease, and Amyotrophic Lateral Sclerosis (ALS).

Multiple Sclerosis (MS) is a typical cognitive and movement disease or disorder according to the present invention. There are four standardized subtype definitions of MS that are also covered by terms as used according to the present invention: relapse-remission, secondary progressive, primary progressive and progressive relapses. The term "relapsing" form of MS is also used and encompasses relapsing-remitting and secondary progressive MS with superimposed relapses. The relapsing-remitting subtype is characterized by unpredictable relapses followed by remissions over a period of months to years with no new signs of clinical disease activity. The defects suffered during an attack (active state) may resolve or leave sequelae. This describes the initial process of exposure to 85% to 90% of the main body of MS. Secondary progressive MS describes those with initial relapsing-remitting MS who then begin to have progressive neurological decline between episodes without any definite period of remission. Occasional relapses and mild remissions may occur. The median time between disease onset and the transition from relapsing remitting to secondary progressive MS is about 19 years. The primary progressive subtype describes about 10% to 15% of subjects who never have remission after their initial MS symptoms. It is characterized by the progression from onset of disability with no or only occasional and mild remission and improvement. The age of onset of the primary progressive subtype is later than the other subtypes. Progressive relapsing MS describes those subjects with stable neurological decline from onset and suffering from clear superimposed episodes. It is now accepted that this latter progressive relapsing phenotype is a variant of Primary Progressive Ms (PPMS), and that the diagnosis of PPMS according to the McDonald 2010 criteria includes progressive relapsing variants.

Symptoms associated with MS include changes in sensation (hypoesthesia and paresthesia), muscle weakness, muscle spasms, difficulty in movement, difficulty with respect to coordination and balance (ataxia), problems in speech (dysarthria) or swallowing (dysphagia), visual problems (nystagmus, optic neuritis and reduced visual acuity, or double vision), fatigue, acute or chronic pain, bladder, sexual and bowel difficulties. Varying degrees of cognitive impairment and affective symptoms of depressed or unstable mood are also frequent symptoms. The primary clinical measure of disability progression and symptom severity is the Expanded Disability Status Scale (EDSS). Further symptoms of MS are well known in the art and are described in standard textbooks of medicine and Neurology, such as, for example, Bradley WG, ethyl.

Progressive MS as used herein refers to the following conditions: wherein one or more of the diseases and/or symptoms thereof become worse over time. Typically, progression is accompanied by the appearance of an active state. The progression may occur in all subtypes of disease. However, according to the present invention, a typical progressive MS should be determined in subjects suffering from relapsing-remitting MS.

However, the method of the invention may be applied in particular in the context of:

identifying clinical disease activity (i.e., recurrence incidence),

-the progression of disability,

primary progressive MS disease processes as defined by established consensus criteria such as, but not exclusively, McDonald criteria 2010 (Polman 2011, Ann Neurol 69: 292-,

secondary progressive MS disease processes, as defined by established consensus criteria such as, but not exclusively, McDonald criteria 2010 (Polman loc. cit.) and/or Lublin et al criteria 2013 (Lublin loc. cit.),

primary progressive MS, as defined by established consensus criteria, such as, but not exclusively, McDonald criteria 2010 (Polman loc. cit.) and/or Lublin et al criteria 2013 (Lublin loc. cit.), and/or

Secondary progressive MS, as defined by established consensus criteria such as, but not exclusively, McDonald criteria 2010 (Polman loc. cit.) and/or Lublin et al criteria 2013 (Lublin loc. cit.).

Furthermore, it is suitable for risk assessment in MS patients, and in particular for:

a risk prediction model that estimates the probability of disease activity (i.e. T2 or FLAIR (fluid attenuation reversal recovery) weighted recurrence and/or new or enlarged lesions on brain or spinal MRI, and/or gadolinium enhanced lesions on brain or spinal MRI),

-a risk prediction model to estimate the probability of disability progression in patients with a diagnosis of Multiple Sclerosis (MS), measured, for example but not exclusively, by the extended disability status scale neurological status (EDSS), multiple sclerosis functional synthesis (MSFC) and its components "timed 25 foot walk test or 9-well stake test", and/or

A risk prediction model that estimates the probability of occurrence of secondary progressive MS disease processes in relapsing-remitting MS as defined by established consensus criteria such as, but not exclusively, McDonald criteria 2010 (Polman loc.cit.) and/or Lublin et al criteria 2013 (Lublin loc.cit.),

-a risk prediction model that estimates the probability of the occurrence of specific MRI signs of a primary or secondary progressive MS disease process, for example but not exclusively defined by the presence of slow-expanding lesions (SEL) on T2 or FLAIR-weighted brain or spinal MRI or signs of meningitis detected on FLAIR-weighted brain or spinal MRI after injection of gadolinium-based contrast agent.

Furthermore, the method may be applied in the context of:

developing an algorithmic solution using, for example, machine learning and pattern recognition techniques to estimate the probability of DMT response or failure as assessed by the risk of ongoing disease activity (i.e., recurrence and/or new or enlarged lesions on T2 or FLAIR weighted brain or spinal MRI, and/or gadolinium enhanced lesions on brain or spinal MRI) in patients diagnosed with Multiple Sclerosis (MS) treated with a particular Disease Modification Therapy (DMT),

-developing algorithmic solutions using, for example, machine learning and pattern recognition techniques to estimate the probability of DMT response or failure as assessed by the risk of ongoing disability progression in patients with diagnosis of Multiple Sclerosis (MS) treated with a specific DMT, for example but not exclusively measured by the Extended Disability Status Scale (EDSS), timed 25 foot walk test or 9-well peg test, and/or

Developing an algorithmic solution using, for example, machine learning and pattern recognition techniques to estimate the probability of DMT response or failure as assessed by the risk of deterioration in brain MRI metrics of neurological tissue damage and neurodegeneration such as, but not exclusively, whole brain volume, fragment of brain parenchyma, whole gray matter volume, cortical gray matter volume, volume of specific cortical regions, deep gray matter volume, thalamic volume, corpus callosum surface, white matter volume, third ventricle volume, total brain T2 lesion volume, total brain T1 lesion volume, total brain FLAIR lesion volume in patients with a diagnosis of Multiple Sclerosis (MS) treated with a specific DMT; algorithmic solutions are developed using, for example, machine learning and pattern recognition techniques to estimate the probability of occurrence of secondary progressive MS disease processes in relapsing-remitting MS as defined by established consensus criteria such as, but not exclusively, McDonald criteria 2010 (Polman loc. cit.) and/or Lublin et al. criteria 2013 (Lublin loc. cit.).

Neuromyelitis optica (NMO, previously known as devike's disease) and neuromyelitis optica spectrum disorders (nmods) are inflammatory disorders of the central nervous system characterized by severe immune-mediated demyelination and axonal damage predominantly targeting the optic nerve and spinal cord. Traditionally considered as a variant of multiple sclerosis, NMO is now recognized as a unique clinical entity based on unique immunological features. The discovery of disease-specific serum NMO-IgG antibodies that selectively bind aquaporin-4 (AQP 4) has led to an increased understanding of the diverse repertoire of disorders. NMO and nmods are characterized by severe recurrent episodes of optic neuritis and transverse myelitis, which, unlike episodes in multiple sclerosis, together homogenize the brain in the early stages. The lineage of NMO has traditionally been limited to the optic nerve and spinal cord. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Stroke, as referred to herein, refers to impairment of blood flow in the central nervous system, particularly in the brain. A stroke may be an ischemic stroke resulting from the occlusion of a blood vessel and the consequent lack of blood flow into a region of brain tissue, or a hemorrhagic stroke resulting from brain injury and subsequent bleeding. The symptoms of stroke depend on the affected brain region, and typically may encompass one or more of the following: instability on the side of movement or feel, problems with understanding or speaking, dizziness, or partial loss of vision. Hemorrhagic stroke can also cover severe headaches. In any case, for the treatment of stroke, the time period between events and treatment is critical, in particular in order to avoid long-term effects on cognitive or other central nervous system functions. In some cases, the symptoms of stroke may be quite mild and may not be easily diagnosed without suitable testing equipment. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Cerebellar disease according to the present invention encompasses any disease that affects the function of the cerebellum. The cerebellum is involved in motor control and learning. Most importantly, animals and humans with cerebellar dysfunction show problems with motor control on the same side of the body as the damaged part of the cerebellum. They continue to be able to generate motor activity, but it loses precision, resulting in irregular, uncoordinated, or incorrectly timed movements. Typical manifestations of motor problems caused by the cerebellum include hypostress, dysdiasia, dysarthria, alternate motor disorder, intention tremor or gait impairment. Typically, the disorder leading to the above-mentioned disability is also known as cerebellar ataxia. Other diseases affecting the cerebellum include degenerative diseases such as olivopontocerebellar atrophy, machado-joseph disease, ataxia telangiectasia, friedreich's ataxia, ramhunter type I syndrome, paraneoplastic cerebellar degeneration or prion diseases, or may be congenital malformations or underdevelopment of the lumbricus cerebellum (hypoplasia), such as dandy-wack syndrome or taebel syndrome. In addition, cerebellar atrophy may also lead to cerebellar disease and may occur in huntington's disease, multiple sclerosis, essential tremor, progressive myoclonic epilepsy, niemann-pick disease due to exposure to toxins including heavy metals or druggable or recreational drugs, or due to acute deficiency of vitamin B1 (thiamine) as seen in beriberi and in west-coxsackie syndrome, or due to vitamin E deficiency. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Spastic paraplegia as used herein refers to the group of genetic diseases accompanied by progressive stiffness and spasticity in the lower limbs. The disease may also affect the optic nerve, retina, cause cataract, ataxia, epilepsy, cognitive impairment, peripheral neuropathy and deafness. The methods of the present invention may also be applied, typically mutatis mutandis, to assess disease, including aspects detailed elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Essential tremor as used herein refers to a movement disorder involving tremor of the arms, hands and fingers. Sometimes, other body parts and speech may also be affected by tremor. Essential tremor is typically either an action tremor (i.e., it occurs where the affected muscle should be used) or a postural tremor (i.e., it exists with sustained muscle tone). The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Muscle weakness as used herein refers to a neuromuscular disease also known as myasthenia gravis characterized by frequently occurring muscle weakness and fatigue. Muscle weakness becomes more pronounced during exercise and less pronounced at rest periods. It is caused by circulating autoantibodies that block nicotinic acetylcholine receptors. These antibodies prevent motor neurons from transmitting signals to muscles. There are other forms of neuromuscular diseases associated with muscle weakness, such as ocular muscle weakness or the bluebotton-eaton muscle weakness syndrome. Such other forms of neuromuscular disorders are also contemplated by the invention as cognitive and movement disorders and diseases. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Muscular dystrophy as mentioned according to the present invention relates to the weakness of muscles caused by the detection or death of muscle cells and tissues. Typically, muscle proteins such as dystrophin proteins may become greatly reduced in muscle dystrophies. Muscle dystrophy as referred to herein encompasses, but is not limited to, becker muscle dystrophy, congenital muscle dystrophy, duchenne muscle dystrophy, peripheral muscle dystrophy, emmeri-delafoss muscle dystrophy, facioscapulohumeral muscle dystrophy, limb-girdle muscle dystrophy, and myotonic muscle dystrophy. Also encompassed according to the invention are forms of myositis or other muscle disorders.

Peripheral neuropathy as referred to herein refers to the following diseases: in which the proper function of the peripheral nerves is impaired. Typically, the nerves envisaged according to the invention are those required for movement or sensation. These neuropathies are also known as motor neuropathy or sensory neuropathy. Motor neuropathy may lead to impaired balance and coordination or most typically muscle weakness. Sensory neuropathy may result in numbness to touch and vibration or reduced position sensation that causes poorer coordination and balance, and may result in reduced sensitivity to temperature changes and pain, spontaneous stinging or burning touch pain, or skin-induced pain. Neuropathy can be further classified as: mononeuropathies, in which essentially a single spirit is affected; and polyneuropathy, affecting various nerves in different parts of the body. Different causes of neuropathy have been described involving serious diseases such as diabetes, immunological diseases, infections, physical injuries, chemotherapy, radiotherapy, cancer, alcoholism, beriberi, hypothyroidism, porphyria, vitamin B12 deficiency or excess vitamin B6. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Polyneuropathy is understood as damage or disorder affecting the peripheral nerves in approximately the same area on all sides of the body. Polyneuropathy can be classified in different ways, such as by cause, by speed of progression, by the body part involved or by the major affected part of the nerve cell (axons, myelin sheath or cell body). Polyneuropathy can be further classified as: acute polyneuropathy, for example caused by infection, autoimmune reaction, toxins, certain drugs or cancer; and chronic polyneuropathy, for example caused by diabetes, excessive alcohol consumption or neurodegeneration. Symptoms of polyneuropathy include weakness, numbness or burning pain that usually starts in the hands and feet and can progress to the arms, legs and sometimes to other parts of the body (Burns 2011, Neurology 76.7 Supplement 2: S6-S13). A number of different disorders are known to cause polyneuropathy, such as diabetes and some types of guillain-barre syndrome. Diagnosis of polyneuropathy is commonly based on physical examination and further clinical tests including, for example, electromyography, nerve conduction studies, muscle biopsies or certain antibody tests. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Cerebral Palsy (CP) is a group of permanent movement disorders. CP usually occurs in early childhood and results from abnormal development or damage to the brain parts that control movement, balance and posture. Symptoms include poor coordination, stiff muscles, weak muscles, tremors, sudden attacks, reduced thinking or reasoning ability, problems with sensation, vision, hearing, swallowing, and speech. According to the center for disease control and prevention (CDC), CP is the most prevalent movement disorder in children and has a prevalence of about 2.11 per 1000 live births. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Extra Pyramidal Syndrome (EPS) is considered a drug-induced movement disorder. The term "extrapyramidal symptoms" derives from the fact that: they are symptoms of disorders in the extrapyramidal system that normally regulate posture and skeletal muscle tone. Symptoms can be acute or delayed and include dystonia (continuous spasms and muscle contractions), akathisia (restlessness), parkinson's disease (characteristic disorders such as rigidity), bradykinesia (bradykinesia), tremor, and tardive dyskinesia (irregular, jerky movements). Extrapyramidal syndrome is most commonly caused by antipsychotics or antidepressants such as haloperidol, fluphenazine, duloxetine, sertraline, escitalopram, fluoxetine and bupropion. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease. The disease process of AD can be divided into four stages, with progressive patterns of cognitive and functional impairment: pre-dementia, early stage, moderate stage, and late stage.

The pre-demented stage of the disease has also been referred to as Mild Cognitive Impairment (MCI) and includes early symptoms of AD, such as short-term memory loss and difficulties in planning or solving problems (Waldemar 2007, European Journal of neurology 14.1: e1-e26; B ä ckman 2004, Journal of internal medicine 256.3: 195-. In the early stages of AD, symptoms such as problems with language, executive function, perception (agnosia), and mobile performance (apraxia) become apparent. As the disease progresses, behavioral and neuropsychiatric changes become more prevalent. Moderate stages of AD include incapacitating vocabulary, loss of reading and writing skills, impairment of coordination of complex motor sequences leading to, for example, increased risk of falls, urinary incontinence, impairment of long-term memory, hallucinations and other delusions. Late stage symptoms of AD include: the reduction of language to simple phrases or even single words, ultimately resulting in complete loss of speech; severe reduction in muscle mass and mobility; and loss of physical function.

AD is considered to be 60% to 70% of the causes of dementia. Behavioral and psychological symptoms of dementia are considered to constitute the major clinical component of AD (Robert 2005, European Psychiatry 20.7: 490-496). Although the rate of progression of AD can vary, the mean life expectancy after diagnosis of AD is about three to nine years (Todd 2013, International journal of geriatric psychiatry 28.11: 1109-1124).

The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Dementia as referred to herein includes a variety of brain diseases that result in decreased thinking and memory, often accompanied by language and motor skills problems. As mentioned above, the most common type of dementia is alzheimer's disease. Other types include, for example, vascular dementia, dementia with lewy bodies, frontotemporal dementia, normal pressure hydrocephalus, Parkinson's disease, syphilis, and Creutzfeldt-Jakob disease. Known risk factors for developing dementia include hypertension, smoking, diabetes and obesity. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Leukodystrophy is a group of disorders characterized by the degeneration of white matter in the brain. Leukodystrophy is thought to result from imperfect growth or development of myelin sheaths or from loss of myelin due to inflammation in the central nervous system. Degeneration of white matter can be seen on MRI and used to diagnose white matter dystrophy in the brain (Cheon 2002, Radiographics 22.3: 461-476). The symptoms of leukodystrophy are usually dependent on the age of onset predominantly in infancy and early childhood and include reduced motor function, muscle rigidity, impairment of vision and hearing, ataxia and mental retardation. Leukodystrophy disorders include, for example, X-linked adrenoleukodystrophy, krabbe's disease, Metachromatic Leukodystrophy (MLD), canavan disease, and alexander disease. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Autism Spectrum Disorder (ASD) characterizes a group of complex neurological and developmental disorders. ASD affects the structure and function of the brain and nervous system. Typical characteristics of ASDs include social issues such as difficulties in communicating and interacting with others, repetitive behaviors, limited interest or activity, and facial expressions, movements, gestures that do not match what is being said. According to the center for disease control and prevention (CDC), approximately 1 of 68 children has been identified with some form of ASD. Diagnosis of ASD can be difficult and is generally based on diagnostic and statistical manuals for mental Disorders (DSM). In the past, the asperger syndrome and the autistic disorder were considered separate disorders. However, in 5 months 2013, a new version of the mental disorder diagnosis and statistics manual (DSM-5), a public manual from the american psychiatric association for diagnosing different mental health conditions, was released. The DSM-5 manual now includes only a range of characteristics and severity within one category known as Autism Spectrum Disorder (ASD), and no longer highlights the subcategories of larger disorders (previous subcategories were: autistic disorder, asberg syndrome, childhood disintegration disorder, broad developmental disorder not otherwise specified). According to DSM-5 guidelines, people whose symptoms were previously diagnosed as yasberg syndrome or autism disorder are now included as part of a category known as Autism Spectrum Disorder (ASD). The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Attention deficit disorder, also known as Attention Deficit Disorder (ADD) or Attention Deficit Hyperactivity Disorder (ADHD), refers to a group of disorders of nerve development.

According to the latest new version of the diagnostic and statistical manual for mental disorders (DSM-5), diagnosis for attention deficit disorder before the age of 12 must present several symptoms. Typical symptoms of ADD or ADHD include: symptoms of inattention, such as difficulty in following instructions or organizing tasks; symptoms of hyperactivity or impulsivity, such as difficulty in keeping seated or waiting for a shift (e.g., answering before a question has been completed, interruption of conversation). The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Intellectual disability as defined by DSM-5. Mental disability (intellectual development disorder) as a diagnostic term for DSM-5 replaces "mental retardation" used in previous releases of handbooks. In DSM-5, the diagnosis of mental disability (intellectual development disorder) was revised based on DSM-IV diagnosis of mental retardation (American society for psychosis. mental disorder diagnosis and statistical Manual (DSM-5.) American psychosis Press, 2013). The revised obstacle reflects the manual's departure from a multi-axis approach to evaluating a condition. Mental disability as defined by DSM-5 relates to impairment of general mental activities affecting adaptive functioning in three domains or domains: (1) concept domains include skills in language, reading, writing, mathematics, reasoning, knowledge, and memory; (2) social domains refer to resonance, social judgment, interpersonal communication skills, ability to form and maintain friendship, and the like; (3) a real-world center of self-management in areas such as personal care, work duties, money management, entertainment, and organizing schools and work tasks. Although intellectual disability does not have a specific age requirement, the symptoms of an individual must begin during the developmental period and be diagnosed based on the severity of the deficit in adaptive functioning. The disorders are considered chronic and often co-occur with other mental conditions like depression, attention deficit/hyperactivity disorder and autism spectrum disorder. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Impairment of cognitive performance and reserve associated with aging refers to any age-related decline in cognitive performance such as thought and memory ability and/or any age-related effect on brain size (also referred to as "brain reserve") or neural count (also referred to as "cognitive reserve"). Cognitive decline in, for example, acceleration ability, executive function and memory is considered typical of normal aging (Gunstad 2006, Journal of Geriatric Psychiatry and Neurology 19.2: 59-64). The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Parkinson's Disease (PD) is a progressive disorder of the central nervous system that primarily affects the motor system. Typical symptoms include trembling, rigidity, slow movement, difficulty with walking. Other symptoms including sensory, sleep and emotional problems, as well as thought and behavioral problems may also occur, and depression and anxiety problems are commonly observed in the late stages of the disease. The cause of parkinson's disease is currently unknown, but the motor symptoms of the disease are thought to be caused by the death of cells in the substantia nigra that leads to a reduction in dopamine in these areas. However, some of the non-motor symptoms are often present at the time of diagnosis, and may precede the motor symptoms. The diagnosis of PD is based primarily on clinical assessment of symptoms in combination with other tests, such as neuroimaging to rule out other diseases. The occurrence of parkinson's disease is most common in people over the age of 60, affecting more men than women. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Huntington's Disease (HD), also known as huntington's disease, is a genetic disorder caused by an autosomal dominant mutation in the huntington gene (HTT). HD is a fatal disease caused by the death of brain cells. Symptoms of huntington's disease may begin at any age from infancy to elderly, although typically become significant between ages 35 and 44. Early symptoms include changes in personality, cognition, and physical skills (Walker 2007, The Lancet 369.9557: 218-. The most unique initial physical symptom is random and uncontrollable movements called chorea. Further symptoms include sudden attacks, abnormal facial expressions, difficulties in chewing, swallowing, and speaking. Diagnosis of HD is typically based on clinical assessment of symptoms as well as genetic testing. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Amyotrophic Lateral Sclerosis (ALS), the most frequent case also known as Motor Neuron Disease (MND), is a late-onset fatal neurodegenerative disease that affects motor neurons. ALS occurs at an incidence of about 1/100000. Most ALS cases are paroxysmal, but 5-10% of cases are familial ALS. Both paroxysmal and familial als (fals) are associated with degeneration of cortical and spinal motor neurons. Typical symptoms include muscle weakness and atrophy throughout the body, impairment of cognitive function. The diagnosis of ALS generally involves clinical examinations and diagnostic test series, often excluding other diseases that resemble ALS. For ALS to be diagnosed, there are often symptoms of both upper and lower motor neuron damage that cannot be attributed to other causes. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

Antipsychotic malignant syndrome (NMS) is a life-threatening neurological disorder most often caused by adverse reactions to antipsychotics or antipsychotics, such as haloperidol, promethazine, chlorpromazine, clozapine, olanzapine, risperidone, quetiapine, or ziprasidone. Symptoms include muscle spasms, tremors, fever, symptoms of autonomic nervous system instability (such as unstable blood pressure), and alterations in mental state (dysphoria, mental disorder, or coma). Muscle symptoms in NMS are most likely caused by obstruction of the dopamine receptor D2, resulting in abnormal function of the basal ganglia similar to that seen in parkinson's disease. Furthermore, elevated levels of plasma creatine kinase correlate with NMS (Strawn 2007, American Journal of Psychiatry 164.6: 870-. The method of the invention can also be applied, typically with necessary modifications, to those objects mentioned in relation to the MS. In particular, the method may be applied to assess disease, including aspects described in detail elsewhere, to make risk assessments, more typically to build risk prediction models and/or to develop algorithmic solutions using, for example, machine learning and pattern recognition techniques.

The term "subject" as used herein refers to an animal, and typically to a mammal. In particular, the subject is a primate, and most typically a human. A subject according to the present invention may suffer or may be suspected of suffering from a cognitive and mobility disease or disorder, i.e. it may have shown some or all of the symptoms associated with the disease.

The term "quality metric activity parameter for cognitive and/or fine motor activity" as used herein refers to a single or composite measure of fluctuations in performance in at least one qualitative feature of cognitive and/or fine motor functioning and integrity during completion of a particular cognitive and/or motor task. Such a quality metric parameter measures how "the nervous system" behaves or behaves during a given task as compared to a performance test that measures only the ability to complete the task with a specific overall performance. Accordingly, a quality metric parameter as referred to herein is typically a measure of the quality of an executable task, e.g., based on the correctness of the task being executed and the time required to execute the task or a series of iterative tasks. Thus, typically, the quality metric parameter may be a time parameter, such as the duration of a task or the time difference between the execution of iterative tasks or a time dependent parameter (such as speed), or it may be a parameter reflecting the accuracy of the movement. Specific cognitive and/or fine motor activities from which quality metric parameters may be derived in accordance with the present invention are listed in greater detail elsewhere herein.

The term "at least one" means: according to the invention, one or more parameters may be determined, such as quality metric activity parameters, i.e. at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine or at least ten or even more different parameters. Therefore, there is no upper limit on the number of different parameters that can be determined by the method according to the invention. Typically, however, there will be between one and three different parameters per dataset for the determined activity measurements.

The term "dataset of activity measurements" refers in principle to the entirety of data acquired by the mobile device from the subject during an activity measurement or any subset of said data useful for deriving quality metric activity parameters. Details are also found elsewhere herein. In particular, activity measurements in connection with the term "dataset of cognitive and/or fine motor activity measurements" as used according to the present invention include Information Processing Speed (IPS) tests, pinch tests performed on a sensor surface of a mobile device and/or measurements from datasets during the performance of U-turn tests (UTT), 2 minute walk tests (2 MWT), Static Balance Tests (SBT) or continuous gait analysis (CAG) from passive monitoring as described in detail elsewhere herein. Typically, the cognitive and/or fine motor activity measured by these respective tests is attention, information processing speed, visual scanning and/or hand motor activity. The data set is a pre-existing data set, meaning that the method of the present invention typically does not require data acquisition from the subject.

In the following, specific envisaged activity tests and means for measurements by a mobile device according to the inventive method are specified:

(1) cognitive quality metric activity parameters from computer-implemented Information Processing Speed (IPS) tests

The purpose of the information processing speed test is: impairment of key neurocognitive functions underlying an iterative visual replacement task including sustained attention, visual scanning and recent memory is detected. The information processing in this example consists of different steps starting with the input of visual information into the sensory system extending secondarily to the output (i.e. responding by pressing a key on the smartphone touch screen). The main steps in the process are as follows: (1) transmission of incoming visual sensory information; (2) completion of cognitive replacement tasks; and (3) execution of outgoing motion output (Costa 2017).

Symbolic digital modal testing (SDMT, Smith1968, 1982) or processing speed testing (PST, Rao 2017) do not account for any measure of relative weight of reaction time or motion output time in overall test performance. According to the present invention, the IPS test was developed to enable the speed of the symbol/digit substitution task to be specifically assessed by subtracting separately measured reaction time, visual processing time, and motion output time from the overall performance.

The symbol set of the IPS test consists of 9 different abstract symbols following a simple design scheme and is assigned to nine keys, i.e. the numbers 1 to 9.

To account for the participant's reaction time and the time it takes to generate the outgoing motion output, a 15 second digit/digit matching workout would be completed after the symbol/digit substitution task. The numbers will be presented in the analog rotation scheme for numbers as symbols in the previous replacement task and will be embedded in the same user interface.

For the symbol/number substitution task of the IPS test, 120 abstract symbols will be displayed sequentially in a total of up to 90 seconds. Legend keys (3 or more versions of polling alternates) showing nine symbols with their corresponding matching numbers from 1 to 9 will be displayed side by side for reference. The learning participant is asked to provide as many correct responses as possible by typing the matching key as quickly as possible on the numeric keypad on the screen of the smartphone during 90 seconds for each iteration symbol.

The number of correct responses to the symbol matches and baseline tests will be displayed to the patient.

Typical cognitive quality metric activity parameters may be derived from Information Processing Speed (IPS) tests aimed at detecting and measuring impairment of key neurocognitive functions that form the basis of iterative visual replacement tasks including sustained attention, visual scanning and recent memory. Digital-to-symbolic substitution tasks are known to be related to brain atrophy in conditions of mild cognitive impairment, and IPS tests performed on mobile devices (as opposed to similar tests such as SDMT (Smith 1968, 1982) or PST (Rao 2017)) enable cognitive substitution task performance to be measured separately, while adjusting for any impact of visual processing and motor execution time.

Typical cognitive quality metric activity parameters derived from IPS tests and captured as continuous outcome variables reflecting intra-test fluctuations measuring cognitive integrity are selected from the group consisting of:

1) (from n-1) the elapsed time before the response,

2) (from n-1) the elapsed time before a correct response,

3) (from n-1) elapsed time before incorrect response,

4) the elapsed time between correct responses (from the previous correct response),

5) (from a previous incorrect response) the elapsed time between incorrect responses, and

6) parameters 1), 2), and 3) applied to a particular symbol or symbol cluster when the symbol sequence is modified to evaluate working memory and learning within the task).

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two, three or all of these parameters are determined.

More typically, the IPS test-derived quality metric parameters of interest are one or more of the following list:

1. number of correct responses

a. Total number of overall Correct Responses (CR) in 90 seconds;

b. number of Correct Responses (CR) from time 0 to 30 seconds_0-30）；

c. Number of Correct Responses (CR) from time 30 to 60 seconds_30-60）；

d. Number of Correct Responses (CR) from time 60 to 90 seconds_60-90）；

e. Number of Correct Responses (CR) from time 0 to 45 seconds_0-45）；

f. Number of Correct Responses (CR) from time 45 to 90 seconds_45-90）；

g. Number of Correct Responses (CR) from time i to j seconds_i-j) Where i, j is between 1 and 90 seconds and i<j，

2. Number of errors

a. A total number of errors in 90 seconds (E);

b. number of errors from time 0 to 30 seconds (E)_0-30）；

c. Number of errors from time 30 to 60 seconds (E)_30-60）；

d. Number of errors from time 60 to 90 seconds (E)_60-90）；

e. Number of errors from time 0 to 45 seconds (E)_0-45）；

f. Number of errors from time 45 to 90 seconds (E)_45-90）；

g. Number of errors from time i to j seconds (E)_i-j) Where i, j is between 1 and 90 seconds and i<j，

3. Number of responses

a. Total number of overall responses (R) in 90 seconds;

b. number of responses (R) from time 0 to 30 seconds_0-30）；

c. Number of responses (R) from time 30 to 60 seconds_30-60）；

d. Number of responses (R) from time 60 to 90 seconds_60-90）；

e. Number of responses (R) from time 0 to 45 seconds_0-45）；

f. Number of responses (R) from time 45 to 90 seconds_45-90），

4. Rate of accuracy

a. Mean Accuracy (AR) over 90 seconds: AR = CR/R;

b. mean Accuracy (AR) from time 0 to 30 seconds: AR_0-30=CR_0-30/R_0-30；

c. Mean Accuracy (AR) from time 30 to 60 seconds: AR_30-60=CR_30-60/R_30-60；

d. Mean Accuracy (AR) from time 60 to 90 seconds: AR_60-90=CR_60-90/R_60-90；

e. Mean Accuracy (AR) from time 0 to 45 seconds: AR_0-45=CR_0-45/R_0-45；

f. Mean Accuracy (AR) from time 45 to 90 seconds: AR_45-90=CR_45-90/R_45-90，

5. Ending of mission fatigue index

a. Speed Fatigue Index (SFI) in last 30 seconds: SFI_60-90=CR_60-90/max (CR_0-30, CR_30-60)；

b. SFI in the last 45 seconds: SFI_45-90=CR_45-90/CR_0-45；

c. Accuracy Fatigue Index (AFI) in last 30 seconds: AFI_60-90=AR_60-90/max (AR_0-30, AR_30-60)；

d. AFI in the last 45 seconds: AFI_45-90=AR_45-90/AR_0-45，

6. Longest sequence of consecutive correct responses

a. The number of correct responses (CCR) within the longest sequence of overall consecutive correct responses in 90 seconds;

b. number of correct responses (CCR) within the longest sequence of consecutive correct responses from time 0 to 30 seconds_0-30）；

c. Number of correct responses (CCR) within the longest sequence of consecutive correct responses from time 30 to 60 seconds_30-60）；

d. Number of correct responses (CCR) within the longest sequence of consecutive correct responses from time 60 to 90 seconds_60-90）；

e. Number of correct responses (CCR) within the longest sequence of consecutive correct responses from time 0 to 45 seconds_0-45）；

f. Number of correct responses (CCR) within the longest sequence of consecutive correct responses from time 45 to 90 seconds_45-90），

7. Time gap between responses

a. Continuous variable analysis of the gap (G) time between two successive responses;

b. a maximum Gap (GM) time elapsed between two successive responses within 90 seconds;

c. maximum gap time (GM) elapsed between two successive responses from time 0 to 30 seconds_0-30）；

d. Maximum gap time (GM) elapsed between two successive responses from time 30 to 60 seconds_30-60）；

e. Maximum gap time (GM) elapsed between two successive responses from time 60 to 90 seconds_60-90）；

f. Maximum gap time (GM) elapsed between two successive responses from time 0 to 45 seconds_0-45）；

g. Maximum gap time (GM) elapsed between two consecutive responses from time 45 to 90 seconds_45-90），

8. Time gap between correct responses

a. Continuous variable analysis of the gap (Gc) time between two successive correct responses;

b. the maximum gap time (GcM) elapsed between two successive correct responses within 90 seconds;

c. maximum gap time (GcM) elapsed between two successive correct responses from time 0 to 30 seconds_0-30）；

d. Maximum gap time (GcM) elapsed between two successive correct responses from time 30 to 60 seconds_30-60）；

e. Maximum gap time (GcM) elapsed between two successive correct responses from time 60 to 90 seconds_60-90）；

f. Maximum gap time (GcM) elapsed between two successive correct responses from time 0 to 45 seconds_0-45）；

g. Maximum gap time (GcM) elapsed between two consecutive correct responses from time 45 to 90 seconds_45-90），

9. Fine finger motor skill functional parameters captured during IPS testing

a. Continuous variable analysis of the duration of the touch screen contact (Tts), the deviation between the touch screen contact and the center of the closest target number key (Dts), and the incorrectly entered touch screen contact (Mts) (i.e., the contact that does not trigger a key hit or that triggers a key hit but is associated with an auxiliary swipe on the screen) while typing the response within 90 seconds;

b. corresponding variables by time period from time 0 to 30 seconds: tts_0-30、Dts_0-30、Mts_0-30；

c. Corresponding variables by time period from time 30 to 60 seconds: tts_30-60、Dts_30-60、Mts_30-60；

d. Corresponding variables for the on-time period from time 60 to 90 seconds: tts_60-90、Dts_60-90、Mts_60-90；

e. Corresponding variables for the on-time period from time 0 to 45 seconds: tts_0-45、Dts_0-45、Mts_0-45；

f. Corresponding variables for the on-time period from time 45 to 90 seconds: tts_45-90、Dts_45-90、Mts_45-90，

10. Symbol-specific analysis of the representation of individual symbols or symbol clusters

a. CR individually for each of the 9 symbols and all their possible cluster combinations;

b. AR individually for each of the 9 symbols and all their possible cluster combinations;

c. a gap time (G) from the previous response to the recorded response individually for each of the 9 symbols and all their possible cluster combinations;

d. pattern analysis that prefers incorrect responses by looking at the type of the mistaken substitute is identified individually for 9 symbols and individually for 9 digital responses,

11. learning and cognitive reserve analysis

a. Change from baseline (baseline is defined as mean performance from the first 2 administrations of the test) in CR (global and sign specific as described in # 9) between successive administrations of IPS test;

b. change from baseline (baseline is defined as mean performance from the first 2 administrations of the test) in AR between successive administrations of IPS test (global and symbolic specific as described in # 9);

c. changes from baseline (baseline is defined as mean performance from the first 2 administrations of the test) in mean G and GM (global and sign specific as described in # 9) between successive administrations of IPS test;

d. mean Gc between successive administrations of IPS test and change from baseline (baseline is defined as mean performance from the first 2 administrations of test) in GcM (global and sign specific as described in # 9);

e. SFI between connection management of IPS test_60-90And SFI_45-90From baseline (baseline is defined as mean performance from the first 2 administrations of the test);

f. AFI between successive management of IPS testing_60-90And AFI_45-90From baseline (baseline is defined as the first 2 from tests)Managed mean performance);

g. change from baseline in Tts between successive administrations of IPS test (baseline is defined as mean performance from the first 2 administrations of the test);

h. change in Dts between successive administrations of IPS test from baseline (baseline is defined as mean performance from the first 2 administrations of test);

i. change in Mts between successive administrations of IPS test from baseline (baseline is defined as mean performance from the first 2 administrations of test).

In yet another embodiment, the IPS test should be applied to determine baseline, cognitive and information processing speeds, and oculomotor nerve and motor functional quality metric activity parameters. Thus, the response time of performing a task on a mobile device may be profiled, and the contribution of individual portions of the nervous system involved in the response may be determined. This is particularly advantageous since it has been found that in conventional SDMT, the portion or function of the nervous system affected by the disease can be compensated by an unaffected portion or function. Thus, a false negative diagnosis can be established based on SDMT data. For example, patients suffering from diseases such as MS can compensate for poor hand motion performance through better cognitive and information processing speeds. When measuring the overall response time to perform an SDMT task, such a patient may not be worse than a healthy subject, or only insignificantly worse than a healthy subject, despite suffering from a disease.

Thus, in yet another embodiment, the cognitive quality metric activity parameters to be analyzed in accordance with the method of the present invention may be derived from a computer-implemented IPS test.

In step i), the computer-implemented IPS test should determine the information processing speed by measuring the response time of the symbol-matching task using test symbols that are unfamiliar (e.g., without naive numbers or symbols) to the subject performing the task. Test symbols useful for the IPS test typically show little resemblance to alphabetic or mathematical notation and thus should also be independent of influences such as cultural background, reading and writing abilities, or educational standards. Thus, such test symbols may also be used for children or subjects with low educational standards (e.g., illiterate). Furthermore, to improve visual recognition, the test symbols should follow a simple design principle in less detail. More typically, the symbol may be designed as a symbol pair having a unique feature (e.g., left/right, up/down feature) at opposite sides of a mirror axis parallel to or orthogonal to the reading direction, or as a recognizable one-piece (singleton) symbol having rotational symmetry, directional orientation, or unique edges. Typical test symbols are described and shown in the following accompanying examples.

Typically, the test is performed by showing a body, a test symbol and a legend on the display, which assigns different test symbols shown during the test to a naive number or other naive symbol (such as a letter). These na iotave numbers or other na iotave symbols are also present on the keypad so that the subject performing the test can press the key bearing the na iotave number or na iotave symbol assigned to the test symbol. It should be understood that the response time in the IPS test for this task depends on the reaction time, the processing time of the hand motion output, and the time of the cognitive information processing.

In step i) of the IPS test described above, an iteration of fixed test symbol matching sequences may be performed, wherein each sequence consists of matching tasks for at least 6 different test symbols. The test symbol matching sequence may also comprise more than 6 and typically 7, 8 or 9 different test symbols.

Typically, the iteration is followed by a new randomized test symbol matching sequence. The improvement in response time between the first and last iterations is indicative of the subject's cognitive learning ability or the response time in the standard test response time and the randomized symbol matching sequence run. Typically, at least two, at least three, at least four iterations of the test symbol matching sequence are performed, and more typically, three test symbol matching sequences are performed. Furthermore, during an iteration, typically test symbol matching may be performed as in standard clinical SDM testing. Typically, the legend of the symbols, the size of the symbols, the keypad and other parameters displayed on the mobile device used to conduct the IPS test are kept in a constant condition in terms of size, appearance, contrast, etc. in order to avoid sensory effects not related to the speed of information processing. A typical example of an implementation of an automated IPS test is described further below in the examples.

The IPS test in step ii) determines the baseline information processing speed by measuring the baseline response time. Typically, in an embodiment, the baseline response time may be determined by measuring the time to match a na iotave number or symbol to a matching na iotave number or symbol on a keypad of the mobile device. More typically, the naive numbers or symbols should be selected such that the individual conducting the test can perform the matching without substantial knowledge. More typically, numbers from 0 to 9 may be used as naive numbers. Such baseline response times using naive numeric or symbolic matching should depend primarily on reaction times and processing times of hand motion outputs. Cognitive tasks should play only a minor role and should not contribute significantly to the baseline response time. Thus, the information processing speed determined in the subsequent step can be deconvoluted into the reaction time and the processing time of the hand motion output and the time of the cognitive information processing by the baseline response time. It is to be understood that the above mentioned step i) may be performed before or after step ii).

Thus, in a computer-implemented IPS test running on a mobile device for obtaining cognitive quality metric activity parameters to be analyzed in accordance with the method of the present invention, the task of determining the time to process including the reaction time, the hand motion output and the cognitive information (testing the different non-na iotave test symbols as described above by pressing the corresponding keys on the keypad to match them to legends that assign said different test symbols shown during the test to na iotave numbers or other na iotave symbols, such as letters) and the task of processing the time to process including the reaction time and the hand motion output (baseline task, typically matching na iotave numbers or symbols to matching na iotave numbers or symbols on the keypad) are taken as a cognitive quality metric activity parameter that is part of the data set to be analyzed by the inventive method. Furthermore, the IPS test also aims to determine the learning ability by comparing the response time required to perform the test task at the end of an iteration of the same test symbol matching sequence with the response time required to perform the running randomized symbol matching sequence.

Thus, in an embodiment of the method of the present invention, a computer-implemented method for automatically assessing Information Processing Speed (IPS) is performed in a test subject, comprising the steps of:

i) determining, in a pre-existing data set of cognitive and/or fine motor activity measurements including cognitive oculomotor neural activity measurements obtained from the test subject, at least one first quality metric activity parameter for perceptual transmission, cognitive and motor output activities and at least one second quality metric activity parameter for perceptual transmission and motor output activities;

ii) determining at least one third quality-metric campaign parameter for cognition by comparing the first and second quality-metric campaign parameters to each other;

iii) assessing the speed of information processing in the subject based on the at least one first, second and third quality metric activity parameters;

iv) providing said information processing speed as a quality metric activity parameter for cognitive and/or fine motor activity in step a) of the method of the invention.

The term "information processing speed" as used herein refers to a neurological parameter indicative of the speed of information processing. The information processing in this example consists of different steps starting with the input of visual information into the sensory system extending secondarily to the output (i.e. responding by pressing a key on the smartphone touch screen). The main steps in the process are as follows: (1) transmission of incoming visual sensory information, i.e., perceptual transmission; (2) completion of a cognitive substitute task, i.e., cognitive information processing; and (3) execution of outgoing motion output, i.e., hand motion output. The speed of information processing may be affected by cognitive impairment associated with neurological diseases or disorders, including those specifically mentioned elsewhere herein, or may be an indicator of the cognitive abilities of the subject.

The term "assessing information processing speed" as used herein refers to assessing the information processing speed in a subject as a quality metric parameter for cognitive and/or fine motor activity. The term includes both absolute and relative determinations of information processing speed. An absolute determination should typically be a determination of a parameter indicative of the actual speed of information processing in the subject, while a relative determination should typically be a determination of the speed of information processing relative to a reference (e.g., relative to a previously determined speed of information processing in a test subject, or relative to a speed of information processing in a reference subject, or a combination thereof). As mentioned herein, the information processing speed typically comprises an assessment of three main contributors, which are the transmission of incoming visual sensory information (i.e. perceptual transmission), the completion of cognitive replacement tasks (i.e. cognitive information processing) and the execution of outgoing motor output (i.e. hand motor output), reflected by at least one first, second and third activity parameter to be determined according to the inventive method.

Typically, the first, second and/or third quality metric activity parameter is a time parameter, such as a performance time required to complete a task or a time parameter indicating a change in speed when performing a task, such as an improvement in speed or a deterioration in speed.

In an embodiment, the at least one third quality metric activity parameter for cognition may thus be determined by comparing the first and second quality metric activity parameters to each other, i.e. the determined activity parameters may be deconvolved by the baseline response time into a reaction time and a processing time of the hand motion output and a time of cognitive information processing. For deconvolution, any suitable mathematical operation may be used. For example, the at least one third parameter may be provided by the following subtraction: subtracting the at least one second quality metric activity parameter from the at least one first quality metric activity parameter. It should be understood that first and second parameters of comparable properties should be used, such as temporal first and second temporal parameters, first and second ratios of temporal parameters or first and second score parameters, etc.

Thus, in a computer-implemented IPS test running on a mobile device for obtaining quality metric activity parameters to be analyzed, the task of determining a time comprising a reaction time, a processing time of a hand motion output and a time of cognitive information processing (testing matching different non-na iotave test symbols as described above by pressing corresponding keys on a keypad to a legend that assigns said different test symbols shown during the test to na iotave or other na iotave symbols, such as letters) and the task comprising a reaction time and a processing time of a hand motion output (baseline task, typically matching na iotave numbers or symbols to matching na iotave numbers or symbols on a keypad) differ in response time as one cognitive quality metric activity parameter. Furthermore, the IPS test also aims to determine the learning ability by comparing the response time required to perform the test task at the end of an iteration of the same test symbol matching sequence with the response time required to perform the running randomized symbol matching sequence. Typically, this time comparison may also be determined as a quality metric activity parameter according to the method of the invention.

Accordingly, the present invention also provides a method for assessing information processing speed, comprising: performing steps a) to c) for a data set of eye movement nerve activity measurements for a first symbol matching task and for a symbol matching task after one or more iterations, and typically four iterations, of the same symbol matching task; and determining a difference in information speed processing assessed for the data set of the first oculomotor nerve activity measurement and the data set taken after the iteration. The difference in speed is an indicator of the cognitive learning ability of the subject. The improvement in speed is an indicator of normal or improved cognitive ability, while deterioration is an indicator of cognitive impairment.

The term "dataset of oculomotor neural activity measurements" as used herein refers to the entirety of data acquired by a mobile device from a subject during cognitive oculomotor neural activity measurements or any subset of said data useful for deriving quality metric activity parameters. Details are also found elsewhere herein. In particular, the activity measurements in connection with the term "dataset of cognitive oculomotor neural activity measurements" as used according to the present invention include measurements of the dataset during execution of an Information Processing Speed (IPS) test as described in the following accompanying examples. The data set is a pre-existing data set, meaning that the method of the present invention typically does not require data acquisition from the subject.

Importantly, the cognitive quality metric activity parameter as mentioned above may be derived from any other cognitive test acquired from the mobile device and comprising a single or composite measure of performance fluctuation in at least one qualitative feature of the cognitive activity.

(2) Hand/arm functional quality metric activity parameters from computer-implemented tests that assess fine motor capabilities (fine motion assessment), in particular, hand/arm motion functions, and in particular, touch screen-based "draw shape" and "pinch" tests.

Typical quality metric parameters for hand/arm function can be derived from the "draw shape" test and the "pinch" test.

In a further embodiment, the mobile device is adapted to perform or obtain data from a fine motion assessment and in particular a hand/arm activity test. Manual agility (hand motion function) characterizes an individual's ability to coordinate the movements of the hand and fingers and manipulate objects in a timely manner. The manual dexterity greatly affects the performance of the subject in daily activities, completing work related tasks and participating in leisure activities.

Manual agility was identified in 2007 as a core construct for incorporation into the National Institute of Health (NIH) toolbox for assessing neurological and behavioral functions as part of the NIH neuroscience research blueprint initiative developing compact and comprehensive instruments to measure motor, cognitive, sensory and emotional functions. After reviewing the existing metrics, the expert recommended two candidate metrics for manual agility: 1) the 9-hole pile test (9 HPT), and 2) the groove splice test (GPT), may be incorporated into the NIH toolbox for their applicability across life spans, psychometric stability, simplicity (relatively short completion time for one test), and applicability in multiple settings.

Fundamentally, 9HPT was chosen because it meets most of the inclusion criteria and the test is easily managed in all age groups (especially younger children). The time to manage the 9-hole pile test is brief (less than 5 minutes to measure for both hands) as required for inclusion in the NIH kit. The existing literature supports 9HPT as a reliable and effective measure of finger agility, and as the ability to assess hand agility in various diagnostic groups (i.e., multiple sclerosis, stroke, cerebral palsy, cerebellar damage, and parkinson's disease).

Normative data for 9HPT has been published across age spans including children and older adults, and since the late 90 s 9HPT represents a key component of functional upper limb assessments from the multiple sclerosis functional synthesis (MSFC) scale.

Furthermore, according to the present invention, two touch screen based application tests (i.e. the so-called "draw shape" and "pinch" tests) were developed, which aim to replicate the characteristics of 9HPT and GPT for enabling remote self-assessment of hand motion functions in neurological disorders on a user-friendly mobile device interface. The "draw shape" and "pinch" tests will assess upper limb motor function and manual dexterity (pinch, draw) and will be sensitive to changes and abnormalities in the pyramidal, extrapyramidal, sensory and cerebellar components of the upper limb nervous system, as well as to neuromuscular and myogenic alterations in upper limb function. The tests are typically performed daily, but may alternatively be performed less frequently (e.g., weekly or bi-weekly).

The purpose of the "draw shape" test is to assess fine finger control and stroke sequencing. The following aspects of the test are believed to cover impaired hand motion function: tremor and spasm and impaired hand-eye coordination. The patient is instructed to hold the mobile device in the untested hand and draw 6 pre-written alternating shapes of increasing complexity (linear, rectangular, circular, sinusoidal and spiral; see below) on the touch screen of the mobile device with the second finger of the tested hand "as quickly and accurately as possible" within a maximum time of, for example, 30 seconds. In order to successfully draw the shape, the patient's finger must continuously slide over the touchscreen and connect through all the indicated checkpoints and keep as much as possible the indicated start and end points within the boundaries of the writing path.

Two linear shapes each having five checkpoints to connect; i.e. four segments. The square shape has nine checkpoints to connect; i.e., eight segments. The circular shape has 14 checkpoints to connect; i.e. 13 segments. The figure 8 shape has 13 checkpoints to connect; i.e. 12 segments. The spiral shape has 22 checkpoints to connect, 21 segments. Completing six shapes then means successfully drawing a total of 62 segments.

The accuracy of the rendering and the time for rendering the shape will be reported to the patient. In addition, the total length of all the graphs made will be reported and depicted using familiar objects (e.g., dog, horse, and building sizes).

The test can be performed with both right and left hands alternated. The user will be instructed in daily turns. Two linear shapes each have a specific number "a" of checkpoints to connect, i.e., "a-1" segments. The square shape has a specific number "b" of checkpoints to connect, i.e., "b-1" segments. The circular shape has a specific number "c" of checkpoints to connect, i.e., "c-1" segments. The figure-8 shape has a specific number "d" of checkpoints to connect, i.e., "d-1" segments. The spiral shape has a specific number "e" of checkpoints to connect, "e-1" segments. Completing 6 shapes then means successfully drawing a total of "(2 a + b + c + d + e-6)" segments.

Based on the shape complexity, linear and square shapes may be associated with a weighting factor (Wf) 1, circular and sinusoidal shapes may be associated with a weighting factor of 2, and spiral shapes may be associated with a weighting factor of 3. The shape that was successfully completed on the second attempt may be associated with a weighting factor of 0.5. These weighting factors are numerical examples that may vary within the context of the present invention.

Typical drawn shape test quality metric parameters of interest are one or more of the following list:

1. shape completion expression score:

a. number of successfully completed shapes per test (0 to 6) (Σ Sh);

b. number of successfully completed shapes (0 to 6) on first attempt (Σ Sh)₁）；

c. Number of successfully completed shapes on second attempt (0 to 6) (Σ Sh₂）；

d. The number of failed/incomplete shapes on all attempts (0 to 12) (Σ F);

e. a shape completion score (0 to 10) reflecting the number of successfully completed shapes adjusted with weighting factors for different complexity levels of the respective shapes (Sh × Wf);

f. reflecting the number of successfully completed shapes adjusted with weighting factors for different complexity levels of the respective shapes and accounting for the successful shape completion score (0-10) of the first to second attempt (Sh [ Sh ])₁*Wf]+Σ[Sh₂*Wf*0.5]）；

g. The shape completion score as defined in #1e, and #1f may account for the speed at which the test is completed, multiplied by 30/t, where t will represent the time in seconds to complete the test;

h. overall and first attempt completion rates for each 6 individual shapes based on multiple tests over a certain time period: (Sigma Sh)₁)/ (ΣSh₁+ΣSh₂+ Σ F) and (Σ Sh₁+ΣSh₂)/ (ΣSh₁+ΣSh₂+ΣF)，

2. Segment completion and agility performance scoring/measuring:

(based on analysis of the best of the two attempts for each shape [ highest number of segments completed ], if applicable.)

a. The number of segments successfully completed per test (0 to [2a + b + c + d + e-6 ]) (Σ Se);

b. mean rapidity of successfully completed segments ([ C ], segments/sec): c = Σ Se/t, where t will represent the time to complete the test in seconds (maximum 30 seconds);

c. a segment completion score (Σ [ Se × Wf ]) reflecting the number of successfully completed segments adjusted with weighting factors for different complexity levels of the respective shape;

d. a speed adjusted and weighted segment completion score (Σ [ Se × Wf ] × 30/t), where t will represent the time in seconds to complete the test;

e. shape specific number of successfully completed segments for linear and square shapes (Σ Se_LS）；

f. Shape specific number of successfully completed segments for circular and sinusoidal shapes (Σ Se_CS）；

g. Shape specific number of successfully completed segments for spiral shape (Σ Se_S）；

h. Shape-specific mean linear agility for successfully completed segments performed in linear and square shape tests: c_L=ΣSe_LST, where t will represent the cumulative epoch time elapsed from the start to the end point for the corresponding successfully completed segment within these particular shapes in seconds;

i. shape-specific mean circular agility for successfully completed segments performed in circular and sinusoidal shape tests: c_C=ΣSe_CST, where t will represent the cumulative epoch time elapsed from the start to the end point for the corresponding successfully completed segment within these particular shapes in seconds;

j. shape-specific mean spiral agility for successfully completed segments performed in spiral shape testing: c_S=ΣSe_ST, where t will represent the cumulative time period elapsed from the start to the end point of the corresponding successfully completed segment within these particular shapes in secondsIn the middle of the furnace, the gas-liquid separation chamber,

3. rendering precision performance score/metric:

(based on analysis of the best of the two attempts for each shape [ highest number of segments completed ], if applicable.)

a. Deviation (Dev) calculated as: the sum of the integrated surface-deviated Area Under Curve (AUC) measures between the plotted trajectory and the reached target plotted path from start to end checkpoint for each particular shape, divided by the total accumulated length of the corresponding target paths within those shapes (reached from start to end checkpoint);

b. linear bias (Dev) calculated as Dev in #3a but specifically from the results of the linear and square shape tests_L）；

c. Circular deviation (Dev) calculated as Dev in #3a but specifically from circular and sinusoidal shape test results_C）；

d. Spiral deviation (Dev) calculated as Dev in #3a but specifically from spiral shape test results_S）；

e. Shape specific bias (Dev) calculated as Dev in #3a but separately from each of 6 different shape test results_1-6) Only those shapes in which at least 3 segments were successfully completed within the best attempt;

f. continuous variable analysis of any other method that calculates shape-specific or shape-agnostic gross deviations from the target trajectory.

The purpose of the pinch test is to assess fine tip motion manipulation (grip and pinch) and control by assessing the accuracy of the finger movements closed by the pinch. The test is considered to cover the following aspects of impaired hand motion function: impaired grip/griping function, muscle weakness and impaired hand-eye coordination. The patient is instructed to hold the mobile device in an untested hand and by touching the screen with two fingers (typically, a thumb and a second finger, or more typically, a thumb and a third finger) from the same hand to squeeze or pinch as many circles (e.g., a picture of a tomato) as possible during 30 seconds. The number of successfully pinched shapes (e.g., tomatoes) will be reported to the patient, and in addition, the total number of pinched tomatoes will be reported in familiar, well understood notation (e.g., tomatoes equivalent to a tomato paste bottle). Impaired fine motor control will affect performance. The test will be performed with the right hand and the left hand alternately. The user will be instructed in daily turns.

Typical hand/arm functional quality metric activity parameters derived from pinch tests and captured as continuous outcome variables reflecting fluctuations within the test measuring hand/arm functional integrity and manual agility are selected from the group consisting of:

1) the time elapsed between 2 successive pinch attempts (defined as a double contact on the touchscreen followed by a pinch attempt),

2) for all detected double contacts, double touch asynchrony measured as the lag time between the first and second fingers touching the screen,

3) for all detected double contacts, the pinching target accuracy measured as the distance from the equidistant point between the starting touch points of the two fingers at the double contact to the center of the tomato shape,

4) pinch finger movement asymmetry, measured as the ratio of the respective distances (shortest/longest) that two fingers slide from the double contact starting point until reaching the pinch gap, for all double contacts that were successfully pinched,

5) for all double contacts successfully pinched, pinch finger velocity measured as the velocity (mm/sec) of each finger and/or both fingers sliding on the screen from the time of the double contact until reaching the pinch gap,

6) for all double contacts successfully pinched, pinch finger asynchrony measured as the ratio of the respective individual finger's time sliding on the screen from the double contact until the speed of the pinch gap is reached (slowest/fastest), and

7) continuous variable analysis of 1) to 6) over time and their analysis over time of variable duration.

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two, three or all of these parameters are determined.

Typical pinch test quality metric parameters of interest are:

1. number of shapes pressed

a. Total number of tomato shapes extruded in 30 seconds (Σ Sh);

b. total number of tomatoes squeezed at first attempt in 30 seconds (Σ Sh)₁) (the first attempt is detected as the first double contact on the screen after a successful squeeze, if not the first attempt tested),

2. and measuring pinching precision:

a. pinching success rate (P)_SR) Defined as Σ Sh divided by the total number of pinch attempts (Σ P) within the total duration of the test (measured as the total number of separately detected two-finger contacts on the screen);

b. dual Touch Asynchrony (DTA) measured as the lag time between the first and second fingers touching the screen for all detected dual contacts;

c. pinching target accuracy (P) measured as the distance from the equidistant point between the initial touch points of the two fingers at the double contact to the center of the tomato shape for all detected double contacts_TP）；

d. Pinch finger movement asymmetry (P) measured as the ratio of the respective distances (shortest/longest) that two fingers slide from a double contact start point until reaching a pinch gap for all double contacts that were successfully pinched (P)_FMA）；

e. Pinch finger velocity (P) measured as the velocity of each finger and/or both fingers sliding on the screen from the time of the double contact until reaching the pinch gap (mm/sec) for all double contacts of a successful pinch_FV）；

f. All double contacts for successful pinching as corresponding individual fingersPinch finger asynchrony (P) measured from time-to-time sliding of double contact on screen until reaching the pinch gap speed (slowest/fastest) ratio_FA）；

g. Continuous variable analysis of 2a to 2f over time and their analysis over time of variable duration (5-15 seconds);

h. continuous variable analysis of integral measures of deviation from target drawn trajectories for all tested shapes (spiral and square in particular).

(3) Walking quality metric parameters from sensor-based (e.g., accelerometer, gyroscope, magnetometer, Global Positioning System (GPS)) and computer-implemented tests for measures of walking performance and gait and step dynamics, in particular, 2-minute walk tests (2 MWT) and U-turn tests (UTT), Static Balance Tests (SBT), and tests for walking performance, step/step dynamics, and upper limb motor function while walking using data collected from passive continuous gait analysis (CAG).

a) Two minutes walk test (2 MWT)

The purpose of this test is to assess difficult, fatiguing, or unusual patterns in long distance walking by capturing gait features in 2 MWT. Data will be captured from the smartphone sensor. Reductions in step and stride length, increases in step duration, increases in stride duration and asymmetry, and less periodic steps and strides can be observed with progression of disability or recurrence of presentation (Hobart 2013). The patient will be instructed to "walk as fast and long as possible for 2 minutes, but walk safely". 2MWT is a simple test that is required to be performed on flat ground, either indoors or outdoors, where patients have identified that they can walk straight up to ≧ 200 meters without making a U-turn. The patient is allowed to wear regular footwear and accessories and/or in-shoe orthotics as desired. The number of steps taken over the course of two minutes and the total number of steps taken over all 2 minute walk tests performed will be reported to the patient.

Typical walking quality metric activity parameters derived from 2MWT and captured as continuous outcome variables reflecting intra-test fluctuations measuring gait and balance integrity are selected from the group consisting of:

1) the duration of the step taken by the user,

2) the walking step speed (steps/second),

3) stride asymmetry ratio (the difference in stride duration between one stride and the next divided by the mean stride duration), and

4) by biomechanically modeling the stride length and total distance of the stride,

5) the index of deceleration by time period is,

6) asymmetry index by time period.

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two, three or all of these parameters are determined.

Further typical 2MWT quality metric parameters of particular interest are one or more of the following list:

1. surrogate for gait speed and spasticity:

a. the total number of steps detected (Σ S) in, for example, 2 minutes;

b. total number of rest stops (Σ Rs) detected in 2 minutes, if any;

c. continuous variable analysis of step duration (WsT) throughout 2 MWT;

d. continuous variable analysis of walking stride speed (WsV) (steps/sec) across 2 MWT;

e. stride asymmetry across 2MWT (mean stride duration difference between one stride and the next divided by mean stride duration): SAR = mean Δ (WsT)_x- WsT_x+1)/(120/ΣS)；

f. Total number of steps detected for each period of 20 seconds (Σ S)_{t, t+20}）；

g. Mean walking stride duration in each period of 20 seconds:WsT_{t, t+20}=20/ΣS_{t, t+20}；

h. mean walking stride speed in each period of 20 seconds: WsV_{t, t+20}= ΣS_{t, t+20}/20；

i. Stride asymmetry ratio in each period of 20 seconds: SAR (synthetic aperture radar)_{t, t+20}= meanΔ_{t, t+20}(WsT_x- WsT_x+1)/(20/ΣS_{t, t+20})；

j. By biomechanically modeling the stride length and total distance of the stride,

2. walking fatigue index:

a. deceleration index: DI = WsV_100-120/max (WsV_0-20, WsV_20-40, WsV_40-60)；

b. Asymmetry index: AI = SAR_100-120/min (SAR_0-20, SAR_20-40, SAR_40-60)。

b) U-turn test (UTT)

The purpose of this test is to assess the difficult or unusual pattern in performing a U-turn when walking short distances at a comfortable pace. UTT is required to be performed indoors or outdoors, on flat ground where the patient is instructed to "walk safely and perform at least five successive U-turns going back and forth between two points spaced several meters apart. Gait feature data (stride count, duration and asymmetry during U-turn, change in U-turn duration) during the task will be captured from the smartphone sensors. The patient is allowed to wear regular footwear and accessories and/or in-shoe orthotics as desired. The speed of the turn will be reported to the patient.

Typical walking quality metric activity parameters indicate continuous gait analysis from passive monitoring and fluctuations in walking quality in UTT, turning speed from UTT, number of daily turns while walking, and average daily turning speed. Intra-subject daily monitoring of these quality metric parameters allows for detection of, for example, multiple sclerosis recurrence. According to the accompanying example, a clear difference in the active test U-turn speed measured with UTT was observed between before and after reporting the recurrence.

Typical UTT quality metric parameters of interest are from the following list:

1. the number of mean steps (Σ Su) required from start to end of a complete U-turn;

2. the mean time (Tu) required from start to end of a complete U-turn;

3. mean walking stride duration: tsu = Tu/Σ Su;

4. turning direction (left/right);

5. turning speed (degrees/second).

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two, three or all of these parameters are determined.

c) Continuous gait analysis (CAG)

Continuous recording of gait feature data (stride count, duration and asymmetry) captured from a smartphone sensor will allow passive monitoring of daily capacity and quality of walking dynamics. The radius of the patient's activity will be reported to the patient. The radius will be expressed in standard dimensions and in familiar layman terms (e.g. football pitch). Continuous recording of gait feature data (stride count, duration and asymmetry, and swing arm dynamics while walking) captured from sensors will allow passive monitoring of daily capacity and quality of walking dynamics. Activity detection is a prior step in gait detection and analysis and activity analysis. It may be based on a different more complex or less complex approach (Rai 2012, Zee: zero-effective horizontal calculation for accelerator localization. Proceedings of the 18th annual communication on Mobile calculation and network. ACM; asheikh, m. a., serim, a., Niyato, d., Doyle, l. 2015, Lin, s., & Tan, h. -p. (r.). DeepActivity recording Models with ternary calculation. arXiv prediction algorithm 1511.04664; or Ord ñ ez, f. j., & Roggen, d. (2016) for correction of the motion of the accelerometer), which may be considered as a high-frequency signal in case of a high-frequency response algorithm (r. g) of the accelerometer 1, 2. g. for the response of the accelerometer, 1. for the activity of the accelerometer, e.1. for the feedback connected environment. The tests are typically performed daily.

Typical walking quality metric activity parameters derived from the CAG and captured as continuous outcome variables reflecting intra-test fluctuations measuring gait and balance integrity are selected from the group consisting of:

1) the frequency distribution of the number of steps detected in each interval of successive strides,

2) the duration/speed of the striding step over time,

3) the change in stride length over time derived by biomechanical modeling,

4) an increasing gain over time, an

5) Frequency distribution of sit/stand transitions and turns.

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two, three or all of these parameters are determined.

Typical further CAG quality metric parameters of interest are one or more of the following list:

daily walking range and speed alternatives:

a. the total number of steps detected for each day of active recording (Σ Sd);

b. total cumulative time of detected walking (Σ T) for each day of active recording;

c. total number of intervals of continuous walking for each day of active recording (Σ Id);

d. a frequency distribution (Δ Si) of the number of detected steps within each interval of consecutive walks for each day of active recording;

e. maximum number of steps in a single interval of continuous walking for each day of active recording (Scmax);

f. mean walking stride duration for each day of active recording: WsT = Σ T/Σ Sd;

g. mean behavioral turning speeds for each day and in a particular walking round or various durations;

h. a mean number of behavior turns detected for each day;

i. mean step power;

j. the number and characteristics of stairs climbed for each day;

k. number and nature of rounds of jogging time;

the number and characteristics of sit/stand transitions per day, the average walking stride speed per day for active recording: WsV = Σ Sd/Σ T (steps/minute);

step length and total distance for each day walking derived by biomechanical modeling;

n. mean circadian pattern of gait activity features based on all combinations of stair climbing, jogging, walking, turning, sitting/standing transition event detection;

o. variables # a-n by time of day.

d) Static Balance Test (SBT)

The objective of this test is to assess the static balance function of a person as in one of the items of the widely used Burger Balance Scale (BBS), i.e. unsupported standing, which is a 14-item objective metric designed to assess static balance and fall risk in the adult population (Berg 1992). Data will be captured from the smartphone sensor. The patient was asked to stand in an unsupported stationary position for 30 seconds, with the arms relaxed straight alongside the body if possible, and with the smartphone held in a running belt in an intermediate forward position. Individuals with increased fall risk and/or impaired static balance function may demonstrate altered postural control [ sway ] (Wai 2014). Changes in the balance shift will be reported to the patient by the rocking path length and depicted in symbols (e.g., solid large rocks, small rocks). The animated rocking path will be shown as an easily understood representation of the equilibrium change.

Typical walking quality metric activity parameters derived from SBT and captured as continuous outcome variables reflecting intra-test fluctuations measuring gait and balance integrity are selected from the group consisting of:

1) swinging and jerking: the time derivative of the acceleration [ mannini 2012],

2) a swinging path: the total length of the track, and

3) the swing range.

It should be understood that one or more of these quality metric parameters may be determined in accordance with the present invention. Typically, one, two or all of these parameters are determined.

Typical further SBT quality metric parameters of interest are one or more of the following list:

1. swinging and jerking: the time derivative of the acceleration (mannini 2012),

2. a swinging path: the total length of the track is such that,

3. the swing range.

It is to be understood that a mobile device to be applied according to the present invention may be adapted to perform one or more of the above mentioned activity tests. In particular, it may be adapted to perform at least one, at least two or all of these tests. Typically, a combination of tests may be implemented on a mobile device.

Furthermore, in the method of the invention, the at least one further parameter may be determined from data comprising general information obtained from a mobile device from the subject. General information can be obtained from the subject in addition to the quality metric parameters mentioned above. This information is typically derived from answering mood scale questions, answering questions related to quality of life and disease symptoms, in particular by performing 29 multiple sclerosis impact scale (MSIS 29) questionnaires and/or Multiple Sclerosis Symptom Tracker (MSST).

Further specific envisaged activity tests and means for measurements by a mobile device according to the inventive method are specified below:

(4) a computer-implemented test to assess emotional state and well being, in particular, the Mood Scale Question (MSQ).

In an embodiment, the mobile device is adapted to perform or obtain data from an emotional scale question (MSQ) questionnaire. Depression in its various forms is a common symptom in MS patients and, if left untreated, it reduces quality of life, makes other symptoms, including fatigue, pain, cognitive changes, feel worse, and may be life threatening (national MS association). Therefore, in order to assess the overall state perceived by the patient, they will be asked on the mobile device about how they feel. Questionnaires are typically performed on a daily basis.

Typical MSQ performance parameters of interest:

1. the proportion of days with excellent mood in the last week, month and year;

2. the proportion of days with good mood is more than or equal to the last week, month and year;

3. the proportion of the days with the emotion more than or equal to the body in the last week, month and year;

4. the proportion of days with very bad mood in the last week, month and year;

5. frequency distribution of response types by time of day between 6-8am, 8-10am, 10-12am, 12-14, 14-16, 16-18, 18-20, 20-24, 0-6am during the last month and during the last year.

(5) A computer-implemented test to assess quality of life, specifically, a 29-item multiple sclerosis impact scale (MSIS 29).

In one embodiment, the mobile device is adapted to perform or obtain data from a Multiple Sclerosis Impact Scale (MSIS) -29 test. To assess the impact of MS on a subject's daily life, they will be asked biweekly on a mobile device to complete MSIS-29 (Hobart 2001, Brain 124: 962-73), MSIS-29 being a 29-item questionnaire (Hobart 2001, loc. cit.) designed to measure the physical (items 1-20) and psychological (items 21-29) impact of MS from the patient's perspective. We will use a second version of MSIS-29 (MSIS-29 v2) that has a four-point response category for each item: "not at all", "somewhat", "moderately" and "extremely". The MSIS-29 score ranges from 29 to 116. The scores on the physical impact scale may range from 20 to 80 and the scores on the psychological impact scale may range from 9 to 36, with lower scores indicating a small impact of MS and higher scores indicating a larger impact. Problem items #4 and #5 and items #2, #6 and #15 of MSIS-29v2, respectively related to walking/lower limb and hand/arm/upper limb bodily functions, will also be subject to separate clustering analyses. The test is typically performed every two weeks.

Typical MSIS-29(v2) performance parameters of interest:

1. MSIS-29 score (29-116);

2. MSIS-29 body impact score (20-80);

3. MSIS-29 psychological impact scores (9-36);

4. MSIS-29 walk/lower limb score (2-10);

5. MSIS-29 hand/arm/upper limb scores (3-15);

6. a time-corrected/filtered MSIS-29 score of 1-5 based on the minimum time required to understand the question posed and provide an answer;

7. 1.-6 based on the number of changes to a given answer and the difference/change between the answers provided, indeed a weighted MSIS-29 score;

8. fine finger motor skill functional parameters captured during MSIS-29

a. Continuous variable analysis of the duration of touch screen contact (Tts);

b. continuous variable analysis of the deviation (Dts) between the touch screen contact and the center of the closest target numeric key;

c. number of touchscreen contacts (Mts) mistakenly typed in response to typing (sum of contacts hit by no trigger key or hit by trigger key but associated with secondary swipe on screen);

9. the ratio of the 6a, 6b and 6c variables during the challenge response variable of eSDMT (6 c transform/normalization representing the planned number of Mts in case of MSIS-29 every 90 seconds).

(6) A computer-implemented test to track emerging new or worsening disease symptoms, in particular, a Multiple Sclerosis Symptom Tracker (MSST).

In yet another embodiment, the mobile device is adapted to execute or acquire data from a Multiple Sclerosis Symptom Tracker (MSST). Since the patient's perception of recurrence incidence and symptom change may be different from clinically relevant symptom exacerbations considered to be relapses, the patient will be asked directly every two weeks on the smartphone a simple question directed toward detecting new/worsening symptoms and synchronized with the MSIS-29 questionnaire. In addition, it is possible for the patient to report symptoms and their corresponding calendar dates of onset at any time. Typically, the MSST may be performed every two weeks or on demand.

Typical MSST performance parameters of interest:

1. the number of reported episodes of "new or significantly worsening symptoms during the last two weeks" in the last month and year (by symptom onset date);

2. the proportion of total reported episodes considered to be "(recurrence(s)" versus "no recurrence" versus "uncertain" new or significantly worsening symptoms during the last two weeks "within the last year.

(7) Computer-implemented passive monitoring of all or a predetermined subset of activities of a subject performed during a certain time window.

In a further embodiment, the mobile device is adapted to perform or obtain data from passive monitoring of all or a subset of activities. In particular, passive monitoring should encompass monitoring one or more activities performed during a predefined window (such as one or more days or one or more weeks) selected from the group consisting of: a measure of gait, generally an amount of movement in a daily routine, a type of movement in a daily routine, general mobility in daily life, and a change in movement behavior.

Typical passive monitoring performance parameters of interest:

a. frequency and/or speed of walking;

b. amount, ability and/or speed of standing up/sitting down, standing still and balancing;

c. the number of visited locations as an indicator of general mobility;

d. the type of location accessed as an indicator of movement behavior.

It is to be understood that the mobile device to be applied according to the present invention may be adapted to perform one or more of the above mentioned further tests. In particular, it may be adapted to perform at least one, at least two or all of these tests.

Furthermore, the mobile device may be adapted to perform further cognitive and movement impairment and disease tests, such as computer-implemented versions of other cognitive tests and/or visual contrast acuity tests (such as low contrast letter acuity or stone tests; stone tests (see, e.g., Bove 2015)).

Further data may also be processed in the method of the invention. These further data are typically adapted to further enhance the identification of the progressing MS in the subject. Typically, such data may be parameters of biochemical biomarkers from MS or data from imaging methods such as whole brain volume, brain parenchyma fragments, whole gray matter volume, cortical gray matter volume, volume of specific cortical regions, deep gray matter volume, thalamic volume, corpus callosum surface or thickness, white matter volume, third ventricle volume, total brain T2 weighted high intensity lesion volume, total cortical lesion volume, total brain T1 weighted high intensity lesion volume, total brain FLAIR (fluid attenuation inversion recovery) lesion volume, cross-sectional and/or longitudinal Magnetic Resonance Imaging (MRI) measures of total new and/or enlarged T2 and FLAIR lesion numbers and volumes, such as using automated algorithmic solution software (such as, but not exclusively, MSmetrix @)^TMOr NeuroQuant^TM) And so on.

The term "mobile device" as used herein refers to any portable device comprising sensors and data recording equipment adapted to obtain a data set of activity measurements. Typically, mobile devices include sensors for measuring activity. This may also require a data processor and storage unit and a display for electronic simulation of the activity test on the mobile device. Furthermore, from the subject's activity, data should be recorded and compiled into a data set that the inventive method is to evaluate on the mobile device itself or on a second device. Depending on the specific arrangement envisaged, it may be necessary for the mobile device to comprise data transmission equipment in order to transmit the acquired data set from the mobile device to one or more further devices. Particularly well suited as a mobile device according to the present invention is a smartphone, a smart watch, a wearable sensor, a portable multimedia device or a tablet computer. Alternatively, a portable sensor with data recording may be used, and optionally processing equipment may be used. Further, depending on the kind of activity test to be performed, the mobile device should be adapted to display instructions for the subject regarding the activity to be performed for the test. The specific contemplated activities that the subject is to perform are described elsewhere herein and encompass the following tests: an Information Processing Speed (IPS) test, a pinch test performed on a sensor surface of a mobile device, and/or a continuous gait analysis (CAG) from U-turn test (UTT), 2-minute walk test (2 MWT), Static Balance Test (SBT), or from passive monitoring, among other tests described in this specification.

Determining at least one parameter, and in particular a quality metric parameter as referred to herein, may be achieved by deriving a desired measurement value directly as said parameter from the data set. Alternatively, the parameter may integrate one or more measurements from the data set, and thus may be derived from the data set by a mathematical operation such as a calculation. Typically, the parameters are derived from the data set by an automated algorithm (e.g., by a computer program that automatically derives the parameters from the data set when tangibly embedded on a data processing device feed by the data set of activity measurements).

The term "reference" as used herein refers to a discriminator that allows identification of subjects suffering from cognitive and mobility diseases or disorders. Such a discriminator may be a value indicative of a parameter of a subject suffering from a cognitive and mobility disorder or disease.

Such values may be derived from one or more parameters (in particular, quality metric parameters as referred to herein) of a subject known to suffer from the cognitive and mobility disease or disorder under study. Typically, in this case, a mean or median may be used as the discriminator. If the determined parameter from the subject is the same as the reference or above a threshold derived from the reference, the subject may be identified as suffering from a cognitive and mobility disease or disorder in such a case. If the determined parameter is different from the reference and in particular below the threshold, the subject should accordingly be identified as not suffering from cognitive and movement diseases or disorders.

Similarly, values may be derived from one or more parameters (in particular, quality metric parameters as referred to herein) of a subject known not to suffer from the cognitive and mobility disease or disorder being studied. Typically, in this case, a mean or median may be used as the discriminator. If the determined parameter from the subject is the same as the reference or below a threshold derived from the reference, the subject may be identified as not suffering from cognitive and mobility diseases or disorders in this case. If the determined parameter is different from the reference and in particular above the threshold, the subject should be identified as suffering from a cognitive and mobility disease or disorder.

As an alternative, the reference may be a previously determined parameter of the data set, in particular a quality metric parameter as referred to herein, which may be a data set from activity measurements already obtained from the same subject prior to the actual data set. In this case, the determined parameters determined from the actual data set different with respect to the previously determined parameters should indicate an improvement or deterioration depending on the previous state of the disease and the kind of activity represented by the parameters. The skilled person knows how the parameters can be used as reference based on the kind of activity and previous parameters.

Comparing the determined at least one parameter (in particular, a quality metric parameter as referred to herein) with a reference may be achieved by an automated comparison algorithm implemented on a data processing device such as a computer. Compared to each other are the values of the determined parameters and the references to said determined parameters as specified in detail elsewhere herein. As a result of the comparison, it may be assessed whether the determined parameter is the same as or different from the reference or is in some relationship with the reference (e.g., greater than or less than the reference). Based on the assessment, subjects may be identified as suffering from a cognitive and movement disease or disorder ("enrollment") or not ("exclusion"). For this assessment, the kind of reference will be considered as described elsewhere in connection with suitable references according to the invention.

Furthermore, by determining the extent of the difference between the determined parameter and the reference, quantitative assessment of cognitive and movement diseases or disorders in the subject should be possible. It will be appreciated that an improved, worsening or unchanged overall disease condition or symptom thereof may be determined by comparing the actual determined parameter with an earlier determined parameter used as a reference. Based on the quantitative difference in the values of the performance parameter, an improved, deteriorated or unchanged condition may be determined and optionally also quantified. If other references are used (such as references from subjects suffering from cognitive and movement diseases or disorders to be studied), it will be appreciated that quantitative differences are meaningful if a certain disease stage can be assigned to a reference set. Relative to this disease stage, a worsening, improved or unchanged disease condition can be determined and optionally also quantified in this case.

The diagnosis (i.e., identifying the subject as a subject suffering from or not suffering from a cognitive and movement disease or disorder) is indicated to the subject or to another person, such as a medical practitioner. Typically, this is achieved by displaying the assessment on a display of the mobile device or the evaluation device. Alternatively, recommendations for a therapy (such as a medication) or for a certain lifestyle (e.g., a certain nutritional diet or rehabilitation measures) are automatically provided to the subject or others. For this purpose, the established diagnosis is compared with recommendations assigned to different diagnoses or assessments in a database. Once the established rating matches one of the stored and assigned diagnoses or ratings, an appropriate recommendation may be identified as a result of assigning a recommendation to the stored diagnosis or rating that matches the established diagnosis or rating. Accordingly, it is typically contemplated that recommendations and diagnostics exist in the form of a relational database. However, other arrangements allowing identification of suitable recommendations are also possible and known to the skilled person.

Further, the one or more parameters may also be stored on the mobile device, or indicated to the subject, typically in real-time. The stored parameters may be assembled into a time course or similar evaluation measure. Such evaluated parameters may be provided to the subject as feedback on the performance of the activity studied according to the method of the invention. Typically, such feedback may be provided in an electronic format on a suitable display of the mobile device, and may be linked to a recommendation for a therapy or rehabilitation measure as specified above.

Further, the evaluated parameters may also be provided to a medical practitioner in a doctor's office or hospital and to other health care providers, such as diagnostic test developers or drug developers in the context of clinical trials, health insurance providers, or other stakeholders of public or private health care systems.

Typically, the method of the invention for assessing a subject suffering from a cognitive and mobility disease or disorder may be carried out as follows:

first, at least one quality metric parameter of cognitive and/or fine motor activity is determined from an existing dataset of activity measurements obtained from the subject using a mobile device. The data set may be transmitted from the mobile device to an evaluation device, such as a computer, or may be processed in the mobile device in order to derive the at least one parameter from the data set.

Second, the determined at least one quality metric parameter of the cognitive and/or fine motor activity is compared to a reference, for example, using a computer-implemented comparison algorithm implemented by a data processor of the mobile device or by an evaluation device (e.g., a computer). The results of the comparison are assessed relative to the reference used in the comparison, and based on the assessment, the subject will be assessed relative to cognitive and movement diseases or disorders.

Third, the assessment (e.g., identifying the subject as a subject suffering from or not suffering from a cognitive and movement disease or disorder) is indicated to the subject or to another person (such as a medical practitioner).

Alternatively, recommendations for a therapy (such as a medication) or for a certain lifestyle (e.g., a certain nutritional diet) are automatically provided to the subject or others. For this purpose, the established rating is compared with recommendations assigned to different ratings in the database. Once the established rating matches one of the stored and assigned ratings, an appropriate recommendation may be identified as a result of assigning a recommendation to the stored rating that matches the established rating. Typical recommendations relate to therapeutic measures as described elsewhere herein.

As yet another alternative or in addition, at least one parameter that forms the basis for the assessment will be stored on the mobile device. Typically, it should be evaluated by a suitable evaluation tool implemented on the mobile device (such as a time course assembly algorithm) along with other stored parameters, which may electronically assist in rehabilitation or therapy recommendations as specified elsewhere herein.

In view of the above, the present invention also specifically contemplates a method of assessing cognitive and mobility diseases or disorders in a subject, comprising the steps of:

a) obtaining, using a mobile device, a dataset of cognitive and/or fine motor activity measurements from a subject during a predetermined activity performed by the subject;

b) determining at least one quality metric parameter of cognitive and/or fine motor activity determined from the dataset of activity measurements obtained from the subject using a mobile device;

c) comparing the determined at least one quality metric parameter of cognitive and/or fine motor activity with a reference; and

d) assessing cognitive and movement diseases or disorders in the subject based on the comparison conducted in step (c).

As used hereinafter, the terms "having," "including," or "containing," or any grammatical variations thereof, are used in a non-exclusive manner. Thus, these terms may refer both to the situation in which no further features than those introduced by these terms are present in the entity described in this context, and to the situation in which one or more further features are present. As an example, the expressions "a has B", "a includes B" and "a includes B" may refer both to the case where no other element than B is present in a (i.e. the case where a consists only and exclusively of B), and to the case where one or more further elements than B are present in the entity a (such as elements C, elements C and D, or even further elements).

Further, it should be noted that the terms "at least one," "one or more," or similar language indicating that a feature or element may be present once or more than once typically will be used only once when introducing the corresponding feature or element. Hereinafter, in most cases, the expression "at least one" or "one or more" will not be repeated when referring to the respective features or elements, despite the fact that the respective features or elements may be present once or more than once.

Further, as used below, the terms "specifically," "more specifically," "typically," and "more typically," or similar terms, are used in connection with additional/alternative features without limiting the alternatives. Thus, the features introduced by these terms are additional/alternative features and are not intended to limit the scope of the claims in any way. As the skilled person will appreciate, the invention may be implemented by using alternative features. Similarly, features introduced by "in embodiments of the invention" or similar expressions are intended as additional/alternative features without any limitation relating to alternative embodiments of the invention, without any limitation relating to the scope of the invention, and without any limitation relating to the possibility of combining features introduced in this way with other additional/alternative or non-additional/alternative features of the invention.

Advantageously, it has been found in the studies that form the basis of the present invention that quality metric parameters of fine motor activity obtained from data sets measured during certain activities of patients suspected to suffer or suffer from cognitive and mobility diseases or disorders (optionally together with other performance parameters of motor and cognitive abilities) can be used as digital biomarkers for assessing (e.g. identifying or monitoring) those patients. In particular, the studies that form the basis of the present invention show: quality metric parameters used as biomarkers are better compared to other (traditional) cognitive or fine motor activity parameters, since they not only serve as a measure for the ability to perform a certain task, but also reflect how the system (e.g., nervous system and/or motor system) performs overall by measuring fluctuations in the performance of a series of tasks. Accordingly, the results obtained are more robust and reliable than those dependent on individual activity parameters. In the studies that form the basis of the present invention, it was found that, in particular: hand/arm functional quality metrology activity parameters measuring fluctuations in manual agility in pinch task performance during pinch tests (see figure 3, interim analysis of clinical trial NCT 02952911) are more capable of detecting abnormal function than in-office performance tests (i.e., 9-hole peg test (9 HPT)). In fact, when patients in the NCT02952911 study with MS who had presumably normal hand/arm function were assessed according to 9HPT, the mass metric parameters of the pinch test enabled the identification of patients with MS from health control (see fig. 3). As a further example, it was found that: advantageously, a walking quality metric activity parameter that measures fluctuations in walking quality (turning speed) in daily UTT (see figure 4, mid-term analysis of clinical trial NCT 02952911) can identify acute disability exacerbations of MS disease progression and/or activity that suggest that performance tests in the clinic were not detected or may not be accurately dated at the time of onset. In this example, a clear difference in the active test U-turn speed measured with 5UTT was observed between before and after reporting a possible "relapse" by the symptom tracker (see fig. 4, panel b). The turn behavior (another walking quality metric parameter) in passive continuous gait analysis (CAG) is also different before versus after the "recurring" onset/report for the number of daily turns (see figure 4, panel c).

The data set studied by the inventive method may have been obtained from the patient in a convenient manner by using a mobile device, such as a smartphone, portable multimedia device or tablet computer throughout. The evaluation of the data set according to the inventive method may be carried out on the same mobile device or it may be carried out on a separate remote device. Furthermore, by using such a mobile device, recommendations for lifestyle or therapy can be provided directly to the patient, i.e. without consulting a medical practitioner in the doctor's office or hospital ambulance. Thanks to the invention, it is possible to more accurately adjust the patient's living condition to the actual disease state due to the use of the actual determined parameters by the inventive method. Thus, more efficient medication may be selected, or a regimen may be adapted to the current state of the patient. It will be appreciated that the method of the invention is typically a data evaluation method requiring an existing data set of data sets from cognitive or fine motor activity measurements of a subject. Within the data set, the method determines at least one cognitive or fine motor activity parameter that can be used to assess cognitive and movement diseases or disorders, i.e. it can be used as a digital biomarker for said diseases or disorders.

Accordingly, the method of the invention may be used to:

-assessing a disease condition;

monitoring the patient, in particular in real-life daily situations and on a large scale;

-supporting the patient with lifestyle and/or therapy recommendations;

study of drug efficacy, e.g. also during clinical trials;

-facilitating and/or aiding therapy decision making;

-support hospital management;

-support rehabilitation measures management;

-support health insurance assessment and management; and/or

-supporting decisions in public health management;

-identification/assessment of sub-clinical subtle changes in information processing speed;

-assessing disease modifying therapies and treatments (DTM); and/or

General assessment of cognitive ability.

The explanations and definitions of the terms made above apply mutatis mutandis to the embodiments described hereinafter.

In the following, specific embodiments of the process of the invention are described:

in an embodiment of the method of the invention, the cognitive and movement disease or disorder is a disease or disorder affecting the central and/or peripheral nervous system of the pyramidal, extrapyramidal, sensory or cerebellar systems, or a neuromuscular disease, or a muscular disease or disorder.

In yet another embodiment of the method of the present invention, the cognitive and mobility diseases or disorders are selected from the group consisting of: multiple sclerosis, stroke, cerebellar disorders, cerebellar ataxia, spastic paraplegia, essential tremor, muscle weakness or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, peripheral neuropathy, cerebral palsy, extra-pyramidal syndrome, alzheimer's disease, other forms of dementia, leukodystrophy, autism spectrum disorders, attention deficit disorder (ADD/ADHD), intellectual disability as defined by DSM-5, impairment of cognitive performance and stores associated with aging, parkinson's disease, huntington's disease, multiple neuropathy and amyotrophic lateral sclerosis.

In particular, it has been found that: subjects suffering from NMO and NMOSD, cerebellar ataxia, spastic paraplegia, essential tremor, muscle weakness or other forms of neuromuscular disorders, muscular dystrophies, myositis or other muscular disorders, peripheral neuropathy can be efficiently identified by using fine motor activity data sets obtained from drawn shape and/or squeezed shape tests. Subjects suffering from cerebral palsy, extra-pyramidal syndrome, alzheimer's disease, other forms of dementia, leukodystrophy, autism spectrum disorders, attention deficit disorder (ADD/ADHD), intellectual disability as defined by DSM-5, cognitive performance and reserve associated with aging can be efficiently identified from the fine motor activity dataset obtained from the eSDMT test. The remaining diseases or disorders can be efficiently identified by fine motor activity data sets from any test or from a combination of all tests. Accordingly, depending on the cognitive and mobility diseases or disorders to be studied, the mobile device may be individually configured to obtain a data set from a suitable combination of tests.

In an embodiment of the method of the present invention, the at least one quality metric activity parameter is a cognitive quality metric activity parameter indicative of fluctuations in neurocognitive function, a hand/arm function quality metric activity parameter indicative of fluctuations in manual agility or a walking quality metric activity parameter indicative of movement fluctuations.

In another embodiment of the inventive method, said cognitive and/or fine motor activity measurements comprise data from an Information Processing Speed (IPS) test, a pinch test performed on a sensor surface of the mobile device and/or from a U-turn test (UTT), a 2 minute walk test (2 MWT), a Static Balance Test (SBT) or continuous gait analysis (CAG) from passive monitoring.

In an embodiment of the inventive method, said data set of cognitive activity measurements comprises data from an Information Processing Speed (IPS) test on a sensor surface of the mobile device.

In a further embodiment of the inventive method, additionally at least one performance parameter is determined from the data set of activity measurements, which is indicative of other motor abilities and functions, walking, color vision, attention, agility and/or cognitive abilities, quality of life, fatigue, mental state, mood, vision and/or cognition of the subject.

In a further embodiment of the inventive method, additionally at least one performance parameter from the data set of activity measurements is determined, which is selected from the group consisting of: visual contrast acuity tests (such as low contrast letter acuity or stone's test) and the emotional scale problem (MSQ), MISI-29.

In an embodiment of the inventive method, the mobile device has been adapted for conducting one or more of the above mentioned tests for cognitive and/or fine motor activity measurements and preferably all of these tests on a subject.

However, in an embodiment of the inventive method, said mobile device is comprised in a smartphone, a smart watch, a wearable sensor, a portable multimedia device or a tablet computer.

In a further embodiment of the method of the present invention, said reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a data set of cognitive and/or fine motor activity measurements obtained from the subject at a point in time before the point in time when the data set of cognitive and/or fine motor activity measurements mentioned in step a) has been obtained from the subject. Typically, a deterioration between the determined at least one quality metric activity parameter and the reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

In another embodiment of the method of the present invention, the reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a data set of cognitive and/or fine motor activity measurements obtained from a subject or group of subjects known to suffer from cognitive and motor diseases or disorders. Typically, the determined at least one quality metric activity parameter being substantially the same as compared to the reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

In a further embodiment of the method of the invention, the reference is at least one quality metric activity parameter for cognitive and/or fine motor activity derived from a data set of cognitive and/or fine motor activity measurements obtained from a subject or group of subjects known not to suffer from cognitive and motor diseases or disorders. Typically, deterioration of the determined at least one quality metric activity parameter compared to a reference is indicative of a subject suffering from a cognitive and mobility disease or disorder.

The invention also contemplates a computer program, a computer program product or a computer-readable storage medium tangibly embodying said computer program, wherein the computer program comprises instructions which, when run on a data processing apparatus or a computer, implement the inventive method as specified above. Specifically, the present disclosure further encompasses:

a computer or computer network comprising at least one processor, wherein the processor is adapted to perform a method according to one of the embodiments described in the present specification,

a computer-loadable data structure adapted to perform a method according to one of the embodiments described in this specification when the data structure is executed on a computer,

-a computer script, wherein the computer program is adapted to perform a method according to one of the embodiments described in the present specification when the program is executed on a computer,

a computer program comprising program means for performing a method according to one of the embodiments described in the present description, when the computer program is executed on a computer or on a computer network,

a computer program comprising program means according to the preceding embodiments, wherein the program means are stored on a storage medium readable by a computer,

a storage medium, wherein a data structure is stored on the storage medium, and wherein the data structure is adapted to perform a method according to one of the embodiments described in the present specification after having been loaded into a main and/or working storage of a computer or computer network,

a computer program product with 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 present specification in case the program code means are executed on a computer or on a computer network,

-a data stream signal, typically encrypted, comprising a data set of cognitive or fine motor activity measurements obtained from a subject using a mobile device, and

-a data stream signal, typically encrypted, comprising at least one quality metric parameter of cognitive or fine motor activity derived from a dataset of cognitive or fine motor activity measurement datasets obtained from a subject using a mobile device.

The invention further relates to a method for determining at least one quality metric parameter of a cognitive or fine motor activity from a data set of cognitive or fine motor activity measurements obtained from said subject using a mobile device,

a) deriving at least one quality metric parameter of cognitive or fine motor activity from a typically pre-existing dataset of cognitive or fine motor activity measurements, the pre-existing dataset being obtained from the subject using a mobile device; and

b) comparing the determined at least one quality metric parameter to a reference, wherein typically the at least one quality metric parameter of cognitive or fine motor activity may assist in assessing cognitive and movement diseases or disorders in the subject.

The present invention also relates to a method for recommending therapy for cognitive and movement diseases or disorders, comprising the steps of the above-mentioned method of the invention (i.e. a method for identifying a subject as suffering from a cognitive and movement disease or disorder) and the further step of recommending therapy if the cognitive and movement disease or disorder is assessed.

The term "therapy for cognitive and movement diseases or disorders" as used herein refers to all kinds of medical treatments, including drug-based therapies, surgery, psychotherapy, physical therapy, and the like. The term also encompasses lifestyle recommendations, rehabilitation measures, and recommendations for nutritional diets. Typically, the method encompasses recommendations for drug-based therapies and in particular recommendations for therapies with drugs known to be useful for the treatment of cognitive and mobility diseases or disorders. Such a drug may be a therapy with one or more drugs selected from the group consisting of: interferon beta-1 a, interferon beta-1B, glatiramer acetate, mitoxantrone, natalizumab, fingolimod, teriflunomide, dimethylbutyrate, alemtuzumab, darlizumab, thrombolytic agents, such as recombinant tissue plasmin activator, acetylcholinesterase inhibitors (such as tacrine, rivastigmine, galantamine, or donepezil), NMDA receptor antagonists (such as memantine), non-steroidal anti-inflammatory drugs, dopa carboxylase inhibitors (such as levodopa, tolcapone, or entacapone), dopamine antagonists (such as bromocriptine, pergolide, pramipexole, ropinirole, piribedil, cabergoline, apomorphine, or lisuride), MAO-B inhibitors (such as safinamide, selegiline, or rasagiline, amantadine, anticholinergic, tetrabenazine, nerve blocking agents, Benzodiazepines and riluzole). Furthermore, the above mentioned method may in a further embodiment comprise the additional step of applying the recommended therapy to the subject.

Also encompassed according to the present invention is a method for determining the efficacy of a therapy against a cognitive and movement disease or disorder, comprising the steps of the above-mentioned method of the present invention (i.e. the method for identifying a subject as suffering from a cognitive and movement disease or disorder) and the following further steps: determining a therapy response if an improvement in cognitive and mobility diseases or disorders occurs in the subject at the time of therapy; or determining a response failure if a worsening of cognitive and movement disease or disorder occurs in the subject at the time of therapy or if the cognitive and movement disease or disorder remains unchanged.

The term "improvement" as referred to according to the present invention relates to any improvement of the overall disease or disorder condition or individual symptoms thereof. Likewise, "exacerbation" means any worsening of the overall disease or disorder condition or individual symptoms thereof. Since the processes of some cognitive and mobility disorders may typically be associated with an exacerbation of the overall disease or disorder condition and its symptoms, the exacerbations mentioned in connection with the above-mentioned methods are unexpected or atypical exacerbations of normal processes beyond the progression of the disease or disorder. Thus, invariant in this context may also mean that the overall disease or disorder condition and the symptoms accompanying it are within normal causes of disease or disorder progression.

Further, the present invention contemplates a method of monitoring cognitive and mobility diseases or disorders in a subject, comprising: whether a cognitive and movement disease or disorder improves, worsens or remains unchanged in a subject is determined by implementing the steps of the above-mentioned method of the present invention (i.e., the method for identifying a subject as suffering from a cognitive and movement disease or disorder) at least twice during a predefined monitoring period.

The term "predefined monitoring period" as used herein refers to a predefined period of time in which at least two activity measurements are conducted. Typically, such a period may range from days to weeks to months to years, depending on the course of disease or disorder progression to be expected for an individual subject. Within the monitoring period, activity measurements and parameters are determined at a first point in time, typically as a start of the monitoring period, and at least one further point in time. However, it is also possible that there is more than one further point in time of the activity measurement and the parameter determination. In any case, the fine motion activity parameter(s) determined from the activity measurements at the first point in time are compared to such parameters at subsequent points in time. Based on such comparison, quantitative differences may be identified that will be used to determine a worsening, improving, or invariant disease condition during the predefined monitoring period.

The invention relates to a mobile device comprising a processor, at least one sensor and a database, and software that is tangibly embedded in said device and that, when run on said device, implements any of the inventive methods.

Further consider a system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database and software tangibly embedded in said device and implementing any of the inventive methods when run on said device, wherein said mobile device and said remote device are operatively linked to each other.

Under "operatively linked to each other", it should be understood that the devices are connected to allow data transfer from one device to another. It is typically envisaged that at least the mobile device that obtains data from the subject is connected to a remote device that implements the steps of the method of the invention, so that the obtained data can be transmitted to the remote device for processing. However, the remote device may also transmit data to the mobile device, such as signals controlling or supervising its appropriate functions. The connection between the mobile device and the remote device may be made by a permanent or temporary physical connection, such as coaxial, fiber optic or twisted pair, 10BASE-T cable. Alternatively, it may be implemented by a temporary or permanent wireless connection (such as Wi-Fi, LTE advanced, or bluetooth) using, for example, radio waves. Further details may be found elsewhere in the specification. For data acquisition, the mobile device may include a user interface, such as a screen or other equipment for data acquisition. Typically, the activity measurement may be performed on a screen comprised by the mobile device, wherein it is to be understood that the screen may have different sizes, including for example a 5.1 inch screen.

Furthermore, the invention relates to the use of the mobile device or system of the invention for identifying a subject suffering from a cognitive and mobility disease or disorder.

The invention also contemplates the use of a mobile device or system according to the invention for monitoring subjects suffering from cognitive and mobility diseases or disorders, in particular in real-life daily situations and on a large scale.

However, it should be understood that the present invention contemplates the use of a mobile device or system according to the present invention for studying drug efficacy (e.g., also during clinical trials) in subjects suffering from cognitive and mobility diseases or disorders.

Further, the present invention contemplates the use of a mobile device or system according to the present invention for facilitating and/or assisting in therapy decision making for subjects suffering from cognitive and mobility diseases or disorders.

Furthermore, the invention provides the use of a mobile device or system according to the invention for supporting hospital management, rehabilitation measures management, health insurance assessment and management and/or for supporting decisions in public health management regarding subjects suffering from cognitive and mobile diseases or disorders.

Furthermore, encompassed by the invention is the use of a mobile device or system according to the invention for utilizing lifestyle and/or therapy recommendations for supporting subjects suffering from cognitive and mobility diseases or disorders.

Further specific examples are listed below:

Drawings

Fig. 1 shows an example of cognitive quality metric activity parameters measuring fluctuations in processing speed and correctness in alternative task performance during IPS testing, the elapsed time between correct responses as depicted in the following graph (mid-term analysis of clinical trial NCT 02952911) illustrates at the population level some degree of in-test "fatigue" as deterioration is observed over time during the 90 second IPS test when performance is monitored and analyzed over a 15 second period in this example.

FIG. 2 shows an example of a variable time profile of intra-test fluctuations in elapsed time between overall symbol-digit replacement responses (left panel) or correct symbol-digit replacement responses (right panel) in 3 categories of subjects relative to variable levels of overall IPS performance, where the total number of correct responses in 90 seconds is < 32 (first row), 32-39 (second row), or > 40 (third row).

Fig. 3 shows an example of a hand/arm functional quality metrology activity parameter measuring fluctuations in manual agility in pinch task performance during pinch tests, depicted in the following graph (mid-term analysis of clinical trial NCT 02952911) is the elapsed time between 2 consecutive pinch attempts to illustrate at the cohort level that this particular feature is more capable of detecting abnormal function than the traditional 9-hole peg test (9 HPT). In fact, when comparing NCT02952911 study patients with MS with presumably normal and impaired hand/arm function (based on a threshold [23.91 seconds ] (Wang 2015) defining an upper limit of normal function corresponding to an average 9HPT time plus two standard deviations for Health Control (HC)), it is possible to identify MS-afflicted patients with normal hand/arm function from HC by their mean time between two consecutive pinching attempts (‡ p < 0.001). Based on the patient's representation of the UTT U-turn velocity profile. P < 0.05; † p < 0.01; ‡ p < 0.001. EDSS, extended disability status scale; MS, multiple sclerosis; t25FW, timed 25 foot walk.

Fig. 4 shows an example of walking quality metric activity parameters measuring fluctuations in walking quality from continuous gait analysis of passive monitoring and UTT, with the turning speed from UTT, the number of daily turns while walking, and the average daily turning speed depicted in the following graph (the mid-term analysis of NCT 02952911) to illustrate the ability of day-by-day monitoring within subjects of these quality metric parameters to detect multiple sclerosis relapses. In this example, a clear difference in active test U-turn speed as measured with UTT was observed between before and after reporting recurrences (Wilcoxon rank sum test; panel b). The turn behavior in passive monitoring also differs before versus after recurring onset/reporting for the number of daily turns (panel c), while the average daily turn speed remains unchanged (panel d).

Fig. 5 shows a graphical representation of pinch test mass metering activity parameters, i.e., pinch test mass metering data. Sub-graph (a) shows an overview of subjects performing the test for 30 seconds. The touch event from the first finger is shown in green and the second finger touch event (b) is shown in red. The blue circle shows when whenever two contact points are made with respect to the display at the same time. The dotted lines show the start and end of the pinching attempt, respectively. Subfigure (c) shows the distance between the two pinching fingers. The velocity of the individual finger is depicted in (d). Panel (e) depicts the location of the 9 th tomato that was successfully pinched with the 13 th pinch on the first attempt. The circle shows the trace of finger movement on the touch screen. The green box indicates that the pinch attempt was successful.

Fig. 6 shows an example of plotting shape test quality metric activity parameters, i.e., touch traces from two subjects in a circular shape. The black circle indicates the waypoint that the body must pass. Each green marker represents the closest trace point to each waypoint. Sub-graph (a) shows a baseline subject selected based on good 9HPT performance. Sub-graph (b) depicts the subject with a difference of 9 HPT.

Fig. 7 shows plotting the shape test quality metric activity parameters, i.e., the trace performance of the example shown in fig. 5. The error distance for each waypoint of the circular shape is shown in sub-diagram (a). Sub-graph (b) shows shape-specific segmentation to sectors and subsequent error for each sector. Sub-graph (c) shows the range of error distances for each subject, including median and IQR.

Fig. 8 shows an example of plotting shape test quality metric activity parameters, i.e., a spiral shaped touch trace from two subjects. The black circle indicates the waypoint that the body must pass. Each green marker represents the closest trace point to each waypoint. Sub-graph (a) shows a baseline subject selected based on good 9HPT performance. Sub-graph (b) depicts the subject with a difference of 9 HPT.

Fig. 9 shows plotting the shape test quality metric activity parameters, i.e., the trace performance of the example shown in fig. 11. The error distance for each waypoint of the spiral shape is shown in sub-diagram (a). Sub-graph (b) shows shape-specific segmentation to sectors and subsequent error for each sector. Sub-graph (c) shows the range of error distances for each subject, including median and IQR.

Figure 10 shows the plot shape test quality metric activity parameters, i.e., the common spatial and temporal characteristics of the subject's performance by visual, velocity and acceleration analysis plots. The velocity is calculated as the change in euclidean distance between successive points over time; acceleration is the rate of change of velocity over time. By subject-specific complementary analysis of the shape and spatial analysis of the plotted points, the fine temporal behaviour of the subject can be studied. (a) Visual tracking of the specified shape; (b) drawing the speed tracking of the shape task along with the time until completion [ s ]; (c) the acceleration of the render shape task over time is tracked to completion s.

Fig. 11 schematically shows the change in total response time and the baseline change during test performance. The difference between baseline and total response time accounts for cognitive activity.

FIG. 12 shows the observed performance changes after several iterations of the matching task. The performance increased in healthy volunteers and patients for the matching task, while baseline performance remained unaffected.

Fig. 13 shows symbols useful for IPS matching tests. a) To c) are symbol pairs and d) to f) are single-piece. a) Rounding the symbols, allowing strong correlation and mirror matching in the reading direction; b) the symbols are segmented, resulting in confusing visual inspection and mirroring in the reading direction; c) the symbol is reinforced by edges, allowing a strong association, with a prominent mirror axis perpendicular to the reading direction; d) the symbol has rotational symmetry allowing easy visual inspection; e) the symbol is oriented and opposite the reading axis; f) the symbol is bordered, with two mirror axes in the reading direction.

FIG. 14 shows the IPS test setup on the display of the mobile device for symbol matching (a) and baseline task performance (b).

example 1: a method for assessing cognitive and mobility diseases or disorders in a subject suspected of suffering therefrom, comprising the steps of:

a) determining at least one quality metric activity parameter for cognitive and/or fine motor activity in a typically pre-existing data set of cognitive and/or fine motor activity measurements obtained from the subject using a mobile device; and

b) the determined at least one quality metric activity parameter is compared to a reference, whereby cognitive and movement diseases or disorders will be assessed.