DIGITAL BIOMARKERS FOR COGNITION AND MOVEMENT DISEASES OR DISORDERS

Abstract

A method for assessing a cognition and movement disease or disorder in a subject suspected to suffer therefrom. A cognition and/or fine motoric activity parameter is determined from a dataset of activity measurements obtained from the subject using a mobile device. The determined activity parameter is compared to a reference, and the cognition and movement disease or disorder is assessed. Also disclosed is a method for identifying whether a subject will benefit from a therapy for a cognition and movement disease or disorder. The steps just described are performed along with the step of identifying the subject as one who benefits from the therapy if the cognition and movement disease or disorder is assessed. Also disclosed is a mobile device comprising a processor, at least one sensor, a database and software which is tangibly embedded in said device and, when running on said device, carries out the disclosed methods.

Claims

1. A method for assessing a cognition and movement disease or disorder in a subject suspected to suffer therefrom, the method comprising: a) determining at least one cognition or fine motoric activity parameter from a dataset of fine motoric activity measurements obtained from said subject using a mobile device; b) comparing the determined at least one cognition or fine motoric activity parameter to a reference; and c) assessing the cognition and movement disease or disorder based on the comparison.

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

3. The method of claim 1, wherein said cognition and movement disease or disorder is selected from the group consisting of: multiple sclerosis (MS), neuromyelitis optica (NMO) and NMO spectrum disorders, stroke, a cerebellar disorder, cerebellar ataxia, spastic paraplegia, essential tremor, myasthenia and myasthenic syndromes or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, a peripheral neuropathy, cerebral palsy, extrapyramidal syndromes, Parkinson's disease, Huntington's disease, Alzheimer's disease, other forms of dementia, leukodystrophies, autism spectrum disorders, attention-deficit disorders (ADD/ADHD), intellectual disabilities as defined by DSM-5, impairment of cognitive performances and reserve related to aging, Parkinson's disease, Huntington's disease, a polyneuropathy, motor neuron diseases and amyotrophic lateral sclerosis (ALS).

4. The method of claim 1, wherein step A0 comprises determining the at least one fine motoric activity parameter and the at least one fine motoric activity parameter is indicative for hand motoric functions.

5. The method of claim 1, wherein the dataset comprises data from a test encompassing drawing shapes with a finger or squeezing shapes with a finger on a sensor surface of the mobile device.

6. The method of claim 1, wherein the said dataset of cognition activity measurements comprises data from a test encompassing performing a eSDMT test on a sensor surface of the mobile device.

7. The method of claim 1, further comprising determining at least one performance parameter from a dataset of activity measurements that is indicative for the subject's other motoric capabilities and function, walking, color vision, attention, dexterity or cognitive capabilities, quality of life, fatigue, mental state, mood, vision or cognition.

8. The method of claim 1, further comprising determining at least one performance parameter from a dataset of activity measurements selected from the group consisting of: 2-Minute Walking Test (2MWT), 5 U-Turn Test (5UTT), Static balance test (SBT), Continuous Analysis of Gait (CAG), visual contrast acuity tests (such as low contrast letter acuity or Ishihara test), Mood Scale Question (MSQ), MSIS-29, and passive monitoring of all or a predetermined subset of activities of a subject performed during a certain time window.

9. The method of claim 1, wherein said mobile device has been adapted for carrying out on the subject one or more of the tests referred to in claim 4.

10. The method of claim 1, wherein said reference is at least one cognition or fine motoric activity parameter derived from a dataset of cognition or fine motoric activity measurements obtained from the subject at a time prior to the time when the dataset of cognition or fine motoric activity measurements referred to in step a) has been obtained from the subject.

11. The method of claim 10, wherein a worsening between the determined at least one cognition or fine motoric activity parameter and the reference is indicative for a subject that suffers from the cognition and movement disease or disorder.

12. The method of claim 1, wherein said reference is at least one cognition or fine motoric activity parameter derived from a dataset of cognition or fine motoric activity measurements obtained from a subject or group of subjects known to suffer from the cognition and movement disease or disorder, or wherein a determined at least one cognition or fine motoric activity parameter being essentially identical compared to the reference is indicative for a subject that suffers from the cognition and movement disease or disorder.

13. The method of claim 1, wherein said reference is at least one cognition or fine motoric activity parameter derived from a dataset of cognition or fine motoric activity measurements obtained from a subject or group of subjects known not to suffer from the cognition and movement disease or disorder.

14. The method of claim 13, wherein a determined at least one cognition or fine motoric activity parameter being worsened compared to the reference is indicative for a subject that suffers from the cognition and movement disease or disorder.

15. The method of claim 1 for use for recommending a therapy for a cognition and movement disease or disorder comprising the further step of recommending the therapy when the cognition and movement disease or disorder is assessed.

16. The method of claim 15, further comprising administering the therapy to a patient.

17. The method of claim 15, wherein the therapy comprises one or more of the following: drug-based therapies, surgery, psychotherapy, physical therapy, life-style recommendations, rehabilitation measures, nutritional diets.

18. The method of claim 15 wherein the therapy includes a drug-based therapy comprising one or more of: Interferon beta-1a, Interferon beta-1b, Glatiramer acetate, Mitoxantrone, Natalizumab, Fingolimod, Teriflunomide, Dimethyl fumarate, Alemtuzumab, Daclizumab, Thrombolytic agents, Acetylcholinesterase inhibitors, NMDA receptor antagonists, non-steroidal anti-inflammatory drugs, dopa carboxylase inhibitors, dopamine antagonists, MAO-B inhibitors, Amantadine, Anticholinergics, Tetrabenazine, Neuroleptics, Benzodiazepines, Riluzole.

19. The method of claim 1 for use in determining efficacy of a therapy against a cognition and movement disease or disorder comprising the further step of determining a therapy response if improvement of the cognition and movement disease or disorder occurs in the subject upon therapy or determining a failure of response if worsening of the cognition and movement disease or disorder occurs in the subject upon therapy or if the cognition and movement disease or disorder remains unchanged.

20. The method of claim 1, comprising carrying out steps a)-c) at least two times during a predefined monitoring period and determining whether the cognition and movement disease or disorder improves, worsens or remains unchanged in a subject.

21. A mobile device comprising a processor, at least one sensor, a database and software which is tangibly embedded in said device and, when running on said device, carries out the method of claim 1.

22. The mobile device of claim 21 for use in identifying a subject suffering from a cognition and movement disease or disorder, or for use in monitoring a subject suffering from a cognition and movement disease or disorder, in particular, in a real life, daily situation and on large scale, for investigating drug efficacy, e.g., also during clinical trials, in a subject suffering from a cognition and movement disease or disorder, for facilitating or aiding therapeutic decision making for a subject suffering from a cognition and movement disease or disorder, for supporting hospital management, rehabilitation measure management, health insurances assessments and management or supporting decisions in public health management with respect to subjects suffering from a cognition and movement disease or disorder or for supporting a subject suffering from a cognition and movement disease or disorder with life style or therapy recommendations.

23. A system comprising a mobile device comprising at least one sensor and a remote device comprising a processor and a database as well as software which is tangibly embedded in said device and, when running on said device, carries out the method of claim 1, wherein said mobile device and said remote device are operatively linked to each other.

24. The system of claim 23 for use in identifying a subject suffering from a cognition and movement disease or disorder, or for use in monitoring a subject suffering from a cognition and movement disease or disorder, in particular, in a real life, daily situation and on large scale, for investigating drug efficacy, e.g., also during clinical trials, in a subject suffering from a cognition and movement disease or disorder, for facilitating or aiding therapeutic decision making for a subject suffering from a cognition and movement disease or disorder, for supporting hospital management, rehabilitation measure management, health insurances assessments and management or supporting decisions in public health management with respect to subjects suffering from a cognition and movement disease or disorder or for supporting a subject suffering from a cognition and movement disease or disorder with life style or therapy recommendations.

25. A method for assessing a cognition and movement disease or disorder in a subject suspected to suffer therefrom, the method comprising: a) prompting a subject to perform fine motoric activities using an electronic; b) collecting with the electronic device a dataset of said fine motoric activities; c) determining at least one cognition or fine motoric activity parameter from the dataset; d) comparing the determined at least one cognition or fine motoric activity parameter to a reference; and e) assessing the cognition and movement disease or disorder based on the comparison.

26. The method of claim 25 for use for recommending a therapy for a cognition and movement disease or disorder comprising the further step of recommending the therapy when the cognition and movement disease or disorder is assessed.

27. The method of claim 26, further comprising administering the therapy to a patient.

28. The method of claim 26, wherein the therapy comprises one or more of the following: drug-based therapies, surgery, psychotherapy, physical therapy, life-style recommendations, rehabilitation measures, nutritional diets.

29. The method of claim 26 wherein the therapy includes a drug-based therapy comprising one or more of: Interferon beta-1a, Interferon beta-1b, Glatiramer acetate, Mitoxantrone, Natalizumab, Fingolimod, Teriflunomide, Dimethyl fumarate, Alemtuzumab, Daclizumab, Thrombolytic agents, Acetylcholinesterase inhibitors, NMDA receptor antagonists, non-steroidal anti-inflammatory drugs, dopa carboxylase inhibitors, dopamine antagonists, MAO-B inhibitors, Amantadine, Anticholinergics, Tetrabenazine, Neuroleptics, Benzodiazepines, Riluzole.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0414] The above-mentioned aspects of exemplary embodiments will become more apparent and will be better understood by reference to the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0415] FIGS. 1A, 1B, 1C and 1D show a smartphone adapted for performing a computer implemented Draw a Shape test. FIG. 1A) Instructions are given to the patient on the screen of the smartphone; FIGS. 1B, 1C and 1D) user interfaces for testing drawing different shapes.

[0416] FIGS. 2A, 2B, 2C and 2D show a smartphone adapted for performing a computer implemented Squeeze a Shape test. FIG. 2A) Instructions are given to the patient on the screen of the smartphone; FIGS. 2B, 2C and 2D) user interface showing the different stages of a squeezing the shape activity.

[0417] FIGS. 3A, 3B and 3C show a smartphone adapted for performing a computer-implemented eSDMT. FIG. 3A) Instructions are given to the patient on the screen of the smartphone; FIG. 3B) user interface for testing matching digits; FIG. 3C) user interface for testing matching symbols.

[0418] FIGS. 4A and 4B show an eSDMT test performance of 30 subjects. FIG. 4A shows the distribution of number of total responses. The accuracy rate is depicted in 4B.

[0419] FIGS. 5A, 5B, 5C, 5D, 5E and 5F show the time elapsed between subsequent responses (R) and subsequent correct responses (CR) in eSDMT tests. FIGS. 5A, 5C and 5E show the elapsed time between subsequent responses (R). FIGS. 5B, 5D and 5F show the elapsed time between subsequent correct responses (CR). The subject population is divided into three groups: FIGS. 5A and 5B stem from subjects providing fewer than 32 (correct) responses (N=9); FIGS. 5C and 5D from subjects providing between 32 and 39 (correct) responses (N=10); and FIGS. 5E and 5F provide 40 or more (correct) responses (N=11) over the course of 90 seconds. The median of the elapsed time is plotted as line and the standard deviation is shown as shaded region.

[0420] FIGS. 6A, 6B, 6C and 6D show examples of responses (R) and correct responses (CR) profile of two subjects with quite distinct performances in eSDMT tests. FIG. 6A shows the cumulative responses (R) profile of two subjects over 90 seconds. FIG. 6C shows the elapsed time between subsequent responses (R) of two patients. FIG. 6B shows the cumulative correct responses (CR) profile of two patients over 90 seconds. FIG. 6D shows the elapsed time between subsequent correct responses (CR) of two patients.

[0421] FIGS. 7A, 7B, 7C, 7D and 7E show an illustration of Squeeze a Shape test data. FIG. 7A shows an overview of a subject performing the Squeeze a Shape test for 30 seconds. Circles 110 in FIG. 7B illustrate the touch events from the first finger and circles 112 show second finger touch. Circles 114 in FIG. 7B show whenever two contact points with the display were made at the same time. The vertical dotted lines show the start and end of a pinch attempt, respectively. Line 116 in FIG. 7C shows the distance between the two pinching fingers. FIG. 7D shows the speed of the first and second fingers. FIG. 7E shows the distance between the two pinching fingers.

[0422] FIGS. 8A and 8B show examples of touch traces for a circle shape from two subjects. Circles 120 along the dashed line indicate waypoints that subjects have to pass through. Circles 122 are the trace points. Each crosshair 118 represents the closest trace point 122 to each waypoint 120. FIG. 8A depicts a subject with poor 9HPT. FIG. 8B shows the baseline subject chosen based on good 9HPT performance.

[0423] FIGS. 9A, 9B and 9C show the tracing performance for examples shown in FIG. 8. Error distances per each waypoint of circle shape are shown in FIG. 9A. FIG. 9B shows shape specific segmentation into sectors, and subsequent error per sector. FIG. 9C shows the range of error distances per subject, including median and IQR.

[0424] FIGS. 10A and 10B show examples of touch traces for spiral shape from two subjects. Circles 120 along the dashed line indicate waypoints that subjects have to pass through. Circles 122 are the trace points. Each crosshair 118 represents the closest trace point 122 to each waypoint 120. FIG. 10A depicts a subject with poor 9HPT. FIG. 10B shows the baseline subject chosen based on good 9HPT performance.

[0425] FIGS. 11A, 11B and 11C show the tracing performance. Error distances per each waypoint of spiral shape are shown in FIG. 11A. FIG. 11B shows shape specific segmentation into sectors, and subsequent error per sector. FIG. 11C shows the range of error distances per subject, including median and IQR.

[0426] FIGS. 12A, 12B and 12C show the collective spatial and temporal characteristics of a subject's drawing performance through visual, velocity and acceleration analysis. Velocity is calculated as the change in Euclidean distance between consecutive points over time; acceleration is the rate of change of velocity over time. Through this shape and subject specific complementary analysis to a spatial analysis of points drawn, a subject's fine temporal performance characteristics can be studied. FIG. 12A) visual tracing of specified shape. FIG. 12B) velocity tracing of draw-a-shape task over time to complete [s]. FIG. 12C) acceleration tracing of Draw-a-Shape task over time to complete [s].

[0427] FIGS. 13A and 13B compare patient adherence to active tests and passive monitoring. Adherence count is based on adherent days per study week, defined as the week starting from the first data point received by the respective subject. Amount of passive monitoring collected is based on the duration of accelerometer recordings with correction for inactivity for smartphones and smartwatches individually. 2MWT, Two-Minute Walking Test.

[0428] FIG. 14 shows an association between PROs conducted on the smartphone and in the clinic. Total scores of paper-based MSIS-29 and smartphone-based MSIS-29 are compared at baseline (screening visit). The identity line is depicted as a dashed line. MSIS-29, Multiple Sclerosis Impact Scale.

[0429] FIG. 15 shows a cross-sectional baseline correlation of oral SDMT vs smartphone-based SDMT. At baseline, the number of correct responses from the smartphone-based SDMT correlated with correct responses from the oral SDMT (Spearman's correlation coefficient=0.72, p<0.001). The patient-level performances on oral SDMT were overall better than on the smartphone-based SDMT.

[0430] FIGS. 16A and 16B show that turning speed while walking correlates with T25FW (FIG. 16A) and EDSS (FIG. 16B). FIG. 16A shows turning speed measured with the 5UTT correlates with the T25FW (Spearman's correlation coefficient=0.62, p<0.001); as well as the ambulation items (items 4 and 5) of the MSIS-29 (Spearman's correlation coefficient=0.57, p=0.001). FIG. 16B shows turning speed measured with the 5UTT correlates with the EDSS score (Spearman's correlation coefficient=0.72, p<0.001).

DESCRIPTION AND EXAMPLES

[0431] The embodiments and examples described below are not intended to be exhaustive or to limit the invention to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may appreciate and understand the principles and practices of this disclosure.

EXAMPLE 1

A Computer-Implemented (Electronic) Symbol Digit Modalities Test (eSDMT)

[0432] Smart phones with a 5.1 inch screen were programmed with suites for performing the eSDMT test. Test persons were asked to carry out the tests on the smart phone according to the instructions shown on the display. 30 subjects were investigated. The determined responses and accuracies are shown in FIGS. 4A-4B.

[0433] The time elapsed between subsequent responses (R) and subsequent correct responses (CR) was also investigated in the implemented eSDMT tests. Results are shown in FIGS. 5A-5F.

[0434] Furthermore, responses (R) and correct responses (CR) profiles were determined. Examples of responses (R) and correct responses (CR) profile of two subjects with quite distinct performances in eSDMT tests are shown in FIGS. 6A-6D.

EXAMPLE 2

A Computer-implemented Test Evaluating Fine Motoric Capabilities (Fine Motoric Assessments), in Particular, Hand Motor Functions and, in Particular, the Touchscreen-Based Draw a Shape and Squeeze a Shape Tests

[0435] Smart phones with a 5.1 inch screen were programmed with suites for performing the Draw a Shape and Squeeze a Shape tests. Test persons were asked to carry out the tests on the smart phone according to the instructions shown on the display.

[0436] In the squeeze a shape set up, touch events from first and second fingers were determined and distances were calculated as well as the speed of the squeezing event (FIGS. 7A-7E). In the draw a shape set up, touch traces for the circle shapes were determined. Results are depicted in FIG. 8 or 10.

[0437] The overall calculated tracing performances are shown in FIGS. 9A-9C and FIGS. 11A-11C, respectively, and detailed data are summarized in Table 1 or 2, below.

TABLE-US-00001 TABLE 1 Circle assessment read-out performance statistics. The table lists performance measures of the two traces depicted in FIGS. 8A-8B. Time to Number Accu- Complete Total Mean Std. of Hits racy Shape [s] Error Error Error Baseline subject 12 85.71% 3.31 sec 195.34 13.95 7.69 Poor performing 9 64.28% 3.52 sec 407.25 29.09 30.56 subject

TABLE-US-00002 TABLE 2 Spiral assessment read-out performance statistics. The table lists performance measures of the two traces depicted in FIG. 10. Time to Number Accu- Complete Total Mean Std. of Hits racy Shape [s] Error Error Error Baseline subject 22 100% 5.77 sec 323.09 14.68 12.36 Poor performing 10 71.4% 7.01 sec 558.025 25.37 15.19 subject

[0438] Finally, spatial and temporal characteristics of a subject drawing a square were determined and results are shown in FIGS. 12A-12C.

EXAMPLE 3

Results from the Prospective Pilot Study (FLOODLIGHT) to Evaluate the Feasibility of Conducting Remote Patient Monitoring with the use of Digital Technology in Patients with Multiple Sclerosis

[0439] A study population will be selected by using the following inclusion and exclusion criteria:

[0440] Key inclusion criteria: [0441] Signed informed consent form [0442] Able to comply with the study protocol, in the investigator's judgment [0443] Age 18-55 years, inclusive [0444] Have a definite diagnosis of MS, confirmed as per the revised McDonald 2010 criteria [0445] EDSS score of 0.0 to 5.5, inclusive [0446] Weight: 45-110 kg [0447] For women of childbearing potential: Agreement to use an acceptable birth control method during the study period

[0448] Key exclusion criteria: [0449] Severely ill and unstable patients as per investigator's discretion [0450] Change in dosing regimen or switch of disease modifying therapy (DMT) in the last 12 weeks prior to enrollment [0451] Pregnant or lactating, or intending to become pregnant during the study

[0452] It is a primary objective of this study to show adherence to smartphone and smartwatch-based assessments quantified as compliance level (%) and to obtain feedback from patients and healthy controls on the smartphone and smartwatch schedule of assessments and the impact on their daily activities using a satisfaction questionnaire. Furthermore, additional objectives are addressed, in particular, the association between assessments conducted using the Floodlight Test and conventional MS clinical outcomes was determined, it was established if Floodlight measures can be used as a marker for disease activity/progression and are associated with changes in MRI and clinical outcomes over time and it was determined if the Floodlight Test Battery can differentiate between patients with and without MS, and between phenotypes in patients with MS.

[0453] In addition to the active tests and passive monitoring, the following assessments will be performed at each scheduled clinic visit: [0454] Oral Version of SDMT [0455] Fatigue Scale for Motor and Cognitive Functions (FSMC) [0456] Timed 25-Foot Walk Test (T25-FW) [0457] Berg Balance Scale (BBS) [0458] 9-Hole Peg Test (9HPT) [0459] Patient Health Questionnaire (PHQ-9) [0460] Patients with MS only: [0461] Brain MRI (MSmetrix) [0462] Expanded Disability Status Scale (EDSS) [0463] Patient Determined Disease Steps (PDDS) [0464] Pen and paper version of MSIS-29

[0465] While performing in-clinic tests, patients and healthy controls will be asked to carry/wear smartphone and smartwatch to collect sensor data along with in-clinic measures.

[0466] Patient adherence to active and passive testing is shown in FIGS. 13A-13B. Furthermore, the association between PROs performed in the hospital and on a mobile device (smart phone) are shown in FIG. 14. A baseline correlation was found between oral SDMT and mobile device implemented eSDMT was found; see FIG. 15. The turning speed while walking correlates with T25FW and EDSS; see FIGS. 16A-16B.

[0467] In summary, these results show that patients are highly engaged with the smartphone- and smartwatch-based assessments. Moreover, there is a correlation between tests and in-clinic clinical outcome measures recorded at baseline which suggests that the smartphone-based Floodlight Test Battery shall become a powerful tool to continuously monitor MS in a real-world scenario. Further, the smartphone-based measurement of turning speed while walking and performing U-turns appeared to correlate with T25FW and EDSS.

[0468] While exemplary embodiments have been disclosed hereinabove, the present invention is not limited to the disclosed embodiments. Instead, this application is intended to cover any variations, uses, or adaptations of this disclosure using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains and which fall within the limits of the appended claims.

CITED LITERATURE

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