Patent Application
20210353209
Aspects described herein generally relate to medical diagnostics and medical devices for improved patient testing and patient analysis. More specifically, aspects described herein provide diagnostic devices, systems and methods for assessing symptom severity and progression of Huntington's disease in a patient by active testing and/or passive monitoring of the patient.
Huntington's disease is an inherited condition that leads to the progressive degeneration of nerve cells in the brain. A diagnosis of Huntington's disease may be based on neurological testing, genetic testing, and imaging, as well as family history and symptoms. The disease causes a wide variety of symptoms associated with motor function, cognitive function, behavioral function and functional capacity of the patient. Symptoms associated with motor function can include both involuntary movement problems and impairments in voluntary movements, such as involuntary jerking or writhing movements (chorea), muscle problems, such as rigidity or muscle contracture (dystonia), slow or abnormal eye movements, impaired gait, posture and balance, difficulty with the physical production of speech or swallowing. Impairments in voluntary movements can impact a person's ability to work, perform daily activities, communicate and remain independent. Although there is no known cure, treatments such as medications, therapies, and life style changes can help the patient cope with the symptoms of the disease. Additionally, the onset, severity and progression of the symptoms of Huntington's disease can vary between individuals. Thus, early detection of even small changes in the severity and progression of symptoms is important for guiding treatment and therapy options.
There are several standardized methods and tests for measuring the symptom severity and progression in patients diagnosed with Huntington's disease. Each of the tests involves a doctor measuring the subject's abilities to perform various mental and physical functions in different ways. These standardized tests can provide an assessment of the various symptoms associated with the patient's cognitive, behavioral, motor functions and capabilities and can help track changes in these symptoms over time. Assessing symptom severity and progression using standardized methods and tests can, therefore, help guide treatment and therapy options.
Currently, assessing the severity and progression of symptoms in a patient diagnosed with Huntington's disease involves in-clinic monitoring and testing of the patient every 6 to 12 months. While monitoring and testing a patient more frequently is ideal, increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient to the patient.
The following presents a simplified summary of various aspects described herein. This summary is not an extensive overview, and is not intended to identify key or critical elements or to delineate the scope of the claims. The following summary merely presents some concepts in a simplified form as an introductory prelude to the more detailed description provided below. Aspects described herein describe specialized medical devices for assessing the severity and progression of symptoms for a patient diagnosed with Huntington's disease. Testing and monitoring may be done remotely and outside of a clinic environment, thereby providing lower cost, increased frequency, and simplified ease and convenience to the patient, resulting in improved detection of symptom progression, which in turn results in better treatment.
According to one aspect, the disclosure relates to a diagnostic device for assessing one or more symptoms of Huntington's disease in a subject. The device includes at least one processor, one or more sensors associated with the device, and memory storing computer-readable instructions that, when executed by the at least one processor, cause the device to receive a plurality of first sensor data via the one or more sensors associated with the device, extract, from the received first sensor data, a first plurality of features associated with the one or more symptoms of Huntington's disease in the subject, and determine a first assessment of the one or more symptoms of Huntington's disease based on the extracted first plurality of features.
A certain embodiment of the invention relates to a diagnostic device for assessing one or more symptoms of Huntington's disease in a subject, the device comprising:
A certain embodiment of the invention relates to the device as described herein, wherein the computer-readable instructions, when executed by the at least one processor, further cause the device to:
A certain embodiment of the invention relates to the device as described herein, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject.
A certain embodiment of the invention relates to the device as described herein, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject, whereby the patient mobility is assessed at least partly based on GPS location data.
A certain embodiment of the invention relates to the device as described herein, wherein the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
A certain embodiment of the invention relates to the device as described herein, wherein the one or more sensors associated with the device comprise at least one of a first sensor disposed within the device or a second sensor worn by the subject and configured to communicate with the device.
A certain embodiment of the invention relates to the device as described herein, wherein prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.
A certain embodiment of the invention relates to the device as described herein, wherein the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test, WPAI-HD test, HD-SDI test, speed tapping test, draw a shape test, chorea test, balance test, u-turn test, SDMT test, and word reading test.
A certain embodiment of the invention relates to a computer-implemented method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:
A certain embodiment of the invention relates to the computer-implemented method as described herein, further comprising:
A certain embodiment of the invention relates to the computer-implemented method as described herein, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject, in particular wherein the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
A certain embodiment of the invention relates to the computer-implemented method as described herein, whereby the patient mobility is assessed at least partly based on GPS location data.
A certain embodiment of the invention relates to the computer-implemented method as described herein, wherein the one or more sensors associated with the device comprise at least one of a first sensor disposed within the device or a second sensor located on the subject and configured to communicate with the device, in particular wherein prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject answer one or more questions or prompting the subject to perform one or more actions.
A certain embodiment of the invention relates to the computer-implemented method as described herein, wherein the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test, WPAI-HD test, HD-SDI test, speed tapping test, draw a shape test, chorea test, balance test, u-turn test, SDMT test, and word reading test.
A certain embodiment of the invention relates to a non-transitory machine readable storage medium comprising machine-readable instructions for causing a processor to execute a method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:
In some embodiments, the computer-readable instructions, when executed by the at least one processor, further cause the device to prompt the subject to perform one or more diagnostic tasks, in response to the subject performing the one or more diagnostic tasks, receive a plurality of second sensor data via the one or more sensors associated with the device, extract, from the received second sensor data, a second plurality of features associated with the one or more symptoms of Huntington's disease, and determine a second assessment of the one or more symptoms of Huntington's disease based on the extracted second plurality of features.
In some embodiments, the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject.
In some embodiments, the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
In some embodiments, the one or more sensors associated with the device comprise at least one of a first sensor disposed within the device or a second sensor worn by the subject and configured to communicate with the device.
In some embodiments, prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.
In some embodiments, the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test (https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/), Work Productivity and Activity Impairment (WPAI)-HD test, HD-Strength Deployment Inventory (SDI) test, speed tapping test, draw a shape test, chorea test, balance test, u-turn test, Symbol Digit Modalities Test (SDMT), and word reading test.
According to one aspect, the disclosure relates to a computer-implemented method for assessing one or more symptoms of Huntington's disease (HD) in a subject. The method includes receiving a plurality of first sensor data via one or more sensors associated with a device, extracting, from the received first sensor data, a first plurality of features associated with the one or more symptoms of Huntington's disease in the subject, and determining a first assessment of the one or more symptoms of Huntington's disease based on the extracted first plurality of features.
In some embodiments, the computer-implemented method further includes prompting the subject to perform one or more diagnostic tasks; in response to the subject performing the one or more diagnostics tasks, receiving, a plurality of second sensor data via the one or more sensors; extracting, from the received second sensor data, a second plurality of features associated with one or more symptoms of Huntington's disease; and determining a second assessment of the one or more symptoms of Huntington's disease based on at least the extracted second sensor data.
In some embodiments, the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject.
In some embodiments, the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
In some embodiments, the one or more sensors associated with the device is at least one of a first sensor disposed within the device or a second sensor located on the subject and configured to communicate with the device.
In some embodiments, prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject answer one or more questions or prompting the subject to perform one or more actions.
In some embodiments, the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test, WPAI-HD test, HD-SDI test, speed tapping test, draw a shape test, chorea test, balance test, a u-turn test, SDMT test, and word reading test.
According to one aspect of the disclosure, a non-transitory machine readable storage medium includes machine-readable instructions for causing a processor to execute a method for assessing one or more symptoms of Huntington's disease in a subject that includes receiving a plurality of sensor data via one or more sensors associated with a device; extracting, from the received sensor data, a plurality of features associated with the one or more symptoms of Huntington's disease in a subject; and determining an assessment of the one or more symptoms of Huntington's disease based on the extracted plurality of features.
A more complete understanding of aspects described herein and the advantages thereof may be acquired by referring to the following description in consideration of the accompanying drawings, in which like reference numbers indicate like features, and wherein:
19A-19E depict example screenshots from an example instructional video for a chorea test according to one or more illustrative aspects described herein.
23A-23F depict example screenshots from an instructional video for an example u-turn test according to one or more illustrative aspects described herein.
In the following description of various aspects, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration various embodiments in which aspects described herein may be practiced. It is to be understood that other aspects and/or embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the described aspects and embodiments. Aspects described herein are capable of other embodiments and of being practiced or being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. Rather, the phrases and terms used herein are to be given their broadest interpretation and meaning. The use of “including” and “comprising” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items and equivalents thereof. The use of the terms “mounted,” “connected,” “coupled,” “positioned,” “engaged” and similar terms, is meant to include both direct and indirect mounting, connecting, coupling, positioning and engaging.
Systems, methods and devices described herein provide a diagnostic for assessing one or more symptoms of Huntington's disease in a patient. In some embodiments, the diagnostic may be provided to the patient as a software application installed on a mobile device.
In some embodiments, systems, methods and devices described herein provide a diagnostic for assessing one or more symptoms of Huntington's disease in a patient based on passive monitoring of the patient. In some embodiments, the diagnostic obtains or receives sensor data from one or more sensors associated with the mobile device as the patient performs activities of daily life. In some embodiments, the sensors may be within the mobile device or wearable sensors. In some embodiments, the sensor features associated with the symptoms of Huntington's disease are extracted from the received or obtained sensor data. In some embodiments, the assessment of the symptom severity and progression of Huntington's disease in the patient is determined based on the extracted sensor features.
In some embodiments, systems, methods and devices according to the present disclosure provide a diagnostic for assessing one or more symptoms of Huntington's disease in a patient based on active testing of the patient. In some embodiments, the diagnostic prompts the patient to perform diagnostic tasks. In some embodiments, the diagnostic tasks are anchored in or modelled after established methods and standardized tests; in others, new tests or methods may be used. In some embodiments, in response to the patient performing the diagnostic task, the diagnostic obtains or receives sensor data via one or more sensors. In some embodiments, the sensors may be within a mobile device or wearable sensors worn by the patient. In some embodiments, sensor features associated with the symptoms of Huntington's disease are extracted from the received or obtained sensor data. In some embodiments, the assessment of the symptom severity and progression of Huntington's disease in the patient is determined based on the extracted features of the sensor data.
Assessments of symptom severity and progression of Huntington's disease using diagnostics according to the present disclosure correlate sufficiently with the assessments based on clinical results and may thus replace clinical patient monitoring and testing. Example diagnostics according to the present disclosure may be used in an out of clinic environment, and therefore have advantages in cost, ease of patient monitoring and convenience to the patient. This facilitates frequent patient monitoring and testing, resulting in a better understanding of the disease stage and provides insights about the disease that are useful to both the clinical and research community. An example diagnostic according to the present disclosure can provide earlier detection of even small changes in symptoms of Huntington's disease in a patient and can therefore be used for better disease management including individualized therapy.
The device 105 extracts, from the received first sensor data and second sensor data, features associated with one or more symptoms of Huntington's disease in the subject 110. In some embodiments, the symptoms of Huntington's disease in the subject 110 may include a symptom indicative of a cognitive function of the subject 110, a symptom indicative of a motor function of the subject 110, a symptom indicative of a behavioral function of the subject 110, or a symptom indicative of a functional capacity of the subject 110. In some embodiments, the one or more symptoms of Huntington's disease in the subject 110 are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
In some embodiments, the first sensor 120a or second sensor 120b (or another sensor altogether) associated with the device 105 may include or interface with a satellite-based radio navigation system, such as may be used with the Global Positioning System (GPS), Galileo, GLONASS, and/or similar systems (collectively referred to herein as GPS), and the plurality of first sensor data received from the first sensor 120b may include location data associated with the device 105. In some embodiments, the device 105 extracts location data, from the received first sensor data and second sensor data, associated with one or more symptoms of Huntington's disease in the subject 110. In some embodiments, an assessment of motor function of the subject 110 may be based at least in part on the extracted location data (e.g., patient mobility may be assessed based in part on GPS location data). In some embodiments, the sensors 120 associated with the device 105 may include sensors associated with Bluetooth and WiFi functionality and the sensor data may include information associated with the Bluetooth and WiFi signals received by the sensors 120. In some embodiments, the device 105 extracts data corresponding to the density of Bluetooth and WiFi signals received or transmitted by the device 105 or sensors, from the received first sensor data and second sensor data. In some embodiments, an assessment of behavioral function or an assessment of the functional capacity of the subject 110 may be based on the extracted Bluetooth and WiFi signal data (e.g., an assessment of patient sociability may be based in part on the density of Bluetooth and WiFi signals picked up).
The device 105 determines an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 send the extracted features over a network 180 to a server 150. The server 150 includes at least one processor 155 and a memory 161 storing computer-instructions for a symptom assessment application 170 that, when executed by the server processor 155, cause the processor 155 to determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features received by the server 150 from the device 105. In some embodiments, the symptom assessment application 170 may determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the sensor data received from the device 105 and a patient database 175 stored in the memory 160. In some embodiments, the patient database 175 may include patient and/or clinical data. In some embodiments, the patient database 175 may include in-clinic and sensor-based measures of motor and cognitive function at baseline and longitudinal from early Huntington's disease patients. In some embodiments, the patient database 175 may include in-clinic and sensor-based measures of behavioral and other symptoms. In some embodiments, the patient database 175 may include data from patients at other stages of Huntington's disease. In some embodiments, the patient database 175 may be independent of the server 150. In some embodiments, the server 150 sends the determined assessment of the one or more symptoms of Huntington's disease in the subject 110 to the device 105. In some embodiments, the device 105 may output the assessment of the one or more symptoms of Huntington's disease. In some embodiments, the device 105 may communicate information to the subject 110 based on the assessment. In some embodiments, the assessment of the one or more symptoms of Huntington's disease may be communicated to a clinician that may determine individualized therapy for the subject 110 based on the assessment.
In some embodiments, the computer-instructions for the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to assess one or more symptoms of Huntington's disease in the subject 110 based on active testing of the subject 110. The device 105 prompts the subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform the one or more diagnostic tasks includes prompting the subject to answer one or more questions or prompting the subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored in or modelled after well-established methods and standardized tests for evaluating and assessing Huntington's disease.
In response to the subject 110 performing the one or more diagnostic tasks, the diagnostic device 105 receives a plurality of sensor data via the one or more sensors associated with the device 105. As mentioned above, the sensors associated with the device 105 may include a first sensor 120a that is disposed within the device 105 and a second sensor 120b that is worn by the subject 110. The device 105 receives a plurality of first sensor data via the first sensor 120a and a plurality of second sensor data via the second sensor 120b. In some embodiments, the one or more diagnostic tasks may be associated with at least one of a EQ-5D-5L test, a WPAI-HD test, HD-SDI test, a speed tapping test, a draw a shape test using a left hand of the subject, a draw a shape test using a right hand of the subject, a chorea test, a balance test, a u-turn test, a SDMT test, and/or a word reading test.
The device 105 extracts, from the received plurality of first sensor data and the received plurality of second sensor data, features associated with one or more symptoms of Huntington's disease in the subject 110. The symptoms of Huntington's disease in the subject 110 may include a symptom indicative of a cognitive function of the subject 110, a symptom indicative of a motor function of the subject 110, a symptom indicative of a behavioral function of the subject 110, or a symptom indicative of a functional capacity of the subject 110. In some embodiments, the one or more symptoms of Huntington's disease in the subject 110 are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia. As discussed above, location-based data from a GPS or similar system may be used to assess symptoms related to the motor function and/or mobility of the subject and other location based assessments. Similarly as discussed above, WiFi and Bluetooth signal density may be used, e.g., to help assess patent sociability.
The device 105 determines an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 sends the extracted features over a network 180 to a server 150. The server 150 may include at least one processor 155 and a memory 161 storing computer-instructions for a symptom assessment application 170 that, when executed by the server processor 155, cause the processor 155 to determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features received by the server 150 from the device 105. In some embodiments, the symptom assessment application 170 may determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the sensor data received from the device 105 and a patient database 175 stored in the memory 160. In some embodiments, the patient database 175 may include patient and/or clinical data. In some embodiments, the patient database 175 may include in-clinic and sensor-based measures of motor and cognitive function at baseline and longitudinal from early Huntington's disease patients. In some embodiments, the patient database 175 may include in-clinic and sensor-based measures of behavioral and other symptoms. In some embodiments, the patient database 175 may include data from patients at other stages of Huntington's disease. In some embodiments, the patient database 175 may be independent of the server 150. In some embodiments, the server 150 sends the determined assessment of the one or more symptoms of Huntington's disease in the subject 110 to the device 105. In some embodiments, the device 105 may output the assessment of the one or more symptoms of Huntington's disease. In some embodiments, the device 105 may communicate information to the subject 110 based on the assessment. In some embodiments, the assessment of the one or more symptoms of Huntington's disease may be communicated to a clinician that may determine individualized therapy for the subject 110 based on the assessment.
In some embodiments, the symptom monitoring application 130 may provide a diagnostic application that includes a user interface (UI) that is displayed on the display screen 160 of the device 105. In some embodiments, the display screen 160 may be a touchscreen and the user interacts with the diagnostic application via the displayed UI.
In some embodiments, the user may specify settings for passive monitoring of the user by the device 105 by selecting the “Settings” menu option of the second pull-down menu shown in
Referring back to
Referring back to
The method 200 proceeds to step 210 which includes extracting, from the received first sensor data, a first plurality of features associated with the one or more symptoms of Huntington's disease in a subject. The device 105 extracts, from the received first sensor data and second sensor data, features associated with one or more symptoms of Huntington's disease in the subject 110. The symptoms of Huntington's disease in the subject 110 may include a symptom indicative of a cognitive function of the subject 110, a symptom indicative of a motor function of the subject 110, a symptom indicative of a behavioral function of the subject 110, or a symptom indicative of a functional capacity of the subject 110. In some embodiments, the extracted features of the plurality of first and second sensor data may be indicative of symptoms of Huntington's disease such as visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
The method 200 proceeds to step 215 which includes determining an assessment of the one or more symptoms of Huntington's disease based on the extracted features. The device 105 determines an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 may send the extracted features over a network 180 to a server 150. The server 150 includes at least one processor 155 and a memory 160 storing computer-instructions for a symptom assessment application 170 that, when executed by the processor 155, determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features received by the server 150 from the device 105. In some embodiments, the symptom assessment application 170 may determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of sensor data received from the device 105 and a patient database 175 stored in the memory 160. The patient database 175 may include various clinical and/or patient data. In some embodiments, the patient database 175 may include data, such as, in-clinic and sensor-based measures of motor and cognitive function at baseline and longitudinal from early Huntington's disease patients. In some embodiments, the patient database 175 may include in-clinic and sensor-based measures of behavioral and other symptoms. In some embodiments, the patient database 175 may include data of patients at other stages of Huntington's disease. In some embodiments, the patient database 175 may be independent of the server 150. In some embodiments, the server 150 sends the determined assessment of the one or more symptoms of Huntington's disease in the subject 110 to the device 105. In some embodiments, the device 105 may output the assessment of the one or more symptoms of Huntington's disease on the display 160 of the device 105. In some embodiments, the assessment of the one or more symptoms of Huntington's disease may be communicated to a clinician that may determine individualized therapy for the subject 110 based on the assessment.
The method 300 begins by proceeding to step 305 which includes prompting the subject to perform one or more diagnostic tasks. The device 105 prompts the subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform the one or more diagnostic tasks includes prompting the subject to answer one or more questions or prompting the subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored in or modelled after well-established methods and standardized tests for evaluating and assessing Huntington's disease.
In some embodiments, the diagnostic tasks may include daily questions to assess the mood and physical health of the patient at the time of the active testing. The patient's response to the daily questions provide an assessment of the patient's daily mood fluctuations and may be used as a control when assessing symptoms associated with motor, cognitive and behavioral functions of the patient.
In some embodiments, the diagnostic tasks may implement a standardized test for measuring generic health status such as the EQ-5D-5L.
In some embodiments, the diagnostic tasks may implement a Work Productivity and Activity Impairment Questionnaire specific to Huntington's disease (WPAI-HD). The diagnostic tasks associated with WPAI-HD measure the effect of Huntington's disease on the subject's ability to work and perform regular activities.
In some embodiments, the diagnostic tasks implement a Huntington's disease Speaking Difficult Item (HD-SDI). The diagnostic tasks associated with a HD-SDI test measure the effect of Huntington's disease on the subject's ability to speak.
In some embodiments, the diagnostic tasks may implement a speeded tapping test. In some embodiments, the diagnostic tasks prompt the subject 110 to tap the display screen 160 of the device 105 as fast and regularly as possible, using the index finger of both the left and right hands. The speeded tapping test measures the speed of finger movements. In some embodiments, the diagnostic tasks associated with the speed tapping test may assess symptoms of bradykinesia, chorea and/or dystonia. In some embodiments, the diagnostic tasks associated with the speed tapping test are modelled on tapping tests that have been shown to be sensitive to symptom changes in early Huntington's disease (Bechtel et al. 2010; Tabrizi et al. 2012). A similar finger tapping task is also included as part of the Unified Huntington's Disease Rating. Scale (UHDRS) assessment (Huntington's Study Group 1996).
In some embodiments, the diagnostic application may include an instructional video for the speeded tapping test that the subject may view on the display screen 160 of the device 105.
In some embodiments, the diagnostic tasks may implement a draw a shape test that prompt the subject 110 to trace a series of increasingly complex shapes on the display screen 160 of the device 105. In some embodiments, the shapes may include lines, a square, a circle, an eight, and a spiral. This test is designed to assess visuo-motor coordination and fine motor impairment in early Huntington's disease patients. The diagnostic tasks are modelled on circle tracing tasks that have been shown to be sensitive to symptom changes in the early stages of Huntington's disease (Say et al. 2011; Tabrizi et al. 2013).
The test is repeated with the left hand. As shown in the screenshot 1655 of
In some embodiments, the diagnostic application may include an instructional video for the draw a shape test that the subject can view on the display screen 160 of the device 105.
In some embodiments, the diagnostic tasks include a chorea test in which the subject 110 is prompted to hold the device 105 still in one hand with the corresponding arm outstretched, while wearing a wrist-worn wearable, such as the second sensor 120b shown in
In some embodiments, the subject 110 the diagnostic application may include an instructional video for the chorea test that the subject can view on the display screen 160 of the device 105. 19A-19E depict example screenshots 1905, 1910, 1915, 1920 and 1925 from an example instructional video for a chorea test according to one or more illustrative aspects described herein.
In some embodiments, the diagnostic tasks implement a balance test. The subject 110 is instructed to stand still while wearing the device 105 and the wrist-worn wearable, such as sensor 120b shown in
In some embodiments, the diagnostic application may include an instructional video for the balance test that the subject 110 may view on the display screen 160 of the device 105.
In some embodiments, the diagnostic tasks may implement a u-turn test. The subject 110 is instructed to walk and turn safely between two points that are at least four steps apart, while wearing the device 105 and the wrist-worn wearable, such as the second sensor 120b shown in
In some embodiments, the diagnostic application may include an instructional video for the u-turn test that the subject 110 may view on the display screen 160 of the device 105.
In some embodiments, the diagnostic tasks may implement a walk test. The subject 110 is instructed to walk as fast as is safely possible for 200 meters or 2 minutes every day. Preferably, the test is performed in a straight path with no obstacles (e.g., in a park). Sensor-based approaches for measuring gait have been shown to be sensitive to differences in symptoms in early HD (Dalton et al. 2013). The test is anchored to the UHDRS gait, bradykinesia body, and tandem walking items (Huntington Study Group 1996).
In some embodiments, the diagnostic tasks include a Symbol Digit Modalities Test (SDMT) that may be modelled on the pen and paper SDMT (Smith, 1968). The subject 110 is prompted to match symbols with numbers according to a key as quickly and accurately as possible. The key, symbols, numbers are displayed on the display screen 160 of the device 105. The SDMT test assesses visuo-motor integration, and measures visual attention and motor speed. The SDMT has been shown to be sensitive to symptom changes in early Huntington's disease patients (Tabrizi 2012) and is part of the Unified Huntington's Disease Rating Scale (UHDRS) assessment (Huntington's Study Group, 1996).
In some embodiments, the diagnostic tasks implement a word reading test. The subject is instructed to read aloud color words written in black font on the display screen 160 of the device 105. The voice of the subject 110 is recorded. This test assesses cognitive processing speed, and is modelled on the “Word Reading” part of the Stroop Word Reading (SWR) Test (Ridley 1935). The “Word Reading” part of the SWR Test has been shown to be sensitive to symptom changes in patients with early Huntington's disease (Tabrizi 2012) and is part of the UHDRS assessment (Huntington's Study Group 1996).
In some embodiments, the diagnostic application may include an instructional video for the word reading test that the subject may view on the display screen 160 of the device 105.
In some embodiments, the diagnostic tasks are automatically scheduled and take about 5 minutes per day. In some embodiments, if the subject 110 does not complete the diagnostic tasks on the scheduled day, the scheduled tasks that occur less frequently than every second day (e.g., EQ-5D-SL, 5 Level Questionnaire, WPAI-HD, HD-SDI, Walk Test) are rolled over to the next time the diagnostic tasks are performed.
In some embodiments, the diagnostic application may display various messages or warnings unrelated to the active tests.
The method 300 proceeds to step 310 which includes in response to the subject performing the one or more diagnostics tasks, receiving, a plurality of second sensor data via the one or more sensors. In response to the subject 110 performing the one or more diagnostic tasks, the diagnostic device 105 receives, a plurality of sensor data via the one or more sensors associated with the device 105. As mentioned above, the sensors associated with the device 105 include a first sensor 120a that is disposed within the device 105 and a second sensor 120b that is worn by the subject 110. The device 105 receives a plurality of first sensor data via the first sensor 120a and a plurality of second sensor data via the second sensor 120b.
The method 300 proceeds to step 315 including extracting, from the received sensor data, a second plurality of features associated with one or more symptoms of Huntington's disease. The device 105 extracts, from the received first sensor data and second sensor data, features associated with one or more symptoms of Huntington's disease in the subject 110. The symptoms of Huntington's disease in the subject 110 may include a symptom indicative of a cognitive function of the subject 110, a symptom indicative of a motor function of the subject 110, a symptom indicative of a behavioral function of the subject 110, or a symptom indicative of a functional capacity of the subject 110. In some embodiments, the extracted features of the plurality of first and second sensor data may be indicative of symptoms of Huntington's disease such as visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia. As discussed above, location-based data from a GPS or similar system may be used to assess symptoms related to the motor function and/or mobility of the subject and other location based assessments. Similarly, WiFi and Bluetooth signal density may be used to help assess patent sociability and the like.
The method 300 proceeds to step 320 which includes determining an assessment of the one or more symptoms of Huntington's disease based on at least the extracted sensor data. The device 105 determines an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 may send the extracted features over a network 180 to a server 150. The server 150 includes at least one processor 155 and a memory 160 storing computer-instructions for a symptom assessment application 170 that, when executed by the processor 155, determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features received by the server 150 from the device 105. In some embodiments, the symptom assessment application 170 may determine an assessment of the one or more symptoms of Huntington's disease in the subject 110 based on the extracted features of sensor data received from the device 105 and a patient database 175 stored in the memory 160. The patient database 175 may include various clinical data. In some embodiments, the second device may be one or more wearable sensors. In some embodiments, the second device may be any device that includes a motion sensor with an inertial measurement unit (IMU). In some embodiments, the second device may be several devices or sensors. In some embodiments, the patient database 175 may be independent of the server 150. In some embodiments, the server 150 sends the determined assessment of the one or more symptoms of Huntington's disease in the subject 110 to the device 105. In some embodiments, such as in
As discussed above, assessments of symptom severity and progression of Huntington's disease using diagnostics according to the present disclosure correlate sufficiently with the assessments based on clinical results and may thus replace clinical patient monitoring and testing. Diagnostics according to the present disclosure were studied in a group of Huntington's disease patients. The patients were provided with a smartphone application that included 7 active tests and continuous passive monitoring. The active tests included SDMT, Stroop word reading test, speeded tapping test, chorea test, balance test, U-turn test and two minutes long walk test. Data was collected over a period of two weeks 2 after in clinic screening visits for each patient. During the in-clinic screening visits, UHDRS scores, Symbol Digit Modalities Test (SDMT), Stroop Word Test and demographics related variables were collected for each patient.
Sensor features were extracted from sensor data measured for each active test and aggregated over two-week periods starting with a baseline clinical visit. Intra-class correlation coefficients and Spearman correlations quantified test-retest reliability and validity compared to equivalent standard in-clinic tests or UHDRS items, respectively.
Test-retest reliabilities of active tests ranged from 0.74-0.97, median 0.95. Diagnostics according to the present disclosure showed correlations with in-clinic testing ranging from r=0.69 (p<0.001, Stroop word reading) to r=0.86 (p<0.001, Speeded tapping). Active tests anchored to specific UHDRS items showed correlations ranging from r=−0.34 (p=0.03, U-Turn) to r=0.64 (p<0.001, Chorea non-dominant hand).
The term “network” as used herein and depicted in the drawings refers not only to systems in which remote storage devices are coupled together via one or more communication paths, but also to stand-alone devices that may be coupled, from time to time, to such systems that have storage capability. Consequently, the term “network” includes not only a “physical network” but also a “content network,” which is comprised of the data—attributable to a single entity—which resides across all physical networks.
The components may include data server 3603, web server 3605, and client computers 3607, 3609. Data server 3603 provides overall access, control and administration of databases and control software for performing one or more illustrative aspects described herein. Data server 3603 may be connected to web server 3605 through which users interact with and obtain data as requested. Alternatively, data server 3603 may act as a web server itself and be directly connected to the Internet. Data server 3603 may be connected to web server 3605 through the network 3601 (e.g., the Internet), via direct or indirect connection, or via some other network. Users may interact with the data server 3603 using remote computers 3607, 3609, e.g., using a web browser to connect to the data server 3603 via one or more externally exposed web sites hosted by web server 3605. Client computers 3607, 3609 may be used in concert with data server 3603 to access data stored therein, or may be used for other purposes. For example, from client device 3607 a user may access web server 3605 using an Internet browser, as is known in the art, or by executing a software application that communicates with web server 3605 and/or data server 3603 over a computer network (such as the Internet). In some embodiments, the client computer 3607 may be a smartphone, smartwatch or other mobile computing device, and may implement a diagnostic device, such as the device 105 shown in
Servers and applications may be combined on the same physical machines, and retain separate virtual or logical addresses, or may reside on separate physical machines.
Each component 3603, 3605, 3607, 3609 may be any type of known computer, server, or data processing device. Data server 3603, e.g., may include a processor 3611 controlling overall operation of the rate server 3603. Data server 3603 may further include RAM 3613, ROM 3615, network interface 3617, input/output interfaces 3619 (e.g., keyboard, mouse, display, printer, etc.), and memory 3621. I/O 3619 may include a variety of interface units and drives for reading, writing, displaying, and/or printing data or files. Memory 3621 may further store operating system software 3623 for controlling overall operation of the data processing device 3603, control logic 3625 for instructing data server 3603 to perform aspects described herein, and other application software 3627 providing secondary, support, and/or other functionality which may or may not be used in conjunction with other aspects described herein. The control logic may also be referred to herein as the data server software 3625. Functionality of the data server software may refer to operations or decisions made automatically based on rules coded into the control logic, made manually by a user providing input into the system, and/or a combination of automatic processing based on user input (e.g., queries, data updates, etc.).
Memory 3621 may also store data used in performance of one or more aspects described herein, including a first database 3629 and a second database 3631. In some embodiments, the first database may include the second database (e.g., as a separate table, report, etc.). That is, the information can be stored in a single database, or separated into different logical, virtual, or physical databases, depending on system design. Devices 3605, 3607, 3609 may have similar or different architecture as described with respect to device 3603. Those of skill in the art will appreciate that the functionality of data processing device 3603 (or device 3605, 3607, 3609) as described herein may be spread across multiple data processing devices, for example, to distribute processing load across multiple computers, to segregate transactions based on geographic location, user access level, quality of service (QoS), etc.
One or more aspects described herein may be embodied in computer-usable or readable data and/or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices as described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The modules may be written in a source code programming language that is subsequently compiled for execution, or may be written in a scripting language such as (but not limited to) HTML or XML. The computer executable instructions may be stored on a computer readable medium such as a hard disk, optical disk, removable storage media, solid state memory, RAM, etc. As will be appreciated by one of skill in the art, the functionality of the program modules may be combined or distributed as desired in various embodiments. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits, field programmable gate arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects, and such data structures are contemplated within the scope of computer executable instructions and computer-usable data described herein.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as illustrative forms of implementing the claims.
Embodiment I-1. A diagnostic device for assessing one or more symptoms of Huntington's disease in a subject, the device comprising:
Embodiment I-2. The device of Embodiment I-1, wherein the computer-readable instructions, when executed by the at least one processor, further cause the device to:
Embodiment I-3. The device of any one of Embodiments I-1 to I-2, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject.
Embodiment I-4. The device of any one of Embodiments I-1 to I-3, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject, whereby the patient mobility is assessed at least partly based on GPS location data.
Embodiment I-5. The device of any one of Embodiments I-1 to I-4, wherein the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
Embodiment I-6. The device of any one of Embodiments I-1 to I-5, wherein the one or more sensors associated with the device comprise at least one of a first sensor disposed within the device or a second sensor worn by the subject and configured to communicate with the device.
Embodiment I-7. The device of any one of Embodiments I-1 to I-6, wherein prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.
Embodiment I-8. The device of any one of Embodiments I-1 to I-6, wherein the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test, WPAI-HD test, HD-SDI test, speed tapping test, draw a shape test, chorea test, balance test, u-turn test, SDMT test, and word reading test.
Embodiment I-9. A computer-implemented method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:
Embodiment I-10. The computer-implemented method of Embodiment I-9, further comprising:
Embodiment I-11. The computer-implemented method of any one of Embodiments I-9 to I-10, wherein the one or more symptoms of Huntington's disease in the subject include at least one of a symptom indicative of a cognitive function of the subject, a symptom indicative of a motor function of the subject, a symptom indicative of a behavioral function of the subject, or a symptom indicative of a functional capacity of the subject, in particular wherein the one or more symptoms of Huntington's disease in the subject are indicative of at least one of visuo-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visuo-motor coordination, fine motor impairment, upper-body or lower-body bradykinesia.
Embodiment I-12. The computer-implemented method of any one of Embodiments I-9 to I-11, whereby the patient mobility is assessed at least partly based on GPS location data.
Embodiment I-13. The computer-implemented method of any one of Embodiments I-9 to I-12, wherein the one or more sensors associated with the device comprise at least one of a first sensor disposed within the device or a second sensor located on the subject and configured to communicate with the device, in particular wherein prompting the subject to perform the one or more diagnostic tasks includes at least one of prompting the subject answer one or more questions or prompting the subject to perform one or more actions.
Embodiment I-14. The computer-implemented method of any one of Embodiments I-9 to I-12, wherein the one or more diagnostic tasks are associated with at least one of a EQ-5D-5L test, WPAI-HD test, HD-SDI test, speed tapping test, draw a shape test, chorea test, balance test, u-turn test, SDMT test, and word reading test.
Embodiment I-15. A non-transitory machine readable storage medium comprising machine-readable instructions for causing a processor to execute a method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:
| Number | Date | Country | Kind |
|---|---|---|---|
| 19154742.1 | Jan 2019 | EP | regional |
This application is a continuation of International Application No. PCT/EP2020/052074, filed Jan. 29, 2020, which claims priority to EP Application No. 19154742.1, filed Jan. 31, 2019, which are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/EP2020/052074 | Jan 2020 | US |
| Child | 17389288 | US |
