WO2019218010A1

WO2019218010A1 - System for determining progression of parkinson's disease

Info

Publication number: WO2019218010A1
Application number: PCT/AU2019/050457
Authority: WO
Inventors: Malcolm Kenneth Horne
Original assignee: Global Kinetics Pty Ltd
Priority date: 2018-05-17
Filing date: 2019-05-15
Publication date: 2019-11-21

Abstract

A system and automated method for determining progression of a movement disorder in a subject includes receiving at a processor time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period. The processor processes the received time-marked motion data and time-marked therapy data to identify automatically changes in one or more movement characteristics of the subject that indicate a response to received therapy and calculating a duration of response to the therapy. The processor calculates a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference duration of response data and generates an output indicative of progression of the movement disorder in the subject. The received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when the calculated change in responsiveness indicates a reduction in the subject's duration of response to the received therapy.

Description

SYSTEM FOR DETERMINING PROGRESSION OF PARKINSON'S DISEASE Technical Field

[0001 ] The present invention relates to automated systems and methods for determining progression of a movement disorder, such as Parkinson's Disease (PD) in a subject. It relates particularly but not exclusively to a method for determining rate of progression of PD to facilitate objective and therefore more effective clinical evaluation and prescription of therapies.

Background of Invention

[0002] A broad range of movement disorders exist. Parkinson's Disease (PD) is one of the most prevalent, affecting over 6 million people globally. PD is a progressive disorder of the nervous system, affecting the frontal lobe of the brain which controls impulsive and non-impulsive movement. People with PD have less dopamine, a neurotransmitter released by brain neurons in the part of the brain which helps regulate movement. People with PD experience movement related symptoms such as bradykinesia, rigidity, tremor and postural instability. Non-movement symptoms may include speech and swallowing difficulties, cognitive impairment or behavioural change, and sleep disturbance.

[0003] Dopamine is synthesised and stored in the terminals (nerve endings) of fibres that emanate from neurones (nerve cells) affected by PD. In the healthy brain, these nerve terminals release dopamine in response to nerve impulses from the neurone. The released dopamine is rapidly taken up again and stored so that in the normal brain, only a small proportion of available dopamine (-10%) is cycled.

Disease progression results in loss of neurones and their nerve terminals so that at the time of presentation, terminal numbers are already reduced and the dopamine available for release is also depleted - this leads to the bradykinesia.

[0004] Treatment with Levodopa or L-Dopa will increase the rate of synthesis of new dopamine and increase the amount stored in the remaining terminals. In these circumstances, the amount of dopamine that can be stored following a single dose will be used over a period of hours, at which time levels will return to the untreated state: this phenomenon is referred to as“wearing-off” as clinically the patient experiences “wearing-off” of the benefit of L-Dopa and re-emergence of bradykinesia. At the time of presentation, the duration between dose and“wearing-off” (duration of benefit) is many hours and even over a day. However, as the disease progresses and terminals are progressively lost, the duration of benefit progressively shortens until it reaches about 3 hours (2.5 - 4 hours depending on the individual).

[0005] L-Dopa as used in the treatment of PD to reduce bradykinesia, the key movement related symptom of PD, is effective in restoring stored levels of dopamine in terminals. However it does not reduce disease progression. The aim of clinical care is to time each dose of L-Dopa so that it precedes the onset of“wearing-off” so that the patient does not experience any of the motor symptoms of PD. This requires careful monitoring to detect the progressive shortening of the duration of benefit and to thus shorten the interval between doses or use treatments to help smooth dopaminergic transmission.

[0006] Skilled neurologists can detect the presence of bradykinesia and

dyskinesia and provide a measure of its severity. However this is inherently subjective and scores may vary between different periods of observation performed by a single neurologist and there can be differences in scores given by different neurologists. Furthermore, this assessment can only be done when the neurologist is with the patient, whereas the response to L-Dopa may fluctuate over the course of the day and from day to day and thus continuous objective monitoring is desirable.

While experienced clinicians can usually detect and estimate the severity of bradykinesia and other movement disorders during a period of observation, these disorders are not easily quantified making dosage control challenging. Moreover, a period of observation in the clinic is finite (typically a period of 10 or so minutes) and undertaken in an environment that is not familiar to the patient, potentially altering or exacerbating symptoms from what is experienced in day-to-day life. Self-reporting of symptoms by patients can do away with the environmental problem but this can be unreliable and is also subjective.

[0007] Objective monitoring of progression of PD would enable clinicians to detect “wearing-off” and modify drug dosage regimens for people with PD more precisely as their disease progressed. It would also assist clinicians to establish e.g. when the management of PD using L-Dopa related oral therapies was failing and required advanced therapies such as deep brain stimulation (DBS) to maintain good control.

[0008] The discussion of the background to the invention included herein including reference to documents, acts, materials, devices, articles and the like is included to explain the context of the present invention. This is not to be taken as an admission or a suggestion that any of the material referred to was published, known or part of the common general knowledge in Australia or in any other country as at the priority date of any of the provisional claims.

Summary of Invention

[0009] Viewed from one aspect, the present invention provides an automated method for determining progression of a movement disorder in a subject, the method including the steps of:

receiving at a processor time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period;

the processor processing the received time-marked motion data and time- marked therapy data to identify automatically changes in one or more movement characteristics of the subject that indicate a response to received therapy and calculating a duration of response to the therapy;

the processor calculating a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference duration of response data; and

the processor generating an output indicative of progression of the movement disorder in the subject;

wherein the received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when the calculated change in responsiveness indicates a reduction in the subject's duration of response to the received therapy.

[0010] In some embodiments, processing the received time-marked motion data to identify changes in one or more movement characteristics of the subject includes the processor: calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject; and performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy.

[0011 ] In some embodiments, one or more of the following are calculated from the time series of scores: a. a peak value or range of values representing a peak response to a received therapy during the observation period; and b. a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy. In some embodiments, the processor automatically calculates the peak value or range of values and the baseline value or range of values. In other embodiments, the processor automatically calculates one or more of: a. a plurality of peak values or ranges of values; and b. a plurality of baseline values or ranges of values; and receives from a user a preferred peak value or range of values and a preferred baseline value or range of values selected from the respective calculated plurality of values or ranges of values.

[0012] In some embodiments, the processor calculates one or more of: a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

[0013] In some embodiments, the processor may determine a rate of progression of the movement disorder in the subject over an extended period, by: receiving time- marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period; wherein the rate of change calculated for one or more of A, B and C over the extended period indicates the rate of progression of the movement disorder.

[0014] Observation periods in the extended period may be separated by any suitable interval, such as an interval of approximately 1 month, 2 months, 3 months, 6 months or 12 months. In some embodiments, the observation period corresponds to the duration that the motion sensor is worn by the subject. Thus in some embodiments, the observation period may be between 3 and 28 days duration, preferably between 5 and 15 days duration and most preferably between 6 and 10 days duration.

[0015] In some embodiments, performing analysis to determine if the movement data contains a statistically significant change includes the processor using analytical tools such as, but not limited to a cumulative sum control chart (CUSUM); and peak detection.

[0016] Typically, the movement disorder is Parkinson's Disease (PD) and the movement characteristic is bradykinesia (BK). Thus in some embodiments the scores calculated by the processor include BK scores indicating an extent or severity of BK in the subject during the observation period.

[0017] In some embodiments, the method further includes the processor:

determining automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy corresponding to the duration of response or longer; and calculating a DBK value being the difference between the calculated basal BK score and a reference basal BK score; wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the processor further determines the movement disorder to have progressed when it detects calculated DBK values increasing over time.

[0018] In some embodiments, the received therapy is selected from a group including L-Dopa, carbidopa and dopamine agonists.

[0019] Aspects of the invention include use of the automated method in an automated advanced therapy analytical tool for determining if a subject is a candidate for advanced therapy, wherein the method includes the processor calculating a rate of progression of the movement disorder and automatically determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of

progression indicates significant wearing off. [0020] Typically, the time-marked motion data is generated by a body-worn motion sensor worn by the subject during the observation period. In some

embodiments, the body-worn motion sensor is configured automatically to generate time-marked therapy data based on one or more therapy inputs received from the subject by the body-worn motion sensor during the observation period.

[0021 ] Viewed from another aspect, the present invention provides a system for determining progression of a movement disorder in a subject, the system including:

an input module for receiving time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period;

a processor configured to i) identify automatically in the time-marked motion data one or more data points indicating a change in motion characteristics of the subject that indicate a response to received therapy; ii) calculate a duration of response to a received therapy; and iii) calculate a change in responsiveness to therapy by comparing one or more calculations of duration of response during the observation period with reference duration of response data; and

an output module generating an output signal for causing a user interface to present an indicator of the progression of the movement disorder in the subject;

wherein the received therapy is for treating a symptom of the movement disorder, and the indicator of progression of the movement disorder is determined from the calculated change in responsiveness to the received therapy.

[0022] In some embodiments, the output module is configured to generate an output signal causing a user interface to produce automatically one or more of: a graphical representation of analysis performed by the processor; a report

summarising analysis performed by the processor; data files representing data processed by the processor; and background information used by the system to generate one or more of the foregoing.

[0023] In some embodiments, the system includes a body-wearable sensor device configured for continuous monitoring of movements of the subject during the observation period and storage and/or transmission of time-marked motion data for receiving by the input module and optionally, configured for storage and transmission of therapy data based on one or more therapy inputs received by the body-worn motion sensor from the subject during the observation period.

[0024] In some embodiments, the output module includes a communication interface configured to communicate signals between the processor and a remotely located device, the signals representing: one or more of data, reports, analysis, protocols and output signals; and one or more of time-marked motion data and therapy data.

[0025] In some embodiments, the input module is configurable to receive data files representing protocols for assessing progression of a movement disorder, the protocols including definitions for one or more of: duration of the observation period; time for delivering therapy to the subject; dosage of therapy; and rules for subject activity before and/or during the observation period.

[0026] In some embodiments, the processor is configurable to identify changes in motion behaviour of the subject by performing steps including: calculating from the time-marked motion data a time series of scores representing a movement

characteristic of the subject; and performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy. The processor is configurable to determine if the movement data contains a statistically significant change using any suitable analytical tool such as for example a cumulative sum control chart (CUSUM); and peak detection.

[0027] In some embodiments, the processor is configurable to calculate

automatically from the time series of scores one or both of: a peak value or range of values representing a peak response to a received therapy during the observation period; and a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy.

[0028] In some embodiments, the processor is configurable to calculate one or more of: a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

[0029] In some embodiments, the processor is configurable to calculate

automatically a rate of progression of the movement disorder in the subject over an extended period, by performing steps including: receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period; wherein the processor determines the rate of progression of the movement disorder to be the rate of change calculated for one or more of A, B and C over the duration of the extended period.

[0030] Typically, the movement characteristic is bradykinesia (BK) and the processor is configured to calculate BK scores indicating an extent or severity of BK in the subject during the observation period. In some embodiments, the processor is configurable to: calculate automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without the therapy, said time period corresponding to the duration of response or longer; and calculate a DBK value being the difference between the calculated basal BK score and a reference basal BK score; wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the processor further determines the movement disorder to have progressed when calculated DBK values increase over time.

[0031 ] In some embodiments, the processor is further configured to determine automatically if the subject is a candidate for advanced therapy, by calculating a rate of progression of the movement disorder for the subject and determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

[0032] Viewed from another aspect, the present invention provides an automated method for determining progression of a movement disorder in a subject, the method including the steps of: receiving at a processor time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period;

the processor calculating from the time-marked motion data a time series of BK scores representing a movement characteristic of the subject being

bradykinesia; and

the processor determining automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy not less than a duration for which the subject is responsive to the therapy;

calculating a DBK value being the difference between the calculated basal BK score and a reference basal BK score; and

wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when calculated DBK values increase over time.

[0033] In some embodiments, the method further includes the processor:

processing the BK scores and the received time-marked therapy data to identify automatically changes in BK scores that indicate a response to the received therapy and calculating a duration of response to the therapy; and calculating a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference duration of response data; wherein the processor further determines the movement disorder to have progressed when the calculated change in responsiveness indicates a reduction in the subject’s duration of response to the received therapy.

[0034] In some embodiments, the method further includes the processor determining a rate of progression of the movement disorder in the subject by calculating the rate of change of at least one of: DBK values; and duration of response; and the processor generating an output that is indicative of the rate of progression. [0035] Viewed from another aspect, the present invention provides a system for determining progression of a movement disorder in a subject, the system including:

a processor configured to calculate from the time-marked therapy data BK scores including a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy not less than a duration for which the subject is responsive to the therapy; and calculate a DBK value being the difference between the calculated basal BK score and a reference basal BK score; and

wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when it detects DBK values increasing over time.

[0036] In some embodiments, the processor is configurable to: process the BK scores and the received time-marked therapy data to identify automatically changes in BK scores that indicate a response to the received therapy and calculating a duration of response to the therapy; and calculate a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference response data; wherein the processor further determines the movement disorder to have progressed when the calculated change in

responsiveness indicates a reduction in the subject’s duration of response to the received therapy.

[0037] In some embodiments, the processor is configurable to determine a rate of progression of the movement disorder in the subject by calculating the rate of change of one or both of DBK values and duration of response, and wherein the output module is configurable to generate an output signal for causing the user interface to present an indicator of the rate of progression. [0038] The background discussion relates to maintaining symptom control.

However, the inventor has discovered that measuring the progressive shortening of the duration of benefit of therapy would also reflect the progression of the disease. Therapies that reduce (or prevent) progression of terminal loss are currently being trialled and actively researched. Measuring disease progression will be important therapeutically to assess whether in an individual, a particular therapy is being effective or whether more aggressive preventative treatment is recommended. It would also aid in the discovery of newer therapies.

[0039] Viewed from one aspect, the present invention provides an automated method for determining progression of a movement disorder in a subject, the method including the steps of: receiving at a processor time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period; the processor processing the received time-marked motion data and time-marked therapy data to identify automatically changes in one or more movement characteristics of the subject that indicate a response to received therapy and calculating a duration of response to the therapy; the processor calculating a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference response data; and the processor generating an output indicative of progression of the movement disorder in the subject; wherein the received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when the calculated change in

responsiveness indicates a reduction in the subject's responsiveness to the received therapy.

[0040] The time-marked motion data may be generated by a body-worn motion sensor worn by the subject during the observation period. In some embodiments the body-worn motion sensor is configured automatically to generate time-marked therapy data based on one or more therapy inputs received from the subject by the body-worn motion sensor during the observation period.

[0041 ] In some embodiments, processing the received time-marked motion data to identify changes in one or more movement characteristics of the subject includes the processor calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject; and performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy.

[0042] In some embodiments, the method includes calculating from the time series of scores one or more of: a peak value or range of values representing a peak response to a received therapy during the observation period; and a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy. In some embodiments, the peak value or range of values and/or the baseline value or range of values may be determined from the scores by a user. In other embodiments, the processor is configured to calculate automatically the peak value or range of values and the baseline value or range of values. In still other embodiments, the processor may automatically calculate a plurality of peak values or ranges of values and/or a plurality of baseline values or ranges of values, and receive from a user a preferred peak value or range of values) and a preferred baseline value or range of values selected from the respective calculated plurality of values or ranges of values.

[0043] In some embodiments, the method further includes the processor calculating one or more of: a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

[0044] In some embodiments, the processor determines a rate of progression of the movement disorder in the subject over an extended period, by receiving time- marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period; wherein the rate of change calculated for one or more of A, B and C over the extended period indicates the rate of progression of the movement disorder. [0045] Observation periods in the extended period may be separated by an interval of e.g. approximately 1 month, 2 months, 3 months, 6 months or 12 months.

In some embodiments, the observation period corresponds to the duration that the motion sensor is worn by the subject. In other embodiments, the observation period is between 3 and 28 days duration, preferably between 5 and 15 days duration and most preferably between 6 and 10 days duration although other durations are contemplated and within the scope of this disclosure.

[0046] In some analysis, the analysis performed by the processor to determine if the movement data contains a statistically significant change includes the processor using an analytical tool such as a cumulative sum control chart (CUSUM), peak detection or the like. The analytical tool may be any tool currently in existence or developed in future which one of skill in the art would regard as suitable for the analytical requirements of the present invention.

[0047] In some embodiments, the movement disorder is Parkinson's Disease (PD) and the movement characteristic may be bradykinesia (BK). Thus, scores calculated by the processor include BK scores indicating an extent or severity of BK in the subject during the observation period. The received therapy is for treating a symptom of the movement disorder and may be selected from a group including for example L- Dopa, carbidopa and dopamine agonists.

[0048] Embodiments of the invention may be used in an automated advanced therapy analytical tool for determining if a subject is a candidate for advanced therapy. The decision tool performs a method including the steps of the processor calculating a rate of progression of the movement disorder and automatically determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

[0049] Viewed from another aspect, the present invention provides a system for determining progression of a movement disorder in a subject, the system including: an input module for receiving time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period; a processor configured to i) identify automatically in the time-marked motion data one or more data points indicating a change in motion characteristics of the subject that indicate a response to received therapy; ii) calculate a duration of response to a received therapy; and iii) calculate a change in responsiveness to therapy by comparing one or more calculations of duration of response during the observation period with reference response data; and an output module generating an output signal for causing a user interface to present an indicator of the progression of the movement disorder in the subject; wherein the received therapy is for treating a symptom of the movement disorder, and the indicator of progression of the movement disorder is determined from the calculated change in responsiveness to the received therapy.

[0050] In some embodiments, the output module is configured to generate an output signal causing a user interface to produce automatically one or more of: a graphical representation of analysis performed by the processor; a report

summarising analysis performed by the processor; data files representing data processed by the processor; and background information used by the system to generate e.g. data analysis, reports, data files, graphical representations and the like.

[0051 ] In some embodiments, the system includes a body-wearable sensor device configured for continuous monitoring of movements of the subject during the observation period and storage and/or transmission of time-marked motion data for receiving by the input module. The body-wearable sensor device may optionally be configured for storage and transmission of therapy data based on one or more therapy inputs received by the body-worn motion sensor from the subject during the observation period.

[0052] In some embodiments, the system output module includes a

communication interface configured to communicate signals between the processor and a remotely located device. The signals may represent, for example: one or more of data, reports, analysis, protocols and output signals; and one or more of time- marked motion data and therapy data.

[0053] In some embodiments, the system input module is configurable to receive data files representing protocols for assessing progression of a movement disorder. The protocols including definitions for one or more of: duration of the observation period; time for delivering therapy to the subject; dosage of therapy; and rules for subject activity before and/or during the observation period. Subject activity may include e.g. a period of rest or task-based activity such as writing, walking in a line, nose tapping or the like.

[0054] In some embodiments, the system processor is configured to identify changes in motion behaviour of the subject by performing steps including: calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject; and performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy.

[0055] In some embodiments, the system processor is configured to calculate automatically from the time series of scores one or both of: a peak value or range of values representing a peak response to a received therapy during the observation period; and a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy. In other embodiments, the system processor is configured to calculate from the time series of scores a plurality of peak values (or ranges of values) and/or a plurality of baseline values (or ranges of values), and receive from the input module data representing a user selection of a preferred peak value (or range of values) and a preferred baseline value (or range of values) selected by the user from the calculated plurality of values (or ranges of values).

[0056] In some embodiments, the system processor is configured to calculate one or more of: a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

[0057] In some embodiments, the system processor is determined to calculate automatically a rate of progression of the movement disorder in the subject over an extended period, by performing steps including: receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period; wherein the processor determines the rate of progression of the movement disorder to be the rate of change calculated for one or more of A, B and C over the duration of the extended period.

[0058] In some embodiments, the system processor is configured to determine if the movement data contains a statistically significant change using an analytical tool selected from a group including but not limited to: cumulative sum control chart (CUSUM); and peak detection.

[0059] In some embodiments, the movement characteristic is bradykinesia (BK) and the processor is configured to calculate BK scores indicating an extent or severity of BK in the subject during the observation period.

[0060] In some embodiments, the system processor is further configured to determine automatically if the subject is a candidate for advanced therapy, by calculating a rate of progression of the movement disorder for the subject and determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

[0061 ] Also described herein is a method for determining progression of a movement disorder in a subject, the method including the steps of: receiving time- marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the observation period; processing the received time-marked motion data and time-marked therapy data to identify changes in one or more movement

characteristics of the subject that indicate a response to received therapy; and calculating a change in responsiveness to therapy by comparing a calculated response during the observation period with reference response data; wherein the received therapy is for treating a symptom of the movement disorder, and the movement disorder is determined to have progressed when the calculated change in responsiveness indicates a reduction in the subject's responsiveness to the received therapy.

[0062] In some embodiments, the processing step includes calculating a duration of response to the therapy, and the step of calculating a change in responsiveness includes comparing a calculated response duration during the observation period with reference response data.

[0063] In some embodiments, processing the time-marked motion data to identify changes in motion behaviour of the subject includes: calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject; and performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy.

[0064] In some embodiments, the method includes calculating from the time series of scores one or both of: a peak value or range of values representing a peak response to a received therapy during the observation period; and a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy. The method may further include the step of calculating one or more of: a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

[0065] In some embodiments, the method further includes the step of determining a rate of progression of the movement disorder in the subject over an extended period, by: receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period; wherein the rate of change calculated for one or more of A, B and C over the extended period indicates the rate of progression of the movement disorder.

[0066] In some embodiments, the movement disorder is Parkinson's Disease (PD), the movement characteristic is bradykinesia (BK) and scores calculated include BK scores indicating an extent or severity of BK in the subject during the observation period. [0067] In some embodiments, the method is to determine if a subject is a candidate for advanced therapy, wherein the method further includes the steps of calculating a rate of progression of the movement disorder and determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

[0068] It is to be noted that any one of the aspects mentioned above may include any of the features of any of the embodiments of other aspects mentioned above and may include any of the features of any of the embodiments described below, as appropriate.

[0069] It is to be understood each of the various aspects described herein may incorporate features, modifications and alternatives described in the context of one or more other aspects, such as but not limited to the various kinetic states, measures of dispersion and other data characteristics used in the determination of a selection score. For efficiency, such features, modifications and alternatives have not been repetitiously disclosed for each and every aspect although one of skill in the art will appreciate that such combinations of features, modifications and alternatives disclosed for some aspects apply similarly for other aspects and are within the scope of and form part of the subject matter of this disclosure.

Brief Description of Drawings

[0070] The present invention will now be described in greater detail with reference to the accompanying drawings. It is to be understood that the embodiments shown are examples only and are not to be taken as limiting the scope of the invention as defined in the provisional claims appended hereto.

[0071 ] Fig. 1 is a schematic illustration showing steps of a method for determining progression of a movement disorder in a subject according to an embodiment of the invention.

[0072] Fig. 2 is a graphical representation showing calculated BK scores according to an embodiment of the invention. [0073] Fig. 3 is a graphical representation showing BK scores calculated for the subject approximately 12 months after the observation period corresponding to the BK scores in Fig. 2.

[0074] Fig. 4 is a graphical representation of a "rate of progression" chart according to an embodiment of the invention.

[0075] Fig. 5 is a schematic illustration of a system for determining progression of a movement disorder in a subject, according to an embodiment of the invention.

[0076] Fig. 6 is a graphical representation showing typical progressive increase in basal bradykinesia, change in bradykinesia and shortening of duration of response following therapy for a subject with Parkinson’s disease.

[0077] Figs. 7A and 7B show increasing DBK values over time and decreasing duration of response over the same time period.

[0078] Fig. 8 shows the time course of extra-synaptic dopamine release in the striatum, before and after administration of L-dopa (20mg/kg) in animals.

Detailed Description

[0079] A Levodopa (L-Dopa) challenge test may be given to subject if a confirmation of a diagnosis of Parkinson’s disease (PD) is needed, or if the subject’s responsiveness to a known dosage of L-Dopa is sought to be determined. The L- Dopa challenge test requires a clinician to perform a detailed question-based assessment of the subject using the Unified Parkinson’s Disease Rating Scale (UPDRS). While experienced neurologists are adept at performing such

assessments, this is nevertheless a largely subjective exercise. Brain imaging can be used to provide detailed images of the dopamine system in the brain providing objective data to supplement an assessment of movement disorders such as PD. Flowever, confirmation by a clinician after a thorough medical examination is still necessary.

[0080] Despite such tests and assessment tools being available for use in PD diagnosis, prior to the current invention there has been no objective test for determining progression of PD and other movement disorders. Thus, there is no objective means for placing the stage of illness in a clinical or pathological context.

[0081 ] Embodiments of the present invention provide novel systems and methods for objective and automated determination of the progression of movement disorders such as PD, so that clinical staging may occur and more effective therapies and care planning prescribed.

[0082] Referring firstly to Fig. 1 , there is shown an automated method 1000 for determining progression of a movement disorder, such as PD, in a subject. In a step 1001 , time-marked motion data indicative of movements of the subject during an observation period, is received at a processor. In a step 1002, the processor receives a time-marked therapy data indicative of a time that a therapy is received by the subject during the observation period, said therapy being for the treatment of a symptom of the movement disorder. Typically, therapy is received by the subject on more than one occasion during the observation period, such as daily, for an observation period of several days. The therapy input may be supplied by the subject or a carer substantially contemporaneously with the therapy being received by the subject or it may be supplied by the subject or carer at a later time. Ideally, therapy inputs are made substantially contemporaneously with therapy being received by the subject for greater accuracy. Therapy inputs may be made using any suitable means such as using a body-worn device of the kind that also generates the time-marked motion data as described below.

[0083] In a step 1003, a processor processes the received time-marked motion data and time-marked therapy data to identify automatically changes in motion characteristics of the subject that indicate a response to the received therapy. Ideally, a duration of the subject’s response to the received therapy is calculated for each dose of therapy received by the subject during the observation period.

[0084] Step 1003 includes calculating a change in responsiveness to therapy. Ideally this is achieved by comparing a calculated duration of response with reference response data. Reference response data may comprise e.g. a calculated duration of response for earlier therapy received by the subject during the observation period, a duration of response calculated for therapy received by the subject during a previous observation period, an average of duration of response values calculated for therapies received by the subject during one or more previous observation periods, or a reference duration of response that has been estimated based on data obtained from a population of individuals being treated for the movement disorder using the same therapy.

[0085] Alternatively/additionally, at step 1003 a basal BK score and DBK value may be calculated. A basal BK score represents basal bradykinetic behaviour of the subject after a time period without therapy not less than a duration for which the subject is responsive to the therapy (e.g. duration of response as calculated by the processor). A DBK value is the difference between the calculated basal BK score and a reference basal BK score representing basal bradykinetic behaviour that is asymptomatic of the movement disorder.

[0086] In a step 1004, the processor generates an output indicative of progression of the movement disorder in the subject. The processor automatically determines the movement disorder to have progressed when the calculated change in duration of response indicates a reduction in the subject’s responsiveness to therapy. In other embodiments, the processor automatically determines the movement disorder to have progressed when calculated DBK values increase over time. In yet other

embodiments, the processor combines these indicators and determines the

movement disorder to have progressed when there is a shortening of duration of response and an increase in DBK values. In some embodiments, the output is a signal configured to cause a user interface, such as a display screen, to present one or more indicators of the progression of the movement disorder in the subject.

[0087] In a preferred embodiment, step 1003 further incudes the processor calculating a rate of progression of the moment disorder by comparing a plurality of duration of response times calculated over a time period, and calculating the rate of change by dividing the change in duration of response across the time period by the duration of the time period over which that change occurred. The time period may be an observation period, or an extended period comprising a plurality of observation periods for which one or more values corresponding to duration of response are calculated. It is to be understood that where a rate of progression is calculated for an extended period, the method may utilise individual duration of response values calculated by the processor, or it may use average, mean or mode values based on a plurality of duration of response values calculated during an observation period. It is to be understood that while rate of progression may be calculated using data collected during a plurality of finite observation periods, some subjects may be monitored continuously for months or even years. The rate of progression may be calculated by the processor using time-marked motion data and therapy data obtained during a continuous observation period which may be regarded as an extended period. The rate of change of DBK values may be used in a corresponding fashion to calculate a rate of progression of the movement disorder over time, either in isolation or in combination with the rate of change in duration of response.

[0088] In a preferred embodiment, processing step 1003 includes the processor calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject and performing analysis of the time series of scores to determine if there is a statistically significant change indicative of a change in responsiveness to the received therapy. In some embodiments, the processor may process the time-marked motion data seeking to identify a statistically significant change, without first calculating a time series of scores representing a movement characteristic of the subject. The processor may be programmed to use any suitable analytical tool as may be currently available, such as but not limited to a cumulative sum control chart (CUSUM) or peak detection, or other tools that one of skill in the art may consider to be appropriate.

[0089] For subjects in which the movement disorder being evaluated is PD, a movement characteristic of interest is bradykinesia (BK) and the processor calculates BK scores indicating an extent or severity of BK in the subject during the observation period. BK scores may be calculated according to any suitable methodology. One suitable methodology is disclosed in W02009/149520 entitled "Detection of

Hypokinetic and/or Hyperkinetic States", the entire disclosure of which is hereby incorporated herein by reference. In that methodology, the algorithm for automated calculation of a BK score arises from knowledge that bradykinetic subjects (such as those suffering from PD) have longer intervals between movement and when they do move it is with lower acceleration. Thus, a lower BK score suggests more severe bradykinesia whereas a high BK score indicates little or no bradykinesia. However, it is to be understood that alternative motion data processing methods and movement scoring regimens may be adopted to determine a change in the subject's movement characteristics and a subject's responsiveness (including duration of response) to therapy.

[0090] According to an embodiment of the invention, BK scores are calculated over an observation period for a subject with PD using the Parkinson's KinetiGraph™ device and associated proprietary software and algorithms (PKG, Global Kinetics Pty Ltd) which provides continuous objective measurements of the subject's movement data. Advantageously these objective measurements are able to be collected during activities of daily living. Fig. 2 is a graphical representation showing the calculated BK scores. The data represented in Fig. 2 is representative of the subject's movement characteristics while L-Dopa therapy is effective in the treatment of the subject's bradykinetic movement disorder symptoms. BK scores are indicated at the trace marked BK. Diamond (2002) represents the time that L-Dopa therapy is administered to the subject. Vertical lines in the graphical representation are separated by 60 minute intervals in the observation period to indicate time scale.

[0091 ] In some embodiments, in step 1003 the processor is configurable to automatically calculate from the time series of BK scores a plurality of indicators used in the determination of progression (or rate of progression) of the movement disorder. In one indicator, BK scores themselves may be used in step 1004 to generate an output indicative of the progression of the movement disorder.

Alternatively/additionally the scores may be used in step 1004 to generate a graphical representation providing a time-scale representation of BK scores calculated by the processor using data collected from the subject after an extended period. Fig. 3 represents BK scores calculated for the subject during an observation period occurring approximately 12 months after the observation period in which the data in Fig. 2 was collected. During the time that elapsed between observation periods, the symptoms of the subject's movement disorder (PD) progressed as is readily visible from the graphical representations of Figs. 2 and 3.

[0092] In Fig. 3 vertical line 3002 represents the time that L-Dopa therapy is administered to the subject for the represented observation period. Vertical lines in the graphical representation are separated by 60 minute intervals in the observation period to indicate time scale. Florizontal line 3003 represents a "baseline" BK score calculated by the processor to be indicative of the subject's movement characteristics before L-Dopa therapy becomes effective. Horizontal broken line 3004 represents a "threshold" value indicating what is clinically regarded as a "good" response to therapy. This may be designated by a clinician, or it may be determined automatically by the system implementing the inventive method. Ideally the processor also calculates from the time series of BK scores a peak value 3005 which indicates the subject's peak response to the L-Dopa therapy administered at 3002. Horizontal line 3006 represents 50% of the peak value represented at 3005.

[0093] In preferred embodiments, step 1003 involves the processor calculating one or more parameters that are indicative of duration of response to therapy and used as an indicator to determine progression (or rate of progression) of the movement disorder. Such parameters may include: a first parameter A being a time duration during which the scores are above the baseline 3003; a second parameter B being a time duration during which the scores are above a percentage (e.g. 50%) of the peak value; and a third parameter C being a time duration during which the scores are above threshold 3004. Parameter B is likely to provide a most reliable estimate of response duration. However it is to be understood that other percentage values (e.g. 60%, 70%, 75% or 80%) and/or a range of percentage values (e.g. 50% to 75%) may be calculated to indicate response duration.

[0094] In Fig. 3 it can be seen that there is a response to L-Dopa therapy commencing about 1 hour after the dose is administered at 3002. The response corresponds to BK scores above baseline 3003 during the period marked A. The response peaks at 3005 approximately 3 hours after the dose is administered. The baseline 3003 to peak 3005 represents the amplitude of the response from which a percentage value (such as 50% as shown at 3006 in Fig. 3) can be calculated.

Calculation of the peak (3005), percentage of peak (3006), threshold (3004) and baseline (3003) BK scores enable calculation of parameters A, B and C by the processor which each indicate the duration of benefit of L-Dopa to this subject and hence the subject's "responsiveness" to the therapy.

[0095] As alluded previously, in some embodiments step 1002 includes the processor determining a rate of progression of the movement disorder in the subject over an extended period, by receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period; calculating one or more of parameters A, B and C for each of the plurality of observation periods; and calculating a rate of change of one or more of parameters A, B and C over the extended period. The rate of change calculated for one or more of A, B and C over the extended period indicates the rate of progression of the movement disorder in the subject.

[0096] Fig. 4 represents graphically a "rate of progression" chart for a subject which may be generated on a user interface for viewing by a clinician, carer or subject. Fig. 4 shows that the rate of change of the duration of response to therapy is high (there is a rapid shortening of duration of response over time) over the first year of data and that the rate of decline of duration of response slows from about 3 years. Presenting the data in a manner that demonstrates the rate of progression, that is the rate at which the subject's duration of response to therapy changes has utility in that it may be regarded as a proxy of the rate of loss of terminals in the striatum and therefore of disease progression. A graphical representation of the kind shown in Fig. 4 could also show therapies received by the subject over the period shown which may be represented by a marker on the time axis or on the curve. Where the user interface includes an operator-controlled cursor, hovering the cursor over the marker may cause a display device of the user interface to show data corresponding to therapy dose information.

[0097] In some embodiments, in step 1003 the processor calculates automatically from the time series of BK scores another indicator, DBK, used in the determination of progression (or rate of progression) of the movement disorder. In this embodiment, the processor first calculates a basal BK score representing the basal bradykinetic behaviour of the subject. In a clinical setting, basal bradykinetic behaviour occurs after a sufficiently long period (corresponding to the duration of response or longer) without therapy. This can be determined automatically by the processor from the received time-marked therapy data and the calculated BK scores since a dose of therapy (L-Dopa in the case of PD) is directly reflected as the effect of bradykinesia in the subject. Thus the processor determines a basal BK score to occur after a period of time without therapy corresponding to the calculated duration of response or longer. [0098] In a step 1003, the processor then calculates a DBK value being the difference between the calculated basal BK score and a reference basal BK score. The reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder (i.e. PD) and in preferred embodiments, is personalised to the subject while asymptomatic. Recognising, however, that asymptomatic data from the subject may not be available, a reference basal BK score may be estimated based on data obtained from one or more individuals who are asymptomatic of the disease. The processor determines the movement disorder to have progressed when it detects DBK values to be increasing over time, representing an increase in basal BK in the subject over time.

[0099] Fig. 6 is an illustrative graphical representation showing progressive increase in basal bradykinesia (as a reflection of basal dopamine levels) as well as the change in bradykinesia and the shortening of duration of response following therapy administered after a period corresponding to at least the subject’s duration of response. Fig. 6 shows a typical response to administration of a dose of therapy (L- Dopa) at time 00 in a subject with Parkinson’s on three occasions separated in time (e.g. by 1 year). The three occasions are represented as Y1 , Y2 and Y3. Broken line A represents the basal BK score while the subject was asymptomatic.

[0100] As shown in Fig. 6, changing DBK values D1 , D2 and D3 show an increase in basal bradykinesia in the subject over time. The curves also show duration of reduction in bradykinesia, which is indicative of duration of response to therapy. The duration of response declines over Y1 , Y2 and Y3 from approximately 190 mins to 135 mins and 115 mins respectively. This is exhibited further in the charts of Figs. 7A and 7B which show increasing DBK values over time, and decreasing duration of response (DOR) over the same time period. It may be that an increase in DBK values may be more or less sensitive (as shown in Figs. 7A and 7B) than DOR. Flowever the inventor envisages that both indicators will provide insight into progression, and more particularly rate of progression, of disease in the subject. While rate of progression is presented graphically in Fig. 4, it is to be understood that numerical representations of the instantaneous or time-based rate of progression may also/alternatively be provided. Qualitative indicators of the rate of progression such as“fast, slow, slowing, quickening, stable" may also be provided on a display device of the user interface. [0101 ] Typically, observation periods in an extended period are separated by an interval of approximately 1 month, 2 months, 3 months, 6 months or 12 months. In a preferred embodiment, observation periods in an extended period are separated by an interval of between 1 and 6 months. Typically, the observation period corresponds to the duration that the motion sensor is worn by the subject. Thus, the observation period may be of e.g. 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 21 or 28 days' duration. In most circumstances, the observation period will be between 5 and 15 days' duration, more typically between 6 and 10 days’ duration.

[0102] In some embodiments, the automated method represented in Fig. 1 may be used in an automated advanced therapy analytical tool for automatically determining if a subject is a candidate for advanced therapy such as deep brain stimulation (DBS). This may involve the processor calculating a rate of progression of disease and detecting when that rate indicates significant wearing off. This may be achieved by the processor determining that there has been progression followed by a period of fluctuations in BK score suggesting significant wearing off and causing the processor to generate an output signalling for the subject to be referred to a clinician for assessment for advanced therapy.

[0103] Ideally, the time-marked motion data processed according to method 1000 shown in Fig. 1 is generated by a body-worn motion sensor worn by the subject during the observation period. Preferably, the body-worn motion sensor is configured to generate and store automatically time-marked therapy data based on one or more therapy inputs received from the subject by the body-worn motion sensor during the observation period. Such a body-worn motion sensor will now be discussed with reference to Fig. 5 which is a schematic illustration of a system for determining progression of a movement disorder in a subject, according to another aspect of the present invention.

[0104] A system 5000 for determining progression of a movement disorder such as PD in a subject includes an input module 5002 for receiving time-marked motion data indicative of movements of the subject during an observation period and time- marked therapy data indicative of therapy received by the subject during the observation period. Therapy is aimed at treating the movement disorder symptom. In some embodiments, input module 5002 is further configurable to receive data files representing protocols for assessing progression of a movement disorder. Such protocols may specify e.g. the duration of the observation period, times or schedules for the subject to receive therapy (doses of medication) and the size of the dose of therapy. Additionally, protocol data may define rules for subject activity before and/or during the observation period.

[0105] A processor 5003 is configurable to identify automatically in the time- marked motion data one or more data points indicating a change in motion

characteristics of the subject that indicate a response to received therapy, calculate a duration of response to a received therapy, and calculate a change in responsiveness to therapy by comparing one or more calculations of duration of response during the observation period with reference duration of response data. Alternatively/additionally, processor 5003 is configurable to calculate automatically a basal BK score and a DBK value and calculate a change in responsiveness to therapy by comparing DBK values over time. An output module 5004 generates an output signal for causing a user interface 5005 in communication with system 5000 to present an indicator of the progression of the movement disorder in the subject, which is determined from the calculated change in duration of response to the received therapy and/or calculated change in DBK values.

[0106] In preferred embodiments, the output module 5004 is configured to generate an output signal causing user interface 5005 to produce automatically a graphical representation (e.g. of the kinds represented in Figs 2 to 4 and 6 to 7B) presenting analysis performed by processor 5003. Additionally/alternatively output module 5004 may be configurable to generate an output signal causing user interface 5005 to produce one or more of a report summarising analysis performed by the processor, data files representing data processed by the processor and background information such as protocols and clinical data relevant to the subject (which may be de-identified) and used by system 5000 to generate the analysis and output signals. Data files may include text-based files, files readable as spreadsheets or other file formats as may be understood by one of skill in the art to be of utility in the context of the present invention.

[0107] In some embodiments the system includes a body-wearable sensor device 5001 configured for continuous monitoring of movements of the subject during the observation period (or on an ongoing basis) and storage or transmission of time- marked motion data for receiving by input module 5002. Where time-marked motion data is collected by body-wearable sensor device 5001 , it is typically stored until the device is removed from the subject and placed in a docking station (not shown) which both charges a battery in the device and transmits stored data via a communication network 5006 to processor 5003. In some embodiments, however, the body-wearable sensor device 5001 contains a communication interface for direct transmission of time-marked motion data from the device to processor 5003 during wear.

[0108] In some embodiments, body-wearable sensor device 5001 is further configured for storage and transmission of therapy data based on one or more therapy inputs received by the body-worn motion sensor from the subject during the observation period. This may be achieved e.g. by the subject providing a slow swipe of the finger or thumb across a screen of body-wearable sensor device 5001 . In some embodiments, body-wearable sensor device 5001 is further configured to provide therapy reminders whereby the subject is instructed to take a dose of medication for example when vibrations are felt from the device. Thus, it may be assumed that therapy inputs provided by the subject using body-wearable sensor device 5001 are provided substantially contemporaneously with therapy being received by the subject.

[0109] Output module 5004 may include a communication interface configured to communicate signals over a communication network 5006 such that output signals may be received by a remotely located user interface device 5005 such as a user display screen associated with a computer, laptop, mobile device or the like and/or printer, via communication network 5006. Signals transmitted over communication network 5006 by system 5000 may include raw or processed data, reports, analysis, protocols and the like so that utility of the system is not limited to an embodiment in which input module 5002, processor 5003 and output module 5004 are physically located together with the user interface (although that may be the case). Rather, data may be collected e.g. by body-wearable sensor device 5001 at a first location, processed by a processor 5003 at a second location and transmitted for display on a user interface 5005 at a third location. Data may be communicated over

communication network 5006 to various entities such as e.g. the patient or subject being monitored, their clinician, a data warehouse and/or a medical authority. [01 10] In preferred embodiments, processor 5003 is configured to identify changes in motion behaviour of the subject by calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject and performing analysis of the time series of scores to determine if the movement data contains a statistically significant change indicative of a change in responsiveness to the received therapy. For PD the movement characteristic of interest is bradykinesia (BK) and the processor is configured to calculate BK scores indicating an extent or severity of BK in the subject during the observation period using any suitable method such as the methods described elsewhere herein.

[01 11 ] Processor 5003 may use an analytical tool such as cumulative sum control chart (CUSUM) and/or peak detection to determine if there is a statistically significant change although other analysis tools may be programmed into the processor, as would be apparent to one of skill in the art.

[01 12] Ideally, processor 5003 is configurable to calculate automatically from the time series of scores one or more of a peak value or range of values representing a peak response to a received therapy during the observation period and a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy. Additionally, in a preferred embodiment processor 5003 is configurable to calculate one or more of a first parameter (A) being a time duration during which the scores are above the baseline, a second parameter (B) being a time duration during which the scores are above a percentage of the peak value, and a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy. Examples of a graphical representation of such values and parameters calculated by processor 5003 are provided in Fig. 3.

[01 13] In preferred embodiments, processor 5003 is further configurable to calculate automatically a rate of progression of the movement disorder in the subject over an extended period. This is achieved by processor 5003 receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period, calculating one or more of parameters A, B and C for each of the plurality of observation periods and calculating a rate of change of one or more of parameters A, B and C over the extended period. Fig. 4 represents graphically progression of the movement disorder, wherein a steep decline represents a rapid rate of progression of the disease in the first year (corresponding to a rapid shortening of duration of benefit of therapy) followed by a period of slowed progression over the following 2 years, and relatively stable response durations over the following time period (corresponding to a slower rate of progression, or disease state stability).

[01 14] In other embodiments, the rate of progression is calculated by calculating a rate of change of DBK values over the extended period. Figs. 7a and 7B represent graphically progression of the movement disorder wherein a gradual increase in DBK represents a slow rate of progression of the disease in the first 12 months followed by a period of faster progression in months 48 to 60.

[01 15] Thus in a preferred embodiment the processor calculates the rate of progression of the movement disorder as the rate of change calculated for one or more of A, B, C and DBK over the duration of the extended period. Rate of

progression may be presented in any suitable manner, e.g. graphically (as in Figs. 4, 7A and 7B), numerically or using other qualitative indicators such as "fast, slow, slowing, quickening, stable".

[01 16] In some embodiments, a graphical representation of the kind shown in Fig. 4 may also show administered therapy over the period shown which may be represented by a marker on the time axis or on the curve as described elsewhere herein.

[01 17] According to some embodiments, processor 5003 may be further configured to determine automatically if the subject is a candidate for advanced therapy such as DBS. In such embodiments, processor 5003 is configured to calculate a rate of progression of the movement disorder for the subject and determine the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off. That is, a change in duration of response indicating that therapy is no longer effective in treating motion symptoms of the disease. This may further include processor 5003 detecting fluctuations in BK score which may mark progression from earlier to more advanced stage disease. [01 18] According to some embodiments, processor 5003 is configurable with an algorithm that can automatically detect basal bradykinesia from the received time- marked motion data and time-marked therapy data in the morning during wearing of a sensor device 5001 . Since therapy is not received by the subject while sleeping, the subject’s response to therapy in the morning represents the response in the presence of basal levels of dopamine in the striatum. Thus, the algorithm may be used as a predictor or substitute for the L-Dopa challenge test. Not only does this reduce the cost of assessment by taking the test out of the clinic and into the subject’s own environment, it may have greater accuracy by objectively monitoring the change in bradykinesia in the subject while the subject is performing everyday tasks rather than during a question-based assessment performed in the clinic according to L-Dopa challenge test protocols.

[01 19] Advantageously, the inventor has determined that markers in kinetic behaviour that are indicative of motion characteristics associated with a movement disorder can be used as an indicator of duration of benefit or "responsiveness" to therapy and more specifically, that changes in responsiveness can indicate disease progression (or remission). The duration of response in PD reflects the number of terminals that can store dopamine until it reaches the half-life of L-Dopa in the blood and the direct conversion of L-Dopa to dopamine by non-terminal sites (with no dopaminergic terminals remaining). Thus the duration of response calculated according to embodiments of the invention reflects the duration of benefit of, therapy which in turn can be utilised as a proxy for the number of remaining terminals. Thus the rate of shortening of the duration of response (or rate of reduction of

responsiveness to therapy) is useful as a proxy for the rate of loss of terminals. As the extent of terminal loss is a reflection of disease progression then also, changes in responsiveness to therapy as represented by calculated change in duration of response is a measure of disease progression.

[0120] Fig. 8 shows the time course of extra-synaptic dopamine release in the striatum, before (-40 to 0 min) and after (20 to 140 min) administration of L-dopa (20mg/kg) in animals with extensive (filled triangle), partial (filled circle), normal density lesioned (filled square) and normal density un-lesioned (unfilled

triangle). Notably, after a sufficiently long period (at least the duration of response) without L-dopa, the basal level of dopamine will be low (the period between -40 and 0 mins). The more denervated the striatum (filled triangles c.f. unfilled triangles) the lower the basal dopamine. A dose of levodopa rapidly raises the basal level of dopamine which the inventor notes is directly reflected as the effect of bradykinesia. Thus, the extent to which the basal level of dopamine falls and the shortening duration of response to L-Dopa are reflections of the same mechanism and may therefore both be a measure of loss of striatal terminals. Thus the rate of change of basal dopamine (represented by the calculated change in DBK) and the rate of change of duration of response are hypothesised by the inventor to be indicators of rate of loss of striatal terminals and hence, either separately or together, are useful as indicators of disease progression and rate of progression.

[0121 ] Sensor based monitoring of kinetic behaviour using methods and systems of the current invention, can be utilised to provide an objective measure of the duration of benefit of therapy in a subject. Repetitive or ongoing monitoring using these inventive approaches enables changes in the duration of response/benefit of therapy over time to be monitored. A decrease in objectively calculated

responsiveness to therapy indicates, in the case of PD and other movement disorders, disease progression. Importantly, the inventive methods and systems thus enable objective measurement of the rate of progression of disease. This enables clinicians and carers to implement effective and evidence based clinical treatment regimens and care plans, and enables subjects themselves to prepare for advanced symptoms. Importantly, the objective assessment techniques of the present invention facilitate measurement of rate of progression and "wearing off" even when patients are not aware of these changes being present.

[0122] Further utility of the present invention lies in its application to the objective evaluation of new therapies being developed not to treat the symptoms of PD and other movement disorders, but to halt or reduce the rate of progression of those diseases.

[0123] Thus, in addition to the benefits associated with treatment of subjects with PD and other movement disorders, the present invention provides a framework for objective, evidence-based decision making in research, healthcare economics, and the development of new therapies including disease modifying pharmaceuticals. Additionally, embodiments of the present invention may be used as an adjunct to or substitute for the L-Dopa challenge test, providing objective data indicating the subject's responsiveness to therapy, thereby supplementing or replacing traditional clinical evaluation performed in accordance with the UPDRS.

[0124] Where the terms“comprise”,“comprises”,“comprised” or“comprising” are used in this specification (including the claims) they are to be interpreted as specifying the presence of the stated features, integers, steps or components, but not precluding the presence of one or more other features, integers, steps or components or group thereof.

[0125] It is to be understood that various modifications, additions and/or alterations may be made to the parts previously described without departing from the ambit of the present invention as defined in the provisional claims appended hereto.

[0126] Future patent applications may be filed in Australia or overseas on the basis of or claiming priority from the present application. It is to be understood that the following claims are provided by way of example only, and are not intended to limit the scope of what may be claimed in any such future application. Features may be added to or omitted from the claims at a later date so as to further define or re-define the invention or inventions.

Claims

Claims:

1. An automated method for determining progression of a movement disorder in a subject, the method including the steps of:

a. receiving at a processor time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the

observation period;

b. the processor processing the received time-marked motion data and time-marked therapy data to identify automatically changes in one or more movement characteristics of the subject that indicate a response to received therapy and calculating a duration of response to the therapy;

c. the processor calculating a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference duration of response data; and

d. the processor generating an output indicative of progression of the movement disorder in the subject;

2. The automated method of claim 1 , wherein processing the received time- marked motion data to identify changes in one or more movement characteristics of the subject includes the processor:

a. calculating from the time-marked motion data a time series of scores representing a movement characteristic of the subject; and

b. performing analysis of the time series of scores to determine if the motion data contains a statistically significant change indicative of a change in responsiveness to the received therapy.

3. The automated method of claim 2, including the step of calculating from the time series of scores one or more of: a. a peak value or range of values representing a peak response to a received therapy during the observation period; and

b. a baseline value or range of values representing movement behaviour of the subject while unaffected by therapy.

4. The automated method of claim 3, wherein the processor automatically calculates the peak value or range of values and the baseline value or range of values.

5. The automated method of claim 3, wherein the processor automatically calculates one or more of:

a. a plurality of peak values or ranges of values; and

b. a plurality of baseline values or ranges of values;

and receives from a user a preferred peak value or range of values and a preferred baseline value or range of values selected from the respective calculated plurality of values or ranges of values.

6. The automated method of any one of claims 3 to 5, further including the step of the processor calculating one or more of:

a. a first parameter (A) being a time duration during which the scores are above the baseline value or range of values;

b. a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and

c. a third parameter (C) being a time duration during which the scores are above a threshold corresponding to a therapeutic response in the subject to the received therapy.

7. The automated method of claim 6 including the step of the processor determining a rate of progression of the movement disorder in the subject over an extended period, by:

a. receiving time-marked motion data and time-marked therapy data for a plurality of observation periods in the extended period;

b. calculating one or more of parameters A, B and C for each of the plurality of observation periods; and c. calculating a rate of change of one or more of parameters A, B and C over the extended period;

wherein the rate of change calculated for one or more of A, B and C over the extended period indicates the rate of progression of the movement disorder.

8. The automated method according to claim 7, wherein observation periods in the extended period are separated by an interval of approximately 1 month, 2 months, 3 months, 6 months or 12 months.

9. The automated method according to any one of the preceding claims wherein the observation period corresponds to the duration that the motion sensor is worn by the subject.

10. The automated method according to any one of the preceding claims wherein the observation period is between 3 and 28 days duration, preferably between 5 and 15 days duration and most preferably between 6 and 10 days duration.

1 1. The automated method according to any one of claims 2 to 8 or claims 9 or 10 when appended to any one of claims 2 to 8, wherein performing analysis to determine if the movement data contains a statistically significant change includes the processor using one or more of the following analytical tools:

a. cumulative sum control chart (CUSUM); and

b. peak detection.

12. The automated method of any one of the preceding claims, wherein the movement disorder is Parkinson's Disease (PD).

13. The automated method according to any one of the preceding claims, wherein the movement characteristic is bradykinesia (BK) and scores calculated by the processor include BK scores indicating an extent or severity of BK in the subject during the observation period.

14. The automated method according to claim 13, further including the processor: a. determining automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy corresponding to the duration of response or longer; and

b. calculating a DBK value being the difference between the calculated basal BK score and a reference basal BK score;

wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the processor further determines the movement disorder to have progressed when it detects calculated DBK values increasing over time.

15. The automated method according to any one of the preceding claims, wherein the received therapy is selected from a group including L-Dopa, carbidopa and dopamine agonists.

16. The automated method according to any one of the preceding claims, for use in an automated advanced therapy analytical tool for determining if a subject is a candidate for advanced therapy, including the steps of the processor calculating a rate of progression of the movement disorder and automatically determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

17. The automated method according to any one of the preceding claims, wherein the time-marked motion data is generated by a body-worn motion sensor worn by the subject during the observation period.

18. The automated method according to claim 17 wherein the body-worn motion sensor is configured automatically to generate time-marked therapy data based on one or more therapy inputs received from the subject by the body-worn motion sensor during the observation period.

19. A system for determining progression of a movement disorder in a subject, the system including: a. an input module for receiving time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the

observation period;

b. a processor configured to i) identify automatically in the time-marked motion data one or more data points indicating a change in motion

characteristics of the subject that indicate a response to received therapy; ii) calculate a duration of response to a received therapy; and iii) calculate a change in responsiveness to therapy by comparing one or more calculations of duration of response during the observation period with reference duration of response data; and

c. an output module generating an output signal for causing a user interface to present an indicator of the progression of the movement disorder in the subject;

20. The system according to claim 19, wherein the output module is configured to generate an output signal causing a user interface to produce automatically one or more of:

a. a graphical representation of analysis performed by the processor; b. a report summarising analysis performed by the processor;

c. data files representing data processed by the processor; and

d. background information used by the system to generate one or more of a. to c.

21. The system according to claim 19 or claim 20, including a body-wearable sensor device configured for continuous monitoring of movements of the subject during the observation period and storage and/or transmission of time-marked motion data for receiving by the input module and optionally, configured for storage and transmission of therapy data based on one or more therapy inputs received by the body-worn motion sensor from the subject during the observation period.

22. The system according to any one of claims 19 to 21 wherein the output module includes a communication interface configured to communicate signals between the processor and a remotely located device, the signals representing:

a. one or more of data, reports, analysis, protocols and output signals; and b. one or more of time-marked motion data and therapy data.

23. The system according to any one of claims 19 to 22 wherein the input module is configurable to receive data files representing protocols for assessing progression of a movement disorder, the protocols including definitions for one or more of:

a. duration of the observation period;

b. time for delivering therapy to the subject;

c. dosage of therapy; and

d. rules for subject activity before and/or during the observation period.

24. The system according to any one of claims 19 to 23, wherein the processor is configurable to identify changes in motion behaviour of the subject by performing steps including:

25. The system according to claim 24, wherein the processor is configurable to calculate automatically from the time series of scores one or both of:

a. a peak value or range of values representing a peak response to a received therapy during the observation period; and

26. The system according to claim 24 or claim 25, wherein the processor is configurable to calculate one or more of:

a. a first parameter (A) being a time duration during which the scores are above the baseline value or range of values; b. a second parameter (B) being a time duration during which the scores are above a percentage of the peak value or range of values; and

27. The system according to claim 26, wherein the processor is configurable to calculate automatically a rate of progression of the movement disorder in the subject over an extended period, by performing steps including:

b. calculating one or more of parameters A, B and C for each of the plurality of observation periods; and

c. calculating a rate of change of one or more of parameters A, B and C over the extended period;

wherein the processor determines the rate of progression of the movement disorder to be the rate of change calculated for one or more of A, B and C over the duration of the extended period.

28. The system according to any one of claims 24 to 27 wherein the processor is configurable to determine if the movement data contains a statistically significant change using an analytical tool selected from a group including but not limited to: a. cumulative sum control chart (CUSUM); and

b. peak detection.

29. The system according to any one of claims 24 to 28, wherein the movement characteristic is bradykinesia (BK) and the processor is configured to calculate BK scores indicating an extent or severity of BK in the subject during the observation period.

30. The system according to any one of claims 24 to 29, wherein the processor is configurable to:

a. calculate automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without the therapy, said time period corresponding to the duration of response or longer; and

b. calculate a DBK value being the difference between the calculated basal BK score and a reference basal BK score;

wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the processor further determines the movement disorder to have progressed when calculated DBK values increase over time.

31. The system according to any one of claims 19 to 30, wherein the processor is further configured to determine automatically if the subject is a candidate for advanced therapy, by calculating a rate of progression of the movement disorder for the subject and determining the subject to be a candidate for clinical assessment for advanced therapy when the rate of progression indicates significant wearing off.

32. An automated method for determining progression of a movement disorder in a subject, the method including the steps of:

observation period;

b. the processor calculating from the time-marked motion data a time series of BK scores representing a movement characteristic of the subject being bradykinesia; and

c. the processor determining automatically from the received time-marked therapy data and calculated BK scores a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy not less than a duration for which the subject is responsive to the therapy;

d. calculating a DBK value being the difference between the calculated basal BK score and a reference basal BK score; and

e. the processor generating an output indicative of progression of the movement disorder in the subject;

33. The automated method according to claim 32, further including the processor: a. processing the BK scores and the received time-marked therapy data to identify automatically changes in BK scores that indicate a response to the received therapy and calculating a duration of response to the therapy; and b. calculating a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference duration of response data;

wherein the processor further determines the movement disorder to have progressed when the calculated change in responsiveness indicates a reduction in the subject’s duration of response to the received therapy.

34. The automated method according to claim 32 or claim 33, further including the processor determining a rate of progression of the movement disorder in the subject by calculating the rate of change of at least one of:

a. DBK values; and

b. duration of response; and

the processor generating an output that is indicative of the rate of progression.

35. A system for determining progression of a movement disorder in a subject, the system including:

a. an input module for receiving time-marked motion data indicative of movements of the subject during an observation period and time-marked therapy data indicative of therapy received by the subject during the

observation period;

b. a processor configured to

i. calculate from the time-marked therapy data BK scores including a basal BK score representing basal bradykinetic behaviour of the subject after a time period without therapy not less than a duration for which the subject is responsive to the therapy; and ii. calculate a DBK value being the difference between the calculated basal BK score and a reference basal BK score; and

c. an output module generating an output signal for causing a user interface to present an indicator of the progression of the movement disorder in the subject; wherein the reference basal BK score corresponds to basal bradykinetic behaviour that is asymptomatic of the movement disorder, and wherein the received therapy is for treating a symptom of the movement disorder, and the processor determines the movement disorder to have progressed when it detects DBK values increasing over time.

36. The system according to claim 35, wherein the processor is configurable to: a. process the BK scores and the received time-marked therapy data to identify automatically changes in BK scores that indicate a response to the received therapy and calculating a duration of response to the therapy; and b. calculate a change in responsiveness to therapy by comparing a calculated duration of response during the observation period with reference response data;

37. The system according to claim 35 or claim 36 wherein the processor is configurable to determine a rate of progression of the movement disorder in the subject by calculating the rate of change of one or both of DBK values and duration of response, and wherein the output module is configurable to generate an output signal for causing the user interface to present an indicator of the rate of progression.