Clinical outcome assessment for multiple sclerosis

Abstract

The present invention relates to a method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, a method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, a method of treating an individual suffering from multiple sclerosis, a computer program product comprising code instructions for the execution of such a method, and a disease modifying drug for use in the treatment of an individual having multiple sclerosis as defined in the description and in the claims.

Description

[0001] F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland

[0002] Case: P39626

[0003] Clinical outcome assessment for multiple sclerosis

[0004] The present invention relates in particular to a method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, a method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, a method of treating an individual suffering from multiple sclerosis, a computer program product comprising code instructions for the execution of such a method, and a disease modifying drug for use in the treatment of an individual having multiple sclerosis.

[0005] Multiple Sclerosis (MS) is an autoimmune, chronic disease of the central nervous system that results in a wide array of signs and symptoms. The manifestation of MS evolution is often categorized into relapsing-remitting forms, which causes symptoms to appear in isolated attacks, and progressive forms, where symptoms build-up progressively over time.

[0006] Physical manifestations of MS include a degradation of the patients’ mobility, which is reported by patients as one of the most important aspects of MS in their daily life.

[0007] Research for treatment in MS through clinical trials requires measures of the ambulatory function of patients in order to quantify the effect of treatment on the patients’ motor function. These measures, also called clinical outcome assessments, need to demonstrate good reliability and sensitivity to change so that they can be trusted to accurately measure any change in the patient’ s capabilities in reaction to the treatment.

[0008] The current standard for the assessment of mobility in MS is a test performed in the clinic under healthcare professional supervision: the Timed-25-Foot-Walk-Test (T25FWT). T25FWT’s reliability and sensitivity to change was evaluated in independent studies (C. I. Coleman, D. M. Sobieraj, and L. N. Marinucci, “Minimally important clinical difference of the Timed 25-Foot Walk Test: results from a randomized controlled

[0009] DP / 19.08.25 trial in patients with multiple sclerosis,” Current Medical Research and Opinion, vol. 28, no. 1, pp. 49-56, Jan. 2012). They highlighted its limitations for both factors, meaning that using this test to assess treatment efficiency requires designing clinical trials involving large numbers of patients. Additionally, tests performed in the clinic are known to be highly dependent on the form, fatigue level and motivation of the patient on the particular day of the evaluation. Such tests provide a single measure evaluated punctually and might not reflect the impact of the disease on the patients’ mobility in their daily life.

[0010] Passive monitoring of the patients’ activity in real-life setting is a solution that has in recent years proven able to address the gaps of classical clinical evaluations described above. Using inertial sensors for the passive monitoring of patients’ mobility in different diseases is now a well-established method to obtain insights into people’s motor function in their daily life.

[0011] The possibility to measure clinical outcomes assessing the gait of patients with altered mobility using inertial, ankle-mounted sensors was further confirmed by the qualification of the stride-velocity 95th percentile (SV95C) as a primary endpoint in ambulatory Duchenne’s Muscular Dystrophy (DMD) studies by the European Medicine’s agency in 2023.

[0012] The international application WO 2019 / 243609 Al describes a method to analyze the gait of a pedestrian in a real- world setting and can be used in the method of the invention to acquire the individual’s lower limb motion. This method notably requires in particular the omission from the recorded data of events corresponding to trampling or that are so slow that they don’t correspond to a normal walking pace or motion

[0013] Surprisingly, the viability of SV95C as an outcome measuring the ambulatory function of MS patients proved to fall short of the expectations described above. It appears that it is due to identified running activities for some of the strongest patients, running activities which are absent in DMD patients, even ambulatory.

[0014] These running activities have the following properties: they include a large quantity of high-velocity strides, which have a large impact on the value of SV95C; SV95C increases by a significant amount when a running episode is present in the analyzed period. In addition, they are performed irregularly by some of the patients, resulting in significantly different values of SV95C between periods when running is present and periods when it is not.

[0015] This causes SV95C to have a high variability for some MS patients compared to what was observed in DMD, resulting in unmet expectations in terms of reliability and ability to detect change. As a result, a new clinical outcome assessment measuring ambulatory function in MS patients was developed. It includes a classification of strides into the walking or running category, and the subsequent exclusion of running strides. One of these new clinical outcome assessments, object of the invention, will be referred to as WSV95C, for walked stride- velocity 95th percentile.

[0016] The present invention solves the above-described problems and provides an efficient and reliable clinical outcome assessment of MS patients that can be measured by a lower limb-worn sensor, for example an ankle-worn sensor, despite the potential occurrence of running episodes.

[0017] The invention thus relates in particular to a method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:

[0018] (a) Acquiring the motion of the individual’s lower limb during a recording period;

[0019] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);

[0020] (c) For each recording period, calculating the velocity of each stride of the individual;

[0021] (d) Regrouping adjacent strides into gait episodes;

[0022] (e) Selecting, in one recording period, strides which are not a running stride;

[0023] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0024] (g) Repeating steps (d)-(f) for several recording periods;

[0025] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; and

[0026] (i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

[0027] Figure 1 describes the different events during a typical symmetrical gait. Figure 2 illustrates the evolution of SV95C and WS95C during 3 consecutive months for patients of the observational study ActiMS. ActiMS (NCT04882891) is a multi-centric, non-commercial, observational study whose goal is to gather real-life longitudinal MS data to develop a robust digital outcome for patients with MS. On the figure, each single line corresponds to one patient evolution during the first three consecutive months of recording at the start of the study. Patients who run are displayed in large dashed lines, while the others are displayed in solid lines.

[0028] Figure 3 illustrates the evolution of SV95C during the first month of the study (indicated as “Baseline” on the x-axis) and after 1-year of follow-up (indicated as “1 Year” on the x-axis), for all patients of the observational ActiMS study. Patients are regrouped into functional groups, depending on the form of MS and the value of their Expanded Disability Status Scale (EDSS) evaluation in the baseline.

[0029] Figure 4 illustrates the evolution of WSV95C during the first month of the study (indicated as “Baseline” on the x-axis) and after 1-year of follow-up (indicated as “1 Year” on the x-axis), for all patients of the observational ActiMS study. Patients are gathered into functional groups, depending on the form of MS and the value of their EDSS evaluation in the baseline.

[0030] A clinical outcome assessment (CO A), as defined in particular by the FDA, is a measure that describes or reflects how a patient feels, functions or survives. Health authorities responsible for the approval of drug products can qualify clinical outcome assessments when they conclude that a COA is a reliable assessment of a patient’s symptoms or functions. An example of such COA is the 95th centile of the stride velocity (SV95C). This COA can be captured using a digital and passive wearable device and system that was developed by the company Sysnav. SV95C represents the maximal speed of a subject’s strides performed in a real-life setting, i.e., 95% of the strides performed by the subject are slower than the SV95C and only 5% of the strides performed are faster. SV95C is the first ever digital endpoint approved by a health authority worldwide in any disease.

[0031] The terms “individual”, “subject” and “patient” are used interchangeably herein. The individual can be pre-diagnosis or post-diagnosis and can be pre-therapy, undergoing therapy or post-therapy.

[0032] Set-up & device

[0033] In clinical trials, time windows are usually defined wherein the assessment of mobility is made, for example a window at baseline and a window one-year later. These time windows can be for example 3-weeks long in order to capture a range of activities representative of the patient’s daily life.

[0034] The motion of the individual’s lower limb, according to step (a) of the invention, can be acquired using a device as follows. Patients are typically asked to wear a device mounted on their lower limb, for example on the ankle, during each recording period. The device can conveniently be a watch-like device but can have other forms. The device usually comprises inertial sensors including a three-axis gyrometer and a three-axis accelerometer. The device can also include a computing unit such as a processor in order to perform data analysis in real-time, or means of data storage in order to enable analysis of the data on an external computing unit at a later stage.

[0035] Step (a) therefore can consist of acquiring the data recorded by the inertial sensor.

[0036] Examples of methods and devices suitable for acquiring the motion of the individual according to the invention are described in FR 3 042266 B 1.

[0037] Data is recorded by the inertial sensors in the form of a discrete, multi-dimensional signal with a fixed sampling rate. The recorded data consist of acceleration and angular velocity measurements of the device associated with a timestamp, which gives access to the date, up to the millisecond, of said measurement.

[0038] Alternatively, step (a) can consist in only receiving the motion of the individual’s lower limb as described below.

[0039] Step (b) can advantageously comprise the steps of stride detection & segmentation and lower limb trajectory estimation.

[0040] Stride detection & segmentation

[0041] Strides are detected during a step aiming at the identification of the starting time and ending time of the strides in the data recorded by the device worn by the patient. This step can be performed according to the process described in WO 2019 / 243609 Al.

[0042] Lower limb trajectory estimation

[0043] The trajectory of the patient’s lower limb, for example the patient’s ankle, during gait can be estimated during a subsequent step, for example according to the process described in FR 3 042 266 Bl, using the segmentation of strides described above.

[0044] Stride parameters estimation For each stride identified during the recording period, the following parameters are estimated:

[0045] • The stride duration D, defined as the difference of time between the exact start of the stride and the exact end of the stride.

[0046] D > ~ T1end — T1start wherein;

[0047] Tstart is the time of the stride’s start; and

[0048] Tend is the time of the stride’s end.

[0049] • The stride cadence C, defined as the inverse of the stride duration.

[0050] • The stride length L, defined as the distance between the position of the lower limb at the end of the stride and the position of the lower limb at the start of the stride. Both positions are obtained from the lower limb trajectory measured above and are projected in the horizontal plan before calculating the distance between them. wherein:

[0051] Xstart and Ystart are the lower limb’s coordinates at the start of the stride; and

[0052] Xend and Yend are the lower limb’s coordinates at the end of the stride.

[0053] • The stride velocity V, defined as the ratio between the stride length L and the stride duration D.

[0054] • The absolute stance duration d, defined as the duration of the period wherein the foot is in contact with the ground. It can for example be measured by the method described B. Mariani, H. Rouhani, X. Crevoisier, et K. Aminian, Quantitative estimation of foot-flat and stance phase of gait using foot- worn inertial sensors,” Gait & Posture, vol. 37, no. 2, pp. 229-234, Feb. 2013. • The stance-ratio or stance percentage A, defined as the proportion of time spent with the foot in contact with the ground during the stride. wherein d and D are as defined above.

[0055] Assignment of strides to gait episodes

[0056] All strides obtained through the means referenced above are grouped into gait episodes (also called gait bouts) on the following criterion: two successive strides are considered to belong to the same gait episode if the duration separating the end of the earlier stride from the start of the later stride is less than a given duration. The skilled person will easily determine this duration. It will typically depend on the nature of the individual’s gait, as well as on the segmentation algorithm chosen for the selection of running strides detailed below. 2.5 seconds is an example of such a duration.

[0057] Stride classification

[0058] Ambulation activities such as walking and running are generally described as a repetition of strides alternating between the left and right legs. Strides on each side can be identified during walk, and successive strides are generally separated by the moment when the foot makes contact with the ground. This particular gait-event is called heel- strike.

[0059] During a stride, from the perspective of one side independently from the other, two different phases are identified: a phase where the foot is in contact with the ground after the heel- strike event, followed by a phase where the foot is not in contact with the ground. The first phase (foot in contact with the ground) is called the stance phase; the second phase (foot not in contact with the ground) is called the swing phase. The transition between these two phases is called the toe-off (sometimes also called foot-off). It corresponds to the instant when the foot stops making contact with the ground.

[0060] During the stance phase, part or all of the body weight rests on the leg. During the swing phase, none of the body weight rests on the leg since there is no ground support on this side.

[0061] The transition from stance to swing is marked by a release of the weight support from the leg. This transition starts near the end of the stance phase and ends at the toe-off. Depending on the type of the stride (walking or running stride), when the toe-off happens on the first leg, then: in the case of a walking stride: the second leg is in the first half of its stance phase, and it accepts the weight as it is being released from the first leg;

[0062] • in the case of a running stride: the second leg is in the second half of its swing phase, and the patient enters a phase when none of the legs provide support from the ground. This phase is called the flight phase.

[0063] The transition from swing to stance is marked by an acceptance of the weight support onto the leg.

[0064] This transition starts when the foot starts making contact with the ground (heel- strike event), and lasts for a certain duration at the start of the stance phase. Depending on the type of the stride (walking or running stride), when the heel- strike happens on the first leg, then:

[0065] • in the case of a walking stride: the second leg is in the second half of its stance phase, and it releases some weight as it is being accepted from the first leg after making contact with the ground;

[0066] • in the case of a running stride: the second leg is in the first half of its swing phase, hence the legs were in flight phase (no legs in contact with the ground). The first leg accepts the weight of the body after making contact with the ground.

[0067] The different events during a typical symmetrical gait are illustrated on Figure 1.

[0068] A few observations result from this:

[0069] There is no flight phase during regular walking strides. One of the feet is always in contact with the ground.

[0070] There is always a phase when both feet are in contact with the ground during regular walking strides, which is called the double- support phase.

[0071] There is no double- support phase during running strides. The feet are never simultaneously on the ground.

[0072] There is always a flight phase during running strides.

[0073] Therefore, from the perspective of one leg, regular walking strides include a stance phase which itself includes time when both feet are in contact with the ground and time when only one foot is in contact with the ground. This imposes that in the case of symmetrical gait (i.e. the behavior of the two legs is similar), the percentage of time spent in the stance phase is more than half of the duration of the stride when walking. It is a well-documented fact that healthy adults walking at a normal average pace have a repartition of around 60% of stance and 40% of swing, which is considered optimal.

[0074] Furthermore, running strides include a flight phase when none of the feet is in contact with the ground, and do not comprise a phase when both feet are simultaneously in contact with the ground. This imposes that in the case of symmetrical running strides, the percentage of time spent in the stance phase is less than half of the duration of the stride when running, therefore a stance percentage below 50%.

[0075] Strides can be classified as a walking stride or a running stride for example using a device (for example as described above) worn on each leg (e.g. ankles or feet). Such a method can be carried out as follows.

[0076] • Use two devices, one attached to each lower limb, e.g. on the ankles;

[0077] • Synchronize the measurements from each device;

[0078] • Detect walking episodes and segment them into strides, on both sides;

[0079] • Detect heel-strikes and toe-off events on both sides (for example according to B. Mariani, op. cit.); they should alternate;

[0080] • For each stride: o during the toe-off event of the stride, determine if the opposite leg is in stance phase or in swing phase:

[0081] ■ if the previous gait-event on the opposite leg is a heel-strike and the next event a toe-off, the opposite leg is considered to be in stance phase;

[0082] ■ if the previous gait-event on the opposite leg is a toe-off and the next event a heel-strike, the opposite leg is considered to be in swing phase; o if the opposite leg is in stance phase during the toe-off event, we consider that the stride is a walking stride; o if the opposite leg is in swing phase during the toe-off event, we consider that the stride is a running stride; • This results in a classification at the stride level of strides into walking or running strides on each side;

[0083] • If reconciliation between both sides is required, this can be done by considering pairs of strides from each side, and applying one of the methods below: o A method favoring the walking strides: o A method favoring the running strides: o A method requiring consensus and using an unknown category otherwise:

[0084] Reconciliation might be needed when the sensors disagree on the classification of successive strides, e.g. the left sensor detects a running stride and the right sensor a walking stride within a walking episode.

[0085] In the above method, the synchronization consists in temporally synchronizing the two devices so that two simultaneous events have the same time stamp. The devices can for example be automatically synchronized if they can communicate with each other or this can be done manually using similarities or correlations in the recorded data. This synchronization method is known to a person of ordinary skill in the art.

[0086] Alternatively, it is possible to classify strides into running and walking strides using a single device worn on one lower limb only (e.g. ankle or foot).

[0087] From the point of view of one leg, as described above, a running stride will typically have a stance percentage inferior to 50% in order to allow the presence of a flight phase. However, observing a stance percentage inferior to 50% during a stride is not sufficient to classify the stride as a running stride. Asymmetrical gait can lead to a stance percentage inferior to 50% on one side, and largely superior to 60% on the other side (compensation phenomena), in the case of a person limping for example (one side of the lower limbs might be affected by a pathology / injury more than the other).

[0088] In order to address this, we designed a classification criterion that takes into account either the stance percentage, the stride velocity and the stride cadence or the stance percentage and the stride velocity.

[0089] Using the stride velocity and the stride cadence on top of the stance percentage eliminates false positives due to limping or other asymmetrical gaits, as these are generally associated with low gait velocities and / or cadence.

[0090] We thus provide below a method to classify strides into walking and running stride using only one device.

[0091] • Use a single inertial device worn on the lower limb, e.g. on the ankle;

[0092] • Detect walking episodes and segment them into strides;

[0093] • Evaluate during each stride; o the stride velocity; o the stride cadence; o the stance percentage;

[0094] • A stride is considered a running stride when: o it has a stance percentage below 50%, and a stride velocity above a certain threshold; or o by computing a score S based on the value of the stance percentage, the stride velocity and the stride cadence, and using a predetermined threshold on this score to classify strides.

[0095] The stride velocity threshold is determined by the skilled person depending on the patient’s capacities and what can reasonably be considered a running stride or not. An example of suitable threshold can be 2 m / s (7.2 km / h).

[0096] In the above method, said score S is calculated for each stride with the following formula: wherein:

[0097] Vmesure, Amesure et Cmesure are the measured stride velocity, stance ratio and stride cadence, respectively

[0098] Vmin, Vmax, Amax, Cmin and Cmax are predetermined parameters; and f is a smoothed step function as follows:

[0099] The predetermined parameters are for example:

[0100] Vmin < Vmesure < Vmax if and only if the measured stride velocity is comprised between 1 m / s and 2 m / s;

[0101] Amin < Amesure < Amax if and only if the measured stance duration is comprised between 50% and 60% of the stride duration; and

[0102] Cmin < Cmeasure < Cmax if and only if the measured stride cadence is comprised between 1 stride per second and 1.5 strides per second.

[0103] The first predetermined threshold is then such that the stride is classified as a walking stride if the score S is lower than or equal to 0.5 and classified as a running stride if the score S is higher than 0.5.

[0104] Alternatively, the classification step can comprise an additional sub-step wherein the stride velocity is compared with a second predetermined threshold. The stride is then classified:

[0105] - as a walking stride if and only if the score is inferior to the first predetermined threshold and the stride velocity is inferior to the second predetermined threshold; and

[0106] - as a running stride in all other cases. The first threshold is, for example, such that, when the score is equal to the first threshold, the value S is substantially equal to 0.5, and the second threshold is, for example, between 2.4 and 2.6 m / s.

[0107] In a further variant, the classification step comprises comparing the stance ratio with a first threshold and comparing the stride velocity with a second threshold, the stride being classified:

[0108] - as a running stride if and only if the stance ratio is inferior to the first threshold and the stride velocity is superior to the second threshold; and

[0109] - as a walking stride in all other cases.

[0110] The first threshold is for example substantially equal to 0.5 and the second threshold is for example between 1.9 and 2.1 m / s.

[0111] It is thus possible to discriminate a walking stride from a running stride of an individual, and thus to discriminate a walking episode from a running episode of this individual, by simply using stride parameters easily deduced from the measurements acquired by an inertial measuring device worn by the individual. This classification is robust, since combining the stance ratio and the stride velocity to make the classification, avoids classifying the walking strides that are typical of limp persons (who have a low stance ratio but a low stride velocity) as running strides. This robustness can further be reinforced by taking into account the stride cadence when making the classification.

[0112] The classification of steps into walking of running steps according to the invention can conveniently be done using a computer.

[0113] The invention further relates to a method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, the method comprising:

[0114] (a) Acquiring the motion of the individual’s lower limb during a recording period;

[0115] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);

[0116] (c) For each recording period, calculating the velocity of each stride of the individual;

[0117] (d) Regrouping adjacent strides into gait episodes;

[0118] (e) Selecting, in one recording period, strides which are not a running stride; (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0119] (g) Repeating steps (d)-(f) for several recording periods; and

[0120] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

[0121] The invention also relates to a method of treating an individual suffering from multiple sclerosis, the method comprising:

[0122] (a) Acquiring the motion of the individual’s lower limb during a recording period;

[0123] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);

[0124] (c) For each recording period, calculating the velocity of each stride of the individual;

[0125] (d) Regrouping adjacent strides into gait episodes;

[0126] (e) Selecting, in one recording period, strides which are not a running stride;

[0127] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0128] (g) Repeating steps (d)-(f) for several recording periods;

[0129] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; and

[0130] (i) Administering an effective amount of a disease-modifying drug to the individual. wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening. The invention relates also to a disease-modifying drug for use in the treatment of an individual having multiple sclerosis comprising

[0131] (a) Receiving, or acquiring during a recording period, the motion of the individual’s lower limb;

[0132] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);

[0133] (c) Calculating the velocity of each stride of the individual;

[0134] (d) Regrouping adjacent strides into gait episodes;

[0135] (e) Selecting strides which are not a running stride;

[0136] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0137] (g) Repeating steps (d)-(f) for several data sets or recording periods; and

[0138] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets or recording periods; wherein the individual is selected for treatment when a decrease of the predetermined percentile of stride velocity obtained in steps (f) as defined above over two or more successive data sets or recording periods is detected.

[0139] The invention further relates to a computer program product comprising code instructions for the execution of a method according to the invention, when said program is executed on a computer.

[0140] The invention further relates to a computer-implemented method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:

[0141] (a) Receiving the motion of the individual’s lower limb;

[0142] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);

[0143] (c) Calculating the velocity of each stride of the individual;

[0144] (d) Regrouping adjacent strides into gait episodes; (e) Selecting strides which are not a running stride;

[0145] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0146] (g) Repeating steps (d)-(f) for several data sets;

[0147] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and

[0148] (i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive data sets indicates that the individual’s multiple sclerosis condition is worsening.

[0149] The invention relates in particular to a method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:

[0150] (a) Receiving the motion of the individual’s lower limb;

[0151] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);

[0152] (c) Calculating the velocity of each stride of the individual;

[0153] (d) Regrouping adjacent strides into gait episodes;

[0154] (e) Selecting strides which are not a running stride;

[0155] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0156] (g) Repeating steps (d)-(f) for several data sets;

[0157] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and

[0158] (i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

[0159] The invention further relates to a method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, the method comprising:

[0160] (a) Receiving the motion of the individual’s lower limb;

[0161] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);

[0162] (c) Calculating the velocity of each stride of the individual;

[0163] (d) Regrouping adjacent strides into gait episodes;

[0164] (e) Selecting strides which are not a running stride;

[0165] (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0166] (g) Repeating steps (d)-(f) for several data sets;

[0167] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

[0168] The invention also relates to a method of treating an individual suffering from multiple sclerosis, the method comprising:

[0169] (a) Receiving the motion of the individual’s lower limb;

[0170] (b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);

[0171] (c) Calculating the velocity of each stride of the individual;

[0172] (d) Regrouping adjacent strides into gait episodes;

[0173] (e) Selecting strides which are not a running stride; (f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;

[0174] (g) Repeating steps (d)-(f) for several data sets;

[0175] (h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and

[0176] (i) Administering an effective amount of a disease-modifying drug to the individual. wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

[0177] The invention also relates to:

[0178] A method, a disease-modifying drug for use or a computer program product according to the invention, wherein the individual in need thereof is treated with a multiple sclerosis disease-modifying drug;

[0179] A method or a disease-modifying drug for use according to the invention, wherein acquiring the motion of the individual’s lower limb is performed by at least one inertial sensor, in particular an accelerometer or a gyrometer;

[0180] A method or a disease-modifying drug for according to the invention, wherein the dosage of the therapy or drug is adapted when the individual’s multiple sclerosis condition is worsening or improving;

[0181] A method or a disease-modifying drug for use according to the invention, wherein the inertial sensor is attached to the individual’s lower limb, in particular on his ankle;

[0182] A method, a disease-modifying drug for use or a computer program product according to the invention, wherein the strides selected in step (e) do not belong to a walking episode of less than 3 strides, particularly less than 5 strides, more particularly strictly less 8 strides;

[0183] A method, a disease-modifying drug for use or a computer program product according to the invention, wherein the strides selected in step (e) are not the first nor the last of the walking episode;

[0184] A method, a disease modifying drug for use or a computer program product according to the invention, wherein recording periods have a cumulative duration greater than 5 hours, particularly greater than 10 hours or 50 hours, more particularly greater than 180 hours;

[0185] A method, a disease-modifying drug for use or a computer program product according to the invention, wherein the predetermined percentile of stride velocity is selected from a range of percentiles comprised between the 80thpercentile and the 100thpercentile, more particularly between the 90thpercentile and the 100thpercentile; and

[0186] A method, a disease-modifying drug for use or a computer program product according to the invention, wherein the predetermined percentile of stride velocity is the 95thpercentile.

[0187] According to the invention, the term “successive recording periods” means that said recording periods follow each other over time, but are not necessarily consecutive to each other. Therefore, a decrease of the predetermined percentile of stride velocity obtained in steps (f) might be detected through a method of the invention over a period covering several recording periods wherein no decrease is detected for two or more directly adjacent recording periods in the period covering them. Even in such an event, the decrease is indicative of a worsening of the patient’s condition. This applies mutatis mutandis for successive data sets.

[0188] The determination of a stride comprises the detection for a given moment of an acceleration of absolute value greater than a predetermined threshold, this moment defining the start of the stride and the end of a previous stride. It can be adjusted by the person practicing the invention. This predetermined threshold can be for example around 1.5 g.

[0189] Regrouping strides into gait episodes according to step (d) and selecting in step (e) strides which do not belong to a gait episode of less than 3 strides, particularly less than 5 strides, more particularly strictly less than 8 strides, advantageously selects data coming only from a continuous and natural gait, when the individual is on his momentum. Thus, the data is not likely biased by a significant amount of trampling, isolated strides or micromovements of the individual, these strides not being representative of the state of the individual. Thus, the strides considered for the determination of the clinical outcome assessment do not comprise the isolated strides or trampling, thereby eliminating the associated measurement bias.

[0190] Moreover, during a sequence of less than for example 8, 5 or 3 consecutive strides, the individual is a priori not in a normal gait pace, but in a slow pace, corresponding to trampling or to a small movement. The length of a stride is the length of the projection of a path of the stride on a horizontal plane, or is the curvilinear length of a path of the stride, said path being estimated based on the motion measurements.

[0191] The motion measurements can be performed by at least one inertial sensor, such as an accelerometer or a gyrometer.

[0192] The inertial sensor can be attached to the lower limb of the pedestrian, for example to an ankle. Such a disposition has, in particular compared to a sensor attached to a foot of the individual, advantages in terms of ergonomics, comfort, aesthetics (sensor concealed under pants) and safety (risk of falling if a sensor attached to the foot gets caught on an external element). It can nevertheless be attached to the foot if preferred.

[0193] The invention can be carried out using an equipment and a recording method as described in WO 2019 / 243609 and FR 3 042 266.

[0194] Step (c) according to the invention allows the monitoring of the clinical outcome assessment’s evolution, and therefore the state of an individual suffering from multiple sclerosis or suspected from suffering from multiple sclerosis over time, recording period after recording period.

[0195] A recording period advantageously has a sufficiently long duration so that the clinical outcome assessment is representative of the state of the individual. According to a preferred embodiment, recording periods have a cumulative recording duration greater than 5 hours, 10 hours or 50 hours, preferably greater than 180 hours.

[0196] The combination of these two criteria, duration between two strides and minimal sequence of consecutive strides, eliminates from processing the strides that are not characteristic of a continuous walk of the individual and of the muscle power he is able to develop. As a variant, only one of these two above criteria can be used to select the strides for steps (d) to (h) of the method according to the invention to be performed.

[0197] It is understood that in step (g), steps (d)-(f) are repeated for several recording periods or data sets, therefore for at least one more recording period or data set, in order to be able make the comparison of step (h) for at least two recording periods or data sets.

[0198] With the same objective of only processing strides representative of the state of an individual, it is possible to identify erroneous strides, for example in the calculation of the path or in the determination of their start and of their end. For this purpose, the path of a stride can be compared with one or several reference path(s). The shape of the path can be studied, the existence of a maximum vertical position around the middle of the stride can be verified and ratios between height and length of the path can be defined. The strides deviating excessively from the expected characteristics in view of the reference paths are then identified as erroneous. It can then be decided that steps (d) to (h) of the method according to the invention are not performed on these strides. Thus, the data analysis may not be biased by possible errors in the calculation of the path, determination of the stride, or the like.

[0199] As a variant, a machine learning algorithm such as a neural network, random forest, support vector machine (SVM) or any other known method, can be used to compare a stride with a reference stride database, and thus identify an erroneous stride.

[0200] It is also possible to identify the strides including a change of course, and exclude these strides from steps (d) to (h) of the method according to the invention. Thus, in this case, only the strides belonging to a walk in a straight line are kept in the method. Indeed, a change of course leads to a slowing down, and a stride including a change of course is therefore not representative of a continuous walk and momentum of an individual. In addition, such a stride is likely to be less accurately measured, the pattern of rotation and acceleration of the stride being altered by the change of course.

[0201] Step (a) of the method according to the invention can be implemented by at least one inertial sensor in a stride analysis equipment worn by the individual, for example of the type described in WO 2019 / 243609.

[0202] Steps (b) to (h) of the method according to the invention, as well as the potential additional steps described above, can be executed by a data processing unit of the stride analysis equipment or can be executed by a computer.

[0203] In step (d), regrouping adjacent strides into gait episodes can conveniently be done by the person of ordinary skill in the art, for example by considering on the time that separates a stride from its neighboring strides.

[0204] A stride can be associated with its date, time, and / or start and / or end moment, and thus with a given recording sub-period. A stride can also be associated with its characteristic quantity (for example its average velocity and / or its length). The strides of a given sub-period can be ordered by increasing values of characteristic quantities.

[0205] A statistical calculation can then be carried out on a set formed by the estimated characteristic stride quantities (for example the average velocities and / or lengths), ordered by increasing values and occurring during a recording sub-period. In particular, it is possible to calculate an average and / or a percentile of this characteristic quantity, with respect to the set of the characteristic quantities of the strides of the recording sub-period. For example, it is possible to calculate the 50th percentile, which corresponds to the median, 80th percentile, 95th percentile or any other percentile value of the characteristic stride quantity for a given sub-period.

[0206] A predetermined range of percentiles can be defined, this range of percentiles being relevant for the stride analysis. Particularly, the high percentiles are representative of the maximum effort and maximum muscle power that an individual is able to develop, as they reflect the fastest and / or longest strides of an individual, the walking velocity being constrained by the muscle’s strength. These high percentiles are therefore particularly sensitive to the fitness state of an individual.

[0207] Such a method of stride analysis from a range of high percentiles is particularly suitable for walking, since an individual statistically achieves a number of longer and / or faster strides in everyday life. Particularly, the longest and / or fastest few percent of strides are representative of the maximum power that the individual is able to develop. For example, studies have shown a correlation between established effort test measurements such as the 6MWT (6 minutes walk test), and the 95th percentile of stride length (SV95C) and / or velocity in the same individual. This led to the qualification of SV95C by the European Medicine Agency.

[0208] Examples of disease-modifying drugs for the treatment of multiple sclerosis include the following: interferon class, IFNbeta-la (REBIF®, Extavia, AVONEX® and PLEGRIDY™) and IFNbeta-lb (BETASERON®); glatiramer acetate (COPAXONE® ), a polypeptide; natalizumab (TYSABRI®), alemtuzumab (LEMTRADA®), both monoclonal antibodies; dimethyl fumarate (TECFIDERA®) and fingolimod (GILENYA®) both small molecules, and mitoxantrone (NOVANTRONE®), a cytotoxic agent; teriflunomide (AUBAGIO®); ocrelizumab (OCREVUS®), an anti-CD20 antibody. Other drugs with an aim of disease modification have been used with varying degrees of success, including methotrexate, cyclophosphamide, azathioprine, and intravenous (IV) immunoglobulin.

[0209] Anti-CD20 antibodies and in particular ocrelizumab are useful disease-modifying drugs according to the invention.

[0210] Ocrelizumab can be for example administered as follows. An initial 600 mg dose is administered as two separate intravenous infusions; first as a 300 mg infusion, followed 2 weeks later by a second 300 mg infusion. Subsequent doses of ocrelizumab are then administered as a single 600 mg intravenous infusion every 6 months. The first subsequent dose of 600 mg is advantageously be administered six months after the first infusion of the initial dose. A minimum interval of 5 months are maintained between each dose of ocrelizumab. The present invention can also be applied to other diseases, for example to other movement disorders like Parkinson’s disease, cerebellar ataxia or Friedreich's ataxia; to neuromuscular diseases like Muscular dystrophy (Duchenne, Beckers, etc...), spinal muscular atrophy, facioscapulohumeral muscular dystrophy, amyotrophic lateral sclerosis (ALS) or Lou Gehrig's Disease, Charcot-Marie-Tooth disease and other inherited neuropathies, chronic inflammatory demyelinating polyneuropathy, Guillain-Barre syndrome, myasthenia gravis, or peripheral neuropathies; to neurological disorders like amyotrophic lateral sclerosis; to neurodevelopmental disorders like Angelman syndrome or Dupl5q syndrome; to neurodegenerative diseases like Huntington's disease or Alzheimer's disease; to metabolic disorders like obesity; or to other diseases like idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, systemic lupus erythematosus, sickle cell disease, hemophilia, arthritis, cerebral palsy, hemiplegia, spinal stenosis, lupus nephritis, stroke, spinal cord injury, cystic fibrosis or Guillain-Barre syndrome.

[0211] The invention will now be illustrated by the following examples that have no limiting character.

[0212] Examples

[0213] With the objective of developing a robust clinical outcome assessment of the evolution of the ambulatory capacities of people with MS, we evaluated different designs of outcomes based on the stride velocity on data from the ActiMS study.

[0214] ActiMS is a multicentric, natural-history study during which patients (N=78) were asked to wear a magneto-inertial device (ActiMyo) for two periods: at baseline and after one- year. Expanded Disability Status Scale (EDSS), Timed 25-feet Walk Test (T25FWT) as well as other classical clinical outcomes were captured at baseline and after one-year. During the baseline examination, patients were categorized as relap sing-remitting or progressive by a neurologist, based on the method described by Lublin in 2014 (Lublin, F.D. (2014) ‘New Multiple Sclerosis Phenotypic Classification’, European Neurology, 72(Suppl. 1), pp. 1-5).

[0215] The viability of a selected digital clinical outcome assessment as a robust measure of a MS patient’s ambulatory function was evaluated based in particular on three criteria consisting of reliability, convergent validity and ability to detect change.

[0216] Reliability was assessed using an intra-class correlation (ICC(2,1), Two-way random, single measures, absolute agreement) coefficient used in a test-retest approach. The 3- months recording period in the baseline of all patients was divided into 3 periods of 1- month in order to evaluate the reliability of the measure when it is evaluated on a 1 -month period. Expectation for the Intra-Class Correlation (ICC(2,1)) is to be superior to 0.9 to provide sufficient reliability on the 1 -month period.

[0217] Convergent validity was assessed through correlation between the variable and another outcome aimed at measuring the stride speed of MS patients: T25FWT. Correlation was measured using a Spearman correlation between T25FWT measured at baseline and the variable evaluated on the first month of the baseline period. Expectation for the correlation is to be < -0.5 with p-value < 10e-3 in order for the clinical outcome assessment to pass the test.

[0218] Ability to detect change was assessed based on the classification of patients as progressive and relapsing-remitting as described above. Change was assessed for the variable by comparing the value of the variable during the first month of baseline to its value during the first month of the one-year period. Comparison was quantified using a paired signed- rank test between the two measurements, for all patients which have data at the one-year point (N=58). It is expected that the clinical outcome assessment is able to detect a significant change in the progressive population (p-value <0.05) and a change of a smaller magnitude for the relapsing-remitting population after one year. Example 1: Evaluation of WSV95C reliability

[0219] The table below presents results of the evaluation of the reliability of SV95C and WS95C on the ActiMS dataset. Reliability for SV95C was 0.884, which is below the expectation for the reliability criterion, whereas reliability for WSV95C was 0.979. This result is driven by the presence of running strides in the computation of SV95C for 3 of the patients. The results on the 3 recording periods are presented on Figure 2. On the figure, each single line corresponds to one patient evolution during the first three consecutive months of recording at the start of the study. Patients who run are displayed in large dashed lines, while the others are displayed in solid lines.

[0220] WSV95C has a superior reliability during the recording period at baseline.

[0221] Example 2: convergent validity

[0222] Correlations of SV95C and WSV95C with T25FWT are reported in the table below.

[0223] Correlations with T25FWT were within expectation for both variables. The inclusion of running strides appears to have a limited impact on the convergent validity.

[0224] Example 3: ability to detect change The ability to detect change of SV95C is impacted by the occurrence of running strides in the same manner described in the reliability section: the presence of a running episode in a period, followed by no running episode in the next period (or the presence of an episode with not enough running strides to impact the 95th percentile of the distribution of velocities) introduces a variation of the clinical outcome assessment which might not be directly related to the evolution of the disease.

[0225] This was identified to occur in ActiMS; this is illustrated in Figures 3 and 4.

[0226] Three of the patients had a drop in SV95C from 50 cm / s to 1 m / s as seen in the box D of Figure 3. Inspection of the stride-level data and the ankle trajectory identified these changes to be mostly caused by the presence of running episodes in the baseline whereas there are no running episodes in the 1-year period. In the box A of Figure 3, 3 patients had high values of SV95C at Baseline period due to running activity. The drop of WS95C for those three patients is within the population standard as seen in the figure 4. This demonstrates the superiority of WS95C on SV95C to measure the patient’s evolution accurately thanks to the addition of the filter which removes running strides from the dataset used to estimate the variable.

Claims

- 27 -Claims1. A method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:(a) Acquiring the motion of the individual’s lower limb during a recording period;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);(c) For each recording period, calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting, in one recording period, strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70thpercentile and the 100thpercentile;(g) Repeating steps (d)-(f) for several recording periods;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; and(i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

2. A method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, the method comprising:(a) Acquiring the motion of the individual’s lower limb during a recording period;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);(c) For each recording period, calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting, in one recording period, strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70thpercentile and the 100thpercentile;(g) Repeating steps (d)-(f) for several recording periods; and(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

3. A method of treating an individual suffering from multiple sclerosis, the method comprising:(a) Acquiring the motion of the individual’s lower limb during a recording period;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion acquired in step (a);(c) For each recording period, calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting, in one recording period, strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70thpercentile and the 100thpercentile;(g) Repeating steps (d)-(f) for several recording periods;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several recording periods; and(i) Administering an effective amount of a disease-modifying drug to the individual. wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

4. A computer-implemented method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:(a) Receiving the motion of the individual’s lower limb;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);(c) Calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;(g) Repeating steps (d)-(f) for several data sets;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and(i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive data sets indicates that the individual’s multiple sclerosis condition is worsening.

5. A method of measuring a clinical outcome assessment of an individual suffering or suspected to be suffering from multiple sclerosis, the method comprising:(a) Receiving the motion of the individual’s lower limb;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);(c) Calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;(g) Repeating steps (d)-(f) for several data sets;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and(i) Providing a clinical outcome assessment of the individual; wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

6. A method of monitoring the efficacy of a therapy in an individual suffering from multiple sclerosis, the method comprising:(a) Receiving the motion of the individual’s lower limb;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);(c) Calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;(g) Repeating steps (d)-(f) for several data sets;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

7. The invention also relates to a method of treating an individual suffering from multiple sclerosis, the method comprising:- 31 -(a) Receiving the motion of the individual’s lower limb;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);(c) Calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting strides which are not a running stride;(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;(g) Repeating steps (d)-(f) for several data sets;(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets; and(i) Administering an effective amount of a disease-modifying drug to the individual. wherein a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive recording periods indicates that the individual’s multiple sclerosis condition is worsening.

8. A method according to any one of claims 1 to 7, wherein the individual in need thereof is treated with a multiple sclerosis disease-modifying drug.

9. A disease-modifying drug for use in the treatment of an individual having multiple sclerosis comprising(a) Receiving, or acquiring during a recording period, the motion of the individual’s lower limb;(b) Determining each stride made by the individual by processing the data of the individual’s lower limb motion received in step (a);(c) Calculating the velocity of each stride of the individual;(d) Regrouping adjacent strides into gait episodes;(e) Selecting strides which are not a running stride;- 32 -(f) Estimating, from the strides selected in step (e), the individual’s stride velocity corresponding to a predetermined percentile selected from a range of percentiles comprised between the 70th percentile and the 100th percentile;(g) Repeating steps (d)-(f) for several data sets or recording periods; and(h) Comparing the individual’s stride velocity corresponding to the predetermined percentile obtained in step (f) for several data sets or recording periods; wherein the individual is selected for treatment when a decrease of the predetermined percentile of stride velocity obtained in steps (f) over two or more successive data sets or recording periods is detected.

10. A computer program product comprising code instructions for the execution of a method according to any one of claims 1 to 8, when said program is executed on a computer.

11. A method or a disease-modifying drug for use according to any one of claims 1 to 3 wherein acquiring the motion of the individual’s lower limb is performed by at least one inertial sensor, in particular an accelerometer or a gyrometer.

12. A method or a disease-modifying drug for use according to any one of claims 2, 3, 6, 7 or 9, wherein the dosage of the therapy or drug is adapted when the individual’s multiple sclerosis condition is worsening or improving.

13. A method or a disease-modifying drug for use according to claim 11, wherein the inertial sensor is attached to the individual’s lower limb, in particular on his ankle.

14. A method, a disease-modifying drug for use or a computer program product according to any one of claims 1 to 13, wherein the strides selected in step (e) do not belong to a walking episode of less than 3 strides, particularly less than 5 strides, more particularly strictly less than 8 strides.

15. A method, a disease-modifying drug for use or a computer program product according to any one of claims 1 to 14, wherein the strides selected in step (e) are not the first nor the last of the walking episode.

16. A method, a disease modifying drug for use or a computer program product according to any one of claims 1 to 15, wherein recording periods have a cumulative duration greater than 5 hours, particularly greater than 10 hours or 50 hours, more particularly greater than 180 hours.- 33 -17. A method, a disease-modifying drug for use or a computer program product according to any one of claims 1 to 16, wherein the predetermined percentile of stride velocity is selected from a range of percentiles comprised between the 80thpercentile and the 100thpercentile, more particularly between the 90thpercentile and the 100thpercentile.

18. A method, a disease-modifying drug for use or a computer program product according to any one of claims 1 to 17, wherein the predetermined percentile of stride velocity is the 95thpercentile.

19. The invention as hereinbefore described.

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