GB2637767A

GB2637767A - Computer-implemented methods for managing chronic pain of a user, and measuring the disease burden thereof

Info

Publication number: GB2637767A
Application number: GB2401421.9A
Authority: GB
Inventors: Lawer Christoper
Original assignee: Ooex Ltd
Current assignee: Ooex Ltd
Priority date: 2024-02-02
Filing date: 2024-02-02
Publication date: 2025-08-06
Also published as: GB202401421D0

Abstract

A computer-implemented method for measuring the disease burden of chronic pain and/or chronic disease for a user (see figure 2) comprising: determining a user experience score associated with a time-bounded event based on at least one indication of the user experience; determining a chronic pain and/or disease score associated with the time-bounded event, based on an indication of chronic pain; storing the respective scores in a data store; outputting a comparison of the scores and using those scores to measure the disease burden of chronic pain for a user. Also claimed is a computer-implemented method for managing chronic pain and/or disease (see figure 3) comprising: obtaining a user profile for a first user comprising stored past experience of chronic pain and/or disease information; obtaining a first user action indication and a user pain indication experienced during the action; comparing the pain indication with the past experience stored information; classifying the action as a negative or a positive activity; selecting at least a second user profile from a plurality of user profiles based on similarity to the first user of stored past experience of chronic pain information; sending a recommendation of at least one positive activity to the second user.

Description

Computer-implemented methods for managing chronic pain of a user, and measuring the disease burden thereof

Field of the invention

The present disclosure relates to a computer-implemented method for managing chronic pain of a use., and a computer-implemented method for measuring the disease burden thereof.

Background

People living with chronic disease and/or chronic pain lack appropriate tools to measure and track changes in their experience of their chronic disease and/or pain. As a result, people living with chronic disease also lack the appropriate tools to act upon the contingent vicissitudes or mutability of their experience of living with one or more acute and chronic disease, illness, condition or syndrome (collectively "diseases").

Healthcare professionals also lack the appropriate tools to evaluate patient experiences of chronic disease, thereby reducing the ability to effectively treat or improve a patient's experience of chronic disease.

Currently, disease experiences are monitored almost exclusively on bodily or mental symptomologies of any individual disease, illness, condition or syndrome. However, this approach often omits to capture the patient's real experience of the disease and the transitions that occur in their everyday life. For more subjective, less observable diseases such as chronic pain, depression or generalised anxiety disorder, the problem is exacerbated. The prevailing frameworks lack knowledge of the dynamic, variable experience of disease, and the impacts of the disease upon a patient's specific quality of life.

The Quality-Adjusted Life Year (QALY) is a measure of the state of health of a person or group in which the benefits, in terms of length of life, are adjusted to reflect the quality of life. One quality-adjusted life year is equal to 1 year of life in perfect health. The QALY calculation is simple: the change in utility value induced by the treatment is multiplied by -2 -the duration of the treatment effect to provide the number of QALYs gained. QALYs can then be incorporated within medical decision-making to measure the potential effectiveness of any treatment.

Despite the advantages of using a single indicator to measure the effectiveness of healthcare interventions, QALYs have been widely criticized on ethical, conceptual and operational grounds.

The is therefore a need for improved methods for measuring the disease burden of chronic 10 diseases, as well as improved methods for managing patient's experiences of chronic disease.

Summary of the invention

Aspects of the invention are as set out in the independent claims and optional features are 15 set out in the dependent claims. Aspects of the invention may be provided in conjunction with each other and features of one aspect may be applied to other aspects.

A first aspect of the invention relates to a computer-implemented method for measuring the disease burden of chronic pain for a user. The method comprises obtaining an indication of a time-bounded event, and obtaining at least one indication of a user experience associated with the time-bounded event. The method then comprises determining a score for the user experience associated with the time-bounded event, wherein the score is based on the at least one indication of the user experience.

Separately to the at least one indication of the user experience, the method further comprises obtaining an indication of chronic pain of the user during the time-bounded event, and deteirnining a score for chronic pain associated with the time-bounded event, based on the obtained indication of chronic pain.

The method then comprises storing, in a data store, (i) the score for the user experience, and (ii) the score for chronic pain, wherein (i) the score for the user experience and (ii) the score for chronic pain are associated within the data store. The method then outputs a comparison of (i) the score for the user experience; and (ii) the score for chronic pain, -3 -wherein the disease burden of chronic pain for a user is configured to be measured based on the comparison of (i) the score for the user experience, and (ii) the score for chronic pain.

This method provides improved measurement of the disease burden of chronic pain for a user, considering both (I) a score for the user experience, and (ii) a score for chronic pain. This therefore captures the real experience of a user, including the disease burden associated with the chronic pain and its impact on the users experience of everyday life. This provides people living with chronic disease and/or chronic pain an effective tool to measure and track changes in their experience of their chronic disease and/or pain over time. The method can also provide a helpful measure for healthcare professionals, for example for QALY calculations which can be used to measure treatment efficacy in relation to a user's overall quality of life which may be used in medical decision making.

Capturing and recording a users experience of an event or activity separately to an indication of the chronic pain experienced during the event may also advantageously improve the management of chronic pain by users, aligned with the principles of Acceptance Commitment Therapy.

Outputting the comparison may further comprise outputting a comparison of (i) the score for the user experience of the user, (H) the score for chronic pain of the user, and (iii) user experience and chronic pain scores of at least a second user for a similar time-bounded event.

Obtaining the at least one indication of a user experience may comprise at least one of obtaining an indication of a users affects in their body and/or movement, associated with the time-bounded event; (ii) obtaining an indication of a physiological parameter of the user, and/or a parameter of the user's movement or activity, associated with the time-bounded event; (iii) obtaining an indication of a user's affects in their environment, associated with the time-bounded event; (iv) obtaining an indication of an environmental parameter associated with the -4 -time-bounded event; (v) obtaining an indication of a users affects of their sources, associated with the time-bounded event: and/or (vi) obtaining an indication of a user's affects of their social parameter of he user associated with the time-bounded event.

Affects may be observed and measured in physiological changes, for example, but not limited to, on facial expressions, in skin conductance, in body heat changes, in heart rate. Alternatively, or in addition, an indication of a user's affects may be obtained based on a users perception of their prior affects. For example, the indications of a user's affects may be derived from prior affects followed by their perceptions. Affects may include sensations forming a quality of experience, for example via a users bodily, environmental, material, social, interactions. Affects may also include a user's capacities to affect those interactions.

Obtaining the at least one indication of a user experience associated with the time-bounded event may comprise: obtaining an indication of a user's affects of their body and/or movement, associated with the time-bounded event: and obtaining an indication of a physiological parameter of the user, and/or a parameter of the users movement or activity, associated with the time-bounded event; wherein determining the score for the user experience associated with the time-bounded event comprises: determining an individual score for the user's affects of their body and/or movement 25 associated with the time-bounded event, wherein the score is based on the indication of the user's affects of their body and/or movement; and applying a weighting to the individual score: wherein the weighting is at least in part based on the obtained indication of the physiological parameter of the user, and/or the parameter of the user's movement or activity, associated with the time-bounded event.

Alternatively, or in addition, the method may comprise determining an individual score for the users affects of their body and/or movement associated with the time-bounded event: wherein the score is based on the indication of the users affects of their body and/or -5 -movement, wherein the method further comprises applying a weighting to the individual score, wherein the weighting is at least in part based on a pre-existing physiological baseline parameter; wherein the pre-existing physiological baseline parameter is stored in the data store. For example, a pre-existing physiological baseline parameter may include, but is not limited to, an indication of a lost limb, a spinal cord injury, or other physical condition or injury.

Obtaining the at least one indication of a user experience associated with the time-bounded event may comprise (i) obtaining an indication of an environmental parameter associated with the time-bounded event; and (ii) obtaining an indication of a user's affect of their environment, associated with the time-bounded event. Determining the score for the user experience associated with the time-bounded event may then comprise determining an individual score for the user's affect of their environment associated with the time-bounded event, wherein the score is based on the indication of the user's affect of their environment; and applying a weighting to the individual score, wherein the weighting is at least in part based on the obtained indication of the environmental parameter associated with the time-bounded event.

Alternatively, or in addition, the method may comprise determining an individual score for the user's affect of their environment associated with the time-bounded event, wherein the score is based on the indication of the user's affect of their environment; and applying a weighting to the individual score, wherein the weighting is at least in part based on an obtained baseline indication of an a priori environmental parameter. For example, an a priori environmental parameter may include, but is not limited to, presence of home, condition of home, location of environment where the time-bounded event occurred, time of day of time-bounded event, etc. The method may further comprise repeating the method described herein for at least a second time--bounded event. The method may then further comprise measuring the disease burden of chronic pain for a user over a time period, based on comparing the (i) the score for the user experience associated with the first time-bounded event and (ii) the score for chronic pain associated with the first time-bounded event, with the (i) the score for the user experience associated with the second time-bounded event and (ii) the score -6 -for chronic pain associated with the second time-bounded event; wherein the time period is defined by the time period between the first time-bounded event, and the second time-bounded event.

Obtaining an indication of a user experience may further comprises obtaining an indication of mental wellness of the user associated with the time-bounded event. Determining the score for the user experience associated with the time-bounded event may then be further based on the indication of mental wellness of the user. For example, determining the score for the user experience associated with the time-bounded event may comprise: aggregating a first score, based on (i) an indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) an indication of an environmental parameter, (iii) an indication of a physical resources of the user, and (iv) an indication of a social parameter; determining a second score for the menial wellness of the user, based on the 15 obtained indication of the mental wellness of the user associated with the time-bounded event: and determining a ratio between the aggregated first score and the second score for the mental wellness of the user, to determine the score for the user experience associated with the time-bounded event.

The method may further comprise determining an individual score for each of (i) an indication of a physiological parameter of the user, and/or the movement parameter of the user', (ii) an indication of an environmental parameter, (iii) an indication of a physical resources of the user, (iv) an indication of a social parameter, based on respective obtained indications. Determining the score for the user experience may then be based on each of the individual scores for (i) the indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter, based on the respective obtained indication. The method may then further comprise storing; in the data store, each individual score associated with (i) the indication of a physiological parameter' of the user, and/or the movement parameter of the user, (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter; wherein said scores are -7 -associated with (i) the score for the user experience and (ii) the score for the impact of chronic pain within the data store, to measure the disease burden of chronic pain for a user.

Determining the score for the user experience may further comprise applying a relative weighting to each individual score for each of (i) the indication of a physiological parameter of the user, and/or the movement parameter of the user; (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter; wherein the weighting is based on an indication of the user, based on a user profile stored to the data store.

Obtaining the indication of a physiological parameter of the user, and/or a movement parameter of the user, associated with the time-bounded event may comprise, but is not limited to obtaining an indication of at least one of heart rate, blood pressure, breathing rate, distance exercised, number of steps, number of stairs climbed, running or walking speed, average running or walking speed, and/or sedentary time.

Obtaining the indication of an environmental parameter associated with the time-bounded event may comprise, but is not limited to, obtaining an indication of at least one of temperature; humidity; precipitation; air quality, pollen count, UV index, decibel level. Obtaining the indication of an environmental parameter associated with the time-bounded event may, additionally or instead, comprise obtaining the indication of location associated with the time-bounded event, for example including but not limited to obtaining an indication of at least one location, country, region, terrain, postcode, altitude, for example based on GPS or GIS data.

Obtaining an indication of a social parameter of the user, associated with the time-bounded event, may comprise, but is not limited to, obtaining an indication of the presence of other 30 users, the movements of other users, or the presence of other persons, the movements of others persons in the time-bounded event.

Obtaining an indication of a physical resource parameter of the user, associated with the -8 -time-bounded event, may comprise, but is not limited to, obtaining an indication of the type of computer equipment used by the user or that is in proximity to the user in their environment, or an indication of the type of food consumed by the user, such as for example its calorific content.

Determining the score for chronic pain associated with the time-bounded event may comprises determining an intensity score for pain experienced by the user during the time-bounded event, based on the obtained indication of chronic pain of the user; and determining an area score for pain experienced across body parts of the user during the time-bounded event, based on the obtained indication of chronic pain of the user. Determining the score for chronic pain may therefore be based on (i) the intensity score for pain, and (ii) the area score for pain.

In another aspect of the invention, there is provided a computer-in-iplemented method for managing chronic pain. The method comprises obtaining a user profile for a first user, the user profile comprising stored infomiation relating to the user's past experience of chronic pain; obtaining an indication of an action performed by the first user: and obtaining an indication of pain experienced by the first user during the action.

The method further comprises comparing the indication of pain experienced by the user during the action with the stored information relating to the user's past experience of chronic pain. Based on the comparison, the method then classifies the action, based on a set of classifications. The set of classifications comprises at least: (i) negative activity, wherein the indication of pain experienced by the user during the action is more severe than the stored information relating to the user's past experience of chronic pain; and (ii) positive activity, wherein the indication of pain experienced by the user during the action is less severe than the stored information relating to the user's past experience of chronic pain.

The method then selects at least a second user profile from a plurality of user profiles, wherein the second user profile is selected based on similarity in stored information relating to the second users past experience of chronic pain compared to the stored inforrnafion relating to the first users past experience of chronic pain. Finally, the method comprises sending a signal to the second user profile to recommend at least one action classified as a positive activity performed by the first user to improve the management of the second user's chronic pain.

This method advantageously provides an effective tool for evaluating patient experiences of chronic disease, including evaluating patient experiences of activities, treatments, and/or therapies, and identifying those which may treat or improve a patient's experience of chronic disease. The method also advantageously provides for recommending said identified activities, treatments, and/or therapies, for a second user group, based on the evaluated experiences of a first user or user group, and identified similarities between the first and second user groups.

Selecting at least a second user profile from a plurality of user profiles may further based on similarity in stored information associated with the second user profile and the first user 15 profile, wherein the stored information comprises at least one of: medical diagnosis, pain diagnosis, duration with pain, age, gender, and/or location.

The stored information relating to the user's past experience of chronic pain may comprise patterns or changes of the user's experience of chronic pan, and/or an indication of the 20 users average experience of chronic pain The method may further comprise, separately to the indication of pain experienced by the first user during the action, obtaining at least one indication of user experience associated with the action performed by the first user. Classifying the action may then be, at least in part, based on the obtained indication of user experience associated with the action performed by the first user.

The skilled person will understand that the indication of user experience associated with the action (e.g. a time-bounded event) may comprise the features of the indication of a 30 user experience associated with the time-bounded event described in relation to the preceding aspect of the invention.

The method may further comprise grouping user profiles based on similarities in the stored -10 -information relating to the user's past experience of chronic pain for each user profile. Thus, the method may send a signal to at least one group of user profiles to recommend at least one action classified as a positive activity performed by the first user, wherein the first user is within said group.

The set of classifications may further comprise a neutral activity, wherein the indication of pain experienced by the user during the action is substantially the same as the stored infori nation relating to the users past experience of chronic pain.

In another aspect of the invention, there is provided a computer program product comprising instructions configured to program a programmable device to perform the method of any preceding aspects of the invention.

Drawings Embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings, in which: Fig. 1 shows a schematic block diagram illustrating a possible system to perform the methods of the present invention.

Fig. 2 illustrates an example a flow diagram of a method for measuring the disease burden of chronic pain for a user.

Fig. 3 illustrates an example a flow diagram of a method for managing chronic pain.

Figs. 4A to 4D illustrate a set of example graphical user interfaces to configured to respectively obtain (i) an indication of a user's affect of their body and/or movement, (ii) an indication of a user's affect of their environment, (iii) an indication of a user's affect of their physical or material resources, and (iv) an indication of a users social affects.

Figs. 5A and 5B illustrate an example graphical user interface configured to obtain an indication of chronic pain of the user. Fig. 5A illustrates the graphical user interface prior to user input; whereas Fig. 5B illustrates the graphical user interface after user input.

Specific description

Embodiments of the claims relate to computer--implemented methods for managing chronic pain of a user, and a computer-implemented method for measuring the disease burden 5 thereof.

It will be appreciated from the discussion above that the embodiments shown in the Figures are merely exemplary, and include features which may be generalised, removed or replaced as described herein and as set out in the claims.

Figs. 2 and 3 show example schematics illustrating computer-implemented methods of the present invention. In the example described herein, the methods of Figs. 2 and 3 are disclosed as being implemented by the system of Fig. 1, however the skilled person will understand that other systems may be used to perform the methods disclosed herein.

The system of Fig. 1 comprises a remote server 106. The remote server 106 comprises a processor 106 and a memory 110. The remote server 106 is preferably hosted in the cloud, for example as a cloud server.

The memory 110 comprises a plurality of data stores; 112A, 112B; 1120, 112..., etc. Each data store is configured to store data related to a user profile, wherein each user profile is associated with a user, such as U", UB, LIG, etc. The system further comprises a plurality of user equipment devices 102A to 102C, each associated with a different user. Each user equipment device 102A to 1020 is configured to wirelessly communicate with the remote server 106, for example via a communication channel 104. The user equipment devices 102A to 102C are shown in Fig. 1 as smartphones, however the skilled person will understand that the user equipment device may be any device configured to wirelessly communicate with the remote server 106, and configured to receive user input. The user equipment device also comprises at least one sensor configured to sense a physiological parameter of the user, or otherwise the user equipment device may be configured to be in wireless communication with an auxiliary sensor configured to sense a physiological parameter of the user, such as a smart watch -12 -or other wearable sensor.

In the example shown in Fig. 1, the system is shown to comprise three user equipment devices, each associated with a different user, however the skilled person will understand 5 that this is purely for illustration, and that other systems comprising at least one user equipment device may be used.

In use, the processor 108 obtains user identification data from a first user equipment device 102A, wherein the user identification data corresponds to a user profile for a user of the 10 user equipment device 102A, UA.

The processor 108 then obtains an indication of a time--bounded event from the user equipment device, 102A (202). In this example, the time-bounded event will be described with reference to a time-bounded event that has already occurred (in the past), however the skilled person will understand that, in other embodiments, obtaining an indication of a time-bounded event may comprise obtaining an indication of a time-bounded event currently in progress (in the present), or an indication of a time-bounded event scheduled in the future. An example of a time-bounded event may be an action, activity, such as a physical activity, or performance, however this is in no way limiting.

Purely for illustration, in this example, the processor 108 obtains, from the user equipment device 102A, an indication of a set of physiotherapy exercises which have just been performed by the user UA within a time-bounded window (202).

The processor 108 also obtains, from the user equipment device 102A, an indication of the user's experience associated with performing the set of physiotherapy exercises within the time-bounded window (204).

In this example, obtaining the indication of the user's experience comprises obtaining an indication of a user's affects of their body and/or movement, associated with the set of physiotherapy exercises via user input via the user equipment device 102A. The indication of a user's affects of their body and/or movement is obtained via user input relative to a predetermined scale. An example graphical user interface for obtaining an indication of a -13 -users affects of their body and/or movement via user input is shown in Fig. 4k In particular, Fig. 4A comprises a sliding scale 400 for receiving user input.

In addition, the processor 108 obtains, from the user equipment device 102A, an indication 5 of a physiological parameter of the user associated within the time-bounded window. The indication of a physiological parameter in this example is heart rate whilst performing the set of physiotherapy exercises, however the skilled person will understand that other physiological parameters may be used, alone or in combination, such as blood pressure, blood oxygen saturation, and/or breathing rate. The physiological parameter, such as 10 heartrate, is determined by a sensor, such as a wearable sensor, for example from a smartwatch worn by the user UA. This sensor data is obtained by the user equipment device 102A and then sent to the processor 108.

In this example, the processor also obtains, from the user equipment device 102A, an indication of a parameter of the user's movement or activity associated within the time-bounded window. The indication of a parameter of the users movement or activity in this example is the time the user is stood up, however the skilled person will understand that other parameters may be used, alone or in combination; such as distance exercised, number of steps, number of stairs climbed, running or walking speed, average running or walking speed, and/or sedentary time.

The processor then determines an individual score for the user's affects of their body arid/or movement associated with the time-bounded event, wherein the score is based on the indication of the user's affects of their body and/or movement obtained via user input.

As an example, the scale 400 provided by the graphical user interface in Fig. 4A is configured to obtain qualitative input from a user; however, the processor 108 is configured to determine a quantitative score based on the qualitative user input into the scale 400. The individual score is then weighted based on the obtained indication of the physiological parameter of the user, and the obtained indication of the activity parameter of the user. In this example, the individual score is also weighted based on a parameter of a pre-existing physiological baseline parameter (such as a lost limb) associated with the user profile. The processor 108 obtains the pre-existing physiological baseline parameter from the data store 1 12A, where it is stored by the user profile 114.

-14 -Obtaining the indication of the user's experience associated with performing the set of physiotherapy exercises within the time-bounded window (204) also comprises obtaining an indication of a user's affects of their environment, associated with the time-bounded event. In this example, this comprises obtaining an indication of a user's affects of their environment, associated with the set of physiotherapy exercises via user input via the user equipment device 102A. The indication of a user's affects to their environment is obtained via user input relative to a predetermined scale, for example as shown in the example graphical user interface of Fig. 4B. In particular, Fig. 48 comprises a sliding scale 402 for receiving user input.

The processor 106 also obtains an indication of an environmental parameter associated with the time-bounded event, from the user equipment device 102A. In this example, the processor 108 obtains (BPS data associated with the time-bounded event. The processor also obtains weather data associated with the time-bounded event from internet data, based on the (BPS data.

The processor then determines an individual score for the user's affect of their environment associated with the time-bounded event, wherein the score is based on the indication of the user's affect of their environment. As described above in relation to Fig. 4A, the scale 402 provided by the graphical user interface in Fig. 4B is configured to obtain qualitative input from a user; however, the processor 108 is then configured to determine a quantitative score based on the qualitative user input into the scale 402. The individuai score is then weighted based on the obtained indication of the environmental parameters associated with the time-bounded event. In this example, the individual score is also weighted based on a parameter of a pre-existing baseline indication of an environmental parameter, associated with the user profile. For example, the obtained GAS data provides location data associated with the time-bounded event. The processor 108 compares the (BPS data to stored indications in the data store 112A associated with the user profile 114. The stored indications may include the location of the home of the user. In the case that the processor 108 identifies that the (BPS data corresponds to the location of the user's home (or another stored location), a weighting may be applied to the individual score for the environment based on the stored indications from the data store 112A relating to the user's home -15 -The processor 108 also obtains an indication of a user's affects of their resources, associated with the time-bounded event In this example, this comprises obtaining an indication of a user's affects of their resources via user input via the user equipment device 102A. The indication of a user's affects to their resources is also obtained via user input a relative to a predetermined scale, for example as shown in the example graphical user interface of Fig. 4C. in particular, Fig. 4C comprises a sliding scale 404 for receiving user input.

The processor 108 may also obtain an indication of a resources parameter associated with the time-bounded event, from the user equipment device 102A, such as administered medication data. The processor 108 then determines an individual score for the user's affects of their resources associated with the time-bounded event, wherein the score is based on the indication of the user's affects of their resources. Similarly to the above, the scale 404 provided by the graphical user interface in Fig. 4C is configured to obtain qualitative input from a user; however; the processor 108 is then configured to determine a quantitative score based on the qualitative user input into the scale 404. The individual score is then weighted based on the obtained indication of the resources parameter associated with the time-bounded event.

The processor 108 also obtains an indication of a user's social affects, associated with the time-bounded event. In this example, this comprises obtaining an indication of a user's social affects via user input via the user equipment device 102A. The indication of a user's social affects is also obtained via user input relative to a predetermined scale, for example as shown in the example graphical user interface of Fig. 4D. in particular, Fig. 4D comprises a sliding scale 406 for receiving user input.

The processor 103 may also obtain an indication of a social parameter associated with the time-bounded event, from the user equipment device 102A, such an indication of the presence of other users during ihe time--bound event, for example wherein the presence of others may be detected by a short-range wireless communications interface of the user equipment device, configured to wirelessly detect the presence of other devices (and thus associated users) in close proximity during the time-bounded window. The processor 108 then determines an individual score for the user's social affects associated with the time- -16 -bounded event, wherein the score is based on the indication of the users affects of their resource, and weighted based on the obtained indication of the resources parameter associated with the time-bounded event.

The processor 108 then determines a combined score for the user experience is based on each of the individual scores for (i) the user's affects of their body and/or movement; OD the user's affect of their environment; (iii) the users affects of their resources; and (iv) the users social affects, and their respective weightings, Separately to obtaining the indication of the user experience, the processor 108 obtains, from the user device 102A, an indication of chronic pain of the user for the time-bounded event (208). Obtaining the indication of chronic pain comprises: 0) obtaining an indication of intensity of chronic pain experienced during the time-bounded event, and (ii) obtaining an indication of localised areas of the users body which experienced the chronic pain. In this example, the indication of (i) the intensity of chronic pain experienced, and (ii) the localised areas of the users body which experienced the chronic pain, are obtained via user input through a graphical user interface displayed on the user equipment device 02A, such as the graphical user interface 500 of Fig. 5k The graphical user interface 500 comprises a visual representation of a human body 502. In the example shown in Fig. 5A, the processor 108 obtains an indication of the localised areas of the users body which experienced the chronic pain based on location-resolved touch data, input by the user; via a touch interface of the user equipment device 502, relative to the representation of the human body 502 displayed by the graphical user interface 500. Purely for illustration, localised touch points are shown by way of example in Fig. 5B, as input by the user via a touch interface of the user equipment device 502.

Based on the obtained location-resolved touch data, the processor 108 determines an area score for pain experienced across body parts of the user during the time-bounded event. For example, this may be determined based on a proportional relationship between the area covered by obtained location-resolved touch data relative to the area of the representation of the human body 502 displayed on the graphical user interface 500, displayed by the user equipment device 102A.

-17 -An indication of the intensity of chronic pain experienced may also be obtained by the processor 108 based on touch data relative to the graphical user interface 500, for example based on the duration of time for which a location is touched or held. For illustration, a long press at a certain location of the graphical user interface generates time-resolved and location-resolved touch data at the user device 102. This data is then sent to the processor 108 where an indication of the intensity of chronic pain may be determined, for example based on a proportional relationship to the intensity of chronic pain experienced at that location.

Based on the obtained time-and location-resolved touch data, the processor 108 determines an intensity score for pain experienced across body parts of the user, for example, based on a proportional relationship between the length of a touch/hold from the touch data.

The processor 108 may then determine a combined score for chronic pain (210), based on (i) the intensity score for pain, and (ii) the area score for pain.

Whilst the graphical user interface 500 of Figs. 5A and 58 is shown to comprise a visual representation of a two-dimensional front view of a human body and a two-dimensional back view of a human body, the skilled person will understand that other visual representations of the human body may be used, for example but not limited a 3D representation of the human body.

Continuing the method of Fig. 2, the processor 108 then signals to the memory 110 to 25 store (i) the score for the user experience and (ii) the score for the impact of chronic pain in data store 112A, associated with the user profile 114 (212). The score for the user experience and the score for the impact of chronic pain may be stored in a volatile or nonvolatile data structure 116, such as a data table, wherein (i) the score for the user experience and (ii) the score for chronic pain are associated within the data store 112A. 30 Finally, the processor 108 then outputs a comparison of (i) the score for the user experience, and (ii) the score for chronic pain, to the user equipment device 102A (214). The disease burden of chronic pain for a user is configured to be measured based on the -18 -comparison of (i) the score for the user experience, and (ii) the score for chronic pain.

Preferably, the method of Fie. 2 is repeated for at least a second time-bounded event, by the same user. The processor 108 is then configured to measure the disease burden of 5 chronic pain for a user over a time period, based on comparing the (i) the score for the user experience associated with the first time-bounded event and (ii) the score for chronic pain associated with the first time-hounded event, with the (I) the score for the user experience associated with the second time-bounded event and (ii) the score for chronic pain associated with the second time-bounded event; wherein the time period is defined 10 by the time period between the first time-bounded event, and the second time-bounded event.

The comparison of (i) the score for the user experience, and (ii) the score for chronic pain over a period of time to measure the disease burden of chronic pain for a user over a time period may also be used for calculations of Quality-Adjusted Life Years (QALY), wherein the change in QUALY for a user over time is based on the recorded change in their 0) the score for the user experience, and (ii) the score for chronic pain. QALY measurements based on these scores may then be incorporated within medical decision-making to quantify the effectiveness, or burden, of potential treatments.

As well as a method for measuring the disease burden of chronic pain for a user, the method of Fig. 3 also provides a method for managing chronic pain of a user. In this example; the method of Fig. 3 is described as a continuation of the method of Fig. 2, however the skilled person will understand that in other embodiments, the method of Fig. 3 may be performed as a standalone method, separately to the method of Fig. 2.

Continuing from the method of Fig. 2, the processor has obtained user identification data from a user equipment device 102A, wherein the user identification data corresponds to a user profile 114 corresponding to the user, UA, of ihe user equipment device 102A (302).

The processor 108 can therefore access a data store 112A within the memory 110, the data store 112/k storing data relating to the user profile 114. The data store 112A comprises stored information relating to the user's past experience of chronic pain, for example wherein the data store 112A comprises (i) scores for the user experience -19 -associated with past time-bounded events, and 0 scores for chronic pain associated with the same past time-bounded events.

The processor 108 then obtains an indication of an action, activity, or performance 5 performed by the first user (304). This may be the same as the obtained indication of a time-bounded event (202) of Fig. 2, such as the performance of a set of physiotherapy exercises.

The processor 108 also obtains an indication of pain experienced by the first user during the action (306). As above, this may be obtained by the processor 108 as the obtained indication of chronic pain of the user associated with the time-bounded event (208) described in relation to Fig. 2. The chronic pain score, determined by the processor 108 as discussed above in relation to Fig. 2, is taken as the indication of pain experienced by the first user for the purposes of this example.

The processor 108 then compares the indication of pain experienced by the user during the activity, such as the chronic pain score, with the stored information relating to the user's past experience of chronic pain (308), such as the scores for chronic pain associated with past activities.

Based on this comparison, the processor 108 classifies the action, from a set of classifications. The set of classifications comprise: (i) negative activity, wherein the chronic pain score experienced by the user during the action is more severe than the chronic pain score relating to the user's past chronic pain scores; and (ii) positive activity, wherein the chronic pain score of the user during the action is less severe than the stored information relating to the user's past chronic pain scores; and (iii) neutral activity, wherein the chronic pain score of the user during the action is substantially the same as the stored information relating to the user's past chronic pain scores.

The processor 108 then compares the first user profile 114 to a plurality of different user profiles. The plurality of user profiles are grouped by the processor 108 based on similarities in the stored information relating to the respective user's past experience of chronic pain for each user profile, and similarities in other stored information relating to the -20 user profiles 114, such as medical information, age, gender, and physical conditions, such as limb loss, etc. The processor 108 then selects at least one group of user profiles, based on the aforementioned similarities with first user profile 114 of the first user UA. The processor 108 then signals to each of the user devices associated with the selected group of user profiles, such as user devices 102B and 102C associated with user profiles for users 1.16 and iJc,, The signal sent by the processor 108 is configured to recommend at least one action which has been classified as a positive activity performed by the first user, such as the set of physiotherapy exercises, thereby implementing effective management of chronic pain for the identified group of users. The disease burden of chronic pain for may also be recorded according to the method of Fig. 2 for each user performing the recommended activity.

Whilst in the example discussed above in relation to Fig. 3, classification of the performed action is disclosed in relation to the indication of pain experienced by the first user, such as a chronic pain score, the skilled person will understand that this is merely one example. In another example, the processor 108 may also obtain an indication of user experience, wherein classification of the performed action is also based at least in part on positive, negative, or neutral comparisons of user experience for the action compared to past user experiences. For example, ciassifying the action may also be based on comparisons of scores for the user experience associated with the action, and past actions; such as the scores for user experience discussed in relation to Fig. 2.

In the context of the present disclosure other examples and variations of the apparatus and methods described herein will be apparent to a person of skill in the art. -21 -

Claims

CLAIMS: 1. A computer-implemented method for measuring the disease burden of chronic pain and/or chronic disease for a user: obtaining an indication of a time-bounded event; obtaining at least one indication of a user experience associated with the time-bounded event; determining a score for the user experience associated with the time-bounded event, wherein the score is based on the at least one indication of the user experience; separately to the at least one indication of the user experience, obtaining an indication of chronic pain and/or disease of the user for the time-bounded event; determining a score for chronic pain and/or disease associated with the time-bounded event, based on the obtained indication of chronic pain and/or disease; and storing in a data store: (i) the score for the user experience, and (ii) the score for 15 chronic pain and/or disease, wherein (i) the score for the user experience and (ii) the score for chronic pain and/or disease are associated within the data store; and outputting a comparison of (i) the score for the user experience, and (ii) the score for chronic pain and/or disease, wherein the disease burden of chronic pain and/or disease for a user is configured to be measured based on the comparison of (i) the score for the 20 user experience, and (ii) the score for chronic pain and/or disease.
2. The computer-implemented method of claim 1, wherein outputting the comparison further comprises outputting a comparison of (i) the score for the user experience of the user, (ii) the score for chronic pain and/or disease of the user, and (iii) user experience and chronic pain and/or disease scores of at least a second user for a similar time-bounded event.
3. The method of any preceding claim wherein obtaining the at least one indication of a user experience comprises at least one of: (i) obtaining an indication of a user's affect in their body and/or movement, associated with the time-bounded event; (ii) obtaining an indication of a physiological parameter of the user, and/or a parameter of the user's movement or activity, associated with the time- -22 -bounded event; (iii) obtaining an indication of a user's affect in their environment, associated with the time-bounded event; (iv) obtaining an indication of an environmental parameter associated with the time-bounded event; (v) obtaining an indication of a user's affect of their resources, associated with the time-bounded event; and/or (vi) obtaining an indication of a user's affect of their social parameter of the user associated with the time-bounded event. 10
4. The method of any preceding claim wherein obtaining at least one indication of a user experience associated with the time-bounded event comprises: obtaining an indication of a user's affect of their body and/or movement, associated with the time-bounded event; and obtaining an indication of a physiological parameter of the user, and/or a parameter of the user's movement or activity, associated with the time-bounded event; wherein determining the score for the user experience associated with the time-bounded event comprises: determining an individual score for the user's affect of their body and/or movement 20 associated with the time-bounded event, wherein the score is based on the indication of the user's affect of their body and/or movement; and applying a weighting to the individual score, wherein the weighting is at least in part based on the obtained indication of the physiological parameter of the user, and/or the parameter of the user's movement or activity, associated with the time-bounded event.
5. The method of any preceding claim wherein obtaining at least one indication of a user experience associated with the time-bounded event comprises: obtaining an indication of an environmental parameter associated with the time-bounded event; and obtaining an indication of a user's affect of their environment, associated with the time-bounded event; wherein determining the score for the user experience associated with the time-bounded event comprises: -23 -determining an individual score for the user's affect of their environment associated with the time-bounded event, wherein the score is based on the indication of the user's affect of their environment; and applying a weighting to the individual score, wherein the weighting is at least in part 5 based on the obtained indication of the environmental parameter associated with the time-bounded event.
6. The computer-implemented method of any preceding claim, further comprising: repeating the method of any preceding claim for at least a second time-bounded 10 event; and measuring the disease burden of chronic pain for a user over a time period, based on comparing the (i) the score for the user experience associated with the first time-bounded event and (ii) the score for chronic pain associated with the first time-bounded event, with the (i) the score for the user experience associated with the second time-bounded event and (ii) the score for chronic pain associated with the second time-bounded event; wherein the time period is defined by the time period between the first time-bounded event, and the second time-bounded event.
7. The computer-implemented method of any preceding claim, wherein obtaining an 20 indication of a user experience further comprises obtaining an indication of mental wellness of the user associated with the time-bounded event; wherein determining the score for the user experience associated with the time-bounded event is further based on the indication of mental wellness of the user.
8. The computer-implemented method of claim 7, wherein determining the score for the user experience associated with the time-bounded event further comprises: aggregating a first score, based on at least two of (i) an indication of a physiological parameter of the user, and/or the movement parameter of the user, 00 an indication of an environmental parameter, (iii) an indication of a physical resources of the user, and (iv) an 30 indication of a social parameter; determining a second score for the mental wellness of the user, based on the obtained indication of the mental wellness of the user associated with the time-bounded event; -24 -determining a ratio between the aggregated first score and the second score for the mental wellness of the user, to determine the score for the user experience associated with the time-bounded event.
9. The computer-implemented method of any preceding claim, further comprising: determining an individual score for each of (i) an indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) an indication of an environmental parameter, (iii) an indication of a physical resources of the user, (iv) an indication of a social parameter, based on respective obtained indications; wherein determining the score for the user experience is based on each of the individual scores for (i) the indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter, based on the respective obtained indication; and the method further comprising: storing, in the data store, each individual score associated with (i) the indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter; wherein said scores are associated with (i) the score for the user experience and 20 (ii) the score for the impact of chronic pain and/or disease within the data store, to measure the disease burden of chronic pain for a user.
10. The computer-implemented method of claim 9, wherein determining the score for the user experience further comprises applying a relative weighting to each individual score for each of (i) the indication of a physiological parameter of the user, and/or the movement parameter of the user, (ii) the indication of an environmental parameter, (iii) the indication of a physical resources of the user, (iv) the indication of a social parameter; wherein the weighting is based on an indication of the user, based on a user profile.
11. The computer-implemented method of any preceding claim, wherein determining the score for chronic pain and/or disease associated with the time-bounded event comprises: determining an intensity score for pain and/or disease experienced by the user -25 -during the time-bounded event, based on the obtained indication of chronic pain and/or disease of the user; determining an area score for pain and/or disease experienced across body parts of the user during the time-bounded event, based on the obtained indication of chronic 5 pain and/or disease of the user; wherein determining the score for chronic pain and/or disease is based on (i) the intensity score for pain and/or disease, and (ii) the area score for pain and/or disease.
12. A computer-implemented method for managing chronic pain and/or disease: obtaining a user profile for a first user, the user profile comprising stored information relating to the user's past experience of chronic pain and/or disease; obtaining an indication of an action performed by the first user; obtaining an indication of pain and/or disease experienced by the first user during the action; comparing the indication of pain and/or disease experienced by the user during the action with the stored information relating to the users past experience of chronic pain and/or disease; classifying the action, based on a set of classifications, wherein the set of classifications comprises: (i) negative activity, wherein the indication of pain and/or disease experienced by the user during the action is more severe than the stored information relating to the user's past experience of chronic pain and/or disease; and (ii) positive activity, wherein the indication of pain and/or disease experienced by the user during the action is less severe than the stored information relating to the user's past experience of chronic pain and/or disease; selecting at least a second user profile from a plurality of user profiles, wherein the second user profile is selected based on similarity in stored information relating to the second user's past experience of chronic pain and/or disease compared to the stored information relating to the first user's past experience of chronic pain and/or disease; sending a signal to the second user profile to recommend at least one action 30 classified as a positive activity performed by the first user to improve the management of the second user's chronic pain and/or disease.
13. The computer-implemented method of claim 12, wherein the set of classifications -26 -further comprises a neutral activity, wherein the indication of pain and/or disease experienced by the user during the action is substantially the same as the stored information relating to the user's past experience of chronic pain and/or disease.
14. The computer-implemented method of claim 12 or 13, wherein selecting at least a second user profile from a plurality of user profiles is further based on similarity in stored information associated with the second user profile and the first user profile, wherein the stored information comprises at least one of: medical diagnosis, pain diagnosis, duration with pain, duration with disease, age, gender, and/or location.
15. The method of any of claims 12 to 14 further comprising: separately to the indication of pain and/or disease experienced by the first user during the action, obtaining at least one indication of user experience associated with the action performed by the first user; wherein classifying the action is at least in part based on the obtained indication of user experience associated with the action performed by the first user.
16. The method of any of claims 12 to 15 further comprising: grouping user profiles based on similarities in the stored information relating to the 20 user's past experience of chronic pain and/or disease for each user profile; and sending a signal to at least one group of user profiles to recommend at least one action classified as a positive activity performed by the first user, wherein the first user is within the group.
17. A computer program product comprising instructions configured to program a programmable device to perform the method of any preceding claim.