A decision support system to facilitate, improve and reinforce self-management of non-specific low back pain
The decision support will be conveyed to the patient via a smartphone app in the form of advice for self-management. This advice will be tailored to each person based on the symptom state and a range of patient characteristics, including information from a physical activity-detecting wristband worn by the patient.
The selfBACK system is a predictive case-based reasoning system, which is a computer model that is based on existing cases. It is the first of its kind, utilizing background data about the patient, patient self-monitoring of pain and functional ability, along with continuous recording of the patient's physical activity and sleep by a wristband, to deduce tailored and personalized recommendations for self-management of low back pain. The wristband will communicate with the developed smartphone app, which will enable the data analysis and personalised decision support. With selfBACK, the patient will be equipped with a tool that is far beyond the state-of-the-art to facilitate, improve and reinforce self-management of non-specific low back pain. A recent study showed that 283 pain-related apps are available in the main app shops App Store, Blackberry App World, Google Play, Nokia Store and Windows Phone Store. However, none of these apps have documented effects by scientific publications and none include a decision support system. It is therefore an urgent need to make personal device systems available that address health issues with a documented effect. To give a general idea of the predicted cost-benefit, we estimate that the total cost of using selfBACK will range between 120-150 EUR per patient (including the activity detecting wristband, app and brief education to enable safe use of selfBACK). The effectiveness of selfBACK in preventing recurrence of episodes with low back pain and disability has yet to be proven but the potential cost-benefit is without doubt substantial.
The selfBACK system is a predictive case-based reasoning system, which is a computer model that is based on existing cases. It is the first of its kind, utilizing background data about the patient, patient self-monitoring of pain and functional ability, along with continuous recording of the patient's physical activity and sleep by a wristband, to deduce tailored and personalized recommendations for self-management of low back pain. The wristband will communicate with the developed smartphone app, which will enable the data analysis and personalised decision support. With selfBACK, the patient will be equipped with a tool that is far beyond the state-of-the-art to facilitate, improve and reinforce self-management of non-specific low back pain. A recent study showed that 283 pain-related apps are available in the main app shops App Store, Blackberry App World, Google Play, Nokia Store and Windows Phone Store. However, none of these apps have documented effects by scientific publications and none include a decision support system. It is therefore an urgent need to make personal device systems available that address health issues with a documented effect. To give a general idea of the predicted cost-benefit, we estimate that the total cost of using selfBACK will range between 120-150 EUR per patient (including the activity detecting wristband, app and brief education to enable safe use of selfBACK). The effectiveness of selfBACK in preventing recurrence of episodes with low back pain and disability has yet to be proven but the potential cost-benefit is without doubt substantial.
Workpackages
WP 1: Data Organisation and Management
The main aim of this WP is to collect, organise and provide the data basis for the decision support system. Data on the selfBACK server is stored securely, and only authorised requests from the self- management app and the co-decision web page will access and update personalised data. Within this WP, the technical and medical teams will create the content needed to provide advice for the user. Further, endpoints for querying this data efficiently will be created.
WP2: Predictive monitoring analysis:
Objectives:
WP 3: Decision Support System Development and Integration:
Objectives:
WP4: User Interface and interaction
Objectives:
WP5: Randomised Controlled Trial
Objectives:
WP6: Innovation Management
Objectives:
WP7: Project Management
Objectives:
The main aim of this WP is to collect, organise and provide the data basis for the decision support system. Data on the selfBACK server is stored securely, and only authorised requests from the self- management app and the co-decision web page will access and update personalised data. Within this WP, the technical and medical teams will create the content needed to provide advice for the user. Further, endpoints for querying this data efficiently will be created.
WP2: Predictive monitoring analysis:
Objectives:
- To conduct a review of physical and sleep activity recognition algorithms and identify those that best addresses selfBACK requirements.
- To develop methods for data abstraction for transforming quantitative data into qualitative states, generate trends, identify correlations in data, and extract relevant features.
- To identify barriers and enablers for predicting the success of a recommended plan for self-management.
- To monitor and compare patient activity against the recommended plan for self-management in order to detect patients whose plans needs to be revised through WP3.
- To align frequent push notification decisions with appropriate behaviour change techniques.
WP 3: Decision Support System Development and Integration:
Objectives:
- To build an ontology and model the inter-relationships between patient features, and implement similarity measures for comparing patient features between cases: each feature type requires its individual similarity measure.
- To implement a case-based reasoning engine for retrieving similar patients and generating individual plans for self-management.
- To develop intelligent question strategies for patient data elicitation that are focusing on high information gains in order to optimize plans for self-management.
- To develop an explanation engine that provides justifications and educational information for patients on their plans for self-management.
- To implement intervention mapping to guide and document the development of selfBACK and its implementation in the intervention.
WP4: User Interface and interaction
Objectives:
- To design and implement the user interface and interactions of the selfBACK app
- To develop the front-end of the mobile device that will be used by the patient
- To implement, in a web-based environment, a questioning strategy to only ask relevant questions
- To create the front-end web application for the clinician providing the view of the patient’s activity records and similar patients
- To define and implementing strategies on how the selfBACK app interacts with the patient via the wristband
- To develop a methodology for motivating patients to adhere, revisit and stay with the selfBACK app over a longer period of time
WP5: Randomised Controlled Trial
Objectives:
- The main aim of this WP is to evaluate the effectiveness of selfBACK in self-management of non- specific low back pain. UoSD will lead this WP and main partners involved will be NTNU, NFA and GLA. This WP uses input from WP1-4 (phase 1) and the results will feed heavily into dissemination and exploitation.
WP6: Innovation Management
Objectives:
- To develop a commercial and academic exploitation strategy for the project results focusing on selected healthcare stakeholders in Europe.
- To design, develop and maintain a dissemination and communication plan.
- To disseminate the progress and findings of selfBACK within the scientific community as well as to early adopters among care givers (general practitioners, physiotherapists and chiropractors), health care service providers, insurance companies, policy makers and decision makers.
- To develop a business and innovation plan and execute the first phase of the go-to-market strategy
- To raise awareness, build consensus and create visible/measurable impact in terms of the ability of all stakeholders, to exploit best practice related to the selfBACK implementation.
WP7: Project Management
Objectives:
- To provide effective coordination and management for all planned activities in the selfBACK project.
- To assure that all activities of project partners are compliant with the European Commission contract and ethical and legal issues.
- To organize regular consortium meetings (General Assembly and management meetings, Advisory Board meetings) and to coordinate exchange of information and results within the consortium.
Deliverables:
- D1.1 Literature review (LIT)
- D1.2 Structuring of information collected by questionnaires (QUEST)
- D1.3 Documentation on the case structure, secure case storage and population (CASEa)
- D1.4 Documentation on the case structure, secure case storage and population (CASEb)
- D1.5 Documentation on the case structure, secure case storage and population (CASEc)
- D1.6 Report on scaleable query processing (SCALE) in selfBACK
- D2.1 Physical Activity Recognition (PAR) software component for selfBACK
- D2.2 Feature extraction algorithm (FEA) optimised for selfBACK data streams
- D2.3 Predictive Monitoring (PM) software component for selfBACK
- D2.4 User Intervention Modelling Framework (UIMF)
- D2.5 Results from WP2 pilot study (PILOT) and empirical evaluations of algorithms
- D3.1 A report on the ontology and a presentation of the similarity measures (SIM)
- D3.2 A demonstrator of the integrated CBR system (CBRa)
- D3.3 A demonstrator of the integrated CBR system (CBRb)
- D3.4 Software component demonstration of the rule engine (RULE)
- D3.5 Software component demonstration of the explanation engine (EXPL)
- D3.6 A demonstrator of the integrated CBR system (CBRc)
- D3.7 A demonstrator of the integrated CBR system (CBRd)
- D3.8 Intervention mapping protocol (IMAP)
- D4.1 Design (DESG) document for the initial questionnaire
- D4.2 Specification and design document for the mobile application (SPECM) with description of persona, and mock-ups
- D4.3 Specification and design document for the selfBACK web front-end (wireframes, SPECW) for the clinician dashboard
- D4.4 API guide (APIG) for back-end communication
- D4.5 First version of smartphone (DEMV1) selfBACK application on iOS
- D4.6 Demonstration of the final connected selfBACK mobile application (DEMCON) on iOS and Android
- D4.7 Demonstration of web-based questionnaire (DEMQ)
- D4.8 Demonstration of web based clinician’s dashboard (DEMCD)
- D4.9 Demonstration of web and mobile software interacting with connected devices such as a wristband that monitors patient activities (DEMFIN)
- D4.10Demonstration of localized software (DEMLOC) in multiple languages
- D5.1 Trial (TRIAL) protocol
- D5.2 Process evaluation (PROEV) and effect study, randomised controlled trial
- D6.1 Dissemination and communication plan (DCP)
- D6.3 Business plan (BP) for exploitation of selected results
- D6.4 Results after the implementation (IMPL) of the business plan
- D6.5 Report on go-to-market (GOTOM) strategy and product development
- D7.1 Project handbook (HANDB) on quality management
- D7.2 Internal project file sharing (IFS) arranged
- D7.3 Preliminary data management plan (DMP1)
- D7.4 Mid-term review (MTR)
- D7.5 Final version data management plan (DMP2)