An App-Delivered Self-Management Program for People With Low Back Pain: Protocol for the selfBACK Randomized Controlled Trial
Background: Low back pain (LBP) is prevalent across all social classes, in all age groups, and across industrialized and developing countries. From a global perspective, LBP is considered the leading cause of disability and negatively impacts everyday life and well-being. Self-management is a recommended first-line treatment, and mobile apps are a promising platform to support self-management of conditions like LBP. In the selfBACK project, we have developed a digital decision support system made available for the user via an app intended to support tailored self-management of nonspecific LBP. Objective: The trial aims to evaluate the effectiveness of using the selfBACK app to support self-management in addition to usual care (intervention group) versus usual care only (control group) in people with nonspecific LBP. Methods: This is a single-blinded, randomized controlled trial (RCT) with two parallel arms. The selfBACK app provides tailored self-management plans consisting of advice on physical activity, physical exercises, and educational content. Tailoring of plans is achieved by using case-based reasoning (CBR) methodology, which is a branch of artificial intelligence. The core of the CBR methodology is to use data about the current case (participant) along with knowledge about previous and similar cases to tailor the self-management plan to the current case. This enables a person-centered intervention based on what has and has not been successful in previous cases. Participants in the RCT are people with LBP who consulted a health care professional in primary care within the preceding 8 weeks. Participants are randomized to using the selfBACK app in addition to usual care versus usual care only. We aim to include a total of 350 participants (175 participants in each arm). Outcomes are collected at baseline, 6 weeks, and 3, 6, and 9 months. The primary end point is difference in pain-related disability between the intervention group and the control group assessed by the Roland-Morris Disability Questionnaire at 3 months. Results: The trial opened for recruitment in February 2019. Data collection is expected to be complete by fall 2020, and the results for the primary outcome are expected to be published in fall 2020. Conclusions: This RCT will provide insights regarding the benefits of supporting tailored self-management of LBP through an app available at times convenient for the user. If successful, the intervention has the potential to become a model for the provision of tailored self-management support to people with nonspecific LBP and inform future interventions for other painful musculoskeletal conditions. Trial Registration: ClinicalTrial.gov NCT03798288; https://clinicaltrials.gov/ct2/show/NCT03798288 International Registered Report Identifier (IRRID): DERR1-10.2196/14720 Published here: JMIR Res Protoc 2019;8(12):e14720doi:10.2196/14720 Read the full article here...... |
Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain
This paper presents the design of a Clinician Dashboard to promote co-decision making between patients and clinicians. Targeted patients are those with non-specific low back pain, a leading cause of discomfort, disability and absence from work throughout the world. Targeted clinicians are those in primary care, including general practitioners, physiotherapists, and chiropractors. Here, the functional specifications for the Clinical Dashboard are delineated, and wireframes illustrating the system interface and flow of control are shown. Representative scenarios are presented to exemplify how the system could be used for co-decision making by a patient and clinician. Also included are a discussion of potential barriers to implementation and use in clinical practice and a look ahead to future work. This work has been conducted as part of the Horizon 2020 selfBACK project, which is funded by the European Commission. Read the full article here.... Published in Journal of Intelligent Information Systems, April 2019, Volume 52, page 269-284 |
A Decision Support System to Enhance Self-Management of Low Back Pain
The selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP. Conclusions: The selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP. Read the full article here... Published in Journal of Medical Internet Research July 2018 |
Backing self-management
The selfBACK project is a healthcare programme centred round self-management and has the potential to save both time and monetary resources in the field of low back pain. The overall aim of the project is to improve the self-management of non-specific low back pain in order to prevent chronicity, recurrence and pain-related disability. To achieve this we are currently developing a decision support system – selfBACK – that will be used by the patients in the self-management of low back pain. The patient receives this information through a smartphone app, providing advice to reinforce their personalised self-management plan.
Read the full article here... Published by impact.pub October 2017 |
Evolutionary Inspired Adaptation of Exercise Plans for Increasing Solution Variety
An initial case base population naturally lacks diversity of solutions. In order to overcome this cold-start problem, we present how genetic algorithms (GA) can be applied. The work presented in this paper is part of the selfBACK EU project and describes a case-based recommendation system that creates exercise plans for patients with non-specific low back pain (LBP). In selfBACK Case-Based Reasoning (CBR) is used as its main methodology for generating patient-specific advice for managing non-specific LBP. The sub-module of selfBACK presented in this work focuses on the adaptation process of exercise plans: A GA inspired method is created to increase the variation of personalized exercise plans, which today are crafted by medical professionals. Experiments are conducted using real patients’ characteristics with expert-crafted solutions and automatically generated solutions. In the evaluation we compare the quality of the GA-generated solutions to null-adaptation solutions. Read more.... |
Diversity of Exercise Plans using Evolutionary Inspired Adaptation
The work presented is part of the selfBACK EU project and describes a case-based recommendation system that creates exer- cise plans for patients with non-specific low back pain (LBP). The sub-module of selfBACK presented in this work focuses on the adaptation process of exercise plans: An evolutionary inspired method is created to increase the variation of personalized exercise plans, which today are crafted by medical professionals. Experiments are conducted using real patients’ characteristics with expert-crafted solutions and automatically generated solutions. In the evaluation we compare the quality of the solutions generated by Genetic Algorithm to null-adaptation solutions. Read More.... |
Digital Support Interventions for the Self-Management of Low Back Pain
Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. There is growing interest in the potential role of digital health as a means of optimising effective treatment strategies. This study aimed to synthesize and critically appraise the published literature regarding the use of interactive digital interventions to support patient self-management of LBP and to determine: 1) What outcome measures have been used in trials of digital self-management interventions in LBP and what effects, if any, have been reported? ;2) What are the key components of reported digital self-management interventions for LBP, including theoretical underpinnings?; and 3) What are the key characteristics of digital self-management interventions that appear to be associated with beneficial effects? Read more....... Published on the Society for Academic Primary Care website, July 2017 |
Digital Support Interventions for the Self-Management of Low Back Pain
The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Read more....
Published 21/5/2107 in: Journal of Medical Internet Research |
Case Representation and Similarity Assessment in the selfBACK Decision Support System
In this paper we introduce the selfBACK decision support system that facilitates, improves and reinforces self-management of non-specific low back pain. The selfBACK system is a predictive case-based reasoning system for personalizing recommendations in order to provide relief for patients with non-specific low back pain and increase their physical functionality over time. We present how case-based reasoning is used for capturing experiences from temporal patient data, and evaluate how to carry out a similarity-based retrieval in order to find the best advice for patients. Specifically, we will show how heterogeneous data received at various frequencies can be captured in cases and used for personalized advice. Read more.....
Published 29/9/2016 In SpringerLink |
A Wristband and an App for a Better Back
Are you doing your back exercises correctly ? Are you following the plan that your doctor or physiotherapist set up for you ? New technology could help you follow up on their advice and adapt the exercises to your needs. Read more...
Published 18/2/2016 in Gemini.no |
Wristband Knows if Patients have done their Exercises
Back pain sufferers are to be fitted with a hi-tech wristband to monitor whether they are making an effort to improve their condition. Researchers at Robert Gordon University in Aberdeen have been awarded around 500,000 Euro (£370,000) to design the device which aims to improve the way that lower back pain is managed. Read more.....
Published 10/6/2015 Beta.scotsman.com |
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Can data driven self-management reduce low back pain?
Low back pain is a common reason for activity limitation, sick leave, and disability. It is the fourth most common diagnosis (after upper respiratory infection, hypertension, and coughing) seen in primary care. An expert group concluded that the most effective approach to manage non-specific low back pain is to discourage bed rest, to use over-the-counter pain killers in the acute stage if necessary (e.g., to be able to sleep), reassure the patient about the favourable prognosis, advise the patient to stay active, and advise strength and/or stretching exercise to prevent recurrence. Read more....
Published January 2016 in Ercim News www.ercim.com |
FundingThis project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689043.
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