scholarly journals Using Intervention Mapping to develop a decision support system-based smartphone app to support self-management of non-specific low back pain (SELFBACK) (Preprint)

Author(s):  
Malene Jagd Svendsen ◽  
Louise Fleng Sandal ◽  
Per Kjær ◽  
Barbara I Nicholl ◽  
Kay Cooper ◽  
...  
2017 ◽  
Author(s):  
Paul Jarle Mork ◽  
Kerstin Bach

BACKGROUND Low back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP. OBJECTIVE The main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data. METHODS The decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK. RESULTS 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. REGISTERED REPORT IDENTIFIER RR1-10.2196/9379


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Louise Fleng Sandal ◽  
Cecilie K. Øverås ◽  
Anne Lovise Nordstoga ◽  
Karen Wood ◽  
Kerstin Bach ◽  
...  

2020 ◽  
Author(s):  
Malene Jagd Svendsen ◽  
Louise Fleng Sandal ◽  
Per Kjær ◽  
Barbara I Nicholl ◽  
Kay Cooper ◽  
...  

BACKGROUND International guidelines consistently endorse promotion of self-management for people with low back pain (LBP), however, implementation of these guidelines remains a challenge. Digital health interventions, such as those that can be provided by smartphone apps, have been proposed as a promising mode to support self-management in people with chronic conditions including LBP. However, the evidence base for digital health interventions to support self-management of LBP is weak and detailed description and documentation of the intervention is lacking. Structured Intervention Mapping (IM) constitutes a six-step process that can be used to guide the development of complex interventions. OBJECTIVE The aim of this paper is to describe the IM process for designing and creating an app-based intervention designed to support self-management of non-specific LBP to reduce pain-related disability. METHODS Five steps of the IM process were systematically applied: the core processes included literature reviews, brainstorming and group discussions, and inclusion of stakeholders and representatives of the target population. Throughout a period of more than two years, the intervention content and technical features of delivery were created, tested and revised through user tests, feasibility studies and a pilot study. RESULTS One behavioural outcome was identified as the proxy for reaching the overall programme goal; increased use of evidence-based self-management strategies. Physical exercises, education and physical activity were the main components of the self-management intervention, designed and produced to be delivered via a smartphone app. All intervention content was theoretically underpinned by behaviour change theory and Normalization Process Theory. CONCLUSIONS We describe a detailed example of the application of the IM approach to the development of a theory-driven, complex, and digital intervention designed to support self-management of LBP. This description provides transparency of the developmental process of the intervention and a possible blue-print for designing and creating future digital health interventions for self-management.


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