Publications

Is selfBACK, an evidence-based, individually tailored self-management support system that is delivered through an artificial intelligence–based app and in conjunction with usual care, effective for pain-related disability in adults with lower back pain?

Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability

Low back pain (LBP) affects up to 80% of the global population at some point in their lifetime and is one of the leading causes of disability worldwide. People with LBP are recommended to self-manage their symptoms for example by undertaking education and exercise but this can be challenging without support. Digital health interventions (DHIs) provide an opportunity to support self-management of LBP and there is some evidence to suggest such interventions can be effective in reducing pain and back pain-related disability. Further, they may be important in reinforcing healthcare providers’ (HCPs) advice on self-management.^‍

The implementation of DHIs for LBP has been studied from patients’ perspectives.However, less is known about HCPs’ engagement in the implementation of DHIs and their views on using such tools in their clinical practice. This is important to understand since HCPs often signpost patients to DHIs, and HCP views can significantly influence patients’ views on self-management activities. Known barriers to recruitment in pragmatic trials are the recruiting HCPs’ lack of experience with research procedures and high workloads or competing tasks. Whether similar or more complex barriers exist regarding the recruitment of patients to randomized controlled trials (RCTs) of DHIs and specifically a DHI for LBP remains unclear.

We evaluated the effectiveness of a knowledge-based artificial intelligence-based app (selfBACK) in an RCT in primary care settings in Denmark and Norway. The selfBACK app was developed to support individually tailored and evidence-based self-management of LBP. Results of the RCT indicated a small but favourable effect of the app-based intervention compared with usual care on LBP-related disability among patients receiving primary care. A process evaluation was conducted alongside the RCT to understand the implementation of selfBACK viewed from the perspective of both patients and HCPs who recruited patients to the study.

This study investigates how physical work demands modify the effectiveness of the SELFBACK app for low-back pain management. Analyzing data from 346 participants, it shows consistent effects for both high and low physical demand workers. Although high-demand workers experience more noticeable benefits, no statistically significant differences emerge. The findings underscore the app’s overall effectiveness across diverse work demands.

Secondary health-economic analysis of the selfBACK randomised controlled trial (RCT) with a 9-month follow-up conducted from a Danish national healthcare perspective (primary scenario) and a societal perspective limited to long-term productivity in the form of long-term absenteeism (secondary scenario)

Supporting people to self-manage their low back pain (LBP) is an important aspect of treatment. Delivery of personalized or tailored support via smartphone apps has shown promising results in reducing both pain and disability in people experiencing LBP. These apps have been mostly tested on middle-aged and well-educated women and we do not know if other groups have the same benefits. To be able to recommend self-management apps for people with LBP, it is important to identify sub-groups that may experience particularly favorable effects of such interventions, or alternatively, sub-groups without benefit or who may experience worse outcomes. In this study we wanted to examine if the effect of tailored support via a smartphone app (selfBACK) on LBP related disability was modified by age, gender and education. Our analysis showed that age, gender, or education did not impact the effect of the selfBACK intervention. However, older participants may have an additional long-term positive effect compared to younger participants. This suggests that the selfBACK intervention may benefit all persons seeking care for LBP in primary care regardless of their age, gender, and education, and be a helpful tool for clinicians and patients to support self-management of LBP.

Clinical guidelines for nonspecific low back pain (LBP) recommend self-management tailored to individual needs and capabilities as a first-line treatment. Mobile health solutions are a promising method for delivering tailored self-management interventions to patients with nonspecific LBP. However, it is not clear if the effectiveness of such self-management interventions depends on patients’ initial pain characteristics. High pain intensity and long-term symptoms of LBP have been associated with an unfavorable prognosis, and current best evidence indicates that long-term LBP (lasting more than 3 months) requires a more extensive treatment approach compared to more acute LBP. The artificial intelligence–based selfback app supports tailored and evidence-based self-management of nonspecific LBP. In a recent randomized controlled trial, we showed that individuals who received the selfBACK app in addition to usual care had lower LBP-related disability at the 3-month follow-up compared to those who received usual care only. This effect was sustained at 6 and 9 months.

selfBACK provides individually tailored self-management support for low back pain (LBP) via an artificial intelligence-based smartphone app. We explore whether those with depressive/stress symptoms can benefit from this technology.

SELFBACK, an artificial intelligence (AI)-based app delivering evidence-based tailored self-management support to people with low back pain (LBP), has been shown to reduce LBP-related disability when added to usual care. LBP commonly co-occurs with multimorbidity (≥ 2 long-term conditions) or pain at other musculoskeletal sites, so this study explores if these factors modify the effect of the SELFBACK app or influence outcome trajectories over time.

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.

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.