Non-specific low back pain (LBP) is one of the leading causes of global disability. Multidisciplinary pain treatment (MPT) programs comprising educational, physical, and psychological interventions have shown positive treatment effects on LBP. Nonetheless, such programs are costly and treatment opportunities are often limited to specialized medical centers. mHealth and other digital interventions may be a promising method to successfully support patient self-management in LBP. To address these issues, we investigated the clinical effects of a multidisciplinary mHealth back pain App (Kaia App) in a randomized controlled trial (registered at German Clinical Trials Register under DRKS00016329). One-hundred one adult patients with non-specific LBP from 6 weeks to 1 year were randomly assigned to an intervention group or a control group. In the intervention group, the Kaia App was provided for 3 months. Control treatment consisted of six individual physiotherapy sessions over 6 weeks and high-quality online education. The primary outcome, pain intensity, was assessed at 12-week follow-up on an 11-point numeric rating scale (NRS). Our per-protocol analysis showed no significant differences between the groups at baseline (Kaia App group: M = 5.10 (SD = 1.07) vs. control group: M = 5.41 (SD = 1.15). At 12-week follow-up the Kaia App group reported significantly lower pain intensity ( M = 2.70 (SD = 1.51)) compared to the control group ( M = 3.40 (SD = 1.63)). Our results indicate that the Kaia App as a multidisciplinary back pain app is an effective treatment in LBP patients and is superior to physiotherapy in combination with online education.

BackgroundEven though modern concepts of disease management of unspecific low back pain (LBP) postulate active participation of patients, this strategy is difficult to adapt unless multidisciplinary pain therapy is applied. Recently, mobile health solutions have proven to be effective aides to foster self-management of many diseases.ObjectiveThe objective of this paper was to report on the retrospective short-term results of a digital multidisciplinary pain app for the treatment of LBP.MethodsKaia is a mobile app that digitalizes multidisciplinary pain treatment and is in the market as a medical product class I. For the current study, the data of anonymized Kaia users was retrospectively analyzed. User data were evaluated for 12 weeks regarding duration of use and effect on in-app user reported pain levels, using the numerical rating scale (NRS), depending on whether LBP was classified as acute, subacute, or chronic back pain according to current guidelines.ResultsData of 180 users were available. The mean age of the users was 33.9 years (SD 10.9). Pain levels decreased from baseline NRS 4.8 to 3.75 for all users at the end of the observation period. Users who completed 4, 8, or 12 weeks showed an even more pronounced decrease in pain level NRS (baseline 4.9 [SD 1.7] versus 3.6 [SD 1.5] at 4 weeks; baseline 4.7 [SD 1.8] versus 3.2 [SD [2.0] at 8 weeks; baseline 4.6 [SD 2.2] versus 2.6 [SD 2.0] at 12 weeks). In addition, subgroup analysis of acute, subacute, or chronic classification revealed no significant main effect of group (P>.30) on the reduction of pain. Conclusions: This retrospective study showed that in a pre-selected population of app users, an app digitalizing multidisciplinary rehabilitation for the self-management of LBP reduced user-reported pain levels significantly. The observed effect size was clinically relevant. Ongoing prospective randomized controlled trials (RCTs) will adjust for potential bias and selection effects.ConclusionsThis retrospective study showed that in a pre-selected population of app users, an app digitalizing multidisciplinary rehabilitation for the self-management of LBP reduced user-reported pain levels significantly. The observed effect size was clinically relevant. Ongoing prospective RCTs will adjust for potential bias and selection effects.

Purpose: Non-specific low back pain (NLBP) causes an enormous burden to patients and tremendous costs for health care systems worldwide. Frequently, treatments are not oriented to existing guidelines. In the future, digital elements may be promising tools to support guidelineoriented treatment in a broader range of patients. The cluster-randomized controlled "Rise-uP" trial aims to support a General Practitioner (GP)-centered back pain treatment (Registration No: DRKS00015048) and includes the following digital elements: 1) electronic case report form (eCRF), 2) a treatment algorithm for guideline-based clinical decision making of GPs, 3) teleconsultation between GPs and pain specialists for patients at risk for development of chronic back pain, and 4) a multidisciplinary mobile back pain app for all patients (Kaia App). Methods: In the Rise-uP trial, 111 GPs throughout Bavaria (southern Germany) were randomized either to the Rise-uP intervention group (IG) or the control group (CG). Rise-uP patients were treated according to the guideline-oriented Rise-uP treatment algorithm. Standard of care was applied to the CG patients with consideration given to the "National guideline for the treatment of non-specific back pain". Pain rating on the numeric rating scale was the primary outcome measure. Psychological measures (anxiety, depression, stress), functional ability, as well as physical and mental wellbeing, served as secondary outcomes. All values were assessed at the beginning of the treatment and at 3-month follow-ups. Results: In total, 1245 patients (IG: 933; CG: 312) with NLBP were included in the study. The Rise-uP group showed a significantly stronger pain reduction compared to the control group after 3 months (IG: M=−33.3% vs CG: M=−14.3%). The Rise-uP group was also superior in secondary outcomes. Furthermore, high-risk patients who received a teleconsultation showed a larger decrease in pain intensity (−43.5%) than CG patients (−14.3%). ANCOVA analysis showed that the impact of teleconsultation was mediated by an increased training activity in the Kaia App. Conclusion: Our results show the superiority of the innovative digital treatment algorithm realized in Rise-uP, even though the CG also received relevant active treatment by their GPs. This provides clear evidence that digital treatment may be a promising tool to improve the quality of treatment of non-specific back pain. In 2021, analyses of routine data from statutory health insurances will enable us to investigate the cost-effectiveness of digital treatment.

We assume that initial vigilance for pain-associated stimuli might reflect an adaptive reaction to detect a potentially harmful stimulus. Subsequently, the pain-associated stimulus might be less attended for the purpose of mood regulation when all clear is given in this situation.