The effect of self-management techniques on relevant outcomes in chronic low back pain: A systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

Among many treatment approaches for chronic low back pain (CLBP), self-management techniques are becoming increasingly important. The aim of this paper was to (a) provide an overview of existing digital self-help interventions for CLBP and (b) examine the effect of these interventions in reducing pain intensity, pain catastrophizing and pain disability.

DATABASES AND DATA TREATMENT

Following the PRISMA guideline, a systematic literature search was conducted in the MEDLINE, EMBASE, PsychInfo, CINAHL and Cochrane databases. We included randomized controlled trials from the last 10 years that examined the impact of digital self-management interventions on at least one of the three outcomes in adult patients with CLBP (duration ≥3 months). The meta-analysis was based on random-effects models. Standardized tools were used to assess the risk of bias (RoB) for each study and the quality of evidence for each outcome.

RESULTS

We included 12 studies (n = 1545). A small but robust and statistically significant pooled effect was found on pain intensity (g = 0.24; 95% CI [0.09, 0.40], k = 12) and pain disability (g = 0.43; 95% CI [0.27, 0.59], k = 11). The effect on pain catastrophizing was not significant (g = 0.38; 95% CI [-0.31, 1.06], k = 4). The overall effect size including all three outcomes was g = 0.33 (95% CI [0.21, 0.44], k = 27). The RoB of the included studies was mixed. The quality of evidence was moderate or high.

CONCLUSION

In summary, we were able to substantiate recent evidence that digital self-management interventions are effective in the treatment of CLBP. Given the heterogeneity of interventions, further research should aim to investigate which patients benefit most from which approach.

SIGNIFICANCE

This meta-analysis examines the effect of digital self-management techniques in patients with CLBP. The results add to the evidence that digital interventions can help patients reduce their pain intensity and disability. A minority of studies point towards the possibility that digital interventions can reduce pain catastrophizing. Future research should further explore which patients benefit most from these kinds of interventions.

European HPC cloud infrastructure for managing SARS-CoV-2 data in compliance with GDPR

 

The Connecting European SARS-CoV-2 Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic (ORCHESTRA) consortium, led by University of Verona (Italy), brings together key European academic experts and research institutions in infectious diseases, data management and High Performance Computing (HPC) from 26 organizations (extending to 37 partners) from 15 countries. The project aims to create a new pan-European cohort built on existing and new large-scale population cohorts in European and non-European countries to significantly impact on the responsiveness to SARS-CoV-2. The integration and analysis of the very heterogeneous characteristics of SARS-CoV-2 data coming from many different sources such as EHR, retrospective and prospective patient registries, and related ‘omics’ data (incl. genomics, proteomics and transcriptomics) can benefit of data analytics enabled by HPC, where both high compute performance and fast storage capabilities are immensely important. During the first year of the project, a dedicated HPC cloud infrastructure have been designed and partially deployed to fulfill the functional requirements for data management ensuring healthcare data confidentiality/privacy, integrity and security in compliance with the European GDPR regulations. The result is an infrastructure for Data Management composed by three main layers: National Data Providers; National Hubs (one for each HPC center involved: CINECA - Italy, CINES - France and HLRS - Germany), to centralize data at national level and to support data storage, sharing and analysis on data ingested from the National Data Providers; ORCHESTRA Data Portal: the pan-European portal for sharing aggregated data and results. Currently data collection is on going; at the end of the project, clinical centers are expected to have enrolled more than 10.000 patients with about 50.000 samples for the prospective studies.

Key messages

• The SARS-CoV-2 crisis made evident the need to manage and analyse very heterogeneous health data coming from many different resources across different countries.

• The HPC cloud infrastructure released for the Orchestra project can act as a model to manage future public health threats.