Boosting treatment outcomes via the patient-practitioner relationship, treatment-beliefs or therapeutic setting. A systematic review with meta-analysis of contextual effects in chronic musculoskeletal pain.

Objective: To ascertain whether manipulating contextual effects (e.g. interaction with patients, or beliefs about treatments) boosted the outcomes of non-pharmacological and non-surgicaltreatments for chronic primary musculoskeletal pain.Design: Systematic review of randomized controlled trialsData Sources: We searched for trials in six databases, citation tracking, and clinical trials registers. We included trials that compared treatments with enhanced contextual effects with the same treatments without enhancement in adults with chronic primary musculoskeletal pain.Data synthesis: The outcomes of interest were pain intensity, physical functioning, global ratings of improvement, quality of life, depression, anxiety, and sleep. We evaluated risk of bias and certainty of the evidence using Cochrane Risk of Bias tool 2.0 and the GRADE approach, respectively.Results: Of 17637 records, we included 10 trials with 990 participants and identified 5 ongoing trials. The treatments were acupuncture, education, exercise training, and physical therapy. The contextual effects that were improved in the enhanced treatments were patient-practitioner relationship, patient beliefs and characteristics, therapeutic setting/environment, and treatment characteristics. Our analysis showed that improving contextual effects in non-pharmacological and non-surgical treatments may not make much difference on pain intensity (mean difference [MD] : -1.77, 95%-CI: [-8.71; 5.16], k = 7 trials, N = 719 participants, Scale: 0-100, GRADE: Low)) or physical functioning (MD: -0.27, 95%-CI: [-1.02; 0.49], 95%-PI: [-2.04; 1.51], k = 6 , N = 567, Scale: 0-10, GRADE: Low) in the short-term and at later follow-ups. Sensitivity analyses revealed similar findings.Conclusion: Whilst evidence gaps exist, per current evidence it may not be possible to achieve meaningful benefit for patients with chronic musculoskeletal pain by manipulating the context of non-pharmacological and non-surgical treatments

Cervical spine and muscle adaptation after spaceflight and relationship to herniation risk:protocol from 'Cervical in Space' trial

BACKGROUND: Astronauts have a higher risk of cervical intervertebral disc herniation. Several mechanisms have been attributed as causative factors for this increased risk. However, most of the previous studies have examined potential causal factors for lumbar intervertebral disc herniation only. Hence, we aim to conduct a study to identify the various changes in the cervical spine that lead to an increased risk of cervical disc herniation after spaceflight. METHODS: A cohort study with astronauts will be conducted. The data collection will involve four main components: a) Magnetic resonance imaging (MRI); b) cervical 3D kinematics; c) an Integrated Protocol consisting of maximal and submaximal voluntary contractions of the neck muscles, endurance testing of the neck muscles, neck muscle fatigue testing and questionnaires; and d) dual energy X-ray absorptiometry (DXA) examination. Measurements will be conducted at several time points before and after astronauts visit the International Space Station. The main outcomes of interest are adaptations in the cervical discs, muscles and bones. DISCUSSION: Astronauts are at higher risk of cervical disc herniation, but contributing factors remain unclear. The results of this study will inform future preventive measures for astronauts and will also contribute to the understanding of intervertebral disc herniation risk in the cervical spine for people on Earth. In addition, we anticipate deeper insight into the aetiology of neck pain with this research project. TRIAL REGISTRATION: German Clinical Trials Register, DRKS00026777. Registered on 08 October 2021. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-022-05684-0

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study.

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The "PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain" (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18-55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The “PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain” (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18–55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs