Telemedicine for Patients with Musculoskeletal Pain Lacks High-Quality Evidence on Delivery Modes and Effectiveness: An Umbrella Review.

Background: Musculoskeletal (MSK) pain is the leading cause of disability worldwide. Telemedicine is of growing importance, yet impacts on treatment efficacy remain unclear. Objective: This umbrella review (CRD42022298047) examined the effectiveness of telemedicine interventions on pain intensity, disability, psychological function, quality of life, self-efficacy, and adverse events in MSK pain. Methods: PubMed, SPORTDiscus, Cochrane Library, EMBASE, and CINAHL were searched from inception to August 9, 2022, for systematic reviews with meta-analysis, including telemedicine-delivered exercise, education, and psychological interventions, in randomized controlled trials (RCTs). AMSTAR-2 was implemented. Standardized mean differences (SMDs; negative favors telemedicine) were extracted as effect estimates. Results: Of 1,135 records, 20 reviews (RCTs: n = 97, participants: n = 15,872) were included. Pain intensity SMDs were -0.66 to 0.10 for mixed pain (estimates: n = 16), -0.64 to -0.01 for low-back pain (n = 9), -0.31 to -0.15 for osteoarthritis (n = 7), -0.29 for knee pain (n = 1), -0.66 to -0.58 for fibromyalgia (n = 2), -0.16 for back pain (n = 1), and -0.09 for rheumatic disorders (n = 1). Disability SMDs were -0.50 to 0.10 for mixed pain (n = 14), -0.39 to 0.00 for low-back pain (n = 8), -0.41 to -0.04 for osteoarthritis (n = 7), -0.22 for knee pain (n = 1), and -0.56 for fibromyalgia (n = 1). Methodological quality was "critically low" for 17 reviews. Effectiveness tended to favor telemedicine for all secondary outcomes. Conclusions: Primary RCTs are required that compare telemedicine interventions with in-person delivery of the intervention (noninferiority trials), consider safety, assess videoconferencing, and combine different treatment approaches.

Effectiveness of Exercise Interventions for Preventing Neck Pain: A Systematic Review With Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: To update the evidence on the effectiveness of exercise interventions to prevent episodes of neck pain. DESIGN: Systematic review with meta-analysis. LITERATURE SEARCH: MEDLINE, Embase, CENTRAL, CINAHL, SPORTDiscus, PEDro, and trial registries from inception to December 2, 2022. Forward and backward citation searches. STUDY SELECTION CRITERIA: Randomized controlled trials (RCTs) that enrolled adults without neck pain at baseline and compared exercise interventions to no intervention, placebo/sham, attention control, or minimal intervention. Military populations and astronauts were excluded. DATA SYNTHESIS: Random-effects meta-analysis. Risk of bias was assessed using the Cochrane RoB 2 tool. The certainty of evidence was judged according to the GRADE approach. RESULTS: Of 4703 records screened, 5 trials (1722 participants at baseline) were included and eligible for meta-analysis. Most (80%) participants were office workers. Risk of bias was rated as some concerns for 2 trials and high for 3 trials. There was moderate-certainty evidence that exercise interventions probably reduce the risk of a new episode of neck pain (OR, 0.49; 95% confidence interval: 0.31, 0.76) compared to no or minimal intervention in the short-term (≤12 months). The results were not robust to sensitivity analyses for missing outcome data. CONCLUSION: There was moderate-certainty evidence supporting exercise interventions for reducing the risk for an episode of neck pain in the next 12 months. The clinical significance of the effect is unclear. J Orthop Sports Phys Ther 2023;53(10):1-16. Epub: 8 September 2023. doi:10.2519/jospt.2023.12063.

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.