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ObjectiveExamine the effectiveness of specific modes of exercise training in non-specific chronic low back pain (NSCLBP).DesignNetwork meta-analysis (NMA).Data sourcesMEDLINE, CINAHL, SPORTDiscus, EMBASE, CENTRAL.Eligibility criteriaExercise training randomised controlled/clinical trials in adults with NSCLBP.ResultsAmong 9543 records, 89 studies (patients=5578) were eligible for qualitative synthesis and 70 (pain), 63 (physical function), 16 (mental health) and 4 (trunk muscle strength) for NMA. The NMA consistency model revealed that the following exercise training modalities had the highest probability (surface under the cumulative ranking (SUCRA)) of being best when compared with true control: Pilates for pain (SUCRA=100%; pooled standardised mean difference (95% CI): −1.86 (–2.54 to –1.19)), resistance (SUCRA=80%; −1.14 (–1.71 to –0.56)) and stabilisation/motor control (SUCRA=80%; −1.13 (–1.53 to –0.74)) for physical function and resistance (SUCRA=80%; −1.26 (–2.10 to –0.41)) and aerobic (SUCRA=80%; −1.18 (–2.20 to –0.15)) for mental health. True control was most likely (SUCRA≤10%) to be the worst treatment for all outcomes, followed by therapist hands-off control for pain (SUCRA=10%; 0.09 (–0.71 to 0.89)) and physical function (SUCRA=20%; −0.31 (–0.94 to 0.32)) and therapist hands-on control for mental health (SUCRA=20%; −0.31 (–1.31 to 0.70)). Stretching and McKenzie exercise effect sizes did not differ to true control for pain or function (p>0.095; SUCRA<40%). NMA was not possible for trunk muscle endurance or analgesic medication. The quality of the synthesised evidence was low according to Grading of Recommendations Assessment, Development and Evaluation criteria.Summary/conclusionThere is low quality evidence that Pilates, stabilisation/motor control, resistance training and aerobic exercise training are the most effective treatments, pending outcome of interest, for adults with NSCLBP. Exercise training may also be more effective than therapist hands-on treatment. Heterogeneity among studies and the fact that there are few studies with low risk of bias are both limitations.

Maximising research productivity is a major focus for universities world-wide. Graduate research programs are an important driver of research outputs. Choosing students with the greatest likelihood of success is considered a key part of improving research outcomes. There has been little empirical investigation of what factors drive the outcomes from a student's PhD and whether ranking procedures are effective in student selection. Here we show that, the research environment had a decisive influence: students who conducted research in one of the University's priority research areas and who had experienced, research-intensive, supervisors had significantly better outcomes from their PhD in terms of number of manuscripts published, citations, average impact factor of journals published in, and reduced attrition rates. In contrast, students' previous academic outcomes and research training was unrelated to outcomes. Furthermore, students who received a scholarship to support their studies generated significantly more publications in higher impact journals, their work was cited more often and they were less likely to withdraw from their PhD. The findings suggest that experienced supervisors researching in a priority research area facilitate PhD student productivity. The findings question the utility of assigning PhD scholarships solely on the basis of student academic merit, once minimum entry requirements are met. Given that citations, publication numbers and publications in higher ranked journals drive university rankings, and that publications from PhD student contribute approximately one-third of all research outputs from universities, strengthening research infrastructure and supervision teams may be more important considerations for maximising the contribution of PhD students to a university's international standing.

The importance of contextual effects and their roles in clinical care controversial. A Cochrane review published in 2010 concluded that placebo interventions lack important clinical effects overall, but that placebo interventions can influence patient-reported outcomes such as pain and nausea. However, systematic reviews published after 2010 estimated greater contextual effects than the Cochrane review, which stems from the inappropriate methods employed to quantify contextual effects. The effects of medical interventions (i.e., the total treatment effect) can be divided into three components: specific, contextual, and non-specific. We propose that the most effective method for quantifying the magnitude of contextual effects is to calculate the difference in outcome measures between a group treated with placebo and a non-treated control group. Here, we show that other methods, such as solely using the placebo control arm or calculation of a ‘proportional contextual effect,’ are limited and should not be applied. The aim of this study is to provide clear guidance on best practices for estimating contextual effects in clinical research.

There are many forms of AI; this editorial discusses natural language model-based AI, such as ChatGPT, and their potential ability to generate academic papers. Nature reported that four manuscripts in preprint credit ChatGPT as an author.3 Also, an article reported that AI had been used to generate an academic paper.4 In this editorial, we discuss the pros and cons of AI for manuscript generation in sports and exercise medicine (SEM), generate an academic paper using AI and bypass AI-generation detection, and discuss potential concerns regarding natural language model-based AI. From this experiment of seeing whether AI is capable of writing a quality academic paper, there are issues regarding whether the produced content is originally interesting, whether the paper shows an advanced and balanced understanding of the topic, and whether it has used critical thinking to generate original thought or if it is simply a summary of knowledge on a topic. The information generated by ChatGPT missed critical steps that may have rendered this PC useless.7 Potentially flawed AI detection It has been found that it can be difficult to discern the difference between AI-generated and original abstracts.8 There are currently a few tools, including GPTZero, GPT-2 Output Detector and AI Detector, to detect whether a current AI language model generated a text.

Muscle mass and function are perturbed by immobilization and remobilization. When muscle mass changes, the quality and quantity of the extracellular matrix protein, particularly the collagens, change with it. In this study, we investigated the temporal profile of three peptide biomarkers derived from turnover of collagen type III and type VI in a long-term immobilization and remobilization study. We also compared individual biomarker levels with Lean body Mass (LBM) and changes therein, hypothesizing that these biomarkers would be biomarkers of the remodeling processes associated with immobilization and/or remobilization. In the Berlin bed rest study, 20 young men were recruited and randomly assigned to 8-week's strict bed rest with or without resistive vibration exercise countermeasure. We measured three neo-epitope ELISA kits in the serum samples of this study: Pro-C3, measured the synthesis of collagen type III; Pro-C6, measured the synthesis of collagen type VI; and C6M measured the degradation of collagen type VI induced by MMP-2 and MMP-9 cleavage. Pro-C3 and Pro-C6 biomarkers are up-regulated with both immobilization and remobilization, whereas C6M is hardly affected at all. We found that Pro-C3 and C6M levels are related to LBM at baseline and that high levels of Pro-C6 are associated with smaller changes in muscle mass during both immobilization and remobilization. The Pro-C3 and-C6 biomarkers change likely reflect remodeling changes in response to unloading or reloading, whereas C6M does not appear to respond to unloading. Pro-C3 and C6M levels correlate with LBM at baseline, while Pro-C6 is related to the anabolic and catabolic responses to unloading and reloading.

There is currently no evidence that the intervertebral discs (IVDs) can respond positively to exercise in humans. Some authors have argued that IVD metabolism in humans is too slow to respond anabolically to exercise within the human lifespan. Here we show that chronic running exercise in men and women is associated with better IVD composition (hydration and proteoglycan content) and with IVD hypertrophy. Via quantitative assessment of physical activity we further find that accelerations at fast walking and slow running (2 m/s), but not high-impact tasks, lower intensity walking or static positions, correlated to positive IVD characteristics. These findings represent the first evidence in humans that exercise can be beneficial for the IVD and provide support for the notion that specific exercise protocols may improve IVD material properties in the spine. We anticipate that our findings will be a starting point to better define exercise protocols and physical activity profiles for IVD anabolism in humans.

This prospectively registered systematic review and meta-analysis examines whether exercise (EX) training has an additive effect to osteoanabolic and/or antiresorptive pharmacological therapy (PT) in people with osteoporosis on bone mineral density (BMD), bone turnover markers (BTMs), fracture healing, and fractures. Four databases (inception to 6 May 2022), 5 trial registries, and reference lists were searched. Included were randomized controlled trials comparing the effect of EX + PT vs. PT with regard to BMD, BTM, fracture healing, and fractures. Risk of bias was assessed using the Cochrane RoB2 and certainty of evidence by the GRADE approach. Random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman adjustment was used to estimate standardized mean differences and 95% confidence intervals. Out of 2593 records, five RCTs with 530 participants were included. Meta-analysis showed with very low certainty evidence and wide confidence intervals that EX + PT compared to PT had larger effect sizes for BMD at 12 months at the hip (SMD [95%CI]: 0.18 [− 1.71; 2.06], n  = 3 studies), tibia (0.25 [− 4.85; 5.34], n  = 2), lumbar spine (0.20 [− 1.15; 1.55], n  = 4), and forearm (0.05 [− 0.35; 0.46], n  = 3), but not femoral neck (− 0.03 [− 1.80; 1.75], n  = 3). Furthermore, no improvement was revealed for BTM such as bone ALP (− 0.68 [− 5.88; 4.53], n  = 3), PINP (− 0.74 [− 10.42; 8.93], n  = 2), and CTX-I (− 0.69 [− 9.61; 8.23], n  = 2), but with very wide confidence intervals. Three potentially relevant ongoing trials were identified via registries. No data were found for fracture healing or fracture outcomes. It remains unclear whether EX has an additive impact to PT in people with osteoporosis. High-quality, adequately powered, targetted RCTs are required. Protocol Registration PROSPERO CRD42022336132.

To measure intervertebral disc (IVD) health parameters in middle-aged long-term runners compared to matched non-physically active controls. Seventeen males aged 44-62yr were included in the study: 9 runners with a running history of >10yr, averaging >50km/week, and eight matched non-physically active controls, the data from one participant had to be excluded. T2-relaxometry, diffusion weighted imaging, T1- and T2-weighted MR scanning, as well as T2 time mapping were performed. Morphological data relating to IVD were extrapolated. Compared to controls on average, runners had 20% greater IVD height (p = 0.002) and seven percentage points greater IVD-vertebral body height ratio (p = 0.001). No significant differences were observed between groups for mean(SD) IVD hydration status, as indicated by similar T2-times (runners: 94.4(11.1)ms, controls: 88.6(23.6)ms), or apparent diffusion coefficients (runners: 249.0(175.2)mm2/s, controls: 202.3(149.5)mm2/s). Average Pfirrmann score for the L5-S1 IVD was 2.2(0.7) for runners and 3.3(1.0) for controls (p = 0.026), average scores for all lumbar levels (L2-S1) were 1.9(0.2) and 2.5(0.7), respectively (p = 0.036). Anterior annulus T2-time and overall average lumbar level Pfirrmann grades were strongly correlated (r = 0.787, p = 0.021 and r = -0.704, p = 0.034, respectively) with greater distances run per week. Average lumbar level Pfirrmann grades were also strongly correlated (r = -0.823, p = 0.006) to total years of running. Middle-aged long-term endurance runners exhibit less age-related decline in their lumbar IVDs. In addition, the measures of IVD morphology appeared to be better in those who had been running for a greater number of years, as well as in those who ran a greater distance per week.

Low back pain (LBP) is a leading cause of disability worldwide, with up to 25% of cases become chronic (cLBP). Whilst multi-factorial, the relative importance of contributors to cLBP remains unclear. We leveraged a comprehensive multi-dimensional data-set and machine learning-based variable importance selection to identify the most effective modalities for differentiating whether a person has cLBP. The dataset included questionnaire data, clinical and functional assessments, and spino-pelvic magnetic resonance imaging (MRI), encompassing a total of 144 parameters from 1,161 adults with (n = 512) and without cLBP (n = 649). Boruta and random forest were utilised for variable importance selection and cLBP classification respectively. A multimodal model including questionnaire, clinical, and MRI data was the most effective in differentiating people with and without cLBP. From this, the most robust variables (n = 9) were psychosocial factors, neck and hip mobility, as well as lower lumbar disc herniation and degeneration. This finding persisted in an unseen holdout dataset. Beyond demonstrating the importance of a multi-dimensional approach to cLBP, our findings will guide the development of targeted diagnostics and personalized treatment strategies for cLBP patients.

Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls (n = 14,927) from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables (loneliness/social isolation and depression) and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35–53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls.