Exercise prescription variables predict reductions in pain intensity in adults with chronic low back pain: secondary analysis of a randomised controlled trial.

Objectives: The relationship between exercise training variables and clinical outcomes in low back pain (LBP) is unclear. The current study aimed to explore the relationship between exercise training parameters and pain intensity in individuals with chronic LBP. Methods: This study is a secondary analysis of a previously reported randomised controlled trial comparing the effects of general strength and conditioning to motor control exercises and manual therapy. This secondary analysis includes adults with chronic LBP (n=20) randomised to the general strength and conditioning programme only. Primary outcomes of this analysis were exercise training parameters (time under tension, rating of perceived exertion (RPE), session duration, session-RPE and training frequency) and pain intensity (0-100 mm visual analogue acale) measured every 2 weeks from baseline to 6 months follow-up. Linear mixed models with random effects (participants) and allowance for heterogeneity of variance (study date) were used to determine the association between pain intensity and training parameters over time. Results: Mean (95% CI) pain intensity decreased over time from baseline to 6 months follow-up by 10.7 (2.8 to 18.7) points (p=0.008). Over the 6-month intervention, lower pain intensity was associated with higher RPE (ß (95% CI) -27.168 (-44.265 to -10.071), p=0.002), greater time under tension (-0.029 (-0.056 to -0.001), p=0.040) and shorter session duration (1.938 (0.011 to 3.865), p=0.049). Conclusion: During 6 months of general strength and conditioning, lower pain intensity was associated with higher participant-reported training intensity, greater volume and shorter session duration. To ensure positive outcomes to exercise training, these variables should be monitored on a short-term basis. Trial registration number: ACTRN12615001270505.

How to Interpret Effect Sizes for Biopsychosocial Outcomes and Implications for Current Research.

Biopsychosocial factors are associated with pain, but they can be difficult to compare. One way of comparing them is to use standardized mean differences. Previously, these effects sizes have been termed as small, medium, or large, if they are bigger than or equal to, respectively, .2, .5, or .8. These cut-offs are arbitrary and recent evidence showed that they need to be reconsidered. We argue it is necessary to determine cut-offs for each biopsychosocial factor. To achieve this, we propose 3 potential approaches: 1) examining, for each factor, how the effect size differs depending upon disease severity; 2) using an existing minimum clinically important difference to anchor the large effect size; and 3) define cut-offs by comparing data from people with and without pain. This is important for pain research, as exploring these methodologies has potential to improve comparability of biopsychosocial factors and lead to more directed treatments. We note assumptions and limitations of these methods that should also be considered. PERSPECTIVE: Standardized mean differences can estimate effect sizes between groups and could theoretically allow for comparison of biopsychosocial factors. However, common thresholds to define effect sizes are arbitrary and likely differ based on outcome. We propose methods that could overcome this and be used to derive biopsychosocial outcome-specific effect sizes.

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.

Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study.

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings.

Classifying Nonspecific Low Back Pain for Better Clinical Outcomes: Current Challenges and Paths Forward.

SYNOPSIS: Low back pain classification systems are structured assessments used to guide choices of more specific treatments. Classification systems examined in randomized controlled trials have limited effects on pain intensity and disability compared to nonclassified interventions. Potential reasons for the lack of efficacy include (1) failing to assess multidimensional factors that contribute to pain, (2) relying on clinician judgement, (3) low accessibility, and (4) poor classification reliability. Overcoming these limitations is critical to deciding whether classification systems can improve clinical practice. Only once these limitations are addressed, can we feel certain about the efficacy, or lack thereof, of classification systems. This Viewpoint guides the reader through some limitations of common classification approaches and presents a path forward to open-access, reliable, and multidimensional precision medicine for managing low back pain. J Orthop Sports Phys Ther 2023;53(5):1-5. Epub: 5 April 2023. doi:10.2519/jospt.2023.11658.

The Interaction Between Psychosocial Factors and Exercise-Induced Hypoalgesia in Pain-Free Nurses.

Purpose: This cross-sectional study aimed to investigate whether psychosocial factors were predictive for exercise-induced hypoalgesia (EIH) in pain-free adults. Methods: A sample of 38 pain-free nurses with a mean (SD) age of 26 (6) years were included in this study. Participants completed psychosocial questionnaires prior to physical tests. Pressure pain threshold (PPT) was assessed bilaterally at the calves (local), lower back (semi-local) and forearm (remote) before and immediately after a maximal graded cycling exercise test. Separate linear mixed effects models were used to determine change in PPT before and after cycling exercise (EIH). Multiple linear regression for all psychosocial variables and best subset regression was used to identify predictors of EIH at all locations. Results: The relative mean increase in PPT at the forearm, lumbar, calf, and globally (all sites pooled) was 6.0% (p<0.001), 10.1% (p<0.001), 13.9% (p<0.001), and 10.2% (p=0.013), respectively. Separate best subset multiple linear regression models at the forearm (predictors; Multidimensional Scale of Perceived Social Support (MSPSS) total), lumbar (predictors; MSPSS total, Pain Catastrophizing Scale (PCS) total, Depression Anxiety Stress Scale (DASS) depression), calf (predictors; MSPSS friends, PCS total), and global (predictors; MSPSS friends, PCS total) accounted for 7.5% (p=0.053), 13% (p=0.052), 24% (p=0.003), and 17% (p=0.015) of the variance, respectively. Conclusion: These findings confirm that cycling exercise produced EIH in young nurses and provided preliminary evidence to support the interaction between perceived social support, pain catastrophizing and EIH. Further investigation is required to better understand psychological and social factors that mediate EIH on a larger sample of adults at high risk of developing chronic musculoskeletal pain.

Assessing safety and treatment efficacy of running on intervertebral discs (ASTEROID) in adults with chronic low back pain: protocol for a randomised controlled trial.

Poor intervertebral disc (IVD) health is associated with low back pain (LBP). This 12-week parallel randomised controlled trial will evaluate the efficacy of a progressive interval running programme on IVD health and other clinical outcomes in adults with chronic LBP. Participants will be randomised to either a digitally delivered progressive interval running programme or waitlist control. Participants randomised to the running programme will receive three individually tailored 30 min community-based sessions per week over 12 weeks. The waitlist control will undergo no formal intervention. All participants will be assessed at baseline, 6 and 12 weeks. Primary outcomes are IVD health (lumbar IVD T2 via MRI), average LBP intensity over the prior week (100-point visual analogue scale) and disability (Oswestry Disability Index). Secondary outcomes include a range of clinical measures. All outcomes will be analysed using linear mixed models. This study has received ethical approval from the Deakin University Human Research Ethics Committee (ID: 2022-162). All participants will provide informed written consent before participation. Regardless of the results, the findings of this study will be disseminated, and anonymised data will be shared via an online repository. This will be the first study to evaluate whether a progressive interval running programme can improve IVD health in adults with chronic LBP. Identifying conservative options to improve IVD health in this susceptible population group has the potential to markedly reduce the burden of disease. This study was registered via the Australian New Zealand Clinical Trials Registry on 29 September 2022 (ACTRN12622001276741).

Effects of pharmacotherapy on sleep-related outcomes in adults with chronic low back pain: A systematic review and meta-analysis of randomised controlled trials.

Background: Adults with chronic low back pain (CLBP) suffer impaired sleep. Medications for CLBP can impact sleep which in turn may influence treatment outcomes. This systematic review and meta-analysis examined the effects of pharmacotherapy (any type) on sleep in adults with CLBP. Methods: In this systematic review and meta-analysis, we searched PubMed, CINAHL, SPORTDiscus, PsycINFO, EMBASE, and CENTRAL from inception to 10 July 2022. Randomised controlled trials that investigated the effects of pharmacotherapy on sleep in adults with CLBP were included. Manual citation search of relevant systematic reviews and included studies were also conducted. Mean change from baseline for sleep outcomes (e.g., sleep quality, total sleep time, wake after sleep onset) was the effect of interest. Pairwise inverse-variance random effect meta-analysis was performed to impute pooled estimates (Hedges' g or risk ratios). The Hartung-Knapp-Sidik-Jonkman method was used where there were ≤5 studies. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used for evaluating the certainty of evidence. This study was registered with PROSPERO (CRD42022309419). Findings: Assessment of 3959 records resulted in nine studies (n = 2927) being included. Pharmacotherapy for CLBP management had a small, yet unlikely clinically significant, effect on improving sleep in adults with CLBP, when compared to placebo (g [95% CI]: -0.23 [-0.37, -0.09], p = .0009; I 2  = 30.1%; n = 1433; studies: n = 8; GRADE: low). Notably, no eligible studies investigated the effect of sleep medications in this population, despite being within the scope of this review. Interpretation: Pharmacotherapy used to manage CLBP provided improvements in sleep in adults with CLBP. Given that these effects were small and unlikely clinically significant, clinicians could consider alternative treatments (e.g., non-pharmacological interventions) for managing sleep in adults with CLBP. However, low to very low certainty of evidence precluded strong conclusions. To improve certainty of evidence and confidence in the effect estimates, future research needs to use robust method to minimise bias. Additional research evaluating multiple sleep characteristics, using both validated objective and subjective measures, is also warranted to further investigate the influence of distinct sleep parameters. Funding: The Summer Research Scholarship from the Appleton Institute, Central Queensland University, Australia.

Interventions for promoting evidence-based guideline-consistent surgery in low back pain: a systematic review and meta-analysis of randomised controlled trials.

PURPOSE: Examine the effectiveness of interventions to approach guideline-adherent surgical referrals for low back pain assessed via systematic review and meta-analysis. METHODS: Five databases (10 September 2021), Google Scholar, reference lists of relevant systematic reviews were searched and forward and backward citation tracking of included studies were implemented. Randomised controlled/clinical trials in adults with low back pain of interventions to optimise surgery rates or referrals to surgery or secondary referral were included. Bias was assessed using the Cochrane ROB2 tool and evidence certainty via Grading of Recommendations Assessment, Development and Evaluation (GRADE). A random effects meta-analysis with a Paule Mandel estimator plus Hartung-Knapp-Sidik-Jonkman method was used to calculate the odds ratio and 95% confidence interval, respectively. RESULTS: Of 886 records, 6 studies were included (N = 258,329) participants; cluster sizes ranged from 4 to 54. Five studies were rated as low risk of bias and one as having some concerns. Two studies reporting spine surgery referral or rates could only be pooled via combination of p values and gave evidence for a reduction (p = 0.021, Fisher's method, risk of bias: low). This did not persist with sensitivity analysis (p = 0.053). For secondary referral, meta-analysis revealed a non-significant odds ratio of 1.07 (95% CI [0.55, 2.06], I2 = 73.0%, n = 4 studies, Grading of Recommendations Assessment, Development and Evaluation [GRADE] evidence certainty: very low). CONCLUSION: Few RCTs exist for interventions to improve guideline-adherent spine surgery rates or referral. Clinician education in isolation may not be effective. Future RCTs should consider organisational and/or policy level interventions. PROSPERO REGISTRATION: CRD42020215137.

Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning.

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.

Reducing Low-Value Imaging for Low Back Pain: Systematic Review With Meta-analysis.

OBJECTIVE: To examine the effectiveness of implementing interventions to improve guideline-recommended imaging referrals in low back pain. DESIGN: Systematic review with meta-analysis. LITERATURE SEARCH: We searched MEDLINE, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, Web of Science Core Collection, and the Cochrane Central Register of Controlled Trials from inception to June 14, 2021, as well as Google Scholar and reference lists of relevant systematic reviews published in the last 10 years. We conducted forward and backward citation tracking. STUDY SELECTION CRITERIA: Randomized controlled or clinical trials in adults with low back pain to improve imaging referrals. DATA SYNTHESIS: Bias was assessed using the Cochrane Risk of Bias 2 tool. Data were synthesized using narrative synthesis and random-effects meta-analysis (Hartung-Knapp-Sidik-Jonkman method). We assessed the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Of the 2719 identified records, 8 trials were included, with 6 studies eligible for meta-analysis (participants: N = 170 460). All trials incorporated clinician education; 4 included audit and/or feedback components. Comparators were no-intervention control and passive dissemination of guidelines. Five trials were rated as low risk of bias, and 2 trials were rated as having some concerns. There was low-certainty evidence that implementing interventions to improve guideline-recommended imaging referrals had no effect (odds ratio [95% confidence interval]: 0.87 [0.72, 1.05]; I2 = 0%; studies: n = 6). The main finding was robust to sensitivity analyses. CONCLUSION: We found low-certainty evidence that interventions to reduce imaging referrals or use in low back pain had no effect. Education interventions are unlikely to be effective. Organizational- and policy-level interventions are more likely to be effective. J Orthop Sports Phys Ther 2022;52(4):175-191. Epub 05 Feb 2022. doi:10.2519/jospt.2022.10731.

Clinician education unlikely effective for guideline-adherent medication prescription in low back pain: systematic review and meta-analysis of RCTs.

BACKGROUND: Effectiveness of implementing interventions to optimise guideline-recommended medical prescription in low back pain is not well established. METHODS: A systematic review and random-effects meta-analyses for dichotomous outcomes with a Paule-Mandel estimator. Five databases and reference lists were searched from inception to 4th August 2021. Randomised controlled/clinical trials in adults with low back pain to optimise medication prescription were included. Cochrane Risk of Bias 2 tool and GRADE were implemented. The review was registered prospectively with PROSPERO (CRD42020219767). FINDINGS: Of 3352 unique records identified in the search, seven studies were included and five were eligible for meta-analysis (N=11339 participants). Six of seven studies incorporated clinician education, three studies included audit/feedback components and one study implemented changes in medical records systems. Via meta-analysis, we estimated a non-significant odds-ratio of 0·94 (95% CI (0·77; 1.16), I² = 0%; n=5 studies, GRADE: low) in favour of the intervention group. The main finding was robust to sensitivity analyses. INTERPRETATION: There is low quality evidence that existing interventions to optimise medication prescription or usage in back pain had no impact. Peer-to-peer education alone does not appear to lead to behaviour change. Organisational and policy interventions may be more effective. FUNDING: This work was supported by internal institutional funding only.

Relative contributions of the nervous system, spinal tissue and psychosocial health to non-specific low back pain: Multivariate meta-analysis.

BACKGROUND AND OBJECTIVES: Nervous system, psychosocial and spinal tissue biomarkers are associated with non-specific low back pain (nsLBP), though relative contributions are unclear. DATABASES AND DATA TREATMENT: MEDLINE, EMBASE, CINAHL, PsycINFO and SPORTDiscus were searched up to 25 March 2020. Related reviews and reference lists were also screened. Observational studies examining structural and functional nervous system biomarkers (e.g. quantitative sensory tests, structural and functional brain measures), psychosocial factors (e.g. mental health, catastrophizing) and structural spinal imaging biomarkers (e.g. intervertebral disc degeneration, paraspinal muscle size) between nsLBP and pain-free controls were included. For multivariate meta-analysis, two of three domains were required in each study. Random-effects pairwise and multivariate meta-analyses were performed. GRADE approach assessed evidence certainty. Newcastle-Ottawa scale assessed risk of bias. Main outcomes were the effect size difference of domains between nsLBP and pain-free controls. RESULTS: Of 4519 unique records identified, 33 studies (LBP = 1552, referents = 1322) were meta-analysed. Psychosocial state (Hedges' g [95%CI]: 0.90 [0.69-1.10], p < 0.001) in nsLBP showed larger effect sizes than nervous system (0.31 [0.13-0.49], p < 0.001; difference: 0.61 [0.36-0.86], p < 0.001) and spine imaging biomarkers (0.55 [0.37-0.73], p < 0.001; difference: 0.36 [0.04-0.67], p = 0.027). The relationship between domains changes depending on if pain duration is acute or chronic. CONCLUSIONS: Psychosocial effect sizes in nsLBP are greater than those for spinal imaging and nervous system biomarkers. Limitations include cross-sectional design of studies included and inference of causality. Future research should investigate the clinical relevance of these effect size differences in relation to pain intensity and disability. STUDY REGISTRATION: PROSPERO-CRD42020159188. SIGNIFICANCE: Spinal structural lesions (e.g. intervertebral disc degeneration), psychosocial (e.g. depression) and nervous system factors (detected by e.g. quantitative sensory tests, structural and functional measures) contribute to non-specific low back pain. However, psychosocial factors may be more compromised than nervous system and spinal imaging biomarkers. This relationship depends on if the pain is acute or chronic. These findings underscore that the 'non-specific' label in back pain should be reconsidered, and more specific multidimensional categories evaluated to guide patient management.

Brain structure, psychosocial, and physical health in acute and chronic back pain: a UK Biobank study.

ABSTRACT: Brain structure, psychosocial, and physical factors underpin back pain conditions; however, less is known about how these factors differ based on pain duration and location. We examined, cross-sectionally, 11,106 individuals from the UK Biobank who (1) were pain-free (n = 5616), (2) had acute back pain (n = 1746), (3) had chronic localised back pain (CBP; n = 1872), or (4) had chronic back pain and additional chronic pain sites (CWP; n = 1872). We found differences in structural brain measures in the chronic pain groups alone. Both CBP and CWP groups had lower primary somatosensory cortex {CBP mean difference (MD) (95% confidence interval [CI]): -250 (-393, -107) mm3, P < 0.001; CWP: -170 (-313, -27)mm3, P = 0.011} and higher caudate gray matter volumes (CBP: 127 [38,216]mm3, P = 0.001; CWP: 122 [33,210]mm3, P = 0.002) compared with pain-free controls. The CBP group also had a lower primary motor cortex volume (-215 [-382, -50]mm3, P = 0.005), whereas the CWP group had a lower amygdala gray matter volume (-27 [-52, -3]mm3, P = 0.021) compared with pain-free controls. Differences in gray matter volumes in some regions may be moderated by sex and body mass index. Psychosocial factors and body mass index differed between all groups and affected those with widespread pain the most (all, P < 0.001), whereas grip strength was only compromised in individuals with widespread pain (-1.0 [-1.4, -0.5] kg, P < 0.001) compared with pain-free controls. Longitudinal research is necessary to confirm these interactions to determine the process of pain development in relation to assessed variables and covariates. However, our results suggest that categorised pain duration and the number of pain sites warrant consideration when assessing markers of brain structure, psychosocial, and physical health.

Classification Approaches for Treating Low Back Pain Have Small Effects That Are Not Clinically Meaningful: A Systematic Review With Meta-analysis.

OBJECTIVE: To determine whether classification systems improve patient-reported outcomes for people with low back pain (LBP). DESIGN: Systematic review with meta-analysis. LITERATURE SEARCH: The MEDLINE, Embase, CINAHL, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials databases were searched from inception to June 21, 2021. Reference lists of prior systematic reviews and included trials were screened. STUDY SELECTION CRITERIA: We included randomized trials comparing a classification system (eg, the McKenzie method or the STarT Back Tool) to any comparator. Studies evaluating participants with specific spinal conditions (eg, fractures or tumors) were excluded. DATA SYNTHESIS: Outcomes were patient-reported LBP intensity, leg pain intensity, and disability. We used the revised Cochrane Collaboration Risk of Bias Tool to assess risk of bias, and the Grading of Recommendations Assessment, Development and Evaluation approach to judge the certainty of evidence. We used random-effects meta-analysis, with the Hartung-Knapp-Sidik- Jonkman adjustment, to estimate the standardized mean difference (SMD; Hedges' g) and 95% confidence interval (CI). Subgroup analyses explored classification system, comparator type, pain type, and pain duration. RESULTS: Twenty-four trials assessing classification systems and 34 assessing subclasses were included. There was low certainty of a small effect at the end of intervention for LBP intensity (SMD, -0.31; 95% CI: -0.54, -0.07; P = .014, n = 4416, n = 21 trials) and disability (SMD, -0.27; 95% CI: -0.46, -0.07; P = .011, n = 4809, n = 24 trials), favoring classified treatments compared to generalized interventions, but not for leg pain intensity. At the end of intervention, no specific type of classification system was superior to generalized interventions for improving pain intensity and disability. None of the estimates exceeded the effect size that one would consider clinically meaningful. CONCLUSION: For patient-reported pain intensity and disability, there is insufficient evidence supporting the use of classification systems over generalized interventions when managing LBP. J Orthop Sports Phys Ther 2022;52(2):67-84. Epub 15 Nov 2021. doi:10.2519/jospt.2022.10761.

Effects of Exercise Training on Fear-Avoidance in Pain and Pain-Free Populations: Systematic Review and Meta-analysis.

BACKGROUND: Fear of pain and movement is an important factor in the development of hypervigilance and avoidance behaviours. OBJECTIVE: We examined the effectiveness of exercise training on improving fear-avoidance beliefs. METHODS: A systematic review (data sources: MEDLINE, CINAHL, SPORTDiscus, EMBASE, CENTRAL) and metaanalysis of randomised controlled/clinical trials of exercise training in adults versus relevant nonexercise comparators that quantified fear-avoidance was conducted. RESULTS: After screening 4603 identified records, 17 (2014 participants) and 13 (1152 participants) studies were eligible for qualitative and quantitative synthesis, respectively. Pairwise meta-analysis showed exercise training was more effective than all non-exercise comparators (standardised mean difference (SMD) [95% CI] - 0.378 [- 0.623, - 0.133], P = 0.002, Grading of Recommendations Assessment, Development and Evaluation [GRADE]: very low) for reducing fear-avoidance. Exercise training was more effective than true control for reducing fear avoidance (- 0.407 [- 0.750, - 0.065], P = 0.020, GRADE: very low), however it was not more effective than other interventions (- 0.243 [- 0.614, 0.128], P = 0.199, GRADE: very low). In people with low back pain, exercise training was more effective than non-exercise comparator groups for reducing fear-avoidance (- 0.530 [- 0.755, - 0.304], P < 0.001, GRADE: very low). For individuals with neck pain, exercise training was not more effective than non-exercise comparator groups for reducing fear-avoidance (0.061 [- 0.360, 0.482], P = 0.777, GRADE: very low). CONCLUSION: There is very low to low-quality evidence that exercise training is effective for reducing fear-avoidance, including in people with low back pain. Exercise training may be more effective than no intervention for reducing fear avoidance, but there is very low-quality evidence that non-exercise interventions are as effective as exercise for fear avoidance. Few studies with low risk of bias is a limitation. TRAIL REGISTRATION: PROSPERO Registration Number: CRD42019139678.

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.

Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test-retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

Randomized Trial of General Strength and Conditioning Versus Motor Control and Manual Therapy for Chronic Low Back Pain on Physical and Self-Report Outcomes.

Exercise and spinal manipulative therapy are commonly used for the treatment of chronic low back pain (CLBP) in Australia. Reduction in pain intensity is a common outcome; however, it is only one measure of intervention efficacy in clinical practice. Therefore, we evaluated the effectiveness of two common clinical interventions on physical and self-report measures in CLBP. Participants were randomized to a 6­month intervention of general strength and conditioning (GSC; n = 20; up to 52 sessions) or motor control exercise plus manual therapy (MCMT; n =20; up to 12 sessions). Pain intensity was measured at baseline and fortnightly throughout the intervention. Trunk extension and flexion endurance, leg muscle strength and endurance, paraspinal muscle volume, cardio­respiratory fitness and self-report measures of kinesiophobia, disability and quality of life were assessed at baseline and 3- and 6-month follow-up. Pain intensity differed favoring MCMT between-groups at week 14 and 16 of treatment (both, p = 0.003), but not at 6-month follow­up. Both GSC (mean change (95%CI): -10.7 (-18.7, -2.8) mm; p = 0.008) and MCMT (-19.2 (-28.1, -10.3) mm; p < 0.001) had within-group reductions in pain intensity at six months, but did not achieve clinically meaningful thresholds (20mm) within- or between­group. At 6-month follow-up, GSC increased trunk extension (mean difference (95% CI): 81.8 (34.8, 128.8) s; p = 0.004) and flexion endurance (51.5 (20.5, 82.6) s; p = 0.004), as well as leg muscle strength (24.7 (3.4, 46.0) kg; p = 0.001) and endurance (9.1 (1.7, 16.4) reps; p = 0.015) compared to MCMT. GSC reduced disability (-5.7 (­11.2, -0.2) pts; p = 0.041) and kinesiophobia (-6.6 (-9.9, -3.2) pts; p < 0.001) compared to MCMT at 6­month follow-up. Multifidus volume increased within-group for GSC (p = 0.003), but not MCMT or between-groups. No other between-group changes were observed at six months. Overall, GSC improved trunk endurance, leg muscle strength and endurance, self-report disability and kinesiophobia compared to MCMT at six months. These results show that GSC may provide a more diverse range of treatment effects compared to MCMT.