Objective Examine the effectiveness of specific modes of exercise training in non-specific chronic low back pain (NSCLBP). Design Network meta-analysis (NMA). Data sources MEDLINE, CINAHL, SPORTDiscus, EMBASE, CENTRAL. Eligibility criteria Exercise training randomised controlled/clinical trials in adults with NSCLBP. Results Among 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/conclusion There 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.

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.

Osteoarthritis is one of the leading causes of pain, disability, and health care resource use worldwide.¹ While the disease can affect any joint, the hip is among the most common sites.^2,3 There is a growing concern that the incidence, in part owing to the aging population, and subsequent financial burden at both the individual and societal level will continue to increase.^1,3