Background Guidelines recommend exercise as a core treatment for osteoarthritis (OA). However, it is unclear which type of exercise is most effective, leading to inconsistency between different recommendations. Objectives The aim of this systematic review and network meta-analysis was to investigate the relative efficacy of different exercises (aerobic, mind–body, strengthening, flexibility/skill, or mixed) for improving pain, function, performance and quality of life (QoL) for knee and hip OA at, or nearest to, 8 weeks. Methods We searched nine electronic databases up until December 2017 for randomised controlled trials that compared exercise with usual care or with another exercise type. Bayesian network meta-analysis was used to estimate the relative effect size (ES) and corresponding 95% credibility interval (CrI) (PROSPERO registration: CRD42016033865). Findings We identified and analysed 103 trials (9134 participants). Aerobic exercise was most beneficial for pain (ES 1.11; 95% CrI 0.69, 1.54) and performance (1.05; 0.63, 1.48). Mind–body exercise, which had pain benefit equivalent to that of aerobic exercise (1.11; 0.63, 1.59), was the best for function (0.81; 0.27, 1.36). Strengthening and flexibility/skill exercises improved multiple outcomes at a moderate level. Mixed exercise was the least effective for all outcomes and had significantly less pain relief than aerobic and mind–body exercises. The trend was significant for pain (p = 0.01), but not for function (p = 0.07), performance (p = 0.06) or QoL (p = 0.65). Conclusion The effect of exercise varies according to the type of exercise and target outcome. Aerobic or mind–body exercise may be the best for pain and function improvements. Strengthening and flexibility/skill exercises may be used for multiple outcomes. Mixed exercise is the least effective and the reason for this merits further investigation.

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3‐dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state‐of‐the‐art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.

Fibromyalgia is a highly heterogeneous condition, but the most common symptoms are widespread pain, fatigue, poor sleep, and low mood. Nonpharmacological interventions are recommended as first-line treatment of fibromyalgia. However which interventions are effective for the different symptoms is not well understood. The objective of this study was to assess the efficacy of nonpharmacological interventions on symptoms and disease-specific quality of life. Seven databases were searched from their inception until June 1, 2020. Randomised controlled trials comparing any nonpharmacological intervention to usual care, waiting list, or placebo in people with fibromyalgia aged >16 years were included without language restriction. Fibromyalgia Impact Questionnaire (FIQ) was the primary outcome measure. Standardised mean difference and 95% confidence interval were calculated using random effects model. The risk of bias was evaluated using the modified Cochrane tool. Of the 16,251 studies identified, 167 randomised controlled trials (n = 11,012) assessing 22 nonpharmacological interventions were included. Exercise, psychological treatments, multidisciplinary modality, balneotherapy, and massage improved FIQ. Subgroup analysis of different exercise interventions found that all forms of exercise improved pain (effect size [ES] −0.72 to −0.96) and depression (ES −0.35 to −1.22) except for flexibility exercise. Mind–body and strengthening exercises improved fatigue (ES −0.77 to −1.00), whereas aerobic and strengthening exercises improved sleep (ES −0.74 to −1.33). Psychological treatments including cognitive behavioural therapy and mindfulness improved FIQ, pain, sleep, and depression (ES −0.35 to −0.55) but not fatigue. The findings of this study suggest that nonpharmacological interventions for fibromyalgia should be individualised according to the predominant symptom.

Objective Clinical guidelines recommend exercise as a core treatment for knee or hip osteoarthritis (OA). However, how its analgesic effect compares to analgesics, for example, oral non-steroidal anti-inflammatory drugs (NSAIDs) and paracetamol—the most commonly used analgesics for OA, remains unknown. Design Network meta-analysis. Data sources PubMed, Embase, Scopus, Cochrane Library and Web of Science from database inception to January 2022. Eligibility criteria for selecting studies Randomised controlled trials (RCTs) comparing exercise therapy with oral NSAIDs and paracetamol directly or indirectly in knee or hip OA. Results A total of n=152 RCTs (17 431 participants) were included. For pain relief, there was no difference between exercise and oral NSAIDs and paracetamol at or nearest to 4 (standardised mean difference (SMD)=−0.12, 95% credibility interval (CrI) −1.74 to 1.50; n=47 RCTs), 8 (SMD=0.22, 95% CrI −0.05 to 0.49; n=2 RCTs) and 24 weeks (SMD=0.17, 95% CrI −0.77 to 1.12; n=9 RCTs). Similarly, there was no difference between exercise and oral NSAIDs and paracetamol in functional improvement at or nearest to 4 (SMD=0.09, 95% CrI −1.69 to 1.85; n=40 RCTs), 8 (SMD=0.06, 95% CrI −0.20 to 0.33; n=2 RCTs) and 24 weeks (SMD=0.05, 95% CrI −1.15 to 1.24; n=9 RCTs). Conclusions Exercise has similar effects on pain and function to that of oral NSAIDs and paracetamol. Given its excellent safety profile, exercise should be given more prominence in clinical care, especially in older people with comorbidity or at higher risk of adverse events related to NSAIDs and paracetamol. CRD42019135166

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.