Autonomy, Resilience and Life Satisfaction among Badminton Paralympians.

Background: People with disabilities are marginalised in mainstream culture and they also experience increased restrictions in a variety of areas, such as sport. The barriers they encounter may adversely affect their life satisfaction, especially if they have a low perceived sense of autonomy and resilience. The purpose of this study was to investigate the relationship between autonomy, resilience and life satisfaction in para-badminton athletes and the mediating role of resilience in the relationship between autonomy and life satisfaction. Methods: Data were collected from 137 para-badminton athletes (male: 65.0% and female: 35.0%). Self-reported measures were used to assess the participants' autonomy, resilience and life satisfaction levels. Results: A structural equation model analysis was performed; the model had sufficient fit indices (comparative fit index [CFI] = 0.94, root mean square error of approximation [RMSEA] = 0.06, standardised root mean square residual [SRMR] = 0.07). The findings showed that autonomy had a significant effect on resilience (ß = 0.32, P = 0.001). Further, resilience had a significant effect on life satisfaction (ß = 0.19, P = 0.011) and significantly mediated the effect of autonomy on life satisfaction (ß = 021, P = 0.033). Conclusion: This study revealed that autonomy fosters resilience among athletes, in turn allowing them to achieve greater life satisfaction. Therefore, society and sport communities should actively seek ways to improve the autonomy and resilience levels of athletes with disabilities.

Perceptions of stigma associated with chronic knee pain: voices of selected women in Thailand and Malaysia.

INTRODUCTION: A higher prevalence of knee pain in Southeast Asian countries, compared with non-Asian countries, is an established fact. This article hypothesizes that this fact, combined with personal, cultural, and environmental factors, may influence attitudes toward illness and treatment-seeking behavior and adherence. OBJECTIVE: This study aimed to determine current attitudes, stigma, and barriers of women to the management of chronic knee pain and treatment in two Southeast Asian countries. METHODS: Fourteen semi-structured interviews explored female lived perceptions of chronic knee pain in Southeast Asia. Using a phenomenological reduction process, open-ended questions allowed participants to voice their perceptions of their experience of this knee condition. Particular foci were potential stigma associated with the perceptions of others, health-seeking attitudes, and attitudes toward exercise. RESULTS: The shared experiences of managing chronic knee pain revealed the impact of their condition on participants' normality of life and their struggles with pain, limitations, and fear for the future. Key individual, interpersonal, organizational and community barriers and facilitators impacted the health seeking attitudes and engagement with conservative rehabilitation programmes. CONCLUSION: Improved socio-cultural competency and consideration for an individuals' intersectional identity and interpersonal relationships are key to designing rehabilitation and conservative management solutions. Co-creating alternative pathways for rehabilitation for individuals that are more distant from health facilities may help reduce socio-cultural barriers at a community level.

A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects.

BACKGROUND: Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. METHODS: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. FINDINGS: Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. INTERPRETATIONS: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.

Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative.

BACKGROUND AND OBJECTIVE: Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography. METHODS: We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers. RESULTS: Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction. CONCLUSIONS: The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.

Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative.

Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions.

Comparative efficacy of exercise therapy and oral non-steroidal anti-inflammatory drugs and paracetamol for knee or hip osteoarthritis: a network meta-analysis of randomised controlled trials.

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.

Test-retest reliability of the single leg stance on a Lafayette stability platform.

The validity and reliability of the Lafayette stability platform are well-established for double leg testing. However, no evaluation of single leg (SL) stance on the platform was discovered yet. Therefore, this study aimed to investigate the reliability of conducting the SL stance on the Lafayette platform. Thirty-six healthy and active university students (age 23.2 ± 3.2 years; BMI 21.1 ± 3.1 kg/m2) were tested twice, one week apart (week 1; W1, week 2; W2). They stood on their dominant leg with eyes-open (EO) and eyes-closed (EC) in random order. Three successful trials of 20 seconds each were recorded. The duration during which the platform was maintained within 0° of tilt was referred to as time in balance (TIB). At all-time points, TIB was consistently longer in EO (EOW1: 17.02 ± 1.04s; EOW2: 17.32 ± 1.03s) compared to EC (ECW1: 11.55 ± 1.73s; ECW2: 13.08 ± 1.82s). A ±10 seconds difference was demonstrated in the Bland-Altman analysis in both EO and EC. Lower standard error of measurement (SEM) and coefficient of variation (CV) indicated consistent output. High intraclass correlation coefficient (ICC) values were seen between weeks (EO = 0.74; EC = 0.76) and within weeks (EOW1 = 0.79; EOW2 = 0.86; ECW1 = 0.71; ECW2 = 0.71). Although statistical measures (i.e., SEM, CV, and ICC) indicated good reliability of Lafayette for SL tasks, the wide agreement interval is yet to be clinically meaningful. Factors underlying the wide variation need to be identified before Lafayette is used for TIB assessment.

Influence and implications of the renin-angiotensin-aldosterone system in obstructive sleep apnea: An updated systematic review and meta-analysis.

Obstructive sleep apnea is a chronic, sleep-related breathing disorder, which is an independent risk factor for cardiovascular disease. The renin-angiotensin-aldosterone system regulates salt and water homeostasis, blood pressure, and cardiovascular remodelling. Elevated aldosterone levels are associated with excess morbidity and mortality. We aimed to analyse the influence and implications of renin-angiotensin-aldosterone system derangement in individuals with and without obstructive sleep apnea. We pooled data from 20 relevant studies involving 2828 participants (1554 with obstructive sleep apnea, 1274 without obstructive sleep apnea). The study outcomes were the levels of renin-angiotensin-aldosterone system hormones, blood pressure and heart rate. Patients with obstructive sleep apnea had higher levels of plasma renin activity (pooled wmd+ 0.25 [95% confidence interval 0.04-0.46], p = 0.0219), plasma aldosterone (pooled wmd+ 30.79 [95% confidence interval 1.05-60.53], p = 0.0424), angiotensin II (pooled wmd+ 5.19 [95% confidence interval 3.11-7.27], p < 0.001), systolic (pooled wmd+ 5.87 [95% confidence interval 1.42-10.32], p = 0.0098) and diastolic (pooled wmd+ 3.40 [95% confidence interval 0.86-5.94], p = 0.0086) blood pressure, and heart rate (pooled wmd+ 3.83 [95% confidence interval 1.57-6.01], p = 0.0009) compared with those without obstructive sleep apnea. The elevation remained significant (except for renin levels) when studies involving patients with resistant hypertension were removed. Sub-group analysis demonstrated that levels of angiotensin II were significantly higher only among the Asian population with obstructive sleep apnea compared with those without obstructive sleep apnea. Body mass index accounted for less than 10% of the between-study variance in elevation of the renin-angiotensin-aldosterone system parameters. Patients with obstructive sleep apnea have higher levels of renin-angiotensin-aldosterone system hormones, blood pressure and heart rate compared with those without obstructive sleep apnea, which remains significant even among patients without resistant hypertension.

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.

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.

Efficacy of nonpharmacological interventions for individual features of fibromyalgia: a systematic review and meta-analysis of randomised controlled trials.

ABSTRACT: 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.

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis.

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.

Transversus Abdominis Plane Block Versus Intraperitoneal Local Anesthetics in Bariatric Surgery: A Systematic Review and Network Meta-analysis.

BACKGROUND: Transversus abdominis plane (TAP) block and intraperitoneal local anesthetics (IPLA) are widely investigated techniques that potentially improve analgesia after bariatric surgery. The analgesic efficacy of TAP block has been shown in previous studies, but the performance of TAP block can be difficult in patients with obesity. We performed a systematic review and meta-analysis to compare the analgesic efficacy of TAP block and IPLA. An alternative technique is useful in clinical setting when TAP block is not feasible. METHODS: We searched PubMed, Embase, and CENTRAL from inception until August 2020 for randomized controlled trials comparing both techniques. The primary outcome was cumulative morphine consumption at 24 h. Secondary pain-related outcomes included pain score at rest and on movement at 2, 6, 12, and 24 h; postoperative nausea and vomiting; and length of hospital stay. RESULTS: We included 23 studies with a total of 2,178 patients. TAP block is superior to control in reducing opioid consumption at 24 h, improving pain scores at all the time points and postoperative nausea and vomiting. The cumulative opioid consumption at 24 h for IPLA is less than control, while the indirect comparison between IPLA with PSI and control showed a significant reduction in pain scores at rest, at 2 h, and on movement at 12 h, and 24 h postoperatively. CONCLUSIONS: Transversus abdominis plane block is effective for reducing pain intensity and has superior opioid-sparing effect compared to control. Current evidence is insufficient to show an equivalent analgesic benefit of IPLA to TAP block.

Efficacy of prolotherapy in comparison to other therapies for chronic soft tissue injuries: A systematic review and network meta-analysis.

INTRODUCTION: Prolotherapy and other injections, primarily acting on pathways associated with maladaptive tissue repair, are recommended for recalcitrant chronic soft tissue injuries (CSTI). However, selection of injection is challenging due to mixed results. This network meta-analysis (NMA) aimed to compare prolotherapy with other therapies, particularly injections, for CSTI and establish robustness of the results. METHODOLOGY: Pubmed, Medline, SPORTDiscus and Google scholar were searched from inception to 4th January 2021 for randomised controlled trials (RCTs) involving injection therapies (e.g. blood derivatives, corticosteroid, hyaluronic acid, botulinum toxin) for CSTI. The primary and secondary outcomes were pain and function, respectively, at (or nearest to) 6 months. Effect size (ES) was presented as standardised mean difference with 95% confidence interval (CI). Frequentist random effect NMA was used to generate the overall estimates, subgroup estimates (by region and measurement time point) and sensitivity analyses. RESULTS: A total of 91 articles (87 RCTs; 5859 participants) involving upper limb (74%), lower limb (23%) and truncal/hip (3%) injuries were included. At all time points, prolotherapy had no statistically significant pain benefits over other therapies. This observation remained unchanged when tested under various assumptions and with exclusion of studies with high risk of bias. Although prolotherapy did not offer statistically significant functional improvement compared to most therapies, its ES was consistently better than non-injections and corticosteroid injection for both outcomes. At selected time points and for selected injuries, prolotherapy demonstrated potentially better pain improvement over placebo (<4 months: shoulder [ES 0.65; 95% CI 0.00 to 1.30]; 4-8 months: elbow [ES 0.91; 95% CI 0.12 to 1.70]; >8 months: shoulder [ES 2.08; 95% CI 1.49, to 2.68]). Injections generally produced greater ES when combined with non-injection therapy. CONCLUSION: While clinical outcomes were generally comparable across types of injection therapy, prolotherapy may be used preferentially for selected conditions at selected times.

Efficacy and potential determinants of exercise therapy in knee and hip osteoarthritis: A systematic review and meta-analysis.

BACKGROUND: Exercise is an effective treatment for osteoarthritis. However, the effect may vary from one patient (or study) to another. OBJECTIVE: To evaluate the efficacy of exercise and its potential determinants for pain, function, performance, and quality of life (QoL) in knee and hip osteoarthritis (OA). METHODS: We searched 9 electronic databases (AMED, CENTRAL, CINAHL, EMBASE, MEDLINE Ovid, PEDro, PubMed, SPORTDiscus and Google Scholar) for reports of randomised controlled trials (RCTs) comparing exercise-only interventions with usual care. The search was performed from inception up to December 2017 with no language restriction. The effect size (ES), with its 95% confidence interval (CI), was calculated on the basis of between-group standardised mean differences. The primary endpoint was at or nearest to 8 weeks. Other outcome time points were grouped into intervals, from<1 month to≥18 months, for time-dependent effects analysis. Potential determinants were explored by subgroup analyses. Level of significance was set at P≤0.10. RESULTS: Data from 77 RCTs (6472 participants) confirmed statistically significant exercise benefits for pain (ES 0.56, 95% CI 0.44-0.68), function (0.50, 0.38-0.63), performance (0.46, 0.35-0.57), and QoL (0.21, 0.11-0.31) at or nearest to 8 weeks. Across all outcomes, the effects appeared to peak around 2 months and then gradually decreased and became no better than usual care after 9 months. Better pain relief was reported by trials investigating participants who were younger (mean age<60 years), had knee OA, and were not awaiting joint replacement surgery. CONCLUSIONS: Exercise significantly reduces pain and improves function, performance and QoL in people with knee and hip OA as compared with usual care at 8 weeks. The effects are maximal around 2 months and thereafter slowly diminish, being no better than usual care at 9 to 18 months. Participants with younger age, knee OA and not awaiting joint replacement may benefit more from exercise therapy. These potential determinants, identified by study-level analyses, may have implied ecological bias and need to be confirmed with individual patient data.

Relative Efficacy of Different Exercises for Pain, Function, Performance and Quality of Life in Knee and Hip Osteoarthritis: Systematic Review and Network Meta-Analysis.

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.

Knee cartilage segmentation and thickness computation from ultrasound images.

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.

Biomechanical and functional efficacy of knee sleeves: A literature review.

BACKGROUND: Knee sleeves are widely used for the symptomatic relief and subjective improvements of knee problems. To date, however, their biomechanical effects have not been well understood. OBJECTIVE: To determine whether knee sleeves can significantly improve the biomechanical variables for knee problems. METHOD: Systematic literature search was conducted on four online databases - PubMed, Web of Science, ScienceDirect and Springer Link - to find peer-reviewed and relevant scientific papers on knee sleeves published from January 2005 to January 2015. Study quality was assessed using the Structured Effectiveness Quality Evaluation Scale (SEQES). RESULTS: Twenty studies on knee sleeves usage identified from the search were included in the review because of their heterogeneous scope of coverage. Twelve studies found significant improvement in gait parameters (3) and functional parameters (9), while eight studies did not find any significant effects of knee sleeves usage. CONCLUSION: Most improvements were observed in: proprioception for healthy knees, gait and balance for osteoarthritic knees, and functional improvement of injured knees. This review suggests that knee sleeves can effect functional improvements to knee problems. However, further work is needed to confirm this hypothesis, due to the lack of homogeneity and rigor of existing studies.

Relative efficacy of topical non-steroidal anti-inflammatory drugs and topical capsaicin in osteoarthritis: protocol for an individual patient data meta-analysis.

BACKGROUND: Pain is the most troubling issue to patients with osteoarthritis (OA), yet current pharmacological treatments offer only small-to-moderate pain reduction. Current guidelines therefore emphasise the need to identify predictors of treatment response. In line with these recommendations, an individual patient data (IPD) meta-analysis will be conducted. The study aims to investigate the relative treatment effects of topical non-steroidal anti-inflammatory drugs (NSAIDs) and topical capsaicin in OA and to identify patient-level predictors of treatment response. METHODS: IPD will be collected from randomised controlled trials (RCTs) of topical NSAIDs and capsaicin in OA. Multilevel regression modelling will be conducted to determine predictors for the specific and the overall treatment effect. DISCUSSION: Through the identification of treatment responders, this IPD meta-analysis may improve the current understanding of the pain mechanisms in OA and guide clinical decision-making. Identifying and prescribing the treatment most likely to be beneficial for an individual with OA will improve the efficiency of patient management. SYSTEMATIC REVIEW REGISTRATION: CRD42016035254.

Identifying placebo responders and predictors of response in osteoarthritis: a protocol for individual patient data meta-analysis.

BACKGROUND: The management of osteoarthritis (OA) is unsatisfactory, as most treatments are not clinically effective over placebo and most drugs have considerable side effects. On average, 75 % of the analgesic effect from OA treatments in clinical trials can be attributed to a placebo response, and this response varies greatly from patient to patient. This individual patient data (IPD) meta-analysis aims to identify placebo responders and the potential determinants of the placebo response in OA. METHODS: This study is undertaken in conjunction with the OA Trial Bank, an ongoing international consortium aiming to collect IPD from randomised controlled trials (RCTs) for all treatments of OA. RCTs for each treatment of OA have been systematically searched for, and authors of the relevant trials have been contacted to request the IPD. We will use the IPD of placebo-controlled RCTs held by the OA Trial Bank for this project. The IPD in placebo groups will be used to investigate the placebo response according to the minimum clinically important difference (MCID) threshold (e.g. 20 % pain reduction). Responders to placebo will be compared with non-responders to identify predictors of response. The quality of the trials will be assessed and potential determinants will be examined using multilevel logistic regression analyses. DISCUSSION: This study explores the varying magnitude of the placebo response and the proportion of participants that experience a clinically important placebo effect in OA RCTs. Potential determinants of the placebo response will also be investigated. These determinants may be useful for future studies as it may allow participants to be stratified into groups based on their likely response to placebo. The results of this study may also be useful for pharmaceutical companies, who could improve the design of their studies in order to separate the specific treatment from the non-specific contextual (i.e. placebo) effects. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016033212.

Relative efficacy of different types of exercise for treatment of knee and hip osteoarthritis: protocol for network meta-analysis of randomised controlled trials.

BACKGROUND: 'Exercise' is universally recommended as a core treatment for knee and hip osteoarthritis (OA). However, there are very few head-to-head comparative trials to determine the relative efficacy between different types of exercise. The aim of this study is to benchmark different types of exercises against each other through the use of a common comparator in a network meta-analysis of randomised controlled trials (RCTs). METHODS: This study will include only RCTs published in peer-reviewed journals. A systematic search will be conducted in several electronic databases and other relevant online resources. No limitations are imposed on language or publication date. Participants must be explicitly identified by authors as having OA. Interventions that involved exercise or comparators in any form will be included. Pain is the primary outcome of interest; secondary outcomes will include function and quality of life measures. Quality assessment of studies will be based on the modified Cochrane's risk of bias assessment tool. At least two investigators will be involved throughout all stages of screening and data acquisition. Conflicts will be resolved through discussion. Conventional meta-analysis will be performed based on random effects model and network meta-analysis on a Bayesian model. Subgroup analysis will also be conducted based on study, patient and disease characteristics. DISCUSSION: This study will provide for the first time comprehensive research evidence for the relative efficacy of different exercise regimens for treatment of OA. We will use network meta-analysis of existing RCT data to answer this question. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016033865.