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Background Low back pain (LBP) is a significant health problem worldwide, with a lifetime prevalence of 84% in the general adult population. To rationalise the management of LBP, clinical practice guidelines (CPGs) have been issued in various countries around the world. This study aims to identify and compare the recommendations of recent CPGs for the management of LBP across the world. Methods MEDLINE, EMBASE, CINAHL, PEDro, and major guideline databases were searched from 2017 to 2022 to identify CPGs. CPGs focusing on information regarding the management and/or treatment of non-specific LBP were considered eligible. The quality of included guidelines was evaluated using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. Results Our analysis identified a total of 22 CPGs that met the inclusion criteria, and were of middle and high methodological quality as assessed by the AGREE II tool. The guidelines exhibited heterogeneity in their recommendations, particularly in the approach to different stages of LBP. For acute LBP, the guidelines recommended the use of non-steroidal anti-inflammatory drugs (NSAIDs), therapeutic exercise, staying active, and spinal manipulation. For subacute LBP, the guidelines recommended the use of NSAIDs, therapeutic exercise, staying active, and spinal manipulation. For chronic LBP, the guidelines recommended therapeutic exercise, the use of NSAIDs, spinal manipulation, and acupuncture. Conclusions Current CPGs provide recommendations for almost all major aspects of the management of LBP, but there is marked heterogeneity between them. Some recommendations lack clarity and overlap with other treatments within the guidelines.

The aim of this review was to understand the use of wearable technology in sport in order to enhance performance and prevent injury. Understanding sports biomechanics is important for injury prevention and performance enhancement and is traditionally assessed using optical motion capture. However, such approaches are limited by capture volume restricting assessment to a laboratory environment, a factor that can be overcome by wearable technology. A systematic search was carried out across seven databases where wearable technology was employed to assess kinetic and kinematic variables in sport. Articles were excluded if they focused on sensor design and did not measure kinetic or kinematic variables or apply the technology on targeted participants. A total of 33 articles were included for full-text analysis where participants took part in a sport and performed dynamic movements relating to performance monitored by wearable technologies. Inertial measurement units, flex sensors and magnetic field and angular rate sensors were among the devices used in over 15 sports to quantify motion. Wearable technology usage is still in an exploratory phase, but there is potential for this technology to positively influence coaching practice and athletes’ technique.

Continuous monitoring of spine movement function could enhance our understanding of low back pain development. Wearable technologies have gained popularity as promising alternative to laboratory systems in allowing ambulatory movement analysis. This paper aims to review the state of art of current use of wearable technology to assess spine kinematics and kinetics. Four electronic databases and reference lists of relevant articles were searched to find studies employing wearable technologies to assess the spine in adults performing dynamic movements. Two reviewers independently identified relevant papers. Customised data extraction and quality appraisal form were developed to extrapolate key details and identify risk of biases of each study. Twenty-two articles were retrieved that met the inclusion criteria: 12 were deemed of medium quality (score 33.4–66.7%), and 10 of high quality (score >66.8%). The majority of articles (19/22) reported validation type studies. Only 6 reported data collection in real-life environments. Multiple sensors type were used: electrogoniometers (3/22), strain gauges based sensors (3/22), textile piezoresistive sensor (1/22) and accelerometers often used with gyroscopes and magnetometers (15/22). Two sensors units were mainly used and placing was commonly reported on the spine lumbar and sacral regions. The sensors were often wired to data transmitter/logger resulting in cumbersome systems. Outcomes were mostly reported relative to the lumbar segment and in the sagittal plane, including angles, range of motion, angular velocity, joint moments and forces. This review demonstrates the applicability of wearable technology to assess the spine, although this technique is still at an early stage of development.

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.

Patients with ongoing symptoms or who had severe covid-19 or a history suggestive of cardiac involvement need further clinical assessment Only return to exercise after at least seven days free of symptoms, and begin with at least two weeks of minimal exertion Use daily self monitoring to track progress, including when to seek further help Our professional experience suggests that, after mild suspected covid-19, a proportion of people experience a prolonged recovery, particularly when trying to return to exercise. [...]there is increasing recognition of potential long term complications of covid-19, including enduring illness (“post-acute” or “long” covid), cardiopulmonary disease, and psychological sequelae in some people.1234 This article offers a pragmatic approach to help patients safely return to physical activity after symptomatic SARS-CoV-2 infection, focusing on those who have lost fitness or had a prolonged period of inactivity but who do not have an enduring post-acute covid-19 illness. The health benefits of being physically active, from cardiovascular to mental health, are well established.56 Conversely, the harms of physical inactivity mean it is a major risk factor for non-communicable disease worldwide, alongside others such as cigarette smoking or obesity.7 Before the covid-19 pandemic, over a third of people in the UK were not physically active enough for good health.8 There is evidence of a further decline in physical activity since the start of the pandemic for people with chronic conditions such as obesity and hypertension9; conditions associated with worse outcomes from covid-19.10 Brief advice in primary care can help people to take up physical activity, with the associated lifelong positive health impacts, and help those recovering from illness to return to previous levels of physical activity or beyond.11 People may feel unsure of how and when to return to physical activity after covid-19, and whether it is safe. A consensus statement from sports clinicians of the European Federation of Sports Medicine Associations from July 2020 recommends a review with a sports and exercise medicine physician after mild symptomatic infection, and investigations including echocardiography and lung function testing where cardiopulmonary symptoms were present.24 Guidance from the Netherlands Society of Cardiology states that, for those with systemic features including fever, electrocardiography testing should be considered before resumption of activity.25 However, the incidence of myocardial injury (box 1) or thromboembolic complications after mild or moderate covid-19 in the community is currently unknown but thought to be low. [...]a balance is needed between obstructing an already inactive population from undertaking physical activity at recommended levels beneficial for their health, and the potential risk of cardiac or other consequences for a small minority. In the natural course of covid-19, deterioration signifying severe infection often occurs at around a week from symptom onset. [...]consensus agreement is that a return to exercise or sporting activity should only occur after an asymptomatic period of at least seven days,21242627 and it would be pragmatic to apply this to any strenuous physical activity (fig 1).

BackgroundChronic low back pain (CLBP) is a highly prevalent musculoskeletal condition affecting 60–80% of the general population within their lifetime. Given the large numbers of people affected, self-management approaches have been introduced as a way to manage this condition with endorsement by the national institute for health and care excellence. Interventions are often termed self-management without defining either content or goals. Our study sought to determine the content, characteristics, and evidence for self-management of CLBP.MethodsThis narrative review was conducted using a systematic approach to search journal articles in English that focused on CLBP self-management. MEDLINE, EMBASE, CINAHL, and PsycINFO databases were used to identify publications with terms relating to back pain and self-management from January 2016 until January 2022.ResultsIn total, 15 studies were found suitable for inclusion in the review. Core components of self-management strategies include exercise, education, and psychological interventions, but there was a lack of consistency with respect to content. Intervention characteristics were either under-reported or varied. Furthermore, outcome measures used to assess these self-management programmes were diverse, mainly focusing on functional disability and pain intensity.ConclusionsInconsistencies in the content of self-management interventions, intervention characteristics, and outcome measures used for assessing self-management programmes were found across the literature. Current self-management approaches do not consider the complex biopsychosocial nature of CLBP. A consensus on the key components of self-management interventions, and how they should be evaluated, will pave the way for research to determine whether self-management can effectively manage CLBP.

Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.

Deep learning biomechanical models perform optimally when trained with large datasets, however these can be challenging to collect in gait labs, while limited augmentation techniques are available. This study presents a data augmentation approach based on generative adversarial networks which generate synthetic motion capture (mocap) datasets of marker trajectories and ground reaction forces (GRFs). The proposed architecture, called adversarial autoencoder, consists of an encoder compressing mocap data to a latent vector, a decoder reconstructing the mocap data from the latent vector and a discriminator distinguishing random vectors from encoded latent vectors. Direct kinematics (DK) and inverse kinematics (IK) joint angles, GRFs, and inverse dynamics (ID) joint moments calculated for real and synthetic trials were compared using statistical parametric mapping to assure realistic data generation and select optimal architectural hyperparameters based on percentage average differences across the gait cycle length. We observed negligible differences for DK computed joint angles and GRFs, but not for inverse methods (IK: 29.2%, ID: 35.5%). When the same architecture was trained also including the joint angles calculated by IK, we found no significant differences in the kinematics and GRFs, and improvements in joint moments estimation (ID: 25.7%). Finally, we showed that our data augmentation approach improved the accuracy of joint kinematics (up to 23%, 0.8°) and vertical GRFs (11%) predicted by standard neural networks using a single simulated pelvic inertial measurement unit. These findings suggest that predictive deep learning models can benefit from the synthetic datasets produced with the proposed technique.

Despite growing interest from both patients and healthcare providers, there is little clinical guidance on how mobile apps should be utilized to add value to patient care. We categorize apps according to their functionality (e.g. preventative behavior change, digital self-management of a specific condition, diagnostic) and discuss evidence for effectiveness from published systematic reviews and meta-analyses and the relevance to patient care. We discuss the limitations of the current literature describing clinical outcomes from mHealth apps, what FDA clearance means now (510(k)/de novo FDA clearance) and in the future. We discuss data security and privacy as a major concern for patients when using mHealth apps. Patients are often not involved in the development of mobile health guidelines, and professionals’ views regarding high-quality health apps may not reflect patients’ views. We discuss efforts to develop guidelines for the development of safe and effective mHealth apps in the US and elsewhere and the role of independent app reviews sites in identifying mHealth apps for patient care. There are only a small number of clinical scenarios where published evidence suggests that mHealth apps may improve patient outcomes.