Quantifying lumbar mobility using a single tri-axial accelerometer.

Background: Lumbar mobility is regarded as important for assessing and managing low back pain (LBP). Inertial Measurement Units (IMUs) are currently the most feasible technology for quantifying lumbar mobility in clinical and research settings. However, their gyroscopes are susceptible to drift errors, limiting their use for long-term remote monitoring. Research question: Can a single tri-axial accelerometer provide an accurate and feasible alternative to a multi-sensor IMU for quantifying lumbar flexion mobility and velocity? Methods: In this cross-sectional study, 18 healthy adults performed nine repetitions of full spinal flexion movements. Lumbar flexion mobility and velocity were quantified using a multi-sensor IMU and just the tri-axial accelerometer within the IMU. Correlations between the two methods were assessed for each percentile of the lumbar flexion movement cycle, and differences in measurements were modelled using a Generalised Additive Model (GAM). Results: Very high correlations (r > 0.90) in flexion angles and velocities were found between the two methods for most of the movement cycle. However, the accelerometer overestimated lumbar flexion angle at the start (-4.7° [95 % CI -7.6° to -1.8°]) and end (-4.8° [95 % CI -7.7° to -1.9°]) of movement cycles, but underestimated angles (maximal difference of 4.3° [95 % CI 1.4° to 7.2°]) between 7 % and 92 % of the movement cycle. For flexion velocity, the accelerometer underestimated at the start (16.6°/s [95%CI 16.0 to 17.2°/s]) and overestimated (-12.3°/s [95%CI -12.9 to -11.7°/s]) at the end of the movement, compared to the IMU. Significance: Despite the observed differences, the study suggests that a single tri-axial accelerometer could be a feasible tool for continuous remote monitoring of lumbar mobility and velocity. This finding has potential implications for the management of LBP, enabling more accessible and cost-effective monitoring of lumbar mobility in both clinical and research settings.

Quantifying lumbar sagittal plane kinematics using a wrist-worn inertial measurement unit.

Wearable sensors like inertial measurement units (IMUs), and those available as smartphone or smartwatch applications, are increasingly used to quantify lumbar mobility. Currently, wearable sensors have to be placed on the back to measure lumbar mobility, meaning it cannot be used in unsupervised environments. This study aims to compare lumbar sagittal plane angles quantified from a wrist-worn against that of a lumbar-worn sensor. Twenty healthy participants were recruited. An IMU was placed on the right wrist and the L3 spinal level. Participants had to position their right forearm on their abdomen, parallel to the floor. Three sets of three consecutive repetitions of flexion, and extension were formed. Linear mixed models were performed to quantify the effect of region (lumbar vs. wrist) on six outcomes [minimum, maximum, range of motion (ROM) of flexion and extension]. Only flexion ROM was significantly different between the wrist and lumbar sensors, with a mean of 4.54° (95% CI = 1.82°-7.27°). Across all outcomes, the maximal difference between a wrist-worn and lumbar-worn sensor was <8°. A wrist-worn IMU sensor could be used to measure gross lumbar sagittal plane mobility in place of a lumbar-worn IMU. This may be useful for remote monitoring during rehabilitation.

Association between fat and fat-free body mass indices on shock attenuation during running.

High amplitudes of shock during running have been thought to be associated with an increased injury risk. This study aimed to quantify the association between dual-energy X-ray absorptiometry (DEXA) quantified body composition, and shock attenuation across the time and frequency domains. Twenty-four active adults participated. A DEXA scan was performed to quantify the fat and fat-free mass of the whole-body, trunk, dominant leg, and viscera. Linear accelerations at the tibia, pelvis, and head were collected whilst participants ran on a treadmill at a fixed dimensionless speed 1.00 Fr. Shock attenuation indices in the time- and frequency-domain (lower frequencies: 3-8 Hz; higher frequencies: 9-20 Hz) were calculated. Pearson correlation analysis was performed for all combinations of DEXA and attenuation indices. Regularised regression was performed to predict shock attenuation indices using DEXA variables. A greater power attenuation between the head and pelvis within the higher frequency range was associated with a greater trunk fat-free mass (r = 0.411, p = 0.046), leg fat-free mass (r = 0.524, p = 0.009), and whole-body fat-free mass (r = 0.480, p = 0.018). For power attenuation of the high-frequency component between the pelvis and head, the strongest predictor was visceral fat mass (ß = 48.79). Passive and active tissues could represent important anatomical factors aiding in shock attenuation during running. Depending on the type and location of these masses, an increase in mass may benefit injury risk reduction. Also, our findings could implicate the injury risk potential during weight loss programs.

Exploring the complexity of commonly held attitudes and beliefs of low back pain-a network analysis.

Objectives: The current study used a network analysis approach to explore the complexity of attitudes and beliefs held in people with and without low back pain (LBP). The study aimed to (1) quantify the adjusted associations between individual items of the Back Pain Attitudes Questionnaire (Back-PAQ), and (2) identify the items with the strongest connectivity within the network. Methods: This is a secondary data analysis of a previously published survey using the Back-PAQ (n = 602). A nonparametric Spearman's rank correlation matrix was used as input to the network analysis. We estimated an unregularised graphical Gaussian model (GGM). Edges were added or removed in a stepwise manner until the extended Bayesian information criterion (EBIC) did not improve. We assessed three measures of centrality measures of betweenness, closeness, and strength. Results: The two pairwise associations with the greatest magnitude of correlation were between Q30-Q31 [0.54 (95% CI 0.44 to 0.60)] and Q15-Q16 [0.52 (95% CI 0.43 to 0.61)]. These two relationships related to the association between items exploring the influence of attentional focus and expectations (Q30-Q31), and feelings and stress (Q15-Q16). The three items with the greatest average centrality values, were Q22, Q25, and Q10. These items reflect beliefs about damaging the back, exercise, and activity avoidance, respectively. Conclusion: Beliefs about back damage, exercise, and activity avoidance are factors most connected to all other beliefs within the network. These three factors may represent candidate targets that clinicians can focus their counseling efforts on to manage unhelpful attitudes and beliefs in people experiencing LBP.

Strategies to optimise machine learning classification performance when using biomechanical features.

Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets - a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved.

Network analyses reveal the interaction between physical features, fear of movement and neck pain and disability in people with acute and chronic whiplash-associated disorders.

BACKGROUND AND OBJECTIVE: A network analysis can be used to quantitatively assess and graphically describe multiple interactions. This study applied network analyses to determine the interaction between physical and pain-related factors and fear of movement in people with whiplash-associated disorders (WAD) during periods of acute and chronic pain. METHODS: Physical measurements, including pressure pain-thresholds (PPT) over neural structures, cervical range of motion, neck flexor and extensor endurance and the cranio-cervical flexion test (CCFT), in addition to subjective reports including the Tampa Scale of Kinesiophobia (TSK-11), Neck Disability Index (NDI) and neck pain and headache intensity, were assessed at baseline in 47 participants with acute WAD. TSK-11, NDI and pain intensity were assessed for the same participants 6 months later (n = 45). Two network analyses were conducted to estimate the associations between features at baseline and at 6 months and their centrality indices. RESULTS: Both network analyses revealed that the greatest weight indices were found for NDI and CCFT at baseline and for neck pain and headache intensity and NDI and TSK-11 at both time points. Associations were also found betweeen cervical muscle endurance and neck pain intensity in the acute phase. Cervical muscle endurance assesssed during the acute phase was also associated with NDI after 6 months - whereas PPT measured at baseline was associsated with headache intensity after 6 months. CONCLUSION: The strongest associations were found for headache and neck pain intensity and neck disability and fear of movement, both during acute pain and when mesured 6 months later. The extent of neck endurance and measures of PPT at baseline may be associated with neck disability and headache, respectively, 6 months after a whiplash injury. SIGNIFICANCE: Through two network analyses, we evaluated the interaction between pain-related factors, fear of movement, neck disability and physical factors in people who had experienced a whiplash injury. We demonstrated that physical factors may be involved in the maintenance and development of chronic pain after a whiplash injury. Nevertheless, the strongest associations were found for headache and neck pain intensity and neck disability and fear of movement, both during acute and chronic phases.

Exploring Interactions Between Sex, Pain Characteristics, Disability, and Quality of Life in People With Chronic Spinal Pain: A Structural Equation Model.

In people with nonspecific chronic spinal pain (nCSP), disability and quality of life are associated with clinical, cognitive, psychophysical, and demographic variables. However, evidence regarding the interactions between these variables is only limited to this population. Therefore, this study aims to explore path models explaining the multivariate contributions of such variables to disability and quality of life in people with nCSP. This secondary analysis uses baseline data from a randomized controlled trial including 120 participants with nCSP. Structural equation modeling was used to explore path models for the Pain Disability Index (PDI), the Short Form 36-item physical (SF-36 PC), and mental (SF-36 MC) component scores. All models included sex, pain catastrophizing, kinesiophobia, hypervigilance, and pain intensity. Additionally, the PDI and SF-36 PC models included pressure pain thresholds (PPTs) at the dominant pain site (ie, neck or low back). Significant associations were found between sex, pain cognitions, pain intensity, and PPTs. Only pain catastrophizing significantly directly influenced the PDI (P ≤ .001) and SF-36 MC (P = .014), while the direct effects on the SF-36 PC from kinesiophobia (P = .008) and pain intensity (P = .006) were also significant. However, only the combined effect of all pain cognitions on the SF-36 PC was mediated by pain intensity (P = .019). Our findings indicate that patients' pain-related cognitions have an adverse effect on their physical health-related quality of life via a negative influence on their pain intensity in people with nCSP. PERSPECTIVE: This secondary analysis details a network analysis confirming significant interactions between sex, pain cognitions, pain intensity, and PPTs in relation to disability and health-related quality of life in people with chronic spinal pain. Moreover, its findings establish the importance of pain cognitions and pain intensity for these outcomes. TRIALS REGISTRATION: Clinicaltrials.gov (NCT02098005).

Prognostic Factors of Perceived Disability and Perceived Recovery After Whiplash: A Longitudinal, Prospective Study With One-year Follow-up.

OBJECTIVES: The understanding of the role that cognitive and emotional factors play in how an individual recovers from a whiplash injury is important. Hence, we sought to evaluate whether pain-related cognitions (self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, and pessimism) and emotions (kinesiophobia) are longitudinally associated with the transition to chronic whiplash-associated disorders in terms of perceived disability and perceived recovery at 6 and 12 months. METHODS: One hundred sixty-one participants with acute or subacute whiplash-associated disorder were included. The predictors were: self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, pessimism, pain intensity, and kinesiophobia. The 2 outcomes were the dichotomized scores of perceived disability and recovery expectations at 6 and 12 months. Stepwise regression with bootstrap resampling was performed to identify the predictors most strongly associated with the outcomes and the stability of such selection. RESULTS: Baseline perceived disability, pain catastrophizing, and expectation of recovery were the most likely to be statistically significant, with an overage frequency of 87.2%, 84.0%, and 84.0%, respectively. CONCLUSION: Individuals with higher expectations of recovery and lower levels of pain catastrophizing and perceived disability at baseline have higher perceived recovery and perceived disability at 6 and 12 months. These results have important clinical implications as both factors are modifiable through health education approaches.

Neuromechanical stabilisation of the centre of mass during running.

BACKGROUND: Stabilisation of the centre of mass (COM) trajectory is thought to be important during running. There is emerging evidence of the importance of leg length and angle regulation during running, which could contribute to stability in the COM trajectory The present study aimed to understand if leg length and angle stabilises the vertical and anterior-posterior (AP) COM displacements, and if the stability alters with running speeds. METHODS: Data for this study came from an open-source treadmill running dataset (n = 28). Leg length (m) was calculated by taking the resultant distance of the two-dimensional sagittal plane leg vector (from pelvis segment to centre of pressure). Leg angle was defined by the angle subtended between the leg vector and the horizontal surface. Leg length and angle were scaled to a standard deviation of one. Uncontrolled manifold analysis (UCM) was used to provide an index of motor abundance (IMA) in the stabilisation of the vertical and AP COM displacement. RESULTS: IMAAP and IMAvertical were largely destabilising and always stabilising, respectively. As speed increased, the peak destabilising effect on IMAAP increased from -0.66(0.18) at 2.5 m/s to -1.12(0.18) at 4.5 m/s, and the peak stabilising effect on IMAvertical increased from 0.69 (0.19) at 2.5 m/s to 1.18 (0.18) at 4.5 m/s. CONCLUSION: Two simple parameters from a simple spring-mass model, leg length and angle, can explain the control behind running. The variability in leg length and angle helped stabilise the vertical COM, whilst maintaining constant running speed may rely more on inter-limb variation to adjust the horizontal COM accelerations.

Characterising the interventions designed to affect the reporting of musculoskeletal imaging: a scoping review protocol using the COM-B model.

INTRODUCTION: Attributing musculoskeletal (MSK) pain to normal and commonly occurring imaging findings, such as tendon, cartilage and spinal disc degeneration, has been shown to increase people's fear of movement, reduce their optimism about recovery and increase healthcare costs. Interventions seeking to reduce the negative effects of MSK imaging reporting have had little effect. To understand the ineffectiveness of these interventions, this study seeks to scope their behavioural targets, intended mechanisms of action and theoretical underpinnings. This information alongside known barriers to helpful reporting can enable researchers to refine or create new more targeted interventions. METHODS AND ANALYSIS: The scoping review will be conducted in accordance with the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Search terms will be devised by the research team. Searches of MEDLINE, EMBASE, CINAHL, AMED and PsycINFO from inception to current day will be performed. The review will include studies, which have developed or evaluated interventions targeting the reporting of MSK imaging. Studies targeting the diagnosis of serious causes of MSK pain will be excluded. Two independent authors will extract study participant data using predefined extraction templates and intervention details using the Template for Intervention Description and Replication checklist. Interventions will be coded and mapped to the technique, mechanism of action and behavioural target according to the Capability, Opportunity, Motivation-Behaviour (COM-B) model categories. Any explicit models or theories used to inform the selection of interventions will be extracted and coded. The study characteristics, behaviour change techniques identified, behavioural targets according to the COM-B and context specific theories within the studies will be presented in narrative and table form. ETHICS AND DISSEMINATION: The information from this review will be used to inform an intervention design process seeking to improve the communication of imaging results. The results will also be disseminated through a peer-reviewed publication, conference presentations and stakeholder events.

Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain.

This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was "having undergone a neuroreflexotherapy intervention" for NP (ß = from 1.987 to 2.296) and AP (ß = from 2.639 to 3.554), and "Imaging findings: spinal stenosis" (ß = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks.

Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating in-vivo forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.

Monocular 3D Human Pose Markerless Systems for Gait Assessment.

Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)'s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T2 score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment.

Tampa Scale of Kinesiophobia may underestimate task-specific fear of movement in people with and without low back pain.

Introduction: The Tampa Scale of Kinesiophobia (TSK) is commonly used to assess fear of movement (FoM) in people with low back pain (LBP). However, the TSK does not provide a task-specific measure of FoM, whereas image-based or video-based methods may do so. Objectives: To compare the magnitude of FoM when assessed using 3 methods (TSK-11, image of lifting, video of lifting) in 3 groups of people: current LBP (LBP), recovered LBP (rLBP), and asymptomatic controls (control). Methods: Fifty-one participants completed the TSK-11 and rated their FoM when viewing images and videos depicting people lifting objects. Low back pain and rLBP participants also completed the Oswestry Disability Index (ODI). Linear mixed models were used to estimate the effects of methods (TSK-11, image, video) and group (control, LBP, rLBP). Linear regression models were used to assess associations between the methods on ODI after adjusting for group. Finally, a linear mixed model was used to understand the effects of method (image, video) and load (light, heavy) on fear. Results: In all groups, viewing images (P = 0.009) and videos (P = 0.038) elicited greater FoM than that captured by the TSK-11. Only the TSK-11 was significantly associated with the ODI (P < 0.001). Finally, there was a significant main effect of load on fear (P < 0.001). Conclusion: Fear of specific movements (eg, lifting) may be better measured using task-specific measures, such as images and videos, than by task-generic questionnaires, such as the TSK-11. Being more strongly associated with the ODI, the TSK-11 still plays an important role in understanding the impact of FoM on disability.

Redirected Attention and Impaired Recognition Memory during Exhaustive Cycling Has Implications for Information Processing Models of Effort-Regulation.

Perception of internal and external cues is an important determinant of pacing behaviour, but little is known about the capacity to attend to such cues as exercise intensity increases. This study investigated whether changes in attentional focus and recognition memory correspond with selected psychophysiological and physiological parameters during exhaustive cycling. METHODS: Twenty male participants performed two laboratory ramped cycling tests beginning at 50 W and increasing by 0.25 W/s until volitional exhaustion. Ratings of perceived exertion, heart rate and respiratory gas exchange measures were recorded during the first test. During the second test, participants listened to a list of spoken words presented through headphones at a rate of one word every 4 s. Afterwards, their recognition memory for the word pool was measured. RESULTS: Recognition memory performance was found to have strong negative correlations with perceived exertion (p < 0.0001), percentage of peak power output (p < 0.0001), percentage of heart rate reserve (p < 0.0001), and percentage of peak oxygen uptake (p < 0.0001). CONCLUSIONS: The results show that, as the physiological and psychophysiological stress of cycling intensified, recognition memory performance deteriorated. This might be due to impairment of memory encoding of the spoken words as they were presented, or because of a diversion of attention away from the headphones, perhaps towards internal physiological sensations as interoceptive sources of attentional load increase with exercise intensity. Information processing models of pacing and performance need to recognise that an athlete's capacity to attend to and process external information is not constant, but changes with exercise intensity.

A cross-sectional investigation of back pain beliefs and fear in physiotherapy and sport undergraduate students.

BACKGROUND: Although low back pain (LBP) beliefs have been well investigated in mainstream healthcare discipline students, the beliefs within sports-related study students, such as Sport and Exercise Science (SES), Sports Therapy (ST), and Sport Performance and Coaching (SPC) programmes have yet to be explored. This study aims to understand any differences in the beliefs and fear associated with movement in students enrolled in four undergraduate study programmes-physiotherapy (PT), ST, SES, and SPC. METHOD: 136 undergraduate students completed an online survey. All participants completed the Tampa Scale of Kinesiophobia (TSK) and Back Beliefs Questionnaire (BBQ). Two sets of two-way between-subjects Analysis of Variance (ANOVA) were conducted for each outcome of TSK and BBQ, with the independent variables of the study programme, study year (1st, 2nd, 3rd), and their interaction. RESULTS: There was a significant interaction between study programme and year for TSK (F(6, 124) = 4.90, P < 0.001) and BBQ (F(6, 124) = 8.18, P < 0.001). Post-hoc analysis revealed that both PT and ST students had lower TSK and higher BBQ scores than SES and SPC students particularly in the 3rd year. CONCLUSIONS: The beliefs of clinicians and trainers managing LBP are known to transfer to patients, and more negative beliefs have been associated with greater disability. This is the first study to understand the beliefs about back pain in various sports study programmes, which is timely, given that the management of injured athletes typically involves a multidisciplinary team.

A network analysis on biopsychosocial factors and pain-related outcomes assessed during a COVID-19 lockdown.

Psychological stress, social isolation, physical inactivity, and reduced access to care during lockdowns throughout a pandemic negatively impact pain and function. In the context of the first COVID-19 lockdown in Spain, we aimed to investigate how different biopsychosocial factors influence chiropractic patients' pain-related outcomes and vice-versa. A total of 648 chiropractic patients completed online questionnaires including variables from the following categories: demographics, pain outcomes, pain beliefs, impact of the COVID-19 pandemic, stress/anxiety and self-efficacy. Twenty-eight variables were considered in a cross-sectional network analysis to examine bidirectional associations between biopsychosocial factors and pain outcomes. Subgroup analyses were conducted to estimate differences according to gender and symptom duration. The greatest associations were observed between pain duration and pain evolution during lockdown. Participants' age, pain symptoms' evolution during lockdown, and generalized anxiety were the variables with the strongest influence over the whole network. Negative emotions evoked by the pandemic were indirectly associated with pain outcomes, possibly via pain catastrophizing. The network structure of patients reporting acute pain showed important differences when compared to patients with chronic pain. These findings will contribute to identify which factors explain the deleterious effects of both the pandemic and the restrictions on patients living with pain.

The effects of plyometric jump training on lower-limb stiffness in healthy individuals: A meta-analytical comparison.

PURPOSE: This study aimed to examine the effects of plyometric jump training (PJT) on lower-limb stiffness. METHODS: Systematic searches were conducted in PubMed, Web of Science, and Scopus. Study participants included healthy males and females who undertook a PJT programme isolated from any other training type. RESULTS: There was a small effect size (ES) of PJT on lower-limb stiffness (ES = 0.33, 95% confidence interval (95%CI): 0.07-0.60, z = 2.47, p = 0.01). Untrained individuals exhibited a larger ES (ES = 0.46, 95%CI: 0.08-0.84, p = 0.02) than trained individuals (ES = 0.15, 95%CI: ‒0.23 to 0.53, p = 0.45). Interventions lasting a greater number of weeks (>7 weeks) had a larger ES (ES = 0.47, 95%CI: 0.06-0.88, p = 0.03) than those lasting fewer weeks (ES = 0.22, 95%CI: ‒0.12 to 0.55, p = 0.20). Programmes with ≤2 sessions per week exhibited a larger ES (ES = 0.39, 95%CI: 0.01-0.77, p = 0.04) than programmes that incorporated >2 sessions per week (ES = 0.20, 95%CI: -0.10 to 0.50, p = 0.18). Programmes with <250 jumps per week (ES = 0.50, 95%CI: 0.02-0.97, p = 0.04) showed a larger effect than programmes with 250-500 jumps per week (ES = 0.36, 95%CI: 0.00-0.72, p = 0.05). Programmes with >500 jumps per week had negative effects (ES = -0.22, 95%CI: -1.10 to 0.67, p = 0.63). Programmes with >7.5 jumps per set showed larger effect sizes (ES = 0.55, 95%CI: 0.02-1.08, p = 0.04) than those with <7.5 jumps per set (ES = 0.32, 95%CI: 0.01-0.62, p = 0.04). CONCLUSION: PJT enhances lower-body stiffness, which can be optimised with lower volumes (<250 jumps per week) over a relatively long period of time (>7 weeks).

Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study.

OBJECTIVES: To understand the physical, activity, pain, and psychological pathways contributing to low back pain (LBP) -related disability, and if these differ between subgroups. METHODS: Data came from the baseline observations (n = 3849) of the "GLA:D Back" intervention program for long-lasting nonspecific LBP. 15 variables comprising demographic, pain, psychological, physical, activity, and disability characteristics were measured. Clustering was used for subgrouping, Bayesian networks (BN) were used for structural learning, and structural equation model (SEM) was used for statistical inference. RESULTS: Two clinical subgroups were identified with those in subgroup 1 having worse symptoms than those in subgroup 2. Psychological factors were directly associated with disability in both subgroups. For subgroup 1, psychological factors were most strongly associated with disability (ß = 0.363). Physical factors were directly associated with disability (ß = -0.077), and indirectly via psychological factors. For subgroup 2, pain was most strongly associated with disability (ß = 0.408). Psychological factors were common predictors of physical factors (ß = 0.078), pain (ß = 0.518), activity (ß = -0.101), and disability (ß = 0.382). CONCLUSIONS: The importance of psychological factors in both subgroups suggests their importance for treatment. Differences in the interaction between physical, pain, and psychological factors and their contribution to disability in different subgroups may open the doors toward more optimal LBP treatments.