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.

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.

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.

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.

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).

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.

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.

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.

Path Analysis Models Integrating Psychological, Psycho-physical and Clinical Variables in Individuals With Tension-Type Headache.

Tension type headache (TTH) is a prevalent but poorly understood pain disease. Current understanding supports the presence of multiple associations underlying its pathogenesis. Our aim was to compare competing multivariate pathway models that explains the complexity of TTH. Headache features (intensity, frequency, or duration - headache diary), headache-related disability (Headache Disability Inventory-HDI), anxiety/depression (Hospital Anxiety and Depression Scale), sleep quality (Pittsburgh Sleep Quality Index), widespread pressure pain thresholds (PPTs) and trigger points (TrPs) were collected in 208 individuals with TTH. Four latent variables were formed from the observed variables - Distress (anxiety, depression), Disability (HDI subscales), Severity (headache features), and Sensitivity (all PPTs). Structural equation modelling (SEM) and Bayesian network (BN) analyses were used to build and compare a theoretical (modeltheory) and a data-driven (modelBN) latent variable model. The modelBN (root mean square error of approximation [RMSEA] = 0.035) provided a better statistical fit than modeltheory (RMSEA = 0.094). The only path common between modelbn and modeltheory was the influence of years with pain on TrPs. The modelBN revealed that the largest coefficient magnitudes were between the latent variables of Distress and Disability (ß=1.524, P = .006). Our theoretical model proposes a relationship whereby psycho-physical and psychological factors result in clinical features of headache and ultimately affect disability. Our data-driven model proposes a more complex relationship where poor sleep, psychological factors, and the number of years with pain takes more relevance at influencing disability. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in TTH. PERSPECTIVE: A theoretical model proposes a relationship where psycho-physical and psychological factors result in clinical manifestations of headache and ultimately affect disability. A data-driven model proposes a more complex relationship where poor sleep, psychological factors, and number of years with pain takes more relevance at influencing disability.

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.

Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study.

Pain can be present in up to 50% of people with post-COVID-19 condition. Understanding the complexity of post-COVID pain can help with better phenotyping of this post-COVID symptom. The aim of this study is to describe the complex associations between sensory-related, psychological, and cognitive variables in previously hospitalized COVID-19 survivors with post-COVID pain, recruited from three hospitals in Madrid (Spain) by using data-driven path analytic modeling. Demographic (i.e., age, height, and weight), sensory-related (intensity or duration of pain, central sensitization-associated symptoms, and neuropathic pain features), psychological (anxiety and depressive levels, and sleep quality), and cognitive (catastrophizing and kinesiophobia) variables were collected in a sample of 149 subjects with post-COVID pain. A Bayesian network was used for structural learning, and the structural model was fitted using structural equation modeling (SEM). The SEM model fit was excellent: RMSEA < 0.001, CFI = 1.000, SRMR = 0.063, and NNFI = 1.008. The only significant predictor of post-COVID pain was the level of depressive symptoms (ß=0.241, p = 0.001). Higher levels of anxiety were associated with greater central sensitization-associated symptoms by a magnitude of ß=0.406 (p = 0.008). Males reported less severe neuropathic pain symptoms (−1.50 SD S-LANSS score, p < 0.001) than females. A higher level of depressive symptoms was associated with worse sleep quality (ß=0.406, p < 0.001), and greater levels of catastrophizing (ß=0.345, p < 0.001). This study presents a model for post-COVID pain where psychological factors were related to central sensitization-associated symptoms and sleep quality. Further, maladaptive cognitions, such as catastrophizing, were also associated with depression. Finally, females reported more neuropathic pain features than males. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in COVID-19 survivors with post-COVID pain and can represent a first step for the development of a theoretical/conceptual framework for post-COVID pain.

Pain-related fear of movement dynamics in individuals with and without low back pain participating in weightlifting and/or powerlifting training.

PURPOSE: Pain-free adults in the general population have been shown to possess unhelpful beliefs that certain movements and postures are harmful to the spine, potentially reinforcing fear-avoidance behaviour. Whether such beliefs occur in individuals undertaking regular powerlifting (PL) and Olympic weightlifting (OWL) training is unclear. METHODS: In a cross-sectional study design, 67 individuals who participate in OWL and PL training completed an online survey. Demographic characteristics, training history, and self-reported perceptions of harm, on the 40-item Photograph Series of Daily Activities shortened electronic version (PHODA-SeV), were collected. After removing collinear variables, 13 items were entered into a network analysis, in which the adjusted correlations between items, and the centrality indices of each item (i.e., the degree of connection with other symptoms in the network) were quantified. RESULTS: Twenty-one (31.3%) participants had LBP symptoms. The pairwise correlations with the greatest magnitudes were between images of 'leg stretch' and 'jumping' (0.32 [95%CI 0.08 to 0.45]) and two images depicting ironing (0.32 [95%CI 0.05 to 0.54]) respectively. The three most Central (connected) items were 'stair ascend', 'walking with groceries', and 'mopping with spine flexion'. CONCLUSIONS: For individuals training in OWL and PL, images reflecting walking, rather than those depicting high spinal flexion angle, had greater connectivity to other activity items. In addition, the strongest correlations were not between items reflecting high spinal flexion angle. Future studies that investigate the relationship between different intensities of OWL and PL training and the dynamics of pain-related fear are warranted.

Psychometric Properties of the Hospital Anxiety and Depression Scale (HADS) in Previously Hospitalized COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is associated with psychological/emotional disturbances. This study aimed to assess internal consistency, reliability, and construct validity of the Hospital Anxiety and Depressive Scale (HADS), as a patient-reported outcome measure (PROM) for evaluating emotional consequences of SARS-CoV-2 in hospitalized COVID-19 survivors with long COVID. The LONG-COVID-EXP-CM is a multicenter cohort study including patients hospitalized by COVID-19 during the first wave of the pandemic in five hospitals in Madrid. A total of 1969 (age: 61 ± 16 years, 46.5% women) COVID-19 survivors experiencing post-COVID symptoms a mean of 8.4 ± 1.5 months after hospital discharge completed HADS. Internal consistency (Cronbach α), reliability (item-internal consistency, item-discriminant validity), construct validity (confirmatory factor analysis), and floor effect and ceiling effect were calculated. The mean time for fulfilling HADS was 65 ± 12 s. A ceiling effect ranging from 1.99% to 13.74% and a floor effect ranging from 43.05% to 77.77% was observed. Based on the item-scale correlation coefficients, the Cronbach's alpha values reflecting the internal consistency reliability were 0.890 for the anxiety scale (HADS-A) and 0.856 for the depressive scale (HADS-D) The correlation coefficient between HADS-A and HADS-D scores was excellent (r: 0.878). The confirmatory factor analysis revealed that five out of the seven fitness indexes were excellent: CFI = 0.969, NNFI = 0.963; TLI = 0.963; AGFI = 0.951; GFI = 0.972), supporting good construct validity. In conclusion, this study indicates that both anxiety and depressive symptoms scales of HADS had overall good psychometric properties to be used for assessing psychological and emotional stress in COVID-19 survivors with long COVID.

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning.

Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment's center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments.