Lower limb joint loading during high-impact activities: implication for bone health.

Osteoporosis results in low-trauma fractures affecting millions globally, in particular elderly populations. Despite the inclusion of physical activity in fracture prevention strategies, the optimal bone-strengthening exercises remain uncertain, highlighting the need for a deeper understanding of lower limb joint loading dynamics across various exercise types and levels. This study examines lower limb joint loading during high-impact exercises across different intensities.

Evaluation of Exercise Program for Overweight and Obese Pediatric Participants in a Single Tertiary Center in Singapore.

Purpose: Pediatric obesity is rising in prevalence. We audited the results of an exercise program for overweight and obese pediatric participants in a tertiary pediatric hospital. The main aim was to determine the program's effectiveness, with the main outcome of change in body mass index (BMI).

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field.

Words matter: Effects of instructional cues on pressure pain threshold values in healthy people.

Background: Pressure pain threshold (PPT) measurements require standardised verbal instructional cues to ensure that the increasing pressure is stopped at the correct time consistently. This study aimed to compare how PPT values and their test-retest reliability were affected by different instructional cues.

Methods: At two separate sessions, two PPT measurements were taken at the anterior knee for each of four different instructional cues: the cue of the German Neuropathic Research Network instructions ('DFNS'), the point where pressure first feels uncomfortable ('Uncomfortable'), 3/10 on the numerical pain rating scale ('3NPRS'), and where pain relates to an image from the pictorial-enhanced NPRS scale ('Pictorial').

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.

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.

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.

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.

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 ( = 602).

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).

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.

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.

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.

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.

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.

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.

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 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.

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).

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

Unlabelled: 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.

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.

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.

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.

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.