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The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management. [ABSTRACT FROM AUTHOR]

The diversity in electromyography (EMG) techniques and their reporting present significant challenges across multiple disciplines in research and clinical practice, where EMG is commonly used. To address these challenges and augment the reproducibility and interpretation of studies using EMG, the Consensus for Experimental Design in Electromyography (CEDE) project has developed a checklist (CEDE-Check) to assist researchers to thoroughly report their EMG methodologies. Development involved a multi-stage Delphi process with seventeen EMG experts from various disciplines. After two rounds, consensus was achieved. The final CEDE-Check consists of forty items that address four critical areas that demand precise reporting when EMG is employed: the task investigated, electrode placement, recording electrode characteristics, and acquisition and pre-processing of EMG signals. This checklist aims to guide researchers to accurately report and critically appraise EMG studies, thereby promoting a standardised critical evaluation, and greater scientific rigor in research that uses EMG signals. This approach not only aims to facilitate interpretation of study results and comparisons between studies, but it is also expected to contribute to advancing research quality and facilitate clinical and other practical applications of knowledge generated through the use of EMG. [ABSTRACT FROM AUTHOR]

Changes in spinal kinematic variability have been observed in people with chronic non-specific LBP (CNSLBP) during the performance of various repetitive functional tasks. However, the direction of these changes (i.e., less or more kinematic variability) is not consistent. This study aimed to assess differences in kinematic variability of the 3D angular displacement of thoracic and lumbar spinal segments in people with CNSLBP compared to asymptomatic individuals during a repetitive lifting task. Eleven people with CNSLBP and 11 asymptomatic volunteers performed 10 cycles of multi-planar lifting movements while spinal kinematics were recorded. For the three planes of motion, point-by-point standard deviations (SDs) were computed across all cycles of lifting and the average was calculated as a measure of kinematic variability for both segments. People with CNSLBP displayed higher thoracic (F = 8.00, p = 0.010, ηp2 = 0.286) and lumbar kinematic variability (F = 5.48, p = 0.030, ηp2 = 0.215) in the sagittal plane. Moreover, group differences were observed in the transversal plane for thoracic (F = 7.62, p = 0.012, ηp2 = 0.276) and lumbar kinematic variability (F = 5.402, p = 0.031, ηp2 = 0.213), as well as in the frontal plane for thoracic (F = 7.27, p = 0.014, ηp2 = 0.267) and lumbar kinematic variability (F = 6.11, p = 0.022, ηp2 = 0.234), all showing higher variability in those with CNSLBP. A significant main effect of group was not detected (p > 0.05) for spinal range of motion (ROM). Thus, people with CNSLBP completed the lifting task with the same ROM in all three planes of motion as observed for asymptomatic individuals, yet they performed the lifting task with higher spinal kinematic cycle-to-cycle variation. [ABSTRACT FROM AUTHOR]

Low back pain (LBP) is a leading cause of disability in the workplace, often caused by manually lifting of heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk of lifting activities. This study aims to verify that, during the execution of fatiguing frequency-dependent lifting, high-density surface electromyography (HDsEMG) allows the discrimination of healthy controls (HC) versus people with LBP and biomechanical risk levels. Fifteen HC and eight people with LBP performed three lifting tasks with a progressively increasing lifting index, each lasting 15 min. Erector spinae (ES) activity was recorded using HDsEMG and amplitude parameters were calculated to characterize the spatial distribution of muscle activity. LBP group showed a less ES activity than HC (lower root mean square across the grid and of the activation region) and an involvement of the same muscular area across the task (lower coefficient of variation of the center of gravity of muscle activity). The results indicate the usefulness of HDsEMG parameters to classify risk levels for both HC and LBP groups and to determine differences between them. The findings suggest that the use of HDsEMG could expand the capabilities of existing instrumental-based tools for biomechanical risk classification during lifting activities. [ABSTRACT FROM AUTHOR]

The analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications. [ABSTRACT FROM AUTHOR]

High-density surface electromyography (HDsEMG) can be used to measure the spatial distribution of electrical muscle activity over the skin. As this distribution is associated with the generation and propagation of muscle fiber action potentials, HDsEMG is processed to extract information on regional muscle activation, muscle fiber characteristics and behaviour of individual motor units. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, summarizes recommendations on the use of HDsEMG in experimental studies. For each application, recommendations are included regarding electrode montage, electrode type and configuration, electrode location and orientation, data analysis, and interpretation. Cautions and reporting standards are also included. The steps of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers when collecting, reporting, and interpreting HDsEMG data. It is hoped that this document will be used to generate new empirical evidence to improve how HDsEMG is used in research and in clinical applications. [ABSTRACT FROM AUTHOR]