A review of methods to assess coactivation in the spine

Coactivation is an important component for understanding the physiological cost of muscular and spinal loads and their associations with spinal pathology and potentially myofascial pain. However, due to the complex and dynamic nature of most activities of daily living, it can be difficult to capture a quantifiable measure of coactivation. Many methods exist to assess coactivation, but most are limited to two-muscle systems, isometric/complex analyses, or dynamic/uniplanar analyses. Hence, a void exists in that coactivation has not been documented or assessed as a multiple-muscle system under realistic complex dynamic loading. Overall, no coactivation index has been capable of assessing coactivation during complex dynamic exertions. The aim of this review is to provide an understanding of the factors that may influence coactivation, document the metrics used to assess coactivity, assess the feasibility of those metrics, and define the necessary variables for a coactivation index that can be used for a variety of tasks. It may also be clinically and practically relevant in the understanding of rehabilitation effectiveness, efficiency during task performance, human-task interactions, and possibly the etiology for a multitude of musculoskeletal conditions

An Exploratory Electromyography-Based Coactivation Index for the Cervical Spine

Objective Develop a coactivation index for the neck and test its effectiveness with complex dynamic head motions. Background Studies describing coactivation for the cervical spine are sparse in the literature. Of those in existence, they were either limited to a priori definitions of agonist/antagonist activity that limited the testing to sagittal and lateral planes or consisted of isometric exertions. Multiplanar movements would allow for a more realistic understanding of naturalistic movements in the cervical spine and propensity for neck pain. However, a gap in the literature exists in which a method to describe coactivation during complex dynamic motions does not exist for the cervical spine. Methods An electromyography-based coactivation index was developed for the cervical spine based on previously tested methodology used on the lumbar spine without a high-end model and tested using a series of different postures and speeds. Results Complex motions involving twisting (i.e., flexion and twisting) and higher speed had higher magnitudes of coactivation than uniplanar motions in the sagittal or lateral plane, which was expected. The coupled motion of flexion and twisting showed four to five times higher coactivation than uniplanar (sagittal or lateral) movements. Conclusion The coactivation index developed accommodates multiplanar, naturalistic movements. Testing of the index showed that motions requiring higher degrees of head control had higher effort due to coactivation, which was expected. Application Overall, this coactivation index may be utilized to understand the neuromuscular effort of various tasks in the cervical spine

The Spinal Instability Neoplastic Score : Impact On Oncologic Decision-Making

STUDY DESIGN.: Systematic literature review OBJECTIVE.: To address the following questions in a systematic literature review: 1. How is spinal neoplastic instability defined or classified in the literature before and after the introduction of the Spinal Instability Neoplastic Score (SINS)? 2. How has SINS affected daily clinical practice? 3. Can SINS be used as a prognostic tool? SUMMARY OF BACKGROUND DATA.: Spinal neoplastic-related instability was defined in 2010 and simultaneously SINS was introduced as a novel tool with criteria agreed upon by expert consensus to assess the degree of spinal stability. METHODS.: Pubmed, Embase, and clinical trial databases were searched with the key words “spinal neoplasm”, “spinal instability”, “spinal instability neoplastic score”, and synonyms. Studies describing spinal neoplastic-related instability were eligible for inclusion. Primary outcomes included studies describing and/or defining neoplastic-related instability, SINS, and studies using SINS as a prognostic factor. RESULTS.: The search identified 1414 articles, of which 51 met the inclusion criteria. No precise definition or validated assessment tool was used specific to spinal neoplastic-related instability prior to the introduction of SINS. Since the publication of SINS in 2010, the vast majority of the literature regarding spinal instability has used SINS to assess or describe instability. Twelve studies specifically investigated the prognostic value of SINS in patients who underwent radiotherapy or surgery. CONCLUSION.: No consensus could be determined regarding the definition, assessment, or reporting of neoplastic-related instability before introduction of SINS. Defining spinal neoplastic-related instability and the introduction of SINS have led to improved uniform reporting within the spinal neoplastic literature. Currently, the prognostic value of SINS is controversial.Level of Evidence: N/

A biologically-assisted curved muscle model of the lumbar spine: Model validation

Biomechanical models have been developed to predict spinal loads in vivo to assess potential risk of injury in workplaces. Most models represent trunk muscles with straight-lines. Even though straight-line muscles behave reasonably well in simple exertions, they could be less reliable during complex dynamic exertions. A curved muscle representation was developed to overcome this issue. However, most curved muscle models have not been validated during dynamic exertions. Thus, the objective of this study was to investigate the fidelity of a curved muscle model during complex dynamic lifting tasks, and to investigate the changes in spine tissue loads. Twelve subjects (7 males and 5 females) participated in this study. Subjects performed lifting tasks as a function of load weight, load origin, and load height to simulate complex exertions. Moment matching measures were recorded to evaluate how well the model predicted spinal moments compared to measured spinal moments from T12/L1 to L5/S1 levels. The biologically-assisted curved muscle model demonstrated better model performance than the straight-line muscle model between various experimental conditions. In general, the curved muscle model predicted at least 80% of the variability in spinal moments, and less than 15% of average absolute error across levels. The model predicted that the compression and anterior–posterior shear load significantly increased as trunk flexion increased, whereas the lateral shear load significantly increased as trunk twisted more asymmetric during lifting tasks. A curved muscle representation in a biologically-assisted model is an empirically reasonable approach to accurately predict spinal moments and spinal tissue loads of the lumbar spine. •The model fidelity of a curved muscle model was evaluated in complex lifting tasks.•The curved muscle model showed good model fidelity between experimental conditions.•Spinal loads were sensitive to the various physical lifting conditions examined.•A curved muscle model will be useful to accurately predict complex spinal loads

Validation of a personalized curved muscle model of the lumbar spine during complex dynamic exertions

Previous curved muscle models have typically examined their robustness only under simple, single-plane static exertions. In addition, the empirical validation of curved muscle models through an entire lumbar spine has not been fully realized. The objective of this study was to empirically validate a personalized biologically-assisted curved muscle model during complex dynamic exertions. Twelve subjects performed a variety of complex lifting tasks as a function of load weight, load origin, and load height. Both a personalized curved muscle model as well as a straight-line muscle model were used to evaluate the model's fidelity and prediction of three-dimensional spine tissue loads under different lifting conditions. The curved muscle model showed better model performance and different spinal loading patterns through an entire lumbar spine compared to the straight-line muscle model. The curved muscle model generally showed good fidelity regardless of lifting condition. The majority of the 600 lifting tasks resulted in a coefficient of determination (R ) greater than 0.8 with an average of 0.83, and the average absolute error less than 15% between measured and predicted dynamic spinal moments. As expected, increased load and asymmetry were generally found to significantly increase spinal loads, demonstrating the ability of the model to differentiate between experimental conditions. A curved muscle model would be useful to estimate precise spine tissue loads under realistic circumstances. This precise assessment tool could aid in understanding biomechanical causal pathways for low back pain

The Spinal Instability Neoplastic Score : Impact On Oncologic Decision-Making

STUDY DESIGN.: Systematic literature review OBJECTIVE.: To address the following questions in a systematic literature review: 1. How is spinal neoplastic instability defined or classified in the literature before and after the introduction of the Spinal Instability Neoplastic Score (SINS)? 2. How has SINS affected daily clinical practice? 3. Can SINS be used as a prognostic tool? SUMMARY OF BACKGROUND DATA.: Spinal neoplastic-related instability was defined in 2010 and simultaneously SINS was introduced as a novel tool with criteria agreed upon by expert consensus to assess the degree of spinal stability. METHODS.: Pubmed, Embase, and clinical trial databases were searched with the key words “spinal neoplasm”, “spinal instability”, “spinal instability neoplastic score”, and synonyms. Studies describing spinal neoplastic-related instability were eligible for inclusion. Primary outcomes included studies describing and/or defining neoplastic-related instability, SINS, and studies using SINS as a prognostic factor. RESULTS.: The search identified 1414 articles, of which 51 met the inclusion criteria. No precise definition or validated assessment tool was used specific to spinal neoplastic-related instability prior to the introduction of SINS. Since the publication of SINS in 2010, the vast majority of the literature regarding spinal instability has used SINS to assess or describe instability. Twelve studies specifically investigated the prognostic value of SINS in patients who underwent radiotherapy or surgery. CONCLUSION.: No consensus could be determined regarding the definition, assessment, or reporting of neoplastic-related instability before introduction of SINS. Defining spinal neoplastic-related instability and the introduction of SINS have led to improved uniform reporting within the spinal neoplastic literature. Currently, the prognostic value of SINS is controversial.Level of Evidence: N/

Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling

Accurate geometry of the trunk musculature is essential for reliably estimating spinal loads in biomechanical models. Currently, many models employ straight muscle path assumptions that yield far less accurate tissue loads, particularly in extreme postures. Precise muscle moment-arms and physiological cross-sectional areas are important when incorporating curved muscle geometry in biomechanical models. The objective of this study was to develop a predictive model of moment arms and physiological cross-sectional areas of trunk musculature at multiple levels in the thoracic/lumbar spine as a function of anthropometric measures. Based on magnetic resonance imaging data from thirty subjects (10 male and 20 female) reported in a previous study, a polynomial regression analysis was conducted to estimate the muscle moment-arms and physiological cross-sectional areas of trunk muscles through thoracic/lumbar spine as a function of vertebral level, gender, age, height, and body mass. Gender, body mass, and height were the best predictors of muscle moment-arms and physiological cross-sectional areas. The predictability of muscle parameters tended to be higher for erector spinae than other muscles. Most muscles showed a curved muscle path along the thoracic/lumbar spine. The polynomial regression model of the muscle geometry in this study generally showed good predictability compared to previous reports. The predictive model in this study will be useful to develop personalized biomechanical models that incorporate curved trunk muscle geometries

Development and testing of a moment-based coactivation index to assess complex dynamic tasks for the lumbar spine

Many methods exist to describe coactivation between muscles. However, most methods have limited capability in the assessment of coactivation during complex dynamic tasks for multi-muscle systems such as the lumbar spine. The ability to assess coactivation is important for the understanding of neuromuscular inefficiency. In the context of this manuscript, inefficiency is defined as the effort or level of coactivation beyond what may be necessary to accomplish a task (e.g., muscle guarding during postural stabilization). The objectives of this study were to describe the development of an index to assess coactivity for the lumbar spine and test its ability to differentiate between various complex dynamic tasks. The development of the coactivation index involved the continuous agonist/antagonist classification of moment contributions for the power-producing muscles of the torso. Different tasks were employed to test the range of the index including lifting, pushing, and Valsalva. The index appeared to be sensitive to conditions where higher coactivation would be expected. These conditions of higher coactivation included tasks involving higher degrees of control. Precision placement tasks required about 20% more coactivation than tasks not requiring precision, lifting at chest height required approximately twice the coactivation as mid-thigh height, and pushing fast speeds with turning also required at least twice the level of coactivity as slow or preferred speeds. Overall, this novel coactivation index could be utilized to describe the neuromuscular effort in the lumbar spine for tasks requiring different degrees of postural control. •A method to assess coactivation from a systems-perspective is proposed.•The method was tested on various complex dynamic manual materials handling tasks.•The index could distinguish between tasks of differing degrees of postural control.•High levels of postural control (i.e., precision tasks) result in a higher index

A biologically-assisted curved muscle model of the lumbar spine: Model structure

Biomechanical models have been developed to assess the spine tissue loads of individuals. However, most models have assumed trunk muscle lines of action as straight-lines, which might be less reliable during occupational tasks that require complex lumbar motions. The objective of this study was to describe the model structure and underlying logic of a biologically-assisted curved muscle model of the lumbar spine. The developed model structure including curved muscle geometry, separation of active and passive muscle forces, and personalization of muscle properties was described. An example of the model procedure including data collection, personalization, and data evaluation was also illustrated. Three-dimensional curved muscle geometry was developed based on a predictive model using magnetic resonance imaging and anthropometric measures to personalize the model for each individual. Calibration algorithms were able to reverse-engineer personalized muscle properties to calculate active and passive muscle forces of each individual. This biologically-assisted curved muscle model will significantly increase the accuracy of spinal tissue load predictions for the entire lumbar spine during complex dynamic occupational tasks. Personalized active and passive muscle force algorithms will help to more robustly investigate person-specific muscle forces and spinal tissue loads. •The structure of a biologically-assisted curved muscle model was described.•Muscle personalization was accounted for estimating active and passive forces.•This model provides person-specific spinal loads during complex task performed