A probabilistic orthopaedic population model to predict fatigue-related subacromial geometric variability

Individuals with rotator cuff tears unsuccessfully treated with exercise therapy have less inferiorly oriented net muscle forces during scapular plane abduction

Exercise therapy for individuals with rotator cuff tears fails in approximately 25.0 % of cases. One reason for failure of exercise therapy may be the inability to strengthen and balance the muscle forces crossing the glenohumeral joint that act to center the humeral head on the glenoid. The objective of the current study was to compare the magnitude and orientation of the net muscle force pre- and post-exercise therapy between subjects successfully and unsuccessfully (e.g. eventually underwent surgery) treated with a 12-week individualized exercise therapy program. Twelve computational musculoskeletal models (n = 6 successful, n = 6 unsuccessful) were developed in OpenSim (v4.0) that incorporated subject specific tear characteristics, muscle peak isometric force, in-vivo kinematics and bony morphology. The models were driven with experimental kinematics and the magnitude and orientation of the net muscle force was determined during scapular plane abduction at pre- and post-exercise therapy timepoints. Subjects unsuccessfully treated had less inferiorly oriented net muscle forces pre- and post-exercise therapy compared to subjects successfully treated (p = 0.039 & 0.045, respectively). No differences were observed in the magnitude of the net muscle force (p > 0.05). The current study developed novel computational musculoskeletal models with subject specific inputs capable of distinguishing between subjects successfully and unsuccessfully treated with exercise therapy. A less inferiorly oriented net muscle force in subjects unsuccessfully treated may increase the risk of superior migration leading to impingement. Adjustments to exercise therapy programs may be warranted to avoid surgery in subjects at risk of unsuccessful treatment.

A comparative probabilistic analysis of human and chimpanzee rotator cuff functional capacity

Computational musculoskeletal modeling represents a valuable approach to examining biological systems in physical anthropology. Probabilistic modeling builds on computational musculoskeletal models by associating mathematical distributions of specific musculoskeletal features within known ranges of biological variability with functional outcomes. The purpose of this study was to determine if overlap in rotator cuff muscle force predictions would occur between species during the performance of an evolutionarily relevant horizontal bimanual arm suspension task. This necessitated creating novel probabilistic models of the human and chimpanzee glenohumeral joint through augmentation of previously published deterministic models. Glenohumeral musculoskeletal features of anthropological interest were probabilistically modeled to produce distributions of predicted human and chimpanzee rotator cuff muscle force that were representative of the specific anatomical manipulations. Musculoskeletal features modeled probabilistically included rotator cuff origins and deltoid insertion, glenoid inclination, and joint stability. Predicted human rotator cuff muscle force distributions were mostly limited to alternating between infraspinatus and teres minor, with both 100% and 0% muscle force predicted for both muscles. The chimpanzee model predicted low-to-moderate muscle force across all rotator cuff muscles. Rotator cuff muscle force predictions were most sensitive to changes of muscle origins and insertions. Results indicate that functional rotator cuff overlap is unlikely between chimpanzees and humans without greater modifications of the glenohumeral musculoskeletal phenotypes. The results also highlight the low efficacy of the human upper extremity in overhead, weight-bearing tasks, and propensity for rotator cuff injury.

Between Two Rocks and in a Hard Place: Reflecting on the Biomechanical Basis of Shoulder Occupational Musculoskeletal Disorders

OBJECTIVE

The aim was to review the biomechanical origins of occupational shoulder damage, while considering the complexity of shoulder mechanics and musculoskeletal consequences of diverse task demands.

BACKGROUND

Accessible measures of physical exposures are the primary focus of occupational shoulder assessments and analyses. This approach has led to guidelines and intervention strategies that are often inadequate for mitigating shoulder disorders amongst the complexity of modern workplace demands. Integration of complex shoulder mechanics into occupational assessments, analyses, and interventions is critical for reducing occupational shoulder injury risk.

METHOD

This narrative review describes shoulder biomechanics in the context of common injury mechanisms and consequent injuries, with a particular focus on subacromial impingement syndrome. Several modulators of shoulder injury risk are reviewed, including fatigue, overhead work, office ergonomics considerations, and pushing and pulling task configurations.

RESULTS

Relationships between work requirements, muscular demands, fatigue, and biomechanical tissue loads exist. This review highlights that consideration of specific workplace factors should be integrated with our knowledge of the intricate arrangement and interpersonal variability of the shoulder complex to proactively evaluate occupational shoulder demands and exposures.

CONCLUSION

A standard method for evaluating shoulder muscle exposures during workplace tasks does not exist. An integrated approach is critical for improved work design and prevention of shoulder tissue damage and accompanying disability.

APPLICATION

This review is particularly relevant for researchers and practitioners, providing guidance for work design and evaluation for shoulder injury prevention by understanding the importance of the unique and complex mechanics of the shoulder.

Joint moment trade-offs across the upper extremity and trunk during repetitive work

Individuals can coordinate small kinematic changes at several degrees of freedom simultaneously in the presence of fatigue, leaving it unclear how overall biomechanical demands at each joint are altered. The purpose of this study was to evaluate trade-offs in joint moments between the trunk, shoulder, and elbow during repetitive upper extremity work. Participants performed four simulated workplace tasks cyclically until meeting fatigue termination criteria. Emergent fatigue-induced adaptations to repetitive work resulted in task-dependent trade-offs in joint moments. In general, reduced shoulder moments were compensated for by increased elbow and trunk joint moment contributions. Although mean joint moment changes were modest (range: 1-3 Nm) across participants, a wide distribution of responses was observed, with standard deviations exceeding 10 Nm. Re-distributing biomechanical demands across joints may alleviate constant tissue loads and facilitate continued task performance with fatigue but may be at the expense of increasing demands at adjacent joints.

Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles

Fall-related hip fractures are a major public health issue. While individual-level risk assessment tools exist, population-level predictive models could catalyze innovation in large-scale interventions. This study presents a hierarchical probabilistic model that predicts population-level hip fracture risk based on Factor of Risk (FOR) principles. Model validation demonstrated that FOR output aligned with a published dataset categorized by sex and hip fracture status. The model predicted normalized FOR for 100000 individuals simulating the Canadian older-adult population. Predicted hip fracture risk was higher for females (by an average of 38%), and increased with age (by15% per decade). Potential applications are discussed.

Scapulothoracic rhythm affects glenohumeral joint force

Hypothesis

Musculoskeletal computer models provide valuable insights into shoulder biomechanics. The shoulder is a complex joint composed of glenohumeral, scapulothoracic, acromioclavicular, and sternoclavicular articulations, whose function is largely dependent on the many muscles spanning these joints. However, the range of patient-to-patient variability in shoulder function is largely unknown. We therefore assessed the sensitivity of glenohumeral forces to population-based model input parameters that were likely to influence shoulder function.

Methods

We constructed musculoskeletal models of the shoulder in the AnyBody Modeling System (AnyBody Technology, Aalborg, Denmark). We used inverse dynamics and static optimization to solve for glenohumeral joint forces during a simulated shoulder elevation. We generated 1000 AnyBody models by uniformly distributing the following input parameters: subject height, scapulohumeral rhythm, humeral head radius, and acromiohumeral interval.

Results

Increasing body height increased glenohumeral joint forces. Increasing the ratio of scapulothoracic to glenohumeral elevation also increased forces. Increasing humeral head radius and acromiohumeral interval decreased forces. The relative sensitivity of glenohumeral joint forces to input parameters was dependent on the angle of shoulder elevation. We developed an efficient method of generating and simulating musculoskeletal models representing a large population of shoulder arthroplasty patients. We found that scapulohumeral rhythm had a significant influence on glenohumeral joint force.

Conclusions

This finding underscores the importance of more accurately measuring and simulating scapulothoracic motion rather than using fixed ratios or average scapulothoracic motion. This modeling approach can be used to generate virtual populations for conducting efficient simulations and generating statistical conclusions.

Scapular Muscle Activity During Static Yoga Postures

Study Design Controlled, cross-sectional laboratory study. Background Despite the growing popularity of yoga, little is known about the muscle activity of the scapular stabilizers during isometric yoga postures and their potential utility in shoulder rehabilitation. Objectives To examine scapular stabilizer muscle activation during various yoga postures. Methods Twenty women with yoga experience and no shoulder pain or injury participated. Electromyography was used to record the muscle activity of the upper, middle, and lower trapezius, as well as of the serratus anterior, during 15 yoga postures. Results Muscle activity varied between yoga postures (3%-57% maximum voluntary isometric contraction [MVIC]). Overall, the "locust arms forward" posture elicited the highest activity from the upper (22.4% MVIC), middle (41.8% MVIC), and lower (56.8% MVIC) trapezius, while several postures elicited moderate activity (greater than 20% MVIC) from the serratus anterior. Conversely, the "dancer's pose right," "reverse tabletop," and "warrior II" postures demonstrated low activity (less than or equal to 15.7% MVIC) of the scapular stabilizers. Conclusion Strengthening the scapular stabilizer muscles is an important component of shoulder rehabilitation. Yoga postures have been identified that activate the scapular stabilizer muscles at varying levels of activity. J Orthop Sports Phys Ther 2018;48(6):504-509. Epub 6 Apr 2018. doi:10.2519/jospt.2018.7311.

Shoulder Bone Geometry Affects the Active and Passive Axial Rotational Range of the Glenohumeral Joint

BACKGROUND

The range of motion of the glenohumeral joint varies substantially among individuals and is dependent on humeral position. How variation in shape of the humerus and scapula affects shoulder axial range of motion at various positions has not been established.

PURPOSE

To quantify variation in the shape of the glenohumeral joint and investigate whether the scapula and humerus geometries affect the axial rotational range of the glenohumeral joint.

STUDY DESIGN

Descriptive laboratory study.

METHODS

The range of active and passive internal-external rotation of the glenohumeral joint was quantified for 10 asymptomatic participants with optical motion tracking at 60º, 90º, and 120º humeral elevations in the coronal, scapular, and sagittal planes. Bone geometrical parameters were acquired from shoulder magnetic resonance image scans, and correlations between geometrical parameters and maximum internal and external rotations were investigated. Three-dimensional participant-specific models of the humerus and scapula were used to identify collisions between bones at the end of range.

RESULTS

Maximum internal and external rotations of the glenohumeral joint were correlated to geometric parameters and were limited by bony collisions. Generally, the active axial rotational range was greater with increased articular cartilage and glenoid curvature, while a shorter acromion resulted in greater passive range. Greater internal rotation was correlated with a greater glenoid depth and curvature in the scapular plane ( r = 0.76, P < .01, at 60° of elevation), a greater subacromial depth in the coronal plane ( r = 0.74, P < .01, at 90° of elevation), and a greater articular cartilage curvature in the sagittal plane ( r = 0.75, P < .01, at 90° of elevation). At higher humeral elevations, a greater subacromial depth and shorter acromion allowed a greater range of motion.

CONCLUSION

The study strongly suggests that specific bony constraints restrict the maximum internal and external rotations achieved in active and passive glenohumeral movement.

CLINICAL RELEVANCE

This study identifies bony constraints that limit the range of motion of the glenohumeral joint. This information can be used to predict full range of motion and set patient-specific rehabilitation targets for those recovering from shoulder disorders. It can improve positioning and choice of shoulder implants during preoperative planning by considering points of collision that could limit range of motion.

Using a Bayesian Network to Predict L5/S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status

Stochastic biomechanical modeling has become a useful tool most commonly implemented using Monte Carlo simulation, advanced mean value theorem, or Markov chain modeling. Bayesian networks are a novel method for probabilistic modeling in artificial intelligence, risk modeling, and machine learning. The purpose of this study was to evaluate the suitability of Bayesian networks for biomechanical modeling using a static biomechanical model of spinal forces during lifting. A 20-node Bayesian network model was used to implement a well-established static two-dimensional biomechanical model for predicting L5/S1 compression and shear forces. The model was also implemented as a Monte Carlo simulation in MATLAB. Mean L5/S1 spinal compression force estimates differed by 0.8%, and shear force estimates were the same. The model was extended to incorporate evidence about disc injury, which can modify the prior probability estimates to provide posterior probability estimates of spinal compression force. An example showed that changing disc injury status from false to true increased the estimate of mean L5/S1 compression force by 14.7%. This work shows that Bayesian networks can be used to implement a whole-body biomechanical model used in occupational biomechanics and incorporate disc injury.