Work-related Low Back Disorders Research Articles

Adaptive Lifting Index (aLI) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results.

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.

The ACGIH TLV for lifting: Estimated TLVs for torso asymmetry beyond 30 degrees.

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for Lifting is a manual material handling (MMH) assessment method to identify weight limits that nearly all workers may be exposed to without developing work-related low back disorders (LBD). However, this assessment method only applies to lifting with the torso within 30° asymmetry of the sagittal plane. Estimate TLV weight limits while lifting with torso asymmetry greater than 30° beyond the sagittal plane. Lifting tasks were performed from various horizontal and vertical locations, at torso asymmetry angles of 0°, 15°, 30°, 45°, 60°, 75° and 90°, using ACGIH identified TLVs. Validated MMH assessment methods (NIOSH Lifting Equation, Ohio State University LBD Risk Model) were utilized to estimate TLVs at torso asymmetries greater than 30°. The current ACGIH TLVs resulted in low- to moderate-risk risk levels for torso asymmetries from 0° to 30°, and the risk incrementally increased as torso asymmetry increased to 90°. With the intention to keep the risk levels to that found at 30° torso asymmetry, lower TLV weight limits in the vertical and horizontal zones investigated were estimated for torso asymmetries from 45° to 90°. The resulting adjusted TLVs were consistent with weight limits identified for similar lifting conditions from other assessment methods that account for torso asymmetry. This research found current ACGIH-defined TLVs possess less than high-risk for LBD, and provided guidance to practitioners for reduced TLVs when torso asymmetry is greater than 30° from the sagittal plane.

Trunk stability in fatiguing frequency-dependent lifting activities

BackgroundWork-related low-back disorders (WLBDs) are one of the most frequent and costly musculoskeletal conditions. It has been showed that WLBDs may occur when intervertebral or torso equilibrium is altered by a biomechanical perturbations or neuromuscular control error. The capacity to react to such disturbances is heavily determined by the spinal stability, provided by active and passive tissues and controlled by the central nervous system. Research questionThis study aims to investigate trunk stability through the Lyapunov’s maximum exponent during repetitive liftings in relation to risk level, as well as to evaluate its ability to discriminate these risk levels. MethodsFifteen healthy volunteers performed fatiguing lifting tasks at three different frequencies corresponding to low, medium, and high risk levels according to the National Institute for Occupational Safety and Health (NIOSH) equation. We investigated changes in spinal stability during fatiguing lifting tasks at different risk levels using the maximum Lyapunov's index (λMax) computed from trunk accelerations recorded by placing three IMUs at pelvis, lower and upper spine levels. A two-way repeated-measures ANOVA was performed to determine if there was any significant effect on λMax among the three risk levels and the time (start, mid, and end of the task). Additionally, we examined the Pearson’s correlation of λMax with the trunk muscle co-activation, computed from trunk sEMG. ResultsOur findings show an increase in trunk stability with increasing risk level and as the lifting task progressed over time. A negative correlation between λMax and trunk co-activation was observed which illustrates that the increase in spinal stability could be partially attributed to increased trunk muscle co-activation. SignificanceThis study highlights the possibility of generating stability measures from kinematic data as risk assessment features in fatiguing tasks which may prove useful to detect the risk of developing work-related low back pain disorders and allow the implementation of early ergonomic interventions.

Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities

Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation. A total of 15 healthy controls (HC) and eight people with LBP performed three lifting tasks with a progressively increasing lifting index (LI), each lasting 15 min. Bilaterally erector spinae longissimus (ESL) activity and rectus abdominis superior (RAS) were recorded using bipolar surface electromyography systems (sEMG), and the time-varying multi-muscle coactivation function (TMCf) was computed. The TMCf can significantly discriminate each pair of LI and it is higher in LBP than HC. Collectively, our findings suggest that it is possible to identify different motor strategies between people with and without LBP. The main finding shows that LBP, to counteract pain, coactivates the trunk muscles more than HC, thereby adopting a strategy that is stiffer and more fatiguing.

A computer-vision method to estimate joint angles and L5/S1 moments during lifting tasks through a single camera

Weight lifting is a risk factor of work-related low-back musculoskeletal disorders (MSD). From the ergonomics perspective, it is important to measure workers’ body motion during a lifting task and estimate low-back joint moments to ensure the low-back biomechanical loadings are within the failure tolerance. With the recent development of advanced deep neural networks, an increasing number of computer vision algorithms have been presented to estimate 3D human poses through videos. In this study, we first performed a 3D pose estimation of lifting tasks using a single RGB camera and VideoPose3D, an open-source library with a fully convolutional model. Joint angle trajectories and L5/S1 joint moment were then calculated following a top-down inverse dynamic biomechanical model. To evaluate the accuracy of the computer-vision-based angular trajectories and L5/S1 joint moments, we conducted an experiment in which participants performed a variety of lifting tasks. The body motions of the participants were concurrently captured by an RGB camera and a laboratory-grade motion tracking system. The body joint angles and L5/S1 joint moments obtained from the camera were compared with those obtained from the motion tracking system. The results showed a strong correlation (r > 0.9, RMSE < 10°) between the two methods for shoulder flexion, trunk flexion, trunk rotation, and elbow flexion. The computer-vision-based method also yielded a good estimate for the total L5/S1 moment and the L5/S1 moment in the sagittal plane (r > 0.9, RMSE < 20 N·m). This study showed computer vision could facilitate safety practitioners to quickly identify the jobs with high MSD risks through field survey videos.

Biomechanical risk in fatiguing frequency-dependent lifting activities: muscle coactivation in people with and without low back pain

Musculoskeletal diseases and disorders from biomechanical overload are very common among workers. In Italy in 2019, occupational diseases of the osteomuscular system and connective tissue accounted for 66% of the total number of diseases reported to INAIL. Many factors can contribute to the establishment of a condition of biomechanical overload and therefore to the onset of work-related musculoskeletal disorders (WMSDs). Among these, work-related low-back disorders (WLBDs), caused mainly by handling heavy loads, are very common. In recent years, several methods have been developed to assess the risk of biomechanical overload, included in several international standards (ISO-11228, ISO-11226, ISO/TR 12295 and 12296) aimed at identifying high-risk work activities and assessing the effectiveness of ergonomic interventions. Among the best known, with regard to the manual lifting of heavy loads, there is the Revised NIOSH Lifting Equation that, while presenting many advantages (cost-effectiveness, non-invasiveness, speed of application ...) at the same time also has limitations concerning mainly the high subjectivity (subject of scientific debate) and the impossibility of these methods to assess all work tasks. From these premises, it is clear the usefulness of being able to use new quantitative risk assessment methodologies, objectifiable and repeatable, which provide for the possibility of assessing the risk from biomechanical overload even in modern working scenarios where the use of exoskeletons by workers and the sharing of working space with cobots is becoming increasingly widespread. In fact, the methods currently used are incomplete and ineffective in assessing the real impact that these technologies have on the health and safety of workers in Industry 4.0. Recent studies (some of which we were involved in) have introduced the possibilities offered by optoelectronic systems, inertial sensors (IMUs) and surface electromyography (sEMG), to integrate the most widely used observational methodologies. These modern technologies, evaluating how a subject moves his joints and uses his muscles during the execution of a work task, can integrate the observational methods, quantify the elements that characterize the risk minimizing the evaluation errors caused by individual subjectivity and allow to carry out the assessment of biomechanical risk even in those areas where the currently most widespread methodologies are not able to give exhaustive answers. In particular, the innovative methodologies based on IMUs and sEMG, allow the instrumental quantitative assessment of biomechanical risk directly in the field thanks to the fact that the sensors are miniaturized, wearable, easily transportable and based on "wireless" transmission of data acquired on the worker who performs the task. These aspects facilitate data recording, allowing accurate signal acquisition even in unfavorable environments and in work situations where the worker interacts with a cobot or uses an exoskeleton. Previous studies have involved studies of non-fatiguing lifts, where the movement and relative risk of single repetitions of lifting were studied. Currently, we wonder what happens when the work activity becomes fatiguing and whether it is still possible to use these methods to classify risk. In addition, another unexplored question concerns the presence of workers who continue to perform work activity during the first phase of onset of musculoskeletal disorders: can the risk to which these workers are exposed be considered the same as that involving workers without pain? To answer these questions, we conducted an experimental campaign at the University of Birmingham in collaboration with Roma Tre University and INAIL in which subjects with and without back disorders performed fatiguing lifts of 15 minutes in three risk levels determined by three different lifting frequencies. We studied trunk muscle activity in terms of muscle coactivation of the trunk flexor and extensor muscles. The results show how coactivation can classify risk during manual load lifting activities by distinguishing not only the level of risk but also the presence or absence of back disorders. These results suggest that the use of electromyographic features to assess the biomechanical risk associated with work activities can also be used in the presence of fatiguing lifting also to distinguish the risk in case of subjects with back pain. This methodology could be used to monitor fatigue and extend the possibilities offered by currently available instrumental-based approaches.

Biomechanical risk in fatiguing frequency-dependent lifting activities: muscle coactivation in people with and without low back pain

Musculoskeletal diseases and disorders from biomechanical overload are very common among workers. In Italy in2019, occupational diseases of the osteomuscular system and connective tissue accounted for 66% of the total number ofdiseases reported to INAIL. Many factors can contribute to the establishment of a condition of biomechanical overloadand therefore to the onset of work-related musculoskeletal disorders (WMSDs). Among these, work-related low-backdisorders (WLBDs), caused mainly by handling heavy loads, are very common.In recent years, several methods have been developed to assess the risk of biomechanical overload, included in severalinternational standards (ISO-11228, ISO-11226, ISO/TR 12295 and 12296) aimed at identifying high-risk work activities and assessing the effectiveness of ergonomic interventions. Among the best known, with regard to the manual liftingof heavy loads, there is the Revised NIOSH Lifting Equation that, while presenting many advantages (cost-effectiveness,non-invasiveness, speed of application ...) at the same time also has limitations concerning mainly the high subjectivity(subject of scientific debate) and the impossibility of these methods to assess all work tasks.From these premises, it is clear the usefulness of being able to use new quantitative risk assessment methodologies,objectifiable and repeatable, which provide for the possibility of assessing the risk from biomechanical overload evenin modern working scenarios where the use of exoskeletons by workers and the sharing of working space with cobots isbecoming increasingly widespread. In fact, the methods currently used are incomplete and ineffective in assessing thereal impact that these technologies have on the health and safety of workers in Industry 4.0.Recent studies (some of which we were involved in) have introduced the possibilities offered by optoelectronic systems, inertial sensors (IMUs) and surface electromyography (sEMG), to integrate the most widely used observationalmethodologies. These modern technologies, evaluating how a subject moves his joints and uses his muscles during theexecution of a work task, can integrate the observational methods, quantify the elements that characterize the risk minimizing the evaluation errors caused by individual subjectivity and allow to carry out the assessment of biomechanicalrisk even in those areas where the currently most widespread methodologies are not able to give exhaustive answers. Inparticular, the innovative methodologies based on IMUs and sEMG, allow the instrumental quantitative assessment ofbiomechanical risk directly in the field thanks to the fact that the sensors are miniaturized, wearable, easily transportableand based on “wireless” transmission of data acquired on the worker who performs the task. These aspects facilitatedata recording, allowing accurate signal acquisition even in unfavorable environments and in work situations where theworker interacts with a cobot or uses an exoskeleton. Previous studies have involved studies of non-fatiguing lifts, wherethe movement and relative risk of single repetitions of lifting were studied. Currently, we wonder what happens when thework activity becomes fatiguing and whether it is still possible to use these methods to classify risk. In addition, anotherunexplored question concerns the presence of workers who continue to perform work activity during the first phase ofonset of musculoskeletal disorders: can the risk to which these workers are exposed be considered the same as thatinvolving workers without pain? To answer these questions, we conducted an experimental campaign at the Universityof Birmingham in collaboration with Roma Tre University and INAIL in which subjects with and without back disordersperformed fatiguing lifts of 15 minutes in three risk levels determined by three different lifting frequencies. We studiedtrunk muscle activity in terms of muscle coactivation of the trunk flexor and extensor muscles. The results show howcoactivation can classify risk during manual load lifting activities by distinguishing not only the level of risk but alsothe presence or absence of back disorders. These results suggest that the use of electromyographic features to assess thebiomechanical risk associated with work activities can also be used in the presence of fatiguing lifting also to distinguishthe risk in case of subjects with back pain. This methodology could be used to monitor fatigue and extend the possibilitiesoffered by currently available instrumental-based approaches.

Combined influence of transfer distance, pace, handled mass and box height on spine loading and posture

Work-related low back disorders are commonly associated with handling tasks. The objective of this study was to determine the combined influence of distance, pace, handled mass and height, on back loading and posture during free box transfer. Kinematics and kinetics of 17 handlers were recorded during a box transfer task between two pallets. Four-way repeated measures ANOVA were conducted on four lift-deposit height conditions (from lift and deposit of 0.16 or 1.16 m), three distances between pallets (1.5, 1.0 and 0.5 m), two handled masses (10 and 20 kg) and two paces (free and faster). The interaction between distance and height on back loading and posture (P < 0.001) showed that increasing distance to more than 1 m is not recommended to avoid unnecessary cumulative loading. The shorter distance of 0.5 m, which generally reduced the most spine loading, may increase it for transfers varying in height. The effect of pace to reduce spine cumulative loading and increase the peak asymmetric loading (P < 0.05) was accentuated by mass, height and distance. The combined factors revealed the importance of tradeoff between peak, cumulative and asymmetric loading.

Lifting Activity Assessment Using Kinematic Features and Neural Networks

Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship between WLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed. The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other lifting conditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this, we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtained in three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within the lifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity center frequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LI and that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used for mapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation during these tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associate with work activities can be integrated with methods already used for biomechanical risk assessment.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship betweenWLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed.The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other liftingconditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this,we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtainedin three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within thelifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity centerfrequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LIand that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used formapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation duringthese tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associatedwith work activities can be integrated with methods already used for biomechanical risk assessment.

Effects of work experience on fatigue-induced biomechanical changes during repetitive asymmetric lifts/lowers

Repetitive lifting/lowering is associated with an increased risk of work-related low back disorders (WRLBDs), and fatigue may exacerbate such risk. Work methods used by experienced workers are potential models for developing worker training to reduce WRLBDs, though whether experience modifies the effects of fatigue on WRLBD risk is largely unknown. Here, six novices and six experienced workers completed 185 cycles of repetitive, asymmetric lifts/lowers. Physical demands, whole-body balance and torso movement stability were assessed using torso kinematics/kinetics, linear/angular momenta and Lyapunov exponents, respectively. Several fatigue-induced changes in movement strategies were evident. Novices decreased and experienced workers increased peak lumbar moments post-fatigue, suggesting lower WRLBD risks among the former in terms of torso kinetics. Other than lumbar moments, though, fatigue substantially reduced group-level differences in torso twisting velocities and accelerations. Post-fatigue movement strategies of experienced workers thus did not appear to be advantageous in terms of WRLBD risk.Practitioner Summary: Fatigue induced changes in movement strategies during a repetitive, asymmetric lifting/lowering task. Novices and experienced workers adapted to fatigue differently, with diminished post-fatigue differences in lifting strategies. Regarding work-related low back disorder risks, the post-fatigue movement strategies of experienced workers did not appear superior to those of novices.

Effects of work experience on work methods during dynamic pushing and pulling

Pushing and pulling are potential risk factors for work-related low back disorders (WRLBDs). While several studies have evaluated differences in work methods related to work experience, such evidence for dynamic pushing and pulling is limited. Eight novices and eight experienced workers completed dynamic push/pull tasks using a cart weighted to 250% of individual body mass in two different configurations (preferred vs. elbow handle heights). Multiple measures [hand forces, torso kinematics and kinetics, and required coefficient of friction (RCOF)] were obtained to assess WRLBD and slip risks. Experienced workers generated higher medio-lateral hand forces, during both pulls and pushes, though with a more substantial difference during pushes (∼74%), and which involved the use of hand force components other than to move the cart in an anterior-posterior direction. Experienced workers also had lower peak torso kinematics in flexion/extension and lateral bending, and lower torso flexion/extension kinetics. The latter is suggestive of a lower risk for WRLBDs, though levels of exposures to WRLBD risk were low to moderate in both groups and were often relatively small and inconsistent across the task configurations. Group-level differences in RCOF were quite small, indicating a comparable slip risk between the two groups. Thus, it was considered inconclusive whether the work methods used by experienced workers during dynamic pushing and pulling are advantageous regarding WRLBD and slip risks. Relevance to industryDistinct movement strategies (work methods) were used by novices vs. experienced workers during dynamic cart pushing/pulling tasks. Regarding WRLBD risks, however, the benefits of the motor control strategies adopted by experienced workers for such tasks were inconsistent and task specific.

Experienced workers may sacrifice peak torso kinematics/kinetics for enhanced balance/stability during repetitive lifting

Work-related low back disorders (WRLBDs) are widely recognized problems, and work experience, while often considered important, has an unclear role with respect to modifying WRLBD risks. For example, some studies have shown that peak torso kinematics/kinetics are higher among experienced workers, suggesting a counterintuitive higher risk. To better understand the movement strategies of experienced workers, additional analyses were conducted using data from a prior study, to assess whole body balance and torso movement stability of 6 experienced workers vs. 6 novices during repetitive lifts/lowers. Dynamic balance and torso movement stability were quantified using peak linear/angular momenta and largest Lyapunov exponent (LLE) of torso flexion/extension, respectively. Peak horizontal linear momenta, all angular momenta, and LLEs were lower among experienced workers, suggestive of superior balance maintenance and more stable torso flexion/extension. Thus, experienced workers seem to sacrifice peak torso kinematics/kinetics to obtain better balance maintenance and torso movement stability, whereas the opposite strategies were evident among novices. These findings highlight that movement strategies can be modified by work experience and have potential implications/applications for worker training or work method analyses.

Classifying the risk of work related low back disorders due to manual material handling tasks

Work related low back disorders (LBDs) due to manual lifting tasks (MLTs) have long been recognized as one of the main occupational disabling injury that affects the quality of life of the industrial working population in the U.S. There have been a number of intensive research efforts devoted to understanding the phenomena of LBDs and building classification models that could effectively distinguish between high risk and low risk MLTs that contribute to LBDs. As of today, however, such models and the occupational exposure limits of different risk factors causing LBDs as well as the guidelines preventing them have not yet been fully proposed. One of the first efforts to comprehend the nature and phenomenon of LBDs was undertaken by Marras et al. (1993). They created a seminal data set and used it to build logistic regression (LR) models to identify significant variables and classify MLTs into high risk and low risk with respect to LBDs. Since then a number of studies have used the same data set to build and test various classifiers to detect the likelihood of LBDs due to manual material handling jobs. This paper summarizes and critiques the previous studies. It also employs this data set to build and test seven classification models, two of which have not been applied in this context yet. The parameters of the models have been calibrated for the best performance, and the models were constructed and validated on the full set and the reduced set of features. Though the performances of our best models are better than those reported in National Institute for Occupational Health and Safety (NIOHS) Guides and two of our previous studies, they are generally less optimistic than those reported in several other studies; this paper proposes a systematic and more reliable approach to creating and validating classifiers to distinguish between low and high risk MLTs that contribute to LBDs.

Multidimensional Training System Evaluation using the Revised Bloom's Taxonomy

Many organizations employ training systems to reduce work-related low back disorders. However, the evaluation of training programs is often not satisfactory, mainly due to the complexity of training systems, the high costs involved, heavy reliance on trainee reactions, and little integration of outcomes-based validation. In this work, we used the Revised Bloom's Taxonomy to classify the learning objectives of a training program. These classified objectives were then examined to predict training performance, and were further compared with trainee reactions using multiple regression and correlation analyses accordingly. Results indicated that the classified learning objectives were better predictors of training performance than trainee reactions. Practical implications of the results are discussed.

Changes in the EMG of Low Back Muscles during Static Lumbar Flexion

Prolonged and/or frequent lumbar flexion posture in industry has been known to contribute to the development of work-related low back pain or disorders but the specific biomechanics of this link has not been fully developed. In this study the effects of static lumbar flexion on back extensor muscles were evaluated by quantifying the changes in the flexion-relaxation onset angle, muscle activity level and the development of muscle fatigue associated with static lumbar flexion. Twenty healthy subjects performed submaximal isometric trunk extension exertions and isokinetic trunk flexion before and immediately after a 5 min continuous lumbar flexion posture while the trunk sagittal flexion angle and the myoelectric activities (EMG) of erector spinae muscles and latissimus dorsi muscles were recorded simultaneously. Results show an increase in the flexion-relaxation onset angle, normalized EMG of isometric extension, and decrease in the median power frequency of EMG as a consequence of the static lumbar flexion. It is suggested that back extensor muscles produce sustained low level contraction during static lumbar flexion, leading to muscular fatigue development. Together with creep deformed passive tissues of the low back and resultant spinal instability, the muscle fatigue of back extensors in static lumbar flexion may play a role in the development of low back pain or disorders.

Why Do Nurses Have a High Incidence of Low Back Disorders, and What Can Be Done to Reduce Their Risk?

Work-related low back disorders (WLBD) are the most frequent and costly musculoskeletal disorder seen, and are related to high working demands. Nurses are among the professionals with the highest rates of WLBD. This paper summarizes the results of research on WLBD among nurses in an acute care teaching hospital. Injury records were reviewed and a questionnaire survey was conducted of 47 nurses from jobs with the highest WLBD incidence rate. The working-life incidence rates of WLBD and point prevalence of low back pain were 65% and 30% for orthopedic nurses and 58% and 25% for intensive care unit (ICU) nurses, respectively. The mean ± standard deviation perceived job exertion on a 10-point scale was 7 ± 2 for the orthopedic nurses and 6 ± 2 for the ICU nurses. Patient transfers (orthopedic nurses) and turning and repositioning patients in bed (ICU nurses) were considered the physically most demanding and risky parts of their occupation. A functional capacity evaluation of 25 nurses and a biomechanical demand analysis of manual handling and patient transfers of 36 nurses were performed. The job simulated forces (78 ± 14% of the maximum) were higher than the preferred force levels (56 ± 21%, P < 0.01). The instantaneous compression at L5/S1 (4754 ± 437 N) and population without sufficient torso strength (37 ± 9%) were highest during the pushing phase of the bed to stretcher transfers by the orthopedic nurses. Evidence-based recommendations for modifications and training programs to reduce the risk of WLBD in nurses have been proposed. Fitness for work, job modifications, and training programs can be designed based on the results presented.

Occupational risks factors identified and interventions suggested by welders and computer numeric control workers to control low back disorders in two steel companies

Work-related low back disorders (WLBD) are common and expensive. This study assessed perceived workload and identified issues and possible improvements to reduce WLBD in 64 welders and 44 computer numeric control workers (CNC workers). Injury records were reviewed and a questionnaire was completed. Discomfort ratings and visual analogue scales were compared using one-way ANOVA with Fisher's least-significant difference post hoc test. The annual incidence of recorded WLBD was lower for welders (3.4%) than for CNC workers (5.4%). On the other hand, the working-life incidences of reported WLBD and the point prevalences of low back pain were higher for the welders (55% and 27%, respectively) than for the CNC workers (36% and 16%). The discomfort scores for the low back region were higher than the scores for the other body parts. The weight manually handled [mean (S.D.)] by the welders [21 (6) kg] was lower than by the CNC workers [35 (11) kg]. Perceived exertion was strong for both groups [5 (1)]. For the welders, the postures, repetitions, and duration contributed more to the total effort than the movements and forces ( p<0.035). For CNC workers, repetitions and duration contributed more to the total effort than postures, movements, and force ( p<0.044). The workers identified relevant problems and possible improvements for their jobs. Recommended improvements included adjustable tables, stretching, and crane use by welders, and training, standing mats, clear limits, stable magnets, and less asymmetrical lifting by CNC workers. Précis This study evaluated reported occupational risk factors for WLBD among welders and CNC workers from two steel companies. A questionnaire was responded by 108 steel workers. The information presented can be used to design participatory ergonomic interventions aimed at reducing WLBD.

Participation in Occupational Health Longitudinal Studies: Predictors of Missed Visits and Dropouts

The purpose of this study is to determine whether demographics, health, and job factors influence continued participation of employed persons in a longitudinal intervention study of tertiary prevention for work-related low-back disorders (WR-LBDs). Four hundred fifty-four actively employed persons had enrolled in an intervention study of back supports and education to promote recovery from a WR-LBD. Baseline values were examined according to whether individuals continued in the study, missed a visit, or dropped out; frequency of missed visits; and early or late dropouts at follow-up intervals of 1, 2, 6, and 12 months. Workers who did not complete all study visits were significantly more likely to be younger and have poorer self-reported health. Individuals with a greater percentage of body fat dropped out early. Significantly more missed visits were observed among those who had arthritis and longer duration of low-back pain symptoms. Job factors did not influence study participation. Continued participation in a longitudinal study of working adults was influenced by age, health status, and factors related to the primary condition targeted by the intervention study. Strategies aimed at those who report lower levels of health and symptoms related to the condition under study may promote follow-up participation in longitudinal studies. Thus, even among actively employed persons, one should not assume that all workers are "healthy."