Workplace psychosocial factors, work organization, and physical exertion as risk factors for low back pain among U.S. workers: Data from the 2015 National Health Interview Survey.

OBJECTIVE: To evaluate the association between workplace psychosocial, organization, and physical risk factors with low back pain (LBP) among U.S. workers. METHODS: 2015 National Health Interview Survey data was analyzed to calculate prevalences and prevalence ratios for LBP across levels of workplace psychosocial and organizational risk factors among 17,464 U.S. adult workers who worked ≥20 hours/week. Results were also stratified by workplace physical exertion. RESULTS: The adjusted prevalences of LBP were significantly elevated for workers reporting high job demand, low job control, work-family imbalance, bullying, job insecurity, working alternate shifts, and physical exertion. Job control and nonstandard shifts were significantly associated with LBP only among those who reported low/no physical exertion. CONCLUSIONS: LBP prevalence was associated with select workplace psychosocial and organization risk factors. Stratification by physical exertion modified multiple associations.

Physical job demands in pregnancy and associated musculoskeletal health and employment outcomes: a systematic review.

OBJECTIVE: A decline in musculoskeletal health during pregnancy is an underappreciated adverse outcome of pregnancy that can have immediate and long-term health consequences. High physical job demands are known risk factors for nontraumatic musculoskeletal disorders in the general working population. Evidence from meta-analyses suggest that occupational lifting and prolonged standing during pregnancy may increase risk of adverse pregnancy outcomes. This systematic review examined associations between occupational lifting or postural load in pregnancy and associated musculoskeletal disorders and related sequalae. DATA SOURCES: Five electronic databases (Medline, Embase, CINAHL, NIOSHTIC-2, and Ergonomic Abstracts) were searched from 1990 to July 2022 for studies in any language. A Web of Science snowball search was performed in December 2022. Reference lists were manually reviewed. STUDY ELIGIBILITY CRITERIA: Eligible studies reported associations between occupational lifting or postural load and musculoskeletal health or sequelae (eg, employment outcomes) among pregnant and postpartum workers. METHODS: Data were extracted using a customized form to document study and sample characteristics; and details of exposures, outcomes, covariates, and analyses. Investigators independently assessed study quality for 7 risk-of-bias domains and overall utility, with discrepant ratings resolved through discussion. A narrative synthesis was conducted due to heterogeneity. RESULTS: Sixteen studies (11 cohort studies, 2 nested case-control studies, and 3 cross-sectional studies) from 8 countries were included (N=142,320 pregnant and N=1744 postpartum workers). Limited but consistent evidence with variable quality ratings, ranging from critical concern to high, suggests that pregnant workers exposed to heavy lifting (usually defined as ≥22 lbs or ≥10 kg) may be at increased risk of functionally limiting pelvic girdle pain and antenatal leave. Moreover, reports of dose-response relationships suggest graded risk levels according to lifting frequency, ranging from 21% to 45% for pelvic girdle pain and 58% to 202% for antenatal leave. Limited but consistent evidence also suggests that postural load increases the risk of employment cessation. CONCLUSION: Limited but consistent evidence suggests that pregnant workers exposed to heavy lifting and postural load are at increased risk of pelvic girdle pain and employment cessation. Job accommodations to reduce exposure levels may promote safe sustainable employment for pregnant workers.

Psychosocial risk factors for low back pain in US workers: Data from the 2002-2018 quality of work life survey.

BACKGROUND: Examining workplace psychosocial risk factors for back pain becomes increasingly important because of the changing nature of work and rising healthcare costs. Some psychosocial risk factors for back pain, such as work and family imbalance, exposure to a hostile work environment, and job insecurity, are understudied for the working population in the United States. METHODS: Data used in this study came from the Quality of Work Life Survey (QWL), a supplementary module of the General Social Survey conducted in the United States. Data from the 2002, 2006, 2010, 2014, and 2018 QWL surveys were used in these analyses, giving a total sample size of 6661. Five domains of workplace psychosocial risk factors for back pain were examined, including job strain, low social support, work-family imbalance, exposure to a hostile work environment (harassment and discrimination), and job insecurity. The adjusted odds ratio (aOR) of each psychosocial risk factor for back pain with 95% confidence intervals (CI) was estimated using a multivariable logistic regression model after controlling for job physical factors, occupation, and demographic and socioeconomic characteristics. RESULTS: Significant associations were found between back pain and several psychosocial factors including job strain (aOR 1.19; CI 1.00,1.41), work-family imbalance (aOR,1.42; CI 1.22,1.64), harassment (aOR 1.40; CI 1.15,1.71), and discrimination (aOR 1.20 CI 1.00,1.44). CONCLUSION: This study contributes to the understanding of the relationship between a variety of workplace psychosocial factors and back pain. Our findings suggest directions in future longitudinal research to examine emerging workplace psychosocial factors for back pain.

Investigating gripping force during lifting tasks using a pressure sensing glove system.

Lifting tasks remain one of the leading causes of musculoskeletal disorders (MSDs), primarily in the low back region. Lifting analysis tools are, therefore, designed for assessing the risk of low back pain. Shoulder musculoskeletal problems have emerged as common MSDs associated with manual handling tasks. It is hypothesized that gripping force is related to lifting conditions and may be used as a supplementary risk metric for MSDs in the shoulder and low back regions, because it measures additional hand exertions for coupling the lifted object during lifting. We assessed the capability tactile gloves for measuring the gripping force during lifting as a means for assessing different task conditions (lifting weight, lifting height, lifting direction, body rotation, and handle). Thirty participants wore the tactile gloves and performed simulated lifting tasks. Regression models were used to analyze the effects of the task variables on estimating the measured gripping force. Results demonstrated that 58% and 70% of the lifting weight variance were explained by the measured gripping force without and with considering the individual difference, respectively. In addition to the lifting risk measures commonly used by practitioners, this study suggests a potential for using gripping force as a supplementary or additional risk metric for MSDs.

Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method.

OBJECTIVE: A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting. BACKGROUND: Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments. METHODS: Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames. RESULTS: The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles (R2 = .80, p < .001). The training error for the kinematics model was 2.3°. CONCLUSION: Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision. APPLICATION: This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

Evidence for Range Expansion and Origins of an Invasive Hornet Vespa bicolor (Hymenoptera, Vespidae) in Taiwan, with Notes on Its Natural Status.

The invasive alien species (IAS) Vespa bicolor is the first reported hornet that has established in Taiwan and is concerning as they prey on honeybee Apis mellifera, which leads to colony losses and public concerns. Thus, the aim of this study was to assess the current status of V. bicolor abundance, dispersal, and impact and to trace the origins of Taiwan's V. bicolor population. Our studies took place in five areas in northern to central Taiwan. We used mtDNA in the phylogenetic analyses. Field survey and ecological niche modeling (ENM) were used to understand the origins and current range of the invasive species. Two main subgroups of V. bicolor in the phylogenetic tree were found, and a clade with short branch lengths in Southeastern China and Taiwan formed a subgroup, which shows that the Taiwan population may have invaded from a single event. Evidence shows that V. bicolor is not a severe pest to honeybees in the study area; however, using ENM, we predict the rapid dispersion of this species to the cooler and hilly mountain areas of Taiwan. The management of V. bicolor should also involve considering it a local pest to reduce loss by beekeepers and public fear in Taiwan. Our findings highlight how the government, beekeepers, and researchers alike should be aware of the implications of V. bicolor's rapid range expansion in Taiwan, or in other countries.

A deep learning approach for lower back-pain risk prediction during manual lifting.

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.

Load Asymmetry Angle Estimation Using Multiple view Videos.

A robust computer vision-based approach is developed to estimate the load asymmetry angle defined in the revised NIOSH lifting equation (RNLE). The angle of asymmetry enables the computation of a recommended weight limit for repetitive lifting operations in a workplace to prevent lower back injuries. An open-source package OpenPose is applied to estimate the 2D locations of skeletal joints of the worker from two synchronous videos. Combining these joint location estimates, a computer vision correspondence and depth estimation method is developed to estimate the 3D coordinates of skeletal joints during lifting. The angle of asymmetry is then deduced from a subset of these 3D positions. Error analysis reveals unreliable angle estimates due to occlusions of upper limbs. A robust angle estimation method that mitigates this challenge is developed. We propose a method to flag unreliable angle estimates based on the average confidence level of 2D joint estimates provided by OpenPose. An optimal threshold is derived that balances the percentage variance reduction of the estimation error and the percentage of angle estimates flagged. Tested with 360 lifting instances in a NIOSH-provided dataset, the standard deviation of angle estimation error is reduced from 10.13° to 4.99°. To realize this error variance reduction, 34% of estimated angles are flagged and require further validation.

Psychosocial Factors and Low Back Pain Outcomes in a Pooled Analysis of Low Back Pain Studies.

OBJECTIVE: Assessment of possible relationships between work-related psychosocial measures and self-reported low back pain (LBP) outcomes in a large pooled dataset of 1929 participants from 82 facilities in the United States. METHODS: Pooled data from three prospective cohort studies were used to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for relationships between psychosocial factors and the LBP outcomes. Personal and occupational confounders were controlled for in adjusted Logistic regression models. RESULTS: Supervisor support and job satisfaction were significantly (P < 0.05) related to all three LBP outcomes. Other psychosocial factors were significantly (P < 0.05) associated with at least one of the LBP outcomes. Adjusted ORs ranged from approximately 1.50 to 3.50 for most associations. CONCLUSIONS: There is a significant relationship between work-related psychosocial measures and LBP outcomes.

Trends in Work-Related Musculoskeletal Disorders From the 2002 to 2014 General Social Survey, Quality of Work Life Supplement.

OBJECTIVE: To update trends in prevalence of back and upper limb musculoskeletal symptoms and risk factors from the 2014 Quality of Work Life (QWL) Survey. METHODS: Quadrennial QWL Surveys, 2002 to 2014 (with N = 1455, 1537, 1019, and 1124 in 2002, 2006, 2010, and 2014 surveys respectively) were analyzed for reports of back pain and pain in arms. RESULTS: In the fourth analysis of this survey, 12-year trends continue to show a decline in back pain and pain in arms. Key physical (heavy lifting, hand movements, very hard physical effort) and psychosocial/work organizational factors (low supervisor support, work is always stressful, not enough time to get work done) remain associated with back and arm pain, with the physical risk factors showing the strongest associations. CONCLUSION: Physical exposure risk factors continue to be strongly associated with low back and arm pain and should be the focus of intervention strategies.

Spinal loading and lift style in confined vertical space.

The objective of this study was to investigate biomechanical loads on the lumbar spine as a function of working in a confined vertical space, consistent with baggage handling inside the baggage compartment of an airplane. Ten male subjects performed baggage handling tasks using confined (kneeling, sitting) and unconfined (stooping) lifting styles. Dependent measures of torso flexion and three-dimensional spinal loads were assessed with an electromyography-driven biomechanical model. Lifting exertions typical to airline baggage handling posed significant risk to the lumbar spine, regardless of lifting style. Statistically significant differences attributable to lift style (stooping, kneeling, sitting) were not observed for peak compressive, lateral shear, or resultant spinal loads, but lifting while kneeling decreased anterior/posterior (A/P) shear spinal loads relative to stooping (p = 0.02). Collectively, kneeling offers the greatest benefit when lifting in confined spaces because of the ability to keep the torso upright, subsequently reducing shear forces on the lumbar spine.

Industrial exoskeletons: Need for intervention effectiveness research.

Exoskeleton devices are being introduced across several industry sectors to augment, amplify, or reinforce the performance of a worker's existing body components-primarily the lower back and the upper extremity. Industrial exoskeletons may play a role in reducing work-related musculoskeletal disorders arising from lifting and handling heavy materials or from supporting heavy tools in overhead work. However, wearing an exoskeleton may pose a number of risks that are currently not well-studied. There are only a few studies about the safety and health implications of wearable exoskeletons and most of those studies involve only a small number of participants. Before the widespread implementation of industrial exoskeletons occurs, there is need for prospective interventional studies to evaluate the safety and health effectiveness of exoskeletons across various industry sectors. Developing a research strategy to fill current safety and health knowledge gaps, understanding the benefits, risks, and barriers to adoption of industrial exoskeletons, determining whether exoskeleton can be considered a type of personal protective equipment, and advancing consensus standards that address exoskeleton safety, should be major interests of both the occupational safety and health research and practice communities.

Understanding outcome metrics of the revised NIOSH lifting equation.

The interpretation of the calculated result of the revised NIOSH Lifting Equation (RNLE) has been problematic because the relationship of the calculated result to back injury risk has not always been either well understood nor consistently interpreted. During the revision of the ISO standard 11228-1 (Manual lifting, lowering and carrying), an extensive literature review was conducted on validation studies of the RNLE. A systematic review of exposure-risk associations between the LI metrics and various low-back health outcomes from peer-reviewed epidemiological studies was conducted. Risk interpretations for different levels of calculated result of the RNLE are added to the ISO standard. Rationale for the risk interpretations is presented in this paper.

The accuracy of a 2D video-based lifting monitor.

A widely used risk prediction tool, the revised NIOSH lifting equation (RNLE), provides the recommended weight limit (RWL), but is limited by analyst subjectivity, experience, and resources. This paper describes a robust, non-intrusive, straightforward approach to automatically extract spatial and temporal factors necessary for the RNLE using a single video camera in the sagittal plane. The participant's silhouette is segmented by motion information and the novel use of a ghosting effect provides accurate detection of lifting instances, and hand and feet location prediction. Laboratory tests using 6 participants, each performing 36 lifts, showed that a nominal 640 pixel × 480 pixel 2D video, in comparison to 3D motion capture, provided RWL estimations within 0.2 kg (SD = 1.0 kg). The linear regression between the video and 3D tracking RWL was R2 = 0.96 (slope = 1.0, intercept = 0.2 kg). Since low definition video was used in order to synchronise with motion capture, better performance is anticipated using high definition video. Practitioner's summary: An algorithm for automatically calculating the revised NIOSH lifting equation using a single video camera was evaluated in comparison to laboratory 3D motion capture. The results indicate that this method has suitable accuracy for practical use and may be, particularly, useful when multiple lifts are evaluated. Abbreviations: 2D: Two-dimensional; 3D: Three-dimensional; ACGIH: American Conference of Governmental Industrial Hygienists; AM: asymmetric multiplier; BOL: beginning of lift; CM: coupling multiplier; DM: distance multiplier; EOL: end of lift; FIRWL: frequency independent recommended weight limit; FM: frequency multiplier; H: horizontal distance; HM: horizontal multiplier; IMU: inertial measurement unit; ISO: International Organization for Standardization; LC: load constant; NIOSH: National Institute for Occupational Safety and Health; RGB: red, green, blue; RGB-D: red, green, blue - depth; RNLE: revised NIOSH lifting equation; RWL: recommended weight limit; SD: standard deviation; TLV: threshold limit value; VM: vertical multiplier; V: vertical distance.

Prevalence of low back pain, seeking medical care, and lost time due to low back pain among manual material handling workers in the United States.

BACKGROUND: Low back pain (LBP) is a common and costly problem throughout the United States. To achieve a greater understanding of the occupational risk factors, the National Institute for Occupational Safety and Health (NIOSH) funded a low back health effects consortium, which performed several surveillance studies throughout the United States. This study combines data from the consortium research groups resulting in a data set with nearly 2000 workers in various regions of the country. The purpose of this paper is to examine prevalence and personal risk factors of low back health effects among these workers. METHODS: There were three common questions regarding history of low back health effects in the past 12 months including 1) have you had LBP lasting 7 days, 2) have you sought medical care for LBP, and 3) have you taken time off work due to LBP. The questionnaire included demographic questions. There were five data collections institutions or sites including NIOSH, Ohio State University, University of Wisconsin-Milwaukee, Texas A&M University, and University of Utah. RESULTS: The 12-month period prevalence of low back pain lasting 7 days, seeking medical care, and lost time due to LBP were 25, 14 and 10%, respectively. There were no statistically significant differences in gender, age or weight between cases and non-cases for any prevalence measure. The height of workers was significantly greater in the cases compared to non-cases for all three prevalence definitions. There were significant differences among the sites on the prevalence of seeking medical care for LBP and lost time due to LBP. The Ohio State University had significantly higher prevalence rates for seeking medical care and lost time due to LBP than University of Wisconsin, University of Utah, or Texas A&M University. CONCLUSION: LBP, the least severe low back health effect studied, had the highest prevalence (25%) and lost time due to LBP, the most severe low back health effect studied, had the lowest prevalence (10%) among nearly 2000 US manual material handling workers. There was a significant site or regional influence in prevalence rates for seeking medical care and lost time due to LBP.

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions.

OBJECTIVE: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. BACKGROUND: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. METHODS: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. RESULTS: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. CONCLUSIONS: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. APPLICATIONS: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.

Effectiveness of a vacuum lifting system in reducing spinal load during airline baggage handling.

Information on spinal loading for using lift assist systems for airport baggage handling is lacking. We conducted a laboratory study to evaluate a vacuum lift system for reducing lumbar spinal loads during baggage loading/unloading tasks. Ten subjects performed the tasks using the industry average baggage weight of 14.5 kg on a typical two-shelved baggage cart with or without using the lift system (i.e. lifting technique). Repeated measures analysis of variance (2 tasks × 2 shelf heights x 2 techniques) was used. Spinal loads were estimated by an electromyography-driven biomechanical model. On average, the vacuum lift system reduced spinal compressive forces on the lumbar spine by 39% and below the 3400 N damage threshold. The system also resulted in a 25% reduction in the anterior-posterior shear force at the L5/S1 inferior endplate level. This study provides evidence for the potential to reduce spinal loads when using a vacuum lift system.