Variable Lifting Index for Manual-Lifting Risk Assessment: A Preliminary Validation Study

Workplace Psychosocial Factors, Work Organization, and Physical Exertion as Risk Factors for Low Back Pain Among US 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 US workers.

METHODS

2015 National Health Interview Survey data were analyzed to calculate the prevalence rates and prevalence ratios for LBP across levels of workplace psychosocial and organizational risk factors among 17,464 US adult workers who worked ≥20 hours per week. Results were also stratified by workplace physical exertion.

RESULTS

The adjusted prevalence rates 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.

Revised NIOSH lifting equation: a critical evaluation

The revised NIOSH lifting equation (RNLE) aims to manage lifting-related lower back pain (LBP), by determining safe load limits. Many researchers have studied the multiplier development criteria, the universal applicability of the equation and its ability to identify an increased risk of LBP in lifting tasks. Although a number of strengths of the equation have been highlighted, many limitations have also been identified. The need for new multipliers, such as worker and environmental characteristics, was highlighted in order to make the equation more adaptable. The RNLE was designed to protect 75% of female workers and is therefore inherently conservative. Additionally, as all multipliers have values less than or equal to 1, the recommended weight limits (RWLs) can be further reduced. Thus, new multipliers may be defined, by combining two or more existing multipliers, to make the RWLs more realistic.

Decision-Making Framework for Implementing Safer Human-Robot Collaboration Workstations: System Dynamics Modeling

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.

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.

Extended compilation of autopsy-material measurements on lumbar ultimate compressive strength for deriving reference values in ergonomic work design: The Revised Dortmund Recommendations

Measures of human physical capacity are required in ergonomic work design. To avoid biomechanical low-back overload, criteria are needed to differentiate load and overload. With respect to the evaluation of manual materials handling and similar physical exposures regarding potential overload, the compression component of the forces transferred via lumbar discs or vertebrae are compared with the ultimate compressive strength of lumbar-spine segments in a common biomechanical approach. As mechanical load-bearing capacity cannot be quantified directly in vivo, forces are applied to dissected spinal elements up to failure, which is interpreted as a measure of ultimate strength or tolerance to compression. Corresponding autopsy-material measurements were collected from literature and examined regarding several conditions: At the very minimum, a specimen consists of a complete vertebra or a disc including the adjacent endplates; failure is identified definitely as lumbar; compressive-force application is quasi-static; results are given as single values etc. This study continues previous collations, the latest is dated on 2001 including 25 usable out of 47 investigations totally. Currently, 66 newly discovered seemingly appropriate studies were collected via a systematic literature search, 11 of them were added for subsequent analysis. Nearly 4,000 values were compiled, 1,192 remained for analysis. Human lumbar ultimate compressive strength varies between 0.6 and 15.6 kN, mean and standard deviation are 4.84 ± 2.50 kN. For data originating from donors of specified gender and aged 20 years or more, the distributions are characterised by 6.09 ± 2.69 kN for male adults (n=305) and 3.95 ± 1.79 kN for female adults (n=205). According to a linear regression model for donors aged 20 years or more, strength significantly decreases with age: 10.43 kN minus 0.923 kN per 10 years of age for males and 7.65 kN minus 0.685 kN per decade for females. Based on these gendered age relations, the "Revised Dortmund Recommendations" were derived ranging between 5.4 kN for males aged 20 years and 2.2 kN for males of 60 years or more. The corresponding recommended limits for females amount to 4.1 and 1.8 kN, respectively. A specific safety margin was implemented for young adults up to 25 years of age as skeletal strength may not be fully developed. Due to the compression-related and biomechanical nature of this approach, other influences like shear or torsion as well as psychological or psychosocial risk factors remain unconsidered despite their undoubted importance for initiating complaints, disorders and diseases at the low-back region.

Cumulative Mass and NIOSH Variable Lifting Index Method for Risk Assessment: Possible Relations

Objective The aim of this study was to explore whether the Variable Lifting Index (VLI) can be corrected for cumulative mass and thus test its efficacy in predicting the risk of low-back pain (LBP). Background A validation study of the VLI method was published in this journal reporting promising results. Although several studies highlighted a positive correlation between cumulative load and LBP, cumulative mass has never been considered in any of the studies investigating the relationship between manual material handling and LBP. Method Both VLI and cumulative mass were calculated for 2,374 exposed subjects using a systematic approach. Due to high variability of cumulative mass values, a stratification within VLI categories was employed. Dummy variables (1-4) were assigned to each class and used as a multiplier factor for the VLI, resulting in a new index (VLI_CMM). Data on LBP were collected by occupational physicians at the study sites. Logistic regression was used to estimate the risk of acute LBP within levels of risk exposure when compared with a control group formed by 1,028 unexposed subjects. Results Data showed greatly variable values of cumulative mass across all VLI classes. The potential effect of cumulative mass on damage emerged as not significant ( p value = .6526). Conclusion When comparing VLI_CMM with raw VLI, the former failed to prove itself as a better predictor of LBP risk. Application To recognize cumulative mass as a modifier, especially for lumbar degenerative spine diseases, authors of future studies should investigate potential association between the VLI and other damage variables.

Non-chemical Risk Assessment for Lifting and Low Back Pain Based on Bayesian Threshold Models

Background

Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor–response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried.

Methods

This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function.

Results

Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found.

Conclusion

A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

Variable Lifting Index (VLI): A New Method for Evaluating Variable Lifting Tasks

OBJECTIVE

We seek to develop a new approach for analyzing the physical demands of highly variable lifting tasks through an adaptation of the Revised NIOSH (National Institute for Occupational Safety and Health) Lifting Equation (RNLE) into a Variable Lifting Index (VLI).

BACKGROUND

There are many jobs that contain individual lifts that vary from lift to lift due to the task requirements. The NIOSH Lifting Equation is not suitable in its present form to analyze variable lifting tasks.

METHOD

In extending the prior work on the VLI, two procedures are presented to allow users to analyze variable lifting tasks. One approach involves the sampling of lifting tasks performed by a worker over a shift and the calculation of the Frequency Independent Lift Index (FILI) for each sampled lift and the aggregation of the FILI values into six categories. The Composite Lift Index (CLI) equation is used with lifting index (LI) category frequency data to calculate the VLI. The second approach employs a detailed systematic collection of lifting task data from production and/or organizational sources. The data are organized into simplified task parameter categories and further aggregated into six FILI categories, which also use the CLI equation to calculate the VLI.

RESULTS

The two procedures will allow practitioners to systematically employ the VLI method to a variety of work situations where highly variable lifting tasks are performed.

CONCLUSIONS

The scientific basis for the VLI procedure is similar to that for the CLI originally presented by NIOSH; however, the VLI method remains to be validated.

APPLICATION

The VLI method allows an analyst to assess highly variable manual lifting jobs in which the task characteristics vary from lift to lift during a shift.