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HFSXXX10.1177/0018720815627405Month XXXXShort Title
SPECIAL SECTION: Impact of Thomas Waters on the Field of Ergonomics
The Cumulative Lifting Index (CULI) for the Revised NIOSH Lifting Equation: Quantifying Risk for Workers With Job Rotation Arun Garg and Jay M. Kapellusch, University of Wisconsin–Milwaukee Objective: The objectives were to: (a) develop a continuous frequency multiplier (FM) for the revised NIOSH lifting equation (RNLE) as a function of lifting frequency and duration of a lifting task, and (b) describe the Cumulative Lifting Index (CULI), a methodology for estimating physical exposure to workers with job rotation. Background: The existing FM for the RNLE (FME) does not differentiate between task duration >2 hr and 2 hr. The CULI is based on an incremental approach to accumulate task exposures into a single, integrated job-level exposure. The procedure for calculating the CULI is similar to the one used for calculating CLI (Waters et al., 1994). In this paper, we have used CLIs to illustrate computations for CULI. Please note that for simple tasks, CLI reduces to LI and is the same as LI. Consider a job with n tasks and with corresponding CLIs (CLI1, CLI2, CLI3, . . . CLIn). Please see page 46 of the Applications Manual for the RNLE for calculating CLIj.
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The duration of these tasks are HRS1, HRS2, HRS3, . . . HRSn hours per day, respectively. The procedure for calculating CULI involves the following steps: First, rearrange and renumber these tasks in order of decreasing physical strain based on CLI (or LI as appropriate) scores for each task. Each CLI is computed using its own task duration, that is, CLIj is computed for HRSj hours using the FMP from Equations (1) and (2) as appropriate. Therefore,
CLI1 ≥ CLI2 ≥ CLI3 ≥ . . . ≥ CLIn,(3) where CLIj = Composite Lifting Index for the jth task performed for HRSj hours per day. Next, calculate Frequency Independent CLIs (FICLIs; or Frequency Independent LIs [FILIs] as appropriate) using the following Equation (4): FICLIj = CLIj × FMj,(4) where FMj = FMP estimated from Equations (1) and (2) for the jth task performed for HRSj hours per day. Finally, the CULI for the entire shift is calculated using Equation (5): CULI = ( CLI1 ) +
∑∆CLI
FICLI 2 FICLI 2 CULI = ( CLI1 ) + − + FM 2,( HRS + HRS ) FM 2,( HRS ) 1 2 1 FICLI3 FICLI3 + (5) − FM 3,( HRS + HRS HRS ) FM 3,( HRS + HRS ) 1 2 1 2+ 3 FICLI n FICLI n + − FM n ,( HRS + HRS HRS HRS ) FM n ,( HRS + HRS HRS HRS ) n + + ... + 2+ 3+...+ n −1 1 2 3 1
In some rare cases, CULI may be greater than peak CLI for a task calculated as though that task were performed for the entire work shift (CLIs). In those situations, the CULI is constrained by the following equation: If CULI > CLIs, then CULI = CLIs, (6)
where CLIs = peak task CLI calculated as if the task were performed for the entire work shift. Duration of task (1, 2, and 8 hr per day) in the RNLE (Waters et al., 1993, Table 7) affects the frequency multiplier differently at different frequencies and does not follow a smooth function. In general, an increase in duration (from 1 to 2 and 8 hr) results in a much greater decrease in the frequency multiplier at higher frequencies than at lower frequencies, and the decrease becomes larger and larger as the frequency increases. Therefore, when two or more tasks performed at different frequencies are combined, as is the case with CULI, the CULI may exceed the hypothetical CLI if only one of the two tasks were performed for the entire work shift. This phenomena generally occurs when the second task has a much higher lifting frequency than the first task. In the simulation presented later (see Comparison of CULI With Time-Weighted and Peak Task CLIs), the CULI exceeded the peak task CLI for 8 hr for 5% of the jobs. For those 5% jobs, the mean difference between CULI and the peak task CLI for 8 hr was 4% (SD = 3%, range = 0%–15%). This difference had little effect in risk classification (CLI ≤ 1.0, 1.0 < CLI ≤ 3.0, and CLI > 3.0) and resulted in a higher risk classification for 1.0 and peak task CLI for 8 hr was ≤1.0 for 0.2% of jobs. Similarly, CULI was >3.0 and the peak task CLI for 8 hr was ≤3.0 for 0.5% of jobs. To illustrate the aforementioned procedure, consider a worker who performs two tasks, A and B, for 4 hr each. The CLIs are 0.3 and 0.93, lifting frequencies (F) are six and two lifts per minute, and FICLIs are 0.22 and 0.34 for Tasks A and B, respectively. We order these tasks as Tasks 1 and 2 corresponding to Tasks B and A. Therefore, CLI1 = 0.93; FICLI1 = 0.34; F1 = 6. CLI2 = 0.3; FICLI2 = 0.22; F2 = 2. The CULI for the 8-hr work shift is CULI = CLI1 + {[FICLI2 / FM2,(4+4 hr)] – [FICLI2 / FM2,(4 hr)]}. CULI = 0.93 + [(0.22 / 0.65) – (0.22 / 0.74)] = 0.93 + 0.04 = 0.97.
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6 Month XXXX - Human Factors
Thus, the CULI for the entire work shift is 0.97, and the job is considered “safe.” In this example, incremental increase in biomechanical stressors by adding Task A to Task B is negligible (∆CLI = 0.04). Using the FME provided in the applications manual (Waters et al., 1994), Tasks A and B have CLIs of 0.35 and 1.24, respectively. In this case, peak task CLI for 8 hr will result in a CLI of 1.24, and time-weightedaverage CLI will result in a CLI of 0.80 (see next section for definitions of peak exposures and time-weighted-average CLIs). Comparison of CULI with time-weighted and peak task CLIs. To compare the performance of CULI with the two commonly used methods, time-weighted average and peak exposures, we simulated jobs with different combinations of FICLI, frequency of lifting, and duration of tasks (hours per day). Each job consisted of two tasks and was performed for an 8-hr work shift. The FICLI for each of the two tasks ranged from 0.2 to 2.5 (in increments of 0.1 for FICLI ≤ 1.0, 0.2 for FICLI ≥ 1.0 and ≤ 2.0, and 0.5 for FICLI ≥ 2.0); frequencies of lifting were 0.2, 0.5, 1.0, and then increments of 1.0 up to 8.0 lifts per minute for each task; and hours per day for each task ranged from 1 to 7 hr per day in 1-hr increments. We assumed total shift duration of 8 hr. Based on these parameters, the simulation produced 157,500 jobs. Table 2 compares absolute and relative (percentage) differences between the CULI, timeweighted-average CLI, peak task CLI using actual task duration (subsequently referred to as peak task CLI for task duration), and peak task CLI calculated as if the tasks were performed for the entire work shift, that is, 8 hr for this simulation (subsequently referred to as peak task CLI for shift). The CULI was calculated using the FMP from Equations (1) and (2), and all other CLIs (time-weighted-average CLI, peak task CLI for task duration, and peak task CLI for shift) were calculated using the FME in the applications manual (Waters et al., 1994). We used two different frequency multipliers to compare the proposed methodology for calculating CULI to the currently available options for calculating CLIs using RNLE for workers with job rotation
that have been used in the past (Garg, Boda, et al., 2014; Lu et al., 2014; Waters et al., 1999). Time-weighted-average CLI was computed by multiplying each task CLI with its duration in hours, adding these CLI × Duration products, and then dividing this sum by duration of the entire job (8 hr in this simulation). Peak task CLI for task duration was determined by first calculating the CLI for each task using frequency multiplier corresponding to its frequency and duration in hours and then by taking the highest of these CLIs. To calculate the peak task CLI for shift, it was assumed that each task was performed for the entire work shift (8 hr in this simulation). We used frequency multiplier corresponding to the task frequency and duration of 8 hr to calculate task CLI for shift for each task. Thus, each task has a CLI as if it were performed for 8 hr. We selected the highest of these CLIs as the peak task CLI for shift. As an example, consider a job with two tasks, A and B. Task A is performed for 2 hr per day, lifting frequency is one per minute (frequency multiplier = 0.88), and it has a CLI of 1.4. Task B is performed for 6 hr per day, lifting frequency is four per minute (frequency multiplier = 0.45), and the CLI is 0.7. For this job, time-weighted-average CLI is (1.4 × 2) + (0.9 × 6) / 8, or 0.88. Task CLI for task duration (2 and 6 hr for Tasks A and B in this example) is 1.4 for Task A and 0.7 for Task B. The higher of these two is 1.4. Therefore, peak task CLI for task duration is 1.4. To calculate peak task CLI for shift, we assume both Tasks A and B have duration of 8 hr. Frequency multipliers (Waters et al., 1993, Table 7) are 0.75 and 0.45. Task A CLI as if it were performed for the entire shift is 1.6 ([1.4 × 0.88] / 0.75). Task B CLI as if it were performed for 8 hr remains unchanged because the frequency multiplier is the same for 6 and 8 hr ([0.7 × 0.45] / 0.45 = 0.7). Therefore, peak task CLI for shift is the higher of 1.6 and 0.7, or 1.6. For 157,500 jobs, mean, standard deviation, and range for peak task CLI for shift, peak task CLI for task duration, timeweighted-average CLI, and CULI were 3.99 ± 2.79 (0.24–13.89), 3.5 ± 2.59 (0.24–13.89), 2.63 ± 1.94 (0.23–13.89), and 3.36 ± 2.35 (0.23–15.9), respectively.
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Table 2: Comparison of Differences Between Time-Weighted-Average CLI (CLITWA), Peak Task CLI for Task Duration (CLITD), Peak Task CLI for Shift (CLIS), and CULI (Horizontal Header CLI Greater than Vertical Header CLI Out of 157,500 Simulated Jobs) Measure Is Greater Than . . . Measure CLITWA n Median IRQ Range CLITD n Median IRQ Range CLIS n Median IRQ Range CULI n Median IRQ Range
CLITWA
CLITD
CLIS
CULI
Absolute
%
Absolute
%
Absolute
%
Absolute
— — — —
— — — —
155,986 0.5 0.2–1.2 0.0–8.5
99 17 8–35 0–559
156,784 0.8 0.3–1.7 0.0–13.2
99 31 14–77 0–1803
153,154 0.5 0.2–1.1 0.0–5.2
0 — — —
0 — — —
— — — —
— — — —
44,560 1.1 0.4–2.5 0.0–9.7
28 60 25–94 0–236
62,949 0.2 0.1–0.4 0.0–4.2
0 — — —
0 — — —
0 — — —
0 — — —
— — — —
— — — —
21,660 0.1 0.0–0.3 0.0–2.0
3,421 0.1 0.0–0.1 0.0–0.4
2 3 1–5 0–14
89,531 0.2 0.1–0.6 0.0–4.9
57 8 2–18 0–55
130,377 0.3 0.1–1.0 0.0–9.1
83 13 4–32 0–187
— — — —
% 97 21 12–40 0–563 40 9 5–17 0–97 14 3 1–6 0–15 — — — —
Note. CLI = Composite Lifting Index; CULI = Cumulative Lifting Index; IRQ = interquartile range. Horizontal header CLI is compared to vertical header CLI. For example, CLIS is greater than CLITD for 44,560 of the 157,500 simulated jobs (28%). CLITWA, CLITD, and CLIS were computed using the existing RNLE frequency multiplier, and CULI was computed using the proposed frequency multiplier.
With few exceptions, time-weighted-average CLI consistently provided the lowest exposure estimates among the four techniques (Table 2). For example, time-weighted-average CLI was lower than peak task CLI for task duration, peak task CLI for shift, and CULI for 99%, 99%, and 97% of simulated jobs, respectively. Similarly, in general, the peak task CLI for shift provided consistently the highest exposure estimates. It was greater than time-weighted-average CLI, peak task CLI for task duration, and CULI for 99%, 28%, and 83% of jobs, respectively. CULI and peak task CLI for task duration provided more moderate estimates, with peak task CLI for
task duration being greater than CULI for 57% of the jobs (89,531 jobs; Table 2). For jobs with peak task CLI for task duration > CULI, the median difference in CLI was 0.2 (8% of CULI), interquartile range (IRQ) was 0.1 to 0.6 (2% to 18% CULI), and the total range was 0.0 to 4.9 (0%–55%; Table 2). To study differences in risk classifications, we classified exposure estimates into low (CLI ≤ 1.0), medium (1.0 < CLI ≤ 3.0), and high risk (CLI > 3.0) using the generally accepted cut points (Marras et al., 1999). Based on CULI, 7.6%, 48.7%, and 43.7% of simulated jobs were in low-, medium-, and high-risk categories.
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8 Month XXXX - Human Factors Table 3: Comparison of Risk Classifications for 157,500 Simulated Jobs Using Time-WeightedAverage CLI (CLITWA), Peak Task CLI for Task Duration (CLITD), Peak Task CLI for Shift (CLIS), and CULI Based on the Generally Accepted Risk Limits (Marras, Fine, Ferguson, & Waters, 1999) CLITWA Risk Category Low (≤1.0) Medium (>1.0, ≤3.0) High (>3.0)
CLITD
CLIS
CULI
n
%
n
%
n
%
n
%
26108 83903 45579
16.5 53.3 30.2
13032 75651 68817
8.3 48.0 43.7
9072 65191 83237
5.8 41.4 52.8
11949 76679 68872
7.6 48.7 43.7
Note. CLI = Composite Lifting Index; CULI = Cumulative Lifting Index.
imilar statistics for time-weighted-average CLI, S peak task CLI for task duration, and peak task CLI for shift are provided in Table 3. As expected, time-weighted-average CLI classified more jobs into the low-risk category (16.5%) and fewer jobs into the high-risk category (30.2%). Among the four methods, peak task CLI for shift had the maximum number of jobs in the high-risk category (52.8%) and the smallest number of jobs in the low-risk category (5.8%). Risk classifications for CULI and peak task CLI for task duration were fairly comparable. Table 4 compares risk classifications for time-weighted-average CLI, peak task CLI for task duration, and peak task CLI for shift using CULI as reference. CULI and time-weightedaverage CLI had nearly perfect agreement for classifying low-risk jobs (99.4%). However, compared to CULI, time-weighted-average CLI classified approximately 18% of medium-risk and 30% of high-risk jobs as low and medium risks, respectively (Table 4). Conversely, CULI and peak task CLI for shift had strong agreement in classifying high-risk jobs (99.6%), but they differed in classifying low- and medium-risk jobs by about 20% or more (Table 4). CULI and peak task CLI for task duration had good agreement in all three risk categories (>90%). Comparison of CULI with Sequential Lifting Index (SLI). For the simulated data, we compared the results obtained from the CULI with those from the SLI suggested by Waters, Lu, and Occhipinti (2007). The SLI was greater than the CULI for 84.4% of the jobs and lower than the CULI for 12.5% of the jobs. For those jobs where the SLI was greater than the CULI, the mean difference was 0.74 (median = 0.38, SD = 0.9, range = 0.01–7.82). Similarly, for those jobs
where the SLI was lower than the CULI, the mean difference was 0.22 (median = 0.12, SD = 0.25, range = 0.01–2.01). Regarding risk classification, the CULI was ≤1.0 and the SLI > 1.0 for 29% of the jobs, and the CULI was ≤3.0 and the SLI > 3.0 for 18.1% of the jobs. Rarely the CULI resulted in a higher risk classification than the SLI: The SLI was ≤1.0 and the CULI > 1.0 for 0.7 % of the jobs, and the SLI was ≤3.0 and the CULI > 3.0 for 0.4% of the jobs. Discussion Frequency Multiplier
We are proposing a frequency multiplier for the RNLE as a continuous function of (a) lifting frequency and (b) continuous duration of a lifting task (hours per day). We believe the FMP will provide health and safety practitioners and engineers with more accurate estimates of biomechanical stressors determined using the RNLE for task durations >2 and 1.0 calculated using the FME may result in a CLI ≤1.0 when calculated using the FMP. This finding may have an impact on resources spent by industry to modify certain existing lifting tasks. Epidemiological studies are needed to determine whether the FMP is superior to the FME. In particular, studies are needed to determine whether the FMP is appropriate for task durations of less than 1 hr and greater than 8 hr. CULI
LI is based upon nonlinear relationships between the six individual biomechanical stressors in the RNLE (e.g., horizontal and vertical location of hands, lifting frequency) and resulting strain on the body (i.e., LI multipliers reflect strain to the low back). The CLI integrates biomechanical stressors from subtasks (LIs) to provide an estimate of biomechanical stressors at the task level (Waters et al., 1994). The proposed CULI further integrates biomechanical stressors from different tasks (LIs or CLIs as applicable) to provide an estimate of total biomechanical stressors to each worker from an entire work shift. We believe that the CULI will provide more precise physical exposure estimates for jobs with multiple tasks. Epidemiological studies are needed to determine the effectiveness of the CULI and the FMP in predicting risk of lowback injuries from lifting of loads. These studies may also suggest appropriate modifications to the FMP and the CULI. Past studies have used several different approaches to estimate biomechanical stressors at the worker level when a worker performs more
than one task in a job (Dempsey, 1999; Garg & Kapellusch, 2009a, 2009b; Mathiassen, 2006; Waters et al., 2007). These include average exposure (Marras et al., 1999), time-weighted average exposure (Gerr et al., 2014; Harris-Adamson, You, & Eisen, 2014; Kapellusch, Gerr, et al., 2014; Lu et al., 2014), simple cumulative exposure (Callaghan, Salewytsch, & Andrews, 2001; Coenen, Kingma, Boot, Bongers, & van Dieën, 2012; Marras, Ferguson, Lavender, Splittstoesser, & Yang, 2014; Norman et al., 1998; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, & Deddens, 2006), or peak exposure (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Herrin, Jaraiedi, & Anderson, 1986; Kapellusch, Garg, et al., 2014; Marras et al., 1993; Norman et al., 1998). However, these alternate approaches may incorrectly summarize exposures for the entire work shift. Using a simple average, timeweighted average, frequency-weighted average, or simple cumulative exposure may dilute the effects of strenuous exertions (Coenen et al., 2012; Dempsey, 1999; Frazer, Norman, Wells, & Neumann, 2003; Garg & Kapellusch, 2009a, 2009b; Mathiassen, 2006; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, & Deddens, 2006). For example, a cumulative approach assumes that a unit increase in force has the same effect on risk of LBP as a unit increase in time (e.g., lifting 7.5 kg for 2 s four times per minute has the same effect as lifting 30 kg for 2 s once per minute; 60 kg-s/min; Coenen et al., 2012; Garg & Kapellusch, 2009a, 2009b). In this regard, Marras et al. (2014) recently reported that none of the cumulative exposures except rest time was associated with a decline in low-back function.
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10 Month XXXX - Human Factors
Peak, average, and time-weighted-average exposure approaches have shown association with increased risk of LBP (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Herrin et al., 1986; Kapellusch, Garg, et al., 2014; Lu et al., 2014; Marras et al., 1993, 1999; Norman et al., 1998). However, there are inherent problems with using these approaches. Under the peak approach, the options are to (a) ignore all exposures (or tasks) other than the peak exposure (or task) to the worker (Herrin et al., 1986; Norman et al., 1998), (b) assume that all exertions occur at the peak exposure level (American Conference of Governmental Industrial Hygienists, 2002; Marras et al., 1993), or (c) assume that the peak task is performed for the entire work shift (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Kapellusch, Garg, et al., 2014). When averaging approaches are used, low exposures dilute the effect of high exposures (Garg & Kapellusch, 2009a, 2009b; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, Abdallah, et al., 2006). Thus, use of peak and average approaches may systematically tend to classify “safe” jobs as “unsafe” or “unsafe” jobs as “safe,” respectively. Based on the construct of CULI and verified with a simulation of 157,500 tasks reported earlier, it appears that in most cases, CULI will be greater than time-weighted-average CLI but less than the peak task CLI calculated, assuming that the task with peak CLI is performed for the entire work shift (peak task CLI for shift). It is possible that the CULI may partially address the underestimation of physical exposure using the time-weighted-average approach and overestimation of exposure using the peak-exposure approach. From our simulation, it appears that the peak task CLI for task duration may be a good approximation to CULI for a vast majority of jobs. The advantage of using peak task CLI for task duration is that it is relatively easier to use than CULI. However, it should be noted that peak task CLI for task duration ignores all tasks in a job rotation other than the peak task. This alternative may be acceptable for those epidemiological studies where a trade-off is necessary between accuracy and sample size, as the less accurate but also less resource-intensive method may provide greater overall statistical power. However, ignoring other tasks would present challenges for job
design and risk analysis prior to intervention, and health and safety professionals and job designers would have to rely on their professional judgments to determine whether ignoring tasks other than the peak task is appropriate when performing an evaluation of an individual job or worker. Ignoring other tasks could lead to underestimation of risk, especially for high-risk jobs. Future studies, and in particular, epidemiological studies, are needed to determine what is the most appropriate method for quantifying risk of musculoskeletal disorders for workers with job rotation. We proposed the CULI rather than using the SLI suggested by Waters et al. (2007) because (a) the SLI is based upon a modified timeweighted-average approach (time-weighed ratios of task LIs to peak task LI with assumed 4-hr duration for tasks); (b) it partially doublecounts exposure from the peak task (Waters et al., 2007, p. 1767); (c) for task durations longer than 2 hr, it assumes the same strain (LI and SLI) on low back irrespective of actual task duration; and (d) it assumes a duration of 4 hr for all SLI and then takes the peak of these SLIs. Based on the results of simulation (157,500 jobs) presented in this paper, the SLI estimated higher exposure than the CULI for a vast majority of jobs (84%), with nearly 50% of jobs resulting in higher risk categories. Conclusion
We provided a methodology, called CULI, to estimate biomechanical stresses for an entire work shift to a worker with job rotation. The CULI integrates biomechanical stressors from different tasks using an incremental approach similar in concept to that used for CLI. The CULI utilizes an FMP that is a continuous function of lifting frequency and task duration. A comprehensive simulation of lifting tasks showed that in most cases, the CULI is greater than the CLI calculated using the time-weightedaverage approach and less than the peak task CLI, assuming the peak task is performed for the entire shift. We believe that the CULI may partially address the systematic underestimation of biomechanical stressors associated with the time-weighted-average approach and systematic overestimation of biomechanical stressors associated with the peak-exposure approach.
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Cumulative Lifting Index Key Points •• Two commonly used methods to estimate job physical exposure for workers with job rotation are time-weighted-average exposure and peak exposure. These methods are believed to underestimate and overestimate biomechanical stressors, respectively. •• A methodology to estimate Cumulative Lifting Index (CULI) is provided to estimate biomechanical stressors for workers with job rotation. •• The CULI utilized a modified frequency multiplier (FM), which is a continuous function of lifting frequency and task duration and is based on existing values in the Applications Manual for the Revised NIOSH Lifting Equation. •• It is believed that the CULI will provide a more accurate estimate of biomechanical stressors for workers with job rotation.
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11 G., Bloswick, D., & Malloy, E. J. (2014). The NIOSH lifting equation and low-back pain, part 1: Association with low-back pain in the Backworks prospective cohort study. Human Factors, 56, 6–28. Garg, A., & Kapellusch, J. M. (2009a). Applications of biomechanics for prevention of work-related musculoskeletal disorders. Ergonomics, 52, 36–59. http://doi.org/10.1080/ 00140130802480794 Garg, A., & Kapellusch, J. M. (2009b, August). Consortium pooled data job physical exposure assessment. Paper presented at the 17th International Ergonomics Association World Conference, Beijing, China. Garg, A., Kapellusch, J. M., Hegmann, K. T., Moore, J. S., Boda, S., Bhoyar, P., Thiese, M. S., Merryweather, A., Deckow-Schaefer, G., Bloswick, D., & Malloy, E. J. (2014). The NIOSH lifting equation and low-back pain, part 2: Association with seeking care in the Backworks prospective cohort study. Human Factors, 56, 44–57. http://doi.org/10.1177/0018720813491284 Gerr, F. E., Fethke, N. B., Anton, D., Merlino, L. A., Rosecrance, J., Marcus, M., & Jones, M. P. (2014). A prospective study of musculoskeletal outcomes among manufacturing workers: II. Effects of psychosocial stress and work organization factors. Human Factors, 56, 178–190. http://doi .org/10.1177/0018720813487201 Harris-Adamson, C., You, D., & Eisen, E. A. (2014). The impact of posture on wrist tendinosis among blue-collar workers: The San Francisco study. Human Factors, 56, 143–150. http://doi .org/10.1177/0018720813502807 Herrin, G. D., Jaraiedi, M., & Anderson, C. K. (1986). Prediction of overexertion injuries using biomechanical and psychophysical models. American Industrial Hygiene Association Journal, 47, 322–330. http://doi.org/10.1080/15298668691389829 Kapellusch, J. M., Garg, A., Boda, S., Hegmann, K. T., Moore, J. S., Thiese, M. S., Merryweather, A., Tomich, S., Foster, J. C., Bloswick, D., & Malloy, E. J. (2014). Association between lifting and use of medication for low back pain. Journal of Occupational and Environmental Medicine, 56, 867–877. http://doi .org/10.1097/JOM.0000000000000197 Kapellusch, J. M., Gerr, F. E., Malloy, E. J., Garg, A., HarrisAdamson, C., Bao, S. S., Burt, S. E., Dale, A. M., Eisen, E. A., Evanoff, B. A., Hegmann, K. T., Silverstein, B. A., Theise, M. S., & Rempel, D. M. (2014). Exposure–response relationships for the ACGIH threshold limit value for hand-activity level: Results from a pooled data study of carpal tunnel syndrome. Scandinavian Journal of Work, Environment & Health, 40, 610–620. http://doi.org/10.5271/sjweh.3456 Kucera, K. L., Loomis, D., Lipscomb, H. J., Marshall, S. W., Mirka, G. A., & Daniels, J. L. (2009). Ergonomic risk factors for low back pain in North Carolina crab pot and gill net commercial fishermen. American Journal of Industrial Medicine, 52, 311–321. http://doi.org/10.1002/ajim.20676 Kuijer, P. P., Verbeek, J. H., Visser, B., & Elders, L. A. (2014). An evidence-based multidisciplinary practice guideline to reduce the workload due to lifting for preventing work-related low back pain. Annals of Occupational & Environmental Medicine, 26(16), 1–9. Liles, D. H., & Mahajan, P. (1985). Using NIOSH lifting guide decreases risks of back injuries. Occupational Health & Safety, 54(2), 57–60. Lu, M. L., Waters, T. R., Krieg, E., & Werren, D. (2014). Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: A one-year prospective study. Human Factors, 56, 73–85. http://doi. org/10.1177/0018720813513608 Marras, W. S., Ferguson, S. A., Lavender, S. A., Splittstoesser, R. E., & Yang, G. (2014). Cumulative spine loading and clinically
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12 Month XXXX - Human Factors meaningful declines in low-back function. Human Factors, 56, 29–43. Marras, W. S., Fine, L. J., Ferguson, S. A., & Waters, T. R. (1999). The effectiveness of commonly used lifting assessment methods to identify industrial jobs associated with elevated risk of low-back disorders. Ergonomics, 42, 229–245. http://doi .org/10.1080/001401399185919 Marras, W. S., Lavender, S. A., Leurgans, S. E., Rajulu, S. L., Allread, W. G., Fathallah, F. A., & Ferguson, S. A. (1993). The role of dynamic three-dimensional trunk motion in occupationally-related low back disorders: The effects of workplace factors, trunk position, and trunk motion characteristics on risk of injury. Spine, 18, 617–628. Mathiassen, S. E. (2006). Diversity and variation in biomechanical exposure: What is it, and why would we like to know? Applied Ergonomics, 37, 419–427. http://doi.org/10.1016/j.apergo .2006.04.006 Mirka, G. A., Kelaher, D. P., Nay, D. T., & Lawrence, B. M. (2000). Continuous assessment of back stress (CABS): A new method to quantify low-back stress in jobs with variable biomechanical demands. Human Factors, 42, 209–225. National Institute of Occupational Safety and Health. (1981). Work practices guide for manual lifting. Cincinnati, OH: U.S. Department of Health and Human Services. Norman, R., Wells, R., Neumann, P., Frank, J., Shannon, H., & Kerr, M. (1998). A comparison of peak vs cumulative physical work exposure risk factors for the reporting of low back pain in the automotive industry. Clinical Biomechanics (Bristol, Avon), 13, 561–573. Russell, S. J., Winnemuller, L., Camp, J. E., & Johnson, P. W. (2007). Comparing the results of five lifting analysis tools. Applied Ergonomics, 38, 91–97. http://doi.org/10.1016/j.apergo .2005.12.006 U.S. Bureau of Labor Statistics. (2012). 2011 nonfatal occupational injuries and illnesses: Cases with days away from work. Washington, DC: Author. van der Beek, A. J., Erik Mathiassen, S., Windhorst, J., & Burdorf, A. (2005). An evaluation of methods assessing the physical demands of manual lifting in scaffolding. Applied Ergonomics, 36, 213–222. http://doi.org/10.1016/j.apergo.2004.10.012 Waddell, G., & Burton, A. K. (2001). Occupational health guidelines for the management of low back pain at work: Evidence review. Occupational Medicine, 51, 124–135. Wang, M.-J. J., Garg, A., Chang, Y.-C., Shih, Y.-C., Yeh, W.-Y., & Lee, C.-L. (1998). The relationship between low back discomfort ratings and the NIOSH lifting index. Human Factors, 40, 509–515. http://doi.org/10.1518/001872098779591377 Waters, T. R., Baron, S. L., Piacitelli, L. A., Anderson, V. P., Skov, T., Haring-Sweeney, M., Wall, D. K., & Fine, L. J. (1999). Evaluation of the revised NIOSH lifting equation: A crosssectional epidemiologic study. Spine, 24, 386–395. Waters, T. R., Dick, R. B., & Krieg, E. F. (2011). Trends in workrelated musculoskeletal disorders: A comparison of risk factors for symptoms using quality of work life data from the 2002 and 2006 General Social Survey. Journal of Occupational and Environmental Medicine, 53, 1013–1024. http://doi .org/10.1097/JOM.0b013e3181fc8493 Waters, T. R., Lu, M. L., & Occhipinti, E. (2007). New procedure for assessing sequential manual lifting jobs using the revised NIOSH lifting equation. Ergonomics, 50, 1761–1770. http:// doi.org/10.1080/00140130701674364 Waters, T. R., Putz-Anderson, V., & Baron, S. (1998). Methods for assessing the physical demands of manual lifting: A review and case study from warehousing. American Industrial
Hygiene Association Journal, 59, 871–881. http://doi .org/10.1080/15428119891011045 Waters, T. R., Putz-Anderson, V., Garg, A., & Fine, L. J. (1993). Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics, 36, 749–776. http://doi .org/10.1080/00140139308967940 Waters, T. R., Putz-Anderson, V., Garg, A., & Fine, L. J. (1994). Applications manual for the revised NIOSH lifting equation (Publication No. 94-110). Cincinnati, OH: U. S. Department of Health and Human Services, National Institute for Occupational Safety and Health. Waters, T. R., Yeung, S., Genaidy, A., Callaghan, J., BarrieraViruet, H., Abdallah, S., & Kumar, S. (2006). Cumulative spinal loading exposure methods for manual material handling tasks: Part 2. Methodological issues and applicability for use in epidemiological studies. Theoretical Issues in Ergonomics Science, 7, 131–148. http://doi.org/10.1080/14639220500111459 Waters, T. R., Yeung, S., Genaidy, A., Callaghan, J., BarrieraViruet, H., & Deddens, J. A. (2006). Cumulative spinal loading exposure methods for manual material handling tasks: Part 1. Is cumulative spinal loading associated with lower back disorders? Theoretical Issues in Ergonomics Science, 7, 113–130. http://doi.org/10.1080/14639220500111392 World Health Organization. (2003). The burden of musculoskeletal conditions at the start of the new millennium (World Health Organization Tech. Rep. Series Vol. 919). Geneva, Switzerland: Author.
Arun Garg is a Distinguished Professor of occupational science and technology at the University of Wisconsin–Milwaukee. He received his PhD from the University of Michigan in 1976, is a Board Certified Professional Ergonomist, and has over 150 publications. He has developed several job analysis methods used worldwide. These include the revised NIOSH lifting equation, 3-D static strength biomechanical model, the energy expenditure model, the strain index, and the human strength prediction model. His areas of expertise include ergonomics, biomechanics, work physiology, office ergonomics, and design of workplace and hand tools to reduce musculoskeletal injuries and illnesses. Jay M. Kapellusch is an associate professor of occupational science and technology at the University of Wisconsin–Milwaukee (UWM). He received his PhD in engineering from UWM in 2010. His interests and expertise are in studying the effects of job physical exposure on incidence of musculoskeletal injuries, job analysis methods, and job design. He has more than 15 years of research and consulting experience in ergonomics and has analyzed in excess of 2,000 jobs in more than 150 companies. Date received: May 29, 2015 Date accepted: December 7, 2015
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HFSXXX10.1177/0018720815627405Month XXXXShort Title
SPECIAL SECTION: Impact of Thomas Waters on the Field of Ergonomics
The Cumulative Lifting Index (CULI) for the Revised NIOSH Lifting Equation: Quantifying Risk for Workers With Job Rotation Arun Garg and Jay M. Kapellusch, University of Wisconsin–Milwaukee Objective: The objectives were to: (a) develop a continuous frequency multiplier (FM) for the revised NIOSH lifting equation (RNLE) as a function of lifting frequency and duration of a lifting task, and (b) describe the Cumulative Lifting Index (CULI), a methodology for estimating physical exposure to workers with job rotation. Background: The existing FM for the RNLE (FME) does not differentiate between task duration >2 hr and 2 hr. The CULI is based on an incremental approach to accumulate task exposures into a single, integrated job-level exposure. The procedure for calculating the CULI is similar to the one used for calculating CLI (Waters et al., 1994). In this paper, we have used CLIs to illustrate computations for CULI. Please note that for simple tasks, CLI reduces to LI and is the same as LI. Consider a job with n tasks and with corresponding CLIs (CLI1, CLI2, CLI3, . . . CLIn). Please see page 46 of the Applications Manual for the RNLE for calculating CLIj.
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The duration of these tasks are HRS1, HRS2, HRS3, . . . HRSn hours per day, respectively. The procedure for calculating CULI involves the following steps: First, rearrange and renumber these tasks in order of decreasing physical strain based on CLI (or LI as appropriate) scores for each task. Each CLI is computed using its own task duration, that is, CLIj is computed for HRSj hours using the FMP from Equations (1) and (2) as appropriate. Therefore,
CLI1 ≥ CLI2 ≥ CLI3 ≥ . . . ≥ CLIn,(3) where CLIj = Composite Lifting Index for the jth task performed for HRSj hours per day. Next, calculate Frequency Independent CLIs (FICLIs; or Frequency Independent LIs [FILIs] as appropriate) using the following Equation (4): FICLIj = CLIj × FMj,(4) where FMj = FMP estimated from Equations (1) and (2) for the jth task performed for HRSj hours per day. Finally, the CULI for the entire shift is calculated using Equation (5): CULI = ( CLI1 ) +
∑∆CLI
FICLI 2 FICLI 2 CULI = ( CLI1 ) + − + FM 2,( HRS + HRS ) FM 2,( HRS ) 1 2 1 FICLI3 FICLI3 + (5) − FM 3,( HRS + HRS HRS ) FM 3,( HRS + HRS ) 1 2 1 2+ 3 FICLI n FICLI n + − FM n ,( HRS + HRS HRS HRS ) FM n ,( HRS + HRS HRS HRS ) n + + ... + 2+ 3+...+ n −1 1 2 3 1
In some rare cases, CULI may be greater than peak CLI for a task calculated as though that task were performed for the entire work shift (CLIs). In those situations, the CULI is constrained by the following equation: If CULI > CLIs, then CULI = CLIs, (6)
where CLIs = peak task CLI calculated as if the task were performed for the entire work shift. Duration of task (1, 2, and 8 hr per day) in the RNLE (Waters et al., 1993, Table 7) affects the frequency multiplier differently at different frequencies and does not follow a smooth function. In general, an increase in duration (from 1 to 2 and 8 hr) results in a much greater decrease in the frequency multiplier at higher frequencies than at lower frequencies, and the decrease becomes larger and larger as the frequency increases. Therefore, when two or more tasks performed at different frequencies are combined, as is the case with CULI, the CULI may exceed the hypothetical CLI if only one of the two tasks were performed for the entire work shift. This phenomena generally occurs when the second task has a much higher lifting frequency than the first task. In the simulation presented later (see Comparison of CULI With Time-Weighted and Peak Task CLIs), the CULI exceeded the peak task CLI for 8 hr for 5% of the jobs. For those 5% jobs, the mean difference between CULI and the peak task CLI for 8 hr was 4% (SD = 3%, range = 0%–15%). This difference had little effect in risk classification (CLI ≤ 1.0, 1.0 < CLI ≤ 3.0, and CLI > 3.0) and resulted in a higher risk classification for 1.0 and peak task CLI for 8 hr was ≤1.0 for 0.2% of jobs. Similarly, CULI was >3.0 and the peak task CLI for 8 hr was ≤3.0 for 0.5% of jobs. To illustrate the aforementioned procedure, consider a worker who performs two tasks, A and B, for 4 hr each. The CLIs are 0.3 and 0.93, lifting frequencies (F) are six and two lifts per minute, and FICLIs are 0.22 and 0.34 for Tasks A and B, respectively. We order these tasks as Tasks 1 and 2 corresponding to Tasks B and A. Therefore, CLI1 = 0.93; FICLI1 = 0.34; F1 = 6. CLI2 = 0.3; FICLI2 = 0.22; F2 = 2. The CULI for the 8-hr work shift is CULI = CLI1 + {[FICLI2 / FM2,(4+4 hr)] – [FICLI2 / FM2,(4 hr)]}. CULI = 0.93 + [(0.22 / 0.65) – (0.22 / 0.74)] = 0.93 + 0.04 = 0.97.
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6 Month XXXX - Human Factors
Thus, the CULI for the entire work shift is 0.97, and the job is considered “safe.” In this example, incremental increase in biomechanical stressors by adding Task A to Task B is negligible (∆CLI = 0.04). Using the FME provided in the applications manual (Waters et al., 1994), Tasks A and B have CLIs of 0.35 and 1.24, respectively. In this case, peak task CLI for 8 hr will result in a CLI of 1.24, and time-weightedaverage CLI will result in a CLI of 0.80 (see next section for definitions of peak exposures and time-weighted-average CLIs). Comparison of CULI with time-weighted and peak task CLIs. To compare the performance of CULI with the two commonly used methods, time-weighted average and peak exposures, we simulated jobs with different combinations of FICLI, frequency of lifting, and duration of tasks (hours per day). Each job consisted of two tasks and was performed for an 8-hr work shift. The FICLI for each of the two tasks ranged from 0.2 to 2.5 (in increments of 0.1 for FICLI ≤ 1.0, 0.2 for FICLI ≥ 1.0 and ≤ 2.0, and 0.5 for FICLI ≥ 2.0); frequencies of lifting were 0.2, 0.5, 1.0, and then increments of 1.0 up to 8.0 lifts per minute for each task; and hours per day for each task ranged from 1 to 7 hr per day in 1-hr increments. We assumed total shift duration of 8 hr. Based on these parameters, the simulation produced 157,500 jobs. Table 2 compares absolute and relative (percentage) differences between the CULI, timeweighted-average CLI, peak task CLI using actual task duration (subsequently referred to as peak task CLI for task duration), and peak task CLI calculated as if the tasks were performed for the entire work shift, that is, 8 hr for this simulation (subsequently referred to as peak task CLI for shift). The CULI was calculated using the FMP from Equations (1) and (2), and all other CLIs (time-weighted-average CLI, peak task CLI for task duration, and peak task CLI for shift) were calculated using the FME in the applications manual (Waters et al., 1994). We used two different frequency multipliers to compare the proposed methodology for calculating CULI to the currently available options for calculating CLIs using RNLE for workers with job rotation
that have been used in the past (Garg, Boda, et al., 2014; Lu et al., 2014; Waters et al., 1999). Time-weighted-average CLI was computed by multiplying each task CLI with its duration in hours, adding these CLI × Duration products, and then dividing this sum by duration of the entire job (8 hr in this simulation). Peak task CLI for task duration was determined by first calculating the CLI for each task using frequency multiplier corresponding to its frequency and duration in hours and then by taking the highest of these CLIs. To calculate the peak task CLI for shift, it was assumed that each task was performed for the entire work shift (8 hr in this simulation). We used frequency multiplier corresponding to the task frequency and duration of 8 hr to calculate task CLI for shift for each task. Thus, each task has a CLI as if it were performed for 8 hr. We selected the highest of these CLIs as the peak task CLI for shift. As an example, consider a job with two tasks, A and B. Task A is performed for 2 hr per day, lifting frequency is one per minute (frequency multiplier = 0.88), and it has a CLI of 1.4. Task B is performed for 6 hr per day, lifting frequency is four per minute (frequency multiplier = 0.45), and the CLI is 0.7. For this job, time-weighted-average CLI is (1.4 × 2) + (0.9 × 6) / 8, or 0.88. Task CLI for task duration (2 and 6 hr for Tasks A and B in this example) is 1.4 for Task A and 0.7 for Task B. The higher of these two is 1.4. Therefore, peak task CLI for task duration is 1.4. To calculate peak task CLI for shift, we assume both Tasks A and B have duration of 8 hr. Frequency multipliers (Waters et al., 1993, Table 7) are 0.75 and 0.45. Task A CLI as if it were performed for the entire shift is 1.6 ([1.4 × 0.88] / 0.75). Task B CLI as if it were performed for 8 hr remains unchanged because the frequency multiplier is the same for 6 and 8 hr ([0.7 × 0.45] / 0.45 = 0.7). Therefore, peak task CLI for shift is the higher of 1.6 and 0.7, or 1.6. For 157,500 jobs, mean, standard deviation, and range for peak task CLI for shift, peak task CLI for task duration, timeweighted-average CLI, and CULI were 3.99 ± 2.79 (0.24–13.89), 3.5 ± 2.59 (0.24–13.89), 2.63 ± 1.94 (0.23–13.89), and 3.36 ± 2.35 (0.23–15.9), respectively.
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Table 2: Comparison of Differences Between Time-Weighted-Average CLI (CLITWA), Peak Task CLI for Task Duration (CLITD), Peak Task CLI for Shift (CLIS), and CULI (Horizontal Header CLI Greater than Vertical Header CLI Out of 157,500 Simulated Jobs) Measure Is Greater Than . . . Measure CLITWA n Median IRQ Range CLITD n Median IRQ Range CLIS n Median IRQ Range CULI n Median IRQ Range
CLITWA
CLITD
CLIS
CULI
Absolute
%
Absolute
%
Absolute
%
Absolute
— — — —
— — — —
155,986 0.5 0.2–1.2 0.0–8.5
99 17 8–35 0–559
156,784 0.8 0.3–1.7 0.0–13.2
99 31 14–77 0–1803
153,154 0.5 0.2–1.1 0.0–5.2
0 — — —
0 — — —
— — — —
— — — —
44,560 1.1 0.4–2.5 0.0–9.7
28 60 25–94 0–236
62,949 0.2 0.1–0.4 0.0–4.2
0 — — —
0 — — —
0 — — —
0 — — —
— — — —
— — — —
21,660 0.1 0.0–0.3 0.0–2.0
3,421 0.1 0.0–0.1 0.0–0.4
2 3 1–5 0–14
89,531 0.2 0.1–0.6 0.0–4.9
57 8 2–18 0–55
130,377 0.3 0.1–1.0 0.0–9.1
83 13 4–32 0–187
— — — —
% 97 21 12–40 0–563 40 9 5–17 0–97 14 3 1–6 0–15 — — — —
Note. CLI = Composite Lifting Index; CULI = Cumulative Lifting Index; IRQ = interquartile range. Horizontal header CLI is compared to vertical header CLI. For example, CLIS is greater than CLITD for 44,560 of the 157,500 simulated jobs (28%). CLITWA, CLITD, and CLIS were computed using the existing RNLE frequency multiplier, and CULI was computed using the proposed frequency multiplier.
With few exceptions, time-weighted-average CLI consistently provided the lowest exposure estimates among the four techniques (Table 2). For example, time-weighted-average CLI was lower than peak task CLI for task duration, peak task CLI for shift, and CULI for 99%, 99%, and 97% of simulated jobs, respectively. Similarly, in general, the peak task CLI for shift provided consistently the highest exposure estimates. It was greater than time-weighted-average CLI, peak task CLI for task duration, and CULI for 99%, 28%, and 83% of jobs, respectively. CULI and peak task CLI for task duration provided more moderate estimates, with peak task CLI for
task duration being greater than CULI for 57% of the jobs (89,531 jobs; Table 2). For jobs with peak task CLI for task duration > CULI, the median difference in CLI was 0.2 (8% of CULI), interquartile range (IRQ) was 0.1 to 0.6 (2% to 18% CULI), and the total range was 0.0 to 4.9 (0%–55%; Table 2). To study differences in risk classifications, we classified exposure estimates into low (CLI ≤ 1.0), medium (1.0 < CLI ≤ 3.0), and high risk (CLI > 3.0) using the generally accepted cut points (Marras et al., 1999). Based on CULI, 7.6%, 48.7%, and 43.7% of simulated jobs were in low-, medium-, and high-risk categories.
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8 Month XXXX - Human Factors Table 3: Comparison of Risk Classifications for 157,500 Simulated Jobs Using Time-WeightedAverage CLI (CLITWA), Peak Task CLI for Task Duration (CLITD), Peak Task CLI for Shift (CLIS), and CULI Based on the Generally Accepted Risk Limits (Marras, Fine, Ferguson, & Waters, 1999) CLITWA Risk Category Low (≤1.0) Medium (>1.0, ≤3.0) High (>3.0)
CLITD
CLIS
CULI
n
%
n
%
n
%
n
%
26108 83903 45579
16.5 53.3 30.2
13032 75651 68817
8.3 48.0 43.7
9072 65191 83237
5.8 41.4 52.8
11949 76679 68872
7.6 48.7 43.7
Note. CLI = Composite Lifting Index; CULI = Cumulative Lifting Index.
imilar statistics for time-weighted-average CLI, S peak task CLI for task duration, and peak task CLI for shift are provided in Table 3. As expected, time-weighted-average CLI classified more jobs into the low-risk category (16.5%) and fewer jobs into the high-risk category (30.2%). Among the four methods, peak task CLI for shift had the maximum number of jobs in the high-risk category (52.8%) and the smallest number of jobs in the low-risk category (5.8%). Risk classifications for CULI and peak task CLI for task duration were fairly comparable. Table 4 compares risk classifications for time-weighted-average CLI, peak task CLI for task duration, and peak task CLI for shift using CULI as reference. CULI and time-weightedaverage CLI had nearly perfect agreement for classifying low-risk jobs (99.4%). However, compared to CULI, time-weighted-average CLI classified approximately 18% of medium-risk and 30% of high-risk jobs as low and medium risks, respectively (Table 4). Conversely, CULI and peak task CLI for shift had strong agreement in classifying high-risk jobs (99.6%), but they differed in classifying low- and medium-risk jobs by about 20% or more (Table 4). CULI and peak task CLI for task duration had good agreement in all three risk categories (>90%). Comparison of CULI with Sequential Lifting Index (SLI). For the simulated data, we compared the results obtained from the CULI with those from the SLI suggested by Waters, Lu, and Occhipinti (2007). The SLI was greater than the CULI for 84.4% of the jobs and lower than the CULI for 12.5% of the jobs. For those jobs where the SLI was greater than the CULI, the mean difference was 0.74 (median = 0.38, SD = 0.9, range = 0.01–7.82). Similarly, for those jobs
where the SLI was lower than the CULI, the mean difference was 0.22 (median = 0.12, SD = 0.25, range = 0.01–2.01). Regarding risk classification, the CULI was ≤1.0 and the SLI > 1.0 for 29% of the jobs, and the CULI was ≤3.0 and the SLI > 3.0 for 18.1% of the jobs. Rarely the CULI resulted in a higher risk classification than the SLI: The SLI was ≤1.0 and the CULI > 1.0 for 0.7 % of the jobs, and the SLI was ≤3.0 and the CULI > 3.0 for 0.4% of the jobs. Discussion Frequency Multiplier
We are proposing a frequency multiplier for the RNLE as a continuous function of (a) lifting frequency and (b) continuous duration of a lifting task (hours per day). We believe the FMP will provide health and safety practitioners and engineers with more accurate estimates of biomechanical stressors determined using the RNLE for task durations >2 and 1.0 calculated using the FME may result in a CLI ≤1.0 when calculated using the FMP. This finding may have an impact on resources spent by industry to modify certain existing lifting tasks. Epidemiological studies are needed to determine whether the FMP is superior to the FME. In particular, studies are needed to determine whether the FMP is appropriate for task durations of less than 1 hr and greater than 8 hr. CULI
LI is based upon nonlinear relationships between the six individual biomechanical stressors in the RNLE (e.g., horizontal and vertical location of hands, lifting frequency) and resulting strain on the body (i.e., LI multipliers reflect strain to the low back). The CLI integrates biomechanical stressors from subtasks (LIs) to provide an estimate of biomechanical stressors at the task level (Waters et al., 1994). The proposed CULI further integrates biomechanical stressors from different tasks (LIs or CLIs as applicable) to provide an estimate of total biomechanical stressors to each worker from an entire work shift. We believe that the CULI will provide more precise physical exposure estimates for jobs with multiple tasks. Epidemiological studies are needed to determine the effectiveness of the CULI and the FMP in predicting risk of lowback injuries from lifting of loads. These studies may also suggest appropriate modifications to the FMP and the CULI. Past studies have used several different approaches to estimate biomechanical stressors at the worker level when a worker performs more
than one task in a job (Dempsey, 1999; Garg & Kapellusch, 2009a, 2009b; Mathiassen, 2006; Waters et al., 2007). These include average exposure (Marras et al., 1999), time-weighted average exposure (Gerr et al., 2014; Harris-Adamson, You, & Eisen, 2014; Kapellusch, Gerr, et al., 2014; Lu et al., 2014), simple cumulative exposure (Callaghan, Salewytsch, & Andrews, 2001; Coenen, Kingma, Boot, Bongers, & van Dieën, 2012; Marras, Ferguson, Lavender, Splittstoesser, & Yang, 2014; Norman et al., 1998; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, & Deddens, 2006), or peak exposure (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Herrin, Jaraiedi, & Anderson, 1986; Kapellusch, Garg, et al., 2014; Marras et al., 1993; Norman et al., 1998). However, these alternate approaches may incorrectly summarize exposures for the entire work shift. Using a simple average, timeweighted average, frequency-weighted average, or simple cumulative exposure may dilute the effects of strenuous exertions (Coenen et al., 2012; Dempsey, 1999; Frazer, Norman, Wells, & Neumann, 2003; Garg & Kapellusch, 2009a, 2009b; Mathiassen, 2006; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, & Deddens, 2006). For example, a cumulative approach assumes that a unit increase in force has the same effect on risk of LBP as a unit increase in time (e.g., lifting 7.5 kg for 2 s four times per minute has the same effect as lifting 30 kg for 2 s once per minute; 60 kg-s/min; Coenen et al., 2012; Garg & Kapellusch, 2009a, 2009b). In this regard, Marras et al. (2014) recently reported that none of the cumulative exposures except rest time was associated with a decline in low-back function.
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10 Month XXXX - Human Factors
Peak, average, and time-weighted-average exposure approaches have shown association with increased risk of LBP (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Herrin et al., 1986; Kapellusch, Garg, et al., 2014; Lu et al., 2014; Marras et al., 1993, 1999; Norman et al., 1998). However, there are inherent problems with using these approaches. Under the peak approach, the options are to (a) ignore all exposures (or tasks) other than the peak exposure (or task) to the worker (Herrin et al., 1986; Norman et al., 1998), (b) assume that all exertions occur at the peak exposure level (American Conference of Governmental Industrial Hygienists, 2002; Marras et al., 1993), or (c) assume that the peak task is performed for the entire work shift (Garg, Boda, et al., 2014; Garg, Kapellusch, et al., 2014; Kapellusch, Garg, et al., 2014). When averaging approaches are used, low exposures dilute the effect of high exposures (Garg & Kapellusch, 2009a, 2009b; Waters, Yeung, Genaidy, Callaghan, Barriera-Viruet, Abdallah, et al., 2006). Thus, use of peak and average approaches may systematically tend to classify “safe” jobs as “unsafe” or “unsafe” jobs as “safe,” respectively. Based on the construct of CULI and verified with a simulation of 157,500 tasks reported earlier, it appears that in most cases, CULI will be greater than time-weighted-average CLI but less than the peak task CLI calculated, assuming that the task with peak CLI is performed for the entire work shift (peak task CLI for shift). It is possible that the CULI may partially address the underestimation of physical exposure using the time-weighted-average approach and overestimation of exposure using the peak-exposure approach. From our simulation, it appears that the peak task CLI for task duration may be a good approximation to CULI for a vast majority of jobs. The advantage of using peak task CLI for task duration is that it is relatively easier to use than CULI. However, it should be noted that peak task CLI for task duration ignores all tasks in a job rotation other than the peak task. This alternative may be acceptable for those epidemiological studies where a trade-off is necessary between accuracy and sample size, as the less accurate but also less resource-intensive method may provide greater overall statistical power. However, ignoring other tasks would present challenges for job
design and risk analysis prior to intervention, and health and safety professionals and job designers would have to rely on their professional judgments to determine whether ignoring tasks other than the peak task is appropriate when performing an evaluation of an individual job or worker. Ignoring other tasks could lead to underestimation of risk, especially for high-risk jobs. Future studies, and in particular, epidemiological studies, are needed to determine what is the most appropriate method for quantifying risk of musculoskeletal disorders for workers with job rotation. We proposed the CULI rather than using the SLI suggested by Waters et al. (2007) because (a) the SLI is based upon a modified timeweighted-average approach (time-weighed ratios of task LIs to peak task LI with assumed 4-hr duration for tasks); (b) it partially doublecounts exposure from the peak task (Waters et al., 2007, p. 1767); (c) for task durations longer than 2 hr, it assumes the same strain (LI and SLI) on low back irrespective of actual task duration; and (d) it assumes a duration of 4 hr for all SLI and then takes the peak of these SLIs. Based on the results of simulation (157,500 jobs) presented in this paper, the SLI estimated higher exposure than the CULI for a vast majority of jobs (84%), with nearly 50% of jobs resulting in higher risk categories. Conclusion
We provided a methodology, called CULI, to estimate biomechanical stresses for an entire work shift to a worker with job rotation. The CULI integrates biomechanical stressors from different tasks using an incremental approach similar in concept to that used for CLI. The CULI utilizes an FMP that is a continuous function of lifting frequency and task duration. A comprehensive simulation of lifting tasks showed that in most cases, the CULI is greater than the CLI calculated using the time-weightedaverage approach and less than the peak task CLI, assuming the peak task is performed for the entire shift. We believe that the CULI may partially address the systematic underestimation of biomechanical stressors associated with the time-weighted-average approach and systematic overestimation of biomechanical stressors associated with the peak-exposure approach.
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Cumulative Lifting Index Key Points •• Two commonly used methods to estimate job physical exposure for workers with job rotation are time-weighted-average exposure and peak exposure. These methods are believed to underestimate and overestimate biomechanical stressors, respectively. •• A methodology to estimate Cumulative Lifting Index (CULI) is provided to estimate biomechanical stressors for workers with job rotation. •• The CULI utilized a modified frequency multiplier (FM), which is a continuous function of lifting frequency and task duration and is based on existing values in the Applications Manual for the Revised NIOSH Lifting Equation. •• It is believed that the CULI will provide a more accurate estimate of biomechanical stressors for workers with job rotation.
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Arun Garg is a Distinguished Professor of occupational science and technology at the University of Wisconsin–Milwaukee. He received his PhD from the University of Michigan in 1976, is a Board Certified Professional Ergonomist, and has over 150 publications. He has developed several job analysis methods used worldwide. These include the revised NIOSH lifting equation, 3-D static strength biomechanical model, the energy expenditure model, the strain index, and the human strength prediction model. His areas of expertise include ergonomics, biomechanics, work physiology, office ergonomics, and design of workplace and hand tools to reduce musculoskeletal injuries and illnesses. Jay M. Kapellusch is an associate professor of occupational science and technology at the University of Wisconsin–Milwaukee (UWM). He received his PhD in engineering from UWM in 2010. His interests and expertise are in studying the effects of job physical exposure on incidence of musculoskeletal injuries, job analysis methods, and job design. He has more than 15 years of research and consulting experience in ergonomics and has analyzed in excess of 2,000 jobs in more than 150 companies. Date received: May 29, 2015 Date accepted: December 7, 2015
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