Calculating Lifetime Cumulative Load to Predict Disc Protrusion
Study Population
This study is a further investigation of the previous study. Recruitment of the participants, measurements of the work exposure, and imaging studies of the lumbar spines were detailed in part I. To obtain a broad spectrum of lifting exposures, the participants were recruited from 2 populations: (1) walk-in clinic patients and (2) workers who carry heavy loads. Patients visited the Internal Medicine Clinic of one National University Hospital and diagnosed with upper respiratory infections (URI), mostly the common cold, were recruited as the background population. The group that carried heavy loads were workers of one fruit and vegetable wholesale market. Lifting is a common task for these workers.
During recruitment, the market workers and the walk-in clinic patients were not
informed of the hypothesis of the study. They were invited to participate in a study regarding spine and bone disorders. The inclusion criteria of this study were between 20 and 65 years and at least 6 months of working experience. Participants previously diagnosed with compression fracture, major back trauma, inflammatory spondylopathy, spinal tumors, cancer, or psychiatric conditions were excluded. We combined these 2 populations to examine the effects of lifting on disc protrusion.
Data Collection
Each participant was asked to complete a questionnaire and to obtain MRI of the lumbar spine. The demographic and occupational data were obtained from an extensive, structured questionnaire. A detailed structured interview with adequate time was implemented to the participants for assessing the relevant work tasks in each job held since they entered the workforce including a complete occupational history, job titles, working tenures, body weights at each job, descriptions of tasks, lifting exposure at work (eg, estimates of the most common weights lifted),
frequency and duration of lifting, numbers of working hours per day and working days per week. The trained interviewers used common milestones in life to help the participants recall the necessary information. The participants were encouraged to recall their body weights during the period of each job. When the job period
was longer than 5 years, the average body weight during this job period was used.
Cigarette exposure was calculated in pack-years by multiplying the number of packs of cigarettes smoked daily by the number of smoking years.
Estimation of Lumbar Disc Compression Load and Calculation of Lifetime Cumulative Lifting Load on the Lumbar Disc
Regarding the estimation of lifetime exposure, the participants recalled all of the jobs held after completing schooling, and the weight, frequency, and duration of each task. The participants performed a typical material handling task to simulate the positions and weights encountered at each job. Lifting activity was divided into a sequence of static postures, including the initial lift-up, transferring, and
unloading postures, and each posture was analyzed. The initial position of the weight lifting task was defined as the lift-up posture, the final position was defined as the unloading posture, and the action of transferring material while walking was defined as the transferring posture. Although the initial and final positions of lifting may have varied during a typical day of materials handling on the job, the selected typical tasks, including the simulated positions and weights, were used to calculate the compression load to represent the job. The compression load on the lumbar disc during lifting was estimated using the 3D Static Strength Prediction Program (3DSSPP, Center for Ergonomics, University of Michigan, Ann Arbor,
Michigan) software system . Anthropometric data such as sex, height, body weight, carried weight, and working posture photograph of each participant were input into the 3DSSPP system to predict the compression load on the lumbar disc. To evaluate the intrarater and interrater reliability of lumbar load estimation by using the 3DSSPP, photographs of the simulated work conditions of the 60 study participants were repeatedly evaluated in 2 rounds, with the second round of evaluation was conducted 4 weeks after the first round.
To investigate the actual cumulative lifting exposure, the participants recalled details regarding lift-up time (tlift-up), transporting time (ttransporting), and unloading time (tunload) of each lifting task at their jobs. Hence, in this study, the lifting exposure of each task was defined as the sum of the products of the lift-up force (Flift-up) and lift-up time, transporting force (Ftransporting) and transporting time, and unloading force (Funload) and unloading time. Only those lift-up forces greater than proposed threshold value were added into lifetime exposure. For each job
described, the lifting exposure was calculated as the product of the lifting load and the duration of lifting in hours (Newton × hour, Nh). The lifetime cumulative load for each participant was then calculated by summing the lifting exposure on the lumbar disc from all jobs.
Threshold Value of Lifting Load
The threshold value in this study was defined as exposure with a lifting load above this proposed value was considered as contributed to disc protrusion over an entire career life, and was included in the lifetime cumulative calculation. The proposed threshold values were set at zero Newton (N), and at 100 N increments from 2000 to 4000 N. For example, if the threshold value is set as 3400 N, only lifting load above 3400 N per lift will be included in the calculation. And, when the threshold value is set at 0 N, every lifting load generated from each activity will be included in the calculation. The calculation can be expressed as the following equation:
Cumulative lifting load =
∑ [(Flift-up*tlift-up + Ftransporting*ttransporting + Funload*tunload)/3600 * frequency of
lifting/day * working days/year * working year]
where F represents the lifting load on the lumbar disc and t represents time (seconds).
The reproducibility of the lifting measurements was tested 6 months after the initial interview with the help of 25 participants. Their current jobs were used for
reliability testing. These measurements included the working tenure, lifting weights, frequency of lifting per day, and lift-up time of the job. After observing and recording the fruit workers’ practices, we found that most of the participants’
lift-up time was almost equal to their unloading time and that the transporting time was zero. Therefore, the reliability of the transporting time and unloading time was not examined. In addition, we determined that pushing or pulling is not a common task for the majority of fruit market workers because they typically drive an electric pedicab to transfer fruit boxes. Therefore, the lumbar load of pushing and pulling was not assessed.
Each intervertebral disc at L4–L5 to L5–S1 was evaluated for disc bulging, protrusion, extrusion, and sequestration using MRI. All MRI examinations were conducted at the National University Hospital. MRI equipment and protocol, definition of disc condition above, the evaluation of intrarater reliability regarding the presence or absence of disc conditions were descripted previously.
Statistical Analysis
The reproducibility of the calculation of the lifting load and lifting measurements was analyzed using SPSS version 16.0 for Windows (SPSS Inc, Chicago, Illinois)
to compute intraclass correlation coefficients (ICCs). Kappa was used to assess the intrarater reliability of disc protrusion. Logistic regression analysis using JMP 5.0 (SAS Institute Inc, Cary, North Carolina) was applied to identify the association between lifetime cumulative lifting load and disc protrusion at either of the lower disc levels, namely, L4-L5 and L5-S1 disc, adjusting for potential risk factors including age, body mass index (BMI), and smoking. P < 0.05 was considered to be statistically significant. To determine the best threshold value of lifting load, four statistical values were used to compare outcome (L4-S1disc protrusion) to lifetime cumulative load while different threshold values was applied, namely, (1) Area under the curve (AUC) of a receiver operating characteristic (ROC) curve, (2) R2, (3) Akaike information criterion (AIC), and (4) Bayesian information criterion (BIC). We compared the AUC in various models that were plotted using MedCalc for Windows Version 9.2.1.0 (MedCalc Software, Mariakerke, Belgium). Models with higher AUC statistics were considered as the optimal model. The amount of cumulative lifting load explained by various threshold values in the model was evaluated based on the R2 statistic. AIC and BIC were obtained using SAS Version 9.1 (SAS Institute Inc.) AIC is closely related to BIC. Given a set of candidate models for the data, the preferred model is the one with the minimal AIC value, and the same applies to BIC.