Although many studies have reported associations between computer use and musculoskeletal disorders (MSDs), the evidence identifying specific physical and individual exposures during computer use that may explain these associations is limited.⁽¹⁾ One challenge in attempting to link exposures and outcomes in epidemiologic studies is the collection of accurate measurements of these exposures.⁽²⁾

Physical exposures such as the muscle activities, postures, velocities, and accelerations experienced during computer use are particularly difficult to measure accurately. Questionnaires, which require participants to self-report their perceived physical exposures during computer use, have the advantage of enabling data collection in large samples of workers at a reasonable cost. However, previous studies have reported low agreement between exposures assessed through self-report methods compared with direct measurements of physical exposures during computer use.^(3–5) Direct measurements, the preferred method of exposure assessment in terms of accuracy, are expensive, time consuming, and often impractical for the large epidemiologic studies needed to make associations with MSDs.⁽²⁾ Direct measurements are also needed to collect enough data to determine the median values of physical exposures, a common metric that has been presented in many previous studies.^(6–8)

Exposure prediction models have been proposed as an alternative means of generating exposure data that is difficult to measure directly. Chen et al.⁽⁹⁾ used mixed-effect modeling to predict taxi drivers’ exposure to whole body vibration based on a set of known predictors of vibration, including engine size, driver’s weight, and seat cushion type, and found acceptably low prediction error. It is possible that predictions can be generated using this strategy for physical exposures including muscle activities, postures, velocities, and accelerations of the upper extremity during computer use.

A task-based approach to exposure prediction modeling takes advantage of the variability in exposures across tasks inherent to many jobs to calculate predictions of exposures. The primary tasks that many modern office workers complete are computer tasks (compared with non-computer work), which can further be broken into keyboard tasks and mouse tasks (prolonged interactions with the computer using each input device), as well as idle tasks (active interaction with the computer without use of an input device).^(10,11) Previous studies have shown that there is variability in physical exposures across computer tasks; for example, keyboard use is associated with increased muscle efforts and wrist velocities and accelerations, and mouse use is associated with non-neutral shoulder postures.^(12,13)

The large inter-subject variability inherent to many physical exposures, which cannot be explained by task, has limited the usefulness of the task-based approach.⁽¹⁴⁾ Thus, factors that could explain some of this variability could be used to improve estimates generated from task-based models. During computer use, physical exposures have varied with hand length,^(6,8) gender,^(7,8) age,⁽¹⁵⁾ and shoulder width.^(8,16)

The goal of this study, therefore, was to develop models based on keyboard and mouse use in combination with individual factors that could be used to predict median upper extremity muscle activities, postures, velocities, and accelerations experienced during computer use. The models were used to determine which keyboard use, mouse use, or individual factors were predictors of median upper extremity muscle activities, postures, velocities, and accelerations, as well as the strength and directions of the associations. The predictive capabilities of the models were also evaluated.

At least one of the keyboard use, mouse use, or individual factors was a significant predictor of every physical exposure except for left wrist flexion (Tables III and IV). Keyboard use, mouse use, or both, were significant predictors in all models. Fewer (9 out of 22) physical exposures also had individual factors as predictors, and no model had only individual factors as predictors without keyboard use or mouse use.

Muscle activity, wrist velocity, and wrist acceleration all tended to increase as the percentage of keyboard use or mouse use increased. Increased keyboard use decreased shoulder flexion and external rotation. Increased mouse use increased shoulder abduction but was not significant in the other shoulder posture multivariate prediction-rule models (Tables III–VI).

Shoulder rotation was the dependent variable most influenced by individual factors, with a decrease in external rotation of 3.8 degrees for every 1-cm increase in shoulder width (Table IV). Gender was not a significant predictor of any muscle activity, posture, velocity, or acceleration, but all other individual factors were significant predictors of at least one muscle activity, posture, velocity, or acceleration. Hand length and shoulder width were significant predictors of several muscle activity and posture measures (Tables III and IV). Age was a significant predictor of velocity and acceleration in the radial and ulnar deviation directions only for both the left and right hands (Table V).

The combination of percentage keyboard use, percentage mouse use, and individual factors explained the largest amount of variability in left and right wrist velocities and accelerations in both the radial/ulnar and flexion/extension directions, with all of the multivariate prediction-rule models having R-squared values of 0.75 or greater and RMS errors of less than 10% of the observed range (Table VI). The models explained less overall variability in shoulder postures (R-squared from 0.21 to 0.50) with relative RMS errors between 8 to 13% and wrist muscle activity (R-squared from 0.06 to 0.46) with relative RMS errors of 8 to 13%. The lowest r-squared values were observed for shoulder muscle activity (R-squared from 0.03 to 0.10) with relative RMS errors of 10 to 12% and wrist posture (R-squared from 0.00 to 0.23) with relative RMS errors of 9 to 12%.

Keyboard use and mouse use were the main determinants of each muscle activity, posture, velocity and acceleration data set, contributing more than any of the individual factors (Table VI). Shoulder rotation was most affected by the removal of individual factors from its prediction model. A 3% decrease was seen in the R-squared for shoulder rotation when all individual factors were removed (reduced model). The RMS errors were not changed by the removal of individual factors from the models.

The goal of this study was to develop models based on keyboard and mouse use in combination with individual factors that could be used to predict median upper extremity muscle activities, postures, velocities, and accelerations experienced during computer use. Percentage keyboard and/or mouse use were significant predictors of each muscle activity, posture, velocity, or acceleration except left wrist flexion. All individual factors except gender were significant predictors of at least one muscle activity, posture, velocity, or acceleration dataset, with the strongest effect of individual factors being observed on shoulder rotation. The large amounts of variability in wrist velocities and accelerations could be explained by keyboard use and/or mouse use (74–81% for “reduced models”). While more variability in wrist muscle activities was explained than in shoulder muscle activities, and more variability in shoulder postures was explained than in wrist postures, the total variability explained in any muscle activity or posture was small (0–50%). Including individual factors in addition to keyboard and mouse use did not explain more variability (changes of less than 3% from “full models” to “reduced models”). The RMS errors were similar across physcial exposures and were not affected by the removal of individual factors.

One advantage of including individual factors as predictors in this study was that it allowed for examination of how specific individual factors influence the muscle activities, postures, velocities, and accelerations during computer use when task is also taken into consideration. Shoulder rotation was the dependent variable most influenced by individual factors, with a decrease in shoulder rotation of 3.8 degrees for every 1-cm increase in shoulder width. Several other studies have also examined the association between shoulder rotation and shoulder width,^(8,16) and in each study an association was reported between shoulder rotation and shoulder width. Computer users with a smaller shoulder width have a narrower base of shoulder rotation and therefore must externally rotate out more to reach the mouse. The current study determined that shoulder width was predictive of shoulder rotation and that the association between shoulder width and shoulder rotation was robust even when controlling for the effect of percentage of keyboard use.

In some cases, the inclusion of individual factors may expose the true relationship between keyboard and/or mouse use and the muscle activity, posture, velocity, or acceleration of interest. For example, Dennerlein and Johnson⁽¹²⁾ report decreased ECU muscle activity with increased mouse use, and in this study we observed increased ECU muscle activity with increased mouse use when hand length is included as a predictor of ECU muscle activity. In the case of these muscles, it seems that increased hand length is the factor driving the decrease in ECU muscle activity, and after controlling for this factor, the true association between muscle activity and mouse use is positive rather than negative. This observation corroborates the results of previous studies, which have shown that mouse use is associated with increased wrist extension,⁽¹²⁾ and that increased hand length is associated with decreased wrist extension.^(6,8) Increased wrist extensor muscle activity is required to increase wrist extension.

Consideration of other factors that contribute to the variability of physical exposures may improve the predictive capabilities of the models described here, especially for muscle activities and postures. We did not include workstation setup⁽²⁶⁾ or psychosocial factors⁽²⁷⁾ as predictors in our models, which have been shown to be associated with physical exposures during computer use. However, these parameters did not vary significantly within our laboratory study – the laboratory workstation was adjusted to match each participant’s anthropometry, and psychosocial stress was not expected to result from this type of study. Another factor that could have affected our results is the amount of variability in the data used. Participants may not have been moving naturally due to the unfamiliar conditions or wearing of the data collection equipment. However, in comparison with a field study, these laboratory data have similar variability as measured in computer workers performing their own work.⁽¹³⁾ Hence, we do not expect that our ability to predict muscle activities or postures is limited by reduced variability in our datasets.

Although the relationships between many upper extremity muscle activities, postures, velocities, accelerations, and MSDs has not yet been reported in epidemiologic studies, they are biologically plausible. Visser and van Dieen⁽²⁸⁾ propose several mechanisms for developing MSDs that could be influenced by physical exposures, including increased levels of muscle activity, which could be an indication of selective loading and damage of small motor units, and postural deviations, which may lead to compression of arteries and loss of blood flow to active muscles. It is possible that with improved assessment of the physical exposures associated with computer use, by means of alternative methods such as those described here, the relationship between physical exposures and MSDs will be realized in epidemiologic studies.

The results of this study must be taken with consideration of its limitations. First, the levels of significance reported here must be interpreted with caution, since all seven computer use and individual factors were allowed to be introduced into the regression models for each muscle effort, posture, velocity, and acceleration dataset. For example, the effect of age on wrist velocities and accelerations reported here has not been previously reported or hypothesized and may be due to chance. The levels of significance also cannot be compared between the computer use and individual factors because the computer use factors were repeated for each participant. However, it is possible that greater effects were observed for keyboard and mouse use than individual factors because we had a larger range of percentages of keyboard and mouse use than of any of the individual factors studied. The ages of the participants in this study were lower than in a previous study of the effect of age on muscle activity⁽¹⁵⁾, which may explain why no effect of age on muscle activity levels was observed in the current study. Second, we only considered the median values of physical exposures. However, within this dataset the 10th, 50th, and 90th percentiles of all physical exposures were highly correlated across tasks, and therefore, we did not expect to find different results than for the median for these metrics.⁽¹²⁾ Also, it is generally believed that an increase in median physical exposures may lead to increased musculoskeletal damage, so predictions of this metric specificially would be useful.⁽²⁸⁾ Other studies may consider whether more variability could be explained using other ways of characterizing physical exposures. Third, we were unable to separate our data into distinct keyboard and mouse tasks and, instead, used percentage keyboard use and percentage mouse use as indicators of task. Future work should confirm our results and explore a task-based approach when the physical exposures can be separated by task.

This study determined that keyboard and mouse use explained a large amount of the variability in wrist velocities and accelerations experienced during computer use. However, keyboard and mouse use did not explain much of the variability in muscle efforts or postures experienced during computer use. The individual factors that have been previously identified in the literature as being associated with physical exposures explained very little of the variability in any of the physical exposures investigated.

The results of this study can inform alternative methods of exposure assessment, including the development of prediction models. The models were able to generate accurate predictions of median wrist velocities and accelerations and may be useful tools for epidemiologic studies. However, the models were not able to generate accurate predictions of median muscle activities or postures.

It is unlikely that the addition of the individual factors investigated in this study will improve the predictions of any physical exposures substantially, although there is a possibility that other, unexplored factors could be used to improve the predictions. Future work in this area should focus on identifying other factors that might be used to create alternative, effective prediction models or other strategies for generating muscle effort and postural data. Ultimately, once accurate predictions are developed, these can be applied to epidemiologic studies investigating the association between physical exposures and MSDs in the computer worker population.