Belinda H.W. Eijckelhof

The effects of workplace stressors on muscle activity in the neck-shoulder and forearm muscles during computer work: a systematic review and meta-analysis

Workplace stressors have been indicated to play a role in the development of neck and upper extremity pain possibly through an increase of sustained (low-level) muscle activity. The aim of this review was to study the effects of workplace stressors on muscle activity in the neck-shoulder and forearm muscles. An additional aim was to find out whether the muscles of the neck-shoulder and the forearm are affected differently by different types of workplace stressors. A systematic literature search was conducted on studies investigating the relation between simulated or realistic workplace stressors and neck-shoulder and forearm muscle activity. For studies meeting the inclusion criteria, a risk of bias assessment was performed and data were extracted for synthesis. Results were pooled when possible and otherwise described. Twenty-eight articles met the inclusion criteria, reporting data of 25 different studies. Except for one field study, all included studies were laboratory studies. Data of 19 articles could be included in the meta-analysis and revealed a statistically significant, medium increase in neck-shoulder and forearm muscle activity as a result of workplace stressors. In subgroup analyses, we found an equal effect of different stressor types (i.e. cognitive/emotional stress, work pace, and precision) on muscle activity in both body regions. In conclusion, simulated workplace stressors result in an increase in neck-shoulder and forearm muscle activity. No indications were found that different types of stressors affect these body regions differently. These conclusions are fully based on laboratory studies, since field studies on this topic are currently lacking.

The effect of over-commitment and reward on trapezius muscle activity and shoulder, head, neck, and torso postures during computer use in the field

BACKGROUND Because of reported associations of psychosocial factors and computer related musculoskeletal symptoms, we investigated the effects of a workplace psychosocial factor, reward, in the presence of over-commitment, on trapezius muscle activity and shoulder, head, neck, and torso postures during computer use. METHODS We measured 120 office workers across four groups (lowest/highest reward/over-commitment), performing their own computer work at their own workstations over a 2-hr period. RESULTS Median trapezius muscle activity (P = 0.04) and median neck flexion (P = 0.03) were largest for participants reporting simultaneously low reward and high over-commitment. No differences were observed for other muscle activities or postures. CONCLUSIONS These data suggest that the interaction of reward and over-commitment can affect upper extremity muscle activity and postures during computer use in the real work environment. This finding aligns with the hypothesized biomechanical pathway connecting workplace psychosocial factors and musculoskeletal symptoms of the neck and shoulder.

The effect of overcommitment and reward on muscle activity, posture, and forces in the arm-wrist-hand region--a field study among computer workers.

OBJECTIVE Office workers with high levels of overcommitment and low levels of reward are thought to be more prone to arm-wrist-hand symptoms, possibly through a higher internal physical exposure. The aim of this study was to examine the effects of high overcommitment and low reward on (i) forearm muscle activity, (ii) wrist posture and kinematics, and (iii) forces applied to computer input devices during computer work in an actual work setting. METHODS We continuously measured wrist extensor muscle activity, wrist posture and kinematics, and forces applied to the keyboard and mouse for two hours during the daily work of 120 office workers with four different levels of overcommitment and reward (low-high, high-high, low-low, and high-low). RESULTS Wrist velocities and accelerations in radial-ulnar direction were higher for workers with high compared to low overcommitment, while their wrist range of motion was similar, possibly indicating a higher work pace. Wrist extensor muscle activity and forces applied to the keyboard and mouse were not increased by high overcommitment and/or low reward. CONCLUSION Overall, our findings provide little support for the proposed pathway of high overcommitment and low reward in the development of arm-wrist-hand symptoms through a higher internal physical exposure.

Prediction of trapezius muscle activity and shoulder, head, neck, and torso postures during computer use: results of a field study

BackgroundDue to difficulties in performing direct measurements as an exposure assessment technique, evidence supporting an association between physical exposures such as neck and shoulder muscle activities and postures and musculoskeletal disorders during computer use is limited. Alternative exposure assessment techniques are needed.MethodsWe predicted the median and range of amplitude (90th-10th percentiles) of trapezius muscle activity and the median and range of motion (90th-10th percentiles) of shoulder, head, neck, and torso postures based on two sets of parameters: the distribution of keyboard/mouse/idle activities only (“task-based” predictions), and a comprehensive set of task, questionnaire, workstation, and anthropometric parameters (“expanded model” predictions). We compared the task-based and expanded model predictions based on R2 values, root mean squared (RMS) errors, and relative RMS errors calculated compared to direct measurements.ResultsThe expanded model predictions of the median and range of amplitude of trapezius muscle activity had consistently better R2 values (range 0.40-0.55 compared to 0.00-0.06), RMS errors (range 2-3%MVC compared to 3-4%MVC), and relative RMS errors (range 10-14%MVC compared to 16-19%MVC) than the task-based predictions. The expanded model predictions of the median and range of amplitude of postures also had consistently better R2 values (range 0.22-0.58 compared to 0.00-0.35), RMS errors (range 2–14 degrees compared to 3–22 degrees), and relative RMS errors (range 9–21 degrees compared to 13–42 degrees) than the task-based predictions.ConclusionsThe variation in physical exposures across users performing the same task is large, especially in comparison to the variation across tasks. Thus, expanded model predictions of physical exposures during computer use should be used rather than task-based predictions to improve exposure assessment for future epidemiological studies. Clinically, this finding also indicates that computer users will have differences in their physical exposures even when performing the same tasks.

Developing a framework for assessing muscle effort and postures during computer work in the field: the effect of computer activities on neck/shoulder muscle effort and postures

The present study, a part of the PROOF (PRedicting Occupational biomechanics in OFfice workers) study, aimed to determine whether trapezius muscle effort was different across computer activities in a field study of computer workers, and also investigated whether head and shoulder postures were different across computer activities. One hundred twenty participants were measured continuously for two hours each while performing their own computer work. Keyboard activities were associated with the highest intensity of left and right trapezius muscle efforts, and mouse activities were associated with the smallest variability in left and right trapezius muscle efforts. Corresponding trends in head and shoulder postures included that the greatest head flexion and left and right shoulder internal rotation was observed during keyboard activities, and that the smallest variability in head flexion, head lateral tilt, and right shoulder internal rotation was observed during mouse activities. Identifying which muscle efforts and postures are different across computer activities is the first essential step for developing prediction rules for muscle efforts and postures, which can be used to link muscle efforts and postures to musculoskeletal symptoms in epidemiological studies.

Predicting Forearm Physical Exposures During Computer Work Using Self-Reports, Software-Recorded Computer Usage Patterns, and Anthropometric and Workstation Measurements

Objectives Alternative techniques to assess physical exposures, such as prediction models, could facilitate more efficient epidemiological assessments in future large cohort studies examining physical exposures in relation to work-related musculoskeletal symptoms. The aim of this study was to evaluate two types of models that predict arm-wrist-hand physical exposures (i.e. muscle activity, wrist postures and kinematics, and keyboard and mouse forces) during computer use, which only differed with respect to the candidate predicting variables; (i) a full set of predicting variables, including self-reported factors, software-recorded computer usage patterns, and worksite measurements of anthropometrics and workstation set-up (full models); and (ii) a practical set of predicting variables, only including the self-reported factors and software-recorded computer usage patterns, that are relatively easy to assess (practical models). Methods Prediction models were build using data from a field study among 117 office workers who were symptom-free at the time of measurement. Arm-wrist-hand physical exposures were measured for approximately two hours while workers performed their own computer work. Each workers anthropometry and workstation set-up were measured by an experimenter, computer usage patterns were recorded using software and self-reported factors (including individual factors, job characteristics, computer work behaviours, psychosocial factors, workstation set-up characteristics, and leisure-time activities) were collected by an online questionnaire. We determined the predictive quality of the models in terms of R2 and root mean squared (RMS) values and exposure classification agreement to low-, medium-, and high-exposure categories (in the practical model only). Results The full models had R2 values that ranged from 0.16 to 0.80, whereas for the practical models values ranged from 0.05 to 0.43. Interquartile ranges were not that different for the two models, indicating that only for some physical exposures the full models performed better. Relative RMS errors ranged between 5% and 19% for the full models, and between 10% and 19% for the practical model. When the predicted physical exposures were classified into low, medium, and high, classification agreement ranged from 26% to 71%. Conclusion The full prediction models, based on self-reported factors, software-recorded computer usage patterns, and additional measurements of anthropometrics and workstation set-up, show a better predictive quality as compared to the practical models based on self-reported factors and recorded computer usage patterns only. However, predictive quality varied largely across different arm-wrist-hand exposure parameters. Future exploration of the relation between predicted physical exposure and symptoms is therefore only recommended for physical exposures that can be reasonably well predicted.

The effects of psychosocial factors on trapezius muscle activity levels during computer use

The goal of the present study, a part of the PROOF (Predicting Occupational biomechanics among OFfice workers) study, was to determine if there was a relationship between psychosocial stress, measured by reward and over-commitment, and trapezius muscle activity while workers performed their own computer work in the field. We observed that workers reporting higher levels of over-commitment and lower reward also experienced approximately 40% higher median trapeizus muscle activity levels than workers reporting lower levels of over-commitment and lower reward (change from 3.5% MVC to 6% MVC), with no difference in muscle activity for workers reporting high reward and either low or high over-commitment. Workers reporting higher levels of over-commitment experienced more variability in trapezius muscle activity. The results of this study may be used to inform interventions targeting reduction of musculoskeletal disorders among office workers.