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 worker's 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.

Office workers with high effort-reward imbalance and overcommitment have greater decreases in heart rate variability over a 2-h working period.

PURPOSE: High levels of workplace psychosocial factors have been associated with adverse cardiovascular outcomes, possibly through the pathway of increasing autonomic arousal. The purpose of this study was to investigate whether the workplace psychosocial factors of effort-reward imbalance (ERI) and overcommitment were associated with greater decreases in heart rate variability (HRV) across a 2-h working period in a cohort of office workers performing their own work at their own workplaces. METHODS: Measurements of HRV in 5-min time epochs across a 2-h morning or afternoon working period, as well as self-reports of ERI and overcommitment, were collected for 91 office workers. RESULTS: There was a negative and significant (p < 0.01) ERI*time interaction for the standard deviation of the interval between normal heart beats (SDNN), the square root of the mean squared differences of successive normal heart beats (RMSSD), and the power in the high-frequency range of the heart rate signal (HF power), and a positive and significant ERI*time interaction for the ratio of power in the low-frequency range of the heart rate signal divided by the HF power (LF/HF ratio). There was a positive and significant overcommitment*time interaction for the LF/HF ratio (p < 0.01) in the morning, and a negative and significant overcommitment*time interaction for SDNN, RMSSD, and HF power (p < 0.01) in the afternoon. CONCLUSIONS: The results indicate that participants exposed to high levels of ERI and overcommitment exhibited a more adverse cardiovascular response (a greater decrease in HRV throughout the 2-h measurement period) compared to their colleagues with lower levels of these factors.

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

BACKGROUND: Due 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: The 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.

Office workers' computer use patterns are associated with workplace stressors.

This field study examined associations between workplace stressors and office workers' computer use patterns. We collected keyboard and mouse activities of 93 office workers (68F, 25M) for approximately two work weeks. Linear regression analyses examined the associations between self-reported effort, reward, overcommitment, and perceived stress and software-recorded computer use duration, number of short and long computer breaks, and pace of input device usage. Daily duration of computer use was, on average, 30 min longer for workers with high compared to low levels of overcommitment and perceived stress. The number of short computer breaks (30 s-5 min long) was approximately 20% lower for those with high compared to low effort and for those with low compared to high reward. These outcomes support the hypothesis that office workers' computer use patterns vary across individuals with different levels of workplace stressors.

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.

Serial postural and motor assessment of Fetal Akinesia Deformation Sequence (FADS).

BACKGROUND: Fetal Akinesia Deformation Sequence (FADS) is a rare, in most cases autosomal recessive, disorder. Its heterogeneous origin results in variable onset and expression of motor and postural anomalies. DNA-diagnostic possibilities are limited, thus prenatal diagnosis is chiefly dependent on sonographic examinations. AIM: To explore postural and motor development from a systematic sonographic protocol for fetuses at high risk for FADS. Specific questions are: which motor (i.e., specific movement patterns, quality and quantity of general movements) and postural aspects are most informative about emerging FADS and is the gestational age of onset range of FADS more limited for siblings? METHODS: Ten families underwent 45, 15-minute sonographic assessments for motility and posture for ten index fetuses with FADS and nine subsequent pregnancies from five families. RESULTS: FADS was diagnosed between 18 and 33 weeks gestation in ten index pregnancies and between 11 and 18 weeks gestation in 4/9 subsequent pregnancies, 1-12 weeks earlier than their index pregnancies. From the four assessment aspects, posture and movement quality were always abnormal, movement quantity in 7/14 and differentiation into specific movement patterns were reduced in comparison with healthy siblings (p<0.01). Deterioration occurred in a 2 week period. CONCLUSIONS: Serial postural and qualitative assessments were most informative diagnosing FADS. Quantity and differentiation into specific movement patterns contributed substantially. Onset range of FADS within siblings was suggested to be more limited than between families.