Effects of Dual Monitor Computer Work Versus Laptop Work on Cervical Muscular and Proprioceptive Characteristics of Males and Females
Bibliographic record
Abstract
OBJECTIVE: The effects of performing a 90-minute computer task with a laptop versus a dual monitor desktop workstation were investigated in healthy young male and female adults. BACKGROUND: Work-related musculoskeletal disorders are common among computer (especially female) users. Laptops have surpassed desktop computer sales, and working with multiple monitors has also become popular. However, few studies have provided objective evidence on how they affect the musculoskeletal system in both genders. METHODS: Twenty-seven healthy participants (mean age = 24.6 years; 13 males) completed a 90-minute computer task while using a laptop or dual monitor (DualMon) desktop. Electromyography (EMG) from eight upper body muscles and visual strain were measured throughout the task. Neck proprioception was tested before and after the computer task using a head-repositioning test. EMG amplitude (root mean square [RMS]), variability (coefficients of variation [CV]), and normalized mutual information (NMI) were computed. RESULTS: Visual strain ( p < .01) and right upper trapezius RMS ( p = .03) increased significantly over time regardless of workstation. Right cervical erector spinae RMS and cervical NMI were smaller, while degrees of overshoot (mean = 4.15°) and end position error (mean = 1.26°) were larger in DualMon regardless of time. Effects on muscle activity were more pronounced in males, whereas effects on proprioception were more pronounced in females. CONCLUSION: Results suggest that compared to laptop, DualMon work is effective in reducing cervical muscle activity, dissociating cervical connectivity, and maintaining more typical neck repositioning patterns, suggesting some health-protective effects. APPLICATION: This evidence could be considered when deciding on computer workstation designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".