Effects of Workstation Type on Mental Stress: FNIRS Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to examine the effect of the workstation type on the severity of mental stress by means of measuring prefrontal cortex (PFC) activation using functional near-infrared spectroscopy. BACKGROUND: Workstation type is known to influence worker's health and performance. Despite the practical implications of ergonomic workstations, limited information is available regarding their impact on brain activity and executive functions. METHOD: Ten healthy participants performed a Montreal imaging stress task (MIST) in ergonomic and nonergonomic workstations to investigate their effects on the severity of the induced mental stress. RESULTS: Cortical hemodynamic changes in the PFC were observed during the MIST in both the ergonomic and nonergonomic workstations. However, the ergonomic workstation exhibited improved MIST performance, which was positively correlated with the cortical activation on the right ventrolateral and the left dorsolateral PFC, as well as a marked decrease in salivary alpha-amylase activity compared with that of the nonergonomic workstation. Further analysis using the NASA Task Load Index revealed a higher weighted workload score in the nonergonomic workstation than that in the ergonomic workstation. CONCLUSION: The findings suggest that ergonomic workstations could significantly improve cognitive functioning and human capabilities at work compared to a nonergonomic workstation. APPLICATION: Such a study could provide critical information on workstation design and development of mental stress that can be overlooked during traditional workstation design and mental stress assessments.
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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) | 0.001 | 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) | 0.002 | 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".