Effect of different seat support characteristics on the neck and trunk muscles and forward head posture of visual display terminal workers
Bibliographic record
Abstract
OBJECTIVE: This study was designed to identify the effect of seat support characteristics on the neck and trunk muscles and forward head posture of visual display terminal (VDT) workers working at computers. PARTICIPANTS: 22 VDT workers with forward head posture were asked to perform computer work. METHODS: Surface electromyography recorded the five neck and trunk muscles Forward head angle was analyzed with a 3-D motion analysis system. The significance of differences in the seat supports (hard, spongy, unstable) was tested by repeated one-way ANOVA with the significance cutoff set at alpha = 0.05. RESULTS: Computer work seated on an unstable cushion-ball as compared to a spongy soft-cushion seat support showed significantly lower midcervical and L5 paraspinal muscle activity and significantly higher lower trapezius and internal oblique abdominal muscle activity. The mean forward head angle decreased in the order of spongy, hard, and unstable seat supports. CONCLUSIONS: An unstable cushion-ball seat support may prevent work-related neck and upper limb disorders associated with forward head posture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".