Stability Ball Versus Office Chair: Comparison of Muscle Activation and Lumbar Spine Posture During Prolonged Sitting
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to evaluate the differences between sitting on a stability ball and in an office chair in terms of trunk muscle activation and lumbar spine posture. BACKGROUND: Stability balls have become increasingly popular as an alternative to office chairs to help reduce the prevalence of low back pain; however, little research has been conducted on their use as office chairs. METHODS: The 14 participants (7 men, 7 women) were required to sit on both a stability ball and an office chair for 1 hour each while performing various computer workstation tasks throughout the sitting periods. The activation of eight muscles and lumbar spine posture were measured and analyzed. RESULTS: Increased muscle activation in thoracic erector spinae (p = .0352), decreased pelvic tilt (p = .0114), and increased perceived discomfort (p < .0001) while sitting on the stability ball were observed. CONCLUSIONS: The small changes in biological responses when sitting on a stability ball as compared with an office chair, combined with the increased reported discomfort while on the ball, suggests its use for prolonged sitting may not be advantageous. APPLICATION: Prolonged sitting on a stability ball does not greatly alter the manner in which an individual sits, yet it appears to increase the level of discomfort. Therefore, it is important to fully explore a new chair design and consult scientific research before implementing its use.
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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) | 0.002 | 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".