Does a break from sitting change biomechanical outcome measures or transient pain? A laboratory-based experimental study
Bibliographic record
Abstract
BACKGROUND: Sitting can induce transient low back pain (LBP) in healthy individuals. A rest from sitting should provide relief, however, the parameters of breaks (activity type, intensity, duration, and timing) are not currently known. OBJECTIVE: The purpose of this study was to examine the effect of 2-minute walking breaks at 40-minute intervals on sitting-induced LBP. METHODS: Thirty-two healthy participants were recruited for a within-control study: two randomly presented sessions of sitting for 2 hours with and without breaks. Outcome measures were compared between condition and pain group using a three-way ANOVA with significance atp > 0.05. RESULTS: Walking breaks at 40-minute intervals result in significantly lower pain ratings than those taken immediately before the break for sitting-induced back pain developers. However, this relief is short lived (<10 minutes), with ratings increasing to pre-break levels once the sitting exposure resumes. There were no differences in biomechanical factors between sessions. Regardless of session type, pain developers displayed higher spine fidget frequency than non-pain developers, females sat with less spine flexion, with greater gluteal activation levels, and with their center of pressure approximately half a centimeter to the left and forward compared to males, and males had significantly greater peak pressures over a smaller area compared to females. CONCLUSION: Walking breaks at 40-minute intervals provide significant, but temporary, relief of sitting-induced back pain for pain developers. Future work should optimize break parameters and examine the longer-term benefit of breaks, especially for individuals that are not able to tolerate sitting for extended durations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".