Disrupting prolonged sitting reduces IL-8 and lower leg swell in active young adults
Bibliographic record
Abstract
Abstract Background Evidence suggests that disrupting prolonged bouts of sitting with short bouts of physical activity can significantly reduce blood glucose and improve insulin sensitivity; however, limited research is available on the impact of such disruptions on inflammation and swelling. The purpose of this study was to determine whether short bouts of exercise performed each hour during a 4 h sitting session were able to negate the effects of prolonged sitting (PS) on several cardiometabolic outcomes. Methods Eligible participants ( n = 10) attended two laboratory sessions: PS (uninterrupted sitting for 4 h) and disrupted sitting (DS; 4 h sitting session disrupted by 3 min of exercise each hour (60-s warm-up at 50 W, 5 s of unloaded cycling, 20-s sprint at 5% body weight, and 95-s cool-down at 50 W)). The exercise bouts were performed at minute 60, 120, and 180. Blood and saliva samples, and measures of heart rate and blood pressure were assessed before (T1) and after (T2) each session; leg swell was measured continuously. Results Concentrations of salivary IL-8 increased during PS (T1: 0.19 ± 0.32; T2: 0.50 ± 1.00 pg/μg of protein) but decreased during DS (T1: 0.41 ± 0.23; T2: 0.22 ± 0.11 pg/μg of protein, d: 0.51, p = 0.002). Leg swell increased and plateaued in PS, but was attenuated during DS. Conclusion It appears that short bouts of exercise significantly reduce swelling in the lower leg and IL-8 levels in the saliva, indicating that even among healthy, active, young adults, disrupting prolonged sitting can significantly reduce swelling and systemic inflammation.
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 | 0.000 |
| 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".