Acute cardiometabolic effects of interrupting sitting with resistance exercise breaks
Bibliographic record
Abstract
Interrupting prolonged sitting with light activity breaks, such as short walks, improves cardiometabolic outcomes, yet less is known about the impact of resistance exercise breaks. This study examined the effects of hourly, guidelines-based simple resistance exercise breaks on acute cardiometabolic health outcomes over a simulated work period. Fourteen adults (age: 53.4 ± 9.5 years, body mass index: 30.9 ± 4.8 kg/m2) completed 2 randomized 4-h conditions: prolonged sitting (SIT) and hourly resistance exercise breaks (REX). Glucose, triglycerides, blood pressure, and heart rate were measured at baseline and then hourly. Pulse wave velocity (PWV) was measured before and after each condition. Linear mixed models evaluated overall condition effects and differences at each hour. Cohen’s d estimated magnitude of effects. Four-hour glucose area under the curve (AUC) did not differ by condition (REX vs. SIT: β = –0.35 mmol/L, p = 0.278, d = 0.51). However, an attenuation of postprandial glucose at 1 h (β = –0.69 mg/dL, p = 0.004, d = 1.02) in REX compared with SIT was observed. Triglyceride AUC, mean blood pressure, and PWV did not differ significantly between REX and SIT overall or any time point (all p > 0.05). Heart rate was higher across the experimental period in REX versus SIT (β = 3.3 bpm, p < 0.001, d = 0.35) and individual time points (β ≥ 3.2 bpm, p ≤ 0.044, d ≥ 0.34). Resistance exercise breaks can potentially improve 1-h postprandial glucose, but may not acutely benefit other cardiometabolic outcomes. Future studies employing guidelines-based resistance exercises to interrupt prolonged sitting with a larger sample and longer follow-up are warranted.
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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 | 0.001 | 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) | 0.001 | 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".