Acute Effects of Whole-Body Electromyostimulation on Energy Expenditure at Resting and during Uphill Walking in Healthy Young Men
Bibliographic record
Abstract
The effects of the different electrical frequencies of whole-body electrical stimulation (WB-EMS) on energy expenditure (EE) and the respiratory exchange ratio (RER) remain poorly understood. This study aimed to determine the effects of different WB-EMS electrical frequencies on EE and the RER during supine resting and uphill walking. A total of 10 healthy and recreationally active men (21.6 ± 3.3 years old) participated in the present study. Participants completed two testing sessions in a randomized order. In each session, a variety of impulse frequencies (1 hertz (Hz), 2 Hz, 4 Hz, 6 Hz, 8 Hz, and 10 Hz) were applied in a randomized order, allowing a 10 min passive recovery between them. Oxygen consumption and carbon dioxide production were measured to calculate EE and the RER. All frequencies increased EE at rest (all p ≤ 0.001), with 4 Hz being the frequency producing the highest increase (Δ = 8.89 ± 1.49 kcal/min), as did 6 Hz (Δ = 8.05 ± 1.52 kcal/min) and 8 Hz (Δ = 7.04 ± 2.16 kcal/min). An increment in the RER at rest was observed with 4 Hz, 6 Hz, 8 Hz and 10 Hz (all p ≤ 0.016), but not with 1 Hz and 2 Hz (p ≥ 0.923). During uphill walking, the frequency that elicited the highest increase in EE was 6 Hz (Δ = 4.87 ± 0.84 kcal/min) compared to the unstimulated condition. None of the impulse frequencies altered the RER during uphill walking. WB-EMS increases EE in healthy young men both during resting and uphill walking.
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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 | 0.000 |
| 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) | 0.001 | 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".