Cycling exercise efficiency and economy: Exploring the role of phase angle
Bibliographic record
Abstract
Background: Phase angle (PhA), derived from bioelectrical impedance analysis (BIA), is considered a non-invasive marker of cellular health and membrane integrity. Whether PhA relates to muscular efficiency during exercise remains unclear. Methods: This pilot study investigated the association between PhA and delta efficiency (DE), gross efficiency (GE), and exercise economy (EC) during submaximal cycling in 30 healthy young adults (15 females, mean age 21.4 ± 3.8 years). Whole-body and lower-body PhA were assessed using multifrequency BIA. Participants completed a graded cycling test (20-80 W) with energy expenditure determined via indirect calorimetry. DE, GE, and EC were calculated using standard procedures, and associations with PhA were examined using Pearson correlations, median-split group comparisons, and multivariable linear regression models adjusting for sex and fat-free mass. Results: Neither whole-body nor lower-body PhA was significantly correlated with DE, GE, or EC (all p > 0.05). Group comparisons based on PhA medians showed no significant differences in performance indicators. The largest observed correlation was between whole-body PhA and GE (r = -0.32, p = 0.081). Regression models adjusting for sex and fat-free mass confirmed that PhA did not independently predict DE, GE, or EC. Conclusions: PhA did not predict submaximal cycling efficiency or economy in healthy young adults. These findings suggest that cellular health, as reflected by PhA, may not directly influence muscular energetics under steady-state conditions. Future studies with larger samples and mechanistic measurements are warranted to clarify this relationship.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".