Listening to music during indoor cycling elicits higher internal loads during prolonged endurance exercise
Bibliographic record
Abstract
BACKGROUND: Dissociation by music may impact the rate of perceived exertion (RPE), which is an indicator of internal loads during exercise. However, it is not clear how music affects the RPE, neuromuscular, and cognitive responses to exercise. AIM: To determine whether listening to preferred music during indoor endurance exercise influences RPE, neuromuscular, and cognitive responses in healthy individuals. METHOD: Thirteen healthy adults performed sessions of prolonged indoor cycling at moderate intensity while listening or not to preferred music. Reaction time, selective attention, and memory were evaluated before, during, and/or after the exercise sessions. RPE, heart rate, muscle activation, pedaling torque, and cadence were recorded during the exercises. RESULTS: RPE (P = 0.004, d = 0.40), heart rate (P = 0.048, d = 0.53) and cadence (P = 0,043; d = 0.51) were higher in the music session compared to no music. Selective attention (P = 0.233), simple reaction time (P = 0.360), working and short-term memory (P > 0.05), as well as torque (P = 0.262) and muscle activation (RMS and MDF, P > 0.05) did not differ between music and no music sessions. CONCLUSION: Indoor cycling while listening to preferred music elicited higher internal loads, which we consider a result of higher cardiovascular demand. However, the effects of music on neuromuscular and cognitive responses were not evident. We conclude that music can be helpful to improve demand during indoor exercise.
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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 | 0.001 |
| 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) | 0.003 | 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".