What is the influence of background noise and exercise on the listening levels of iPod users?
Bibliographic record
Abstract
The widespread use of portable listening devices (PLDs) has increased concern about the potential for hearing impairment caused by their use. The current study investigated the effects of external noise and exercise on the use of PLDs. The 24 participants listened to the same song on an iPod during rest-in-quiet, rest-in-noise, and exercise-in-noise conditions. Preferred listening levels (PLLs) were recorded and participants’ maximum noise doses were calculated. Participants selected significantly higher listening levels in both noise conditions than in the quiet condition. The variability of volume selection was reduced significantly in the noise conditions. The maximum daily noise dose would have been exceeded by seven participants in the rest-in-noise condition and by eight in the exercise-in-noise condition compared to one participant in the rest-in-quiet condition. These results indicated that increased background noise causes individuals to increase the volume on their PLDs to potentially dangerous levels and that increased noise alone was not the only factor affecting the participants as the addition of exercise induced even further increases in PLLs.SumarioEl amplio uso de instrumentos portátiles de escucha (PLDs) ha incrementado la preocupación sobre el potencial que tiene su uso como causa de discapacidad auditiva. En este estudio se investigaron los efectos del ruido externo y del ejercicio sobre el uso de iPods. Los 24 participantes escucharon en un iPod la misma canción, en condiciones de descanso-en-silencio, descanso-con-ruido y ejercicio-en condiciones ruidosas. Se registraron los niveles de escucha preferidos (PLLs) y se calcularon las dosis máximas de ruido en los participantes. Estos seleccionaron niveles de escucha significativamente mayores en las dos condiciones con ruido pero no en la de silencio. La dosis diaria máxima de sonido se excedió en siete participantes, en la condición de descanso-en-silencio y por ocho en la de ejercicio-con ruido, comparada con 1 en la condición de descanso- en-silencio. Estos resultados indican que el aumento del ruido de fondo hace que los individuos incrementen el volumen de sus iPods hasta niveles potencialmente peligrosos y que el incremento de ruido en forma aislada no es el único factor que afecta a los participantes, como lo es la adición de ejercicio que incluso induce incrementos adicionales en los PLLs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".