Intensity and type of physical activity and semen quality in healthy young men
Notice bibliographique
Résumé
OBJECTIVE: To study the relationship between intensity of physical activity (PA) and semen quality in healthy young men. DESIGN: A prospective cohort study with repeated measures for each subject. PATIENT(S): Healthy high school and university students who did not regularly smoke tobacco, drink alcohol, or take drugs or medicine, with normal body mass index and abdominal circumference. EXPOSURE: The participants underwent urologic visit, fasting blood and semen sampling, and anthropometric measurements, and filled in the International Physical Activity Questionnaire, at enrollment and after 4 and 8 months. Duration and frequency of walking, moderate-intensity, and vigorous-intensity activities in the last week were assessed, and a score was computed for total PA. MAIN OUTCOME MEASURE(S): Semen specimens were taken at each visit through masturbation, after 3-5 days of abstinence, and analyzed by an expert urologist. Sperm concentration, total and progressive motility, and proportion of spermatozoa with normal morphology were measured. Linear and generalized linear mixed models with the Poisson family were fitted to assess the relationships between PA variables and sperm parameters, after adjusting for season, time, and study arm. The shape of the relationship was modeled through restricted cubic spline regression. RESULT(S): A total of 143 male subjects, aged 18-23 years (median, 20 years), were enrolled. They had a median PA of 1,960 (95% confidence interval, 1,055-3,182) Metabolic Equivalent of Tasks in min/wk. Statistically significant differences were found for total, progressive motility, and percent of cell with normal morphology across categories of total PA; the highest medians of total (47%) and progressive motility (34%) and of the percentage of normal morphology cells (7%) were found for medium PA. Positive associations of sperm total motility and normal morphology with medium levels of PA, and negative associations with walking and vigorous-intensity activity emerged. Spline regression analysis confirmed these findings, showing an inverse U-shape relationship, with the highest value of total motility and normal morphology for medium PA, and the lowest values for lower and higher activity. CONCLUSION(S): These findings support the present recommendations to practice moderate PA for health improvement, including semen quality.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».