Analysis of NHANES 2005–2016 Data Showed Significant Association Between Micro and Macronutrient Intake and Various Sleep Variables (P06-103-19)
Notice bibliographique
Résumé
To understand the association between micro and macronutrient intake and sleep variables from the National Health and Nutrition Evaluation Survey (NHANES, 2005–2016). Data analysis was performed using SAS 9.4; regression analysis was used to assess the relationship (p < 0.05) of nutrient intake with sleep variables. All nutrients were individual usual intakes determined using the National Cancer Institute method from food plus supplements; covariates included age, gender, ethnicity, poverty income ration, current smoking status and physical activity level. Individuals 16+ years of age were included in the analysis; pregnant or lactating females and those with unreliable dietary recalls were excluded in the analysis. Seven (7) Sleep variables included in the analysis were short sleep hours (<7 hrs of sleep) and trouble sleeping (NHANES 2005–2016), sleep disorder (NHANES, 2005–2014) and poor sleep quality, insomnia, sleep latency, and use of sleeping pills >5 times in the last month (NHANES 2005–2008). In adults (males and females) 19+ years, 32.7% experienced short sleep; 47.3% poor sleep quality; 8.94% a sleep disorder; 37.9% sleep latency; 9.30% used sleeping pills; 15.1% exhibited insomnia; and 27.7% experienced sleep trouble. Within this population, short sleep was significantly (p < 0.05) associated with the greatest number of nutrients; showing an inverse association with magnesium, niacin, vitamin D, calcium, and dietary fiber intake. Across all seven sleep variables, however, magnesium, niacin and vitamin D demonstrated significant (p < 0.05) inverse association within this population. Inverse associations were also found for dietary fiber intake and short sleep and sleep disorder; phosphorus intake and poor sleep quality, sleep latency and sleep pill use; and vitamin K intake and poor sleep quality, sleep disorder, sleep latency and sleep pill use in the gender combined adults 19+ years. Within this population however, there were direct associations for the intakes of protein and vitamin B6 and short sleep, sleep disorder and sleep trouble; for the intakes of sodium and vitamin A and poor sleep quality, sleep latency and sleep pill use; for the intake of vitamin B12 and poor ADL and insomnia; and for the intake of zinc and sleep quality, sleep latency, sleep pill use, poor ADL and insomnia. Among female adults 19+ years, dietary fiber was the only nutrient that showed an inverse association with all seven sleep variables. These findings demonstrate the importance of micro and macronutrient intake on numerous sleep variables. This analysis was funded by Pharmavite, LLC.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».