Heterogeneous effects of physical activity on physiological stress during pregnancy
Notice bibliographique
Résumé
Abstract Pregnancy is a critical period characterized by profound physiological and psychological adaptations that can significantly impact both maternal and fetal health outcomes. Thus, it is imperative to implement targeted and evidence-based interventions to enhance maternal well-being during the prenatal period. Mobile health (mHealth) technologies enable continuous, real-time monitoring of both physiological and psychological states, providing detailed insights into health behaviors and individual responses in natural settings. This study leveraged mHealth technologies, including the Oura smart ring and ecological momentary assessment (EMA) via a mobile app, to examine how emotional distress influences the relationship between physical activity (PA) and heart rate variability (HRV), an indicator of physiological stress during pregnancy. Consenting participants, aged 18-40 years, with a healthy singleton pregnancy in the second trimester, were enrolled in the study. Our findings revealed that among participants experiencing emotional distress, increased PA was associated with higher HRV, indicating lower physiological stress. In particular, this beneficial effect was more pronounced on days with elevated emotional distress. These findings suggest that engaging in physical activity may help protect pregnant women against autonomic dysregulation associated with emotional distress, potentially supporting maternal cardiovascular health. By utilizing mHealth technologies for real-time data collection, our study highlights the potential for personalized and adaptive interventions that promote maternal well-being by encouraging physical activity as a strategy to mitigate the physiological effects of emotional distress during pregnancy. Author summary Pregnancy involves significant physiological and psychological changes that impact maternal and fetal health. Heart rate variability (HRV), a key biomarker of autonomic function and cardiovascular health, reflects the body’s ability to regulate physiological stress. In this study, we used mobile health (mHealth) technologies, including the Oura smart ring and ecological momentary assessment (EMA) through a mobile app, to examine how emotional distress influences the relationship between physical activity (PA) and HRV during pregnancy. Our findings indicate that PA protects against autonomic dysregulation linked to emotional distress. On days with emotional distress, engaging in more PA was associated with higher HRV, indicating lower physiological stress. This positive effect was even stronger on days with increased emotional distress. By leveraging real-time monitoring, our study highlights the value of mHealth in capturing dynamic interactions between emotional and physiological states. These insights demonstrate the potential for personalized interventions using mHealth technologies to support maternal well-being by encouraging PA as a strategy to better manage physiological stress during pregnancy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».