Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps
Notice bibliographique
Résumé
Abstract Wearables, apps and other remote smart devices can capture rich, objective physiologic, metabolic, and behavioral information that is particularly relevant to pregnancy. The objectives of this paper were to 1) characterize individual level pregnancy self-reported symptoms and objective features from wearables compared to the aggregate; 2) determine whether pregnancy self-reported symptoms and objective features can differentiate pregnancy-related conditions; and 3) describe associations between self-reported symptoms and objective features. Data are from the Better Understanding the Metamorphosis of Pregnancy study, which followed individuals from preconception to three-months postpartum. Participants (18-40 years) were provided with an Oura smart ring, a Garmin smartwatch, and a Bodyport Cardiac Scale. They also used a study smartphone app with surveys and tasks to measure symptoms. Analyses included descriptive spaghetti plots for both individual-level data and cohort averages for select weekly reported symptoms and objective measures from wearables. This data was further stratified by pregnancy-related clinical conditions such as preeclampsia and preterm birth. Mean Spearman correlations between pairs of self-reported symptoms and objective features were estimated. Self-reported symptoms and objective features during pregnancy were highly heterogeneous between individuals. While some aggregate trends were notable, including an inflection in heart rate variability approximately eight weeks prior to delivery, these average trends were highly variable at the n-of-1 level, even among healthy individuals. Pregnancy conditions were not well differentiated by objective features. With the exception of self-reported swelling and body fluid volume, self-reported symptoms and objective features were weakly correlated (mean Spearman correlations <0.1). High heterogeneity and complexities of associations between subjective experiences and objective features across individuals pose challenges for researchers and highlights the dangers in reliance on aggregate approaches in the use of wearable data in pregnant individuals. Innovation in machine learning and AI approaches at the n-of-1 level could help to accelerate the field. Author Summary The objective physiological and behavioral information from wearable and other smart devices is uniquely relevant to pregnancy. The objectives of this study were to: 1) describe the individual-level variability of pregnancy self-reported symptoms and objective wearable measures; 2) determine whether this variability can be explained by pregnancy clinical conditions; and 3) determine whether pregnancy self-reported symptoms are associated with objective wearable measures. Data are from the Better Understanding the Metamorphosis of Pregnancy study, which followed individuals from preconception to three-months postpartum. Participants (18-40 years) used an Oura smartring, a Garmin smartwatch, and a Bodyport Cardiac Scale alongside a study app to track self-reported symptoms. High heterogeneity was observed in self-reported pregnancy symptoms, and objective measures such as heart rate variability, activity and sleep over pregnancy that were dissimilar to the population average of these measures. Pregnancy clinical conditions did not explain well the observed high variability in objective wearable measures while self-reported symptoms were weakly correlated with objective wearable measures over pregnancy. In sum, high heterogeneity and complexities of associations between subjective experiences and objective measures from wearables across pregnant individuals pose challenges for researchers. Innovation in machine learning and AI individual level approaches will help to accelerate the field.
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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 | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».