The quantified baby: real-world use of infant sleep monitoring technologies and its impact on parent mental health and medical decision-making
Notice bibliographique
Résumé
Abstract Background Managing sleep is a challenging experience in early parenthood, and infant sleep problems are associated with negative outcomes within the family. A large market of devices to monitor infants’ real-time health information during sleep has emerged, including smart cameras, under-mattress sensors, and wearable devices. The impacts of these products on maternal and parental mental health and medical decision-making are poorly understood. Methods We performed a systematic search for products detecting health data from sleeping children on the global retail platform Amazon in March 2023. A total of 11,262 unique reviews from 48 eligible products were retrieved from the US, Canada, UK, and Australia sites and subjected to sentiment and thematic analyses to capture the characteristics of user families, contexts of device use, and impacts on maternal and child health. Results Parental anxiety and infants’ high-risk medical status were cited as main reasons to purchase products. When devices worked well, their use was associated with improved parental sleep quality and decreased anxiety. However, poor device performance was commonly reported and was linked to increased parental stress and anxiety and disrupted child sleep. Users reported making medical decisions based on device output. Price, privacy, and unsafe use of devices emerged as ethical issues. Conclusions Use of a smart sleep device in the home is common and has implications for the health of both children and adults. Benefits and harms must be understood by parents and healthcare providers in order to support evidence-based decision-making around their use. Key Messages What is already known Many households monitor sleeping children’s health using emerging “smart” commercial products such as cameras, sensors, and wearables. The medical community is not yet prepared to offer evidence-based recommendations about these practices, despite their widespread adoption and potential intersection with the healthcare system. What this study adds By conducting a content analysis of a large corpus of consumer reviews, we find evidence that these devices impact maternal and parental mental health, children’s sleep quality and safety, and family usage of healthcare services. How this study might affect research, practice or policy These results will inform future development of recommendations for sleep device use. With a better understanding of how these devices affect user families, parents can be more equipped to make evidence-informed decisions for their family’s health.
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 | 0,010 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».