The quantified baby: real-world use of infant sleep monitoring technologies and its impact on parent mental health and medical decision-making
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".