Insights into maternal sleep: a large-scale longitudinal analysis of real-world wearable device data before, during, and after pregnancy
Bibliographic record
Abstract
BACKGROUND: Current understanding of pregnancy and postpartum sleep is driven by limited lab or self-reported data. Our goal is to use consumer wearable devices through an observational study to reveal longitudinal, real-world sleep patterns in this population. METHODS: We analysed retrospective, de-identified Fitbit device data from 2540 users in the United States and Canada who met strict wear-time requirements (≥80% daily usage for ≥80% of the time periods of interest [12 weeks prepregnancy, throughout pregnancy, and 20 weeks immediately postpartum]). We tracked sleep time and stages using Fitbit devices. FINDINGS: Pregnant participants experienced a peak in total sleep time (TST) at 10 weeks (447.6 ± 47.6 min), exceeding their prepregnancy average (425.3 ± 43.5 min) before declining throughout pregnancy. This initial TST increase, mirrored by time in bed (TIB), was driven by more light sleep. Deep and rapid-eye movement sleep decreased significantly throughout pregnancy, with maximum reductions of 19.2 ± 13.8 min and 9.0 ± 19.2 min respectively by pregnancy end (two-sided t-test, p < 0.001 for both). Sleep efficiency also slightly declined during pregnancy (median drop: 88.3%-86.8%). Postpartum, TIB remained below prepregnancy levels by 14.7 ± 45.7 min one year after birth and 15.2 ± 47.7 min at 1.5 years after birth. INTERPRETATION: This study revealed a previously unquantified initial increase in sleep followed by decreases in both quantity and quality as pregnancy progresses. Sleep deficits persist for at least 1.5 years postpartum. These quantified trends can assist clinicians and patients in understanding what to expect through their pregnancy and postpartum journey. FUNDING: Google, LLC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".