Prospective User Perceptions and Acceptability of a Wearable Fetal Heart Monitor: A Cross-Sectional Survey (Preprint)
Bibliographic record
Abstract
BACKGROUND: Limited access to prenatal care, particularly in maternity care deserts and other underserved communities, contributes to maternal and fetal health disparities. Advances in telehealth and wearable fetal monitoring technologies, including fetal electrocardiography, offer opportunities to support remote fetal assessment and supplement traditional prenatal care. Understanding the acceptability of these technologies among potential users is important for informing future device development and implementation. OBJECTIVE: This study aims to evaluate perceptions of acceptability and user preferences regarding a wireless fetal heart monitor among women of reproductive age. METHODS: A survey was designed to assess the acceptability and user preferences of a wireless fetal monitoring device among women of reproductive age to guide device development. The survey was administered using the Qualtrics XM online survey platform. The survey was distributed using snowball recruitment through community and online postering. Compensation was offered to survey respondents. Data were analyzed using SPSS (version 29). RESULTS: A total of 163 participants completed the survey, with 103 responses to acceptability questions. Older women aged 30 to 49 years showed higher rates of acceptability (40/54, 74.1%) than young women aged 18 to 29 years (26/49, 53.1%). In total, 70.7% (73/103) of the participants preferred a device measuring 2.5 × 7.6 cm or smaller. CONCLUSIONS: Designing a device that incorporates patient preferences may increase the likelihood of patient adoption and consistent use. This study examined perceived acceptability of a hypothetical monitoring device, providing a preliminary foundation to inform future device development and acceptability assessment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".