Women’s Attitudes Toward Self-Monitoring of Their Pregnancy Using Noninvasive Electronic Devices: Cross-Sectional Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Pregnancy can be distressing, particularly if expectant mothers are worried about the well-being of their fetus. Consequently, the desire for reassurance and frequent fetal monitoring is often pronounced. Smart wearable devices and telemedicine are promising tools that could assist women in self-monitoring their pregnancy at home, hence disburdening emergency departments (EDs). They present the possibility to clarify the need for urgent care remotely and offer tighter pregnancy monitoring. However, patients' acceptance of such new technologies for fetal monitoring has not yet been explored extensively. OBJECTIVE: This survey aimed to elucidate the attitudes of women toward self-monitoring of their pregnancy using noninvasive electronic devices. The technical details of the proposed devices were not specified. METHODS: A cross-sectional multicenter study was conducted at the departments of obstetrics of the University Hospitals of Heidelberg and Leipzig, Germany. All patients seen in the obstetrics clinic who were above 18 years were offered participation. We designed a survey questionnaire including validated instruments covering population characteristics, issues in current and past pregnancies, as well as attitudes toward self-monitoring of pregnancy with smart devices. RESULTS: A total of 509 pregnant women with no previous experience in telemedicine participated. Only a small minority of 5.9% (29/493) regarded self-monitoring with wearable devices as an alternative to consulting their physicians. Along these lines, only 7.7% (38/496) strongly believed they would visit the ED less often if such devices were readily available. However, if the procedure were combined with a Web-based telemetric physician consult, 13.5% (66/487) would be highly motivated to use the devices. Furthermore, significantly more women regarded it as an alternative prior to seeing a doctor when they perceived a decline in fetal movements (P<.001). Interestingly, women with university degrees had a higher propensity to engage in pregnancy self-monitoring compared with women without one (37% vs 23%; P=.001). Of the participants, 77.9% (381/489) would like smart wearable devices to measure fetal heart sounds, and 62.6% (306/489) wished to use the devices on their own. Feedback from a doctor or midwife was also very important in their choice of such devices (61.8%, 301/487 wished feedback). The intended frequency of use differed vastly among women, ranging from 13.8% (65/471) who would like to use such a device several times per day to 31.6% (149/471) who favored once per week at most. CONCLUSIONS: Our results point to a skeptical attitude toward pregnancy self-monitoring among pregnant women. Nevertheless, many women are open to using devices for pregnancy monitoring in parallel to consulting their physician. The intention to use such devices several times daily or weekly, expressed by more than half of the participants, highlights the potential of such technologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".