Wireless Vital Sign Monitoring in Pregnant Women: A Functionality and Acceptability Study
Abstract
Objective: To test the functionality and acceptability of a wireless vital sign monitor in an inpatient obstetric unit. Materials and Methods: Pregnant women at a U.S. tertiary-care hospital wore a wireless vital sign sensor that captures heart rate, respiratory rate, and temperature. Measurements were compared with vital signs obtained by standard devices. We defined continuous capture of vital signs for 30 min with wireless data transfer to a central monitor as functional success. Acceptability was assessed per the pregnant women and nurses observing the device. Bland–Altman plots were constructed to assess agreement between the wireless sensor and standard measurements. Results: Thirty of 32 enrolled pregnant women had successful monitoring; 2 cases were stopped early for non–study-related reasons. Comparing wireless sensor and standard measurements, the mean difference (limits of agreement) values at the 25th and 75th percentiles were 1.6 (±13.2) and 4.2 (±18.6) heartbeats/min, 4.2 (±6.1) and 0.7 (±5.4) respirations/min, and 0.02°C (±1.5) and 0.5°C (±1.8), respectively. Most pregnant women found the device comfortable, likeable, and useful (78%, 81%, and 97%, respectively); 80% of nurses found the monitor easy to use, and 84% would recommend it to a patient. Conclusions: We successfully obtained maternal vital signs using a simple wireless monitor with high acceptability. Well-validated monitors of this nature could significantly alleviate the human resource burden of monitoring during labor and confer greatly desired mobility to laboring pregnant women, although incorporation of blood pressure monitoring will be critical.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Additional monitoring equipment designed to assess maternal or fetal status, such as pulse oximetry, blood pressure monitors, and electrocardiography, are typically used in a noncontinuous manner and place a relatively high burden on providers.
“Additional monitoring equipment designed to assess maternal or fetal status, such as pulse oximetry, blood pressure (BP) monitors, and electrocardiography (ECG), are typically used in a noncontinuous manner and place a relatively high burden on providers (11).”
From Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings (2021) · cites this paper as [11]
Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Measuring agreement in method comparison studies
Bland, J. M., & Altman, D. G. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800204 · Cited by 9,619 · see citing works
Clinical Antecedents to In-Hospital Cardiopulmonary Arrest
Schein, R. M. H., Hazday, N., Pena, M., Ruben, B. H., & Sprung, C. L. (1990) · CHEST Journal · doi:10.1378/chest.98.6.1388 · Cited by 902 · see citing works
Developing strategies to prevent inhospital cardiac arrest
Franklin, C. M., & Mathew, J. (1994) · Critical Care Medicine · doi:10.1097/00003246-199402000-00014 · Cited by 618 · see citing works
Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare
Steele, R., Lo, A., Secombe, C., & Wong, Y. K. (2009) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2009.08.001 · Cited by 508 · see citing works
The Current State of Telemonitoring: A Comment on the Literature
Meystre, S. M. (2005) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2005.11.63 · Cited by 272 · see citing works
Lovett, P. B., Buchwald, J. M., Stürmann, K., & Bijur, P. E. (2004) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2004.06.016 · Cited by 243 · see citing works
Body-Worn Sensor Design: What Do Patients and Clinicians Want?
Bergmann, J., & McGregor, A. H. (2011) · Annals of Biomedical Engineering · doi:10.1007/s10439-011-0339-9 · Cited by 216 · see citing works
Why don't nurses monitor the respiratory rates of patients?
Hogan, J. (2006) · British Journal of Nursing · doi:10.12968/bjon.2006.15.9.21087 · Cited by 204 · see citing works
Estimation of human core temperature from sequential heart rate observations
Buller, M. J., Tharion, W. J., Cheuvront, S. N., Montain, S. J., Kenefick, R. W., Castellani, J. W., Latzka, W. A., Roberts, W. S., Richter, M., Jenkins, O. C., et al. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/7/781 · Cited by 172 · see citing works
Patients’ perceptions of a home telecare system
Rahimpour, M. R., Lovell, N. H., Celler, B. G., & McCormick, J. (2007) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2007.10.006 · Cited by 160 · see citing works
A Remote Patient Monitoring System for Congestive Heart Failure
Suh, M.-K., Chen, C.-A., Woodbridge, J., Tu, M. K., Kim, J. I., Nahapetian, A., Evangelista, L. S., & Sarrafzadeh, M. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9733-y · Cited by 144 · see citing works
Harrison, G. A., Jacques, T. C., Kilborn, G., et al. (2005) · Resuscitation · doi:10.1016/j.resuscitation.2004.11.017 · Cited by 132 · see citing works
Rifkin, D. E., Abdelmalek, J. A., Miracle, C. M., Low, C. L., Barsotti, R., Rios, P., Stepnowsky, C. J., & Agha, Z. (2012) · Blood Pressure Monitoring · doi:10.1097/mbp.0b013e32835d126c · Cited by 93 · see citing works
Mobile phone-based surveillance of cardiac patients at home
Scherr, D., Zweiker, R., Kollmann, A., Kästner, P., Schreier, G., & Fruhwald, F. M. (2006) · Journal of Telemedicine and Telecare · doi:10.1258/135763306777889046 · Cited by 85 · see citing works
Respiratory rates in emergency department patients
Hooker, E. A., O’Brien, D. J., Danzl, D. F., Barefoot, J. A., & Brown, J. E. (1989) · Journal of Emergency Medicine · doi:10.1016/0736-4679(89)90257-6 · Cited by 73 · see citing works
Staff perceptions of respiratory rate measurement in a general hospital
Philip, K. E. J., Richardson, R., & Cohen, M. (2013) · British Journal of Nursing · doi:10.12968/bjon.2013.22.10.570 · Cited by 63 · see citing works
Noninvasive Measurement of Body Temperature in Critically Ill Patients
Bridges, E. J., & Thomas, K. A. (2009) · Critical Care Nurse · doi:10.4037/ccn2009132 · Cited by 46 · see citing works
Rauf, Z., O׳Brien, E., Stampalija, T., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028129 · Cited by 39 · see citing works
Santhosh, S., & Kamaraj, R. (2018) · Research Journal of Pharmacy and Technology · doi:10.5958/0974-360x.2018.01028.4 · Cited by 33 · see citing works
The Impact of Using Mobile-Enabled Devices on Patient Engagement in Remote Monitoring Programs
Agboola, S., Havasy, R., Myint-U, K., Kvedar, J., & Jethwani, K. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700306 · Cited by 25 · see citing works
Boatin, A. A., Wylie, B. J., Goldfarb, I. T., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117043 · Cited by 24 · see citing works
Self-citationWang, Q., Zhao, H., Chen, W., Li, N., & Wan, Y. (2013) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000017 · Cited by 19 · see citing works
Metrics for Assessing the Reliability of a Telemedicine Remote Monitoring System
Charness, N. H., Fox, M. R., Papadopoulos, A., & Crump, C. (2013) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2012.0143 · Cited by 17 · see citing works
Zeng, W., Huang, Q.-F., Sheng, C., et al. (2012) · Blood Pressure Monitoring · doi:10.1097/mbp.0b013e328359c426 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ryu, D., Kim, D. H., Price, J. T., Lee, J. Y., Chung, H. U., Allen, E., Walter, J. R., Jeong, H., Cao, J., Kulikova, E., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2100466118 · Cited by 105 · see citing works
Innovations in vital signs measurement for the detection of hypertension and shock in pregnancy
Vousden, N., Nathan, H. L., & Shennan, A. H. (2018) · Reproductive Health · doi:10.1186/s12978-018-0533-4 · Cited by 29 · see citing works
- Authors
- Boatin, A. A., Wylie, B. J., Goldfarb, I., Azevedo, R., Pittel, E., Ng, C., & Haberer, J. E.
- Affiliations
- Harvard University (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Boatin, Adeline Adwoa
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2016-07
- Volume
- 22
- Issue
- 7
- Pages
- 564-571
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- OpenAlex
- W2349429054
- Cited by
- 30
- References
- 25
- Citations by year
- 2024: 4 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 1 · 2018: 2 · 2017: 3 · 2016: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Telemedicine Journal and e-Health (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Massachusetts General Hospital
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- pregnant women, heart rate, respiratory rate, body temperature, device acceptability, sensor accuracy, labor monitoring, nurses' perceptions, patient comfort
- MeSH terms
- Adolescent, Adult, Cross-Sectional Studies, Female, Humans, Pregnancy, United States, Patient Satisfaction, Young Adult, Vital Signs, Remote Sensing Technology, Wireless Technology, Tertiary Care Centers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
