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A Pilot Study of the Womb Watch App: Fetal Assessment Using the Microphone of the Smartphone

Status: Recruiting
Location: See all (3) locations...
Intervention Type: Device
Study Type: Observational
SUMMARY

The surveillance of pregnancies at risk for fetal loss secondary to high-risk maternal or fetal conditions remains a mainstay of perinatal care. Current testing to prevent fetal loss includes the regular use of ultrasound (biophysical profile) or fetal heart rate monitoring (non-stress test) in an outpatient clinic setting once or twice weekly. A patient may also be asked to subjectively assess daily fetal movements during the time between routine antepartum testing appointments. However, there are no good systems for pregnant women to objectively measure fetal movements. Smartphones have allowed for the development of applications that utilize various embedded devices including the camera and microphone. In our recent pilot STUDY00001552 of 205 pregnant patients, placement of the iPhone10 microphone directly on the maternal abdominal wall was utilized to detect fetal movements. AI assessment of the audio recordings proved superior to maternal perception of fetal movements that were recorded during simultaneous ultrasound (gross fetal movements: 64% audio vs 18% maternal; breathing: 93% vs 3%, hiccups: 73% vs 3%). This trial is a prospective, observational, feasibility study of 60 patients that includes both low-risk and high-risk pregnant women to examine the usability of the Womb Watch smartphone application. The study will involve introduction of the Womb Watch app to a population of pregnant patients. Features of the app will be modified based on participant feedback. Anxiety levels of the patient will be tracked serially using a survey tool. The various types and versions of smartphones will be assessed to see if they affect the AI model. Finally, patients will be asked to determine the strength of fetal movements to see if this parameter can be assessed by the AI model. Amniotic fluid data will assessed through clinical ultrasounds to see if this also has any effect on the AI model's ability to detect fetal movements.

Eligibility
Participation Requirements
Sex: Female
Minimum Age: 18
Maximum Age: 45
Healthy Volunteers: f
View:

• Ability to understand and voluntarily provide written signed informed consent to participate in the study.

• English speaking (the alpha version of the Womb Watch app is only available in English)

• Singleton intrauterine pregnancy

• Estimated gestational age at enrollment 28 weeks to 32 weeks

• Low-risk pregnancy with normal fetal growth and anatomy and no maternal co-morbidities of note.

• High-risk pregnancy including fetal anomalies and maternal co-morbidities including but not limited to cardiovascular, pulmonary, hepatic, renal, hematologic, gastrointestinal, endocrine/metabolic, immunologic, dermatologic, neurologic, or oncologic.

• No prior diagnosis of an anxiety mood disorder or a psychiatric illness

• Access to internet

• A functioning email address

• Owns personal smartphone; iPhone or Android of any generation

Locations
United States
Texas
Dell Medical School- University of Texas at Austin
NOT_YET_RECRUITING
Austin
University of Texas Medical Branch at Galveston - UTMB Health
RECRUITING
Galveston
McGovern Medical School - UTHealth Houston
RECRUITING
Houston
Contact Information
Primary
Kenneth Moise, MD
kmoise@austin.utexas.edu
713-444-7603
Time Frame
Start Date: 2026-08-06
Estimated Completion Date: 2027-09-30
Participants
Target number of participants: 60
Treatments
Single Group Assignment
Pregnant women between 28 - 40 weeks gestation will record sounds coming from their pregnant abdomen daily for 15 minutes with the Womb Watch smartphone application. Participants must have a singleton intrauterine pregnancy, diagnosed as a low-risk or high-risk pregnancy, no prior diagnosis of an anxiety mood disorder or a psychiatric illness, access to the internet, a functioning email address, owns a personal smartphone, and be English speaking.
Related Therapeutic Areas
Sponsors
Collaborators: The University of Texas Health Science Center, Houston, The University of Texas Medical Branch, Galveston
Leads: Kenneth Moise MD

This content was sourced from clinicaltrials.gov

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