Investigating the Physiology of Labour and Labour Onset Through Longitudinal Measurements Performed With a Wearable Sensor

Investigating the Physiology of Labour, Labour Onset and Pregnancy Outcomes in Pregnant Women, Through Longitudinal Measurements Performed With a Wearable Sensor

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT03982654

Enrollment

145

Registered

2019-06-11

Start date

2018-03-30

Completion date

2019-03-05

Last updated

2019-06-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Labour Onset

Brief summary

The purpose of the study is to collect data to support the development and validation of a machine learning model for the automatic detection, prediction and monitoring of labour, through the use of a wearable sensor (Bloomlife sensor) that can be easily used at home. Women participating in the study will be asked to regularly record data with the Bloomlife sensor, from inclusion in the study until delivery. In addition, clinical information related to their pregnancy and delivery will be collected.

Interventions

DEVICEBloomlife

Women use the Bloomlife sensor to collect data throughout their pregnancy. The collected data is stored locally in the device, but it is not used to provide any feedback to the patients or to the clinical team. Therefore the use of the device doesn't have an impact on clinical decision making and patients' care.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

OTHER

Masking

NONE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

* Pregnant * Gestational age between 20 weeks and 0 days and 30 weeks and 0 days * Willingness to participate in the study

Exclusion criteria

* Implanted pacemaker or any other implanted electrical device * History of allergies to silicone-based adhesives * Any health condition resulting in higher chance of C-section (meeting one of these criteria is sufficient to be excluded): previous history of C-section, placental abnormality, being primipara and older than 40

Design outcomes

Primary

Measure	Time frame	Description
Accuracy	At delivery	Accuracy of the machine learning model for the automatic detection of labor, measured in terms of sensitivity and specificity in detecting labour

Secondary

Measure	Time frame	Description
Labour probability	At delivery	Probability of being in labour, computed using electrophysiological signals collected with the Bloomlife sensor

Countries

Belgium

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026