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Rebooting Infant Pain Care: Using Machine Learning and Skin-to-Skin Contact to Exponentially Improve Neonatal Intensive Care Unit Practice

NCT05579496 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2026-07-13

No results posted yet for this study

Summary

To address the current limitations related to infant pain assessment in the NICU, our international team of knowledge users and health/natural science/engineering/social science researchers have come together to build a machine learning algorithm that will learn how to discriminate invasive and non-invasive distress. Furthermore, to improve the use of current pain management practices, our team seeks to better understand the developmental mechanisms underlying skin-to-skin contact over time and factors that may influence its efficacy in mitigating pain responses in preterm infants. This is an ongoing naturalistic observational study.

Conditions

Sponsors & Collaborators

  • MOUNT SINAI HOSPITAL

    collaborator OTHER

  • University College, London

    collaborator OTHER

  • University College London Hospitals

    collaborator OTHER

  • York University

    lead OTHER

Principal Investigators

  • Rebecca Pillai Riddell, PhD · York University/Mount Sinai Hospital

Eligibility

Min Age
25 Weeks
Max Age
33 Weeks
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-11-01
Primary Completion
2030-03-31
Completion
2031-03-31

Countries

  • Canada
  • United Kingdom

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05579496 on ClinicalTrials.gov