Dose-Response Effects of a Brief Audio-Guided Mindfulness Intervention for Acute Pain
NCT07562412
Acute Pain, Agnosia
Tallahassee, Florida, United States
View Trial DetailsNCT Number: NCT05579496
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.
Interested in participating?
Request Info25 week–33 week
All sexes
Observational
Mount Sinai Hospital, Toronto, Ontario, Canada
Unmanaged pain in hospitalized infants has serious long-term complications. Existing infant pain assessment approaches demonstrate several key flaws (e.g., dependent on human cognitive capacity to simply combine data from multiple indicators into a total pain score, none have passed a critical discriminant validity test, bias introduced from human caregivers). Thus, the complexity of preterm pain assessment necessitates a machine learning approach. 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, better understanding how skin-to-skin contact works in caregiver-infant dyads and factors that influence the effectiveness of this pain management strategy is a critical step in improving infant pain care in NICUs. Relatedly, the design and sample of our current study (acute pain paradigm while infant is either in skin-to-skin contact with the birthing parent or in the cot) allows us to not only test the influence of skin-to-skin contact vs. cot on preterm newborn pain responding, but also interrogate potential mechanisms underlying the effectiveness of skin-to-skin contact (i.e., cardiac regulation attunement between caregivers and their infants during the procedure; influence of birthing parent perceived stress given the particularly elevated stress levels of NICU parents). A sample of 400 preterm infants (300 from Mount Sinai Hospital and 100 from University College London Hospital [UCLH]) and their birthing parents (if available) will be followed during a routine painful procedure (heel lance). Pain indicators (facial grimacing [behavioural indicators], heart rate, respiration rate, oxygen saturation levels [physiologic indicators], brain electrical activity) during the painful procedure will be used to train the algorithm to discriminate between different types of distress (pain-related and non-pain related). Heart rate and respiration rate, as well as maternal-reported perceived stress levels, will be collected from the birthing parent to examine factors impacting the effectiveness of skin-to-skin contact.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
QUALITATIVE INTERVIEWS
Inclusion criteria
Exclusion criteria
*Participants who cannot communicate fluently in English
QUANTITITATIVE DATA CAPTURE (video, eeg, ecg, RR, SPo2)
Inclusion criteria
Exclusion criteria
Time frame: NFCS-P coded in 1-5 minute epochs, over 2 hour surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
To be analyzed using machine learning via bedside videography: Facial Grimacing using Neonatal Facial Coding System (NFCS-P subset; Bucsea et al., 2022, 10.1097/j.pain.0000000000002798)
Time frame: For 2 hours surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
To be analyzed using machine learning via bedside monitoring: Continuous EEG data capture
Time frame: Over 2 hours surrounding painful procedure (time locked to heel lance)
To be analyzed using machine learning via bedside monitoring: Heart Rate, Heart Rate Variability
Time frame: Over 2 hours surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)
To be analyzed using machine learning via bedside monitoring: amount of oxygen-carrying hemoglobin in the blood relative to the amount of hemoglobin not carrying oxygen
Time frame: [Time Frame: Over 2 hours surrounding painful procedure (time locked to heel lance; approximately 1 hour before to 1 hour after heel lance)]
To be analyzed using machine learning via bedside monitoring: Respiratory patterns
Time frame: [Time Frame: Over 1.5 hours surrounding painful procedure (time locked to heel lance)]
We will measure parent cardiac stress in order to control for the effect of the parent context on infant pain processing. We will collect ECG data with 3 leads and a thin belt for respiration from the CNS monitor.
Time frame: Extracted from participants' medical charts following study completion
Time frame: Extracted from participants' medical charts following study completion
We will collect data on the cumulative exposure of painful and/or invasive procedures that the infant experienced since birth. A published tool will be used to calculate total pain burden during their NICU stay (Laudiano-Dray et al., 2020; 10.1097/j.pain.0000000000001814).
Time frame: Completed at the beginning of the study while EEG electrodes are being applied to the baby
Four questionnaires will be completed to understand the perspectives and experiences of birthing parents at time of study:
Time frame: Documented following the study completion
Time frame: These interviews are occurring at the beginning of the study and will be qualitatively analyzed. They are not linked to infants whose data we are collecting primary outcomes.
Health Professionals and Caregivers will be asked about their thoughts on using AI for infant pain assessment
Contact information is provided by the study sponsor or research team.
York University
Other
Acronym: BabyAIBabyCalm
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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