Randomized Controlled Trial Protocol on the Effects of a Sensory Motor Intervention Associated with Kangaroo Skin-to-Skin Contact in Preterm Newborns.
Mariane de Oliveira Nunes RecoDaniele Almeida Soares-MarangoniPublished in: International journal of environmental research and public health (2024)
There is still very limited evidence on the effects of neonatal interventions on infant neurodevelopmental outcomes, including general movements (GMs). This research will primarily assess the effects of a sensory motor physical therapy intervention combined with kangaroo skin-to-skin contact on the GMs of hospitalized preterm newborns. Secondary outcomes include body weight, posture and muscle tone, behavioral state, length of hospital stay, and breastfeeding. This study protocol details a two-arm parallel clinical trial methodology, involving participants with a postmenstrual age of 34-35 weeks admitted to a Neonatal Intermediate Care Unit (NInCU) with poor repertoire GMs. Thirty-four participants will be randomly assigned to either the experimental group, receiving a 10-day sensory motor physical therapy associated with kangaroo skin-to-skin contact, or the control group, which will only receive kangaroo skin-to-skin contact. The study will measure GMs (primary outcome), and body weight, posture and muscle tone, behavioral state, length of hospital stay, and breastfeeding (secondary outcomes). Data collection occurs in the NInCU before and after the intervention, with follow-up measurements post discharge at 2-4 weeks and 12-15 weeks post-term. SPSS will be used for data analyses. The results will provide novel information on how sensory motor experiences may affect early neurodevelopment and clinical variables in preterm newborns.
Keyphrases
- randomized controlled trial
- gestational age
- soft tissue
- body weight
- study protocol
- wound healing
- low birth weight
- clinical trial
- preterm infants
- healthcare
- preterm birth
- pregnant women
- skeletal muscle
- type diabetes
- electronic health record
- emergency department
- mental health
- metabolic syndrome
- machine learning
- quality improvement
- adverse drug
- health information
- double blind