BACKGROUND: Preterm infant experiences neuromotor disturbances and neonatal pain during NICU stay. Optimal brain development and pain tolerance require both sensory and motor experiences. We aimed to explore the effect of multimodal stimulation (MMS) including multi-sensory and movement therapy on neuromotor behaviour and neonatal pain among hospitalized preterm infants. METHODS: 32 recruited preterm infants [gestational age (GA): 28 to 36 weeks] by convenience sampling technique for pre-test-post-test control group, single-center randomized controlled study were randomly allocated into two groups, MMS group, and control group. MMS group received the interventions for 5 days period, totalling five sessions with 30 minutes/session. Infant Neurological International Battery (INFANIB), Neonatal Infant Pain Scale (NIPS), weight, length, and Occipitofrontal circumference (OFC) were recorded at baseline and 6th day to analyze the changes observed neuromotor behaviour, neonatal pain, and changes in the anthropometric parameters respectively. RESULTS: GA, weight, length and OFC of preterm infants recruited were, 34.4±2 weeks, 2.11±0.48 kg, 45±3.8 cm and 32.3±1.7 cm respectively. INFANIB and NIPS change scores in MMS group were [4 (2, 6); p = 0.001] and NIPS scores [2 (2,3); p < 0.001] respectively when compared to INFANIB [– 4 (– 6, – 2); p = 0.001] and NIPS scores [1 (– 1,1); p = 0.778] in control group. Significant difference p < 0.001 in INFANIB and NIPS scores while non-significant difference in weight (p = 0.166), length (p = 0.443) and OFC (p = 0.413) were observed between the groups. CONCLUSION: Administration of MMS has a positive clinically significant impact on neuromotor behaviour and neonatal pain on preterm infants.
Objectives:Motor cognitive processing speed (MCPS) is often reported in terms of reaction time. In spite of being a significant indicator of function, behavior, and performance, MCPS is rarely used in clinics and schools to identify kids with slowed motor cognitive processing. The reason behind this is the lack of availability of convenient formula to estimate MCPS. Thereby, the aim of this study is to estimate the MCPS in the primary schoolchildren.Materials and Methods:Two hundred and four primary schoolchildren, aged 6–12 years, were recruited by the cluster sampling method for this cross-sectional study. MCPS was estimated by the ruler drop method (RDM). By this method, a metallic stainless steel ruler was suspended vertically such that 5 cm graduation of the lower was aligned between the web space of the child's hand, and the child was asked to catch the moving ruler as quickly as possible, once released from the examiner's hand. Distance the ruler traveled was recorded and converted into time, which is the MCPS. Multiple regression analysis of variables was performed to determine the influence of independent variables on MCPS.Results:Mean MCPS of the entire sample of 204 primary schoolchildren is 230.01 ms ± 26.5 standard deviation (95% confidence interval; 226.4–233.7 ms) that ranged from 162.9 to 321.6 ms. By stepwise regression analysis, we derived the regression equation, MCPS (ms) = 279.625–5.495 × age, with 41.3% (R = 0.413) predictability and 17.1% (R2 = 0.171 and adjusted R2 = 0.166) variability.Conclusion:MCPS prediction formula through RDM in the primary schoolchildren has been established.
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