Inappropriate auditory stimulants lead to neonatal stress and changes in physiological parameters. Nowadays, particular emphasis is placed on the developmental aspects of the care of preterm infants. The lullaby is a suitable auditory stimulus for preterm infants, which as a subtype of developmental care decreases stress responses. This study investigates the effects of lullabies in the mothers' own voices on preterm infants' physiological parameters. Materials and Methods:This single group study is a randomized clinical trial. Forty study-qualified hospitalized infants were included in the study, during a lullaby stage and a non-lullaby stage. Their physiological parameters including respiratory rate, heart rate and oxygen saturation level were recorded. Their mothers' lullabies were played for them during the lullaby stage. No intervention was performed in the non-lullaby stage and only the infants' variables were recorded. Infants were assessed for four successive days, two days for each stage. Data was collected and recorded every 2 minute. Data was statistically analysed after gathering and entering into the SPSS.22 by means of Friedman's and paired sample t-test.Results: In this study of 40 case studies, 45% were female and 55% were male, with an average gestational age of new-borns of 32.43 weeks and mean birth weight of 2,189.36 gr. In the intervention group, during the time that the lullaby was played, mean rates of heart beat were significantly decreased (p=0.03) and SaO 2 was increased (p=0.039), which were significantly different from their base recorded levels at the beginning and those of the control stage, but there was no significant difference between two stages in the mean of respiratory rates (p=0.070). Conclusion:Since a mother's lullaby has significant effects on physiological parameters, we hope that nurses will tell mothers to use the lullaby as a supportive developmental care for infants to assist improving the physiological state of preterm new-borns.

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Background: Nowadays, outcome prediction models using logistic regression (LR) and artificial neural network (ANN) analysis have been developed in many areas of healthcare research.

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