Imitation of 'bye-bye' in very low-birthweight infants.

BACKGROUND: The aim of this study was to clarify the development of imitation skills in very low-birthweight (VLBW) infants compared with full-term infants with regard to the onset of 'bye-bye'. METHODS: A total of 597 full-term infants (age, 6-21 months) and 95 VLBW infants (corrected age, 6-21 months) participated in this study. The time at which the infants began to imitate bye-bye and how they moved their hands were investigated by direct observations of their behaviors. RESULT: Some full-term infants began to imitate bye-bye at 9 months, and all full-term infants could imitate bye-bye by 16 months old. The imitation of bye-bye was delayed in VLBW infants, but all of them could imitate it at 17 months old. Bye-bye motions were divided into five types. The 'moving wrist up and down' motion was observed most frequently at the initial bye-bye in both groups, but it was more frequent in VLBW infants at the early stage. The motion types changed with age, with the 'palm facing others' motion observed exclusively at 16 months in full-term infants. All infants of both groups could imitate this type at 17 months old. CONCLUSION: The development of the ability to imitate bye-bye was delayed in VLBW infants even after correction for gestational age. It was suspected that the fine motor development delay might contribute to the late appearance of bye-bye in VLBW infants. Further follow-up study is required to clarify the clinical significance.

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.