BMC Digital Health (Nov 2023)

A novel telehealth tool using a snack activity to identify autism spectrum disorder

  • Zenghui Ma,
  • Yan Jin,
  • Ruoying He,
  • Qinyi Liu,
  • Xing Su,
  • Jialu Chen,
  • Disha Xu,
  • Jianhong Cheng,
  • Tiantian Zheng,
  • Yanqing Guo,
  • Xue Li,
  • Jing Liu

Journal volume & issue
Vol. 1, no. 1
pp. 1 – 13


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Abstract Background The COVID-19 pandemic has caused an unprecedented need for accessible health care services and significantly accelerated the development processes of telehealth tools for autism spectrum disorder (ASD) early screening and diagnosis. This study aimed to examine the feasibility and utility of a time-efficient telehealth tool combining a structured snack time assessment activity and a novel behaviour coding scheme for identifying ASD. Methods A total of 134 1–6-year-old individuals with ASD (age in months: mean = 51.3, SD = 13.1) and 134 age- and sex-matched typically developing individuals (TD) (age in months: mean = 54, SD = 9.44) completed a 1-min snack time interaction assessment with examiners. The recorded videos were then coded by trained coders for 17 ASD-related behaviours; the beginning and end points and the form and function of each behaviour were recorded, which took 10–15 min. Coded details were transformed into 62 indicators representing the count, duration, rate, and proportion of those behaviours. Results Twenty indicators with good reliability were selected for group difference, univariate and multivariate analyses. Fifteen behaviour indicators differed significantly between the ASD and TD groups and remained significant after Bonferroni correction, including the children’s response to the examiner’s initiation, eye gaze, pointing, facial expressions, vocalization and verbalization, and giving behaviours. Five indicators were included in the final prediction model: total counts of eye gaze, counts of standard pointing divided by the total counts of pointing, counts of appropriate facial expressions, counts of socially oriented vocalizations and verbalizations divided by the total counts of vocalizations and verbalizations, and counts of children using giving behaviours to respond to the examiner's initiations divided by the total counts of the examiner's initiation of snack requisitions. The ROC curve revealed a good prediction performance with an area under the curve (AUC) of 0.955, a sensitivity of 92.5% and a specificity of 84.3%. Conclusion Our results suggest that the snack activity-based ASD telehealth approach shows promise in primary health care settings for early ASD screening.