A Digital Tool for Assessing the Distinct Effects of Depression, Anxiety, and ADHD on Children’s Emotional Cognitive Bias: A Cross-Sectional Study (Preprint)

Background: Emotional wellness and healthy neurocognitive development are crucial from early childhood. An imbalance in the development of the attentional and emotional regulation system is associated with an increased risk of depression, anxiety, and ADHD. Assessing these potential risks from a young age is essential but it is difficult to conduct cognitive tests that are both child-friendly and able to dissociate amongst various kinds of behavioral biases. Objective: This study aims to develop an app-based tool to objectively monitor the interaction between cognition and affect that can be used by young children and to measure the risk of anxiety, depression, and ADHD. Methods: We assessed 78 children aged 4-10 using an animated emotional Flanker task, emotional Stroop task, and emotional Go/No-Go task to examine their emotional regulation and attentional control. As measures of children’s current mental health, we collected self-reported depression and anxiety states through the CES-DC and STAI-CH scales, while the ADHD risk was reported by parents using the K-ARS scale. Results: Using the numerous behavioral measures collected across the tasks, we extracted three abstract scores representing different aspects of cognitive function (i.e., attention, selective inhibition, and emotional sensitivity). There was a significant improvement in general attention across development but not emotion-attention interactions. Performance was also correlated with mental health scales: first, the children with higher depression symptoms were slower in their responses in general. Second, both anxious and depressed children demonstrated reduced attention selectively to the emotional stimuli as indicated by elongated RT and lower accuracy. Lastly, children with higher ADHD scales showed lower accuracy across the three tasks, which may potentially be induced by their high impulsiveness, leading to difficulties regulating their responses to emotional stimuli. Conclusions: By combining the predictive power of well-established emotional cognitive tasks with our dimensionality reduction techniques, we were able to successfully extract individual affective-cognitive characteristics from diverse but noisy behavioral patterns. Together, these results demonstrate the advantages and applicability of our child-friendly, digital assessment tool for monitoring young children’s affective and cognitive health.