UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety

Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening

Background

Depression and anxiety are prevalent mental health concerns among children and adolescents. The application of conventional assessment methods, such as survey questionnaires to children, may lead to self-reporting issues. Digital biomarkers provide extensive data, reducing bias in mental health self-reporting, and significantly influence patient screening. Our primary objectives were to accurately assess children's mental health and to investigate the feasibility of using various digital biomarkers.

Methods

This study included a total of 54 boys and girls aged between 7 to 11 years. Each participant's mental state was assessed using the Depression, Anxiety, and Stress Scale. Subsequently, the subjects participated in digital biomarker collection tasks. Heart rate variability (HRV) data were collected using a camera sensor. Eye-tracking data were collected through tasks displaying emotion-face stimuli. Voice data were obtained by recording the participants' voices while they engaged in free speech and description tasks.

Results

Depressive symptoms were positively correlated with low frequency (LF, 0.04-0.15 Hz of HRV) in HRV and negatively associated with eye-tracking variables. Anxiety symptoms had a negative correlation with high frequency (HF, 0.15-0.40 Hz of HRV) in HRV and a positive association with LF/HF. Regarding stress, eye-tracking variables indicated a positive correlation, while pNN50, which represents the proportion of NN50 (the number of pairs of successive R-R intervals differing by more than 50 milliseconds) divided by the total number of NN (R-R) intervals, exhibited a negative association. Variables identified for childhood depression included LF and the total time spent looking at a sad face. Those variables recognized for anxiety were LF/HF, heart rate (HR), and pNN50. For childhood stress, HF, LF, and Jitter showed different correlation patterns between the two grade groups.

Discussion

We examined the potential of multimodal biomarkers in children, identifying features linked to childhood depression, particularly LF and the Sad.TF:time. Anxiety was most effectively explained by HRV features. To explore reasons for non-replication of previous studies, we categorized participants by elementary school grades into lower grades (1st, 2nd, 3rd) and upper grades (4th, 5th, 6th).

Conclusion

This study confirmed the potential use of multimodal digital biomarkers for children's mental health screening, serving as foundational research.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.