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

Loftness, Bryn C.; Halvorson-Phelan, Julia; O'Leary, Aisling; Cheney, Nick; McGinnis, Ellen; McGinnis, Ryan S.

doi:10.1109/embc48229.2022.9871090

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…For this analysis, we consider data from the Kiddie Internalizing Disorder (KID) Study [14], [15]. Children in the KID Study participate in an administrator-led, RDoC-motivated battery of behavioral mood induction tasks that relate to a variety of internalizing and externalizing disorders [16], [17].…”

Section: Methodsmentioning

confidence: 99%

“…These efforts inform the potential for future clustering and predictive modeling in child mental health using robust feature sets of wearable sensor-derived data. For this analysis, we consider data from the Kiddie Internalizing Disorder (KID) Study [14], [15]. Children in the KID Study participate in an administrator-led, RDoCmotivated battery of behavioral mood induction tasks that relate to a variety of internalizing and externalizing disorders [16], [17].…”

Section: Introductionmentioning

confidence: 99%

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

Loftness

Rizzo

Halvorson-Phelan

et al. 2023

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Loftness

Rizzo

Halvorson-Phelan

et al. 2023

Preprint

Self Cite

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“…This occurs because discerning a child’s thoughts and emotions can be challenging, even for adults who are intimately familiar with the child’s behavior ( 17 , 18 ). These challenges underscore the need for an objective, accurate, and easily applicable mental health screening tool to detect anxiety and depression in young children ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation of children’s mental health using movement and voice targeted children aged 4-8 years, with the study by Loftness et al. ( 19 ) being a notable exception. In that study, active phenotype data were obtained by using tasks that children can perform, similar to our study.…”

Section: Introductionmentioning

confidence: 99%

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

Choo,

Park,

Cho

et al. 2024

Front. Psychiatry

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BackgroundDepression 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.MethodsThis 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.ResultsDepressive 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.DiscussionWe 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).ConclusionThis study confirmed the potential use of multimodal digital biomarkers for children’s mental health screening, serving as foundational research.

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“…These digital biomarkers can be combined to form digital phenotypes of specific mental health conditions that may be assessed and monitored over time. For instance, our prior work has demonstrated digital phenotyping derived from child movement and speech recorded during adapted behavior paradigms can identify young children with anxiety and depression with good accuracy (75%-81%) 4–8 . However, it can be difficult for other researchers to replicate this data as a broadly interdisciplinary team is required.…”

Section: Introductionmentioning

confidence: 99%

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

Loftness

Halvorson-Phelan

O'Leary

et al. 2023

Preprint

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Objective: Internalizing disorders, such as anxiety and depression, in pre-school aged children are common and often go undiagnosed into adulthood. To augment traditional parental-reports, we have previously presented an objective assessment for early childhood anxiety and depression which leverages movement and vocal biomarkers measured via wearable sensors during brief mood induction tasks that achieves good accuracy (75%-81%). However, these methods required specialized equipment and expertise in data and sensor engineering to administer and analyze. Method: To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes an Android mobile application for collecting movement and audio data during mood induction tasks and an open-source data analysis platform for extracting digital biomarkers and discovering digital phenotypes. As proof of principle, we present data collected using the ChAMP System from 50 children ages 5-8, with and without anxiety or depressive disorders. Results: Movement and vocal features derived from the ChAMP System support the consideration of theory-driven temporal phases within mood induction tasks, and the use of an assessment battery for characterizing childhood internalizing disorders. Results also demonstrate that features significantly differ between diagnostic groups and correlate with symptom severity implying their potential use as digital biomarkers. Conclusions: The ChAMP System provides clinically relevant digital biomarkers of childhood anxiety and depression. This new open-source tool lowers the barrier to entry for those interested in exploring digital phenotyping of childhood mental health.

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

Cited by 8 publications

References 24 publications

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

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

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

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

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