Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery

McGinnis, Ellen; Scism, Jordyn; Hruschak, Jessica; Muzik, Maria; Rosenblum, Katherine L.; Fitzgerald, Kate D.; Copeland, William E.; McGinnis, Ryan S.

doi:10.1109/jbhi.2021.3053846

Cited by 10 publications

(19 citation statements)

References 87 publications

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“…A few studies (3/19, 16%) [ 31 , 32 , 37 ] investigated the identification children with IDs using wearable devices and predicted their adverse behavioral outcomes through machine learning and AI.…”

Section: Resultsmentioning

confidence: 99%

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Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review

Welch¹,

Wy²,

Ligęzka³

et al. 2022

J Med Internet Res

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Background Mental health disorders are a leading cause of medical disabilities across an individual’s lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising. Objective This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry. Methods This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus. Results The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals. Conclusions Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.

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Section: Resultsmentioning

confidence: 99%

“…McGinnis et al [ 31 ] aimed to develop a digital phenotype for childhood internalizing psychopathology based on data collected from a wearable inertial sensor. Data were recorded while the child completed three different tasks: the bubbles task (induced positivity), snake task (induced anxiety), and speech task (induced fear).…”

Section: Resultsmentioning

confidence: 99%

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review

Welch¹,

Wy²,

Ligęzka³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Each of the three behavior tasks (Approach 5,6,13 , Speech 7,14,15 , and Bubbles 8,16,17 , see Table 1) is meant to induce a construct of negative or positive valence from the Research Domain Criteria framework 18 , which have associations with anxiety and depressive symptoms 19– 22 . The ChAMP app facilitates the protocols, collects timely objective data, and provides output accordingly.…”

Section: Methodsmentioning

confidence: 99%

“…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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“…Digital medicine technologies and wearables can make a significant impact on conditions wherein symptoms cannot be reported reliably. For example, children under 8 years old are not able to accurately describe abstract emotions and thus are unable to report their own mental health problems [19][20][21][22][23], and adults with dementia [24] or psychosis have an impaired capacity to reliably report on their symptoms [25,26] as a product of the disease. In these cases, digital medicine technologies can capture objective metrics of their symptoms and convey that information directly to clinical decision makers.…”

Section: Areas Of Opportunity For Digital Medicinementioning

confidence: 99%

Advancing Digital Medicine with Wearables in the Wild

McGinnis

2022

Sensors

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Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery

Cited by 10 publications

References 87 publications

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review

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

Advancing Digital Medicine with Wearables in the Wild

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