ArticlePloS one2019
Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Scoping review of precision child and youth mental health research: dwelling in possibility.Frontiers in psychiatry · 2025Pooled it
- Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.Sensors (Basel, Switzerland) · 2019Pooled it
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- Targeting neurobehavioral substrates of effortful control to reduce childhood anxiety: The Camp Kidpower study protocol.Research square · 2025Article
- Wearables and behavioral coding show promise for measuring and predicting severe emotional outbursts in children.Frontiers in digital health · 2025Article
- Exploring emotions beyond the laboratory: A review of emotional and physiological ecological momentary assessment methods in children and youth.Psychophysiology · 2024Review
- Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges.Sensors (Basel, Switzerland) · 2024Review
- The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.medRxiv : the preprint server for health sciences · 2023Article
- The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.IEEE journal of biomedical and health informatics · 2023Article
- Evaluation of the Michigan Clinical Consultation and Care Program: An Evidence-Based Approach to Perinatal Mental Healthcare.Journal of clinical medicine · 2023Article
- Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.Journal of medical Internet research · 2023Article
- Wearable devices for anxiety & depression: A scoping review.Computer methods and programs in biomedicine update · 2023Article
- How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway.Sensors (Basel, Switzerland) · 2022Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Advancing Digital Medicine with Wearables in the Wild.Sensors (Basel, Switzerland) · 2022Article
- Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.Journal of medical Internet research · 2022Article
- Article
- Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery.IEEE journal of biomedical and health informatics · 2021Article
- A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression.Frontiers in psychiatry · 2021Article
- Inertial Sensors-Applications and Challenges in a Nutshell.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause children to go unassessed-suffering in silence because they never exhibiting the disruptive behaviors that would lead to a referral for diagnostic assessment. If left untreated these disorders are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying children with internalizing disorders using an instrumented 90-second mood induction task. Participant motion during the task is monitored using a commercially available wearable sensor. We show that machine learning can be used to differentiate children with an internalizing diagnosis from controls with 81% accuracy (67% sensitivity, 88% specificity). We provide a detailed description of the modeling methodology used to arrive at these results and explore further the predictive ability of each temporal phase of the mood induction task. Kinematical measures most discriminative of internalizing diagnosis are analyzed in detail, showing affected children exhibit significantly more avoidance of ambiguous threat. Performance of the proposed approach is compared to clinical thresholds on parent-reported child symptoms which differentiate children with an internalizing diagnosis from controls with slightly lower accuracy (.68-.75 vs. .81), slightly higher specificity (.88-1.00 vs. .88), and lower sensitivity (.00-.42 vs. .67) than the proposed, instrumented method. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.