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O’Leary A, Lahey T, Lovato J, Loftness B, Douglas A, Skelton J, Cohen JG, Copeland WE, McGinnis RS, McGinnis EW. Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges. SENSORS (BASEL, SWITZERLAND) 2024; 24:3214. [PMID: 38794067 PMCID: PMC11125700 DOI: 10.3390/s24103214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.
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Affiliation(s)
- Aisling O’Leary
- Department of Philosophy, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA;
| | - Timothy Lahey
- University of Vermont Medical Center, Burlington, VT 05401, USA; (T.L.); (A.D.)
| | - Juniper Lovato
- Complex Systems Center, University of Vermont, Burlington VT 05405, USA; (J.L.); (B.L.)
| | - Bryn Loftness
- Complex Systems Center, University of Vermont, Burlington VT 05405, USA; (J.L.); (B.L.)
| | - Antranig Douglas
- University of Vermont Medical Center, Burlington, VT 05401, USA; (T.L.); (A.D.)
| | - Joseph Skelton
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA;
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
| | - Jenna G. Cohen
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington VT 05405, USA;
| | | | - Ryan S. McGinnis
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
| | - Ellen W. McGinnis
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA;
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
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Loftness BC, Halvorson-Phelan J, OLeary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. IEEE J Biomed Health Inform 2023; PP:10.1109/JBHI.2023.3337649. [PMID: 38019617 PMCID: PMC11133764 DOI: 10.1109/jbhi.2023.3337649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
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.
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Loftness BC, Halvorson-Phelan J, O'Leary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.19.23284753. [PMID: 38076802 PMCID: PMC10705626 DOI: 10.1101/2023.01.19.23284753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
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.
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Affiliation(s)
- Bryn C Loftness
- University of Vermont's Complex Systems Center and M-Sense Research Group
| | | | | | - Carter Bradshaw
- University of Vermont Medical Center Department of Psychiatry
| | | | - Isabel Berman
- University of Vermont Medical Center Department of Psychiatry
| | - John Torous
- Digital Psychiatry Division for Beth Israel Deaconess Medical Center at Harvard Medical School
| | | | - Nick Cheney
- University of Vermont Complex Systems Center
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Loftness BC, Rizzo DM, Halvorson-Phelan J, O'Leary A, Prytherch S, Bradshaw C, Brown AJ, Cheney N, McGinnis EW, McGinnis RS. Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082795 DOI: 10.1109/embc40787.2023.10340806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
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.
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Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Abu Serhan H, Abd-Alrazaq AA, Solaiman B, Househ M. Wearable devices for anxiety & depression: A scoping review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2023; 3:100095. [PMID: 36743720 PMCID: PMC9884643 DOI: 10.1016/j.cmpbup.2023.100095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Background The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.
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Affiliation(s)
- Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Mahmood Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | | | - Alaa A Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Barry Solaiman
- College of Law, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Loftness BC, Halvorson-Phelan J, O'Leary A, Cheney N, McGinnis EW, McGinnis RS. UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1141-1144. [PMID: 36085630 DOI: 10.1109/embc48229.2022.9871090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems including substance abuse, development of comorbid psychopathology, increased risk for suicide, and substantial functional impairment. Current diagnostic procedures require access to clinical experts, take considerable time to complete, and inherently assume that child symptoms are observable by caregivers. Multi-modal wearable sensors may enable development of rapid point-of-care diagnostics that address these challenges. Building on our prior work, here we present an assessment battery for the development of a digital phenotype for internalizing disorders in young children and an early feasibility case study of multi-modal wearable sensor data from two participants, one of whom has been clinically diagnosed with an internalizing disorder. Results lend support that sacral movement responses and R-R interval during a short stress-induction task may facilitate child diagnosis. Multi-modal sensors measuring movement and surface biopotentials of the chest and trapezius are also shown to have significant redundancy, introducing the potential for sensor optimization moving forward. Future work aims to further optimize sensor placement, signals, features, and assessments to enable deployment in clinical practice. Clinical Relevance- This work considers the development and optimization of technologies for improving the identification of children with internalizing disorders.
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Advancing Digital Medicine with Wearables in the Wild. SENSORS 2022; 22:s22124576. [PMID: 35746358 PMCID: PMC9227612 DOI: 10.3390/s22124576] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023]
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Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health 2021; 3:662811. [PMID: 34713137 PMCID: PMC8521964 DOI: 10.3389/fdgth.2021.662811] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/21/2022] Open
Abstract
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.
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Affiliation(s)
- Mahsa Sheikh
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - M Qassem
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
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Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Abu Serhan H, Abd-alrazaq A, Solaiman B, Househ M. Features of wearable devices used for Anxiety & Depression: A scoping review (Preprint).. [DOI: 10.2196/preprints.33287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
The rates of mental health disorders such as anxiety and depression are at an all time high and the need for readily available digital health care solutions has never been greater. Wearable devices (WD) have seen a steady rise in the usage of sensors previously reserved for hospital settings. The availibity of features that make use of WDs for anxiety and depression is in its infancy, but we are seeing the potential for consumers to self monitor moods and behaviours with everyday commercially available devices and the ability to self-regulate their health needs.
OBJECTIVE
This study aims to explore features of wearable devices (WDs) used for anxiety and depression
METHODS
We have searched the following six bibliographic databases while conducting this review: MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar. Two reviewers independently performed study selection and data extraction; two other individual reviewers justified cross-checking of extracted data. We utilized a narrative approach for synthesizing the data.
RESULTS
From an initial 2,408 studies we assess and report the features in 58 studies that were highlighted according to our inclusion criteria. Wrist worn devices were identified in the bulk of our studies (n=42 or 71%). Depression was assessed in most of the studies (n=27 or 47%), whereas anxiety was assessed in n=15 or 25% of studies. More than a quarter (n=16 or 27%) of the included studies assessed both mental disorders. Finally n=26 or 46% of studies highlighted the wearable device host device as a smartphone.
CONCLUSIONS
The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies such as anxiety and depression. We see WDs having real potential in aiding with self-care and with purposefully designed WDs that combine the expertise of technologists and clinical experts WDs could play a key role in self-care monitoring and diagnosis.
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McGinnis EW, Scism J, Hruschak J, Muzik M, Rosenblum KL, Fitzgerald K, Copeland W, McGinnis RS. Digital Phenotype for Childhood Internalizing Disorders: Less Positive Play and Promise for a Brief Assessment Battery. IEEE J Biomed Health Inform 2021; 25:3176-3184. [PMID: 33481724 PMCID: PMC8384142 DOI: 10.1109/jbhi.2021.3053846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Childhood internalizing disorders, like anxiety and depression, are common, impairing, and difficult to detect. Universal childhood mental health screening has been recommended, but new technologies are needed to provide objective detection. Instrumented mood induction tasks, designed to press children for specific behavioral responses, have emerged as means for detecting childhood internalizing psychopathology. In our previous work, we leveraged machine learning to identify digital phenotypes of childhood internalizing psychopathology from movement and voice data collected during negative valence tasks (pressing for anxiety and fear). In this work, we develop a digital phenotype for childhood internalizing disorders based on wearable inertial sensor data recorded from a Positive Valence task during which a child plays with bubbles. We find that a phenotype derived from features that capture reward responsiveness is able to accurately detect children with underlying internalizing psychopathology (AUC = 0.81). In so doing, we explore the impact of a variety of feature sets computed from wearable sensors deployed to two body locations on phenotype performance across two phases of the task. We further consider this novel digital phenotype in the context of our previous Negative Valence digital phenotypes and find that each task brings unique information to the problem of detecting childhood internalizing psychopathology, capturing different problems and disorder subtypes. Collectively, these results provide preliminary evidence for a mood induction task battery to develop a novel diagnostic for childhood internalizing disorders.
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McGinnis EW, Anderau SP, Hruschak J, Gurchiek RD, Lopez-Duran NL, Fitzgerald K, Rosenblum KL, Muzik M, McGinnis RS. Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood. IEEE J Biomed Health Inform 2019; 23:2294-2301. [PMID: 31034426 PMCID: PMC7484854 DOI: 10.1109/jbhi.2019.2913590] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning analysis of audio data from the task can be used to identify children with an internalizing disorder with 80% accuracy (54% sensitivity, 93% specificity). The speech features most discriminative of internalizing disorder are analyzed in detail, showing that affected children exhibit especially low-pitch voices, with repeatable speech inflections and content, and high-pitched response to surprising stimuli relative to controls. This new tool is shown to outperform clinical thresholds on parent-reported child symptoms, which identify children with an internalizing disorder with lower accuracy (67-77% versus 80%), and similar specificity (85-100% versus 93%), and sensitivity (0-58% versus 54%) in this sample. 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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McGinnis RS, McGinnis EW, Hruschak J, Lopez-Duran NL, Fitzgerald K, Rosenblum KL, Muzik M. Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning. PLoS One 2019; 14:e0210267. [PMID: 30650109 PMCID: PMC6334916 DOI: 10.1371/journal.pone.0210267] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 12/18/2018] [Indexed: 01/21/2023] Open
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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Affiliation(s)
- Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, United States of America
| | - Ellen W. McGinnis
- Department of Psychiatry, University of Vermont, Burlington, VT, United States of America
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States of America
| | - Jessica Hruschak
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States of America
| | - Nestor L. Lopez-Duran
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States of America
| | - Kate Fitzgerald
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States of America
| | - Katherine L. Rosenblum
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States of America
| | - Maria Muzik
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States of America
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