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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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Abd-Alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. J Med Internet Res 2023; 25:e42672. [PMID: 36656625 PMCID: PMC9896355 DOI: 10.2196/42672] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/18/2022] [Accepted: 12/11/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. OBJECTIVE This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. METHODS We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. RESULTS Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. CONCLUSIONS Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Kerstin Denecke
- Institute for Medical Informatics, Bern University of Applied Science, Bern, Switzerland
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute, Hamad bin Khalifa University, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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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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Welch V, Wy TJ, Ligezka A, Hassett LC, Croarkin PE, Athreya AP, Romanowicz M. The Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry – A Scoping Review (Preprint). J Med Internet Res 2021; 24:e33560. [PMID: 35285812 PMCID: PMC8961347 DOI: 10.2196/33560] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/13/2022] [Accepted: 01/26/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Victoria Welch
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Tom Joshua Wy
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Anna Ligezka
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN, United States
| | - Leslie C Hassett
- Mayo Clinic Libraries, Mayo Clinic, Rochester, MN, United States
| | - Paul E Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Arjun P Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Magdalena Romanowicz
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
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