ArticleCell2025
Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.
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Article in Cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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26citing papers in PubMed, 1 pooled it
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26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- Towards Human-Centered Digital Health Interventions.The Psychiatric clinics of North America · 2026Review
- Wearable measured physiological stress and activity patterns changes after 2025 Los Angeles wildfire among older adults: findings from a pilot prospective cohort study.Environmental research, health : ERH · 2026Article
- The ABCD Study at 10: The NIH perspective on progress, impact, and the road ahead.Developmental cognitive neuroscience · 2026Article
- Building the foundations for global data banking in digital phenotyping for mental health.NPP - digital psychiatry and neuroscience · 2026Review
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Integrating speech biomarkers and large language models for adolescent suicide risk detection with mobile application for real-world evaluation.Cell reports. Medicine · 2026Article
- A decade of discovery: Leveraging genomic data in the ABCD study to illuminate adolescent neurodevelopment.Developmental cognitive neuroscience · 2026Review
- Sensor wide association studies in digital medicine.NPJ digital medicine · 2026Review
- Smartphone gaze-tracking for accessible psychiatric assessment.Npj mental health research · 2026Article
- AI-driven complex systems redefine cognitive science.Innovation (Cambridge (Mass.)) · 2026Review
- Machine learning-enabled implantable plant biomarker sensor for early detection and classification of acid and salt stress.Nature communications · 2026Article
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Health Care: Qualitative Study.JMIR mental health · 2026Article
- Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment.npj women's health · 2026Article
- Soft and Strong: Elastic Conductors with Bio-Inspired Self-Protection.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Trans-synaptic Interaction with mGluR6 Contributes to ELFN1 Presynaptic Enrichment in Rod Photoreceptors.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
Corrections and comments
- Update of
Authors and funding
15 authors.
Jason J LiuProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Beatrice BorsariProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Yunyang LiDepartment of Computer Science, Yale University, New Haven, CT 06511, USA.
Susanna X LiuProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Yuan GaoProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Xin XinProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Shaoke LouProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Matthew JensenProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA.
Diego Garrido-MartínDepartment of Genetics, Microbiology and Statistics, Universitat de Barcelona (UB), Barcelona 08028, Spain.
Terril L VerplaetseDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.
Garrett AshSection of General Internal Medicine, Yale University School of Medicine, New Haven, CT 06511, USA; Center for Pain, Research, Informatics, Medical Comorbidities and Education Center (PRIME), VA Connecticut Healthcare System, West Haven, CT 06516, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA.
Jing ZhangDepartment of Computer Science, University of California, Irvine, Irvine, CA 92697, USA.
Matthew J GirgentiDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.
Walter RobertsDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA. Electronic address: walter.roberts@yale.edu.
Mark GersteinProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA; Department of Computer Science, Yale University, New Haven, CT 06511, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA. Electronic address: mark@gersteinlab.org.
Funding
ABCD-USA Consortium: Coordinating CenterU24DA041147 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SANDRA A BROWN, TERRY L. JERNIGAN · 2015 to 2026
$54.7MABCD-USA Consortium: Data Analysis, Informatics and Resource CenterU24DA041123 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANDERS M DALE · 2015 to 2026
$51.5MAdolescent Substance Use Initiation: Disentangling neurocognitive risks from consequences using longitudinal and genetically-informed methodsU01DA041120 · NIDA · UNIVERSITY OF MINNESOTA · PI Monica Luciana, Sylia Wilson · 2015 to 2026
$34.5MABCD-USA Consortium: Research ProjectU01DA041089 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Joanna Jacobus, Susan F. Tapert · 2015 to 2026
$31.7MProspective Research Studies of Maturation (PRISM)- Research ProjectU01DA041134 · NIDA · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI ERIN MCGLADE, PERRY FRANKLIN RENSHAW · 2015 to 2026
$29.2MABCD-USA CONSORTIUM: RESEARCH PROJECTU01DA041048 · NIDA · CHILDREN'S HOSPITAL OF LOS ANGELES · PI Megan Marie Herting, ELIZABETH R SOWELL · 2015 to 2026
$28.7MABCD-USA Consortium: Research ProjectU01DA041106 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mary M Heitzeg, Chandra Sekhar Sripada · 2015 to 2026
$24.9MFIU-ABCD: Pathways and Mechanisms to Addiction in the Latino Youth of South FloridaU01DA041156 · NIDA · FLORIDA INTERNATIONAL UNIVERSITY · PI Raul Gonzalez, Angela R Laird · 2015 to 2026
$22.8MABCD-USA Consortium: Research ProjectU01DA041148 · NIDA · OREGON HEALTH & SCIENCE UNIVERSITY · PI Damien A Fair, Rebekah S Huber · 2015 to 2026
$22.3MABCD-USA: NYC Research ProjectU01DA041174 · NIDA · YALE UNIVERSITY · PI Arielle Ryan Baskin-Sommers, Betty J Casey · 2015 to 2026
$19.7MAdolescent Brain Cognitive Development (ABCD) Prospective Research in Studies of Maturation (PRISM) ConsortiumU01DA041117 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI LINDA CHANG, THOMAS M ERNST · 2015 to 2026
$19.5M15/21 ABCD-USA Consortium: Research Project Site at LIBRU01DA050989 · NIDA · LAUREATE INSTITUTE FOR BRAIN RESEARCH · PI ROBIN L AUPPERLE, MARTIN P. PAULUS · 2020 to 2026
$14.7MNIAAA NIH HHS K01 AA025670NIAAA NIH HHS K23 AA026890NIAAA NIH HHS R01 AA030971NIAAA NIH HHS R01 AA031959NIAAA NIH HHS U54 AA027989NIDA NIH HHS U01 DA041022NIDA NIH HHS U01 DA041025NIDA NIH HHS U01 DA041028NIDA NIH HHS U01 DA041048NIDA NIH HHS U01 DA041089NIDA NIH HHS U01 DA041093NIDA NIH HHS U01 DA041106NIDA NIH HHS U01 DA041117NIDA NIH HHS U01 DA041120NIDA NIH HHS U01 DA041134NIDA NIH HHS U01 DA041148NIDA NIH HHS U01 DA041156NIDA NIH HHS U01 DA041174NIDA NIH HHS U01 DA050987NIDA NIH HHS U01 DA050988NIDA NIH HHS U01 DA050989NIDA NIH HHS U01 DA051016NIDA NIH HHS U01 DA051018NIDA NIH HHS U01 DA051037NIDA NIH HHS U01 DA051038NIDA NIH HHS U01 DA051039NIDA NIH HHS U24 DA041123NIDA NIH HHS U24 DA041147
Abstract
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion. Here, we analyze wearable and genetic data from the Adolescent Brain Cognitive Development (ABCD) study. Leveraging >250 wearable-derived features as digital phenotypes, we show that an interpretable AI framework can objectively classify adolescents with psychiatric disorders more accurately than previously possible. To relate digital phenotypes to the underlying genetics, we show how they can be employed in univariate and multivariate genome-wide association studies (GWASs). Doing so, we identify 16 significant genetic loci and 37 psychiatric-associated genes, including ELFN1 and ADORA3, demonstrating that continuous, wearable-derived features give greater detection power than traditional case-control GWASs. Overall, we show how wearable technology can help uncover new linkages between behavior and genetics.
Indexed as
Artificial IntelligenceMental DisordersWearable Electronic DevicesAdolescentFemaleGenome-Wide Association StudyHumansMalePhenotypeAIbraindeep learningdigital phenotypinggeneticsgenomicsGWASpersonal healthpsychiatrywearable biosensors
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