This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (744) Ubiquitous Health (uHealth) (134) Wearable Devices and Sensors (1133) mHealth for Data Collection and Research (1196) Machine Learning (2076) Depression and Mood Disorders; Suicide Prevention (1691) Diagnostic Tools in Mental Health (326)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35807, first published .
Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Authors of this article:

Tahsin Mullick1 Author Orcid Image ;   Ana Radovic2 Author Orcid Image ;   Sam Shaaban3 Author Orcid Image ;   Afsaneh Doryab1 Author Orcid Image
Article Authors Cited by (47) Tweetations (4) Metrics

Journals

  1. 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. Journal of Medical Internet Research 2023;25:e42672 View
  2. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View
  3. Barzilay S, Fine S, Akhavan S, Haruvi-Catalan L, Apter A, Brunstein-Klomek A, Carmi L, Zohar M, Kinarty I, Friedman T, Fennig S. Real-Time Real-World Digital Monitoring of Adolescent Suicide Risk During the Six Months Following Emergency Department Discharge: Protocol for an Intensive Longitudinal Study. JMIR Research Protocols 2023;12:e46464 View
  4. Kadirvelu B, Bellido Bel T, Wu X, Burmester V, Ananth S, Cabral C C Branco B, Girela-Serrano B, Gledhill J, Di Simplicio M, Nicholls D, Faisal A. Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study. JMIR Formative Research 2023;7:e44877 View
  5. Sieberts S, Burn A, Carey E, Carlson S, Fernandes B, Kalha J, Lindani S, Marten C, Neelakantan L, Ranganathan S, Sams N, Scanlan E, Shah H, Sumant S, Suver C, Tummalacherla M, Velloza J, Areán P, Collins P, Fazel M, Ford T, Freeman M, Pathare S, Zingela Z, Doerr M. Targeted recruitment and the role of choice in the engagement of youth in a randomised smartphone-based mental health study in India, South Africa, and the UK: results from the MindKind Study. Wellcome Open Research 2023;8:334 View
  6. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  7. Jiang A, Al-Dajani N, King C, Hong V, Koo H, Czyz E. Acceptability and feasibility of ecological momentary assessment with augmentation of passive sensor data in young adults at high risk for suicide. Psychiatry Research 2023;326:115347 View
  8. Moukaddam N, Lamichhane B, Salas R, Goodman W, Sabharwal A, Emanuele E. Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder. Behavioural Neurology 2023;2023:1 View
  9. Shende C, Sahoo S, Sam S, Patel P, Morillo R, Wang X, Ware S, Bi J, Kamath J, Russell A, Song D, Wang B. Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
  10. Piccin J, Viduani A, Buchweitz C, Pereira R, Zimerman A, Amando G, Cosenza V, Ferreira L, McMahon N, Melo R, Richter D, Reckziegel F, Rohrsetzer F, Souza L, Tonon A, Costa-Valle M, Zajkowska Z, Araújo R, Hauser T, van Heerden A, Hidalgo M, Kohrt B, Mondelli V, Swartz J, Fisher H, Kieling C. Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments. JAACAP Open 2024;2(2):145 View
  11. Jiang Z, Van Zoest V, Deng W, Ngai E, Liu J. Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey. IEEE Internet of Things Journal 2023;10(24):21959 View
  12. Adi G, Park I. Emerging Machine Learning in Wearable Healthcare Sensors. JOURNAL OF SENSOR SCIENCE AND TECHNOLOGY 2023;32(6):378 View
  13. Stamatis C, Meyerhoff J, Meng Y, Lin Z, Cho Y, Liu T, Karr C, Liu T, Curtis B, Ungar L, Mohr D. Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. npj Mental Health Research 2024;3(1) View
  14. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  15. Pillai A, Nepal S, Wang W, Nemesure M, Heinz M, Price G, Lekkas D, Collins A, Griffin T, Buck B, Preum S, Cohen T, Jacobson N, Ben-Zeev D, Campbell A. Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(4):1 View
  16. Mudra Rakshasa-Loots A, Naidoo S, Hamana T, Fanqa B, van Wyhe K, Lindani F, van der Kouwe A, Glashoff R, Kruger S, Robertson F, Cox S, Meintjes E, Laughton B, Vera J. Multi-modal analysis of inflammation as a potential mediator of depressive symptoms in young people with HIV: The GOLD depression study. PLOS ONE 2024;19(2):e0298787 View
  17. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  18. Liu L, Dai Y, Liu Z. Combining Data Mining Algorithms for 6G Integrated Cyber-Physical Health Assessment and Exercise Ability Optimization Intervention in Young Children. Wireless Personal Communications 2024 View
  19. Kirshenbaum J, Pagliaccio D, Bitran A, Xu E, Auerbach R. Why do adolescents attempt suicide? Insights from leading ideation-to-action suicide theories: a systematic review. Translational Psychiatry 2024;14(1) View
  20. Wu Y, Liu Z, Yuan J, Chen B, Cai H, Liu L, Zhao Y, Mei H, Deng J, Bao Y, Hu B. PIE: A Personalized Information Embedded model for text-based depression detection. Information Processing & Management 2024;61(6):103830 View
  21. Ng M, Frederick J, Fisher A, Allen N, Pettit J, McMakin D. Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study. JMIR Research Protocols 2024;13:e43931 View
  22. Shvetcov A, Funke Kupper J, Zheng W, Slade A, Han J, Whitton A, Spoelma M, Hoon L, Mouzakis K, Vasa R, Gupta S, Venkatesh S, Newby J, Christensen H. Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping. Frontiers in Psychiatry 2024;15 View
  23. Walschots Q, Zarchev M, Unkel M, Kamperman A. Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder—A Scoping Review and Introductory Text for Clinical Professionals. Algorithms 2024;17(9):408 View
  24. Yoo A, Li F, Youn J, Guan J, Guyer A, Hostinar C, Tagkopoulos I. Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning. Scientific Reports 2024;14(1) View
  25. Lipschitz J, Lin S, Saghafian S, Pike C, Burdick K. Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology. Acta Psychiatrica Scandinavica 2025;151(3):434 View
  26. Alt A, Pascher A, Seizer L, von Fraunberg M, Conzelmann A, Renner T. Psychotherapy 2.0 - Application context and effectiveness of sensor technology in psychotherapy with children and adolescents: A systematic review. Internet Interventions 2024;38:100785 View
  27. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View
  28. Ikäheimonen A, Luong N, Baryshnikov I, Darst R, Heikkilä R, Holmen J, Martikkala A, Riihimäki K, Saleva O, Isometsä E, Aledavood T. Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study. Journal of Medical Internet Research 2024;26:e56874 View
  29. LoParo D, Matos A, Arnarson E, Craighead W. Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach. Journal of Psychiatric Research 2025;182:235 View
  30. Liu J, Li K, Li S, Liu S, Wang C, Huang S, Tu Y, Wang B, Zhang P, Luo Y, Sun G, Chen T. A new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing: an exploratory study. Frontiers in Computational Neuroscience 2025;19 View
  31. Kargarandehkordi A, Li S, Lin K, Phillips K, Benzo R, Washington P. Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors 2025;15(4):202 View
  32. Zhang G, Li S. Personalized prediction and intervention for adolescent mental health: multimodal temporal modeling using transformer. Frontiers in Psychiatry 2025;16 View
  33. Shin Y, Kim A, Kim S, Shin M, Choi J, Lee K, Lee J, Byun S, Kim S, Lee H, Cho C. Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea. BMJ Open 2025;15(6):e096773 View
  34. Vlad A, Iovanovici E. THE IMPACT OF FEATURE SELECTION AND DATA PRE-PROCESSING ON ML MODELS. Romanian Journal of Petroleum & Gas Technology 2025;6 (77)(1):163 View
  35. Shen S, Qi W, Zeng J, Li S, Liu X, Zhu X, Dong C, Wang B, Shi Y, Yao J, Wang B, Lou X, Gu S, Li P, Wang J, Jiang G, Cao S. Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review. Journal of Medical Internet Research 2025;27:e77066 View
  36. Amin R, Schreynemackers S, Oppenheimer H, Petrovic M, Hegerl U, Reich H. Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review. Journal of Medical Internet Research 2025;27:e57418 View
  37. Hoang H, Ha H, Nguyen H, Watton P, Ngo L. Advancing mental health diagnostics: a review on the role of smartphones, wearable devices, and artificial intelligence in depression and anxiety detection. Biomedical Engineering Letters 2025;15(6):1003 View
  38. Löchner J. From Data to Care. Kindheit und Entwicklung 2025;34(2):63 View
  39. Löchner J, Bolivar M, Booth L, Canella S, Calobro M, Firth J, Garcia-Palacios A, Kyritsaka A, Sander L, Seiferth C, Seizer L, Teesson M, Tyrowicz J, Vogel L, Wheeler E, Wolstein J, Schuller B. Multidisciplinary perspectives on personalised prevention in youth mental health. Frontiers in Digital Health 2025;7 View
  40. Demrozi F, Farmanbar M, Engan K. Multimodal AI (MMAI) for next-generation healthcare: data domains, algorithms, challenges, and future perspectives. Current Opinion in Biomedical Engineering 2026;37:100632 View
  41. Ni S, Lu C, Wu C, Hsieh M, Chen I, Lin C, Lai F, Chien Y. An exploratory study on predicting depressive symptoms in autistic individuals using wearable devices and machine learning. Journal of the Formosan Medical Association 2025 View
  42. Zhao C, Cardei M, Clark M, Yan R, Doryab A. Biobehavioral Rhythms in Everyday Life: Data and Models for Capturing Cyclic Behavior in Naturalistic Settings. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2025;9(4):1 View

Books/Policy Documents

  1. Avasthi S, Sanwal T, Prakash A, Tripathi S. Multimodal Biometric and Machine Learning Technologies. View

Conference Proceedings

  1. Li Y, Huang A, Sanku B, He J. 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Designing an Empathy Training for Depression Prevention Using Virtual Reality and a Preliminary Study View
  2. Rajesh N, Ankitha D. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). Application of Multi-Sensor based Audio Wearable Device in Sleep Analysis, Wellness Tracking and Prediction View
  3. Saathwik B, Ramachandran A. 3RD INTERNATIONAL CONFERENCE ON ENGINEERING AND SCIENCE TO ACHIEVE THE SUSTAINABLE DEVELOPMENT GOALS. Improving accuracy in screen time prediction using decision tree algorithm compared with support vector regression algorithm View
  4. Zhang E, Clark M, Doryab A. Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies. Predicting The Severity of Anxiety in Adolescents Through Passively Sensed Behaviors View