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

Development and Evaluation of Research Methods, Instruments and Tools (481) Ubiquitous Health (uHealth) (123) Wearable Devices and Sensors (888) mHealth for Data Collection and Research (955) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363) Diagnostic Tools in Mental Health (242)

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 (29) Tweetations (4) Metrics

Journals

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  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
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  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
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  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
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Books/Policy Documents

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