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

mHealth for Data Collection and Research (1196)

Published on in Vol 7, No 12 (2019): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14473, first published .
Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Authors of this article:

Sungkyu Park1 Author Orcid Image ;   Sang Won Lee2 Author Orcid Image ;   Sungwon Han3 Author Orcid Image ;   Meeyoung Cha3, 4 Author Orcid Image
Article Authors Cited by (27) Tweetations (12) Metrics

Journals

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  6. Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V, Sarmiento R. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS Digital Health 2023;2(10):e0000347 View
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  10. Ingle M, Sharma M, Kumar K, Kumar P, Bhurane A, Elphick H, Joshi D, Acharya U. A systematic review on automatic identification of insomnia. Physiological Measurement 2024;45(3):03TR01 View
  11. Groninger H, Arem H, Ayangma L, Gong L, Zhou E, Greenberg D. Development of a Voice-Activated Virtual Assistant to Improve Insomnia Among Young Adult Cancer Survivors: Mixed Methods Feasibility and Acceptability Study. JMIR Formative Research 2025;9:e64869 View
  12. Chatur A, Haghi M, Ganapathy N, TaheriNejad N, Seepold R, Madrid N. Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset. IEEE Access 2024;12:150664 View
  13. Bhatt A, Sengupta S, Abolhassani A, Brower D, Forehand C, Keats K, Kwon Y, Healy W. Awakening Sleep Medicine: The Transformative Role of Artificial Intelligence in Sleep Health. Current Sleep Medicine Reports 2025;11(1) View
  14. Zhang P, An X, Yang R, Qi M, Gao Z, Zhang X, Wu Z, Zheng Z, Dong X, Wang W, Wang X, Zha D. Echoes in the night: How sleep quality influences auditory health. Neuroscience 2025;577:200 View
  15. Aziz S, A M Ali A, Aslam H, A Abd-alrazaq A, AlSaad R, Alajlani M, Ahmad R, Khalil L, Ahmed A, Sheikh J. Wearable Artificial Intelligence for Sleep Disorders: Scoping Review. Journal of Medical Internet Research 2025;27:e65272 View
  16. Jeong J, Jeon Y, Kim H, Yeom J, Shin Y, Kim S, Pack S, Lee H, Cheong T, Cho C. Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data. Scientific Reports 2025;15(1) View
  17. Yan A, Speed T, Taylor C. Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders. Scientific Reports 2025;15(1) View
  18. Baigutanova A, Park S, Constantinides M, Lee S, Quercia D, Cha M. A continuous real-world dataset comprising wearable-based heart rate variability alongside sleep diaries. Scientific Data 2025;12(1) View
  19. Biedebach L, Ferreira-Santos D, Stefanos M, Lindhagen A, Pires G, Arnardóttir E, Islind A. Unsupervised machine learning in sleep research: a scoping review. SLEEPJ 2025;48(11) View
  20. He J, Cheng J, Su C, Zhang J. Research on the effect of TMS on insomnia patients: EEG changes and prognostic modeling. Frontiers in Neuroscience 2025;19 View
  21. McCully L, Cao H, Wachowicz M, Champion S, Williams P. Discovering self-quantified patterns using multi-time window models. Applied Computing and Informatics 2025;21(3-4):330 View

Books/Policy Documents

  1. Barrachina M, Valenzuela López L. Advancing the Investigation and Treatment of Sleep Disorders Using AI. View
  2. Sureja N, Mehta K, Shah V, Patel G. Machine Learning for Advanced Functional Materials. View
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  4. Zan X, Liu F, Xian X, Pardalos P. Handbook of AI and Data Sciences for Sleep Disorders. View

Conference Proceedings

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