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

New Methods (944) Wearable Devices and Sensors (888) Fitness Trackers and Smart Pedometers/Accelerometers (561) mHealth for Data Collection and Research (955) Machine Learning (1465) mHealth for Wellness, Behavior Change and Prevention (2871) Sleep Monitoring, Sleep Quality, Sleep Disorders (183)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18403, first published .
Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

Authors of this article:

Xinyue Li1, 2 Author Orcid Image ;   Michael Kane3 Author Orcid Image ;   Yunting Zhang2, 4 Author Orcid Image ;   Wanqi Sun5 Author Orcid Image ;   Yuanjin Song5 Author Orcid Image ;   Shumei Dong5 Author Orcid Image ;   Qingmin Lin5 Author Orcid Image ;   Qi Zhu5 Author Orcid Image ;   Fan Jiang4, 5 Author Orcid Image ;   Hongyu Zhao3, 6 Author Orcid Image
Article Authors Cited by (6) Tweetations (9) Metrics

Journals

  1. Cui S, Lin Q, Gui Y, Zhang Y, Lu H, Zhao H, Wang X, Li X, Jiang F. CARE as a wearable derived feature linking circadian amplitude to human cognitive functions. npj Digital Medicine 2023;6(1) View
  2. Liu J, Zhang X, Lin T, Chen R, Zhong Y, Chen T, Wu T, Liu C, Huang A, Nguyen T, Lee E, Jeste D, Tu X. A new paradigm for high‐dimensional data: Distance‐based semiparametric feature aggregation framework via between‐subject attributes. Scandinavian Journal of Statistics 2024;51(2):672 View
  3. Reinhart L, Bischops A, Kerth J, Hagemeister M, Heinrichs B, Eickhoff S, Dukart J, Konrad K, Mayatepek E, Meissner T. Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance. Intelligence-Based Medicine 2024;9:100134 View
  4. Corponi F, Li B, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie S, Whalley H, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Translational Psychiatry 2024;14(1) View
  5. Veldman S, Gubbels J, Singh A, Koedijker J, Chinapaw M, Altenburg T. Correlates of Fundamental Motor Skills in the Early Years (0–4 Years): A Systematic Review. Journal of Motor Learning and Development 2024;12(1):1 View
  6. Hong M, Kang R, Yang J, Rhee S, Lee H, Kim Y, Lee K, Kim H, Lee Y, Youn T, Kim S, Ahn Y. Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study. Journal of Medical Internet Research 2024;26:e65994 View