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

AI in Older Adult Care (47) Assisted Living for the Elderly and Nursing Home Care (211) Machine Learning (2229) Dementia and Cognitive Decline (741) Insomnia and Sleep Hygiene (261) Sleep Monitoring, Sleep Quality, Sleep Disorders (285) Wearable Devices and Sensors (1212)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72338, first published .
Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation

Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation

Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation

Authors of this article:

Shevvaa Beiglary1 Author Orcid Image ;   Yanxiao Feng2 Author Orcid Image ;   Nan Wang3 Author Orcid Image ;   Neda Ghaeili1 Author Orcid Image ;   Ying-Ling Jao4 Author Orcid Image ;   Yo-Jen Liao4 Author Orcid Image ;   Yuxin Li1 Author Orcid Image ;   Julian Wang1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Wang Y, Zhang Y, Cheng S, Zhang Y, Zhang X. Towards a predictive paradigm for individual light exposure history: A trajectory-based modeling framework. Building and Environment 2026;294:114422 View
  2. Ribino P, Potenza G, Baglivo C, Bonomolo M. Intelligent lighting systems: Leveraging LSTM and time-series clustering for optimal PhotoSensor deployment. Journal of Building Engineering 2026;122:115761 View