ArticleFrontiers in public health2022
Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Association between circadian rhythm disturbances and cognitive decline in the elderly: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Repetitive Transcranial Magnetic Stimulation and Tai Chi Chuan for Older Adults With Sleep Disorders and Mild Cognitive Impairment: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Passive digital health technologies for Alzheimer's disease screening and diagnosis: a systematic review.NPJ digital medicine · 2026Article
- miR-181d coordinates homologous recombination and anti-tumor immune responses in glioblastoma.iScience · 2026Article
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Wearable EEG devices in the detection of mild cognitive impairment: a systematic review.NPJ digital medicine · 2026Article
- Association between physical activity levels, sedentary time, and mild cognitive impairment in older adults.Frontiers in public health · 2026Article
- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine.JMIR medical informatics · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Using wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics. Methods: 31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method. Results: The low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 ( Conclusion: By collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.