Predicting long-term sleep deprivation using wearable sensors and health surveys.

Autor: Trujillo R; Florida International University - Knight Foundation School of Computing and Information Sciences, 11200 SW 8th St, Miami, FL, 33199, USA. Electronic address: rtruj023@fiu.edu., Zhang E; Florida International University - Knight Foundation School of Computing and Information Sciences, 11200 SW 8th St, Miami, FL, 33199, USA. Electronic address: ezhan004@fiu.edu., Templeton JM; University of South Florida - Department of Computer Science and Engineering, 4202 E Fowler Ave, Tampa, FL, 33620, USA. Electronic address: jtemplet@usf.edu., Poellabauer C; Florida International University - Knight Foundation School of Computing and Information Sciences, 11200 SW 8th St, Miami, FL, 33199, USA. Electronic address: cpoellab@fiu.edu.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Sep; Vol. 179, pp. 108749. Date of Electronic Publication: 2024 Jul 02.
DOI: 10.1016/j.compbiomed.2024.108749
Abstrakt: Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognition, and mood. The measurement of sleep from in-bed physiological signals has progressed to where commercial devices already incorporate this functionality. However, the prediction of sleep duration from previous awake activity is less studied. Previous studies have used daily exercise summaries, actigraph data, and pedometer data to predict sleep during individual nights. Building upon these, this article demonstrates how to predict a person's long-term average sleep length over the course of 30 days from Fitbit-recorded physical activity data alongside self-report surveys. Recursive Feature Elimination with Random Forest (RFE-RF) is used to extract the feature sets used by the machine learning models, and sex differences in the feature sets and performances of different machine learning models are then examined. The feature selection process demonstrates that previous sleep patterns and physical exercise are the most relevant kind of features for predicting sleep. Personality and depression metrics were also found to be relevant. When attempting to classify individuals as being long-term sleep-deprived, good performance was achieved across both the male, female, and combined data sets, with the highest-performing model achieving an AUC of 0.9762. The best-performing regression model for predicting the average nightly sleep time achieved an R-squared of 0.6861, with other models achieving similar results. When attempting to predict if a person who previously was obtaining sufficient sleep would become sleep-deprived, the best-performing model obtained an AUC of 0.9448.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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