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Objective prediction of siesta based on machine learning and association with obesity

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dc.contributor.authorRodríguez Martín, María
dc.contributor.authorMoreno Caballero, Fernando
dc.contributor.authorDashti, Hassan S.
dc.contributor.authorSaxena, Richa
dc.contributor.authorScheer, Frank A. J. L.
dc.contributor.authorFernández Breis, Jesualdo Tomás
dc.contributor.authorGaraulet Aza, Marta
dc.contributor.departmentFisiología
dc.contributor.otherFacultades de la UMU::Facultad de Biología
dc.date.accessioned2026-06-04T12:30:13Z
dc.date.available2026-06-04T12:30:13Z
dc.date.copyright© 2026 The Author(s)
dc.date.issued2026-04-09
dc.description.abstractObjectives To predict siesta behavior using machine learning models trained on self-reported and objective data—temperature (T), activity (A), position (P), and the integrated TAP variable—and to explore its associations with obesity-related traits. Methods From ONTIME-MT, 889 adults wore wrist sensors for 7 days to continuously record temperature, activity, and position, and self-reported daily siesta. Machine learning models were developed to classify 30-second epoch siesta data, to reconstruct weekly siesta behavior. Anthropometric and metabolic parameters were assessed. Associations were analyzed using linear and logistic regression. Model generalizability was evaluated in an independent Mediterranean cohort (n = 70). Results The machine learning model allowed to obtain 83% of success in siesta patterns prediction. Among the input variables, activity was the most discriminative by the decision tree (threshold: 27 Δ°/min), followed by TAP (0.51 AU) and position (4.7°). In an independent external validation cohort, success in prediction reached 77%, indicating strong alignment between algorithm-based and self-reported siesta patterns detection. Predicted siesta—but not self-reported alone—was significantly associated with obesity-related traits. Later siesta timing was linked to increased waist circumference in women (β = 0.769 cm per hour; P = 0.026). Longer siesta duration was associated with increased obesity risk (OR=2.081; P=0.002), BMI (β=0.013 kg/m²/h; P = 0.034), and systolic blood pressure (β = 3.540 mmHg/h; P = 0.049). Greater siesta frequency was associated with lower corrected insulin response (β = −0.037 AU/day; P = 0.012). Conclusion Objective data from temperature, activity, position, and TAP, combined with ML models, accurately predict siesta behavior and its metabolic relevance. These findings support the use of machine learning approaches based on temperature, activity, position, and the integrated TAP, to assess siesta under free-living conditions. ClinicalTrials.gov identifier NCT03036592
dc.formatapplication/pdf
dc.format.extent9
dc.identifier.citationSleep Health, Available online 9 April 2026
dc.identifier.doihttps://doi.org/10.1016/j.sleh.2026.02.007
dc.identifier.eissn2352-7218
dc.identifier.urihttp://hdl.handle.net/10201/236821
dc.languageeng
dc.publisherElsevier
dc.relationThis study was funded by the Spanish Ministry of Science and Innovation (MCINN/AEI/10.13039/501100011033) under grant PID2020-112768RB-I00. ONTIME-MT was funded by National Institute of Health grant R01DK105072. MRM was supported by the MICINN grant PRE2021-100760. FAJLS was supported in part by the National Institute of Health under grants R01 HL140574 and R01 HL153969. HSD was supported in part by the National Institute of Health under grant R00 HL153795.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2352721826000185
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSiesta
dc.subjectNapping
dc.subjectObesity
dc.subjectMachine learning
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleObjective prediction of siesta based on machine learning and association with obesity
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
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relation.isAuthorOfPublication5679cbb2-785d-4082-b5ad-dce1c8f21252
relation.isAuthorOfPublicationb40dbac2-8d22-43cf-89fb-fd6df8d82836
relation.isAuthorOfPublication.latestForDiscovery872e5ce3-d831-4bd6-9c07-040f38085e1b
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