Unmasking the nuances of loneliness: Using digital biomarkers to understand social and emotional loneliness in college students

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dc.contributor.authorQirtas, Malik Muhammad
dc.contributor.authorZafeiridi, Evi
dc.contributor.authorPesch, Dirk
dc.contributor.authorWhite, Eleanor Bantry
dc.date.accessioned2026-03-11T10:50:06Z
dc.date.available2026-03-11T10:50:06Z
dc.date.issued2024
dc.description.abstractBackground: Loneliness among students is increasing across the world, with potential consequences for mental health and academic success. To address this growing problem, accurate methods of detection are needed to identify loneliness and to differentiate social and emotional loneliness so that intervention can be personalized to individual need. Passive sensing technology provides a unique technique to capture behavioral patterns linked with distinct loneliness forms, allowing for more nuanced understanding and interventions for loneliness. Methods: To differentiate between social and emotional loneliness using digital biomarkers, our study included statistical tests, machine learning for predictive modeling, and SHAP values for feature importance analysis, revealing important factors in loneliness classification. Results: Our analysis revealed significant behavioral differences between socially and emotionally lonely groups, particularly in terms of phone usage and location-based features , with machine learning models demonstrating substantial predictive power in classifying loneliness levels. The XGBoost model, in particular, showed high accuracy and was effective in identifying key digital biomarkers, including phone usage duration and location-based features, as significant predictors of loneliness categories. Conclusion: This study underscores the potential of passive sensing data, combined with machine learning techniques, to provide insights into the behavioral manifestations of social and emotional loneliness among students. The identification of key digital biomarkers paves the way for targeted interventions aimed at mitigating loneliness in this population.en
dc.description.statusPeer revieweden
dc.description.versionSubmitted Versionen
dc.format.extent15
dc.format.extent460955
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidQirtas, Malik Muhammad
dc.identifier.authororcidZafeiridi, Evi
dc.identifier.authororcidPesch, Dirk§0000-0001-9706-5705
dc.identifier.authororcidWhite, Eleanor Bantry§0000-0002-7663-6836
dc.identifier.citationQirtas, M. M., Zafeiridi, E., Pesch, D. and Bantry White, E. (2024) 'Unmasking the nuances of loneliness: Using digital biomarkers to understand social and emotional loneliness in college students', arXiv, pp. 1-15. https://doi.org/10.48550/arXiv.2404.01845
dc.identifier.doi10.48550/arXiv.2404.01845
dc.identifier.endpage15
dc.identifier.otherArXiv: http://arxiv.org/abs/2404.01845v1
dc.identifier.otherORCID: /0000-0002-7663-6836/work/208199607
dc.identifier.otherORCID: /0000-0001-9706-5705/work/208199784
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18620
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-85191029136&partnerID=MN8TOARS
dc.language.isoen
dc.publisherarXiv
dc.rights© 2024, the Authors.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectPassive sensing data
dc.subjectIreland
dc.subjectStudents
dc.subjectLoneliness
dc.subjectMachine learning techniques
dc.subject[ComputerScience]
dc.titleUnmasking the nuances of loneliness: Using digital biomarkers to understand social and emotional loneliness in college studentsen
dc.typePreprint
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